diff --git a/.gitignore b/.gitignore index a6338f3..01188d9 100644 --- a/.gitignore +++ b/.gitignore @@ -4,4 +4,5 @@ logs/* .DS_Store sandbox slurm* -data \ No newline at end of file +data +bld \ No newline at end of file diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 0000000..135524a --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,5 @@ +{ + "python.terminal.activateEnvInCurrentTerminal": true, + "quarto.path": "", + "quarto.usePipQuarto": false +} \ No newline at end of file diff --git a/LICENSE b/LICENSE index 3b106e8..f987f3d 100644 --- a/LICENSE +++ b/LICENSE @@ -1,201 +1,395 @@ - Apache License - Version 2.0, January 2004 - http://www.apache.org/licenses/ - - TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION - - 1. 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Obtain an API key for the ERA5 datastore from [here](https://cds.climate.copernicus.eu/how-to-api), and ask Tinashe for access to the Golden Lab `googledriver` API key +## era5_sandbox -1. Clone this repository to your workspace on FASRC +> Sandbox environment for era5 development -2. Create a conda environment with `conda create -n era5_sandbox python=3.10` and install all of the necessary dependencies for the package with `pip install -e .` +This package documents the development and implementation of functions +and code for the Madagascar ERA5 dataset project. The goal is for +exposure data to be made available at the daily resolution when +possible. Finer resolutions shouldn’t ever be needed for our purposes, +and it should then be relatively easy to aggregate at coarser +resolutions, such as weekly or monthly. Additionally, we’ve extended +this work to Nepal as well. -3. Run the `core` module to test your API key and setup the data -directory structure +Variables should generally be made available from 2010 onward, as that’s +where our clinic data starts. -`python src/era5_sandbox/core.py` +All data are ideally made available at the “healthshed” geographical +level. Healthsheds are defined as geographical areas where people who +live all go to the same clinic. There are a total of ~2700 public +clinics in Madagascar, hence ~2700 healthsheds, with each healthshed +containing ~10000 people on average. -4. Symlink your local data directory to the original work -`ln -s [YOUR WORKING DIRECTORY]/data /n/dominici_lab/lab/data_processing/csph-era5_sandbox/data` +Preliminary list of environmental variables -5. Dry run by removing a file from data `snakemake --dry-run` +- [x] 2-m air temperature from ERA5: daily min, max, mean -6. Run the pipeline `sbatch snakemake.sbatch` +- [x] 2-m air dew point temperature from ERA5: daily min, max, mean + +- [x] Precipitation: daily total (ERA5) + +- [x] Soil moisture: daily average (ERA5) + +Variables from other sources: + +- [ ] Sea surface temperature: daily average and maximum in the nearest + neighbor for each healthshed. + +- [ ] Precipitation: daily total (CHIRPS) + +- [ ] Chlorophyll-A (Giacomo) + +- [ ] Wealth index: Available from Giacomo + +- [ ] NDVI + +- [ ] Tropical storm + +- [ ] Flooding + +- [ ] Deforestation + +- [ ] Linking/segmenting healthsheds into climate zones and other + +- [ ] Relative humidity: daily average (lower priority) + +Those from the ERA5 dataset will be housed here, but we may likely +develop a separate repository for the other datasets. + +## Developer Guide + +This package is built and maintained with `nbdev`. If you are new to +using `nbdev` here are some useful pointers to get you started. + +### Install era5_sandbox in Development mode + +``` sh +# make sure era5_sandbox package is installed in development mode +$ pip install -e . +``` + +To make changes, go to the “notes” directory and edit the notebooks as +necessary. Each notebook refers to a module in the era5_sandbox package. +Cells are exported to the module when the notebook is saved and you run +the following command: + +``` sh +$ nbdev_export +``` + +For e.g., to change functionality of the +[`testAPI()`](https://TinasheMTapera.github.io/era5_sandbox/core.html#testapi) +function in the testAPI Hydra rule, you would edit the +[`testAPI`](https://TinasheMTapera.github.io/era5_sandbox/core.html#testapi) +notebook in the `notes` directory `notes/testAPI.ipynb`, and then save +that notebook and run `nbdev_export` to update the `core` module in the +package. + +### How to Run the Pipeline + +The pipeline downloads ERA5 variables for a given date range and +geographical bounding box. You can learn how each of these steps was by +following the notebooks in `notes` in numerical order. + +> [!IMPORTANT] +> +> The pipeline has two implementations: one using `snakemake` and +> `hydra`, and another using `pytask`. The `pytask` implementation is +> the more recent one, and is recommended for future use. The +> `snakemake` implementation is left here for reference to legacy code. + +#### Using `pytask` + +To run the pipeline, the `pytask` config at `note/20_pytask_config.qmd` +should be reviewed and updated if necessary. The pipeline can then be +run with the following command: + +``` sh +$ sbatch pytask.sbatch +``` + +#### Using `snakemake` and `hydra` + +To run the pipeline, the config at `config/config.yaml` should be +updated with the desired date range and geographical bounding box. The +pipeline can then be run with the following command: + +``` sh +sbatch snakemake.sbatch +``` + +### What Does the Pipeline Produce? + +Using `pytask`’s data catalog, you can investigate the downloaded raw +data with python, eg.: + +``` python +import xarray as xr +from era5_sandbox.config import data_catalog +from era5_sandbox.core import ClimateDataFileHandler + +ex_nc = list(data_catalog['download']['outputs']._entries).pop() +ex_nc_path = data_catalog['download']['outputs'][ex_nc].load() + +with ClimateDataFileHandler(ex_nc_path) as handler: + ds = xr.open_dataset(handler.get_dataset("instant")) + +ds +``` + +
+ + + + + + + + + + + + + + +
<xarray.Dataset> Size: 53MB
+Dimensions:     (valid_time: 744, latitude: 49, longitude: 91)
+Coordinates:
+    number      int64 8B ...
+  * valid_time  (valid_time) datetime64[ns] 6kB 2024-03-01 ... 2024-03-31T23:...
+  * latitude    (latitude) float64 392B 30.8 30.7 30.6 30.5 ... 26.2 26.1 26.0
+  * longitude   (longitude) float64 728B 79.6 79.7 79.8 79.9 ... 88.4 88.5 88.6
+    expver      (valid_time) <U4 12kB ...
+Data variables:
+    d2m         (valid_time, latitude, longitude) float32 13MB ...
+    t2m         (valid_time, latitude, longitude) float32 13MB ...
+    tp          (valid_time, latitude, longitude) float32 13MB ...
+    swvl1       (valid_time, latitude, longitude) float32 13MB ...
+Attributes:
+    GRIB_centre:             ecmf
+    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts
+    GRIB_subCentre:          0
+    Conventions:             CF-1.7
+    institution:             European Centre for Medium-Range Weather Forecasts
+    history:                 2025-09-16T20:55 GRIB to CDM+CF via cfgrib-0.9.1...
+ +And plot it with cartopy, eg.: + +``` python +import matplotlib.pyplot as plt +import cartopy.crs as ccrs +import cartopy.feature as cfeature + +temperature = ds["t2m"] + +# Select a specific time step +temperature_at_time = temperature.isel(valid_time=0) + +# Plot the data on a map +plt.figure(figsize=(12, 8)) +ax = plt.axes(projection=ccrs.PlateCarree()) +temperature_at_time.plot(ax=ax, cmap="coolwarm", transform=ccrs.PlateCarree(), cbar_kwargs={"label": "Temperature (K)"}) +ax.coastlines() +ax.add_feature(cfeature.BORDERS, linestyle=":") +ax.set_title("Temperature at Time Step 0") +plt.show() +``` + + + +You can also load the aggregated data: + +``` python +import pandas as pd +import geopandas as gpd +from era5_sandbox.config import data_catalog + +ex_agg_path = data_catalog['aggregate']['outputs']['2019_08_madagascar_night_d2m_max.parquet'].load() + +gpd.read_parquet(ex_agg_path).describe() +``` + +
+ + +| | day_01 | day_02 | day_03 | day_04 | day_05 | day_06 | day_07 | day_08 | day_09 | day_10 | day_11 | day_12 | day_13 | day_14 | day_15 | day_16 | day_17 | day_18 | day_19 | day_20 | day_21 | day_22 | day_23 | day_24 | day_25 | day_26 | day_27 | day_28 | day_29 | day_30 | day_31 | day_32 | +|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----| +| count | 2701.000000 | 2701.000000 | 2701.000000 | 2701.000000 | 2701.000000 | 2701.000000 | 2701.000000 | 2701.000000 | 2701.000000 | 2701.000000 | 2701.000000 | 2701.000000 | 2701.000000 | 2701.000000 | 2701.000000 | 2701.000000 | 2701.000000 | 2701.000000 | 2701.000000 | 2701.000000 | 2701.000000 | 2701.000000 | 2701.000000 | 2701.000000 | 2701.000000 | 2701.000000 | 2701.000000 | 2701.000000 | 2701.000000 | 2701.000000 | 2701.000000 | 2701.000000 | +| mean | 290.493048 | 290.145274 | 288.953153 | 288.503714 | 288.439820 | 288.304426 | 286.940995 | 287.186512 | 287.453656 | 287.843029 | 288.301938 | 288.778014 | 288.813762 | 288.667253 | 288.796892 | 288.547945 | 288.197632 | 287.882440 | 287.659818 | 289.291587 | 289.911503 | 288.760939 | 288.257644 | 288.271450 | 287.746390 | 288.379399 | 288.504720 | 287.665699 | 288.149861 | 288.266861 | 288.644028 | 288.224829 | +| std | 2.616922 | 2.832083 | 3.215642 | 3.566019 | 4.401416 | 4.198817 | 5.235795 | 4.444031 | 4.346305 | 3.435444 | 2.735781 | 2.864494 | 2.841268 | 3.080593 | 3.306217 | 2.938165 | 3.018303 | 2.849850 | 2.817690 | 2.600946 | 2.584079 | 3.161855 | 3.171827 | 2.983778 | 3.223380 | 2.918867 | 2.844314 | 3.052635 | 3.077292 | 3.093706 | 3.335983 | 3.296264 | +| min | 284.295898 | 281.673340 | 280.566406 | 280.509521 | 277.348145 | 279.243164 | 274.955078 | 274.682129 | 275.397461 | 279.498291 | 282.339111 | 282.188721 | 282.470703 | 281.371582 | 280.724609 | 280.093506 | 280.849121 | 281.123535 | 281.952148 | 282.186768 | 284.168945 | 282.519287 | 282.015381 | 280.578857 | 281.183838 | 281.146973 | 281.977539 | 281.014648 | 280.787842 | 281.631348 | 281.349854 | 280.615967 | +| 25% | 288.031494 | 287.739014 | 286.978271 | 285.750488 | 284.326904 | 284.071289 | 281.695068 | 283.710449 | 284.153076 | 285.459717 | 286.141846 | 286.444092 | 286.505859 | 286.104004 | 286.114014 | 286.730225 | 286.005371 | 285.420166 | 285.230713 | 287.408203 | 287.744873 | 286.101318 | 285.243652 | 285.488281 | 285.170166 | 285.876465 | 286.145508 | 285.243164 | 285.579346 | 285.322754 | 285.930908 | 285.565186 | +| 50% | 290.674316 | 290.331543 | 288.916260 | 288.472168 | 289.635742 | 289.390381 | 288.382568 | 287.926758 | 288.173096 | 287.859375 | 287.797852 | 288.716064 | 288.806641 | 288.789307 | 289.210938 | 288.769287 | 288.085205 | 287.698975 | 287.252930 | 289.310547 | 289.878418 | 288.511719 | 288.420166 | 288.263916 | 287.717041 | 288.661621 | 288.999023 | 287.485107 | 288.326416 | 288.429199 | 288.576416 | 288.093018 | +| 75% | 292.828369 | 292.707764 | 291.609375 | 291.655762 | 291.987305 | 291.845459 | 291.671631 | 291.051758 | 291.288574 | 291.000244 | 290.813721 | 291.365967 | 291.540039 | 291.393799 | 291.756592 | 291.094727 | 290.893311 | 290.266602 | 290.166748 | 291.649902 | 291.970459 | 291.342285 | 290.443848 | 290.660400 | 290.400146 | 290.360840 | 290.854004 | 290.328125 | 290.827881 | 290.999268 | 291.598877 | 291.072754 | +| max | 296.467285 | 295.717529 | 295.837158 | 295.693604 | 295.723389 | 296.195557 | 295.589600 | 295.345703 | 294.754639 | 294.483154 | 294.952148 | 294.815430 | 294.623779 | 295.088135 | 295.036621 | 294.847900 | 294.224609 | 294.522949 | 294.728760 | 295.268066 | 295.507324 | 295.797363 | 296.297119 | 296.222900 | 295.492432 | 295.406006 | 294.629883 | 295.211670 | 295.363037 | 295.263184 | 295.446533 | 295.408691 | + +
diff --git a/_docs/00_core.html b/_docs/00_core.html new file mode 100644 index 0000000..5a54931 --- /dev/null +++ b/_docs/00_core.html @@ -0,0 +1,1132 @@ + + + + + + + + + +Core Module: Internal functions and testing – era5_sandbox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Core Module: Internal functions and testing

+
+ + + +
+ + + + +
+ + + +
+ + +
+

core

+
+

This is a core library for the ERA5 dataset pipeline. It defines a few helpful functions such as an API tester to test your API key and connection.

+
+ +
+
+Exported source +
import os
+import cdsapi
+import hydra
+import json
+import tempfile
+import argparse
+import zipfile
+import shutil
+import geopandas as gpd
+from pathlib import Path
+from pydrive2.auth import GoogleAuth
+from pydrive2.drive import GoogleDrive
+from omegaconf import DictConfig, OmegaConf
+from pyprojroot import here
+from importlib import import_module
+
+
+
+
+

Utilities

+

Some utilities are provided to help you with the ERA5 dataset.

+
+

source

+
+

describe

+
+
 describe (cfg:omegaconf.dictconfig.DictConfig=None)
+
+

Describe the configuration file used by Hydra for the pipeline

+ + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
cfgDictConfigNoneConfiguration file
ReturnsNone
+
+
+Exported source +
def describe(
+    cfg: DictConfig=None,  # Configuration file
+    )-> None:
+    "Describe the configuration file used by Hydra for the pipeline"
+    
+    if cfg is None:
+        print("No configuration file provided. Generating default configuration file.")
+        cfg = OmegaConf.create()
+        
+    print("This package fetches ERA5 data. The following is the config file used by Hydra for the pipeline:\n")
+    print(OmegaConf.to_yaml(cfg))
+
+
+

In addition, we’ve defined 3 private functions to help with path expansion _expand_path, dynamic function importing _get_callable, and directory structure creation _create_directory_structure.

+
+
+

A Simple Temperature Conversion Function

+
+

source

+
+
+

kelvin_to_celsius

+
+
 kelvin_to_celsius (kelvin:float)
+
+

Convert temperature from Kelvin to Celsius.

+ + + + + + + + + + + + + + + + + + + + +
TypeDetails
kelvinfloatTemperature in Kelvin
ReturnsfloatTemperature in Celsius
+
+
+

A Class for Authenticating Google Drive

+

We’re going to use a class to authenticate and interact with google drive. The goal is to have a simple interface to fetch the healthshed files dynamically from google drive in the pipeline.

+
+
+
+ +
+
+Important +
+
+
+

This class was implemented when all of our data was stored on a private Google Drive. Since we have moved all of our data to FASRC, this will likely be deprecated in the near future.

+
+
+
+

source

+
+
+

GoogleDriver

+
+
 GoogleDriver (json_key_path=None)
+
+

*A class to handle Google Drive authentication and file management. This class uses the PyDrive2 library to authenticate with Google Drive using a service account.

+

It provides three methods: authenticating the account, getting the drive object, and downloading the healthshed files for madagascar.*

+

Here’s how we use it. The credentials for the data-pipeline service account are available in the sandbox folder, and the path to said folder is set in the config:

+
+
from hydra import initialize, compose
+from omegaconf import OmegaConf
+
+
+
# unfortunately, we have to use the initialize function to load the config file
+# this is because the @hydra decorator does not work with Notebooks very well
+# this is a known issue with Hydra: https://gist.github.com/bdsaglam/586704a98336a0cf0a65a6e7c247d248
+# 
+# just use the relative path from the notebook to the config dir
+try:
+    with initialize(version_base=None, config_path="../conf"):
+        cfg = compose(config_name='config.yaml')
+except Exception as e:
+    print(f"Error initializing Hydra: {e}")
+    with initialize(version_base=None, config_path="conf"):
+        cfg = compose(config_name='config.yaml')
+
+
+
+
+ +
+
+Important +
+
+
+

If we continue with pytask, we will not need to use hydra at all, and so the above strategy may get deprecated.

+
+
+
+
auth = GoogleDriver(json_key_path=here() / cfg.GOOGLE_DRIVE_AUTH_JSON.path)
+drive = auth.get_drive()
+
+

Here’s how we might check that the healthsheds are accessible in the drive:

+
+
# we're using the madagascar healthshed folder as an example
+folder_id = cfg.geographies.madagascar.healthsheds
+folder_name = "healthsheds2022.zip"
+file_list = drive.ListFile({'q': f" title='{folder_name}' and trashed = false "}).GetList()
+
+for file in file_list:
+    print(f"{file['title']} - {file['mimeType']}")
+
+

That being said, we can read in the healthsheds into geopandas by downloading them to a temp directory. The healthsheds must be a zipped shapefiles package with the files at the root of the zip directory.

+
+
with tempfile.TemporaryDirectory() as temp_dir:
+    # Create a temporary directory to store the downloaded file
+    zip_path = os.path.join(temp_dir, folder_name)
+
+    # Download file from Google Drive
+    file_obj = drive.CreateFile({'id': file_list[0]['id']})
+    file_obj.GetContentFile(zip_path)
+
+    # Read shapefile directly from ZIP
+    gdf = gpd.read_file(f"zip://{zip_path}")
+
+

That works! So now we can patch the class to include this workflow:

+
+

source

+
+
+

GoogleDriver.read_healthsheds

+
+
 GoogleDriver.read_healthsheds (healthshed_zip_name)
+
+

And to check that it works:

+
+
driver = GoogleDriver(json_key_path=here() / cfg.GOOGLE_DRIVE_AUTH_JSON.path)
+drive = driver.get_drive()
+healthsheds = driver.read_healthsheds("healthsheds2022.zip")
+
+healthsheds.describe()
+
+
+
+
+

CDS File Handler Type

+
+
+
+ +
+
+Important +
+
+
+

This section may also be deprecated. Since adding swvl1 to the pipeline, we have not needed to use this class. We leave it here for now for reference.

+
+
+

We’re going to make a file handler type to help deal with CDS files. This is to fix NSAPH-Data-Processing/era5_sandbox#13.

+

Usually, when you download data, it comes out as a simple .nc file that can be opened with xarray. However, the CDS API has a few different file types that are not .nc files. For example, the ERA5 data is stored in a .grib file format. This is a common format for meteorological data, and it is used by the ECMWF. When a query has multiple variables, sometimes they are downloaded as a .zip file to separat the grib from the netcdf.

+

So, below, we define a class that can handle the file no matter what the type is. It will check the file type and then use the appropriate method to open it. The class will also have a method to check if the file is a .zip file, and if so, it will unzip it and return the path to the unzipped file.

+
+

source

+
+

ClimateDataFileHandler

+
+
 ClimateDataFileHandler (input_path:str)
+
+

A class to handle file operations for the Climate Data Store (CDS). This class provides unpack files downloaded from the CDS API. It must be able to handle the unpacking of files downloaded from the CDS API. This means that if the file is a basic netcdf, it should be passed to the netcdf handler. If the file is a zip, it should be handled by the zip handler in temp and the data returned as required.

+
+
import xarray as xr
+from fastcore.test import test_fail
+
+
+
eg_file = here() / "bld/2019_5_madagascar.nc"
+
+# this fails because the nc file downloaded has grib and netcdf in it, so
+# xr cannot handle it.
+def wont_work(multilayer_file):
+
+    ds = xr.open_dataset(multilayer_file)
+
+test_fail(
+    wont_work,
+    args=(eg_file)
+)
+
+# equivalent to saying try: wont_work(eg_file) Except: some error handling
+
+

The above fails because the download contains temperature and precipitation data, which get encoded silently as different formats. Even though it is one file, it contains both grib and netcdf data and is encoded as a .zip file. So we use the class to read it instead:

+
+
handler = ClimateDataFileHandler(eg_file)
+handler.prepare()
+ds1 = xr.open_dataset(handler.get_dataset("instant"))
+#ds2 = xr.open_dataset(handler.get_dataset("accum"))
+
+
+
+
+ +
+
+Important +
+
+
+

The above line for ds2 is commented out because the example file does not separate accumulation data.

+
+
+
+
ds1
+
+
+
#ds2
+
+
+
handler.cleanup()
+
+

Great! Let’s add a context handler and this can be added to the pipeline, so that with the entry and exit methods, we can now use the class in a with statement:

+
+
with ClimateDataFileHandler(eg_file) as handler:
+    ds1 = xr.open_dataset(handler.get_dataset("instant"))
+    #ds2 = xr.open_dataset(handler.get_dataset("accum"))
+
+    print(ds1)
+    #print(ds2)
+
+
+
+
+

Tests and Main

+

In nbdev, our tests are embedded in the notebook. Whenever you export the notebook, all the cells that are specified to run are run, and hence, the tests are executed. The tests are also exported. This is a great way to ensure that your documentation is always up-to-date. For this module, we’re using the testAPI() function as our main test.

+
+

source

+
+

testAPI

+
+
 testAPI (cfg:omegaconf.dictconfig.DictConfig=None,
+          dataset:str='reanalysis-era5-pressure-levels')
+
+
+
+Exported source +
def testAPI(
+    cfg: DictConfig=None,
+    dataset:str="reanalysis-era5-pressure-levels"
+    )-> bool:    
+    
+    # parse config
+    testing=cfg.development_mode
+    output_path=here("data") / "testing"
+
+    print(OmegaConf.to_yaml(cfg))
+
+    try:
+        client = cdsapi.Client()
+
+        # build request
+        request = {
+            'product_type': ['reanalysis'],
+            'variable': ['geopotential'],
+            'year': ['2024'],
+            'month': ['03'],
+            'day': ['01'],
+            'time': ['13:00'],
+            'pressure_level': ['1000'],
+            'data_format': 'grib',
+        }
+
+        target = output_path / 'test_download.grib'
+        
+        print("Testing API connection by downloading a dummy dataset to {}...".format(output_path))
+
+        client.retrieve(dataset, request, target)
+
+        if not testing:
+            os.remove(target)
+        
+        print("API connection test successful.")
+        return True
+
+    except Exception as e:
+        print("API connection test failed.")
+        print("Did you set up your API key with CDS? If not, please visit https://cds.climate.copernicus.eu/how-to-api#install-the-cds-api-client")
+        print("Error: {}".format(e))
+        return False
+
+
+

We can see that this API tester tool works with Hydra configuration:

+
+
from hydra import initialize, compose
+from omegaconf import OmegaConf
+
+
+
# unfortunately, we have to use the initialize function to load the config file
+# this is because the @hydra decorator does not work with Notebooks very well
+# this is a known issue with Hydra: https://gist.github.com/bdsaglam/586704a98336a0cf0a65a6e7c247d248
+# 
+# just use the relative path from the notebook to the config dir
+try:
+    with initialize(version_base=None, config_path="../conf"):
+        cfg = compose(config_name='config.yaml')
+except Exception as e:
+    print(f"Error initializing Hydra: {e}")
+    with initialize(version_base=None, config_path="conf"):
+        cfg = compose(config_name='config.yaml')
+
+describe(cfg)
+
+
+
+

Importing the Main Function

+
+
+
+ +
+
+Important +
+
+
+

As mentioned, if we continue with pytask, we will not need to use hydra at all, and so the main function may get deprecated as pytask will handle the pipeline execution without __main__ scripts.

+
+
+

Important: using __main__ in nbdev and Hydra is a little bit tricky. We need to define the main function in the module ONLY ONCE and then when we export the notebook to script, we need to add the nbdev.imports.IN_NOTEBOOK variable. This way, the main function will only be executed when we run the notebook and not when we import the module.

+
from nbdev.imports import IN_NOTEBOOK
+

You’ll see this listed throughout the notebooks.

+
+

source

+
+
+

main

+
+
 main (cfg:omegaconf.dictconfig.DictConfig)
+
+
+
+Exported source +
@hydra.main(version_base=None, config_path="../../conf", config_name="config")
+def main(cfg: DictConfig) -> None:
+
+    # Create the directory structure
+    _create_directory_structure(here() / "data", cfg.datapaths)
+
+    # test the api
+    testAPI(cfg=cfg)
+
+
+
+
try: from nbdev.imports import IN_NOTEBOOK
+except: IN_NOTEBOOK=False
+
+if __name__ == "__main__" and not IN_NOTEBOOK:
+    main()
+
+ + +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/_docs/00_core.md b/_docs/00_core.md new file mode 100644 index 0000000..43d541c --- /dev/null +++ b/_docs/00_core.md @@ -0,0 +1,529 @@ +# Core Module: Internal functions and testing + + +## core + +> This is a core library for the ERA5 dataset pipeline. It defines a few +> helpful functions such as an API tester to test your API key and +> connection. + + + +
+Exported source + +``` python +import os +import cdsapi +import hydra +import json +import tempfile +import argparse +import zipfile +import shutil +import geopandas as gpd +from pathlib import Path +from pydrive2.auth import GoogleAuth +from pydrive2.drive import GoogleDrive +from omegaconf import DictConfig, OmegaConf +from pyprojroot import here +from importlib import import_module +``` + +
+ +## Utilities + +Some utilities are provided to help you with the ERA5 dataset. + +------------------------------------------------------------------------ + +source + +### describe + +> describe (cfg:omegaconf.dictconfig.DictConfig=None) + +*Describe the configuration file used by Hydra for the pipeline* + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
cfgDictConfigNoneConfiguration file
ReturnsNone
+ +
+Exported source + +``` python +def describe( + cfg: DictConfig=None, # Configuration file + )-> None: + "Describe the configuration file used by Hydra for the pipeline" + + if cfg is None: + print("No configuration file provided. Generating default configuration file.") + cfg = OmegaConf.create() + + print("This package fetches ERA5 data. The following is the config file used by Hydra for the pipeline:\n") + print(OmegaConf.to_yaml(cfg)) +``` + +
+ +In addition, we’ve defined 3 private functions to help with path +expansion +[`_expand_path`](https://TinasheMTapera.github.io/era5_sandbox/core.html#_expand_path), +dynamic function importing +[`_get_callable`](https://TinasheMTapera.github.io/era5_sandbox/core.html#_get_callable), +and directory structure creation +[`_create_directory_structure`](https://TinasheMTapera.github.io/era5_sandbox/core.html#_create_directory_structure). + +### A Simple Temperature Conversion Function + +------------------------------------------------------------------------ + +source + +### kelvin_to_celsius + +> kelvin_to_celsius (kelvin:float) + +*Convert temperature from Kelvin to Celsius.* + + + + + + + + + + + + + + + + + + + + + +
TypeDetails
kelvinfloatTemperature in Kelvin
ReturnsfloatTemperature in Celsius
+ +### A Class for Authenticating Google Drive + +We’re going to use a class to authenticate and interact with google +drive. The goal is to have a simple interface to fetch the healthshed +files dynamically from google drive in the pipeline. + +
+ +> **Important** +> +> This class was implemented when all of our data was stored on a +> private Google Drive. Since we have moved all of our data to FASRC, +> this will likely be deprecated in the near future. + +
+ +------------------------------------------------------------------------ + +source + +### GoogleDriver + +> GoogleDriver (json_key_path=None) + +\*A class to handle Google Drive authentication and file management. +This class uses the PyDrive2 library to authenticate with Google Drive +using a service account. + +It provides three methods: authenticating the account, getting the drive +object, and downloading the healthshed files for madagascar.\* + +Here’s how we use it. The credentials for the data-pipeline service +account are available in the sandbox folder, and the path to said folder +is set in the config: + +``` python +from hydra import initialize, compose +from omegaconf import OmegaConf +``` + +``` python +# unfortunately, we have to use the initialize function to load the config file +# this is because the @hydra decorator does not work with Notebooks very well +# this is a known issue with Hydra: https://gist.github.com/bdsaglam/586704a98336a0cf0a65a6e7c247d248 +# +# just use the relative path from the notebook to the config dir +try: + with initialize(version_base=None, config_path="../conf"): + cfg = compose(config_name='config.yaml') +except Exception as e: + print(f"Error initializing Hydra: {e}") + with initialize(version_base=None, config_path="conf"): + cfg = compose(config_name='config.yaml') +``` + +
+ +> **Important** +> +> If we continue with `pytask`, we will not need to use hydra at all, +> and so the above strategy may get deprecated. + +
+ +``` python +auth = GoogleDriver(json_key_path=here() / cfg.GOOGLE_DRIVE_AUTH_JSON.path) +drive = auth.get_drive() +``` + +Here’s how we might check that the healthsheds are accessible in the +drive: + +``` python +# we're using the madagascar healthshed folder as an example +folder_id = cfg.geographies.madagascar.healthsheds +folder_name = "healthsheds2022.zip" +file_list = drive.ListFile({'q': f" title='{folder_name}' and trashed = false "}).GetList() + +for file in file_list: + print(f"{file['title']} - {file['mimeType']}") +``` + +That being said, we can read in the healthsheds into geopandas by +downloading them to a temp directory. The healthsheds must be a zipped +shapefiles package with the files at the root of the zip directory. + +``` python +with tempfile.TemporaryDirectory() as temp_dir: + # Create a temporary directory to store the downloaded file + zip_path = os.path.join(temp_dir, folder_name) + + # Download file from Google Drive + file_obj = drive.CreateFile({'id': file_list[0]['id']}) + file_obj.GetContentFile(zip_path) + + # Read shapefile directly from ZIP + gdf = gpd.read_file(f"zip://{zip_path}") +``` + +That works! So now we can patch the class to include this workflow: + +------------------------------------------------------------------------ + +source + +### GoogleDriver.read_healthsheds + +> GoogleDriver.read_healthsheds (healthshed_zip_name) + +And to check that it works: + +``` python +driver = GoogleDriver(json_key_path=here() / cfg.GOOGLE_DRIVE_AUTH_JSON.path) +drive = driver.get_drive() +healthsheds = driver.read_healthsheds("healthsheds2022.zip") + +healthsheds.describe() +``` + +## CDS File Handler Type + +
+ +> **Important** +> +> This section may also be deprecated. Since adding `swvl1` to the +> pipeline, we have not needed to use this class. We leave it here for +> now for reference. + +
+ +We’re going to make a file handler type to help deal with CDS files. +This is to fix +[NSAPH-Data-Processing/era5_sandbox#13](https://github.com/NSAPH-Data-Processing/era5_sandbox/issues/13). + +Usually, when you download data, it comes out as a simple .nc file that +can be opened with xarray. However, the CDS API has a few different file +types that are not .nc files. For example, the ERA5 data is stored in a +.grib file format. This is a common format for meteorological data, and +it is used by the ECMWF. When a query has multiple variables, sometimes +they are downloaded as a .zip file to separat the grib from the netcdf. + +So, below, we define a class that can handle the file no matter what the +type is. It will check the file type and then use the appropriate method +to open it. The class will also have a method to check if the file is a +.zip file, and if so, it will unzip it and return the path to the +unzipped file. + +------------------------------------------------------------------------ + +source + +### ClimateDataFileHandler + +> ClimateDataFileHandler (input_path:str) + +*A class to handle file operations for the Climate Data Store (CDS). +This class provides unpack files downloaded from the CDS API. It must be +able to handle the unpacking of files downloaded from the CDS API. This +means that if the file is a basic netcdf, it should be passed to the +netcdf handler. If the file is a zip, it should be handled by the zip +handler in temp and the data returned as required.* + +``` python +import xarray as xr +from fastcore.test import test_fail +``` + +``` python +eg_file = here() / "bld/2019_5_madagascar.nc" + +# this fails because the nc file downloaded has grib and netcdf in it, so +# xr cannot handle it. +def wont_work(multilayer_file): + + ds = xr.open_dataset(multilayer_file) + +test_fail( + wont_work, + args=(eg_file) +) + +# equivalent to saying try: wont_work(eg_file) Except: some error handling +``` + +The above fails because the download contains temperature and +precipitation data, which get encoded silently as different formats. +Even though it is one file, it contains both grib and netcdf data and is +encoded as a .zip file. So we use the class to read it instead: + +``` python +handler = ClimateDataFileHandler(eg_file) +handler.prepare() +ds1 = xr.open_dataset(handler.get_dataset("instant")) +#ds2 = xr.open_dataset(handler.get_dataset("accum")) +``` + +
+ +> **Important** +> +> The above line for `ds2` is commented out because the example file +> does not separate accumulation data. + +
+ +``` python +ds1 +``` + +``` python +#ds2 +``` + +``` python +handler.cleanup() +``` + +Great! Let’s add a context handler and this can be added to the +pipeline, so that with the entry and exit methods, we can now use the +class in a `with` statement: + +``` python +with ClimateDataFileHandler(eg_file) as handler: + ds1 = xr.open_dataset(handler.get_dataset("instant")) + #ds2 = xr.open_dataset(handler.get_dataset("accum")) + + print(ds1) + #print(ds2) +``` + +## Tests and Main + +In `nbdev`, our tests are embedded in the notebook. Whenever you export +the notebook, all the cells that are specified to run are run, and +hence, the tests are executed. The tests are also exported. This is a +great way to ensure that your documentation is always up-to-date. For +this module, we’re using the +[`testAPI()`](https://TinasheMTapera.github.io/era5_sandbox/core.html#testapi) +function as our main test. + +------------------------------------------------------------------------ + +source + +### testAPI + +> testAPI (cfg:omegaconf.dictconfig.DictConfig=None, +> dataset:str='reanalysis-era5-pressure-levels') + +
+Exported source + +``` python +def testAPI( + cfg: DictConfig=None, + dataset:str="reanalysis-era5-pressure-levels" + )-> bool: + + # parse config + testing=cfg.development_mode + output_path=here("data") / "testing" + + print(OmegaConf.to_yaml(cfg)) + + try: + client = cdsapi.Client() + + # build request + request = { + 'product_type': ['reanalysis'], + 'variable': ['geopotential'], + 'year': ['2024'], + 'month': ['03'], + 'day': ['01'], + 'time': ['13:00'], + 'pressure_level': ['1000'], + 'data_format': 'grib', + } + + target = output_path / 'test_download.grib' + + print("Testing API connection by downloading a dummy dataset to {}...".format(output_path)) + + client.retrieve(dataset, request, target) + + if not testing: + os.remove(target) + + print("API connection test successful.") + return True + + except Exception as e: + print("API connection test failed.") + print("Did you set up your API key with CDS? If not, please visit https://cds.climate.copernicus.eu/how-to-api#install-the-cds-api-client") + print("Error: {}".format(e)) + return False +``` + +
+ +We can see that this API tester tool works with Hydra configuration: + +``` python +from hydra import initialize, compose +from omegaconf import OmegaConf +``` + +``` python +# unfortunately, we have to use the initialize function to load the config file +# this is because the @hydra decorator does not work with Notebooks very well +# this is a known issue with Hydra: https://gist.github.com/bdsaglam/586704a98336a0cf0a65a6e7c247d248 +# +# just use the relative path from the notebook to the config dir +try: + with initialize(version_base=None, config_path="../conf"): + cfg = compose(config_name='config.yaml') +except Exception as e: + print(f"Error initializing Hydra: {e}") + with initialize(version_base=None, config_path="conf"): + cfg = compose(config_name='config.yaml') + +describe(cfg) +``` + +### Importing the Main Function + +
+ +> **Important** +> +> As mentioned, if we continue with `pytask`, we will not need to use +> hydra at all, and so the main function may get deprecated as `pytask` +> will handle the pipeline execution without `__main__` scripts. + +
+ +Important: using `__main__` in nbdev and Hydra is a little bit tricky. +We need to define the main function in the module ONLY ONCE and then +when we export the notebook to script, we need to add the +`nbdev.imports.IN_NOTEBOOK` variable. This way, the main function will +only be executed when we run the notebook and not when we import the +module. + +``` python +from nbdev.imports import IN_NOTEBOOK +``` + +You’ll see this listed throughout the notebooks. + +------------------------------------------------------------------------ + +source + +### main + +> main (cfg:omegaconf.dictconfig.DictConfig) + +
+Exported source + +``` python +@hydra.main(version_base=None, config_path="../../conf", config_name="config") +def main(cfg: DictConfig) -> None: + + # Create the directory structure + _create_directory_structure(here() / "data", cfg.datapaths) + + # test the api + testAPI(cfg=cfg) +``` + +
+ +``` python +try: from nbdev.imports import IN_NOTEBOOK +except: IN_NOTEBOOK=False + +if __name__ == "__main__" and not IN_NOTEBOOK: + main() +``` diff --git a/_docs/01_download_raw_data.html b/_docs/01_download_raw_data.html new file mode 100644 index 0000000..37dfd00 --- /dev/null +++ b/_docs/01_download_raw_data.html @@ -0,0 +1,969 @@ + + + + + + + + + +Download Module: Downloading Raw Data from CDSAPI – era5_sandbox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Download Module: Downloading Raw Data from CDSAPI

+
+ + + +
+ + + + +
+ + + +
+ + +
+

download

+
+

This module downloads the raw data from CDS and saves it in the local directory

+
+ +

We use a similar approach to the one in the tutorial to download the data to local storage.

+

The background functionality in this module involves downloading the bounding box of a region of interest, and sending that to the CDS API query. As such, we define two helper functions to fetch the OCHA/HDX shapefiles for a geographic region, and another to create the bounding box from the files.

+
+

source

+
+

fetch_GADM

+
+
 fetch_GADM
+             (url:str='https://geodata.ucdavis.edu/gadm/gadm4.1/gpkg/gadm4
+             1_MDG.gpkg', output_file:str='gadm41_MDG.gpkg')
+
+

Fetch the GADM bounding box for geographic region

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
urlstrhttps://geodata.ucdavis.edu/gadm/gadm4.1/gpkg/gadm41_MDG.gpkgURL to fetch the GADM data for Madagascar
output_filestrgadm41_MDG.gpkgfile path to save the GADM data
Returnsstr
+
+

source

+
+
+

create_bounding_box

+
+
 create_bounding_box (zip_url_or_path:str, buffer_km:float=50,
+                      round_to:int=1)
+
+

Create a bounding box from OCHA/HDX shapefile data with a buffer.

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
zip_url_or_pathstrURL or local path to the zipped shapefile.
buffer_kmfloat50Buffer distance in kilometers to expand the bounding box.
round_toint1Number of decimal places to round the bounding box coordinates.
ReturnslistBounding box in the CDS API area format [North, West, South, East]
+
+
+Exported source +
def create_bounding_box(
+    zip_url_or_path: str, # URL or local path to the zipped shapefile.
+    buffer_km: float = 50, # Buffer distance in kilometers to expand the bounding box.
+    round_to: int = 1 # Number of decimal places to round the bounding box coordinates.
+) -> list: # Bounding box in the CDS API area format [North, West, South, East]
+    '''
+    Create a bounding box from OCHA/HDX shapefile data with a buffer.
+    '''
+    with tempfile.TemporaryDirectory() as tmpdir:
+        # Download if it's a URL
+        if zip_url_or_path.startswith("http"):
+            response = requests.get(zip_url_or_path)
+            zip_path = os.path.join(tmpdir, "ocha_data.zip")
+            with open(zip_path, "wb") as f:
+                f.write(response.content)
+        else:
+            zip_path = zip_url_or_path
+
+        # Unzip
+        with zipfile.ZipFile(zip_path, 'r') as zip_ref:
+            zip_ref.extractall(tmpdir)
+
+        # Find the .shp file
+        shp_files = list(Path(tmpdir).rglob("*.shp"))
+        if not shp_files:
+            raise FileNotFoundError("No shapefile (.shp) found in the extracted archive.")
+        shp_path = str(shp_files[0])  # Use first found .shp
+
+        # Read shapefile
+        shape = gpd.read_file(shp_path)
+
+        # Reproject to projected CRS (you may want to detect the correct UTM zone)
+        shape_proj = shape.to_crs(epsg=32738)
+
+        # Apply buffer
+        buffered = shape_proj.geometry.buffer(buffer_km * 1000)
+
+        # Convert back to geographic coordinates
+        buffered_geo = gpd.GeoSeries(buffered, crs=shape_proj.crs).to_crs(epsg=4326)
+
+        # Get bounding box
+        bounds = buffered_geo.total_bounds  # [min_x, min_y, max_x, max_y]
+        bbox = [
+            round(bounds[3], round_to),  # North
+            round(bounds[0], round_to),  # West
+            round(bounds[1], round_to),  # South
+            round(bounds[2], round_to)   # East
+        ]
+
+        return bbox
+
+
+

The primary function to download the data from CDSAPI is defined below.

+
+

source

+
+
+

download_raw_era5

+
+
 download_raw_era5 (cfg:omegaconf.dictconfig.DictConfig)
+
+

Send the query to the API and download the data

+ + + + + + + + + + + + + + + + + + + + +
TypeDetails
cfgDictConfighydra configuration file
ReturnsNone
+
+
+Exported source +
def download_raw_era5(
+        cfg: DictConfig  # hydra configuration file
+    )->None:
+    '''
+    Send the query to the API and download the data
+    '''
+
+    # parse the cfg
+    testing = cfg.development_mode # for testing
+    output_dir = here("data/input") # output directory
+    
+    geography = cfg.query.geography
+
+    target = os.path.join(_expand_path(output_dir), "{}_{}_{}.nc".format(geography, cfg.query['year'], cfg.query['month']))
+    
+    client = cdsapi.Client()
+    
+    query = _validate_query(cfg.query)
+
+    dataset = cfg.dataset
+    # to make sure the query is valid at the end
+    del query['geography']
+    
+    # Send the query to the client
+    if not testing:
+        bounds = create_bounding_box(cfg.geographies[geography]['shapefile'])
+        query['area'] = bounds
+        client.retrieve(dataset, query).download(target)
+
+        print("Downloaded file to: {}".format(target))
+    else:
+        print(f"Testing mode. Not downloading data. Query is {query}")
+
+    print("Done")
+
+
+
+
+
+

Tests and Main

+

Here we define some tests and the main function that will be used to download the data.

+
+
from hydra import initialize, compose
+from omegaconf import OmegaConf
+
+# unfortunately, we have to use the initialize function to load the config file
+# this is because the @hydra decorator does not work with Notebooks very well
+# this is a known issue with Hydra: https://gist.github.com/bdsaglam/586704a98336a0cf0a65a6e7c247d248
+# 
+# just use the relative path from the notebook to the config dir
+try:
+    with initialize(version_base=None, config_path="../conf"):
+        cfg = compose(config_name='config.yaml')
+except Exception as e:
+    print(f"Error initializing Hydra: {e}")
+    with initialize(version_base=None, config_path="conf"):
+        cfg = compose(config_name='config.yaml')
+
+cfg.development_mode = False
+cfg.query['year'] = 2017
+cfg.query['month'] = 11
+#cfg.query['day'] = 1
+#cfg.query['time'] = "00:00"
+cfg.query['geography'] = "nepal"
+download_raw_era5(cfg)
+
+
+

source

+
+

main

+
+
 main (cfg:omegaconf.dictconfig.DictConfig)
+
+
+
+Exported source +
@hydra.main(config_path="../../conf", config_name="config", version_base=None)
+def main(cfg: DictConfig) -> None:
+    download_raw_era5(cfg=cfg)
+    # better approach would be to have the function only use the specific arguments of the config
+
+
+ + +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/_docs/01_download_raw_data.md b/_docs/01_download_raw_data.md new file mode 100644 index 0000000..cb8d8b0 --- /dev/null +++ b/_docs/01_download_raw_data.md @@ -0,0 +1,315 @@ +# Download Module: Downloading Raw Data from CDSAPI + + +## download + +> This module downloads the raw data from CDS and saves it in the local +> directory + + + +We use a similar approach to the one in the tutorial to download the +data to local storage. + +The background functionality in this module involves downloading the +bounding box of a region of interest, and sending that to the CDS API +query. As such, we define two helper functions to fetch the OCHA/HDX +shapefiles for a geographic region, and another to create the bounding +box from the files. + +------------------------------------------------------------------------ + +source + +### fetch_GADM + +> fetch_GADM +> (url:str='https://geodata.ucdavis.edu/gadm/gadm4.1/gpkg/gadm4 +> 1_MDG.gpkg', output_file:str='gadm41_MDG.gpkg') + +*Fetch the GADM bounding box for geographic region* + + ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
urlstrhttps://geodata.ucdavis.edu/gadm/gadm4.1/gpkg/gadm41_MDG.gpkgURL to fetch the GADM data for Madagascar
output_filestrgadm41_MDG.gpkgfile path to save the GADM data
Returnsstr
+ +------------------------------------------------------------------------ + +source + +### create_bounding_box + +> create_bounding_box (zip_url_or_path:str, buffer_km:float=50, +> round_to:int=1) + +*Create a bounding box from OCHA/HDX shapefile data with a buffer.* + + ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
zip_url_or_pathstrURL or local path to the zipped shapefile.
buffer_kmfloat50Buffer distance in kilometers to expand the bounding box.
round_toint1Number of decimal places to round the bounding box coordinates.
ReturnslistBounding box in the CDS API area format [North, West, South, +East]
+ +
+Exported source + +``` python +def create_bounding_box( + zip_url_or_path: str, # URL or local path to the zipped shapefile. + buffer_km: float = 50, # Buffer distance in kilometers to expand the bounding box. + round_to: int = 1 # Number of decimal places to round the bounding box coordinates. +) -> list: # Bounding box in the CDS API area format [North, West, South, East] + ''' + Create a bounding box from OCHA/HDX shapefile data with a buffer. + ''' + with tempfile.TemporaryDirectory() as tmpdir: + # Download if it's a URL + if zip_url_or_path.startswith("http"): + response = requests.get(zip_url_or_path) + zip_path = os.path.join(tmpdir, "ocha_data.zip") + with open(zip_path, "wb") as f: + f.write(response.content) + else: + zip_path = zip_url_or_path + + # Unzip + with zipfile.ZipFile(zip_path, 'r') as zip_ref: + zip_ref.extractall(tmpdir) + + # Find the .shp file + shp_files = list(Path(tmpdir).rglob("*.shp")) + if not shp_files: + raise FileNotFoundError("No shapefile (.shp) found in the extracted archive.") + shp_path = str(shp_files[0]) # Use first found .shp + + # Read shapefile + shape = gpd.read_file(shp_path) + + # Reproject to projected CRS (you may want to detect the correct UTM zone) + shape_proj = shape.to_crs(epsg=32738) + + # Apply buffer + buffered = shape_proj.geometry.buffer(buffer_km * 1000) + + # Convert back to geographic coordinates + buffered_geo = gpd.GeoSeries(buffered, crs=shape_proj.crs).to_crs(epsg=4326) + + # Get bounding box + bounds = buffered_geo.total_bounds # [min_x, min_y, max_x, max_y] + bbox = [ + round(bounds[3], round_to), # North + round(bounds[0], round_to), # West + round(bounds[1], round_to), # South + round(bounds[2], round_to) # East + ] + + return bbox +``` + +
+ +The primary function to download the data from CDSAPI is defined below. + +------------------------------------------------------------------------ + +source + +### download_raw_era5 + +> download_raw_era5 (cfg:omegaconf.dictconfig.DictConfig) + +*Send the query to the API and download the data* + + + + + + + + + + + + + + + + + + + + + +
TypeDetails
cfgDictConfighydra configuration file
ReturnsNone
+ +
+Exported source + +``` python +def download_raw_era5( + cfg: DictConfig # hydra configuration file + )->None: + ''' + Send the query to the API and download the data + ''' + + # parse the cfg + testing = cfg.development_mode # for testing + output_dir = here("data/input") # output directory + + geography = cfg.query.geography + + target = os.path.join(_expand_path(output_dir), "{}_{}_{}.nc".format(geography, cfg.query['year'], cfg.query['month'])) + + client = cdsapi.Client() + + query = _validate_query(cfg.query) + + dataset = cfg.dataset + # to make sure the query is valid at the end + del query['geography'] + + # Send the query to the client + if not testing: + bounds = create_bounding_box(cfg.geographies[geography]['shapefile']) + query['area'] = bounds + client.retrieve(dataset, query).download(target) + + print("Downloaded file to: {}".format(target)) + else: + print(f"Testing mode. Not downloading data. Query is {query}") + + print("Done") +``` + +
+ +## Tests and Main + +Here we define some tests and the main function that will be used to +download the data. + +``` python +from hydra import initialize, compose +from omegaconf import OmegaConf + +# unfortunately, we have to use the initialize function to load the config file +# this is because the @hydra decorator does not work with Notebooks very well +# this is a known issue with Hydra: https://gist.github.com/bdsaglam/586704a98336a0cf0a65a6e7c247d248 +# +# just use the relative path from the notebook to the config dir +try: + with initialize(version_base=None, config_path="../conf"): + cfg = compose(config_name='config.yaml') +except Exception as e: + print(f"Error initializing Hydra: {e}") + with initialize(version_base=None, config_path="conf"): + cfg = compose(config_name='config.yaml') + +cfg.development_mode = False +cfg.query['year'] = 2017 +cfg.query['month'] = 11 +#cfg.query['day'] = 1 +#cfg.query['time'] = "00:00" +cfg.query['geography'] = "nepal" +download_raw_era5(cfg) +``` + +------------------------------------------------------------------------ + +source + +### main + +> main (cfg:omegaconf.dictconfig.DictConfig) + +
+Exported source + +``` python +@hydra.main(config_path="../../conf", config_name="config", version_base=None) +def main(cfg: DictConfig) -> None: + download_raw_era5(cfg=cfg) + # better approach would be to have the function only use the specific arguments of the config +``` + +
diff --git a/_docs/02_aggregate.html b/_docs/02_aggregate.html new file mode 100644 index 0000000..762b66f --- /dev/null +++ b/_docs/02_aggregate.html @@ -0,0 +1,1556 @@ + + + + + + + + + +Aggregate Module: Spatial Aggregation to Healthsheds – era5_sandbox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Aggregate Module: Spatial Aggregation to Healthsheds

+
+ + + +
+ + + + +
+ + + +
+ + +
+

aggregate

+
+

This module aggregates the downloaded data into the respective output dataframes.

+
+ +

We prototyped the code in this module using a Jupyter notebook. The notebook is available in notes/prototypes/learning_aggregations_w_michelle_20250328.ipynb. The code in this module is a cleaned-up version of the code in that notebook. The notebook contains additional comments and explanations of the code, which may be helpful for understanding the code in this module.

+

The basic process is as follows:

+
    +
  1. Load the netCDF data in memory
    2. +
  2. Statistically aggregate the hourly data to daily data per exposure using resample()
    3. +
  3. Write out the data to tiff
    4. +
  4. Read the tiff data back in
    5. +
  5. Read in the shapefile that defines the healthsheds
    6. +
  6. Spatially aggregate the exposure data to the healthsheds
    7. +
  7. Quality check the aggregations
    8. +
  8. Write out final aggregations to tiff
    9. +
+
+
+Exported source +
import tempfile
+import rasterio
+import hydra
+import argparse
+import os
+
+import pandas as pd
+import geopandas as gpd
+import numpy as np
+import xarray as xr
+import matplotlib.pyplot as plt
+
+from dataclasses import dataclass, field
+from typing import Optional, Tuple
+from pyprojroot import here
+from hydra import initialize, compose
+from omegaconf import OmegaConf, DictConfig
+from tqdm import tqdm
+from math import ceil, floor
+from rasterstats.io import Raster
+from rasterstats.utils import boxify_points, rasterize_geom
+
+try: from era5_sandbox.core import GoogleDriver, _get_callable, describe, ClimateDataFileHandler, kelvin_to_celsius
+except: from core import GoogleDriver, _get_callable, describe, ClimateDataFileHandler, kelvin_to_celsius
+
+
+
+
try:
+    with initialize(version_base=None, config_path="../conf"):
+        cfg = compose(config_name='config.yaml')
+except Exception as e:
+    print(f"Error initializing Hydra: {e}")
+    with initialize(version_base=None, config_path="conf"):
+        cfg = compose(config_name='config.yaml')
+
+

We’re going to write a function that aggregates the data for a single exposure from a file. This file should be the single month data we got from the previous step in the pipeline.

+
+
eg_file = here() / "bld/2009_01_nepal.nc"
+
+
+

source

+
+

resample_netcdf

+
+
 resample_netcdf (fpath:str, resample:str='1D', agg_func:<built-
+                  infunctioncallable>=<function mean at 0x145cb6c3b930>,
+                  time_dim:str='valid_time', **xr_open_kwargs)
+
+

*Resample a netCDF file to a specified frequency and aggregation method.

+

Args: fpath (str): Path to the netCDF file. resample (str): Resampling frequency (e.g., ‘1H’, ‘1D’). agg_func (callable): Aggregation function (e.g., np.mean, np.sum).

+

Returns: xarray.Dataset: Resampled dataset.*

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
fpathstrPath to the netCDF file.
resamplestr1DResampling frequency (e.g., ‘1H’, ‘1D’)
agg_funccallablemeanAggregation function (e.g., np.mean, np.sum).
time_dimstrvalid_timeName of the time dimension in the dataset.
xr_open_kwargsVAR_KEYWORD
ReturnsDatasetkeywords for python’s xarray module
+

We pull the aggregation function from the config file:

+
+
var = 'swvl1'
+agg_func = _get_callable(cfg['aggregation']['aggregation'][var]['hourly_to_daily'][0]['function'])
+
+
+
with ClimateDataFileHandler(eg_file) as handler:
+
+    ds_path = handler.get_dataset("instant")
+    resampled_data = resample_netcdf(ds_path, agg_func=agg_func)
+
+

I’m going to use a dataclass to represent the tiff data. This will allow us to easily pass around the data and metadata associated with the tiff file. Why? I’ve never used dataclasses and I’m curious about them — ChatGPT thinks this will make the code cleaner and easier to read.

+
+

source

+
+
+

RasterFile

+
+
 RasterFile (path:str, band:int)
+
+
+
+Exported source +
@dataclass
+class RasterFile:
+    path: str
+    band: int # note that this is 1-indexed
+    data: Optional[np.ndarray] = field(default=None, init=False)
+    transform: Optional[rasterio.Affine] = field(default=None, init=False)
+    crs: Optional[str] = field(default=None, init=False)
+    nodata: Optional[float] = field(default=None, init=False)
+    bounds: Optional[Tuple[float, float, float, float]] = field(default=None, init=False)
+
+    def load(self):
+        """Load raster data and basic metadata."""
+        with rasterio.open(self.path) as src:
+            self.data = src.read(self.band)  # each day gets one rasterfile
+            self.transform = src.transform
+            self.crs = src.crs
+            self.nodata = src.nodata
+            self.bounds = src.bounds
+        return self
+
+    def shape(self) -> Optional[Tuple[int, int]]:
+        """Return the shape of the raster data."""
+        return self.data.shape if self.data is not None else None
+
+    def __str__(self):
+        return f"RasterFile(path='{self.path}', shape={self.shape()}, crs='{self.crs}')"
+
+
+

Next, a function to write and read the netCDF to tiff:

+
+

source

+
+
+

netcdf_to_tiff

+
+
 netcdf_to_tiff (ds:xarray.core.dataset.Dataset, band:int, variable:str,
+                 crs:str='EPSG:4326')
+
+

Convert a netCDF file to a GeoTIFF file.

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
dsDatasetThe aggregated xarray dataset to convert.
bandintThe day to rasterise; 1 indexed just like human english
variablestrThe variable name to convert.
crsstrEPSG:4326Coordinate reference system (default is WGS84).
+
+
+Exported source +
def netcdf_to_tiff(
+    ds: xr.Dataset, # The aggregated xarray dataset to convert.    
+    band: int,      # The day to rasterise; 1 indexed just like human english
+    variable: str, # The variable name to convert.
+    crs: str = "EPSG:4326", # Coordinate reference system (default is WGS84).    
+    ):
+
+    """
+    Convert a netCDF file to a GeoTIFF file.
+    """
+
+    with tempfile.TemporaryDirectory() as tmpdirname:
+
+        # Select the variable and time index
+        variable = ds[variable]
+        ds_ = variable.rio.set_spatial_dims(x_dim="longitude", y_dim="latitude")
+        ds_ = ds_.rio.write_crs(crs)
+        # Save as GeoTIFF
+        ds_.rio.to_raster(f"{tmpdirname}/output.tif")
+        # Load the raster file
+        raster_file = RasterFile(path=f"{tmpdirname}/output.tif", band=band).load()
+
+    return raster_file
+
+
+

Now to test it:

+
+
with ClimateDataFileHandler(eg_file) as handler:
+    ds_path = handler.get_dataset("instant")
+    resampled_nc = resample_netcdf(ds_path)
+
+print(resampled_nc)
+resampled_tiff = netcdf_to_tiff(
+    ds=resampled_nc,
+    band=28,
+    variable="swvl1",
+    crs="EPSG:4326"
+)
+
+
+
resampled_tiff.data.shape, resampled_tiff.transform, resampled_tiff.crs, resampled_tiff.bounds
+
+

Super cool! The tiff file is created and the data is read back in correctly. Now we can move on to the next step, which is to aggregate the data by healthshed.

+
+
+
+

Polygon to Raster Cells

+

This function was initially shared from a previous NSAPH aggregation pipeline here. To better understand this, here is a ChatGPT explanation of the code:

+
+

This function, polygon_to_raster_cells, is doing a crucial first step in spatial alignment: it determines which raster cells are “touched” by each polygon geometry (e.g., administrative areas, watersheds, etc.).
+Essentially, this function helps figure out which pixels from a raster image fall inside each polygon (like a district, region, or shape). It does this by looking at each polygon one by one, zooming in on just the part of the raster that overlaps with that shape, and marking the pixels that are inside. This is kind of like placing a cookie cutter (the polygon) on a pixelated map (the raster) and seeing which pixels get cut.
+The result is a list where each item tells you the pixel locations that match a specific polygon. You can then use those pixel locations to pull out data from the raster, like temperatures or rainfall, and calculate statistics (like the average) for each shape. This is a key step when you want to summarize raster data within specific regions, like figuring out the average temperature in each county or how much vegetation is in each park.

+
+
+

source

+
+

polygon_to_raster_cells

+
+
 polygon_to_raster_cells (vectors, raster, nodata=None, affine=None,
+                          all_touched=False, verbose=False, **kwargs)
+
+

Returns an index map for each vector geometry to indices in the raster source.

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
vectorslist of geometries from a shapefile
rasterthe raster data as a numpy array
nodataNoneTypeNonethe nodata value of the raster
affineNoneTypeNonethe affine transform of the raster
all_touchedboolFalsewhether to include all touched pixels
verboseboolFalse
kwargsVAR_KEYWORD
ReturnslistA dictionary mapping vector the ids of geometries to locations (indices) in the raster source.
+
+
+Exported source +
def polygon_to_raster_cells(
+    vectors, # list of geometries from a shapefile
+    raster, # the raster data as a numpy array
+    nodata=None, # the nodata value of the raster
+    affine=None, # the affine transform of the raster
+    all_touched=False, # whether to include all touched pixels
+    verbose=False, 
+    **kwargs,
+) -> list: # A dictionary mapping vector the ids of geometries to locations (indices) in the raster source.
+    """Returns an index map for each vector geometry to indices in the raster source."""
+
+    cell_map = []
+
+    with Raster(raster, affine, nodata) as rast:
+        # used later to crop raster and find start row and col
+        min_lon, dlon = affine.c, affine.a
+        max_lat, dlat = affine.f, -affine.e
+        H, W = rast.shape
+
+        for geom in tqdm(vectors, disable=(not verbose)):
+            if "Point" in geom.geom_type:
+                geom = boxify_points(geom, rast)
+
+            # find geometry bounds to crop raster
+            # the raster and geometry must be in the same lon/lat coordinate system
+            start_row = max(0, min(H - 1, floor((max_lat - geom.bounds[3]) / dlat)))
+            start_col = min(W - 1, max(0, floor((geom.bounds[0] - min_lon) / dlon)))
+            end_col = max(0, min(W - 1, ceil((geom.bounds[2] - min_lon) / dlon)))
+            end_row = min(H - 1, max(0, ceil((max_lat - geom.bounds[1]) / dlat)))
+            geom_bounds = (
+                min_lon + dlon * start_col,  # left
+                max_lat - dlat * end_row - 1e-12,  # bottom
+                min_lon + dlon * end_col + 1e-12,  # right
+                max_lat - dlat * start_row,  # top
+            )
+
+            # crop raster to area of interest and rasterize
+            fsrc = rast.read(bounds=geom_bounds)
+            rv_array = rasterize_geom(geom, like=fsrc, all_touched=all_touched)
+            indices = np.nonzero(rv_array)
+
+            if len(indices[0]) > 0:
+                indices = (indices[0] + start_row, indices[1] + start_col)
+                assert 0 <= indices[0].min() < rast.shape[0]
+                assert 0 <= indices[1].min() < rast.shape[1]
+            else:
+                pass  # stop here for debug
+
+            cell_map.append(indices)
+
+        return cell_map
+
+
+

To use this, we must define the polygon and raster data. The polygon data is the healthshed shapefile, and the raster data is the tiff file we created earlier. We can use the GoogleDriver class we defined in core to read in the shapefile.

+
+
try:
+    with initialize(version_base=None, config_path="../conf"):
+        cfg = compose(config_name='config.yaml')
+except Exception as e:
+    print(f"Error initializing Hydra: {e}")
+    with initialize(version_base=None, config_path="conf"):
+        cfg = compose(config_name='config.yaml')
+
+driver = GoogleDriver(json_key_path=here() / cfg.GOOGLE_DRIVE_AUTH_JSON.path)
+drive = driver.get_drive()
+healthsheds = driver.read_healthsheds("Nepal_Healthsheds2024.zip")
+
+
+
res_poly2cell=polygon_to_raster_cells(
+    vectors = healthsheds.geometry.values, # the geometries of the shapefile of the regions
+    raster=resampled_tiff.data, # the raster data above
+    nodata=resampled_tiff.nodata, # any intersections with no data, may have to be np.nan
+    affine=resampled_tiff.transform, # some math thing need to revise
+    all_touched=True, 
+    verbose=True
+)
+
+

The data below maps which grid entries fall into each of the regions in the shapefile (e.g. which pixel is in which state)

+
+
res_poly2cell[:5]
+
+

Last but not least, we aggregate these data to the healthshed level. We can use the rasterstats package to do this.

+
+

source

+
+
+

aggregate_to_healthsheds

+
+
 aggregate_to_healthsheds (res_poly2cell:list, raster:__main__.RasterFile,
+                           shapes:geopandas.geodataframe.GeoDataFrame,
+                           names_column:str='fs_uid',
+                           aggregation_func:<built-
+                           infunctioncallable>=<function nanmean at
+                           0x145cb6bbbdf0>, aggregation_name:str='mean')
+
+

Aggregate the raster data to the health sheds.

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
res_poly2celllistthe result of polygon_to_raster_cells
rasterRasterFilethe raster data
shapesGeoDataFramethe shapes of the health sheds
names_columnstrfs_uidthe unique identifier column name of the health sheds
aggregation_funccallablenanmeanthe aggregation function
aggregation_namestrmeanthe name of the aggregation function
ReturnsGeoDataFrame
+
+
+Exported source +
def aggregate_to_healthsheds(
+    res_poly2cell: list, # the result of polygon_to_raster_cells    
+    raster: RasterFile, # the raster data
+    shapes: gpd.GeoDataFrame, # the shapes of the health sheds
+    names_column: str = "fs_uid", # the unique identifier column name of the health sheds
+    aggregation_func: callable = np.nanmean, # the aggregation function
+    aggregation_name: str = "mean" # the name of the aggregation function
+    ) -> gpd.GeoDataFrame:
+    """
+    Aggregate the raster data to the health sheds.
+    """
+
+    stats = []
+
+    for indices in res_poly2cell:
+        if len(indices[0]) == 0:
+            # no cells found for this polygon
+            stats.append(np.nan)
+        else:
+            cells = raster.data[indices]
+            if sum(~np.isnan(cells)) == 0:
+                # no valid cells found for this polygon
+                stats.append(np.nan)
+                continue
+            else:
+                # compute MEAN of valid cells
+                # but this stat can be ANYTHING
+                stats.append(aggregation_func(cells))
+
+    # clean up the result into a dataframe
+    stats = pd.Series(stats)
+    shapes[aggregation_name] = stats
+    df = pd.DataFrame(
+            {"healthshed": shapes[names_column], aggregation_name: stats}
+        )
+    gdf = gpd.GeoDataFrame(df, geometry=shapes.geometry.values, crs=shapes.crs)
+    return gdf
+
+
+

And now we apply it:

+
+
result = aggregate_to_healthsheds(
+    res_poly2cell=res_poly2cell,
+    raster=resampled_tiff,
+    shapes=healthsheds,
+    names_column="fid",
+    aggregation_func=np.nanmean,
+    aggregation_name="mean_soil_moisture"
+)
+result.head()
+
+

And plot for QA:

+
+
result.plot(column="mean_soil_moisture", legend=True)
+plt.title("Mean Soil Moisture (m^3 m^-3) by Health Shed Nov 2017 day 1")
+plt.show()
+
+

That looks great! The data is aggregated to the healthshed level, and we can see the differences in exposure across the healthsheds. We can also see that the data is not uniform across the healthsheds, which is what we expect.

+
+
+
+

Tests and Main

+

Now we can wrap this up in a main function that will simply take in the input file and generate this output. We can also add some tests to make sure the data is aggregated correctly; tests will run automatically in this notebook.

+
+
import random
+
+
+
# variables = ["t2m", "d2m"]
+# years = ["20{:02d}".format(m) for m in range(9, 24)]
+# months = [str(m) for m in range(1, 13)]
+# aggregations = [
+#     ("Mean", np.nanmean),
+#     ("Max", np.nanmax),
+#     ("Min", np.nanmin)
+# ]
+
+# exposure_variable = random.choice(variables)
+# year = random.choice(years)
+# month = random.choice(months)
+# aggregation_str, agg_func = random.choice(aggregations)
+# input_file = here() / "data/input/{}_{}.nc".format(year, month)
+
+# with initialize(version_base=None, config_path="../conf"):
+#     cfg = compose(config_name='config.yaml')
+
+# driver = GoogleDriver(json_key_path=here() / cfg.GOOGLE_DRIVE_AUTH_JSON.path)
+# drive = driver.get_drive()
+# healthsheds = driver.read_healthsheds(cfg.GOOGLE_DRIVE_AUTH_JSON.healthsheds_id)
+
+# with ClimateDataFileHandler(input_file) as handler:
+#     ds_path = handler.get_dataset("instant")
+#     resampled_nc_file = resample_netcdf(ds_path, agg_func=agg_func)
+
+# days = len(resampled_nc_file.valid_time.values)
+# day = random.choice(range(1, days + 1))
+
+# resampled_tiff = netcdf_to_tiff(
+#     ds=resampled_nc_file,
+#     band=day, # the day we're aggregating
+#     variable=exposure_variable,
+#     crs="EPSG:4326"
+# )
+
+# res_poly2cell=polygon_to_raster_cells(
+#     vectors = healthsheds.geometry.values, # the geometries of the shapefile of the regions
+#     raster=resampled_tiff.data, # the raster data above
+#     nodata=resampled_tiff.nodata, # any intersections with no data, may have to be np.nan
+#     affine=resampled_tiff.transform, # some math thing need to revise
+#     all_touched=True, 
+#     verbose=True
+# )
+
+# result = aggregate_to_healthsheds(
+#     res_poly2cell=res_poly2cell,
+#     raster=resampled_tiff,
+#     shapes=healthsheds,
+#     names_column="fs_uid",
+#     aggregation_func=agg_func,
+#     aggregation_name=exposure_variable
+# )
+
+# result.plot(column=exposure_variable, legend=True)
+# plt.title("{} {} (K) by Health Shed {}".format(aggregation_str, exposure_variable, input_file.stem))
+# plt.suptitle("Aggregation: {}, Day: {}".format(aggregation_str, str(day)))
+# plt.show()
+
+
+
+
+ +
+
+Note +
+
+
+

Note: The above code is commented out to prevent execution during documentation generation. You can uncomment and run it in an appropriate environment to test the aggregation process.

+
+
+

3.2 seconds per aggregation is pretty cool!

+
+
result.to_parquet(here() / "data/testing/test_aggregation.parquet")
+
+
+

source

+
+

aggregate_data

+
+
 aggregate_data (cfg:omegaconf.dictconfig.DictConfig, input_file:str,
+                 output_file:str, exposure_variable:str)
+
+

Aggregate raster data day-by-day and store all days and statistics as separate columns in a single Parquet file.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDetails
cfgDictConfigthe hydra config
input_filestrthe input netcdf file
output_filestrthe output parquet file
exposure_variablestrWhich variable in the dataset to aggregate
ReturnsNone
+
+
+Exported source +
def aggregate_data(
+        cfg: DictConfig, # the hydra config
+        input_file: str, # the input netcdf file
+        output_file: str, # the output parquet file
+        exposure_variable: str # Which variable in the dataset to aggregate
+    ) -> None:
+    '''
+    Aggregate raster data day-by-day and store all days and statistics as separate columns in a single Parquet file.
+    '''
+
+    if cfg.development_mode:
+        describe(cfg)
+        return None
+
+    geography = cfg['query'].geography
+    year = cfg['query']['year']
+    month = cfg['query']['month']
+    daily_aggs = cfg['aggregation']['aggregation'][exposure_variable]['hourly_to_daily']
+    healthshed_aggs = cfg['aggregation']['aggregation'][exposure_variable]['daily_to_healthshed']
+
+    # Load healthsheds
+    driver = GoogleDriver(json_key_path=here() / cfg.GOOGLE_DRIVE_AUTH_JSON.path)
+    drive = driver.get_drive()
+    healthsheds = driver.read_healthsheds(cfg.geographies[geography].healthsheds)
+    
+    # Initialize output DataFrame
+    result_df = healthsheds[[cfg.geographies[geography].unique_id, "geometry"]].copy()
+
+    for daily_agg in daily_aggs:
+        print(f"Processing daily aggregation: {daily_agg['name']}...")
+    
+        daily_agg_func = _get_callable(daily_agg['function'])
+
+        with ClimateDataFileHandler(input_file) as handler:
+            if exposure_variable in ["t2m", "d2m", "swvl1"]:
+                ds_path = handler.get_dataset("instant")
+            else:
+                ds_path = handler.get_dataset("accum")
+            resampled_nc_file = resample_netcdf(ds_path, agg_func=daily_agg_func)
+        
+        for healthshed_agg in healthshed_aggs:
+            print(f"Aggregating to healthshed by: {healthshed_agg['name']}...")
+
+            # Get the number of days in the dataset
+            days = len(resampled_nc_file.valid_time.values)
+
+            # Get the aggregation function for healthshed
+            healthshed_agg_func = _get_callable(healthshed_agg['function'])
+            days = len(resampled_nc_file.valid_time.values)
+
+            for day in range(1, days + 1):
+                print(f"Processing day {day}...")
+                
+                day_col = f"day_{day:02d}_daily_{daily_agg['name']}"
+                resampled_tiff = netcdf_to_tiff(
+                    ds=resampled_nc_file,
+                    band=day,
+                    variable=exposure_variable,
+                    crs="EPSG:4326"
+                )
+
+                result_poly2cell = polygon_to_raster_cells(
+                    vectors=healthsheds.geometry.values,
+                    raster=resampled_tiff.data,
+                    nodata=resampled_tiff.nodata,
+                    affine=resampled_tiff.transform,
+                    all_touched=True,
+                    verbose=True
+                )
+
+                res = aggregate_to_healthsheds(
+                    res_poly2cell=result_poly2cell,
+                    raster=resampled_tiff,
+                    shapes=healthsheds,
+                    names_column=cfg.geographies[geography].unique_id,
+                    aggregation_func=healthshed_agg_func,
+                    aggregation_name=exposure_variable
+                )
+
+                result_df[day_col] = res[exposure_variable]
+
+    print(f"Saving final monthly parquet file: {output_file}")
+    result_df.to_parquet(output_file, compression="snappy")
+    # return(result_df)
+
+
+
+
try:
+    with initialize(version_base=None, config_path="../conf"):
+        cfg = compose(config_name='config.yaml')
+except Exception as e:
+    print(f"Error initializing Hydra: {e}")
+    with initialize(version_base=None, config_path="conf"):
+        cfg = compose(config_name='config.yaml')
+
+cfg.development_mode = False
+cfg.query['year'] = 2017
+cfg.query['month'] = 11
+cfg.query['geography'] = "nepal"
+
+variable = "swvl1"
+
+aggregate_data(cfg, here() / "bld/2017_11_nepal.nc", here() / "data/testing/test_nepal_aggregation.parquet", exposure_variable=variable)
+
+
+
parquet_file = gpd.read_parquet(here() / "data/testing/test_nepal_aggregation.parquet")
+
+
+
parquet_file
+
+
+
parquet_file.plot(column="day_22_daily_mean", legend=True)
+
+
+

source

+
+
+

main

+
+
 main (cfg:omegaconf.dictconfig.DictConfig)
+
+
+
+Exported source +
@hydra.main(version_base=None, config_path="../../conf", config_name="config")
+def main(cfg: DictConfig) -> None:
+    # Parse command-line arguments
+    input_file = str(snakemake.input[0])  # First input file
+    output_file = str(snakemake.output[0])
+    geography = str(snakemake.params.geography)
+    aggregation_variable = str(snakemake.params.variable)
+
+    variables_dict = {
+        "2m_temperature": "t2m",
+        "2m_dewpoint_temperature": "d2m",
+        "volumetric_soil_water_layer_1": "swvl1",
+        "total_precipitation": "tp"
+    }
+
+    cfg['query']['geography'] = geography
+    
+    aggregate_data(cfg, input_file=input_file, output_file=output_file, exposure_variable=variables_dict[aggregation_variable])
+
+
+ + +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/_docs/02_aggregate.md b/_docs/02_aggregate.md new file mode 100644 index 0000000..0291c2a --- /dev/null +++ b/_docs/02_aggregate.md @@ -0,0 +1,988 @@ +# Aggregate Module: Spatial Aggregation to Healthsheds + + +## aggregate + +> This module aggregates the downloaded data into the respective output +> dataframes. + + + +We prototyped the code in this module using a Jupyter notebook. The +notebook is available in +`notes/prototypes/learning_aggregations_w_michelle_20250328.ipynb`. The +code in this module is a cleaned-up version of the code in that +notebook. The notebook contains additional comments and explanations of +the code, which may be helpful for understanding the code in this +module. + +The basic process is as follows: + +1. Load the netCDF data in memory +2. Statistically aggregate the hourly data to daily data per exposure + using resample() +3. Write out the data to tiff +4. Read the tiff data back in +5. Read in the shapefile that defines the healthsheds +6. Spatially aggregate the exposure data to the healthsheds +7. Quality check the aggregations +8. Write out final aggregations to tiff + +
+Exported source + +``` python +import tempfile +import rasterio +import hydra +import argparse +import os + +import pandas as pd +import geopandas as gpd +import numpy as np +import xarray as xr +import matplotlib.pyplot as plt + +from dataclasses import dataclass, field +from typing import Optional, Tuple +from pyprojroot import here +from hydra import initialize, compose +from omegaconf import OmegaConf, DictConfig +from tqdm import tqdm +from math import ceil, floor +from rasterstats.io import Raster +from rasterstats.utils import boxify_points, rasterize_geom + +try: from era5_sandbox.core import GoogleDriver, _get_callable, describe, ClimateDataFileHandler, kelvin_to_celsius +except: from core import GoogleDriver, _get_callable, describe, ClimateDataFileHandler, kelvin_to_celsius +``` + +
+ +``` python +try: + with initialize(version_base=None, config_path="../conf"): + cfg = compose(config_name='config.yaml') +except Exception as e: + print(f"Error initializing Hydra: {e}") + with initialize(version_base=None, config_path="conf"): + cfg = compose(config_name='config.yaml') +``` + +We’re going to write a function that aggregates the data for a single +exposure from a file. This file should be the single month data we got +from the previous step in the pipeline. + +``` python +eg_file = here() / "bld/2009_01_nepal.nc" +``` + +------------------------------------------------------------------------ + +source + +### resample_netcdf + +> resample_netcdf (fpath:str, resample:str='1D', agg_func: infunctioncallable>=, +> time_dim:str='valid_time', **xr_open_kwargs) + +\*Resample a netCDF file to a specified frequency and aggregation +method. + +Args: fpath (str): Path to the netCDF file. resample (str): Resampling +frequency (e.g., ‘1H’, ‘1D’). agg_func (callable): Aggregation function +(e.g., np.mean, np.sum). + +Returns: xarray.Dataset: Resampled dataset.\* + + ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
fpathstrPath to the netCDF file.
resamplestr1DResampling frequency (e.g., ‘1H’, ‘1D’)
agg_funccallablemeanAggregation function (e.g., np.mean, np.sum).
time_dimstrvalid_timeName of the time dimension in the dataset.
xr_open_kwargsVAR_KEYWORD
ReturnsDatasetkeywords for python’s xarray module
+ +We pull the aggregation function from the config file: + +``` python +var = 'swvl1' +agg_func = _get_callable(cfg['aggregation']['aggregation'][var]['hourly_to_daily'][0]['function']) +``` + +``` python +with ClimateDataFileHandler(eg_file) as handler: + + ds_path = handler.get_dataset("instant") + resampled_data = resample_netcdf(ds_path, agg_func=agg_func) +``` + +I’m going to use a dataclass to represent the tiff data. This will allow +us to easily pass around the data and metadata associated with the tiff +file. Why? I’ve never used dataclasses and I’m curious about them — +ChatGPT thinks this will make the code cleaner and easier to read. + +------------------------------------------------------------------------ + +source + +### RasterFile + +> RasterFile (path:str, band:int) + +
+Exported source + +``` python +@dataclass +class RasterFile: + path: str + band: int # note that this is 1-indexed + data: Optional[np.ndarray] = field(default=None, init=False) + transform: Optional[rasterio.Affine] = field(default=None, init=False) + crs: Optional[str] = field(default=None, init=False) + nodata: Optional[float] = field(default=None, init=False) + bounds: Optional[Tuple[float, float, float, float]] = field(default=None, init=False) + + def load(self): + """Load raster data and basic metadata.""" + with rasterio.open(self.path) as src: + self.data = src.read(self.band) # each day gets one rasterfile + self.transform = src.transform + self.crs = src.crs + self.nodata = src.nodata + self.bounds = src.bounds + return self + + def shape(self) -> Optional[Tuple[int, int]]: + """Return the shape of the raster data.""" + return self.data.shape if self.data is not None else None + + def __str__(self): + return f"RasterFile(path='{self.path}', shape={self.shape()}, crs='{self.crs}')" +``` + +
+ +Next, a function to write and read the netCDF to tiff: + +------------------------------------------------------------------------ + +source + +### netcdf_to_tiff + +> netcdf_to_tiff (ds:xarray.core.dataset.Dataset, band:int, variable:str, +> crs:str='EPSG:4326') + +*Convert a netCDF file to a GeoTIFF file.* + + ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
dsDatasetThe aggregated xarray dataset to convert.
bandintThe day to rasterise; 1 indexed just like human english
variablestrThe variable name to convert.
crsstrEPSG:4326Coordinate reference system (default is WGS84).
+ +
+Exported source + +``` python +def netcdf_to_tiff( + ds: xr.Dataset, # The aggregated xarray dataset to convert. + band: int, # The day to rasterise; 1 indexed just like human english + variable: str, # The variable name to convert. + crs: str = "EPSG:4326", # Coordinate reference system (default is WGS84). + ): + + """ + Convert a netCDF file to a GeoTIFF file. + """ + + with tempfile.TemporaryDirectory() as tmpdirname: + + # Select the variable and time index + variable = ds[variable] + ds_ = variable.rio.set_spatial_dims(x_dim="longitude", y_dim="latitude") + ds_ = ds_.rio.write_crs(crs) + # Save as GeoTIFF + ds_.rio.to_raster(f"{tmpdirname}/output.tif") + # Load the raster file + raster_file = RasterFile(path=f"{tmpdirname}/output.tif", band=band).load() + + return raster_file +``` + +
+ +Now to test it: + +``` python +with ClimateDataFileHandler(eg_file) as handler: + ds_path = handler.get_dataset("instant") + resampled_nc = resample_netcdf(ds_path) + +print(resampled_nc) +resampled_tiff = netcdf_to_tiff( + ds=resampled_nc, + band=28, + variable="swvl1", + crs="EPSG:4326" +) +``` + +``` python +resampled_tiff.data.shape, resampled_tiff.transform, resampled_tiff.crs, resampled_tiff.bounds +``` + +Super cool! The tiff file is created and the data is read back in +correctly. Now we can move on to the next step, which is to aggregate +the data by healthshed. + +## Polygon to Raster Cells + +This function was initially shared from a previous NSAPH aggregation +pipeline +[here](https://github.com/NSAPH-Data-Processing/air_pollution__aqdh/blob/2a8109075fe7a8fbf7c435cc34ffa97b63f5e133/utils/faster_zonal_stats.py#L17). +To better understand this, here is a ChatGPT explanation of the code: + +> This function, +> [`polygon_to_raster_cells`](https://TinasheMTapera.github.io/era5_sandbox/aggregate.html#polygon_to_raster_cells), +> is doing a crucial first step in spatial alignment: it determines +> which raster cells are “touched” by each polygon geometry (e.g., +> administrative areas, watersheds, etc.). +> Essentially, this function helps figure out which pixels from a raster +> image fall inside each polygon (like a district, region, or shape). It +> does this by looking at each polygon one by one, zooming in on just +> the part of the raster that overlaps with that shape, and marking the +> pixels that are inside. This is kind of like placing a cookie cutter +> (the polygon) on a pixelated map (the raster) and seeing which pixels +> get cut. +> The result is a list where each item tells you the pixel locations +> that match a specific polygon. You can then use those pixel locations +> to pull out data from the raster, like temperatures or rainfall, and +> calculate statistics (like the average) for each shape. This is a key +> step when you want to summarize raster data within specific regions, +> like figuring out the average temperature in each county or how much +> vegetation is in each park. + +------------------------------------------------------------------------ + +source + +### polygon_to_raster_cells + +> polygon_to_raster_cells (vectors, raster, nodata=None, affine=None, +> all_touched=False, verbose=False, **kwargs) + +*Returns an index map for each vector geometry to indices in the raster +source.* + + ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
vectorslist of geometries from a shapefile
rasterthe raster data as a numpy array
nodataNoneTypeNonethe nodata value of the raster
affineNoneTypeNonethe affine transform of the raster
all_touchedboolFalsewhether to include all touched pixels
verboseboolFalse
kwargsVAR_KEYWORD
ReturnslistA dictionary mapping vector the ids of geometries to +locations (indices) in the raster source.
+ +
+Exported source + +``` python +def polygon_to_raster_cells( + vectors, # list of geometries from a shapefile + raster, # the raster data as a numpy array + nodata=None, # the nodata value of the raster + affine=None, # the affine transform of the raster + all_touched=False, # whether to include all touched pixels + verbose=False, + **kwargs, +) -> list: # A dictionary mapping vector the ids of geometries to locations (indices) in the raster source. + """Returns an index map for each vector geometry to indices in the raster source.""" + + cell_map = [] + + with Raster(raster, affine, nodata) as rast: + # used later to crop raster and find start row and col + min_lon, dlon = affine.c, affine.a + max_lat, dlat = affine.f, -affine.e + H, W = rast.shape + + for geom in tqdm(vectors, disable=(not verbose)): + if "Point" in geom.geom_type: + geom = boxify_points(geom, rast) + + # find geometry bounds to crop raster + # the raster and geometry must be in the same lon/lat coordinate system + start_row = max(0, min(H - 1, floor((max_lat - geom.bounds[3]) / dlat))) + start_col = min(W - 1, max(0, floor((geom.bounds[0] - min_lon) / dlon))) + end_col = max(0, min(W - 1, ceil((geom.bounds[2] - min_lon) / dlon))) + end_row = min(H - 1, max(0, ceil((max_lat - geom.bounds[1]) / dlat))) + geom_bounds = ( + min_lon + dlon * start_col, # left + max_lat - dlat * end_row - 1e-12, # bottom + min_lon + dlon * end_col + 1e-12, # right + max_lat - dlat * start_row, # top + ) + + # crop raster to area of interest and rasterize + fsrc = rast.read(bounds=geom_bounds) + rv_array = rasterize_geom(geom, like=fsrc, all_touched=all_touched) + indices = np.nonzero(rv_array) + + if len(indices[0]) > 0: + indices = (indices[0] + start_row, indices[1] + start_col) + assert 0 <= indices[0].min() < rast.shape[0] + assert 0 <= indices[1].min() < rast.shape[1] + else: + pass # stop here for debug + + cell_map.append(indices) + + return cell_map +``` + +
+ +To use this, we must define the polygon and raster data. The polygon +data is the healthshed shapefile, and the raster data is the tiff file +we created earlier. We can use the +[`GoogleDriver`](https://TinasheMTapera.github.io/era5_sandbox/core.html#googledriver) +class we defined in `core` to read in the shapefile. + +``` python +try: + with initialize(version_base=None, config_path="../conf"): + cfg = compose(config_name='config.yaml') +except Exception as e: + print(f"Error initializing Hydra: {e}") + with initialize(version_base=None, config_path="conf"): + cfg = compose(config_name='config.yaml') + +driver = GoogleDriver(json_key_path=here() / cfg.GOOGLE_DRIVE_AUTH_JSON.path) +drive = driver.get_drive() +healthsheds = driver.read_healthsheds("Nepal_Healthsheds2024.zip") +``` + +``` python +res_poly2cell=polygon_to_raster_cells( + vectors = healthsheds.geometry.values, # the geometries of the shapefile of the regions + raster=resampled_tiff.data, # the raster data above + nodata=resampled_tiff.nodata, # any intersections with no data, may have to be np.nan + affine=resampled_tiff.transform, # some math thing need to revise + all_touched=True, + verbose=True +) +``` + +The data below maps which grid entries fall into each of the regions in +the shapefile (e.g. which pixel is in which state) + +``` python +res_poly2cell[:5] +``` + +Last but not least, we aggregate these data to the healthshed level. We +can use the `rasterstats` package to do this. + +------------------------------------------------------------------------ + +source + +### aggregate_to_healthsheds + +> aggregate_to_healthsheds (res_poly2cell:list, raster:__main__.RasterFile, +> shapes:geopandas.geodataframe.GeoDataFrame, +> names_column:str='fs_uid', +> aggregation_func: infunctioncallable>= 0x145cb6bbbdf0>, aggregation_name:str='mean') + +*Aggregate the raster data to the health sheds.* + + ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
res_poly2celllistthe result of polygon_to_raster_cells
rasterRasterFilethe raster data
shapesGeoDataFramethe shapes of the health sheds
names_columnstrfs_uidthe unique identifier column name of the health sheds
aggregation_funccallablenanmeanthe aggregation function
aggregation_namestrmeanthe name of the aggregation function
ReturnsGeoDataFrame
+ +
+Exported source + +``` python +def aggregate_to_healthsheds( + res_poly2cell: list, # the result of polygon_to_raster_cells + raster: RasterFile, # the raster data + shapes: gpd.GeoDataFrame, # the shapes of the health sheds + names_column: str = "fs_uid", # the unique identifier column name of the health sheds + aggregation_func: callable = np.nanmean, # the aggregation function + aggregation_name: str = "mean" # the name of the aggregation function + ) -> gpd.GeoDataFrame: + """ + Aggregate the raster data to the health sheds. + """ + + stats = [] + + for indices in res_poly2cell: + if len(indices[0]) == 0: + # no cells found for this polygon + stats.append(np.nan) + else: + cells = raster.data[indices] + if sum(~np.isnan(cells)) == 0: + # no valid cells found for this polygon + stats.append(np.nan) + continue + else: + # compute MEAN of valid cells + # but this stat can be ANYTHING + stats.append(aggregation_func(cells)) + + # clean up the result into a dataframe + stats = pd.Series(stats) + shapes[aggregation_name] = stats + df = pd.DataFrame( + {"healthshed": shapes[names_column], aggregation_name: stats} + ) + gdf = gpd.GeoDataFrame(df, geometry=shapes.geometry.values, crs=shapes.crs) + return gdf +``` + +
+ +And now we apply it: + +``` python +result = aggregate_to_healthsheds( + res_poly2cell=res_poly2cell, + raster=resampled_tiff, + shapes=healthsheds, + names_column="fid", + aggregation_func=np.nanmean, + aggregation_name="mean_soil_moisture" +) +result.head() +``` + +And plot for QA: + +``` python +result.plot(column="mean_soil_moisture", legend=True) +plt.title("Mean Soil Moisture (m^3 m^-3) by Health Shed Nov 2017 day 1") +plt.show() +``` + +That looks great! The data is aggregated to the healthshed level, and we +can see the differences in exposure across the healthsheds. We can also +see that the data is not uniform across the healthsheds, which is what +we expect. + +## Tests and Main + +Now we can wrap this up in a main function that will simply take in the +input file and generate this output. We can also add some tests to make +sure the data is aggregated correctly; tests will run automatically in +this notebook. + +``` python +import random +``` + +``` python +# variables = ["t2m", "d2m"] +# years = ["20{:02d}".format(m) for m in range(9, 24)] +# months = [str(m) for m in range(1, 13)] +# aggregations = [ +# ("Mean", np.nanmean), +# ("Max", np.nanmax), +# ("Min", np.nanmin) +# ] + +# exposure_variable = random.choice(variables) +# year = random.choice(years) +# month = random.choice(months) +# aggregation_str, agg_func = random.choice(aggregations) +# input_file = here() / "data/input/{}_{}.nc".format(year, month) + +# with initialize(version_base=None, config_path="../conf"): +# cfg = compose(config_name='config.yaml') + +# driver = GoogleDriver(json_key_path=here() / cfg.GOOGLE_DRIVE_AUTH_JSON.path) +# drive = driver.get_drive() +# healthsheds = driver.read_healthsheds(cfg.GOOGLE_DRIVE_AUTH_JSON.healthsheds_id) + +# with ClimateDataFileHandler(input_file) as handler: +# ds_path = handler.get_dataset("instant") +# resampled_nc_file = resample_netcdf(ds_path, agg_func=agg_func) + +# days = len(resampled_nc_file.valid_time.values) +# day = random.choice(range(1, days + 1)) + +# resampled_tiff = netcdf_to_tiff( +# ds=resampled_nc_file, +# band=day, # the day we're aggregating +# variable=exposure_variable, +# crs="EPSG:4326" +# ) + +# res_poly2cell=polygon_to_raster_cells( +# vectors = healthsheds.geometry.values, # the geometries of the shapefile of the regions +# raster=resampled_tiff.data, # the raster data above +# nodata=resampled_tiff.nodata, # any intersections with no data, may have to be np.nan +# affine=resampled_tiff.transform, # some math thing need to revise +# all_touched=True, +# verbose=True +# ) + +# result = aggregate_to_healthsheds( +# res_poly2cell=res_poly2cell, +# raster=resampled_tiff, +# shapes=healthsheds, +# names_column="fs_uid", +# aggregation_func=agg_func, +# aggregation_name=exposure_variable +# ) + +# result.plot(column=exposure_variable, legend=True) +# plt.title("{} {} (K) by Health Shed {}".format(aggregation_str, exposure_variable, input_file.stem)) +# plt.suptitle("Aggregation: {}, Day: {}".format(aggregation_str, str(day))) +# plt.show() +``` + +
+ +> **Note** +> +> **Note:** The above code is commented out to prevent execution during +> documentation generation. You can uncomment and run it in an +> appropriate environment to test the aggregation process. + +
+ +3.2 seconds per aggregation is pretty cool! + +``` python +result.to_parquet(here() / "data/testing/test_aggregation.parquet") +``` + +------------------------------------------------------------------------ + +source + +### aggregate_data + +> aggregate_data (cfg:omegaconf.dictconfig.DictConfig, input_file:str, +> output_file:str, exposure_variable:str) + +*Aggregate raster data day-by-day and store all days and statistics as +separate columns in a single Parquet file.* + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDetails
cfgDictConfigthe hydra config
input_filestrthe input netcdf file
output_filestrthe output parquet file
exposure_variablestrWhich variable in the dataset to aggregate
ReturnsNone
+ +
+Exported source + +``` python +def aggregate_data( + cfg: DictConfig, # the hydra config + input_file: str, # the input netcdf file + output_file: str, # the output parquet file + exposure_variable: str # Which variable in the dataset to aggregate + ) -> None: + ''' + Aggregate raster data day-by-day and store all days and statistics as separate columns in a single Parquet file. + ''' + + if cfg.development_mode: + describe(cfg) + return None + + geography = cfg['query'].geography + year = cfg['query']['year'] + month = cfg['query']['month'] + daily_aggs = cfg['aggregation']['aggregation'][exposure_variable]['hourly_to_daily'] + healthshed_aggs = cfg['aggregation']['aggregation'][exposure_variable]['daily_to_healthshed'] + + # Load healthsheds + driver = GoogleDriver(json_key_path=here() / cfg.GOOGLE_DRIVE_AUTH_JSON.path) + drive = driver.get_drive() + healthsheds = driver.read_healthsheds(cfg.geographies[geography].healthsheds) + + # Initialize output DataFrame + result_df = healthsheds[[cfg.geographies[geography].unique_id, "geometry"]].copy() + + for daily_agg in daily_aggs: + print(f"Processing daily aggregation: {daily_agg['name']}...") + + daily_agg_func = _get_callable(daily_agg['function']) + + with ClimateDataFileHandler(input_file) as handler: + if exposure_variable in ["t2m", "d2m", "swvl1"]: + ds_path = handler.get_dataset("instant") + else: + ds_path = handler.get_dataset("accum") + resampled_nc_file = resample_netcdf(ds_path, agg_func=daily_agg_func) + + for healthshed_agg in healthshed_aggs: + print(f"Aggregating to healthshed by: {healthshed_agg['name']}...") + + # Get the number of days in the dataset + days = len(resampled_nc_file.valid_time.values) + + # Get the aggregation function for healthshed + healthshed_agg_func = _get_callable(healthshed_agg['function']) + days = len(resampled_nc_file.valid_time.values) + + for day in range(1, days + 1): + print(f"Processing day {day}...") + + day_col = f"day_{day:02d}_daily_{daily_agg['name']}" + resampled_tiff = netcdf_to_tiff( + ds=resampled_nc_file, + band=day, + variable=exposure_variable, + crs="EPSG:4326" + ) + + result_poly2cell = polygon_to_raster_cells( + vectors=healthsheds.geometry.values, + raster=resampled_tiff.data, + nodata=resampled_tiff.nodata, + affine=resampled_tiff.transform, + all_touched=True, + verbose=True + ) + + res = aggregate_to_healthsheds( + res_poly2cell=result_poly2cell, + raster=resampled_tiff, + shapes=healthsheds, + names_column=cfg.geographies[geography].unique_id, + aggregation_func=healthshed_agg_func, + aggregation_name=exposure_variable + ) + + result_df[day_col] = res[exposure_variable] + + print(f"Saving final monthly parquet file: {output_file}") + result_df.to_parquet(output_file, compression="snappy") + # return(result_df) +``` + +
+ +``` python +try: + with initialize(version_base=None, config_path="../conf"): + cfg = compose(config_name='config.yaml') +except Exception as e: + print(f"Error initializing Hydra: {e}") + with initialize(version_base=None, config_path="conf"): + cfg = compose(config_name='config.yaml') + +cfg.development_mode = False +cfg.query['year'] = 2017 +cfg.query['month'] = 11 +cfg.query['geography'] = "nepal" + +variable = "swvl1" + +aggregate_data(cfg, here() / "bld/2017_11_nepal.nc", here() / "data/testing/test_nepal_aggregation.parquet", exposure_variable=variable) +``` + +``` python +parquet_file = gpd.read_parquet(here() / "data/testing/test_nepal_aggregation.parquet") +``` + +``` python +parquet_file +``` + +``` python +parquet_file.plot(column="day_22_daily_mean", legend=True) +``` + +------------------------------------------------------------------------ + +source + +### main + +> main (cfg:omegaconf.dictconfig.DictConfig) + +
+Exported source + +``` python +@hydra.main(version_base=None, config_path="../../conf", config_name="config") +def main(cfg: DictConfig) -> None: + # Parse command-line arguments + input_file = str(snakemake.input[0]) # First input file + output_file = str(snakemake.output[0]) + geography = str(snakemake.params.geography) + aggregation_variable = str(snakemake.params.variable) + + variables_dict = { + "2m_temperature": "t2m", + "2m_dewpoint_temperature": "d2m", + "volumetric_soil_water_layer_1": "swvl1", + "total_precipitation": "tp" + } + + cfg['query']['geography'] = geography + + aggregate_data(cfg, input_file=input_file, output_file=output_file, exposure_variable=variables_dict[aggregation_variable]) +``` + +
diff --git a/_docs/03_publish.html b/_docs/03_publish.html new file mode 100644 index 0000000..9793d35 --- /dev/null +++ b/_docs/03_publish.html @@ -0,0 +1,1098 @@ + + + + + + + + + +Publish: Gather the Aggregated Data and Publish to DataVerse – era5_sandbox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+

Publish: Gather the Aggregated Data and Publish to DataVerse

+
+ + + +
+ + + + +
+ + + +
+ + +
+

publish

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+

This is the publish module for the ERA5 dataset pipeline. It defines a functions that make use of the pyDataverse library and API to publish our outputs to the Harvard Dataverse.

+
+ +

First, we’ll test out the API by pinging the Harvard DataVerse

+
+
+Exported source +
import hydra
+import yaml
+import json
+from tqdm import tqdm
+from pyprojroot import here
+
+
+
+
api_token_file = here() / "sandbox/dataverse_api_key.yml"
+with open(api_token_file, "r") as f:
+    config = yaml.load(f, Loader=yaml.BaseLoader)
+
+

Now, following the docs for the dataverse tutorial, load a NativeAPI up:

+
+
+Exported source +
from pyDataverse.api import NativeApi
+
+
+

The NativeAPI is a catchall API object to be able to do general stuff:

+
+
api = NativeApi(config['base_url'], config['api_token'])
+resp=api.get_info_version()
+#resp.text()
+
+
+
resp.json()
+
+

Looks good! Now that we know that it works, we can think more about how to publish data there.

+
+
+

Harvard Dataverse

+

Let’s create a dummy dataset with the components we’re planning to upload, and then upload and promptly delete it.

+

To do that, we must import the models module and create a Dataset object:

+
+
from pyDataverse.models import Dataset
+
+
+
ds = Dataset()
+
+

This ds object is pretty straightforward since it doesn’t contain anything yet:

+
+
ds.get()
+
+

We can populate the object from the dummy data on the github repo:

+
+
from pyDataverse.utils import read_file
+from urllib.request import urlretrieve
+import tempfile
+
+
+
# url for dummy data
+url = "https://raw.githubusercontent.com/gdcc/pyDataverse/refs/heads/main/tests/data/user-guide/dataset.json"
+
+
+with tempfile.NamedTemporaryFile(mode='w+') as tmp:
+    urlretrieve(url, tmp.name)
+    ds.from_json(read_file(tmp.name))
+
+

We have to validate the JSON correctly:

+
+
ds.validate_json()
+
+

Modifying it is easy:

+
+
ds.set({"title": "Youth from Austria 2005"})
+ds.get()
+
+

Now, to create the dataset we use the API:

+
+
# this is only run in interactive sessions for demo purposes
+resp = api.create_dataset(":root", ds.json())
+
+

If you caught the resp object, it contains the PID for the newly created dataset.

+

However, if you didn’t you can use the SearchAPI to find it:

+
+
+Exported source +
from pyDataverse.api import SearchApi
+
+
+
+
search_api = SearchApi(config['base_url'], config['api_token'])
+
+
+
#
+
+resp = search_api.search("Youth from Austria", data_type="dataset")
+results = resp.json()['data']['items']
+result = [x for x in results if "Youth from Austria" in x['name']][0]
+result
+
+
+
pid = result['global_id']
+
+

Now to look at the data we created using the NativeAPI again, and delete the dataset:

+
+
uploaded_ds = api.get_dataset(pid)
+uploaded_ds.json()['data']
+
+resp = api.delete_dataset(pid)
+resp.json()
+
+

With that understanding, we can develop a quick module to do the following:

+
    +
  1. Make the dataset LEGO Compatible
    2. +
  2. Upload and publish the data to dataverse
    3. +
+
+
+

LEGO Compatibility

+

Let’s take an example file to use as a model for LEGO compatibility

+
+
+Exported source +
import geopandas as gpd
+import pandas as pd
+import re
+import glob
+
+
+
+
ex = gpd.read_parquet(here() / "bld/2009_06_madagascar_day_swvl1_mean.parquet")
+ex.describe()
+
+

We know that the LEGO data model should look like this:

+
<main lab folder>/lego
+├── <domain>
+│   ├── <subdomain>__<data_source>
+│   │   ├── <geo_resolution>__<time_resolution>
+│   │   │   ├── <filename>_yyyy.parquet
+

So, for the above file, we’ll end up with the LEGO path data/environmental/exposures_era5/healthshed_monthly/dewpoint_2024.parquet. In it, we should have the following columns:

+
healthshed_id  year month day stat_1 stat_2 ... stat_n
+

This means we should read in all of the exposures for a single timepoint at once. I think the smart thing to do is use a glob string to gather all of the pertinent files. This will be the first function we export to the library:

+
+

source

+
+

gather_exposure_geodataframes

+
+
 gather_exposure_geodataframes (glob_string:str, polygon_id:str,
+                                exposure:str)
+
+

Read in a list of geo dataframes from the same time frame and merge them

+ +++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDetails
glob_stringstrstring for the path to search for the pertinent files
polygon_idstrthe string signifying the healthshed ID of the polygon
exposurestrthe exposure name
Returnslist
+
+
+Exported source +
def gather_exposure_geodataframes(
+    glob_string: str,   # string for the path to search for the pertinent files
+    polygon_id: str,    # the string signifying the healthshed ID of the polygon
+    exposure: str       # the exposure name
+    )-> list:
+    "Read in a list of geo dataframes from the same time frame and merge them"
+
+    # first get the initial one so we have the polygon ID and geometry
+    frames = glob.glob(str(glob_string))
+    initial_gdf=gpd.read_parquet(frames[0])
+    merged_df = []
+  
+    for f in tqdm(frames, desc="Processing files"):
+        # read in as a regular dataframe by ignoring geometry
+        df = gpd.read_parquet(f).drop(["geometry"], axis=1) 
+        
+        # get the year and month
+        # Extract year and month
+        search_str = rf'_{exposure}_(\d{{4}})_(\d{{1,2}})\.parquet$'
+        match = re.search(search_str, f)
+
+        if match:
+            year = int(match.group(1))
+            month = int(match.group(2))
+            #print(f"Year: {year}, Month: {month}")
+        else:
+            raise ValueError(f"Could not extract year and month from filename: {search_str} {f}")
+            
+        df['exposure'] = exposure
+        df['month'] = month
+        df['year'] = year
+
+        # Step 1: Melt all day columns (leave 'month' and 'year' as identifiers)
+        df_long = df.melt(id_vars=[polygon_id, "exposure", "year", "month"], var_name="day_stat", value_name="value")
+
+        # Step 2: Extract day and stat type from column names
+        # Example column: "day_01_daily_mean"
+        df_long[["day", "stat"]] = df_long["day_stat"].str.extract(r"day_(\d{2})_daily_(mean|max|min|total)")
+
+        # Optional: convert 'day' and month to integer
+        df_long["day"] = df_long["day"].astype(int)
+        df_long["month"] = df_long["month"].astype(int)
+
+        # Drop the original combined column
+        df_long = df_long.drop(columns="day_stat")
+
+        # Reorder columns
+        df_long = df_long[[polygon_id, "exposure", "year", "month", "day", "stat", "value"]]
+
+        df_long = df_long.sort_values(by=["year", "month", "day"])
+        df_clean = df_long.pivot(index=[polygon_id, "exposure", "year", "month", "day"], columns="stat", values="value").reset_index()
+        merged_df.append(df_clean)
+
+    return [pd.concat(merged_df).reset_index(drop=True), initial_gdf[[polygon_id, "geometry"]]]
+
+
+
+
frames = here() / "data" / "testing" / "*madagascar*"
+
+merged = gather_exposure_geodataframes(frames, "fs_uid", "2m_dewpoint_temperature")
+merged[0].describe()
+
+

This returns one file with all of the geometries and one file with the statistics and exposures.

+

Now, with this, we can move on. The dataset was created in the UI and is available via search and test out how to upload it:

+
+
resp = search_api.search("ERA5", data_type="dataset")
+
+results = resp.json()['data']['items']
+
+result = [x for x in results if "ERA5" in x['name']][0]
+era5_pid = result['global_id']
+result
+
+
+
+Exported source +
from pyDataverse.models import Datafile
+import os
+import pathlib
+
+
+

We’ll upload directly from file. In the case of ERA5 vs. LEGO, we store the file on disk as LEGO hierarchy, but to upload it to dataverse using a flat filename (since creating subdatasets to represent directories might be a bit of a hassle)

+
+
# assuming the file has a path on disk like:
+f_out = "environmental/exposures_era5/healthshed_daily/dewpoint_2024.parquet"
+os.makedirs(here() / "data" / "testing" / os.path.dirname(f_out), exist_ok=True)
+aggregations, geo = merged
+aggregations.to_parquet(here() / "data" / "testing" / f_out, index=False)
+
+datafile = Datafile()
+datafile.set({
+    # the id of the era5 dataset 
+    "pid": era5_pid,
+    # the path to the file on disk goes here
+    "filename": str(here() / "data" / "testing" / f_out),
+    # use the "label" to name the file
+    "label": f_out.replace("/", "-")
+})
+
+
+
resp = api.upload_datafile(era5_pid, str(here() / "data" / "testing" / f_out), datafile.json())
+
+

Pretty simple!

+

Now, we just need a main function to upload this data. The final upload is one file per exposure per year, so these should be the variables we gather data for.

+

We should get some functionality to gather the groups of these files automatically, based on the hydra config:

+
+
+Exported source +
from hydra import initialize, compose
+from omegaconf import OmegaConf, DictConfig
+from tqdm import tqdm
+
+
+
+
target_dir = here() / "data" / "intermediate"
+
+try:
+    with initialize(version_base=None, config_path="../conf"):
+        cfg = compose(config_name='config.yaml')
+except Exception as e:
+    print(f"Error initializing Hydra: {e}")
+    with initialize(version_base=None, config_path="conf"):
+        cfg = compose(config_name='config.yaml')
+
+cfg.development_mode = False
+#cfg.query['year'] = 2017
+#cfg.query['month'] = 11
+#cfg.query['geography'] = "nepal"
+
+
+

source

+
+
+

main

+
+
 main (cfg:omegaconf.dictconfig.DictConfig)
+
+
+
+Exported source +
@hydra.main(version_base=None, config_path="../../conf", config_name="config")
+def main(cfg: DictConfig) -> None:
+
+    variables_dict = {
+        "2m_temperature": "t2m",
+        "2m_dewpoint_temperature": "d2m",
+        "volumetric_soil_water_layer_1": "swvl1",
+        "total_precipitation": "tp"
+    }
+
+    print(OmegaConf.to_yaml(cfg))
+
+    #prep dataverse
+    api_token_file = here() / "sandbox/dataverse_api_key.yml"
+    with open(api_token_file, "r") as f:
+        apiconfig = yaml.load(f, Loader=yaml.BaseLoader)
+    api = NativeApi(apiconfig['base_url'], apiconfig['api_token'])
+    search_api = SearchApi(apiconfig['base_url'], apiconfig['api_token'])
+    resp = search_api.search("ERA5", data_type="dataset")
+
+    results = resp.json()['data']['items']
+
+    result = [x for x in results if "ERA5" in x['name']][0]
+    era5_pid = result['global_id']
+
+    for geography in cfg.geographies:
+        for year in cfg.query['year']:
+            for variable, v in variables_dict.items():
+                
+                print(f"Processing {geography} for {variable} in {year}")
+                glob_string = here() / "data" / "intermediate" / f"*{geography}*{variable}*{year}*"
+                print(f"Glob: {glob_string}")
+                polygon_id = cfg.geographies[geography]['unique_id']
+                print(f"polygon_id: {polygon_id}")
+                merged = gather_exposure_geodataframes(glob_string, polygon_id, variable)
+                print(merged[0].head())
+                print(merged[1].head())
+
+                output_dir = here() / "data" / "output" 
+                
+                f_out = f"environmental/exposures_era5/healthshed_daily/{geography}_{v}_{year}.parquet"
+                os.makedirs(output_dir / os.path.dirname(f_out), exist_ok=True)
+                output_path = output_dir / f_out
+
+                print(f"Writing to {output_path}")
+                merged[0].to_parquet(output_path, index=False)
+                
+
+                print(f"Uploading {f_out.replace('/', '-')} to Dataverse...")
+                # upload to dataverse
+                datafile = Datafile()
+                datafile.set({
+                    "pid": era5_pid,
+                    "filename": str(output_path),
+                    "label": f_out.replace("/", "-")
+                })
+
+                resp = api.upload_datafile(era5_pid, output_path, datafile.json())
+                assert resp.json()['status'] == "OK", f"Failed to upload datafile: {resp.text}"
+        
+        # also save the geometry for the region 
+        merged[1].to_parquet(output_path.parent / f"{geography}_geometry.parquet", index=False)
+
+        # and upload it to dataverse
+        datafile = Datafile()
+        datafile.set({
+            "pid": era5_pid,
+            "filename": str(output_path.parent / f"{geography}_geometry.parquet"),
+            "label": f"{geography}_geometry.parquet"
+        })
+
+        resp = api.upload_datafile(era5_pid, output_path.parent / f"{geography}_geometry.parquet", datafile.json())
+        assert resp.json()['status'] == "OK", f"Failed to upload geometry datafile: {resp.text}"
+
+    print("All files processed and uploaded successfully.")
+
+
+ + +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/_docs/03_publish.md b/_docs/03_publish.md new file mode 100644 index 0000000..ee32f98 --- /dev/null +++ b/_docs/03_publish.md @@ -0,0 +1,511 @@ +# Publish: Gather the Aggregated Data and Publish to DataVerse + + +## publish + +> This is the `publish` module for the ERA5 dataset pipeline. It defines +> a functions that make use of the `pyDataverse` library and API to +> publish our outputs to the Harvard Dataverse. + + + +First, we’ll test out the API by pinging the Harvard DataVerse + +
+Exported source + +``` python +import hydra +import yaml +import json +from tqdm import tqdm +from pyprojroot import here +``` + +
+ +``` python +api_token_file = here() / "sandbox/dataverse_api_key.yml" +with open(api_token_file, "r") as f: + config = yaml.load(f, Loader=yaml.BaseLoader) +``` + +Now, following the [docs]() for the dataverse tutorial, load a NativeAPI +up: + +
+Exported source + +``` python +from pyDataverse.api import NativeApi +``` + +
+ +The NativeAPI is a catchall API object to be able to do general stuff: + +``` python +api = NativeApi(config['base_url'], config['api_token']) +resp=api.get_info_version() +#resp.text() +``` + +``` python +resp.json() +``` + +Looks good! Now that we know that it works, we can think more about how +to publish data there. + +## Harvard Dataverse + +Let’s create a dummy dataset with the components we’re planning to +upload, and then upload and promptly delete it. + +To do that, we must import the `models` module and create a Dataset +object: + +``` python +from pyDataverse.models import Dataset +``` + +``` python +ds = Dataset() +``` + +This `ds` object is pretty straightforward since it doesn’t contain +anything yet: + +``` python +ds.get() +``` + +We can populate the object from the dummy data on the github repo: + +``` python +from pyDataverse.utils import read_file +from urllib.request import urlretrieve +import tempfile +``` + +``` python +# url for dummy data +url = "https://raw.githubusercontent.com/gdcc/pyDataverse/refs/heads/main/tests/data/user-guide/dataset.json" + + +with tempfile.NamedTemporaryFile(mode='w+') as tmp: + urlretrieve(url, tmp.name) + ds.from_json(read_file(tmp.name)) +``` + +We have to validate the JSON correctly: + +``` python +ds.validate_json() +``` + +Modifying it is easy: + +``` python +ds.set({"title": "Youth from Austria 2005"}) +ds.get() +``` + +Now, to create the dataset we use the API: + +``` python +# this is only run in interactive sessions for demo purposes +resp = api.create_dataset(":root", ds.json()) +``` + +If you caught the `resp` object, it contains the PID for the newly +created dataset. + +However, if you didn’t you can use the SearchAPI to find it: + +
+Exported source + +``` python +from pyDataverse.api import SearchApi +``` + +
+ +``` python +search_api = SearchApi(config['base_url'], config['api_token']) +``` + +``` python +# + +resp = search_api.search("Youth from Austria", data_type="dataset") +results = resp.json()['data']['items'] +result = [x for x in results if "Youth from Austria" in x['name']][0] +result +``` + +``` python +pid = result['global_id'] +``` + +Now to look at the data we created using the NativeAPI again, and delete +the dataset: + +``` python +uploaded_ds = api.get_dataset(pid) +uploaded_ds.json()['data'] + +resp = api.delete_dataset(pid) +resp.json() +``` + +With that understanding, we can develop a quick module to do the +following: + +1. Make the dataset LEGO Compatible +2. Upload and publish the data to dataverse + +## LEGO Compatibility + +Let’s take an example file to use as a model for LEGO compatibility + +
+Exported source + +``` python +import geopandas as gpd +import pandas as pd +import re +import glob +``` + +
+ +``` python +ex = gpd.read_parquet(here() / "bld/2009_06_madagascar_day_swvl1_mean.parquet") +ex.describe() +``` + +We know that the LEGO data model should look like this: + +
/lego + ├── + │ ├── __ + │ │ ├── __ + │ │ │ ├── _yyyy.parquet + +So, for the above file, we’ll end up with the LEGO path +`data/environmental/exposures_era5/healthshed_monthly/dewpoint_2024.parquet`. +In it, we should have the following columns: + + healthshed_id year month day stat_1 stat_2 ... stat_n + +This means we should read in all of the exposures for a single timepoint +at once. I think the smart thing to do is use a glob string to gather +all of the pertinent files. This will be the first function we export to +the library: + +------------------------------------------------------------------------ + +source + +### gather_exposure_geodataframes + +> gather_exposure_geodataframes (glob_string:str, polygon_id:str, +> exposure:str) + +*Read in a list of geo dataframes from the same time frame and merge +them* + + +++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDetails
glob_stringstrstring for the path to search for the pertinent files
polygon_idstrthe string signifying the healthshed ID of the polygon
exposurestrthe exposure name
Returnslist
+ +
+Exported source + +``` python +def gather_exposure_geodataframes( + glob_string: str, # string for the path to search for the pertinent files + polygon_id: str, # the string signifying the healthshed ID of the polygon + exposure: str # the exposure name + )-> list: + "Read in a list of geo dataframes from the same time frame and merge them" + + # first get the initial one so we have the polygon ID and geometry + frames = glob.glob(str(glob_string)) + initial_gdf=gpd.read_parquet(frames[0]) + merged_df = [] + + for f in tqdm(frames, desc="Processing files"): + # read in as a regular dataframe by ignoring geometry + df = gpd.read_parquet(f).drop(["geometry"], axis=1) + + # get the year and month + # Extract year and month + search_str = rf'_{exposure}_(\d{{4}})_(\d{{1,2}})\.parquet$' + match = re.search(search_str, f) + + if match: + year = int(match.group(1)) + month = int(match.group(2)) + #print(f"Year: {year}, Month: {month}") + else: + raise ValueError(f"Could not extract year and month from filename: {search_str} {f}") + + df['exposure'] = exposure + df['month'] = month + df['year'] = year + + # Step 1: Melt all day columns (leave 'month' and 'year' as identifiers) + df_long = df.melt(id_vars=[polygon_id, "exposure", "year", "month"], var_name="day_stat", value_name="value") + + # Step 2: Extract day and stat type from column names + # Example column: "day_01_daily_mean" + df_long[["day", "stat"]] = df_long["day_stat"].str.extract(r"day_(\d{2})_daily_(mean|max|min|total)") + + # Optional: convert 'day' and month to integer + df_long["day"] = df_long["day"].astype(int) + df_long["month"] = df_long["month"].astype(int) + + # Drop the original combined column + df_long = df_long.drop(columns="day_stat") + + # Reorder columns + df_long = df_long[[polygon_id, "exposure", "year", "month", "day", "stat", "value"]] + + df_long = df_long.sort_values(by=["year", "month", "day"]) + df_clean = df_long.pivot(index=[polygon_id, "exposure", "year", "month", "day"], columns="stat", values="value").reset_index() + merged_df.append(df_clean) + + return [pd.concat(merged_df).reset_index(drop=True), initial_gdf[[polygon_id, "geometry"]]] +``` + +
+ +``` python +frames = here() / "data" / "testing" / "*madagascar*" + +merged = gather_exposure_geodataframes(frames, "fs_uid", "2m_dewpoint_temperature") +merged[0].describe() +``` + +This returns one file with all of the geometries and one file with the +statistics and exposures. + +Now, with this, we can move on. The dataset was created in the UI and is +available via search and test out how to upload it: + +``` python +resp = search_api.search("ERA5", data_type="dataset") + +results = resp.json()['data']['items'] + +result = [x for x in results if "ERA5" in x['name']][0] +era5_pid = result['global_id'] +result +``` + +
+Exported source + +``` python +from pyDataverse.models import Datafile +import os +import pathlib +``` + +
+ +We’ll upload directly from file. In the case of ERA5 vs. LEGO, we store +the file on disk as LEGO hierarchy, but to upload it to dataverse using +a flat filename (since creating subdatasets to represent directories +might be a bit of a hassle) + +``` python +# assuming the file has a path on disk like: +f_out = "environmental/exposures_era5/healthshed_daily/dewpoint_2024.parquet" +os.makedirs(here() / "data" / "testing" / os.path.dirname(f_out), exist_ok=True) +aggregations, geo = merged +aggregations.to_parquet(here() / "data" / "testing" / f_out, index=False) + +datafile = Datafile() +datafile.set({ + # the id of the era5 dataset + "pid": era5_pid, + # the path to the file on disk goes here + "filename": str(here() / "data" / "testing" / f_out), + # use the "label" to name the file + "label": f_out.replace("/", "-") +}) +``` + +``` python +resp = api.upload_datafile(era5_pid, str(here() / "data" / "testing" / f_out), datafile.json()) +``` + +Pretty simple! + +Now, we just need a main function to upload this data. The final upload +is one file per exposure per year, so these should be the variables we +gather data for. + +We should get some functionality to gather the groups of these files +automatically, based on the hydra config: + +
+Exported source + +``` python +from hydra import initialize, compose +from omegaconf import OmegaConf, DictConfig +from tqdm import tqdm +``` + +
+ +``` python +target_dir = here() / "data" / "intermediate" + +try: + with initialize(version_base=None, config_path="../conf"): + cfg = compose(config_name='config.yaml') +except Exception as e: + print(f"Error initializing Hydra: {e}") + with initialize(version_base=None, config_path="conf"): + cfg = compose(config_name='config.yaml') + +cfg.development_mode = False +#cfg.query['year'] = 2017 +#cfg.query['month'] = 11 +#cfg.query['geography'] = "nepal" +``` + +------------------------------------------------------------------------ + +source + +### main + +> main (cfg:omegaconf.dictconfig.DictConfig) + +
+Exported source + +``` python +@hydra.main(version_base=None, config_path="../../conf", config_name="config") +def main(cfg: DictConfig) -> None: + + variables_dict = { + "2m_temperature": "t2m", + "2m_dewpoint_temperature": "d2m", + "volumetric_soil_water_layer_1": "swvl1", + "total_precipitation": "tp" + } + + print(OmegaConf.to_yaml(cfg)) + + #prep dataverse + api_token_file = here() / "sandbox/dataverse_api_key.yml" + with open(api_token_file, "r") as f: + apiconfig = yaml.load(f, Loader=yaml.BaseLoader) + api = NativeApi(apiconfig['base_url'], apiconfig['api_token']) + search_api = SearchApi(apiconfig['base_url'], apiconfig['api_token']) + resp = search_api.search("ERA5", data_type="dataset") + + results = resp.json()['data']['items'] + + result = [x for x in results if "ERA5" in x['name']][0] + era5_pid = result['global_id'] + + for geography in cfg.geographies: + for year in cfg.query['year']: + for variable, v in variables_dict.items(): + + print(f"Processing {geography} for {variable} in {year}") + glob_string = here() / "data" / "intermediate" / f"*{geography}*{variable}*{year}*" + print(f"Glob: {glob_string}") + polygon_id = cfg.geographies[geography]['unique_id'] + print(f"polygon_id: {polygon_id}") + merged = gather_exposure_geodataframes(glob_string, polygon_id, variable) + print(merged[0].head()) + print(merged[1].head()) + + output_dir = here() / "data" / "output" + + f_out = f"environmental/exposures_era5/healthshed_daily/{geography}_{v}_{year}.parquet" + os.makedirs(output_dir / os.path.dirname(f_out), exist_ok=True) + output_path = output_dir / f_out + + print(f"Writing to {output_path}") + merged[0].to_parquet(output_path, index=False) + + + print(f"Uploading {f_out.replace('/', '-')} to Dataverse...") + # upload to dataverse + datafile = Datafile() + datafile.set({ + "pid": era5_pid, + "filename": str(output_path), + "label": f_out.replace("/", "-") + }) + + resp = api.upload_datafile(era5_pid, output_path, datafile.json()) + assert resp.json()['status'] == "OK", f"Failed to upload datafile: {resp.text}" + + # also save the geometry for the region + merged[1].to_parquet(output_path.parent / f"{geography}_geometry.parquet", index=False) + + # and upload it to dataverse + datafile = Datafile() + datafile.set({ + "pid": era5_pid, + "filename": str(output_path.parent / f"{geography}_geometry.parquet"), + "label": f"{geography}_geometry.parquet" + }) + + resp = api.upload_datafile(era5_pid, output_path.parent / f"{geography}_geometry.parquet", datafile.json()) + assert resp.json()['status'] == "OK", f"Failed to upload geometry datafile: {resp.text}" + + print("All files processed and uploaded successfully.") +``` + +
diff --git a/_docs/10_pytask_demo.html b/_docs/10_pytask_demo.html new file mode 100644 index 0000000..ca9be4b --- /dev/null +++ b/_docs/10_pytask_demo.html @@ -0,0 +1,1114 @@ + + + + + + + + + +Demo: How to Create Pipelines with pytask – era5_sandbox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Demo: How to Create Pipelines with pytask

+
+ + + +
+ + + + +
+ + + +
+ + +
+

Data Preparation Demo

+
+

Data preparation task for pytask demo

+
+

In this notebook, we are demonstrating how to convert our snakemake workflow into a pytask workflow. We use the basic tutorial to demonstrate this, but continue to use nbdev for development of functions in notebooks.

+

pytask is a task management system that allows you to define tasks and their dependencies, similar to Snakemake. It is particularly useful for data science workflows.

+

There are a number of reasons to use pytask over snakemake: - Pythonic: pytask is designed to be purely Pythonic by default, allowing you to write tasks and entire pipelines as Python functions. - Flexibility: pytask allows you to define tasks and their dependencies in a more flexible way, using Python functions and decorators, as opposed to orchestrating numerous scripts. - Integration: pytask integrates well with other Python libraries, such as nbdev here, or hydra configurations if you need, allowing you to use your existing code, notebooks, or configs in your workflows. - Parallelism: pytask supports parallel execution of tasks with pytask-parallel, which can speed up your workflows significantly, especially for data processing tasks.

+

We’ll use nbdev to define the task functions, and then export them to the src directory. pytask is then invoked at the command line to run the tasks.

+ +

This demo task is taken from the tutorial at pytask documentation. At minimum, you need your package to contain the following in a config.py file:

+
my_project
+│
+├───.pytask
+│
+├───bld
+│   └────...
+│
+├───src
+│   └───my_project
+│       ├────__init__.py
+│       ├────config.py
+│       └────...
+│
+└───pyproject.toml
+
#contents of `era5_sandbox.config` module
+from pathlib import Path
+
+
+SRC = Path(__file__).parent.resolve()
+BLD = SRC.joinpath("..", "..", "bld").resolve()
+

Additionally, your pyproject.toml file should contain the following at minimum:

+
[tool.pytask.ini_options]
+paths = ["src/era5_sandbox"]
+

The former tells Python where to find the source code and build directory for pytask objects and shims, while the latter tells pytask where to find the task definitions and dependency DAG.

+
+
+Exported source +
import os
+from pathlib import Path
+from typing import Annotated
+
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+from era5_sandbox.config import BLD
+from era5_sandbox.config import data_catalog, demo_catalog
+
+from pytask import PickleNode
+from pytask import Product
+
+
+
+

Defining Tasks

+

To define a task, simply use the task_ prefix in the function name (or, if you are familiar and comfortable with decorators, use @pytask.mark.task). Be verbose and expressive in your use of type hints to specify the input and output data, so that pytask can automatically detect and handle the dependencies between tasks.

+
+
+

Defining Tracked Outputs

+

To define something as a tracked output, you can annotate the input of the task with Annotated[Path, Product], where Product is imported from pytask. This tells pytask that this is a product of the task and should be saved in the build directory.

+

In this example, we’re generating random data into a data frame and saving the object as a pickle in the bld directory (bld is the default build directory for pytask’s intermediate data). To get that directory, we use the BLD variable from the era5_sandbox.config module as above. This module itself could also be generated using nbdev if you want to keep your configuration in notebooks.

+

Using nbdev, we can also include all of the bells and whistles of function documentation.

+
+

source

+
+
+

task_create_random_data

+
+
 task_create_random_data (seed:typing.Annotated[int,42], path_to_data:typi
+                          ng.Annotated[pathlib.Path,ProductType()]=Path('/
+                          net/rcstorenfs02/ifs/rc_labs/dominici_lab/lab/da
+                          ta_processing/csph-era5_sandbox/bld/data.pkl'))
+
+

Create a random data set and save it as a pickle file. Return the path to the saved file.

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
seedAnnotatedDefault seed for reproducibility
path_to_dataAnnotated/net/rcstorenfs02/ifs/rc_labs/dominici_lab/lab/data_processing/csph-era5_sandbox/bld/data.pklPath to the object in the build directory
ReturnsNone
+
+
+Exported source +
def task_create_random_data(
+        seed: Annotated[int, 42],                                   # Default seed for reproducibility
+        path_to_data: Annotated[Path, Product] = BLD / "data.pkl" # Path to the object in the build directory
+    ) -> None:
+    "Create a random data set and save it as a pickle file. Return the path to the saved file."
+    rng = np.random.default_rng(seed)
+    beta = 2
+
+    x = rng.normal(loc=5, scale=10, size=1_000)
+    epsilon = rng.standard_normal(1_000)
+
+    y = beta * x + epsilon
+
+    df = pd.DataFrame({"x": x, "y": y})
+
+    # this is a tracked output, so we annotate the return value with `Annotated[Path, Product]`
+    df.to_pickle(path_to_data)
+
+
+

We can test the function directly in the notebook:

+
+
task_create_random_data(42)
+
+

Once this module and function are exported with nbdev_export, the functions are in a python package. We can then use the command line to look at the registered tasks:

+
+
pytask collect
+
+

Let’s add another task in the same module. This task plots the data we generated. To link the previous task to this one as a dependency, we can list the output of the previous task as an input to this one. This way, pytask will know that it needs to run the first task before this one.

+
+

source

+
+
+

task_plot_data

+
+
 task_plot_data (path_to_data:typing.Annotated[pathlib.Path,Path('/net/rcs
+                 torenfs02/ifs/rc_labs/dominici_lab/lab/data_processing/cs
+                 ph-era5_sandbox/bld/data.pkl')], path_to_plot:typing.Anno
+                 tated[pathlib.Path,ProductType()]=Path('/net/rcstorenfs02
+                 /ifs/rc_labs/dominici_lab/lab/data_processing/csph-
+                 era5_sandbox/bld/plot.png'))
+
+

Plot the data from the pickle file and save the plot. Note that this task: 1. depends on the data.pkl file created by the previous task, 2. does not return any value, but saves a plot to the build directory. So the side effect of the task is what we are interested in here (though this is probably bad practice).

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
path_to_dataAnnotatedPath to the data file created by the previous task
path_to_plotAnnotated/net/rcstorenfs02/ifs/rc_labs/dominici_lab/lab/data_processing/csph-era5_sandbox/bld/plot.pngPath to the build directory for the plot
ReturnsNone
+
+
+Exported source +
def task_plot_data(
+    path_to_data: Annotated[Path, BLD / "data.pkl"], # Path to the data file created by the previous task
+    path_to_plot: Annotated[Path, Product] = BLD / "plot.png"  # Path to the build directory for the plot
+) -> None:
+    """
+    Plot the data from the pickle file and save the plot. Note that this task:
+        1. depends on the data.pkl file created by the previous task,
+        2. does not return any value, but saves a plot to the build directory. So the side effect of the task is what we are interested in here (though this is probably bad practice).
+    """
+
+    df = pd.read_pickle(path_to_data)
+    
+    _, ax = plt.subplots()
+    df.plot(x="x", y="y", ax=ax, kind="scatter")
+
+    plt.savefig(path_to_plot)
+    plt.close()
+
+
+

We now have a DAG of tasks that pytask can execute. To see the tasks, we can use the command line to create a pygraphviz graph of the tasks:

+
pytask dag
+

The DAG is saved as a pdf file, and you can view it using any viewer. Now, to run the pipeline, just invoke pytask at the command line:

+
pytask
+

In Jupyter or iPython, you can interact with the task outputs directly:

+
+
# list all the files in the build directory
+for file in os.listdir(BLD):
+    print(file)
+
+

We can use these to build subsequent tasks later.

+
+
+
+

More Complex Tasks & The Data Catalog

+

As we define more complex tasks, we can use the pytask data catalog to manage the inputs and outputs of our tasks. The data catalog allows us to imperatively name the data and their formats, making it easier to manage the data flow in our tasks. Importantly, we can define the data pythonically, which allows us to use the full power of Python to manipulate and transform our data. This is particularly more useful than snakemake’s approach, which requires you to define the data in a more static way using paths and a separate pseudo-language.

+

The content of the era5_sandbox.config module can be extended to include a data catalog:

+
from pathlib import Path
+from pytask import DataCatalog, Product
+
+SRC = Path(__file__).parent.resolve()
+BLD = SRC.joinpath("..", "..", "bld").resolve()
+
+demo_catalog = DataCatalog()
+

With just this definition, we’re now able to refer directly to data by name in our tasks, and pytask will handle the paths and formats for us. This allows us to focus on the logic of our tasks rather than the details of data management.

+
+
+
+ +
+
+Note +
+
+
+

This is a major advantage of pytask over snakemake, as it allows you to define the data in a more flexible and Pythonic way, while still maintaining the benefits of a task management system. It is a similar approach to building pipelines in R with targets, which allows you to define the data in a more flexible way.

+
+
+

Let’s create a task that modifies the data frame by adding a new column. This task will depend on the previous task’s output, and we will use the data catalog to define the input and output data.

+
+

source

+
+

task_add_one

+
+
 task_add_one (path_to_data:typing.Annotated[pathlib.Path,Path('/net/rcsto
+               renfs02/ifs/rc_labs/dominici_lab/lab/data_processing/csph-
+               era5_sandbox/bld/data.pkl')], node:typing.Annotated[_pytask
+               .nodes.PickleNode,ProductType()]=PickleNode(path=Path('/net
+               /rcstorenfs02/ifs/rc_labs/dominici_lab/lab/data_processing/
+               csph-era5_sandbox/.pytask/data_catalogs/default/1eef510d81e
+               ea49161cd821b318aa999e630bdd292b093aa9a9319e9f282b984.pkl')
+               , name='mydata', attributes={'catalog_name': 'default'},
+               serializer=<built-in function dump>, deserializer=<built-in
+               function load>))
+
+

Add one to the ‘y’ column of the data frame and save it as a new pickle file.

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
path_to_dataAnnotatedPath to the data file created by the previous task
nodeAnnotatedPickleNode(path=Path(‘/net/rcstorenfs02/ifs/rc_labs/dominici_lab/lab/data_processing/csph-era5_sandbox/.pytask/data_catalogs/default/1eef510d81eea49161cd821b318aa999e630bdd292b093aa9a9319e9f282b984.pkl’), name=‘mydata’, attributes={‘catalog_name’: ‘default’}, serializer=, deserializer=)
ReturnsNone
+
+
+Exported source +
def task_add_one(
+    path_to_data: Annotated[Path, BLD / "data.pkl"],  # Path to the data file created by the previous task
+    node: Annotated[PickleNode, Product] = demo_catalog["mydata"]
+) -> None:
+    """
+    Add one to the 'y' column of the data frame and save it as a new pickle file.
+    """
+    df = pd.read_pickle(path_to_data)
+    df['z'] = df['y'] + 1
+    
+    node.save(df)
+
+
+

In this function, we’ve defined that the task relies on the output of the first task being there, the data.pkl file. But importantly, we’ve also defined our product as a node from the PickleNode module. This will allow pytask to handle the serialization and deserialization of the data frame automatically, so we don’t have to worry about the details of how the data is stored. We create the datacatalog in our config file, and then tell this task to create a Node in that catalog called mydata. Whatever we save with the node.save() method will be saved in the build directory, but more importantly will be indexed and hashed by pytask. This means that if the data changes, pytask will know to rerun the task.

+

To make this even more pythonic, we can modify the format of our task function so that the return type annotator is used as a node in the data catalog. This allows us to define the output of the task as a PickleNode, which will automatically handle the serialization and deserialization of the data frame.

+
+
+
+ +
+
+Note +
+
+
+

This is another trick I’m deriving from {targets}. By formatting tasks as pure functions where inputs are parameters and targets are return type annotations, we can define the output of the task as a PickleNode, which will automatically handle the serialization and deserialization of the data frame. This again allows us to focus on the logic of our tasks rather than the details of data management.

+
+
+

So below, we’re directly accessing the data_catalog to get the mydata node, and then modifying it by adding a new column. It feels like we are doing this in place, such as in an iPython session, because we are allowing pytask to handle the serialization of the file on disk for us.

+
+

source

+
+
+

task_add_another_column

+
+
 task_add_another_column (df:typing.Annotated[pandas.core.frame.DataFrame,
+                          PickleNode(path=Path('/net/rcstorenfs02/ifs/rc_l
+                          abs/dominici_lab/lab/data_processing/csph-era5_s
+                          andbox/.pytask/data_catalogs/default/1eef510d81e
+                          ea49161cd821b318aa999e630bdd292b093aa9a9319e9f28
+                          2b984.pkl'),name='mydata',attributes={'catalog_n
+                          ame':'default'},serializer=<built-
+                          infunctiondump>,deserializer=<built-
+                          infunctionload>)])
+
+

Add another column to the data frame stored in the PickleNode.

+ +++++ + + + + + + + + + + + + + + + + + + + +
TypeDetails
dfAnnotatedwhich object in the catalog to fetch from the catalog with node.load()
ReturnsAnnotatedwhich object in the catalog to save the return value to
+
+
+Exported source +
def task_add_another_column(
+    df: Annotated[pd.DataFrame, demo_catalog["mydata"]] # which object in the catalog to fetch from the catalog with node.load()
+) -> Annotated[pd.DataFrame, demo_catalog["mydata2"]]:  # which object in the catalog to save the return value to
+    """
+    Add another column to the data frame stored in the PickleNode.
+    """
+
+    # use the datacatalog directly to access the node
+    # this is a bit like accessing the node in an iPython session, but pytask
+    # will handle the serialization and deserialization for us
+    df['w'] = df['z'] * df['y']
+    
+    return df
+
+
+

To test this interactively, we’d have to import the data catalog’s object

+
+
df = demo_catalog["mydata"].load()  # load the data frame from the PickleNode
+result = task_add_another_column(df)  # call the task function with the loaded data frame
+
+
+
result
+
+

Now that we know it will work, we can invoke pytask:

+
+
pytask
+
+

Notice that the outputs are cached and not recomputed unless the inputs change. This is a key feature of pytask and other DAGs, allowing you to efficiently manage your data processing tasks without unnecessary recomputation.

+
+
+
+

Conclusion

+

The takeaway here is that with pytask, you can define pure functions that take inputs and return outputs, and build a DAG of tasks that can be executed in a flexible and efficient way. This allows you to focus on the logic of your tasks rather than the details of data management, while still maintaining the benefits of a task management system. The key elements are:

+
    +
  • Task annotation: You define your tasks by creating pure functions that take inputs and return outputs, and use decorators or naming conventions to mark them as “tasks” in a dag
    • +
  • Input and output annotation: You define the inputs and outputs of your tasksusing type hints, and allow pytask to automatically detect and handle the dependencies between tasks.
    • +
  • Data catalog: You define your data in a Pythonic object in your config called data_catalog. As you iteratively develop your DAG, you add objects to the data catalog, which are called nodes. As long as a node is a pythonic object and has a pickle method, pytask will handle the serialization and deserialization of the data for you.
    • +
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/_docs/10_pytask_demo.md b/_docs/10_pytask_demo.md new file mode 100644 index 0000000..d0a20e4 --- /dev/null +++ b/_docs/10_pytask_demo.md @@ -0,0 +1,603 @@ +# Demo: How to Create Pipelines with `pytask` + + +## Data Preparation Demo + +> Data preparation task for `pytask` demo + +In this notebook, we are demonstrating how to convert our snakemake +workflow into a `pytask` workflow. We use the basic tutorial to +demonstrate this, but continue to use nbdev for development of functions +in notebooks. + +`pytask` is a task management system that allows you to define tasks and +their dependencies, similar to `Snakemake`. It is particularly useful +for data science workflows. + +There are a number of reasons to use `pytask` over `snakemake`: - +**Pythonic**: `pytask` is designed to be purely Pythonic by default, +allowing you to write tasks and entire pipelines as Python functions. - +**Flexibility**: `pytask` allows you to define tasks and their +dependencies in a more flexible way, using Python functions and +decorators, as opposed to orchestrating numerous scripts. - +**Integration**: `pytask` integrates well with other Python libraries, +such as `nbdev` here, or `hydra` configurations if you need, allowing +you to use your existing code, notebooks, or configs in your +workflows. - **Parallelism**: `pytask` supports parallel execution of +tasks with `pytask-parallel`, which can speed up your workflows +significantly, especially for data processing tasks. + +We’ll use nbdev to define the task functions, and then export them to +the `src` directory. `pytask` is then invoked at the command line to run +the tasks. + + + +This demo task is taken from the tutorial at [pytask +documentation](https://pytask-dev.readthedocs.io/en/stable/tutorials/write_a_task.html). +At minimum, you need your package to contain the following in a +config.py file: + +``` md +my_project +│ +├───.pytask +│ +├───bld +│ └────... +│ +├───src +│ └───my_project +│ ├────__init__.py +│ ├────config.py +│ └────... +│ +└───pyproject.toml +``` + +``` python +#contents of `era5_sandbox.config` module +from pathlib import Path + + +SRC = Path(__file__).parent.resolve() +BLD = SRC.joinpath("..", "..", "bld").resolve() +``` + +Additionally, your pyproject.toml file should contain the following at +minimum: + +``` toml +[tool.pytask.ini_options] +paths = ["src/era5_sandbox"] +``` + +The former tells Python where to find the source code and build +directory for `pytask` objects and shims, while the latter tells +`pytask` where to find the task definitions and dependency DAG. + +
+Exported source + +``` python +import os +from pathlib import Path +from typing import Annotated + +import numpy as np +import matplotlib.pyplot as plt +import pandas as pd +from era5_sandbox.config import BLD +from era5_sandbox.config import data_catalog, demo_catalog + +from pytask import PickleNode +from pytask import Product +``` + +
+ +### Defining Tasks + +To define a task, simply use the `task_` prefix in the function name +(or, if you are familiar and comfortable with decorators, use +`@pytask.mark.task`). Be verbose and expressive in your use of type +hints to specify the input and output data, so that `pytask` can +automatically detect and handle the dependencies between tasks. + +### Defining Tracked Outputs + +To define something as a tracked output, you can annotate the input of +the task with `Annotated[Path, Product]`, where `Product` is imported +from `pytask`. This tells `pytask` that this is a product of the task +and should be saved in the build directory. + +In this example, we’re generating random data into a data frame and +saving the object as a pickle in the `bld` directory (`bld` is the +default build directory for `pytask`’s intermediate data). To get that +directory, we use the `BLD` variable from the `era5_sandbox.config` +module as above. This module itself could also be generated using +`nbdev` if you want to keep your configuration in notebooks. + +Using `nbdev`, we can also include all of the bells and whistles of +function documentation. + +------------------------------------------------------------------------ + +source + +### task_create_random_data + +> task_create_random_data (seed:typing.Annotated[int,42], path_to_data:typi +> ng.Annotated[pathlib.Path,ProductType()]=Path('/ +> net/rcstorenfs02/ifs/rc_labs/dominici_lab/lab/da +> ta_processing/csph-era5_sandbox/bld/data.pkl')) + +*Create a random data set and save it as a pickle file. Return the path +to the saved file.* + + ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
seedAnnotatedDefault seed for reproducibility
path_to_dataAnnotated/net/rcstorenfs02/ifs/rc_labs/dominici_lab/lab/data_processing/csph-era5_sandbox/bld/data.pklPath to the object in the build directory
ReturnsNone
+ +
+Exported source + +``` python +def task_create_random_data( + seed: Annotated[int, 42], # Default seed for reproducibility + path_to_data: Annotated[Path, Product] = BLD / "data.pkl" # Path to the object in the build directory + ) -> None: + "Create a random data set and save it as a pickle file. Return the path to the saved file." + rng = np.random.default_rng(seed) + beta = 2 + + x = rng.normal(loc=5, scale=10, size=1_000) + epsilon = rng.standard_normal(1_000) + + y = beta * x + epsilon + + df = pd.DataFrame({"x": x, "y": y}) + + # this is a tracked output, so we annotate the return value with `Annotated[Path, Product]` + df.to_pickle(path_to_data) +``` + +
+ +We can test the function directly in the notebook: + +``` python +task_create_random_data(42) +``` + +Once this module and function are exported with `nbdev_export`, the +functions are in a python package. We can then use the command line to +look at the registered tasks: + +``` sh +pytask collect +``` + +Let’s add another task in the same module. This task plots the data we +generated. To link the previous task to this one as a dependency, we can +list the output of the previous task as an input to this one. This way, +`pytask` will know that it needs to run the first task before this one. + +------------------------------------------------------------------------ + +source + +### task_plot_data + +> task_plot_data (path_to_data:typing.Annotated[pathlib.Path,Path('/net/rcs +> torenfs02/ifs/rc_labs/dominici_lab/lab/data_processing/cs +> ph-era5_sandbox/bld/data.pkl')], path_to_plot:typing.Anno +> tated[pathlib.Path,ProductType()]=Path('/net/rcstorenfs02 +> /ifs/rc_labs/dominici_lab/lab/data_processing/csph- +> era5_sandbox/bld/plot.png')) + +*Plot the data from the pickle file and save the plot. Note that this +task: 1. depends on the data.pkl file created by the previous task, 2. +does not return any value, but saves a plot to the build directory. So +the side effect of the task is what we are interested in here (though +this is probably bad practice).* + + ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
path_to_dataAnnotatedPath to the data file created by the previous task
path_to_plotAnnotated/net/rcstorenfs02/ifs/rc_labs/dominici_lab/lab/data_processing/csph-era5_sandbox/bld/plot.pngPath to the build directory for the plot
ReturnsNone
+ +
+Exported source + +``` python +def task_plot_data( + path_to_data: Annotated[Path, BLD / "data.pkl"], # Path to the data file created by the previous task + path_to_plot: Annotated[Path, Product] = BLD / "plot.png" # Path to the build directory for the plot +) -> None: + """ + Plot the data from the pickle file and save the plot. Note that this task: + 1. depends on the data.pkl file created by the previous task, + 2. does not return any value, but saves a plot to the build directory. So the side effect of the task is what we are interested in here (though this is probably bad practice). + """ + + df = pd.read_pickle(path_to_data) + + _, ax = plt.subplots() + df.plot(x="x", y="y", ax=ax, kind="scatter") + + plt.savefig(path_to_plot) + plt.close() +``` + +
+ +We now have a DAG of tasks that `pytask` can execute. To see the tasks, +we can use the command line to create a pygraphviz graph of the tasks: + +``` bash +pytask dag +``` + +The DAG is saved as a pdf file, and you can view it using any viewer. +Now, to run the pipeline, just invoke `pytask` at the command line: + +``` bash +pytask +``` + +In Jupyter or iPython, you can interact with the task outputs directly: + +``` python +# list all the files in the build directory +for file in os.listdir(BLD): + print(file) +``` + +We can use these to build subsequent tasks later. + +## More Complex Tasks & The Data Catalog + +As we define more complex tasks, we can use the `pytask` data catalog to +manage the inputs and outputs of our tasks. The data catalog allows us +to imperatively name the data and their formats, making it easier to +manage the data flow in our tasks. Importantly, we can define the data +pythonically, which allows us to use the full power of Python to +manipulate and transform our data. This is particularly more useful than +snakemake’s approach, which requires you to define the data in a more +static way using paths and a separate pseudo-language. + +The content of the `era5_sandbox.config` module can be extended to +include a data catalog: + +``` python +from pathlib import Path +from pytask import DataCatalog, Product + +SRC = Path(__file__).parent.resolve() +BLD = SRC.joinpath("..", "..", "bld").resolve() + +demo_catalog = DataCatalog() +``` + +With just this definition, we’re now able to refer directly to data by +name in our tasks, and `pytask` will handle the paths and formats for +us. This allows us to focus on the logic of our tasks rather than the +details of data management. + +
+ +> **Note** +> +> This is a major advantage of `pytask` over `snakemake`, as it allows +> you to define the data in a more flexible and Pythonic way, while +> still maintaining the benefits of a task management system. It is a +> similar approach to building pipelines in R with targets, which allows +> you to define the data in a more flexible way. + +
+ +Let’s create a task that modifies the data frame by adding a new column. +This task will depend on the previous task’s output, and we will use the +data catalog to define the input and output data. + +------------------------------------------------------------------------ + +source + +### task_add_one + +> task_add_one (path_to_data:typing.Annotated[pathlib.Path,Path('/net/rcsto +> renfs02/ifs/rc_labs/dominici_lab/lab/data_processing/csph- +> era5_sandbox/bld/data.pkl')], node:typing.Annotated[_pytask +> .nodes.PickleNode,ProductType()]=PickleNode(path=Path('/net +> /rcstorenfs02/ifs/rc_labs/dominici_lab/lab/data_processing/ +> csph-era5_sandbox/.pytask/data_catalogs/default/1eef510d81e +> ea49161cd821b318aa999e630bdd292b093aa9a9319e9f282b984.pkl') +> , name='mydata', attributes={'catalog_name': 'default'}, +> serializer=, deserializer= function load>)) + +*Add one to the ‘y’ column of the data frame and save it as a new pickle +file.* + + ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TypeDefaultDetails
path_to_dataAnnotatedPath to the data file created by the previous task
nodeAnnotatedPickleNode(path=Path(‘/net/rcstorenfs02/ifs/rc_labs/dominici_lab/lab/data_processing/csph-era5_sandbox/.pytask/data_catalogs/default/1eef510d81eea49161cd821b318aa999e630bdd292b093aa9a9319e9f282b984.pkl’), +name=‘mydata’, attributes={‘catalog_name’: ‘default’}, +serializer=, +deserializer=)
ReturnsNone
+ +
+Exported source + +``` python +def task_add_one( + path_to_data: Annotated[Path, BLD / "data.pkl"], # Path to the data file created by the previous task + node: Annotated[PickleNode, Product] = demo_catalog["mydata"] +) -> None: + """ + Add one to the 'y' column of the data frame and save it as a new pickle file. + """ + df = pd.read_pickle(path_to_data) + df['z'] = df['y'] + 1 + + node.save(df) +``` + +
+ +In this function, we’ve defined that the task relies on the output of +the first task being there, the `data.pkl` file. But importantly, we’ve +also defined our product as a `node` from the `PickleNode` module. This +will allow `pytask` to handle the serialization and deserialization of +the data frame automatically, so we don’t have to worry about the +details of how the data is stored. We create the datacatalog in our +config file, and then tell this task to create a Node in that catalog +called `mydata`. Whatever we save with the `node.save()` method will be +saved in the build directory, but more importantly *will be indexed and +hashed by `pytask`*. This means that if the data changes, `pytask` will +know to rerun the task. + +To make this even more pythonic, we can modify the format of our task +function so that the return type annotator is used as a node in the data +catalog. This allows us to define the output of the task as a +`PickleNode`, which will automatically handle the serialization and +deserialization of the data frame. + +
+ +> **Note** +> +> This is another trick I’m deriving from {targets}. By formatting tasks +> as pure functions where inputs are parameters and targets are return +> type annotations, we can define the output of the task as a +> `PickleNode`, which will automatically handle the serialization and +> deserialization of the data frame. This again allows us to focus on +> the logic of our tasks rather than the details of data management. + +
+ +So below, we’re directly accessing the `data_catalog` to get the +`mydata` node, and then modifying it by adding a new column. It *feels* +like we are doing this in place, such as in an iPython session, because +we are allowing `pytask` to handle the serialization of the file on disk +for us. + +------------------------------------------------------------------------ + +source + +### task_add_another_column + +> task_add_another_column (df:typing.Annotated[pandas.core.frame.DataFrame, +> PickleNode(path=Path('/net/rcstorenfs02/ifs/rc_l +> abs/dominici_lab/lab/data_processing/csph-era5_s +> andbox/.pytask/data_catalogs/default/1eef510d81e +> ea49161cd821b318aa999e630bdd292b093aa9a9319e9f28 +> 2b984.pkl'),name='mydata',attributes={'catalog_n +> ame':'default'},serializer= infunctiondump>,deserializer= infunctionload>)]) + +*Add another column to the data frame stored in the PickleNode.* + + +++++ + + + + + + + + + + + + + + + + + + + +
TypeDetails
dfAnnotatedwhich object in the catalog to fetch from the catalog with +node.load()
ReturnsAnnotatedwhich object in the catalog to save the return value +to
+ +
+Exported source + +``` python +def task_add_another_column( + df: Annotated[pd.DataFrame, demo_catalog["mydata"]] # which object in the catalog to fetch from the catalog with node.load() +) -> Annotated[pd.DataFrame, demo_catalog["mydata2"]]: # which object in the catalog to save the return value to + """ + Add another column to the data frame stored in the PickleNode. + """ + + # use the datacatalog directly to access the node + # this is a bit like accessing the node in an iPython session, but pytask + # will handle the serialization and deserialization for us + df['w'] = df['z'] * df['y'] + + return df +``` + +
+ +To test this interactively, we’d have to import the data catalog’s +object + +``` python +df = demo_catalog["mydata"].load() # load the data frame from the PickleNode +result = task_add_another_column(df) # call the task function with the loaded data frame +``` + +``` python +result +``` + +Now that we know it will work, we can invoke pytask: + +``` sh +pytask +``` + +Notice that the outputs are cached and not recomputed unless the inputs +change. This is a key feature of `pytask` and other DAGs, allowing you +to efficiently manage your data processing tasks without unnecessary +recomputation. + +## Conclusion + +The takeaway here is that with `pytask`, you can define pure functions +that take inputs and return outputs, and build a DAG of tasks that can +be executed in a flexible and efficient way. This allows you to focus on +the logic of your tasks rather than the details of data management, +while still maintaining the benefits of a task management system. The +key elements are: + +- **Task annotation**: You define your tasks by creating pure functions + that take inputs and return outputs, and use decorators or naming + conventions to mark them as “tasks” in a dag +- **Input and output annotation**: You define the inputs and outputs of + your tasksusing type hints, and allow `pytask` to automatically detect + and handle the dependencies between tasks. +- **Data catalog**: You define your data in a Pythonic object in your + config called `data_catalog`. As you iteratively develop your DAG, you + add objects to the data catalog, which are called nodes. As long as a + node is a pythonic object and has a pickle method, `pytask` will + handle the serialization and deserialization of the data for you. diff --git a/_docs/20_pytask_config.html b/_docs/20_pytask_config.html new file mode 100644 index 0000000..aaf6c2a --- /dev/null +++ b/_docs/20_pytask_config.html @@ -0,0 +1,927 @@ + + + + + + + + + +pytask Config: Defining the Pipeline Internals in pytask – era5_sandbox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

pytask Config: Defining the Pipeline Internals in pytask

+
+ + + +
+ + + + +
+ + + +
+ + +
+

config

+
+

This is the config module for the pytask pipeline. This module defines the data catalog(s) and any hard-coded parameters that are used throughout the pipeline.

+
+ +
+
+Exported source +
import pandas as pd
+
+from pathlib import Path
+from pyprojroot import here
+from pytask import DataCatalog
+
+
+SRC = here() / "src" / "era5_sandbox"
+BLD = here() / "bld"
+
+demo_catalog = DataCatalog()
+
+
+
+
+

DEV_MODE: A Quick Development Flag

+

I’m adding a flag to the config that can be used for quick development. If you import this boolean variable, it can be used to skip tasks, setup samples, etc. on the fly by marking a task with the pytask.mark.skipif decorator. Change this to False when you’re ready to run the full pipeline.

+
+
+Exported source +
DEV_MODE=True
+
+
+
+
+

The Data Catalog

+

To manage our pipeline, we’re going to use a nested data catalog structure. This way, we can easily return specific entries to specific tasks without having to manage multiple different data catalogs. Specifically, we’ll have a data catalog for each stage of the pipeline, and each catalog will have entries for the inputs, outputs, and any other parameters needed for that stage. This is similar to how we used Hydra configs, but using the pytask data catalog, we can more easily gather the data for a specific task in structured manner entirely in Python.

+
+
+Exported source +
stages = ["mydata", 'mydata2', # from the demo, ignore
+    "download", # download task
+    "aggregate", # aggregation task
+    "publish", # publishing task
+    "viz"] # visualization task
+
+buckets = [
+    "inputs", # any specific inputs, eg for carrying over between tasks
+    "outputs", # specific output task returns
+    "jobs", # job parameters as a dataframe
+    "params" # any lingering hardcoded parameters
+    ]
+
+data_catalog = {
+
+    stage: {bucket: DataCatalog(name=f"{stage}_{bucket}") for bucket in buckets}
+    for stage in stages
+}
+
+
+
+
+

The Download Task

+

A good strategy may be to set pipeline stage parameters in the config file, and then use the pytask data catalog to manage the data. This way, we can easily change the parameters without having to modify the code. This is especially useful for the API query, where we need to be able to set the parameter grid for the years and data types we want to download data for. So, let’s create an entry in the data catalog specifically for the download task.

+

A good strategy I thought about for grid parameter comprehension is to create a dataframe expands all the combinations of parameters, and then uses each combination to create the tasks which are then easily added to the data catalog. This way, we can still easily inspect the pipeline and see what tasks are being run, while also being able to easily change the parameters in the config file without too much hassle.

+

An important framework decision I’m making here is that each ROW of the dataframe corresponds to a single task, so that we can quickly understand at a glance what the task is doing, and also easily develop the code for the task itself. This is different from the hydra approach where a job is first specified by a default config, and then the parameters are swept over in multiple config files. This is a more flexible approach, IMO, because:

+
    +
  1. each row defines a single task run, so it’s easy to understand what the run is doing
    2. +
  2. it’s easy to add or remove runs by simply expanding the list of parameters and using dataframe filters to remove irrelevant parameter combinations
    3. +
  3. we don’t have to independently inspect and manage multiple different/overriding config files
    4. +
  4. it’s all in Python, so we can use the full power of the language to define the parameters and the tasks in a single sweep, not through the need of hydra+snakemake multi stage/multi-lingual config system
    5. +
+

So, to do this, we define one job as a query to the CDS API that must contain: - The dataset (re-analysis) - The year - The month - All days in the month - All times of day (hour) - The geography (region), which will need: - The URL to the shapefile to calculate the bounding box

+

Given one combination of all of these, a single SLURM job can complete the first “task” in parallel by having a run assigned to each row of the dataframe.

+
+
+Exported source +
# Dimensions
+years = [str(x) for x in range(2009, 2025)]  # 16 years
+months = [str(x).zfill(2) for x in range(1, 13)]      # 12 months
+geographies = ["madagascar", "nepal"]  # 2 geographies
+
+# nested values; we want ALL days, times, and variables for each job
+days = [str(x).zfill(2) for x in range(1, 32)]
+times = [f"{x:02d}:00" for x in range(24)]
+variables = ["2m_dewpoint_temperature", "2m_temperature", "total_precipitation", "volumetric_soil_water_layer_1"]
+
+product_type = "reanalysis"
+
+# Map shapefiles to geography
+shapefiles = {
+    "madagascar": "https://data.humdata.org/dataset/26fa506b-0727-4d9d-a590-d2abee21ee22/resource/ed94d52e-349e-41be-80cb-62dc0435bd34/download/mdg_adm_bngrc_ocha_20181031_shp.zip",
+    "nepal": "https://data.humdata.org/dataset/07db728a-4f0f-4e98-8eb0-8fa9df61f01c/resource/2eb4c47f-fd6e-425d-b623-d35be1a7640e/download/npl_adm_nd_20240314_ab_shp.zip"
+}
+
+# Build row-wise combinations of (year, month, geography)
+rows = []
+for year in years:
+    for month in months:
+        for geo in geographies:
+            rows.append({
+                "year": year,
+                "month": month,
+                "geography": geo,
+                "shapefile": shapefiles[geo],
+                "product_type": product_type,
+                "day": days,
+                "time": times,
+                "variables": variables,
+                "output": f"{year}_{month}_{geo}"
+            })
+
+# Create dataframe
+query_df = pd.DataFrame(rows)
+
+
+
+
query_df
+
+
+
print(f"Number of estimated jobs: {query_df.shape[0]}. Examples...")
+
+for i, row in query_df.sample(3).iterrows():
+    print(f"Year: {row['year']}, Month: {row['month']}, Geography: {row['geography']}, Link: {row['shapefile']}, Variables: {row['variables']}")
+
+

Now add them to the catalog. We’re going to use a dictionary to nest data catalogs so that we can return specific task products to named data catalog nodes.

+

Our data catalog now has a download|jobs node with a queries_df entry that contains the dataframe of all the jobs to be run in this task.

+
+
data_catalog['download']['jobs']['queries_df'].load().head()
+
+
+
+

The Aggregation Task

+

To carry out the aggregation, we will follow similar logic to the original pipeline and use xarray to aggregate data into spatial and temporal averages. The aggregation task will take the downloaded data and compute the mean over the specified time period and spatial region. However, in this case, we want to aggregate the data diurnally, so we will need to fetch the sundown and sunrise times for the region and use them to compute the diurnal averages.

+

Once again, we will use a dataframe to define the parameters for the aggregation task.

+

Here we will use a dataframe with the jobs as rows; the first column is “input” which is the list of query names from the download task, and the last column is the output object name. Columns in between can be the parameters needed for the aggregation task, which then get expanded to the full list of jobs with itertools.product, explode or similar, and filtered as necessary.

+

For explanations of the parameters, see the Aggregation Task notebook’s final task_aggregate_data_diurnal function.

+
+
+Exported source +
inputs = query_df["output"].tolist()
+outputs = [f"{i}_agg" for i in inputs]
+
+variable_dict = {
+    "2m_dewpoint_temperature": "d2m",
+    "2m_temperature": "t2m",
+    "total_precipitation": "tp",
+    "volumetric_soil_water_layer_1": "swvl1"
+}
+
+# list of params that get fed into the task functions
+agg_params = {
+    "time": ["day", "night"],
+    "solar_classification": ["before"],
+    "variables": variables,
+    "variables_short": [variable_dict[x] for x in variables],
+    "aggregation_name": ["mean", "sum", "max", "min"]
+}
+
+from itertools import product
+import pandas as pd
+
+# expand all the params
+agg_params = pd.DataFrame(list(product(*agg_params.values())), columns=agg_params.keys())
+
+
+

Inspecting it:

+
+
agg_params
+
+

Let’s keep only rows where the variables and variables_short match

+
+
+Exported source +
agg_params = agg_params[agg_params.apply(lambda x: variable_dict[x['variables']] == x['variables_short'], axis=1)]
+
+
+
+
agg_params
+
+

Great, and now keeping sum only for total precipitation (we don’t need mean, max, min for that variable), and removing sum for all other variables (we don’t need sum for temperature or soil moisture):

+
+
+Exported source +
mask = (agg_params['variables_short'] == "tp") & (agg_params['aggregation_name'] != "sum")
+agg_params = agg_params[~mask]
+
+# remove rows where non-tp aggregation is sum
+mask = (agg_params['variables_short'] != "tp") & (agg_params['aggregation_name'] == "sum")
+agg_params = agg_params[~mask]
+
+
+
+
agg_params
+
+

Now we add the input and output columns by joining:

+
+
+Exported source +
inputs = pd.DataFrame({"input": inputs})
+aggregate_jobs = inputs.merge(agg_params, how="cross")
+
+
+

This result gives us the full list of jobs for the aggregation task. 20 rows for the parameters, and 384 inputs/outputs, giving a total of 7680 jobs:

+
+
assert aggregate_jobs.shape[0] == 20 * len(inputs)
+aggregate_jobs
+
+

A few more configuration items need to be added, like the local timezone for each geography, the healthshed filename, the healthshed unique ID variable name in the shapefile, and whether the variable is instantaneous or accumulated:

+
+
+Exported source +
aggregate_jobs['local_tz'] = aggregate_jobs['input'].apply(
+    lambda x: "Asia/Kathmandu" if "nepal" in x else "Indian/Antananarivo"
+)
+aggregate_jobs['shapefile'] = aggregate_jobs['input'].apply(
+    lambda x: "Nepal_Healthsheds2024.zip" if "nepal" in x else "healthsheds2022.zip"
+)
+
+aggregate_jobs['hshd_unique_id'] = aggregate_jobs['input'].apply(
+    lambda x: "fid" if "nepal" in x else "fs_uid"
+)
+
+aggregate_jobs['climate_handler_var'] = aggregate_jobs['variables_short'].apply(
+    lambda x: "accum" if x == "tp" else "instant"
+)
+
+
+
+
aggregate_jobs
+
+

Now we add this to the data catalog:

+
+
+Exported source +
data_catalog['aggregate']['jobs'].add("jobs_df", aggregate_jobs)
+
+
+

Our data catalog now has an aggregate|jobs node with a jobs_df entry that contains the dataframe of all the jobs to be run in this task.

+
+
data_catalog['aggregate']['jobs']['jobs_df'].load().head()
+
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/_docs/20_pytask_config.md b/_docs/20_pytask_config.md new file mode 100644 index 0000000..689d80c --- /dev/null +++ b/_docs/20_pytask_config.md @@ -0,0 +1,365 @@ +# `pytask` Config: Defining the Pipeline Internals in `pytask` + + +## config + +> This is the config module for the `pytask` pipeline. This module +> defines the data catalog(s) and any hard-coded parameters that are +> used throughout the pipeline. + + + +
+Exported source + +``` python +import pandas as pd + +from pathlib import Path +from pyprojroot import here +from pytask import DataCatalog + + +SRC = here() / "src" / "era5_sandbox" +BLD = here() / "bld" + +demo_catalog = DataCatalog() +``` + +
+ +## `DEV_MODE`: A Quick Development Flag + +I’m adding a flag to the config that can be used for quick development. +If you import this boolean variable, it can be used to skip tasks, setup +samples, etc. on the fly by `marking` a task with the +`pytask.mark.skipif` decorator. Change this to `False` when you’re ready +to run the full pipeline. + +
+Exported source + +``` python +DEV_MODE=True +``` + +
+ +## The Data Catalog + +To manage our pipeline, we’re going to use a nested data catalog +structure. This way, we can easily return specific entries to specific +tasks without having to manage multiple different data catalogs. +Specifically, we’ll have a data catalog for each stage of the pipeline, +and each catalog will have entries for the inputs, outputs, and any +other parameters needed for that stage. This is similar to how we used +Hydra configs, but using the `pytask` data catalog, we can more easily +gather the data for a specific task in structured manner entirely in +Python. + +
+Exported source + +``` python +stages = ["mydata", 'mydata2', # from the demo, ignore + "download", # download task + "aggregate", # aggregation task + "publish", # publishing task + "viz"] # visualization task + +buckets = [ + "inputs", # any specific inputs, eg for carrying over between tasks + "outputs", # specific output task returns + "jobs", # job parameters as a dataframe + "params" # any lingering hardcoded parameters + ] + +data_catalog = { + + stage: {bucket: DataCatalog(name=f"{stage}_{bucket}") for bucket in buckets} + for stage in stages +} +``` + +
+ +## The Download Task + +A good strategy may be to set pipeline stage parameters in the config +file, and then use the `pytask` data catalog to manage the data. This +way, we can easily change the parameters without having to modify the +code. This is especially useful for the API query, where we need to be +able to set the parameter grid for the years and data types we want to +download data for. So, let’s create an entry in the data catalog +specifically for the download task. + +A good strategy I thought about for grid parameter comprehension is to +create a dataframe expands all the combinations of parameters, and then +uses each combination to create the tasks which are then easily added to +the data catalog. This way, we can still easily inspect the pipeline and +see what tasks are being run, while also being able to easily change the +parameters in the config file without too much hassle. + +An important framework decision I’m making here is that each ROW of the +dataframe corresponds to a single task, so that we can quickly +understand at a glance what the task is doing, and also easily develop +the code for the task itself. This is different from the hydra approach +where a job is first specified by a default config, and then the +parameters are swept over in multiple config files. This is a more +flexible approach, IMO, because: + +1. each row defines a single task run, so it’s easy to understand what + the run is doing +2. it’s easy to add or remove runs by simply expanding the list of + parameters and using dataframe filters to remove irrelevant + parameter combinations +3. we don’t have to independently inspect and manage multiple + different/overriding config files +4. it’s all in Python, so we can use the full power of the language to + define the parameters and the tasks in a single sweep, not through + the need of hydra+snakemake multi stage/multi-lingual config system + +So, to do this, we define one job as a query to the CDS API that must +contain: - The dataset (re-analysis) - The year - The month - All days +in the month - All times of day (hour) - The geography (region), which +will need: - The URL to the shapefile to calculate the bounding box + +Given one combination of all of these, a single SLURM job can complete +the first “task” in parallel by having a run assigned to each row of the +dataframe. + +
+Exported source + +``` python +# Dimensions +years = [str(x) for x in range(2009, 2025)] # 16 years +months = [str(x).zfill(2) for x in range(1, 13)] # 12 months +geographies = ["madagascar", "nepal"] # 2 geographies + +# nested values; we want ALL days, times, and variables for each job +days = [str(x).zfill(2) for x in range(1, 32)] +times = [f"{x:02d}:00" for x in range(24)] +variables = ["2m_dewpoint_temperature", "2m_temperature", "total_precipitation", "volumetric_soil_water_layer_1"] + +product_type = "reanalysis" + +# Map shapefiles to geography +shapefiles = { + "madagascar": "https://data.humdata.org/dataset/26fa506b-0727-4d9d-a590-d2abee21ee22/resource/ed94d52e-349e-41be-80cb-62dc0435bd34/download/mdg_adm_bngrc_ocha_20181031_shp.zip", + "nepal": "https://data.humdata.org/dataset/07db728a-4f0f-4e98-8eb0-8fa9df61f01c/resource/2eb4c47f-fd6e-425d-b623-d35be1a7640e/download/npl_adm_nd_20240314_ab_shp.zip" +} + +# Build row-wise combinations of (year, month, geography) +rows = [] +for year in years: + for month in months: + for geo in geographies: + rows.append({ + "year": year, + "month": month, + "geography": geo, + "shapefile": shapefiles[geo], + "product_type": product_type, + "day": days, + "time": times, + "variables": variables, + "output": f"{year}_{month}_{geo}" + }) + +# Create dataframe +query_df = pd.DataFrame(rows) +``` + +
+ +``` python +query_df +``` + +``` python +print(f"Number of estimated jobs: {query_df.shape[0]}. Examples...") + +for i, row in query_df.sample(3).iterrows(): + print(f"Year: {row['year']}, Month: {row['month']}, Geography: {row['geography']}, Link: {row['shapefile']}, Variables: {row['variables']}") +``` + +Now add them to the catalog. We’re going to use a dictionary to nest +data catalogs so that we can return specific task products to named data +catalog nodes. + +Our data catalog now has a `download|jobs` node with a `queries_df` +entry that contains the dataframe of all the jobs to be run in this +task. + +``` python +data_catalog['download']['jobs']['queries_df'].load().head() +``` + +## The Aggregation Task + +To carry out the aggregation, we will follow similar logic to the +original pipeline and use xarray to aggregate data into spatial and +temporal averages. The aggregation task will take the downloaded data +and compute the mean over the specified time period and spatial region. +However, in this case, we want to aggregate the data diurnally, so we +will need to fetch the sundown and sunrise times for the region and use +them to compute the diurnal averages. + +Once again, we will use a dataframe to define the parameters for the +aggregation task. + +Here we will use a dataframe with the jobs as rows; the first column is +“input” which is the list of query names from the download task, and the +last column is the output object name. Columns in between can be the +parameters needed for the aggregation task, which then get expanded to +the full list of jobs with `itertools.product`, `explode` or similar, +and filtered as necessary. + +For explanations of the parameters, see the Aggregation Task notebook’s +final `task_aggregate_data_diurnal` function. + +
+Exported source + +``` python +inputs = query_df["output"].tolist() +outputs = [f"{i}_agg" for i in inputs] + +variable_dict = { + "2m_dewpoint_temperature": "d2m", + "2m_temperature": "t2m", + "total_precipitation": "tp", + "volumetric_soil_water_layer_1": "swvl1" +} + +# list of params that get fed into the task functions +agg_params = { + "time": ["day", "night"], + "solar_classification": ["before"], + "variables": variables, + "variables_short": [variable_dict[x] for x in variables], + "aggregation_name": ["mean", "sum", "max", "min"] +} + +from itertools import product +import pandas as pd + +# expand all the params +agg_params = pd.DataFrame(list(product(*agg_params.values())), columns=agg_params.keys()) +``` + +
+ +Inspecting it: + +``` python +agg_params +``` + +Let’s keep only rows where the variables and variables_short match + +
+Exported source + +``` python +agg_params = agg_params[agg_params.apply(lambda x: variable_dict[x['variables']] == x['variables_short'], axis=1)] +``` + +
+ +``` python +agg_params +``` + +Great, and now keeping `sum` only for total precipitation (we don’t need +mean, max, min for that variable), and removing `sum` for all other +variables (we don’t need sum for temperature or soil moisture): + +
+Exported source + +``` python +mask = (agg_params['variables_short'] == "tp") & (agg_params['aggregation_name'] != "sum") +agg_params = agg_params[~mask] + +# remove rows where non-tp aggregation is sum +mask = (agg_params['variables_short'] != "tp") & (agg_params['aggregation_name'] == "sum") +agg_params = agg_params[~mask] +``` + +
+ +``` python +agg_params +``` + +Now we add the input and output columns by joining: + +
+Exported source + +``` python +inputs = pd.DataFrame({"input": inputs}) +aggregate_jobs = inputs.merge(agg_params, how="cross") +``` + +
+ +This result gives us the full list of jobs for the aggregation task. 20 +rows for the parameters, and 384 inputs/outputs, giving a total of 7680 +jobs: + +``` python +assert aggregate_jobs.shape[0] == 20 * len(inputs) +aggregate_jobs +``` + +A few more configuration items need to be added, like the local timezone +for each geography, the healthshed filename, the healthshed unique ID +variable name in the shapefile, and whether the variable is +instantaneous or accumulated: + +
+Exported source + +``` python +aggregate_jobs['local_tz'] = aggregate_jobs['input'].apply( + lambda x: "Asia/Kathmandu" if "nepal" in x else "Indian/Antananarivo" +) +aggregate_jobs['shapefile'] = aggregate_jobs['input'].apply( + lambda x: "Nepal_Healthsheds2024.zip" if "nepal" in x else "healthsheds2022.zip" +) + +aggregate_jobs['hshd_unique_id'] = aggregate_jobs['input'].apply( + lambda x: "fid" if "nepal" in x else "fs_uid" +) + +aggregate_jobs['climate_handler_var'] = aggregate_jobs['variables_short'].apply( + lambda x: "accum" if x == "tp" else "instant" +) +``` + +
+ +``` python +aggregate_jobs +``` + +Now we add this to the data catalog: + +
+Exported source + +``` python +data_catalog['aggregate']['jobs'].add("jobs_df", aggregate_jobs) +``` + +
+ +Our data catalog now has an `aggregate|jobs` node with a `jobs_df` entry +that contains the dataframe of all the jobs to be run in this task. + +``` python +data_catalog['aggregate']['jobs']['jobs_df'].load().head() +``` diff --git a/_docs/20_pytask_logger.html b/_docs/20_pytask_logger.html new file mode 100644 index 0000000..bb3016d --- /dev/null +++ b/_docs/20_pytask_logger.html @@ -0,0 +1,734 @@ + + + + + + + + + +Logging: A simple logger to inject into pytask jobs – era5_sandbox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Logging: A simple logger to inject into pytask jobs

+
+ + + +
+ + + + +
+ + + +
+ + +
+

logger

+
+

A simple logger module for the pytask tasks

+
+ +
+

source

+
+

setup_logger

+
+
 setup_logger (name:str, log_path:pathlib.Path=Path('/net/rcstorenfs02/ifs
+               /rc_labs/dominici_lab/lab/data_processing/csph-
+               era5_sandbox/logs/2025-09-25/13-57-20'), level=20)
+
+
+
+Exported source +
import logging
+from pathlib import Path
+from pyprojroot import here
+from datetime import datetime
+
+LOG_DIR = here("logs")
+# get the date & time for the log file name
+log_date = datetime.now().strftime("%Y-%m-%d")
+log_time = datetime.now().strftime("%H-%M-%S")
+LOG_DIR = here("logs") / log_date / log_time
+
+
+
+
+Exported source +
def setup_logger(name: str, log_path: Path=LOG_DIR, level=logging.INFO) -> logging.Logger:
+    log_path.mkdir(parents=True, exist_ok=True)
+    formatter = logging.Formatter('%(asctime)s — %(name)s — %(levelname)s — %(message)s')
+
+    handler = logging.FileHandler(log_path / f"{name}.log", mode='a')
+    handler.setFormatter(formatter)
+
+    logger = logging.getLogger(name)
+    logger.setLevel(level)
+    logger.addHandler(handler)
+    logger.propagate = False
+
+    return logger
+
+
+ + +
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/_docs/20_pytask_logger.md b/_docs/20_pytask_logger.md new file mode 100644 index 0000000..315040d --- /dev/null +++ b/_docs/20_pytask_logger.md @@ -0,0 +1,59 @@ +# Logging: A simple logger to inject into `pytask` jobs + + +## logger + +> A simple logger module for the pytask tasks + + + +------------------------------------------------------------------------ + +source + +### setup_logger + +> setup_logger (name:str, log_path:pathlib.Path=Path('/net/rcstorenfs02/ifs +> /rc_labs/dominici_lab/lab/data_processing/csph- +> era5_sandbox/logs/2025-09-25/13-57-20'), level=20) + +
+Exported source + +``` python +import logging +from pathlib import Path +from pyprojroot import here +from datetime import datetime + +LOG_DIR = here("logs") +# get the date & time for the log file name +log_date = datetime.now().strftime("%Y-%m-%d") +log_time = datetime.now().strftime("%H-%M-%S") +LOG_DIR = here("logs") / log_date / log_time +``` + +
+ +
+Exported source + +``` python +def setup_logger(name: str, log_path: Path=LOG_DIR, level=logging.INFO) -> logging.Logger: + log_path.mkdir(parents=True, exist_ok=True) + formatter = logging.Formatter('%(asctime)s — %(name)s — %(levelname)s — %(message)s') + + handler = logging.FileHandler(log_path / f"{name}.log", mode='a') + handler.setFormatter(formatter) + + logger = logging.getLogger(name) + logger.setLevel(level) + logger.addHandler(handler) + logger.propagate = False + + return logger +``` + +
diff --git a/_docs/21_pytask_download.html b/_docs/21_pytask_download.html new file mode 100644 index 0000000..14934ce --- /dev/null +++ b/_docs/21_pytask_download.html @@ -0,0 +1,748 @@ + + + + + + + + + +Download: download Module as a pytask Task – era5_sandbox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Download: download Module as a pytask Task

+
+ + + +
+ + + + +
+ + + +
+ + +
+

task_download

+
+

This module downloads the raw era5 data from the CDS API. It is similar to the original script, refactored for pytask.

+
+ +

We’re going to quickly refactor the pipeline to use pytask instead of hydra and snakemake. This will hopefully demonstrate a simpler and more flexible way to manage data pipelines in Python.

+

To start off, we need to create a function that queries the CDS API with one job. This function will be used to download the data for each query in the range specified in the data catalog in the config file.

+

Let’s take a look at the data catalog we created in the config module:

+

You can see the queries entry we created in the data catalog. Each query is a row of a dataframe that contains the parameters for the CDS API query.

+
+
queries = data_catalog['download']['jobs']['queries_df'].load()
+queries
+
+

We can test this query like we did in the original work:

+
+
example_query = queries.iloc[0]
+
+create_bounding_box(example_query['shapefile'])
+
+

In this way, we have a similar approach as Hydra configs, but, using the pytask data catalog, we can more easily gather the data for a specific task in structured manner entirely in Python.

+
+
client = cdsapi.Client()
+
+ex_bounding_box = create_bounding_box(example_query['shapefile'])
+
+request = {
+            "product_type": example_query['product_type'],
+            "variable": example_query['variables'], 
+            "year": str(example_query['year']),
+            "month": str(example_query['month']),
+            "day": example_query['day'],
+            "time": example_query['time'],
+            "data_format": "netcdf",
+            "download_format": "unarchived",
+            "area": ex_bounding_box
+        }
+
+target = f"{example_query['output']}.nc"
+
+client.retrieve("reanalysis-era5-single-levels", request).download(target)
+
+

This works! So now we just need to create a task_ function that pytask will recognise to parallelise the download of queries over:

+
+

How this works (with some help from GPT):

+
+

🧠 How pytask Discovers and Executes Tasks

+

When you run pytask, it automatically scans your project for Python files named task_*.py. In these files, it looks for: - Functions decorated with @task, or - Functions prefixed with task_

+

These functions are not executed immediately. Instead, pytask: 1. Imports each task_*.py module (just like Python would) 2. Registers any matching task functions as nodes in a directed acyclic graph (DAG) 3. Resolves dependencies by analyzing: - Input annotations (e.g., Annotated[x, DependsOn]) - Output declarations (e.g., return values or Product annotations) 4. Builds the DAG, where each task function is a node 5. Executes the tasks, respecting dependency order and skipping up-to-date nodes

+

So even though the task functions aren’t explicitly “run” in the Python code itself, pytask knows how and when to execute them — based on their position in the DAG.

+
+
+

🔄 How This Differs from Snakemake

+

In snakemake, you’re expected to define a series of explicitly executable rules, often using shell commands or Python scripts. You “stitch together” rules using filenames and wildcard matching.

+

In contrast: - 🐍 pytask is Python-native — tasks are just regular Python functions - ⚙️ It builds a DAG from those functions and tracks inputs/outputs automatically - 🧱 You are declaring nodes, not scripting execution

+

Think of your Python files not as scripts to run, but as a way to define and wire together declarative tasks that will be executed by the pytask engine.

+
+

Because we defined this task in a function and loop, we can easily debug a node in the DAG by simply calling it:

+
+
task_download_raw_data()
+
+ + +
+
+
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/_docs/21_pytask_download.md b/_docs/21_pytask_download.md new file mode 100644 index 0000000..6ff9682 --- /dev/null +++ b/_docs/21_pytask_download.md @@ -0,0 +1,110 @@ +# Download: `download` Module as a `pytask` Task + + +## task_download + +> This module downloads the raw era5 data from the CDS API. It is +> similar to the original script, refactored for `pytask`. + + + +We’re going to quickly refactor the pipeline to use pytask instead of +hydra and snakemake. This will hopefully demonstrate a simpler and more +flexible way to manage data pipelines in Python. + +To start off, we need to create a function that queries the CDS API with +one job. This function will be used to download the data for each query +in the range specified in the data catalog in the config file. + +Let’s take a look at the data catalog we created in the config module: + +You can see the queries entry we created in the data catalog. Each query +is a row of a dataframe that contains the parameters for the CDS API +query. + +``` python +queries = data_catalog['download']['jobs']['queries_df'].load() +queries +``` + +We can test this query like we did in the original work: + +``` python +example_query = queries.iloc[0] + +create_bounding_box(example_query['shapefile']) +``` + +In this way, we have a similar approach as Hydra configs, but, using the +`pytask` data catalog, we can more easily gather the data for a specific +task in structured manner entirely in Python. + +``` python +client = cdsapi.Client() + +ex_bounding_box = create_bounding_box(example_query['shapefile']) + +request = { + "product_type": example_query['product_type'], + "variable": example_query['variables'], + "year": str(example_query['year']), + "month": str(example_query['month']), + "day": example_query['day'], + "time": example_query['time'], + "data_format": "netcdf", + "download_format": "unarchived", + "area": ex_bounding_box + } + +target = f"{example_query['output']}.nc" + +client.retrieve("reanalysis-era5-single-levels", request).download(target) +``` + +This works! So now we just need to create a `task_` function that pytask +will recognise to parallelise the download of queries over: + +### How this works (with some help from GPT): + +#### 🧠 How pytask Discovers and Executes Tasks + +When you run pytask, it automatically scans your project for Python +files named `task_*.py`. In these files, it looks for: - Functions +decorated with `@task`, or - Functions prefixed with `task_` + +These functions are not executed immediately. Instead, `pytask`: 1. +Imports each task\_\*.py module (just like Python would) 2. Registers +any matching task functions as nodes in a directed acyclic graph (DAG) +3. Resolves dependencies by analyzing: - Input annotations (e.g., +`Annotated[x, DependsOn]`) - Output declarations (e.g., `return` values +or `Product` annotations) 4. Builds the DAG, where each task function is +a node 5. Executes the tasks, respecting dependency order and skipping +up-to-date nodes + +So even though the task functions aren’t explicitly “run” in the Python +code itself, pytask knows how and when to execute them — based on their +position in the DAG. + +#### 🔄 How This Differs from Snakemake + +In `snakemake`, you’re expected to define a series of explicitly +executable rules, often using shell commands or Python scripts. You +“stitch together” rules using filenames and wildcard matching. + +In contrast: - 🐍 pytask is Python-native — tasks are just regular +Python functions - ⚙️ It builds a DAG from those functions and tracks +inputs/outputs automatically - 🧱 You are declaring nodes, not scripting +execution + +Think of your Python files not as scripts to run, but as a way to define +and wire together declarative tasks that will be executed by the pytask +engine. + +------------------------------------------------------------------------ + +Because we defined this task in a function and loop, we can easily debug +a node in the DAG by simply calling it: + +``` python +task_download_raw_data() +``` diff --git a/_docs/22_pytask_aggregate.html b/_docs/22_pytask_aggregate.html new file mode 100644 index 0000000..221b794 --- /dev/null +++ b/_docs/22_pytask_aggregate.html @@ -0,0 +1,1189 @@ + + + + + + + + + +Aggregation: The aggregation Module as a pytask Task – era5_sandbox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

Aggregation: The aggregation Module as a pytask Task

+
+ + + +
+ + + + +
+ + + +
+ + +
+

task_aggregate

+
+

This task aggregates the downloaded data into spatial and temporal averages. It uses xarray to compute summary statistics over the specified time period and spatial region. The aggregation is done diurnally, so we will fetch the sundown and sunrise times for the region and use them to compute the diurnal averages.

+
+ +
+

Diurnal Classification Based on Sun Position

+

To do diurnal classificaiton, we will need to fetch the sundown and sunrise times for the region and use them to compute the diurnal averages. We will use the astral library to get the sunrise and sunset times for the specified latitude and longitude. The aggregation will be done using xarray, which allows us to compute the mean over the specified time period and spatial region.

+

Here’s our example file:

+
+
eg_file = data_catalog['download']['outputs']['2009_01_nepal'].load()
+with ClimateDataFileHandler(eg_file) as handler:
+    
+    ds = xr.open_dataset(handler.get_dataset("instant"))
+    #ds = xr.open_dataset(handler.get_dataset("accum"))
+
+ds
+
+

We can see the astral library in action below:

+
+
+Exported source +
from astral import Observer, sun
+import pandas as pd
+import numpy as np
+from tqdm import tqdm
+import random
+import datetime
+from pytz import UTC
+
+
+
+
# get the location of a datapoint in the dataset
+lat, long = ds.coords["latitude"].values[0], ds.coords["longitude"].values[0]
+time = ds['valid_time'].values[0]
+dt = pd.to_datetime(time, utc=True)
+
+
+
dt
+
+
+
observer = Observer(latitude=lat, longitude=long, elevation=0)
+sun_info = sun.sun(observer, date=dt)
+sun_info
+
+

Astral is very fast:

+
+
#fetch a random time from valid_time
+options = ds['valid_time'].values
+
+random_time = random.choice(options)
+dt = pd.to_datetime(random_time, utc=True)
+sun_info = sun.sun(observer, date=dt)
+if dt < sun_info['sunrise']:
+    print(f"Randomly selected time: {dt} is pre_dawn")
+elif dt >= sun_info['sunrise'] and dt < sun_info['sunset']:
+    print(f"Randomly selected time: {dt} is day")
+else:
+    print(f"Randomly selected time: {dt} is post_dusk")
+
+

This tells us that we can use the valid time for the specific location of each data point in the query and know based on the sun whether it was daytime or nighttime. The runtime will be limited only by the looping. Let’s put this in a function so that we can use the resampling in xarray.

+

The resampling approach will be a single function that can resample in three ways:

+
    +
  • By calendar date, default (1 value per calendar date)
    • +
  • By diurnal class by calendar date (3 values, pre-dawn, day, post-dusk)
    • +
  • By solar date (2 values per calendar date, with night classified as “before” or “after”)
    • +
+

Therefore, we’ll need 2 internal functions; one to do diurnal, and one to do solar date bins.

+

Essentially, we are going to create an array-shaped index/mask, (time, latitude, longitude). As a demonstration, this loop goes through the first 24 time points in the dataset, and calculates the sun info for each latitude and longitude, assigning the values to an array:

+
+
times = ds['valid_time'].values[:24]
+lats = ds.coords['latitude'].values
+lons = ds.coords['longitude'].values
+
+result = np.full((len(times), len(lats), len(lons)), "", dtype=object)
+
+for i, dt in enumerate(times):
+
+    for j, lat in enumerate(lats):
+
+        for k, lon in enumerate(lons):
+            
+            # set the geographical position
+            observer = Observer(latitude=lat, longitude=lon, elevation=0)
+            
+            # use the time
+            dt = pd.to_datetime(dt, utc=True)
+
+            # where/when is the sun at this time for this position
+            sun_info = sun.sun(observer, date=dt)
+            result[i, j, k] = sun_info
+
+

So we know that in the first hour, the sun goes up and comes down at slightly different times based on latitude and longitude. Take the first hour, for example:

+
+
print(result.shape)
+hour_1 = 0 # 0th index of the results
+
+min_lat = 0
+min_lon = 0
+max_lat = 48
+max_lon = 90
+print(f"Even though the reading came from the first HOUR of data UTC, the sun info at the minimum latitude/longitude is: {result[hour_1, min_lat, min_lon]}")
+
+print(f"this is different from the sun info at the maximum latitude/longitude is: {result[hour_1, max_lat, max_lon]}")
+
+
+

source

+
+

compute_diurnal_class_bins

+
+
 compute_diurnal_class_bins (ds:xarray.core.dataset.Dataset)
+
+

Compute the diurnal value for each data point in the dataset. This function iterates over each data point in the dataset, calculates the sunrise and sunset times for the given time, latitude and longitude, and returns whether or not that data point is before dawn, during the day, or after dusk.

+
+
ex=compute_diurnal_class_bins(ds)
+
+

So, for our 720 time points, we should find that if we take the set() of all the classifications within that slice, there should be a few of them with 2 classes. In other words, at any given hour, almost all of the readings are “day”, because it is daytime across all of Madagascar, but at certain timepoints, the sun is rising or setting in the northern part of the country and so some portion of the slice is classified differently:

+
+
+

+
illustrated
+
+
+
+
for x in range(720):
+    print(set(ex[x].flatten()))
+
+

This works! Now we can do a similar, but slightly more complicated function to define “night” and “day”, where “night” includes all of the values after the sun goes down.

+
+

source

+
+
+

compute_solar_day_night_class_bins

+
+
 compute_solar_day_night_class_bins (ds:xarray.core.dataset.Dataset,
+                                     night_direction:Literal['before','aft
+                                     er'])
+
+

Compute the diurnal value for each data point in the dataset. This function iterates over each data point in the dataset, calculates the sunrise and sunset times for the given time, latitude and longitude, and returns whether or not that data point is daytime or nighttime. The definition of “nighttime” can be set to be all the darkness before the sun came up (before), or all the darkness after it went down (after).

+
+
+Exported source +
def compute_solar_day_night_class_bins(
+        ds: xr.Dataset,
+        night_direction: Literal["before", "after"],
+    )-> list:
+    """
+    Compute the diurnal value for each data point in the dataset.
+    This function iterates over each data point in the dataset,
+    calculates the sunrise and sunset times for the given time, latitude and longitude,
+    and returns whether or not that data point is daytime or nighttime.
+    The definition of "nighttime" can be set to be all the darkness before the sun
+    came up (before), or all the darkness after it went down (after).
+    """
+
+    times = ds['valid_time'].values
+    lats = ds.coords['latitude'].values
+    lons = ds.coords['longitude'].values
+
+    result = np.full((len(times), len(lats), len(lons)), "", dtype=object)
+    datetimes = np.full((len(times), len(lats), len(lons)), "", dtype=object)
+
+    for i, dt in enumerate(tqdm(times, desc="Classifying data points by sun position")):
+        # use the time
+        dt = pd.to_datetime(dt, utc=True)
+
+        for j, lat in enumerate(lats):
+
+            for k, lon in enumerate(lons):
+                
+                # set the geographical position
+                observer = Observer(latitude=lat, longitude=lon, elevation=0)
+                if night_direction == "before":
+                    # Night is from previous sunset to today's sunrise
+                    sun_today = sun.sun(observer, date=dt.date())
+                    sun_prev = sun.sun(observer, date=(dt - pd.Timedelta(days=1)).date())
+                    night_start = sun_prev["sunset"].astimezone(pd.Timestamp.utcnow().tz)
+                    night_end = sun_today["sunrise"].astimezone(pd.Timestamp.utcnow().tz)
+                    
+                    # the reading is from yesterday's nighttime
+                    if night_start <= dt < night_end:
+                        result[i, j, k] = "night"
+                        # the date counts as today
+                        datetimes[i, j, k] = dt.date()
+
+                    # the reading is from daytime
+                    elif sun_today["sunrise"] <= dt < sun_today["sunset"]:
+                        result[i, j, k] = "day"
+                        # the date counts as today
+                        datetimes[i, j, k] = dt.date()
+                    
+                    # the reading is from today's nighttime, but counts as tomorrow's night
+                    else:
+                        result[i, j, k] = "night"
+                        # the date is tomorrow
+                        datetimes[i, j, k] = (dt + pd.Timedelta(days=1)).date()
+
+                elif night_direction == "after":
+                    # Night is from today's sunset to next sunrise
+                    sun_today = sun.sun(observer, date=dt.date())
+                    sun_next = sun.sun(observer, date=(dt + pd.Timedelta(days=1)).date())
+                    night_start = sun_today["sunset"].astimezone(pd.Timestamp.utcnow().tz)
+                    night_end = sun_next["sunrise"].astimezone(pd.Timestamp.utcnow().tz)
+
+                    # the reading is from daytime
+                    if sun_today["sunrise"] <= dt < sun_today["sunset"]:
+                        result[i, j, k] = "day"
+                        # the date counts as today
+                        datetimes[i, j, k] = dt.date()
+                    # the reading is from tonight
+                    elif night_start <= dt < night_end:
+                        result[i, j, k] = "night"
+                        # the date counts as today
+                        datetimes[i, j, k] = dt.date()
+
+                    # the reading is from yesterday night
+                    else:
+                        # the date counts as yesterday
+                        result[i, j, k] = "day"
+                        datetimes[i, j, k] = (dt - pd.Timedelta(days=1)).date()
+                else:
+                    raise ValueError(f"Invalid night_direction: {night_direction}")
+
+    return result, datetimes
+
+
+
+
ex_class, ex_dt = compute_solar_day_night_class_bins(ds, "before")
+
+
+
ex_class
+
+

As before, we should see that most slices are homogenous, meaning most of the time, all the readings are from the day, but some slices should have day and night values:

+
+
for slice_ in range(720):
+    print(set(ex_class[slice_].flatten()))
+
+

The returned array can serve as new “variable indexes” for the dataset:

+
+
ds_masked = ds.copy()
+ds_masked['solar_class'] = (('valid_time', 'latitude', 'longitude'), ex_class)
+ds_masked["solar_date"] = (("valid_time", "latitude", "longitude"), ex_dt)
+
+
+
+
+

Diurnal Resampling

+

Now, to see if it will resample by both solar day and diurnal class. Let’s try by masking and making copies with NaN in the masked values:

+
+
ds_day = ds_masked.where(ds_masked["solar_class"] == "day").drop_vars(["solar_class", "solar_date"])
+ds_night = ds_masked.where(ds_masked["solar_class"] == "night").drop_vars(["solar_class", "solar_date"])
+
+

Next, we set the time zone for Madagascar since, to resample by day and night, we should observe the local time:

+
+
ds_day = ds_day.assign_coords(valid_time=pd.to_datetime(ds["valid_time"].values).tz_localize("UTC").tz_convert("Asia/Kathmandu"))
+ds_night = ds_night.assign_coords(valid_time=pd.to_datetime(ds["valid_time"].values).tz_localize("UTC").tz_convert("Asia/Kathmandu"))
+
+

Now if we can resample by day…

+
+
ds_day_rs = ds_day.resample(valid_time="1D").reduce(np.nanmean)
+ds_night_rs = ds_night.resample(valid_time="1D").reduce(np.nanmean)
+ds_day_rs
+
+

Can we successfully convert this to a tiff?

+
+
from era5_sandbox.aggregate import netcdf_to_tiff
+
+
+
raster_day = netcdf_to_tiff(ds_day_rs, band=1, variable="d2m")
+raster_night = netcdf_to_tiff(ds_night_rs, band=1, variable="d2m")
+
+

Looks great! These two rasters represent one calendar day of daytime and nighttime values.

+
+

Testing Polygon to Raster Cells & Healthshed Aggregation

+

The penultimate step of the aggregate pipeline in the original version is assigning each datapoint to the respective healthshed. The vectors argument comes from the healthshed, and represents each geographic polygon on the ground that we want to aggregate data to.

+
+
from hydra import initialize, compose
+
+
+
try:
+    with initialize(version_base=None, config_path="../conf"):
+        cfg = compose(config_name='config.yaml')
+except Exception as e:
+    print(f"Error initializing Hydra: {e}")
+    with initialize(version_base=None, config_path="conf"):
+        cfg = compose(config_name='config.yaml')
+
+driver = GoogleDriver(json_key_path=here() / cfg.GOOGLE_DRIVE_AUTH_JSON.path)
+drive = driver.get_drive()
+healthsheds = driver.read_healthsheds("Nepal_Healthsheds2024.zip")
+
+
+
res_poly2cell=polygon_to_raster_cells(
+    vectors = healthsheds.geometry.values, # the geometries of the shapefile of the regions
+    raster=raster_day.data, # the raster data above
+    nodata=np.nan, # any intersections with no data, may have to be np.nan
+    affine=raster_day.transform, # some math thing need to revise
+    all_touched=True, 
+    verbose=True
+)
+
+

This works fine. Finally, we aggregate to healthsheds:

+
+
from era5_sandbox.aggregate import aggregate_to_healthsheds
+
+
+
result_day = aggregate_to_healthsheds(
+    res_poly2cell=res_poly2cell,
+    raster=raster_day,
+    shapes=healthsheds,
+    names_column="fid",
+    aggregation_func=np.nanmean,
+    aggregation_name="mean_dewpoint_day"
+)
+
+result_night = aggregate_to_healthsheds(
+    res_poly2cell=res_poly2cell,
+    raster=raster_night,
+    shapes=healthsheds,
+    names_column="fid",
+    aggregation_func=np.nanmean,
+    aggregation_name="mean_dewpoint_night"
+)
+
+

Below shows the result of aggregating the daytime dewpoint temperature to the healthshed level:

+
+
result_day
+
+
+
result_night
+
+

So from one input, we will have two outputs, one for daytime and one for nighttime, and this will have to loop over the bands (ie each day in the month).

+
+
+
+
+

Putting it all together in a pytask task

+

Below we define our pytask task to aggregate data to the healthshed level.

+
+
+Exported source +
job_rows = data_catalog['aggregate']['jobs']['jobs_df'].load()
+
+aggregation_funcs = {
+    "mean": np.nanmean,
+    "sum": np.nansum,
+    "max": np.nanmax,
+    "min": np.nanmin
+}
+
+for i, job in job_rows.iterrows():
+    #print(f"Job {i+1}: variable={job['variables']}, time={job['time']}, aggregation={job['aggregation_name']}")
+
+    # parse the row into function parameters
+    input_file = data_catalog['download']['outputs'][job['input']]
+    solar_classification = job['solar_classification']
+    variable = job['variables_short']
+    time = job['time']
+    aggregation_func = aggregation_funcs[job['aggregation_name']]
+    aggregation_name = job['aggregation_name']
+
+    climate_handler_var = job['climate_handler_var']
+    local_tz = job['local_tz']
+
+    shapefile = job['shapefile']
+    hshd_unique_id = job['hshd_unique_id']
+
+    output_file = job['input'] + "_" + job['time'] + "_" + job['variables_short'] + "_" + job['aggregation_name'] + ".parquet"
+
+    @task(id=output_file, name=f"Aggregate {output_file}", after="task_download_raw_data")
+    def task_aggregate_data_diurnal(
+            input_file: Path = data_catalog['download']['outputs'][job['input']], # input data Path from the download task
+            aggregation_func: callable = aggregation_func, # the aggregation function
+            aggregation_name: str = aggregation_name, # the name of the aggregation function
+            time: Literal["day", "night"] = time, # whether to aggregate by day or night
+            night_direction: Literal["before", "after"] = solar_classification, # how to define night
+            variable: str = variable, # the variable to aggregate,
+            climate_handler_var: Literal["instant", "accum"] = climate_handler_var, # whether the variable is instant or accum,
+            local_tz: str = local_tz, # the local timezone for resampling
+            shapefile: str = shapefile, # the shapefile for the healthsheds,
+            hshd_unique_id: str = hshd_unique_id, # the unique id column in the shapefile,
+            output_file: str = output_file # the output file name
+        ) -> Annotated[Path, data_catalog['aggregate']['outputs'][output_file]]:
+        """
+        Task to aggregate data from a CDSAPI Query to the healthshed
+        level. Returns path to parquet file with aggregated data.
+        """
+
+        logger = setup_logger(output_file)
+
+        logger.info(f"Aggregating: {output_file}")
+
+        # check if the string path exists
+        # if os.path.exists(output_file):
+        #     logger.info(f"File {output_file} already exists. Skipping aggregation.")
+        #     return output_file
+
+        # get input data
+        logger.info("Reading input data...")
+        with ClimateDataFileHandler(input_file) as handler:
+            ds = xr.open_dataset(handler.get_dataset('instant'))
+
+        #get the healthshed shapefile
+        logger.info(f"Reading healthshed shapefile from yaml {here()}...")
+        with open(here() / "conf" / "config.yaml") as f:
+            healthshed_config = yaml.safe_load(f)
+
+            key_path = here() / healthshed_config['GOOGLE_DRIVE_AUTH_JSON']['path']
+
+            driver = GoogleDriver(json_key_path=key_path)
+            drive = driver.get_drive()
+            healthsheds = driver.read_healthsheds(shapefile)
+
+        # compute the diurnal classification bins
+        logger.info("Computing diurnal classification bins...")
+        class_bins, class_dts = compute_solar_day_night_class_bins(ds, night_direction)
+
+        ds_masked = ds.copy()
+
+        # assign classifications
+        logger.info("Assigning classification bins to dataset...")
+        ds['solar_class'] = (('valid_time', 'latitude', 'longitude'), class_bins)
+        ds["solar_date"] = (("valid_time", "latitude", "longitude"), class_dts)
+
+        # mask the dataset to the requested time
+        mask = ds["solar_class"] == time
+        ds_masked = ds_masked.where(mask)
+
+        # set the local timezone
+        ds_masked = ds_masked.assign_coords(valid_time=pd.to_datetime(ds["valid_time"].values).tz_localize("UTC").tz_convert(local_tz))
+
+        # resample by local date
+        logger.info("Resampling by local date...")
+        ds_rs = ds_masked.resample(valid_time="1D").reduce(aggregation_func)
+
+        # convert to tiff
+        logger.info("Rasterizing resampled data...")
+        n_bands = ds_rs.dims['valid_time']
+
+        # polygon to raster cells for the first band
+        logger.info("Converting polygons to raster cells...")
+        raster = netcdf_to_tiff(ds_rs, band=1, variable=variable)
+        res_poly2cell=polygon_to_raster_cells(
+            vectors = healthsheds.geometry.values, # the geometries of the shapefile of the regions
+            raster=raster.data, # the raster data above
+            nodata=np.nan, # any intersections with no data, may have to be np.nan
+            affine=raster.transform, # some math thing need to revise
+            all_touched=True, 
+            verbose=True
+        )
+
+        result_df = healthsheds[[hshd_unique_id, "geometry"]].copy()
+
+        # loop over bands and aggregate to healthsheds
+        for band in tqdm(range(1, n_bands + 1)):
+            logger.info(f"Processing band {band} of {n_bands}...")
+            
+            day = band  # band is 1-indexed
+
+            day_col = f"day_{day:02d}"
+
+            # calculate raster for this band
+            raster = netcdf_to_tiff(ds_rs, band=band, variable=variable)
+
+            # aggregate to healthsheds
+            result = aggregate_to_healthsheds(
+                res_poly2cell=res_poly2cell,
+                raster=raster,
+                shapes=healthsheds,
+                names_column=hshd_unique_id,
+                aggregation_func=aggregation_func,
+                aggregation_name=variable
+            )
+            
+            # add band to result dataframe
+            result_df[day_col] = result[variable]
+
+        # save to parquet
+        result_df.to_parquet(f"{BLD}/{output_file}")
+
+        logger.info("Aggregation complete.")
+        
+        return Path(f"{BLD}/{output_file}")
+
+
+

That should wrap it up! To test, we can run a single job:

+
+
# runs the last defined job only
+task_aggregate_data_diurnal()
+
+

Or we can run the task in pytask:

+
pytask build -k "nepal and 2009" --dry-run
+ + +
+ +
+ +
+ + + + + \ No newline at end of file diff --git a/_docs/IMG_740012467778-1.jpeg b/_docs/IMG_740012467778-1.jpeg new file mode 100644 index 0000000..52886eb Binary files /dev/null and b/_docs/IMG_740012467778-1.jpeg differ diff --git a/_docs/index.html b/_docs/index.html new file mode 100644 index 0000000..bc8f5d1 --- /dev/null +++ b/_docs/index.html @@ -0,0 +1,1616 @@ + + + + + + + + + +The ERA5 Spatial Aggregation Pipeline – era5_sandbox + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+
+ + +
+ +
+ + +
+ + + +
+ +
+
+

The ERA5 Spatial Aggregation Pipeline

+
+ + + +
+ + + + +
+ + + +
+ + + +
+
from era5_sandbox.core import *
+
+
+

era5_sandbox

+
+

Sandbox environment for era5 development

+
+

This package documents the development and implementation of functions and code for the Madagascar ERA5 dataset project. The goal is for exposure data to be made available at the daily resolution when possible. Finer resolutions shouldn’t ever be needed for our purposes, and it should then be relatively easy to aggregate at coarser resolutions, such as weekly or monthly. Additionally, we’ve extended this work to Nepal as well.

+

Variables should generally be made available from 2010 onward, as that’s where our clinic data starts.

+

All data are ideally made available at the “healthshed” geographical level. Healthsheds are defined as geographical areas where people who live all go to the same clinic. There are a total of ~2700 public clinics in Madagascar, hence ~2700 healthsheds, with each healthshed containing ~10000 people on average.

+

Preliminary list of environmental variables

+
    +

    • +

    • +

    • +

    • +
+

Variables from other sources:

+
    +

    • +

    • +

    • +

    • +

    • +

    • +

    • +

    • +

    • +

    • +
+

Those from the ERA5 dataset will be housed here, but we may likely develop a separate repository for the other datasets.

+
+
+

Developer Guide

+

This package is built and maintained with nbdev. If you are new to using nbdev here are some useful pointers to get you started.

+
+

Install era5_sandbox in Development mode

+
# make sure era5_sandbox package is installed in development mode
+$ pip install -e .
+

To make changes, go to the “notes” directory and edit the notebooks as necessary. Each notebook refers to a module in the era5_sandbox package. Cells are exported to the module when the notebook is saved and you run the following command:

+
$ nbdev_export
+

For e.g., to change functionality of the testAPI() function in the testAPI Hydra rule, you would edit the testAPI notebook in the notes directory notes/testAPI.ipynb, and then save that notebook and run nbdev_export to update the core module in the package.

+
+
+

How to Run the Pipeline

+

The pipeline downloads ERA5 variables for a given date range and geographical bounding box. You can learn how each of these steps was by following the notebooks in notes in numerical order.

+
+
+
+ +
+
+Important +
+
+
+

The pipeline has two implementations: one using snakemake and hydra, and another using pytask. The pytask implementation is the more recent one, and is recommended for future use. The snakemake implementation is left here for reference to legacy code.

+
+
+
+

Using pytask

+

To run the pipeline, the pytask config at note/20_pytask_config.qmd should be reviewed and updated if necessary. The pipeline can then be run with the following command:

+
$ sbatch pytask.sbatch
+
+
+

Using snakemake and hydra

+

To run the pipeline, the config at config/config.yaml should be updated with the desired date range and geographical bounding box. The pipeline can then be run with the following command:

+
sbatch snakemake.sbatch
+
+
+
+

What Does the Pipeline Produce?

+

Using pytask’s data catalog, you can investigate the downloaded raw data with python, eg.:

+
+
import xarray as xr
+from era5_sandbox.config import data_catalog
+from era5_sandbox.core import ClimateDataFileHandler
+
+ex_nc = list(data_catalog['download']['outputs']._entries).pop()
+ex_nc_path = data_catalog['download']['outputs'][ex_nc].load()
+
+with ClimateDataFileHandler(ex_nc_path) as handler:
+    ds = xr.open_dataset(handler.get_dataset("instant"))
+
+ds
+
+
+ + + + + + + + + + + + + + +
<xarray.Dataset> Size: 53MB
+Dimensions:     (valid_time: 744, latitude: 49, longitude: 91)
+Coordinates:
+    number      int64 8B ...
+  * valid_time  (valid_time) datetime64[ns] 6kB 2024-03-01 ... 2024-03-31T23:...
+  * latitude    (latitude) float64 392B 30.8 30.7 30.6 30.5 ... 26.2 26.1 26.0
+  * longitude   (longitude) float64 728B 79.6 79.7 79.8 79.9 ... 88.4 88.5 88.6
+    expver      (valid_time) <U4 12kB ...
+Data variables:
+    d2m         (valid_time, latitude, longitude) float32 13MB ...
+    t2m         (valid_time, latitude, longitude) float32 13MB ...
+    tp          (valid_time, latitude, longitude) float32 13MB ...
+    swvl1       (valid_time, latitude, longitude) float32 13MB ...
+Attributes:
+    GRIB_centre:             ecmf
+    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts
+    GRIB_subCentre:          0
+    Conventions:             CF-1.7
+    institution:             European Centre for Medium-Range Weather Forecasts
+    history:                 2025-09-16T20:55 GRIB to CDM+CF via cfgrib-0.9.1...
+
+
+

And plot it with cartopy, eg.:

+
+
import matplotlib.pyplot as plt
+import cartopy.crs as ccrs
+import cartopy.feature as cfeature
+
+temperature = ds["t2m"]
+
+# Select a specific time step
+temperature_at_time = temperature.isel(valid_time=0)
+
+# Plot the data on a map
+plt.figure(figsize=(12, 8))
+ax = plt.axes(projection=ccrs.PlateCarree())
+temperature_at_time.plot(ax=ax, cmap="coolwarm", transform=ccrs.PlateCarree(), cbar_kwargs={"label": "Temperature (K)"})
+ax.coastlines()
+ax.add_feature(cfeature.BORDERS, linestyle=":")
+ax.set_title("Temperature at Time Step 0")
+plt.show()
+
+
+
+

+
+
+
+
+

You can also load the aggregated data:

+
+
import pandas as pd
+import geopandas as gpd
+from era5_sandbox.config import data_catalog
+
+ex_agg_path = data_catalog['aggregate']['outputs']['2019_08_madagascar_night_d2m_max.parquet'].load()
+
+gpd.read_parquet(ex_agg_path).describe()
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ + + + + \ No newline at end of file diff --git a/_docs/index.md b/_docs/index.md new file mode 100644 index 0000000..97efafe --- /dev/null +++ b/_docs/index.md @@ -0,0 +1,928 @@ +# The ERA5 Spatial Aggregation Pipeline + + + + +``` python +from era5_sandbox.core import * +``` + +## era5_sandbox + +> Sandbox environment for era5 development + +This package documents the development and implementation of functions +and code for the Madagascar ERA5 dataset project. The goal is for +exposure data to be made available at the daily resolution when +possible. Finer resolutions shouldn’t ever be needed for our purposes, +and it should then be relatively easy to aggregate at coarser +resolutions, such as weekly or monthly. Additionally, we’ve extended +this work to Nepal as well. + +Variables should generally be made available from 2010 onward, as that’s +where our clinic data starts. + +All data are ideally made available at the “healthshed” geographical +level. Healthsheds are defined as geographical areas where people who +live all go to the same clinic. There are a total of ~2700 public +clinics in Madagascar, hence ~2700 healthsheds, with each healthshed +containing ~10000 people on average. + +Preliminary list of environmental variables + +- ☒ 2-m air temperature from ERA5: daily min, max, mean + +- ☒ 2-m air dew point temperature from ERA5: daily min, max, mean + +- ☒ Precipitation: daily total (ERA5) + +- ☒ Soil moisture: daily average (ERA5) + +Variables from other sources: + +- ☐ Sea surface temperature: daily average and maximum in the nearest + neighbor for each healthshed. + +- ☐ Precipitation: daily total (CHIRPS) + +- ☐ Chlorophyll-A (Giacomo) + +- ☐ Wealth index: Available from Giacomo + +- ☐ NDVI + +- ☐ Tropical storm + +- ☐ Flooding + +- ☐ Deforestation + +- ☐ Linking/segmenting healthsheds into climate zones and other + +- ☐ Relative humidity: daily average (lower priority) + +Those from the ERA5 dataset will be housed here, but we may likely +develop a separate repository for the other datasets. + +## Developer Guide + +This package is built and maintained with `nbdev`. If you are new to +using `nbdev` here are some useful pointers to get you started. + +### Install era5_sandbox in Development mode + +``` sh +# make sure era5_sandbox package is installed in development mode +$ pip install -e . +``` + +To make changes, go to the “notes” directory and edit the notebooks as +necessary. Each notebook refers to a module in the era5_sandbox package. +Cells are exported to the module when the notebook is saved and you run +the following command: + +``` sh +$ nbdev_export +``` + +For e.g., to change functionality of the +[`testAPI()`](https://TinasheMTapera.github.io/era5_sandbox/core.html#testapi) +function in the testAPI Hydra rule, you would edit the +[`testAPI`](https://TinasheMTapera.github.io/era5_sandbox/core.html#testapi) +notebook in the `notes` directory `notes/testAPI.ipynb`, and then save +that notebook and run `nbdev_export` to update the `core` module in the +package. + +### How to Run the Pipeline + +The pipeline downloads ERA5 variables for a given date range and +geographical bounding box. You can learn how each of these steps was by +following the notebooks in `notes` in numerical order. + +
+ +> **Important** +> +> The pipeline has two implementations: one using `snakemake` and +> `hydra`, and another using `pytask`. The `pytask` implementation is +> the more recent one, and is recommended for future use. The +> `snakemake` implementation is left here for reference to legacy code. + +
+ +#### Using `pytask` + +To run the pipeline, the `pytask` config at `note/20_pytask_config.qmd` +should be reviewed and updated if necessary. The pipeline can then be +run with the following command: + +``` sh +$ sbatch pytask.sbatch +``` + +#### Using `snakemake` and `hydra` + +To run the pipeline, the config at `config/config.yaml` should be +updated with the desired date range and geographical bounding box. The +pipeline can then be run with the following command: + +``` sh +sbatch snakemake.sbatch +``` + +### What Does the Pipeline Produce? + +Using `pytask`’s data catalog, you can investigate the downloaded raw +data with python, eg.: + +``` python +import xarray as xr +from era5_sandbox.config import data_catalog +from era5_sandbox.core import ClimateDataFileHandler + +ex_nc = list(data_catalog['download']['outputs']._entries).pop() +ex_nc_path = data_catalog['download']['outputs'][ex_nc].load() + +with ClimateDataFileHandler(ex_nc_path) as handler: + ds = xr.open_dataset(handler.get_dataset("instant")) + +ds +``` + +
+ + + + + + + + + + + + + + +
<xarray.Dataset> Size: 53MB
+Dimensions:     (valid_time: 744, latitude: 49, longitude: 91)
+Coordinates:
+    number      int64 8B ...
+  * valid_time  (valid_time) datetime64[ns] 6kB 2024-03-01 ... 2024-03-31T23:...
+  * latitude    (latitude) float64 392B 30.8 30.7 30.6 30.5 ... 26.2 26.1 26.0
+  * longitude   (longitude) float64 728B 79.6 79.7 79.8 79.9 ... 88.4 88.5 88.6
+    expver      (valid_time) <U4 12kB ...
+Data variables:
+    d2m         (valid_time, latitude, longitude) float32 13MB ...
+    t2m         (valid_time, latitude, longitude) float32 13MB ...
+    tp          (valid_time, latitude, longitude) float32 13MB ...
+    swvl1       (valid_time, latitude, longitude) float32 13MB ...
+Attributes:
+    GRIB_centre:             ecmf
+    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts
+    GRIB_subCentre:          0
+    Conventions:             CF-1.7
+    institution:             European Centre for Medium-Range Weather Forecasts
+    history:                 2025-09-16T20:55 GRIB to CDM+CF via cfgrib-0.9.1...
+ +And plot it with cartopy, eg.: + +``` python +import matplotlib.pyplot as plt +import cartopy.crs as ccrs +import cartopy.feature as cfeature + +temperature = ds["t2m"] + +# Select a specific time step +temperature_at_time = temperature.isel(valid_time=0) + +# Plot the data on a map +plt.figure(figsize=(12, 8)) +ax = plt.axes(projection=ccrs.PlateCarree()) +temperature_at_time.plot(ax=ax, cmap="coolwarm", transform=ccrs.PlateCarree(), cbar_kwargs={"label": "Temperature (K)"}) +ax.coastlines() +ax.add_feature(cfeature.BORDERS, linestyle=":") +ax.set_title("Temperature at Time Step 0") +plt.show() +``` + + + +You can also load the aggregated data: + +``` python +import pandas as pd +import geopandas as gpd +from era5_sandbox.config import data_catalog + +ex_agg_path = data_catalog['aggregate']['outputs']['2019_08_madagascar_night_d2m_max.parquet'].load() + +gpd.read_parquet(ex_agg_path).describe() +``` + +
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diff --git a/_docs/index_files/figure-commonmark/cell-4-output-1.png b/_docs/index_files/figure-commonmark/cell-4-output-1.png new file mode 100644 index 0000000..6044c3d Binary files /dev/null and b/_docs/index_files/figure-commonmark/cell-4-output-1.png differ diff --git a/_docs/index_files/figure-html/cell-4-output-1.png b/_docs/index_files/figure-html/cell-4-output-1.png new file mode 100644 index 0000000..6044c3d Binary files /dev/null and b/_docs/index_files/figure-html/cell-4-output-1.png differ diff --git a/_docs/robots.txt b/_docs/robots.txt new file mode 100644 index 0000000..816a507 --- /dev/null +++ b/_docs/robots.txt @@ -0,0 +1 @@ +Sitemap: https://TinasheMTapera.github.io/era5_sandbox/sitemap.xml diff --git a/_docs/search.json b/_docs/search.json new file mode 100644 index 0000000..6e771fe --- /dev/null +++ b/_docs/search.json @@ -0,0 +1,362 @@ +[ + { + "objectID": "01_download_raw_data.html", + "href": "01_download_raw_data.html", + "title": "Download Module: Downloading Raw Data from CDSAPI", + "section": "", + "text": "This module downloads the raw data from CDS and saves it in the local directory\n\n\nWe use a similar approach to the one in the tutorial to download the data to local storage.\nThe background functionality in this module involves downloading the bounding box of a region of interest, and sending that to the CDS API query. As such, we define two helper functions to fetch the OCHA/HDX shapefiles for a geographic region, and another to create the bounding box from the files.\n\nsource\n\n\n\n fetch_GADM\n (url:str='https://geodata.ucdavis.edu/gadm/gadm4.1/gpkg/gadm4\n 1_MDG.gpkg', output_file:str='gadm41_MDG.gpkg')\n\nFetch the GADM bounding box for geographic region\n\n\n\n\n\n\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\nurl\nstr\nhttps://geodata.ucdavis.edu/gadm/gadm4.1/gpkg/gadm41_MDG.gpkg\nURL to fetch the GADM data for Madagascar\n\n\noutput_file\nstr\ngadm41_MDG.gpkg\nfile path to save the GADM data\n\n\nReturns\nstr\n\n\n\n\n\n\nsource\n\n\n\n\n create_bounding_box (zip_url_or_path:str, buffer_km:float=50,\n round_to:int=1)\n\nCreate a bounding box from OCHA/HDX shapefile data with a buffer.\n\n\n\n\n\n\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\nzip_url_or_path\nstr\n\nURL or local path to the zipped shapefile.\n\n\nbuffer_km\nfloat\n50\nBuffer distance in kilometers to expand the bounding box.\n\n\nround_to\nint\n1\nNumber of decimal places to round the bounding box coordinates.\n\n\nReturns\nlist\n\nBounding box in the CDS API area format [North, West, South, East]\n\n\n\n\n\nExported source\ndef create_bounding_box(\n zip_url_or_path: str, # URL or local path to the zipped shapefile.\n buffer_km: float = 50, # Buffer distance in kilometers to expand the bounding box.\n round_to: int = 1 # Number of decimal places to round the bounding box coordinates.\n) -> list: # Bounding box in the CDS API area format [North, West, South, East]\n '''\n Create a bounding box from OCHA/HDX shapefile data with a buffer.\n '''\n with tempfile.TemporaryDirectory() as tmpdir:\n # Download if it's a URL\n if zip_url_or_path.startswith(\"http\"):\n response = requests.get(zip_url_or_path)\n zip_path = os.path.join(tmpdir, \"ocha_data.zip\")\n with open(zip_path, \"wb\") as f:\n f.write(response.content)\n else:\n zip_path = zip_url_or_path\n\n # Unzip\n with zipfile.ZipFile(zip_path, 'r') as zip_ref:\n zip_ref.extractall(tmpdir)\n\n # Find the .shp file\n shp_files = list(Path(tmpdir).rglob(\"*.shp\"))\n if not shp_files:\n raise FileNotFoundError(\"No shapefile (.shp) found in the extracted archive.\")\n shp_path = str(shp_files[0]) # Use first found .shp\n\n # Read shapefile\n shape = gpd.read_file(shp_path)\n\n # Reproject to projected CRS (you may want to detect the correct UTM zone)\n shape_proj = shape.to_crs(epsg=32738)\n\n # Apply buffer\n buffered = shape_proj.geometry.buffer(buffer_km * 1000)\n\n # Convert back to geographic coordinates\n buffered_geo = gpd.GeoSeries(buffered, crs=shape_proj.crs).to_crs(epsg=4326)\n\n # Get bounding box\n bounds = buffered_geo.total_bounds # [min_x, min_y, max_x, max_y]\n bbox = [\n round(bounds[3], round_to), # North\n round(bounds[0], round_to), # West\n round(bounds[1], round_to), # South\n round(bounds[2], round_to) # East\n ]\n\n return bbox\n\n\nThe primary function to download the data from CDSAPI is defined below.\n\nsource\n\n\n\n\n download_raw_era5 (cfg:omegaconf.dictconfig.DictConfig)\n\nSend the query to the API and download the data\n\n\n\n\nType\nDetails\n\n\n\n\ncfg\nDictConfig\nhydra configuration file\n\n\nReturns\nNone\n\n\n\n\n\n\nExported source\ndef download_raw_era5(\n cfg: DictConfig # hydra configuration file\n )->None:\n '''\n Send the query to the API and download the data\n '''\n\n # parse the cfg\n testing = cfg.development_mode # for testing\n output_dir = here(\"data/input\") # output directory\n \n geography = cfg.query.geography\n\n target = os.path.join(_expand_path(output_dir), \"{}_{}_{}.nc\".format(geography, cfg.query['year'], cfg.query['month']))\n \n client = cdsapi.Client()\n \n query = _validate_query(cfg.query)\n\n dataset = cfg.dataset\n # to make sure the query is valid at the end\n del query['geography']\n \n # Send the query to the client\n if not testing:\n bounds = create_bounding_box(cfg.geographies[geography]['shapefile'])\n query['area'] = bounds\n client.retrieve(dataset, query).download(target)\n\n print(\"Downloaded file to: {}\".format(target))\n else:\n print(f\"Testing mode. Not downloading data. Query is {query}\")\n\n print(\"Done\")", + "crumbs": [ + "Download Module: Downloading Raw Data from CDSAPI" + ] + }, + { + "objectID": "01_download_raw_data.html#download", + "href": "01_download_raw_data.html#download", + "title": "Download Module: Downloading Raw Data from CDSAPI", + "section": "", + "text": "This module downloads the raw data from CDS and saves it in the local directory\n\n\nWe use a similar approach to the one in the tutorial to download the data to local storage.\nThe background functionality in this module involves downloading the bounding box of a region of interest, and sending that to the CDS API query. As such, we define two helper functions to fetch the OCHA/HDX shapefiles for a geographic region, and another to create the bounding box from the files.\n\nsource\n\n\n\n fetch_GADM\n (url:str='https://geodata.ucdavis.edu/gadm/gadm4.1/gpkg/gadm4\n 1_MDG.gpkg', output_file:str='gadm41_MDG.gpkg')\n\nFetch the GADM bounding box for geographic region\n\n\n\n\n\n\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\nurl\nstr\nhttps://geodata.ucdavis.edu/gadm/gadm4.1/gpkg/gadm41_MDG.gpkg\nURL to fetch the GADM data for Madagascar\n\n\noutput_file\nstr\ngadm41_MDG.gpkg\nfile path to save the GADM data\n\n\nReturns\nstr\n\n\n\n\n\n\nsource\n\n\n\n\n create_bounding_box (zip_url_or_path:str, buffer_km:float=50,\n round_to:int=1)\n\nCreate a bounding box from OCHA/HDX shapefile data with a buffer.\n\n\n\n\n\n\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\nzip_url_or_path\nstr\n\nURL or local path to the zipped shapefile.\n\n\nbuffer_km\nfloat\n50\nBuffer distance in kilometers to expand the bounding box.\n\n\nround_to\nint\n1\nNumber of decimal places to round the bounding box coordinates.\n\n\nReturns\nlist\n\nBounding box in the CDS API area format [North, West, South, East]\n\n\n\n\n\nExported source\ndef create_bounding_box(\n zip_url_or_path: str, # URL or local path to the zipped shapefile.\n buffer_km: float = 50, # Buffer distance in kilometers to expand the bounding box.\n round_to: int = 1 # Number of decimal places to round the bounding box coordinates.\n) -> list: # Bounding box in the CDS API area format [North, West, South, East]\n '''\n Create a bounding box from OCHA/HDX shapefile data with a buffer.\n '''\n with tempfile.TemporaryDirectory() as tmpdir:\n # Download if it's a URL\n if zip_url_or_path.startswith(\"http\"):\n response = requests.get(zip_url_or_path)\n zip_path = os.path.join(tmpdir, \"ocha_data.zip\")\n with open(zip_path, \"wb\") as f:\n f.write(response.content)\n else:\n zip_path = zip_url_or_path\n\n # Unzip\n with zipfile.ZipFile(zip_path, 'r') as zip_ref:\n zip_ref.extractall(tmpdir)\n\n # Find the .shp file\n shp_files = list(Path(tmpdir).rglob(\"*.shp\"))\n if not shp_files:\n raise FileNotFoundError(\"No shapefile (.shp) found in the extracted archive.\")\n shp_path = str(shp_files[0]) # Use first found .shp\n\n # Read shapefile\n shape = gpd.read_file(shp_path)\n\n # Reproject to projected CRS (you may want to detect the correct UTM zone)\n shape_proj = shape.to_crs(epsg=32738)\n\n # Apply buffer\n buffered = shape_proj.geometry.buffer(buffer_km * 1000)\n\n # Convert back to geographic coordinates\n buffered_geo = gpd.GeoSeries(buffered, crs=shape_proj.crs).to_crs(epsg=4326)\n\n # Get bounding box\n bounds = buffered_geo.total_bounds # [min_x, min_y, max_x, max_y]\n bbox = [\n round(bounds[3], round_to), # North\n round(bounds[0], round_to), # West\n round(bounds[1], round_to), # South\n round(bounds[2], round_to) # East\n ]\n\n return bbox\n\n\nThe primary function to download the data from CDSAPI is defined below.\n\nsource\n\n\n\n\n download_raw_era5 (cfg:omegaconf.dictconfig.DictConfig)\n\nSend the query to the API and download the data\n\n\n\n\nType\nDetails\n\n\n\n\ncfg\nDictConfig\nhydra configuration file\n\n\nReturns\nNone\n\n\n\n\n\n\nExported source\ndef download_raw_era5(\n cfg: DictConfig # hydra configuration file\n )->None:\n '''\n Send the query to the API and download the data\n '''\n\n # parse the cfg\n testing = cfg.development_mode # for testing\n output_dir = here(\"data/input\") # output directory\n \n geography = cfg.query.geography\n\n target = os.path.join(_expand_path(output_dir), \"{}_{}_{}.nc\".format(geography, cfg.query['year'], cfg.query['month']))\n \n client = cdsapi.Client()\n \n query = _validate_query(cfg.query)\n\n dataset = cfg.dataset\n # to make sure the query is valid at the end\n del query['geography']\n \n # Send the query to the client\n if not testing:\n bounds = create_bounding_box(cfg.geographies[geography]['shapefile'])\n query['area'] = bounds\n client.retrieve(dataset, query).download(target)\n\n print(\"Downloaded file to: {}\".format(target))\n else:\n print(f\"Testing mode. Not downloading data. Query is {query}\")\n\n print(\"Done\")", + "crumbs": [ + "Download Module: Downloading Raw Data from CDSAPI" + ] + }, + { + "objectID": "01_download_raw_data.html#tests-and-main", + "href": "01_download_raw_data.html#tests-and-main", + "title": "Download Module: Downloading Raw Data from CDSAPI", + "section": "Tests and Main", + "text": "Tests and Main\nHere we define some tests and the main function that will be used to download the data.\n\nfrom hydra import initialize, compose\nfrom omegaconf import OmegaConf\n\n# unfortunately, we have to use the initialize function to load the config file\n# this is because the @hydra decorator does not work with Notebooks very well\n# this is a known issue with Hydra: https://gist.github.com/bdsaglam/586704a98336a0cf0a65a6e7c247d248\n# \n# just use the relative path from the notebook to the config dir\ntry:\n with initialize(version_base=None, config_path=\"../conf\"):\n cfg = compose(config_name='config.yaml')\nexcept Exception as e:\n print(f\"Error initializing Hydra: {e}\")\n with initialize(version_base=None, config_path=\"conf\"):\n cfg = compose(config_name='config.yaml')\n\ncfg.development_mode = False\ncfg.query['year'] = 2017\ncfg.query['month'] = 11\n#cfg.query['day'] = 1\n#cfg.query['time'] = \"00:00\"\ncfg.query['geography'] = \"nepal\"\ndownload_raw_era5(cfg)\n\n\nsource\n\nmain\n\n main (cfg:omegaconf.dictconfig.DictConfig)\n\n\n\nExported source\n@hydra.main(config_path=\"../../conf\", config_name=\"config\", version_base=None)\ndef main(cfg: DictConfig) -> None:\n download_raw_era5(cfg=cfg)\n # better approach would be to have the function only use the specific arguments of the config", + "crumbs": [ + "Download Module: Downloading Raw Data from CDSAPI" + ] + }, + { + "objectID": "22_pytask_aggregate.html", + "href": "22_pytask_aggregate.html", + "title": "Aggregation: The aggregation Module as a pytask Task", + "section": "", + "text": "This task aggregates the downloaded data into spatial and temporal averages. It uses xarray to compute summary statistics over the specified time period and spatial region. The aggregation is done diurnally, so we will fetch the sundown and sunrise times for the region and use them to compute the diurnal averages.\n\n\n\n\nTo do diurnal classificaiton, we will need to fetch the sundown and sunrise times for the region and use them to compute the diurnal averages. We will use the astral library to get the sunrise and sunset times for the specified latitude and longitude. The aggregation will be done using xarray, which allows us to compute the mean over the specified time period and spatial region.\nHere’s our example file:\n\neg_file = data_catalog['download']['outputs']['2009_01_nepal'].load()\nwith ClimateDataFileHandler(eg_file) as handler:\n \n ds = xr.open_dataset(handler.get_dataset(\"instant\"))\n #ds = xr.open_dataset(handler.get_dataset(\"accum\"))\n\nds\n\nWe can see the astral library in action below:\n\n\nExported source\nfrom astral import Observer, sun\nimport pandas as pd\nimport numpy as np\nfrom tqdm import tqdm\nimport random\nimport datetime\nfrom pytz import UTC\n\n\n\n# get the location of a datapoint in the dataset\nlat, long = ds.coords[\"latitude\"].values[0], ds.coords[\"longitude\"].values[0]\ntime = ds['valid_time'].values[0]\ndt = pd.to_datetime(time, utc=True)\n\n\ndt\n\n\nobserver = Observer(latitude=lat, longitude=long, elevation=0)\nsun_info = sun.sun(observer, date=dt)\nsun_info\n\nAstral is very fast:\n\n#fetch a random time from valid_time\noptions = ds['valid_time'].values\n\nrandom_time = random.choice(options)\ndt = pd.to_datetime(random_time, utc=True)\nsun_info = sun.sun(observer, date=dt)\nif dt < sun_info['sunrise']:\n print(f\"Randomly selected time: {dt} is pre_dawn\")\nelif dt >= sun_info['sunrise'] and dt < sun_info['sunset']:\n print(f\"Randomly selected time: {dt} is day\")\nelse:\n print(f\"Randomly selected time: {dt} is post_dusk\")\n\nThis tells us that we can use the valid time for the specific location of each data point in the query and know based on the sun whether it was daytime or nighttime. The runtime will be limited only by the looping. Let’s put this in a function so that we can use the resampling in xarray.\nThe resampling approach will be a single function that can resample in three ways:\n\nBy calendar date, default (1 value per calendar date)\nBy diurnal class by calendar date (3 values, pre-dawn, day, post-dusk)\nBy solar date (2 values per calendar date, with night classified as “before” or “after”)\n\nTherefore, we’ll need 2 internal functions; one to do diurnal, and one to do solar date bins.\nEssentially, we are going to create an array-shaped index/mask, (time, latitude, longitude). As a demonstration, this loop goes through the first 24 time points in the dataset, and calculates the sun info for each latitude and longitude, assigning the values to an array:\n\ntimes = ds['valid_time'].values[:24]\nlats = ds.coords['latitude'].values\nlons = ds.coords['longitude'].values\n\nresult = np.full((len(times), len(lats), len(lons)), \"\", dtype=object)\n\nfor i, dt in enumerate(times):\n\n for j, lat in enumerate(lats):\n\n for k, lon in enumerate(lons):\n \n # set the geographical position\n observer = Observer(latitude=lat, longitude=lon, elevation=0)\n \n # use the time\n dt = pd.to_datetime(dt, utc=True)\n\n # where/when is the sun at this time for this position\n sun_info = sun.sun(observer, date=dt)\n result[i, j, k] = sun_info\n\nSo we know that in the first hour, the sun goes up and comes down at slightly different times based on latitude and longitude. Take the first hour, for example:\n\nprint(result.shape)\nhour_1 = 0 # 0th index of the results\n\nmin_lat = 0\nmin_lon = 0\nmax_lat = 48\nmax_lon = 90\nprint(f\"Even though the reading came from the first HOUR of data UTC, the sun info at the minimum latitude/longitude is: {result[hour_1, min_lat, min_lon]}\")\n\nprint(f\"this is different from the sun info at the maximum latitude/longitude is: {result[hour_1, max_lat, max_lon]}\")\n\n\nsource\n\n\n\n compute_diurnal_class_bins (ds:xarray.core.dataset.Dataset)\n\nCompute the diurnal value for each data point in the dataset. This function iterates over each data point in the dataset, calculates the sunrise and sunset times for the given time, latitude and longitude, and returns whether or not that data point is before dawn, during the day, or after dusk.\n\nex=compute_diurnal_class_bins(ds)\n\nSo, for our 720 time points, we should find that if we take the set() of all the classifications within that slice, there should be a few of them with 2 classes. In other words, at any given hour, almost all of the readings are “day”, because it is daytime across all of Madagascar, but at certain timepoints, the sun is rising or setting in the northern part of the country and so some portion of the slice is classified differently:\n\n\n\nillustrated\n\n\n\nfor x in range(720):\n print(set(ex[x].flatten()))\n\nThis works! Now we can do a similar, but slightly more complicated function to define “night” and “day”, where “night” includes all of the values after the sun goes down.\n\nsource\n\n\n\n\n compute_solar_day_night_class_bins (ds:xarray.core.dataset.Dataset,\n night_direction:Literal['before','aft\n er'])\n\nCompute the diurnal value for each data point in the dataset. This function iterates over each data point in the dataset, calculates the sunrise and sunset times for the given time, latitude and longitude, and returns whether or not that data point is daytime or nighttime. The definition of “nighttime” can be set to be all the darkness before the sun came up (before), or all the darkness after it went down (after).\n\n\nExported source\ndef compute_solar_day_night_class_bins(\n ds: xr.Dataset,\n night_direction: Literal[\"before\", \"after\"],\n )-> list:\n \"\"\"\n Compute the diurnal value for each data point in the dataset.\n This function iterates over each data point in the dataset,\n calculates the sunrise and sunset times for the given time, latitude and longitude,\n and returns whether or not that data point is daytime or nighttime.\n The definition of \"nighttime\" can be set to be all the darkness before the sun\n came up (before), or all the darkness after it went down (after).\n \"\"\"\n\n times = ds['valid_time'].values\n lats = ds.coords['latitude'].values\n lons = ds.coords['longitude'].values\n\n result = np.full((len(times), len(lats), len(lons)), \"\", dtype=object)\n datetimes = np.full((len(times), len(lats), len(lons)), \"\", dtype=object)\n\n for i, dt in enumerate(tqdm(times, desc=\"Classifying data points by sun position\")):\n # use the time\n dt = pd.to_datetime(dt, utc=True)\n\n for j, lat in enumerate(lats):\n\n for k, lon in enumerate(lons):\n \n # set the geographical position\n observer = Observer(latitude=lat, longitude=lon, elevation=0)\n if night_direction == \"before\":\n # Night is from previous sunset to today's sunrise\n sun_today = sun.sun(observer, date=dt.date())\n sun_prev = sun.sun(observer, date=(dt - pd.Timedelta(days=1)).date())\n night_start = sun_prev[\"sunset\"].astimezone(pd.Timestamp.utcnow().tz)\n night_end = sun_today[\"sunrise\"].astimezone(pd.Timestamp.utcnow().tz)\n \n # the reading is from yesterday's nighttime\n if night_start <= dt < night_end:\n result[i, j, k] = \"night\"\n # the date counts as today\n datetimes[i, j, k] = dt.date()\n\n # the reading is from daytime\n elif sun_today[\"sunrise\"] <= dt < sun_today[\"sunset\"]:\n result[i, j, k] = \"day\"\n # the date counts as today\n datetimes[i, j, k] = dt.date()\n \n # the reading is from today's nighttime, but counts as tomorrow's night\n else:\n result[i, j, k] = \"night\"\n # the date is tomorrow\n datetimes[i, j, k] = (dt + pd.Timedelta(days=1)).date()\n\n elif night_direction == \"after\":\n # Night is from today's sunset to next sunrise\n sun_today = sun.sun(observer, date=dt.date())\n sun_next = sun.sun(observer, date=(dt + pd.Timedelta(days=1)).date())\n night_start = sun_today[\"sunset\"].astimezone(pd.Timestamp.utcnow().tz)\n night_end = sun_next[\"sunrise\"].astimezone(pd.Timestamp.utcnow().tz)\n\n # the reading is from daytime\n if sun_today[\"sunrise\"] <= dt < sun_today[\"sunset\"]:\n result[i, j, k] = \"day\"\n # the date counts as today\n datetimes[i, j, k] = dt.date()\n # the reading is from tonight\n elif night_start <= dt < night_end:\n result[i, j, k] = \"night\"\n # the date counts as today\n datetimes[i, j, k] = dt.date()\n\n # the reading is from yesterday night\n else:\n # the date counts as yesterday\n result[i, j, k] = \"day\"\n datetimes[i, j, k] = (dt - pd.Timedelta(days=1)).date()\n else:\n raise ValueError(f\"Invalid night_direction: {night_direction}\")\n\n return result, datetimes\n\n\n\nex_class, ex_dt = compute_solar_day_night_class_bins(ds, \"before\")\n\n\nex_class\n\nAs before, we should see that most slices are homogenous, meaning most of the time, all the readings are from the day, but some slices should have day and night values:\n\nfor slice_ in range(720):\n print(set(ex_class[slice_].flatten()))\n\nThe returned array can serve as new “variable indexes” for the dataset:\n\nds_masked = ds.copy()\nds_masked['solar_class'] = (('valid_time', 'latitude', 'longitude'), ex_class)\nds_masked[\"solar_date\"] = ((\"valid_time\", \"latitude\", \"longitude\"), ex_dt)\n\n\n\n\n\nNow, to see if it will resample by both solar day and diurnal class. Let’s try by masking and making copies with NaN in the masked values:\n\nds_day = ds_masked.where(ds_masked[\"solar_class\"] == \"day\").drop_vars([\"solar_class\", \"solar_date\"])\nds_night = ds_masked.where(ds_masked[\"solar_class\"] == \"night\").drop_vars([\"solar_class\", \"solar_date\"])\n\nNext, we set the time zone for Madagascar since, to resample by day and night, we should observe the local time:\n\nds_day = ds_day.assign_coords(valid_time=pd.to_datetime(ds[\"valid_time\"].values).tz_localize(\"UTC\").tz_convert(\"Asia/Kathmandu\"))\nds_night = ds_night.assign_coords(valid_time=pd.to_datetime(ds[\"valid_time\"].values).tz_localize(\"UTC\").tz_convert(\"Asia/Kathmandu\"))\n\nNow if we can resample by day…\n\nds_day_rs = ds_day.resample(valid_time=\"1D\").reduce(np.nanmean)\nds_night_rs = ds_night.resample(valid_time=\"1D\").reduce(np.nanmean)\nds_day_rs\n\nCan we successfully convert this to a tiff?\n\nfrom era5_sandbox.aggregate import netcdf_to_tiff\n\n\nraster_day = netcdf_to_tiff(ds_day_rs, band=1, variable=\"d2m\")\nraster_night = netcdf_to_tiff(ds_night_rs, band=1, variable=\"d2m\")\n\nLooks great! These two rasters represent one calendar day of daytime and nighttime values.\n\n\nThe penultimate step of the aggregate pipeline in the original version is assigning each datapoint to the respective healthshed. The vectors argument comes from the healthshed, and represents each geographic polygon on the ground that we want to aggregate data to.\n\nfrom hydra import initialize, compose\n\n\ntry:\n with initialize(version_base=None, config_path=\"../conf\"):\n cfg = compose(config_name='config.yaml')\nexcept Exception as e:\n print(f\"Error initializing Hydra: {e}\")\n with initialize(version_base=None, config_path=\"conf\"):\n cfg = compose(config_name='config.yaml')\n\ndriver = GoogleDriver(json_key_path=here() / cfg.GOOGLE_DRIVE_AUTH_JSON.path)\ndrive = driver.get_drive()\nhealthsheds = driver.read_healthsheds(\"Nepal_Healthsheds2024.zip\")\n\n\nres_poly2cell=polygon_to_raster_cells(\n vectors = healthsheds.geometry.values, # the geometries of the shapefile of the regions\n raster=raster_day.data, # the raster data above\n nodata=np.nan, # any intersections with no data, may have to be np.nan\n affine=raster_day.transform, # some math thing need to revise\n all_touched=True, \n verbose=True\n)\n\nThis works fine. Finally, we aggregate to healthsheds:\n\nfrom era5_sandbox.aggregate import aggregate_to_healthsheds\n\n\nresult_day = aggregate_to_healthsheds(\n res_poly2cell=res_poly2cell,\n raster=raster_day,\n shapes=healthsheds,\n names_column=\"fid\",\n aggregation_func=np.nanmean,\n aggregation_name=\"mean_dewpoint_day\"\n)\n\nresult_night = aggregate_to_healthsheds(\n res_poly2cell=res_poly2cell,\n raster=raster_night,\n shapes=healthsheds,\n names_column=\"fid\",\n aggregation_func=np.nanmean,\n aggregation_name=\"mean_dewpoint_night\"\n)\n\nBelow shows the result of aggregating the daytime dewpoint temperature to the healthshed level:\n\nresult_day\n\n\nresult_night\n\nSo from one input, we will have two outputs, one for daytime and one for nighttime, and this will have to loop over the bands (ie each day in the month).", + "crumbs": [ + "Aggregation: The `aggregation` Module as a `pytask` Task" + ] + }, + { + "objectID": "22_pytask_aggregate.html#diurnal-classification-based-on-sun-position", + "href": "22_pytask_aggregate.html#diurnal-classification-based-on-sun-position", + "title": "Aggregation: The aggregation Module as a pytask Task", + "section": "", + "text": "To do diurnal classificaiton, we will need to fetch the sundown and sunrise times for the region and use them to compute the diurnal averages. We will use the astral library to get the sunrise and sunset times for the specified latitude and longitude. The aggregation will be done using xarray, which allows us to compute the mean over the specified time period and spatial region.\nHere’s our example file:\n\neg_file = data_catalog['download']['outputs']['2009_01_nepal'].load()\nwith ClimateDataFileHandler(eg_file) as handler:\n \n ds = xr.open_dataset(handler.get_dataset(\"instant\"))\n #ds = xr.open_dataset(handler.get_dataset(\"accum\"))\n\nds\n\nWe can see the astral library in action below:\n\n\nExported source\nfrom astral import Observer, sun\nimport pandas as pd\nimport numpy as np\nfrom tqdm import tqdm\nimport random\nimport datetime\nfrom pytz import UTC\n\n\n\n# get the location of a datapoint in the dataset\nlat, long = ds.coords[\"latitude\"].values[0], ds.coords[\"longitude\"].values[0]\ntime = ds['valid_time'].values[0]\ndt = pd.to_datetime(time, utc=True)\n\n\ndt\n\n\nobserver = Observer(latitude=lat, longitude=long, elevation=0)\nsun_info = sun.sun(observer, date=dt)\nsun_info\n\nAstral is very fast:\n\n#fetch a random time from valid_time\noptions = ds['valid_time'].values\n\nrandom_time = random.choice(options)\ndt = pd.to_datetime(random_time, utc=True)\nsun_info = sun.sun(observer, date=dt)\nif dt < sun_info['sunrise']:\n print(f\"Randomly selected time: {dt} is pre_dawn\")\nelif dt >= sun_info['sunrise'] and dt < sun_info['sunset']:\n print(f\"Randomly selected time: {dt} is day\")\nelse:\n print(f\"Randomly selected time: {dt} is post_dusk\")\n\nThis tells us that we can use the valid time for the specific location of each data point in the query and know based on the sun whether it was daytime or nighttime. The runtime will be limited only by the looping. Let’s put this in a function so that we can use the resampling in xarray.\nThe resampling approach will be a single function that can resample in three ways:\n\nBy calendar date, default (1 value per calendar date)\nBy diurnal class by calendar date (3 values, pre-dawn, day, post-dusk)\nBy solar date (2 values per calendar date, with night classified as “before” or “after”)\n\nTherefore, we’ll need 2 internal functions; one to do diurnal, and one to do solar date bins.\nEssentially, we are going to create an array-shaped index/mask, (time, latitude, longitude). As a demonstration, this loop goes through the first 24 time points in the dataset, and calculates the sun info for each latitude and longitude, assigning the values to an array:\n\ntimes = ds['valid_time'].values[:24]\nlats = ds.coords['latitude'].values\nlons = ds.coords['longitude'].values\n\nresult = np.full((len(times), len(lats), len(lons)), \"\", dtype=object)\n\nfor i, dt in enumerate(times):\n\n for j, lat in enumerate(lats):\n\n for k, lon in enumerate(lons):\n \n # set the geographical position\n observer = Observer(latitude=lat, longitude=lon, elevation=0)\n \n # use the time\n dt = pd.to_datetime(dt, utc=True)\n\n # where/when is the sun at this time for this position\n sun_info = sun.sun(observer, date=dt)\n result[i, j, k] = sun_info\n\nSo we know that in the first hour, the sun goes up and comes down at slightly different times based on latitude and longitude. Take the first hour, for example:\n\nprint(result.shape)\nhour_1 = 0 # 0th index of the results\n\nmin_lat = 0\nmin_lon = 0\nmax_lat = 48\nmax_lon = 90\nprint(f\"Even though the reading came from the first HOUR of data UTC, the sun info at the minimum latitude/longitude is: {result[hour_1, min_lat, min_lon]}\")\n\nprint(f\"this is different from the sun info at the maximum latitude/longitude is: {result[hour_1, max_lat, max_lon]}\")\n\n\nsource\n\n\n\n compute_diurnal_class_bins (ds:xarray.core.dataset.Dataset)\n\nCompute the diurnal value for each data point in the dataset. This function iterates over each data point in the dataset, calculates the sunrise and sunset times for the given time, latitude and longitude, and returns whether or not that data point is before dawn, during the day, or after dusk.\n\nex=compute_diurnal_class_bins(ds)\n\nSo, for our 720 time points, we should find that if we take the set() of all the classifications within that slice, there should be a few of them with 2 classes. In other words, at any given hour, almost all of the readings are “day”, because it is daytime across all of Madagascar, but at certain timepoints, the sun is rising or setting in the northern part of the country and so some portion of the slice is classified differently:\n\n\n\nillustrated\n\n\n\nfor x in range(720):\n print(set(ex[x].flatten()))\n\nThis works! Now we can do a similar, but slightly more complicated function to define “night” and “day”, where “night” includes all of the values after the sun goes down.\n\nsource\n\n\n\n\n compute_solar_day_night_class_bins (ds:xarray.core.dataset.Dataset,\n night_direction:Literal['before','aft\n er'])\n\nCompute the diurnal value for each data point in the dataset. This function iterates over each data point in the dataset, calculates the sunrise and sunset times for the given time, latitude and longitude, and returns whether or not that data point is daytime or nighttime. The definition of “nighttime” can be set to be all the darkness before the sun came up (before), or all the darkness after it went down (after).\n\n\nExported source\ndef compute_solar_day_night_class_bins(\n ds: xr.Dataset,\n night_direction: Literal[\"before\", \"after\"],\n )-> list:\n \"\"\"\n Compute the diurnal value for each data point in the dataset.\n This function iterates over each data point in the dataset,\n calculates the sunrise and sunset times for the given time, latitude and longitude,\n and returns whether or not that data point is daytime or nighttime.\n The definition of \"nighttime\" can be set to be all the darkness before the sun\n came up (before), or all the darkness after it went down (after).\n \"\"\"\n\n times = ds['valid_time'].values\n lats = ds.coords['latitude'].values\n lons = ds.coords['longitude'].values\n\n result = np.full((len(times), len(lats), len(lons)), \"\", dtype=object)\n datetimes = np.full((len(times), len(lats), len(lons)), \"\", dtype=object)\n\n for i, dt in enumerate(tqdm(times, desc=\"Classifying data points by sun position\")):\n # use the time\n dt = pd.to_datetime(dt, utc=True)\n\n for j, lat in enumerate(lats):\n\n for k, lon in enumerate(lons):\n \n # set the geographical position\n observer = Observer(latitude=lat, longitude=lon, elevation=0)\n if night_direction == \"before\":\n # Night is from previous sunset to today's sunrise\n sun_today = sun.sun(observer, date=dt.date())\n sun_prev = sun.sun(observer, date=(dt - pd.Timedelta(days=1)).date())\n night_start = sun_prev[\"sunset\"].astimezone(pd.Timestamp.utcnow().tz)\n night_end = sun_today[\"sunrise\"].astimezone(pd.Timestamp.utcnow().tz)\n \n # the reading is from yesterday's nighttime\n if night_start <= dt < night_end:\n result[i, j, k] = \"night\"\n # the date counts as today\n datetimes[i, j, k] = dt.date()\n\n # the reading is from daytime\n elif sun_today[\"sunrise\"] <= dt < sun_today[\"sunset\"]:\n result[i, j, k] = \"day\"\n # the date counts as today\n datetimes[i, j, k] = dt.date()\n \n # the reading is from today's nighttime, but counts as tomorrow's night\n else:\n result[i, j, k] = \"night\"\n # the date is tomorrow\n datetimes[i, j, k] = (dt + pd.Timedelta(days=1)).date()\n\n elif night_direction == \"after\":\n # Night is from today's sunset to next sunrise\n sun_today = sun.sun(observer, date=dt.date())\n sun_next = sun.sun(observer, date=(dt + pd.Timedelta(days=1)).date())\n night_start = sun_today[\"sunset\"].astimezone(pd.Timestamp.utcnow().tz)\n night_end = sun_next[\"sunrise\"].astimezone(pd.Timestamp.utcnow().tz)\n\n # the reading is from daytime\n if sun_today[\"sunrise\"] <= dt < sun_today[\"sunset\"]:\n result[i, j, k] = \"day\"\n # the date counts as today\n datetimes[i, j, k] = dt.date()\n # the reading is from tonight\n elif night_start <= dt < night_end:\n result[i, j, k] = \"night\"\n # the date counts as today\n datetimes[i, j, k] = dt.date()\n\n # the reading is from yesterday night\n else:\n # the date counts as yesterday\n result[i, j, k] = \"day\"\n datetimes[i, j, k] = (dt - pd.Timedelta(days=1)).date()\n else:\n raise ValueError(f\"Invalid night_direction: {night_direction}\")\n\n return result, datetimes\n\n\n\nex_class, ex_dt = compute_solar_day_night_class_bins(ds, \"before\")\n\n\nex_class\n\nAs before, we should see that most slices are homogenous, meaning most of the time, all the readings are from the day, but some slices should have day and night values:\n\nfor slice_ in range(720):\n print(set(ex_class[slice_].flatten()))\n\nThe returned array can serve as new “variable indexes” for the dataset:\n\nds_masked = ds.copy()\nds_masked['solar_class'] = (('valid_time', 'latitude', 'longitude'), ex_class)\nds_masked[\"solar_date\"] = ((\"valid_time\", \"latitude\", \"longitude\"), ex_dt)", + "crumbs": [ + "Aggregation: The `aggregation` Module as a `pytask` Task" + ] + }, + { + "objectID": "22_pytask_aggregate.html#diurnal-resampling", + "href": "22_pytask_aggregate.html#diurnal-resampling", + "title": "Aggregation: The aggregation Module as a pytask Task", + "section": "", + "text": "Now, to see if it will resample by both solar day and diurnal class. Let’s try by masking and making copies with NaN in the masked values:\n\nds_day = ds_masked.where(ds_masked[\"solar_class\"] == \"day\").drop_vars([\"solar_class\", \"solar_date\"])\nds_night = ds_masked.where(ds_masked[\"solar_class\"] == \"night\").drop_vars([\"solar_class\", \"solar_date\"])\n\nNext, we set the time zone for Madagascar since, to resample by day and night, we should observe the local time:\n\nds_day = ds_day.assign_coords(valid_time=pd.to_datetime(ds[\"valid_time\"].values).tz_localize(\"UTC\").tz_convert(\"Asia/Kathmandu\"))\nds_night = ds_night.assign_coords(valid_time=pd.to_datetime(ds[\"valid_time\"].values).tz_localize(\"UTC\").tz_convert(\"Asia/Kathmandu\"))\n\nNow if we can resample by day…\n\nds_day_rs = ds_day.resample(valid_time=\"1D\").reduce(np.nanmean)\nds_night_rs = ds_night.resample(valid_time=\"1D\").reduce(np.nanmean)\nds_day_rs\n\nCan we successfully convert this to a tiff?\n\nfrom era5_sandbox.aggregate import netcdf_to_tiff\n\n\nraster_day = netcdf_to_tiff(ds_day_rs, band=1, variable=\"d2m\")\nraster_night = netcdf_to_tiff(ds_night_rs, band=1, variable=\"d2m\")\n\nLooks great! These two rasters represent one calendar day of daytime and nighttime values.\n\n\nThe penultimate step of the aggregate pipeline in the original version is assigning each datapoint to the respective healthshed. The vectors argument comes from the healthshed, and represents each geographic polygon on the ground that we want to aggregate data to.\n\nfrom hydra import initialize, compose\n\n\ntry:\n with initialize(version_base=None, config_path=\"../conf\"):\n cfg = compose(config_name='config.yaml')\nexcept Exception as e:\n print(f\"Error initializing Hydra: {e}\")\n with initialize(version_base=None, config_path=\"conf\"):\n cfg = compose(config_name='config.yaml')\n\ndriver = GoogleDriver(json_key_path=here() / cfg.GOOGLE_DRIVE_AUTH_JSON.path)\ndrive = driver.get_drive()\nhealthsheds = driver.read_healthsheds(\"Nepal_Healthsheds2024.zip\")\n\n\nres_poly2cell=polygon_to_raster_cells(\n vectors = healthsheds.geometry.values, # the geometries of the shapefile of the regions\n raster=raster_day.data, # the raster data above\n nodata=np.nan, # any intersections with no data, may have to be np.nan\n affine=raster_day.transform, # some math thing need to revise\n all_touched=True, \n verbose=True\n)\n\nThis works fine. Finally, we aggregate to healthsheds:\n\nfrom era5_sandbox.aggregate import aggregate_to_healthsheds\n\n\nresult_day = aggregate_to_healthsheds(\n res_poly2cell=res_poly2cell,\n raster=raster_day,\n shapes=healthsheds,\n names_column=\"fid\",\n aggregation_func=np.nanmean,\n aggregation_name=\"mean_dewpoint_day\"\n)\n\nresult_night = aggregate_to_healthsheds(\n res_poly2cell=res_poly2cell,\n raster=raster_night,\n shapes=healthsheds,\n names_column=\"fid\",\n aggregation_func=np.nanmean,\n aggregation_name=\"mean_dewpoint_night\"\n)\n\nBelow shows the result of aggregating the daytime dewpoint temperature to the healthshed level:\n\nresult_day\n\n\nresult_night\n\nSo from one input, we will have two outputs, one for daytime and one for nighttime, and this will have to loop over the bands (ie each day in the month).", + "crumbs": [ + "Aggregation: The `aggregation` Module as a `pytask` Task" + ] + }, + { + "objectID": "10_pytask_demo.html", + "href": "10_pytask_demo.html", + "title": "Demo: How to Create Pipelines with pytask", + "section": "", + "text": "Data preparation task for pytask demo\n\nIn this notebook, we are demonstrating how to convert our snakemake workflow into a pytask workflow. We use the basic tutorial to demonstrate this, but continue to use nbdev for development of functions in notebooks.\npytask is a task management system that allows you to define tasks and their dependencies, similar to Snakemake. It is particularly useful for data science workflows.\nThere are a number of reasons to use pytask over snakemake: - Pythonic: pytask is designed to be purely Pythonic by default, allowing you to write tasks and entire pipelines as Python functions. - Flexibility: pytask allows you to define tasks and their dependencies in a more flexible way, using Python functions and decorators, as opposed to orchestrating numerous scripts. - Integration: pytask integrates well with other Python libraries, such as nbdev here, or hydra configurations if you need, allowing you to use your existing code, notebooks, or configs in your workflows. - Parallelism: pytask supports parallel execution of tasks with pytask-parallel, which can speed up your workflows significantly, especially for data processing tasks.\nWe’ll use nbdev to define the task functions, and then export them to the src directory. pytask is then invoked at the command line to run the tasks.\n\nThis demo task is taken from the tutorial at pytask documentation. At minimum, you need your package to contain the following in a config.py file:\nmy_project\n│\n├───.pytask\n│\n├───bld\n│ └────...\n│\n├───src\n│ └───my_project\n│ ├────__init__.py\n│ ├────config.py\n│ └────...\n│\n└───pyproject.toml\n#contents of `era5_sandbox.config` module\nfrom pathlib import Path\n\n\nSRC = Path(__file__).parent.resolve()\nBLD = SRC.joinpath(\"..\", \"..\", \"bld\").resolve()\nAdditionally, your pyproject.toml file should contain the following at minimum:\n[tool.pytask.ini_options]\npaths = [\"src/era5_sandbox\"]\nThe former tells Python where to find the source code and build directory for pytask objects and shims, while the latter tells pytask where to find the task definitions and dependency DAG.\n\n\nExported source\nimport os\nfrom pathlib import Path\nfrom typing import Annotated\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nfrom era5_sandbox.config import BLD\nfrom era5_sandbox.config import data_catalog, demo_catalog\n\nfrom pytask import PickleNode\nfrom pytask import Product\n\n\n\n\nTo define a task, simply use the task_ prefix in the function name (or, if you are familiar and comfortable with decorators, use @pytask.mark.task). Be verbose and expressive in your use of type hints to specify the input and output data, so that pytask can automatically detect and handle the dependencies between tasks.\n\n\n\nTo define something as a tracked output, you can annotate the input of the task with Annotated[Path, Product], where Product is imported from pytask. This tells pytask that this is a product of the task and should be saved in the build directory.\nIn this example, we’re generating random data into a data frame and saving the object as a pickle in the bld directory (bld is the default build directory for pytask’s intermediate data). To get that directory, we use the BLD variable from the era5_sandbox.config module as above. This module itself could also be generated using nbdev if you want to keep your configuration in notebooks.\nUsing nbdev, we can also include all of the bells and whistles of function documentation.\n\nsource\n\n\n\n\n task_create_random_data (seed:typing.Annotated[int,42], path_to_data:typi\n ng.Annotated[pathlib.Path,ProductType()]=Path('/\n net/rcstorenfs02/ifs/rc_labs/dominici_lab/lab/da\n ta_processing/csph-era5_sandbox/bld/data.pkl'))\n\nCreate a random data set and save it as a pickle file. Return the path to the saved file.\n\n\n\n\n\n\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\nseed\nAnnotated\n\nDefault seed for reproducibility\n\n\npath_to_data\nAnnotated\n/net/rcstorenfs02/ifs/rc_labs/dominici_lab/lab/data_processing/csph-era5_sandbox/bld/data.pkl\nPath to the object in the build directory\n\n\nReturns\nNone\n\n\n\n\n\n\n\nExported source\ndef task_create_random_data(\n seed: Annotated[int, 42], # Default seed for reproducibility\n path_to_data: Annotated[Path, Product] = BLD / \"data.pkl\" # Path to the object in the build directory\n ) -> None:\n \"Create a random data set and save it as a pickle file. Return the path to the saved file.\"\n rng = np.random.default_rng(seed)\n beta = 2\n\n x = rng.normal(loc=5, scale=10, size=1_000)\n epsilon = rng.standard_normal(1_000)\n\n y = beta * x + epsilon\n\n df = pd.DataFrame({\"x\": x, \"y\": y})\n\n # this is a tracked output, so we annotate the return value with `Annotated[Path, Product]`\n df.to_pickle(path_to_data)\n\n\nWe can test the function directly in the notebook:\n\ntask_create_random_data(42)\n\nOnce this module and function are exported with nbdev_export, the functions are in a python package. We can then use the command line to look at the registered tasks:\n\npytask collect\n\nLet’s add another task in the same module. This task plots the data we generated. To link the previous task to this one as a dependency, we can list the output of the previous task as an input to this one. This way, pytask will know that it needs to run the first task before this one.\n\nsource\n\n\n\n\n task_plot_data (path_to_data:typing.Annotated[pathlib.Path,Path('/net/rcs\n torenfs02/ifs/rc_labs/dominici_lab/lab/data_processing/cs\n ph-era5_sandbox/bld/data.pkl')], path_to_plot:typing.Anno\n tated[pathlib.Path,ProductType()]=Path('/net/rcstorenfs02\n /ifs/rc_labs/dominici_lab/lab/data_processing/csph-\n era5_sandbox/bld/plot.png'))\n\nPlot the data from the pickle file and save the plot. Note that this task: 1. depends on the data.pkl file created by the previous task, 2. does not return any value, but saves a plot to the build directory. So the side effect of the task is what we are interested in here (though this is probably bad practice).\n\n\n\n\n\n\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\npath_to_data\nAnnotated\n\nPath to the data file created by the previous task\n\n\npath_to_plot\nAnnotated\n/net/rcstorenfs02/ifs/rc_labs/dominici_lab/lab/data_processing/csph-era5_sandbox/bld/plot.png\nPath to the build directory for the plot\n\n\nReturns\nNone\n\n\n\n\n\n\n\nExported source\ndef task_plot_data(\n path_to_data: Annotated[Path, BLD / \"data.pkl\"], # Path to the data file created by the previous task\n path_to_plot: Annotated[Path, Product] = BLD / \"plot.png\" # Path to the build directory for the plot\n) -> None:\n \"\"\"\n Plot the data from the pickle file and save the plot. Note that this task:\n 1. depends on the data.pkl file created by the previous task,\n 2. does not return any value, but saves a plot to the build directory. So the side effect of the task is what we are interested in here (though this is probably bad practice).\n \"\"\"\n\n df = pd.read_pickle(path_to_data)\n \n _, ax = plt.subplots()\n df.plot(x=\"x\", y=\"y\", ax=ax, kind=\"scatter\")\n\n plt.savefig(path_to_plot)\n plt.close()\n\n\nWe now have a DAG of tasks that pytask can execute. To see the tasks, we can use the command line to create a pygraphviz graph of the tasks:\npytask dag\nThe DAG is saved as a pdf file, and you can view it using any viewer. Now, to run the pipeline, just invoke pytask at the command line:\npytask\nIn Jupyter or iPython, you can interact with the task outputs directly:\n\n# list all the files in the build directory\nfor file in os.listdir(BLD):\n print(file)\n\nWe can use these to build subsequent tasks later.", + "crumbs": [ + "Demo: How to Create Pipelines with `pytask`" + ] + }, + { + "objectID": "10_pytask_demo.html#data-preparation-demo", + "href": "10_pytask_demo.html#data-preparation-demo", + "title": "Demo: How to Create Pipelines with pytask", + "section": "", + "text": "Data preparation task for pytask demo\n\nIn this notebook, we are demonstrating how to convert our snakemake workflow into a pytask workflow. We use the basic tutorial to demonstrate this, but continue to use nbdev for development of functions in notebooks.\npytask is a task management system that allows you to define tasks and their dependencies, similar to Snakemake. It is particularly useful for data science workflows.\nThere are a number of reasons to use pytask over snakemake: - Pythonic: pytask is designed to be purely Pythonic by default, allowing you to write tasks and entire pipelines as Python functions. - Flexibility: pytask allows you to define tasks and their dependencies in a more flexible way, using Python functions and decorators, as opposed to orchestrating numerous scripts. - Integration: pytask integrates well with other Python libraries, such as nbdev here, or hydra configurations if you need, allowing you to use your existing code, notebooks, or configs in your workflows. - Parallelism: pytask supports parallel execution of tasks with pytask-parallel, which can speed up your workflows significantly, especially for data processing tasks.\nWe’ll use nbdev to define the task functions, and then export them to the src directory. pytask is then invoked at the command line to run the tasks.\n\nThis demo task is taken from the tutorial at pytask documentation. At minimum, you need your package to contain the following in a config.py file:\nmy_project\n│\n├───.pytask\n│\n├───bld\n│ └────...\n│\n├───src\n│ └───my_project\n│ ├────__init__.py\n│ ├────config.py\n│ └────...\n│\n└───pyproject.toml\n#contents of `era5_sandbox.config` module\nfrom pathlib import Path\n\n\nSRC = Path(__file__).parent.resolve()\nBLD = SRC.joinpath(\"..\", \"..\", \"bld\").resolve()\nAdditionally, your pyproject.toml file should contain the following at minimum:\n[tool.pytask.ini_options]\npaths = [\"src/era5_sandbox\"]\nThe former tells Python where to find the source code and build directory for pytask objects and shims, while the latter tells pytask where to find the task definitions and dependency DAG.\n\n\nExported source\nimport os\nfrom pathlib import Path\nfrom typing import Annotated\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nfrom era5_sandbox.config import BLD\nfrom era5_sandbox.config import data_catalog, demo_catalog\n\nfrom pytask import PickleNode\nfrom pytask import Product\n\n\n\n\nTo define a task, simply use the task_ prefix in the function name (or, if you are familiar and comfortable with decorators, use @pytask.mark.task). Be verbose and expressive in your use of type hints to specify the input and output data, so that pytask can automatically detect and handle the dependencies between tasks.\n\n\n\nTo define something as a tracked output, you can annotate the input of the task with Annotated[Path, Product], where Product is imported from pytask. This tells pytask that this is a product of the task and should be saved in the build directory.\nIn this example, we’re generating random data into a data frame and saving the object as a pickle in the bld directory (bld is the default build directory for pytask’s intermediate data). To get that directory, we use the BLD variable from the era5_sandbox.config module as above. This module itself could also be generated using nbdev if you want to keep your configuration in notebooks.\nUsing nbdev, we can also include all of the bells and whistles of function documentation.\n\nsource\n\n\n\n\n task_create_random_data (seed:typing.Annotated[int,42], path_to_data:typi\n ng.Annotated[pathlib.Path,ProductType()]=Path('/\n net/rcstorenfs02/ifs/rc_labs/dominici_lab/lab/da\n ta_processing/csph-era5_sandbox/bld/data.pkl'))\n\nCreate a random data set and save it as a pickle file. Return the path to the saved file.\n\n\n\n\n\n\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\nseed\nAnnotated\n\nDefault seed for reproducibility\n\n\npath_to_data\nAnnotated\n/net/rcstorenfs02/ifs/rc_labs/dominici_lab/lab/data_processing/csph-era5_sandbox/bld/data.pkl\nPath to the object in the build directory\n\n\nReturns\nNone\n\n\n\n\n\n\n\nExported source\ndef task_create_random_data(\n seed: Annotated[int, 42], # Default seed for reproducibility\n path_to_data: Annotated[Path, Product] = BLD / \"data.pkl\" # Path to the object in the build directory\n ) -> None:\n \"Create a random data set and save it as a pickle file. Return the path to the saved file.\"\n rng = np.random.default_rng(seed)\n beta = 2\n\n x = rng.normal(loc=5, scale=10, size=1_000)\n epsilon = rng.standard_normal(1_000)\n\n y = beta * x + epsilon\n\n df = pd.DataFrame({\"x\": x, \"y\": y})\n\n # this is a tracked output, so we annotate the return value with `Annotated[Path, Product]`\n df.to_pickle(path_to_data)\n\n\nWe can test the function directly in the notebook:\n\ntask_create_random_data(42)\n\nOnce this module and function are exported with nbdev_export, the functions are in a python package. We can then use the command line to look at the registered tasks:\n\npytask collect\n\nLet’s add another task in the same module. This task plots the data we generated. To link the previous task to this one as a dependency, we can list the output of the previous task as an input to this one. This way, pytask will know that it needs to run the first task before this one.\n\nsource\n\n\n\n\n task_plot_data (path_to_data:typing.Annotated[pathlib.Path,Path('/net/rcs\n torenfs02/ifs/rc_labs/dominici_lab/lab/data_processing/cs\n ph-era5_sandbox/bld/data.pkl')], path_to_plot:typing.Anno\n tated[pathlib.Path,ProductType()]=Path('/net/rcstorenfs02\n /ifs/rc_labs/dominici_lab/lab/data_processing/csph-\n era5_sandbox/bld/plot.png'))\n\nPlot the data from the pickle file and save the plot. Note that this task: 1. depends on the data.pkl file created by the previous task, 2. does not return any value, but saves a plot to the build directory. So the side effect of the task is what we are interested in here (though this is probably bad practice).\n\n\n\n\n\n\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\npath_to_data\nAnnotated\n\nPath to the data file created by the previous task\n\n\npath_to_plot\nAnnotated\n/net/rcstorenfs02/ifs/rc_labs/dominici_lab/lab/data_processing/csph-era5_sandbox/bld/plot.png\nPath to the build directory for the plot\n\n\nReturns\nNone\n\n\n\n\n\n\n\nExported source\ndef task_plot_data(\n path_to_data: Annotated[Path, BLD / \"data.pkl\"], # Path to the data file created by the previous task\n path_to_plot: Annotated[Path, Product] = BLD / \"plot.png\" # Path to the build directory for the plot\n) -> None:\n \"\"\"\n Plot the data from the pickle file and save the plot. Note that this task:\n 1. depends on the data.pkl file created by the previous task,\n 2. does not return any value, but saves a plot to the build directory. So the side effect of the task is what we are interested in here (though this is probably bad practice).\n \"\"\"\n\n df = pd.read_pickle(path_to_data)\n \n _, ax = plt.subplots()\n df.plot(x=\"x\", y=\"y\", ax=ax, kind=\"scatter\")\n\n plt.savefig(path_to_plot)\n plt.close()\n\n\nWe now have a DAG of tasks that pytask can execute. To see the tasks, we can use the command line to create a pygraphviz graph of the tasks:\npytask dag\nThe DAG is saved as a pdf file, and you can view it using any viewer. Now, to run the pipeline, just invoke pytask at the command line:\npytask\nIn Jupyter or iPython, you can interact with the task outputs directly:\n\n# list all the files in the build directory\nfor file in os.listdir(BLD):\n print(file)\n\nWe can use these to build subsequent tasks later.", + "crumbs": [ + "Demo: How to Create Pipelines with `pytask`" + ] + }, + { + "objectID": "10_pytask_demo.html#more-complex-tasks-the-data-catalog", + "href": "10_pytask_demo.html#more-complex-tasks-the-data-catalog", + "title": "Demo: How to Create Pipelines with pytask", + "section": "More Complex Tasks & The Data Catalog", + "text": "More Complex Tasks & The Data Catalog\nAs we define more complex tasks, we can use the pytask data catalog to manage the inputs and outputs of our tasks. The data catalog allows us to imperatively name the data and their formats, making it easier to manage the data flow in our tasks. Importantly, we can define the data pythonically, which allows us to use the full power of Python to manipulate and transform our data. This is particularly more useful than snakemake’s approach, which requires you to define the data in a more static way using paths and a separate pseudo-language.\nThe content of the era5_sandbox.config module can be extended to include a data catalog:\nfrom pathlib import Path\nfrom pytask import DataCatalog, Product\n\nSRC = Path(__file__).parent.resolve()\nBLD = SRC.joinpath(\"..\", \"..\", \"bld\").resolve()\n\ndemo_catalog = DataCatalog()\nWith just this definition, we’re now able to refer directly to data by name in our tasks, and pytask will handle the paths and formats for us. This allows us to focus on the logic of our tasks rather than the details of data management.\n\n\n\n\n\n\nNote\n\n\n\nThis is a major advantage of pytask over snakemake, as it allows you to define the data in a more flexible and Pythonic way, while still maintaining the benefits of a task management system. It is a similar approach to building pipelines in R with targets, which allows you to define the data in a more flexible way.\n\n\nLet’s create a task that modifies the data frame by adding a new column. This task will depend on the previous task’s output, and we will use the data catalog to define the input and output data.\n\nsource\n\ntask_add_one\n\n task_add_one (path_to_data:typing.Annotated[pathlib.Path,Path('/net/rcsto\n renfs02/ifs/rc_labs/dominici_lab/lab/data_processing/csph-\n era5_sandbox/bld/data.pkl')], node:typing.Annotated[_pytask\n .nodes.PickleNode,ProductType()]=PickleNode(path=Path('/net\n /rcstorenfs02/ifs/rc_labs/dominici_lab/lab/data_processing/\n csph-era5_sandbox/.pytask/data_catalogs/default/1eef510d81e\n ea49161cd821b318aa999e630bdd292b093aa9a9319e9f282b984.pkl')\n , name='mydata', attributes={'catalog_name': 'default'},\n serializer=<built-in function dump>, deserializer=<built-in\n function load>))\n\nAdd one to the ‘y’ column of the data frame and save it as a new pickle file.\n\n\n\n\n\n\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\npath_to_data\nAnnotated\n\nPath to the data file created by the previous task\n\n\nnode\nAnnotated\nPickleNode(path=Path(‘/net/rcstorenfs02/ifs/rc_labs/dominici_lab/lab/data_processing/csph-era5_sandbox/.pytask/data_catalogs/default/1eef510d81eea49161cd821b318aa999e630bdd292b093aa9a9319e9f282b984.pkl’), name=‘mydata’, attributes={‘catalog_name’: ‘default’}, serializer=, deserializer=)\n\n\n\nReturns\nNone\n\n\n\n\n\n\n\nExported source\ndef task_add_one(\n path_to_data: Annotated[Path, BLD / \"data.pkl\"], # Path to the data file created by the previous task\n node: Annotated[PickleNode, Product] = demo_catalog[\"mydata\"]\n) -> None:\n \"\"\"\n Add one to the 'y' column of the data frame and save it as a new pickle file.\n \"\"\"\n df = pd.read_pickle(path_to_data)\n df['z'] = df['y'] + 1\n \n node.save(df)\n\n\nIn this function, we’ve defined that the task relies on the output of the first task being there, the data.pkl file. But importantly, we’ve also defined our product as a node from the PickleNode module. This will allow pytask to handle the serialization and deserialization of the data frame automatically, so we don’t have to worry about the details of how the data is stored. We create the datacatalog in our config file, and then tell this task to create a Node in that catalog called mydata. Whatever we save with the node.save() method will be saved in the build directory, but more importantly will be indexed and hashed by pytask. This means that if the data changes, pytask will know to rerun the task.\nTo make this even more pythonic, we can modify the format of our task function so that the return type annotator is used as a node in the data catalog. This allows us to define the output of the task as a PickleNode, which will automatically handle the serialization and deserialization of the data frame.\n\n\n\n\n\n\nNote\n\n\n\nThis is another trick I’m deriving from {targets}. By formatting tasks as pure functions where inputs are parameters and targets are return type annotations, we can define the output of the task as a PickleNode, which will automatically handle the serialization and deserialization of the data frame. This again allows us to focus on the logic of our tasks rather than the details of data management.\n\n\nSo below, we’re directly accessing the data_catalog to get the mydata node, and then modifying it by adding a new column. It feels like we are doing this in place, such as in an iPython session, because we are allowing pytask to handle the serialization of the file on disk for us.\n\nsource\n\n\ntask_add_another_column\n\n task_add_another_column (df:typing.Annotated[pandas.core.frame.DataFrame,\n PickleNode(path=Path('/net/rcstorenfs02/ifs/rc_l\n abs/dominici_lab/lab/data_processing/csph-era5_s\n andbox/.pytask/data_catalogs/default/1eef510d81e\n ea49161cd821b318aa999e630bdd292b093aa9a9319e9f28\n 2b984.pkl'),name='mydata',attributes={'catalog_n\n ame':'default'},serializer=<built-\n infunctiondump>,deserializer=<built-\n infunctionload>)])\n\nAdd another column to the data frame stored in the PickleNode.\n\n\n\n\n\n\n\n\n\nType\nDetails\n\n\n\n\ndf\nAnnotated\nwhich object in the catalog to fetch from the catalog with node.load()\n\n\nReturns\nAnnotated\nwhich object in the catalog to save the return value to\n\n\n\n\n\nExported source\ndef task_add_another_column(\n df: Annotated[pd.DataFrame, demo_catalog[\"mydata\"]] # which object in the catalog to fetch from the catalog with node.load()\n) -> Annotated[pd.DataFrame, demo_catalog[\"mydata2\"]]: # which object in the catalog to save the return value to\n \"\"\"\n Add another column to the data frame stored in the PickleNode.\n \"\"\"\n\n # use the datacatalog directly to access the node\n # this is a bit like accessing the node in an iPython session, but pytask\n # will handle the serialization and deserialization for us\n df['w'] = df['z'] * df['y']\n \n return df\n\n\nTo test this interactively, we’d have to import the data catalog’s object\n\ndf = demo_catalog[\"mydata\"].load() # load the data frame from the PickleNode\nresult = task_add_another_column(df) # call the task function with the loaded data frame\n\n\nresult\n\nNow that we know it will work, we can invoke pytask:\n\npytask\n\nNotice that the outputs are cached and not recomputed unless the inputs change. This is a key feature of pytask and other DAGs, allowing you to efficiently manage your data processing tasks without unnecessary recomputation.", + "crumbs": [ + "Demo: How to Create Pipelines with `pytask`" + ] + }, + { + "objectID": "10_pytask_demo.html#conclusion", + "href": "10_pytask_demo.html#conclusion", + "title": "Demo: How to Create Pipelines with pytask", + "section": "Conclusion", + "text": "Conclusion\nThe takeaway here is that with pytask, you can define pure functions that take inputs and return outputs, and build a DAG of tasks that can be executed in a flexible and efficient way. This allows you to focus on the logic of your tasks rather than the details of data management, while still maintaining the benefits of a task management system. The key elements are:\n\nTask annotation: You define your tasks by creating pure functions that take inputs and return outputs, and use decorators or naming conventions to mark them as “tasks” in a dag\nInput and output annotation: You define the inputs and outputs of your tasksusing type hints, and allow pytask to automatically detect and handle the dependencies between tasks.\nData catalog: You define your data in a Pythonic object in your config called data_catalog. As you iteratively develop your DAG, you add objects to the data catalog, which are called nodes. As long as a node is a pythonic object and has a pickle method, pytask will handle the serialization and deserialization of the data for you.", + "crumbs": [ + "Demo: How to Create Pipelines with `pytask`" + ] + }, + { + "objectID": "20_pytask_config.html", + "href": "20_pytask_config.html", + "title": "pytask Config: Defining the Pipeline Internals in pytask", + "section": "", + "text": "This is the config module for the pytask pipeline. This module defines the data catalog(s) and any hard-coded parameters that are used throughout the pipeline.\n\n\n\n\nExported source\nimport pandas as pd\n\nfrom pathlib import Path\nfrom pyprojroot import here\nfrom pytask import DataCatalog\n\n\nSRC = here() / \"src\" / \"era5_sandbox\"\nBLD = here() / \"bld\"\n\ndemo_catalog = DataCatalog()", + "crumbs": [ + "`pytask` Config: Defining the Pipeline Internals in `pytask`" + ] + }, + { + "objectID": "20_pytask_config.html#config", + "href": "20_pytask_config.html#config", + "title": "pytask Config: Defining the Pipeline Internals in pytask", + "section": "", + "text": "This is the config module for the pytask pipeline. This module defines the data catalog(s) and any hard-coded parameters that are used throughout the pipeline.\n\n\n\n\nExported source\nimport pandas as pd\n\nfrom pathlib import Path\nfrom pyprojroot import here\nfrom pytask import DataCatalog\n\n\nSRC = here() / \"src\" / \"era5_sandbox\"\nBLD = here() / \"bld\"\n\ndemo_catalog = DataCatalog()", + "crumbs": [ + "`pytask` Config: Defining the Pipeline Internals in `pytask`" + ] + }, + { + "objectID": "20_pytask_config.html#dev_mode-a-quick-development-flag", + "href": "20_pytask_config.html#dev_mode-a-quick-development-flag", + "title": "pytask Config: Defining the Pipeline Internals in pytask", + "section": "DEV_MODE: A Quick Development Flag", + "text": "DEV_MODE: A Quick Development Flag\nI’m adding a flag to the config that can be used for quick development. If you import this boolean variable, it can be used to skip tasks, setup samples, etc. on the fly by marking a task with the pytask.mark.skipif decorator. Change this to False when you’re ready to run the full pipeline.\n\n\nExported source\nDEV_MODE=True", + "crumbs": [ + "`pytask` Config: Defining the Pipeline Internals in `pytask`" + ] + }, + { + "objectID": "20_pytask_config.html#the-data-catalog", + "href": "20_pytask_config.html#the-data-catalog", + "title": "pytask Config: Defining the Pipeline Internals in pytask", + "section": "The Data Catalog", + "text": "The Data Catalog\nTo manage our pipeline, we’re going to use a nested data catalog structure. This way, we can easily return specific entries to specific tasks without having to manage multiple different data catalogs. Specifically, we’ll have a data catalog for each stage of the pipeline, and each catalog will have entries for the inputs, outputs, and any other parameters needed for that stage. This is similar to how we used Hydra configs, but using the pytask data catalog, we can more easily gather the data for a specific task in structured manner entirely in Python.\n\n\nExported source\nstages = [\"mydata\", 'mydata2', # from the demo, ignore\n \"download\", # download task\n \"aggregate\", # aggregation task\n \"publish\", # publishing task\n \"viz\"] # visualization task\n\nbuckets = [\n \"inputs\", # any specific inputs, eg for carrying over between tasks\n \"outputs\", # specific output task returns\n \"jobs\", # job parameters as a dataframe\n \"params\" # any lingering hardcoded parameters\n ]\n\ndata_catalog = {\n\n stage: {bucket: DataCatalog(name=f\"{stage}_{bucket}\") for bucket in buckets}\n for stage in stages\n}", + "crumbs": [ + "`pytask` Config: Defining the Pipeline Internals in `pytask`" + ] + }, + { + "objectID": "20_pytask_config.html#the-download-task", + "href": "20_pytask_config.html#the-download-task", + "title": "pytask Config: Defining the Pipeline Internals in pytask", + "section": "The Download Task", + "text": "The Download Task\nA good strategy may be to set pipeline stage parameters in the config file, and then use the pytask data catalog to manage the data. This way, we can easily change the parameters without having to modify the code. This is especially useful for the API query, where we need to be able to set the parameter grid for the years and data types we want to download data for. So, let’s create an entry in the data catalog specifically for the download task.\nA good strategy I thought about for grid parameter comprehension is to create a dataframe expands all the combinations of parameters, and then uses each combination to create the tasks which are then easily added to the data catalog. This way, we can still easily inspect the pipeline and see what tasks are being run, while also being able to easily change the parameters in the config file without too much hassle.\nAn important framework decision I’m making here is that each ROW of the dataframe corresponds to a single task, so that we can quickly understand at a glance what the task is doing, and also easily develop the code for the task itself. This is different from the hydra approach where a job is first specified by a default config, and then the parameters are swept over in multiple config files. This is a more flexible approach, IMO, because:\n\neach row defines a single task run, so it’s easy to understand what the run is doing\nit’s easy to add or remove runs by simply expanding the list of parameters and using dataframe filters to remove irrelevant parameter combinations\nwe don’t have to independently inspect and manage multiple different/overriding config files\nit’s all in Python, so we can use the full power of the language to define the parameters and the tasks in a single sweep, not through the need of hydra+snakemake multi stage/multi-lingual config system\n\nSo, to do this, we define one job as a query to the CDS API that must contain: - The dataset (re-analysis) - The year - The month - All days in the month - All times of day (hour) - The geography (region), which will need: - The URL to the shapefile to calculate the bounding box\nGiven one combination of all of these, a single SLURM job can complete the first “task” in parallel by having a run assigned to each row of the dataframe.\n\n\nExported source\n# Dimensions\nyears = [str(x) for x in range(2009, 2025)] # 16 years\nmonths = [str(x).zfill(2) for x in range(1, 13)] # 12 months\ngeographies = [\"madagascar\", \"nepal\"] # 2 geographies\n\n# nested values; we want ALL days, times, and variables for each job\ndays = [str(x).zfill(2) for x in range(1, 32)]\ntimes = [f\"{x:02d}:00\" for x in range(24)]\nvariables = [\"2m_dewpoint_temperature\", \"2m_temperature\", \"total_precipitation\", \"volumetric_soil_water_layer_1\"]\n\nproduct_type = \"reanalysis\"\n\n# Map shapefiles to geography\nshapefiles = {\n \"madagascar\": \"https://data.humdata.org/dataset/26fa506b-0727-4d9d-a590-d2abee21ee22/resource/ed94d52e-349e-41be-80cb-62dc0435bd34/download/mdg_adm_bngrc_ocha_20181031_shp.zip\",\n \"nepal\": \"https://data.humdata.org/dataset/07db728a-4f0f-4e98-8eb0-8fa9df61f01c/resource/2eb4c47f-fd6e-425d-b623-d35be1a7640e/download/npl_adm_nd_20240314_ab_shp.zip\"\n}\n\n# Build row-wise combinations of (year, month, geography)\nrows = []\nfor year in years:\n for month in months:\n for geo in geographies:\n rows.append({\n \"year\": year,\n \"month\": month,\n \"geography\": geo,\n \"shapefile\": shapefiles[geo],\n \"product_type\": product_type,\n \"day\": days,\n \"time\": times,\n \"variables\": variables,\n \"output\": f\"{year}_{month}_{geo}\"\n })\n\n# Create dataframe\nquery_df = pd.DataFrame(rows)\n\n\n\nquery_df\n\n\nprint(f\"Number of estimated jobs: {query_df.shape[0]}. Examples...\")\n\nfor i, row in query_df.sample(3).iterrows():\n print(f\"Year: {row['year']}, Month: {row['month']}, Geography: {row['geography']}, Link: {row['shapefile']}, Variables: {row['variables']}\")\n\nNow add them to the catalog. We’re going to use a dictionary to nest data catalogs so that we can return specific task products to named data catalog nodes.\nOur data catalog now has a download|jobs node with a queries_df entry that contains the dataframe of all the jobs to be run in this task.\n\ndata_catalog['download']['jobs']['queries_df'].load().head()", + "crumbs": [ + "`pytask` Config: Defining the Pipeline Internals in `pytask`" + ] + }, + { + "objectID": "20_pytask_config.html#the-aggregation-task", + "href": "20_pytask_config.html#the-aggregation-task", + "title": "pytask Config: Defining the Pipeline Internals in pytask", + "section": "The Aggregation Task", + "text": "The Aggregation Task\nTo carry out the aggregation, we will follow similar logic to the original pipeline and use xarray to aggregate data into spatial and temporal averages. The aggregation task will take the downloaded data and compute the mean over the specified time period and spatial region. However, in this case, we want to aggregate the data diurnally, so we will need to fetch the sundown and sunrise times for the region and use them to compute the diurnal averages.\nOnce again, we will use a dataframe to define the parameters for the aggregation task.\nHere we will use a dataframe with the jobs as rows; the first column is “input” which is the list of query names from the download task, and the last column is the output object name. Columns in between can be the parameters needed for the aggregation task, which then get expanded to the full list of jobs with itertools.product, explode or similar, and filtered as necessary.\nFor explanations of the parameters, see the Aggregation Task notebook’s final task_aggregate_data_diurnal function.\n\n\nExported source\ninputs = query_df[\"output\"].tolist()\noutputs = [f\"{i}_agg\" for i in inputs]\n\nvariable_dict = {\n \"2m_dewpoint_temperature\": \"d2m\",\n \"2m_temperature\": \"t2m\",\n \"total_precipitation\": \"tp\",\n \"volumetric_soil_water_layer_1\": \"swvl1\"\n}\n\n# list of params that get fed into the task functions\nagg_params = {\n \"time\": [\"day\", \"night\"],\n \"solar_classification\": [\"before\"],\n \"variables\": variables,\n \"variables_short\": [variable_dict[x] for x in variables],\n \"aggregation_name\": [\"mean\", \"sum\", \"max\", \"min\"]\n}\n\nfrom itertools import product\nimport pandas as pd\n\n# expand all the params\nagg_params = pd.DataFrame(list(product(*agg_params.values())), columns=agg_params.keys())\n\n\nInspecting it:\n\nagg_params\n\nLet’s keep only rows where the variables and variables_short match\n\n\nExported source\nagg_params = agg_params[agg_params.apply(lambda x: variable_dict[x['variables']] == x['variables_short'], axis=1)]\n\n\n\nagg_params\n\nGreat, and now keeping sum only for total precipitation (we don’t need mean, max, min for that variable), and removing sum for all other variables (we don’t need sum for temperature or soil moisture):\n\n\nExported source\nmask = (agg_params['variables_short'] == \"tp\") & (agg_params['aggregation_name'] != \"sum\")\nagg_params = agg_params[~mask]\n\n# remove rows where non-tp aggregation is sum\nmask = (agg_params['variables_short'] != \"tp\") & (agg_params['aggregation_name'] == \"sum\")\nagg_params = agg_params[~mask]\n\n\n\nagg_params\n\nNow we add the input and output columns by joining:\n\n\nExported source\ninputs = pd.DataFrame({\"input\": inputs})\naggregate_jobs = inputs.merge(agg_params, how=\"cross\")\n\n\nThis result gives us the full list of jobs for the aggregation task. 20 rows for the parameters, and 384 inputs/outputs, giving a total of 7680 jobs:\n\nassert aggregate_jobs.shape[0] == 20 * len(inputs)\naggregate_jobs\n\nA few more configuration items need to be added, like the local timezone for each geography, the healthshed filename, the healthshed unique ID variable name in the shapefile, and whether the variable is instantaneous or accumulated:\n\n\nExported source\naggregate_jobs['local_tz'] = aggregate_jobs['input'].apply(\n lambda x: \"Asia/Kathmandu\" if \"nepal\" in x else \"Indian/Antananarivo\"\n)\naggregate_jobs['shapefile'] = aggregate_jobs['input'].apply(\n lambda x: \"Nepal_Healthsheds2024.zip\" if \"nepal\" in x else \"healthsheds2022.zip\"\n)\n\naggregate_jobs['hshd_unique_id'] = aggregate_jobs['input'].apply(\n lambda x: \"fid\" if \"nepal\" in x else \"fs_uid\"\n)\n\naggregate_jobs['climate_handler_var'] = aggregate_jobs['variables_short'].apply(\n lambda x: \"accum\" if x == \"tp\" else \"instant\"\n)\n\n\n\naggregate_jobs\n\nNow we add this to the data catalog:\n\n\nExported source\ndata_catalog['aggregate']['jobs'].add(\"jobs_df\", aggregate_jobs)\n\n\nOur data catalog now has an aggregate|jobs node with a jobs_df entry that contains the dataframe of all the jobs to be run in this task.\n\ndata_catalog['aggregate']['jobs']['jobs_df'].load().head()", + "crumbs": [ + "`pytask` Config: Defining the Pipeline Internals in `pytask`" + ] + }, + { + "objectID": "20_pytask_logger.html", + "href": "20_pytask_logger.html", + "title": "Logging: A simple logger to inject into pytask jobs", + "section": "", + "text": "A simple logger module for the pytask tasks\n\n\n\nsource\n\n\n\n setup_logger (name:str, log_path:pathlib.Path=Path('/net/rcstorenfs02/ifs\n /rc_labs/dominici_lab/lab/data_processing/csph-\n era5_sandbox/logs/2025-09-25/13-57-20'), level=20)\n\n\n\nExported source\nimport logging\nfrom pathlib import Path\nfrom pyprojroot import here\nfrom datetime import datetime\n\nLOG_DIR = here(\"logs\")\n# get the date & time for the log file name\nlog_date = datetime.now().strftime(\"%Y-%m-%d\")\nlog_time = datetime.now().strftime(\"%H-%M-%S\")\nLOG_DIR = here(\"logs\") / log_date / log_time\n\n\n\n\nExported source\ndef setup_logger(name: str, log_path: Path=LOG_DIR, level=logging.INFO) -> logging.Logger:\n log_path.mkdir(parents=True, exist_ok=True)\n formatter = logging.Formatter('%(asctime)s — %(name)s — %(levelname)s — %(message)s')\n\n handler = logging.FileHandler(log_path / f\"{name}.log\", mode='a')\n handler.setFormatter(formatter)\n\n logger = logging.getLogger(name)\n logger.setLevel(level)\n logger.addHandler(handler)\n logger.propagate = False\n\n return logger", + "crumbs": [ + "Logging: A simple logger to inject into `pytask` jobs" + ] + }, + { + "objectID": "20_pytask_logger.html#logger", + "href": "20_pytask_logger.html#logger", + "title": "Logging: A simple logger to inject into pytask jobs", + "section": "", + "text": "A simple logger module for the pytask tasks\n\n\n\nsource\n\n\n\n setup_logger (name:str, log_path:pathlib.Path=Path('/net/rcstorenfs02/ifs\n /rc_labs/dominici_lab/lab/data_processing/csph-\n era5_sandbox/logs/2025-09-25/13-57-20'), level=20)\n\n\n\nExported source\nimport logging\nfrom pathlib import Path\nfrom pyprojroot import here\nfrom datetime import datetime\n\nLOG_DIR = here(\"logs\")\n# get the date & time for the log file name\nlog_date = datetime.now().strftime(\"%Y-%m-%d\")\nlog_time = datetime.now().strftime(\"%H-%M-%S\")\nLOG_DIR = here(\"logs\") / log_date / log_time\n\n\n\n\nExported source\ndef setup_logger(name: str, log_path: Path=LOG_DIR, level=logging.INFO) -> logging.Logger:\n log_path.mkdir(parents=True, exist_ok=True)\n formatter = logging.Formatter('%(asctime)s — %(name)s — %(levelname)s — %(message)s')\n\n handler = logging.FileHandler(log_path / f\"{name}.log\", mode='a')\n handler.setFormatter(formatter)\n\n logger = logging.getLogger(name)\n logger.setLevel(level)\n logger.addHandler(handler)\n logger.propagate = False\n\n return logger", + "crumbs": [ + "Logging: A simple logger to inject into `pytask` jobs" + ] + }, + { + "objectID": "21_pytask_download.html", + "href": "21_pytask_download.html", + "title": "Download: download Module as a pytask Task", + "section": "", + "text": "This module downloads the raw era5 data from the CDS API. It is similar to the original script, refactored for pytask.\n\n\nWe’re going to quickly refactor the pipeline to use pytask instead of hydra and snakemake. This will hopefully demonstrate a simpler and more flexible way to manage data pipelines in Python.\nTo start off, we need to create a function that queries the CDS API with one job. This function will be used to download the data for each query in the range specified in the data catalog in the config file.\nLet’s take a look at the data catalog we created in the config module:\nYou can see the queries entry we created in the data catalog. Each query is a row of a dataframe that contains the parameters for the CDS API query.\n\nqueries = data_catalog['download']['jobs']['queries_df'].load()\nqueries\n\nWe can test this query like we did in the original work:\n\nexample_query = queries.iloc[0]\n\ncreate_bounding_box(example_query['shapefile'])\n\nIn this way, we have a similar approach as Hydra configs, but, using the pytask data catalog, we can more easily gather the data for a specific task in structured manner entirely in Python.\n\nclient = cdsapi.Client()\n\nex_bounding_box = create_bounding_box(example_query['shapefile'])\n\nrequest = {\n \"product_type\": example_query['product_type'],\n \"variable\": example_query['variables'], \n \"year\": str(example_query['year']),\n \"month\": str(example_query['month']),\n \"day\": example_query['day'],\n \"time\": example_query['time'],\n \"data_format\": \"netcdf\",\n \"download_format\": \"unarchived\",\n \"area\": ex_bounding_box\n }\n\ntarget = f\"{example_query['output']}.nc\"\n\nclient.retrieve(\"reanalysis-era5-single-levels\", request).download(target)\n\nThis works! So now we just need to create a task_ function that pytask will recognise to parallelise the download of queries over:\n\n\n\n\nWhen you run pytask, it automatically scans your project for Python files named task_*.py. In these files, it looks for: - Functions decorated with @task, or - Functions prefixed with task_\nThese functions are not executed immediately. Instead, pytask: 1. Imports each task_*.py module (just like Python would) 2. Registers any matching task functions as nodes in a directed acyclic graph (DAG) 3. Resolves dependencies by analyzing: - Input annotations (e.g., Annotated[x, DependsOn]) - Output declarations (e.g., return values or Product annotations) 4. Builds the DAG, where each task function is a node 5. Executes the tasks, respecting dependency order and skipping up-to-date nodes\nSo even though the task functions aren’t explicitly “run” in the Python code itself, pytask knows how and when to execute them — based on their position in the DAG.\n\n\n\nIn snakemake, you’re expected to define a series of explicitly executable rules, often using shell commands or Python scripts. You “stitch together” rules using filenames and wildcard matching.\nIn contrast: - 🐍 pytask is Python-native — tasks are just regular Python functions - ⚙️ It builds a DAG from those functions and tracks inputs/outputs automatically - 🧱 You are declaring nodes, not scripting execution\nThink of your Python files not as scripts to run, but as a way to define and wire together declarative tasks that will be executed by the pytask engine.\n\nBecause we defined this task in a function and loop, we can easily debug a node in the DAG by simply calling it:\n\ntask_download_raw_data()", + "crumbs": [ + "Download: `download` Module as a `pytask` Task" + ] + }, + { + "objectID": "21_pytask_download.html#task_download", + "href": "21_pytask_download.html#task_download", + "title": "Download: download Module as a pytask Task", + "section": "", + "text": "This module downloads the raw era5 data from the CDS API. It is similar to the original script, refactored for pytask.\n\n\nWe’re going to quickly refactor the pipeline to use pytask instead of hydra and snakemake. This will hopefully demonstrate a simpler and more flexible way to manage data pipelines in Python.\nTo start off, we need to create a function that queries the CDS API with one job. This function will be used to download the data for each query in the range specified in the data catalog in the config file.\nLet’s take a look at the data catalog we created in the config module:\nYou can see the queries entry we created in the data catalog. Each query is a row of a dataframe that contains the parameters for the CDS API query.\n\nqueries = data_catalog['download']['jobs']['queries_df'].load()\nqueries\n\nWe can test this query like we did in the original work:\n\nexample_query = queries.iloc[0]\n\ncreate_bounding_box(example_query['shapefile'])\n\nIn this way, we have a similar approach as Hydra configs, but, using the pytask data catalog, we can more easily gather the data for a specific task in structured manner entirely in Python.\n\nclient = cdsapi.Client()\n\nex_bounding_box = create_bounding_box(example_query['shapefile'])\n\nrequest = {\n \"product_type\": example_query['product_type'],\n \"variable\": example_query['variables'], \n \"year\": str(example_query['year']),\n \"month\": str(example_query['month']),\n \"day\": example_query['day'],\n \"time\": example_query['time'],\n \"data_format\": \"netcdf\",\n \"download_format\": \"unarchived\",\n \"area\": ex_bounding_box\n }\n\ntarget = f\"{example_query['output']}.nc\"\n\nclient.retrieve(\"reanalysis-era5-single-levels\", request).download(target)\n\nThis works! So now we just need to create a task_ function that pytask will recognise to parallelise the download of queries over:\n\n\n\n\nWhen you run pytask, it automatically scans your project for Python files named task_*.py. In these files, it looks for: - Functions decorated with @task, or - Functions prefixed with task_\nThese functions are not executed immediately. Instead, pytask: 1. Imports each task_*.py module (just like Python would) 2. Registers any matching task functions as nodes in a directed acyclic graph (DAG) 3. Resolves dependencies by analyzing: - Input annotations (e.g., Annotated[x, DependsOn]) - Output declarations (e.g., return values or Product annotations) 4. Builds the DAG, where each task function is a node 5. Executes the tasks, respecting dependency order and skipping up-to-date nodes\nSo even though the task functions aren’t explicitly “run” in the Python code itself, pytask knows how and when to execute them — based on their position in the DAG.\n\n\n\nIn snakemake, you’re expected to define a series of explicitly executable rules, often using shell commands or Python scripts. You “stitch together” rules using filenames and wildcard matching.\nIn contrast: - 🐍 pytask is Python-native — tasks are just regular Python functions - ⚙️ It builds a DAG from those functions and tracks inputs/outputs automatically - 🧱 You are declaring nodes, not scripting execution\nThink of your Python files not as scripts to run, but as a way to define and wire together declarative tasks that will be executed by the pytask engine.\n\nBecause we defined this task in a function and loop, we can easily debug a node in the DAG by simply calling it:\n\ntask_download_raw_data()", + "crumbs": [ + "Download: `download` Module as a `pytask` Task" + ] + }, + { + "objectID": "index.html", + "href": "index.html", + "title": "The ERA5 Spatial Aggregation Pipeline", + "section": "", + "text": "from era5_sandbox.core import *", + "crumbs": [ + "The ERA5 Spatial Aggregation Pipeline" + ] + }, + { + "objectID": "index.html#era5_sandbox", + "href": "index.html#era5_sandbox", + "title": "The ERA5 Spatial Aggregation Pipeline", + "section": "era5_sandbox", + "text": "era5_sandbox\n\nSandbox environment for era5 development\n\nThis package documents the development and implementation of functions and code for the Madagascar ERA5 dataset project. The goal is for exposure data to be made available at the daily resolution when possible. Finer resolutions shouldn’t ever be needed for our purposes, and it should then be relatively easy to aggregate at coarser resolutions, such as weekly or monthly. Additionally, we’ve extended this work to Nepal as well.\nVariables should generally be made available from 2010 onward, as that’s where our clinic data starts.\nAll data are ideally made available at the “healthshed” geographical level. Healthsheds are defined as geographical areas where people who live all go to the same clinic. There are a total of ~2700 public clinics in Madagascar, hence ~2700 healthsheds, with each healthshed containing ~10000 people on average.\nPreliminary list of environmental variables\n\n2-m air temperature from ERA5: daily min, max, mean\n2-m air dew point temperature from ERA5: daily min, max, mean\nPrecipitation: daily total (ERA5)\nSoil moisture: daily average (ERA5)\n\nVariables from other sources:\n\nSea surface temperature: daily average and maximum in the nearest neighbor for each healthshed.\nPrecipitation: daily total (CHIRPS)\nChlorophyll-A (Giacomo)\nWealth index: Available from Giacomo\nNDVI\nTropical storm\nFlooding\nDeforestation\nLinking/segmenting healthsheds into climate zones and other\nRelative humidity: daily average (lower priority)\n\nThose from the ERA5 dataset will be housed here, but we may likely develop a separate repository for the other datasets.", + "crumbs": [ + "The ERA5 Spatial Aggregation Pipeline" + ] + }, + { + "objectID": "index.html#developer-guide", + "href": "index.html#developer-guide", + "title": "The ERA5 Spatial Aggregation Pipeline", + "section": "Developer Guide", + "text": "Developer Guide\nThis package is built and maintained with nbdev. If you are new to using nbdev here are some useful pointers to get you started.\n\nInstall era5_sandbox in Development mode\n# make sure era5_sandbox package is installed in development mode\n$ pip install -e .\nTo make changes, go to the “notes” directory and edit the notebooks as necessary. Each notebook refers to a module in the era5_sandbox package. Cells are exported to the module when the notebook is saved and you run the following command:\n$ nbdev_export\nFor e.g., to change functionality of the testAPI() function in the testAPI Hydra rule, you would edit the testAPI notebook in the notes directory notes/testAPI.ipynb, and then save that notebook and run nbdev_export to update the core module in the package.\n\n\nHow to Run the Pipeline\nThe pipeline downloads ERA5 variables for a given date range and geographical bounding box. You can learn how each of these steps was by following the notebooks in notes in numerical order.\n\n\n\n\n\n\nImportant\n\n\n\nThe pipeline has two implementations: one using snakemake and hydra, and another using pytask. The pytask implementation is the more recent one, and is recommended for future use. The snakemake implementation is left here for reference to legacy code.\n\n\n\nUsing pytask\nTo run the pipeline, the pytask config at note/20_pytask_config.qmd should be reviewed and updated if necessary. The pipeline can then be run with the following command:\n$ sbatch pytask.sbatch\n\n\nUsing snakemake and hydra\nTo run the pipeline, the config at config/config.yaml should be updated with the desired date range and geographical bounding box. The pipeline can then be run with the following command:\nsbatch snakemake.sbatch\n\n\n\nWhat Does the Pipeline Produce?\nUsing pytask’s data catalog, you can investigate the downloaded raw data with python, eg.:\n\nimport xarray as xr\nfrom era5_sandbox.config import data_catalog\nfrom era5_sandbox.core import ClimateDataFileHandler\n\nex_nc = list(data_catalog['download']['outputs']._entries).pop()\nex_nc_path = data_catalog['download']['outputs'][ex_nc].load()\n\nwith ClimateDataFileHandler(ex_nc_path) as handler:\n ds = xr.open_dataset(handler.get_dataset(\"instant\"))\n\nds\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n<xarray.Dataset> Size: 53MB\nDimensions: (valid_time: 744, latitude: 49, longitude: 91)\nCoordinates:\n number int64 8B ...\n * valid_time (valid_time) datetime64[ns] 6kB 2024-03-01 ... 2024-03-31T23:...\n * latitude (latitude) float64 392B 30.8 30.7 30.6 30.5 ... 26.2 26.1 26.0\n * longitude (longitude) float64 728B 79.6 79.7 79.8 79.9 ... 88.4 88.5 88.6\n expver (valid_time) <U4 12kB ...\nData variables:\n d2m (valid_time, latitude, longitude) float32 13MB ...\n t2m (valid_time, latitude, longitude) float32 13MB ...\n tp (valid_time, latitude, longitude) float32 13MB ...\n swvl1 (valid_time, latitude, longitude) float32 13MB ...\nAttributes:\n GRIB_centre: ecmf\n GRIB_centreDescription: European Centre for Medium-Range Weather Forecasts\n GRIB_subCentre: 0\n Conventions: CF-1.7\n institution: European Centre for Medium-Range Weather Forecasts\n history: 2025-09-16T20:55 GRIB to CDM+CF via cfgrib-0.9.1...xarray.DatasetDimensions:valid_time: 744latitude: 49longitude: 91Coordinates: (5)number()int64...long_name :ensemble member numerical idunits :1standard_name :realization[1 values with dtype=int64]valid_time(valid_time)datetime64[ns]2024-03-01 ... 2024-03-31T23:00:00long_name :timestandard_name :timearray(['2024-03-01T00:00:00.000000000', '2024-03-01T01:00:00.000000000',\n '2024-03-01T02:00:00.000000000', ..., '2024-03-31T21:00:00.000000000',\n '2024-03-31T22:00:00.000000000', '2024-03-31T23:00:00.000000000'],\n shape=(744,), dtype='datetime64[ns]')latitude(latitude)float6430.8 30.7 30.6 ... 26.2 26.1 26.0units :degrees_northstandard_name :latitudelong_name :latitudestored_direction :decreasingarray([30.8, 30.7, 30.6, 30.5, 30.4, 30.3, 30.2, 30.1, 30. , 29.9, 29.8, 29.7,\n 29.6, 29.5, 29.4, 29.3, 29.2, 29.1, 29. , 28.9, 28.8, 28.7, 28.6, 28.5,\n 28.4, 28.3, 28.2, 28.1, 28. , 27.9, 27.8, 27.7, 27.6, 27.5, 27.4, 27.3,\n 27.2, 27.1, 27. , 26.9, 26.8, 26.7, 26.6, 26.5, 26.4, 26.3, 26.2, 26.1,\n 26. ])longitude(longitude)float6479.6 79.7 79.8 ... 88.4 88.5 88.6units :degrees_eaststandard_name :longitudelong_name :longitudearray([79.6, 79.7, 79.8, 79.9, 80. , 80.1, 80.2, 80.3, 80.4, 80.5, 80.6, 80.7,\n 80.8, 80.9, 81. , 81.1, 81.2, 81.3, 81.4, 81.5, 81.6, 81.7, 81.8, 81.9,\n 82. , 82.1, 82.2, 82.3, 82.4, 82.5, 82.6, 82.7, 82.8, 82.9, 83. , 83.1,\n 83.2, 83.3, 83.4, 83.5, 83.6, 83.7, 83.8, 83.9, 84. , 84.1, 84.2, 84.3,\n 84.4, 84.5, 84.6, 84.7, 84.8, 84.9, 85. , 85.1, 85.2, 85.3, 85.4, 85.5,\n 85.6, 85.7, 85.8, 85.9, 86. , 86.1, 86.2, 86.3, 86.4, 86.5, 86.6, 86.7,\n 86.8, 86.9, 87. , 87.1, 87.2, 87.3, 87.4, 87.5, 87.6, 87.7, 87.8, 87.9,\n 88. , 88.1, 88.2, 88.3, 88.4, 88.5, 88.6])expver(valid_time)<U4...[744 values with dtype=<U4]Data variables: (4)d2m(valid_time, latitude, longitude)float32...GRIB_paramId :168GRIB_dataType :fcGRIB_numberOfPoints :4459GRIB_typeOfLevel :surfaceGRIB_stepUnits :1GRIB_stepType :instantGRIB_gridType :regular_llGRIB_uvRelativeToGrid :0GRIB_NV :0GRIB_Nx :91GRIB_Ny :49GRIB_cfName :unknownGRIB_cfVarName :d2mGRIB_gridDefinitionDescription :Latitude/Longitude GridGRIB_iDirectionIncrementInDegrees :0.1GRIB_iScansNegatively :0GRIB_jDirectionIncrementInDegrees :0.1GRIB_jPointsAreConsecutive :0GRIB_jScansPositively :0GRIB_latitudeOfFirstGridPointInDegrees :30.8GRIB_latitudeOfLastGridPointInDegrees :26.0GRIB_longitudeOfFirstGridPointInDegrees :79.6GRIB_longitudeOfLastGridPointInDegrees :88.6GRIB_missingValue :3.4028234663852886e+38GRIB_name :2 metre dewpoint temperatureGRIB_shortName :2dGRIB_totalNumber :0GRIB_units :Klong_name :2 metre dewpoint temperatureunits :Kstandard_name :unknownGRIB_surface :0.0[3317496 values with dtype=float32]t2m(valid_time, latitude, longitude)float32...GRIB_paramId :167GRIB_dataType :fcGRIB_numberOfPoints :4459GRIB_typeOfLevel :surfaceGRIB_stepUnits :1GRIB_stepType :instantGRIB_gridType :regular_llGRIB_uvRelativeToGrid :0GRIB_NV :0GRIB_Nx :91GRIB_Ny :49GRIB_cfName :unknownGRIB_cfVarName :t2mGRIB_gridDefinitionDescription :Latitude/Longitude GridGRIB_iDirectionIncrementInDegrees :0.1GRIB_iScansNegatively :0GRIB_jDirectionIncrementInDegrees :0.1GRIB_jPointsAreConsecutive :0GRIB_jScansPositively :0GRIB_latitudeOfFirstGridPointInDegrees :30.8GRIB_latitudeOfLastGridPointInDegrees :26.0GRIB_longitudeOfFirstGridPointInDegrees :79.6GRIB_longitudeOfLastGridPointInDegrees :88.6GRIB_missingValue :3.4028234663852886e+38GRIB_name :2 metre temperatureGRIB_shortName :2tGRIB_totalNumber :0GRIB_units :Klong_name :2 metre temperatureunits :Kstandard_name :unknownGRIB_surface :0.0[3317496 values with dtype=float32]tp(valid_time, latitude, longitude)float32...GRIB_paramId :228GRIB_dataType :fcGRIB_numberOfPoints :4459GRIB_typeOfLevel :surfaceGRIB_stepUnits :1GRIB_stepType :accumGRIB_gridType :regular_llGRIB_uvRelativeToGrid :0GRIB_NV :0GRIB_Nx :91GRIB_Ny :49GRIB_cfName :unknownGRIB_cfVarName :tpGRIB_gridDefinitionDescription :Latitude/Longitude GridGRIB_iDirectionIncrementInDegrees :0.1GRIB_iScansNegatively :0GRIB_jDirectionIncrementInDegrees :0.1GRIB_jPointsAreConsecutive :0GRIB_jScansPositively :0GRIB_latitudeOfFirstGridPointInDegrees :30.8GRIB_latitudeOfLastGridPointInDegrees :26.0GRIB_longitudeOfFirstGridPointInDegrees :79.6GRIB_longitudeOfLastGridPointInDegrees :88.6GRIB_missingValue :3.4028234663852886e+38GRIB_name :Total precipitationGRIB_shortName :tpGRIB_totalNumber :0GRIB_units :mlong_name :Total precipitationunits :mstandard_name :unknownGRIB_surface :0.0[3317496 values with dtype=float32]swvl1(valid_time, latitude, longitude)float32...GRIB_paramId :39GRIB_dataType :anGRIB_numberOfPoints :4459GRIB_typeOfLevel :depthBelowLandLayerGRIB_stepUnits :1GRIB_stepType :instantGRIB_gridType :regular_llGRIB_uvRelativeToGrid :0GRIB_NV :0GRIB_Nx :91GRIB_Ny :49GRIB_cfName :unknownGRIB_cfVarName :swvl1GRIB_gridDefinitionDescription :Latitude/Longitude GridGRIB_iDirectionIncrementInDegrees :0.1GRIB_iScansNegatively :0GRIB_jDirectionIncrementInDegrees :0.1GRIB_jPointsAreConsecutive :0GRIB_jScansPositively :0GRIB_latitudeOfFirstGridPointInDegrees :30.8GRIB_latitudeOfLastGridPointInDegrees :26.0GRIB_longitudeOfFirstGridPointInDegrees :79.6GRIB_longitudeOfLastGridPointInDegrees :88.6GRIB_missingValue :3.4028234663852886e+38GRIB_name :Volumetric soil water layer 1GRIB_shortName :swvl1GRIB_totalNumber :0GRIB_units :m**3 m**-3long_name :Volumetric soil water layer 1units :m**3 m**-3standard_name :unknownGRIB_depthBelowLandLayer :0.0[3317496 values with dtype=float32]Indexes: (3)valid_timePandasIndexPandasIndex(DatetimeIndex(['2024-03-01 00:00:00', '2024-03-01 01:00:00',\n '2024-03-01 02:00:00', '2024-03-01 03:00:00',\n '2024-03-01 04:00:00', '2024-03-01 05:00:00',\n '2024-03-01 06:00:00', '2024-03-01 07:00:00',\n '2024-03-01 08:00:00', '2024-03-01 09:00:00',\n ...\n '2024-03-31 14:00:00', '2024-03-31 15:00:00',\n '2024-03-31 16:00:00', '2024-03-31 17:00:00',\n '2024-03-31 18:00:00', '2024-03-31 19:00:00',\n '2024-03-31 20:00:00', '2024-03-31 21:00:00',\n '2024-03-31 22:00:00', '2024-03-31 23:00:00'],\n dtype='datetime64[ns]', name='valid_time', length=744, freq=None))latitudePandasIndexPandasIndex(Index([ 30.8, 30.7, 30.599999999999998,\n 30.499999999999996, 30.399999999999995, 30.299999999999994,\n 30.199999999999992, 30.09999999999999, 29.99999999999999,\n 29.899999999999988, 29.799999999999986, 29.699999999999985,\n 29.599999999999984, 29.499999999999982, 29.39999999999998,\n 29.29999999999998, 29.199999999999978, 29.099999999999977,\n 28.999999999999975, 28.899999999999974, 28.799999999999972,\n 28.69999999999997, 28.59999999999997, 28.499999999999968,\n 28.399999999999967, 28.299999999999965, 28.199999999999964,\n 28.099999999999962, 27.99999999999996, 27.89999999999996,\n 27.799999999999958, 27.699999999999957, 27.599999999999955,\n 27.499999999999954, 27.399999999999952, 27.29999999999995,\n 27.19999999999995, 27.099999999999948, 26.999999999999947,\n 26.899999999999945, 26.799999999999944, 26.699999999999942,\n 26.59999999999994, 26.49999999999994, 26.399999999999938,\n 26.299999999999937, 26.199999999999935, 26.099999999999934,\n 26.0],\n dtype='float64', name='latitude'))longitudePandasIndexPandasIndex(Index([ 79.6, 79.69999999999999, 79.79999999999998,\n 79.89999999999998, 79.99999999999997, 80.09999999999997,\n 80.19999999999996, 80.29999999999995, 80.39999999999995,\n 80.49999999999994, 80.59999999999994, 80.69999999999993,\n 80.79999999999993, 80.89999999999992, 80.99999999999991,\n 81.09999999999991, 81.1999999999999, 81.2999999999999,\n 81.39999999999989, 81.49999999999989, 81.59999999999988,\n 81.69999999999987, 81.79999999999987, 81.89999999999986,\n 81.99999999999986, 82.09999999999985, 82.19999999999985,\n 82.29999999999984, 82.39999999999984, 82.49999999999983,\n 82.59999999999982, 82.69999999999982, 82.79999999999981,\n 82.8999999999998, 82.9999999999998, 83.0999999999998,\n 83.19999999999979, 83.29999999999978, 83.39999999999978,\n 83.49999999999977, 83.59999999999977, 83.69999999999976,\n 83.79999999999976, 83.89999999999975, 83.99999999999974,\n 84.09999999999974, 84.19999999999973, 84.29999999999973,\n 84.39999999999972, 84.49999999999972, 84.59999999999971,\n 84.6999999999997, 84.7999999999997, 84.8999999999997,\n 84.99999999999969, 85.09999999999968, 85.19999999999968,\n 85.29999999999967, 85.39999999999966, 85.49999999999966,\n 85.59999999999965, 85.69999999999965, 85.79999999999964,\n 85.89999999999964, 85.99999999999963, 86.09999999999962,\n 86.19999999999962, 86.29999999999961, 86.39999999999961,\n 86.4999999999996, 86.5999999999996, 86.69999999999959,\n 86.79999999999959, 86.89999999999958, 86.99999999999957,\n 87.09999999999957, 87.19999999999956, 87.29999999999956,\n 87.39999999999955, 87.49999999999955, 87.59999999999954,\n 87.69999999999953, 87.79999999999953, 87.89999999999952,\n 87.99999999999952, 88.09999999999951, 88.1999999999995,\n 88.2999999999995, 88.3999999999995, 88.49999999999949,\n 88.6],\n dtype='float64', name='longitude'))Attributes: (6)GRIB_centre :ecmfGRIB_centreDescription :European Centre for Medium-Range Weather ForecastsGRIB_subCentre :0Conventions :CF-1.7institution :European Centre for Medium-Range Weather Forecastshistory :2025-09-16T20:55 GRIB to CDM+CF via cfgrib-0.9.15.0/ecCodes-2.42.0 with {\"source\": \"data.grib\", \"filter_by_keys\": {}, \"encode_cf\": [\"parameter\", \"time\", \"geography\", \"vertical\"]}\n\n\nAnd plot it with cartopy, eg.:\n\nimport matplotlib.pyplot as plt\nimport cartopy.crs as ccrs\nimport cartopy.feature as cfeature\n\ntemperature = ds[\"t2m\"]\n\n# Select a specific time step\ntemperature_at_time = temperature.isel(valid_time=0)\n\n# Plot the data on a map\nplt.figure(figsize=(12, 8))\nax = plt.axes(projection=ccrs.PlateCarree())\ntemperature_at_time.plot(ax=ax, cmap=\"coolwarm\", transform=ccrs.PlateCarree(), cbar_kwargs={\"label\": \"Temperature (K)\"})\nax.coastlines()\nax.add_feature(cfeature.BORDERS, linestyle=\":\")\nax.set_title(\"Temperature at Time Step 0\")\nplt.show()\n\n\n\n\n\n\n\n\nYou can also load the aggregated data:\n\nimport pandas as pd\nimport geopandas as gpd\nfrom era5_sandbox.config import data_catalog\n\nex_agg_path = data_catalog['aggregate']['outputs']['2019_08_madagascar_night_d2m_max.parquet'].load()\n\ngpd.read_parquet(ex_agg_path).describe()\n\n\n\n\n\n\n\n\nday_01\nday_02\nday_03\nday_04\nday_05\nday_06\nday_07\nday_08\nday_09\nday_10\nday_11\nday_12\nday_13\nday_14\nday_15\nday_16\nday_17\nday_18\nday_19\nday_20\nday_21\nday_22\nday_23\nday_24\nday_25\nday_26\nday_27\nday_28\nday_29\nday_30\nday_31\nday_32\n\n\n\n\ncount\n2701.000000\n2701.000000\n2701.000000\n2701.000000\n2701.000000\n2701.000000\n2701.000000\n2701.000000\n2701.000000\n2701.000000\n2701.000000\n2701.000000\n2701.000000\n2701.000000\n2701.000000\n2701.000000\n2701.000000\n2701.000000\n2701.000000\n2701.000000\n2701.000000\n2701.000000\n2701.000000\n2701.000000\n2701.000000\n2701.000000\n2701.000000\n2701.000000\n2701.000000\n2701.000000\n2701.000000\n2701.000000\n\n\nmean\n290.493048\n290.145274\n288.953153\n288.503714\n288.439820\n288.304426\n286.940995\n287.186512\n287.453656\n287.843029\n288.301938\n288.778014\n288.813762\n288.667253\n288.796892\n288.547945\n288.197632\n287.882440\n287.659818\n289.291587\n289.911503\n288.760939\n288.257644\n288.271450\n287.746390\n288.379399\n288.504720\n287.665699\n288.149861\n288.266861\n288.644028\n288.224829\n\n\nstd\n2.616922\n2.832083\n3.215642\n3.566019\n4.401416\n4.198817\n5.235795\n4.444031\n4.346305\n3.435444\n2.735781\n2.864494\n2.841268\n3.080593\n3.306217\n2.938165\n3.018303\n2.849850\n2.817690\n2.600946\n2.584079\n3.161855\n3.171827\n2.983778\n3.223380\n2.918867\n2.844314\n3.052635\n3.077292\n3.093706\n3.335983\n3.296264\n\n\nmin\n284.295898\n281.673340\n280.566406\n280.509521\n277.348145\n279.243164\n274.955078\n274.682129\n275.397461\n279.498291\n282.339111\n282.188721\n282.470703\n281.371582\n280.724609\n280.093506\n280.849121\n281.123535\n281.952148\n282.186768\n284.168945\n282.519287\n282.015381\n280.578857\n281.183838\n281.146973\n281.977539\n281.014648\n280.787842\n281.631348\n281.349854\n280.615967\n\n\n25%\n288.031494\n287.739014\n286.978271\n285.750488\n284.326904\n284.071289\n281.695068\n283.710449\n284.153076\n285.459717\n286.141846\n286.444092\n286.505859\n286.104004\n286.114014\n286.730225\n286.005371\n285.420166\n285.230713\n287.408203\n287.744873\n286.101318\n285.243652\n285.488281\n285.170166\n285.876465\n286.145508\n285.243164\n285.579346\n285.322754\n285.930908\n285.565186\n\n\n50%\n290.674316\n290.331543\n288.916260\n288.472168\n289.635742\n289.390381\n288.382568\n287.926758\n288.173096\n287.859375\n287.797852\n288.716064\n288.806641\n288.789307\n289.210938\n288.769287\n288.085205\n287.698975\n287.252930\n289.310547\n289.878418\n288.511719\n288.420166\n288.263916\n287.717041\n288.661621\n288.999023\n287.485107\n288.326416\n288.429199\n288.576416\n288.093018\n\n\n75%\n292.828369\n292.707764\n291.609375\n291.655762\n291.987305\n291.845459\n291.671631\n291.051758\n291.288574\n291.000244\n290.813721\n291.365967\n291.540039\n291.393799\n291.756592\n291.094727\n290.893311\n290.266602\n290.166748\n291.649902\n291.970459\n291.342285\n290.443848\n290.660400\n290.400146\n290.360840\n290.854004\n290.328125\n290.827881\n290.999268\n291.598877\n291.072754\n\n\nmax\n296.467285\n295.717529\n295.837158\n295.693604\n295.723389\n296.195557\n295.589600\n295.345703\n294.754639\n294.483154\n294.952148\n294.815430\n294.623779\n295.088135\n295.036621\n294.847900\n294.224609\n294.522949\n294.728760\n295.268066\n295.507324\n295.797363\n296.297119\n296.222900\n295.492432\n295.406006\n294.629883\n295.211670\n295.363037\n295.263184\n295.446533\n295.408691", + "crumbs": [ + "The ERA5 Spatial Aggregation Pipeline" + ] + }, + { + "objectID": "03_publish.html", + "href": "03_publish.html", + "title": "Publish: Gather the Aggregated Data and Publish to DataVerse", + "section": "", + "text": "This is the publish module for the ERA5 dataset pipeline. It defines a functions that make use of the pyDataverse library and API to publish our outputs to the Harvard Dataverse.\n\n\nFirst, we’ll test out the API by pinging the Harvard DataVerse\n\n\nExported source\nimport hydra\nimport yaml\nimport json\nfrom tqdm import tqdm\nfrom pyprojroot import here\n\n\n\napi_token_file = here() / \"sandbox/dataverse_api_key.yml\"\nwith open(api_token_file, \"r\") as f:\n config = yaml.load(f, Loader=yaml.BaseLoader)\n\nNow, following the docs for the dataverse tutorial, load a NativeAPI up:\n\n\nExported source\nfrom pyDataverse.api import NativeApi\n\n\nThe NativeAPI is a catchall API object to be able to do general stuff:\n\napi = NativeApi(config['base_url'], config['api_token'])\nresp=api.get_info_version()\n#resp.text()\n\n\nresp.json()\n\nLooks good! Now that we know that it works, we can think more about how to publish data there.", + "crumbs": [ + "Publish: Gather the Aggregated Data and Publish to DataVerse" + ] + }, + { + "objectID": "03_publish.html#publish", + "href": "03_publish.html#publish", + "title": "Publish: Gather the Aggregated Data and Publish to DataVerse", + "section": "", + "text": "This is the publish module for the ERA5 dataset pipeline. It defines a functions that make use of the pyDataverse library and API to publish our outputs to the Harvard Dataverse.\n\n\nFirst, we’ll test out the API by pinging the Harvard DataVerse\n\n\nExported source\nimport hydra\nimport yaml\nimport json\nfrom tqdm import tqdm\nfrom pyprojroot import here\n\n\n\napi_token_file = here() / \"sandbox/dataverse_api_key.yml\"\nwith open(api_token_file, \"r\") as f:\n config = yaml.load(f, Loader=yaml.BaseLoader)\n\nNow, following the docs for the dataverse tutorial, load a NativeAPI up:\n\n\nExported source\nfrom pyDataverse.api import NativeApi\n\n\nThe NativeAPI is a catchall API object to be able to do general stuff:\n\napi = NativeApi(config['base_url'], config['api_token'])\nresp=api.get_info_version()\n#resp.text()\n\n\nresp.json()\n\nLooks good! Now that we know that it works, we can think more about how to publish data there.", + "crumbs": [ + "Publish: Gather the Aggregated Data and Publish to DataVerse" + ] + }, + { + "objectID": "03_publish.html#harvard-dataverse", + "href": "03_publish.html#harvard-dataverse", + "title": "Publish: Gather the Aggregated Data and Publish to DataVerse", + "section": "Harvard Dataverse", + "text": "Harvard Dataverse\nLet’s create a dummy dataset with the components we’re planning to upload, and then upload and promptly delete it.\nTo do that, we must import the models module and create a Dataset object:\n\nfrom pyDataverse.models import Dataset\n\n\nds = Dataset()\n\nThis ds object is pretty straightforward since it doesn’t contain anything yet:\n\nds.get()\n\nWe can populate the object from the dummy data on the github repo:\n\nfrom pyDataverse.utils import read_file\nfrom urllib.request import urlretrieve\nimport tempfile\n\n\n# url for dummy data\nurl = \"https://raw.githubusercontent.com/gdcc/pyDataverse/refs/heads/main/tests/data/user-guide/dataset.json\"\n\n\nwith tempfile.NamedTemporaryFile(mode='w+') as tmp:\n urlretrieve(url, tmp.name)\n ds.from_json(read_file(tmp.name))\n\nWe have to validate the JSON correctly:\n\nds.validate_json()\n\nModifying it is easy:\n\nds.set({\"title\": \"Youth from Austria 2005\"})\nds.get()\n\nNow, to create the dataset we use the API:\n\n# this is only run in interactive sessions for demo purposes\nresp = api.create_dataset(\":root\", ds.json())\n\nIf you caught the resp object, it contains the PID for the newly created dataset.\nHowever, if you didn’t you can use the SearchAPI to find it:\n\n\nExported source\nfrom pyDataverse.api import SearchApi\n\n\n\nsearch_api = SearchApi(config['base_url'], config['api_token'])\n\n\n#\n\nresp = search_api.search(\"Youth from Austria\", data_type=\"dataset\")\nresults = resp.json()['data']['items']\nresult = [x for x in results if \"Youth from Austria\" in x['name']][0]\nresult\n\n\npid = result['global_id']\n\nNow to look at the data we created using the NativeAPI again, and delete the dataset:\n\nuploaded_ds = api.get_dataset(pid)\nuploaded_ds.json()['data']\n\nresp = api.delete_dataset(pid)\nresp.json()\n\nWith that understanding, we can develop a quick module to do the following:\n\nMake the dataset LEGO Compatible\nUpload and publish the data to dataverse", + "crumbs": [ + "Publish: Gather the Aggregated Data and Publish to DataVerse" + ] + }, + { + "objectID": "03_publish.html#lego-compatibility", + "href": "03_publish.html#lego-compatibility", + "title": "Publish: Gather the Aggregated Data and Publish to DataVerse", + "section": "LEGO Compatibility", + "text": "LEGO Compatibility\nLet’s take an example file to use as a model for LEGO compatibility\n\n\nExported source\nimport geopandas as gpd\nimport pandas as pd\nimport re\nimport glob\n\n\n\nex = gpd.read_parquet(here() / \"bld/2009_06_madagascar_day_swvl1_mean.parquet\")\nex.describe()\n\nWe know that the LEGO data model should look like this:\n<main lab folder>/lego\n├── <domain>\n│ ├── <subdomain>__<data_source>\n│ │ ├── <geo_resolution>__<time_resolution>\n│ │ │ ├── <filename>_yyyy.parquet\nSo, for the above file, we’ll end up with the LEGO path data/environmental/exposures_era5/healthshed_monthly/dewpoint_2024.parquet. In it, we should have the following columns:\nhealthshed_id year month day stat_1 stat_2 ... stat_n \nThis means we should read in all of the exposures for a single timepoint at once. I think the smart thing to do is use a glob string to gather all of the pertinent files. This will be the first function we export to the library:\n\nsource\n\ngather_exposure_geodataframes\n\n gather_exposure_geodataframes (glob_string:str, polygon_id:str,\n exposure:str)\n\nRead in a list of geo dataframes from the same time frame and merge them\n\n\n\n\n\n\n\n\n\nType\nDetails\n\n\n\n\nglob_string\nstr\nstring for the path to search for the pertinent files\n\n\npolygon_id\nstr\nthe string signifying the healthshed ID of the polygon\n\n\nexposure\nstr\nthe exposure name\n\n\nReturns\nlist\n\n\n\n\n\n\nExported source\ndef gather_exposure_geodataframes(\n glob_string: str, # string for the path to search for the pertinent files\n polygon_id: str, # the string signifying the healthshed ID of the polygon\n exposure: str # the exposure name\n )-> list:\n \"Read in a list of geo dataframes from the same time frame and merge them\"\n\n # first get the initial one so we have the polygon ID and geometry\n frames = glob.glob(str(glob_string))\n initial_gdf=gpd.read_parquet(frames[0])\n merged_df = []\n \n for f in tqdm(frames, desc=\"Processing files\"):\n # read in as a regular dataframe by ignoring geometry\n df = gpd.read_parquet(f).drop([\"geometry\"], axis=1) \n \n # get the year and month\n # Extract year and month\n search_str = rf'_{exposure}_(\\d{{4}})_(\\d{{1,2}})\\.parquet$'\n match = re.search(search_str, f)\n\n if match:\n year = int(match.group(1))\n month = int(match.group(2))\n #print(f\"Year: {year}, Month: {month}\")\n else:\n raise ValueError(f\"Could not extract year and month from filename: {search_str} {f}\")\n \n df['exposure'] = exposure\n df['month'] = month\n df['year'] = year\n\n # Step 1: Melt all day columns (leave 'month' and 'year' as identifiers)\n df_long = df.melt(id_vars=[polygon_id, \"exposure\", \"year\", \"month\"], var_name=\"day_stat\", value_name=\"value\")\n\n # Step 2: Extract day and stat type from column names\n # Example column: \"day_01_daily_mean\"\n df_long[[\"day\", \"stat\"]] = df_long[\"day_stat\"].str.extract(r\"day_(\\d{2})_daily_(mean|max|min|total)\")\n\n # Optional: convert 'day' and month to integer\n df_long[\"day\"] = df_long[\"day\"].astype(int)\n df_long[\"month\"] = df_long[\"month\"].astype(int)\n\n # Drop the original combined column\n df_long = df_long.drop(columns=\"day_stat\")\n\n # Reorder columns\n df_long = df_long[[polygon_id, \"exposure\", \"year\", \"month\", \"day\", \"stat\", \"value\"]]\n\n df_long = df_long.sort_values(by=[\"year\", \"month\", \"day\"])\n df_clean = df_long.pivot(index=[polygon_id, \"exposure\", \"year\", \"month\", \"day\"], columns=\"stat\", values=\"value\").reset_index()\n merged_df.append(df_clean)\n\n return [pd.concat(merged_df).reset_index(drop=True), initial_gdf[[polygon_id, \"geometry\"]]]\n\n\n\nframes = here() / \"data\" / \"testing\" / \"*madagascar*\"\n\nmerged = gather_exposure_geodataframes(frames, \"fs_uid\", \"2m_dewpoint_temperature\")\nmerged[0].describe()\n\nThis returns one file with all of the geometries and one file with the statistics and exposures.\nNow, with this, we can move on. The dataset was created in the UI and is available via search and test out how to upload it:\n\nresp = search_api.search(\"ERA5\", data_type=\"dataset\")\n\nresults = resp.json()['data']['items']\n\nresult = [x for x in results if \"ERA5\" in x['name']][0]\nera5_pid = result['global_id']\nresult\n\n\n\nExported source\nfrom pyDataverse.models import Datafile\nimport os\nimport pathlib\n\n\nWe’ll upload directly from file. In the case of ERA5 vs. LEGO, we store the file on disk as LEGO hierarchy, but to upload it to dataverse using a flat filename (since creating subdatasets to represent directories might be a bit of a hassle)\n\n# assuming the file has a path on disk like:\nf_out = \"environmental/exposures_era5/healthshed_daily/dewpoint_2024.parquet\"\nos.makedirs(here() / \"data\" / \"testing\" / os.path.dirname(f_out), exist_ok=True)\naggregations, geo = merged\naggregations.to_parquet(here() / \"data\" / \"testing\" / f_out, index=False)\n\ndatafile = Datafile()\ndatafile.set({\n # the id of the era5 dataset \n \"pid\": era5_pid,\n # the path to the file on disk goes here\n \"filename\": str(here() / \"data\" / \"testing\" / f_out),\n # use the \"label\" to name the file\n \"label\": f_out.replace(\"/\", \"-\")\n})\n\n\nresp = api.upload_datafile(era5_pid, str(here() / \"data\" / \"testing\" / f_out), datafile.json())\n\nPretty simple!\nNow, we just need a main function to upload this data. The final upload is one file per exposure per year, so these should be the variables we gather data for.\nWe should get some functionality to gather the groups of these files automatically, based on the hydra config:\n\n\nExported source\nfrom hydra import initialize, compose\nfrom omegaconf import OmegaConf, DictConfig\nfrom tqdm import tqdm\n\n\n\ntarget_dir = here() / \"data\" / \"intermediate\"\n\ntry:\n with initialize(version_base=None, config_path=\"../conf\"):\n cfg = compose(config_name='config.yaml')\nexcept Exception as e:\n print(f\"Error initializing Hydra: {e}\")\n with initialize(version_base=None, config_path=\"conf\"):\n cfg = compose(config_name='config.yaml')\n\ncfg.development_mode = False\n#cfg.query['year'] = 2017\n#cfg.query['month'] = 11\n#cfg.query['geography'] = \"nepal\"\n\n\nsource\n\n\nmain\n\n main (cfg:omegaconf.dictconfig.DictConfig)\n\n\n\nExported source\n@hydra.main(version_base=None, config_path=\"../../conf\", config_name=\"config\")\ndef main(cfg: DictConfig) -> None:\n\n variables_dict = {\n \"2m_temperature\": \"t2m\",\n \"2m_dewpoint_temperature\": \"d2m\",\n \"volumetric_soil_water_layer_1\": \"swvl1\",\n \"total_precipitation\": \"tp\"\n }\n\n print(OmegaConf.to_yaml(cfg))\n\n #prep dataverse\n api_token_file = here() / \"sandbox/dataverse_api_key.yml\"\n with open(api_token_file, \"r\") as f:\n apiconfig = yaml.load(f, Loader=yaml.BaseLoader)\n api = NativeApi(apiconfig['base_url'], apiconfig['api_token'])\n search_api = SearchApi(apiconfig['base_url'], apiconfig['api_token'])\n resp = search_api.search(\"ERA5\", data_type=\"dataset\")\n\n results = resp.json()['data']['items']\n\n result = [x for x in results if \"ERA5\" in x['name']][0]\n era5_pid = result['global_id']\n\n for geography in cfg.geographies:\n for year in cfg.query['year']:\n for variable, v in variables_dict.items():\n \n print(f\"Processing {geography} for {variable} in {year}\")\n glob_string = here() / \"data\" / \"intermediate\" / f\"*{geography}*{variable}*{year}*\"\n print(f\"Glob: {glob_string}\")\n polygon_id = cfg.geographies[geography]['unique_id']\n print(f\"polygon_id: {polygon_id}\")\n merged = gather_exposure_geodataframes(glob_string, polygon_id, variable)\n print(merged[0].head())\n print(merged[1].head())\n\n output_dir = here() / \"data\" / \"output\" \n \n f_out = f\"environmental/exposures_era5/healthshed_daily/{geography}_{v}_{year}.parquet\"\n os.makedirs(output_dir / os.path.dirname(f_out), exist_ok=True)\n output_path = output_dir / f_out\n\n print(f\"Writing to {output_path}\")\n merged[0].to_parquet(output_path, index=False)\n \n\n print(f\"Uploading {f_out.replace('/', '-')} to Dataverse...\")\n # upload to dataverse\n datafile = Datafile()\n datafile.set({\n \"pid\": era5_pid,\n \"filename\": str(output_path),\n \"label\": f_out.replace(\"/\", \"-\")\n })\n\n resp = api.upload_datafile(era5_pid, output_path, datafile.json())\n assert resp.json()['status'] == \"OK\", f\"Failed to upload datafile: {resp.text}\"\n \n # also save the geometry for the region \n merged[1].to_parquet(output_path.parent / f\"{geography}_geometry.parquet\", index=False)\n\n # and upload it to dataverse\n datafile = Datafile()\n datafile.set({\n \"pid\": era5_pid,\n \"filename\": str(output_path.parent / f\"{geography}_geometry.parquet\"),\n \"label\": f\"{geography}_geometry.parquet\"\n })\n\n resp = api.upload_datafile(era5_pid, output_path.parent / f\"{geography}_geometry.parquet\", datafile.json())\n assert resp.json()['status'] == \"OK\", f\"Failed to upload geometry datafile: {resp.text}\"\n\n print(\"All files processed and uploaded successfully.\")", + "crumbs": [ + "Publish: Gather the Aggregated Data and Publish to DataVerse" + ] + }, + { + "objectID": "00_core.html", + "href": "00_core.html", + "title": "Core Module: Internal functions and testing", + "section": "", + "text": "This is a core library for the ERA5 dataset pipeline. It defines a few helpful functions such as an API tester to test your API key and connection.\n\n\n\n\nExported source\nimport os\nimport cdsapi\nimport hydra\nimport json\nimport tempfile\nimport argparse\nimport zipfile\nimport shutil\nimport geopandas as gpd\nfrom pathlib import Path\nfrom pydrive2.auth import GoogleAuth\nfrom pydrive2.drive import GoogleDrive\nfrom omegaconf import DictConfig, OmegaConf\nfrom pyprojroot import here\nfrom importlib import import_module", + "crumbs": [ + "Core Module: Internal functions and testing" + ] + }, + { + "objectID": "00_core.html#core", + "href": "00_core.html#core", + "title": "Core Module: Internal functions and testing", + "section": "", + "text": "This is a core library for the ERA5 dataset pipeline. It defines a few helpful functions such as an API tester to test your API key and connection.\n\n\n\n\nExported source\nimport os\nimport cdsapi\nimport hydra\nimport json\nimport tempfile\nimport argparse\nimport zipfile\nimport shutil\nimport geopandas as gpd\nfrom pathlib import Path\nfrom pydrive2.auth import GoogleAuth\nfrom pydrive2.drive import GoogleDrive\nfrom omegaconf import DictConfig, OmegaConf\nfrom pyprojroot import here\nfrom importlib import import_module", + "crumbs": [ + "Core Module: Internal functions and testing" + ] + }, + { + "objectID": "00_core.html#utilities", + "href": "00_core.html#utilities", + "title": "Core Module: Internal functions and testing", + "section": "Utilities", + "text": "Utilities\nSome utilities are provided to help you with the ERA5 dataset.\n\nsource\n\ndescribe\n\n describe (cfg:omegaconf.dictconfig.DictConfig=None)\n\nDescribe the configuration file used by Hydra for the pipeline\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\ncfg\nDictConfig\nNone\nConfiguration file\n\n\nReturns\nNone\n\n\n\n\n\n\n\nExported source\ndef describe(\n cfg: DictConfig=None, # Configuration file\n )-> None:\n \"Describe the configuration file used by Hydra for the pipeline\"\n \n if cfg is None:\n print(\"No configuration file provided. Generating default configuration file.\")\n cfg = OmegaConf.create()\n \n print(\"This package fetches ERA5 data. The following is the config file used by Hydra for the pipeline:\\n\")\n print(OmegaConf.to_yaml(cfg))\n\n\nIn addition, we’ve defined 3 private functions to help with path expansion _expand_path, dynamic function importing _get_callable, and directory structure creation _create_directory_structure.\n\n\nA Simple Temperature Conversion Function\n\nsource\n\n\nkelvin_to_celsius\n\n kelvin_to_celsius (kelvin:float)\n\nConvert temperature from Kelvin to Celsius.\n\n\n\n\nType\nDetails\n\n\n\n\nkelvin\nfloat\nTemperature in Kelvin\n\n\nReturns\nfloat\nTemperature in Celsius\n\n\n\n\n\nA Class for Authenticating Google Drive\nWe’re going to use a class to authenticate and interact with google drive. The goal is to have a simple interface to fetch the healthshed files dynamically from google drive in the pipeline.\n\n\n\n\n\n\nImportant\n\n\n\nThis class was implemented when all of our data was stored on a private Google Drive. Since we have moved all of our data to FASRC, this will likely be deprecated in the near future.\n\n\n\nsource\n\n\nGoogleDriver\n\n GoogleDriver (json_key_path=None)\n\n*A class to handle Google Drive authentication and file management. This class uses the PyDrive2 library to authenticate with Google Drive using a service account.\nIt provides three methods: authenticating the account, getting the drive object, and downloading the healthshed files for madagascar.*\nHere’s how we use it. The credentials for the data-pipeline service account are available in the sandbox folder, and the path to said folder is set in the config:\n\nfrom hydra import initialize, compose\nfrom omegaconf import OmegaConf\n\n\n# unfortunately, we have to use the initialize function to load the config file\n# this is because the @hydra decorator does not work with Notebooks very well\n# this is a known issue with Hydra: https://gist.github.com/bdsaglam/586704a98336a0cf0a65a6e7c247d248\n# \n# just use the relative path from the notebook to the config dir\ntry:\n with initialize(version_base=None, config_path=\"../conf\"):\n cfg = compose(config_name='config.yaml')\nexcept Exception as e:\n print(f\"Error initializing Hydra: {e}\")\n with initialize(version_base=None, config_path=\"conf\"):\n cfg = compose(config_name='config.yaml')\n\n\n\n\n\n\n\nImportant\n\n\n\nIf we continue with pytask, we will not need to use hydra at all, and so the above strategy may get deprecated.\n\n\n\nauth = GoogleDriver(json_key_path=here() / cfg.GOOGLE_DRIVE_AUTH_JSON.path)\ndrive = auth.get_drive()\n\nHere’s how we might check that the healthsheds are accessible in the drive:\n\n# we're using the madagascar healthshed folder as an example\nfolder_id = cfg.geographies.madagascar.healthsheds\nfolder_name = \"healthsheds2022.zip\"\nfile_list = drive.ListFile({'q': f\" title='{folder_name}' and trashed = false \"}).GetList()\n\nfor file in file_list:\n print(f\"{file['title']} - {file['mimeType']}\")\n\nThat being said, we can read in the healthsheds into geopandas by downloading them to a temp directory. The healthsheds must be a zipped shapefiles package with the files at the root of the zip directory.\n\nwith tempfile.TemporaryDirectory() as temp_dir:\n # Create a temporary directory to store the downloaded file\n zip_path = os.path.join(temp_dir, folder_name)\n\n # Download file from Google Drive\n file_obj = drive.CreateFile({'id': file_list[0]['id']})\n file_obj.GetContentFile(zip_path)\n\n # Read shapefile directly from ZIP\n gdf = gpd.read_file(f\"zip://{zip_path}\")\n\nThat works! So now we can patch the class to include this workflow:\n\nsource\n\n\nGoogleDriver.read_healthsheds\n\n GoogleDriver.read_healthsheds (healthshed_zip_name)\n\nAnd to check that it works:\n\ndriver = GoogleDriver(json_key_path=here() / cfg.GOOGLE_DRIVE_AUTH_JSON.path)\ndrive = driver.get_drive()\nhealthsheds = driver.read_healthsheds(\"healthsheds2022.zip\")\n\nhealthsheds.describe()", + "crumbs": [ + "Core Module: Internal functions and testing" + ] + }, + { + "objectID": "00_core.html#cds-file-handler-type", + "href": "00_core.html#cds-file-handler-type", + "title": "Core Module: Internal functions and testing", + "section": "CDS File Handler Type", + "text": "CDS File Handler Type\n\n\n\n\n\n\nImportant\n\n\n\nThis section may also be deprecated. Since adding swvl1 to the pipeline, we have not needed to use this class. We leave it here for now for reference.\n\n\nWe’re going to make a file handler type to help deal with CDS files. This is to fix NSAPH-Data-Processing/era5_sandbox#13.\nUsually, when you download data, it comes out as a simple .nc file that can be opened with xarray. However, the CDS API has a few different file types that are not .nc files. For example, the ERA5 data is stored in a .grib file format. This is a common format for meteorological data, and it is used by the ECMWF. When a query has multiple variables, sometimes they are downloaded as a .zip file to separat the grib from the netcdf.\nSo, below, we define a class that can handle the file no matter what the type is. It will check the file type and then use the appropriate method to open it. The class will also have a method to check if the file is a .zip file, and if so, it will unzip it and return the path to the unzipped file.\n\nsource\n\nClimateDataFileHandler\n\n ClimateDataFileHandler (input_path:str)\n\nA class to handle file operations for the Climate Data Store (CDS). This class provides unpack files downloaded from the CDS API. It must be able to handle the unpacking of files downloaded from the CDS API. This means that if the file is a basic netcdf, it should be passed to the netcdf handler. If the file is a zip, it should be handled by the zip handler in temp and the data returned as required.\n\nimport xarray as xr\nfrom fastcore.test import test_fail\n\n\neg_file = here() / \"bld/2019_5_madagascar.nc\"\n\n# this fails because the nc file downloaded has grib and netcdf in it, so\n# xr cannot handle it.\ndef wont_work(multilayer_file):\n\n ds = xr.open_dataset(multilayer_file)\n\ntest_fail(\n wont_work,\n args=(eg_file)\n)\n\n# equivalent to saying try: wont_work(eg_file) Except: some error handling\n\nThe above fails because the download contains temperature and precipitation data, which get encoded silently as different formats. Even though it is one file, it contains both grib and netcdf data and is encoded as a .zip file. So we use the class to read it instead:\n\nhandler = ClimateDataFileHandler(eg_file)\nhandler.prepare()\nds1 = xr.open_dataset(handler.get_dataset(\"instant\"))\n#ds2 = xr.open_dataset(handler.get_dataset(\"accum\"))\n\n\n\n\n\n\n\nImportant\n\n\n\nThe above line for ds2 is commented out because the example file does not separate accumulation data.\n\n\n\nds1\n\n\n#ds2\n\n\nhandler.cleanup()\n\nGreat! Let’s add a context handler and this can be added to the pipeline, so that with the entry and exit methods, we can now use the class in a with statement:\n\nwith ClimateDataFileHandler(eg_file) as handler:\n ds1 = xr.open_dataset(handler.get_dataset(\"instant\"))\n #ds2 = xr.open_dataset(handler.get_dataset(\"accum\"))\n\n print(ds1)\n #print(ds2)", + "crumbs": [ + "Core Module: Internal functions and testing" + ] + }, + { + "objectID": "00_core.html#tests-and-main", + "href": "00_core.html#tests-and-main", + "title": "Core Module: Internal functions and testing", + "section": "Tests and Main", + "text": "Tests and Main\nIn nbdev, our tests are embedded in the notebook. Whenever you export the notebook, all the cells that are specified to run are run, and hence, the tests are executed. The tests are also exported. This is a great way to ensure that your documentation is always up-to-date. For this module, we’re using the testAPI() function as our main test.\n\nsource\n\ntestAPI\n\n testAPI (cfg:omegaconf.dictconfig.DictConfig=None,\n dataset:str='reanalysis-era5-pressure-levels')\n\n\n\nExported source\ndef testAPI(\n cfg: DictConfig=None,\n dataset:str=\"reanalysis-era5-pressure-levels\"\n )-> bool: \n \n # parse config\n testing=cfg.development_mode\n output_path=here(\"data\") / \"testing\"\n\n print(OmegaConf.to_yaml(cfg))\n\n try:\n client = cdsapi.Client()\n\n # build request\n request = {\n 'product_type': ['reanalysis'],\n 'variable': ['geopotential'],\n 'year': ['2024'],\n 'month': ['03'],\n 'day': ['01'],\n 'time': ['13:00'],\n 'pressure_level': ['1000'],\n 'data_format': 'grib',\n }\n\n target = output_path / 'test_download.grib'\n \n print(\"Testing API connection by downloading a dummy dataset to {}...\".format(output_path))\n\n client.retrieve(dataset, request, target)\n\n if not testing:\n os.remove(target)\n \n print(\"API connection test successful.\")\n return True\n\n except Exception as e:\n print(\"API connection test failed.\")\n print(\"Did you set up your API key with CDS? If not, please visit https://cds.climate.copernicus.eu/how-to-api#install-the-cds-api-client\")\n print(\"Error: {}\".format(e))\n return False\n\n\nWe can see that this API tester tool works with Hydra configuration:\n\nfrom hydra import initialize, compose\nfrom omegaconf import OmegaConf\n\n\n# unfortunately, we have to use the initialize function to load the config file\n# this is because the @hydra decorator does not work with Notebooks very well\n# this is a known issue with Hydra: https://gist.github.com/bdsaglam/586704a98336a0cf0a65a6e7c247d248\n# \n# just use the relative path from the notebook to the config dir\ntry:\n with initialize(version_base=None, config_path=\"../conf\"):\n cfg = compose(config_name='config.yaml')\nexcept Exception as e:\n print(f\"Error initializing Hydra: {e}\")\n with initialize(version_base=None, config_path=\"conf\"):\n cfg = compose(config_name='config.yaml')\n\ndescribe(cfg)\n\n\n\nImporting the Main Function\n\n\n\n\n\n\nImportant\n\n\n\nAs mentioned, if we continue with pytask, we will not need to use hydra at all, and so the main function may get deprecated as pytask will handle the pipeline execution without __main__ scripts.\n\n\nImportant: using __main__ in nbdev and Hydra is a little bit tricky. We need to define the main function in the module ONLY ONCE and then when we export the notebook to script, we need to add the nbdev.imports.IN_NOTEBOOK variable. This way, the main function will only be executed when we run the notebook and not when we import the module.\nfrom nbdev.imports import IN_NOTEBOOK\nYou’ll see this listed throughout the notebooks.\n\nsource\n\n\nmain\n\n main (cfg:omegaconf.dictconfig.DictConfig)\n\n\n\nExported source\n@hydra.main(version_base=None, config_path=\"../../conf\", config_name=\"config\")\ndef main(cfg: DictConfig) -> None:\n\n # Create the directory structure\n _create_directory_structure(here() / \"data\", cfg.datapaths)\n\n # test the api\n testAPI(cfg=cfg)\n\n\n\ntry: from nbdev.imports import IN_NOTEBOOK\nexcept: IN_NOTEBOOK=False\n\nif __name__ == \"__main__\" and not IN_NOTEBOOK:\n main()", + "crumbs": [ + "Core Module: Internal functions and testing" + ] + }, + { + "objectID": "02_aggregate.html", + "href": "02_aggregate.html", + "title": "Aggregate Module: Spatial Aggregation to Healthsheds", + "section": "", + "text": "This module aggregates the downloaded data into the respective output dataframes.\n\n\nWe prototyped the code in this module using a Jupyter notebook. The notebook is available in notes/prototypes/learning_aggregations_w_michelle_20250328.ipynb. The code in this module is a cleaned-up version of the code in that notebook. The notebook contains additional comments and explanations of the code, which may be helpful for understanding the code in this module.\nThe basic process is as follows:\n\nLoad the netCDF data in memory\nStatistically aggregate the hourly data to daily data per exposure using resample()\nWrite out the data to tiff\nRead the tiff data back in\nRead in the shapefile that defines the healthsheds\nSpatially aggregate the exposure data to the healthsheds\nQuality check the aggregations\nWrite out final aggregations to tiff\n\n\n\nExported source\nimport tempfile\nimport rasterio\nimport hydra\nimport argparse\nimport os\n\nimport pandas as pd\nimport geopandas as gpd\nimport numpy as np\nimport xarray as xr\nimport matplotlib.pyplot as plt\n\nfrom dataclasses import dataclass, field\nfrom typing import Optional, Tuple\nfrom pyprojroot import here\nfrom hydra import initialize, compose\nfrom omegaconf import OmegaConf, DictConfig\nfrom tqdm import tqdm\nfrom math import ceil, floor\nfrom rasterstats.io import Raster\nfrom rasterstats.utils import boxify_points, rasterize_geom\n\ntry: from era5_sandbox.core import GoogleDriver, _get_callable, describe, ClimateDataFileHandler, kelvin_to_celsius\nexcept: from core import GoogleDriver, _get_callable, describe, ClimateDataFileHandler, kelvin_to_celsius\n\n\n\ntry:\n with initialize(version_base=None, config_path=\"../conf\"):\n cfg = compose(config_name='config.yaml')\nexcept Exception as e:\n print(f\"Error initializing Hydra: {e}\")\n with initialize(version_base=None, config_path=\"conf\"):\n cfg = compose(config_name='config.yaml')\n\nWe’re going to write a function that aggregates the data for a single exposure from a file. This file should be the single month data we got from the previous step in the pipeline.\n\neg_file = here() / \"bld/2009_01_nepal.nc\"\n\n\nsource\n\n\n\n resample_netcdf (fpath:str, resample:str='1D', agg_func:<built-\n infunctioncallable>=<function mean at 0x145cb6c3b930>,\n time_dim:str='valid_time', **xr_open_kwargs)\n\n*Resample a netCDF file to a specified frequency and aggregation method.\nArgs: fpath (str): Path to the netCDF file. resample (str): Resampling frequency (e.g., ‘1H’, ‘1D’). agg_func (callable): Aggregation function (e.g., np.mean, np.sum).\nReturns: xarray.Dataset: Resampled dataset.*\n\n\n\n\n\n\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\nfpath\nstr\n\nPath to the netCDF file.\n\n\nresample\nstr\n1D\nResampling frequency (e.g., ‘1H’, ‘1D’)\n\n\nagg_func\ncallable\nmean\nAggregation function (e.g., np.mean, np.sum).\n\n\ntime_dim\nstr\nvalid_time\nName of the time dimension in the dataset.\n\n\nxr_open_kwargs\nVAR_KEYWORD\n\n\n\n\nReturns\nDataset\n\nkeywords for python’s xarray module\n\n\n\nWe pull the aggregation function from the config file:\n\nvar = 'swvl1'\nagg_func = _get_callable(cfg['aggregation']['aggregation'][var]['hourly_to_daily'][0]['function'])\n\n\nwith ClimateDataFileHandler(eg_file) as handler:\n\n ds_path = handler.get_dataset(\"instant\")\n resampled_data = resample_netcdf(ds_path, agg_func=agg_func)\n\nI’m going to use a dataclass to represent the tiff data. This will allow us to easily pass around the data and metadata associated with the tiff file. Why? I’ve never used dataclasses and I’m curious about them — ChatGPT thinks this will make the code cleaner and easier to read.\n\nsource\n\n\n\n\n RasterFile (path:str, band:int)\n\n\n\nExported source\n@dataclass\nclass RasterFile:\n path: str\n band: int # note that this is 1-indexed\n data: Optional[np.ndarray] = field(default=None, init=False)\n transform: Optional[rasterio.Affine] = field(default=None, init=False)\n crs: Optional[str] = field(default=None, init=False)\n nodata: Optional[float] = field(default=None, init=False)\n bounds: Optional[Tuple[float, float, float, float]] = field(default=None, init=False)\n\n def load(self):\n \"\"\"Load raster data and basic metadata.\"\"\"\n with rasterio.open(self.path) as src:\n self.data = src.read(self.band) # each day gets one rasterfile\n self.transform = src.transform\n self.crs = src.crs\n self.nodata = src.nodata\n self.bounds = src.bounds\n return self\n\n def shape(self) -> Optional[Tuple[int, int]]:\n \"\"\"Return the shape of the raster data.\"\"\"\n return self.data.shape if self.data is not None else None\n\n def __str__(self):\n return f\"RasterFile(path='{self.path}', shape={self.shape()}, crs='{self.crs}')\"\n\n\nNext, a function to write and read the netCDF to tiff:\n\nsource\n\n\n\n\n netcdf_to_tiff (ds:xarray.core.dataset.Dataset, band:int, variable:str,\n crs:str='EPSG:4326')\n\nConvert a netCDF file to a GeoTIFF file.\n\n\n\n\n\n\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\nds\nDataset\n\nThe aggregated xarray dataset to convert.\n\n\nband\nint\n\nThe day to rasterise; 1 indexed just like human english\n\n\nvariable\nstr\n\nThe variable name to convert.\n\n\ncrs\nstr\nEPSG:4326\nCoordinate reference system (default is WGS84).\n\n\n\n\n\nExported source\ndef netcdf_to_tiff(\n ds: xr.Dataset, # The aggregated xarray dataset to convert. \n band: int, # The day to rasterise; 1 indexed just like human english\n variable: str, # The variable name to convert.\n crs: str = \"EPSG:4326\", # Coordinate reference system (default is WGS84). \n ):\n\n \"\"\"\n Convert a netCDF file to a GeoTIFF file.\n \"\"\"\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n\n # Select the variable and time index\n variable = ds[variable]\n ds_ = variable.rio.set_spatial_dims(x_dim=\"longitude\", y_dim=\"latitude\")\n ds_ = ds_.rio.write_crs(crs)\n # Save as GeoTIFF\n ds_.rio.to_raster(f\"{tmpdirname}/output.tif\")\n # Load the raster file\n raster_file = RasterFile(path=f\"{tmpdirname}/output.tif\", band=band).load()\n\n return raster_file\n\n\nNow to test it:\n\nwith ClimateDataFileHandler(eg_file) as handler:\n ds_path = handler.get_dataset(\"instant\")\n resampled_nc = resample_netcdf(ds_path)\n\nprint(resampled_nc)\nresampled_tiff = netcdf_to_tiff(\n ds=resampled_nc,\n band=28,\n variable=\"swvl1\",\n crs=\"EPSG:4326\"\n)\n\n\nresampled_tiff.data.shape, resampled_tiff.transform, resampled_tiff.crs, resampled_tiff.bounds\n\nSuper cool! The tiff file is created and the data is read back in correctly. Now we can move on to the next step, which is to aggregate the data by healthshed.", + "crumbs": [ + "Aggregate Module: Spatial Aggregation to Healthsheds" + ] + }, + { + "objectID": "02_aggregate.html#aggregate", + "href": "02_aggregate.html#aggregate", + "title": "Aggregate Module: Spatial Aggregation to Healthsheds", + "section": "", + "text": "This module aggregates the downloaded data into the respective output dataframes.\n\n\nWe prototyped the code in this module using a Jupyter notebook. The notebook is available in notes/prototypes/learning_aggregations_w_michelle_20250328.ipynb. The code in this module is a cleaned-up version of the code in that notebook. The notebook contains additional comments and explanations of the code, which may be helpful for understanding the code in this module.\nThe basic process is as follows:\n\nLoad the netCDF data in memory\nStatistically aggregate the hourly data to daily data per exposure using resample()\nWrite out the data to tiff\nRead the tiff data back in\nRead in the shapefile that defines the healthsheds\nSpatially aggregate the exposure data to the healthsheds\nQuality check the aggregations\nWrite out final aggregations to tiff\n\n\n\nExported source\nimport tempfile\nimport rasterio\nimport hydra\nimport argparse\nimport os\n\nimport pandas as pd\nimport geopandas as gpd\nimport numpy as np\nimport xarray as xr\nimport matplotlib.pyplot as plt\n\nfrom dataclasses import dataclass, field\nfrom typing import Optional, Tuple\nfrom pyprojroot import here\nfrom hydra import initialize, compose\nfrom omegaconf import OmegaConf, DictConfig\nfrom tqdm import tqdm\nfrom math import ceil, floor\nfrom rasterstats.io import Raster\nfrom rasterstats.utils import boxify_points, rasterize_geom\n\ntry: from era5_sandbox.core import GoogleDriver, _get_callable, describe, ClimateDataFileHandler, kelvin_to_celsius\nexcept: from core import GoogleDriver, _get_callable, describe, ClimateDataFileHandler, kelvin_to_celsius\n\n\n\ntry:\n with initialize(version_base=None, config_path=\"../conf\"):\n cfg = compose(config_name='config.yaml')\nexcept Exception as e:\n print(f\"Error initializing Hydra: {e}\")\n with initialize(version_base=None, config_path=\"conf\"):\n cfg = compose(config_name='config.yaml')\n\nWe’re going to write a function that aggregates the data for a single exposure from a file. This file should be the single month data we got from the previous step in the pipeline.\n\neg_file = here() / \"bld/2009_01_nepal.nc\"\n\n\nsource\n\n\n\n resample_netcdf (fpath:str, resample:str='1D', agg_func:<built-\n infunctioncallable>=<function mean at 0x145cb6c3b930>,\n time_dim:str='valid_time', **xr_open_kwargs)\n\n*Resample a netCDF file to a specified frequency and aggregation method.\nArgs: fpath (str): Path to the netCDF file. resample (str): Resampling frequency (e.g., ‘1H’, ‘1D’). agg_func (callable): Aggregation function (e.g., np.mean, np.sum).\nReturns: xarray.Dataset: Resampled dataset.*\n\n\n\n\n\n\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\nfpath\nstr\n\nPath to the netCDF file.\n\n\nresample\nstr\n1D\nResampling frequency (e.g., ‘1H’, ‘1D’)\n\n\nagg_func\ncallable\nmean\nAggregation function (e.g., np.mean, np.sum).\n\n\ntime_dim\nstr\nvalid_time\nName of the time dimension in the dataset.\n\n\nxr_open_kwargs\nVAR_KEYWORD\n\n\n\n\nReturns\nDataset\n\nkeywords for python’s xarray module\n\n\n\nWe pull the aggregation function from the config file:\n\nvar = 'swvl1'\nagg_func = _get_callable(cfg['aggregation']['aggregation'][var]['hourly_to_daily'][0]['function'])\n\n\nwith ClimateDataFileHandler(eg_file) as handler:\n\n ds_path = handler.get_dataset(\"instant\")\n resampled_data = resample_netcdf(ds_path, agg_func=agg_func)\n\nI’m going to use a dataclass to represent the tiff data. This will allow us to easily pass around the data and metadata associated with the tiff file. Why? I’ve never used dataclasses and I’m curious about them — ChatGPT thinks this will make the code cleaner and easier to read.\n\nsource\n\n\n\n\n RasterFile (path:str, band:int)\n\n\n\nExported source\n@dataclass\nclass RasterFile:\n path: str\n band: int # note that this is 1-indexed\n data: Optional[np.ndarray] = field(default=None, init=False)\n transform: Optional[rasterio.Affine] = field(default=None, init=False)\n crs: Optional[str] = field(default=None, init=False)\n nodata: Optional[float] = field(default=None, init=False)\n bounds: Optional[Tuple[float, float, float, float]] = field(default=None, init=False)\n\n def load(self):\n \"\"\"Load raster data and basic metadata.\"\"\"\n with rasterio.open(self.path) as src:\n self.data = src.read(self.band) # each day gets one rasterfile\n self.transform = src.transform\n self.crs = src.crs\n self.nodata = src.nodata\n self.bounds = src.bounds\n return self\n\n def shape(self) -> Optional[Tuple[int, int]]:\n \"\"\"Return the shape of the raster data.\"\"\"\n return self.data.shape if self.data is not None else None\n\n def __str__(self):\n return f\"RasterFile(path='{self.path}', shape={self.shape()}, crs='{self.crs}')\"\n\n\nNext, a function to write and read the netCDF to tiff:\n\nsource\n\n\n\n\n netcdf_to_tiff (ds:xarray.core.dataset.Dataset, band:int, variable:str,\n crs:str='EPSG:4326')\n\nConvert a netCDF file to a GeoTIFF file.\n\n\n\n\n\n\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\nds\nDataset\n\nThe aggregated xarray dataset to convert.\n\n\nband\nint\n\nThe day to rasterise; 1 indexed just like human english\n\n\nvariable\nstr\n\nThe variable name to convert.\n\n\ncrs\nstr\nEPSG:4326\nCoordinate reference system (default is WGS84).\n\n\n\n\n\nExported source\ndef netcdf_to_tiff(\n ds: xr.Dataset, # The aggregated xarray dataset to convert. \n band: int, # The day to rasterise; 1 indexed just like human english\n variable: str, # The variable name to convert.\n crs: str = \"EPSG:4326\", # Coordinate reference system (default is WGS84). \n ):\n\n \"\"\"\n Convert a netCDF file to a GeoTIFF file.\n \"\"\"\n\n with tempfile.TemporaryDirectory() as tmpdirname:\n\n # Select the variable and time index\n variable = ds[variable]\n ds_ = variable.rio.set_spatial_dims(x_dim=\"longitude\", y_dim=\"latitude\")\n ds_ = ds_.rio.write_crs(crs)\n # Save as GeoTIFF\n ds_.rio.to_raster(f\"{tmpdirname}/output.tif\")\n # Load the raster file\n raster_file = RasterFile(path=f\"{tmpdirname}/output.tif\", band=band).load()\n\n return raster_file\n\n\nNow to test it:\n\nwith ClimateDataFileHandler(eg_file) as handler:\n ds_path = handler.get_dataset(\"instant\")\n resampled_nc = resample_netcdf(ds_path)\n\nprint(resampled_nc)\nresampled_tiff = netcdf_to_tiff(\n ds=resampled_nc,\n band=28,\n variable=\"swvl1\",\n crs=\"EPSG:4326\"\n)\n\n\nresampled_tiff.data.shape, resampled_tiff.transform, resampled_tiff.crs, resampled_tiff.bounds\n\nSuper cool! The tiff file is created and the data is read back in correctly. Now we can move on to the next step, which is to aggregate the data by healthshed.", + "crumbs": [ + "Aggregate Module: Spatial Aggregation to Healthsheds" + ] + }, + { + "objectID": "02_aggregate.html#polygon-to-raster-cells", + "href": "02_aggregate.html#polygon-to-raster-cells", + "title": "Aggregate Module: Spatial Aggregation to Healthsheds", + "section": "Polygon to Raster Cells", + "text": "Polygon to Raster Cells\nThis function was initially shared from a previous NSAPH aggregation pipeline here. To better understand this, here is a ChatGPT explanation of the code:\n\nThis function, polygon_to_raster_cells, is doing a crucial first step in spatial alignment: it determines which raster cells are “touched” by each polygon geometry (e.g., administrative areas, watersheds, etc.).\nEssentially, this function helps figure out which pixels from a raster image fall inside each polygon (like a district, region, or shape). It does this by looking at each polygon one by one, zooming in on just the part of the raster that overlaps with that shape, and marking the pixels that are inside. This is kind of like placing a cookie cutter (the polygon) on a pixelated map (the raster) and seeing which pixels get cut.\nThe result is a list where each item tells you the pixel locations that match a specific polygon. You can then use those pixel locations to pull out data from the raster, like temperatures or rainfall, and calculate statistics (like the average) for each shape. This is a key step when you want to summarize raster data within specific regions, like figuring out the average temperature in each county or how much vegetation is in each park.\n\n\nsource\n\npolygon_to_raster_cells\n\n polygon_to_raster_cells (vectors, raster, nodata=None, affine=None,\n all_touched=False, verbose=False, **kwargs)\n\nReturns an index map for each vector geometry to indices in the raster source.\n\n\n\n\n\n\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\nvectors\n\n\nlist of geometries from a shapefile\n\n\nraster\n\n\nthe raster data as a numpy array\n\n\nnodata\nNoneType\nNone\nthe nodata value of the raster\n\n\naffine\nNoneType\nNone\nthe affine transform of the raster\n\n\nall_touched\nbool\nFalse\nwhether to include all touched pixels\n\n\nverbose\nbool\nFalse\n\n\n\nkwargs\nVAR_KEYWORD\n\n\n\n\nReturns\nlist\n\nA dictionary mapping vector the ids of geometries to locations (indices) in the raster source.\n\n\n\n\n\nExported source\ndef polygon_to_raster_cells(\n vectors, # list of geometries from a shapefile\n raster, # the raster data as a numpy array\n nodata=None, # the nodata value of the raster\n affine=None, # the affine transform of the raster\n all_touched=False, # whether to include all touched pixels\n verbose=False, \n **kwargs,\n) -> list: # A dictionary mapping vector the ids of geometries to locations (indices) in the raster source.\n \"\"\"Returns an index map for each vector geometry to indices in the raster source.\"\"\"\n\n cell_map = []\n\n with Raster(raster, affine, nodata) as rast:\n # used later to crop raster and find start row and col\n min_lon, dlon = affine.c, affine.a\n max_lat, dlat = affine.f, -affine.e\n H, W = rast.shape\n\n for geom in tqdm(vectors, disable=(not verbose)):\n if \"Point\" in geom.geom_type:\n geom = boxify_points(geom, rast)\n\n # find geometry bounds to crop raster\n # the raster and geometry must be in the same lon/lat coordinate system\n start_row = max(0, min(H - 1, floor((max_lat - geom.bounds[3]) / dlat)))\n start_col = min(W - 1, max(0, floor((geom.bounds[0] - min_lon) / dlon)))\n end_col = max(0, min(W - 1, ceil((geom.bounds[2] - min_lon) / dlon)))\n end_row = min(H - 1, max(0, ceil((max_lat - geom.bounds[1]) / dlat)))\n geom_bounds = (\n min_lon + dlon * start_col, # left\n max_lat - dlat * end_row - 1e-12, # bottom\n min_lon + dlon * end_col + 1e-12, # right\n max_lat - dlat * start_row, # top\n )\n\n # crop raster to area of interest and rasterize\n fsrc = rast.read(bounds=geom_bounds)\n rv_array = rasterize_geom(geom, like=fsrc, all_touched=all_touched)\n indices = np.nonzero(rv_array)\n\n if len(indices[0]) > 0:\n indices = (indices[0] + start_row, indices[1] + start_col)\n assert 0 <= indices[0].min() < rast.shape[0]\n assert 0 <= indices[1].min() < rast.shape[1]\n else:\n pass # stop here for debug\n\n cell_map.append(indices)\n\n return cell_map\n\n\nTo use this, we must define the polygon and raster data. The polygon data is the healthshed shapefile, and the raster data is the tiff file we created earlier. We can use the GoogleDriver class we defined in core to read in the shapefile.\n\ntry:\n with initialize(version_base=None, config_path=\"../conf\"):\n cfg = compose(config_name='config.yaml')\nexcept Exception as e:\n print(f\"Error initializing Hydra: {e}\")\n with initialize(version_base=None, config_path=\"conf\"):\n cfg = compose(config_name='config.yaml')\n\ndriver = GoogleDriver(json_key_path=here() / cfg.GOOGLE_DRIVE_AUTH_JSON.path)\ndrive = driver.get_drive()\nhealthsheds = driver.read_healthsheds(\"Nepal_Healthsheds2024.zip\")\n\n\nres_poly2cell=polygon_to_raster_cells(\n vectors = healthsheds.geometry.values, # the geometries of the shapefile of the regions\n raster=resampled_tiff.data, # the raster data above\n nodata=resampled_tiff.nodata, # any intersections with no data, may have to be np.nan\n affine=resampled_tiff.transform, # some math thing need to revise\n all_touched=True, \n verbose=True\n)\n\nThe data below maps which grid entries fall into each of the regions in the shapefile (e.g. which pixel is in which state)\n\nres_poly2cell[:5]\n\nLast but not least, we aggregate these data to the healthshed level. We can use the rasterstats package to do this.\n\nsource\n\n\naggregate_to_healthsheds\n\n aggregate_to_healthsheds (res_poly2cell:list, raster:__main__.RasterFile,\n shapes:geopandas.geodataframe.GeoDataFrame,\n names_column:str='fs_uid',\n aggregation_func:<built-\n infunctioncallable>=<function nanmean at\n 0x145cb6bbbdf0>, aggregation_name:str='mean')\n\nAggregate the raster data to the health sheds.\n\n\n\n\n\n\n\n\n\n\nType\nDefault\nDetails\n\n\n\n\nres_poly2cell\nlist\n\nthe result of polygon_to_raster_cells\n\n\nraster\nRasterFile\n\nthe raster data\n\n\nshapes\nGeoDataFrame\n\nthe shapes of the health sheds\n\n\nnames_column\nstr\nfs_uid\nthe unique identifier column name of the health sheds\n\n\naggregation_func\ncallable\nnanmean\nthe aggregation function\n\n\naggregation_name\nstr\nmean\nthe name of the aggregation function\n\n\nReturns\nGeoDataFrame\n\n\n\n\n\n\n\nExported source\ndef aggregate_to_healthsheds(\n res_poly2cell: list, # the result of polygon_to_raster_cells \n raster: RasterFile, # the raster data\n shapes: gpd.GeoDataFrame, # the shapes of the health sheds\n names_column: str = \"fs_uid\", # the unique identifier column name of the health sheds\n aggregation_func: callable = np.nanmean, # the aggregation function\n aggregation_name: str = \"mean\" # the name of the aggregation function\n ) -> gpd.GeoDataFrame:\n \"\"\"\n Aggregate the raster data to the health sheds.\n \"\"\"\n\n stats = []\n\n for indices in res_poly2cell:\n if len(indices[0]) == 0:\n # no cells found for this polygon\n stats.append(np.nan)\n else:\n cells = raster.data[indices]\n if sum(~np.isnan(cells)) == 0:\n # no valid cells found for this polygon\n stats.append(np.nan)\n continue\n else:\n # compute MEAN of valid cells\n # but this stat can be ANYTHING\n stats.append(aggregation_func(cells))\n\n # clean up the result into a dataframe\n stats = pd.Series(stats)\n shapes[aggregation_name] = stats\n df = pd.DataFrame(\n {\"healthshed\": shapes[names_column], aggregation_name: stats}\n )\n gdf = gpd.GeoDataFrame(df, geometry=shapes.geometry.values, crs=shapes.crs)\n return gdf\n\n\nAnd now we apply it:\n\nresult = aggregate_to_healthsheds(\n res_poly2cell=res_poly2cell,\n raster=resampled_tiff,\n shapes=healthsheds,\n names_column=\"fid\",\n aggregation_func=np.nanmean,\n aggregation_name=\"mean_soil_moisture\"\n)\nresult.head()\n\nAnd plot for QA:\n\nresult.plot(column=\"mean_soil_moisture\", legend=True)\nplt.title(\"Mean Soil Moisture (m^3 m^-3) by Health Shed Nov 2017 day 1\")\nplt.show()\n\nThat looks great! The data is aggregated to the healthshed level, and we can see the differences in exposure across the healthsheds. We can also see that the data is not uniform across the healthsheds, which is what we expect.", + "crumbs": [ + "Aggregate Module: Spatial Aggregation to Healthsheds" + ] + }, + { + "objectID": "02_aggregate.html#tests-and-main", + "href": "02_aggregate.html#tests-and-main", + "title": "Aggregate Module: Spatial Aggregation to Healthsheds", + "section": "Tests and Main", + "text": "Tests and Main\nNow we can wrap this up in a main function that will simply take in the input file and generate this output. We can also add some tests to make sure the data is aggregated correctly; tests will run automatically in this notebook.\n\nimport random\n\n\n# variables = [\"t2m\", \"d2m\"]\n# years = [\"20{:02d}\".format(m) for m in range(9, 24)]\n# months = [str(m) for m in range(1, 13)]\n# aggregations = [\n# (\"Mean\", np.nanmean),\n# (\"Max\", np.nanmax),\n# (\"Min\", np.nanmin)\n# ]\n\n# exposure_variable = random.choice(variables)\n# year = random.choice(years)\n# month = random.choice(months)\n# aggregation_str, agg_func = random.choice(aggregations)\n# input_file = here() / \"data/input/{}_{}.nc\".format(year, month)\n\n# with initialize(version_base=None, config_path=\"../conf\"):\n# cfg = compose(config_name='config.yaml')\n\n# driver = GoogleDriver(json_key_path=here() / cfg.GOOGLE_DRIVE_AUTH_JSON.path)\n# drive = driver.get_drive()\n# healthsheds = driver.read_healthsheds(cfg.GOOGLE_DRIVE_AUTH_JSON.healthsheds_id)\n\n# with ClimateDataFileHandler(input_file) as handler:\n# ds_path = handler.get_dataset(\"instant\")\n# resampled_nc_file = resample_netcdf(ds_path, agg_func=agg_func)\n\n# days = len(resampled_nc_file.valid_time.values)\n# day = random.choice(range(1, days + 1))\n\n# resampled_tiff = netcdf_to_tiff(\n# ds=resampled_nc_file,\n# band=day, # the day we're aggregating\n# variable=exposure_variable,\n# crs=\"EPSG:4326\"\n# )\n\n# res_poly2cell=polygon_to_raster_cells(\n# vectors = healthsheds.geometry.values, # the geometries of the shapefile of the regions\n# raster=resampled_tiff.data, # the raster data above\n# nodata=resampled_tiff.nodata, # any intersections with no data, may have to be np.nan\n# affine=resampled_tiff.transform, # some math thing need to revise\n# all_touched=True, \n# verbose=True\n# )\n\n# result = aggregate_to_healthsheds(\n# res_poly2cell=res_poly2cell,\n# raster=resampled_tiff,\n# shapes=healthsheds,\n# names_column=\"fs_uid\",\n# aggregation_func=agg_func,\n# aggregation_name=exposure_variable\n# )\n\n# result.plot(column=exposure_variable, legend=True)\n# plt.title(\"{} {} (K) by Health Shed {}\".format(aggregation_str, exposure_variable, input_file.stem))\n# plt.suptitle(\"Aggregation: {}, Day: {}\".format(aggregation_str, str(day)))\n# plt.show()\n\n\n\n\n\n\n\nNote\n\n\n\nNote: The above code is commented out to prevent execution during documentation generation. You can uncomment and run it in an appropriate environment to test the aggregation process.\n\n\n3.2 seconds per aggregation is pretty cool!\n\nresult.to_parquet(here() / \"data/testing/test_aggregation.parquet\")\n\n\nsource\n\naggregate_data\n\n aggregate_data (cfg:omegaconf.dictconfig.DictConfig, input_file:str,\n output_file:str, exposure_variable:str)\n\nAggregate raster data day-by-day and store all days and statistics as separate columns in a single Parquet file.\n\n\n\n\nType\nDetails\n\n\n\n\ncfg\nDictConfig\nthe hydra config\n\n\ninput_file\nstr\nthe input netcdf file\n\n\noutput_file\nstr\nthe output parquet file\n\n\nexposure_variable\nstr\nWhich variable in the dataset to aggregate\n\n\nReturns\nNone\n\n\n\n\n\n\nExported source\ndef aggregate_data(\n cfg: DictConfig, # the hydra config\n input_file: str, # the input netcdf file\n output_file: str, # the output parquet file\n exposure_variable: str # Which variable in the dataset to aggregate\n ) -> None:\n '''\n Aggregate raster data day-by-day and store all days and statistics as separate columns in a single Parquet file.\n '''\n\n if cfg.development_mode:\n describe(cfg)\n return None\n\n geography = cfg['query'].geography\n year = cfg['query']['year']\n month = cfg['query']['month']\n daily_aggs = cfg['aggregation']['aggregation'][exposure_variable]['hourly_to_daily']\n healthshed_aggs = cfg['aggregation']['aggregation'][exposure_variable]['daily_to_healthshed']\n\n # Load healthsheds\n driver = GoogleDriver(json_key_path=here() / cfg.GOOGLE_DRIVE_AUTH_JSON.path)\n drive = driver.get_drive()\n healthsheds = driver.read_healthsheds(cfg.geographies[geography].healthsheds)\n \n # Initialize output DataFrame\n result_df = healthsheds[[cfg.geographies[geography].unique_id, \"geometry\"]].copy()\n\n for daily_agg in daily_aggs:\n print(f\"Processing daily aggregation: {daily_agg['name']}...\")\n \n daily_agg_func = _get_callable(daily_agg['function'])\n\n with ClimateDataFileHandler(input_file) as handler:\n if exposure_variable in [\"t2m\", \"d2m\", \"swvl1\"]:\n ds_path = handler.get_dataset(\"instant\")\n else:\n ds_path = handler.get_dataset(\"accum\")\n resampled_nc_file = resample_netcdf(ds_path, agg_func=daily_agg_func)\n \n for healthshed_agg in healthshed_aggs:\n print(f\"Aggregating to healthshed by: {healthshed_agg['name']}...\")\n\n # Get the number of days in the dataset\n days = len(resampled_nc_file.valid_time.values)\n\n # Get the aggregation function for healthshed\n healthshed_agg_func = _get_callable(healthshed_agg['function'])\n days = len(resampled_nc_file.valid_time.values)\n\n for day in range(1, days + 1):\n print(f\"Processing day {day}...\")\n \n day_col = f\"day_{day:02d}_daily_{daily_agg['name']}\"\n resampled_tiff = netcdf_to_tiff(\n ds=resampled_nc_file,\n band=day,\n variable=exposure_variable,\n crs=\"EPSG:4326\"\n )\n\n result_poly2cell = polygon_to_raster_cells(\n vectors=healthsheds.geometry.values,\n raster=resampled_tiff.data,\n nodata=resampled_tiff.nodata,\n affine=resampled_tiff.transform,\n all_touched=True,\n verbose=True\n )\n\n res = aggregate_to_healthsheds(\n res_poly2cell=result_poly2cell,\n raster=resampled_tiff,\n shapes=healthsheds,\n names_column=cfg.geographies[geography].unique_id,\n aggregation_func=healthshed_agg_func,\n aggregation_name=exposure_variable\n )\n\n result_df[day_col] = res[exposure_variable]\n\n print(f\"Saving final monthly parquet file: {output_file}\")\n result_df.to_parquet(output_file, compression=\"snappy\")\n # return(result_df)\n\n\n\ntry:\n with initialize(version_base=None, config_path=\"../conf\"):\n cfg = compose(config_name='config.yaml')\nexcept Exception as e:\n print(f\"Error initializing Hydra: {e}\")\n with initialize(version_base=None, config_path=\"conf\"):\n cfg = compose(config_name='config.yaml')\n\ncfg.development_mode = False\ncfg.query['year'] = 2017\ncfg.query['month'] = 11\ncfg.query['geography'] = \"nepal\"\n\nvariable = \"swvl1\"\n\naggregate_data(cfg, here() / \"bld/2017_11_nepal.nc\", here() / \"data/testing/test_nepal_aggregation.parquet\", exposure_variable=variable)\n\n\nparquet_file = gpd.read_parquet(here() / \"data/testing/test_nepal_aggregation.parquet\")\n\n\nparquet_file\n\n\nparquet_file.plot(column=\"day_22_daily_mean\", legend=True)\n\n\nsource\n\n\nmain\n\n main (cfg:omegaconf.dictconfig.DictConfig)\n\n\n\nExported source\n@hydra.main(version_base=None, config_path=\"../../conf\", config_name=\"config\")\ndef main(cfg: DictConfig) -> None:\n # Parse command-line arguments\n input_file = str(snakemake.input[0]) # First input file\n output_file = str(snakemake.output[0])\n geography = str(snakemake.params.geography)\n aggregation_variable = str(snakemake.params.variable)\n\n variables_dict = {\n \"2m_temperature\": \"t2m\",\n \"2m_dewpoint_temperature\": \"d2m\",\n \"volumetric_soil_water_layer_1\": \"swvl1\",\n \"total_precipitation\": \"tp\"\n }\n\n cfg['query']['geography'] = geography\n \n aggregate_data(cfg, input_file=input_file, output_file=output_file, exposure_variable=variables_dict[aggregation_variable])", + "crumbs": [ + "Aggregate Module: Spatial Aggregation to Healthsheds" + ] + } +] \ No newline at end of file diff --git a/_docs/site_libs/bootstrap/bootstrap-20da06d658d047a248207d3462be1903.min.css b/_docs/site_libs/bootstrap/bootstrap-20da06d658d047a248207d3462be1903.min.css new file mode 100644 index 0000000..60e6eeb --- /dev/null +++ b/_docs/site_libs/bootstrap/bootstrap-20da06d658d047a248207d3462be1903.min.css @@ -0,0 +1,12 @@ +/*! + * Bootstrap v5.3.1 (https://getbootstrap.com/) + * Copyright 2011-2023 The Bootstrap Authors + * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE) + */@import"https://fonts.googleapis.com/css2?family=Source+Sans+Pro:wght@300;400;700&display=swap";:root,[data-bs-theme=light]{--bs-blue: #2780e3;--bs-indigo: #6610f2;--bs-purple: #613d7c;--bs-pink: #e83e8c;--bs-red: #ff0039;--bs-orange: 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none}.btn-outline-primary{--bs-btn-color: #2780e3;--bs-btn-border-color: #2780e3;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #2780e3;--bs-btn-hover-border-color: #2780e3;--bs-btn-focus-shadow-rgb: 39, 128, 227;--bs-btn-active-color: #fff;--bs-btn-active-bg: #2780e3;--bs-btn-active-border-color: #2780e3;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #2780e3;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #2780e3;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-secondary{--bs-btn-color: #343a40;--bs-btn-border-color: #343a40;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #343a40;--bs-btn-hover-border-color: #343a40;--bs-btn-focus-shadow-rgb: 52, 58, 64;--bs-btn-active-color: #fff;--bs-btn-active-bg: #343a40;--bs-btn-active-border-color: #343a40;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #343a40;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #343a40;--bs-btn-bg: 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#ff0039;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #ff0039;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-light{--bs-btn-color: #f8f9fa;--bs-btn-border-color: #f8f9fa;--bs-btn-hover-color: #000;--bs-btn-hover-bg: #f8f9fa;--bs-btn-hover-border-color: #f8f9fa;--bs-btn-focus-shadow-rgb: 248, 249, 250;--bs-btn-active-color: #000;--bs-btn-active-bg: #f8f9fa;--bs-btn-active-border-color: #f8f9fa;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #f8f9fa;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #f8f9fa;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-dark{--bs-btn-color: #343a40;--bs-btn-border-color: #343a40;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #343a40;--bs-btn-hover-border-color: #343a40;--bs-btn-focus-shadow-rgb: 52, 58, 64;--bs-btn-active-color: #fff;--bs-btn-active-bg: #343a40;--bs-btn-active-border-color: #343a40;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #343a40;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #343a40;--bs-btn-bg: transparent;--bs-gradient: none}.btn-link{--bs-btn-font-weight: 400;--bs-btn-color: #2761e3;--bs-btn-bg: transparent;--bs-btn-border-color: transparent;--bs-btn-hover-color: #1f4eb6;--bs-btn-hover-border-color: transparent;--bs-btn-active-color: #1f4eb6;--bs-btn-active-border-color: transparent;--bs-btn-disabled-color: #6c757d;--bs-btn-disabled-border-color: transparent;--bs-btn-box-shadow: 0 0 0 #000;--bs-btn-focus-shadow-rgb: 71, 121, 231;text-decoration:underline;-webkit-text-decoration:underline;-moz-text-decoration:underline;-ms-text-decoration:underline;-o-text-decoration:underline}.btn-link:focus-visible{color:var(--bs-btn-color)}.btn-link:hover{color:var(--bs-btn-hover-color)}.btn-lg,.btn-group-lg>.btn{--bs-btn-padding-y: 0.5rem;--bs-btn-padding-x: 1rem;--bs-btn-font-size:1.25rem;--bs-btn-border-radius: 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auto}.btn-group>.btn-check:checked+.btn,.btn-group>.btn-check:focus+.btn,.btn-group>.btn:hover,.btn-group>.btn:focus,.btn-group>.btn:active,.btn-group>.btn.active,.btn-group-vertical>.btn-check:checked+.btn,.btn-group-vertical>.btn-check:focus+.btn,.btn-group-vertical>.btn:hover,.btn-group-vertical>.btn:focus,.btn-group-vertical>.btn:active,.btn-group-vertical>.btn.active{z-index:1}.btn-toolbar{display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;justify-content:flex-start;-webkit-justify-content:flex-start}.btn-toolbar .input-group{width:auto}.btn-group>:not(.btn-check:first-child)+.btn,.btn-group>.btn-group:not(:first-child){margin-left:calc(1px*-1)}.dropdown-toggle-split{padding-right:.5625rem;padding-left:.5625rem}.dropdown-toggle-split::after,.dropup .dropdown-toggle-split::after,.dropend .dropdown-toggle-split::after{margin-left:0}.dropstart .dropdown-toggle-split::before{margin-right:0}.btn-sm+.dropdown-toggle-split,.btn-group-sm>.btn+.dropdown-toggle-split{padding-right:.375rem;padding-left:.375rem}.btn-lg+.dropdown-toggle-split,.btn-group-lg>.btn+.dropdown-toggle-split{padding-right:.75rem;padding-left:.75rem}.btn-group-vertical{flex-direction:column;-webkit-flex-direction:column;align-items:flex-start;-webkit-align-items:flex-start;justify-content:center;-webkit-justify-content:center}.btn-group-vertical>.btn,.btn-group-vertical>.btn-group{width:100%}.btn-group-vertical>.btn:not(:first-child),.btn-group-vertical>.btn-group:not(:first-child){margin-top:calc(1px*-1)}.nav{--bs-nav-link-padding-x: 1rem;--bs-nav-link-padding-y: 0.5rem;--bs-nav-link-font-weight: ;--bs-nav-link-color: #2761e3;--bs-nav-link-hover-color: #1f4eb6;--bs-nav-link-disabled-color: rgba(52, 58, 64, 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var(--bs-navbar-hover-color);--bs-nav-link-disabled-color: var(--bs-navbar-disabled-color);display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;list-style:none}.navbar-nav .nav-link.active,.navbar-nav .nav-link.show{color:var(--bs-navbar-active-color)}.navbar-nav .dropdown-menu{position:static}.navbar-text{padding-top:.5rem;padding-bottom:.5rem;color:var(--bs-navbar-color)}.navbar-text a,.navbar-text a:hover,.navbar-text a:focus{color:var(--bs-navbar-active-color)}.navbar-collapse{flex-basis:100%;-webkit-flex-basis:100%;flex-grow:1;-webkit-flex-grow:1;align-items:center;-webkit-align-items:center}.navbar-toggler{padding:var(--bs-navbar-toggler-padding-y) var(--bs-navbar-toggler-padding-x);font-size:var(--bs-navbar-toggler-font-size);line-height:1;color:var(--bs-navbar-color);background-color:rgba(0,0,0,0);border:var(--bs-border-width) solid 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992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas 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.offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand .navbar-toggler{display:none}.navbar-expand .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand .offcanvas .offcanvas-header{display:none}.navbar-expand .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}.navbar-dark,.navbar[data-bs-theme=dark]{--bs-navbar-color: #fdfeff;--bs-navbar-hover-color: rgba(253, 253, 255, 0.8);--bs-navbar-disabled-color: rgba(253, 254, 255, 0.75);--bs-navbar-active-color: #fdfdff;--bs-navbar-brand-color: #fdfeff;--bs-navbar-brand-hover-color: #fdfdff;--bs-navbar-toggler-border-color: rgba(253, 254, 255, 0);--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 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0.75rem;position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card>hr{margin-right:0;margin-left:0}.card>.list-group{border-top:inherit;border-bottom:inherit}.card>.list-group:first-child{border-top-width:0}.card>.list-group:last-child{border-bottom-width:0}.card>.card-header+.list-group,.card>.list-group+.card-footer{border-top:0}.card-body{flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-card-spacer-y) var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header-tabs{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-bottom:calc(-1*var(--bs-card-cap-padding-y));margin-left:calc(-0.5*var(--bs-card-cap-padding-x));border-bottom:0}.card-header-tabs .nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}}.accordion{--bs-accordion-color: #343a40;--bs-accordion-bg: #fff;--bs-accordion-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, border-radius 0.15s ease;--bs-accordion-border-color: #dee2e6;--bs-accordion-border-width: 1px;--bs-accordion-border-radius: 0.25rem;--bs-accordion-inner-border-radius: calc(0.25rem - 1px);--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: #343a40;--bs-accordion-btn-bg: #fff;--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23343a40'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%2310335b'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #93c0f1;--bs-accordion-btn-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: #10335b;--bs-accordion-active-bg: #d4e6f9}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media(prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:not(:first-of-type){border-top:0}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237db3ee'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237db3ee'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: rgba(52, 58, 64, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(52, 58, 64, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #2761e3;--bs-pagination-bg: #fff;--bs-pagination-border-width: 1px;--bs-pagination-border-color: #dee2e6;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #1f4eb6;--bs-pagination-hover-bg: #f8f9fa;--bs-pagination-hover-border-color: #dee2e6;--bs-pagination-focus-color: #1f4eb6;--bs-pagination-focus-bg: #e9ecef;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #2780e3;--bs-pagination-active-border-color: #2780e3;--bs-pagination-disabled-color: rgba(52, 58, 64, 0.75);--bs-pagination-disabled-bg: #e9ecef;--bs-pagination-disabled-border-color: #dee2e6;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(1px*-1)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 0 solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:.5rem}}.progress,.progress-stacked{--bs-progress-height: 0.5rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #e9ecef;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #2780e3;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #343a40;--bs-list-group-bg: #fff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(52, 58, 64, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #f8f9fa;--bs-list-group-action-active-color: #343a40;--bs-list-group-action-active-bg: #e9ecef;--bs-list-group-disabled-color: rgba(52, 58, 64, 0.75);--bs-list-group-disabled-bg: #fff;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #2780e3;--bs-list-group-active-border-color: #2780e3;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.5;--bs-btn-close-hover-opacity: 0.75;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(255, 255, 255, 0.85);--bs-toast-border-width: 1px;--bs-toast-border-color: rgba(0, 0, 0, 0.175);--bs-toast-border-radius: 0.25rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(52, 58, 64, 0.75);--bs-toast-header-bg: rgba(255, 255, 255, 0.85);--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color)}.toast-header .btn-close{margin-right:calc(-0.5*var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: 0.5rem;--bs-modal-color: ;--bs-modal-bg: #fff;--bs-modal-border-color: rgba(0, 0, 0, 0.175);--bs-modal-border-width: 1px;--bs-modal-border-radius: 0.5rem;--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-modal-inner-border-radius: calc(0.5rem - 1px);--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: #dee2e6;--bs-modal-header-border-width: 1px;--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: #dee2e6;--bs-modal-footer-border-width: 1px;position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform .3s ease-out;transform:translate(0, -50px)}@media(prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin)*2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;min-height:calc(100% - var(--bs-modal-margin)*2)}.modal-content{position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;width:100%;color:var(--bs-modal-color);pointer-events:auto;background-color:var(--bs-modal-bg);background-clip:padding-box;border:var(--bs-modal-border-width) solid var(--bs-modal-border-color);outline:0}.modal-backdrop{--bs-backdrop-zindex: 1050;--bs-backdrop-bg: #000;--bs-backdrop-opacity: 0.5;position:fixed;top:0;left:0;z-index:var(--bs-backdrop-zindex);width:100vw;height:100vh;background-color:var(--bs-backdrop-bg)}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:var(--bs-backdrop-opacity)}.modal-header{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-modal-header-padding);border-bottom:var(--bs-modal-header-border-width) solid var(--bs-modal-header-border-color)}.modal-header .btn-close{padding:calc(var(--bs-modal-header-padding-y)*.5) calc(var(--bs-modal-header-padding-x)*.5);margin:calc(-0.5*var(--bs-modal-header-padding-y)) calc(-0.5*var(--bs-modal-header-padding-x)) calc(-0.5*var(--bs-modal-header-padding-y)) auto}.modal-title{margin-bottom:0;line-height:var(--bs-modal-title-line-height)}.modal-body{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-modal-padding)}.modal-footer{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:flex-end;-webkit-justify-content:flex-end;padding:calc(var(--bs-modal-padding) - var(--bs-modal-footer-gap)*.5);background-color:var(--bs-modal-footer-bg);border-top:var(--bs-modal-footer-border-width) solid var(--bs-modal-footer-border-color)}.modal-footer>*{margin:calc(var(--bs-modal-footer-gap)*.5)}@media(min-width: 576px){.modal{--bs-modal-margin: 1.75rem;--bs-modal-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15)}.modal-dialog{max-width:var(--bs-modal-width);margin-right:auto;margin-left:auto}.modal-sm{--bs-modal-width: 300px}}@media(min-width: 992px){.modal-lg,.modal-xl{--bs-modal-width: 800px}}@media(min-width: 1200px){.modal-xl{--bs-modal-width: 1140px}}.modal-fullscreen{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen .modal-content{height:100%;border:0}.modal-fullscreen .modal-body{overflow-y:auto}@media(max-width: 575.98px){.modal-fullscreen-sm-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-sm-down .modal-content{height:100%;border:0}.modal-fullscreen-sm-down .modal-body{overflow-y:auto}}@media(max-width: 767.98px){.modal-fullscreen-md-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-md-down .modal-content{height:100%;border:0}.modal-fullscreen-md-down .modal-body{overflow-y:auto}}@media(max-width: 991.98px){.modal-fullscreen-lg-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-lg-down .modal-content{height:100%;border:0}.modal-fullscreen-lg-down .modal-body{overflow-y:auto}}@media(max-width: 1199.98px){.modal-fullscreen-xl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xl-down .modal-content{height:100%;border:0}.modal-fullscreen-xl-down .modal-body{overflow-y:auto}}@media(max-width: 1399.98px){.modal-fullscreen-xxl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xxl-down .modal-content{height:100%;border:0}.modal-fullscreen-xxl-down .modal-body{overflow-y:auto}}.tooltip{--bs-tooltip-zindex: 1080;--bs-tooltip-max-width: 200px;--bs-tooltip-padding-x: 0.5rem;--bs-tooltip-padding-y: 0.25rem;--bs-tooltip-margin: ;--bs-tooltip-font-size:0.875rem;--bs-tooltip-color: #fff;--bs-tooltip-bg: #000;--bs-tooltip-border-radius: 0.25rem;--bs-tooltip-opacity: 0.9;--bs-tooltip-arrow-width: 0.8rem;--bs-tooltip-arrow-height: 0.4rem;z-index:var(--bs-tooltip-zindex);display:block;margin:var(--bs-tooltip-margin);font-family:"Source Sans Pro",-apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,"Helvetica Neue",Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI 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;--quarto-scss-export-btn-white-space: ;--quarto-scss-export-btn-link-color: #2761e3;--quarto-scss-export-btn-link-hover-color: #1f4eb6;--quarto-scss-export-btn-link-disabled-color: #6c757d;--quarto-scss-export-form-text-font-style: ;--quarto-scss-export-form-text-font-weight: ;--quarto-scss-export-form-text-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-form-label-font-size: ;--quarto-scss-export-form-label-font-style: ;--quarto-scss-export-form-label-font-weight: ;--quarto-scss-export-form-label-color: ;--quarto-scss-export-input-font-family: ;--quarto-scss-export-input-disabled-color: ;--quarto-scss-export-input-disabled-bg: #e9ecef;--quarto-scss-export-input-disabled-border-color: ;--quarto-scss-export-input-color: #343a40;--quarto-scss-export-input-focus-bg: #fff;--quarto-scss-export-input-focus-border-color: #93c0f1;--quarto-scss-export-input-focus-color: #343a40;--quarto-scss-export-input-placeholder-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-input-plaintext-color: #343a40;--quarto-scss-export-form-check-label-color: ;--quarto-scss-export-form-check-transition: ;--quarto-scss-export-form-check-input-bg: #fff;--quarto-scss-export-form-check-input-focus-border: #93c0f1;--quarto-scss-export-form-check-input-checked-color: #fff;--quarto-scss-export-form-check-input-checked-bg-color: #2780e3;--quarto-scss-export-form-check-input-checked-border-color: #2780e3;--quarto-scss-export-form-check-input-indeterminate-color: #fff;--quarto-scss-export-form-check-input-indeterminate-bg-color: #2780e3;--quarto-scss-export-form-check-input-indeterminate-border-color: #2780e3;--quarto-scss-export-form-switch-color: rgba(0, 0, 0, 0.25);--quarto-scss-export-form-switch-focus-color: #93c0f1;--quarto-scss-export-form-switch-checked-color: #fff;--quarto-scss-export-input-group-addon-color: #343a40;--quarto-scss-export-input-group-addon-bg: #f8f9fa;--quarto-scss-export-input-group-addon-border-color: #dee2e6;--quarto-scss-export-form-select-font-family: ;--quarto-scss-export-form-select-color: #343a40;--quarto-scss-export-form-select-bg: #fff;--quarto-scss-export-form-select-disabled-color: ;--quarto-scss-export-form-select-disabled-bg: #e9ecef;--quarto-scss-export-form-select-disabled-border-color: ;--quarto-scss-export-form-select-indicator-color: #343a40;--quarto-scss-export-form-select-border-color: #dee2e6;--quarto-scss-export-form-select-focus-border-color: #93c0f1;--quarto-scss-export-form-range-track-bg: #f8f9fa;--quarto-scss-export-form-range-thumb-bg: #2780e3;--quarto-scss-export-form-range-thumb-active-bg: #bed9f7;--quarto-scss-export-form-range-thumb-disabled-bg: rgba(52, 58, 64, 0.75);--quarto-scss-export-form-file-button-color: #343a40;--quarto-scss-export-form-file-button-bg: #f8f9fa;--quarto-scss-export-form-file-button-hover-bg: #e9ecef;--quarto-scss-export-form-floating-label-disabled-color: #6c757d;--quarto-scss-export-form-feedback-font-style: ;--quarto-scss-export-form-feedback-valid-color: #3fb618;--quarto-scss-export-form-feedback-invalid-color: #ff0039;--quarto-scss-export-form-feedback-icon-valid-color: #3fb618;--quarto-scss-export-form-feedback-icon-invalid-color: #ff0039;--quarto-scss-export-form-valid-color: #3fb618;--quarto-scss-export-form-valid-border-color: #3fb618;--quarto-scss-export-form-invalid-color: #ff0039;--quarto-scss-export-form-invalid-border-color: #ff0039;--quarto-scss-export-nav-link-font-size: ;--quarto-scss-export-nav-link-font-weight: ;--quarto-scss-export-nav-link-color: #2761e3;--quarto-scss-export-nav-link-hover-color: #1f4eb6;--quarto-scss-export-nav-link-disabled-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-nav-tabs-border-color: #dee2e6;--quarto-scss-export-nav-tabs-link-hover-border-color: #e9ecef #e9ecef #dee2e6;--quarto-scss-export-nav-tabs-link-active-color: #000;--quarto-scss-export-nav-tabs-link-active-bg: #fff;--quarto-scss-export-nav-pills-link-active-bg: #2780e3;--quarto-scss-export-nav-pills-link-active-color: #fff;--quarto-scss-export-nav-underline-link-active-color: #000;--quarto-scss-export-navbar-padding-x: ;--quarto-scss-export-navbar-light-contrast: #fff;--quarto-scss-export-navbar-dark-contrast: #fff;--quarto-scss-export-navbar-light-icon-color: rgba(255, 255, 255, 0.75);--quarto-scss-export-navbar-dark-icon-color: rgba(255, 255, 255, 0.75);--quarto-scss-export-dropdown-color: #343a40;--quarto-scss-export-dropdown-bg: #fff;--quarto-scss-export-dropdown-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-divider-bg: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-link-color: #343a40;--quarto-scss-export-dropdown-link-hover-color: #343a40;--quarto-scss-export-dropdown-link-hover-bg: #f8f9fa;--quarto-scss-export-dropdown-link-active-bg: #2780e3;--quarto-scss-export-dropdown-link-active-color: #fff;--quarto-scss-export-dropdown-link-disabled-color: rgba(52, 58, 64, 0.5);--quarto-scss-export-dropdown-header-color: #6c757d;--quarto-scss-export-dropdown-dark-color: #dee2e6;--quarto-scss-export-dropdown-dark-bg: #343a40;--quarto-scss-export-dropdown-dark-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-dark-divider-bg: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-dark-box-shadow: ;--quarto-scss-export-dropdown-dark-link-color: #dee2e6;--quarto-scss-export-dropdown-dark-link-hover-color: #fff;--quarto-scss-export-dropdown-dark-link-hover-bg: rgba(255, 255, 255, 0.15);--quarto-scss-export-dropdown-dark-link-active-color: #fff;--quarto-scss-export-dropdown-dark-link-active-bg: #2780e3;--quarto-scss-export-dropdown-dark-link-disabled-color: #adb5bd;--quarto-scss-export-dropdown-dark-header-color: #adb5bd;--quarto-scss-export-pagination-color: #2761e3;--quarto-scss-export-pagination-bg: #fff;--quarto-scss-export-pagination-border-color: #dee2e6;--quarto-scss-export-pagination-focus-color: #1f4eb6;--quarto-scss-export-pagination-focus-bg: #e9ecef;--quarto-scss-export-pagination-hover-color: #1f4eb6;--quarto-scss-export-pagination-hover-bg: #f8f9fa;--quarto-scss-export-pagination-hover-border-color: #dee2e6;--quarto-scss-export-pagination-active-color: #fff;--quarto-scss-export-pagination-active-bg: #2780e3;--quarto-scss-export-pagination-active-border-color: #2780e3;--quarto-scss-export-pagination-disabled-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-pagination-disabled-bg: #e9ecef;--quarto-scss-export-pagination-disabled-border-color: #dee2e6;--quarto-scss-export-card-title-color: ;--quarto-scss-export-card-subtitle-color: ;--quarto-scss-export-card-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-card-box-shadow: ;--quarto-scss-export-card-cap-color: ;--quarto-scss-export-card-height: ;--quarto-scss-export-card-color: ;--quarto-scss-export-card-bg: #fff;--quarto-scss-export-accordion-color: #343a40;--quarto-scss-export-accordion-bg: #fff;--quarto-scss-export-accordion-border-color: #dee2e6;--quarto-scss-export-accordion-button-color: #343a40;--quarto-scss-export-accordion-button-bg: #fff;--quarto-scss-export-accordion-button-active-bg: #d4e6f9;--quarto-scss-export-accordion-button-active-color: #10335b;--quarto-scss-export-accordion-button-focus-border-color: #93c0f1;--quarto-scss-export-accordion-icon-color: #343a40;--quarto-scss-export-accordion-icon-active-color: #10335b;--quarto-scss-export-tooltip-color: #fff;--quarto-scss-export-tooltip-bg: #000;--quarto-scss-export-tooltip-margin: ;--quarto-scss-export-tooltip-arrow-color: ;--quarto-scss-export-form-feedback-tooltip-line-height: ;--quarto-scss-export-popover-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-popover-header-bg: #e9ecef;--quarto-scss-export-popover-body-color: #343a40;--quarto-scss-export-popover-arrow-color: #fff;--quarto-scss-export-popover-arrow-outer-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-toast-color: ;--quarto-scss-export-toast-background-color: rgba(255, 255, 255, 0.85);--quarto-scss-export-toast-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-toast-header-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-toast-header-background-color: rgba(255, 255, 255, 0.85);--quarto-scss-export-toast-header-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-badge-color: #fff;--quarto-scss-export-modal-content-color: ;--quarto-scss-export-modal-content-bg: #fff;--quarto-scss-export-modal-content-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-modal-backdrop-bg: #000;--quarto-scss-export-modal-header-border-color: #dee2e6;--quarto-scss-export-modal-footer-bg: ;--quarto-scss-export-modal-footer-border-color: #dee2e6;--quarto-scss-export-progress-bg: #e9ecef;--quarto-scss-export-progress-bar-color: #fff;--quarto-scss-export-progress-bar-bg: #2780e3;--quarto-scss-export-list-group-color: #343a40;--quarto-scss-export-list-group-bg: #fff;--quarto-scss-export-list-group-border-color: #dee2e6;--quarto-scss-export-list-group-hover-bg: #f8f9fa;--quarto-scss-export-list-group-active-bg: #2780e3;--quarto-scss-export-list-group-active-color: #fff;--quarto-scss-export-list-group-active-border-color: #2780e3;--quarto-scss-export-list-group-disabled-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-list-group-disabled-bg: #fff;--quarto-scss-export-list-group-action-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-list-group-action-hover-color: #000;--quarto-scss-export-list-group-action-active-color: #343a40;--quarto-scss-export-list-group-action-active-bg: #e9ecef;--quarto-scss-export-thumbnail-bg: #fff;--quarto-scss-export-thumbnail-border-color: #dee2e6;--quarto-scss-export-figure-caption-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-breadcrumb-font-size: ;--quarto-scss-export-breadcrumb-bg: ;--quarto-scss-export-breadcrumb-divider-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-breadcrumb-active-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-breadcrumb-border-radius: ;--quarto-scss-export-carousel-control-color: #fff;--quarto-scss-export-carousel-indicator-active-bg: #fff;--quarto-scss-export-carousel-caption-color: #fff;--quarto-scss-export-carousel-dark-indicator-active-bg: #000;--quarto-scss-export-carousel-dark-caption-color: #000;--quarto-scss-export-btn-close-color: #000;--quarto-scss-export-offcanvas-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-offcanvas-bg-color: #fff;--quarto-scss-export-offcanvas-color: #343a40;--quarto-scss-export-offcanvas-backdrop-bg: #000;--quarto-scss-export-code-color-dark: white;--quarto-scss-export-kbd-color: #fff;--quarto-scss-export-kbd-bg: #343a40;--quarto-scss-export-nested-kbd-font-weight: ;--quarto-scss-export-pre-bg: #f8f9fa;--quarto-scss-export-pre-color: #000;--quarto-scss-export-bslib-page-sidebar-title-bg: #2780e3;--quarto-scss-export-bslib-page-sidebar-title-color: #fff;--quarto-scss-export-bslib-sidebar-bg: rgba(var(--bs-emphasis-color-rgb, 0, 0, 0), 0.05);--quarto-scss-export-bslib-sidebar-toggle-bg: rgba(var(--bs-emphasis-color-rgb, 0, 0, 0), 0.1);--quarto-scss-export-sidebar-color: #595959;--quarto-scss-export-sidebar-hover-color: rgba(33, 81, 191, 0.8);--quarto-scss-export-sidebar-disabled-color: rgba(89, 89, 89, 0.75);--quarto-scss-export-valuebox-bg-primary: #5397e9;--quarto-scss-export-valuebox-bg-secondary: #343a40;--quarto-scss-export-valuebox-bg-success: #3aa716;--quarto-scss-export-valuebox-bg-info: rgba(153, 84, 187, 0.7019607843);--quarto-scss-export-valuebox-bg-warning: #fa6400;--quarto-scss-export-valuebox-bg-danger: rgba(255, 0, 57, 0.7019607843);--quarto-scss-export-valuebox-bg-light: #f8f9fa;--quarto-scss-export-valuebox-bg-dark: #343a40;--quarto-scss-export-mermaid-bg-color: #fff;--quarto-scss-export-mermaid-edge-color: #343a40;--quarto-scss-export-mermaid-node-fg-color: #343a40;--quarto-scss-export-mermaid-fg-color: #343a40;--quarto-scss-export-mermaid-fg-color--lighter: #4b545c;--quarto-scss-export-mermaid-fg-color--lightest: #626d78;--quarto-scss-export-mermaid-label-bg-color: #fff;--quarto-scss-export-mermaid-label-fg-color: #2780e3;--quarto-scss-export-mermaid-node-bg-color: rgba(39, 128, 227, 0.1);--quarto-scss-export-code-block-border-left-color: #dee2e6;--quarto-scss-export-callout-color-note: #2780e3;--quarto-scss-export-callout-color-tip: #3fb618;--quarto-scss-export-callout-color-important: #ff0039;--quarto-scss-export-callout-color-caution: #f0ad4e;--quarto-scss-export-callout-color-warning: #ff7518} \ No newline at end of file diff --git a/_docs/site_libs/bootstrap/bootstrap-icons.css b/_docs/site_libs/bootstrap/bootstrap-icons.css new file mode 100644 index 0000000..285e444 --- /dev/null +++ b/_docs/site_libs/bootstrap/bootstrap-icons.css @@ -0,0 +1,2078 @@ +/*! + * Bootstrap Icons v1.11.1 (https://icons.getbootstrap.com/) + * Copyright 2019-2023 The Bootstrap Authors + * Licensed under MIT (https://github.com/twbs/icons/blob/main/LICENSE) + */ + +@font-face { + font-display: block; + font-family: "bootstrap-icons"; + src: +url("./bootstrap-icons.woff?2820a3852bdb9a5832199cc61cec4e65") format("woff"); +} + +.bi::before, +[class^="bi-"]::before, +[class*=" bi-"]::before { + display: inline-block; + font-family: bootstrap-icons !important; + font-style: normal; + font-weight: normal !important; + font-variant: normal; + text-transform: none; + line-height: 1; + vertical-align: -.125em; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; +} + +.bi-123::before { content: "\f67f"; } +.bi-alarm-fill::before { content: "\f101"; } +.bi-alarm::before { content: "\f102"; } +.bi-align-bottom::before { content: "\f103"; } +.bi-align-center::before { content: "\f104"; } +.bi-align-end::before { content: "\f105"; } +.bi-align-middle::before { content: "\f106"; } +.bi-align-start::before { content: "\f107"; } +.bi-align-top::before { content: "\f108"; } +.bi-alt::before { content: "\f109"; } +.bi-app-indicator::before { content: "\f10a"; } +.bi-app::before { content: "\f10b"; } +.bi-archive-fill::before { content: "\f10c"; } +.bi-archive::before { content: "\f10d"; } +.bi-arrow-90deg-down::before { content: "\f10e"; } +.bi-arrow-90deg-left::before { content: "\f10f"; } +.bi-arrow-90deg-right::before { content: "\f110"; } +.bi-arrow-90deg-up::before { content: "\f111"; } +.bi-arrow-bar-down::before { content: "\f112"; } +.bi-arrow-bar-left::before { content: "\f113"; } +.bi-arrow-bar-right::before { content: "\f114"; } +.bi-arrow-bar-up::before { content: "\f115"; } +.bi-arrow-clockwise::before { content: "\f116"; } +.bi-arrow-counterclockwise::before { content: "\f117"; } +.bi-arrow-down-circle-fill::before { content: "\f118"; } +.bi-arrow-down-circle::before { content: "\f119"; } +.bi-arrow-down-left-circle-fill::before { content: "\f11a"; } +.bi-arrow-down-left-circle::before { content: "\f11b"; } +.bi-arrow-down-left-square-fill::before { content: "\f11c"; } +.bi-arrow-down-left-square::before { content: "\f11d"; } +.bi-arrow-down-left::before { content: "\f11e"; } +.bi-arrow-down-right-circle-fill::before { content: "\f11f"; } +.bi-arrow-down-right-circle::before { content: "\f120"; } +.bi-arrow-down-right-square-fill::before { content: "\f121"; } +.bi-arrow-down-right-square::before { content: "\f122"; } +.bi-arrow-down-right::before { content: "\f123"; } +.bi-arrow-down-short::before { content: "\f124"; } +.bi-arrow-down-square-fill::before { content: "\f125"; } +.bi-arrow-down-square::before { content: "\f126"; } +.bi-arrow-down-up::before { content: "\f127"; } +.bi-arrow-down::before { content: "\f128"; } +.bi-arrow-left-circle-fill::before { content: "\f129"; } +.bi-arrow-left-circle::before { content: "\f12a"; } +.bi-arrow-left-right::before { content: "\f12b"; } +.bi-arrow-left-short::before { content: "\f12c"; } +.bi-arrow-left-square-fill::before { content: "\f12d"; } +.bi-arrow-left-square::before { content: "\f12e"; } +.bi-arrow-left::before { content: "\f12f"; } +.bi-arrow-repeat::before { content: "\f130"; } 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this._getItems().indexOf(t)}_setActiveIndicatorElement(t){if(!this._indicatorsElement)return;const e=z.findOne(wt,this._indicatorsElement);e.classList.remove(yt),e.removeAttribute("aria-current");const i=z.findOne(`[data-bs-slide-to="${t}"]`,this._indicatorsElement);i&&(i.classList.add(yt),i.setAttribute("aria-current","true"))}_updateInterval(){const t=this._activeElement||this._getActive();if(!t)return;const e=Number.parseInt(t.getAttribute("data-bs-interval"),10);this._config.interval=e||this._config.defaultInterval}_slide(t,e=null){if(this._isSliding)return;const i=this._getActive(),n=t===at,s=e||b(this._getItems(),i,n,this._config.wrap);if(s===i)return;const o=this._getItemIndex(s),r=e=>N.trigger(this._element,e,{relatedTarget:s,direction:this._orderToDirection(t),from:this._getItemIndex(i),to:o});if(r(dt).defaultPrevented)return;if(!i||!s)return;const a=Boolean(this._interval);this.pause(),this._isSliding=!0,this._setActiveIndicatorElement(o),this._activeElement=s;const 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Object.keys(f).sort((function(t,e){return f[t]-f[e]}))}const si={name:"flip",enabled:!0,phase:"main",fn:function(t){var e=t.state,i=t.options,n=t.name;if(!e.modifiersData[n]._skip){for(var s=i.mainAxis,o=void 0===s||s,r=i.altAxis,a=void 0===r||r,l=i.fallbackPlacements,c=i.padding,h=i.boundary,d=i.rootBoundary,u=i.altBoundary,f=i.flipVariations,p=void 0===f||f,m=i.allowedAutoPlacements,g=e.options.placement,_=be(g),b=l||(_!==g&&p?function(t){if(be(t)===Kt)return[];var e=Ve(t);return[Qe(t),e,Qe(e)]}(g):[Ve(g)]),v=[g].concat(b).reduce((function(t,i){return t.concat(be(i)===Kt?ni(e,{placement:i,boundary:h,rootBoundary:d,padding:c,flipVariations:p,allowedAutoPlacements:m}):i)}),[]),y=e.rects.reference,w=e.rects.popper,A=new Map,E=!0,T=v[0],C=0;C=0,S=L?"width":"height",D=ii(e,{placement:O,boundary:h,rootBoundary:d,altBoundary:u,padding:c}),$=L?k?qt:Vt:k?Rt:zt;y[S]>w[S]&&($=Ve($));var I=Ve($),N=[];if(o&&N.push(D[x]<=0),a&&N.push(D[$]<=0,D[I]<=0),N.every((function(t){return t}))){T=O,E=!1;break}A.set(O,N)}if(E)for(var P=function(t){var e=v.find((function(e){var i=A.get(e);if(i)return i.slice(0,t).every((function(t){return t}))}));if(e)return T=e,"break"},M=p?3:1;M>0&&"break"!==P(M);M--);e.placement!==T&&(e.modifiersData[n]._skip=!0,e.placement=T,e.reset=!0)}},requiresIfExists:["offset"],data:{_skip:!1}};function oi(t,e,i){return void 0===i&&(i={x:0,y:0}),{top:t.top-e.height-i.y,right:t.right-e.width+i.x,bottom:t.bottom-e.height+i.y,left:t.left-e.width-i.x}}function ri(t){return[zt,qt,Rt,Vt].some((function(e){return t[e]>=0}))}const ai={name:"hide",enabled:!0,phase:"main",requiresIfExists:["preventOverflow"],fn:function(t){var 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m?m(Object.assign({},e.rects,{placement:e.placement})):m,O="number"==typeof C?{mainAxis:C,altAxis:C}:Object.assign({mainAxis:0,altAxis:0},C),x=e.modifiersData.offset?e.modifiersData.offset[e.placement]:null,k={x:0,y:0};if(A){if(o){var L,S="y"===y?zt:Vt,D="y"===y?Rt:qt,$="y"===y?"height":"width",I=A[y],N=I+g[S],P=I-g[D],M=f?-T[$]/2:0,j=b===Xt?E[$]:T[$],F=b===Xt?-T[$]:-E[$],H=e.elements.arrow,W=f&&H?Ce(H):{width:0,height:0},B=e.modifiersData["arrow#persistent"]?e.modifiersData["arrow#persistent"].padding:{top:0,right:0,bottom:0,left:0},z=B[S],R=B[D],q=Ne(0,E[$],W[$]),V=v?E[$]/2-M-q-z-O.mainAxis:j-q-z-O.mainAxis,K=v?-E[$]/2+M+q+R+O.mainAxis:F+q+R+O.mainAxis,Q=e.elements.arrow&&$e(e.elements.arrow),X=Q?"y"===y?Q.clientTop||0:Q.clientLeft||0:0,Y=null!=(L=null==x?void 0:x[y])?L:0,U=I+K-Y,G=Ne(f?ye(N,I+V-Y-X):N,I,f?ve(P,U):P);A[y]=G,k[y]=G-I}if(a){var J,Z="x"===y?zt:Vt,tt="x"===y?Rt:qt,et=A[w],it="y"===w?"height":"width",nt=et+g[Z],st=et-g[tt],ot=-1!==[zt,Vt].indexOf(_),rt=null!=(J=null==x?void 0:x[w])?J:0,at=ot?nt:et-E[it]-T[it]-rt+O.altAxis,lt=ot?et+E[it]+T[it]-rt-O.altAxis:st,ct=f&&ot?function(t,e,i){var n=Ne(t,e,i);return n>i?i:n}(at,et,lt):Ne(f?at:nt,et,f?lt:st);A[w]=ct,k[w]=ct-et}e.modifiersData[n]=k}},requiresIfExists:["offset"]};function di(t,e,i){void 0===i&&(i=!1);var n,s,o=me(e),r=me(e)&&function(t){var e=t.getBoundingClientRect(),i=we(e.width)/t.offsetWidth||1,n=we(e.height)/t.offsetHeight||1;return 1!==i||1!==n}(e),a=Le(e),l=Te(t,r,i),c={scrollLeft:0,scrollTop:0},h={x:0,y:0};return(o||!o&&!i)&&(("body"!==ue(e)||Ue(a))&&(c=(n=e)!==fe(n)&&me(n)?{scrollLeft:(s=n).scrollLeft,scrollTop:s.scrollTop}:Xe(n)),me(e)?((h=Te(e,!0)).x+=e.clientLeft,h.y+=e.clientTop):a&&(h.x=Ye(a))),{x:l.left+c.scrollLeft-h.x,y:l.top+c.scrollTop-h.y,width:l.width,height:l.height}}function ui(t){var e=new Map,i=new Set,n=[];function s(t){i.add(t.name),[].concat(t.requires||[],t.requiresIfExists||[]).forEach((function(t){if(!i.has(t)){var n=e.get(t);n&&s(n)}})),n.push(t)}return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||s(t)})),n}var fi={placement:"bottom",modifiers:[],strategy:"absolute"};function pi(){for(var t=arguments.length,e=new Array(t),i=0;iNumber.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(F.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,...g(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=z.find(".dropdown-menu 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e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ei,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ii)?this:z.prev(this,Ii)[0]||z.next(this,Ii)[0]||z.findOne(Ii,t.delegateTarget.parentNode),o=qi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}N.on(document,Si,Ii,qi.dataApiKeydownHandler),N.on(document,Si,Pi,qi.dataApiKeydownHandler),N.on(document,Li,qi.clearMenus),N.on(document,Di,qi.clearMenus),N.on(document,Li,Ii,(function(t){t.preventDefault(),qi.getOrCreateInstance(this).toggle()})),m(qi);const Vi="backdrop",Ki="show",Qi=`mousedown.bs.${Vi}`,Xi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Yi={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Ui extends H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}N.on(document,yn,'[data-bs-toggle="modal"]',(function(t){const e=z.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),N.one(e,pn,(t=>{t.defaultPrevented||N.one(e,fn,(()=>{a(this)&&this.focus()}))}));const i=z.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),R(On),m(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Wn=`click${xn}${kn}`,Bn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"
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")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),N.off(this._element.closest(is),ns,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=N.trigger(this._element,this.constructor.eventName("show")),e=(c(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),N.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ts),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Jn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ts)}_isShown(){return this.tip&&this.tip.classList.contains(es)}_createPopper(t){const e=g(this._config.placement,[this,t,this._element]),i=rs[e.toUpperCase()];return bi(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return g(t,[this._element])}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,...g(this._config.popperConfig,[e])}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)N.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if("manual"!==e){const t=e===ss?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i=e===ss?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");N.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?os:ss]=!0,e._enter()})),N.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?os:ss]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},N.on(this._element.closest(is),ns,this._hideModalHandler)}_fixTitle(){const t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=F.getDataAttributes(this._element);for(const t of Object.keys(e))Zn.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const[e,i]of Object.entries(this._config))this.constructor.Default[e]!==i&&(t[e]=i);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=cs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}m(cs);const hs={...cs.Default,content:"",offset:[0,8],placement:"right",template:'',trigger:"click"},ds={...cs.DefaultType,content:"(null|string|element|function)"};class us extends cs{static get Default(){return hs}static get DefaultType(){return ds}static get 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e=this._observableSections.get(t.target.hash);if(e){t.preventDefault();const i=this._rootElement||window,n=e.offsetTop-this._element.offsetTop;if(i.scrollTo)return void i.scrollTo({top:n,behavior:"smooth"});i.scrollTop=n}})))}_getNewObserver(){const t={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver((t=>this._observerCallback(t)),t)}_observerCallback(t){const e=t=>this._targetLinks.get(`#${t.target.id}`),i=t=>{this._previousScrollData.visibleEntryTop=t.target.offsetTop,this._process(e(t))},n=(this._rootElement||document.documentElement).scrollTop,s=n>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=n;for(const o of t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=z.find(bs,this._config.target);for(const e of t){if(!e.hash||l(e))continue;const t=z.findOne(decodeURI(e.hash),this._element);a(t)&&(this._targetLinks.set(decodeURI(e.hash),e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add(_s),this._activateParents(t),N.trigger(this._element,ps,{relatedTarget:t}))}_activateParents(t){if(t.classList.contains("dropdown-item"))z.findOne(".dropdown-toggle",t.closest(".dropdown")).classList.add(_s);else for(const e of z.parents(t,".nav, .list-group"))for(const t of z.prev(e,ys))t.classList.add(_s)}_clearActiveClass(t){t.classList.remove(_s);const e=z.find(`${bs}.${_s}`,t);for(const t of e)t.classList.remove(_s)}static jQueryInterface(t){return this.each((function(){const e=Es.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}N.on(window,gs,(()=>{for(const t of z.find('[data-bs-spy="scroll"]'))Es.getOrCreateInstance(t)})),m(Es);const Ts=".bs.tab",Cs=`hide${Ts}`,Os=`hidden${Ts}`,xs=`show${Ts}`,ks=`shown${Ts}`,Ls=`click${Ts}`,Ss=`keydown${Ts}`,Ds=`load${Ts}`,$s="ArrowLeft",Is="ArrowRight",Ns="ArrowUp",Ps="ArrowDown",Ms="Home",js="End",Fs="active",Hs="fade",Ws="show",Bs=":not(.dropdown-toggle)",zs='[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',Rs=`.nav-link${Bs}, .list-group-item${Bs}, [role="tab"]${Bs}, ${zs}`,qs=`.${Fs}[data-bs-toggle="tab"], .${Fs}[data-bs-toggle="pill"], .${Fs}[data-bs-toggle="list"]`;class Vs extends W{constructor(t){super(t),this._parent=this._element.closest('.list-group, .nav, [role="tablist"]'),this._parent&&(this._setInitialAttributes(this._parent,this._getChildren()),N.on(this._element,Ss,(t=>this._keydown(t))))}static get NAME(){return"tab"}show(){const t=this._element;if(this._elemIsActive(t))return;const e=this._getActiveElem(),i=e?N.trigger(e,Cs,{relatedTarget:t}):null;N.trigger(t,xs,{relatedTarget:e}).defaultPrevented||i&&i.defaultPrevented||(this._deactivate(e,t),this._activate(t,e))}_activate(t,e){t&&(t.classList.add(Fs),this._activate(z.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.removeAttribute("tabindex"),t.setAttribute("aria-selected",!0),this._toggleDropDown(t,!0),N.trigger(t,ks,{relatedTarget:e})):t.classList.add(Ws)}),t,t.classList.contains(Hs)))}_deactivate(t,e){t&&(t.classList.remove(Fs),t.blur(),this._deactivate(z.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.setAttribute("aria-selected",!1),t.setAttribute("tabindex","-1"),this._toggleDropDown(t,!1),N.trigger(t,Os,{relatedTarget:e})):t.classList.remove(Ws)}),t,t.classList.contains(Hs)))}_keydown(t){if(![$s,Is,Ns,Ps,Ms,js].includes(t.key))return;t.stopPropagation(),t.preventDefault();const e=this._getChildren().filter((t=>!l(t)));let i;if([Ms,js].includes(t.key))i=e[t.key===Ms?0:e.length-1];else{const n=[Is,Ps].includes(t.key);i=b(e,t.target,n,!0)}i&&(i.focus({preventScroll:!0}),Vs.getOrCreateInstance(i).show())}_getChildren(){return z.find(Rs,this._parent)}_getActiveElem(){return this._getChildren().find((t=>this._elemIsActive(t)))||null}_setInitialAttributes(t,e){this._setAttributeIfNotExists(t,"role","tablist");for(const t of e)this._setInitialAttributesOnChild(t)}_setInitialAttributesOnChild(t){t=this._getInnerElement(t);const e=this._elemIsActive(t),i=this._getOuterElement(t);t.setAttribute("aria-selected",e),i!==t&&this._setAttributeIfNotExists(i,"role","presentation"),e||t.setAttribute("tabindex","-1"),this._setAttributeIfNotExists(t,"role","tab"),this._setInitialAttributesOnTargetPanel(t)}_setInitialAttributesOnTargetPanel(t){const 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t=e.state,n=e.name,r=t.rects.reference,o=t.rects.popper,i=t.modifiersData.preventOverflow,a=J(t,{elementContext:"reference"}),s=J(t,{altBoundary:!0}),f=ve(a,r),c=ve(s,o,i),p=ye(f),u=ye(c);t.modifiersData[n]={referenceClippingOffsets:f,popperEscapeOffsets:c,isReferenceHidden:p,hasPopperEscaped:u},t.attributes.popper=Object.assign({},t.attributes.popper,{"data-popper-reference-hidden":p,"data-popper-escaped":u})}},be=Z({defaultModifiers:[ee,te,oe,ie]}),xe=[ee,te,oe,ie,ae,le,he,me,ge],we=Z({defaultModifiers:xe});e.applyStyles=ie,e.arrow=me,e.computeStyles=oe,e.createPopper=we,e.createPopperLite=be,e.defaultModifiers=xe,e.detectOverflow=J,e.eventListeners=ee,e.flip=le,e.hide=ge,e.offset=ae,e.popperGenerator=Z,e.popperOffsets=te,e.preventOverflow=he,Object.defineProperty(e,"__esModule",{value:!0})})); + diff --git a/_docs/site_libs/quarto-html/quarto-syntax-highlighting-2f5df379a58b258e96c21c0638c20c03.css b/_docs/site_libs/quarto-html/quarto-syntax-highlighting-2f5df379a58b258e96c21c0638c20c03.css new file mode 100644 index 0000000..48bb62a --- /dev/null +++ b/_docs/site_libs/quarto-html/quarto-syntax-highlighting-2f5df379a58b258e96c21c0638c20c03.css @@ -0,0 +1,205 @@ +/* quarto syntax highlight colors */ +:root { + --quarto-hl-ot-color: #003B4F; + --quarto-hl-at-color: #657422; + --quarto-hl-ss-color: #20794D; + --quarto-hl-an-color: #5E5E5E; + --quarto-hl-fu-color: #4758AB; + --quarto-hl-st-color: #20794D; + --quarto-hl-cf-color: #003B4F; + --quarto-hl-op-color: #5E5E5E; + --quarto-hl-er-color: #AD0000; + --quarto-hl-bn-color: #AD0000; + --quarto-hl-al-color: #AD0000; + --quarto-hl-va-color: #111111; + --quarto-hl-bu-color: inherit; + --quarto-hl-ex-color: inherit; + --quarto-hl-pp-color: #AD0000; + --quarto-hl-in-color: #5E5E5E; + --quarto-hl-vs-color: #20794D; + --quarto-hl-wa-color: #5E5E5E; + --quarto-hl-do-color: #5E5E5E; + --quarto-hl-im-color: #00769E; + --quarto-hl-ch-color: #20794D; + --quarto-hl-dt-color: #AD0000; + --quarto-hl-fl-color: #AD0000; + --quarto-hl-co-color: #5E5E5E; + --quarto-hl-cv-color: #5E5E5E; + --quarto-hl-cn-color: #8f5902; + --quarto-hl-sc-color: #5E5E5E; + --quarto-hl-dv-color: #AD0000; + --quarto-hl-kw-color: #003B4F; +} + +/* other quarto variables */ +:root { + --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; +} + +pre > code.sourceCode > span { + color: #003B4F; +} + +code span { + color: #003B4F; +} + +code.sourceCode > span { + color: #003B4F; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #003B4F; +} + +code span.ot { + color: #003B4F; + font-style: inherit; +} + +code span.at { + color: #657422; + font-style: inherit; +} + +code span.ss { + color: #20794D; + font-style: inherit; +} + +code span.an { + color: #5E5E5E; + font-style: inherit; +} + +code span.fu { + color: #4758AB; + font-style: inherit; +} + +code span.st { + color: #20794D; + font-style: inherit; +} + +code span.cf { + color: #003B4F; + font-weight: bold; + font-style: inherit; +} + +code span.op { + color: #5E5E5E; + font-style: inherit; +} + +code span.er { + color: #AD0000; + font-style: inherit; +} + +code span.bn { + color: #AD0000; + font-style: inherit; +} + +code span.al { + color: #AD0000; + font-style: inherit; +} + +code span.va { + color: #111111; + font-style: inherit; +} + +code span.bu { + font-style: inherit; +} + +code span.ex { + font-style: inherit; +} + +code span.pp { + color: #AD0000; + font-style: inherit; +} + +code span.in { + color: #5E5E5E; + font-style: inherit; +} + +code span.vs { + color: #20794D; + font-style: inherit; +} + +code span.wa { + color: #5E5E5E; + font-style: italic; +} + +code span.do { + color: #5E5E5E; + font-style: italic; +} + +code span.im { + color: #00769E; + font-style: inherit; +} + +code span.ch { + color: #20794D; + font-style: inherit; +} + +code span.dt { + color: #AD0000; + font-style: inherit; +} + +code span.fl { + color: #AD0000; + font-style: inherit; +} + +code span.co { + color: #5E5E5E; + font-style: inherit; +} + +code span.cv { + color: #5E5E5E; + font-style: italic; +} + +code span.cn { + color: #8f5902; + font-style: inherit; +} + +code span.sc { + color: #5E5E5E; + font-style: inherit; +} + +code span.dv { + color: #AD0000; + font-style: inherit; +} + +code span.kw { + color: #003B4F; + font-weight: bold; + font-style: inherit; +} + +.prevent-inlining { + content: " { + // Find any conflicting margin elements and add margins to the + // top to prevent overlap + const marginChildren = window.document.querySelectorAll( + ".column-margin.column-container > *, .margin-caption, .aside" + ); + + let lastBottom = 0; + for (const marginChild of marginChildren) { + if (marginChild.offsetParent !== null) { + // clear the top margin so we recompute it + marginChild.style.marginTop = null; + const top = marginChild.getBoundingClientRect().top + window.scrollY; + if (top < lastBottom) { + const marginChildStyle = window.getComputedStyle(marginChild); + const marginBottom = parseFloat(marginChildStyle["marginBottom"]); + const margin = lastBottom - top + marginBottom; + marginChild.style.marginTop = `${margin}px`; + } + const styles = window.getComputedStyle(marginChild); + const marginTop = parseFloat(styles["marginTop"]); + lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; + } + } +}; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Recompute the position of margin elements anytime the body size changes + if (window.ResizeObserver) { + const resizeObserver = new window.ResizeObserver( + throttle(() => { + layoutMarginEls(); + if ( + window.document.body.getBoundingClientRect().width < 990 && + isReaderMode() + ) { + quartoToggleReader(); + } + }, 50) + ); + resizeObserver.observe(window.document.body); + } + + const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]'); + const sidebarEl = window.document.getElementById("quarto-sidebar"); + const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left"); + const marginSidebarEl = window.document.getElementById( + "quarto-margin-sidebar" + ); + // function to determine whether the element has a previous sibling that is active + const prevSiblingIsActiveLink = (el) => { + const sibling = el.previousElementSibling; + if (sibling && sibling.tagName === "A") { + return sibling.classList.contains("active"); + } else { + return false; + } + }; + + // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior) + function fireSlideEnter(e) { + const event = window.document.createEvent("Event"); + event.initEvent("slideenter", true, true); + window.document.dispatchEvent(event); + } + const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); + tabs.forEach((tab) => { + tab.addEventListener("shown.bs.tab", fireSlideEnter); + }); + + // fire slideEnter for tabby tab activations (for htmlwidget resize behavior) + document.addEventListener("tabby", fireSlideEnter, false); + + // Track scrolling and mark TOC links as active + // get table of contents and sidebar (bail if we don't have at least one) + const tocLinks = tocEl + ? [...tocEl.querySelectorAll("a[data-scroll-target]")] + : []; + const makeActive = (link) => tocLinks[link].classList.add("active"); + const removeActive = (link) => tocLinks[link].classList.remove("active"); + const removeAllActive = () => + [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); + + // activate the anchor for a section associated with this TOC entry + tocLinks.forEach((link) => { + link.addEventListener("click", () => { + if (link.href.indexOf("#") !== -1) { + const anchor = link.href.split("#")[1]; + const heading = window.document.querySelector( + `[data-anchor-id="${anchor}"]` + ); + if (heading) { + // Add the class + heading.classList.add("reveal-anchorjs-link"); + + // function to show the anchor + const handleMouseout = () => { + heading.classList.remove("reveal-anchorjs-link"); + heading.removeEventListener("mouseout", handleMouseout); + }; + + // add a function to clear the anchor when the user mouses out of it + heading.addEventListener("mouseout", handleMouseout); + } + } + }); + }); + + const sections = tocLinks.map((link) => { + const target = link.getAttribute("data-scroll-target"); + if (target.startsWith("#")) { + return window.document.getElementById(decodeURI(`${target.slice(1)}`)); + } else { + return window.document.querySelector(decodeURI(`${target}`)); + } + }); + + const sectionMargin = 200; + let currentActive = 0; + // track whether we've initialized state the first time + let init = false; + + const updateActiveLink = () => { + // The index from bottom to top (e.g. reversed list) + let sectionIndex = -1; + if ( + window.innerHeight + window.pageYOffset >= + window.document.body.offsetHeight + ) { + // This is the no-scroll case where last section should be the active one + sectionIndex = 0; + } else { + // This finds the last section visible on screen that should be made active + sectionIndex = [...sections].reverse().findIndex((section) => { + if (section) { + return window.pageYOffset >= section.offsetTop - sectionMargin; + } else { + return false; + } + }); + } + if (sectionIndex > -1) { + const current = sections.length - sectionIndex - 1; + if (current !== currentActive) { + removeAllActive(); + currentActive = current; + makeActive(current); + if (init) { + window.dispatchEvent(sectionChanged); + } + init = true; + } + } + }; + + const inHiddenRegion = (top, bottom, hiddenRegions) => { + for (const region of hiddenRegions) { + if (top <= region.bottom && bottom >= region.top) { + return true; + } + } + return false; + }; + + const categorySelector = "header.quarto-title-block .quarto-category"; + const activateCategories = (href) => { + // Find any categories + // Surround them with a link pointing back to: + // #category=Authoring + try { + const categoryEls = window.document.querySelectorAll(categorySelector); + for (const categoryEl of categoryEls) { + const categoryText = categoryEl.textContent; + if (categoryText) { + const link = `${href}#category=${encodeURIComponent(categoryText)}`; + const linkEl = window.document.createElement("a"); + linkEl.setAttribute("href", link); + for (const child of categoryEl.childNodes) { + linkEl.append(child); + } + categoryEl.appendChild(linkEl); + } + } + } catch { + // Ignore errors + } + }; + function hasTitleCategories() { + return window.document.querySelector(categorySelector) !== null; + } + + function offsetRelativeUrl(url) { + const offset = getMeta("quarto:offset"); + return offset ? offset + url : url; + } + + function offsetAbsoluteUrl(url) { + const offset = getMeta("quarto:offset"); + const baseUrl = new URL(offset, window.location); + + const projRelativeUrl = url.replace(baseUrl, ""); + if (projRelativeUrl.startsWith("/")) { + return projRelativeUrl; + } else { + return "/" + projRelativeUrl; + } + } + + // read a meta tag value + function getMeta(metaName) { + const metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; + } + + async function findAndActivateCategories() { + // Categories search with listing only use path without query + const currentPagePath = offsetAbsoluteUrl( + window.location.origin + window.location.pathname + ); + const response = await fetch(offsetRelativeUrl("listings.json")); + if (response.status == 200) { + return response.json().then(function (listingPaths) { + const listingHrefs = []; + for (const listingPath of listingPaths) { + const pathWithoutLeadingSlash = listingPath.listing.substring(1); + for (const item of listingPath.items) { + if ( + item === currentPagePath || + item === currentPagePath + "index.html" + ) { + // Resolve this path against the offset to be sure + // we already are using the correct path to the listing + // (this adjusts the listing urls to be rooted against + // whatever root the page is actually running against) + const relative = offsetRelativeUrl(pathWithoutLeadingSlash); + const baseUrl = window.location; + const resolvedPath = new URL(relative, baseUrl); + listingHrefs.push(resolvedPath.pathname); + break; + } + } + } + + // Look up the tree for a nearby linting and use that if we find one + const nearestListing = findNearestParentListing( + offsetAbsoluteUrl(window.location.pathname), + listingHrefs + ); + if (nearestListing) { + activateCategories(nearestListing); + } else { + // See if the referrer is a listing page for this item + const referredRelativePath = offsetAbsoluteUrl(document.referrer); + const referrerListing = listingHrefs.find((listingHref) => { + const isListingReferrer = + listingHref === referredRelativePath || + listingHref === referredRelativePath + "index.html"; + return isListingReferrer; + }); + + if (referrerListing) { + // Try to use the referrer if possible + activateCategories(referrerListing); + } else if (listingHrefs.length > 0) { + // Otherwise, just fall back to the first listing + activateCategories(listingHrefs[0]); + } + } + }); + } + } + if (hasTitleCategories()) { + findAndActivateCategories(); + } + + const findNearestParentListing = (href, listingHrefs) => { + if (!href || !listingHrefs) { + return undefined; + } + // Look up the tree for a nearby linting and use that if we find one + const relativeParts = href.substring(1).split("/"); + while (relativeParts.length > 0) { + const path = relativeParts.join("/"); + for (const listingHref of listingHrefs) { + if (listingHref.startsWith(path)) { + return listingHref; + } + } + relativeParts.pop(); + } + + return undefined; + }; + + const manageSidebarVisiblity = (el, placeholderDescriptor) => { + let isVisible = true; + let elRect; + + return (hiddenRegions) => { + if (el === null) { + return; + } + + // Find the last element of the TOC + const lastChildEl = el.lastElementChild; + + if (lastChildEl) { + // Converts the sidebar to a menu + const convertToMenu = () => { + for (const child of el.children) { + child.style.opacity = 0; + child.style.overflow = "hidden"; + child.style.pointerEvents = "none"; + } + + nexttick(() => { + const toggleContainer = window.document.createElement("div"); + toggleContainer.style.width = "100%"; + toggleContainer.classList.add("zindex-over-content"); + toggleContainer.classList.add("quarto-sidebar-toggle"); + toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom + toggleContainer.id = placeholderDescriptor.id; + toggleContainer.style.position = "fixed"; + + const toggleIcon = window.document.createElement("i"); + toggleIcon.classList.add("quarto-sidebar-toggle-icon"); + toggleIcon.classList.add("bi"); + toggleIcon.classList.add("bi-caret-down-fill"); + + const toggleTitle = window.document.createElement("div"); + const titleEl = window.document.body.querySelector( + placeholderDescriptor.titleSelector + ); + if (titleEl) { + toggleTitle.append( + titleEl.textContent || titleEl.innerText, + toggleIcon + ); + } + toggleTitle.classList.add("zindex-over-content"); + toggleTitle.classList.add("quarto-sidebar-toggle-title"); + toggleContainer.append(toggleTitle); + + const toggleContents = window.document.createElement("div"); + toggleContents.classList = el.classList; + toggleContents.classList.add("zindex-over-content"); + toggleContents.classList.add("quarto-sidebar-toggle-contents"); + for (const child of el.children) { + if (child.id === "toc-title") { + continue; + } + + const clone = child.cloneNode(true); + clone.style.opacity = 1; + clone.style.pointerEvents = null; + clone.style.display = null; + toggleContents.append(clone); + } + toggleContents.style.height = "0px"; + const positionToggle = () => { + // position the element (top left of parent, same width as parent) + if (!elRect) { + elRect = el.getBoundingClientRect(); + } + toggleContainer.style.left = `${elRect.left}px`; + toggleContainer.style.top = `${elRect.top}px`; + toggleContainer.style.width = `${elRect.width}px`; + }; + positionToggle(); + + toggleContainer.append(toggleContents); + el.parentElement.prepend(toggleContainer); + + // Process clicks + let tocShowing = false; + // Allow the caller to control whether this is dismissed + // when it is clicked (e.g. sidebar navigation supports + // opening and closing the nav tree, so don't dismiss on click) + const clickEl = placeholderDescriptor.dismissOnClick + ? toggleContainer + : toggleTitle; + + const closeToggle = () => { + if (tocShowing) { + toggleContainer.classList.remove("expanded"); + toggleContents.style.height = "0px"; + tocShowing = false; + } + }; + + // Get rid of any expanded toggle if the user scrolls + window.document.addEventListener( + "scroll", + throttle(() => { + closeToggle(); + }, 50) + ); + + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + positionToggle(); + }, 50) + ); + + window.addEventListener("quarto-hrChanged", () => { + elRect = undefined; + }); + + // Process the click + clickEl.onclick = () => { + if (!tocShowing) { + toggleContainer.classList.add("expanded"); + toggleContents.style.height = null; + tocShowing = true; + } else { + closeToggle(); + } + }; + }); + }; + + // Converts a sidebar from a menu back to a sidebar + const convertToSidebar = () => { + for (const child of el.children) { + child.style.opacity = 1; + child.style.overflow = null; + child.style.pointerEvents = null; + } + + const placeholderEl = window.document.getElementById( + placeholderDescriptor.id + ); + if (placeholderEl) { + placeholderEl.remove(); + } + + el.classList.remove("rollup"); + }; + + if (isReaderMode()) { + convertToMenu(); + isVisible = false; + } else { + // Find the top and bottom o the element that is being managed + const elTop = el.offsetTop; + const elBottom = + elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; + + if (!isVisible) { + // If the element is current not visible reveal if there are + // no conflicts with overlay regions + if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToSidebar(); + isVisible = true; + } + } else { + // If the element is visible, hide it if it conflicts with overlay regions + // and insert a placeholder toggle (or if we're in reader mode) + if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToMenu(); + isVisible = false; + } + } + } + } + }; + }; + + const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); + for (const tabEl of tabEls) { + const id = tabEl.getAttribute("data-bs-target"); + if (id) { + const columnEl = document.querySelector( + `${id} .column-margin, .tabset-margin-content` + ); + if (columnEl) + tabEl.addEventListener("shown.bs.tab", function (event) { + const el = event.srcElement; + if (el) { + const visibleCls = `${el.id}-margin-content`; + // walk up until we find a parent tabset + let panelTabsetEl = el.parentElement; + while (panelTabsetEl) { + if (panelTabsetEl.classList.contains("panel-tabset")) { + break; + } + panelTabsetEl = panelTabsetEl.parentElement; + } + + if (panelTabsetEl) { + const prevSib = panelTabsetEl.previousElementSibling; + if ( + prevSib && + prevSib.classList.contains("tabset-margin-container") + ) { + const childNodes = prevSib.querySelectorAll( + ".tabset-margin-content" + ); + for (const childEl of childNodes) { + if (childEl.classList.contains(visibleCls)) { + childEl.classList.remove("collapse"); + } else { + childEl.classList.add("collapse"); + } + } + } + } + } + + layoutMarginEls(); + }); + } + } + + // Manage the visibility of the toc and the sidebar + const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { + id: "quarto-toc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { + id: "quarto-sidebarnav-toggle", + titleSelector: ".title", + dismissOnClick: false, + }); + let tocLeftScrollVisibility; + if (leftTocEl) { + tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { + id: "quarto-lefttoc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + } + + // Find the first element that uses formatting in special columns + const conflictingEls = window.document.body.querySelectorAll( + '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' + ); + + // Filter all the possibly conflicting elements into ones + // the do conflict on the left or ride side + const arrConflictingEls = Array.from(conflictingEls); + const leftSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return false; + } + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + className.startsWith("column-") && + !className.endsWith("right") && + !className.endsWith("container") && + className !== "column-margin" + ); + }); + }); + const rightSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return true; + } + + const hasMarginCaption = Array.from(el.classList).find((className) => { + return className == "margin-caption"; + }); + if (hasMarginCaption) { + return true; + } + + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + !className.endsWith("container") && + className.startsWith("column-") && + !className.endsWith("left") + ); + }); + }); + + const kOverlapPaddingSize = 10; + function toRegions(els) { + return els.map((el) => { + const boundRect = el.getBoundingClientRect(); + const top = + boundRect.top + + document.documentElement.scrollTop - + kOverlapPaddingSize; + return { + top, + bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, + }; + }); + } + + let hasObserved = false; + const visibleItemObserver = (els) => { + let visibleElements = [...els]; + const intersectionObserver = new IntersectionObserver( + (entries, _observer) => { + entries.forEach((entry) => { + if (entry.isIntersecting) { + if (visibleElements.indexOf(entry.target) === -1) { + visibleElements.push(entry.target); + } + } else { + visibleElements = visibleElements.filter((visibleEntry) => { + return visibleEntry !== entry; + }); + } + }); + + if (!hasObserved) { + hideOverlappedSidebars(); + } + hasObserved = true; + }, + {} + ); + els.forEach((el) => { + intersectionObserver.observe(el); + }); + + return { + getVisibleEntries: () => { + return visibleElements; + }, + }; + }; + + const rightElementObserver = visibleItemObserver(rightSideConflictEls); + const leftElementObserver = visibleItemObserver(leftSideConflictEls); + + const hideOverlappedSidebars = () => { + marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); + sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); + if (tocLeftScrollVisibility) { + tocLeftScrollVisibility( + toRegions(leftElementObserver.getVisibleEntries()) + ); + } + }; + + window.quartoToggleReader = () => { + // Applies a slow class (or removes it) + // to update the transition speed + const slowTransition = (slow) => { + const manageTransition = (id, slow) => { + const el = document.getElementById(id); + if (el) { + if (slow) { + el.classList.add("slow"); + } else { + el.classList.remove("slow"); + } + } + }; + + manageTransition("TOC", slow); + manageTransition("quarto-sidebar", slow); + }; + const readerMode = !isReaderMode(); + setReaderModeValue(readerMode); + + // If we're entering reader mode, slow the transition + if (readerMode) { + slowTransition(readerMode); + } + highlightReaderToggle(readerMode); + hideOverlappedSidebars(); + + // If we're exiting reader mode, restore the non-slow transition + if (!readerMode) { + slowTransition(!readerMode); + } + }; + + const highlightReaderToggle = (readerMode) => { + const els = document.querySelectorAll(".quarto-reader-toggle"); + if (els) { + els.forEach((el) => { + if (readerMode) { + el.classList.add("reader"); + } else { + el.classList.remove("reader"); + } + }); + } + }; + + const setReaderModeValue = (val) => { + if (window.location.protocol !== "file:") { + window.localStorage.setItem("quarto-reader-mode", val); + } else { + localReaderMode = val; + } + }; + + const isReaderMode = () => { + if (window.location.protocol !== "file:") { + return window.localStorage.getItem("quarto-reader-mode") === "true"; + } else { + return localReaderMode; + } + }; + let localReaderMode = null; + + const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); + const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; + + // Walk the TOC and collapse/expand nodes + // Nodes are expanded if: + // - they are top level + // - they have children that are 'active' links + // - they are directly below an link that is 'active' + const walk = (el, depth) => { + // Tick depth when we enter a UL + if (el.tagName === "UL") { + depth = depth + 1; + } + + // It this is active link + let isActiveNode = false; + if (el.tagName === "A" && el.classList.contains("active")) { + isActiveNode = true; + } + + // See if there is an active child to this element + let hasActiveChild = false; + for (child of el.children) { + hasActiveChild = walk(child, depth) || hasActiveChild; + } + + // Process the collapse state if this is an UL + if (el.tagName === "UL") { + if (tocOpenDepth === -1 && depth > 1) { + // toc-expand: false + el.classList.add("collapse"); + } else if ( + depth <= tocOpenDepth || + hasActiveChild || + prevSiblingIsActiveLink(el) + ) { + el.classList.remove("collapse"); + } else { + el.classList.add("collapse"); + } + + // untick depth when we leave a UL + depth = depth - 1; + } + return hasActiveChild || isActiveNode; + }; + + // walk the TOC and expand / collapse any items that should be shown + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + + // Throttle the scroll event and walk peridiocally + window.document.addEventListener( + "scroll", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 5) + ); + window.addEventListener( + "resize", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 10) + ); + hideOverlappedSidebars(); + highlightReaderToggle(isReaderMode()); +}); + +// grouped tabsets +window.addEventListener("pageshow", (_event) => { + function getTabSettings() { + const data = localStorage.getItem("quarto-persistent-tabsets-data"); + if (!data) { + localStorage.setItem("quarto-persistent-tabsets-data", "{}"); + return {}; + } + if (data) { + return JSON.parse(data); + } + } + + function setTabSettings(data) { + localStorage.setItem( + "quarto-persistent-tabsets-data", + JSON.stringify(data) + ); + } + + function setTabState(groupName, groupValue) { + const data = getTabSettings(); + data[groupName] = groupValue; + setTabSettings(data); + } + + function toggleTab(tab, active) { + const tabPanelId = tab.getAttribute("aria-controls"); + const tabPanel = document.getElementById(tabPanelId); + if (active) { + tab.classList.add("active"); + tabPanel.classList.add("active"); + } else { + tab.classList.remove("active"); + tabPanel.classList.remove("active"); + } + } + + function toggleAll(selectedGroup, selectorsToSync) { + for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { + const active = selectedGroup === thisGroup; + for (const tab of tabs) { + toggleTab(tab, active); + } + } + } + + function findSelectorsToSyncByLanguage() { + const result = {}; + const tabs = Array.from( + document.querySelectorAll(`div[data-group] a[id^='tabset-']`) + ); + for (const item of tabs) { + const div = item.parentElement.parentElement.parentElement; + const group = div.getAttribute("data-group"); + if (!result[group]) { + result[group] = {}; + } + const selectorsToSync = result[group]; + const value = item.innerHTML; + if (!selectorsToSync[value]) { + selectorsToSync[value] = []; + } + selectorsToSync[value].push(item); + } + return result; + } + + function setupSelectorSync() { + const selectorsToSync = findSelectorsToSyncByLanguage(); + Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { + Object.entries(tabSetsByValue).forEach(([value, items]) => { + items.forEach((item) => { + item.addEventListener("click", (_event) => { + setTabState(group, value); + toggleAll(value, selectorsToSync[group]); + }); + }); + }); + }); + return selectorsToSync; + } + + const selectorsToSync 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"))},removeClass:function(t){this.elem.classList.remove.apply(this.elem.classList,this.classes[t].split(" "))},hasClass:function(t){return this.classes[t].split(" ").every(function(t){return this.classList.contains(t)},this.elem)},update:function(t){t.isOutOfBounds||!0!==this.frozen&&(t.top?this.top():this.notTop(),t.bottom?this.bottom():this.notBottom(),this.shouldUnpin(t)?this.unpin():this.shouldPin(t)&&this.pin())}},s.options={tolerance:{up:0,down:0},offset:0,scroller:t()?window:null,classes:{frozen:"headroom--frozen",pinned:"headroom--pinned",unpinned:"headroom--unpinned",top:"headroom--top",notTop:"headroom--not-top",bottom:"headroom--bottom",notBottom:"headroom--not-bottom",initial:"headroom"}},s.cutsTheMustard=!!(t()&&function(){}.bind&&"classList"in document.documentElement&&Object.assign&&Object.keys&&requestAnimationFrame),s}); diff --git a/_docs/site_libs/quarto-nav/quarto-nav.js b/_docs/site_libs/quarto-nav/quarto-nav.js new file mode 100644 index 0000000..38cc430 --- /dev/null +++ b/_docs/site_libs/quarto-nav/quarto-nav.js @@ -0,0 +1,325 @@ +const headroomChanged = new CustomEvent("quarto-hrChanged", { + detail: {}, + bubbles: true, + cancelable: false, + composed: false, +}); + +const announceDismiss = () => { + const annEl = window.document.getElementById("quarto-announcement"); + if (annEl) { + annEl.remove(); + + const annId = annEl.getAttribute("data-announcement-id"); + window.localStorage.setItem(`quarto-announce-${annId}`, "true"); + } +}; + +const announceRegister = () => { + const annEl = window.document.getElementById("quarto-announcement"); + if (annEl) { + const annId = annEl.getAttribute("data-announcement-id"); + const isDismissed = + window.localStorage.getItem(`quarto-announce-${annId}`) || false; + if (isDismissed) { + announceDismiss(); + return; + } else { + annEl.classList.remove("hidden"); + } + + const actionEl = annEl.querySelector(".quarto-announcement-action"); + if (actionEl) { + actionEl.addEventListener("click", function (e) { + e.preventDefault(); + // Hide the bar immediately + announceDismiss(); + }); + } + } +}; + +window.document.addEventListener("DOMContentLoaded", function () { + let init = false; + + announceRegister(); + + // Manage the back to top button, if one is present. + let lastScrollTop = window.pageYOffset || document.documentElement.scrollTop; + const scrollDownBuffer = 5; + const scrollUpBuffer = 35; + const btn = document.getElementById("quarto-back-to-top"); + const hideBackToTop = () => { + btn.style.display = "none"; + }; + const showBackToTop = () => { + btn.style.display = "inline-block"; + }; + if (btn) { + window.document.addEventListener( + "scroll", + function () { + const currentScrollTop = + window.pageYOffset || document.documentElement.scrollTop; + + // Shows and hides the button 'intelligently' as the user scrolls + if (currentScrollTop - scrollDownBuffer > lastScrollTop) { + hideBackToTop(); + lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; + } else if (currentScrollTop < lastScrollTop - scrollUpBuffer) { + showBackToTop(); + lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; + } + + // Show the button at the bottom, hides it at the top + if (currentScrollTop <= 0) { + hideBackToTop(); + } else if ( + window.innerHeight + currentScrollTop >= + document.body.offsetHeight + ) { + showBackToTop(); + } + }, + false + ); + } + + function throttle(func, wait) { + var timeout; + return function () { + const context = this; + const args = arguments; + const later = function () { + clearTimeout(timeout); + timeout = null; + func.apply(context, args); + }; + + if (!timeout) { + timeout = setTimeout(later, wait); + } + }; + } + + function headerOffset() { + // Set an offset if there is are fixed top navbar + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl) { + return headerEl.clientHeight; + } else { + return 0; + } + } + + function footerOffset() { + const footerEl = window.document.querySelector("footer.footer"); + if (footerEl) { + return footerEl.clientHeight; + } else { + return 0; + } + } + + function dashboardOffset() { + const dashboardNavEl = window.document.getElementById( + "quarto-dashboard-header" + ); + if (dashboardNavEl !== null) { + return dashboardNavEl.clientHeight; + } else { + return 0; + } + } + + function updateDocumentOffsetWithoutAnimation() { + updateDocumentOffset(false); + } + + function updateDocumentOffset(animated) { + // set body offset + const topOffset = headerOffset(); + const bodyOffset = topOffset + footerOffset() + dashboardOffset(); + const bodyEl = window.document.body; + bodyEl.setAttribute("data-bs-offset", topOffset); + bodyEl.style.paddingTop = topOffset + "px"; + + // deal with sidebar offsets + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + if (!animated) { + sidebar.classList.add("notransition"); + // Remove the no transition class after the animation has time to complete + setTimeout(function () { + sidebar.classList.remove("notransition"); + }, 201); + } + + if (window.Headroom && sidebar.classList.contains("sidebar-unpinned")) { + sidebar.style.top = "0"; + sidebar.style.maxHeight = "100vh"; + } else { + sidebar.style.top = topOffset + "px"; + sidebar.style.maxHeight = "calc(100vh - " + topOffset + "px)"; + } + }); + + // allow space for footer + const mainContainer = window.document.querySelector(".quarto-container"); + if (mainContainer) { + mainContainer.style.minHeight = "calc(100vh - " + bodyOffset + "px)"; + } + + // link offset + let linkStyle = window.document.querySelector("#quarto-target-style"); + if (!linkStyle) { + linkStyle = window.document.createElement("style"); + linkStyle.setAttribute("id", "quarto-target-style"); + window.document.head.appendChild(linkStyle); + } + while (linkStyle.firstChild) { + linkStyle.removeChild(linkStyle.firstChild); + } + if (topOffset > 0) { + linkStyle.appendChild( + window.document.createTextNode(` + section:target::before { + content: ""; + display: block; + height: ${topOffset}px; + margin: -${topOffset}px 0 0; + }`) + ); + } + if (init) { + window.dispatchEvent(headroomChanged); + } + init = true; + } + + // initialize headroom + var header = window.document.querySelector("#quarto-header"); + if (header && window.Headroom) { + const headroom = new window.Headroom(header, { + tolerance: 5, + onPin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.remove("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + onUnpin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.add("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + }); + headroom.init(); + + let frozen = false; + window.quartoToggleHeadroom = function () { + if (frozen) { + headroom.unfreeze(); + frozen = false; + } else { + headroom.freeze(); + frozen = true; + } + }; + } + + window.addEventListener( + "hashchange", + function (e) { + if ( + getComputedStyle(document.documentElement).scrollBehavior !== "smooth" + ) { + window.scrollTo(0, window.pageYOffset - headerOffset()); + } + }, + false + ); + + // Observe size changed for the header + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl && window.ResizeObserver) { + const observer = new window.ResizeObserver(() => { + setTimeout(updateDocumentOffsetWithoutAnimation, 0); + }); + observer.observe(headerEl, { + attributes: true, + childList: true, + characterData: true, + }); + } else { + window.addEventListener( + "resize", + throttle(updateDocumentOffsetWithoutAnimation, 50) + ); + } + setTimeout(updateDocumentOffsetWithoutAnimation, 250); + + // fixup index.html links if we aren't on the filesystem + if (window.location.protocol !== "file:") { + const links = window.document.querySelectorAll("a"); + for (let i = 0; i < links.length; i++) { + if (links[i].href) { + links[i].dataset.originalHref = links[i].href; + links[i].href = links[i].href.replace(/\/index\.html/, "/"); + } + } + + // Fixup any sharing links that require urls + // Append url to any sharing urls + const sharingLinks = window.document.querySelectorAll( + "a.sidebar-tools-main-item, a.quarto-navigation-tool, a.quarto-navbar-tools, a.quarto-navbar-tools-item" + ); + for (let i = 0; i < sharingLinks.length; i++) { + const sharingLink = sharingLinks[i]; + const href = sharingLink.getAttribute("href"); + if (href) { + sharingLink.setAttribute( + "href", + href.replace("|url|", window.location.href) + ); + } + } + + // Scroll the active navigation item into view, if necessary + const navSidebar = window.document.querySelector("nav#quarto-sidebar"); + if (navSidebar) { + // Find the active item + const activeItem = navSidebar.querySelector("li.sidebar-item a.active"); + if (activeItem) { + // Wait for the scroll height and height to resolve by observing size changes on the + // nav element that is scrollable + const resizeObserver = new ResizeObserver((_entries) => { + // The bottom of the element + const elBottom = activeItem.offsetTop; + const viewBottom = navSidebar.scrollTop + navSidebar.clientHeight; + + // The element height and scroll height are the same, then we are still loading + if (viewBottom !== navSidebar.scrollHeight) { + // Determine if the item isn't visible and scroll to it + if (elBottom >= viewBottom) { + navSidebar.scrollTop = elBottom; + } + + // stop observing now since we've completed the scroll + resizeObserver.unobserve(navSidebar); + } + }); + resizeObserver.observe(navSidebar); + } + } + } +}); diff --git a/_docs/site_libs/quarto-search/autocomplete.umd.js b/_docs/site_libs/quarto-search/autocomplete.umd.js new file mode 100644 index 0000000..ae0063a --- /dev/null +++ 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0!==arguments[1]?arguments[1]:{},n=t.limit,r=void 0===n?-1:n,i=this.options,o=i.includeMatches,c=i.includeScore,a=i.shouldSort,s=i.sortFn,u=i.ignoreFieldNorm,h=g(e)?g(this._docs[0])?this._searchStringList(e):this._searchObjectList(e):this._searchLogical(e);return fe(h,{ignoreFieldNorm:u}),a&&h.sort(s),y(r)&&r>-1&&(h=h.slice(0,r)),ge(h,this._docs,{includeMatches:o,includeScore:c})}},{key:"_searchStringList",value:function(e){var t=re(e,this.options),n=this._myIndex.records,r=[];return n.forEach((function(e){var n=e.v,i=e.i,o=e.n;if(k(n)){var c=t.searchIn(n),a=c.isMatch,s=c.score,u=c.indices;a&&r.push({item:n,idx:i,matches:[{score:s,value:n,norm:o,indices:u}]})}})),r}},{key:"_searchLogical",value:function(e){var t=this,n=function(e,t){var n=(arguments.length>2&&void 0!==arguments[2]?arguments[2]:{}).auto,r=void 0===n||n,i=function e(n){var i=Object.keys(n),o=ue(n);if(!o&&i.length>1&&!se(n))return e(le(n));if(he(n)){var c=o?n[ce]:i[0],a=o?n[ae]:n[c];if(!g(a))throw new Error(x(c));var s={keyId:j(c),pattern:a};return r&&(s.searcher=re(a,t)),s}var u={children:[],operator:i[0]};return i.forEach((function(t){var r=n[t];v(r)&&r.forEach((function(t){u.children.push(e(t))}))})),u};return se(e)||(e=le(e)),i(e)}(e,this.options),r=function e(n,r,i){if(!n.children){var o=n.keyId,c=n.searcher,a=t._findMatches({key:t._keyStore.get(o),value:t._myIndex.getValueForItemAtKeyId(r,o),searcher:c});return a&&a.length?[{idx:i,item:r,matches:a}]:[]}for(var s=[],u=0,h=n.children.length;u1&&void 0!==arguments[1]?arguments[1]:{},n=t.getFn,r=void 0===n?I.getFn:n,i=t.fieldNormWeight,o=void 0===i?I.fieldNormWeight:i,c=e.keys,a=e.records,s=new $({getFn:r,fieldNormWeight:o});return s.setKeys(c),s.setIndexRecords(a),s},ye.config=I,function(){ne.push.apply(ne,arguments)}(te),ye},"object"==typeof exports&&"undefined"!=typeof module?module.exports=t():"function"==typeof define&&define.amd?define(t):(e="undefined"!=typeof globalThis?globalThis:e||self).Fuse=t(); \ No newline at end of file diff --git a/_docs/site_libs/quarto-search/quarto-search.js b/_docs/site_libs/quarto-search/quarto-search.js new file mode 100644 index 0000000..d788a95 --- /dev/null +++ b/_docs/site_libs/quarto-search/quarto-search.js @@ -0,0 +1,1290 @@ +const kQueryArg = "q"; +const kResultsArg = "show-results"; + +// If items don't provide a URL, then both the navigator and the onSelect +// function aren't called (and therefore, the default implementation is used) +// +// We're using this sentinel URL to signal to those handlers that this +// item is a more item (along with the type) and can be handled appropriately +const kItemTypeMoreHref = "0767FDFD-0422-4E5A-BC8A-3BE11E5BBA05"; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Ensure that search is available on this page. If it isn't, + // should return early and not do anything + var searchEl = window.document.getElementById("quarto-search"); + if (!searchEl) return; + + const { autocomplete } = window["@algolia/autocomplete-js"]; + + let quartoSearchOptions = {}; + let language = {}; + const searchOptionEl = window.document.getElementById( + "quarto-search-options" + ); + if (searchOptionEl) { + const jsonStr = searchOptionEl.textContent; + quartoSearchOptions = JSON.parse(jsonStr); + language = quartoSearchOptions.language; + } + + // note the search mode + if (quartoSearchOptions.type === "overlay") { + searchEl.classList.add("type-overlay"); + } else { + searchEl.classList.add("type-textbox"); + } + + // Used to determine highlighting behavior for this page + // A `q` query param is expected when the user follows a search + // to this page + const currentUrl = new URL(window.location); + const query = currentUrl.searchParams.get(kQueryArg); + const showSearchResults = currentUrl.searchParams.get(kResultsArg); + const mainEl = window.document.querySelector("main"); + + // highlight matches on the page + if (query && mainEl) { + // perform any highlighting + highlight(escapeRegExp(query), mainEl); + + // fix up the URL to remove the q query param + const replacementUrl = new URL(window.location); + replacementUrl.searchParams.delete(kQueryArg); + window.history.replaceState({}, "", replacementUrl); + } + + // function to clear highlighting on the page when the search query changes + // (e.g. if the user edits the query or clears it) + let highlighting = true; + const resetHighlighting = (searchTerm) => { + if (mainEl && highlighting && query && searchTerm !== query) { + clearHighlight(query, mainEl); + highlighting = false; + } + }; + + // Clear search highlighting when the user scrolls sufficiently + const resetFn = () => { + resetHighlighting(""); + window.removeEventListener("quarto-hrChanged", resetFn); + window.removeEventListener("quarto-sectionChanged", resetFn); + }; + + // Register this event after the initial scrolling and settling of events + // on the page + window.addEventListener("quarto-hrChanged", resetFn); + window.addEventListener("quarto-sectionChanged", resetFn); + + // Responsively switch to overlay mode if the search is present on the navbar + // Note that switching the sidebar to overlay mode requires more coordinate (not just + // the media query since we generate different HTML for sidebar overlays than we do + // for sidebar input UI) + const detachedMediaQuery = + quartoSearchOptions.type === "overlay" ? "all" : "(max-width: 991px)"; + + // If configured, include the analytics client to send insights + const plugins = configurePlugins(quartoSearchOptions); + + let lastState = null; + const { setIsOpen, setQuery, setCollections } = autocomplete({ + container: searchEl, + detachedMediaQuery: detachedMediaQuery, + defaultActiveItemId: 0, + panelContainer: "#quarto-search-results", + panelPlacement: quartoSearchOptions["panel-placement"], + debug: false, + openOnFocus: true, + plugins, + classNames: { + form: "d-flex", + }, + placeholder: language["search-text-placeholder"], + translations: { + clearButtonTitle: language["search-clear-button-title"], + detachedCancelButtonText: language["search-detached-cancel-button-title"], + submitButtonTitle: language["search-submit-button-title"], + }, + initialState: { + query, + }, + getItemUrl({ item }) { + return item.href; + }, + onStateChange({ state }) { + // If this is a file URL, note that + + // Perhaps reset highlighting + resetHighlighting(state.query); + + // If the panel just opened, ensure the panel is positioned properly + if (state.isOpen) { + if (lastState && !lastState.isOpen) { + setTimeout(() => { + positionPanel(quartoSearchOptions["panel-placement"]); + }, 150); + } + } + + // Perhaps show the copy link + showCopyLink(state.query, quartoSearchOptions); + + lastState = state; + }, + reshape({ sources, state }) { + return sources.map((source) => { + try { + const items = source.getItems(); + + // Validate the items + validateItems(items); + + // group the items by document + const groupedItems = new Map(); + items.forEach((item) => { + const hrefParts = item.href.split("#"); + const baseHref = hrefParts[0]; + const isDocumentItem = hrefParts.length === 1; + + const items = groupedItems.get(baseHref); + if (!items) { + groupedItems.set(baseHref, [item]); + } else { + // If the href for this item matches the document + // exactly, place this item first as it is the item that represents + // the document itself + if (isDocumentItem) { + items.unshift(item); + } else { + items.push(item); + } + groupedItems.set(baseHref, items); + } + }); + + const reshapedItems = []; + let count = 1; + for (const [_key, value] of groupedItems) { + const firstItem = value[0]; + reshapedItems.push({ + ...firstItem, + type: kItemTypeDoc, + }); + + const collapseMatches = quartoSearchOptions["collapse-after"]; + const collapseCount = + typeof collapseMatches === "number" ? collapseMatches : 1; + + if (value.length > 1) { + const target = `search-more-${count}`; + const isExpanded = + state.context.expanded && + state.context.expanded.includes(target); + + const remainingCount = value.length - collapseCount; + + for (let i = 1; i < value.length; i++) { + if (collapseMatches && i === collapseCount) { + reshapedItems.push({ + target, + title: isExpanded + ? language["search-hide-matches-text"] + : remainingCount === 1 + ? `${remainingCount} ${language["search-more-match-text"]}` + : `${remainingCount} ${language["search-more-matches-text"]}`, + type: kItemTypeMore, + href: kItemTypeMoreHref, + }); + } + + if (isExpanded || !collapseMatches || i < collapseCount) { + reshapedItems.push({ + ...value[i], + type: kItemTypeItem, + target, + }); + } + } + } + count += 1; + } + + return { + ...source, + getItems() { + return reshapedItems; + }, + }; + } catch (error) { + // Some form of error occurred + return { + ...source, + getItems() { + return [ + { + title: error.name || "An Error Occurred While Searching", + text: + error.message || + "An unknown error occurred while attempting to perform the requested search.", + type: kItemTypeError, + }, + ]; + }, + }; + } + }); + }, + navigator: { + navigate({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.location.assign(itemUrl); + } + }, + navigateNewTab({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + const windowReference = window.open(itemUrl, "_blank", "noopener"); + if (windowReference) { + windowReference.focus(); + } + } + }, + navigateNewWindow({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.open(itemUrl, "_blank", "noopener"); + } + }, + }, + getSources({ state, setContext, setActiveItemId, refresh }) { + return [ + { + sourceId: "documents", + getItemUrl({ item }) { + if (item.href) { + return offsetURL(item.href); + } else { + return undefined; + } + }, + onSelect({ + item, + state, + setContext, + setIsOpen, + setActiveItemId, + refresh, + }) { + if (item.type === kItemTypeMore) { + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + + // Toggle more + setIsOpen(true); + } + }, + getItems({ query }) { + if (query === null || query === "") { + return []; + } + + const limit = quartoSearchOptions.limit; + if (quartoSearchOptions.algolia) { + return algoliaSearch(query, limit, quartoSearchOptions.algolia); + } else { + // Fuse search options + const fuseSearchOptions = { + isCaseSensitive: false, + shouldSort: true, + minMatchCharLength: 2, + limit: limit, + }; + + return readSearchData().then(function (fuse) { + return fuseSearch(query, fuse, fuseSearchOptions); + }); + } + }, + templates: { + noResults({ createElement }) { + const hasQuery = lastState.query; + + return createElement( + "div", + { + class: `quarto-search-no-results${ + hasQuery ? "" : " no-query" + }`, + }, + language["search-no-results-text"] + ); + }, + header({ items, createElement }) { + // count the documents + const count = items.filter((item) => { + return item.type === kItemTypeDoc; + }).length; + + if (count > 0) { + return createElement( + "div", + { class: "search-result-header" }, + `${count} ${language["search-matching-documents-text"]}` + ); + } else { + return createElement( + "div", + { class: "search-result-header-no-results" }, + `` + ); + } + }, + footer({ _items, createElement }) { + if ( + quartoSearchOptions.algolia && + quartoSearchOptions.algolia["show-logo"] + ) { + const libDir = quartoSearchOptions.algolia["libDir"]; + const logo = createElement("img", { + src: offsetURL( + `${libDir}/quarto-search/search-by-algolia.svg` + ), + class: "algolia-search-logo", + }); + return createElement( + "a", + { href: "http://www.algolia.com/" }, + logo + ); + } + }, + + item({ item, createElement }) { + return renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh, + quartoSearchOptions + ); + }, + }, + }, + ]; + }, + }); + + window.quartoOpenSearch = () => { + setIsOpen(false); + setIsOpen(true); + focusSearchInput(); + }; + + document.addEventListener("keyup", (event) => { + const { key } = event; + const kbds = quartoSearchOptions["keyboard-shortcut"]; + const focusedEl = document.activeElement; + + const isFormElFocused = [ + "input", + "select", + "textarea", + "button", + "option", + ].find((tag) => { + return focusedEl.tagName.toLowerCase() === tag; + }); + + if ( + kbds && + kbds.includes(key) && + !isFormElFocused && + !document.activeElement.isContentEditable + ) { + event.preventDefault(); + window.quartoOpenSearch(); + } + }); + + // Remove the labeleledby attribute since it is pointing + // to a non-existent label + if (quartoSearchOptions.type === "overlay") { + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + if (inputEl) { + inputEl.removeAttribute("aria-labelledby"); + } + } + + function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; + } + + // If the main document scrolls dismiss the search results + // (otherwise, since they're floating in the document they can scroll with the document) + window.document.body.onscroll = throttle(() => { + // Only do this if we're not detached + // Bug #7117 + // This will happen when the keyboard is shown on ios (resulting in a scroll) + // which then closed the search UI + if (!window.matchMedia(detachedMediaQuery).matches) { + setIsOpen(false); + } + }, 50); + + if (showSearchResults) { + setIsOpen(true); + focusSearchInput(); + } +}); + +function configurePlugins(quartoSearchOptions) { + const autocompletePlugins = []; + const algoliaOptions = quartoSearchOptions.algolia; + if ( + algoliaOptions && + algoliaOptions["analytics-events"] && + algoliaOptions["search-only-api-key"] && + algoliaOptions["application-id"] + ) { + const apiKey = algoliaOptions["search-only-api-key"]; + const appId = algoliaOptions["application-id"]; + + // Aloglia insights may not be loaded because they require cookie consent + // Use deferred loading so events will start being recorded when/if consent + // is granted. + const algoliaInsightsDeferredPlugin = deferredLoadPlugin(() => { + if ( + window.aa && + window["@algolia/autocomplete-plugin-algolia-insights"] + ) { + window.aa("init", { + appId, + apiKey, + useCookie: true, + }); + + const { createAlgoliaInsightsPlugin } = + window["@algolia/autocomplete-plugin-algolia-insights"]; + // Register the insights client + const algoliaInsightsPlugin = createAlgoliaInsightsPlugin({ + insightsClient: window.aa, + onItemsChange({ insights, insightsEvents }) { + const events = insightsEvents.flatMap((event) => { + // This API limits the number of items per event to 20 + const chunkSize = 20; + const itemChunks = []; + const eventItems = event.items; + for (let i = 0; i < eventItems.length; i += chunkSize) { + itemChunks.push(eventItems.slice(i, i + chunkSize)); + } + // Split the items into multiple events that can be sent + const events = itemChunks.map((items) => { + return { + ...event, + items, + }; + }); + return events; + }); + + for (const event of events) { + insights.viewedObjectIDs(event); + } + }, + }); + return algoliaInsightsPlugin; + } + }); + + // Add the plugin + autocompletePlugins.push(algoliaInsightsDeferredPlugin); + return autocompletePlugins; + } +} + +// For plugins that may not load immediately, create a wrapper +// plugin and forward events and plugin data once the plugin +// is initialized. This is useful for cases like cookie consent +// which may prevent the analytics insights event plugin from initializing +// immediately. +function deferredLoadPlugin(createPlugin) { + let plugin = undefined; + let subscribeObj = undefined; + const wrappedPlugin = () => { + if (!plugin && subscribeObj) { + plugin = createPlugin(); + if (plugin && plugin.subscribe) { + plugin.subscribe(subscribeObj); + } + } + return plugin; + }; + + return { + subscribe: (obj) => { + subscribeObj = obj; + }, + onStateChange: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onStateChange) { + plugin.onStateChange(obj); + } + }, + onSubmit: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onSubmit) { + plugin.onSubmit(obj); + } + }, + onReset: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onReset) { + plugin.onReset(obj); + } + }, + getSources: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.getSources) { + return plugin.getSources(obj); + } else { + return Promise.resolve([]); + } + }, + data: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.data) { + plugin.data(obj); + } + }, + }; +} + +function validateItems(items) { + // Validate the first item + if (items.length > 0) { + const item = items[0]; + const missingFields = []; + if (item.href == undefined) { + missingFields.push("href"); + } + if (!item.title == undefined) { + missingFields.push("title"); + } + if (!item.text == undefined) { + missingFields.push("text"); + } + + if (missingFields.length === 1) { + throw { + name: `Error: Search index is missing the ${missingFields[0]} field.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items include the ${missingFields[0]} field or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } else if (missingFields.length > 1) { + const missingFieldList = missingFields + .map((field) => { + return `${field}`; + }) + .join(", "); + + throw { + name: `Error: Search index is missing the following fields: ${missingFieldList}.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items includes the following fields: ${missingFieldList}, or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } + } +} + +let lastQuery = null; +function showCopyLink(query, options) { + const language = options.language; + lastQuery = query; + // Insert share icon + const inputSuffixEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix" + ); + + if (inputSuffixEl) { + let copyButtonEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix .aa-CopyButton" + ); + + if (copyButtonEl === null) { + copyButtonEl = window.document.createElement("button"); + copyButtonEl.setAttribute("class", "aa-CopyButton"); + copyButtonEl.setAttribute("type", "button"); + copyButtonEl.setAttribute("title", language["search-copy-link-title"]); + copyButtonEl.onmousedown = (e) => { + e.preventDefault(); + e.stopPropagation(); + }; + + const linkIcon = "bi-clipboard"; + const checkIcon = "bi-check2"; + + const shareIconEl = window.document.createElement("i"); + shareIconEl.setAttribute("class", `bi ${linkIcon}`); + copyButtonEl.appendChild(shareIconEl); + inputSuffixEl.prepend(copyButtonEl); + + const clipboard = new window.ClipboardJS(".aa-CopyButton", { + text: function (_trigger) { + const copyUrl = new URL(window.location); + copyUrl.searchParams.set(kQueryArg, lastQuery); + copyUrl.searchParams.set(kResultsArg, "1"); + return copyUrl.toString(); + }, + }); + clipboard.on("success", function (e) { + // Focus the input + + // button target + const button = e.trigger; + const icon = button.querySelector("i.bi"); + + // flash "checked" + icon.classList.add(checkIcon); + icon.classList.remove(linkIcon); + setTimeout(function () { + icon.classList.remove(checkIcon); + icon.classList.add(linkIcon); + }, 1000); + }); + } + + // If there is a query, show the link icon + if (copyButtonEl) { + if (lastQuery && options["copy-button"]) { + copyButtonEl.style.display = "flex"; + } else { + copyButtonEl.style.display = "none"; + } + } + } +} + +/* Search Index Handling */ +// create the index +var fuseIndex = undefined; +var shownWarning = false; + +// fuse index options +const kFuseIndexOptions = { + keys: [ + { name: "title", weight: 20 }, + { name: "section", weight: 20 }, + { name: "text", weight: 10 }, + ], + ignoreLocation: true, + threshold: 0.1, +}; + +async function readSearchData() { + // Initialize the search index on demand + if (fuseIndex === undefined) { + if (window.location.protocol === "file:" && !shownWarning) { + window.alert( + "Search requires JavaScript features disabled when running in file://... URLs. In order to use search, please run this document in a web server." + ); + shownWarning = true; + return; + } + const fuse = new window.Fuse([], kFuseIndexOptions); + + // fetch the main search.json + const response = await fetch(offsetURL("search.json")); + if (response.status == 200) { + return response.json().then(function (searchDocs) { + searchDocs.forEach(function (searchDoc) { + fuse.add(searchDoc); + }); + fuseIndex = fuse; + return fuseIndex; + }); + } else { + return Promise.reject( + new Error( + "Unexpected status from search index request: " + response.status + ) + ); + } + } + + return fuseIndex; +} + +function inputElement() { + return window.document.body.querySelector(".aa-Form .aa-Input"); +} + +function focusSearchInput() { + setTimeout(() => { + const inputEl = inputElement(); + if (inputEl) { + inputEl.focus(); + } + }, 50); +} + +/* Panels */ +const kItemTypeDoc = "document"; +const kItemTypeMore = "document-more"; +const kItemTypeItem = "document-item"; +const kItemTypeError = "error"; + +function renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh, + quartoSearchOptions +) { + switch (item.type) { + case kItemTypeDoc: + return createDocumentCard( + createElement, + "file-richtext", + item.title, + item.section, + item.text, + item.href, + item.crumbs, + quartoSearchOptions + ); + case kItemTypeMore: + return createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh + ); + case kItemTypeItem: + return createSectionCard( + createElement, + item.section, + item.text, + item.href + ); + case kItemTypeError: + return createErrorCard(createElement, item.title, item.text); + default: + return undefined; + } +} + +function createDocumentCard( + createElement, + icon, + title, + section, + text, + href, + crumbs, + quartoSearchOptions +) { + const iconEl = createElement("i", { + class: `bi bi-${icon} search-result-icon`, + }); + const titleEl = createElement("p", { class: "search-result-title" }, title); + const titleContents = [iconEl, titleEl]; + const showParent = quartoSearchOptions["show-item-context"]; + if (crumbs && showParent) { + let crumbsOut = undefined; + const crumbClz = ["search-result-crumbs"]; + if (showParent === "root") { + crumbsOut = crumbs.length > 1 ? crumbs[0] : undefined; + } else if (showParent === "parent") { + crumbsOut = crumbs.length > 1 ? crumbs[crumbs.length - 2] : undefined; + } else { + crumbsOut = crumbs.length > 1 ? crumbs.join(" > ") : undefined; + crumbClz.push("search-result-crumbs-wrap"); + } + + const crumbEl = createElement( + "p", + { class: crumbClz.join(" ") }, + crumbsOut + ); + titleContents.push(crumbEl); + } + + const titleContainerEl = createElement( + "div", + { class: "search-result-title-container" }, + titleContents + ); + + const textEls = []; + if (section) { + const sectionEl = createElement( + "p", + { class: "search-result-section" }, + section + ); + textEls.push(sectionEl); + } + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + textEls.push(descEl); + + const textContainerEl = createElement( + "div", + { class: "search-result-text-container" }, + textEls + ); + + const containerEl = createElement( + "div", + { + class: "search-result-container", + }, + [titleContainerEl, textContainerEl] + ); + + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + containerEl + ); + + const classes = ["search-result-doc", "search-item"]; + if (!section) { + classes.push("document-selectable"); + } + + return createElement( + "div", + { + class: classes.join(" "), + }, + linkEl + ); +} + +function createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh +) { + const moreCardEl = createElement( + "div", + { + class: "search-result-more search-item", + onClick: (e) => { + // Handle expanding the sections by adding the expanded + // section to the list of expanded sections + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + e.stopPropagation(); + }, + }, + item.title + ); + + return moreCardEl; +} + +function toggleExpanded(item, state, setContext, setActiveItemId, refresh) { + const expanded = state.context.expanded || []; + if (expanded.includes(item.target)) { + setContext({ + expanded: expanded.filter((target) => target !== item.target), + }); + } else { + setContext({ expanded: [...expanded, item.target] }); + } + + refresh(); + setActiveItemId(item.__autocomplete_id); +} + +function createSectionCard(createElement, section, text, href) { + const sectionEl = createSection(createElement, section, text, href); + return createElement( + "div", + { + class: "search-result-doc-section search-item", + }, + sectionEl + ); +} + +function createSection(createElement, title, text, href) { + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { class: "search-result-section" }, title); + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + [titleEl, descEl] + ); + return linkEl; +} + +function createErrorCard(createElement, title, text) { + const descEl = createElement("p", { + class: "search-error-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { + class: "search-error-title", + dangerouslySetInnerHTML: { + __html: ` ${title}`, + }, + }); + const errorEl = createElement("div", { class: "search-error" }, [ + titleEl, + descEl, + ]); + return errorEl; +} + +function positionPanel(pos) { + const panelEl = window.document.querySelector( + "#quarto-search-results .aa-Panel" + ); + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + + if (panelEl && inputEl) { + panelEl.style.top = `${Math.round(panelEl.offsetTop)}px`; + if (pos === "start") { + panelEl.style.left = `${Math.round(inputEl.left)}px`; + } else { + panelEl.style.right = `${Math.round(inputEl.offsetRight)}px`; + } + } +} + +/* Highlighting */ +// highlighting functions +function highlightMatch(query, text) { + if (text) { + const start = text.toLowerCase().indexOf(query.toLowerCase()); + if (start !== -1) { + const startMark = ""; + const endMark = ""; + + const end = start + query.length; + text = + text.slice(0, start) + + startMark + + text.slice(start, end) + + endMark + + text.slice(end); + const startInfo = clipStart(text, start); + const endInfo = clipEnd( + text, + startInfo.position + startMark.length + endMark.length + ); + text = + startInfo.prefix + + text.slice(startInfo.position, endInfo.position) + + endInfo.suffix; + + return text; + } else { + return text; + } + } else { + return text; + } +} + +function clipStart(text, pos) { + const clipStart = pos - 50; + if (clipStart < 0) { + // This will just return the start of the string + return { + position: 0, + prefix: "", + }; + } else { + // We're clipping before the start of the string, walk backwards to the first space. + const spacePos = findSpace(text, pos, -1); + return { + position: spacePos.position, + prefix: "", + }; + } +} + +function clipEnd(text, pos) { + const clipEnd = pos + 200; + if (clipEnd > text.length) { + return { + position: text.length, + suffix: "", + }; + } else { + const spacePos = findSpace(text, clipEnd, 1); + return { + position: spacePos.position, + suffix: spacePos.clipped ? "…" : "", + }; + } +} + +function findSpace(text, start, step) { + let stepPos = start; + while (stepPos > -1 && stepPos < text.length) { + const char = text[stepPos]; + if (char === " " || char === "," || char === ":") { + return { + position: step === 1 ? stepPos : stepPos - step, + clipped: stepPos > 1 && stepPos < text.length, + }; + } + stepPos = stepPos + step; + } + + return { + position: stepPos - step, + clipped: false, + }; +} + +// removes highlighting as implemented by the mark tag +function clearHighlight(searchterm, el) { + const childNodes = el.childNodes; + for (let i = childNodes.length - 1; i >= 0; i--) { + const node = childNodes[i]; + if (node.nodeType === Node.ELEMENT_NODE) { + if ( + node.tagName === "MARK" && + node.innerText.toLowerCase() === searchterm.toLowerCase() + ) { + el.replaceChild(document.createTextNode(node.innerText), node); + } else { + clearHighlight(searchterm, node); + } + } + } +} + +function escapeRegExp(string) { + return string.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string +} + +// highlight matches +function highlight(term, el) { + const termRegex = new RegExp(term, "ig"); + const childNodes = el.childNodes; + + // walk back to front avoid mutating elements in front of us + for (let i = childNodes.length - 1; i >= 0; i--) { + const node = childNodes[i]; + + if (node.nodeType === Node.TEXT_NODE) { + // Search text nodes for text to highlight + const text = node.nodeValue; + + let startIndex = 0; + let matchIndex = text.search(termRegex); + if (matchIndex > -1) { + const markFragment = document.createDocumentFragment(); + while (matchIndex > -1) { + const prefix = text.slice(startIndex, matchIndex); + markFragment.appendChild(document.createTextNode(prefix)); + + const mark = document.createElement("mark"); + mark.appendChild( + document.createTextNode( + text.slice(matchIndex, matchIndex + term.length) + ) + ); + markFragment.appendChild(mark); + + startIndex = matchIndex + term.length; + matchIndex = text.slice(startIndex).search(new RegExp(term, "ig")); + if (matchIndex > -1) { + matchIndex = startIndex + matchIndex; + } + } + if (startIndex < text.length) { + markFragment.appendChild( + document.createTextNode(text.slice(startIndex, text.length)) + ); + } + + el.replaceChild(markFragment, node); + } + } else if (node.nodeType === Node.ELEMENT_NODE) { + // recurse through elements + highlight(term, node); + } + } +} + +/* Link Handling */ +// get the offset from this page for a given site root relative url +function offsetURL(url) { + var offset = getMeta("quarto:offset"); + return offset ? offset + url : url; +} + +// read a meta tag value +function getMeta(metaName) { + var metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; +} + +function algoliaSearch(query, limit, algoliaOptions) { + const { getAlgoliaResults } = window["@algolia/autocomplete-preset-algolia"]; + + const applicationId = algoliaOptions["application-id"]; + const searchOnlyApiKey = algoliaOptions["search-only-api-key"]; + const indexName = algoliaOptions["index-name"]; + const indexFields = algoliaOptions["index-fields"]; + const searchClient = window.algoliasearch(applicationId, searchOnlyApiKey); + const searchParams = algoliaOptions["params"]; + const searchAnalytics = !!algoliaOptions["analytics-events"]; + + return getAlgoliaResults({ + searchClient, + queries: [ + { + indexName: indexName, + query, + params: { + hitsPerPage: limit, + clickAnalytics: searchAnalytics, + ...searchParams, + }, + }, + ], + transformResponse: (response) => { + if (!indexFields) { + return response.hits.map((hit) => { + return hit.map((item) => { + return { + ...item, + text: highlightMatch(query, item.text), + }; + }); + }); + } else { + const remappedHits = response.hits.map((hit) => { + return hit.map((item) => { + const newItem = { ...item }; + ["href", "section", "title", "text", "crumbs"].forEach( + (keyName) => { + const mappedName = indexFields[keyName]; + if ( + mappedName && + item[mappedName] !== undefined && + mappedName !== keyName + ) { + newItem[keyName] = item[mappedName]; + delete newItem[mappedName]; + } + } + ); + newItem.text = highlightMatch(query, newItem.text); + return newItem; + }); + }); + return remappedHits; + } + }, + }); +} + +let subSearchTerm = undefined; +let subSearchFuse = undefined; +const kFuseMaxWait = 125; + +async function fuseSearch(query, fuse, fuseOptions) { + let index = fuse; + // Fuse.js using the Bitap algorithm for text matching which runs in + // O(nm) time (no matter the structure of the text). In our case this + // means that long search terms mixed with large index gets very slow + // + // This injects a subIndex that will be used once the terms get long enough + // Usually making this subindex is cheap since there will typically be + // a subset of results matching the existing query + if (subSearchFuse !== undefined && query.startsWith(subSearchTerm)) { + // Use the existing subSearchFuse + index = subSearchFuse; + } else if (subSearchFuse !== undefined) { + // The term changed, discard the existing fuse + subSearchFuse = undefined; + subSearchTerm = undefined; + } + + // Search using the active fuse + const then = performance.now(); + const resultsRaw = await index.search(query, fuseOptions); + const now = performance.now(); + + const results = resultsRaw.map((result) => { + const addParam = (url, name, value) => { + const anchorParts = url.split("#"); + const baseUrl = anchorParts[0]; + const sep = baseUrl.search("\\?") > 0 ? "&" : "?"; + anchorParts[0] = baseUrl + sep + name + "=" + value; + return anchorParts.join("#"); + }; + + return { + title: result.item.title, + section: result.item.section, + href: addParam(result.item.href, kQueryArg, query), + text: highlightMatch(query, result.item.text), + crumbs: result.item.crumbs, + }; + }); + + // If we don't have a subfuse and the query is long enough, go ahead + // and create a subfuse to use for subsequent queries + if ( + now - then > kFuseMaxWait && + subSearchFuse === undefined && + resultsRaw.length < fuseOptions.limit + ) { + subSearchTerm = query; + subSearchFuse = new window.Fuse([], kFuseIndexOptions); + resultsRaw.forEach((rr) => { + subSearchFuse.add(rr.item); + }); + } + return results; +} diff --git a/_docs/sitemap.xml b/_docs/sitemap.xml new file mode 100644 index 0000000..2a2019a --- /dev/null +++ b/_docs/sitemap.xml @@ -0,0 +1,43 @@ + + + + https://TinasheMTapera.github.io/era5_sandbox/01_download_raw_data.html + 2025-09-25T17:57:25.286Z + + + https://TinasheMTapera.github.io/era5_sandbox/22_pytask_aggregate.html + 2025-09-25T17:57:48.656Z + + + https://TinasheMTapera.github.io/era5_sandbox/10_pytask_demo.html + 2025-09-25T17:57:45.163Z + + + https://TinasheMTapera.github.io/era5_sandbox/20_pytask_config.html + 2025-09-25T17:57:45.082Z + + + https://TinasheMTapera.github.io/era5_sandbox/20_pytask_logger.html + 2025-09-25T17:57:20.956Z + + + https://TinasheMTapera.github.io/era5_sandbox/21_pytask_download.html + 2025-09-25T17:57:47.560Z + + + https://TinasheMTapera.github.io/era5_sandbox/index.html + 2025-09-25T18:01:05.301Z + + + https://TinasheMTapera.github.io/era5_sandbox/03_publish.html + 2025-09-25T17:57:24.841Z + + + https://TinasheMTapera.github.io/era5_sandbox/00_core.html + 2025-09-25T17:48:40.698Z + + + https://TinasheMTapera.github.io/era5_sandbox/02_aggregate.html + 2025-09-25T17:44:11.424Z + + diff --git a/_docs/styles.css b/_docs/styles.css new file mode 100644 index 0000000..66ccc49 --- /dev/null +++ b/_docs/styles.css @@ -0,0 +1,37 @@ +.cell { + margin-bottom: 1rem; +} + +.cell > .sourceCode { + margin-bottom: 0; +} + +.cell-output > pre { + margin-bottom: 0; +} + +.cell-output > pre, .cell-output > .sourceCode > pre, .cell-output-stdout > pre { + margin-left: 0.8rem; + margin-top: 0; + background: none; + border-left: 2px solid lightsalmon; + border-top-left-radius: 0; + border-top-right-radius: 0; +} + +.cell-output > .sourceCode { + border: none; +} + +.cell-output > .sourceCode { + background: none; + margin-top: 0; +} + +div.description { + padding-left: 2px; + padding-top: 5px; + font-style: italic; + font-size: 135%; + opacity: 70%; +} diff --git a/_extensions/mcanouil/github/LICENSE b/_extensions/mcanouil/github/LICENSE new file mode 100644 index 0000000..43fb0c2 --- /dev/null +++ b/_extensions/mcanouil/github/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2025 Mickaël Canouil + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/_extensions/mcanouil/github/_extension.yml b/_extensions/mcanouil/github/_extension.yml new file mode 100644 index 0000000..537e712 --- /dev/null +++ b/_extensions/mcanouil/github/_extension.yml @@ -0,0 +1,8 @@ +title: GitHub +author: Mickaël Canouil +version: 1.0.1 +quarto-required: ">=1.3.450" +contributes: + filters: + - github.lua +source: mcanouil/quarto-github@1.0.1 \ No newline at end of file diff --git a/_extensions/mcanouil/github/github.lua b/_extensions/mcanouil/github/github.lua new file mode 100644 index 0000000..a9c1a4b --- /dev/null +++ b/_extensions/mcanouil/github/github.lua @@ -0,0 +1,165 @@ +--[[ +# MIT License +# +# Copyright (c) 2025 Mickaël Canouil +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. +]] + +local function is_empty(s) + return s == nil or s == '' +end + +local function github_uri(text, uri) + if not is_empty(uri) and not is_empty(text) then + return pandoc.Link(text, uri) + end +end + +local github_repository = nil + +function get_repository(meta) + local meta_github_repository = nil + if not is_empty(meta['repository-name']) then + meta_github_repository = pandoc.utils.stringify(meta['repository-name']) + end + github_repository = meta_github_repository + return meta +end + +function issues(elem) + local user_repo = nil + local issue_number = nil + local type = nil + local short_link = nil + + if elem.text:match("^#(%d+)$") then + issue_number = elem.text:match("^#(%d+)$") + user_repo = github_repository + type = "issues" + short_link = "#" .. issue_number + elseif elem.text:match("^([^/]+/[^/#]+)#(%d+)$") then + user_repo, issue_number = elem.text:match("^([^/]+/[^/#]+)#(%d+)$") + type = "issues" + short_link = user_repo .. "#" .. issue_number + elseif elem.text:match("^GH%-(%d+)$") then + issue_number = elem.text:match("^GH%-(%d+)$") + user_repo = github_repository + type = "issues" + short_link = "#" .. issue_number + elseif elem.text:match("^https://github.com/([^/]+/[^/]+)/([^/]+)/(%d+)$") then + user_repo, type, issue_number = elem.text:match("^https://github.com/([^/]+/[^/]+)/([^/]+)/(%d+)$") + if user_repo == github_repository then + short_link = "#" .. issue_number + else + short_link = user_repo .. "#" .. issue_number + end + end + + local uri = nil + local text = nil + if not is_empty(short_link) and not is_empty(issue_number) and not is_empty(user_repo) and not is_empty(type) then + if type == "issues" or type == "discussions" or type == "pull" then + uri = "https://github.com/" .. user_repo .. '/' .. type .. '/' .. issue_number + text = pandoc.utils.stringify(short_link) + end + end + + return github_uri(text, uri) +end + +function commits(elem) + local user_repo = nil + local commit_sha = nil + local type = nil + local short_link = nil + + if elem.text:match("^(%w+)$") and elem.text:len() == 40 then + commit_sha = elem.text:match("^(%w+)$") + user_repo = github_repository + type = "commit" + short_link = commit_sha:sub(1, 7) + elseif elem.text:match("^([^/]+/[^/@]+)@(%w+)$") then + user_repo, commit_sha = elem.text:match("^([^/]+/[^/@]+)@(%w+)$") + type = "commit" + short_link = user_repo .. "@" .. commit_sha:sub(1, 7) + elseif elem.text:match("^(%w+)@(%w+)$") then + user_repo, commit_sha = elem.text:match("^(%w+)@(%w+)$") + if commit_sha:len() == 40 then + type = "commit" + short_link = user_repo .. "@" .. commit_sha:sub(1, 7) + end + elseif elem.text:match("^https://github.com/([^/]+/[^/]+)/([^/]+)/(%w+)$") then + user_repo, type, commit_sha = elem.text:match("^https://github.com/([^/]+/[^/]+)/([^/]+)/(%w+)$") + if user_repo == github_repository then + short_link = commit_sha:sub(1, 7) + else + short_link = user_repo .. "@" .. commit_sha:sub(1, 7) + end + end + + local uri = nil + local text = nil + if not is_empty(short_link) and not is_empty(commit_sha) and not is_empty(user_repo) and not is_empty(type) then + if type == "commit" and commit_sha:len() == 40 then + uri = "https://github.com/" .. user_repo .. '/' .. type .. '/' .. commit_sha + text = pandoc.utils.stringify(short_link) + end + end + + return github_uri(text, uri) +end + +function mentions(elem) + local uri = nil + local text = nil + if elem.text:match("^@(%w+)$") then + local mention = elem.text:match("^@(%w+)$") + uri = "https://github.com/" .. mention + text = pandoc.utils.stringify(elem.text) + end + + return github_uri(text, uri) +end + +function github(elem) + local link = nil + if is_empty(link) then + link = issues(elem) + end + + if is_empty(link) then + link = commits(elem) + end + + -- if is_empty(link) then + -- link = mentions(elem) + -- end + + if is_empty(link) then + return elem + else + return link + end +end + +return { + {Meta = get_repository}, + {Str = github} +} diff --git a/_extensions/produnis/quarto-cheatsheet/_extension.yml b/_extensions/produnis/quarto-cheatsheet/_extension.yml new file mode 100644 index 0000000..1db6466 --- /dev/null +++ b/_extensions/produnis/quarto-cheatsheet/_extension.yml @@ -0,0 +1,29 @@ +title: quarto-cheatsheet +author: Joe Slam +version: 1.0.0 +quarto-required: ">=1.3.0" +contributes: + formats: + pdf: + documentclass: article + geometry: + - a4paper + - landscape + - bottom=78mm + df-print: kable + code-line-numbers: true # false + keep-tex: false # true # false + lang: de + date-format: "DD.MM.YYYY" + linkcolor: black + nodecolor: "185191" # HTML color only + linecolor: "185191" # HTML color only + headcolor: "FFFFFF" # HTML color only + textcolor: "000000" # HTML color only + include-in-header: in-header.tex # header + template-partials: + - before-body.tex + - after-body.tex + filters: + - cheatsheet.lua +source: produnis/quarto-cheatsheet \ No newline at end of file diff --git a/_extensions/produnis/quarto-cheatsheet/after-body.tex b/_extensions/produnis/quarto-cheatsheet/after-body.tex new file mode 100644 index 0000000..a70f4d1 --- /dev/null +++ b/_extensions/produnis/quarto-cheatsheet/after-body.tex @@ -0,0 +1,2 @@ +%--- ENDE ------------ +\end{multicols*} \ No newline at end of file diff --git a/_extensions/produnis/quarto-cheatsheet/before-body.tex b/_extensions/produnis/quarto-cheatsheet/before-body.tex new file mode 100644 index 0000000..129e18c --- /dev/null +++ b/_extensions/produnis/quarto-cheatsheet/before-body.tex @@ -0,0 +1,12 @@ +\begin{center}{\huge{\textbf{$title$}}}\\ +\end{center} +\begin{multicols*}{3} + +\definecolor{NODECOL}{HTML}{$nodecolor$} +\definecolor{LINECOL}{HTML}{$linecolor$} +\definecolor{HEADCOL}{HTML}{$headcolor$} +\definecolor{TEXTCOL}{HTML}{$textcolor$} + +\tikzstyle{mybox} = [draw=LINECOL, fill=white, very thick, text=TEXTCOL, + rectangle, rounded corners, inner sep=10pt, inner ysep=11pt, text width=0.3\textwidth] +\tikzstyle{fancytitle} =[fill=NODECOL, text=HEADCOL, font=\bfseries] \ No newline at end of file diff --git a/_extensions/produnis/quarto-cheatsheet/cheatsheet.lua b/_extensions/produnis/quarto-cheatsheet/cheatsheet.lua new file mode 100644 index 0000000..88a710b --- /dev/null +++ b/_extensions/produnis/quarto-cheatsheet/cheatsheet.lua @@ -0,0 +1,34 @@ +function generateCheatBlockLatex(block) + local title = pandoc.utils.stringify(block.attributes["title"]) + + -- create a small Pandoc-Dokument with block.content + local pandocDocument = pandoc.Pandoc(block.content, {}) + + -- use pandoc.write to convert into LaTeX + local latexContent = pandoc.write(pandocDocument, "latex") + + local latexCode = "\\begin{tikzpicture}\n" + latexCode = latexCode .. " \\node [mybox] (box){%\n" + latexCode = latexCode .. " " .. latexContent .. "\n" + latexCode = latexCode .. " };\n" + latexCode = latexCode .. " %------------ Neues Semester Header ---------------------\n" + latexCode = latexCode .. " \\node[fancytitle, right=10pt] at (box.north west) {" .. title .. "};\n" + latexCode = latexCode .. " \\end{tikzpicture}" + + return pandoc.RawBlock('latex', latexCode) +end + +function replaceCheatBlock(block) + local blockType = block.classes[1] + + if blockType == "cheat" then + return generateCheatBlockLatex(block) + else + return block + end +end + +-- add filter to Pandoc +return { + { Div = replaceCheatBlock } +} diff --git a/_extensions/produnis/quarto-cheatsheet/in-header.tex b/_extensions/produnis/quarto-cheatsheet/in-header.tex new file mode 100644 index 0000000..3c980b8 --- /dev/null +++ b/_extensions/produnis/quarto-cheatsheet/in-header.tex @@ -0,0 +1,31 @@ +\usepackage{tikz} +\usepackage{url} +\usepackage{multicol} +\usepackage{amsmath} +\usepackage{esint} +\usepackage{amsfonts} +\usepackage{tikz} +\usetikzlibrary{decorations.pathmorphing} +\usepackage{amsmath,amssymb} + +\usepackage{colortbl} +\usepackage{xcolor} +\usepackage{mathtools} +\usepackage{amsmath,amssymb} +\usepackage{enumitem} +\makeatletter + +\newcommand*\bigcdot{\mathpalette\bigcdot@{.5}} +\newcommand*\bigcdot@[2]{\mathbin{\vcenter{\hbox{\scalebox{#2}{$\m@th#1\bullet$}}}}} +\makeatother + +\title{$title$} + +\advance\topmargin-.8in +\advance\textheight3in +\advance\textwidth3in +\advance\oddsidemargin-1.5in +\advance\evensidemargin-1.5in +\parindent0pt +\parskip2pt +\newcommand{\hr}{\centerline{\rule{3.5in}{1pt}}} diff --git a/_extensions/quarto-ext/fontawesome/_extension.yml b/_extensions/quarto-ext/fontawesome/_extension.yml new file mode 100644 index 0000000..d326efd --- /dev/null +++ b/_extensions/quarto-ext/fontawesome/_extension.yml @@ -0,0 +1,8 @@ +title: Font Awesome support +author: Carlos Scheidegger +version: 1.2.0 +quarto-required: ">=1.2.269" +contributes: + shortcodes: + - fontawesome.lua +source: quarto-ext/fontawesome@v1.2.0 \ No newline at end of file diff --git a/_extensions/quarto-ext/fontawesome/assets/css/all.css b/_extensions/quarto-ext/fontawesome/assets/css/all.css new file mode 100644 index 0000000..7e4dfe1 --- /dev/null +++ b/_extensions/quarto-ext/fontawesome/assets/css/all.css @@ -0,0 +1,8030 @@ +/*! + * Font Awesome Free 6.5.2 by @fontawesome - https://fontawesome.com + * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) + * Copyright 2024 Fonticons, Inc. + */ +.fa { + font-family: var(--fa-style-family, "Font Awesome 6 Free"); + font-weight: var(--fa-style, 900); } + +.fa, +.fa-classic, +.fa-sharp, +.fas, +.fa-solid, +.far, +.fa-regular, +.fab, +.fa-brands { + -moz-osx-font-smoothing: grayscale; + -webkit-font-smoothing: antialiased; + display: var(--fa-display, inline-block); + font-style: normal; + font-variant: normal; + line-height: 1; + text-rendering: auto; } + +.fas, +.fa-classic, +.fa-solid, +.far, +.fa-regular { + font-family: 'Font Awesome 6 Free'; } + +.fab, +.fa-brands { + font-family: 'Font Awesome 6 Brands'; } + +.fa-1x { + font-size: 1em; } + +.fa-2x { + font-size: 2em; } + +.fa-3x { + font-size: 3em; } + +.fa-4x { + font-size: 4em; } + +.fa-5x { + font-size: 5em; } + +.fa-6x { + font-size: 6em; } + +.fa-7x { + font-size: 7em; } + +.fa-8x { + font-size: 8em; } + +.fa-9x { + font-size: 9em; } + +.fa-10x { + font-size: 10em; } + +.fa-2xs { + font-size: 0.625em; + line-height: 0.1em; + vertical-align: 0.225em; } + +.fa-xs { + font-size: 0.75em; + line-height: 0.08333em; + vertical-align: 0.125em; } + +.fa-sm { + font-size: 0.875em; + line-height: 0.07143em; + vertical-align: 0.05357em; } + +.fa-lg { + font-size: 1.25em; + line-height: 0.05em; + vertical-align: -0.075em; } + +.fa-xl { + font-size: 1.5em; + line-height: 0.04167em; + vertical-align: -0.125em; } + +.fa-2xl { + font-size: 2em; + line-height: 0.03125em; + vertical-align: -0.1875em; } + +.fa-fw { + text-align: center; + width: 1.25em; } + +.fa-ul { + list-style-type: none; + margin-left: var(--fa-li-margin, 2.5em); + padding-left: 0; } + .fa-ul > li { + position: relative; } + +.fa-li { + left: calc(var(--fa-li-width, 2em) * -1); + position: absolute; + text-align: center; + width: var(--fa-li-width, 2em); + line-height: inherit; } + +.fa-border { + border-color: var(--fa-border-color, #eee); + border-radius: var(--fa-border-radius, 0.1em); + border-style: var(--fa-border-style, solid); + border-width: var(--fa-border-width, 0.08em); + padding: var(--fa-border-padding, 0.2em 0.25em 0.15em); } + +.fa-pull-left { + float: left; + margin-right: var(--fa-pull-margin, 0.3em); } + +.fa-pull-right { + float: right; + margin-left: var(--fa-pull-margin, 0.3em); } + +.fa-beat { + -webkit-animation-name: fa-beat; + animation-name: fa-beat; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out); + animation-timing-function: var(--fa-animation-timing, ease-in-out); } + +.fa-bounce { + -webkit-animation-name: fa-bounce; + animation-name: fa-bounce; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1)); + animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1)); } + +.fa-fade { + -webkit-animation-name: fa-fade; + animation-name: fa-fade; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); + animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); } + +.fa-beat-fade { + -webkit-animation-name: fa-beat-fade; + animation-name: fa-beat-fade; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); + animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); } + +.fa-flip { + -webkit-animation-name: fa-flip; + animation-name: fa-flip; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out); + animation-timing-function: var(--fa-animation-timing, ease-in-out); } + +.fa-shake { + -webkit-animation-name: fa-shake; + animation-name: fa-shake; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, linear); + animation-timing-function: var(--fa-animation-timing, linear); } + +.fa-spin { + -webkit-animation-name: fa-spin; + animation-name: fa-spin; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 2s); + animation-duration: var(--fa-animation-duration, 2s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, linear); + animation-timing-function: var(--fa-animation-timing, linear); } + +.fa-spin-reverse { + --fa-animation-direction: reverse; } + +.fa-pulse, +.fa-spin-pulse { + -webkit-animation-name: fa-spin; + animation-name: fa-spin; + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, steps(8)); + animation-timing-function: var(--fa-animation-timing, steps(8)); } + +@media (prefers-reduced-motion: reduce) { + .fa-beat, + .fa-bounce, + .fa-fade, + .fa-beat-fade, + .fa-flip, + .fa-pulse, + .fa-shake, + .fa-spin, + .fa-spin-pulse { + -webkit-animation-delay: -1ms; + animation-delay: -1ms; + -webkit-animation-duration: 1ms; + animation-duration: 1ms; + -webkit-animation-iteration-count: 1; + animation-iteration-count: 1; + -webkit-transition-delay: 0s; + transition-delay: 0s; + -webkit-transition-duration: 0s; + transition-duration: 0s; } } + +@-webkit-keyframes fa-beat { + 0%, 90% { + -webkit-transform: scale(1); + transform: scale(1); } + 45% { + -webkit-transform: scale(var(--fa-beat-scale, 1.25)); + transform: scale(var(--fa-beat-scale, 1.25)); } } + +@keyframes fa-beat { + 0%, 90% { + -webkit-transform: scale(1); + transform: scale(1); } + 45% { + -webkit-transform: scale(var(--fa-beat-scale, 1.25)); + transform: scale(var(--fa-beat-scale, 1.25)); } } + +@-webkit-keyframes fa-bounce { + 0% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 10% { + -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); + transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); } + 30% { + -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); + transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); } + 50% { + -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); + transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); } + 57% { + -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); + transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); } + 64% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 100% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } } + +@keyframes fa-bounce { + 0% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 10% { + -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); + transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); } + 30% { + -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); + transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); } + 50% { + -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); + transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); } + 57% { + -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); + transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); } + 64% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 100% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } } + +@-webkit-keyframes fa-fade { + 50% { + opacity: var(--fa-fade-opacity, 0.4); } } + +@keyframes fa-fade { + 50% { + opacity: var(--fa-fade-opacity, 0.4); } } + +@-webkit-keyframes fa-beat-fade { + 0%, 100% { + opacity: var(--fa-beat-fade-opacity, 0.4); + -webkit-transform: scale(1); + transform: scale(1); } + 50% { + opacity: 1; + -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125)); + transform: scale(var(--fa-beat-fade-scale, 1.125)); } } + +@keyframes fa-beat-fade { + 0%, 100% { + opacity: var(--fa-beat-fade-opacity, 0.4); + -webkit-transform: scale(1); + transform: scale(1); } + 50% { + opacity: 1; + -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125)); + transform: scale(var(--fa-beat-fade-scale, 1.125)); } } + +@-webkit-keyframes fa-flip { + 50% { + -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); + transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } } + +@keyframes fa-flip { + 50% { + -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); + transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } } + +@-webkit-keyframes fa-shake { + 0% { + -webkit-transform: rotate(-15deg); + transform: rotate(-15deg); } + 4% { + -webkit-transform: rotate(15deg); + transform: rotate(15deg); } + 8%, 24% { + -webkit-transform: rotate(-18deg); + transform: rotate(-18deg); } + 12%, 28% { + -webkit-transform: rotate(18deg); + transform: rotate(18deg); } + 16% { + -webkit-transform: rotate(-22deg); + transform: rotate(-22deg); } + 20% { + -webkit-transform: rotate(22deg); + transform: rotate(22deg); } + 32% { + -webkit-transform: rotate(-12deg); + transform: rotate(-12deg); } + 36% { + -webkit-transform: rotate(12deg); + transform: rotate(12deg); } + 40%, 100% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } } + +@keyframes fa-shake { + 0% { + -webkit-transform: rotate(-15deg); + transform: rotate(-15deg); } + 4% { + -webkit-transform: rotate(15deg); + transform: rotate(15deg); } + 8%, 24% { + -webkit-transform: rotate(-18deg); + transform: rotate(-18deg); } + 12%, 28% { + -webkit-transform: rotate(18deg); + transform: rotate(18deg); } + 16% { + -webkit-transform: rotate(-22deg); + transform: rotate(-22deg); } + 20% { + -webkit-transform: rotate(22deg); + transform: rotate(22deg); } + 32% { + -webkit-transform: rotate(-12deg); + transform: rotate(-12deg); } + 36% { + -webkit-transform: rotate(12deg); + transform: rotate(12deg); } + 40%, 100% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } } + +@-webkit-keyframes fa-spin { + 0% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } + 100% { + -webkit-transform: rotate(360deg); + transform: rotate(360deg); } } + +@keyframes fa-spin { + 0% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } + 100% { + -webkit-transform: rotate(360deg); + transform: rotate(360deg); } } + +.fa-rotate-90 { + -webkit-transform: rotate(90deg); + transform: rotate(90deg); } + +.fa-rotate-180 { + -webkit-transform: rotate(180deg); + transform: rotate(180deg); } + +.fa-rotate-270 { + -webkit-transform: rotate(270deg); + transform: rotate(270deg); } + +.fa-flip-horizontal { + -webkit-transform: scale(-1, 1); + transform: scale(-1, 1); } + +.fa-flip-vertical { + -webkit-transform: scale(1, -1); + transform: scale(1, -1); } + +.fa-flip-both, +.fa-flip-horizontal.fa-flip-vertical { + -webkit-transform: scale(-1, -1); + transform: scale(-1, -1); } + +.fa-rotate-by { + -webkit-transform: rotate(var(--fa-rotate-angle, 0)); + transform: rotate(var(--fa-rotate-angle, 0)); } + +.fa-stack { + display: inline-block; + height: 2em; + line-height: 2em; + position: relative; + vertical-align: middle; + width: 2.5em; } + +.fa-stack-1x, +.fa-stack-2x { + left: 0; + position: absolute; + text-align: center; + width: 100%; + z-index: var(--fa-stack-z-index, auto); } + +.fa-stack-1x { + line-height: inherit; } + +.fa-stack-2x { + font-size: 2em; } + +.fa-inverse { + color: var(--fa-inverse, #fff); } + +/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen +readers do not read off random characters that represent icons */ + +.fa-0::before { + content: "\30"; } + +.fa-1::before { + content: "\31"; } + +.fa-2::before { + content: "\32"; } + +.fa-3::before { + content: "\33"; } + +.fa-4::before { + content: "\34"; } + +.fa-5::before { + content: "\35"; } + +.fa-6::before { + content: "\36"; } + +.fa-7::before { + content: "\37"; } + +.fa-8::before { + content: "\38"; } + +.fa-9::before { + content: "\39"; } + +.fa-fill-drip::before { + content: "\f576"; } + +.fa-arrows-to-circle::before { + content: "\e4bd"; } + +.fa-circle-chevron-right::before { + content: "\f138"; } + +.fa-chevron-circle-right::before { + content: "\f138"; } + +.fa-at::before { + content: "\40"; } + +.fa-trash-can::before { + content: "\f2ed"; } + +.fa-trash-alt::before { + content: "\f2ed"; } + +.fa-text-height::before { + content: "\f034"; } + +.fa-user-xmark::before { + content: "\f235"; } + +.fa-user-times::before { + content: "\f235"; } + +.fa-stethoscope::before { + content: "\f0f1"; } + +.fa-message::before { + content: "\f27a"; } + +.fa-comment-alt::before { + content: "\f27a"; } + +.fa-info::before { + content: "\f129"; } + +.fa-down-left-and-up-right-to-center::before { + content: "\f422"; } + +.fa-compress-alt::before { + content: "\f422"; } + +.fa-explosion::before { + content: "\e4e9"; } + +.fa-file-lines::before { + content: "\f15c"; } + +.fa-file-alt::before { + content: "\f15c"; } + +.fa-file-text::before { + content: "\f15c"; } + +.fa-wave-square::before { + content: "\f83e"; } + +.fa-ring::before { + content: "\f70b"; } + +.fa-building-un::before { + content: "\e4d9"; } + +.fa-dice-three::before { + content: "\f527"; } + +.fa-calendar-days::before { + content: "\f073"; } + +.fa-calendar-alt::before { + content: "\f073"; } + +.fa-anchor-circle-check::before { + content: "\e4aa"; } + +.fa-building-circle-arrow-right::before { + content: "\e4d1"; } + +.fa-volleyball::before { + content: "\f45f"; } + +.fa-volleyball-ball::before { + content: "\f45f"; } + +.fa-arrows-up-to-line::before { + content: "\e4c2"; } + +.fa-sort-down::before { + content: "\f0dd"; } + +.fa-sort-desc::before { + content: "\f0dd"; } + +.fa-circle-minus::before { + content: "\f056"; } + +.fa-minus-circle::before { + content: "\f056"; } + +.fa-door-open::before { + content: "\f52b"; } + +.fa-right-from-bracket::before { + content: "\f2f5"; } + +.fa-sign-out-alt::before { + content: "\f2f5"; } + +.fa-atom::before { + content: "\f5d2"; } + +.fa-soap::before { + content: "\e06e"; } + +.fa-icons::before { + content: "\f86d"; } + +.fa-heart-music-camera-bolt::before { + content: "\f86d"; } + +.fa-microphone-lines-slash::before { + content: "\f539"; } + +.fa-microphone-alt-slash::before { + content: "\f539"; } + +.fa-bridge-circle-check::before { + content: "\e4c9"; } + +.fa-pump-medical::before { + content: "\e06a"; } + +.fa-fingerprint::before { + content: "\f577"; } + +.fa-hand-point-right::before { + content: "\f0a4"; } + +.fa-magnifying-glass-location::before { + content: "\f689"; } + +.fa-search-location::before { + content: "\f689"; } + +.fa-forward-step::before { + content: "\f051"; } + +.fa-step-forward::before { + content: "\f051"; } + +.fa-face-smile-beam::before { + content: "\f5b8"; } + +.fa-smile-beam::before { + content: "\f5b8"; } + +.fa-flag-checkered::before { + content: "\f11e"; } + +.fa-football::before { + content: "\f44e"; } + +.fa-football-ball::before { + content: "\f44e"; } + +.fa-school-circle-exclamation::before { + content: "\e56c"; } + +.fa-crop::before { + content: "\f125"; } + +.fa-angles-down::before { + content: 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"\f410"; } + +.fa-window-close::before { + content: "\f410"; } + +.fa-baht-sign::before { + content: "\e0ac"; } + +.fa-book-open::before { + content: "\f518"; } + +.fa-book-journal-whills::before { + content: "\f66a"; } + +.fa-journal-whills::before { + content: "\f66a"; } + +.fa-handcuffs::before { + content: "\e4f8"; } + +.fa-triangle-exclamation::before { + content: "\f071"; } + +.fa-exclamation-triangle::before { + content: "\f071"; } + +.fa-warning::before { + content: "\f071"; } + +.fa-database::before { + content: "\f1c0"; } + +.fa-share::before { + content: "\f064"; } + +.fa-mail-forward::before { + content: "\f064"; } + +.fa-bottle-droplet::before { + content: "\e4c4"; } + +.fa-mask-face::before { + content: "\e1d7"; } + +.fa-hill-rockslide::before { + content: "\e508"; } + +.fa-right-left::before { + content: "\f362"; } + +.fa-exchange-alt::before { + content: "\f362"; } + +.fa-paper-plane::before { + content: "\f1d8"; } + +.fa-road-circle-exclamation::before { + content: 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"\f48e"; } + +.fa-cloud-sun::before { + content: "\f6c4"; } + +.fa-stopwatch-20::before { + content: "\e06f"; } + +.fa-square-full::before { + content: "\f45c"; } + +.fa-magnet::before { + content: "\f076"; } + +.fa-jar::before { + content: "\e516"; } + +.fa-note-sticky::before { + content: "\f249"; } + +.fa-sticky-note::before { + content: "\f249"; } + +.fa-bug-slash::before { + content: "\e490"; } + +.fa-arrow-up-from-water-pump::before { + content: "\e4b6"; } + +.fa-bone::before { + content: "\f5d7"; } + +.fa-user-injured::before { + content: "\f728"; } + +.fa-face-sad-tear::before { + content: "\f5b4"; } + +.fa-sad-tear::before { + content: "\f5b4"; } + +.fa-plane::before { + content: "\f072"; } + +.fa-tent-arrows-down::before { + content: "\e581"; } + +.fa-exclamation::before { + content: "\21"; } + +.fa-arrows-spin::before { + content: "\e4bb"; } + +.fa-print::before { + content: "\f02f"; } + +.fa-turkish-lira-sign::before { + content: "\e2bb"; } + +.fa-try::before { + content: "\e2bb"; } + +.fa-turkish-lira::before { + content: "\e2bb"; } + +.fa-dollar-sign::before { + content: "\24"; } + +.fa-dollar::before { + content: "\24"; } + +.fa-usd::before { + content: "\24"; } + +.fa-x::before { + content: "\58"; } + +.fa-magnifying-glass-dollar::before { + content: "\f688"; } + +.fa-search-dollar::before { + content: "\f688"; } + +.fa-users-gear::before { + content: "\f509"; } + +.fa-users-cog::before { + content: "\f509"; } + +.fa-person-military-pointing::before { + content: "\e54a"; } + +.fa-building-columns::before { + content: "\f19c"; } + +.fa-bank::before { + content: "\f19c"; } + +.fa-institution::before { + content: "\f19c"; } + +.fa-museum::before { + content: "\f19c"; } + +.fa-university::before { + content: "\f19c"; } + +.fa-umbrella::before { + content: "\f0e9"; } + +.fa-trowel::before { + content: "\e589"; } + +.fa-d::before { + content: "\44"; } + +.fa-stapler::before { + content: "\e5af"; } + +.fa-masks-theater::before { + content: "\f630"; } + 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content: "\f3ca"; } + +.fa-qq:before { + content: "\f1d6"; } + +.fa-orcid:before { + content: "\f8d2"; } + +.fa-java:before { + content: "\f4e4"; } + +.fa-invision:before { + content: "\f7b0"; } + +.fa-creative-commons-pd-alt:before { + content: "\f4ed"; } + +.fa-centercode:before { + content: "\f380"; } + +.fa-glide-g:before { + content: "\f2a6"; } + +.fa-drupal:before { + content: "\f1a9"; } + +.fa-jxl:before { + content: "\e67b"; } + +.fa-hire-a-helper:before { + content: "\f3b0"; } + +.fa-creative-commons-by:before { + content: "\f4e7"; } + +.fa-unity:before { + content: "\e049"; } + +.fa-whmcs:before { + content: "\f40d"; } + +.fa-rocketchat:before { + content: "\f3e8"; } + +.fa-vk:before { + content: "\f189"; } + +.fa-untappd:before { + content: "\f405"; } + +.fa-mailchimp:before { + content: "\f59e"; } + +.fa-css3-alt:before { + content: "\f38b"; } + +.fa-square-reddit:before { + content: "\f1a2"; } + +.fa-reddit-square:before { + content: "\f1a2"; } + +.fa-vimeo-v:before { + content: "\f27d"; } + +.fa-contao:before { + content: "\f26d"; } + +.fa-square-font-awesome:before { + content: "\e5ad"; } + +.fa-deskpro:before { + content: "\f38f"; } + +.fa-brave:before { + content: "\e63c"; } + +.fa-sistrix:before { + content: "\f3ee"; } + +.fa-square-instagram:before { + content: "\e055"; } + +.fa-instagram-square:before { + content: "\e055"; } + +.fa-battle-net:before { + content: "\f835"; } + +.fa-the-red-yeti:before { + content: "\f69d"; } + +.fa-square-hacker-news:before { + content: "\f3af"; } + +.fa-hacker-news-square:before { + content: "\f3af"; } + +.fa-edge:before { + content: "\f282"; } + +.fa-threads:before { + content: "\e618"; } + +.fa-napster:before { + content: "\f3d2"; } + +.fa-square-snapchat:before { + content: "\f2ad"; } + +.fa-snapchat-square:before { + content: "\f2ad"; } + +.fa-google-plus-g:before { + content: "\f0d5"; } + +.fa-artstation:before { + content: "\f77a"; } + +.fa-markdown:before { + content: "\f60f"; } + +.fa-sourcetree:before { + content: "\f7d3"; } + +.fa-google-plus:before { + content: "\f2b3"; } + +.fa-diaspora:before { + content: "\f791"; } + +.fa-foursquare:before { + content: "\f180"; } + +.fa-stack-overflow:before { + content: "\f16c"; } + +.fa-github-alt:before { + content: "\f113"; } + +.fa-phoenix-squadron:before { + content: "\f511"; } + +.fa-pagelines:before { + content: "\f18c"; } + +.fa-algolia:before { + content: "\f36c"; } + +.fa-red-river:before { + content: "\f3e3"; } + +.fa-creative-commons-sa:before { + content: "\f4ef"; } + +.fa-safari:before { + content: "\f267"; } + +.fa-google:before { + content: "\f1a0"; } + +.fa-square-font-awesome-stroke:before { + content: "\f35c"; } + +.fa-font-awesome-alt:before { + content: "\f35c"; } + +.fa-atlassian:before { + content: "\f77b"; } + +.fa-linkedin-in:before { + content: "\f0e1"; } + +.fa-digital-ocean:before { + content: "\f391"; } + +.fa-nimblr:before { + content: "\f5a8"; } + +.fa-chromecast:before { + content: "\f838"; } + 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file mode 100644 index 0000000..8c5864c Binary files /dev/null and b/_extensions/quarto-ext/fontawesome/assets/webfonts/fa-v4compatibility.ttf differ diff --git a/_extensions/quarto-ext/fontawesome/assets/webfonts/fa-v4compatibility.woff2 b/_extensions/quarto-ext/fontawesome/assets/webfonts/fa-v4compatibility.woff2 new file mode 100644 index 0000000..f94bec2 Binary files /dev/null and b/_extensions/quarto-ext/fontawesome/assets/webfonts/fa-v4compatibility.woff2 differ diff --git a/_extensions/quarto-ext/fontawesome/fontawesome.lua b/_extensions/quarto-ext/fontawesome/fontawesome.lua new file mode 100644 index 0000000..ea44681 --- /dev/null +++ b/_extensions/quarto-ext/fontawesome/fontawesome.lua @@ -0,0 +1,84 @@ +local function ensureLatexDeps() + quarto.doc.use_latex_package("fontawesome5") +end + +local function ensureHtmlDeps() + quarto.doc.add_html_dependency({ + name = 'fontawesome6', + version = '1.2.0', + stylesheets = {'assets/css/all.min.css', 'assets/css/latex-fontsize.css'} + }) +end + +local function isEmpty(s) + return s == nil or s == '' +end + +local function isValidSize(size) + local validSizes = { + "tiny", + "scriptsize", + "footnotesize", + "small", + "normalsize", + "large", + "Large", + "LARGE", + "huge", + "Huge" + } + for _, v in ipairs(validSizes) do + if v == size then + return size + end + end + return "" +end + +return { + ["fa"] = function(args, kwargs) + + local group = "solid" + local icon = pandoc.utils.stringify(args[1]) + if #args > 1 then + group = icon + icon = pandoc.utils.stringify(args[2]) + end + + local title = pandoc.utils.stringify(kwargs["title"]) + if not isEmpty(title) then + title = " title=\"" .. title .. "\"" + end + + local label = pandoc.utils.stringify(kwargs["label"]) + if isEmpty(label) then + label = " aria-label=\"" .. icon .. "\"" + else + label = " aria-label=\"" .. label .. "\"" + end + + local size = pandoc.utils.stringify(kwargs["size"]) + + -- detect html (excluding epub which won't handle fa) + if quarto.doc.is_format("html:js") then + ensureHtmlDeps() + if not isEmpty(size) then + size = " fa-" .. size + end + return pandoc.RawInline( + 'html', + "" + ) + -- detect pdf / beamer / latex / etc + elseif quarto.doc.is_format("pdf") then + ensureLatexDeps() + if isEmpty(isValidSize(size)) then + return pandoc.RawInline('tex', "\\faIcon{" .. icon .. "}") + else + return pandoc.RawInline('tex', "{\\" .. size .. "\\faIcon{" .. icon .. "}}") + end + else + return pandoc.Null() + end + end +} diff --git a/_extensions/shafayetshafee/collapse-callout/_extension.yml b/_extensions/shafayetshafee/collapse-callout/_extension.yml new file mode 100644 index 0000000..252c9c7 --- /dev/null +++ b/_extensions/shafayetshafee/collapse-callout/_extension.yml @@ -0,0 +1,8 @@ +title: Make the callout blocks collapsible on `html` output format +author: Shafayet Khan Shafee +version: 1.0.0 +quarto-required: ">=1.2.0" +contributes: + filters: + - collapse-callout.lua +source: shafayetshafee/collapse-callout@v1.0.0 \ No newline at end of file diff --git a/_extensions/shafayetshafee/collapse-callout/collapse-callout.lua b/_extensions/shafayetshafee/collapse-callout/collapse-callout.lua new file mode 100644 index 0000000..9e4ec70 --- /dev/null +++ b/_extensions/shafayetshafee/collapse-callout/collapse-callout.lua @@ -0,0 +1,56 @@ +-- defining all possible callout types +local callouts_all = { + caution = 'callout-caution', + important = 'callout-important', + tip = 'callout-tip', + note = 'callout-note', + warning = 'callout-warning' + } + +-- function for adding collapse attributes to callout divs +function collapse_callout(callouts, bool) + local callout_filter = { + Div = function(el) + for key, val in pairs(callouts) do + if el.classes:includes(val) then + if el.attributes["collapse"] == nil then + el.attributes["collapse"] = bool + return el + end + end + end + end + } + return callout_filter +end + +-- make changes to input file if the format is html +if quarto.doc.isFormat("html:js") then + function Pandoc (doc) + local collapse = doc.meta['collapse-callout'] + if not collapse then + return nil + end + + if collapse.all == false then + return doc:walk(collapse_callout(callouts_all,'false')) + elseif collapse.all == true then + return doc:walk(collapse_callout(callouts_all, 'true')) + else + filtered_doc = doc + for k, v in pairs{"caution", "important", "tip", "note", "warning"} do + if collapse[v] == true then + filtered_doc = filtered_doc:walk( + collapse_callout({callouts_all[v]}, 'true') + ) + elseif collapse[v] == false then + filtered_doc = filtered_doc:walk( + collapse_callout({callouts_all[v]}, 'false') + ) + end + end + return filtered_doc + end + return nil + end +end \ No newline at end of file diff --git a/_proc/.gitignore b/_proc/.gitignore new file mode 100644 index 0000000..075b254 --- /dev/null +++ b/_proc/.gitignore @@ -0,0 +1 @@ +/.quarto/ diff --git a/_proc/00_core.html.md b/_proc/00_core.html.md new file mode 100644 index 0000000..12bef05 --- /dev/null +++ b/_proc/00_core.html.md @@ -0,0 +1,465 @@ +--- +title: "Core Module: Internal functions and testing" +exec_all: true +--- + + +## core + +> This is a core library for the ERA5 dataset pipeline. It defines a few helpful functions such as an API tester to test your API key and connection. + + + +::: {#cell-2 .cell exports='null'} +``` {.python .cell-code code-fold="show" code-summary="Exported source"} +import os +import cdsapi +import hydra +import json +import tempfile +import argparse +import zipfile +import shutil +import geopandas as gpd +from pathlib import Path +from pydrive2.auth import GoogleAuth +from pydrive2.drive import GoogleDrive +from omegaconf import DictConfig, OmegaConf +from pyprojroot import here +from importlib import import_module +``` +::: + + +## Utilities + +Some utilities are provided to help you with the ERA5 dataset. + +--- + +[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/core.py#L26){target="_blank" style="float:right; font-size:smaller"} + +### describe + +> describe (cfg:omegaconf.dictconfig.DictConfig=None) + +*Describe the configuration file used by Hydra for the pipeline* + +| | **Type** | **Default** | **Details** | +| -- | -------- | ----------- | ----------- | +| cfg | DictConfig | None | Configuration file | +| **Returns** | **None** | | | + + +::: {#cell-5 .cell exports='null'} +``` {.python .cell-code code-fold="show" code-summary="Exported source"} +def describe( + cfg: DictConfig=None, # Configuration file + )-> None: + "Describe the configuration file used by Hydra for the pipeline" + + if cfg is None: + print("No configuration file provided. Generating default configuration file.") + cfg = OmegaConf.create() + + print("This package fetches ERA5 data. The following is the config file used by Hydra for the pipeline:\n") + print(OmegaConf.to_yaml(cfg)) +``` +::: + + +In addition, we've defined 3 private functions to help with path expansion [`_expand_path`](https://TinasheMTapera.github.io/era5_sandbox/core.html#_expand_path), dynamic function importing [`_get_callable`](https://TinasheMTapera.github.io/era5_sandbox/core.html#_get_callable), and directory structure creation [`_create_directory_structure`](https://TinasheMTapera.github.io/era5_sandbox/core.html#_create_directory_structure). + +### A Simple Temperature Conversion Function + +--- + +[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/core.py#L81){target="_blank" style="float:right; font-size:smaller"} + +### kelvin_to_celsius + +> kelvin_to_celsius (kelvin:float) + +*Convert temperature from Kelvin to Celsius.* + +| | **Type** | **Details** | +| -- | -------- | ----------- | +| kelvin | float | Temperature in Kelvin | +| **Returns** | **float** | **Temperature in Celsius** | + + +### A Class for Authenticating Google Drive + +We're going to use a class to authenticate and interact with google drive. The goal is to have a simple interface to fetch the healthshed files dynamically from google drive in the pipeline. + +::: {.callout-important} +This class was implemented when all of our data +was stored on a private Google Drive. Since we +have moved all of our data to FASRC, this will +likely be deprecated in the near future. +::: + +--- + +[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/core.py#L91){target="_blank" style="float:right; font-size:smaller"} + +### GoogleDriver + +> GoogleDriver (json_key_path=None) + +*A class to handle Google Drive authentication and file management. +This class uses the PyDrive2 library to authenticate with Google Drive using a service account. + +It provides three methods: authenticating the account, getting the drive object, and downloading the healthshed files for madagascar.* + + +Here's how we use it. The credentials for the data-pipeline service account are +available in the sandbox folder, and the path to said folder is set in the config: + +::: {#cell-11 .cell} +``` {.python .cell-code} +from hydra import initialize, compose +from omegaconf import OmegaConf +``` +::: + + +::: {#cell-12 .cell} +``` {.python .cell-code} +# unfortunately, we have to use the initialize function to load the config file +# this is because the @hydra decorator does not work with Notebooks very well +# this is a known issue with Hydra: https://gist.github.com/bdsaglam/586704a98336a0cf0a65a6e7c247d248 +# +# just use the relative path from the notebook to the config dir +try: + with initialize(version_base=None, config_path="../conf"): + cfg = compose(config_name='config.yaml') +except Exception as e: + print(f"Error initializing Hydra: {e}") + with initialize(version_base=None, config_path="conf"): + cfg = compose(config_name='config.yaml') +``` +::: + + +::: {.callout-important} +If we continue with `pytask`, we will not need to +use hydra at all, and so the above strategy +may get deprecated. +::: + +::: {#cell-14 .cell} +``` {.python .cell-code} +auth = GoogleDriver(json_key_path=here() / cfg.GOOGLE_DRIVE_AUTH_JSON.path) +drive = auth.get_drive() +``` +::: + + +Here's how we might check that the healthsheds are accessible in the drive: + +::: {#cell-16 .cell} +``` {.python .cell-code} +# we're using the madagascar healthshed folder as an example +folder_id = cfg.geographies.madagascar.healthsheds +folder_name = "healthsheds2022.zip" +file_list = drive.ListFile({'q': f" title='{folder_name}' and trashed = false "}).GetList() + +for file in file_list: + print(f"{file['title']} - {file['mimeType']}") +``` +::: + + +That being said, we can read in the healthsheds into geopandas by downloading them to a temp directory. The healthsheds must be a zipped shapefiles package with the files at the root of the zip directory. + +::: {#cell-18 .cell} +``` {.python .cell-code} +with tempfile.TemporaryDirectory() as temp_dir: + # Create a temporary directory to store the downloaded file + zip_path = os.path.join(temp_dir, folder_name) + + # Download file from Google Drive + file_obj = drive.CreateFile({'id': file_list[0]['id']}) + file_obj.GetContentFile(zip_path) + + # Read shapefile directly from ZIP + gdf = gpd.read_file(f"zip://{zip_path}") +``` +::: + + +That works! So now we can patch the class to include this workflow: + +--- + +[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/core.py#L128){target="_blank" style="float:right; font-size:smaller"} + +### GoogleDriver.read_healthsheds + +> GoogleDriver.read_healthsheds (healthshed_zip_name) + + +And to check that it works: + +::: {#cell-22 .cell} +``` {.python .cell-code} +driver = GoogleDriver(json_key_path=here() / cfg.GOOGLE_DRIVE_AUTH_JSON.path) +drive = driver.get_drive() +healthsheds = driver.read_healthsheds("healthsheds2022.zip") + +healthsheds.describe() +``` +::: + + +## CDS File Handler Type + +::: {.callout-important} +This section may also be deprecated. Since adding `swvl1` to the pipeline, we have not needed to use this class. We leave it here for now for reference. +::: + +We're going to make a file handler type to help deal with CDS files. This is to fix [NSAPH-Data-Processing/era5_sandbox#13](https://github.com/NSAPH-Data-Processing/era5_sandbox/issues/13). + +Usually, when you download data, it comes out as a simple .nc file that can be opened with xarray. However, the CDS API has a few different file types that are not .nc files. For example, the ERA5 data is stored in a .grib file format. This is a common format for meteorological data, and it is used by the ECMWF. When a query has multiple variables, sometimes they are downloaded as a .zip file to separat the grib from the netcdf. + +So, below, we define a class that can handle the file no matter what the type is. It will check the file type and then use the appropriate method to open it. The class will also have a method to check if the file is a .zip file, and if so, it will unzip it and return the path to the unzipped file. + +--- + +[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/core.py#L151){target="_blank" style="float:right; font-size:smaller"} + +### ClimateDataFileHandler + +> ClimateDataFileHandler (input_path:str) + +*A class to handle file operations for the Climate Data Store (CDS). +This class provides unpack files downloaded from the CDS API. It must be able to +handle the unpacking of files downloaded from the CDS API. This means that +if the file is a basic netcdf, it should be passed to the netcdf handler. If +the file is a zip, it should be handled by the zip handler in temp and the +data returned as required.* + + +::: {#cell-25 .cell} +``` {.python .cell-code} +import xarray as xr +from fastcore.test import test_fail +``` +::: + + +::: {#cell-26 .cell} +``` {.python .cell-code} +eg_file = here() / "bld/2019_5_madagascar.nc" + +# this fails because the nc file downloaded has grib and netcdf in it, so +# xr cannot handle it. +def wont_work(multilayer_file): + + ds = xr.open_dataset(multilayer_file) + +test_fail( + wont_work, + args=(eg_file) +) + +# equivalent to saying try: wont_work(eg_file) Except: some error handling +``` +::: + + +The above fails because the download contains temperature and precipitation data, which get encoded silently as different formats. Even though it is one file, it contains both grib and netcdf data and is encoded as a .zip file. So we use the class to read it instead: + +::: {#cell-28 .cell} +``` {.python .cell-code} +handler = ClimateDataFileHandler(eg_file) +handler.prepare() +ds1 = xr.open_dataset(handler.get_dataset("instant")) +#ds2 = xr.open_dataset(handler.get_dataset("accum")) +``` +::: + + +::: {.callout-important} +The above line for `ds2` is commented out because the example file does not separate accumulation data. +::: + +::: {#cell-30 .cell} +``` {.python .cell-code} +ds1 +``` +::: + + +::: {#cell-31 .cell} +``` {.python .cell-code} +#ds2 +``` +::: + + +::: {#cell-32 .cell} +``` {.python .cell-code} +handler.cleanup() +``` +::: + + +Great! Let's add a context handler and this can be added to the pipeline, +so that with the entry and exit methods, we can now use the class in a `with` statement: + +::: {#cell-34 .cell} +``` {.python .cell-code} +with ClimateDataFileHandler(eg_file) as handler: + ds1 = xr.open_dataset(handler.get_dataset("instant")) + #ds2 = xr.open_dataset(handler.get_dataset("accum")) + + print(ds1) + #print(ds2) +``` +::: + + +## Tests and Main + +In `nbdev`, our tests are embedded in the notebook. Whenever you export the notebook, all the cells that are specified to run are run, and hence, the tests are executed. The tests are also exported. This is a great way to ensure that your documentation is always up-to-date. For this module, we're using the [`testAPI()`](https://TinasheMTapera.github.io/era5_sandbox/core.html#testapi) function as our main test. + +--- + +[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/core.py#L254){target="_blank" style="float:right; font-size:smaller"} + +### testAPI + +> testAPI (cfg:omegaconf.dictconfig.DictConfig=None, +> dataset:str='reanalysis-era5-pressure-levels') + + +::: {#cell-37 .cell exports='null'} +``` {.python .cell-code code-fold="show" code-summary="Exported source"} +def testAPI( + cfg: DictConfig=None, + dataset:str="reanalysis-era5-pressure-levels" + )-> bool: + + # parse config + testing=cfg.development_mode + output_path=here("data") / "testing" + + print(OmegaConf.to_yaml(cfg)) + + try: + client = cdsapi.Client() + + # build request + request = { + 'product_type': ['reanalysis'], + 'variable': ['geopotential'], + 'year': ['2024'], + 'month': ['03'], + 'day': ['01'], + 'time': ['13:00'], + 'pressure_level': ['1000'], + 'data_format': 'grib', + } + + target = output_path / 'test_download.grib' + + print("Testing API connection by downloading a dummy dataset to {}...".format(output_path)) + + client.retrieve(dataset, request, target) + + if not testing: + os.remove(target) + + print("API connection test successful.") + return True + + except Exception as e: + print("API connection test failed.") + print("Did you set up your API key with CDS? If not, please visit https://cds.climate.copernicus.eu/how-to-api#install-the-cds-api-client") + print("Error: {}".format(e)) + return False +``` +::: + + +We can see that this API tester tool works with Hydra configuration: + +::: {#cell-39 .cell} +``` {.python .cell-code} +from hydra import initialize, compose +from omegaconf import OmegaConf +``` +::: + + +::: {#cell-40 .cell} +``` {.python .cell-code} +# unfortunately, we have to use the initialize function to load the config file +# this is because the @hydra decorator does not work with Notebooks very well +# this is a known issue with Hydra: https://gist.github.com/bdsaglam/586704a98336a0cf0a65a6e7c247d248 +# +# just use the relative path from the notebook to the config dir +try: + with initialize(version_base=None, config_path="../conf"): + cfg = compose(config_name='config.yaml') +except Exception as e: + print(f"Error initializing Hydra: {e}") + with initialize(version_base=None, config_path="conf"): + cfg = compose(config_name='config.yaml') + +describe(cfg) +``` +::: + + +### Importing the Main Function + +::: {.callout-important} +As mentioned, if we continue with `pytask`, we will not need to use hydra at all, and so the main function +may get deprecated as `pytask` will handle the pipeline execution without `__main__` scripts. +::: + +Important: using `__main__` in nbdev and Hydra is a little bit tricky. We need to define the main function in the module ONLY ONCE and then when we export the notebook to script, we need to add the `nbdev.imports.IN_NOTEBOOK` variable. This way, the main function will only be executed when we run the notebook and not when we import the module. + +```python +from nbdev.imports import IN_NOTEBOOK +``` + +You'll see this listed throughout the notebooks. + +--- + +[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/aggregate.py#L302){target="_blank" style="float:right; font-size:smaller"} + +### main + +> main (cfg:omegaconf.dictconfig.DictConfig) + + +::: {#cell-43 .cell exports='null'} +``` {.python .cell-code code-fold="show" code-summary="Exported source"} +@hydra.main(version_base=None, config_path="../../conf", config_name="config") +def main(cfg: DictConfig) -> None: + + # Create the directory structure + _create_directory_structure(here() / "data", cfg.datapaths) + + # test the api + testAPI(cfg=cfg) +``` +::: + + +::: {#cell-44 .cell export='null'} +``` {.python .cell-code} +try: from nbdev.imports import IN_NOTEBOOK +except: IN_NOTEBOOK=False + +if __name__ == "__main__" and not IN_NOTEBOOK: + main() +``` +::: + + diff --git a/_proc/00_core.ipynb b/_proc/00_core.ipynb new file mode 100644 index 0000000..2b6b95c --- /dev/null +++ b/_proc/00_core.ipynb @@ -0,0 +1,891 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "title: \"Core Module: Internal functions and testing\"\n", + "exec_all: true\n", + "---\n", + "\n", + "## core\n", + "\n", + "> This is a core library for the ERA5 dataset pipeline. It defines a few helpful functions such as an API tester to test your API key and connection." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| code-fold: show\n", + "#| code-summary: \"Exported source\"\n", + "#| exports: #\n", + "import os\n", + "import cdsapi\n", + "import hydra\n", + "import json\n", + "import tempfile\n", + "import argparse\n", + "import zipfile\n", + "import shutil\n", + "import geopandas as gpd\n", + "from pathlib import Path\n", + "from pydrive2.auth import GoogleAuth\n", + "from pydrive2.drive import GoogleDrive\n", + "from omegaconf import DictConfig, OmegaConf\n", + "from pyprojroot import here\n", + "from importlib import import_module" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Utilities\n", + "\n", + "Some utilities are provided to help you with the ERA5 dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "has_sd": true, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/core.py#L26){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### describe\n", + "\n", + "> describe (cfg:omegaconf.dictconfig.DictConfig=None)\n", + "\n", + "*Describe the configuration file used by Hydra for the pipeline*\n", + "\n", + "| | **Type** | **Default** | **Details** |\n", + "| -- | -------- | ----------- | ----------- |\n", + "| cfg | DictConfig | None | Configuration file |\n", + "| **Returns** | **None** | | |" + ], + "text/plain": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/core.py#L26){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### describe\n", + "\n", + "> describe (cfg:omegaconf.dictconfig.DictConfig=None)\n", + "\n", + "*Describe the configuration file used by Hydra for the pipeline*\n", + "\n", + "| | **Type** | **Default** | **Details** |\n", + "| -- | -------- | ----------- | ----------- |\n", + "| cfg | DictConfig | None | Configuration file |\n", + "| **Returns** | **None** | | |" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| echo: false\n", + "#| output: asis\n", + "show_doc(describe)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| code-fold: show\n", + "#| code-summary: \"Exported source\"\n", + "#| exports: #\n", + "def describe(\n", + " cfg: DictConfig=None, # Configuration file\n", + " )-> None:\n", + " \"Describe the configuration file used by Hydra for the pipeline\"\n", + " \n", + " if cfg is None:\n", + " print(\"No configuration file provided. Generating default configuration file.\")\n", + " cfg = OmegaConf.create()\n", + " \n", + " print(\"This package fetches ERA5 data. The following is the config file used by Hydra for the pipeline:\\n\")\n", + " print(OmegaConf.to_yaml(cfg))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In addition, we've defined 3 private functions to help with path expansion [`_expand_path`](https://TinasheMTapera.github.io/era5_sandbox/core.html#_expand_path), dynamic function importing [`_get_callable`](https://TinasheMTapera.github.io/era5_sandbox/core.html#_get_callable), and directory structure creation [`_create_directory_structure`](https://TinasheMTapera.github.io/era5_sandbox/core.html#_create_directory_structure).\n", + "\n", + "### A Simple Temperature Conversion Function" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "has_sd": true, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/core.py#L81){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### kelvin_to_celsius\n", + "\n", + "> kelvin_to_celsius (kelvin:float)\n", + "\n", + "*Convert temperature from Kelvin to Celsius.*\n", + "\n", + "| | **Type** | **Details** |\n", + "| -- | -------- | ----------- |\n", + "| kelvin | float | Temperature in Kelvin |\n", + "| **Returns** | **float** | **Temperature in Celsius** |" + ], + "text/plain": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/core.py#L81){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### kelvin_to_celsius\n", + "\n", + "> kelvin_to_celsius (kelvin:float)\n", + "\n", + "*Convert temperature from Kelvin to Celsius.*\n", + "\n", + "| | **Type** | **Details** |\n", + "| -- | -------- | ----------- |\n", + "| kelvin | float | Temperature in Kelvin |\n", + "| **Returns** | **float** | **Temperature in Celsius** |" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| echo: false\n", + "#| output: asis\n", + "show_doc(kelvin_to_celsius)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### A Class for Authenticating Google Drive\n", + "\n", + "We're going to use a class to authenticate and interact with google drive. The goal is to have a simple interface to fetch the healthshed files dynamically from google drive in the pipeline.\n", + "\n", + "::: {.callout-important}\n", + "This class was implemented when all of our data\n", + "was stored on a private Google Drive. Since we\n", + "have moved all of our data to FASRC, this will\n", + "likely be deprecated in the near future.\n", + ":::" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "has_sd": true, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/core.py#L91){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### GoogleDriver\n", + "\n", + "> GoogleDriver (json_key_path=None)\n", + "\n", + "*A class to handle Google Drive authentication and file management.\n", + "This class uses the PyDrive2 library to authenticate with Google Drive using a service account.\n", + "\n", + "It provides three methods: authenticating the account, getting the drive object, and downloading the healthshed files for madagascar.*" + ], + "text/plain": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/core.py#L91){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### GoogleDriver\n", + "\n", + "> GoogleDriver (json_key_path=None)\n", + "\n", + "*A class to handle Google Drive authentication and file management.\n", + "This class uses the PyDrive2 library to authenticate with Google Drive using a service account.\n", + "\n", + "It provides three methods: authenticating the account, getting the drive object, and downloading the healthshed files for madagascar.*" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| echo: false\n", + "#| output: asis\n", + "show_doc(GoogleDriver)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's how we use it. The credentials for the data-pipeline service account are\n", + "available in the sandbox folder, and the path to said folder is set in the config:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "from hydra import initialize, compose\n", + "from omegaconf import OmegaConf" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "# unfortunately, we have to use the initialize function to load the config file\n", + "# this is because the @hydra decorator does not work with Notebooks very well\n", + "# this is a known issue with Hydra: https://gist.github.com/bdsaglam/586704a98336a0cf0a65a6e7c247d248\n", + "# \n", + "# just use the relative path from the notebook to the config dir\n", + "try:\n", + " with initialize(version_base=None, config_path=\"../conf\"):\n", + " cfg = compose(config_name='config.yaml')\n", + "except Exception as e:\n", + " print(f\"Error initializing Hydra: {e}\")\n", + " with initialize(version_base=None, config_path=\"conf\"):\n", + " cfg = compose(config_name='config.yaml')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "::: {.callout-important}\n", + "If we continue with `pytask`, we will not need to\n", + "use hydra at all, and so the above strategy\n", + "may get deprecated.\n", + ":::" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "auth = GoogleDriver(json_key_path=here() / cfg.GOOGLE_DRIVE_AUTH_JSON.path)\n", + "drive = auth.get_drive()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's how we might check that the healthsheds are accessible in the drive:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "# we're using the madagascar healthshed folder as an example\n", + "folder_id = cfg.geographies.madagascar.healthsheds\n", + "folder_name = \"healthsheds2022.zip\"\n", + "file_list = drive.ListFile({'q': f\" title='{folder_name}' and trashed = false \"}).GetList()\n", + "\n", + "for file in file_list:\n", + " print(f\"{file['title']} - {file['mimeType']}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That being said, we can read in the healthsheds into geopandas by downloading them to a temp directory. The healthsheds must be a zipped shapefiles package with the files at the root of the zip directory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "with tempfile.TemporaryDirectory() as temp_dir:\n", + " # Create a temporary directory to store the downloaded file\n", + " zip_path = os.path.join(temp_dir, folder_name)\n", + "\n", + " # Download file from Google Drive\n", + " file_obj = drive.CreateFile({'id': file_list[0]['id']})\n", + " file_obj.GetContentFile(zip_path)\n", + "\n", + " # Read shapefile directly from ZIP\n", + " gdf = gpd.read_file(f\"zip://{zip_path}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That works! So now we can patch the class to include this workflow:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "has_sd": true, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/core.py#L128){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### GoogleDriver.read_healthsheds\n", + "\n", + "> GoogleDriver.read_healthsheds (healthshed_zip_name)" + ], + "text/plain": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/core.py#L128){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### GoogleDriver.read_healthsheds\n", + "\n", + "> GoogleDriver.read_healthsheds (healthshed_zip_name)" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| echo: false\n", + "#| output: asis\n", + "show_doc(GoogleDriver.read_healthsheds)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And to check that it works:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "driver = GoogleDriver(json_key_path=here() / cfg.GOOGLE_DRIVE_AUTH_JSON.path)\n", + "drive = driver.get_drive()\n", + "healthsheds = driver.read_healthsheds(\"healthsheds2022.zip\")\n", + "\n", + "healthsheds.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## CDS File Handler Type\n", + "\n", + "::: {.callout-important}\n", + "This section may also be deprecated. Since adding `swvl1` to the pipeline, we have not needed to use this class. We leave it here for now for reference.\n", + ":::\n", + "\n", + "We're going to make a file handler type to help deal with CDS files. This is to fix [NSAPH-Data-Processing/era5_sandbox#13](https://github.com/NSAPH-Data-Processing/era5_sandbox/issues/13). \n", + "\n", + "Usually, when you download data, it comes out as a simple .nc file that can be opened with xarray. However, the CDS API has a few different file types that are not .nc files. For example, the ERA5 data is stored in a .grib file format. This is a common format for meteorological data, and it is used by the ECMWF. When a query has multiple variables, sometimes they are downloaded as a .zip file to separat the grib from the netcdf.\n", + "\n", + "So, below, we define a class that can handle the file no matter what the type is. It will check the file type and then use the appropriate method to open it. The class will also have a method to check if the file is a .zip file, and if so, it will unzip it and return the path to the unzipped file." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "has_sd": true, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/core.py#L151){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### ClimateDataFileHandler\n", + "\n", + "> ClimateDataFileHandler (input_path:str)\n", + "\n", + "*A class to handle file operations for the Climate Data Store (CDS).\n", + "This class provides unpack files downloaded from the CDS API. It must be able to\n", + "handle the unpacking of files downloaded from the CDS API. This means that\n", + "if the file is a basic netcdf, it should be passed to the netcdf handler. If\n", + "the file is a zip, it should be handled by the zip handler in temp and the\n", + "data returned as required.*" + ], + "text/plain": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/core.py#L151){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### ClimateDataFileHandler\n", + "\n", + "> ClimateDataFileHandler (input_path:str)\n", + "\n", + "*A class to handle file operations for the Climate Data Store (CDS).\n", + "This class provides unpack files downloaded from the CDS API. It must be able to\n", + "handle the unpacking of files downloaded from the CDS API. This means that\n", + "if the file is a basic netcdf, it should be passed to the netcdf handler. If\n", + "the file is a zip, it should be handled by the zip handler in temp and the\n", + "data returned as required.*" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| echo: false\n", + "#| output: asis\n", + "show_doc(ClimateDataFileHandler)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "import xarray as xr\n", + "from fastcore.test import test_fail" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "eg_file = here() / \"bld/2019_5_madagascar.nc\"\n", + "\n", + "# this fails because the nc file downloaded has grib and netcdf in it, so\n", + "# xr cannot handle it.\n", + "def wont_work(multilayer_file):\n", + "\n", + " ds = xr.open_dataset(multilayer_file)\n", + "\n", + "test_fail(\n", + " wont_work,\n", + " args=(eg_file)\n", + ")\n", + "\n", + "# equivalent to saying try: wont_work(eg_file) Except: some error handling" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The above fails because the download contains temperature and precipitation data, which get encoded silently as different formats. Even though it is one file, it contains both grib and netcdf data and is encoded as a .zip file. So we use the class to read it instead:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "handler = ClimateDataFileHandler(eg_file)\n", + "handler.prepare()\n", + "ds1 = xr.open_dataset(handler.get_dataset(\"instant\"))\n", + "#ds2 = xr.open_dataset(handler.get_dataset(\"accum\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "::: {.callout-important}\n", + "The above line for `ds2` is commented out because the example file does not separate accumulation data. \n", + ":::" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "ds1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#ds2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "handler.cleanup()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Great! Let's add a context handler and this can be added to the pipeline,\n", + "so that with the entry and exit methods, we can now use the class in a `with` statement:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "with ClimateDataFileHandler(eg_file) as handler:\n", + " ds1 = xr.open_dataset(handler.get_dataset(\"instant\"))\n", + " #ds2 = xr.open_dataset(handler.get_dataset(\"accum\"))\n", + "\n", + " print(ds1)\n", + " #print(ds2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tests and Main\n", + "\n", + "In `nbdev`, our tests are embedded in the notebook. Whenever you export the notebook, all the cells that are specified to run are run, and hence, the tests are executed. The tests are also exported. This is a great way to ensure that your documentation is always up-to-date. For this module, we're using the [`testAPI()`](https://TinasheMTapera.github.io/era5_sandbox/core.html#testapi) function as our main test." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "has_sd": true, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/core.py#L254){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### testAPI\n", + "\n", + "> testAPI (cfg:omegaconf.dictconfig.DictConfig=None,\n", + "> dataset:str='reanalysis-era5-pressure-levels')" + ], + "text/plain": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/core.py#L254){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### testAPI\n", + "\n", + "> testAPI (cfg:omegaconf.dictconfig.DictConfig=None,\n", + "> dataset:str='reanalysis-era5-pressure-levels')" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| echo: false\n", + "#| output: asis\n", + "show_doc(testAPI)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| code-fold: show\n", + "#| code-summary: \"Exported source\"\n", + "#| exports: #\n", + "def testAPI(\n", + " cfg: DictConfig=None,\n", + " dataset:str=\"reanalysis-era5-pressure-levels\"\n", + " )-> bool: \n", + " \n", + " # parse config\n", + " testing=cfg.development_mode\n", + " output_path=here(\"data\") / \"testing\"\n", + "\n", + " print(OmegaConf.to_yaml(cfg))\n", + "\n", + " try:\n", + " client = cdsapi.Client()\n", + "\n", + " # build request\n", + " request = {\n", + " 'product_type': ['reanalysis'],\n", + " 'variable': ['geopotential'],\n", + " 'year': ['2024'],\n", + " 'month': ['03'],\n", + " 'day': ['01'],\n", + " 'time': ['13:00'],\n", + " 'pressure_level': ['1000'],\n", + " 'data_format': 'grib',\n", + " }\n", + "\n", + " target = output_path / 'test_download.grib'\n", + " \n", + " print(\"Testing API connection by downloading a dummy dataset to {}...\".format(output_path))\n", + "\n", + " client.retrieve(dataset, request, target)\n", + "\n", + " if not testing:\n", + " os.remove(target)\n", + " \n", + " print(\"API connection test successful.\")\n", + " return True\n", + "\n", + " except Exception as e:\n", + " print(\"API connection test failed.\")\n", + " print(\"Did you set up your API key with CDS? If not, please visit https://cds.climate.copernicus.eu/how-to-api#install-the-cds-api-client\")\n", + " print(\"Error: {}\".format(e))\n", + " return False" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that this API tester tool works with Hydra configuration:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "from hydra import initialize, compose\n", + "from omegaconf import OmegaConf" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "# unfortunately, we have to use the initialize function to load the config file\n", + "# this is because the @hydra decorator does not work with Notebooks very well\n", + "# this is a known issue with Hydra: https://gist.github.com/bdsaglam/586704a98336a0cf0a65a6e7c247d248\n", + "# \n", + "# just use the relative path from the notebook to the config dir\n", + "try:\n", + " with initialize(version_base=None, config_path=\"../conf\"):\n", + " cfg = compose(config_name='config.yaml')\n", + "except Exception as e:\n", + " print(f\"Error initializing Hydra: {e}\")\n", + " with initialize(version_base=None, config_path=\"conf\"):\n", + " cfg = compose(config_name='config.yaml')\n", + "\n", + "describe(cfg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Importing the Main Function\n", + "\n", + "::: {.callout-important}\n", + "As mentioned, if we continue with `pytask`, we will not need to use hydra at all, and so the main function\n", + "may get deprecated as `pytask` will handle the pipeline execution without `__main__` scripts.\n", + ":::\n", + "\n", + "Important: using `__main__` in nbdev and Hydra is a little bit tricky. We need to define the main function in the module ONLY ONCE and then when we export the notebook to script, we need to add the `nbdev.imports.IN_NOTEBOOK` variable. This way, the main function will only be executed when we run the notebook and not when we import the module.\n", + "\n", + "```python\n", + "from nbdev.imports import IN_NOTEBOOK\n", + "```\n", + "\n", + "You'll see this listed throughout the notebooks." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "has_sd": true, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/aggregate.py#L302){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### main\n", + "\n", + "> main (cfg:omegaconf.dictconfig.DictConfig)" + ], + "text/plain": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/aggregate.py#L302){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### main\n", + "\n", + "> main (cfg:omegaconf.dictconfig.DictConfig)" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| echo: false\n", + "#| output: asis\n", + "show_doc(main)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| code-fold: show\n", + "#| code-summary: \"Exported source\"\n", + "#| exports: #\n", + "@hydra.main(version_base=None, config_path=\"../../conf\", config_name=\"config\")\n", + "def main(cfg: DictConfig) -> None:\n", + "\n", + " # Create the directory structure\n", + " _create_directory_structure(here() / \"data\", cfg.datapaths)\n", + "\n", + " # test the api\n", + " testAPI(cfg=cfg)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| export: null\n", + "#| eval: false\n", + "try: from nbdev.imports import IN_NOTEBOOK\n", + "except: IN_NOTEBOOK=False\n", + "\n", + "if __name__ == \"__main__\" and not IN_NOTEBOOK:\n", + " main()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/_proc/01_download_raw_data.html.md b/_proc/01_download_raw_data.html.md new file mode 100644 index 0000000..dc7e233 --- /dev/null +++ b/_proc/01_download_raw_data.html.md @@ -0,0 +1,225 @@ +--- +title: "Download Module: Downloading Raw Data from CDSAPI" +engine: jupyter +--- + + +## download + +> This module downloads the raw data from CDS and saves it in the local directory + + + +We use a similar approach to the one in the tutorial to download the data +to local storage. + +The background functionality in this module involves downloading the +bounding box of a region of interest, and sending that to the +CDS API query. As such, we define two helper functions to +fetch the OCHA/HDX shapefiles for a geographic region, and +another to create the bounding box from the files. + +--- + +[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/download.py#L58){target="_blank" style="float:right; font-size:smaller"} + +### fetch_GADM + +> fetch_GADM +> (url:str='https://geodata.ucdavis.edu/gadm/gadm4.1/gpkg/gadm4 +> 1_MDG.gpkg', output_file:str='gadm41_MDG.gpkg') + +*Fetch the GADM bounding box for geographic region* + +| | **Type** | **Default** | **Details** | +| -- | -------- | ----------- | ----------- | +| url | str | https://geodata.ucdavis.edu/gadm/gadm4.1/gpkg/gadm41_MDG.gpkg | URL to fetch the GADM data for Madagascar | +| output_file | str | gadm41_MDG.gpkg | file path to save the GADM data | +| **Returns** | **str** | | | + + +--- + +[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/download.py#L79){target="_blank" style="float:right; font-size:smaller"} + +### create_bounding_box + +> create_bounding_box (zip_url_or_path:str, buffer_km:float=50, +> round_to:int=1) + +*Create a bounding box from OCHA/HDX shapefile data with a buffer.* + +| | **Type** | **Default** | **Details** | +| -- | -------- | ----------- | ----------- | +| zip_url_or_path | str | | URL or local path to the zipped shapefile. | +| buffer_km | float | 50 | Buffer distance in kilometers to expand the bounding box. | +| round_to | int | 1 | Number of decimal places to round the bounding box coordinates. | +| **Returns** | **list** | | **Bounding box in the CDS API area format [North, West, South, East]** | + + +::: {#cell-6 .cell exports='null'} +``` {.python .cell-code code-fold="show" code-summary="Exported source"} +def create_bounding_box( + zip_url_or_path: str, # URL or local path to the zipped shapefile. + buffer_km: float = 50, # Buffer distance in kilometers to expand the bounding box. + round_to: int = 1 # Number of decimal places to round the bounding box coordinates. +) -> list: # Bounding box in the CDS API area format [North, West, South, East] + ''' + Create a bounding box from OCHA/HDX shapefile data with a buffer. + ''' + with tempfile.TemporaryDirectory() as tmpdir: + # Download if it's a URL + if zip_url_or_path.startswith("http"): + response = requests.get(zip_url_or_path) + zip_path = os.path.join(tmpdir, "ocha_data.zip") + with open(zip_path, "wb") as f: + f.write(response.content) + else: + zip_path = zip_url_or_path + + # Unzip + with zipfile.ZipFile(zip_path, 'r') as zip_ref: + zip_ref.extractall(tmpdir) + + # Find the .shp file + shp_files = list(Path(tmpdir).rglob("*.shp")) + if not shp_files: + raise FileNotFoundError("No shapefile (.shp) found in the extracted archive.") + shp_path = str(shp_files[0]) # Use first found .shp + + # Read shapefile + shape = gpd.read_file(shp_path) + + # Reproject to projected CRS (you may want to detect the correct UTM zone) + shape_proj = shape.to_crs(epsg=32738) + + # Apply buffer + buffered = shape_proj.geometry.buffer(buffer_km * 1000) + + # Convert back to geographic coordinates + buffered_geo = gpd.GeoSeries(buffered, crs=shape_proj.crs).to_crs(epsg=4326) + + # Get bounding box + bounds = buffered_geo.total_bounds # [min_x, min_y, max_x, max_y] + bbox = [ + round(bounds[3], round_to), # North + round(bounds[0], round_to), # West + round(bounds[1], round_to), # South + round(bounds[2], round_to) # East + ] + + return bbox +``` +::: + + +The primary function to download the data from CDSAPI is defined below. + +--- + +[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/download.py#L132){target="_blank" style="float:right; font-size:smaller"} + +### download_raw_era5 + +> download_raw_era5 (cfg:omegaconf.dictconfig.DictConfig) + +*Send the query to the API and download the data* + +| | **Type** | **Details** | +| -- | -------- | ----------- | +| cfg | DictConfig | hydra configuration file | +| **Returns** | **None** | | + + +::: {#cell-9 .cell exports='null'} +``` {.python .cell-code code-fold="show" code-summary="Exported source"} +def download_raw_era5( + cfg: DictConfig # hydra configuration file + )->None: + ''' + Send the query to the API and download the data + ''' + + # parse the cfg + testing = cfg.development_mode # for testing + output_dir = here("data/input") # output directory + + geography = cfg.query.geography + + target = os.path.join(_expand_path(output_dir), "{}_{}_{}.nc".format(geography, cfg.query['year'], cfg.query['month'])) + + client = cdsapi.Client() + + query = _validate_query(cfg.query) + + dataset = cfg.dataset + # to make sure the query is valid at the end + del query['geography'] + + # Send the query to the client + if not testing: + bounds = create_bounding_box(cfg.geographies[geography]['shapefile']) + query['area'] = bounds + client.retrieve(dataset, query).download(target) + + print("Downloaded file to: {}".format(target)) + else: + print(f"Testing mode. Not downloading data. Query is {query}") + + print("Done") +``` +::: + + +## Tests and Main + +Here we define some tests and the main function that will be used to download the data. + +::: {#cell-11 .cell} +``` {.python .cell-code} +from hydra import initialize, compose +from omegaconf import OmegaConf + +# unfortunately, we have to use the initialize function to load the config file +# this is because the @hydra decorator does not work with Notebooks very well +# this is a known issue with Hydra: https://gist.github.com/bdsaglam/586704a98336a0cf0a65a6e7c247d248 +# +# just use the relative path from the notebook to the config dir +try: + with initialize(version_base=None, config_path="../conf"): + cfg = compose(config_name='config.yaml') +except Exception as e: + print(f"Error initializing Hydra: {e}") + with initialize(version_base=None, config_path="conf"): + cfg = compose(config_name='config.yaml') + +cfg.development_mode = False +cfg.query['year'] = 2017 +cfg.query['month'] = 11 +#cfg.query['day'] = 1 +#cfg.query['time'] = "00:00" +cfg.query['geography'] = "nepal" +download_raw_era5(cfg) +``` +::: + + +--- + +[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/aggregate.py#L302){target="_blank" style="float:right; font-size:smaller"} + +### main + +> main (cfg:omegaconf.dictconfig.DictConfig) + + +::: {#cell-13 .cell exports='null'} +``` {.python .cell-code code-fold="show" code-summary="Exported source"} +@hydra.main(config_path="../../conf", config_name="config", version_base=None) +def main(cfg: DictConfig) -> None: + download_raw_era5(cfg=cfg) + # better approach would be to have the function only use the specific arguments of the config +``` +::: + + diff --git a/_proc/01_download_raw_data.ipynb b/_proc/01_download_raw_data.ipynb new file mode 100644 index 0000000..8545aff --- /dev/null +++ b/_proc/01_download_raw_data.ipynb @@ -0,0 +1,436 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "title: \"Download Module: Downloading Raw Data from CDSAPI\"\n", + "engine: jupyter\n", + "---\n", + "\n", + "## download\n", + "\n", + "> This module downloads the raw data from CDS and saves it in the local directory" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We use a similar approach to the one in the tutorial to download the data\n", + "to local storage." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The background functionality in this module involves downloading the\n", + "bounding box of a region of interest, and sending that to the\n", + "CDS API query. As such, we define two helper functions to\n", + "fetch the OCHA/HDX shapefiles for a geographic region, and\n", + "another to create the bounding box from the files." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "has_sd": true, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/download.py#L58){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### fetch_GADM\n", + "\n", + "> fetch_GADM\n", + "> (url:str='https://geodata.ucdavis.edu/gadm/gadm4.1/gpkg/gadm4\n", + "> 1_MDG.gpkg', output_file:str='gadm41_MDG.gpkg')\n", + "\n", + "*Fetch the GADM bounding box for geographic region*\n", + "\n", + "| | **Type** | **Default** | **Details** |\n", + "| -- | -------- | ----------- | ----------- |\n", + "| url | str | https://geodata.ucdavis.edu/gadm/gadm4.1/gpkg/gadm41_MDG.gpkg | URL to fetch the GADM data for Madagascar |\n", + "| output_file | str | gadm41_MDG.gpkg | file path to save the GADM data |\n", + "| **Returns** | **str** | | |" + ], + "text/plain": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/download.py#L58){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### fetch_GADM\n", + "\n", + "> fetch_GADM\n", + "> (url:str='https://geodata.ucdavis.edu/gadm/gadm4.1/gpkg/gadm4\n", + "> 1_MDG.gpkg', output_file:str='gadm41_MDG.gpkg')\n", + "\n", + "*Fetch the GADM bounding box for geographic region*\n", + "\n", + "| | **Type** | **Default** | **Details** |\n", + "| -- | -------- | ----------- | ----------- |\n", + "| url | str | https://geodata.ucdavis.edu/gadm/gadm4.1/gpkg/gadm41_MDG.gpkg | URL to fetch the GADM data for Madagascar |\n", + "| output_file | str | gadm41_MDG.gpkg | file path to save the GADM data |\n", + "| **Returns** | **str** | | |" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| echo: false\n", + "#| output: asis\n", + "show_doc(fetch_GADM)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "has_sd": true, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/download.py#L79){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### create_bounding_box\n", + "\n", + "> create_bounding_box (zip_url_or_path:str, buffer_km:float=50,\n", + "> round_to:int=1)\n", + "\n", + "*Create a bounding box from OCHA/HDX shapefile data with a buffer.*\n", + "\n", + "| | **Type** | **Default** | **Details** |\n", + "| -- | -------- | ----------- | ----------- |\n", + "| zip_url_or_path | str | | URL or local path to the zipped shapefile. |\n", + "| buffer_km | float | 50 | Buffer distance in kilometers to expand the bounding box. |\n", + "| round_to | int | 1 | Number of decimal places to round the bounding box coordinates. |\n", + "| **Returns** | **list** | | **Bounding box in the CDS API area format [North, West, South, East]** |" + ], + "text/plain": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/download.py#L79){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### create_bounding_box\n", + "\n", + "> create_bounding_box (zip_url_or_path:str, buffer_km:float=50,\n", + "> round_to:int=1)\n", + "\n", + "*Create a bounding box from OCHA/HDX shapefile data with a buffer.*\n", + "\n", + "| | **Type** | **Default** | **Details** |\n", + "| -- | -------- | ----------- | ----------- |\n", + "| zip_url_or_path | str | | URL or local path to the zipped shapefile. |\n", + "| buffer_km | float | 50 | Buffer distance in kilometers to expand the bounding box. |\n", + "| round_to | int | 1 | Number of decimal places to round the bounding box coordinates. |\n", + "| **Returns** | **list** | | **Bounding box in the CDS API area format [North, West, South, East]** |" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| echo: false\n", + "#| output: asis\n", + "show_doc(create_bounding_box)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| code-fold: show\n", + "#| code-summary: \"Exported source\"\n", + "#| exports: #\n", + "def create_bounding_box(\n", + " zip_url_or_path: str, # URL or local path to the zipped shapefile.\n", + " buffer_km: float = 50, # Buffer distance in kilometers to expand the bounding box.\n", + " round_to: int = 1 # Number of decimal places to round the bounding box coordinates.\n", + ") -> list: # Bounding box in the CDS API area format [North, West, South, East]\n", + " '''\n", + " Create a bounding box from OCHA/HDX shapefile data with a buffer.\n", + " '''\n", + " with tempfile.TemporaryDirectory() as tmpdir:\n", + " # Download if it's a URL\n", + " if zip_url_or_path.startswith(\"http\"):\n", + " response = requests.get(zip_url_or_path)\n", + " zip_path = os.path.join(tmpdir, \"ocha_data.zip\")\n", + " with open(zip_path, \"wb\") as f:\n", + " f.write(response.content)\n", + " else:\n", + " zip_path = zip_url_or_path\n", + "\n", + " # Unzip\n", + " with zipfile.ZipFile(zip_path, 'r') as zip_ref:\n", + " zip_ref.extractall(tmpdir)\n", + "\n", + " # Find the .shp file\n", + " shp_files = list(Path(tmpdir).rglob(\"*.shp\"))\n", + " if not shp_files:\n", + " raise FileNotFoundError(\"No shapefile (.shp) found in the extracted archive.\")\n", + " shp_path = str(shp_files[0]) # Use first found .shp\n", + "\n", + " # Read shapefile\n", + " shape = gpd.read_file(shp_path)\n", + "\n", + " # Reproject to projected CRS (you may want to detect the correct UTM zone)\n", + " shape_proj = shape.to_crs(epsg=32738)\n", + "\n", + " # Apply buffer\n", + " buffered = shape_proj.geometry.buffer(buffer_km * 1000)\n", + "\n", + " # Convert back to geographic coordinates\n", + " buffered_geo = gpd.GeoSeries(buffered, crs=shape_proj.crs).to_crs(epsg=4326)\n", + "\n", + " # Get bounding box\n", + " bounds = buffered_geo.total_bounds # [min_x, min_y, max_x, max_y]\n", + " bbox = [\n", + " round(bounds[3], round_to), # North\n", + " round(bounds[0], round_to), # West\n", + " round(bounds[1], round_to), # South\n", + " round(bounds[2], round_to) # East\n", + " ]\n", + "\n", + " return bbox" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The primary function to download the data from CDSAPI is defined below." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "has_sd": true, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/download.py#L132){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### download_raw_era5\n", + "\n", + "> download_raw_era5 (cfg:omegaconf.dictconfig.DictConfig)\n", + "\n", + "*Send the query to the API and download the data*\n", + "\n", + "| | **Type** | **Details** |\n", + "| -- | -------- | ----------- |\n", + "| cfg | DictConfig | hydra configuration file |\n", + "| **Returns** | **None** | |" + ], + "text/plain": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/download.py#L132){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### download_raw_era5\n", + "\n", + "> download_raw_era5 (cfg:omegaconf.dictconfig.DictConfig)\n", + "\n", + "*Send the query to the API and download the data*\n", + "\n", + "| | **Type** | **Details** |\n", + "| -- | -------- | ----------- |\n", + "| cfg | DictConfig | hydra configuration file |\n", + "| **Returns** | **None** | |" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| echo: false\n", + "#| output: asis\n", + "show_doc(download_raw_era5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| code-fold: show\n", + "#| code-summary: \"Exported source\"\n", + "#| exports: #\n", + "def download_raw_era5(\n", + " cfg: DictConfig # hydra configuration file\n", + " )->None:\n", + " '''\n", + " Send the query to the API and download the data\n", + " '''\n", + "\n", + " # parse the cfg\n", + " testing = cfg.development_mode # for testing\n", + " output_dir = here(\"data/input\") # output directory\n", + " \n", + " geography = cfg.query.geography\n", + "\n", + " target = os.path.join(_expand_path(output_dir), \"{}_{}_{}.nc\".format(geography, cfg.query['year'], cfg.query['month']))\n", + " \n", + " client = cdsapi.Client()\n", + " \n", + " query = _validate_query(cfg.query)\n", + "\n", + " dataset = cfg.dataset\n", + " # to make sure the query is valid at the end\n", + " del query['geography']\n", + " \n", + " # Send the query to the client\n", + " if not testing:\n", + " bounds = create_bounding_box(cfg.geographies[geography]['shapefile'])\n", + " query['area'] = bounds\n", + " client.retrieve(dataset, query).download(target)\n", + "\n", + " print(\"Downloaded file to: {}\".format(target))\n", + " else:\n", + " print(f\"Testing mode. Not downloading data. Query is {query}\")\n", + "\n", + " print(\"Done\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tests and Main\n", + "\n", + "Here we define some tests and the main function that will be used to download the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| eval: false\n", + "from hydra import initialize, compose\n", + "from omegaconf import OmegaConf\n", + "\n", + "# unfortunately, we have to use the initialize function to load the config file\n", + "# this is because the @hydra decorator does not work with Notebooks very well\n", + "# this is a known issue with Hydra: https://gist.github.com/bdsaglam/586704a98336a0cf0a65a6e7c247d248\n", + "# \n", + "# just use the relative path from the notebook to the config dir\n", + "try:\n", + " with initialize(version_base=None, config_path=\"../conf\"):\n", + " cfg = compose(config_name='config.yaml')\n", + "except Exception as e:\n", + " print(f\"Error initializing Hydra: {e}\")\n", + " with initialize(version_base=None, config_path=\"conf\"):\n", + " cfg = compose(config_name='config.yaml')\n", + "\n", + "cfg.development_mode = False\n", + "cfg.query['year'] = 2017\n", + "cfg.query['month'] = 11\n", + "#cfg.query['day'] = 1\n", + "#cfg.query['time'] = \"00:00\"\n", + "cfg.query['geography'] = \"nepal\"\n", + "download_raw_era5(cfg)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "has_sd": true, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/aggregate.py#L302){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### main\n", + "\n", + "> main (cfg:omegaconf.dictconfig.DictConfig)" + ], + "text/plain": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/aggregate.py#L302){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### main\n", + "\n", + "> main (cfg:omegaconf.dictconfig.DictConfig)" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| echo: false\n", + "#| output: asis\n", + "show_doc(main)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| code-fold: show\n", + "#| code-summary: \"Exported source\"\n", + "#| exports: #\n", + "@hydra.main(config_path=\"../../conf\", config_name=\"config\", version_base=None)\n", + "def main(cfg: DictConfig) -> None:\n", + " download_raw_era5(cfg=cfg)\n", + " # better approach would be to have the function only use the specific arguments of the config" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/_proc/02_aggregate.html.md b/_proc/02_aggregate.html.md new file mode 100644 index 0000000..65e29e8 --- /dev/null +++ b/_proc/02_aggregate.html.md @@ -0,0 +1,755 @@ +--- +title: "Aggregate Module: Spatial Aggregation to Healthsheds" +execute: + freeze: auto +engine: jupyter +--- + + +## aggregate + +> This module aggregates the downloaded data into the respective output dataframes. + + + +We prototyped the code in this module using a Jupyter notebook. The notebook is available in `notes/prototypes/learning_aggregations_w_michelle_20250328.ipynb`. The code in this module is a cleaned-up version of the code in that notebook. The notebook contains additional comments and explanations of the code, which may be helpful for understanding the code in this module. + +The basic process is as follows: + +1. Load the netCDF data in memory +2. Statistically aggregate the hourly data to daily data per exposure using resample() +3. Write out the data to tiff +4. Read the tiff data back in +5. Read in the shapefile that defines the healthsheds +6. Spatially aggregate the exposure data to the healthsheds +7. Quality check the aggregations +8. Write out final aggregations to tiff + +::: {#cell-3 .cell exports='null'} +``` {.python .cell-code code-fold="show" code-summary="Exported source"} +import tempfile +import rasterio +import hydra +import argparse +import os + +import pandas as pd +import geopandas as gpd +import numpy as np +import xarray as xr +import matplotlib.pyplot as plt + +from dataclasses import dataclass, field +from typing import Optional, Tuple +from pyprojroot import here +from hydra import initialize, compose +from omegaconf import OmegaConf, DictConfig +from tqdm import tqdm +from math import ceil, floor +from rasterstats.io import Raster +from rasterstats.utils import boxify_points, rasterize_geom + +try: from era5_sandbox.core import GoogleDriver, _get_callable, describe, ClimateDataFileHandler, kelvin_to_celsius +except: from core import GoogleDriver, _get_callable, describe, ClimateDataFileHandler, kelvin_to_celsius +``` +::: + + +::: {#cell-4 .cell} +``` {.python .cell-code} +try: + with initialize(version_base=None, config_path="../conf"): + cfg = compose(config_name='config.yaml') +except Exception as e: + print(f"Error initializing Hydra: {e}") + with initialize(version_base=None, config_path="conf"): + cfg = compose(config_name='config.yaml') +``` +::: + + +We're going to write a function that aggregates the data for a single exposure from a file. This file should be the single month data we got from the previous step in the pipeline. + +::: {#cell-6 .cell} +``` {.python .cell-code} +eg_file = here() / "bld/2009_01_nepal.nc" +``` +::: + + +--- + +[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/aggregate.py#L36){target="_blank" style="float:right; font-size:smaller"} + +### resample_netcdf + +> resample_netcdf (fpath:str, resample:str='1D', agg_func: infunctioncallable>=, +> time_dim:str='valid_time', **xr_open_kwargs) + +*Resample a netCDF file to a specified frequency and aggregation method. + +Args: + fpath (str): Path to the netCDF file. + resample (str): Resampling frequency (e.g., '1H', '1D'). + agg_func (callable): Aggregation function (e.g., np.mean, np.sum). + +Returns: + xarray.Dataset: Resampled dataset.* + +| | **Type** | **Default** | **Details** | +| -- | -------- | ----------- | ----------- | +| fpath | str | | Path to the netCDF file. | +| resample | str | 1D | Resampling frequency (e.g., '1H', '1D') | +| agg_func | callable | mean | Aggregation function (e.g., np.mean, np.sum). | +| time_dim | str | valid_time | Name of the time dimension in the dataset. | +| xr_open_kwargs | VAR_KEYWORD | | | +| **Returns** | **Dataset** | | **keywords for python's xarray module** | + + +We pull the aggregation function from the config file: + +::: {#cell-9 .cell} +``` {.python .cell-code} +var = 'swvl1' +agg_func = _get_callable(cfg['aggregation']['aggregation'][var]['hourly_to_daily'][0]['function']) +``` +::: + + +::: {#cell-10 .cell} +``` {.python .cell-code} +with ClimateDataFileHandler(eg_file) as handler: + + ds_path = handler.get_dataset("instant") + resampled_data = resample_netcdf(ds_path, agg_func=agg_func) +``` +::: + + +I'm going to use a dataclass to represent the tiff data. This will allow us to easily pass around the data and metadata associated with the tiff file. Why? I've never used dataclasses and I'm curious about them — ChatGPT thinks this will make the code cleaner and easier to read. + +--- + +[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/aggregate.py#L66){target="_blank" style="float:right; font-size:smaller"} + +### RasterFile + +> RasterFile (path:str, band:int) + + +::: {#cell-13 .cell exports='null'} +``` {.python .cell-code code-fold="show" code-summary="Exported source"} +@dataclass +class RasterFile: + path: str + band: int # note that this is 1-indexed + data: Optional[np.ndarray] = field(default=None, init=False) + transform: Optional[rasterio.Affine] = field(default=None, init=False) + crs: Optional[str] = field(default=None, init=False) + nodata: Optional[float] = field(default=None, init=False) + bounds: Optional[Tuple[float, float, float, float]] = field(default=None, init=False) + + def load(self): + """Load raster data and basic metadata.""" + with rasterio.open(self.path) as src: + self.data = src.read(self.band) # each day gets one rasterfile + self.transform = src.transform + self.crs = src.crs + self.nodata = src.nodata + self.bounds = src.bounds + return self + + def shape(self) -> Optional[Tuple[int, int]]: + """Return the shape of the raster data.""" + return self.data.shape if self.data is not None else None + + def __str__(self): + return f"RasterFile(path='{self.path}', shape={self.shape()}, crs='{self.crs}')" +``` +::: + + +Next, a function to write and read the netCDF to tiff: + +--- + +[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/aggregate.py#L94){target="_blank" style="float:right; font-size:smaller"} + +### netcdf_to_tiff + +> netcdf_to_tiff (ds:xarray.core.dataset.Dataset, band:int, variable:str, +> crs:str='EPSG:4326') + +*Convert a netCDF file to a GeoTIFF file.* + +| | **Type** | **Default** | **Details** | +| -- | -------- | ----------- | ----------- | +| ds | Dataset | | The aggregated xarray dataset to convert. | +| band | int | | The day to rasterise; 1 indexed just like human english | +| variable | str | | The variable name to convert. | +| crs | str | EPSG:4326 | Coordinate reference system (default is WGS84). | + + +::: {#cell-16 .cell exports='null'} +``` {.python .cell-code code-fold="show" code-summary="Exported source"} +def netcdf_to_tiff( + ds: xr.Dataset, # The aggregated xarray dataset to convert. + band: int, # The day to rasterise; 1 indexed just like human english + variable: str, # The variable name to convert. + crs: str = "EPSG:4326", # Coordinate reference system (default is WGS84). + ): + + """ + Convert a netCDF file to a GeoTIFF file. + """ + + with tempfile.TemporaryDirectory() as tmpdirname: + + # Select the variable and time index + variable = ds[variable] + ds_ = variable.rio.set_spatial_dims(x_dim="longitude", y_dim="latitude") + ds_ = ds_.rio.write_crs(crs) + # Save as GeoTIFF + ds_.rio.to_raster(f"{tmpdirname}/output.tif") + # Load the raster file + raster_file = RasterFile(path=f"{tmpdirname}/output.tif", band=band).load() + + return raster_file +``` +::: + + +Now to test it: + +::: {#cell-18 .cell} +``` {.python .cell-code} +with ClimateDataFileHandler(eg_file) as handler: + ds_path = handler.get_dataset("instant") + resampled_nc = resample_netcdf(ds_path) + +print(resampled_nc) +resampled_tiff = netcdf_to_tiff( + ds=resampled_nc, + band=28, + variable="swvl1", + crs="EPSG:4326" +) +``` +::: + + +::: {#cell-19 .cell} +``` {.python .cell-code} +resampled_tiff.data.shape, resampled_tiff.transform, resampled_tiff.crs, resampled_tiff.bounds +``` +::: + + +Super cool! The tiff file is created and the data is read back in correctly. Now we can move on to the next step, which is to aggregate the data by healthshed. + +## Polygon to Raster Cells + +This function was initially shared from a previous NSAPH aggregation pipeline [here](https://github.com/NSAPH-Data-Processing/air_pollution__aqdh/blob/2a8109075fe7a8fbf7c435cc34ffa97b63f5e133/utils/faster_zonal_stats.py#L17). To better understand this, here is a ChatGPT explanation of the code: + +> This function, [`polygon_to_raster_cells`](https://TinasheMTapera.github.io/era5_sandbox/aggregate.html#polygon_to_raster_cells), is doing a crucial first step in spatial alignment: it determines which raster cells are “touched” by each polygon geometry (e.g., administrative areas, watersheds, etc.). +Essentially, this function helps figure out which pixels from a raster image fall inside each polygon (like a district, region, or shape). It does this by looking at each polygon one by one, zooming in on just the part of the raster that overlaps with that shape, and marking the pixels that are inside. This is kind of like placing a cookie cutter (the polygon) on a pixelated map (the raster) and seeing which pixels get cut. +The result is a list where each item tells you the pixel locations that match a specific polygon. You can then use those pixel locations to pull out data from the raster, like temperatures or rainfall, and calculate statistics (like the average) for each shape. This is a key step when you want to summarize raster data within specific regions, like figuring out the average temperature in each county or how much vegetation is in each park. + +--- + +[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/aggregate.py#L120){target="_blank" style="float:right; font-size:smaller"} + +### polygon_to_raster_cells + +> polygon_to_raster_cells (vectors, raster, nodata=None, affine=None, +> all_touched=False, verbose=False, **kwargs) + +*Returns an index map for each vector geometry to indices in the raster source.* + +| | **Type** | **Default** | **Details** | +| -- | -------- | ----------- | ----------- | +| vectors | | | list of geometries from a shapefile | +| raster | | | the raster data as a numpy array | +| nodata | NoneType | None | the nodata value of the raster | +| affine | NoneType | None | the affine transform of the raster | +| all_touched | bool | False | whether to include all touched pixels | +| verbose | bool | False | | +| kwargs | VAR_KEYWORD | | | +| **Returns** | **list** | | **A dictionary mapping vector the ids of geometries to locations (indices) in the raster source.** | + + +::: {#cell-22 .cell exports='null'} +``` {.python .cell-code code-fold="show" code-summary="Exported source"} +def polygon_to_raster_cells( + vectors, # list of geometries from a shapefile + raster, # the raster data as a numpy array + nodata=None, # the nodata value of the raster + affine=None, # the affine transform of the raster + all_touched=False, # whether to include all touched pixels + verbose=False, + **kwargs, +) -> list: # A dictionary mapping vector the ids of geometries to locations (indices) in the raster source. + """Returns an index map for each vector geometry to indices in the raster source.""" + + cell_map = [] + + with Raster(raster, affine, nodata) as rast: + # used later to crop raster and find start row and col + min_lon, dlon = affine.c, affine.a + max_lat, dlat = affine.f, -affine.e + H, W = rast.shape + + for geom in tqdm(vectors, disable=(not verbose)): + if "Point" in geom.geom_type: + geom = boxify_points(geom, rast) + + # find geometry bounds to crop raster + # the raster and geometry must be in the same lon/lat coordinate system + start_row = max(0, min(H - 1, floor((max_lat - geom.bounds[3]) / dlat))) + start_col = min(W - 1, max(0, floor((geom.bounds[0] - min_lon) / dlon))) + end_col = max(0, min(W - 1, ceil((geom.bounds[2] - min_lon) / dlon))) + end_row = min(H - 1, max(0, ceil((max_lat - geom.bounds[1]) / dlat))) + geom_bounds = ( + min_lon + dlon * start_col, # left + max_lat - dlat * end_row - 1e-12, # bottom + min_lon + dlon * end_col + 1e-12, # right + max_lat - dlat * start_row, # top + ) + + # crop raster to area of interest and rasterize + fsrc = rast.read(bounds=geom_bounds) + rv_array = rasterize_geom(geom, like=fsrc, all_touched=all_touched) + indices = np.nonzero(rv_array) + + if len(indices[0]) > 0: + indices = (indices[0] + start_row, indices[1] + start_col) + assert 0 <= indices[0].min() < rast.shape[0] + assert 0 <= indices[1].min() < rast.shape[1] + else: + pass # stop here for debug + + cell_map.append(indices) + + return cell_map +``` +::: + + +To use this, we must define the polygon and raster data. The polygon data is the healthshed shapefile, and the raster data is the tiff file we created earlier. We can use the [`GoogleDriver`](https://TinasheMTapera.github.io/era5_sandbox/core.html#googledriver) class we defined in `core` to read in the shapefile. + +::: {#cell-24 .cell} +``` {.python .cell-code} +try: + with initialize(version_base=None, config_path="../conf"): + cfg = compose(config_name='config.yaml') +except Exception as e: + print(f"Error initializing Hydra: {e}") + with initialize(version_base=None, config_path="conf"): + cfg = compose(config_name='config.yaml') + +driver = GoogleDriver(json_key_path=here() / cfg.GOOGLE_DRIVE_AUTH_JSON.path) +drive = driver.get_drive() +healthsheds = driver.read_healthsheds("Nepal_Healthsheds2024.zip") +``` +::: + + +::: {#cell-25 .cell} +``` {.python .cell-code} +res_poly2cell=polygon_to_raster_cells( + vectors = healthsheds.geometry.values, # the geometries of the shapefile of the regions + raster=resampled_tiff.data, # the raster data above + nodata=resampled_tiff.nodata, # any intersections with no data, may have to be np.nan + affine=resampled_tiff.transform, # some math thing need to revise + all_touched=True, + verbose=True +) +``` +::: + + +The data below maps which grid entries fall into each of the regions in the shapefile (e.g. which pixel is in which state) + +::: {#cell-27 .cell} +``` {.python .cell-code} +res_poly2cell[:5] +``` +::: + + +Last but not least, we aggregate these data to the healthshed level. We can use the `rasterstats` package to do this. + +--- + +[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/aggregate.py#L174){target="_blank" style="float:right; font-size:smaller"} + +### aggregate_to_healthsheds + +> aggregate_to_healthsheds (res_poly2cell:list, raster:__main__.RasterFile, +> shapes:geopandas.geodataframe.GeoDataFrame, +> names_column:str='fs_uid', +> aggregation_func: infunctioncallable>= 0x145cb6bbbdf0>, aggregation_name:str='mean') + +*Aggregate the raster data to the health sheds.* + +| | **Type** | **Default** | **Details** | +| -- | -------- | ----------- | ----------- | +| res_poly2cell | list | | the result of polygon_to_raster_cells | +| raster | RasterFile | | the raster data | +| shapes | GeoDataFrame | | the shapes of the health sheds | +| names_column | str | fs_uid | the unique identifier column name of the health sheds | +| aggregation_func | callable | nanmean | the aggregation function | +| aggregation_name | str | mean | the name of the aggregation function | +| **Returns** | **GeoDataFrame** | | | + + +::: {#cell-30 .cell exports='null'} +``` {.python .cell-code code-fold="show" code-summary="Exported source"} +def aggregate_to_healthsheds( + res_poly2cell: list, # the result of polygon_to_raster_cells + raster: RasterFile, # the raster data + shapes: gpd.GeoDataFrame, # the shapes of the health sheds + names_column: str = "fs_uid", # the unique identifier column name of the health sheds + aggregation_func: callable = np.nanmean, # the aggregation function + aggregation_name: str = "mean" # the name of the aggregation function + ) -> gpd.GeoDataFrame: + """ + Aggregate the raster data to the health sheds. + """ + + stats = [] + + for indices in res_poly2cell: + if len(indices[0]) == 0: + # no cells found for this polygon + stats.append(np.nan) + else: + cells = raster.data[indices] + if sum(~np.isnan(cells)) == 0: + # no valid cells found for this polygon + stats.append(np.nan) + continue + else: + # compute MEAN of valid cells + # but this stat can be ANYTHING + stats.append(aggregation_func(cells)) + + # clean up the result into a dataframe + stats = pd.Series(stats) + shapes[aggregation_name] = stats + df = pd.DataFrame( + {"healthshed": shapes[names_column], aggregation_name: stats} + ) + gdf = gpd.GeoDataFrame(df, geometry=shapes.geometry.values, crs=shapes.crs) + return gdf +``` +::: + + +And now we apply it: + +::: {#cell-32 .cell} +``` {.python .cell-code} +result = aggregate_to_healthsheds( + res_poly2cell=res_poly2cell, + raster=resampled_tiff, + shapes=healthsheds, + names_column="fid", + aggregation_func=np.nanmean, + aggregation_name="mean_soil_moisture" +) +result.head() +``` +::: + + +And plot for QA: + +::: {#cell-34 .cell} +``` {.python .cell-code} +result.plot(column="mean_soil_moisture", legend=True) +plt.title("Mean Soil Moisture (m^3 m^-3) by Health Shed Nov 2017 day 1") +plt.show() +``` +::: + + +That looks great! The data is aggregated to the healthshed level, and we can see the differences in exposure across the healthsheds. We can also see that the data is not uniform across the healthsheds, which is what we expect. + +## Tests and Main + +Now we can wrap this up in a main function that will simply take in the input file and generate this output. We can also add some tests to make sure the data is aggregated correctly; tests will run automatically in this notebook. + +::: {#cell-36 .cell} +``` {.python .cell-code} +import random +``` +::: + + +::: {#cell-37 .cell} +``` {.python .cell-code} +# variables = ["t2m", "d2m"] +# years = ["20{:02d}".format(m) for m in range(9, 24)] +# months = [str(m) for m in range(1, 13)] +# aggregations = [ +# ("Mean", np.nanmean), +# ("Max", np.nanmax), +# ("Min", np.nanmin) +# ] + +# exposure_variable = random.choice(variables) +# year = random.choice(years) +# month = random.choice(months) +# aggregation_str, agg_func = random.choice(aggregations) +# input_file = here() / "data/input/{}_{}.nc".format(year, month) + +# with initialize(version_base=None, config_path="../conf"): +# cfg = compose(config_name='config.yaml') + +# driver = GoogleDriver(json_key_path=here() / cfg.GOOGLE_DRIVE_AUTH_JSON.path) +# drive = driver.get_drive() +# healthsheds = driver.read_healthsheds(cfg.GOOGLE_DRIVE_AUTH_JSON.healthsheds_id) + +# with ClimateDataFileHandler(input_file) as handler: +# ds_path = handler.get_dataset("instant") +# resampled_nc_file = resample_netcdf(ds_path, agg_func=agg_func) + +# days = len(resampled_nc_file.valid_time.values) +# day = random.choice(range(1, days + 1)) + +# resampled_tiff = netcdf_to_tiff( +# ds=resampled_nc_file, +# band=day, # the day we're aggregating +# variable=exposure_variable, +# crs="EPSG:4326" +# ) + +# res_poly2cell=polygon_to_raster_cells( +# vectors = healthsheds.geometry.values, # the geometries of the shapefile of the regions +# raster=resampled_tiff.data, # the raster data above +# nodata=resampled_tiff.nodata, # any intersections with no data, may have to be np.nan +# affine=resampled_tiff.transform, # some math thing need to revise +# all_touched=True, +# verbose=True +# ) + +# result = aggregate_to_healthsheds( +# res_poly2cell=res_poly2cell, +# raster=resampled_tiff, +# shapes=healthsheds, +# names_column="fs_uid", +# aggregation_func=agg_func, +# aggregation_name=exposure_variable +# ) + +# result.plot(column=exposure_variable, legend=True) +# plt.title("{} {} (K) by Health Shed {}".format(aggregation_str, exposure_variable, input_file.stem)) +# plt.suptitle("Aggregation: {}, Day: {}".format(aggregation_str, str(day))) +# plt.show() +``` +::: + + +::: {.callout-note} +**Note:** The above code is commented out to prevent execution during documentation generation. You can uncomment and run it in an appropriate environment to test the aggregation process. +::: + +3.2 seconds per aggregation is pretty cool! + +::: {#cell-39 .cell} +``` {.python .cell-code} +result.to_parquet(here() / "data/testing/test_aggregation.parquet") +``` +::: + + +--- + +[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/aggregate.py#L214){target="_blank" style="float:right; font-size:smaller"} + +### aggregate_data + +> aggregate_data (cfg:omegaconf.dictconfig.DictConfig, input_file:str, +> output_file:str, exposure_variable:str) + +*Aggregate raster data day-by-day and store all days and statistics as separate columns in a single Parquet file.* + +| | **Type** | **Details** | +| -- | -------- | ----------- | +| cfg | DictConfig | the hydra config | +| input_file | str | the input netcdf file | +| output_file | str | the output parquet file | +| exposure_variable | str | Which variable in the dataset to aggregate | +| **Returns** | **None** | | + + +::: {#cell-41 .cell exports='null'} +``` {.python .cell-code code-fold="show" code-summary="Exported source"} +def aggregate_data( + cfg: DictConfig, # the hydra config + input_file: str, # the input netcdf file + output_file: str, # the output parquet file + exposure_variable: str # Which variable in the dataset to aggregate + ) -> None: + ''' + Aggregate raster data day-by-day and store all days and statistics as separate columns in a single Parquet file. + ''' + + if cfg.development_mode: + describe(cfg) + return None + + geography = cfg['query'].geography + year = cfg['query']['year'] + month = cfg['query']['month'] + daily_aggs = cfg['aggregation']['aggregation'][exposure_variable]['hourly_to_daily'] + healthshed_aggs = cfg['aggregation']['aggregation'][exposure_variable]['daily_to_healthshed'] + + # Load healthsheds + driver = GoogleDriver(json_key_path=here() / cfg.GOOGLE_DRIVE_AUTH_JSON.path) + drive = driver.get_drive() + healthsheds = driver.read_healthsheds(cfg.geographies[geography].healthsheds) + + # Initialize output DataFrame + result_df = healthsheds[[cfg.geographies[geography].unique_id, "geometry"]].copy() + + for daily_agg in daily_aggs: + print(f"Processing daily aggregation: {daily_agg['name']}...") + + daily_agg_func = _get_callable(daily_agg['function']) + + with ClimateDataFileHandler(input_file) as handler: + if exposure_variable in ["t2m", "d2m", "swvl1"]: + ds_path = handler.get_dataset("instant") + else: + ds_path = handler.get_dataset("accum") + resampled_nc_file = resample_netcdf(ds_path, agg_func=daily_agg_func) + + for healthshed_agg in healthshed_aggs: + print(f"Aggregating to healthshed by: {healthshed_agg['name']}...") + + # Get the number of days in the dataset + days = len(resampled_nc_file.valid_time.values) + + # Get the aggregation function for healthshed + healthshed_agg_func = _get_callable(healthshed_agg['function']) + days = len(resampled_nc_file.valid_time.values) + + for day in range(1, days + 1): + print(f"Processing day {day}...") + + day_col = f"day_{day:02d}_daily_{daily_agg['name']}" + resampled_tiff = netcdf_to_tiff( + ds=resampled_nc_file, + band=day, + variable=exposure_variable, + crs="EPSG:4326" + ) + + result_poly2cell = polygon_to_raster_cells( + vectors=healthsheds.geometry.values, + raster=resampled_tiff.data, + nodata=resampled_tiff.nodata, + affine=resampled_tiff.transform, + all_touched=True, + verbose=True + ) + + res = aggregate_to_healthsheds( + res_poly2cell=result_poly2cell, + raster=resampled_tiff, + shapes=healthsheds, + names_column=cfg.geographies[geography].unique_id, + aggregation_func=healthshed_agg_func, + aggregation_name=exposure_variable + ) + + result_df[day_col] = res[exposure_variable] + + print(f"Saving final monthly parquet file: {output_file}") + result_df.to_parquet(output_file, compression="snappy") + # return(result_df) +``` +::: + + +::: {#cell-42 .cell} +``` {.python .cell-code} +try: + with initialize(version_base=None, config_path="../conf"): + cfg = compose(config_name='config.yaml') +except Exception as e: + print(f"Error initializing Hydra: {e}") + with initialize(version_base=None, config_path="conf"): + cfg = compose(config_name='config.yaml') + +cfg.development_mode = False +cfg.query['year'] = 2017 +cfg.query['month'] = 11 +cfg.query['geography'] = "nepal" + +variable = "swvl1" + +aggregate_data(cfg, here() / "bld/2017_11_nepal.nc", here() / "data/testing/test_nepal_aggregation.parquet", exposure_variable=variable) +``` +::: + + +::: {#cell-43 .cell} +``` {.python .cell-code} +parquet_file = gpd.read_parquet(here() / "data/testing/test_nepal_aggregation.parquet") +``` +::: + + +::: {#cell-44 .cell} +``` {.python .cell-code} +parquet_file +``` +::: + + +::: {#cell-45 .cell} +``` {.python .cell-code} +parquet_file.plot(column="day_22_daily_mean", legend=True) +``` +::: + + +--- + +[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/aggregate.py#L302){target="_blank" style="float:right; font-size:smaller"} + +### main + +> main (cfg:omegaconf.dictconfig.DictConfig) + + +::: {#cell-47 .cell exports='null'} +``` {.python .cell-code code-fold="show" code-summary="Exported source"} +@hydra.main(version_base=None, config_path="../../conf", config_name="config") +def main(cfg: DictConfig) -> None: + # Parse command-line arguments + input_file = str(snakemake.input[0]) # First input file + output_file = str(snakemake.output[0]) + geography = str(snakemake.params.geography) + aggregation_variable = str(snakemake.params.variable) + + variables_dict = { + "2m_temperature": "t2m", + "2m_dewpoint_temperature": "d2m", + "volumetric_soil_water_layer_1": "swvl1", + "total_precipitation": "tp" + } + + cfg['query']['geography'] = geography + + aggregate_data(cfg, input_file=input_file, output_file=output_file, exposure_variable=variables_dict[aggregation_variable]) +``` +::: + + diff --git a/_proc/02_aggregate.ipynb b/_proc/02_aggregate.ipynb new file mode 100644 index 0000000..85c673d --- /dev/null +++ b/_proc/02_aggregate.ipynb @@ -0,0 +1,1250 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "title: \"Aggregate Module: Spatial Aggregation to Healthsheds\"\n", + "execute:\n", + " freeze: auto\n", + "engine: jupyter\n", + "---\n", + "\n", + "## aggregate\n", + "\n", + "> This module aggregates the downloaded data into the respective output dataframes." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We prototyped the code in this module using a Jupyter notebook. The notebook is available in `notes/prototypes/learning_aggregations_w_michelle_20250328.ipynb`. The code in this module is a cleaned-up version of the code in that notebook. The notebook contains additional comments and explanations of the code, which may be helpful for understanding the code in this module.\n", + "\n", + "The basic process is as follows:\n", + "\n", + "1. Load the netCDF data in memory\n", + "2. Statistically aggregate the hourly data to daily data per exposure using resample()\n", + "3. Write out the data to tiff\n", + "4. Read the tiff data back in\n", + "5. Read in the shapefile that defines the healthsheds\n", + "6. Spatially aggregate the exposure data to the healthsheds\n", + "7. Quality check the aggregations\n", + "8. Write out final aggregations to tiff" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| code-fold: show\n", + "#| code-summary: \"Exported source\"\n", + "#| exports: #\n", + "import tempfile\n", + "import rasterio\n", + "import hydra\n", + "import argparse\n", + "import os\n", + "\n", + "import pandas as pd\n", + "import geopandas as gpd\n", + "import numpy as np\n", + "import xarray as xr\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from dataclasses import dataclass, field\n", + "from typing import Optional, Tuple\n", + "from pyprojroot import here\n", + "from hydra import initialize, compose\n", + "from omegaconf import OmegaConf, DictConfig\n", + "from tqdm import tqdm\n", + "from math import ceil, floor\n", + "from rasterstats.io import Raster\n", + "from rasterstats.utils import boxify_points, rasterize_geom\n", + "\n", + "try: from era5_sandbox.core import GoogleDriver, _get_callable, describe, ClimateDataFileHandler, kelvin_to_celsius\n", + "except: from core import GoogleDriver, _get_callable, describe, ClimateDataFileHandler, kelvin_to_celsius" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "try:\n", + " with initialize(version_base=None, config_path=\"../conf\"):\n", + " cfg = compose(config_name='config.yaml')\n", + "except Exception as e:\n", + " print(f\"Error initializing Hydra: {e}\")\n", + " with initialize(version_base=None, config_path=\"conf\"):\n", + " cfg = compose(config_name='config.yaml')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We're going to write a function that aggregates the data for a single exposure from a file. This file should be the single month data we got from the previous step in the pipeline." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "eg_file = here() / \"bld/2009_01_nepal.nc\"" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "has_sd": true, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/aggregate.py#L36){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### resample_netcdf\n", + "\n", + "> resample_netcdf (fpath:str, resample:str='1D', agg_func: infunctioncallable>=,\n", + "> time_dim:str='valid_time', **xr_open_kwargs)\n", + "\n", + "*Resample a netCDF file to a specified frequency and aggregation method.\n", + "\n", + "Args:\n", + " fpath (str): Path to the netCDF file.\n", + " resample (str): Resampling frequency (e.g., '1H', '1D').\n", + " agg_func (callable): Aggregation function (e.g., np.mean, np.sum).\n", + "\n", + "Returns:\n", + " xarray.Dataset: Resampled dataset.*\n", + "\n", + "| | **Type** | **Default** | **Details** |\n", + "| -- | -------- | ----------- | ----------- |\n", + "| fpath | str | | Path to the netCDF file. |\n", + "| resample | str | 1D | Resampling frequency (e.g., '1H', '1D') |\n", + "| agg_func | callable | mean | Aggregation function (e.g., np.mean, np.sum). |\n", + "| time_dim | str | valid_time | Name of the time dimension in the dataset. |\n", + "| xr_open_kwargs | VAR_KEYWORD | | |\n", + "| **Returns** | **Dataset** | | **keywords for python's xarray module** |" + ], + "text/plain": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/aggregate.py#L36){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### resample_netcdf\n", + "\n", + "> resample_netcdf (fpath:str, resample:str='1D', agg_func: infunctioncallable>=,\n", + "> time_dim:str='valid_time', **xr_open_kwargs)\n", + "\n", + "*Resample a netCDF file to a specified frequency and aggregation method.\n", + "\n", + "Args:\n", + " fpath (str): Path to the netCDF file.\n", + " resample (str): Resampling frequency (e.g., '1H', '1D').\n", + " agg_func (callable): Aggregation function (e.g., np.mean, np.sum).\n", + "\n", + "Returns:\n", + " xarray.Dataset: Resampled dataset.*\n", + "\n", + "| | **Type** | **Default** | **Details** |\n", + "| -- | -------- | ----------- | ----------- |\n", + "| fpath | str | | Path to the netCDF file. |\n", + "| resample | str | 1D | Resampling frequency (e.g., '1H', '1D') |\n", + "| agg_func | callable | mean | Aggregation function (e.g., np.mean, np.sum). |\n", + "| time_dim | str | valid_time | Name of the time dimension in the dataset. |\n", + "| xr_open_kwargs | VAR_KEYWORD | | |\n", + "| **Returns** | **Dataset** | | **keywords for python's xarray module** |" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| echo: false\n", + "#| output: asis\n", + "show_doc(resample_netcdf)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We pull the aggregation function from the config file:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "var = 'swvl1'\n", + "agg_func = _get_callable(cfg['aggregation']['aggregation'][var]['hourly_to_daily'][0]['function'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "with ClimateDataFileHandler(eg_file) as handler:\n", + "\n", + " ds_path = handler.get_dataset(\"instant\")\n", + " resampled_data = resample_netcdf(ds_path, agg_func=agg_func)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "I'm going to use a dataclass to represent the tiff data. This will allow us to easily pass around the data and metadata associated with the tiff file. Why? I've never used dataclasses and I'm curious about them — ChatGPT thinks this will make the code cleaner and easier to read." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "has_sd": true, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/aggregate.py#L66){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### RasterFile\n", + "\n", + "> RasterFile (path:str, band:int)" + ], + "text/plain": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/aggregate.py#L66){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### RasterFile\n", + "\n", + "> RasterFile (path:str, band:int)" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| echo: false\n", + "#| output: asis\n", + "show_doc(RasterFile)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| code-fold: show\n", + "#| code-summary: \"Exported source\"\n", + "#| exports: #\n", + "@dataclass\n", + "class RasterFile:\n", + " path: str\n", + " band: int # note that this is 1-indexed\n", + " data: Optional[np.ndarray] = field(default=None, init=False)\n", + " transform: Optional[rasterio.Affine] = field(default=None, init=False)\n", + " crs: Optional[str] = field(default=None, init=False)\n", + " nodata: Optional[float] = field(default=None, init=False)\n", + " bounds: Optional[Tuple[float, float, float, float]] = field(default=None, init=False)\n", + "\n", + " def load(self):\n", + " \"\"\"Load raster data and basic metadata.\"\"\"\n", + " with rasterio.open(self.path) as src:\n", + " self.data = src.read(self.band) # each day gets one rasterfile\n", + " self.transform = src.transform\n", + " self.crs = src.crs\n", + " self.nodata = src.nodata\n", + " self.bounds = src.bounds\n", + " return self\n", + "\n", + " def shape(self) -> Optional[Tuple[int, int]]:\n", + " \"\"\"Return the shape of the raster data.\"\"\"\n", + " return self.data.shape if self.data is not None else None\n", + "\n", + " def __str__(self):\n", + " return f\"RasterFile(path='{self.path}', shape={self.shape()}, crs='{self.crs}')\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, a function to write and read the netCDF to tiff:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "has_sd": true, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/aggregate.py#L94){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### netcdf_to_tiff\n", + "\n", + "> netcdf_to_tiff (ds:xarray.core.dataset.Dataset, band:int, variable:str,\n", + "> crs:str='EPSG:4326')\n", + "\n", + "*Convert a netCDF file to a GeoTIFF file.*\n", + "\n", + "| | **Type** | **Default** | **Details** |\n", + "| -- | -------- | ----------- | ----------- |\n", + "| ds | Dataset | | The aggregated xarray dataset to convert. |\n", + "| band | int | | The day to rasterise; 1 indexed just like human english |\n", + "| variable | str | | The variable name to convert. |\n", + "| crs | str | EPSG:4326 | Coordinate reference system (default is WGS84). |" + ], + "text/plain": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/aggregate.py#L94){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### netcdf_to_tiff\n", + "\n", + "> netcdf_to_tiff (ds:xarray.core.dataset.Dataset, band:int, variable:str,\n", + "> crs:str='EPSG:4326')\n", + "\n", + "*Convert a netCDF file to a GeoTIFF file.*\n", + "\n", + "| | **Type** | **Default** | **Details** |\n", + "| -- | -------- | ----------- | ----------- |\n", + "| ds | Dataset | | The aggregated xarray dataset to convert. |\n", + "| band | int | | The day to rasterise; 1 indexed just like human english |\n", + "| variable | str | | The variable name to convert. |\n", + "| crs | str | EPSG:4326 | Coordinate reference system (default is WGS84). |" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| echo: false\n", + "#| output: asis\n", + "show_doc(netcdf_to_tiff)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| code-fold: show\n", + "#| code-summary: \"Exported source\"\n", + "#| exports: #\n", + "def netcdf_to_tiff(\n", + " ds: xr.Dataset, # The aggregated xarray dataset to convert. \n", + " band: int, # The day to rasterise; 1 indexed just like human english\n", + " variable: str, # The variable name to convert.\n", + " crs: str = \"EPSG:4326\", # Coordinate reference system (default is WGS84). \n", + " ):\n", + "\n", + " \"\"\"\n", + " Convert a netCDF file to a GeoTIFF file.\n", + " \"\"\"\n", + "\n", + " with tempfile.TemporaryDirectory() as tmpdirname:\n", + "\n", + " # Select the variable and time index\n", + " variable = ds[variable]\n", + " ds_ = variable.rio.set_spatial_dims(x_dim=\"longitude\", y_dim=\"latitude\")\n", + " ds_ = ds_.rio.write_crs(crs)\n", + " # Save as GeoTIFF\n", + " ds_.rio.to_raster(f\"{tmpdirname}/output.tif\")\n", + " # Load the raster file\n", + " raster_file = RasterFile(path=f\"{tmpdirname}/output.tif\", band=band).load()\n", + "\n", + " return raster_file" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now to test it:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "with ClimateDataFileHandler(eg_file) as handler:\n", + " ds_path = handler.get_dataset(\"instant\")\n", + " resampled_nc = resample_netcdf(ds_path)\n", + "\n", + "print(resampled_nc)\n", + "resampled_tiff = netcdf_to_tiff(\n", + " ds=resampled_nc,\n", + " band=28,\n", + " variable=\"swvl1\",\n", + " crs=\"EPSG:4326\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "resampled_tiff.data.shape, resampled_tiff.transform, resampled_tiff.crs, resampled_tiff.bounds" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Super cool! The tiff file is created and the data is read back in correctly. Now we can move on to the next step, which is to aggregate the data by healthshed.\n", + "\n", + "## Polygon to Raster Cells\n", + "\n", + "This function was initially shared from a previous NSAPH aggregation pipeline [here](https://github.com/NSAPH-Data-Processing/air_pollution__aqdh/blob/2a8109075fe7a8fbf7c435cc34ffa97b63f5e133/utils/faster_zonal_stats.py#L17). To better understand this, here is a ChatGPT explanation of the code:\n", + "\n", + "> This function, [`polygon_to_raster_cells`](https://TinasheMTapera.github.io/era5_sandbox/aggregate.html#polygon_to_raster_cells), is doing a crucial first step in spatial alignment: it determines which raster cells are “touched” by each polygon geometry (e.g., administrative areas, watersheds, etc.). \n", + "Essentially, this function helps figure out which pixels from a raster image fall inside each polygon (like a district, region, or shape). It does this by looking at each polygon one by one, zooming in on just the part of the raster that overlaps with that shape, and marking the pixels that are inside. This is kind of like placing a cookie cutter (the polygon) on a pixelated map (the raster) and seeing which pixels get cut. \n", + "The result is a list where each item tells you the pixel locations that match a specific polygon. You can then use those pixel locations to pull out data from the raster, like temperatures or rainfall, and calculate statistics (like the average) for each shape. This is a key step when you want to summarize raster data within specific regions, like figuring out the average temperature in each county or how much vegetation is in each park." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "has_sd": true, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/aggregate.py#L120){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### polygon_to_raster_cells\n", + "\n", + "> polygon_to_raster_cells (vectors, raster, nodata=None, affine=None,\n", + "> all_touched=False, verbose=False, **kwargs)\n", + "\n", + "*Returns an index map for each vector geometry to indices in the raster source.*\n", + "\n", + "| | **Type** | **Default** | **Details** |\n", + "| -- | -------- | ----------- | ----------- |\n", + "| vectors | | | list of geometries from a shapefile |\n", + "| raster | | | the raster data as a numpy array |\n", + "| nodata | NoneType | None | the nodata value of the raster |\n", + "| affine | NoneType | None | the affine transform of the raster |\n", + "| all_touched | bool | False | whether to include all touched pixels |\n", + "| verbose | bool | False | |\n", + "| kwargs | VAR_KEYWORD | | |\n", + "| **Returns** | **list** | | **A dictionary mapping vector the ids of geometries to locations (indices) in the raster source.** |" + ], + "text/plain": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/aggregate.py#L120){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### polygon_to_raster_cells\n", + "\n", + "> polygon_to_raster_cells (vectors, raster, nodata=None, affine=None,\n", + "> all_touched=False, verbose=False, **kwargs)\n", + "\n", + "*Returns an index map for each vector geometry to indices in the raster source.*\n", + "\n", + "| | **Type** | **Default** | **Details** |\n", + "| -- | -------- | ----------- | ----------- |\n", + "| vectors | | | list of geometries from a shapefile |\n", + "| raster | | | the raster data as a numpy array |\n", + "| nodata | NoneType | None | the nodata value of the raster |\n", + "| affine | NoneType | None | the affine transform of the raster |\n", + "| all_touched | bool | False | whether to include all touched pixels |\n", + "| verbose | bool | False | |\n", + "| kwargs | VAR_KEYWORD | | |\n", + "| **Returns** | **list** | | **A dictionary mapping vector the ids of geometries to locations (indices) in the raster source.** |" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| echo: false\n", + "#| output: asis\n", + "show_doc(polygon_to_raster_cells)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| code-fold: show\n", + "#| code-summary: \"Exported source\"\n", + "#| exports: #\n", + "def polygon_to_raster_cells(\n", + " vectors, # list of geometries from a shapefile\n", + " raster, # the raster data as a numpy array\n", + " nodata=None, # the nodata value of the raster\n", + " affine=None, # the affine transform of the raster\n", + " all_touched=False, # whether to include all touched pixels\n", + " verbose=False, \n", + " **kwargs,\n", + ") -> list: # A dictionary mapping vector the ids of geometries to locations (indices) in the raster source.\n", + " \"\"\"Returns an index map for each vector geometry to indices in the raster source.\"\"\"\n", + "\n", + " cell_map = []\n", + "\n", + " with Raster(raster, affine, nodata) as rast:\n", + " # used later to crop raster and find start row and col\n", + " min_lon, dlon = affine.c, affine.a\n", + " max_lat, dlat = affine.f, -affine.e\n", + " H, W = rast.shape\n", + "\n", + " for geom in tqdm(vectors, disable=(not verbose)):\n", + " if \"Point\" in geom.geom_type:\n", + " geom = boxify_points(geom, rast)\n", + "\n", + " # find geometry bounds to crop raster\n", + " # the raster and geometry must be in the same lon/lat coordinate system\n", + " start_row = max(0, min(H - 1, floor((max_lat - geom.bounds[3]) / dlat)))\n", + " start_col = min(W - 1, max(0, floor((geom.bounds[0] - min_lon) / dlon)))\n", + " end_col = max(0, min(W - 1, ceil((geom.bounds[2] - min_lon) / dlon)))\n", + " end_row = min(H - 1, max(0, ceil((max_lat - geom.bounds[1]) / dlat)))\n", + " geom_bounds = (\n", + " min_lon + dlon * start_col, # left\n", + " max_lat - dlat * end_row - 1e-12, # bottom\n", + " min_lon + dlon * end_col + 1e-12, # right\n", + " max_lat - dlat * start_row, # top\n", + " )\n", + "\n", + " # crop raster to area of interest and rasterize\n", + " fsrc = rast.read(bounds=geom_bounds)\n", + " rv_array = rasterize_geom(geom, like=fsrc, all_touched=all_touched)\n", + " indices = np.nonzero(rv_array)\n", + "\n", + " if len(indices[0]) > 0:\n", + " indices = (indices[0] + start_row, indices[1] + start_col)\n", + " assert 0 <= indices[0].min() < rast.shape[0]\n", + " assert 0 <= indices[1].min() < rast.shape[1]\n", + " else:\n", + " pass # stop here for debug\n", + "\n", + " cell_map.append(indices)\n", + "\n", + " return cell_map" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To use this, we must define the polygon and raster data. The polygon data is the healthshed shapefile, and the raster data is the tiff file we created earlier. We can use the [`GoogleDriver`](https://TinasheMTapera.github.io/era5_sandbox/core.html#googledriver) class we defined in `core` to read in the shapefile." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "try:\n", + " with initialize(version_base=None, config_path=\"../conf\"):\n", + " cfg = compose(config_name='config.yaml')\n", + "except Exception as e:\n", + " print(f\"Error initializing Hydra: {e}\")\n", + " with initialize(version_base=None, config_path=\"conf\"):\n", + " cfg = compose(config_name='config.yaml')\n", + "\n", + "driver = GoogleDriver(json_key_path=here() / cfg.GOOGLE_DRIVE_AUTH_JSON.path)\n", + "drive = driver.get_drive()\n", + "healthsheds = driver.read_healthsheds(\"Nepal_Healthsheds2024.zip\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "res_poly2cell=polygon_to_raster_cells(\n", + " vectors = healthsheds.geometry.values, # the geometries of the shapefile of the regions\n", + " raster=resampled_tiff.data, # the raster data above\n", + " nodata=resampled_tiff.nodata, # any intersections with no data, may have to be np.nan\n", + " affine=resampled_tiff.transform, # some math thing need to revise\n", + " all_touched=True, \n", + " verbose=True\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The data below maps which grid entries fall into each of the regions in the shapefile (e.g. which pixel is in which state)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "res_poly2cell[:5]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Last but not least, we aggregate these data to the healthshed level. We can use the `rasterstats` package to do this." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "has_sd": true, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/aggregate.py#L174){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### aggregate_to_healthsheds\n", + "\n", + "> aggregate_to_healthsheds (res_poly2cell:list, raster:__main__.RasterFile,\n", + "> shapes:geopandas.geodataframe.GeoDataFrame,\n", + "> names_column:str='fs_uid',\n", + "> aggregation_func: infunctioncallable>= 0x14b8f1ccf8f0>, aggregation_name:str='mean')\n", + "\n", + "*Aggregate the raster data to the health sheds.*\n", + "\n", + "| | **Type** | **Default** | **Details** |\n", + "| -- | -------- | ----------- | ----------- |\n", + "| res_poly2cell | list | | the result of polygon_to_raster_cells |\n", + "| raster | RasterFile | | the raster data |\n", + "| shapes | GeoDataFrame | | the shapes of the health sheds |\n", + "| names_column | str | fs_uid | the unique identifier column name of the health sheds |\n", + "| aggregation_func | callable | nanmean | the aggregation function |\n", + "| aggregation_name | str | mean | the name of the aggregation function |\n", + "| **Returns** | **GeoDataFrame** | | |" + ], + "text/plain": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/aggregate.py#L174){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### aggregate_to_healthsheds\n", + "\n", + "> aggregate_to_healthsheds (res_poly2cell:list, raster:__main__.RasterFile,\n", + "> shapes:geopandas.geodataframe.GeoDataFrame,\n", + "> names_column:str='fs_uid',\n", + "> aggregation_func: infunctioncallable>= 0x14b8f1ccf8f0>, aggregation_name:str='mean')\n", + "\n", + "*Aggregate the raster data to the health sheds.*\n", + "\n", + "| | **Type** | **Default** | **Details** |\n", + "| -- | -------- | ----------- | ----------- |\n", + "| res_poly2cell | list | | the result of polygon_to_raster_cells |\n", + "| raster | RasterFile | | the raster data |\n", + "| shapes | GeoDataFrame | | the shapes of the health sheds |\n", + "| names_column | str | fs_uid | the unique identifier column name of the health sheds |\n", + "| aggregation_func | callable | nanmean | the aggregation function |\n", + "| aggregation_name | str | mean | the name of the aggregation function |\n", + "| **Returns** | **GeoDataFrame** | | |" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| echo: false\n", + "#| output: asis\n", + "show_doc(aggregate_to_healthsheds)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| code-fold: show\n", + "#| code-summary: \"Exported source\"\n", + "#| exports: #\n", + "def aggregate_to_healthsheds(\n", + " res_poly2cell: list, # the result of polygon_to_raster_cells \n", + " raster: RasterFile, # the raster data\n", + " shapes: gpd.GeoDataFrame, # the shapes of the health sheds\n", + " names_column: str = \"fs_uid\", # the unique identifier column name of the health sheds\n", + " aggregation_func: callable = np.nanmean, # the aggregation function\n", + " aggregation_name: str = \"mean\" # the name of the aggregation function\n", + " ) -> gpd.GeoDataFrame:\n", + " \"\"\"\n", + " Aggregate the raster data to the health sheds.\n", + " \"\"\"\n", + "\n", + " stats = []\n", + "\n", + " for indices in res_poly2cell:\n", + " if len(indices[0]) == 0:\n", + " # no cells found for this polygon\n", + " stats.append(np.nan)\n", + " else:\n", + " cells = raster.data[indices]\n", + " if sum(~np.isnan(cells)) == 0:\n", + " # no valid cells found for this polygon\n", + " stats.append(np.nan)\n", + " continue\n", + " else:\n", + " # compute MEAN of valid cells\n", + " # but this stat can be ANYTHING\n", + " stats.append(aggregation_func(cells))\n", + "\n", + " # clean up the result into a dataframe\n", + " stats = pd.Series(stats)\n", + " shapes[aggregation_name] = stats\n", + " df = pd.DataFrame(\n", + " {\"healthshed\": shapes[names_column], aggregation_name: stats}\n", + " )\n", + " gdf = gpd.GeoDataFrame(df, geometry=shapes.geometry.values, crs=shapes.crs)\n", + " return gdf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And now we apply it:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "result = aggregate_to_healthsheds(\n", + " res_poly2cell=res_poly2cell,\n", + " raster=resampled_tiff,\n", + " shapes=healthsheds,\n", + " names_column=\"fid\",\n", + " aggregation_func=np.nanmean,\n", + " aggregation_name=\"mean_soil_moisture\"\n", + ")\n", + "result.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And plot for QA:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "result.plot(column=\"mean_soil_moisture\", legend=True)\n", + "plt.title(\"Mean Soil Moisture (m^3 m^-3) by Health Shed Nov 2017 day 1\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That looks great! The data is aggregated to the healthshed level, and we can see the differences in exposure across the healthsheds. We can also see that the data is not uniform across the healthsheds, which is what we expect.\n", + "\n", + "## Tests and Main\n", + "\n", + "Now we can wrap this up in a main function that will simply take in the input file and generate this output. We can also add some tests to make sure the data is aggregated correctly; tests will run automatically in this notebook." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "import random" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| eval: false\n", + "# variables = [\"t2m\", \"d2m\"]\n", + "# years = [\"20{:02d}\".format(m) for m in range(9, 24)]\n", + "# months = [str(m) for m in range(1, 13)]\n", + "# aggregations = [\n", + "# (\"Mean\", np.nanmean),\n", + "# (\"Max\", np.nanmax),\n", + "# (\"Min\", np.nanmin)\n", + "# ]\n", + "\n", + "# exposure_variable = random.choice(variables)\n", + "# year = random.choice(years)\n", + "# month = random.choice(months)\n", + "# aggregation_str, agg_func = random.choice(aggregations)\n", + "# input_file = here() / \"data/input/{}_{}.nc\".format(year, month)\n", + "\n", + "# with initialize(version_base=None, config_path=\"../conf\"):\n", + "# cfg = compose(config_name='config.yaml')\n", + "\n", + "# driver = GoogleDriver(json_key_path=here() / cfg.GOOGLE_DRIVE_AUTH_JSON.path)\n", + "# drive = driver.get_drive()\n", + "# healthsheds = driver.read_healthsheds(cfg.GOOGLE_DRIVE_AUTH_JSON.healthsheds_id)\n", + "\n", + "# with ClimateDataFileHandler(input_file) as handler:\n", + "# ds_path = handler.get_dataset(\"instant\")\n", + "# resampled_nc_file = resample_netcdf(ds_path, agg_func=agg_func)\n", + "\n", + "# days = len(resampled_nc_file.valid_time.values)\n", + "# day = random.choice(range(1, days + 1))\n", + "\n", + "# resampled_tiff = netcdf_to_tiff(\n", + "# ds=resampled_nc_file,\n", + "# band=day, # the day we're aggregating\n", + "# variable=exposure_variable,\n", + "# crs=\"EPSG:4326\"\n", + "# )\n", + "\n", + "# res_poly2cell=polygon_to_raster_cells(\n", + "# vectors = healthsheds.geometry.values, # the geometries of the shapefile of the regions\n", + "# raster=resampled_tiff.data, # the raster data above\n", + "# nodata=resampled_tiff.nodata, # any intersections with no data, may have to be np.nan\n", + "# affine=resampled_tiff.transform, # some math thing need to revise\n", + "# all_touched=True, \n", + "# verbose=True\n", + "# )\n", + "\n", + "# result = aggregate_to_healthsheds(\n", + "# res_poly2cell=res_poly2cell,\n", + "# raster=resampled_tiff,\n", + "# shapes=healthsheds,\n", + "# names_column=\"fs_uid\",\n", + "# aggregation_func=agg_func,\n", + "# aggregation_name=exposure_variable\n", + "# )\n", + "\n", + "# result.plot(column=exposure_variable, legend=True)\n", + "# plt.title(\"{} {} (K) by Health Shed {}\".format(aggregation_str, exposure_variable, input_file.stem))\n", + "# plt.suptitle(\"Aggregation: {}, Day: {}\".format(aggregation_str, str(day)))\n", + "# plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "::: {.callout-note}\n", + "**Note:** The above code is commented out to prevent execution during documentation generation. You can uncomment and run it in an appropriate environment to test the aggregation process.\n", + ":::\n", + "\n", + "3.2 seconds per aggregation is pretty cool!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| eval: false\n", + "result.to_parquet(here() / \"data/testing/test_aggregation.parquet\")" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "has_sd": true, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/aggregate.py#L214){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### aggregate_data\n", + "\n", + "> aggregate_data (cfg:omegaconf.dictconfig.DictConfig, input_file:str,\n", + "> output_file:str, exposure_variable:str)\n", + "\n", + "*Aggregate raster data day-by-day and store all days and statistics as separate columns in a single Parquet file.*\n", + "\n", + "| | **Type** | **Details** |\n", + "| -- | -------- | ----------- |\n", + "| cfg | DictConfig | the hydra config |\n", + "| input_file | str | the input netcdf file |\n", + "| output_file | str | the output parquet file |\n", + "| exposure_variable | str | Which variable in the dataset to aggregate |\n", + "| **Returns** | **None** | |" + ], + "text/plain": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/aggregate.py#L214){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### aggregate_data\n", + "\n", + "> aggregate_data (cfg:omegaconf.dictconfig.DictConfig, input_file:str,\n", + "> output_file:str, exposure_variable:str)\n", + "\n", + "*Aggregate raster data day-by-day and store all days and statistics as separate columns in a single Parquet file.*\n", + "\n", + "| | **Type** | **Details** |\n", + "| -- | -------- | ----------- |\n", + "| cfg | DictConfig | the hydra config |\n", + "| input_file | str | the input netcdf file |\n", + "| output_file | str | the output parquet file |\n", + "| exposure_variable | str | Which variable in the dataset to aggregate |\n", + "| **Returns** | **None** | |" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| echo: false\n", + "#| output: asis\n", + "show_doc(aggregate_data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| code-fold: show\n", + "#| code-summary: \"Exported source\"\n", + "#| exports: #\n", + "def aggregate_data(\n", + " cfg: DictConfig, # the hydra config\n", + " input_file: str, # the input netcdf file\n", + " output_file: str, # the output parquet file\n", + " exposure_variable: str # Which variable in the dataset to aggregate\n", + " ) -> None:\n", + " '''\n", + " Aggregate raster data day-by-day and store all days and statistics as separate columns in a single Parquet file.\n", + " '''\n", + "\n", + " if cfg.development_mode:\n", + " describe(cfg)\n", + " return None\n", + "\n", + " geography = cfg['query'].geography\n", + " year = cfg['query']['year']\n", + " month = cfg['query']['month']\n", + " daily_aggs = cfg['aggregation']['aggregation'][exposure_variable]['hourly_to_daily']\n", + " healthshed_aggs = cfg['aggregation']['aggregation'][exposure_variable]['daily_to_healthshed']\n", + "\n", + " # Load healthsheds\n", + " driver = GoogleDriver(json_key_path=here() / cfg.GOOGLE_DRIVE_AUTH_JSON.path)\n", + " drive = driver.get_drive()\n", + " healthsheds = driver.read_healthsheds(cfg.geographies[geography].healthsheds)\n", + " \n", + " # Initialize output DataFrame\n", + " result_df = healthsheds[[cfg.geographies[geography].unique_id, \"geometry\"]].copy()\n", + "\n", + " for daily_agg in daily_aggs:\n", + " print(f\"Processing daily aggregation: {daily_agg['name']}...\")\n", + " \n", + " daily_agg_func = _get_callable(daily_agg['function'])\n", + "\n", + " with ClimateDataFileHandler(input_file) as handler:\n", + " if exposure_variable in [\"t2m\", \"d2m\", \"swvl1\"]:\n", + " ds_path = handler.get_dataset(\"instant\")\n", + " else:\n", + " ds_path = handler.get_dataset(\"accum\")\n", + " resampled_nc_file = resample_netcdf(ds_path, agg_func=daily_agg_func)\n", + " \n", + " for healthshed_agg in healthshed_aggs:\n", + " print(f\"Aggregating to healthshed by: {healthshed_agg['name']}...\")\n", + "\n", + " # Get the number of days in the dataset\n", + " days = len(resampled_nc_file.valid_time.values)\n", + "\n", + " # Get the aggregation function for healthshed\n", + " healthshed_agg_func = _get_callable(healthshed_agg['function'])\n", + " days = len(resampled_nc_file.valid_time.values)\n", + "\n", + " for day in range(1, days + 1):\n", + " print(f\"Processing day {day}...\")\n", + " \n", + " day_col = f\"day_{day:02d}_daily_{daily_agg['name']}\"\n", + " resampled_tiff = netcdf_to_tiff(\n", + " ds=resampled_nc_file,\n", + " band=day,\n", + " variable=exposure_variable,\n", + " crs=\"EPSG:4326\"\n", + " )\n", + "\n", + " result_poly2cell = polygon_to_raster_cells(\n", + " vectors=healthsheds.geometry.values,\n", + " raster=resampled_tiff.data,\n", + " nodata=resampled_tiff.nodata,\n", + " affine=resampled_tiff.transform,\n", + " all_touched=True,\n", + " verbose=True\n", + " )\n", + "\n", + " res = aggregate_to_healthsheds(\n", + " res_poly2cell=result_poly2cell,\n", + " raster=resampled_tiff,\n", + " shapes=healthsheds,\n", + " names_column=cfg.geographies[geography].unique_id,\n", + " aggregation_func=healthshed_agg_func,\n", + " aggregation_name=exposure_variable\n", + " )\n", + "\n", + " result_df[day_col] = res[exposure_variable]\n", + "\n", + " print(f\"Saving final monthly parquet file: {output_file}\")\n", + " result_df.to_parquet(output_file, compression=\"snappy\")\n", + " # return(result_df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| eval: false\n", + "try:\n", + " with initialize(version_base=None, config_path=\"../conf\"):\n", + " cfg = compose(config_name='config.yaml')\n", + "except Exception as e:\n", + " print(f\"Error initializing Hydra: {e}\")\n", + " with initialize(version_base=None, config_path=\"conf\"):\n", + " cfg = compose(config_name='config.yaml')\n", + "\n", + "cfg.development_mode = False\n", + "cfg.query['year'] = 2017\n", + "cfg.query['month'] = 11\n", + "cfg.query['geography'] = \"nepal\"\n", + "\n", + "variable = \"swvl1\"\n", + "\n", + "aggregate_data(cfg, here() / \"bld/2017_11_nepal.nc\", here() / \"data/testing/test_nepal_aggregation.parquet\", exposure_variable=variable)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| eval: false\n", + "parquet_file = gpd.read_parquet(here() / \"data/testing/test_nepal_aggregation.parquet\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| eval: false\n", + "parquet_file" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| eval: false\n", + "parquet_file.plot(column=\"day_22_daily_mean\", legend=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "has_sd": true, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/aggregate.py#L302){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### main\n", + "\n", + "> main (cfg:omegaconf.dictconfig.DictConfig)" + ], + "text/plain": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/aggregate.py#L302){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### main\n", + "\n", + "> main (cfg:omegaconf.dictconfig.DictConfig)" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| echo: false\n", + "#| output: asis\n", + "show_doc(main)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| code-fold: show\n", + "#| code-summary: \"Exported source\"\n", + "#| exports: #\n", + "@hydra.main(version_base=None, config_path=\"../../conf\", config_name=\"config\")\n", + "def main(cfg: DictConfig) -> None:\n", + " # Parse command-line arguments\n", + " input_file = str(snakemake.input[0]) # First input file\n", + " output_file = str(snakemake.output[0])\n", + " geography = str(snakemake.params.geography)\n", + " aggregation_variable = str(snakemake.params.variable)\n", + "\n", + " variables_dict = {\n", + " \"2m_temperature\": \"t2m\",\n", + " \"2m_dewpoint_temperature\": \"d2m\",\n", + " \"volumetric_soil_water_layer_1\": \"swvl1\",\n", + " \"total_precipitation\": \"tp\"\n", + " }\n", + "\n", + " cfg['query']['geography'] = geography\n", + " \n", + " aggregate_data(cfg, input_file=input_file, output_file=output_file, exposure_variable=variables_dict[aggregation_variable])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/_proc/03_publish.html.md b/_proc/03_publish.html.md new file mode 100644 index 0000000..8b5c1b2 --- /dev/null +++ b/_proc/03_publish.html.md @@ -0,0 +1,522 @@ +--- +title: "Publish: Gather the Aggregated Data and Publish to DataVerse" +engine: jupyter +--- + + +## publish + +> This is the `publish` module for the ERA5 dataset pipeline. It defines a functions that make use of the `pyDataverse` library and API to publish our outputs to the Harvard Dataverse. + + + +First, we'll test out the API by pinging the Harvard DataVerse + +::: {#cell-3 .cell exports='null'} +``` {.python .cell-code code-fold="show" code-summary="Exported source"} +import hydra +import yaml +import json +from tqdm import tqdm +from pyprojroot import here +``` +::: + + +::: {#cell-4 .cell} +``` {.python .cell-code} +api_token_file = here() / "sandbox/dataverse_api_key.yml" +with open(api_token_file, "r") as f: + config = yaml.load(f, Loader=yaml.BaseLoader) +``` +::: + + +Now, following the [docs]() for the dataverse tutorial, load a NativeAPI up: + +::: {#cell-6 .cell exports='null'} +``` {.python .cell-code code-fold="show" code-summary="Exported source"} +from pyDataverse.api import NativeApi +``` +::: + + +The NativeAPI is a catchall API object to be able to do general stuff: + +::: {#cell-8 .cell} +``` {.python .cell-code} +api = NativeApi(config['base_url'], config['api_token']) +resp=api.get_info_version() +#resp.text() +``` +::: + + +::: {#cell-9 .cell} +``` {.python .cell-code} +resp.json() +``` +::: + + +Looks good! Now that we know that it works, we can think more +about how to publish data there. + +## Harvard Dataverse + +Let's create a dummy dataset with the components we're +planning to upload, and then upload and promptly delete it. + +To do that, we must import the `models` module and create a Dataset object: + +::: {#cell-11 .cell} +``` {.python .cell-code} +from pyDataverse.models import Dataset +``` +::: + + +::: {#cell-12 .cell} +``` {.python .cell-code} +ds = Dataset() +``` +::: + + +This `ds` object is pretty straightforward since it doesn't contain anything yet: + +::: {#cell-14 .cell} +``` {.python .cell-code} +ds.get() +``` +::: + + +We can populate the object from the dummy data on the github repo: + +::: {#cell-16 .cell} +``` {.python .cell-code} +from pyDataverse.utils import read_file +from urllib.request import urlretrieve +import tempfile +``` +::: + + +::: {#cell-17 .cell} +``` {.python .cell-code} +# url for dummy data +url = "https://raw.githubusercontent.com/gdcc/pyDataverse/refs/heads/main/tests/data/user-guide/dataset.json" + + +with tempfile.NamedTemporaryFile(mode='w+') as tmp: + urlretrieve(url, tmp.name) + ds.from_json(read_file(tmp.name)) +``` +::: + + +We have to validate the JSON correctly: + +::: {#cell-19 .cell} +``` {.python .cell-code} +ds.validate_json() +``` +::: + + +Modifying it is easy: + +::: {#cell-21 .cell} +``` {.python .cell-code} +ds.set({"title": "Youth from Austria 2005"}) +ds.get() +``` +::: + + +Now, to create the dataset we use the API: + +::: {#cell-23 .cell} +``` {.python .cell-code} +# this is only run in interactive sessions for demo purposes +resp = api.create_dataset(":root", ds.json()) +``` +::: + + +If you caught the `resp` object, it contains the PID for the newly created dataset. + +However, if you didn't you can use the SearchAPI to find it: + +::: {#cell-25 .cell exports='null'} +``` {.python .cell-code code-fold="show" code-summary="Exported source"} +from pyDataverse.api import SearchApi +``` +::: + + +::: {#cell-26 .cell} +``` {.python .cell-code} +search_api = SearchApi(config['base_url'], config['api_token']) +``` +::: + + +::: {#cell-27 .cell} +``` {.python .cell-code} +# + +resp = search_api.search("Youth from Austria", data_type="dataset") +results = resp.json()['data']['items'] +result = [x for x in results if "Youth from Austria" in x['name']][0] +result +``` +::: + + +::: {#cell-28 .cell} +``` {.python .cell-code} +pid = result['global_id'] +``` +::: + + +Now to look at the data we created using the NativeAPI again, and delete the dataset: + +::: {#cell-30 .cell} +``` {.python .cell-code} +uploaded_ds = api.get_dataset(pid) +uploaded_ds.json()['data'] + +resp = api.delete_dataset(pid) +resp.json() +``` +::: + + +With that understanding, we can develop a quick module to do the following: + +1. Make the dataset LEGO Compatible +2. Upload and publish the data to dataverse + +## LEGO Compatibility + +Let's take an example file to use as a model for LEGO compatibility + +::: {#cell-32 .cell exports='null'} +``` {.python .cell-code code-fold="show" code-summary="Exported source"} +import geopandas as gpd +import pandas as pd +import re +import glob +``` +::: + + +::: {#cell-33 .cell} +``` {.python .cell-code} +ex = gpd.read_parquet(here() / "bld/2009_06_madagascar_day_swvl1_mean.parquet") +ex.describe() +``` +::: + + +We know that the LEGO data model should look like this: + +``` +
/lego +├── +│ ├── __ +│ │ ├── __ +│ │ │ ├── _yyyy.parquet +``` + +So, for the above file, we'll end up with the LEGO path `data/environmental/exposures_era5/healthshed_monthly/dewpoint_2024.parquet`. In it, we should have the following columns: + + +``` +healthshed_id year month day stat_1 stat_2 ... stat_n +``` + + +This means we should read in all of the exposures for a single timepoint at once. +I think the smart thing to do is use a glob string to gather all of the pertinent files. +This will be the first function we export to the library: + +--- + +[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/publish.py#L32){target="_blank" style="float:right; font-size:smaller"} + +### gather_exposure_geodataframes + +> gather_exposure_geodataframes (glob_string:str, polygon_id:str, +> exposure:str) + +*Read in a list of geo dataframes from the same time frame and merge them* + +| | **Type** | **Details** | +| -- | -------- | ----------- | +| glob_string | str | string for the path to search for the pertinent files | +| polygon_id | str | the string signifying the healthshed ID of the polygon | +| exposure | str | the exposure name | +| **Returns** | **list** | | + + +::: {#cell-36 .cell exports='null'} +``` {.python .cell-code code-fold="show" code-summary="Exported source"} +def gather_exposure_geodataframes( + glob_string: str, # string for the path to search for the pertinent files + polygon_id: str, # the string signifying the healthshed ID of the polygon + exposure: str # the exposure name + )-> list: + "Read in a list of geo dataframes from the same time frame and merge them" + + # first get the initial one so we have the polygon ID and geometry + frames = glob.glob(str(glob_string)) + initial_gdf=gpd.read_parquet(frames[0]) + merged_df = [] + + for f in tqdm(frames, desc="Processing files"): + # read in as a regular dataframe by ignoring geometry + df = gpd.read_parquet(f).drop(["geometry"], axis=1) + + # get the year and month + # Extract year and month + search_str = rf'_{exposure}_(\d{{4}})_(\d{{1,2}})\.parquet$' + match = re.search(search_str, f) + + if match: + year = int(match.group(1)) + month = int(match.group(2)) + #print(f"Year: {year}, Month: {month}") + else: + raise ValueError(f"Could not extract year and month from filename: {search_str} {f}") + + df['exposure'] = exposure + df['month'] = month + df['year'] = year + + # Step 1: Melt all day columns (leave 'month' and 'year' as identifiers) + df_long = df.melt(id_vars=[polygon_id, "exposure", "year", "month"], var_name="day_stat", value_name="value") + + # Step 2: Extract day and stat type from column names + # Example column: "day_01_daily_mean" + df_long[["day", "stat"]] = df_long["day_stat"].str.extract(r"day_(\d{2})_daily_(mean|max|min|total)") + + # Optional: convert 'day' and month to integer + df_long["day"] = df_long["day"].astype(int) + df_long["month"] = df_long["month"].astype(int) + + # Drop the original combined column + df_long = df_long.drop(columns="day_stat") + + # Reorder columns + df_long = df_long[[polygon_id, "exposure", "year", "month", "day", "stat", "value"]] + + df_long = df_long.sort_values(by=["year", "month", "day"]) + df_clean = df_long.pivot(index=[polygon_id, "exposure", "year", "month", "day"], columns="stat", values="value").reset_index() + merged_df.append(df_clean) + + return [pd.concat(merged_df).reset_index(drop=True), initial_gdf[[polygon_id, "geometry"]]] +``` +::: + + +::: {#cell-37 .cell} +``` {.python .cell-code} +frames = here() / "data" / "testing" / "*madagascar*" + +merged = gather_exposure_geodataframes(frames, "fs_uid", "2m_dewpoint_temperature") +merged[0].describe() +``` +::: + + +This returns one file with all of the geometries and one file +with the statistics and exposures. + +Now, with this, we can move on. The dataset was created in the UI and is available via search and test out how to upload it: + +::: {#cell-39 .cell} +``` {.python .cell-code} +resp = search_api.search("ERA5", data_type="dataset") + +results = resp.json()['data']['items'] + +result = [x for x in results if "ERA5" in x['name']][0] +era5_pid = result['global_id'] +result +``` +::: + + +::: {#cell-40 .cell exports='null'} +``` {.python .cell-code code-fold="show" code-summary="Exported source"} +from pyDataverse.models import Datafile +import os +import pathlib +``` +::: + + +We'll upload directly from file. In the case of ERA5 vs. LEGO, we +store the file on disk as LEGO hierarchy, but to upload it to dataverse +using a flat filename (since creating subdatasets to represent directories might be +a bit of a hassle) + +::: {#cell-42 .cell} +``` {.python .cell-code} +# assuming the file has a path on disk like: +f_out = "environmental/exposures_era5/healthshed_daily/dewpoint_2024.parquet" +os.makedirs(here() / "data" / "testing" / os.path.dirname(f_out), exist_ok=True) +aggregations, geo = merged +aggregations.to_parquet(here() / "data" / "testing" / f_out, index=False) + +datafile = Datafile() +datafile.set({ + # the id of the era5 dataset + "pid": era5_pid, + # the path to the file on disk goes here + "filename": str(here() / "data" / "testing" / f_out), + # use the "label" to name the file + "label": f_out.replace("/", "-") +}) +``` +::: + + +::: {#cell-43 .cell} +``` {.python .cell-code} +resp = api.upload_datafile(era5_pid, str(here() / "data" / "testing" / f_out), datafile.json()) +``` +::: + + +Pretty simple! + +Now, we just need a main function to upload this data. The final upload is one file per +exposure per year, so these should be the variables we gather data for. + +We should get some functionality to gather the groups of these files automatically, based on +the hydra config: + +::: {#cell-45 .cell exports='null'} +``` {.python .cell-code code-fold="show" code-summary="Exported source"} +from hydra import initialize, compose +from omegaconf import OmegaConf, DictConfig +from tqdm import tqdm +``` +::: + + +::: {#cell-46 .cell} +``` {.python .cell-code} +target_dir = here() / "data" / "intermediate" + +try: + with initialize(version_base=None, config_path="../conf"): + cfg = compose(config_name='config.yaml') +except Exception as e: + print(f"Error initializing Hydra: {e}") + with initialize(version_base=None, config_path="conf"): + cfg = compose(config_name='config.yaml') + +cfg.development_mode = False +#cfg.query['year'] = 2017 +#cfg.query['month'] = 11 +#cfg.query['geography'] = "nepal" +``` +::: + + +--- + +[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/aggregate.py#L302){target="_blank" style="float:right; font-size:smaller"} + +### main + +> main (cfg:omegaconf.dictconfig.DictConfig) + + +::: {#cell-48 .cell exports='null'} +``` {.python .cell-code code-fold="show" code-summary="Exported source"} +@hydra.main(version_base=None, config_path="../../conf", config_name="config") +def main(cfg: DictConfig) -> None: + + variables_dict = { + "2m_temperature": "t2m", + "2m_dewpoint_temperature": "d2m", + "volumetric_soil_water_layer_1": "swvl1", + "total_precipitation": "tp" + } + + print(OmegaConf.to_yaml(cfg)) + + #prep dataverse + api_token_file = here() / "sandbox/dataverse_api_key.yml" + with open(api_token_file, "r") as f: + apiconfig = yaml.load(f, Loader=yaml.BaseLoader) + api = NativeApi(apiconfig['base_url'], apiconfig['api_token']) + search_api = SearchApi(apiconfig['base_url'], apiconfig['api_token']) + resp = search_api.search("ERA5", data_type="dataset") + + results = resp.json()['data']['items'] + + result = [x for x in results if "ERA5" in x['name']][0] + era5_pid = result['global_id'] + + for geography in cfg.geographies: + for year in cfg.query['year']: + for variable, v in variables_dict.items(): + + print(f"Processing {geography} for {variable} in {year}") + glob_string = here() / "data" / "intermediate" / f"*{geography}*{variable}*{year}*" + print(f"Glob: {glob_string}") + polygon_id = cfg.geographies[geography]['unique_id'] + print(f"polygon_id: {polygon_id}") + merged = gather_exposure_geodataframes(glob_string, polygon_id, variable) + print(merged[0].head()) + print(merged[1].head()) + + output_dir = here() / "data" / "output" + + f_out = f"environmental/exposures_era5/healthshed_daily/{geography}_{v}_{year}.parquet" + os.makedirs(output_dir / os.path.dirname(f_out), exist_ok=True) + output_path = output_dir / f_out + + print(f"Writing to {output_path}") + merged[0].to_parquet(output_path, index=False) + + + print(f"Uploading {f_out.replace('/', '-')} to Dataverse...") + # upload to dataverse + datafile = Datafile() + datafile.set({ + "pid": era5_pid, + "filename": str(output_path), + "label": f_out.replace("/", "-") + }) + + resp = api.upload_datafile(era5_pid, output_path, datafile.json()) + assert resp.json()['status'] == "OK", f"Failed to upload datafile: {resp.text}" + + # also save the geometry for the region + merged[1].to_parquet(output_path.parent / f"{geography}_geometry.parquet", index=False) + + # and upload it to dataverse + datafile = Datafile() + datafile.set({ + "pid": era5_pid, + "filename": str(output_path.parent / f"{geography}_geometry.parquet"), + "label": f"{geography}_geometry.parquet" + }) + + resp = api.upload_datafile(era5_pid, output_path.parent / f"{geography}_geometry.parquet", datafile.json()) + assert resp.json()['status'] == "OK", f"Failed to upload geometry datafile: {resp.text}" + + print("All files processed and uploaded successfully.") +``` +::: + + diff --git a/_proc/03_publish.ipynb b/_proc/03_publish.ipynb new file mode 100644 index 0000000..abb4f71 --- /dev/null +++ b/_proc/03_publish.ipynb @@ -0,0 +1,840 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "title: \"Publish: Gather the Aggregated Data and Publish to DataVerse\"\n", + "engine: jupyter\n", + "---\n", + "\n", + "## publish \n", + "\n", + "> This is the `publish` module for the ERA5 dataset pipeline. It defines a functions that make use of the `pyDataverse` library and API to publish our outputs to the Harvard Dataverse." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, we'll test out the API by pinging the Harvard DataVerse" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| code-fold: show\n", + "#| code-summary: \"Exported source\"\n", + "#| exports: #\n", + "import hydra\n", + "import yaml\n", + "import json\n", + "from tqdm import tqdm\n", + "from pyprojroot import here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "api_token_file = here() / \"sandbox/dataverse_api_key.yml\"\n", + "with open(api_token_file, \"r\") as f:\n", + " config = yaml.load(f, Loader=yaml.BaseLoader)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, following the [docs]() for the dataverse tutorial, load a NativeAPI up:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| code-fold: show\n", + "#| code-summary: \"Exported source\"\n", + "#| exports: #\n", + "from pyDataverse.api import NativeApi" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The NativeAPI is a catchall API object to be able to do general stuff:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "api = NativeApi(config['base_url'], config['api_token'])\n", + "resp=api.get_info_version()\n", + "#resp.text()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "resp.json()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looks good! Now that we know that it works, we can think more\n", + "about how to publish data there.\n", + "\n", + "## Harvard Dataverse\n", + "\n", + "Let's create a dummy dataset with the components we're\n", + "planning to upload, and then upload and promptly delete it.\n", + "\n", + "To do that, we must import the `models` module and create a Dataset object:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "from pyDataverse.models import Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "ds = Dataset()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This `ds` object is pretty straightforward since it doesn't contain anything yet:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "ds.get()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can populate the object from the dummy data on the github repo:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "from pyDataverse.utils import read_file\n", + "from urllib.request import urlretrieve\n", + "import tempfile" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "# url for dummy data\n", + "url = \"https://raw.githubusercontent.com/gdcc/pyDataverse/refs/heads/main/tests/data/user-guide/dataset.json\"\n", + "\n", + "\n", + "with tempfile.NamedTemporaryFile(mode='w+') as tmp:\n", + " urlretrieve(url, tmp.name)\n", + " ds.from_json(read_file(tmp.name))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have to validate the JSON correctly:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "ds.validate_json()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Modifying it is easy:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "ds.set({\"title\": \"Youth from Austria 2005\"})\n", + "ds.get()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, to create the dataset we use the API:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| eval: false\n", + "# this is only run in interactive sessions for demo purposes\n", + "resp = api.create_dataset(\":root\", ds.json())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you caught the `resp` object, it contains the PID for the newly created dataset.\n", + "\n", + "However, if you didn't you can use the SearchAPI to find it:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| code-fold: show\n", + "#| code-summary: \"Exported source\"\n", + "#| exports: #\n", + "from pyDataverse.api import SearchApi" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "search_api = SearchApi(config['base_url'], config['api_token'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| eval: false\n", + "#\n", + "\n", + "resp = search_api.search(\"Youth from Austria\", data_type=\"dataset\")\n", + "results = resp.json()['data']['items']\n", + "result = [x for x in results if \"Youth from Austria\" in x['name']][0]\n", + "result" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| eval: false\n", + "pid = result['global_id']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now to look at the data we created using the NativeAPI again, and delete the dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| eval: false\n", + "uploaded_ds = api.get_dataset(pid)\n", + "uploaded_ds.json()['data']\n", + "\n", + "resp = api.delete_dataset(pid)\n", + "resp.json()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With that understanding, we can develop a quick module to do the following:\n", + "\n", + "1. Make the dataset LEGO Compatible\n", + "2. Upload and publish the data to dataverse\n", + "\n", + "## LEGO Compatibility\n", + "\n", + "Let's take an example file to use as a model for LEGO compatibility" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| code-fold: show\n", + "#| code-summary: \"Exported source\"\n", + "#| exports: #\n", + "import geopandas as gpd\n", + "import pandas as pd\n", + "import re\n", + "import glob" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "ex = gpd.read_parquet(here() / \"bld/2009_06_madagascar_day_swvl1_mean.parquet\")\n", + "ex.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We know that the LEGO data model should look like this:\n", + "\n", + "```\n", + "
/lego\n", + "├── \n", + "│ ├── __\n", + "│ │ ├── __\n", + "│ │ │ ├── _yyyy.parquet\n", + "```\n", + "\n", + "So, for the above file, we'll end up with the LEGO path `data/environmental/exposures_era5/healthshed_monthly/dewpoint_2024.parquet`. In it, we should have the following columns:\n", + "\n", + "\n", + "```\n", + "healthshed_id year month day stat_1 stat_2 ... stat_n \n", + "```\n", + "\n", + "\n", + "This means we should read in all of the exposures for a single timepoint at once. \n", + "I think the smart thing to do is use a glob string to gather all of the pertinent files.\n", + "This will be the first function we export to the library:" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "has_sd": true, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/publish.py#L32){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### gather_exposure_geodataframes\n", + "\n", + "> gather_exposure_geodataframes (glob_string:str, polygon_id:str,\n", + "> exposure:str)\n", + "\n", + "*Read in a list of geo dataframes from the same time frame and merge them*\n", + "\n", + "| | **Type** | **Details** |\n", + "| -- | -------- | ----------- |\n", + "| glob_string | str | string for the path to search for the pertinent files |\n", + "| polygon_id | str | the string signifying the healthshed ID of the polygon |\n", + "| exposure | str | the exposure name |\n", + "| **Returns** | **list** | |" + ], + "text/plain": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/publish.py#L32){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### gather_exposure_geodataframes\n", + "\n", + "> gather_exposure_geodataframes (glob_string:str, polygon_id:str,\n", + "> exposure:str)\n", + "\n", + "*Read in a list of geo dataframes from the same time frame and merge them*\n", + "\n", + "| | **Type** | **Details** |\n", + "| -- | -------- | ----------- |\n", + "| glob_string | str | string for the path to search for the pertinent files |\n", + "| polygon_id | str | the string signifying the healthshed ID of the polygon |\n", + "| exposure | str | the exposure name |\n", + "| **Returns** | **list** | |" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| echo: false\n", + "#| output: asis\n", + "show_doc(gather_exposure_geodataframes)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| code-fold: show\n", + "#| code-summary: \"Exported source\"\n", + "#| exports: # \n", + "\n", + "def gather_exposure_geodataframes(\n", + " glob_string: str, # string for the path to search for the pertinent files\n", + " polygon_id: str, # the string signifying the healthshed ID of the polygon\n", + " exposure: str # the exposure name\n", + " )-> list:\n", + " \"Read in a list of geo dataframes from the same time frame and merge them\"\n", + "\n", + " # first get the initial one so we have the polygon ID and geometry\n", + " frames = glob.glob(str(glob_string))\n", + " initial_gdf=gpd.read_parquet(frames[0])\n", + " merged_df = []\n", + " \n", + " for f in tqdm(frames, desc=\"Processing files\"):\n", + " # read in as a regular dataframe by ignoring geometry\n", + " df = gpd.read_parquet(f).drop([\"geometry\"], axis=1) \n", + " \n", + " # get the year and month\n", + " # Extract year and month\n", + " search_str = rf'_{exposure}_(\\d{{4}})_(\\d{{1,2}})\\.parquet$'\n", + " match = re.search(search_str, f)\n", + "\n", + " if match:\n", + " year = int(match.group(1))\n", + " month = int(match.group(2))\n", + " #print(f\"Year: {year}, Month: {month}\")\n", + " else:\n", + " raise ValueError(f\"Could not extract year and month from filename: {search_str} {f}\")\n", + " \n", + " df['exposure'] = exposure\n", + " df['month'] = month\n", + " df['year'] = year\n", + "\n", + " # Step 1: Melt all day columns (leave 'month' and 'year' as identifiers)\n", + " df_long = df.melt(id_vars=[polygon_id, \"exposure\", \"year\", \"month\"], var_name=\"day_stat\", value_name=\"value\")\n", + "\n", + " # Step 2: Extract day and stat type from column names\n", + " # Example column: \"day_01_daily_mean\"\n", + " df_long[[\"day\", \"stat\"]] = df_long[\"day_stat\"].str.extract(r\"day_(\\d{2})_daily_(mean|max|min|total)\")\n", + "\n", + " # Optional: convert 'day' and month to integer\n", + " df_long[\"day\"] = df_long[\"day\"].astype(int)\n", + " df_long[\"month\"] = df_long[\"month\"].astype(int)\n", + "\n", + " # Drop the original combined column\n", + " df_long = df_long.drop(columns=\"day_stat\")\n", + "\n", + " # Reorder columns\n", + " df_long = df_long[[polygon_id, \"exposure\", \"year\", \"month\", \"day\", \"stat\", \"value\"]]\n", + "\n", + " df_long = df_long.sort_values(by=[\"year\", \"month\", \"day\"])\n", + " df_clean = df_long.pivot(index=[polygon_id, \"exposure\", \"year\", \"month\", \"day\"], columns=\"stat\", values=\"value\").reset_index()\n", + " merged_df.append(df_clean)\n", + "\n", + " return [pd.concat(merged_df).reset_index(drop=True), initial_gdf[[polygon_id, \"geometry\"]]]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "frames = here() / \"data\" / \"testing\" / \"*madagascar*\"\n", + "\n", + "merged = gather_exposure_geodataframes(frames, \"fs_uid\", \"2m_dewpoint_temperature\")\n", + "merged[0].describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This returns one file with all of the geometries and one file\n", + "with the statistics and exposures.\n", + "\n", + "Now, with this, we can move on. The dataset was created in the UI and is available via search and test out how to upload it:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "resp = search_api.search(\"ERA5\", data_type=\"dataset\")\n", + "\n", + "results = resp.json()['data']['items']\n", + "\n", + "result = [x for x in results if \"ERA5\" in x['name']][0]\n", + "era5_pid = result['global_id']\n", + "result" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| code-fold: show\n", + "#| code-summary: \"Exported source\"\n", + "#| exports: #\n", + "\n", + "from pyDataverse.models import Datafile\n", + "import os\n", + "import pathlib" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll upload directly from file. In the case of ERA5 vs. LEGO, we\n", + "store the file on disk as LEGO hierarchy, but to upload it to dataverse\n", + "using a flat filename (since creating subdatasets to represent directories might be \n", + "a bit of a hassle)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "# assuming the file has a path on disk like:\n", + "f_out = \"environmental/exposures_era5/healthshed_daily/dewpoint_2024.parquet\"\n", + "os.makedirs(here() / \"data\" / \"testing\" / os.path.dirname(f_out), exist_ok=True)\n", + "aggregations, geo = merged\n", + "aggregations.to_parquet(here() / \"data\" / \"testing\" / f_out, index=False)\n", + "\n", + "datafile = Datafile()\n", + "datafile.set({\n", + " # the id of the era5 dataset \n", + " \"pid\": era5_pid,\n", + " # the path to the file on disk goes here\n", + " \"filename\": str(here() / \"data\" / \"testing\" / f_out),\n", + " # use the \"label\" to name the file\n", + " \"label\": f_out.replace(\"/\", \"-\")\n", + "})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| eval: false\n", + "resp = api.upload_datafile(era5_pid, str(here() / \"data\" / \"testing\" / f_out), datafile.json())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pretty simple!\n", + "\n", + "Now, we just need a main function to upload this data. The final upload is one file per\n", + "exposure per year, so these should be the variables we gather data for.\n", + "\n", + "We should get some functionality to gather the groups of these files automatically, based on\n", + "the hydra config:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| code-fold: show\n", + "#| code-summary: \"Exported source\"\n", + "#| exports: #\n", + "from hydra import initialize, compose\n", + "from omegaconf import OmegaConf, DictConfig\n", + "from tqdm import tqdm" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "target_dir = here() / \"data\" / \"intermediate\"\n", + "\n", + "try:\n", + " with initialize(version_base=None, config_path=\"../conf\"):\n", + " cfg = compose(config_name='config.yaml')\n", + "except Exception as e:\n", + " print(f\"Error initializing Hydra: {e}\")\n", + " with initialize(version_base=None, config_path=\"conf\"):\n", + " cfg = compose(config_name='config.yaml')\n", + "\n", + "cfg.development_mode = False\n", + "#cfg.query['year'] = 2017\n", + "#cfg.query['month'] = 11\n", + "#cfg.query['geography'] = \"nepal\"" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "has_sd": true, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/aggregate.py#L302){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### main\n", + "\n", + "> main (cfg:omegaconf.dictconfig.DictConfig)" + ], + "text/plain": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/aggregate.py#L302){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### main\n", + "\n", + "> main (cfg:omegaconf.dictconfig.DictConfig)" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| echo: false\n", + "#| output: asis\n", + "show_doc(main)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| code-fold: show\n", + "#| code-summary: \"Exported source\"\n", + "#| exports: #\n", + "\n", + "@hydra.main(version_base=None, config_path=\"../../conf\", config_name=\"config\")\n", + "def main(cfg: DictConfig) -> None:\n", + "\n", + " variables_dict = {\n", + " \"2m_temperature\": \"t2m\",\n", + " \"2m_dewpoint_temperature\": \"d2m\",\n", + " \"volumetric_soil_water_layer_1\": \"swvl1\",\n", + " \"total_precipitation\": \"tp\"\n", + " }\n", + "\n", + " print(OmegaConf.to_yaml(cfg))\n", + "\n", + " #prep dataverse\n", + " api_token_file = here() / \"sandbox/dataverse_api_key.yml\"\n", + " with open(api_token_file, \"r\") as f:\n", + " apiconfig = yaml.load(f, Loader=yaml.BaseLoader)\n", + " api = NativeApi(apiconfig['base_url'], apiconfig['api_token'])\n", + " search_api = SearchApi(apiconfig['base_url'], apiconfig['api_token'])\n", + " resp = search_api.search(\"ERA5\", data_type=\"dataset\")\n", + "\n", + " results = resp.json()['data']['items']\n", + "\n", + " result = [x for x in results if \"ERA5\" in x['name']][0]\n", + " era5_pid = result['global_id']\n", + "\n", + " for geography in cfg.geographies:\n", + " for year in cfg.query['year']:\n", + " for variable, v in variables_dict.items():\n", + " \n", + " print(f\"Processing {geography} for {variable} in {year}\")\n", + " glob_string = here() / \"data\" / \"intermediate\" / f\"*{geography}*{variable}*{year}*\"\n", + " print(f\"Glob: {glob_string}\")\n", + " polygon_id = cfg.geographies[geography]['unique_id']\n", + " print(f\"polygon_id: {polygon_id}\")\n", + " merged = gather_exposure_geodataframes(glob_string, polygon_id, variable)\n", + " print(merged[0].head())\n", + " print(merged[1].head())\n", + "\n", + " output_dir = here() / \"data\" / \"output\" \n", + " \n", + " f_out = f\"environmental/exposures_era5/healthshed_daily/{geography}_{v}_{year}.parquet\"\n", + " os.makedirs(output_dir / os.path.dirname(f_out), exist_ok=True)\n", + " output_path = output_dir / f_out\n", + "\n", + " print(f\"Writing to {output_path}\")\n", + " merged[0].to_parquet(output_path, index=False)\n", + " \n", + "\n", + " print(f\"Uploading {f_out.replace('/', '-')} to Dataverse...\")\n", + " # upload to dataverse\n", + " datafile = Datafile()\n", + " datafile.set({\n", + " \"pid\": era5_pid,\n", + " \"filename\": str(output_path),\n", + " \"label\": f_out.replace(\"/\", \"-\")\n", + " })\n", + "\n", + " resp = api.upload_datafile(era5_pid, output_path, datafile.json())\n", + " assert resp.json()['status'] == \"OK\", f\"Failed to upload datafile: {resp.text}\"\n", + " \n", + " # also save the geometry for the region \n", + " merged[1].to_parquet(output_path.parent / f\"{geography}_geometry.parquet\", index=False)\n", + "\n", + " # and upload it to dataverse\n", + " datafile = Datafile()\n", + " datafile.set({\n", + " \"pid\": era5_pid,\n", + " \"filename\": str(output_path.parent / f\"{geography}_geometry.parquet\"),\n", + " \"label\": f\"{geography}_geometry.parquet\"\n", + " })\n", + "\n", + " resp = api.upload_datafile(era5_pid, output_path.parent / f\"{geography}_geometry.parquet\", datafile.json())\n", + " assert resp.json()['status'] == \"OK\", f\"Failed to upload geometry datafile: {resp.text}\"\n", + "\n", + " print(\"All files processed and uploaded successfully.\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/_proc/10_pytask_demo.html.md b/_proc/10_pytask_demo.html.md new file mode 100644 index 0000000..02b722b --- /dev/null +++ b/_proc/10_pytask_demo.html.md @@ -0,0 +1,383 @@ +--- +title: "Demo: How to Create Pipelines with `pytask`" +engine: jupyter +--- + + +## Data Preparation Demo + +> Data preparation task for `pytask` demo + +In this notebook, we are demonstrating how to convert our snakemake workflow into a `pytask` workflow. We use the basic tutorial to demonstrate this, but continue +to use nbdev for development of functions in notebooks. + +`pytask` is a task management system that allows you to define tasks and their dependencies, similar to `Snakemake`. It is particularly useful for data science workflows. + +There are a number of reasons to use `pytask` over `snakemake`: +- **Pythonic**: `pytask` is designed to be purely Pythonic by default, allowing you to write tasks and entire pipelines as Python functions. +- **Flexibility**: `pytask` allows you to define tasks and their dependencies in a more flexible way, using Python functions and decorators, as opposed to orchestrating numerous scripts. +- **Integration**: `pytask` integrates well with other Python libraries, such as `nbdev` here, or `hydra` configurations if you need, allowing you to use your existing code, notebooks, or configs in your workflows. +- **Parallelism**: `pytask` supports parallel execution of tasks with `pytask-parallel`, which can speed up your workflows significantly, especially for data processing tasks. + +We'll use nbdev to define the task functions, and then export them to the `src` directory. `pytask` is then invoked at the command line to run the tasks. + + + +This demo task is taken from the tutorial at [pytask documentation](https://pytask-dev.readthedocs.io/en/stable/tutorials/write_a_task.html). At minimum, you need your package to contain the following in a config.py file: + +```md +my_project +│ +├───.pytask +│ +├───bld +│ └────... +│ +├───src +│ └───my_project +│ ├────__init__.py +│ ├────config.py +│ └────... +│ +└───pyproject.toml +``` + +```python +#contents of `era5_sandbox.config` module +from pathlib import Path + + +SRC = Path(__file__).parent.resolve() +BLD = SRC.joinpath("..", "..", "bld").resolve() +``` + +Additionally, your pyproject.toml file should contain the following at minimum: + +```toml +[tool.pytask.ini_options] +paths = ["src/era5_sandbox"] +``` + +The former tells Python where to find the source code and build directory for `pytask` objects and shims, while the latter tells `pytask` where to find the task definitions and dependency DAG. + +::: {#cell-3 .cell exports='null'} +``` {.python .cell-code code-fold="show" code-summary="Exported source"} +import os +from pathlib import Path +from typing import Annotated + +import numpy as np +import matplotlib.pyplot as plt +import pandas as pd +from era5_sandbox.config import BLD +from era5_sandbox.config import data_catalog, demo_catalog + +from pytask import PickleNode +from pytask import Product +``` +::: + + +### Defining Tasks + +To define a task, simply use the `task_` prefix in the function name (or, if you are familiar and comfortable with decorators, use `@pytask.mark.task`). Be verbose and expressive in your use of type hints to specify the input and output data, so that `pytask` can automatically detect and handle the dependencies between tasks. + +### Defining Tracked Outputs + +To define something as a tracked output, you can annotate the input of the task with `Annotated[Path, Product]`, where `Product` is imported from `pytask`. This tells `pytask` that this is a product of the task and should be saved in the build directory. + +In this example, we're generating random data into a data frame and saving the object as a pickle in the `bld` directory (`bld` is the default build directory for `pytask`'s intermediate data). To get that directory, we use the `BLD` variable from the `era5_sandbox.config` module as above. This module itself could also be generated using `nbdev` if you want to keep your configuration in notebooks. + +Using `nbdev`, we can also include all of the bells and whistles of function documentation. + +--- + +[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/task_data_preparation.py#L24){target="_blank" style="float:right; font-size:smaller"} + +### task_create_random_data + +> task_create_random_data (seed:typing.Annotated[int,42], path_to_data:typi +> ng.Annotated[pathlib.Path,ProductType()]=Path('/ +> net/rcstorenfs02/ifs/rc_labs/dominici_lab/lab/da +> ta_processing/csph-era5_sandbox/bld/data.pkl')) + +*Create a random data set and save it as a pickle file. Return the path to the saved file.* + +| | **Type** | **Default** | **Details** | +| -- | -------- | ----------- | ----------- | +| seed | Annotated | | Default seed for reproducibility | +| path_to_data | Annotated | /net/rcstorenfs02/ifs/rc_labs/dominici_lab/lab/data_processing/csph-era5_sandbox/bld/data.pkl | Path to the object in the build directory | +| **Returns** | **None** | | | + + +::: {#cell-6 .cell exports='null'} +``` {.python .cell-code code-fold="show" code-summary="Exported source"} +def task_create_random_data( + seed: Annotated[int, 42], # Default seed for reproducibility + path_to_data: Annotated[Path, Product] = BLD / "data.pkl" # Path to the object in the build directory + ) -> None: + "Create a random data set and save it as a pickle file. Return the path to the saved file." + rng = np.random.default_rng(seed) + beta = 2 + + x = rng.normal(loc=5, scale=10, size=1_000) + epsilon = rng.standard_normal(1_000) + + y = beta * x + epsilon + + df = pd.DataFrame({"x": x, "y": y}) + + # this is a tracked output, so we annotate the return value with `Annotated[Path, Product]` + df.to_pickle(path_to_data) +``` +::: + + +We can test the function directly in the notebook: + +::: {#cell-8 .cell} +``` {.python .cell-code} +task_create_random_data(42) +``` +::: + + +Once this module and function are exported with `nbdev_export`, the functions are in a python package. We can then use the command line to look at the registered tasks: + +::: {#cell-10 .cell} +``` {.sh .cell-code} +pytask collect +``` +::: + + +Let's add another task in the same module. This task plots the data we generated. To link the previous task to this one as a dependency, we can list the output of the previous task as an input to this one. This way, `pytask` will know that it needs to run the first task before this one. + +--- + +[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/task_data_preparation.py#L45){target="_blank" style="float:right; font-size:smaller"} + +### task_plot_data + +> task_plot_data (path_to_data:typing.Annotated[pathlib.Path,Path('/net/rcs +> torenfs02/ifs/rc_labs/dominici_lab/lab/data_processing/cs +> ph-era5_sandbox/bld/data.pkl')], path_to_plot:typing.Anno +> tated[pathlib.Path,ProductType()]=Path('/net/rcstorenfs02 +> /ifs/rc_labs/dominici_lab/lab/data_processing/csph- +> era5_sandbox/bld/plot.png')) + +*Plot the data from the pickle file and save the plot. Note that this task: + 1. depends on the data.pkl file created by the previous task, + 2. does not return any value, but saves a plot to the build directory. So the side effect of the task is what we are interested in here (though this is probably bad practice).* + +| | **Type** | **Default** | **Details** | +| -- | -------- | ----------- | ----------- | +| path_to_data | Annotated | | Path to the data file created by the previous task | +| path_to_plot | Annotated | /net/rcstorenfs02/ifs/rc_labs/dominici_lab/lab/data_processing/csph-era5_sandbox/bld/plot.png | Path to the build directory for the plot | +| **Returns** | **None** | | | + + +::: {#cell-13 .cell exports='null'} +``` {.python .cell-code code-fold="show" code-summary="Exported source"} +def task_plot_data( + path_to_data: Annotated[Path, BLD / "data.pkl"], # Path to the data file created by the previous task + path_to_plot: Annotated[Path, Product] = BLD / "plot.png" # Path to the build directory for the plot +) -> None: + """ + Plot the data from the pickle file and save the plot. Note that this task: + 1. depends on the data.pkl file created by the previous task, + 2. does not return any value, but saves a plot to the build directory. So the side effect of the task is what we are interested in here (though this is probably bad practice). + """ + + df = pd.read_pickle(path_to_data) + + _, ax = plt.subplots() + df.plot(x="x", y="y", ax=ax, kind="scatter") + + plt.savefig(path_to_plot) + plt.close() +``` +::: + + +We now have a DAG of tasks that `pytask` can execute. To see the tasks, we can use the command line to create a pygraphviz graph of the tasks: + +```bash +pytask dag +``` + +The DAG is saved as a pdf file, and you can view it using any viewer. Now, to run the pipeline, just invoke `pytask` at the command line: + +```bash +pytask +``` + +In Jupyter or iPython, you can interact with the task outputs directly: + +::: {#cell-15 .cell} +``` {.python .cell-code} +# list all the files in the build directory +for file in os.listdir(BLD): + print(file) +``` +::: + + +We can use these to build subsequent tasks later. + +## More Complex Tasks & The Data Catalog + +As we define more complex tasks, we can use the `pytask` data catalog to manage the inputs and outputs of our tasks. The data catalog allows us to imperatively name the data and their formats, making it easier to manage the data flow in our tasks. Importantly, we can define the data pythonically, which allows us to use the full power of Python to manipulate and transform our data. This is particularly more useful than snakemake's approach, which requires you to define the data in a more static way using paths and a separate pseudo-language. + +The content of the `era5_sandbox.config` module can be extended to include a data catalog: + +```python +from pathlib import Path +from pytask import DataCatalog, Product + +SRC = Path(__file__).parent.resolve() +BLD = SRC.joinpath("..", "..", "bld").resolve() + +demo_catalog = DataCatalog() +``` + +With just this definition, we're now able to refer directly to data by name in our tasks, and `pytask` will handle the paths and formats for us. This allows us to focus on the logic of our tasks rather than the details of data management. + +:::{.callout-note} +This is a major advantage of `pytask` over `snakemake`, as it allows you to define the data in a more flexible and Pythonic way, while still maintaining the benefits of a task management system. It is a similar approach to building pipelines in R with targets, which allows you to define the data in a more flexible way. +::: + +Let's create a task that modifies the data frame by adding a new column. This task will depend on the previous task's output, and we will use the data catalog to define the input and output data. + +--- + +[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/task_data_preparation.py#L66){target="_blank" style="float:right; font-size:smaller"} + +### task_add_one + +> task_add_one (path_to_data:typing.Annotated[pathlib.Path,Path('/net/rcsto +> renfs02/ifs/rc_labs/dominici_lab/lab/data_processing/csph- +> era5_sandbox/bld/data.pkl')], node:typing.Annotated[_pytask +> .nodes.PickleNode,ProductType()]=PickleNode(path=Path('/net +> /rcstorenfs02/ifs/rc_labs/dominici_lab/lab/data_processing/ +> csph-era5_sandbox/.pytask/data_catalogs/default/1eef510d81e +> ea49161cd821b318aa999e630bdd292b093aa9a9319e9f282b984.pkl') +> , name='mydata', attributes={'catalog_name': 'default'}, +> serializer=, deserializer= function load>)) + +*Add one to the 'y' column of the data frame and save it as a new pickle file.* + +| | **Type** | **Default** | **Details** | +| -- | -------- | ----------- | ----------- | +| path_to_data | Annotated | | Path to the data file created by the previous task | +| node | Annotated | PickleNode(path=Path('/net/rcstorenfs02/ifs/rc_labs/dominici_lab/lab/data_processing/csph-era5_sandbox/.pytask/data_catalogs/default/1eef510d81eea49161cd821b318aa999e630bdd292b093aa9a9319e9f282b984.pkl'), name='mydata', attributes={'catalog_name': 'default'}, serializer=, deserializer=) | | +| **Returns** | **None** | | | + + +::: {#cell-18 .cell exports='null'} +``` {.python .cell-code code-fold="show" code-summary="Exported source"} +def task_add_one( + path_to_data: Annotated[Path, BLD / "data.pkl"], # Path to the data file created by the previous task + node: Annotated[PickleNode, Product] = demo_catalog["mydata"] +) -> None: + """ + Add one to the 'y' column of the data frame and save it as a new pickle file. + """ + df = pd.read_pickle(path_to_data) + df['z'] = df['y'] + 1 + + node.save(df) +``` +::: + + +In this function, we've defined that the task relies on the output of the first task being there, the `data.pkl` file. But importantly, we've also defined our product as a `node` from the `PickleNode` module. This will allow `pytask` to handle the serialization and deserialization of the data frame automatically, so we don't have to worry about the details of how the data is stored. We create the datacatalog in our config file, and then tell this task to create a Node in that catalog called `mydata`. Whatever we save with the `node.save()` method will be saved in the build directory, but more importantly _will be indexed and hashed by `pytask`_. This means that if the data changes, `pytask` will know to rerun the task. + +To make this even more pythonic, we can modify the format of our task function so that the return type annotator is used as a node in the data catalog. This allows us to define the output of the task as a `PickleNode`, which will automatically handle the serialization and deserialization of the data frame. + +:::{.callout-note} +This is another trick I'm deriving from {targets}. By formatting tasks as pure functions where inputs are parameters and targets are return type annotations, we can define the output of the task as a `PickleNode`, which will automatically handle the serialization and deserialization of the data frame. This again allows us to focus on the logic of our tasks rather than the details of data management. +::: + +So below, we're directly accessing the `data_catalog` to get the `mydata` node, and then modifying it by adding a new column. It _feels_ like we are doing this in place, such as in an iPython session, because we are allowing `pytask` to handle the serialization of the file on disk for us. + +--- + +[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/task_data_preparation.py#L81){target="_blank" style="float:right; font-size:smaller"} + +### task_add_another_column + +> task_add_another_column (df:typing.Annotated[pandas.core.frame.DataFrame, +> PickleNode(path=Path('/net/rcstorenfs02/ifs/rc_l +> abs/dominici_lab/lab/data_processing/csph-era5_s +> andbox/.pytask/data_catalogs/default/1eef510d81e +> ea49161cd821b318aa999e630bdd292b093aa9a9319e9f28 +> 2b984.pkl'),name='mydata',attributes={'catalog_n +> ame':'default'},serializer= infunctiondump>,deserializer= infunctionload>)]) + +*Add another column to the data frame stored in the PickleNode.* + +| | **Type** | **Details** | +| -- | -------- | ----------- | +| df | Annotated | which object in the catalog to fetch from the catalog with node.load() | +| **Returns** | **Annotated** | **which object in the catalog to save the return value to** | + + +::: {#cell-21 .cell exports='null'} +``` {.python .cell-code code-fold="show" code-summary="Exported source"} +def task_add_another_column( + df: Annotated[pd.DataFrame, demo_catalog["mydata"]] # which object in the catalog to fetch from the catalog with node.load() +) -> Annotated[pd.DataFrame, demo_catalog["mydata2"]]: # which object in the catalog to save the return value to + """ + Add another column to the data frame stored in the PickleNode. + """ + + # use the datacatalog directly to access the node + # this is a bit like accessing the node in an iPython session, but pytask + # will handle the serialization and deserialization for us + df['w'] = df['z'] * df['y'] + + return df +``` +::: + + +To test this interactively, we'd have to import the data catalog's object + +::: {#cell-23 .cell} +``` {.python .cell-code} +df = demo_catalog["mydata"].load() # load the data frame from the PickleNode +result = task_add_another_column(df) # call the task function with the loaded data frame +``` +::: + + +::: {#cell-24 .cell} +``` {.python .cell-code} +result +``` +::: + + +Now that we know it will work, we can invoke pytask: + +::: {#cell-26 .cell} +``` {.sh .cell-code} +pytask +``` +::: + + +Notice that the outputs are cached and not recomputed unless the inputs change. This is a key feature of `pytask` and other DAGs, allowing you to efficiently manage your data processing tasks without unnecessary recomputation. + +## Conclusion + +The takeaway here is that with `pytask`, you can define pure functions that take inputs and return outputs, and build a DAG of tasks that can be executed in a flexible and efficient way. This allows you to focus on the logic of your tasks rather than the details of data management, while still maintaining the benefits of a task management system. The key elements are: + +- **Task annotation**: You define your tasks by creating pure functions that take inputs and return outputs, and use decorators or naming conventions to mark them as "tasks" in a dag +- **Input and output annotation**: You define the inputs and outputs of your tasksusing type hints, and allow `pytask` to automatically detect and handle the dependencies between tasks. +- **Data catalog**: You define your data in a Pythonic object in your config called `data_catalog`. As you iteratively develop your DAG, you add objects to the data catalog, which are called nodes. As long as a node is a pythonic object and has a pickle method, `pytask` will handle the serialization and deserialization of the data for you. + diff --git a/_proc/10_pytask_demo.ipynb b/_proc/10_pytask_demo.ipynb new file mode 100644 index 0000000..abe3aff --- /dev/null +++ b/_proc/10_pytask_demo.ipynb @@ -0,0 +1,702 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "title: \"Demo: How to Create Pipelines with `pytask`\"\n", + "engine: jupyter\n", + "---\n", + "\n", + "## Data Preparation Demo\n", + "\n", + "> Data preparation task for `pytask` demo\n", + "\n", + "In this notebook, we are demonstrating how to convert our snakemake workflow into a `pytask` workflow. We use the basic tutorial to demonstrate this, but continue\n", + "to use nbdev for development of functions in notebooks.\n", + "\n", + "`pytask` is a task management system that allows you to define tasks and their dependencies, similar to `Snakemake`. It is particularly useful for data science workflows.\n", + "\n", + "There are a number of reasons to use `pytask` over `snakemake`:\n", + "- **Pythonic**: `pytask` is designed to be purely Pythonic by default, allowing you to write tasks and entire pipelines as Python functions.\n", + "- **Flexibility**: `pytask` allows you to define tasks and their dependencies in a more flexible way, using Python functions and decorators, as opposed to orchestrating numerous scripts.\n", + "- **Integration**: `pytask` integrates well with other Python libraries, such as `nbdev` here, or `hydra` configurations if you need, allowing you to use your existing code, notebooks, or configs in your workflows.\n", + "- **Parallelism**: `pytask` supports parallel execution of tasks with `pytask-parallel`, which can speed up your workflows significantly, especially for data processing tasks.\n", + "\n", + "We'll use nbdev to define the task functions, and then export them to the `src` directory. `pytask` is then invoked at the command line to run the tasks." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This demo task is taken from the tutorial at [pytask documentation](https://pytask-dev.readthedocs.io/en/stable/tutorials/write_a_task.html). At minimum, you need your package to contain the following in a config.py file:\n", + "\n", + "```md\n", + "my_project\n", + "│\n", + "├───.pytask\n", + "│\n", + "├───bld\n", + "│ └────...\n", + "│\n", + "├───src\n", + "│ └───my_project\n", + "│ ├────__init__.py\n", + "│ ├────config.py\n", + "│ └────...\n", + "│\n", + "└───pyproject.toml\n", + "```\n", + "\n", + "```python\n", + "#contents of `era5_sandbox.config` module\n", + "from pathlib import Path\n", + "\n", + "\n", + "SRC = Path(__file__).parent.resolve()\n", + "BLD = SRC.joinpath(\"..\", \"..\", \"bld\").resolve()\n", + "```\n", + "\n", + "Additionally, your pyproject.toml file should contain the following at minimum:\n", + "\n", + "```toml\n", + "[tool.pytask.ini_options]\n", + "paths = [\"src/era5_sandbox\"]\n", + "```\n", + "\n", + "The former tells Python where to find the source code and build directory for `pytask` objects and shims, while the latter tells `pytask` where to find the task definitions and dependency DAG." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| code-fold: show\n", + "#| code-summary: \"Exported source\"\n", + "#| exports: #\n", + "import os\n", + "from pathlib import Path\n", + "from typing import Annotated\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "from era5_sandbox.config import BLD\n", + "from era5_sandbox.config import data_catalog, demo_catalog\n", + "\n", + "from pytask import PickleNode\n", + "from pytask import Product" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Defining Tasks\n", + "\n", + "To define a task, simply use the `task_` prefix in the function name (or, if you are familiar and comfortable with decorators, use `@pytask.mark.task`). Be verbose and expressive in your use of type hints to specify the input and output data, so that `pytask` can automatically detect and handle the dependencies between tasks. \n", + "\n", + "### Defining Tracked Outputs\n", + "\n", + "To define something as a tracked output, you can annotate the input of the task with `Annotated[Path, Product]`, where `Product` is imported from `pytask`. This tells `pytask` that this is a product of the task and should be saved in the build directory.\n", + "\n", + "In this example, we're generating random data into a data frame and saving the object as a pickle in the `bld` directory (`bld` is the default build directory for `pytask`'s intermediate data). To get that directory, we use the `BLD` variable from the `era5_sandbox.config` module as above. This module itself could also be generated using `nbdev` if you want to keep your configuration in notebooks.\n", + "\n", + "Using `nbdev`, we can also include all of the bells and whistles of function documentation." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "has_sd": true, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/task_data_preparation.py#L24){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### task_create_random_data\n", + "\n", + "> task_create_random_data (seed:typing.Annotated[int,42], path_to_data:typi\n", + "> ng.Annotated[pathlib.Path,ProductType()]=Path('/\n", + "> net/rcstorenfs02/ifs/rc_labs/dominici_lab/lab/da\n", + "> ta_processing/csph-era5_sandbox/bld/data.pkl'))\n", + "\n", + "*Create a random data set and save it as a pickle file. Return the path to the saved file.*\n", + "\n", + "| | **Type** | **Default** | **Details** |\n", + "| -- | -------- | ----------- | ----------- |\n", + "| seed | Annotated | | Default seed for reproducibility |\n", + "| path_to_data | Annotated | /net/rcstorenfs02/ifs/rc_labs/dominici_lab/lab/data_processing/csph-era5_sandbox/bld/data.pkl | Path to the object in the build directory |\n", + "| **Returns** | **None** | | |" + ], + "text/plain": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/task_data_preparation.py#L24){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### task_create_random_data\n", + "\n", + "> task_create_random_data (seed:typing.Annotated[int,42], path_to_data:typi\n", + "> ng.Annotated[pathlib.Path,ProductType()]=Path('/\n", + "> net/rcstorenfs02/ifs/rc_labs/dominici_lab/lab/da\n", + "> ta_processing/csph-era5_sandbox/bld/data.pkl'))\n", + "\n", + "*Create a random data set and save it as a pickle file. Return the path to the saved file.*\n", + "\n", + "| | **Type** | **Default** | **Details** |\n", + "| -- | -------- | ----------- | ----------- |\n", + "| seed | Annotated | | Default seed for reproducibility |\n", + "| path_to_data | Annotated | /net/rcstorenfs02/ifs/rc_labs/dominici_lab/lab/data_processing/csph-era5_sandbox/bld/data.pkl | Path to the object in the build directory |\n", + "| **Returns** | **None** | | |" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| echo: false\n", + "#| output: asis\n", + "show_doc(task_create_random_data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| code-fold: show\n", + "#| code-summary: \"Exported source\"\n", + "#| exports: #\n", + "\n", + "def task_create_random_data(\n", + " seed: Annotated[int, 42], # Default seed for reproducibility\n", + " path_to_data: Annotated[Path, Product] = BLD / \"data.pkl\" # Path to the object in the build directory\n", + " ) -> None:\n", + " \"Create a random data set and save it as a pickle file. Return the path to the saved file.\"\n", + " rng = np.random.default_rng(seed)\n", + " beta = 2\n", + "\n", + " x = rng.normal(loc=5, scale=10, size=1_000)\n", + " epsilon = rng.standard_normal(1_000)\n", + "\n", + " y = beta * x + epsilon\n", + "\n", + " df = pd.DataFrame({\"x\": x, \"y\": y})\n", + "\n", + " # this is a tracked output, so we annotate the return value with `Annotated[Path, Product]`\n", + " df.to_pickle(path_to_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can test the function directly in the notebook:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "task_create_random_data(42)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once this module and function are exported with `nbdev_export`, the functions are in a python package. We can then use the command line to look at the registered tasks:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "sh" + }, + "outputs": [], + "source": [ + "#| eval: false\n", + "\n", + "pytask collect" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's add another task in the same module. This task plots the data we generated. To link the previous task to this one as a dependency, we can list the output of the previous task as an input to this one. This way, `pytask` will know that it needs to run the first task before this one." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "has_sd": true, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/task_data_preparation.py#L45){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### task_plot_data\n", + "\n", + "> task_plot_data (path_to_data:typing.Annotated[pathlib.Path,Path('/net/rcs\n", + "> torenfs02/ifs/rc_labs/dominici_lab/lab/data_processing/cs\n", + "> ph-era5_sandbox/bld/data.pkl')], path_to_plot:typing.Anno\n", + "> tated[pathlib.Path,ProductType()]=Path('/net/rcstorenfs02\n", + "> /ifs/rc_labs/dominici_lab/lab/data_processing/csph-\n", + "> era5_sandbox/bld/plot.png'))\n", + "\n", + "*Plot the data from the pickle file and save the plot. Note that this task:\n", + " 1. depends on the data.pkl file created by the previous task,\n", + " 2. does not return any value, but saves a plot to the build directory. So the side effect of the task is what we are interested in here (though this is probably bad practice).*\n", + "\n", + "| | **Type** | **Default** | **Details** |\n", + "| -- | -------- | ----------- | ----------- |\n", + "| path_to_data | Annotated | | Path to the data file created by the previous task |\n", + "| path_to_plot | Annotated | /net/rcstorenfs02/ifs/rc_labs/dominici_lab/lab/data_processing/csph-era5_sandbox/bld/plot.png | Path to the build directory for the plot |\n", + "| **Returns** | **None** | | |" + ], + "text/plain": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/task_data_preparation.py#L45){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### task_plot_data\n", + "\n", + "> task_plot_data (path_to_data:typing.Annotated[pathlib.Path,Path('/net/rcs\n", + "> torenfs02/ifs/rc_labs/dominici_lab/lab/data_processing/cs\n", + "> ph-era5_sandbox/bld/data.pkl')], path_to_plot:typing.Anno\n", + "> tated[pathlib.Path,ProductType()]=Path('/net/rcstorenfs02\n", + "> /ifs/rc_labs/dominici_lab/lab/data_processing/csph-\n", + "> era5_sandbox/bld/plot.png'))\n", + "\n", + "*Plot the data from the pickle file and save the plot. Note that this task:\n", + " 1. depends on the data.pkl file created by the previous task,\n", + " 2. does not return any value, but saves a plot to the build directory. So the side effect of the task is what we are interested in here (though this is probably bad practice).*\n", + "\n", + "| | **Type** | **Default** | **Details** |\n", + "| -- | -------- | ----------- | ----------- |\n", + "| path_to_data | Annotated | | Path to the data file created by the previous task |\n", + "| path_to_plot | Annotated | /net/rcstorenfs02/ifs/rc_labs/dominici_lab/lab/data_processing/csph-era5_sandbox/bld/plot.png | Path to the build directory for the plot |\n", + "| **Returns** | **None** | | |" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| echo: false\n", + "#| output: asis\n", + "show_doc(task_plot_data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| code-fold: show\n", + "#| code-summary: \"Exported source\"\n", + "#| exports: #\n", + "\n", + "def task_plot_data(\n", + " path_to_data: Annotated[Path, BLD / \"data.pkl\"], # Path to the data file created by the previous task\n", + " path_to_plot: Annotated[Path, Product] = BLD / \"plot.png\" # Path to the build directory for the plot\n", + ") -> None:\n", + " \"\"\"\n", + " Plot the data from the pickle file and save the plot. Note that this task:\n", + " 1. depends on the data.pkl file created by the previous task,\n", + " 2. does not return any value, but saves a plot to the build directory. So the side effect of the task is what we are interested in here (though this is probably bad practice).\n", + " \"\"\"\n", + "\n", + " df = pd.read_pickle(path_to_data)\n", + " \n", + " _, ax = plt.subplots()\n", + " df.plot(x=\"x\", y=\"y\", ax=ax, kind=\"scatter\")\n", + "\n", + " plt.savefig(path_to_plot)\n", + " plt.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now have a DAG of tasks that `pytask` can execute. To see the tasks, we can use the command line to create a pygraphviz graph of the tasks:\n", + "\n", + "```bash\n", + "pytask dag\n", + "```\n", + "\n", + "The DAG is saved as a pdf file, and you can view it using any viewer. Now, to run the pipeline, just invoke `pytask` at the command line:\n", + "\n", + "```bash\n", + "pytask\n", + "```\n", + "\n", + "In Jupyter or iPython, you can interact with the task outputs directly:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| eval: false\n", + "# list all the files in the build directory\n", + "for file in os.listdir(BLD):\n", + " print(file)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can use these to build subsequent tasks later.\n", + "\n", + "## More Complex Tasks & The Data Catalog\n", + "\n", + "As we define more complex tasks, we can use the `pytask` data catalog to manage the inputs and outputs of our tasks. The data catalog allows us to imperatively name the data and their formats, making it easier to manage the data flow in our tasks. Importantly, we can define the data pythonically, which allows us to use the full power of Python to manipulate and transform our data. This is particularly more useful than snakemake's approach, which requires you to define the data in a more static way using paths and a separate pseudo-language.\n", + "\n", + "The content of the `era5_sandbox.config` module can be extended to include a data catalog:\n", + "\n", + "```python\n", + "from pathlib import Path\n", + "from pytask import DataCatalog, Product\n", + "\n", + "SRC = Path(__file__).parent.resolve()\n", + "BLD = SRC.joinpath(\"..\", \"..\", \"bld\").resolve()\n", + "\n", + "demo_catalog = DataCatalog()\n", + "```\n", + "\n", + "With just this definition, we're now able to refer directly to data by name in our tasks, and `pytask` will handle the paths and formats for us. This allows us to focus on the logic of our tasks rather than the details of data management.\n", + "\n", + ":::{.callout-note}\n", + "This is a major advantage of `pytask` over `snakemake`, as it allows you to define the data in a more flexible and Pythonic way, while still maintaining the benefits of a task management system. It is a similar approach to building pipelines in R with targets, which allows you to define the data in a more flexible way.\n", + ":::\n", + "\n", + "Let's create a task that modifies the data frame by adding a new column. This task will depend on the previous task's output, and we will use the data catalog to define the input and output data." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "has_sd": true, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/task_data_preparation.py#L66){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### task_add_one\n", + "\n", + "> task_add_one (path_to_data:typing.Annotated[pathlib.Path,Path('/net/rcsto\n", + "> renfs02/ifs/rc_labs/dominici_lab/lab/data_processing/csph-\n", + "> era5_sandbox/bld/data.pkl')], node:typing.Annotated[_pytask\n", + "> .nodes.PickleNode,ProductType()]=PickleNode(path=Path('/net\n", + "> /rcstorenfs02/ifs/rc_labs/dominici_lab/lab/data_processing/\n", + "> csph-era5_sandbox/.pytask/data_catalogs/default/1eef510d81e\n", + "> ea49161cd821b318aa999e630bdd292b093aa9a9319e9f282b984.pkl')\n", + "> , name='mydata', attributes={'catalog_name': 'default'},\n", + "> serializer=, deserializer= function load>))\n", + "\n", + "*Add one to the 'y' column of the data frame and save it as a new pickle file.*\n", + "\n", + "| | **Type** | **Default** | **Details** |\n", + "| -- | -------- | ----------- | ----------- |\n", + "| path_to_data | Annotated | | Path to the data file created by the previous task |\n", + "| node | Annotated | PickleNode(path=Path('/net/rcstorenfs02/ifs/rc_labs/dominici_lab/lab/data_processing/csph-era5_sandbox/.pytask/data_catalogs/default/1eef510d81eea49161cd821b318aa999e630bdd292b093aa9a9319e9f282b984.pkl'), name='mydata', attributes={'catalog_name': 'default'}, serializer=, deserializer=) | |\n", + "| **Returns** | **None** | | |" + ], + "text/plain": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/task_data_preparation.py#L66){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### task_add_one\n", + "\n", + "> task_add_one (path_to_data:typing.Annotated[pathlib.Path,Path('/net/rcsto\n", + "> renfs02/ifs/rc_labs/dominici_lab/lab/data_processing/csph-\n", + "> era5_sandbox/bld/data.pkl')], node:typing.Annotated[_pytask\n", + "> .nodes.PickleNode,ProductType()]=PickleNode(path=Path('/net\n", + "> /rcstorenfs02/ifs/rc_labs/dominici_lab/lab/data_processing/\n", + "> csph-era5_sandbox/.pytask/data_catalogs/default/1eef510d81e\n", + "> ea49161cd821b318aa999e630bdd292b093aa9a9319e9f282b984.pkl')\n", + "> , name='mydata', attributes={'catalog_name': 'default'},\n", + "> serializer=, deserializer= function load>))\n", + "\n", + "*Add one to the 'y' column of the data frame and save it as a new pickle file.*\n", + "\n", + "| | **Type** | **Default** | **Details** |\n", + "| -- | -------- | ----------- | ----------- |\n", + "| path_to_data | Annotated | | Path to the data file created by the previous task |\n", + "| node | Annotated | PickleNode(path=Path('/net/rcstorenfs02/ifs/rc_labs/dominici_lab/lab/data_processing/csph-era5_sandbox/.pytask/data_catalogs/default/1eef510d81eea49161cd821b318aa999e630bdd292b093aa9a9319e9f282b984.pkl'), name='mydata', attributes={'catalog_name': 'default'}, serializer=, deserializer=) | |\n", + "| **Returns** | **None** | | |" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| echo: false\n", + "#| output: asis\n", + "show_doc(task_add_one)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| code-fold: show\n", + "#| code-summary: \"Exported source\"\n", + "#| exports: #\n", + "\n", + "def task_add_one(\n", + " path_to_data: Annotated[Path, BLD / \"data.pkl\"], # Path to the data file created by the previous task\n", + " node: Annotated[PickleNode, Product] = demo_catalog[\"mydata\"]\n", + ") -> None:\n", + " \"\"\"\n", + " Add one to the 'y' column of the data frame and save it as a new pickle file.\n", + " \"\"\"\n", + " df = pd.read_pickle(path_to_data)\n", + " df['z'] = df['y'] + 1\n", + " \n", + " node.save(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this function, we've defined that the task relies on the output of the first task being there, the `data.pkl` file. But importantly, we've also defined our product as a `node` from the `PickleNode` module. This will allow `pytask` to handle the serialization and deserialization of the data frame automatically, so we don't have to worry about the details of how the data is stored. We create the datacatalog in our config file, and then tell this task to create a Node in that catalog called `mydata`. Whatever we save with the `node.save()` method will be saved in the build directory, but more importantly _will be indexed and hashed by `pytask`_. This means that if the data changes, `pytask` will know to rerun the task.\n", + "\n", + "To make this even more pythonic, we can modify the format of our task function so that the return type annotator is used as a node in the data catalog. This allows us to define the output of the task as a `PickleNode`, which will automatically handle the serialization and deserialization of the data frame.\n", + "\n", + ":::{.callout-note}\n", + "This is another trick I'm deriving from {targets}. By formatting tasks as pure functions where inputs are parameters and targets are return type annotations, we can define the output of the task as a `PickleNode`, which will automatically handle the serialization and deserialization of the data frame. This again allows us to focus on the logic of our tasks rather than the details of data management.\n", + ":::\n", + "\n", + "So below, we're directly accessing the `data_catalog` to get the `mydata` node, and then modifying it by adding a new column. It _feels_ like we are doing this in place, such as in an iPython session, because we are allowing `pytask` to handle the serialization of the file on disk for us." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "has_sd": true, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/task_data_preparation.py#L81){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### task_add_another_column\n", + "\n", + "> task_add_another_column (df:typing.Annotated[pandas.core.frame.DataFrame,\n", + "> PickleNode(path=Path('/net/rcstorenfs02/ifs/rc_l\n", + "> abs/dominici_lab/lab/data_processing/csph-era5_s\n", + "> andbox/.pytask/data_catalogs/default/1eef510d81e\n", + "> ea49161cd821b318aa999e630bdd292b093aa9a9319e9f28\n", + "> 2b984.pkl'),name='mydata',attributes={'catalog_n\n", + "> ame':'default'},serializer= infunctiondump>,deserializer= infunctionload>)])\n", + "\n", + "*Add another column to the data frame stored in the PickleNode.*\n", + "\n", + "| | **Type** | **Details** |\n", + "| -- | -------- | ----------- |\n", + "| df | Annotated | which object in the catalog to fetch from the catalog with node.load() |\n", + "| **Returns** | **Annotated** | **which object in the catalog to save the return value to** |" + ], + "text/plain": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/task_data_preparation.py#L81){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### task_add_another_column\n", + "\n", + "> task_add_another_column (df:typing.Annotated[pandas.core.frame.DataFrame,\n", + "> PickleNode(path=Path('/net/rcstorenfs02/ifs/rc_l\n", + "> abs/dominici_lab/lab/data_processing/csph-era5_s\n", + "> andbox/.pytask/data_catalogs/default/1eef510d81e\n", + "> ea49161cd821b318aa999e630bdd292b093aa9a9319e9f28\n", + "> 2b984.pkl'),name='mydata',attributes={'catalog_n\n", + "> ame':'default'},serializer= infunctiondump>,deserializer= infunctionload>)])\n", + "\n", + "*Add another column to the data frame stored in the PickleNode.*\n", + "\n", + "| | **Type** | **Details** |\n", + "| -- | -------- | ----------- |\n", + "| df | Annotated | which object in the catalog to fetch from the catalog with node.load() |\n", + "| **Returns** | **Annotated** | **which object in the catalog to save the return value to** |" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| echo: false\n", + "#| output: asis\n", + "show_doc(task_add_another_column)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| code-fold: show\n", + "#| code-summary: \"Exported source\"\n", + "#| exports: #\n", + "\n", + "def task_add_another_column(\n", + " df: Annotated[pd.DataFrame, demo_catalog[\"mydata\"]] # which object in the catalog to fetch from the catalog with node.load()\n", + ") -> Annotated[pd.DataFrame, demo_catalog[\"mydata2\"]]: # which object in the catalog to save the return value to\n", + " \"\"\"\n", + " Add another column to the data frame stored in the PickleNode.\n", + " \"\"\"\n", + "\n", + " # use the datacatalog directly to access the node\n", + " # this is a bit like accessing the node in an iPython session, but pytask\n", + " # will handle the serialization and deserialization for us\n", + " df['w'] = df['z'] * df['y']\n", + " \n", + " return df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To test this interactively, we'd have to import the data catalog's object" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "df = demo_catalog[\"mydata\"].load() # load the data frame from the PickleNode\n", + "result = task_add_another_column(df) # call the task function with the loaded data frame" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "result" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we know it will work, we can invoke pytask:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "sh" + }, + "outputs": [], + "source": [ + "#| eval: false\n", + "\n", + "pytask" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that the outputs are cached and not recomputed unless the inputs change. This is a key feature of `pytask` and other DAGs, allowing you to efficiently manage your data processing tasks without unnecessary recomputation.\n", + "\n", + "## Conclusion\n", + "\n", + "The takeaway here is that with `pytask`, you can define pure functions that take inputs and return outputs, and build a DAG of tasks that can be executed in a flexible and efficient way. This allows you to focus on the logic of your tasks rather than the details of data management, while still maintaining the benefits of a task management system. The key elements are:\n", + "\n", + "- **Task annotation**: You define your tasks by creating pure functions that take inputs and return outputs, and use decorators or naming conventions to mark them as \"tasks\" in a dag\n", + "- **Input and output annotation**: You define the inputs and outputs of your tasksusing type hints, and allow `pytask` to automatically detect and handle the dependencies between tasks.\n", + "- **Data catalog**: You define your data in a Pythonic object in your config called `data_catalog`. As you iteratively develop your DAG, you add objects to the data catalog, which are called nodes. As long as a node is a pythonic object and has a pickle method, `pytask` will handle the serialization and deserialization of the data for you." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/_proc/20_pytask_config.html.md b/_proc/20_pytask_config.html.md new file mode 100644 index 0000000..c33410b --- /dev/null +++ b/_proc/20_pytask_config.html.md @@ -0,0 +1,348 @@ +--- +title: "`pytask` Config: Defining the Pipeline Internals in `pytask`" +engine: jupyter +--- + + +## config + +> This is the config module for the `pytask` pipeline. +This module defines the data catalog(s) and any hard-coded parameters that are used throughout the pipeline. + + + +::: {#cell-2 .cell exports='null'} +``` {.python .cell-code code-fold="show" code-summary="Exported source"} +import pandas as pd + +from pathlib import Path +from pyprojroot import here +from pytask import DataCatalog + + +SRC = here() / "src" / "era5_sandbox" +BLD = here() / "bld" + +demo_catalog = DataCatalog() +``` +::: + + +## `DEV_MODE`: A Quick Development Flag + +I'm adding a flag to the config that can be used for quick development. +If you import this boolean variable, it can be used to skip tasks, +setup samples, etc. on the fly by `marking` a task with the `pytask.mark.skipif` +decorator. Change this to `False` when you're ready to run the full pipeline. + +::: {#cell-4 .cell exports='null'} +``` {.python .cell-code code-fold="show" code-summary="Exported source"} +DEV_MODE=True +``` +::: + + +## The Data Catalog + +To manage our pipeline, we're going to use a nested data catalog structure. +This way, we can easily return specific entries to specific tasks +without having to manage multiple different data catalogs. Specifically, +we'll have a data catalog for each stage of the pipeline, and each catalog +will have entries for the inputs, outputs, and any other parameters needed +for that stage. This is similar to how we used Hydra configs, but +using the `pytask` data catalog, we can more easily gather the data +for a specific task in structured manner entirely in Python. + +::: {#cell-6 .cell exports='null'} +``` {.python .cell-code code-fold="show" code-summary="Exported source"} +stages = ["mydata", 'mydata2', # from the demo, ignore + "download", # download task + "aggregate", # aggregation task + "publish", # publishing task + "viz"] # visualization task + +buckets = [ + "inputs", # any specific inputs, eg for carrying over between tasks + "outputs", # specific output task returns + "jobs", # job parameters as a dataframe + "params" # any lingering hardcoded parameters + ] + +data_catalog = { + + stage: {bucket: DataCatalog(name=f"{stage}_{bucket}") for bucket in buckets} + for stage in stages +} +``` +::: + + +## The Download Task + +A good strategy may be to set pipeline stage parameters in the config file, +and then use the `pytask` data catalog to manage the data. This way, we can +easily change the parameters without having to modify the code. This is especially +useful for the API query, where we need to be able to set the parameter grid for +the years and data types we want to download data for. So, let's create an entry in the data catalog specifically for the download task. + +A good strategy I thought about for grid parameter comprehension is to create a dataframe expands all the combinations of +parameters, and then uses each combination to create the tasks which are then +easily added to the data catalog. This way, we can still easily inspect the +pipeline and see what tasks are being run, while also being able to easily +change the parameters in the config file without too much hassle. + +An important framework decision I'm making here is that each ROW of the dataframe corresponds to a single task, so that we can quickly understand at a glance what the task is doing, and also easily develop the code for the task itself. This is different from the hydra approach where a job is first specified by a default config, and then the parameters are swept over in multiple config files. This is a more flexible approach, IMO, because: + +1. each row defines a single task run, so it's easy to understand what the run is doing +2. it's easy to add or remove runs by simply expanding the list of parameters and using dataframe filters to remove irrelevant parameter combinations +3. we don't have to independently inspect and manage multiple different/overriding config files +4. it's all in Python, so we can use the full power of the language to define + the parameters and the tasks in a single sweep, not through the need of + hydra+snakemake multi stage/multi-lingual config system + +So, to do this, we define one job as a query to the CDS API that must contain: +- The dataset (re-analysis) +- The year +- The month +- All days in the month +- All times of day (hour) +- The geography (region), which will need: + - The URL to the shapefile to calculate the bounding box + +Given one combination of all of these, a single SLURM job can complete the first "task" in parallel by having a run assigned to each row of the dataframe. + +::: {#cell-8 .cell exports='null'} +``` {.python .cell-code code-fold="show" code-summary="Exported source"} +# Dimensions +years = [str(x) for x in range(2009, 2025)] # 16 years +months = [str(x).zfill(2) for x in range(1, 13)] # 12 months +geographies = ["madagascar", "nepal"] # 2 geographies + +# nested values; we want ALL days, times, and variables for each job +days = [str(x).zfill(2) for x in range(1, 32)] +times = [f"{x:02d}:00" for x in range(24)] +variables = ["2m_dewpoint_temperature", "2m_temperature", "total_precipitation", "volumetric_soil_water_layer_1"] + +product_type = "reanalysis" + +# Map shapefiles to geography +shapefiles = { + "madagascar": "https://data.humdata.org/dataset/26fa506b-0727-4d9d-a590-d2abee21ee22/resource/ed94d52e-349e-41be-80cb-62dc0435bd34/download/mdg_adm_bngrc_ocha_20181031_shp.zip", + "nepal": "https://data.humdata.org/dataset/07db728a-4f0f-4e98-8eb0-8fa9df61f01c/resource/2eb4c47f-fd6e-425d-b623-d35be1a7640e/download/npl_adm_nd_20240314_ab_shp.zip" +} + +# Build row-wise combinations of (year, month, geography) +rows = [] +for year in years: + for month in months: + for geo in geographies: + rows.append({ + "year": year, + "month": month, + "geography": geo, + "shapefile": shapefiles[geo], + "product_type": product_type, + "day": days, + "time": times, + "variables": variables, + "output": f"{year}_{month}_{geo}" + }) + +# Create dataframe +query_df = pd.DataFrame(rows) +``` +::: + + +::: {#cell-9 .cell} +``` {.python .cell-code} +query_df +``` +::: + + +::: {#cell-10 .cell} +``` {.python .cell-code} +print(f"Number of estimated jobs: {query_df.shape[0]}. Examples...") + +for i, row in query_df.sample(3).iterrows(): + print(f"Year: {row['year']}, Month: {row['month']}, Geography: {row['geography']}, Link: {row['shapefile']}, Variables: {row['variables']}") +``` +::: + + +Now add them to the catalog. We're going to use a dictionary to +nest data catalogs so that we can return specific task products to +named data catalog nodes. + +Our data catalog now has a `download|jobs` node with a `queries_df` entry that contains the dataframe of all the jobs to be run in this task. + +::: {#cell-13 .cell} +``` {.python .cell-code} +data_catalog['download']['jobs']['queries_df'].load().head() +``` +::: + + +## The Aggregation Task + +To carry out the aggregation, we will follow similar logic to the original pipeline and use xarray to aggregate data into spatial and temporal averages. The aggregation task will take the downloaded data and compute the mean over the specified time period and spatial region. However, in this case, we want to aggregate the data diurnally, so we will need to fetch the sundown and sunrise times for the region and use them to compute the diurnal averages. + +Once again, we will use a dataframe to define the parameters for the aggregation task. + +Here we will use a dataframe with the jobs as rows; +the first column is "input" which is the list of query names from +the download task, and the last column is the output object name. Columns +in between can be the parameters needed for the aggregation task, which +then get expanded to the full list of jobs with `itertools.product`, `explode` or similar, +and filtered as necessary. + +For explanations of the parameters, see the Aggregation Task notebook's final `task_aggregate_data_diurnal` function. + +::: {#cell-15 .cell exports='null'} +``` {.python .cell-code code-fold="show" code-summary="Exported source"} +inputs = query_df["output"].tolist() +outputs = [f"{i}_agg" for i in inputs] + +variable_dict = { + "2m_dewpoint_temperature": "d2m", + "2m_temperature": "t2m", + "total_precipitation": "tp", + "volumetric_soil_water_layer_1": "swvl1" +} + +# list of params that get fed into the task functions +agg_params = { + "time": ["day", "night"], + "solar_classification": ["before"], + "variables": variables, + "variables_short": [variable_dict[x] for x in variables], + "aggregation_name": ["mean", "sum", "max", "min"] +} + +from itertools import product +import pandas as pd + +# expand all the params +agg_params = pd.DataFrame(list(product(*agg_params.values())), columns=agg_params.keys()) +``` +::: + + +Inspecting it: + +::: {#cell-17 .cell} +``` {.python .cell-code} +agg_params +``` +::: + + +Let's keep only rows where the variables and variables_short match + +::: {#cell-19 .cell exports='null'} +``` {.python .cell-code code-fold="show" code-summary="Exported source"} +agg_params = agg_params[agg_params.apply(lambda x: variable_dict[x['variables']] == x['variables_short'], axis=1)] +``` +::: + + +::: {#cell-20 .cell} +``` {.python .cell-code} +agg_params +``` +::: + + +Great, and now keeping `sum` only for total precipitation (we don't need mean, max, min for that variable), and removing `sum` for all other variables (we don't need sum for temperature or soil moisture): + +::: {#cell-22 .cell exports='null'} +``` {.python .cell-code code-fold="show" code-summary="Exported source"} +mask = (agg_params['variables_short'] == "tp") & (agg_params['aggregation_name'] != "sum") +agg_params = agg_params[~mask] + +# remove rows where non-tp aggregation is sum +mask = (agg_params['variables_short'] != "tp") & (agg_params['aggregation_name'] == "sum") +agg_params = agg_params[~mask] +``` +::: + + +::: {#cell-23 .cell} +``` {.python .cell-code} +agg_params +``` +::: + + +Now we add the input and output columns by joining: + +::: {#cell-25 .cell exports='null'} +``` {.python .cell-code code-fold="show" code-summary="Exported source"} +inputs = pd.DataFrame({"input": inputs}) +aggregate_jobs = inputs.merge(agg_params, how="cross") +``` +::: + + +This result gives us the full list of jobs for the aggregation task. 20 rows for the parameters, +and 384 inputs/outputs, giving a total of 7680 jobs: + +::: {#cell-27 .cell} +``` {.python .cell-code} +assert aggregate_jobs.shape[0] == 20 * len(inputs) +aggregate_jobs +``` +::: + + +A few more configuration items need to be added, like +the local timezone for each geography, the healthshed filename, +the healthshed unique ID variable name in the shapefile, +and whether the variable is instantaneous or accumulated: + +::: {#cell-29 .cell exports='null'} +``` {.python .cell-code code-fold="show" code-summary="Exported source"} +aggregate_jobs['local_tz'] = aggregate_jobs['input'].apply( + lambda x: "Asia/Kathmandu" if "nepal" in x else "Indian/Antananarivo" +) +aggregate_jobs['shapefile'] = aggregate_jobs['input'].apply( + lambda x: "Nepal_Healthsheds2024.zip" if "nepal" in x else "healthsheds2022.zip" +) + +aggregate_jobs['hshd_unique_id'] = aggregate_jobs['input'].apply( + lambda x: "fid" if "nepal" in x else "fs_uid" +) + +aggregate_jobs['climate_handler_var'] = aggregate_jobs['variables_short'].apply( + lambda x: "accum" if x == "tp" else "instant" +) +``` +::: + + +::: {#cell-30 .cell} +``` {.python .cell-code} +aggregate_jobs +``` +::: + + +Now we add this to the data catalog: + +::: {#cell-32 .cell exports='null'} +``` {.python .cell-code code-fold="show" code-summary="Exported source"} +data_catalog['aggregate']['jobs'].add("jobs_df", aggregate_jobs) +``` +::: + + +Our data catalog now has an `aggregate|jobs` node with a `jobs_df` entry that contains the dataframe of all the jobs to be run in this task. + +::: {#cell-34 .cell} +``` {.python .cell-code} +data_catalog['aggregate']['jobs']['jobs_df'].load().head() +``` +::: + + diff --git a/_proc/20_pytask_config.ipynb b/_proc/20_pytask_config.ipynb new file mode 100644 index 0000000..5a23f8d --- /dev/null +++ b/_proc/20_pytask_config.ipynb @@ -0,0 +1,550 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "title: \"`pytask` Config: Defining the Pipeline Internals in `pytask`\"\n", + "engine: jupyter\n", + "---\n", + "\n", + "## config\n", + "\n", + "> This is the config module for the `pytask` pipeline. \n", + "This module defines the data catalog(s) and any hard-coded parameters that are used throughout the pipeline." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| code-fold: show\n", + "#| code-summary: \"Exported source\"\n", + "#| exports: #\n", + "\n", + "import pandas as pd\n", + "\n", + "from pathlib import Path\n", + "from pyprojroot import here\n", + "from pytask import DataCatalog\n", + "\n", + "\n", + "SRC = here() / \"src\" / \"era5_sandbox\"\n", + "BLD = here() / \"bld\"\n", + "\n", + "demo_catalog = DataCatalog()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## `DEV_MODE`: A Quick Development Flag\n", + "\n", + "I'm adding a flag to the config that can be used for quick development. \n", + "If you import this boolean variable, it can be used to skip tasks,\n", + "setup samples, etc. on the fly by `marking` a task with the `pytask.mark.skipif`\n", + "decorator. Change this to `False` when you're ready to run the full pipeline." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| code-fold: show\n", + "#| code-summary: \"Exported source\"\n", + "#| exports: #\n", + "DEV_MODE=True" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The Data Catalog\n", + "\n", + "To manage our pipeline, we're going to use a nested data catalog structure.\n", + "This way, we can easily return specific entries to specific tasks\n", + "without having to manage multiple different data catalogs. Specifically,\n", + "we'll have a data catalog for each stage of the pipeline, and each catalog\n", + "will have entries for the inputs, outputs, and any other parameters needed\n", + "for that stage. This is similar to how we used Hydra configs, but\n", + "using the `pytask` data catalog, we can more easily gather the data\n", + "for a specific task in structured manner entirely in Python." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| code-fold: show\n", + "#| code-summary: \"Exported source\"\n", + "#| exports: #\n", + "\n", + "stages = [\"mydata\", 'mydata2', # from the demo, ignore\n", + " \"download\", # download task\n", + " \"aggregate\", # aggregation task\n", + " \"publish\", # publishing task\n", + " \"viz\"] # visualization task\n", + "\n", + "buckets = [\n", + " \"inputs\", # any specific inputs, eg for carrying over between tasks\n", + " \"outputs\", # specific output task returns\n", + " \"jobs\", # job parameters as a dataframe\n", + " \"params\" # any lingering hardcoded parameters\n", + " ]\n", + "\n", + "data_catalog = {\n", + "\n", + " stage: {bucket: DataCatalog(name=f\"{stage}_{bucket}\") for bucket in buckets}\n", + " for stage in stages\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The Download Task\n", + "\n", + "A good strategy may be to set pipeline stage parameters in the config file, \n", + "and then use the `pytask` data catalog to manage the data. This way, we can\n", + "easily change the parameters without having to modify the code. This is especially \n", + "useful for the API query, where we need to be able to set the parameter grid for\n", + "the years and data types we want to download data for. So, let's create an entry in the data catalog specifically for the download task.\n", + "\n", + "A good strategy I thought about for grid parameter comprehension is to create a dataframe expands all the combinations of\n", + "parameters, and then uses each combination to create the tasks which are then \n", + "easily added to the data catalog. This way, we can still easily inspect the \n", + "pipeline and see what tasks are being run, while also being able to easily \n", + "change the parameters in the config file without too much hassle.\n", + "\n", + "An important framework decision I'm making here is that each ROW of the dataframe corresponds to a single task, so that we can quickly understand at a glance what the task is doing, and also easily develop the code for the task itself. This is different from the hydra approach where a job is first specified by a default config, and then the parameters are swept over in multiple config files. This is a more flexible approach, IMO, because:\n", + "\n", + "1. each row defines a single task run, so it's easy to understand what the run is doing\n", + "2. it's easy to add or remove runs by simply expanding the list of parameters and using dataframe filters to remove irrelevant parameter combinations\n", + "3. we don't have to independently inspect and manage multiple different/overriding config files\n", + "4. it's all in Python, so we can use the full power of the language to define\n", + " the parameters and the tasks in a single sweep, not through the need of\n", + " hydra+snakemake multi stage/multi-lingual config system\n", + "\n", + "So, to do this, we define one job as a query to the CDS API that must contain:\n", + "- The dataset (re-analysis)\n", + "- The year\n", + "- The month\n", + "- All days in the month\n", + "- All times of day (hour)\n", + "- The geography (region), which will need:\n", + " - The URL to the shapefile to calculate the bounding box\n", + "\n", + "Given one combination of all of these, a single SLURM job can complete the first \"task\" in parallel by having a run assigned to each row of the dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| code-fold: show\n", + "#| code-summary: \"Exported source\"\n", + "#| exports: # a dataframe for the query parameters, with nested entries for days, times, and variables\n", + "# Dimensions\n", + "years = [str(x) for x in range(2009, 2025)] # 16 years\n", + "months = [str(x).zfill(2) for x in range(1, 13)] # 12 months\n", + "geographies = [\"madagascar\", \"nepal\"] # 2 geographies\n", + "\n", + "# nested values; we want ALL days, times, and variables for each job\n", + "days = [str(x).zfill(2) for x in range(1, 32)]\n", + "times = [f\"{x:02d}:00\" for x in range(24)]\n", + "variables = [\"2m_dewpoint_temperature\", \"2m_temperature\", \"total_precipitation\", \"volumetric_soil_water_layer_1\"]\n", + "\n", + "product_type = \"reanalysis\"\n", + "\n", + "# Map shapefiles to geography\n", + "shapefiles = {\n", + " \"madagascar\": \"https://data.humdata.org/dataset/26fa506b-0727-4d9d-a590-d2abee21ee22/resource/ed94d52e-349e-41be-80cb-62dc0435bd34/download/mdg_adm_bngrc_ocha_20181031_shp.zip\",\n", + " \"nepal\": \"https://data.humdata.org/dataset/07db728a-4f0f-4e98-8eb0-8fa9df61f01c/resource/2eb4c47f-fd6e-425d-b623-d35be1a7640e/download/npl_adm_nd_20240314_ab_shp.zip\"\n", + "}\n", + "\n", + "# Build row-wise combinations of (year, month, geography)\n", + "rows = []\n", + "for year in years:\n", + " for month in months:\n", + " for geo in geographies:\n", + " rows.append({\n", + " \"year\": year,\n", + " \"month\": month,\n", + " \"geography\": geo,\n", + " \"shapefile\": shapefiles[geo],\n", + " \"product_type\": product_type,\n", + " \"day\": days,\n", + " \"time\": times,\n", + " \"variables\": variables,\n", + " \"output\": f\"{year}_{month}_{geo}\"\n", + " })\n", + "\n", + "# Create dataframe\n", + "query_df = pd.DataFrame(rows)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "query_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "print(f\"Number of estimated jobs: {query_df.shape[0]}. Examples...\")\n", + "\n", + "for i, row in query_df.sample(3).iterrows():\n", + " print(f\"Year: {row['year']}, Month: {row['month']}, Geography: {row['geography']}, Link: {row['shapefile']}, Variables: {row['variables']}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now add them to the catalog. We're going to use a dictionary to\n", + "nest data catalogs so that we can return specific task products to\n", + "named data catalog nodes." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our data catalog now has a `download|jobs` node with a `queries_df` entry that contains the dataframe of all the jobs to be run in this task." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "data_catalog['download']['jobs']['queries_df'].load().head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The Aggregation Task\n", + "\n", + "To carry out the aggregation, we will follow similar logic to the original pipeline and use xarray to aggregate data into spatial and temporal averages. The aggregation task will take the downloaded data and compute the mean over the specified time period and spatial region. However, in this case, we want to aggregate the data diurnally, so we will need to fetch the sundown and sunrise times for the region and use them to compute the diurnal averages.\n", + "\n", + "Once again, we will use a dataframe to define the parameters for the aggregation task.\n", + "\n", + "Here we will use a dataframe with the jobs as rows;\n", + "the first column is \"input\" which is the list of query names from\n", + "the download task, and the last column is the output object name. Columns\n", + "in between can be the parameters needed for the aggregation task, which\n", + "then get expanded to the full list of jobs with `itertools.product`, `explode` or similar,\n", + "and filtered as necessary.\n", + "\n", + "For explanations of the parameters, see the Aggregation Task notebook's final `task_aggregate_data_diurnal` function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| code-fold: show\n", + "#| code-summary: \"Exported source\"\n", + "#| exports: # aggregate task parameters\n", + "\n", + "inputs = query_df[\"output\"].tolist()\n", + "outputs = [f\"{i}_agg\" for i in inputs]\n", + "\n", + "variable_dict = {\n", + " \"2m_dewpoint_temperature\": \"d2m\",\n", + " \"2m_temperature\": \"t2m\",\n", + " \"total_precipitation\": \"tp\",\n", + " \"volumetric_soil_water_layer_1\": \"swvl1\"\n", + "}\n", + "\n", + "# list of params that get fed into the task functions\n", + "agg_params = {\n", + " \"time\": [\"day\", \"night\"],\n", + " \"solar_classification\": [\"before\"],\n", + " \"variables\": variables,\n", + " \"variables_short\": [variable_dict[x] for x in variables],\n", + " \"aggregation_name\": [\"mean\", \"sum\", \"max\", \"min\"]\n", + "}\n", + "\n", + "from itertools import product\n", + "import pandas as pd\n", + "\n", + "# expand all the params\n", + "agg_params = pd.DataFrame(list(product(*agg_params.values())), columns=agg_params.keys())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Inspecting it:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "agg_params" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's keep only rows where the variables and variables_short match" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| code-fold: show\n", + "#| code-summary: \"Exported source\"\n", + "#| exports: # quick filter to keep only matching rows\n", + "\n", + "agg_params = agg_params[agg_params.apply(lambda x: variable_dict[x['variables']] == x['variables_short'], axis=1)]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "agg_params" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Great, and now keeping `sum` only for total precipitation (we don't need mean, max, min for that variable), and removing `sum` for all other variables (we don't need sum for temperature or soil moisture):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| code-fold: show\n", + "#| code-summary: \"Exported source\"\n", + "#| exports: # remove rows where tp aggregation is not sum\n", + "mask = (agg_params['variables_short'] == \"tp\") & (agg_params['aggregation_name'] != \"sum\")\n", + "agg_params = agg_params[~mask]\n", + "\n", + "# remove rows where non-tp aggregation is sum\n", + "mask = (agg_params['variables_short'] != \"tp\") & (agg_params['aggregation_name'] == \"sum\")\n", + "agg_params = agg_params[~mask]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "agg_params" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we add the input and output columns by joining:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| code-fold: show\n", + "#| code-summary: \"Exported source\"\n", + "#| exports: # set up inputs and parameters\n", + "inputs = pd.DataFrame({\"input\": inputs})\n", + "aggregate_jobs = inputs.merge(agg_params, how=\"cross\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This result gives us the full list of jobs for the aggregation task. 20 rows for the parameters,\n", + "and 384 inputs/outputs, giving a total of 7680 jobs:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "assert aggregate_jobs.shape[0] == 20 * len(inputs)\n", + "aggregate_jobs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A few more configuration items need to be added, like\n", + "the local timezone for each geography, the healthshed filename,\n", + "the healthshed unique ID variable name in the shapefile,\n", + "and whether the variable is instantaneous or accumulated:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| code-fold: show\n", + "#| code-summary: \"Exported source\"\n", + "#| exports: # add a few more columns\n", + "aggregate_jobs['local_tz'] = aggregate_jobs['input'].apply(\n", + " lambda x: \"Asia/Kathmandu\" if \"nepal\" in x else \"Indian/Antananarivo\"\n", + ")\n", + "aggregate_jobs['shapefile'] = aggregate_jobs['input'].apply(\n", + " lambda x: \"Nepal_Healthsheds2024.zip\" if \"nepal\" in x else \"healthsheds2022.zip\"\n", + ")\n", + "\n", + "aggregate_jobs['hshd_unique_id'] = aggregate_jobs['input'].apply(\n", + " lambda x: \"fid\" if \"nepal\" in x else \"fs_uid\"\n", + ")\n", + "\n", + "aggregate_jobs['climate_handler_var'] = aggregate_jobs['variables_short'].apply(\n", + " lambda x: \"accum\" if x == \"tp\" else \"instant\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "aggregate_jobs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we add this to the data catalog:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| code-fold: show\n", + "#| code-summary: \"Exported source\"\n", + "#| exports: # update catalog\n", + "data_catalog['aggregate']['jobs'].add(\"jobs_df\", aggregate_jobs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our data catalog now has an `aggregate|jobs` node with a `jobs_df` entry that contains the dataframe of all the jobs to be run in this task." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "data_catalog['aggregate']['jobs']['jobs_df'].load().head()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/_proc/20_pytask_logger.html.md b/_proc/20_pytask_logger.html.md new file mode 100644 index 0000000..0bb0c7f --- /dev/null +++ b/_proc/20_pytask_logger.html.md @@ -0,0 +1,58 @@ +--- +title: "Logging: A simple logger to inject into `pytask` jobs" +engine: jupyter +--- + + +## logger + +> A simple logger module for the pytask tasks + + + +--- + +[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/pytask_logger.py#L25){target="_blank" style="float:right; font-size:smaller"} + +### setup_logger + +> setup_logger (name:str, log_path:pathlib.Path=Path('/net/rcstorenfs02/ifs +> /rc_labs/dominici_lab/lab/data_processing/csph- +> era5_sandbox/logs/2025-09-25/13-57-20'), level=20) + + +::: {#cell-3 .cell exports='null'} +``` {.python .cell-code code-fold="show" code-summary="Exported source"} +import logging +from pathlib import Path +from pyprojroot import here +from datetime import datetime + +LOG_DIR = here("logs") +# get the date & time for the log file name +log_date = datetime.now().strftime("%Y-%m-%d") +log_time = datetime.now().strftime("%H-%M-%S") +LOG_DIR = here("logs") / log_date / log_time +``` +::: + + +::: {#cell-4 .cell exports='null'} +``` {.python .cell-code code-fold="show" code-summary="Exported source"} +def setup_logger(name: str, log_path: Path=LOG_DIR, level=logging.INFO) -> logging.Logger: + log_path.mkdir(parents=True, exist_ok=True) + formatter = logging.Formatter('%(asctime)s — %(name)s — %(levelname)s — %(message)s') + + handler = logging.FileHandler(log_path / f"{name}.log", mode='a') + handler.setFormatter(formatter) + + logger = logging.getLogger(name) + logger.setLevel(level) + logger.addHandler(handler) + logger.propagate = False + + return logger +``` +::: + + diff --git a/_proc/20_pytask_logger.ipynb b/_proc/20_pytask_logger.ipynb new file mode 100644 index 0000000..5e079ad --- /dev/null +++ b/_proc/20_pytask_logger.ipynb @@ -0,0 +1,129 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "title: \"Logging: A simple logger to inject into `pytask` jobs\"\n", + "engine: jupyter\n", + "---\n", + "\n", + "## logger\n", + "\n", + "> A simple logger module for the pytask tasks" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "has_sd": true, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/pytask_logger.py#L25){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### setup_logger\n", + "\n", + "> setup_logger (name:str, log_path:pathlib.Path=Path('/net/rcstorenfs02/ifs\n", + "> /rc_labs/dominici_lab/lab/data_processing/csph-\n", + "> era5_sandbox/logs/2025-09-25/15-52-18'), level=20)" + ], + "text/plain": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/pytask_logger.py#L25){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### setup_logger\n", + "\n", + "> setup_logger (name:str, log_path:pathlib.Path=Path('/net/rcstorenfs02/ifs\n", + "> /rc_labs/dominici_lab/lab/data_processing/csph-\n", + "> era5_sandbox/logs/2025-09-25/15-52-18'), level=20)" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| echo: false\n", + "#| output: asis\n", + "show_doc(setup_logger)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| code-fold: show\n", + "#| code-summary: \"Exported source\"\n", + "#| exports: # imports \n", + "\n", + "import logging\n", + "from pathlib import Path\n", + "from pyprojroot import here\n", + "from datetime import datetime\n", + "\n", + "LOG_DIR = here(\"logs\")\n", + "# get the date & time for the log file name\n", + "log_date = datetime.now().strftime(\"%Y-%m-%d\")\n", + "log_time = datetime.now().strftime(\"%H-%M-%S\")\n", + "LOG_DIR = here(\"logs\") / log_date / log_time" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| code-fold: show\n", + "#| code-summary: \"Exported source\"\n", + "#| exports: # main function to setup a logger\n", + "\n", + "\n", + "\n", + "def setup_logger(name: str, log_path: Path=LOG_DIR, level=logging.INFO) -> logging.Logger:\n", + " log_path.mkdir(parents=True, exist_ok=True)\n", + " formatter = logging.Formatter('%(asctime)s — %(name)s — %(levelname)s — %(message)s')\n", + "\n", + " handler = logging.FileHandler(log_path / f\"{name}.log\", mode='a')\n", + " handler.setFormatter(formatter)\n", + "\n", + " logger = logging.getLogger(name)\n", + " logger.setLevel(level)\n", + " logger.addHandler(handler)\n", + " logger.propagate = False\n", + "\n", + " return logger" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/_proc/21_pytask_download.html.md b/_proc/21_pytask_download.html.md new file mode 100644 index 0000000..8c96760 --- /dev/null +++ b/_proc/21_pytask_download.html.md @@ -0,0 +1,109 @@ +--- +title: "Download: `download` Module as a `pytask` Task" +engine: jupyter +--- + + +## task_download + +> This module downloads the raw era5 data from the CDS API. It is similar to the original script, refactored for `pytask`. + + + +We're going to quickly refactor the pipeline to use pytask instead of hydra and snakemake. This will hopefully demonstrate a simpler and more flexible way to manage data pipelines in Python. + +To start off, we need to create a function that queries the CDS API with one job. This function will be used to download the data for each query in the range specified in the data catalog in the config file. + +Let's take a look at the data catalog we created in the config module: + +You can see the queries entry we created in the data catalog. Each query is a row of a dataframe that contains the parameters for the CDS API query. + +::: {#cell-4 .cell} +``` {.python .cell-code} +queries = data_catalog['download']['jobs']['queries_df'].load() +queries +``` +::: + + +We can test this query like we did in the original work: + +::: {#cell-6 .cell} +``` {.python .cell-code} +example_query = queries.iloc[0] + +create_bounding_box(example_query['shapefile']) +``` +::: + + +In this way, we have a similar approach as Hydra configs, but, using the `pytask` data catalog, we can more easily gather the data for a specific task in structured manner entirely in Python. + +::: {#cell-8 .cell} +``` {.python .cell-code} +client = cdsapi.Client() + +ex_bounding_box = create_bounding_box(example_query['shapefile']) + +request = { + "product_type": example_query['product_type'], + "variable": example_query['variables'], + "year": str(example_query['year']), + "month": str(example_query['month']), + "day": example_query['day'], + "time": example_query['time'], + "data_format": "netcdf", + "download_format": "unarchived", + "area": ex_bounding_box + } + +target = f"{example_query['output']}.nc" + +client.retrieve("reanalysis-era5-single-levels", request).download(target) +``` +::: + + +This works! So now we just need to create a `task_` function that pytask will recognise to parallelise the download of queries over: + +### How this works (with some help from GPT): + +#### 🧠 How pytask Discovers and Executes Tasks + +When you run pytask, it automatically scans your project for Python files named `task_*.py`. In these files, it looks for: +- Functions decorated with `@task`, or +- Functions prefixed with `task_` + +These functions are not executed immediately. Instead, `pytask`: +1. Imports each task_*.py module (just like Python would) +2. Registers any matching task functions as nodes in a directed acyclic graph (DAG) +3. Resolves dependencies by analyzing: + - Input annotations (e.g., `Annotated[x, DependsOn]`) + - Output declarations (e.g., `return` values or `Product` annotations) +4. Builds the DAG, where each task function is a node +5. Executes the tasks, respecting dependency order and skipping up-to-date nodes + +So even though the task functions aren’t explicitly “run” in the Python code itself, pytask knows how and when to execute them — based on their position in the DAG. + +#### 🔄 How This Differs from Snakemake + +In `snakemake`, you’re expected to define a series of explicitly executable rules, often using shell commands or Python scripts. You “stitch together” rules using filenames and wildcard matching. + +In contrast: +- 🐍 pytask is Python-native — tasks are just regular Python functions +- ⚙️ It builds a DAG from those functions and tracks inputs/outputs automatically +- 🧱 You are declaring nodes, not scripting execution + +Think of your Python files not as scripts to run, but as a way to define and wire together declarative tasks that will be executed by the pytask engine. + +--- + +Because we defined this task in a function and loop, we can easily debug a node in the DAG by simply calling it: + +::: {#cell-11 .cell} +``` {.python .cell-code} +task_download_raw_data() +``` +::: + + diff --git a/_proc/21_pytask_download.ipynb b/_proc/21_pytask_download.ipynb new file mode 100644 index 0000000..5feaa36 --- /dev/null +++ b/_proc/21_pytask_download.ipynb @@ -0,0 +1,179 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "title: \"Download: `download` Module as a `pytask` Task\"\n", + "engine: jupyter\n", + "---\n", + "\n", + "## task_download \n", + "\n", + "> This module downloads the raw era5 data from the CDS API. It is similar to the original script, refactored for `pytask`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We're going to quickly refactor the pipeline to use pytask instead of hydra and snakemake. This will hopefully demonstrate a simpler and more flexible way to manage data pipelines in Python.\n", + "\n", + "To start off, we need to create a function that queries the CDS API with one job. This function will be used to download the data for each query in the range specified in the data catalog in the config file.\n", + "\n", + "Let's take a look at the data catalog we created in the config module:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can see the queries entry we created in the data catalog. Each query is a row of a dataframe that contains the parameters for the CDS API query." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "queries = data_catalog['download']['jobs']['queries_df'].load()\n", + "queries" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can test this query like we did in the original work:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "example_query = queries.iloc[0]\n", + "\n", + "create_bounding_box(example_query['shapefile'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this way, we have a similar approach as Hydra configs, but, using the `pytask` data catalog, we can more easily gather the data for a specific task in structured manner entirely in Python." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| eval: false\n", + "client = cdsapi.Client()\n", + "\n", + "ex_bounding_box = create_bounding_box(example_query['shapefile'])\n", + "\n", + "request = {\n", + " \"product_type\": example_query['product_type'],\n", + " \"variable\": example_query['variables'], \n", + " \"year\": str(example_query['year']),\n", + " \"month\": str(example_query['month']),\n", + " \"day\": example_query['day'],\n", + " \"time\": example_query['time'],\n", + " \"data_format\": \"netcdf\",\n", + " \"download_format\": \"unarchived\",\n", + " \"area\": ex_bounding_box\n", + " }\n", + "\n", + "target = f\"{example_query['output']}.nc\"\n", + "\n", + "client.retrieve(\"reanalysis-era5-single-levels\", request).download(target)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This works! So now we just need to create a `task_` function that pytask will recognise to parallelise the download of queries over:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### How this works (with some help from GPT):\n", + "\n", + "#### 🧠 How pytask Discovers and Executes Tasks\n", + "\n", + "When you run pytask, it automatically scans your project for Python files named `task_*.py`. In these files, it looks for:\n", + "- Functions decorated with `@task`, or\n", + "- Functions prefixed with `task_`\n", + "\n", + "These functions are not executed immediately. Instead, `pytask`:\n", + "1.\tImports each task_*.py module (just like Python would)\n", + "2.\tRegisters any matching task functions as nodes in a directed acyclic graph (DAG)\n", + "3.\tResolves dependencies by analyzing:\n", + " - Input annotations (e.g., `Annotated[x, DependsOn]`)\n", + " - Output declarations (e.g., `return` values or `Product` annotations)\n", + "4.\tBuilds the DAG, where each task function is a node\n", + "5.\tExecutes the tasks, respecting dependency order and skipping up-to-date nodes\n", + "\n", + "So even though the task functions aren’t explicitly “run” in the Python code itself, pytask knows how and when to execute them — based on their position in the DAG.\n", + "\n", + "#### 🔄 How This Differs from Snakemake\n", + "\n", + "In `snakemake`, you’re expected to define a series of explicitly executable rules, often using shell commands or Python scripts. You “stitch together” rules using filenames and wildcard matching.\n", + "\n", + "In contrast:\n", + "- 🐍 pytask is Python-native — tasks are just regular Python functions\n", + "- ⚙️ It builds a DAG from those functions and tracks inputs/outputs automatically\n", + "- 🧱 You are declaring nodes, not scripting execution\n", + "\n", + "Think of your Python files not as scripts to run, but as a way to define and wire together declarative tasks that will be executed by the pytask engine.\n", + "\n", + "---\n", + "\n", + "Because we defined this task in a function and loop, we can easily debug a node in the DAG by simply calling it:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| eval: false\n", + "task_download_raw_data()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/_proc/22_pytask_aggregate.html.md b/_proc/22_pytask_aggregate.html.md new file mode 100644 index 0000000..b1bf48b --- /dev/null +++ b/_proc/22_pytask_aggregate.html.md @@ -0,0 +1,657 @@ +--- +title: "Aggregation: The `aggregation` Module as a `pytask` Task" +format: html +engine: jupyter +--- + + +# task_aggregate + +> This task aggregates the downloaded data into spatial and temporal averages. It uses xarray to compute summary statistics over the specified time period and spatial region. The aggregation is done diurnally, so we will fetch the sundown and sunrise times for the region and use them to compute the diurnal averages. + + + +## Diurnal Classification Based on Sun Position + +To do diurnal classificaiton, we will need to fetch the sundown and sunrise times for the region and use them to compute the diurnal averages. We will use the [astral library](https://astral.readthedocs.io/en/latest/) to get the sunrise and sunset times for the specified latitude and longitude. The aggregation will be done using xarray, which allows us to compute the mean over the specified time period and spatial region. + +Here's our example file: + +::: {#cell-3 .cell} +``` {.python .cell-code} +eg_file = data_catalog['download']['outputs']['2009_01_nepal'].load() +with ClimateDataFileHandler(eg_file) as handler: + + ds = xr.open_dataset(handler.get_dataset("instant")) + #ds = xr.open_dataset(handler.get_dataset("accum")) + +ds +``` +::: + + +We can see the astral library in action below: + +::: {#cell-5 .cell exports='null'} +``` {.python .cell-code code-fold="show" code-summary="Exported source"} +from astral import Observer, sun +import pandas as pd +import numpy as np +from tqdm import tqdm +import random +import datetime +from pytz import UTC +``` +::: + + +::: {#cell-6 .cell} +``` {.python .cell-code} +# get the location of a datapoint in the dataset +lat, long = ds.coords["latitude"].values[0], ds.coords["longitude"].values[0] +time = ds['valid_time'].values[0] +dt = pd.to_datetime(time, utc=True) +``` +::: + + +::: {#cell-7 .cell} +``` {.python .cell-code} +dt +``` +::: + + +::: {#cell-8 .cell} +``` {.python .cell-code} +observer = Observer(latitude=lat, longitude=long, elevation=0) +sun_info = sun.sun(observer, date=dt) +sun_info +``` +::: + + +Astral is very fast: + +::: {#cell-10 .cell} +``` {.python .cell-code} +#fetch a random time from valid_time +options = ds['valid_time'].values + +random_time = random.choice(options) +dt = pd.to_datetime(random_time, utc=True) +sun_info = sun.sun(observer, date=dt) +if dt < sun_info['sunrise']: + print(f"Randomly selected time: {dt} is pre_dawn") +elif dt >= sun_info['sunrise'] and dt < sun_info['sunset']: + print(f"Randomly selected time: {dt} is day") +else: + print(f"Randomly selected time: {dt} is post_dusk") +``` +::: + + +This tells us that we can use the valid time for the specific location of each data point in the query and know based on the sun whether it was daytime or nighttime. The runtime will be limited only by the looping. +Let's put this in a function so that we can use the resampling in `xarray`. + +The resampling approach will be a single function that can resample in three ways: + +- By calendar date, default (1 value per calendar date) +- By diurnal class by calendar date (3 values, pre-dawn, day, post-dusk) +- By solar date (2 values per calendar date, with night classified as "before" or "after") + +Therefore, we'll need 2 internal functions; one to do diurnal, and one to do solar date bins. + +Essentially, we are going to create an array-shaped index/mask, (time, latitude, longitude). As a +demonstration, this loop goes through the first 24 time points in the dataset, +and calculates the sun info for each latitude and longitude, assigning the values to an array: + +::: {#cell-12 .cell} +``` {.python .cell-code} +times = ds['valid_time'].values[:24] +lats = ds.coords['latitude'].values +lons = ds.coords['longitude'].values + +result = np.full((len(times), len(lats), len(lons)), "", dtype=object) + +for i, dt in enumerate(times): + + for j, lat in enumerate(lats): + + for k, lon in enumerate(lons): + + # set the geographical position + observer = Observer(latitude=lat, longitude=lon, elevation=0) + + # use the time + dt = pd.to_datetime(dt, utc=True) + + # where/when is the sun at this time for this position + sun_info = sun.sun(observer, date=dt) + result[i, j, k] = sun_info +``` +::: + + +So we know that in the first hour, the sun goes up and comes down at slightly different +times based on latitude and longitude. Take the first hour, for example: + +::: {#cell-14 .cell} +``` {.python .cell-code} +print(result.shape) +hour_1 = 0 # 0th index of the results + +min_lat = 0 +min_lon = 0 +max_lat = 48 +max_lon = 90 +print(f"Even though the reading came from the first HOUR of data UTC, the sun info at the minimum latitude/longitude is: {result[hour_1, min_lat, min_lon]}") + +print(f"this is different from the sun info at the maximum latitude/longitude is: {result[hour_1, max_lat, max_lon]}") +``` +::: + + +--- + +[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/task_aggregate.py#L41){target="_blank" style="float:right; font-size:smaller"} + +### compute_diurnal_class_bins + +> compute_diurnal_class_bins (ds:xarray.core.dataset.Dataset) + +*Compute the diurnal value for each data point in the dataset. +This function iterates over each data point in the dataset, +calculates the sunrise and sunset times for the given time, latitude and longitude, +and returns whether or not that data point is before dawn, during the day, or after dusk.* + + +::: {#cell-16 .cell} +``` {.python .cell-code} +ex=compute_diurnal_class_bins(ds) +``` +::: + + +So, for our 720 time points, we should find that +if we take the `set()` of all the classifications within that slice, +there should be a few of them with 2 classes. +In other words, at any given hour, almost all of +the readings are "day", because it is daytime across all +of Madagascar, _but_ at certain timepoints, the sun is rising +or setting in the northern part of the country and so some +portion of the slice is classified differently: + +![illustrated](./IMG_740012467778-1.jpeg) + +::: {#cell-18 .cell} +``` {.python .cell-code} +for x in range(720): + print(set(ex[x].flatten())) +``` +::: + + +This works! Now we can do a similar, but slightly more +complicated function to define "night" and "day", +where "night" includes all of the values after the sun goes down. + +--- + +[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/task_aggregate.py#L83){target="_blank" style="float:right; font-size:smaller"} + +### compute_solar_day_night_class_bins + +> compute_solar_day_night_class_bins (ds:xarray.core.dataset.Dataset, +> night_direction:Literal['before','aft +> er']) + +*Compute the diurnal value for each data point in the dataset. +This function iterates over each data point in the dataset, +calculates the sunrise and sunset times for the given time, latitude and longitude, +and returns whether or not that data point is daytime or nighttime. +The definition of "nighttime" can be set to be all the darkness before the sun +came up (before), or all the darkness after it went down (after).* + + +::: {#cell-21 .cell exports='null'} +``` {.python .cell-code code-fold="show" code-summary="Exported source"} +def compute_solar_day_night_class_bins( + ds: xr.Dataset, + night_direction: Literal["before", "after"], + )-> list: + """ + Compute the diurnal value for each data point in the dataset. + This function iterates over each data point in the dataset, + calculates the sunrise and sunset times for the given time, latitude and longitude, + and returns whether or not that data point is daytime or nighttime. + The definition of "nighttime" can be set to be all the darkness before the sun + came up (before), or all the darkness after it went down (after). + """ + + times = ds['valid_time'].values + lats = ds.coords['latitude'].values + lons = ds.coords['longitude'].values + + result = np.full((len(times), len(lats), len(lons)), "", dtype=object) + datetimes = np.full((len(times), len(lats), len(lons)), "", dtype=object) + + for i, dt in enumerate(tqdm(times, desc="Classifying data points by sun position")): + # use the time + dt = pd.to_datetime(dt, utc=True) + + for j, lat in enumerate(lats): + + for k, lon in enumerate(lons): + + # set the geographical position + observer = Observer(latitude=lat, longitude=lon, elevation=0) + if night_direction == "before": + # Night is from previous sunset to today's sunrise + sun_today = sun.sun(observer, date=dt.date()) + sun_prev = sun.sun(observer, date=(dt - pd.Timedelta(days=1)).date()) + night_start = sun_prev["sunset"].astimezone(pd.Timestamp.utcnow().tz) + night_end = sun_today["sunrise"].astimezone(pd.Timestamp.utcnow().tz) + + # the reading is from yesterday's nighttime + if night_start <= dt < night_end: + result[i, j, k] = "night" + # the date counts as today + datetimes[i, j, k] = dt.date() + + # the reading is from daytime + elif sun_today["sunrise"] <= dt < sun_today["sunset"]: + result[i, j, k] = "day" + # the date counts as today + datetimes[i, j, k] = dt.date() + + # the reading is from today's nighttime, but counts as tomorrow's night + else: + result[i, j, k] = "night" + # the date is tomorrow + datetimes[i, j, k] = (dt + pd.Timedelta(days=1)).date() + + elif night_direction == "after": + # Night is from today's sunset to next sunrise + sun_today = sun.sun(observer, date=dt.date()) + sun_next = sun.sun(observer, date=(dt + pd.Timedelta(days=1)).date()) + night_start = sun_today["sunset"].astimezone(pd.Timestamp.utcnow().tz) + night_end = sun_next["sunrise"].astimezone(pd.Timestamp.utcnow().tz) + + # the reading is from daytime + if sun_today["sunrise"] <= dt < sun_today["sunset"]: + result[i, j, k] = "day" + # the date counts as today + datetimes[i, j, k] = dt.date() + # the reading is from tonight + elif night_start <= dt < night_end: + result[i, j, k] = "night" + # the date counts as today + datetimes[i, j, k] = dt.date() + + # the reading is from yesterday night + else: + # the date counts as yesterday + result[i, j, k] = "day" + datetimes[i, j, k] = (dt - pd.Timedelta(days=1)).date() + else: + raise ValueError(f"Invalid night_direction: {night_direction}") + + return result, datetimes +``` +::: + + +::: {#cell-22 .cell} +``` {.python .cell-code} +ex_class, ex_dt = compute_solar_day_night_class_bins(ds, "before") +``` +::: + + +::: {#cell-23 .cell} +``` {.python .cell-code} +ex_class +``` +::: + + +As before, we should see that most slices are homogenous, +meaning most of the time, all the readings are from the day, +but some slices should have day and night values: + +::: {#cell-25 .cell} +``` {.python .cell-code} +for slice_ in range(720): + print(set(ex_class[slice_].flatten())) +``` +::: + + +The returned array can serve as new "variable indexes" for the dataset: + +::: {#cell-27 .cell} +``` {.python .cell-code} +ds_masked = ds.copy() +ds_masked['solar_class'] = (('valid_time', 'latitude', 'longitude'), ex_class) +ds_masked["solar_date"] = (("valid_time", "latitude", "longitude"), ex_dt) +``` +::: + + +## Diurnal Resampling + +Now, to see if it will resample by both solar day and diurnal class. Let's try by masking and making copies with NaN in the masked values: + +::: {#cell-29 .cell} +``` {.python .cell-code} +ds_day = ds_masked.where(ds_masked["solar_class"] == "day").drop_vars(["solar_class", "solar_date"]) +ds_night = ds_masked.where(ds_masked["solar_class"] == "night").drop_vars(["solar_class", "solar_date"]) +``` +::: + + +Next, we set the time zone for Madagascar since, to resample by day and night, +we should observe the local time: + +::: {#cell-31 .cell} +``` {.python .cell-code} +ds_day = ds_day.assign_coords(valid_time=pd.to_datetime(ds["valid_time"].values).tz_localize("UTC").tz_convert("Asia/Kathmandu")) +ds_night = ds_night.assign_coords(valid_time=pd.to_datetime(ds["valid_time"].values).tz_localize("UTC").tz_convert("Asia/Kathmandu")) +``` +::: + + +Now if we can resample by day... + +::: {#cell-33 .cell} +``` {.python .cell-code} +ds_day_rs = ds_day.resample(valid_time="1D").reduce(np.nanmean) +ds_night_rs = ds_night.resample(valid_time="1D").reduce(np.nanmean) +ds_day_rs +``` +::: + + +Can we successfully convert this to a tiff? + +::: {#cell-35 .cell} +``` {.python .cell-code} +from era5_sandbox.aggregate import netcdf_to_tiff +``` +::: + + +::: {#cell-36 .cell} +``` {.python .cell-code} +raster_day = netcdf_to_tiff(ds_day_rs, band=1, variable="d2m") +raster_night = netcdf_to_tiff(ds_night_rs, band=1, variable="d2m") +``` +::: + + +Looks great! These two rasters represent one calendar day of daytime and nighttime values. + +### Testing Polygon to Raster Cells & Healthshed Aggregation + +The penultimate step of the aggregate pipeline in the original version is +assigning each datapoint to the respective healthshed. The `vectors` argument +comes from the healthshed, and represents each geographic polygon on the ground +that we want to aggregate data to. + +::: {#cell-38 .cell} +``` {.python .cell-code} +from hydra import initialize, compose +``` +::: + + +::: {#cell-39 .cell} +``` {.python .cell-code} +try: + with initialize(version_base=None, config_path="../conf"): + cfg = compose(config_name='config.yaml') +except Exception as e: + print(f"Error initializing Hydra: {e}") + with initialize(version_base=None, config_path="conf"): + cfg = compose(config_name='config.yaml') + +driver = GoogleDriver(json_key_path=here() / cfg.GOOGLE_DRIVE_AUTH_JSON.path) +drive = driver.get_drive() +healthsheds = driver.read_healthsheds("Nepal_Healthsheds2024.zip") +``` +::: + + +::: {#cell-40 .cell} +``` {.python .cell-code} +res_poly2cell=polygon_to_raster_cells( + vectors = healthsheds.geometry.values, # the geometries of the shapefile of the regions + raster=raster_day.data, # the raster data above + nodata=np.nan, # any intersections with no data, may have to be np.nan + affine=raster_day.transform, # some math thing need to revise + all_touched=True, + verbose=True +) +``` +::: + + +This works fine. Finally, we aggregate to healthsheds: + +::: {#cell-42 .cell} +``` {.python .cell-code} +from era5_sandbox.aggregate import aggregate_to_healthsheds +``` +::: + + +::: {#cell-43 .cell} +``` {.python .cell-code} +result_day = aggregate_to_healthsheds( + res_poly2cell=res_poly2cell, + raster=raster_day, + shapes=healthsheds, + names_column="fid", + aggregation_func=np.nanmean, + aggregation_name="mean_dewpoint_day" +) + +result_night = aggregate_to_healthsheds( + res_poly2cell=res_poly2cell, + raster=raster_night, + shapes=healthsheds, + names_column="fid", + aggregation_func=np.nanmean, + aggregation_name="mean_dewpoint_night" +) +``` +::: + + +Below shows the result of aggregating the daytime dewpoint temperature to the healthshed level: + +::: {#cell-45 .cell} +``` {.python .cell-code} +result_day +``` +::: + + +::: {#cell-46 .cell} +``` {.python .cell-code} +result_night +``` +::: + + +So from one input, we will have two outputs, one for daytime and one for nighttime, and this will have to loop over the bands (ie each day in the month). + +# Putting it all together in a `pytask` task + +Below we define our `pytask` task to aggregate data to the healthshed level. + +::: {#cell-48 .cell exports='null'} +``` {.python .cell-code code-fold="show" code-summary="Exported source"} +job_rows = data_catalog['aggregate']['jobs']['jobs_df'].load() + +aggregation_funcs = { + "mean": np.nanmean, + "sum": np.nansum, + "max": np.nanmax, + "min": np.nanmin +} + +for i, job in job_rows.iterrows(): + #print(f"Job {i+1}: variable={job['variables']}, time={job['time']}, aggregation={job['aggregation_name']}") + + # parse the row into function parameters + input_file = data_catalog['download']['outputs'][job['input']] + solar_classification = job['solar_classification'] + variable = job['variables_short'] + time = job['time'] + aggregation_func = aggregation_funcs[job['aggregation_name']] + aggregation_name = job['aggregation_name'] + + climate_handler_var = job['climate_handler_var'] + local_tz = job['local_tz'] + + shapefile = job['shapefile'] + hshd_unique_id = job['hshd_unique_id'] + + output_file = job['input'] + "_" + job['time'] + "_" + job['variables_short'] + "_" + job['aggregation_name'] + ".parquet" + + @task(id=output_file, name=f"Aggregate {output_file}", after="task_download_raw_data") + def task_aggregate_data_diurnal( + input_file: Path = data_catalog['download']['outputs'][job['input']], # input data Path from the download task + aggregation_func: callable = aggregation_func, # the aggregation function + aggregation_name: str = aggregation_name, # the name of the aggregation function + time: Literal["day", "night"] = time, # whether to aggregate by day or night + night_direction: Literal["before", "after"] = solar_classification, # how to define night + variable: str = variable, # the variable to aggregate, + climate_handler_var: Literal["instant", "accum"] = climate_handler_var, # whether the variable is instant or accum, + local_tz: str = local_tz, # the local timezone for resampling + shapefile: str = shapefile, # the shapefile for the healthsheds, + hshd_unique_id: str = hshd_unique_id, # the unique id column in the shapefile, + output_file: str = output_file # the output file name + ) -> Annotated[Path, data_catalog['aggregate']['outputs'][output_file]]: + """ + Task to aggregate data from a CDSAPI Query to the healthshed + level. Returns path to parquet file with aggregated data. + """ + + logger = setup_logger(output_file) + + logger.info(f"Aggregating: {output_file}") + + # check if the string path exists + # if os.path.exists(output_file): + # logger.info(f"File {output_file} already exists. Skipping aggregation.") + # return output_file + + # get input data + logger.info("Reading input data...") + with ClimateDataFileHandler(input_file) as handler: + ds = xr.open_dataset(handler.get_dataset('instant')) + + #get the healthshed shapefile + logger.info(f"Reading healthshed shapefile from yaml {here()}...") + with open(here() / "conf" / "config.yaml") as f: + healthshed_config = yaml.safe_load(f) + + key_path = here() / healthshed_config['GOOGLE_DRIVE_AUTH_JSON']['path'] + + driver = GoogleDriver(json_key_path=key_path) + drive = driver.get_drive() + healthsheds = driver.read_healthsheds(shapefile) + + # compute the diurnal classification bins + logger.info("Computing diurnal classification bins...") + class_bins, class_dts = compute_solar_day_night_class_bins(ds, night_direction) + + ds_masked = ds.copy() + + # assign classifications + logger.info("Assigning classification bins to dataset...") + ds['solar_class'] = (('valid_time', 'latitude', 'longitude'), class_bins) + ds["solar_date"] = (("valid_time", "latitude", "longitude"), class_dts) + + # mask the dataset to the requested time + mask = ds["solar_class"] == time + ds_masked = ds_masked.where(mask) + + # set the local timezone + ds_masked = ds_masked.assign_coords(valid_time=pd.to_datetime(ds["valid_time"].values).tz_localize("UTC").tz_convert(local_tz)) + + # resample by local date + logger.info("Resampling by local date...") + ds_rs = ds_masked.resample(valid_time="1D").reduce(aggregation_func) + + # convert to tiff + logger.info("Rasterizing resampled data...") + n_bands = ds_rs.dims['valid_time'] + + # polygon to raster cells for the first band + logger.info("Converting polygons to raster cells...") + raster = netcdf_to_tiff(ds_rs, band=1, variable=variable) + res_poly2cell=polygon_to_raster_cells( + vectors = healthsheds.geometry.values, # the geometries of the shapefile of the regions + raster=raster.data, # the raster data above + nodata=np.nan, # any intersections with no data, may have to be np.nan + affine=raster.transform, # some math thing need to revise + all_touched=True, + verbose=True + ) + + result_df = healthsheds[[hshd_unique_id, "geometry"]].copy() + + # loop over bands and aggregate to healthsheds + for band in tqdm(range(1, n_bands + 1)): + logger.info(f"Processing band {band} of {n_bands}...") + + day = band # band is 1-indexed + + day_col = f"day_{day:02d}" + + # calculate raster for this band + raster = netcdf_to_tiff(ds_rs, band=band, variable=variable) + + # aggregate to healthsheds + result = aggregate_to_healthsheds( + res_poly2cell=res_poly2cell, + raster=raster, + shapes=healthsheds, + names_column=hshd_unique_id, + aggregation_func=aggregation_func, + aggregation_name=variable + ) + + # add band to result dataframe + result_df[day_col] = result[variable] + + # save to parquet + result_df.to_parquet(f"{BLD}/{output_file}") + + logger.info("Aggregation complete.") + + return Path(f"{BLD}/{output_file}") +``` +::: + + +That should wrap it up! To test, we can run a single job: + +::: {#cell-50 .cell} +``` {.python .cell-code} +# runs the last defined job only +task_aggregate_data_diurnal() +``` +::: + + +Or we can run the task in `pytask`: + +```bash +pytask build -k "nepal and 2009" --dry-run +``` + diff --git a/_proc/22_pytask_aggregate.ipynb b/_proc/22_pytask_aggregate.ipynb new file mode 100644 index 0000000..84f7f1e --- /dev/null +++ b/_proc/22_pytask_aggregate.ipynb @@ -0,0 +1,974 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "title: \"Aggregation: The `aggregation` Module as a `pytask` Task\"\n", + "format: html\n", + "engine: jupyter\n", + "---\n", + "\n", + "# task_aggregate\n", + "\n", + "> This task aggregates the downloaded data into spatial and temporal averages. It uses xarray to compute summary statistics over the specified time period and spatial region. The aggregation is done diurnally, so we will fetch the sundown and sunrise times for the region and use them to compute the diurnal averages." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Diurnal Classification Based on Sun Position\n", + "\n", + "To do diurnal classificaiton, we will need to fetch the sundown and sunrise times for the region and use them to compute the diurnal averages. We will use the [astral library](https://astral.readthedocs.io/en/latest/) to get the sunrise and sunset times for the specified latitude and longitude. The aggregation will be done using xarray, which allows us to compute the mean over the specified time period and spatial region.\n", + "\n", + "Here's our example file:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "eg_file = data_catalog['download']['outputs']['2009_01_nepal'].load()\n", + "with ClimateDataFileHandler(eg_file) as handler:\n", + " \n", + " ds = xr.open_dataset(handler.get_dataset(\"instant\"))\n", + " #ds = xr.open_dataset(handler.get_dataset(\"accum\"))\n", + "\n", + "ds" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see the astral library in action below:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| code-fold: show\n", + "#| code-summary: \"Exported source\"\n", + "#| exports: #\n", + "from astral import Observer, sun\n", + "import pandas as pd\n", + "import numpy as np\n", + "from tqdm import tqdm\n", + "import random\n", + "import datetime\n", + "from pytz import UTC" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "# get the location of a datapoint in the dataset\n", + "lat, long = ds.coords[\"latitude\"].values[0], ds.coords[\"longitude\"].values[0]\n", + "time = ds['valid_time'].values[0]\n", + "dt = pd.to_datetime(time, utc=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "dt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "observer = Observer(latitude=lat, longitude=long, elevation=0)\n", + "sun_info = sun.sun(observer, date=dt)\n", + "sun_info" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Astral is very fast:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#fetch a random time from valid_time\n", + "options = ds['valid_time'].values\n", + "\n", + "random_time = random.choice(options)\n", + "dt = pd.to_datetime(random_time, utc=True)\n", + "sun_info = sun.sun(observer, date=dt)\n", + "if dt < sun_info['sunrise']:\n", + " print(f\"Randomly selected time: {dt} is pre_dawn\")\n", + "elif dt >= sun_info['sunrise'] and dt < sun_info['sunset']:\n", + " print(f\"Randomly selected time: {dt} is day\")\n", + "else:\n", + " print(f\"Randomly selected time: {dt} is post_dusk\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This tells us that we can use the valid time for the specific location of each data point in the query and know based on the sun whether it was daytime or nighttime. The runtime will be limited only by the looping.\n", + "Let's put this in a function so that we can use the resampling in `xarray`.\n", + "\n", + "The resampling approach will be a single function that can resample in three ways:\n", + "\n", + "- By calendar date, default (1 value per calendar date)\n", + "- By diurnal class by calendar date (3 values, pre-dawn, day, post-dusk)\n", + "- By solar date (2 values per calendar date, with night classified as \"before\" or \"after\")\n", + "\n", + "Therefore, we'll need 2 internal functions; one to do diurnal, and one to do solar date bins.\n", + "\n", + "Essentially, we are going to create an array-shaped index/mask, (time, latitude, longitude). As a\n", + "demonstration, this loop goes through the first 24 time points in the dataset,\n", + "and calculates the sun info for each latitude and longitude, assigning the values to an array:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "times = ds['valid_time'].values[:24]\n", + "lats = ds.coords['latitude'].values\n", + "lons = ds.coords['longitude'].values\n", + "\n", + "result = np.full((len(times), len(lats), len(lons)), \"\", dtype=object)\n", + "\n", + "for i, dt in enumerate(times):\n", + "\n", + " for j, lat in enumerate(lats):\n", + "\n", + " for k, lon in enumerate(lons):\n", + " \n", + " # set the geographical position\n", + " observer = Observer(latitude=lat, longitude=lon, elevation=0)\n", + " \n", + " # use the time\n", + " dt = pd.to_datetime(dt, utc=True)\n", + "\n", + " # where/when is the sun at this time for this position\n", + " sun_info = sun.sun(observer, date=dt)\n", + " result[i, j, k] = sun_info" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So we know that in the first hour, the sun goes up and comes down at slightly different\n", + "times based on latitude and longitude. Take the first hour, for example:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "print(result.shape)\n", + "hour_1 = 0 # 0th index of the results\n", + "\n", + "min_lat = 0\n", + "min_lon = 0\n", + "max_lat = 48\n", + "max_lon = 90\n", + "print(f\"Even though the reading came from the first HOUR of data UTC, the sun info at the minimum latitude/longitude is: {result[hour_1, min_lat, min_lon]}\")\n", + "\n", + "print(f\"this is different from the sun info at the maximum latitude/longitude is: {result[hour_1, max_lat, max_lon]}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "has_sd": true, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/task_aggregate.py#L41){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### compute_diurnal_class_bins\n", + "\n", + "> compute_diurnal_class_bins (ds:xarray.core.dataset.Dataset)\n", + "\n", + "*Compute the diurnal value for each data point in the dataset.\n", + "This function iterates over each data point in the dataset,\n", + "calculates the sunrise and sunset times for the given time, latitude and longitude,\n", + "and returns whether or not that data point is before dawn, during the day, or after dusk.*" + ], + "text/plain": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/task_aggregate.py#L41){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### compute_diurnal_class_bins\n", + "\n", + "> compute_diurnal_class_bins (ds:xarray.core.dataset.Dataset)\n", + "\n", + "*Compute the diurnal value for each data point in the dataset.\n", + "This function iterates over each data point in the dataset,\n", + "calculates the sunrise and sunset times for the given time, latitude and longitude,\n", + "and returns whether or not that data point is before dawn, during the day, or after dusk.*" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| echo: false\n", + "#| output: asis\n", + "show_doc(compute_diurnal_class_bins)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "ex=compute_diurnal_class_bins(ds)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So, for our 720 time points, we should find that\n", + "if we take the `set()` of all the classifications within that slice,\n", + "there should be a few of them with 2 classes.\n", + "In other words, at any given hour, almost all of\n", + "the readings are \"day\", because it is daytime across all\n", + "of Madagascar, _but_ at certain timepoints, the sun is rising\n", + "or setting in the northern part of the country and so some\n", + "portion of the slice is classified differently:\n", + "\n", + "![illustrated](./IMG_740012467778-1.jpeg)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "for x in range(720):\n", + " print(set(ex[x].flatten()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This works! Now we can do a similar, but slightly more\n", + "complicated function to define \"night\" and \"day\",\n", + "where \"night\" includes all of the values after the sun goes down." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "has_sd": true, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/task_aggregate.py#L83){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### compute_solar_day_night_class_bins\n", + "\n", + "> compute_solar_day_night_class_bins (ds:xarray.core.dataset.Dataset,\n", + "> night_direction:Literal['before','aft\n", + "> er'])\n", + "\n", + "*Compute the diurnal value for each data point in the dataset.\n", + "This function iterates over each data point in the dataset,\n", + "calculates the sunrise and sunset times for the given time, latitude and longitude,\n", + "and returns whether or not that data point is daytime or nighttime.\n", + "The definition of \"nighttime\" can be set to be all the darkness before the sun\n", + "came up (before), or all the darkness after it went down (after).*" + ], + "text/plain": [ + "---\n", + "\n", + "[source](https://github.com/TinasheMTapera/era5_sandbox/blob/main/era5_sandbox/task_aggregate.py#L83){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n", + "\n", + "### compute_solar_day_night_class_bins\n", + "\n", + "> compute_solar_day_night_class_bins (ds:xarray.core.dataset.Dataset,\n", + "> night_direction:Literal['before','aft\n", + "> er'])\n", + "\n", + "*Compute the diurnal value for each data point in the dataset.\n", + "This function iterates over each data point in the dataset,\n", + "calculates the sunrise and sunset times for the given time, latitude and longitude,\n", + "and returns whether or not that data point is daytime or nighttime.\n", + "The definition of \"nighttime\" can be set to be all the darkness before the sun\n", + "came up (before), or all the darkness after it went down (after).*" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#| echo: false\n", + "#| output: asis\n", + "show_doc(compute_solar_day_night_class_bins)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| code-fold: show\n", + "#| code-summary: \"Exported source\"\n", + "#| exports: # \n", + "\n", + "def compute_solar_day_night_class_bins(\n", + " ds: xr.Dataset,\n", + " night_direction: Literal[\"before\", \"after\"],\n", + " )-> list:\n", + " \"\"\"\n", + " Compute the diurnal value for each data point in the dataset.\n", + " This function iterates over each data point in the dataset,\n", + " calculates the sunrise and sunset times for the given time, latitude and longitude,\n", + " and returns whether or not that data point is daytime or nighttime.\n", + " The definition of \"nighttime\" can be set to be all the darkness before the sun\n", + " came up (before), or all the darkness after it went down (after).\n", + " \"\"\"\n", + "\n", + " times = ds['valid_time'].values\n", + " lats = ds.coords['latitude'].values\n", + " lons = ds.coords['longitude'].values\n", + "\n", + " result = np.full((len(times), len(lats), len(lons)), \"\", dtype=object)\n", + " datetimes = np.full((len(times), len(lats), len(lons)), \"\", dtype=object)\n", + "\n", + " for i, dt in enumerate(tqdm(times, desc=\"Classifying data points by sun position\")):\n", + " # use the time\n", + " dt = pd.to_datetime(dt, utc=True)\n", + "\n", + " for j, lat in enumerate(lats):\n", + "\n", + " for k, lon in enumerate(lons):\n", + " \n", + " # set the geographical position\n", + " observer = Observer(latitude=lat, longitude=lon, elevation=0)\n", + " if night_direction == \"before\":\n", + " # Night is from previous sunset to today's sunrise\n", + " sun_today = sun.sun(observer, date=dt.date())\n", + " sun_prev = sun.sun(observer, date=(dt - pd.Timedelta(days=1)).date())\n", + " night_start = sun_prev[\"sunset\"].astimezone(pd.Timestamp.utcnow().tz)\n", + " night_end = sun_today[\"sunrise\"].astimezone(pd.Timestamp.utcnow().tz)\n", + " \n", + " # the reading is from yesterday's nighttime\n", + " if night_start <= dt < night_end:\n", + " result[i, j, k] = \"night\"\n", + " # the date counts as today\n", + " datetimes[i, j, k] = dt.date()\n", + "\n", + " # the reading is from daytime\n", + " elif sun_today[\"sunrise\"] <= dt < sun_today[\"sunset\"]:\n", + " result[i, j, k] = \"day\"\n", + " # the date counts as today\n", + " datetimes[i, j, k] = dt.date()\n", + " \n", + " # the reading is from today's nighttime, but counts as tomorrow's night\n", + " else:\n", + " result[i, j, k] = \"night\"\n", + " # the date is tomorrow\n", + " datetimes[i, j, k] = (dt + pd.Timedelta(days=1)).date()\n", + "\n", + " elif night_direction == \"after\":\n", + " # Night is from today's sunset to next sunrise\n", + " sun_today = sun.sun(observer, date=dt.date())\n", + " sun_next = sun.sun(observer, date=(dt + pd.Timedelta(days=1)).date())\n", + " night_start = sun_today[\"sunset\"].astimezone(pd.Timestamp.utcnow().tz)\n", + " night_end = sun_next[\"sunrise\"].astimezone(pd.Timestamp.utcnow().tz)\n", + "\n", + " # the reading is from daytime\n", + " if sun_today[\"sunrise\"] <= dt < sun_today[\"sunset\"]:\n", + " result[i, j, k] = \"day\"\n", + " # the date counts as today\n", + " datetimes[i, j, k] = dt.date()\n", + " # the reading is from tonight\n", + " elif night_start <= dt < night_end:\n", + " result[i, j, k] = \"night\"\n", + " # the date counts as today\n", + " datetimes[i, j, k] = dt.date()\n", + "\n", + " # the reading is from yesterday night\n", + " else:\n", + " # the date counts as yesterday\n", + " result[i, j, k] = \"day\"\n", + " datetimes[i, j, k] = (dt - pd.Timedelta(days=1)).date()\n", + " else:\n", + " raise ValueError(f\"Invalid night_direction: {night_direction}\")\n", + "\n", + " return result, datetimes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "ex_class, ex_dt = compute_solar_day_night_class_bins(ds, \"before\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "ex_class" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As before, we should see that most slices are homogenous,\n", + "meaning most of the time, all the readings are from the day,\n", + "but some slices should have day and night values:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "for slice_ in range(720):\n", + " print(set(ex_class[slice_].flatten()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The returned array can serve as new \"variable indexes\" for the dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "ds_masked = ds.copy()\n", + "ds_masked['solar_class'] = (('valid_time', 'latitude', 'longitude'), ex_class)\n", + "ds_masked[\"solar_date\"] = ((\"valid_time\", \"latitude\", \"longitude\"), ex_dt)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Diurnal Resampling\n", + "\n", + "Now, to see if it will resample by both solar day and diurnal class. Let's try by masking and making copies with NaN in the masked values:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "ds_day = ds_masked.where(ds_masked[\"solar_class\"] == \"day\").drop_vars([\"solar_class\", \"solar_date\"])\n", + "ds_night = ds_masked.where(ds_masked[\"solar_class\"] == \"night\").drop_vars([\"solar_class\", \"solar_date\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we set the time zone for Madagascar since, to resample by day and night,\n", + "we should observe the local time:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "ds_day = ds_day.assign_coords(valid_time=pd.to_datetime(ds[\"valid_time\"].values).tz_localize(\"UTC\").tz_convert(\"Asia/Kathmandu\"))\n", + "ds_night = ds_night.assign_coords(valid_time=pd.to_datetime(ds[\"valid_time\"].values).tz_localize(\"UTC\").tz_convert(\"Asia/Kathmandu\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now if we can resample by day..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "ds_day_rs = ds_day.resample(valid_time=\"1D\").reduce(np.nanmean)\n", + "ds_night_rs = ds_night.resample(valid_time=\"1D\").reduce(np.nanmean)\n", + "ds_day_rs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Can we successfully convert this to a tiff?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "from era5_sandbox.aggregate import netcdf_to_tiff" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "raster_day = netcdf_to_tiff(ds_day_rs, band=1, variable=\"d2m\")\n", + "raster_night = netcdf_to_tiff(ds_night_rs, band=1, variable=\"d2m\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looks great! These two rasters represent one calendar day of daytime and nighttime values.\n", + "\n", + "### Testing Polygon to Raster Cells & Healthshed Aggregation\n", + "\n", + "The penultimate step of the aggregate pipeline in the original version is\n", + "assigning each datapoint to the respective healthshed. The `vectors` argument\n", + "comes from the healthshed, and represents each geographic polygon on the ground\n", + "that we want to aggregate data to." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "from hydra import initialize, compose" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "try:\n", + " with initialize(version_base=None, config_path=\"../conf\"):\n", + " cfg = compose(config_name='config.yaml')\n", + "except Exception as e:\n", + " print(f\"Error initializing Hydra: {e}\")\n", + " with initialize(version_base=None, config_path=\"conf\"):\n", + " cfg = compose(config_name='config.yaml')\n", + "\n", + "driver = GoogleDriver(json_key_path=here() / cfg.GOOGLE_DRIVE_AUTH_JSON.path)\n", + "drive = driver.get_drive()\n", + "healthsheds = driver.read_healthsheds(\"Nepal_Healthsheds2024.zip\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "res_poly2cell=polygon_to_raster_cells(\n", + " vectors = healthsheds.geometry.values, # the geometries of the shapefile of the regions\n", + " raster=raster_day.data, # the raster data above\n", + " nodata=np.nan, # any intersections with no data, may have to be np.nan\n", + " affine=raster_day.transform, # some math thing need to revise\n", + " all_touched=True, \n", + " verbose=True\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This works fine. Finally, we aggregate to healthsheds:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "from era5_sandbox.aggregate import aggregate_to_healthsheds" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "result_day = aggregate_to_healthsheds(\n", + " res_poly2cell=res_poly2cell,\n", + " raster=raster_day,\n", + " shapes=healthsheds,\n", + " names_column=\"fid\",\n", + " aggregation_func=np.nanmean,\n", + " aggregation_name=\"mean_dewpoint_day\"\n", + ")\n", + "\n", + "result_night = aggregate_to_healthsheds(\n", + " res_poly2cell=res_poly2cell,\n", + " raster=raster_night,\n", + " shapes=healthsheds,\n", + " names_column=\"fid\",\n", + " aggregation_func=np.nanmean,\n", + " aggregation_name=\"mean_dewpoint_night\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below shows the result of aggregating the daytime dewpoint temperature to the healthshed level:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "result_day" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "result_night" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So from one input, we will have two outputs, one for daytime and one for nighttime, and this will have to loop over the bands (ie each day in the month).\n", + "\n", + "# Putting it all together in a `pytask` task\n", + "\n", + "Below we define our `pytask` task to aggregate data to the healthshed level." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| code-fold: show\n", + "#| code-summary: \"Exported source\"\n", + "#| exports: #\n", + "\n", + "job_rows = data_catalog['aggregate']['jobs']['jobs_df'].load()\n", + "\n", + "aggregation_funcs = {\n", + " \"mean\": np.nanmean,\n", + " \"sum\": np.nansum,\n", + " \"max\": np.nanmax,\n", + " \"min\": np.nanmin\n", + "}\n", + "\n", + "for i, job in job_rows.iterrows():\n", + " #print(f\"Job {i+1}: variable={job['variables']}, time={job['time']}, aggregation={job['aggregation_name']}\")\n", + "\n", + " # parse the row into function parameters\n", + " input_file = data_catalog['download']['outputs'][job['input']]\n", + " solar_classification = job['solar_classification']\n", + " variable = job['variables_short']\n", + " time = job['time']\n", + " aggregation_func = aggregation_funcs[job['aggregation_name']]\n", + " aggregation_name = job['aggregation_name']\n", + "\n", + " climate_handler_var = job['climate_handler_var']\n", + " local_tz = job['local_tz']\n", + "\n", + " shapefile = job['shapefile']\n", + " hshd_unique_id = job['hshd_unique_id']\n", + "\n", + " output_file = job['input'] + \"_\" + job['time'] + \"_\" + job['variables_short'] + \"_\" + job['aggregation_name'] + \".parquet\"\n", + "\n", + " @task(id=output_file, name=f\"Aggregate {output_file}\", after=\"task_download_raw_data\")\n", + " def task_aggregate_data_diurnal(\n", + " input_file: Path = data_catalog['download']['outputs'][job['input']], # input data Path from the download task\n", + " aggregation_func: callable = aggregation_func, # the aggregation function\n", + " aggregation_name: str = aggregation_name, # the name of the aggregation function\n", + " time: Literal[\"day\", \"night\"] = time, # whether to aggregate by day or night\n", + " night_direction: Literal[\"before\", \"after\"] = solar_classification, # how to define night\n", + " variable: str = variable, # the variable to aggregate,\n", + " climate_handler_var: Literal[\"instant\", \"accum\"] = climate_handler_var, # whether the variable is instant or accum,\n", + " local_tz: str = local_tz, # the local timezone for resampling\n", + " shapefile: str = shapefile, # the shapefile for the healthsheds,\n", + " hshd_unique_id: str = hshd_unique_id, # the unique id column in the shapefile,\n", + " output_file: str = output_file # the output file name\n", + " ) -> Annotated[Path, data_catalog['aggregate']['outputs'][output_file]]:\n", + " \"\"\"\n", + " Task to aggregate data from a CDSAPI Query to the healthshed\n", + " level. Returns path to parquet file with aggregated data.\n", + " \"\"\"\n", + "\n", + " logger = setup_logger(output_file)\n", + "\n", + " logger.info(f\"Aggregating: {output_file}\")\n", + "\n", + " # check if the string path exists\n", + " # if os.path.exists(output_file):\n", + " # logger.info(f\"File {output_file} already exists. Skipping aggregation.\")\n", + " # return output_file\n", + "\n", + " # get input data\n", + " logger.info(\"Reading input data...\")\n", + " with ClimateDataFileHandler(input_file) as handler:\n", + " ds = xr.open_dataset(handler.get_dataset('instant'))\n", + "\n", + " #get the healthshed shapefile\n", + " logger.info(f\"Reading healthshed shapefile from yaml {here()}...\")\n", + " with open(here() / \"conf\" / \"config.yaml\") as f:\n", + " healthshed_config = yaml.safe_load(f)\n", + "\n", + " key_path = here() / healthshed_config['GOOGLE_DRIVE_AUTH_JSON']['path']\n", + "\n", + " driver = GoogleDriver(json_key_path=key_path)\n", + " drive = driver.get_drive()\n", + " healthsheds = driver.read_healthsheds(shapefile)\n", + "\n", + " # compute the diurnal classification bins\n", + " logger.info(\"Computing diurnal classification bins...\")\n", + " class_bins, class_dts = compute_solar_day_night_class_bins(ds, night_direction)\n", + "\n", + " ds_masked = ds.copy()\n", + "\n", + " # assign classifications\n", + " logger.info(\"Assigning classification bins to dataset...\")\n", + " ds['solar_class'] = (('valid_time', 'latitude', 'longitude'), class_bins)\n", + " ds[\"solar_date\"] = ((\"valid_time\", \"latitude\", \"longitude\"), class_dts)\n", + "\n", + " # mask the dataset to the requested time\n", + " mask = ds[\"solar_class\"] == time\n", + " ds_masked = ds_masked.where(mask)\n", + "\n", + " # set the local timezone\n", + " ds_masked = ds_masked.assign_coords(valid_time=pd.to_datetime(ds[\"valid_time\"].values).tz_localize(\"UTC\").tz_convert(local_tz))\n", + "\n", + " # resample by local date\n", + " logger.info(\"Resampling by local date...\")\n", + " ds_rs = ds_masked.resample(valid_time=\"1D\").reduce(aggregation_func)\n", + "\n", + " # convert to tiff\n", + " logger.info(\"Rasterizing resampled data...\")\n", + " n_bands = ds_rs.dims['valid_time']\n", + "\n", + " # polygon to raster cells for the first band\n", + " logger.info(\"Converting polygons to raster cells...\")\n", + " raster = netcdf_to_tiff(ds_rs, band=1, variable=variable)\n", + " res_poly2cell=polygon_to_raster_cells(\n", + " vectors = healthsheds.geometry.values, # the geometries of the shapefile of the regions\n", + " raster=raster.data, # the raster data above\n", + " nodata=np.nan, # any intersections with no data, may have to be np.nan\n", + " affine=raster.transform, # some math thing need to revise\n", + " all_touched=True, \n", + " verbose=True\n", + " )\n", + "\n", + " result_df = healthsheds[[hshd_unique_id, \"geometry\"]].copy()\n", + "\n", + " # loop over bands and aggregate to healthsheds\n", + " for band in tqdm(range(1, n_bands + 1)):\n", + " logger.info(f\"Processing band {band} of {n_bands}...\")\n", + " \n", + " day = band # band is 1-indexed\n", + "\n", + " day_col = f\"day_{day:02d}\"\n", + "\n", + " # calculate raster for this band\n", + " raster = netcdf_to_tiff(ds_rs, band=band, variable=variable)\n", + "\n", + " # aggregate to healthsheds\n", + " result = aggregate_to_healthsheds(\n", + " res_poly2cell=res_poly2cell,\n", + " raster=raster,\n", + " shapes=healthsheds,\n", + " names_column=hshd_unique_id,\n", + " aggregation_func=aggregation_func,\n", + " aggregation_name=variable\n", + " )\n", + " \n", + " # add band to result dataframe\n", + " result_df[day_col] = result[variable]\n", + "\n", + " # save to parquet\n", + " result_df.to_parquet(f\"{BLD}/{output_file}\")\n", + "\n", + " logger.info(\"Aggregation complete.\")\n", + " \n", + " return Path(f\"{BLD}/{output_file}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That should wrap it up! To test, we can run a single job:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| eval: false\n", + "# runs the last defined job only\n", + "task_aggregate_data_diurnal()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Or we can run the task in `pytask`:\n", + "\n", + "```bash\n", + "pytask build -k \"nepal and 2009\" --dry-run\n", + "```" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/_proc/IMG_740012467778-1.jpeg b/_proc/IMG_740012467778-1.jpeg new file mode 100644 index 0000000..52886eb Binary files /dev/null and b/_proc/IMG_740012467778-1.jpeg differ diff --git a/_proc/_docs/index.html b/_proc/_docs/index.html new file mode 100644 index 0000000..35a8225 --- /dev/null +++ b/_proc/_docs/index.html @@ -0,0 +1,8 @@ + + + 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0.75rem;position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card>hr{margin-right:0;margin-left:0}.card>.list-group{border-top:inherit;border-bottom:inherit}.card>.list-group:first-child{border-top-width:0}.card>.list-group:last-child{border-bottom-width:0}.card>.card-header+.list-group,.card>.list-group+.card-footer{border-top:0}.card-body{flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-card-spacer-y) var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header-tabs{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-bottom:calc(-1*var(--bs-card-cap-padding-y));margin-left:calc(-0.5*var(--bs-card-cap-padding-x));border-bottom:0}.card-header-tabs .nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}}.accordion{--bs-accordion-color: #343a40;--bs-accordion-bg: #fff;--bs-accordion-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, border-radius 0.15s ease;--bs-accordion-border-color: #dee2e6;--bs-accordion-border-width: 1px;--bs-accordion-border-radius: 0.25rem;--bs-accordion-inner-border-radius: calc(0.25rem - 1px);--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: #343a40;--bs-accordion-btn-bg: #fff;--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23343a40'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%2310335b'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #93c0f1;--bs-accordion-btn-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: #10335b;--bs-accordion-active-bg: #d4e6f9}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media(prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:not(:first-of-type){border-top:0}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237db3ee'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%237db3ee'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: rgba(52, 58, 64, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(52, 58, 64, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #2761e3;--bs-pagination-bg: #fff;--bs-pagination-border-width: 1px;--bs-pagination-border-color: #dee2e6;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #1f4eb6;--bs-pagination-hover-bg: #f8f9fa;--bs-pagination-hover-border-color: #dee2e6;--bs-pagination-focus-color: #1f4eb6;--bs-pagination-focus-bg: #e9ecef;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #2780e3;--bs-pagination-active-border-color: #2780e3;--bs-pagination-disabled-color: rgba(52, 58, 64, 0.75);--bs-pagination-disabled-bg: #e9ecef;--bs-pagination-disabled-border-color: #dee2e6;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(1px*-1)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 0 solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:.5rem}}.progress,.progress-stacked{--bs-progress-height: 0.5rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #e9ecef;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #2780e3;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #343a40;--bs-list-group-bg: #fff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(52, 58, 64, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #f8f9fa;--bs-list-group-action-active-color: #343a40;--bs-list-group-action-active-bg: #e9ecef;--bs-list-group-disabled-color: rgba(52, 58, 64, 0.75);--bs-list-group-disabled-bg: #fff;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #2780e3;--bs-list-group-active-border-color: #2780e3;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.5;--bs-btn-close-hover-opacity: 0.75;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(39, 128, 227, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(255, 255, 255, 0.85);--bs-toast-border-width: 1px;--bs-toast-border-color: rgba(0, 0, 0, 0.175);--bs-toast-border-radius: 0.25rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(52, 58, 64, 0.75);--bs-toast-header-bg: rgba(255, 255, 255, 0.85);--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color)}.toast-header .btn-close{margin-right:calc(-0.5*var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: 0.5rem;--bs-modal-color: ;--bs-modal-bg: #fff;--bs-modal-border-color: rgba(0, 0, 0, 0.175);--bs-modal-border-width: 1px;--bs-modal-border-radius: 0.5rem;--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-modal-inner-border-radius: calc(0.5rem - 1px);--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: #dee2e6;--bs-modal-header-border-width: 1px;--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: #dee2e6;--bs-modal-footer-border-width: 1px;position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform .3s ease-out;transform:translate(0, -50px)}@media(prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin)*2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;min-height:calc(100% - var(--bs-modal-margin)*2)}.modal-content{position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;width:100%;color:var(--bs-modal-color);pointer-events:auto;background-color:var(--bs-modal-bg);background-clip:padding-box;border:var(--bs-modal-border-width) solid var(--bs-modal-border-color);outline:0}.modal-backdrop{--bs-backdrop-zindex: 1050;--bs-backdrop-bg: #000;--bs-backdrop-opacity: 0.5;position:fixed;top:0;left:0;z-index:var(--bs-backdrop-zindex);width:100vw;height:100vh;background-color:var(--bs-backdrop-bg)}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:var(--bs-backdrop-opacity)}.modal-header{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-modal-header-padding);border-bottom:var(--bs-modal-header-border-width) solid var(--bs-modal-header-border-color)}.modal-header .btn-close{padding:calc(var(--bs-modal-header-padding-y)*.5) calc(var(--bs-modal-header-padding-x)*.5);margin:calc(-0.5*var(--bs-modal-header-padding-y)) calc(-0.5*var(--bs-modal-header-padding-x)) calc(-0.5*var(--bs-modal-header-padding-y)) auto}.modal-title{margin-bottom:0;line-height:var(--bs-modal-title-line-height)}.modal-body{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-modal-padding)}.modal-footer{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:flex-end;-webkit-justify-content:flex-end;padding:calc(var(--bs-modal-padding) - var(--bs-modal-footer-gap)*.5);background-color:var(--bs-modal-footer-bg);border-top:var(--bs-modal-footer-border-width) solid var(--bs-modal-footer-border-color)}.modal-footer>*{margin:calc(var(--bs-modal-footer-gap)*.5)}@media(min-width: 576px){.modal{--bs-modal-margin: 1.75rem;--bs-modal-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15)}.modal-dialog{max-width:var(--bs-modal-width);margin-right:auto;margin-left:auto}.modal-sm{--bs-modal-width: 300px}}@media(min-width: 992px){.modal-lg,.modal-xl{--bs-modal-width: 800px}}@media(min-width: 1200px){.modal-xl{--bs-modal-width: 1140px}}.modal-fullscreen{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen .modal-content{height:100%;border:0}.modal-fullscreen .modal-body{overflow-y:auto}@media(max-width: 575.98px){.modal-fullscreen-sm-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-sm-down .modal-content{height:100%;border:0}.modal-fullscreen-sm-down .modal-body{overflow-y:auto}}@media(max-width: 767.98px){.modal-fullscreen-md-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-md-down .modal-content{height:100%;border:0}.modal-fullscreen-md-down .modal-body{overflow-y:auto}}@media(max-width: 991.98px){.modal-fullscreen-lg-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-lg-down .modal-content{height:100%;border:0}.modal-fullscreen-lg-down .modal-body{overflow-y:auto}}@media(max-width: 1199.98px){.modal-fullscreen-xl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xl-down .modal-content{height:100%;border:0}.modal-fullscreen-xl-down .modal-body{overflow-y:auto}}@media(max-width: 1399.98px){.modal-fullscreen-xxl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xxl-down .modal-content{height:100%;border:0}.modal-fullscreen-xxl-down .modal-body{overflow-y:auto}}.tooltip{--bs-tooltip-zindex: 1080;--bs-tooltip-max-width: 200px;--bs-tooltip-padding-x: 0.5rem;--bs-tooltip-padding-y: 0.25rem;--bs-tooltip-margin: ;--bs-tooltip-font-size:0.875rem;--bs-tooltip-color: #fff;--bs-tooltip-bg: #000;--bs-tooltip-border-radius: 0.25rem;--bs-tooltip-opacity: 0.9;--bs-tooltip-arrow-width: 0.8rem;--bs-tooltip-arrow-height: 0.4rem;z-index:var(--bs-tooltip-zindex);display:block;margin:var(--bs-tooltip-margin);font-family:"Source Sans Pro",-apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,"Helvetica Neue",Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol";font-style:normal;font-weight:400;line-height:1.5;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;white-space:normal;word-spacing:normal;line-break:auto;font-size:var(--bs-tooltip-font-size);word-wrap:break-word;opacity:0}.tooltip.show{opacity:var(--bs-tooltip-opacity)}.tooltip .tooltip-arrow{display:block;width:var(--bs-tooltip-arrow-width);height:var(--bs-tooltip-arrow-height)}.tooltip .tooltip-arrow::before{position:absolute;content:"";border-color:rgba(0,0,0,0);border-style:solid}.bs-tooltip-top .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=top] .tooltip-arrow{bottom:calc(-1*var(--bs-tooltip-arrow-height))}.bs-tooltip-top .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=top] .tooltip-arrow::before{top:-1px;border-width:var(--bs-tooltip-arrow-height) calc(var(--bs-tooltip-arrow-width)*.5) 0;border-top-color:var(--bs-tooltip-bg)}.bs-tooltip-end .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=right] .tooltip-arrow{left:calc(-1*var(--bs-tooltip-arrow-height));width:var(--bs-tooltip-arrow-height);height:var(--bs-tooltip-arrow-width)}.bs-tooltip-end .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=right] .tooltip-arrow::before{right:-1px;border-width:calc(var(--bs-tooltip-arrow-width)*.5) var(--bs-tooltip-arrow-height) calc(var(--bs-tooltip-arrow-width)*.5) 0;border-right-color:var(--bs-tooltip-bg)}.bs-tooltip-bottom .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=bottom] .tooltip-arrow{top:calc(-1*var(--bs-tooltip-arrow-height))}.bs-tooltip-bottom .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=bottom] .tooltip-arrow::before{bottom:-1px;border-width:0 calc(var(--bs-tooltip-arrow-width)*.5) var(--bs-tooltip-arrow-height);border-bottom-color:var(--bs-tooltip-bg)}.bs-tooltip-start .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=left] .tooltip-arrow{right:calc(-1*var(--bs-tooltip-arrow-height));width:var(--bs-tooltip-arrow-height);height:var(--bs-tooltip-arrow-width)}.bs-tooltip-start .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=left] .tooltip-arrow::before{left:-1px;border-width:calc(var(--bs-tooltip-arrow-width)*.5) 0 calc(var(--bs-tooltip-arrow-width)*.5) var(--bs-tooltip-arrow-height);border-left-color:var(--bs-tooltip-bg)}.tooltip-inner{max-width:var(--bs-tooltip-max-width);padding:var(--bs-tooltip-padding-y) var(--bs-tooltip-padding-x);color:var(--bs-tooltip-color);text-align:center;background-color:var(--bs-tooltip-bg)}.popover{--bs-popover-zindex: 1070;--bs-popover-max-width: 276px;--bs-popover-font-size:0.875rem;--bs-popover-bg: #fff;--bs-popover-border-width: 1px;--bs-popover-border-color: rgba(0, 0, 0, 0.175);--bs-popover-border-radius: 0.5rem;--bs-popover-inner-border-radius: calc(0.5rem - 1px);--bs-popover-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-popover-header-padding-x: 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;--quarto-scss-export-btn-white-space: ;--quarto-scss-export-btn-link-color: #2761e3;--quarto-scss-export-btn-link-hover-color: #1f4eb6;--quarto-scss-export-btn-link-disabled-color: #6c757d;--quarto-scss-export-form-text-font-style: ;--quarto-scss-export-form-text-font-weight: ;--quarto-scss-export-form-text-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-form-label-font-size: ;--quarto-scss-export-form-label-font-style: ;--quarto-scss-export-form-label-font-weight: ;--quarto-scss-export-form-label-color: ;--quarto-scss-export-input-font-family: ;--quarto-scss-export-input-disabled-color: ;--quarto-scss-export-input-disabled-bg: #e9ecef;--quarto-scss-export-input-disabled-border-color: ;--quarto-scss-export-input-color: #343a40;--quarto-scss-export-input-focus-bg: #fff;--quarto-scss-export-input-focus-border-color: #93c0f1;--quarto-scss-export-input-focus-color: #343a40;--quarto-scss-export-input-placeholder-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-input-plaintext-color: #343a40;--quarto-scss-export-form-check-label-color: ;--quarto-scss-export-form-check-transition: ;--quarto-scss-export-form-check-input-bg: #fff;--quarto-scss-export-form-check-input-focus-border: #93c0f1;--quarto-scss-export-form-check-input-checked-color: #fff;--quarto-scss-export-form-check-input-checked-bg-color: #2780e3;--quarto-scss-export-form-check-input-checked-border-color: #2780e3;--quarto-scss-export-form-check-input-indeterminate-color: #fff;--quarto-scss-export-form-check-input-indeterminate-bg-color: #2780e3;--quarto-scss-export-form-check-input-indeterminate-border-color: #2780e3;--quarto-scss-export-form-switch-color: rgba(0, 0, 0, 0.25);--quarto-scss-export-form-switch-focus-color: #93c0f1;--quarto-scss-export-form-switch-checked-color: #fff;--quarto-scss-export-input-group-addon-color: #343a40;--quarto-scss-export-input-group-addon-bg: #f8f9fa;--quarto-scss-export-input-group-addon-border-color: #dee2e6;--quarto-scss-export-form-select-font-family: ;--quarto-scss-export-form-select-color: #343a40;--quarto-scss-export-form-select-bg: #fff;--quarto-scss-export-form-select-disabled-color: ;--quarto-scss-export-form-select-disabled-bg: #e9ecef;--quarto-scss-export-form-select-disabled-border-color: ;--quarto-scss-export-form-select-indicator-color: #343a40;--quarto-scss-export-form-select-border-color: #dee2e6;--quarto-scss-export-form-select-focus-border-color: #93c0f1;--quarto-scss-export-form-range-track-bg: #f8f9fa;--quarto-scss-export-form-range-thumb-bg: #2780e3;--quarto-scss-export-form-range-thumb-active-bg: #bed9f7;--quarto-scss-export-form-range-thumb-disabled-bg: rgba(52, 58, 64, 0.75);--quarto-scss-export-form-file-button-color: #343a40;--quarto-scss-export-form-file-button-bg: #f8f9fa;--quarto-scss-export-form-file-button-hover-bg: #e9ecef;--quarto-scss-export-form-floating-label-disabled-color: #6c757d;--quarto-scss-export-form-feedback-font-style: ;--quarto-scss-export-form-feedback-valid-color: #3fb618;--quarto-scss-export-form-feedback-invalid-color: #ff0039;--quarto-scss-export-form-feedback-icon-valid-color: #3fb618;--quarto-scss-export-form-feedback-icon-invalid-color: #ff0039;--quarto-scss-export-form-valid-color: #3fb618;--quarto-scss-export-form-valid-border-color: #3fb618;--quarto-scss-export-form-invalid-color: #ff0039;--quarto-scss-export-form-invalid-border-color: #ff0039;--quarto-scss-export-nav-link-font-size: ;--quarto-scss-export-nav-link-font-weight: ;--quarto-scss-export-nav-link-color: #2761e3;--quarto-scss-export-nav-link-hover-color: #1f4eb6;--quarto-scss-export-nav-link-disabled-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-nav-tabs-border-color: #dee2e6;--quarto-scss-export-nav-tabs-link-hover-border-color: #e9ecef #e9ecef #dee2e6;--quarto-scss-export-nav-tabs-link-active-color: #000;--quarto-scss-export-nav-tabs-link-active-bg: #fff;--quarto-scss-export-nav-pills-link-active-bg: #2780e3;--quarto-scss-export-nav-pills-link-active-color: #fff;--quarto-scss-export-nav-underline-link-active-color: #000;--quarto-scss-export-navbar-padding-x: ;--quarto-scss-export-navbar-light-contrast: #fff;--quarto-scss-export-navbar-dark-contrast: #fff;--quarto-scss-export-navbar-light-icon-color: rgba(255, 255, 255, 0.75);--quarto-scss-export-navbar-dark-icon-color: rgba(255, 255, 255, 0.75);--quarto-scss-export-dropdown-color: #343a40;--quarto-scss-export-dropdown-bg: #fff;--quarto-scss-export-dropdown-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-divider-bg: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-link-color: #343a40;--quarto-scss-export-dropdown-link-hover-color: #343a40;--quarto-scss-export-dropdown-link-hover-bg: #f8f9fa;--quarto-scss-export-dropdown-link-active-bg: #2780e3;--quarto-scss-export-dropdown-link-active-color: #fff;--quarto-scss-export-dropdown-link-disabled-color: rgba(52, 58, 64, 0.5);--quarto-scss-export-dropdown-header-color: #6c757d;--quarto-scss-export-dropdown-dark-color: #dee2e6;--quarto-scss-export-dropdown-dark-bg: #343a40;--quarto-scss-export-dropdown-dark-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-dark-divider-bg: rgba(0, 0, 0, 0.175);--quarto-scss-export-dropdown-dark-box-shadow: ;--quarto-scss-export-dropdown-dark-link-color: #dee2e6;--quarto-scss-export-dropdown-dark-link-hover-color: #fff;--quarto-scss-export-dropdown-dark-link-hover-bg: rgba(255, 255, 255, 0.15);--quarto-scss-export-dropdown-dark-link-active-color: #fff;--quarto-scss-export-dropdown-dark-link-active-bg: #2780e3;--quarto-scss-export-dropdown-dark-link-disabled-color: #adb5bd;--quarto-scss-export-dropdown-dark-header-color: #adb5bd;--quarto-scss-export-pagination-color: #2761e3;--quarto-scss-export-pagination-bg: #fff;--quarto-scss-export-pagination-border-color: #dee2e6;--quarto-scss-export-pagination-focus-color: #1f4eb6;--quarto-scss-export-pagination-focus-bg: #e9ecef;--quarto-scss-export-pagination-hover-color: #1f4eb6;--quarto-scss-export-pagination-hover-bg: #f8f9fa;--quarto-scss-export-pagination-hover-border-color: #dee2e6;--quarto-scss-export-pagination-active-color: #fff;--quarto-scss-export-pagination-active-bg: #2780e3;--quarto-scss-export-pagination-active-border-color: #2780e3;--quarto-scss-export-pagination-disabled-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-pagination-disabled-bg: #e9ecef;--quarto-scss-export-pagination-disabled-border-color: #dee2e6;--quarto-scss-export-card-title-color: ;--quarto-scss-export-card-subtitle-color: ;--quarto-scss-export-card-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-card-box-shadow: ;--quarto-scss-export-card-cap-color: ;--quarto-scss-export-card-height: ;--quarto-scss-export-card-color: ;--quarto-scss-export-card-bg: #fff;--quarto-scss-export-accordion-color: #343a40;--quarto-scss-export-accordion-bg: #fff;--quarto-scss-export-accordion-border-color: #dee2e6;--quarto-scss-export-accordion-button-color: #343a40;--quarto-scss-export-accordion-button-bg: #fff;--quarto-scss-export-accordion-button-active-bg: #d4e6f9;--quarto-scss-export-accordion-button-active-color: #10335b;--quarto-scss-export-accordion-button-focus-border-color: #93c0f1;--quarto-scss-export-accordion-icon-color: #343a40;--quarto-scss-export-accordion-icon-active-color: #10335b;--quarto-scss-export-tooltip-color: #fff;--quarto-scss-export-tooltip-bg: #000;--quarto-scss-export-tooltip-margin: ;--quarto-scss-export-tooltip-arrow-color: ;--quarto-scss-export-form-feedback-tooltip-line-height: ;--quarto-scss-export-popover-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-popover-header-bg: #e9ecef;--quarto-scss-export-popover-body-color: #343a40;--quarto-scss-export-popover-arrow-color: #fff;--quarto-scss-export-popover-arrow-outer-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-toast-color: ;--quarto-scss-export-toast-background-color: rgba(255, 255, 255, 0.85);--quarto-scss-export-toast-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-toast-header-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-toast-header-background-color: rgba(255, 255, 255, 0.85);--quarto-scss-export-toast-header-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-badge-color: #fff;--quarto-scss-export-modal-content-color: ;--quarto-scss-export-modal-content-bg: #fff;--quarto-scss-export-modal-content-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-modal-backdrop-bg: #000;--quarto-scss-export-modal-header-border-color: #dee2e6;--quarto-scss-export-modal-footer-bg: ;--quarto-scss-export-modal-footer-border-color: #dee2e6;--quarto-scss-export-progress-bg: #e9ecef;--quarto-scss-export-progress-bar-color: #fff;--quarto-scss-export-progress-bar-bg: #2780e3;--quarto-scss-export-list-group-color: #343a40;--quarto-scss-export-list-group-bg: #fff;--quarto-scss-export-list-group-border-color: #dee2e6;--quarto-scss-export-list-group-hover-bg: #f8f9fa;--quarto-scss-export-list-group-active-bg: #2780e3;--quarto-scss-export-list-group-active-color: #fff;--quarto-scss-export-list-group-active-border-color: #2780e3;--quarto-scss-export-list-group-disabled-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-list-group-disabled-bg: #fff;--quarto-scss-export-list-group-action-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-list-group-action-hover-color: #000;--quarto-scss-export-list-group-action-active-color: #343a40;--quarto-scss-export-list-group-action-active-bg: #e9ecef;--quarto-scss-export-thumbnail-bg: #fff;--quarto-scss-export-thumbnail-border-color: #dee2e6;--quarto-scss-export-figure-caption-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-breadcrumb-font-size: ;--quarto-scss-export-breadcrumb-bg: ;--quarto-scss-export-breadcrumb-divider-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-breadcrumb-active-color: rgba(52, 58, 64, 0.75);--quarto-scss-export-breadcrumb-border-radius: ;--quarto-scss-export-carousel-control-color: #fff;--quarto-scss-export-carousel-indicator-active-bg: #fff;--quarto-scss-export-carousel-caption-color: #fff;--quarto-scss-export-carousel-dark-indicator-active-bg: #000;--quarto-scss-export-carousel-dark-caption-color: #000;--quarto-scss-export-btn-close-color: #000;--quarto-scss-export-offcanvas-border-color: rgba(0, 0, 0, 0.175);--quarto-scss-export-offcanvas-bg-color: #fff;--quarto-scss-export-offcanvas-color: #343a40;--quarto-scss-export-offcanvas-backdrop-bg: #000;--quarto-scss-export-code-color-dark: white;--quarto-scss-export-kbd-color: #fff;--quarto-scss-export-kbd-bg: #343a40;--quarto-scss-export-nested-kbd-font-weight: ;--quarto-scss-export-pre-bg: #f8f9fa;--quarto-scss-export-pre-color: #000;--quarto-scss-export-bslib-page-sidebar-title-bg: #2780e3;--quarto-scss-export-bslib-page-sidebar-title-color: #fff;--quarto-scss-export-bslib-sidebar-bg: rgba(var(--bs-emphasis-color-rgb, 0, 0, 0), 0.05);--quarto-scss-export-bslib-sidebar-toggle-bg: rgba(var(--bs-emphasis-color-rgb, 0, 0, 0), 0.1);--quarto-scss-export-sidebar-color: #595959;--quarto-scss-export-sidebar-hover-color: rgba(33, 81, 191, 0.8);--quarto-scss-export-sidebar-disabled-color: rgba(89, 89, 89, 0.75);--quarto-scss-export-valuebox-bg-primary: #5397e9;--quarto-scss-export-valuebox-bg-secondary: #343a40;--quarto-scss-export-valuebox-bg-success: #3aa716;--quarto-scss-export-valuebox-bg-info: rgba(153, 84, 187, 0.7019607843);--quarto-scss-export-valuebox-bg-warning: #fa6400;--quarto-scss-export-valuebox-bg-danger: rgba(255, 0, 57, 0.7019607843);--quarto-scss-export-valuebox-bg-light: #f8f9fa;--quarto-scss-export-valuebox-bg-dark: #343a40;--quarto-scss-export-mermaid-bg-color: #fff;--quarto-scss-export-mermaid-edge-color: #343a40;--quarto-scss-export-mermaid-node-fg-color: #343a40;--quarto-scss-export-mermaid-fg-color: #343a40;--quarto-scss-export-mermaid-fg-color--lighter: #4b545c;--quarto-scss-export-mermaid-fg-color--lightest: #626d78;--quarto-scss-export-mermaid-label-bg-color: #fff;--quarto-scss-export-mermaid-label-fg-color: #2780e3;--quarto-scss-export-mermaid-node-bg-color: rgba(39, 128, 227, 0.1);--quarto-scss-export-code-block-border-left-color: #dee2e6;--quarto-scss-export-callout-color-note: #2780e3;--quarto-scss-export-callout-color-tip: #3fb618;--quarto-scss-export-callout-color-important: #ff0039;--quarto-scss-export-callout-color-caution: #f0ad4e;--quarto-scss-export-callout-color-warning: #ff7518} \ No newline at end of file diff --git a/_proc/_docs/site_libs/bootstrap/bootstrap-icons.css b/_proc/_docs/site_libs/bootstrap/bootstrap-icons.css new file mode 100644 index 0000000..285e444 --- /dev/null +++ b/_proc/_docs/site_libs/bootstrap/bootstrap-icons.css @@ -0,0 +1,2078 @@ +/*! + * Bootstrap Icons v1.11.1 (https://icons.getbootstrap.com/) + * Copyright 2019-2023 The Bootstrap Authors + * Licensed under MIT (https://github.com/twbs/icons/blob/main/LICENSE) + */ + +@font-face { + font-display: block; + font-family: "bootstrap-icons"; + src: +url("./bootstrap-icons.woff?2820a3852bdb9a5832199cc61cec4e65") format("woff"); +} + +.bi::before, +[class^="bi-"]::before, +[class*=" bi-"]::before { + display: inline-block; + font-family: bootstrap-icons !important; + font-style: normal; + font-weight: normal !important; + font-variant: normal; + text-transform: none; + line-height: 1; + vertical-align: -.125em; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; +} + +.bi-123::before { content: "\f67f"; } +.bi-alarm-fill::before { content: "\f101"; } +.bi-alarm::before { content: "\f102"; } +.bi-align-bottom::before { content: "\f103"; } +.bi-align-center::before { content: "\f104"; } +.bi-align-end::before { content: "\f105"; } +.bi-align-middle::before { content: "\f106"; } +.bi-align-start::before { content: "\f107"; } +.bi-align-top::before { content: "\f108"; } +.bi-alt::before { content: "\f109"; } +.bi-app-indicator::before { content: "\f10a"; } +.bi-app::before { content: "\f10b"; } +.bi-archive-fill::before { content: "\f10c"; } +.bi-archive::before { content: "\f10d"; } +.bi-arrow-90deg-down::before { content: "\f10e"; } +.bi-arrow-90deg-left::before { content: "\f10f"; } +.bi-arrow-90deg-right::before { content: "\f110"; } +.bi-arrow-90deg-up::before { content: "\f111"; } +.bi-arrow-bar-down::before { content: "\f112"; } +.bi-arrow-bar-left::before { content: "\f113"; } +.bi-arrow-bar-right::before { content: "\f114"; } +.bi-arrow-bar-up::before { content: "\f115"; } +.bi-arrow-clockwise::before { content: "\f116"; } +.bi-arrow-counterclockwise::before { content: "\f117"; } +.bi-arrow-down-circle-fill::before { content: "\f118"; } +.bi-arrow-down-circle::before { content: "\f119"; } +.bi-arrow-down-left-circle-fill::before { content: "\f11a"; } +.bi-arrow-down-left-circle::before { content: "\f11b"; } +.bi-arrow-down-left-square-fill::before { content: "\f11c"; } +.bi-arrow-down-left-square::before { content: "\f11d"; } +.bi-arrow-down-left::before { content: "\f11e"; } +.bi-arrow-down-right-circle-fill::before { content: "\f11f"; } +.bi-arrow-down-right-circle::before { content: "\f120"; } +.bi-arrow-down-right-square-fill::before { content: "\f121"; } +.bi-arrow-down-right-square::before { content: "\f122"; } +.bi-arrow-down-right::before { content: "\f123"; } +.bi-arrow-down-short::before { content: "\f124"; } +.bi-arrow-down-square-fill::before { content: "\f125"; } +.bi-arrow-down-square::before { content: "\f126"; } +.bi-arrow-down-up::before { content: "\f127"; } +.bi-arrow-down::before { content: "\f128"; } +.bi-arrow-left-circle-fill::before { content: "\f129"; } +.bi-arrow-left-circle::before { content: "\f12a"; } +.bi-arrow-left-right::before { content: "\f12b"; } +.bi-arrow-left-short::before { content: "\f12c"; } +.bi-arrow-left-square-fill::before { content: "\f12d"; } +.bi-arrow-left-square::before { content: "\f12e"; } +.bi-arrow-left::before { content: "\f12f"; } +.bi-arrow-repeat::before { content: "\f130"; } 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this._getItems().indexOf(t)}_setActiveIndicatorElement(t){if(!this._indicatorsElement)return;const e=z.findOne(wt,this._indicatorsElement);e.classList.remove(yt),e.removeAttribute("aria-current");const i=z.findOne(`[data-bs-slide-to="${t}"]`,this._indicatorsElement);i&&(i.classList.add(yt),i.setAttribute("aria-current","true"))}_updateInterval(){const t=this._activeElement||this._getActive();if(!t)return;const e=Number.parseInt(t.getAttribute("data-bs-interval"),10);this._config.interval=e||this._config.defaultInterval}_slide(t,e=null){if(this._isSliding)return;const i=this._getActive(),n=t===at,s=e||b(this._getItems(),i,n,this._config.wrap);if(s===i)return;const o=this._getItemIndex(s),r=e=>N.trigger(this._element,e,{relatedTarget:s,direction:this._orderToDirection(t),from:this._getItemIndex(i),to:o});if(r(dt).defaultPrevented)return;if(!i||!s)return;const a=Boolean(this._interval);this.pause(),this._isSliding=!0,this._setActiveIndicatorElement(o),this._activeElement=s;const 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Object.keys(f).sort((function(t,e){return f[t]-f[e]}))}const si={name:"flip",enabled:!0,phase:"main",fn:function(t){var e=t.state,i=t.options,n=t.name;if(!e.modifiersData[n]._skip){for(var s=i.mainAxis,o=void 0===s||s,r=i.altAxis,a=void 0===r||r,l=i.fallbackPlacements,c=i.padding,h=i.boundary,d=i.rootBoundary,u=i.altBoundary,f=i.flipVariations,p=void 0===f||f,m=i.allowedAutoPlacements,g=e.options.placement,_=be(g),b=l||(_!==g&&p?function(t){if(be(t)===Kt)return[];var e=Ve(t);return[Qe(t),e,Qe(e)]}(g):[Ve(g)]),v=[g].concat(b).reduce((function(t,i){return t.concat(be(i)===Kt?ni(e,{placement:i,boundary:h,rootBoundary:d,padding:c,flipVariations:p,allowedAutoPlacements:m}):i)}),[]),y=e.rects.reference,w=e.rects.popper,A=new Map,E=!0,T=v[0],C=0;C=0,S=L?"width":"height",D=ii(e,{placement:O,boundary:h,rootBoundary:d,altBoundary:u,padding:c}),$=L?k?qt:Vt:k?Rt:zt;y[S]>w[S]&&($=Ve($));var I=Ve($),N=[];if(o&&N.push(D[x]<=0),a&&N.push(D[$]<=0,D[I]<=0),N.every((function(t){return t}))){T=O,E=!1;break}A.set(O,N)}if(E)for(var P=function(t){var e=v.find((function(e){var i=A.get(e);if(i)return i.slice(0,t).every((function(t){return t}))}));if(e)return T=e,"break"},M=p?3:1;M>0&&"break"!==P(M);M--);e.placement!==T&&(e.modifiersData[n]._skip=!0,e.placement=T,e.reset=!0)}},requiresIfExists:["offset"],data:{_skip:!1}};function oi(t,e,i){return void 0===i&&(i={x:0,y:0}),{top:t.top-e.height-i.y,right:t.right-e.width+i.x,bottom:t.bottom-e.height+i.y,left:t.left-e.width-i.x}}function ri(t){return[zt,qt,Rt,Vt].some((function(e){return t[e]>=0}))}const ai={name:"hide",enabled:!0,phase:"main",requiresIfExists:["preventOverflow"],fn:function(t){var 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m?m(Object.assign({},e.rects,{placement:e.placement})):m,O="number"==typeof C?{mainAxis:C,altAxis:C}:Object.assign({mainAxis:0,altAxis:0},C),x=e.modifiersData.offset?e.modifiersData.offset[e.placement]:null,k={x:0,y:0};if(A){if(o){var L,S="y"===y?zt:Vt,D="y"===y?Rt:qt,$="y"===y?"height":"width",I=A[y],N=I+g[S],P=I-g[D],M=f?-T[$]/2:0,j=b===Xt?E[$]:T[$],F=b===Xt?-T[$]:-E[$],H=e.elements.arrow,W=f&&H?Ce(H):{width:0,height:0},B=e.modifiersData["arrow#persistent"]?e.modifiersData["arrow#persistent"].padding:{top:0,right:0,bottom:0,left:0},z=B[S],R=B[D],q=Ne(0,E[$],W[$]),V=v?E[$]/2-M-q-z-O.mainAxis:j-q-z-O.mainAxis,K=v?-E[$]/2+M+q+R+O.mainAxis:F+q+R+O.mainAxis,Q=e.elements.arrow&&$e(e.elements.arrow),X=Q?"y"===y?Q.clientTop||0:Q.clientLeft||0:0,Y=null!=(L=null==x?void 0:x[y])?L:0,U=I+K-Y,G=Ne(f?ye(N,I+V-Y-X):N,I,f?ve(P,U):P);A[y]=G,k[y]=G-I}if(a){var J,Z="x"===y?zt:Vt,tt="x"===y?Rt:qt,et=A[w],it="y"===w?"height":"width",nt=et+g[Z],st=et-g[tt],ot=-1!==[zt,Vt].indexOf(_),rt=null!=(J=null==x?void 0:x[w])?J:0,at=ot?nt:et-E[it]-T[it]-rt+O.altAxis,lt=ot?et+E[it]+T[it]-rt-O.altAxis:st,ct=f&&ot?function(t,e,i){var n=Ne(t,e,i);return n>i?i:n}(at,et,lt):Ne(f?at:nt,et,f?lt:st);A[w]=ct,k[w]=ct-et}e.modifiersData[n]=k}},requiresIfExists:["offset"]};function di(t,e,i){void 0===i&&(i=!1);var n,s,o=me(e),r=me(e)&&function(t){var e=t.getBoundingClientRect(),i=we(e.width)/t.offsetWidth||1,n=we(e.height)/t.offsetHeight||1;return 1!==i||1!==n}(e),a=Le(e),l=Te(t,r,i),c={scrollLeft:0,scrollTop:0},h={x:0,y:0};return(o||!o&&!i)&&(("body"!==ue(e)||Ue(a))&&(c=(n=e)!==fe(n)&&me(n)?{scrollLeft:(s=n).scrollLeft,scrollTop:s.scrollTop}:Xe(n)),me(e)?((h=Te(e,!0)).x+=e.clientLeft,h.y+=e.clientTop):a&&(h.x=Ye(a))),{x:l.left+c.scrollLeft-h.x,y:l.top+c.scrollTop-h.y,width:l.width,height:l.height}}function ui(t){var e=new Map,i=new Set,n=[];function s(t){i.add(t.name),[].concat(t.requires||[],t.requiresIfExists||[]).forEach((function(t){if(!i.has(t)){var n=e.get(t);n&&s(n)}})),n.push(t)}return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||s(t)})),n}var fi={placement:"bottom",modifiers:[],strategy:"absolute"};function pi(){for(var t=arguments.length,e=new Array(t),i=0;iNumber.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(F.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,...g(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=z.find(".dropdown-menu 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e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ei,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ii)?this:z.prev(this,Ii)[0]||z.next(this,Ii)[0]||z.findOne(Ii,t.delegateTarget.parentNode),o=qi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}N.on(document,Si,Ii,qi.dataApiKeydownHandler),N.on(document,Si,Pi,qi.dataApiKeydownHandler),N.on(document,Li,qi.clearMenus),N.on(document,Di,qi.clearMenus),N.on(document,Li,Ii,(function(t){t.preventDefault(),qi.getOrCreateInstance(this).toggle()})),m(qi);const Vi="backdrop",Ki="show",Qi=`mousedown.bs.${Vi}`,Xi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Yi={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Ui extends H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}N.on(document,yn,'[data-bs-toggle="modal"]',(function(t){const e=z.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),N.one(e,pn,(t=>{t.defaultPrevented||N.one(e,fn,(()=>{a(this)&&this.focus()}))}));const i=z.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),R(On),m(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Wn=`click${xn}${kn}`,Bn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"
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")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),N.off(this._element.closest(is),ns,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=N.trigger(this._element,this.constructor.eventName("show")),e=(c(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),N.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ts),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Jn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ts)}_isShown(){return this.tip&&this.tip.classList.contains(es)}_createPopper(t){const e=g(this._config.placement,[this,t,this._element]),i=rs[e.toUpperCase()];return bi(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return g(t,[this._element])}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,...g(this._config.popperConfig,[e])}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)N.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if("manual"!==e){const t=e===ss?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i=e===ss?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");N.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?os:ss]=!0,e._enter()})),N.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?os:ss]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},N.on(this._element.closest(is),ns,this._hideModalHandler)}_fixTitle(){const t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=F.getDataAttributes(this._element);for(const t of Object.keys(e))Zn.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const[e,i]of Object.entries(this._config))this.constructor.Default[e]!==i&&(t[e]=i);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=cs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}m(cs);const hs={...cs.Default,content:"",offset:[0,8],placement:"right",template:'',trigger:"click"},ds={...cs.DefaultType,content:"(null|string|element|function)"};class us extends cs{static get Default(){return hs}static get DefaultType(){return ds}static get 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e=this._observableSections.get(t.target.hash);if(e){t.preventDefault();const i=this._rootElement||window,n=e.offsetTop-this._element.offsetTop;if(i.scrollTo)return void i.scrollTo({top:n,behavior:"smooth"});i.scrollTop=n}})))}_getNewObserver(){const t={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver((t=>this._observerCallback(t)),t)}_observerCallback(t){const e=t=>this._targetLinks.get(`#${t.target.id}`),i=t=>{this._previousScrollData.visibleEntryTop=t.target.offsetTop,this._process(e(t))},n=(this._rootElement||document.documentElement).scrollTop,s=n>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=n;for(const o of t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=z.find(bs,this._config.target);for(const e of t){if(!e.hash||l(e))continue;const t=z.findOne(decodeURI(e.hash),this._element);a(t)&&(this._targetLinks.set(decodeURI(e.hash),e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add(_s),this._activateParents(t),N.trigger(this._element,ps,{relatedTarget:t}))}_activateParents(t){if(t.classList.contains("dropdown-item"))z.findOne(".dropdown-toggle",t.closest(".dropdown")).classList.add(_s);else for(const e of z.parents(t,".nav, .list-group"))for(const t of z.prev(e,ys))t.classList.add(_s)}_clearActiveClass(t){t.classList.remove(_s);const e=z.find(`${bs}.${_s}`,t);for(const t of e)t.classList.remove(_s)}static jQueryInterface(t){return this.each((function(){const e=Es.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}N.on(window,gs,(()=>{for(const t of z.find('[data-bs-spy="scroll"]'))Es.getOrCreateInstance(t)})),m(Es);const Ts=".bs.tab",Cs=`hide${Ts}`,Os=`hidden${Ts}`,xs=`show${Ts}`,ks=`shown${Ts}`,Ls=`click${Ts}`,Ss=`keydown${Ts}`,Ds=`load${Ts}`,$s="ArrowLeft",Is="ArrowRight",Ns="ArrowUp",Ps="ArrowDown",Ms="Home",js="End",Fs="active",Hs="fade",Ws="show",Bs=":not(.dropdown-toggle)",zs='[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',Rs=`.nav-link${Bs}, .list-group-item${Bs}, [role="tab"]${Bs}, ${zs}`,qs=`.${Fs}[data-bs-toggle="tab"], .${Fs}[data-bs-toggle="pill"], .${Fs}[data-bs-toggle="list"]`;class Vs extends W{constructor(t){super(t),this._parent=this._element.closest('.list-group, .nav, [role="tablist"]'),this._parent&&(this._setInitialAttributes(this._parent,this._getChildren()),N.on(this._element,Ss,(t=>this._keydown(t))))}static get NAME(){return"tab"}show(){const t=this._element;if(this._elemIsActive(t))return;const e=this._getActiveElem(),i=e?N.trigger(e,Cs,{relatedTarget:t}):null;N.trigger(t,xs,{relatedTarget:e}).defaultPrevented||i&&i.defaultPrevented||(this._deactivate(e,t),this._activate(t,e))}_activate(t,e){t&&(t.classList.add(Fs),this._activate(z.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.removeAttribute("tabindex"),t.setAttribute("aria-selected",!0),this._toggleDropDown(t,!0),N.trigger(t,ks,{relatedTarget:e})):t.classList.add(Ws)}),t,t.classList.contains(Hs)))}_deactivate(t,e){t&&(t.classList.remove(Fs),t.blur(),this._deactivate(z.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.setAttribute("aria-selected",!1),t.setAttribute("tabindex","-1"),this._toggleDropDown(t,!1),N.trigger(t,Os,{relatedTarget:e})):t.classList.remove(Ws)}),t,t.classList.contains(Hs)))}_keydown(t){if(![$s,Is,Ns,Ps,Ms,js].includes(t.key))return;t.stopPropagation(),t.preventDefault();const e=this._getChildren().filter((t=>!l(t)));let i;if([Ms,js].includes(t.key))i=e[t.key===Ms?0:e.length-1];else{const n=[Is,Ps].includes(t.key);i=b(e,t.target,n,!0)}i&&(i.focus({preventScroll:!0}),Vs.getOrCreateInstance(i).show())}_getChildren(){return z.find(Rs,this._parent)}_getActiveElem(){return this._getChildren().find((t=>this._elemIsActive(t)))||null}_setInitialAttributes(t,e){this._setAttributeIfNotExists(t,"role","tablist");for(const t of e)this._setInitialAttributesOnChild(t)}_setInitialAttributesOnChild(t){t=this._getInnerElement(t);const e=this._elemIsActive(t),i=this._getOuterElement(t);t.setAttribute("aria-selected",e),i!==t&&this._setAttributeIfNotExists(i,"role","presentation"),e||t.setAttribute("tabindex","-1"),this._setAttributeIfNotExists(t,"role","tab"),this._setInitialAttributesOnTargetPanel(t)}_setInitialAttributesOnTargetPanel(t){const 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t=e.state,n=e.name,r=t.rects.reference,o=t.rects.popper,i=t.modifiersData.preventOverflow,a=J(t,{elementContext:"reference"}),s=J(t,{altBoundary:!0}),f=ve(a,r),c=ve(s,o,i),p=ye(f),u=ye(c);t.modifiersData[n]={referenceClippingOffsets:f,popperEscapeOffsets:c,isReferenceHidden:p,hasPopperEscaped:u},t.attributes.popper=Object.assign({},t.attributes.popper,{"data-popper-reference-hidden":p,"data-popper-escaped":u})}},be=Z({defaultModifiers:[ee,te,oe,ie]}),xe=[ee,te,oe,ie,ae,le,he,me,ge],we=Z({defaultModifiers:xe});e.applyStyles=ie,e.arrow=me,e.computeStyles=oe,e.createPopper=we,e.createPopperLite=be,e.defaultModifiers=xe,e.detectOverflow=J,e.eventListeners=ee,e.flip=le,e.hide=ge,e.offset=ae,e.popperGenerator=Z,e.popperOffsets=te,e.preventOverflow=he,Object.defineProperty(e,"__esModule",{value:!0})})); + diff --git a/_proc/_docs/site_libs/quarto-html/quarto-syntax-highlighting-2f5df379a58b258e96c21c0638c20c03.css b/_proc/_docs/site_libs/quarto-html/quarto-syntax-highlighting-2f5df379a58b258e96c21c0638c20c03.css new file mode 100644 index 0000000..48bb62a --- /dev/null +++ b/_proc/_docs/site_libs/quarto-html/quarto-syntax-highlighting-2f5df379a58b258e96c21c0638c20c03.css @@ -0,0 +1,205 @@ +/* quarto syntax highlight colors */ +:root { + --quarto-hl-ot-color: #003B4F; + --quarto-hl-at-color: #657422; + --quarto-hl-ss-color: #20794D; + --quarto-hl-an-color: #5E5E5E; + --quarto-hl-fu-color: #4758AB; + --quarto-hl-st-color: #20794D; + --quarto-hl-cf-color: #003B4F; + --quarto-hl-op-color: #5E5E5E; + --quarto-hl-er-color: #AD0000; + --quarto-hl-bn-color: #AD0000; + --quarto-hl-al-color: #AD0000; + --quarto-hl-va-color: #111111; + --quarto-hl-bu-color: inherit; + --quarto-hl-ex-color: inherit; + --quarto-hl-pp-color: #AD0000; + --quarto-hl-in-color: #5E5E5E; + --quarto-hl-vs-color: #20794D; + --quarto-hl-wa-color: #5E5E5E; + --quarto-hl-do-color: #5E5E5E; + --quarto-hl-im-color: #00769E; + --quarto-hl-ch-color: #20794D; + --quarto-hl-dt-color: #AD0000; + --quarto-hl-fl-color: #AD0000; + --quarto-hl-co-color: #5E5E5E; + --quarto-hl-cv-color: #5E5E5E; + --quarto-hl-cn-color: #8f5902; + --quarto-hl-sc-color: #5E5E5E; + --quarto-hl-dv-color: #AD0000; + --quarto-hl-kw-color: #003B4F; +} + +/* other quarto variables */ +:root { + --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; +} + +pre > code.sourceCode > span { + color: #003B4F; +} + +code span { + color: #003B4F; +} + +code.sourceCode > span { + color: #003B4F; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #003B4F; +} + +code span.ot { + color: #003B4F; + font-style: inherit; +} + +code span.at { + color: #657422; + font-style: inherit; +} + +code span.ss { + color: #20794D; + font-style: inherit; +} + +code span.an { + color: #5E5E5E; + font-style: inherit; +} + +code span.fu { + color: #4758AB; + font-style: inherit; +} + +code span.st { + color: #20794D; + font-style: inherit; +} + +code span.cf { + color: #003B4F; + font-weight: bold; + font-style: inherit; +} + +code span.op { + color: #5E5E5E; + font-style: inherit; +} + +code span.er { + color: #AD0000; + font-style: inherit; +} + +code span.bn { + color: #AD0000; + font-style: inherit; +} + +code span.al { + color: #AD0000; + font-style: inherit; +} + +code span.va { + color: #111111; + font-style: inherit; +} + +code span.bu { + font-style: inherit; +} + +code span.ex { + font-style: inherit; +} + +code span.pp { + color: #AD0000; + font-style: inherit; +} + +code span.in { + color: #5E5E5E; + font-style: inherit; +} + +code span.vs { + color: #20794D; + font-style: inherit; +} + +code span.wa { + color: #5E5E5E; + font-style: italic; +} + +code span.do { + color: #5E5E5E; + font-style: italic; +} + +code span.im { + color: #00769E; + font-style: inherit; +} + +code span.ch { + color: #20794D; + font-style: inherit; +} + +code span.dt { + color: #AD0000; + font-style: inherit; +} + +code span.fl { + color: #AD0000; + font-style: inherit; +} + +code span.co { + color: #5E5E5E; + font-style: inherit; +} + +code span.cv { + color: #5E5E5E; + font-style: italic; +} + +code span.cn { + color: #8f5902; + font-style: inherit; +} + +code span.sc { + color: #5E5E5E; + font-style: inherit; +} + +code span.dv { + color: #AD0000; + font-style: inherit; +} + +code span.kw { + color: #003B4F; + font-weight: bold; + font-style: inherit; +} + +.prevent-inlining { + content: " { + // Find any conflicting margin elements and add margins to the + // top to prevent overlap + const marginChildren = window.document.querySelectorAll( + ".column-margin.column-container > *, .margin-caption, .aside" + ); + + let lastBottom = 0; + for (const marginChild of marginChildren) { + if (marginChild.offsetParent !== null) { + // clear the top margin so we recompute it + marginChild.style.marginTop = null; + const top = marginChild.getBoundingClientRect().top + window.scrollY; + if (top < lastBottom) { + const marginChildStyle = window.getComputedStyle(marginChild); + const marginBottom = parseFloat(marginChildStyle["marginBottom"]); + const margin = lastBottom - top + marginBottom; + marginChild.style.marginTop = `${margin}px`; + } + const styles = window.getComputedStyle(marginChild); + const marginTop = parseFloat(styles["marginTop"]); + lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; + } + } +}; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Recompute the position of margin elements anytime the body size changes + if (window.ResizeObserver) { + const resizeObserver = new window.ResizeObserver( + throttle(() => { + layoutMarginEls(); + if ( + window.document.body.getBoundingClientRect().width < 990 && + isReaderMode() + ) { + quartoToggleReader(); + } + }, 50) + ); + resizeObserver.observe(window.document.body); + } + + const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]'); + const sidebarEl = window.document.getElementById("quarto-sidebar"); + const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left"); + const marginSidebarEl = window.document.getElementById( + "quarto-margin-sidebar" + ); + // function to determine whether the element has a previous sibling that is active + const prevSiblingIsActiveLink = (el) => { + const sibling = el.previousElementSibling; + if (sibling && sibling.tagName === "A") { + return sibling.classList.contains("active"); + } else { + return false; + } + }; + + // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior) + function fireSlideEnter(e) { + const event = window.document.createEvent("Event"); + event.initEvent("slideenter", true, true); + window.document.dispatchEvent(event); + } + const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); + tabs.forEach((tab) => { + tab.addEventListener("shown.bs.tab", fireSlideEnter); + }); + + // fire slideEnter for tabby tab activations (for htmlwidget resize behavior) + document.addEventListener("tabby", fireSlideEnter, false); + + // Track scrolling and mark TOC links as active + // get table of contents and sidebar (bail if we don't have at least one) + const tocLinks = tocEl + ? [...tocEl.querySelectorAll("a[data-scroll-target]")] + : []; + const makeActive = (link) => tocLinks[link].classList.add("active"); + const removeActive = (link) => tocLinks[link].classList.remove("active"); + const removeAllActive = () => + [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); + + // activate the anchor for a section associated with this TOC entry + tocLinks.forEach((link) => { + link.addEventListener("click", () => { + if (link.href.indexOf("#") !== -1) { + const anchor = link.href.split("#")[1]; + const heading = window.document.querySelector( + `[data-anchor-id="${anchor}"]` + ); + if (heading) { + // Add the class + heading.classList.add("reveal-anchorjs-link"); + + // function to show the anchor + const handleMouseout = () => { + heading.classList.remove("reveal-anchorjs-link"); + heading.removeEventListener("mouseout", handleMouseout); + }; + + // add a function to clear the anchor when the user mouses out of it + heading.addEventListener("mouseout", handleMouseout); + } + } + }); + }); + + const sections = tocLinks.map((link) => { + const target = link.getAttribute("data-scroll-target"); + if (target.startsWith("#")) { + return window.document.getElementById(decodeURI(`${target.slice(1)}`)); + } else { + return window.document.querySelector(decodeURI(`${target}`)); + } + }); + + const sectionMargin = 200; + let currentActive = 0; + // track whether we've initialized state the first time + let init = false; + + const updateActiveLink = () => { + // The index from bottom to top (e.g. reversed list) + let sectionIndex = -1; + if ( + window.innerHeight + window.pageYOffset >= + window.document.body.offsetHeight + ) { + // This is the no-scroll case where last section should be the active one + sectionIndex = 0; + } else { + // This finds the last section visible on screen that should be made active + sectionIndex = [...sections].reverse().findIndex((section) => { + if (section) { + return window.pageYOffset >= section.offsetTop - sectionMargin; + } else { + return false; + } + }); + } + if (sectionIndex > -1) { + const current = sections.length - sectionIndex - 1; + if (current !== currentActive) { + removeAllActive(); + currentActive = current; + makeActive(current); + if (init) { + window.dispatchEvent(sectionChanged); + } + init = true; + } + } + }; + + const inHiddenRegion = (top, bottom, hiddenRegions) => { + for (const region of hiddenRegions) { + if (top <= region.bottom && bottom >= region.top) { + return true; + } + } + return false; + }; + + const categorySelector = "header.quarto-title-block .quarto-category"; + const activateCategories = (href) => { + // Find any categories + // Surround them with a link pointing back to: + // #category=Authoring + try { + const categoryEls = window.document.querySelectorAll(categorySelector); + for (const categoryEl of categoryEls) { + const categoryText = categoryEl.textContent; + if (categoryText) { + const link = `${href}#category=${encodeURIComponent(categoryText)}`; + const linkEl = window.document.createElement("a"); + linkEl.setAttribute("href", link); + for (const child of categoryEl.childNodes) { + linkEl.append(child); + } + categoryEl.appendChild(linkEl); + } + } + } catch { + // Ignore errors + } + }; + function hasTitleCategories() { + return window.document.querySelector(categorySelector) !== null; + } + + function offsetRelativeUrl(url) { + const offset = getMeta("quarto:offset"); + return offset ? offset + url : url; + } + + function offsetAbsoluteUrl(url) { + const offset = getMeta("quarto:offset"); + const baseUrl = new URL(offset, window.location); + + const projRelativeUrl = url.replace(baseUrl, ""); + if (projRelativeUrl.startsWith("/")) { + return projRelativeUrl; + } else { + return "/" + projRelativeUrl; + } + } + + // read a meta tag value + function getMeta(metaName) { + const metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; + } + + async function findAndActivateCategories() { + // Categories search with listing only use path without query + const currentPagePath = offsetAbsoluteUrl( + window.location.origin + window.location.pathname + ); + const response = await fetch(offsetRelativeUrl("listings.json")); + if (response.status == 200) { + return response.json().then(function (listingPaths) { + const listingHrefs = []; + for (const listingPath of listingPaths) { + const pathWithoutLeadingSlash = listingPath.listing.substring(1); + for (const item of listingPath.items) { + if ( + item === currentPagePath || + item === currentPagePath + "index.html" + ) { + // Resolve this path against the offset to be sure + // we already are using the correct path to the listing + // (this adjusts the listing urls to be rooted against + // whatever root the page is actually running against) + const relative = offsetRelativeUrl(pathWithoutLeadingSlash); + const baseUrl = window.location; + const resolvedPath = new URL(relative, baseUrl); + listingHrefs.push(resolvedPath.pathname); + break; + } + } + } + + // Look up the tree for a nearby linting and use that if we find one + const nearestListing = findNearestParentListing( + offsetAbsoluteUrl(window.location.pathname), + listingHrefs + ); + if (nearestListing) { + activateCategories(nearestListing); + } else { + // See if the referrer is a listing page for this item + const referredRelativePath = offsetAbsoluteUrl(document.referrer); + const referrerListing = listingHrefs.find((listingHref) => { + const isListingReferrer = + listingHref === referredRelativePath || + listingHref === referredRelativePath + "index.html"; + return isListingReferrer; + }); + + if (referrerListing) { + // Try to use the referrer if possible + activateCategories(referrerListing); + } else if (listingHrefs.length > 0) { + // Otherwise, just fall back to the first listing + activateCategories(listingHrefs[0]); + } + } + }); + } + } + if (hasTitleCategories()) { + findAndActivateCategories(); + } + + const findNearestParentListing = (href, listingHrefs) => { + if (!href || !listingHrefs) { + return undefined; + } + // Look up the tree for a nearby linting and use that if we find one + const relativeParts = href.substring(1).split("/"); + while (relativeParts.length > 0) { + const path = relativeParts.join("/"); + for (const listingHref of listingHrefs) { + if (listingHref.startsWith(path)) { + return listingHref; + } + } + relativeParts.pop(); + } + + return undefined; + }; + + const manageSidebarVisiblity = (el, placeholderDescriptor) => { + let isVisible = true; + let elRect; + + return (hiddenRegions) => { + if (el === null) { + return; + } + + // Find the last element of the TOC + const lastChildEl = el.lastElementChild; + + if (lastChildEl) { + // Converts the sidebar to a menu + const convertToMenu = () => { + for (const child of el.children) { + child.style.opacity = 0; + child.style.overflow = "hidden"; + child.style.pointerEvents = "none"; + } + + nexttick(() => { + const toggleContainer = window.document.createElement("div"); + toggleContainer.style.width = "100%"; + toggleContainer.classList.add("zindex-over-content"); + toggleContainer.classList.add("quarto-sidebar-toggle"); + toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom + toggleContainer.id = placeholderDescriptor.id; + toggleContainer.style.position = "fixed"; + + const toggleIcon = window.document.createElement("i"); + toggleIcon.classList.add("quarto-sidebar-toggle-icon"); + toggleIcon.classList.add("bi"); + toggleIcon.classList.add("bi-caret-down-fill"); + + const toggleTitle = window.document.createElement("div"); + const titleEl = window.document.body.querySelector( + placeholderDescriptor.titleSelector + ); + if (titleEl) { + toggleTitle.append( + titleEl.textContent || titleEl.innerText, + toggleIcon + ); + } + toggleTitle.classList.add("zindex-over-content"); + toggleTitle.classList.add("quarto-sidebar-toggle-title"); + toggleContainer.append(toggleTitle); + + const toggleContents = window.document.createElement("div"); + toggleContents.classList = el.classList; + toggleContents.classList.add("zindex-over-content"); + toggleContents.classList.add("quarto-sidebar-toggle-contents"); + for (const child of el.children) { + if (child.id === "toc-title") { + continue; + } + + const clone = child.cloneNode(true); + clone.style.opacity = 1; + clone.style.pointerEvents = null; + clone.style.display = null; + toggleContents.append(clone); + } + toggleContents.style.height = "0px"; + const positionToggle = () => { + // position the element (top left of parent, same width as parent) + if (!elRect) { + elRect = el.getBoundingClientRect(); + } + toggleContainer.style.left = `${elRect.left}px`; + toggleContainer.style.top = `${elRect.top}px`; + toggleContainer.style.width = `${elRect.width}px`; + }; + positionToggle(); + + toggleContainer.append(toggleContents); + el.parentElement.prepend(toggleContainer); + + // Process clicks + let tocShowing = false; + // Allow the caller to control whether this is dismissed + // when it is clicked (e.g. sidebar navigation supports + // opening and closing the nav tree, so don't dismiss on click) + const clickEl = placeholderDescriptor.dismissOnClick + ? toggleContainer + : toggleTitle; + + const closeToggle = () => { + if (tocShowing) { + toggleContainer.classList.remove("expanded"); + toggleContents.style.height = "0px"; + tocShowing = false; + } + }; + + // Get rid of any expanded toggle if the user scrolls + window.document.addEventListener( + "scroll", + throttle(() => { + closeToggle(); + }, 50) + ); + + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + positionToggle(); + }, 50) + ); + + window.addEventListener("quarto-hrChanged", () => { + elRect = undefined; + }); + + // Process the click + clickEl.onclick = () => { + if (!tocShowing) { + toggleContainer.classList.add("expanded"); + toggleContents.style.height = null; + tocShowing = true; + } else { + closeToggle(); + } + }; + }); + }; + + // Converts a sidebar from a menu back to a sidebar + const convertToSidebar = () => { + for (const child of el.children) { + child.style.opacity = 1; + child.style.overflow = null; + child.style.pointerEvents = null; + } + + const placeholderEl = window.document.getElementById( + placeholderDescriptor.id + ); + if (placeholderEl) { + placeholderEl.remove(); + } + + el.classList.remove("rollup"); + }; + + if (isReaderMode()) { + convertToMenu(); + isVisible = false; + } else { + // Find the top and bottom o the element that is being managed + const elTop = el.offsetTop; + const elBottom = + elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; + + if (!isVisible) { + // If the element is current not visible reveal if there are + // no conflicts with overlay regions + if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToSidebar(); + isVisible = true; + } + } else { + // If the element is visible, hide it if it conflicts with overlay regions + // and insert a placeholder toggle (or if we're in reader mode) + if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToMenu(); + isVisible = false; + } + } + } + } + }; + }; + + const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); + for (const tabEl of tabEls) { + const id = tabEl.getAttribute("data-bs-target"); + if (id) { + const columnEl = document.querySelector( + `${id} .column-margin, .tabset-margin-content` + ); + if (columnEl) + tabEl.addEventListener("shown.bs.tab", function (event) { + const el = event.srcElement; + if (el) { + const visibleCls = `${el.id}-margin-content`; + // walk up until we find a parent tabset + let panelTabsetEl = el.parentElement; + while (panelTabsetEl) { + if (panelTabsetEl.classList.contains("panel-tabset")) { + break; + } + panelTabsetEl = panelTabsetEl.parentElement; + } + + if (panelTabsetEl) { + const prevSib = panelTabsetEl.previousElementSibling; + if ( + prevSib && + prevSib.classList.contains("tabset-margin-container") + ) { + const childNodes = prevSib.querySelectorAll( + ".tabset-margin-content" + ); + for (const childEl of childNodes) { + if (childEl.classList.contains(visibleCls)) { + childEl.classList.remove("collapse"); + } else { + childEl.classList.add("collapse"); + } + } + } + } + } + + layoutMarginEls(); + }); + } + } + + // Manage the visibility of the toc and the sidebar + const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { + id: "quarto-toc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { + id: "quarto-sidebarnav-toggle", + titleSelector: ".title", + dismissOnClick: false, + }); + let tocLeftScrollVisibility; + if (leftTocEl) { + tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { + id: "quarto-lefttoc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + } + + // Find the first element that uses formatting in special columns + const conflictingEls = window.document.body.querySelectorAll( + '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' + ); + + // Filter all the possibly conflicting elements into ones + // the do conflict on the left or ride side + const arrConflictingEls = Array.from(conflictingEls); + const leftSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return false; + } + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + className.startsWith("column-") && + !className.endsWith("right") && + !className.endsWith("container") && + className !== "column-margin" + ); + }); + }); + const rightSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return true; + } + + const hasMarginCaption = Array.from(el.classList).find((className) => { + return className == "margin-caption"; + }); + if (hasMarginCaption) { + return true; + } + + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + !className.endsWith("container") && + className.startsWith("column-") && + !className.endsWith("left") + ); + }); + }); + + const kOverlapPaddingSize = 10; + function toRegions(els) { + return els.map((el) => { + const boundRect = el.getBoundingClientRect(); + const top = + boundRect.top + + document.documentElement.scrollTop - + kOverlapPaddingSize; + return { + top, + bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, + }; + }); + } + + let hasObserved = false; + const visibleItemObserver = (els) => { + let visibleElements = [...els]; + const intersectionObserver = new IntersectionObserver( + (entries, _observer) => { + entries.forEach((entry) => { + if (entry.isIntersecting) { + if (visibleElements.indexOf(entry.target) === -1) { + visibleElements.push(entry.target); + } + } else { + visibleElements = visibleElements.filter((visibleEntry) => { + return visibleEntry !== entry; + }); + } + }); + + if (!hasObserved) { + hideOverlappedSidebars(); + } + hasObserved = true; + }, + {} + ); + els.forEach((el) => { + intersectionObserver.observe(el); + }); + + return { + getVisibleEntries: () => { + return visibleElements; + }, + }; + }; + + const rightElementObserver = visibleItemObserver(rightSideConflictEls); + const leftElementObserver = visibleItemObserver(leftSideConflictEls); + + const hideOverlappedSidebars = () => { + marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); + sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); + if (tocLeftScrollVisibility) { + tocLeftScrollVisibility( + toRegions(leftElementObserver.getVisibleEntries()) + ); + } + }; + + window.quartoToggleReader = () => { + // Applies a slow class (or removes it) + // to update the transition speed + const slowTransition = (slow) => { + const manageTransition = (id, slow) => { + const el = document.getElementById(id); + if (el) { + if (slow) { + el.classList.add("slow"); + } else { + el.classList.remove("slow"); + } + } + }; + + manageTransition("TOC", slow); + manageTransition("quarto-sidebar", slow); + }; + const readerMode = !isReaderMode(); + setReaderModeValue(readerMode); + + // If we're entering reader mode, slow the transition + if (readerMode) { + slowTransition(readerMode); + } + highlightReaderToggle(readerMode); + hideOverlappedSidebars(); + + // If we're exiting reader mode, restore the non-slow transition + if (!readerMode) { + slowTransition(!readerMode); + } + }; + + const highlightReaderToggle = (readerMode) => { + const els = document.querySelectorAll(".quarto-reader-toggle"); + if (els) { + els.forEach((el) => { + if (readerMode) { + el.classList.add("reader"); + } else { + el.classList.remove("reader"); + } + }); + } + }; + + const setReaderModeValue = (val) => { + if (window.location.protocol !== "file:") { + window.localStorage.setItem("quarto-reader-mode", val); + } else { + localReaderMode = val; + } + }; + + const isReaderMode = () => { + if (window.location.protocol !== "file:") { + return window.localStorage.getItem("quarto-reader-mode") === "true"; + } else { + return localReaderMode; + } + }; + let localReaderMode = null; + + const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); + const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; + + // Walk the TOC and collapse/expand nodes + // Nodes are expanded if: + // - they are top level + // - they have children that are 'active' links + // - they are directly below an link that is 'active' + const walk = (el, depth) => { + // Tick depth when we enter a UL + if (el.tagName === "UL") { + depth = depth + 1; + } + + // It this is active link + let isActiveNode = false; + if (el.tagName === "A" && el.classList.contains("active")) { + isActiveNode = true; + } + + // See if there is an active child to this element + let hasActiveChild = false; + for (child of el.children) { + hasActiveChild = walk(child, depth) || hasActiveChild; + } + + // Process the collapse state if this is an UL + if (el.tagName === "UL") { + if (tocOpenDepth === -1 && depth > 1) { + // toc-expand: false + el.classList.add("collapse"); + } else if ( + depth <= tocOpenDepth || + hasActiveChild || + prevSiblingIsActiveLink(el) + ) { + el.classList.remove("collapse"); + } else { + el.classList.add("collapse"); + } + + // untick depth when we leave a UL + depth = depth - 1; + } + return hasActiveChild || isActiveNode; + }; + + // walk the TOC and expand / collapse any items that should be shown + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + + // Throttle the scroll event and walk peridiocally + window.document.addEventListener( + "scroll", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 5) + ); + window.addEventListener( + "resize", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 10) + ); + hideOverlappedSidebars(); + highlightReaderToggle(isReaderMode()); +}); + +// grouped tabsets +window.addEventListener("pageshow", (_event) => { + function getTabSettings() { + const data = localStorage.getItem("quarto-persistent-tabsets-data"); + if (!data) { + localStorage.setItem("quarto-persistent-tabsets-data", "{}"); + return {}; + } + if (data) { + return JSON.parse(data); + } + } + + function setTabSettings(data) { + localStorage.setItem( + "quarto-persistent-tabsets-data", + JSON.stringify(data) + ); + } + + function setTabState(groupName, groupValue) { + const data = getTabSettings(); + data[groupName] = groupValue; + setTabSettings(data); + } + + function toggleTab(tab, active) { + const tabPanelId = tab.getAttribute("aria-controls"); + const tabPanel = document.getElementById(tabPanelId); + if (active) { + tab.classList.add("active"); + tabPanel.classList.add("active"); + } else { + tab.classList.remove("active"); + tabPanel.classList.remove("active"); + } + } + + function toggleAll(selectedGroup, selectorsToSync) { + for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { + const active = selectedGroup === thisGroup; + for (const tab of tabs) { + toggleTab(tab, active); + } + } + } + + function findSelectorsToSyncByLanguage() { + const result = {}; + const tabs = Array.from( + document.querySelectorAll(`div[data-group] a[id^='tabset-']`) + ); + for (const item of tabs) { + const div = item.parentElement.parentElement.parentElement; + const group = div.getAttribute("data-group"); + if (!result[group]) { + result[group] = {}; + } + const selectorsToSync = result[group]; + const value = item.innerHTML; + if (!selectorsToSync[value]) { + selectorsToSync[value] = []; + } + selectorsToSync[value].push(item); + } + return result; + } + + function setupSelectorSync() { + const selectorsToSync = findSelectorsToSyncByLanguage(); + Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { + Object.entries(tabSetsByValue).forEach(([value, items]) => { + items.forEach((item) => { + item.addEventListener("click", (_event) => { + setTabState(group, value); + toggleAll(value, selectorsToSync[group]); + }); + }); + }); + }); + return selectorsToSync; + } + + const selectorsToSync 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s.cutsTheMustard&&!this.initialised&&(this.addClass("initial"),this.initialised=!0,setTimeout(function(t){t.scrollTracker=n(t.scroller,{offset:t.offset,tolerance:t.tolerance},t.update.bind(t))},100,this)),this},destroy:function(){this.initialised=!1,Object.keys(this.classes).forEach(this.removeClass,this),this.scrollTracker.destroy()},unpin:function(){!this.hasClass("pinned")&&this.hasClass("unpinned")||(this.addClass("unpinned"),this.removeClass("pinned"),this.onUnpin&&this.onUnpin.call(this))},pin:function(){this.hasClass("unpinned")&&(this.addClass("pinned"),this.removeClass("unpinned"),this.onPin&&this.onPin.call(this))},freeze:function(){this.frozen=!0,this.addClass("frozen")},unfreeze:function(){this.frozen=!1,this.removeClass("frozen")},top:function(){this.hasClass("top")||(this.addClass("top"),this.removeClass("notTop"),this.onTop&&this.onTop.call(this))},notTop:function(){this.hasClass("notTop")||(this.addClass("notTop"),this.removeClass("top"),this.onNotTop&&this.onNotTop.call(this))},bottom:function(){this.hasClass("bottom")||(this.addClass("bottom"),this.removeClass("notBottom"),this.onBottom&&this.onBottom.call(this))},notBottom:function(){this.hasClass("notBottom")||(this.addClass("notBottom"),this.removeClass("bottom"),this.onNotBottom&&this.onNotBottom.call(this))},shouldUnpin:function(t){return"down"===t.direction&&!t.top&&t.toleranceExceeded},shouldPin:function(t){return"up"===t.direction&&t.toleranceExceeded||t.top},addClass:function(t){this.elem.classList.add.apply(this.elem.classList,this.classes[t].split(" "))},removeClass:function(t){this.elem.classList.remove.apply(this.elem.classList,this.classes[t].split(" "))},hasClass:function(t){return this.classes[t].split(" ").every(function(t){return this.classList.contains(t)},this.elem)},update:function(t){t.isOutOfBounds||!0!==this.frozen&&(t.top?this.top():this.notTop(),t.bottom?this.bottom():this.notBottom(),this.shouldUnpin(t)?this.unpin():this.shouldPin(t)&&this.pin())}},s.options={tolerance:{up:0,down:0},offset:0,scroller:t()?window:null,classes:{frozen:"headroom--frozen",pinned:"headroom--pinned",unpinned:"headroom--unpinned",top:"headroom--top",notTop:"headroom--not-top",bottom:"headroom--bottom",notBottom:"headroom--not-bottom",initial:"headroom"}},s.cutsTheMustard=!!(t()&&function(){}.bind&&"classList"in document.documentElement&&Object.assign&&Object.keys&&requestAnimationFrame),s}); diff --git a/_proc/_docs/site_libs/quarto-nav/quarto-nav.js b/_proc/_docs/site_libs/quarto-nav/quarto-nav.js new file mode 100644 index 0000000..38cc430 --- /dev/null +++ b/_proc/_docs/site_libs/quarto-nav/quarto-nav.js @@ -0,0 +1,325 @@ +const headroomChanged = new CustomEvent("quarto-hrChanged", { + detail: {}, + bubbles: true, + cancelable: false, + composed: false, +}); + +const announceDismiss = () => { + const annEl = window.document.getElementById("quarto-announcement"); + if (annEl) { + annEl.remove(); + + const annId = annEl.getAttribute("data-announcement-id"); + window.localStorage.setItem(`quarto-announce-${annId}`, "true"); + } +}; + +const announceRegister = () => { + const annEl = window.document.getElementById("quarto-announcement"); + if (annEl) { + const annId = annEl.getAttribute("data-announcement-id"); + const isDismissed = + window.localStorage.getItem(`quarto-announce-${annId}`) || false; + if (isDismissed) { + announceDismiss(); + return; + } else { + annEl.classList.remove("hidden"); + } + + const actionEl = annEl.querySelector(".quarto-announcement-action"); + if (actionEl) { + actionEl.addEventListener("click", function (e) { + e.preventDefault(); + // Hide the bar immediately + announceDismiss(); + }); + } + } +}; + +window.document.addEventListener("DOMContentLoaded", function () { + let init = false; + + announceRegister(); + + // Manage the back to top button, if one is present. + let lastScrollTop = window.pageYOffset || document.documentElement.scrollTop; + const scrollDownBuffer = 5; + const scrollUpBuffer = 35; + const btn = document.getElementById("quarto-back-to-top"); + const hideBackToTop = () => { + btn.style.display = "none"; + }; + const showBackToTop = () => { + btn.style.display = "inline-block"; + }; + if (btn) { + window.document.addEventListener( + "scroll", + function () { + const currentScrollTop = + window.pageYOffset || document.documentElement.scrollTop; + + // Shows and hides the button 'intelligently' as the user scrolls + if (currentScrollTop - scrollDownBuffer > lastScrollTop) { + hideBackToTop(); + lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; + } else if (currentScrollTop < lastScrollTop - scrollUpBuffer) { + showBackToTop(); + lastScrollTop = currentScrollTop <= 0 ? 0 : currentScrollTop; + } + + // Show the button at the bottom, hides it at the top + if (currentScrollTop <= 0) { + hideBackToTop(); + } else if ( + window.innerHeight + currentScrollTop >= + document.body.offsetHeight + ) { + showBackToTop(); + } + }, + false + ); + } + + function throttle(func, wait) { + var timeout; + return function () { + const context = this; + const args = arguments; + const later = function () { + clearTimeout(timeout); + timeout = null; + func.apply(context, args); + }; + + if (!timeout) { + timeout = setTimeout(later, wait); + } + }; + } + + function headerOffset() { + // Set an offset if there is are fixed top navbar + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl) { + return headerEl.clientHeight; + } else { + return 0; + } + } + + function footerOffset() { + const footerEl = window.document.querySelector("footer.footer"); + if (footerEl) { + return footerEl.clientHeight; + } else { + return 0; + } + } + + function dashboardOffset() { + const dashboardNavEl = window.document.getElementById( + "quarto-dashboard-header" + ); + if (dashboardNavEl !== null) { + return dashboardNavEl.clientHeight; + } else { + return 0; + } + } + + function updateDocumentOffsetWithoutAnimation() { + updateDocumentOffset(false); + } + + function updateDocumentOffset(animated) { + // set body offset + const topOffset = headerOffset(); + const bodyOffset = topOffset + footerOffset() + dashboardOffset(); + const bodyEl = window.document.body; + bodyEl.setAttribute("data-bs-offset", topOffset); + bodyEl.style.paddingTop = topOffset + "px"; + + // deal with sidebar offsets + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + if (!animated) { + sidebar.classList.add("notransition"); + // Remove the no transition class after the animation has time to complete + setTimeout(function () { + sidebar.classList.remove("notransition"); + }, 201); + } + + if (window.Headroom && sidebar.classList.contains("sidebar-unpinned")) { + sidebar.style.top = "0"; + sidebar.style.maxHeight = "100vh"; + } else { + sidebar.style.top = topOffset + "px"; + sidebar.style.maxHeight = "calc(100vh - " + topOffset + "px)"; + } + }); + + // allow space for footer + const mainContainer = window.document.querySelector(".quarto-container"); + if (mainContainer) { + mainContainer.style.minHeight = "calc(100vh - " + bodyOffset + "px)"; + } + + // link offset + let linkStyle = window.document.querySelector("#quarto-target-style"); + if (!linkStyle) { + linkStyle = window.document.createElement("style"); + linkStyle.setAttribute("id", "quarto-target-style"); + window.document.head.appendChild(linkStyle); + } + while (linkStyle.firstChild) { + linkStyle.removeChild(linkStyle.firstChild); + } + if (topOffset > 0) { + linkStyle.appendChild( + window.document.createTextNode(` + section:target::before { + content: ""; + display: block; + height: ${topOffset}px; + margin: -${topOffset}px 0 0; + }`) + ); + } + if (init) { + window.dispatchEvent(headroomChanged); + } + init = true; + } + + // initialize headroom + var header = window.document.querySelector("#quarto-header"); + if (header && window.Headroom) { + const headroom = new window.Headroom(header, { + tolerance: 5, + onPin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.remove("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + onUnpin: function () { + const sidebars = window.document.querySelectorAll( + ".sidebar, .headroom-target" + ); + sidebars.forEach((sidebar) => { + sidebar.classList.add("sidebar-unpinned"); + }); + updateDocumentOffset(); + }, + }); + headroom.init(); + + let frozen = false; + window.quartoToggleHeadroom = function () { + if (frozen) { + headroom.unfreeze(); + frozen = false; + } else { + headroom.freeze(); + frozen = true; + } + }; + } + + window.addEventListener( + "hashchange", + function (e) { + if ( + getComputedStyle(document.documentElement).scrollBehavior !== "smooth" + ) { + window.scrollTo(0, window.pageYOffset - headerOffset()); + } + }, + false + ); + + // Observe size changed for the header + const headerEl = window.document.querySelector("header.fixed-top"); + if (headerEl && window.ResizeObserver) { + const observer = new window.ResizeObserver(() => { + setTimeout(updateDocumentOffsetWithoutAnimation, 0); + }); + observer.observe(headerEl, { + attributes: true, + childList: true, + characterData: true, + }); + } else { + window.addEventListener( + "resize", + throttle(updateDocumentOffsetWithoutAnimation, 50) + ); + } + setTimeout(updateDocumentOffsetWithoutAnimation, 250); + + // fixup index.html links if we aren't on the filesystem + if (window.location.protocol !== "file:") { + const links = window.document.querySelectorAll("a"); + for (let i = 0; i < links.length; i++) { + if (links[i].href) { + links[i].dataset.originalHref = links[i].href; + links[i].href = links[i].href.replace(/\/index\.html/, "/"); + } + } + + // Fixup any sharing links that require urls + // Append url to any sharing urls + const sharingLinks = window.document.querySelectorAll( + "a.sidebar-tools-main-item, a.quarto-navigation-tool, a.quarto-navbar-tools, a.quarto-navbar-tools-item" + ); + for (let i = 0; i < sharingLinks.length; i++) { + const sharingLink = sharingLinks[i]; + const href = sharingLink.getAttribute("href"); + if (href) { + sharingLink.setAttribute( + "href", + href.replace("|url|", window.location.href) + ); + } + } + + // Scroll the active navigation item into view, if necessary + const navSidebar = window.document.querySelector("nav#quarto-sidebar"); + if (navSidebar) { + // Find the active item + const activeItem = navSidebar.querySelector("li.sidebar-item a.active"); + if (activeItem) { + // Wait for the scroll height and height to resolve by observing size changes on the + // nav element that is scrollable + const resizeObserver = new ResizeObserver((_entries) => { + // The bottom of the element + const elBottom = activeItem.offsetTop; + const viewBottom = navSidebar.scrollTop + navSidebar.clientHeight; + + // The element height and scroll height are the same, then we are still loading + if (viewBottom !== navSidebar.scrollHeight) { + // Determine if the item isn't visible and scroll to it + if (elBottom >= viewBottom) { + navSidebar.scrollTop = elBottom; + } + + // stop observing now since we've completed the scroll + resizeObserver.unobserve(navSidebar); + } + }); + resizeObserver.observe(navSidebar); + } + } + } +}); diff --git a/_proc/_docs/site_libs/quarto-search/autocomplete.umd.js b/_proc/_docs/site_libs/quarto-search/autocomplete.umd.js new file mode 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0!==arguments[1]?arguments[1]:{},n=t.limit,r=void 0===n?-1:n,i=this.options,o=i.includeMatches,c=i.includeScore,a=i.shouldSort,s=i.sortFn,u=i.ignoreFieldNorm,h=g(e)?g(this._docs[0])?this._searchStringList(e):this._searchObjectList(e):this._searchLogical(e);return fe(h,{ignoreFieldNorm:u}),a&&h.sort(s),y(r)&&r>-1&&(h=h.slice(0,r)),ge(h,this._docs,{includeMatches:o,includeScore:c})}},{key:"_searchStringList",value:function(e){var t=re(e,this.options),n=this._myIndex.records,r=[];return n.forEach((function(e){var n=e.v,i=e.i,o=e.n;if(k(n)){var c=t.searchIn(n),a=c.isMatch,s=c.score,u=c.indices;a&&r.push({item:n,idx:i,matches:[{score:s,value:n,norm:o,indices:u}]})}})),r}},{key:"_searchLogical",value:function(e){var t=this,n=function(e,t){var n=(arguments.length>2&&void 0!==arguments[2]?arguments[2]:{}).auto,r=void 0===n||n,i=function e(n){var i=Object.keys(n),o=ue(n);if(!o&&i.length>1&&!se(n))return e(le(n));if(he(n)){var c=o?n[ce]:i[0],a=o?n[ae]:n[c];if(!g(a))throw new Error(x(c));var s={keyId:j(c),pattern:a};return r&&(s.searcher=re(a,t)),s}var u={children:[],operator:i[0]};return i.forEach((function(t){var r=n[t];v(r)&&r.forEach((function(t){u.children.push(e(t))}))})),u};return se(e)||(e=le(e)),i(e)}(e,this.options),r=function e(n,r,i){if(!n.children){var o=n.keyId,c=n.searcher,a=t._findMatches({key:t._keyStore.get(o),value:t._myIndex.getValueForItemAtKeyId(r,o),searcher:c});return a&&a.length?[{idx:i,item:r,matches:a}]:[]}for(var s=[],u=0,h=n.children.length;u1&&void 0!==arguments[1]?arguments[1]:{},n=t.getFn,r=void 0===n?I.getFn:n,i=t.fieldNormWeight,o=void 0===i?I.fieldNormWeight:i,c=e.keys,a=e.records,s=new $({getFn:r,fieldNormWeight:o});return s.setKeys(c),s.setIndexRecords(a),s},ye.config=I,function(){ne.push.apply(ne,arguments)}(te),ye},"object"==typeof exports&&"undefined"!=typeof module?module.exports=t():"function"==typeof define&&define.amd?define(t):(e="undefined"!=typeof globalThis?globalThis:e||self).Fuse=t(); \ No newline at end of file diff --git a/_proc/_docs/site_libs/quarto-search/quarto-search.js b/_proc/_docs/site_libs/quarto-search/quarto-search.js new file mode 100644 index 0000000..d788a95 --- /dev/null +++ b/_proc/_docs/site_libs/quarto-search/quarto-search.js @@ -0,0 +1,1290 @@ +const kQueryArg = "q"; +const kResultsArg = "show-results"; + +// If items don't provide a URL, then both the navigator and the onSelect +// function aren't called (and therefore, the default implementation is used) +// +// We're using this sentinel URL to signal to those handlers that this +// item is a more item (along with the type) and can be handled appropriately +const kItemTypeMoreHref = "0767FDFD-0422-4E5A-BC8A-3BE11E5BBA05"; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Ensure that search is available on this page. If it isn't, + // should return early and not do anything + var searchEl = window.document.getElementById("quarto-search"); + if (!searchEl) return; + + const { autocomplete } = window["@algolia/autocomplete-js"]; + + let quartoSearchOptions = {}; + let language = {}; + const searchOptionEl = window.document.getElementById( + "quarto-search-options" + ); + if (searchOptionEl) { + const jsonStr = searchOptionEl.textContent; + quartoSearchOptions = JSON.parse(jsonStr); + language = quartoSearchOptions.language; + } + + // note the search mode + if (quartoSearchOptions.type === "overlay") { + searchEl.classList.add("type-overlay"); + } else { + searchEl.classList.add("type-textbox"); + } + + // Used to determine highlighting behavior for this page + // A `q` query param is expected when the user follows a search + // to this page + const currentUrl = new URL(window.location); + const query = currentUrl.searchParams.get(kQueryArg); + const showSearchResults = currentUrl.searchParams.get(kResultsArg); + const mainEl = window.document.querySelector("main"); + + // highlight matches on the page + if (query && mainEl) { + // perform any highlighting + highlight(escapeRegExp(query), mainEl); + + // fix up the URL to remove the q query param + const replacementUrl = new URL(window.location); + replacementUrl.searchParams.delete(kQueryArg); + window.history.replaceState({}, "", replacementUrl); + } + + // function to clear highlighting on the page when the search query changes + // (e.g. if the user edits the query or clears it) + let highlighting = true; + const resetHighlighting = (searchTerm) => { + if (mainEl && highlighting && query && searchTerm !== query) { + clearHighlight(query, mainEl); + highlighting = false; + } + }; + + // Clear search highlighting when the user scrolls sufficiently + const resetFn = () => { + resetHighlighting(""); + window.removeEventListener("quarto-hrChanged", resetFn); + window.removeEventListener("quarto-sectionChanged", resetFn); + }; + + // Register this event after the initial scrolling and settling of events + // on the page + window.addEventListener("quarto-hrChanged", resetFn); + window.addEventListener("quarto-sectionChanged", resetFn); + + // Responsively switch to overlay mode if the search is present on the navbar + // Note that switching the sidebar to overlay mode requires more coordinate (not just + // the media query since we generate different HTML for sidebar overlays than we do + // for sidebar input UI) + const detachedMediaQuery = + quartoSearchOptions.type === "overlay" ? "all" : "(max-width: 991px)"; + + // If configured, include the analytics client to send insights + const plugins = configurePlugins(quartoSearchOptions); + + let lastState = null; + const { setIsOpen, setQuery, setCollections } = autocomplete({ + container: searchEl, + detachedMediaQuery: detachedMediaQuery, + defaultActiveItemId: 0, + panelContainer: "#quarto-search-results", + panelPlacement: quartoSearchOptions["panel-placement"], + debug: false, + openOnFocus: true, + plugins, + classNames: { + form: "d-flex", + }, + placeholder: language["search-text-placeholder"], + translations: { + clearButtonTitle: language["search-clear-button-title"], + detachedCancelButtonText: language["search-detached-cancel-button-title"], + submitButtonTitle: language["search-submit-button-title"], + }, + initialState: { + query, + }, + getItemUrl({ item }) { + return item.href; + }, + onStateChange({ state }) { + // If this is a file URL, note that + + // Perhaps reset highlighting + resetHighlighting(state.query); + + // If the panel just opened, ensure the panel is positioned properly + if (state.isOpen) { + if (lastState && !lastState.isOpen) { + setTimeout(() => { + positionPanel(quartoSearchOptions["panel-placement"]); + }, 150); + } + } + + // Perhaps show the copy link + showCopyLink(state.query, quartoSearchOptions); + + lastState = state; + }, + reshape({ sources, state }) { + return sources.map((source) => { + try { + const items = source.getItems(); + + // Validate the items + validateItems(items); + + // group the items by document + const groupedItems = new Map(); + items.forEach((item) => { + const hrefParts = item.href.split("#"); + const baseHref = hrefParts[0]; + const isDocumentItem = hrefParts.length === 1; + + const items = groupedItems.get(baseHref); + if (!items) { + groupedItems.set(baseHref, [item]); + } else { + // If the href for this item matches the document + // exactly, place this item first as it is the item that represents + // the document itself + if (isDocumentItem) { + items.unshift(item); + } else { + items.push(item); + } + groupedItems.set(baseHref, items); + } + }); + + const reshapedItems = []; + let count = 1; + for (const [_key, value] of groupedItems) { + const firstItem = value[0]; + reshapedItems.push({ + ...firstItem, + type: kItemTypeDoc, + }); + + const collapseMatches = quartoSearchOptions["collapse-after"]; + const collapseCount = + typeof collapseMatches === "number" ? collapseMatches : 1; + + if (value.length > 1) { + const target = `search-more-${count}`; + const isExpanded = + state.context.expanded && + state.context.expanded.includes(target); + + const remainingCount = value.length - collapseCount; + + for (let i = 1; i < value.length; i++) { + if (collapseMatches && i === collapseCount) { + reshapedItems.push({ + target, + title: isExpanded + ? language["search-hide-matches-text"] + : remainingCount === 1 + ? `${remainingCount} ${language["search-more-match-text"]}` + : `${remainingCount} ${language["search-more-matches-text"]}`, + type: kItemTypeMore, + href: kItemTypeMoreHref, + }); + } + + if (isExpanded || !collapseMatches || i < collapseCount) { + reshapedItems.push({ + ...value[i], + type: kItemTypeItem, + target, + }); + } + } + } + count += 1; + } + + return { + ...source, + getItems() { + return reshapedItems; + }, + }; + } catch (error) { + // Some form of error occurred + return { + ...source, + getItems() { + return [ + { + title: error.name || "An Error Occurred While Searching", + text: + error.message || + "An unknown error occurred while attempting to perform the requested search.", + type: kItemTypeError, + }, + ]; + }, + }; + } + }); + }, + navigator: { + navigate({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.location.assign(itemUrl); + } + }, + navigateNewTab({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + const windowReference = window.open(itemUrl, "_blank", "noopener"); + if (windowReference) { + windowReference.focus(); + } + } + }, + navigateNewWindow({ itemUrl }) { + if (itemUrl !== offsetURL(kItemTypeMoreHref)) { + window.open(itemUrl, "_blank", "noopener"); + } + }, + }, + getSources({ state, setContext, setActiveItemId, refresh }) { + return [ + { + sourceId: "documents", + getItemUrl({ item }) { + if (item.href) { + return offsetURL(item.href); + } else { + return undefined; + } + }, + onSelect({ + item, + state, + setContext, + setIsOpen, + setActiveItemId, + refresh, + }) { + if (item.type === kItemTypeMore) { + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + + // Toggle more + setIsOpen(true); + } + }, + getItems({ query }) { + if (query === null || query === "") { + return []; + } + + const limit = quartoSearchOptions.limit; + if (quartoSearchOptions.algolia) { + return algoliaSearch(query, limit, quartoSearchOptions.algolia); + } else { + // Fuse search options + const fuseSearchOptions = { + isCaseSensitive: false, + shouldSort: true, + minMatchCharLength: 2, + limit: limit, + }; + + return readSearchData().then(function (fuse) { + return fuseSearch(query, fuse, fuseSearchOptions); + }); + } + }, + templates: { + noResults({ createElement }) { + const hasQuery = lastState.query; + + return createElement( + "div", + { + class: `quarto-search-no-results${ + hasQuery ? "" : " no-query" + }`, + }, + language["search-no-results-text"] + ); + }, + header({ items, createElement }) { + // count the documents + const count = items.filter((item) => { + return item.type === kItemTypeDoc; + }).length; + + if (count > 0) { + return createElement( + "div", + { class: "search-result-header" }, + `${count} ${language["search-matching-documents-text"]}` + ); + } else { + return createElement( + "div", + { class: "search-result-header-no-results" }, + `` + ); + } + }, + footer({ _items, createElement }) { + if ( + quartoSearchOptions.algolia && + quartoSearchOptions.algolia["show-logo"] + ) { + const libDir = quartoSearchOptions.algolia["libDir"]; + const logo = createElement("img", { + src: offsetURL( + `${libDir}/quarto-search/search-by-algolia.svg` + ), + class: "algolia-search-logo", + }); + return createElement( + "a", + { href: "http://www.algolia.com/" }, + logo + ); + } + }, + + item({ item, createElement }) { + return renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh, + quartoSearchOptions + ); + }, + }, + }, + ]; + }, + }); + + window.quartoOpenSearch = () => { + setIsOpen(false); + setIsOpen(true); + focusSearchInput(); + }; + + document.addEventListener("keyup", (event) => { + const { key } = event; + const kbds = quartoSearchOptions["keyboard-shortcut"]; + const focusedEl = document.activeElement; + + const isFormElFocused = [ + "input", + "select", + "textarea", + "button", + "option", + ].find((tag) => { + return focusedEl.tagName.toLowerCase() === tag; + }); + + if ( + kbds && + kbds.includes(key) && + !isFormElFocused && + !document.activeElement.isContentEditable + ) { + event.preventDefault(); + window.quartoOpenSearch(); + } + }); + + // Remove the labeleledby attribute since it is pointing + // to a non-existent label + if (quartoSearchOptions.type === "overlay") { + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + if (inputEl) { + inputEl.removeAttribute("aria-labelledby"); + } + } + + function throttle(func, wait) { + let waiting = false; + return function () { + if (!waiting) { + func.apply(this, arguments); + waiting = true; + setTimeout(function () { + waiting = false; + }, wait); + } + }; + } + + // If the main document scrolls dismiss the search results + // (otherwise, since they're floating in the document they can scroll with the document) + window.document.body.onscroll = throttle(() => { + // Only do this if we're not detached + // Bug #7117 + // This will happen when the keyboard is shown on ios (resulting in a scroll) + // which then closed the search UI + if (!window.matchMedia(detachedMediaQuery).matches) { + setIsOpen(false); + } + }, 50); + + if (showSearchResults) { + setIsOpen(true); + focusSearchInput(); + } +}); + +function configurePlugins(quartoSearchOptions) { + const autocompletePlugins = []; + const algoliaOptions = quartoSearchOptions.algolia; + if ( + algoliaOptions && + algoliaOptions["analytics-events"] && + algoliaOptions["search-only-api-key"] && + algoliaOptions["application-id"] + ) { + const apiKey = algoliaOptions["search-only-api-key"]; + const appId = algoliaOptions["application-id"]; + + // Aloglia insights may not be loaded because they require cookie consent + // Use deferred loading so events will start being recorded when/if consent + // is granted. + const algoliaInsightsDeferredPlugin = deferredLoadPlugin(() => { + if ( + window.aa && + window["@algolia/autocomplete-plugin-algolia-insights"] + ) { + window.aa("init", { + appId, + apiKey, + useCookie: true, + }); + + const { createAlgoliaInsightsPlugin } = + window["@algolia/autocomplete-plugin-algolia-insights"]; + // Register the insights client + const algoliaInsightsPlugin = createAlgoliaInsightsPlugin({ + insightsClient: window.aa, + onItemsChange({ insights, insightsEvents }) { + const events = insightsEvents.flatMap((event) => { + // This API limits the number of items per event to 20 + const chunkSize = 20; + const itemChunks = []; + const eventItems = event.items; + for (let i = 0; i < eventItems.length; i += chunkSize) { + itemChunks.push(eventItems.slice(i, i + chunkSize)); + } + // Split the items into multiple events that can be sent + const events = itemChunks.map((items) => { + return { + ...event, + items, + }; + }); + return events; + }); + + for (const event of events) { + insights.viewedObjectIDs(event); + } + }, + }); + return algoliaInsightsPlugin; + } + }); + + // Add the plugin + autocompletePlugins.push(algoliaInsightsDeferredPlugin); + return autocompletePlugins; + } +} + +// For plugins that may not load immediately, create a wrapper +// plugin and forward events and plugin data once the plugin +// is initialized. This is useful for cases like cookie consent +// which may prevent the analytics insights event plugin from initializing +// immediately. +function deferredLoadPlugin(createPlugin) { + let plugin = undefined; + let subscribeObj = undefined; + const wrappedPlugin = () => { + if (!plugin && subscribeObj) { + plugin = createPlugin(); + if (plugin && plugin.subscribe) { + plugin.subscribe(subscribeObj); + } + } + return plugin; + }; + + return { + subscribe: (obj) => { + subscribeObj = obj; + }, + onStateChange: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onStateChange) { + plugin.onStateChange(obj); + } + }, + onSubmit: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onSubmit) { + plugin.onSubmit(obj); + } + }, + onReset: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.onReset) { + plugin.onReset(obj); + } + }, + getSources: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.getSources) { + return plugin.getSources(obj); + } else { + return Promise.resolve([]); + } + }, + data: (obj) => { + const plugin = wrappedPlugin(); + if (plugin && plugin.data) { + plugin.data(obj); + } + }, + }; +} + +function validateItems(items) { + // Validate the first item + if (items.length > 0) { + const item = items[0]; + const missingFields = []; + if (item.href == undefined) { + missingFields.push("href"); + } + if (!item.title == undefined) { + missingFields.push("title"); + } + if (!item.text == undefined) { + missingFields.push("text"); + } + + if (missingFields.length === 1) { + throw { + name: `Error: Search index is missing the ${missingFields[0]} field.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items include the ${missingFields[0]} field or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } else if (missingFields.length > 1) { + const missingFieldList = missingFields + .map((field) => { + return `${field}`; + }) + .join(", "); + + throw { + name: `Error: Search index is missing the following fields: ${missingFieldList}.`, + message: `The items being returned for this search do not include all the required fields. Please ensure that your index items includes the following fields: ${missingFieldList}, or use index-fields in your _quarto.yml file to specify the field names.`, + }; + } + } +} + +let lastQuery = null; +function showCopyLink(query, options) { + const language = options.language; + lastQuery = query; + // Insert share icon + const inputSuffixEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix" + ); + + if (inputSuffixEl) { + let copyButtonEl = window.document.body.querySelector( + ".aa-Form .aa-InputWrapperSuffix .aa-CopyButton" + ); + + if (copyButtonEl === null) { + copyButtonEl = window.document.createElement("button"); + copyButtonEl.setAttribute("class", "aa-CopyButton"); + copyButtonEl.setAttribute("type", "button"); + copyButtonEl.setAttribute("title", language["search-copy-link-title"]); + copyButtonEl.onmousedown = (e) => { + e.preventDefault(); + e.stopPropagation(); + }; + + const linkIcon = "bi-clipboard"; + const checkIcon = "bi-check2"; + + const shareIconEl = window.document.createElement("i"); + shareIconEl.setAttribute("class", `bi ${linkIcon}`); + copyButtonEl.appendChild(shareIconEl); + inputSuffixEl.prepend(copyButtonEl); + + const clipboard = new window.ClipboardJS(".aa-CopyButton", { + text: function (_trigger) { + const copyUrl = new URL(window.location); + copyUrl.searchParams.set(kQueryArg, lastQuery); + copyUrl.searchParams.set(kResultsArg, "1"); + return copyUrl.toString(); + }, + }); + clipboard.on("success", function (e) { + // Focus the input + + // button target + const button = e.trigger; + const icon = button.querySelector("i.bi"); + + // flash "checked" + icon.classList.add(checkIcon); + icon.classList.remove(linkIcon); + setTimeout(function () { + icon.classList.remove(checkIcon); + icon.classList.add(linkIcon); + }, 1000); + }); + } + + // If there is a query, show the link icon + if (copyButtonEl) { + if (lastQuery && options["copy-button"]) { + copyButtonEl.style.display = "flex"; + } else { + copyButtonEl.style.display = "none"; + } + } + } +} + +/* Search Index Handling */ +// create the index +var fuseIndex = undefined; +var shownWarning = false; + +// fuse index options +const kFuseIndexOptions = { + keys: [ + { name: "title", weight: 20 }, + { name: "section", weight: 20 }, + { name: "text", weight: 10 }, + ], + ignoreLocation: true, + threshold: 0.1, +}; + +async function readSearchData() { + // Initialize the search index on demand + if (fuseIndex === undefined) { + if (window.location.protocol === "file:" && !shownWarning) { + window.alert( + "Search requires JavaScript features disabled when running in file://... URLs. In order to use search, please run this document in a web server." + ); + shownWarning = true; + return; + } + const fuse = new window.Fuse([], kFuseIndexOptions); + + // fetch the main search.json + const response = await fetch(offsetURL("search.json")); + if (response.status == 200) { + return response.json().then(function (searchDocs) { + searchDocs.forEach(function (searchDoc) { + fuse.add(searchDoc); + }); + fuseIndex = fuse; + return fuseIndex; + }); + } else { + return Promise.reject( + new Error( + "Unexpected status from search index request: " + response.status + ) + ); + } + } + + return fuseIndex; +} + +function inputElement() { + return window.document.body.querySelector(".aa-Form .aa-Input"); +} + +function focusSearchInput() { + setTimeout(() => { + const inputEl = inputElement(); + if (inputEl) { + inputEl.focus(); + } + }, 50); +} + +/* Panels */ +const kItemTypeDoc = "document"; +const kItemTypeMore = "document-more"; +const kItemTypeItem = "document-item"; +const kItemTypeError = "error"; + +function renderItem( + item, + createElement, + state, + setActiveItemId, + setContext, + refresh, + quartoSearchOptions +) { + switch (item.type) { + case kItemTypeDoc: + return createDocumentCard( + createElement, + "file-richtext", + item.title, + item.section, + item.text, + item.href, + item.crumbs, + quartoSearchOptions + ); + case kItemTypeMore: + return createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh + ); + case kItemTypeItem: + return createSectionCard( + createElement, + item.section, + item.text, + item.href + ); + case kItemTypeError: + return createErrorCard(createElement, item.title, item.text); + default: + return undefined; + } +} + +function createDocumentCard( + createElement, + icon, + title, + section, + text, + href, + crumbs, + quartoSearchOptions +) { + const iconEl = createElement("i", { + class: `bi bi-${icon} search-result-icon`, + }); + const titleEl = createElement("p", { class: "search-result-title" }, title); + const titleContents = [iconEl, titleEl]; + const showParent = quartoSearchOptions["show-item-context"]; + if (crumbs && showParent) { + let crumbsOut = undefined; + const crumbClz = ["search-result-crumbs"]; + if (showParent === "root") { + crumbsOut = crumbs.length > 1 ? crumbs[0] : undefined; + } else if (showParent === "parent") { + crumbsOut = crumbs.length > 1 ? crumbs[crumbs.length - 2] : undefined; + } else { + crumbsOut = crumbs.length > 1 ? crumbs.join(" > ") : undefined; + crumbClz.push("search-result-crumbs-wrap"); + } + + const crumbEl = createElement( + "p", + { class: crumbClz.join(" ") }, + crumbsOut + ); + titleContents.push(crumbEl); + } + + const titleContainerEl = createElement( + "div", + { class: "search-result-title-container" }, + titleContents + ); + + const textEls = []; + if (section) { + const sectionEl = createElement( + "p", + { class: "search-result-section" }, + section + ); + textEls.push(sectionEl); + } + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + textEls.push(descEl); + + const textContainerEl = createElement( + "div", + { class: "search-result-text-container" }, + textEls + ); + + const containerEl = createElement( + "div", + { + class: "search-result-container", + }, + [titleContainerEl, textContainerEl] + ); + + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + containerEl + ); + + const classes = ["search-result-doc", "search-item"]; + if (!section) { + classes.push("document-selectable"); + } + + return createElement( + "div", + { + class: classes.join(" "), + }, + linkEl + ); +} + +function createMoreCard( + createElement, + item, + state, + setActiveItemId, + setContext, + refresh +) { + const moreCardEl = createElement( + "div", + { + class: "search-result-more search-item", + onClick: (e) => { + // Handle expanding the sections by adding the expanded + // section to the list of expanded sections + toggleExpanded(item, state, setContext, setActiveItemId, refresh); + e.stopPropagation(); + }, + }, + item.title + ); + + return moreCardEl; +} + +function toggleExpanded(item, state, setContext, setActiveItemId, refresh) { + const expanded = state.context.expanded || []; + if (expanded.includes(item.target)) { + setContext({ + expanded: expanded.filter((target) => target !== item.target), + }); + } else { + setContext({ expanded: [...expanded, item.target] }); + } + + refresh(); + setActiveItemId(item.__autocomplete_id); +} + +function createSectionCard(createElement, section, text, href) { + const sectionEl = createSection(createElement, section, text, href); + return createElement( + "div", + { + class: "search-result-doc-section search-item", + }, + sectionEl + ); +} + +function createSection(createElement, title, text, href) { + const descEl = createElement("p", { + class: "search-result-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { class: "search-result-section" }, title); + const linkEl = createElement( + "a", + { + href: offsetURL(href), + class: "search-result-link", + }, + [titleEl, descEl] + ); + return linkEl; +} + +function createErrorCard(createElement, title, text) { + const descEl = createElement("p", { + class: "search-error-text", + dangerouslySetInnerHTML: { + __html: text, + }, + }); + + const titleEl = createElement("p", { + class: "search-error-title", + dangerouslySetInnerHTML: { + __html: ` ${title}`, + }, + }); + const errorEl = createElement("div", { class: "search-error" }, [ + titleEl, + descEl, + ]); + return errorEl; +} + +function positionPanel(pos) { + const panelEl = window.document.querySelector( + "#quarto-search-results .aa-Panel" + ); + const inputEl = window.document.querySelector( + "#quarto-search .aa-Autocomplete" + ); + + if (panelEl && inputEl) { + panelEl.style.top = `${Math.round(panelEl.offsetTop)}px`; + if (pos === "start") { + panelEl.style.left = `${Math.round(inputEl.left)}px`; + } else { + panelEl.style.right = `${Math.round(inputEl.offsetRight)}px`; + } + } +} + +/* Highlighting */ +// highlighting functions +function highlightMatch(query, text) { + if (text) { + const start = text.toLowerCase().indexOf(query.toLowerCase()); + if (start !== -1) { + const startMark = ""; + const endMark = ""; + + const end = start + query.length; + text = + text.slice(0, start) + + startMark + + text.slice(start, end) + + endMark + + text.slice(end); + const startInfo = clipStart(text, start); + const endInfo = clipEnd( + text, + startInfo.position + startMark.length + endMark.length + ); + text = + startInfo.prefix + + text.slice(startInfo.position, endInfo.position) + + endInfo.suffix; + + return text; + } else { + return text; + } + } else { + return text; + } +} + +function clipStart(text, pos) { + const clipStart = pos - 50; + if (clipStart < 0) { + // This will just return the start of the string + return { + position: 0, + prefix: "", + }; + } else { + // We're clipping before the start of the string, walk backwards to the first space. + const spacePos = findSpace(text, pos, -1); + return { + position: spacePos.position, + prefix: "", + }; + } +} + +function clipEnd(text, pos) { + const clipEnd = pos + 200; + if (clipEnd > text.length) { + return { + position: text.length, + suffix: "", + }; + } else { + const spacePos = findSpace(text, clipEnd, 1); + return { + position: spacePos.position, + suffix: spacePos.clipped ? "…" : "", + }; + } +} + +function findSpace(text, start, step) { + let stepPos = start; + while (stepPos > -1 && stepPos < text.length) { + const char = text[stepPos]; + if (char === " " || char === "," || char === ":") { + return { + position: step === 1 ? stepPos : stepPos - step, + clipped: stepPos > 1 && stepPos < text.length, + }; + } + stepPos = stepPos + step; + } + + return { + position: stepPos - step, + clipped: false, + }; +} + +// removes highlighting as implemented by the mark tag +function clearHighlight(searchterm, el) { + const childNodes = el.childNodes; + for (let i = childNodes.length - 1; i >= 0; i--) { + const node = childNodes[i]; + if (node.nodeType === Node.ELEMENT_NODE) { + if ( + node.tagName === "MARK" && + node.innerText.toLowerCase() === searchterm.toLowerCase() + ) { + el.replaceChild(document.createTextNode(node.innerText), node); + } else { + clearHighlight(searchterm, node); + } + } + } +} + +function escapeRegExp(string) { + return string.replace(/[.*+?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string +} + +// highlight matches +function highlight(term, el) { + const termRegex = new RegExp(term, "ig"); + const childNodes = el.childNodes; + + // walk back to front avoid mutating elements in front of us + for (let i = childNodes.length - 1; i >= 0; i--) { + const node = childNodes[i]; + + if (node.nodeType === Node.TEXT_NODE) { + // Search text nodes for text to highlight + const text = node.nodeValue; + + let startIndex = 0; + let matchIndex = text.search(termRegex); + if (matchIndex > -1) { + const markFragment = document.createDocumentFragment(); + while (matchIndex > -1) { + const prefix = text.slice(startIndex, matchIndex); + markFragment.appendChild(document.createTextNode(prefix)); + + const mark = document.createElement("mark"); + mark.appendChild( + document.createTextNode( + text.slice(matchIndex, matchIndex + term.length) + ) + ); + markFragment.appendChild(mark); + + startIndex = matchIndex + term.length; + matchIndex = text.slice(startIndex).search(new RegExp(term, "ig")); + if (matchIndex > -1) { + matchIndex = startIndex + matchIndex; + } + } + if (startIndex < text.length) { + markFragment.appendChild( + document.createTextNode(text.slice(startIndex, text.length)) + ); + } + + el.replaceChild(markFragment, node); + } + } else if (node.nodeType === Node.ELEMENT_NODE) { + // recurse through elements + highlight(term, node); + } + } +} + +/* Link Handling */ +// get the offset from this page for a given site root relative url +function offsetURL(url) { + var offset = getMeta("quarto:offset"); + return offset ? offset + url : url; +} + +// read a meta tag value +function getMeta(metaName) { + var metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; +} + +function algoliaSearch(query, limit, algoliaOptions) { + const { getAlgoliaResults } = window["@algolia/autocomplete-preset-algolia"]; + + const applicationId = algoliaOptions["application-id"]; + const searchOnlyApiKey = algoliaOptions["search-only-api-key"]; + const indexName = algoliaOptions["index-name"]; + const indexFields = algoliaOptions["index-fields"]; + const searchClient = window.algoliasearch(applicationId, searchOnlyApiKey); + const searchParams = algoliaOptions["params"]; + const searchAnalytics = !!algoliaOptions["analytics-events"]; + + return getAlgoliaResults({ + searchClient, + queries: [ + { + indexName: indexName, + query, + params: { + hitsPerPage: limit, + clickAnalytics: searchAnalytics, + ...searchParams, + }, + }, + ], + transformResponse: (response) => { + if (!indexFields) { + return response.hits.map((hit) => { + return hit.map((item) => { + return { + ...item, + text: highlightMatch(query, item.text), + }; + }); + }); + } else { + const remappedHits = response.hits.map((hit) => { + return hit.map((item) => { + const newItem = { ...item }; + ["href", "section", "title", "text", "crumbs"].forEach( + (keyName) => { + const mappedName = indexFields[keyName]; + if ( + mappedName && + item[mappedName] !== undefined && + mappedName !== keyName + ) { + newItem[keyName] = item[mappedName]; + delete newItem[mappedName]; + } + } + ); + newItem.text = highlightMatch(query, newItem.text); + return newItem; + }); + }); + return remappedHits; + } + }, + }); +} + +let subSearchTerm = undefined; +let subSearchFuse = undefined; +const kFuseMaxWait = 125; + +async function fuseSearch(query, fuse, fuseOptions) { + let index = fuse; + // Fuse.js using the Bitap algorithm for text matching which runs in + // O(nm) time (no matter the structure of the text). In our case this + // means that long search terms mixed with large index gets very slow + // + // This injects a subIndex that will be used once the terms get long enough + // Usually making this subindex is cheap since there will typically be + // a subset of results matching the existing query + if (subSearchFuse !== undefined && query.startsWith(subSearchTerm)) { + // Use the existing subSearchFuse + index = subSearchFuse; + } else if (subSearchFuse !== undefined) { + // The term changed, discard the existing fuse + subSearchFuse = undefined; + subSearchTerm = undefined; + } + + // Search using the active fuse + const then = performance.now(); + const resultsRaw = await index.search(query, fuseOptions); + const now = performance.now(); + + const results = resultsRaw.map((result) => { + const addParam = (url, name, value) => { + const anchorParts = url.split("#"); + const baseUrl = anchorParts[0]; + const sep = baseUrl.search("\\?") > 0 ? "&" : "?"; + anchorParts[0] = baseUrl + sep + name + "=" + value; + return anchorParts.join("#"); + }; + + return { + title: result.item.title, + section: result.item.section, + href: addParam(result.item.href, kQueryArg, query), + text: highlightMatch(query, result.item.text), + crumbs: result.item.crumbs, + }; + }); + + // If we don't have a subfuse and the query is long enough, go ahead + // and create a subfuse to use for subsequent queries + if ( + now - then > kFuseMaxWait && + subSearchFuse === undefined && + resultsRaw.length < fuseOptions.limit + ) { + subSearchTerm = query; + subSearchFuse = new window.Fuse([], kFuseIndexOptions); + resultsRaw.forEach((rr) => { + subSearchFuse.add(rr.item); + }); + } + return results; +} diff --git a/_proc/_docs/sitemap.xml b/_proc/_docs/sitemap.xml new file mode 100644 index 0000000..7962ef4 --- /dev/null +++ b/_proc/_docs/sitemap.xml @@ -0,0 +1,3 @@ + + + diff --git a/_proc/index.html.md b/_proc/index.html.md new file mode 100644 index 0000000..032eb4b --- /dev/null +++ b/_proc/index.html.md @@ -0,0 +1,983 @@ +--- +title: "The ERA5 Spatial Aggregation Pipeline" +exec_all: true +--- + + + + +::: {#cell-2 .cell hide='null'} +``` {.python .cell-code} +from era5_sandbox.core import * +``` +::: + + +## era5_sandbox + +> Sandbox environment for era5 development + +This package documents the development and implementation of functions and code for the Madagascar ERA5 dataset project. The goal is for exposure data to be made available at the daily resolution when possible. Finer resolutions shouldn’t ever be needed for our purposes, and it should then be relatively easy to aggregate at coarser resolutions, such as weekly or monthly. Additionally, we've extended this work to Nepal as well. + +Variables should generally be made available from 2010 onward, as that’s where our clinic data starts. + +All data are ideally made available at the “healthshed” geographical level. Healthsheds are defined as geographical areas where people who live all go to the same clinic. There are a total of ~2700 public clinics in Madagascar, hence ~2700 healthsheds, with each healthshed containing ~10000 people on average. + +Preliminary list of environmental variables + +- [x] 2-m air temperature from ERA5: daily min, max, mean + +- [x] 2-m air dew point temperature from ERA5: daily min, max, mean + +- [x] Precipitation: daily total (ERA5) + +- [x] Soil moisture: daily average (ERA5) + +Variables from other sources: + +- [ ] Sea surface temperature: daily average and maximum in the nearest neighbor for each healthshed. + +- [ ] Precipitation: daily total (CHIRPS) + +- [ ] Chlorophyll-A (Giacomo) + +- [ ] Wealth index: Available from Giacomo + +- [ ] NDVI + +- [ ] Tropical storm + +- [ ] Flooding + +- [ ] Deforestation + +- [ ] Linking/segmenting healthsheds into climate zones and other + +- [ ] Relative humidity: daily average (lower priority) + +Those from the ERA5 dataset will be housed here, but we may likely develop a separate repository for the other datasets. + +## Developer Guide + +This package is built and maintained with `nbdev`. If you are new to using `nbdev` here are some useful pointers to get you started. + +### Install era5_sandbox in Development mode + +```sh +# make sure era5_sandbox package is installed in development mode +$ pip install -e . +``` + +To make changes, go to the "notes" directory and edit the notebooks as necessary. +Each notebook refers to a module in the era5_sandbox package. Cells are exported to the module +when the notebook is saved and you run the following command: + +```sh +$ nbdev_export +``` + +For e.g., to change functionality of the [`testAPI()`](https://TinasheMTapera.github.io/era5_sandbox/core.html#testapi) function in the testAPI Hydra rule, you would edit the [`testAPI`](https://TinasheMTapera.github.io/era5_sandbox/core.html#testapi) notebook in the `notes` directory `notes/testAPI.ipynb`, and then save that notebook and run `nbdev_export` to update the `core` module in the package. + +### How to Run the Pipeline + +The pipeline downloads ERA5 variables for a given date range and geographical bounding box. You can learn how each of these steps was by following the notebooks in `notes` in numerical order. + +::: {.callout-important} +The pipeline has two implementations: one using `snakemake` and `hydra`, and another using `pytask`. The `pytask` implementation is the more recent one, and is recommended for future use. The `snakemake` implementation is left here for reference to legacy code. +::: + +#### Using `pytask` + +To run the pipeline, the `pytask` config at `note/20_pytask_config.qmd` should be reviewed +and updated if necessary. The pipeline can then be run with the following command: + +```sh +$ sbatch pytask.sbatch +``` + +#### Using `snakemake` and `hydra` + +To run the pipeline, the config at `config/config.yaml` should be updated with the desired date range and geographical bounding box. The pipeline can then be run with the following command: + +```sh +sbatch snakemake.sbatch +``` + +### What Does the Pipeline Produce? + +Using `pytask`'s data catalog, you can investigate the downloaded raw data with python, eg.: + +::: {#cell-4 .cell exec_doc='null'} +``` {.python .cell-code} +import xarray as xr +from era5_sandbox.config import data_catalog +from era5_sandbox.core import ClimateDataFileHandler + +ex_nc = list(data_catalog['download']['outputs']._entries).pop() +ex_nc_path = data_catalog['download']['outputs'][ex_nc].load() + +with ClimateDataFileHandler(ex_nc_path) as handler: + ds = xr.open_dataset(handler.get_dataset("instant")) + +ds +``` + +::: {.cell-output .cell-output-display} + +```{=html} +
+ + + + + + + + + + + + + + +
<xarray.Dataset> Size: 53MB
+Dimensions:     (valid_time: 744, latitude: 49, longitude: 91)
+Coordinates:
+    number      int64 8B ...
+  * valid_time  (valid_time) datetime64[ns] 6kB 2024-03-01 ... 2024-03-31T23:...
+  * latitude    (latitude) float64 392B 30.8 30.7 30.6 30.5 ... 26.2 26.1 26.0
+  * longitude   (longitude) float64 728B 79.6 79.7 79.8 79.9 ... 88.4 88.5 88.6
+    expver      (valid_time) <U4 12kB ...
+Data variables:
+    d2m         (valid_time, latitude, longitude) float32 13MB ...
+    t2m         (valid_time, latitude, longitude) float32 13MB ...
+    tp          (valid_time, latitude, longitude) float32 13MB ...
+    swvl1       (valid_time, latitude, longitude) float32 13MB ...
+Attributes:
+    GRIB_centre:             ecmf
+    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts
+    GRIB_subCentre:          0
+    Conventions:             CF-1.7
+    institution:             European Centre for Medium-Range Weather Forecasts
+    history:                 2025-09-16T20:55 GRIB to CDM+CF via cfgrib-0.9.1...
+``` + +::: +::: + + +And plot it with cartopy, eg.: + +::: {#cell-6 .cell exec_doc='null'} +``` {.python .cell-code} +import matplotlib.pyplot as plt +import cartopy.crs as ccrs +import cartopy.feature as cfeature + +temperature = ds["t2m"] + +# Select a specific time step +temperature_at_time = temperature.isel(valid_time=0) + +# Plot the data on a map +plt.figure(figsize=(12, 8)) +ax = plt.axes(projection=ccrs.PlateCarree()) +temperature_at_time.plot(ax=ax, cmap="coolwarm", transform=ccrs.PlateCarree(), cbar_kwargs={"label": "Temperature (K)"}) +ax.coastlines() +ax.add_feature(cfeature.BORDERS, linestyle=":") +ax.set_title("Temperature at Time Step 0") +plt.show() +``` + +::: {.cell-output .cell-output-display} +![](index_files/figure-html/cell-4-output-1.png){width=897 height=640} +::: +::: + + +You can also load the aggregated data: + +::: {#cell-8 .cell exec_doc='null'} +``` {.python .cell-code} +import pandas as pd +import geopandas as gpd +from era5_sandbox.config import data_catalog + +ex_agg_path = data_catalog['aggregate']['outputs']['2019_08_madagascar_night_d2m_max.parquet'].load() + +gpd.read_parquet(ex_agg_path).describe() +``` + +::: {.cell-output .cell-output-display} + +```{=html} +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
day_01day_02day_03day_04day_05day_06day_07day_08day_09day_10day_11day_12day_13day_14day_15day_16day_17day_18day_19day_20day_21day_22day_23day_24day_25day_26day_27day_28day_29day_30day_31day_32
count2701.0000002701.0000002701.0000002701.0000002701.0000002701.0000002701.0000002701.0000002701.0000002701.0000002701.0000002701.0000002701.0000002701.0000002701.0000002701.0000002701.0000002701.0000002701.0000002701.0000002701.0000002701.0000002701.0000002701.0000002701.0000002701.0000002701.0000002701.0000002701.0000002701.0000002701.0000002701.000000
mean290.493048290.145274288.953153288.503714288.439820288.304426286.940995287.186512287.453656287.843029288.301938288.778014288.813762288.667253288.796892288.547945288.197632287.882440287.659818289.291587289.911503288.760939288.257644288.271450287.746390288.379399288.504720287.665699288.149861288.266861288.644028288.224829
std2.6169222.8320833.2156423.5660194.4014164.1988175.2357954.4440314.3463053.4354442.7357812.8644942.8412683.0805933.3062172.9381653.0183032.8498502.8176902.6009462.5840793.1618553.1718272.9837783.2233802.9188672.8443143.0526353.0772923.0937063.3359833.296264
min284.295898281.673340280.566406280.509521277.348145279.243164274.955078274.682129275.397461279.498291282.339111282.188721282.470703281.371582280.724609280.093506280.849121281.123535281.952148282.186768284.168945282.519287282.015381280.578857281.183838281.146973281.977539281.014648280.787842281.631348281.349854280.615967
25%288.031494287.739014286.978271285.750488284.326904284.071289281.695068283.710449284.153076285.459717286.141846286.444092286.505859286.104004286.114014286.730225286.005371285.420166285.230713287.408203287.744873286.101318285.243652285.488281285.170166285.876465286.145508285.243164285.579346285.322754285.930908285.565186
50%290.674316290.331543288.916260288.472168289.635742289.390381288.382568287.926758288.173096287.859375287.797852288.716064288.806641288.789307289.210938288.769287288.085205287.698975287.252930289.310547289.878418288.511719288.420166288.263916287.717041288.661621288.999023287.485107288.326416288.429199288.576416288.093018
75%292.828369292.707764291.609375291.655762291.987305291.845459291.671631291.051758291.288574291.000244290.813721291.365967291.540039291.393799291.756592291.094727290.893311290.266602290.166748291.649902291.970459291.342285290.443848290.660400290.400146290.360840290.854004290.328125290.827881290.999268291.598877291.072754
max296.467285295.717529295.837158295.693604295.723389296.195557295.589600295.345703294.754639294.483154294.952148294.815430294.623779295.088135295.036621294.847900294.224609294.522949294.728760295.268066295.507324295.797363296.297119296.222900295.492432295.406006294.629883295.211670295.363037295.263184295.446533295.408691
+
+``` + +::: +::: + + diff --git a/_proc/index.ipynb b/_proc/index.ipynb index b608fd6..43c2671 100644 --- a/_proc/index.ipynb +++ b/_proc/index.ipynb @@ -1,16 +1,13 @@ { "cells": [ { - "cell_type": "raw", + "cell_type": "markdown", "metadata": {}, "source": [ "---\n", - "description: Sandbox environment for era5 development\n", - "output-file: index.html\n", - "title: era5_sandbox\n", - "\n", - "---\n", - "\n" + "title: \"The ERA5 Spatial Aggregation Pipeline\"\n", + "exec_all: true\n", + "---" ] }, { @@ -20,11 +17,27 @@ "" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [], + "source": [ + "#| hide: null\n", + "from era5_sandbox.core import *" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Here we are developing functions and code for the Madagascar ERA5 dataset project. The goal is for exposure data to be made available at the daily resolution when possible. Finer resolutions shouldn’t ever be needed for our purposes, and it should then be relatively easy to aggregate at coarser resolutions, such as weekly or monthly.\n", + "## era5_sandbox\n", + "\n", + "> Sandbox environment for era5 development\n", + "\n", + "This package documents the development and implementation of functions and code for the Madagascar ERA5 dataset project. The goal is for exposure data to be made available at the daily resolution when possible. Finer resolutions shouldn’t ever be needed for our purposes, and it should then be relatively easy to aggregate at coarser resolutions, such as weekly or monthly. Additionally, we've extended this work to Nepal as well.\n", "\n", "Variables should generally be made available from 2010 onward, as that’s where our clinic data starts.\n", "\n", @@ -32,11 +45,15 @@ "\n", "Preliminary list of environmental variables\n", "\n", - "- [ ] 2-m air temperature from ERA5: daily min, max, mean\n", + "- [x] 2-m air temperature from ERA5: daily min, max, mean\n", " \n", - "- [ ] 2-m air dew point temperature from ERA5: daily min, max, mean\n", + "- [x] 2-m air dew point temperature from ERA5: daily min, max, mean\n", "\n", - "- [ ] Precipitation: daily total (ERA5)\n", + "- [x] Precipitation: daily total (ERA5)\n", + "\n", + "- [x] Soil moisture: daily average (ERA5)\n", + "\n", + "Variables from other sources:\n", "\n", "- [ ] Sea surface temperature: daily average and maximum in the nearest neighbor for each healthshed.\n", "\n", @@ -56,132 +73,615 @@ "\n", "- [ ] Linking/segmenting healthsheds into climate zones and other \n", "\n", - "- [ ] Relative humidity: daily average (lower priority)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Developer Guide" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you are new to using `nbdev` here are some useful pointers to get you started." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Install era5_sandbox in Development mode" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ + "- [ ] Relative humidity: daily average (lower priority)\n", + "\n", + "Those from the ERA5 dataset will be housed here, but we may likely develop a separate repository for the other datasets.\n", + "\n", + "## Developer Guide\n", + "\n", + "This package is built and maintained with `nbdev`. If you are new to using `nbdev` here are some useful pointers to get you started.\n", + "\n", + "### Install era5_sandbox in Development mode\n", + "\n", "```sh\n", "# make sure era5_sandbox package is installed in development mode\n", "$ pip install -e .\n", + "```\n", "\n", - "# To make changes, go to the \"notes\" directory and edit the notebooks as necessary.\n", - "# Each notebook refers to a module in the era5_sandbox package. Cells are exported to the module\n", - "# when the notebook is saved and you run the following command:\n", + "To make changes, go to the \"notes\" directory and edit the notebooks as necessary.\n", + "Each notebook refers to a module in the era5_sandbox package. Cells are exported to the module\n", + "when the notebook is saved and you run the following command:\n", "\n", + "```sh\n", "$ nbdev_export\n", "```\n", "\n", - "For e.g., to change functionality of the [`testAPI()`](https://TinasheMTapera.github.io/era5_sandbox/core.html#testapi) function in the testAPI Hydra rule, you would edit the [`testAPI`](https://TinasheMTapera.github.io/era5_sandbox/core.html#testapi) notebook in the `notes` directory `notes/testAPI.ipynb`, and then save that notebook and run `nbdev_export` to update the `core` module in the package." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Usage" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Installation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Install latest from the GitHub [repository][repo]:\n", + "For e.g., to change functionality of the [`testAPI()`](https://TinasheMTapera.github.io/era5_sandbox/core.html#testapi) function in the testAPI Hydra rule, you would edit the [`testAPI`](https://TinasheMTapera.github.io/era5_sandbox/core.html#testapi) notebook in the `notes` directory `notes/testAPI.ipynb`, and then save that notebook and run `nbdev_export` to update the `core` module in the package.\n", + "\n", + "### How to Run the Pipeline\n", + "\n", + "The pipeline downloads ERA5 variables for a given date range and geographical bounding box. You can learn how each of these steps was by following the notebooks in `notes` in numerical order.\n", + "\n", + "::: {.callout-important}\n", + "The pipeline has two implementations: one using `snakemake` and `hydra`, and another using `pytask`. The `pytask` implementation is the more recent one, and is recommended for future use. The `snakemake` implementation is left here for reference to legacy code.\n", + ":::\n", + "\n", + "#### Using `pytask`\n", + "\n", + "To run the pipeline, the `pytask` config at `note/20_pytask_config.qmd` should be reviewed\n", + "and updated if necessary. The pipeline can then be run with the following command:\n", "\n", "```sh\n", - "$ pip install git+https://github.com/NSAPH-Data-Processing/era5_sandbox\n", + "$ sbatch pytask.sbatch\n", "```\n", "\n", - "or clone and install in development mode:\n", + "#### Using `snakemake` and `hydra`\n", + "\n", + "To run the pipeline, the config at `config/config.yaml` should be updated with the desired date range and geographical bounding box. The pipeline can then be run with the following command:\n", "\n", "```sh\n", - "$ git clone https://github.com/NSAPH-Data-Processing/era5_sandbox\n", - "$ pip install -e .\n", + "sbatch snakemake.sbatch\n", "```\n", "\n", + "### What Does the Pipeline Produce?\n", "\n", - "[repo]: https://github.com/NSAPH-Data-Processing/era5_sandbox" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Documentation" + "Using `pytask`'s data catalog, you can investigate the downloaded raw data with python, eg.:" ] }, { - "cell_type": "markdown", - "metadata": {}, + "cell_type": "code", + "execution_count": null, + "metadata": { + "language": "python" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset> Size: 53MB\n",
+       "Dimensions:     (valid_time: 744, latitude: 49, longitude: 91)\n",
+       "Coordinates:\n",
+       "    number      int64 8B ...\n",
+       "  * valid_time  (valid_time) datetime64[ns] 6kB 2024-03-01 ... 2024-03-31T23:...\n",
+       "  * latitude    (latitude) float64 392B 30.8 30.7 30.6 30.5 ... 26.2 26.1 26.0\n",
+       "  * longitude   (longitude) float64 728B 79.6 79.7 79.8 79.9 ... 88.4 88.5 88.6\n",
+       "    expver      (valid_time) <U4 12kB ...\n",
+       "Data variables:\n",
+       "    d2m         (valid_time, latitude, longitude) float32 13MB ...\n",
+       "    t2m         (valid_time, latitude, longitude) float32 13MB ...\n",
+       "    tp          (valid_time, latitude, longitude) float32 13MB ...\n",
+       "    swvl1       (valid_time, latitude, longitude) float32 13MB ...\n",
+       "Attributes:\n",
+       "    GRIB_centre:             ecmf\n",
+       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
+       "    GRIB_subCentre:          0\n",
+       "    Conventions:             CF-1.7\n",
+       "    institution:             European Centre for Medium-Range Weather Forecasts\n",
+       "    history:                 2025-09-16T20:55 GRIB to CDM+CF via cfgrib-0.9.1...
" + ], + "text/plain": [ + " Size: 53MB\n", + "Dimensions: (valid_time: 744, latitude: 49, longitude: 91)\n", + "Coordinates:\n", + " number int64 8B ...\n", + " * valid_time (valid_time) datetime64[ns] 6kB 2024-03-01 ... 2024-03-31T23:...\n", + " * latitude (latitude) float64 392B 30.8 30.7 30.6 30.5 ... 26.2 26.1 26.0\n", + " * longitude (longitude) float64 728B 79.6 79.7 79.8 79.9 ... 88.4 88.5 88.6\n", + " expver (valid_time) " + ] + }, + "metadata": { + "image/png": { + "height": 640, + "width": 897 + } + }, + "output_type": "display_data" + } + ], "source": [ - "The pipeline currently downloads ERA5 temperature and dew point temperature data for a given date range and geographical bounding box. You can learn each of these steps by following the notebooks in `notes` in numerical order.\n", - "\n", - "To run the pipeline, the config at `config/config.yaml` should be updated with the desired date range and geographical bounding box. The pipeline can then be run with the following command:\n", - "\n", - "```sh\n", - "sbatch snakemake.sbatch\n", - "```\n", - "\n", - "You can investigate the downloaded raw data with python, eg.:\n", - "\n", - "```python\n", - "import xarray as xr\n", + "#| exec_doc: #\n", "import matplotlib.pyplot as plt\n", "import cartopy.crs as ccrs\n", "import cartopy.feature as cfeature\n", "\n", - "### the path to any of the downloaded files\n", - "file_path = \"/n/dominici_lab/lab/data_processing/csph-era5_sandbox/data/input/2010_01.nc\"\n", - "data = xr.open_dataset(file_path)\n", - "\n", - "\n", - "temperature = data[\"t2m\"]\n", - "\n", - "\n", + "temperature = ds[\"t2m\"]\n", "\n", "# Select a specific time step\n", "temperature_at_time = temperature.isel(valid_time=0)\n", @@ -193,8 +693,14 @@ "ax.coastlines()\n", "ax.add_feature(cfeature.BORDERS, linestyle=\":\")\n", "ax.set_title(\"Temperature at Time Step 0\")\n", - "plt.show()\n", - "```" + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can also load the aggregated data:" ] }, { @@ -206,8 +712,356 @@ "outputs": [ { "data": { + "text/html": [ + "
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std2.6169222.8320833.2156423.5660194.4014164.1988175.2357954.4440314.3463053.4354442.7357812.8644942.8412683.0805933.3062172.9381653.0183032.8498502.8176902.6009462.5840793.1618553.1718272.9837783.2233802.9188672.8443143.0526353.0772923.0937063.3359833.296264
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It defines\n", - "a few helpful functions such as an API tester to test your API key and connection." + "## core\n", + "\n", + "> This is a core library for the ERA5 dataset pipeline. It defines a few helpful functions such as an API tester to test your API key and connection." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "#| default_exp core" + "#| default_exp core:\n", + "#" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "#| hide\n", + "#| hide:\n", + "#\n", "from nbdev.showdoc import *" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "#| export\n", + "#| exports:\n", + "#\n", "import os\n", "import cdsapi\n", "import hydra\n", @@ -50,7 +57,7 @@ "from pydrive2.drive import GoogleDrive\n", "from omegaconf import DictConfig, OmegaConf\n", "from pyprojroot import here\n", - "from importlib import import_module\n" + "from importlib import import_module" ] }, { @@ -64,11 +71,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "#| export\n", + "#| exports:\n", + "#\n", "def describe(\n", " cfg: DictConfig=None, # Configuration file\n", " )-> None:\n", @@ -84,11 +92,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "#| exporti\n", + "#| exporti:\n", + "#\n", "def _expand_path(\n", " path: str # Path on user's machine\n", " )-> str: # Expanded path\n", @@ -105,11 +114,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "#| exporti\n", + "#| exporti:\n", + "#\n", "def _get_callable(func_path):\n", " \"\"\"Dynamically import a callable from a string path.\"\"\"\n", " module_name, func_name = func_path.rsplit(\".\", 1)\n", @@ -119,22 +129,18 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "#| exporti\n", - "\n", + "#| exporti:\n", + "# a directory structure creator\n", "def _create_directory_structure(\n", " base_path: str, # The base directory where the structure will be created\n", " structure: dict # A dictionary representing the directory structure\n", " )->None:\n", " \"\"\"\n", " Recursively creates a directory structure from a dictionary.\n", - "\n", - " Args:\n", - " base_path (str): The base directory where the structure will be created.\n", - " structure (dict): A dictionary representing the directory structure.\n", " \"\"\"\n", " for folder, substructure in structure.items():\n", " # Create the current directory\n", @@ -146,23 +152,28 @@ " _create_directory_structure(current_path, substructure)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In addition, we've defined 3 private functions to help with path expansion `_expand_path`, dynamic function importing `_get_callable`, and directory structure creation `_create_directory_structure`.\n", + "\n", + "### A Simple Temperature Conversion Function" + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "#| export\n", - "\n", - "def kelvin_to_celsius(kelvin):\n", + "#| export:\n", + "#\n", + "def kelvin_to_celsius(\n", + " kelvin: float # Temperature in Kelvin\n", + " ) -> float: # Temperature in Celsius\n", " \"\"\"\n", " Convert temperature from Kelvin to Celsius.\n", - " \n", - " Args:\n", - " kelvin (float): Temperature in Kelvin.\n", - " \n", - " Returns:\n", - " float: Temperature in Celsius.\n", " \"\"\"\n", " return kelvin - 273.15" ] @@ -173,17 +184,24 @@ "source": [ "### A Class for Authenticating Google Drive\n", "\n", - "We're going to use a class to authenticate and interact with google drive. The goal is to have a simple interface to fetch the healthshed files dynamically from google drive in the pipeline." + "We're going to use a class to authenticate and interact with google drive. The goal is to have a simple interface to fetch the healthshed files dynamically from google drive in the pipeline.\n", + "\n", + "::: {.callout-important}\n", + "This class was implemented when all of our data\n", + "was stored on a private Google Drive. Since we\n", + "have moved all of our data to FASRC, this will\n", + "likely be deprecated in the near future.\n", + ":::" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "#| export\n", - "\n", + "#| export:\n", + "#\n", "class GoogleDriver:\n", " \"\"\"\n", " A class to handle Google Drive authentication and file management.\n", @@ -225,25 +243,48 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from hydra import initialize, compose\n", - "from omegaconf import OmegaConf\n", - "\n", + "from omegaconf import OmegaConf" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "# unfortunately, we have to use the initialize function to load the config file\n", "# this is because the @hydra decorator does not work with Notebooks very well\n", "# this is a known issue with Hydra: https://gist.github.com/bdsaglam/586704a98336a0cf0a65a6e7c247d248\n", "# \n", "# just use the relative path from the notebook to the config dir\n", - "with initialize(version_base=None, config_path=\"../conf\"):\n", - " cfg = compose(config_name='config.yaml')" + "try:\n", + " with initialize(version_base=None, config_path=\"../conf\"):\n", + " cfg = compose(config_name='config.yaml')\n", + "except Exception as e:\n", + " print(f\"Error initializing Hydra: {e}\")\n", + " with initialize(version_base=None, config_path=\"conf\"):\n", + " cfg = compose(config_name='config.yaml')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "::: {.callout-important}\n", + "If we continue with `pytask`, we will not need to\n", + "use hydra at all, and so the above strategy\n", + "may get deprecated.\n", + ":::" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -260,17 +301,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "healthsheds2022.zip - application/zip\n" - ] - } - ], + "outputs": [], "source": [ "# we're using the madagascar healthshed folder as an example\n", "folder_id = cfg.geographies.madagascar.healthsheds\n", @@ -290,7 +323,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -303,9 +336,7 @@ " file_obj.GetContentFile(zip_path)\n", "\n", " # Read shapefile directly from ZIP\n", - " gdf = gpd.read_file(f\"zip://{zip_path}\")\n", - "\n", - "\n" + " gdf = gpd.read_file(f\"zip://{zip_path}\")" ] }, { @@ -317,22 +348,23 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "#| export\n", + "#| export:\n", + "#\n", "from fastcore.basics import patch" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "#| export\n", - "\n", + "#| export:\n", + "#\n", "@patch\n", "def read_healthsheds(self:GoogleDriver, healthshed_zip_name):\n", "\n", @@ -365,123 +397,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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-       "    institution:             European Centre for Medium-Range Weather Forecasts\n",
-       "    history:                 2025-04-30T16:38 GRIB to CDM+CF via cfgrib-0.9.1...
" - ], - "text/plain": [ - " Size: 9kB\n", - "Dimensions: (valid_time: 1, latitude: 59, longitude: 33)\n", - "Coordinates:\n", - " number int64 8B ...\n", - " * valid_time (valid_time) datetime64[ns] 8B 2023-10-01\n", - " * latitude (latitude) float64 472B -11.6 -11.85 -12.1 ... -25.85 -26.1\n", - " * longitude (longitude) float64 264B 42.7 42.95 43.2 ... 50.2 50.45 50.7\n", - " expver Size: 16kB\n", - "Dimensions: (valid_time: 1, latitude: 59, longitude: 33)\n", - "Coordinates:\n", - " number int64 8B ...\n", - " * valid_time (valid_time) datetime64[ns] 8B 2023-10-01\n", - " * latitude (latitude) float64 472B -11.6 -11.85 -12.1 ... -25.85 -26.1\n", - " * longitude (longitude) float64 264B 42.7 42.95 43.2 ... 50.2 50.45 50.7\n", - " expver Size: 9kB\n", - "Dimensions: (valid_time: 1, latitude: 59, longitude: 33)\n", - "Coordinates:\n", - " number int64 8B ...\n", - " * valid_time (valid_time) datetime64[ns] 8B 2023-10-01\n", - " * latitude (latitude) float64 472B -11.6 -11.85 -12.1 ... -25.85 -26.1\n", - " * longitude (longitude) float64 264B 42.7 42.95 43.2 ... 50.2 50.45 50.7\n", - " expver None:\n", "\n", @@ -1854,7 +796,7 @@ "metadata": {}, "outputs": [], "source": [ - "#| export\n", + "#| export: null\n", "#| eval: false\n", "try: from nbdev.imports import IN_NOTEBOOK\n", "except: IN_NOTEBOOK=False\n", @@ -1869,30 +811,19 @@ "metadata": {}, "outputs": [], "source": [ - "#| hide\n", + "#| hide:\n", + "#\n", "import nbdev; nbdev.nbdev_export()" ] } ], "metadata": { "kernelspec": { - "display_name": "era5_sandbox", + "display_name": "python3", "language": "python", "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.11" } }, "nbformat": 4, - "nbformat_minor": 4 + "nbformat_minor": 5 } diff --git a/notes/01_download_raw_data.ipynb b/notes/01_download_raw_data.ipynb index cc3de90..fd13e99 100644 --- a/notes/01_download_raw_data.ipynb +++ b/notes/01_download_raw_data.ipynb @@ -4,27 +4,34 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# download\n", + "---\n", + "title: \"Download Module: Downloading Raw Data from CDSAPI\"\n", + "engine: jupyter\n", + "---\n", "\n", - "> This module downloads the raw data from CDS and saves it in the local directory\n" + "## download\n", + "\n", + "> This module downloads the raw data from CDS and saves it in the local directory" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "#| default_exp download\n" + "#| default_exp download:\n", + "#" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "#| hide\n", + "#| hide:\n", + "#\n", "from nbdev.showdoc import *" ] }, @@ -38,11 +45,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "#| export\n", + "#| export:\n", + "#\n", "import os\n", "import hydra\n", "import cdsapi\n", @@ -61,16 +69,19 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "#| exporti\n", + "#| exporti:\n", + "#\n", "def _validate_query(\n", " query_body: DictConfig\n", " )->bool:\n", " '''\n", " Check that the query is valid\n", + " ###TODO Not a good idea to overwrite components of the query body because the user may believe something and the function may give somehting else back\n", + " Better to just tell them something is wrong\n", " '''\n", "\n", " required_keys = ['product_type', 'variable', 'year', 'month', 'day', 'time', 'area', 'data_format', 'download_format']\n", @@ -96,20 +107,31 @@ " return OmegaConf.to_container(query_body, resolve=True)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The background functionality in this module involves downloading the\n", + "bounding box of a region of interest, and sending that to the\n", + "CDS API query. As such, we define two helper functions to\n", + "fetch the OCHA/HDX shapefiles for a geographic region, and\n", + "another to create the bounding box from the files." + ] + }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "#| export\n", + "#| export:\n", + "#\n", "def fetch_GADM(\n", - " url: str=\"https://geodata.ucdavis.edu/gadm/gadm4.1/gpkg/gadm41_MDG.gpkg\",\n", + " url: str=\"https://geodata.ucdavis.edu/gadm/gadm4.1/gpkg/gadm41_MDG.gpkg\", # URL to fetch the GADM data for Madagascar\n", " output_file: str=\"gadm41_MDG.gpkg\" # file path to save the GADM data\n", " )-> str:\n", " '''\n", - " Fetch the GADM data for Madagascar\n", - " https://geodata.ucdavis.edu/gadm/gadm4.1/gpkg/gadm41_MDG.gpkg\n", + " Fetch the GADM bounding box for geographic region\n", " '''\n", "\n", " output_file_path = _expand_path(output_file)\n", @@ -126,27 +148,19 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "#| export\n", - "\n", + "#| exports:\n", + "#\n", "def create_bounding_box(\n", - " zip_url_or_path: str,\n", - " buffer_km: float = 50,\n", - " round_to: int = 1\n", - ") -> list:\n", + " zip_url_or_path: str, # URL or local path to the zipped shapefile.\n", + " buffer_km: float = 50, # Buffer distance in kilometers to expand the bounding box.\n", + " round_to: int = 1 # Number of decimal places to round the bounding box coordinates.\n", + ") -> list: # Bounding box in the CDS API area format [North, West, South, East]\n", " '''\n", " Create a bounding box from OCHA/HDX shapefile data with a buffer.\n", - "\n", - " Parameters:\n", - " zip_url_or_path (str): URL or local path to the zipped shapefile.\n", - " buffer_km (float): Buffer distance in kilometers to expand the bounding box.\n", - " round_to (int): Number of decimal places to round the bounding box coordinates.\n", - "\n", - " Returns:\n", - " list: Bounding box in the CDS API area format [North, West, South, East].\n", " '''\n", " with tempfile.TemporaryDirectory() as tmpdir:\n", " # Download if it's a URL\n", @@ -189,16 +203,24 @@ " round(bounds[2], round_to) # East\n", " ]\n", "\n", - " return bbox\n" + " return bbox" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The primary function to download the data from CDSAPI is defined below." ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "#| export\n", + "#| exports:\n", + "#\n", "def download_raw_era5(\n", " cfg: DictConfig # hydra configuration file\n", " )->None:\n", @@ -246,39 +268,11 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-05-14 16:45:08,085 INFO [2024-09-26T00:00:00] Watch our [Forum](https://forum.ecmwf.int/) for Announcements, news and other discussed topics.\n", - "2025-05-14 16:45:08,085 WARNING [2024-06-16T00:00:00] CDS API syntax is changed and some keys or parameter names may have also changed. To avoid requests failing, please use the \"Show API request code\" tool on the dataset Download Form to check you are using the correct syntax for your API request.\n", - "2025-05-14 16:45:11,277 INFO Request ID is 9ccc342d-9b35-4535-9558-032e1d7efe3d\n", - "2025-05-14 16:45:11,406 INFO status has been updated to accepted\n", - "2025-05-14 16:45:25,389 INFO status has been updated to running\n", - "2025-05-14 16:51:31,605 INFO status has been updated to successful\n", - " " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Downloaded file to: /net/rcstorenfs02/ifs/rc_labs/dominici_lab/lab/data_processing/csph-era5_sandbox/data/input/nepal_2017_11.nc\n", - "Done\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r" - ] - } - ], + "outputs": [], "source": [ + "#| eval: false\n", "from hydra import initialize, compose\n", "from omegaconf import OmegaConf\n", "\n", @@ -287,8 +281,13 @@ "# this is a known issue with Hydra: https://gist.github.com/bdsaglam/586704a98336a0cf0a65a6e7c247d248\n", "# \n", "# just use the relative path from the notebook to the config dir\n", - "with initialize(version_base=None, config_path=\"../conf\"):\n", - " cfg = compose(config_name='config.yaml')\n", + "try:\n", + " with initialize(version_base=None, config_path=\"../conf\"):\n", + " cfg = compose(config_name='config.yaml')\n", + "except Exception as e:\n", + " print(f\"Error initializing Hydra: {e}\")\n", + " with initialize(version_base=None, config_path=\"conf\"):\n", + " cfg = compose(config_name='config.yaml')\n", "\n", "cfg.development_mode = False\n", "cfg.query['year'] = 2017\n", @@ -305,10 +304,12 @@ "metadata": {}, "outputs": [], "source": [ - "#| export\n", + "#| exports:\n", + "#\n", "@hydra.main(config_path=\"../../conf\", config_name=\"config\", version_base=None)\n", "def main(cfg: DictConfig) -> None:\n", - " download_raw_era5(cfg=cfg)" + " download_raw_era5(cfg=cfg)\n", + " # better approach would be to have the function only use the specific arguments of the config" ] }, { @@ -317,7 +318,7 @@ "metadata": {}, "outputs": [], "source": [ - "#| export\n", + "#| export:\n", "#| eval: false\n", "try: from nbdev.imports import IN_NOTEBOOK\n", "except: IN_NOTEBOOK=False\n", @@ -334,30 +335,19 @@ "metadata": {}, "outputs": [], "source": [ - "#| hide\n", + "#| hide:\n", + "#\n", "import nbdev; nbdev.nbdev_export()" ] } ], "metadata": { "kernelspec": { - "display_name": "era5_sandbox", + "display_name": "python3", "language": "python", "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.11" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 5 } diff --git a/notes/02_aggregate.ipynb b/notes/02_aggregate.ipynb index 3fe8268..74fc1c0 100644 --- a/notes/02_aggregate.ipynb +++ b/notes/02_aggregate.ipynb @@ -4,6 +4,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "---\n", + "title: \"Aggregate Module: Spatial Aggregation to Healthsheds\"\n", + "execute:\n", + " freeze: auto\n", + "engine: jupyter\n", + "---\n", + "\n", "## aggregate\n", "\n", "> This module aggregates the downloaded data into the respective output dataframes." @@ -11,20 +18,22 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "#| default_exp aggregate\n" + "#| default_exp aggregate:\n", + "#" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "#| hide\n", + "#| hide:\n", + "#\n", "from nbdev.showdoc import *" ] }, @@ -48,12 +57,12 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "#| export\n", - "\n", + "#| exports:\n", + "#\n", "import tempfile\n", "import rasterio\n", "import hydra\n", @@ -82,12 +91,17 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "with initialize(version_base=None, config_path=\"../conf\"):\n", - " cfg = compose(config_name='config.yaml')" + "try:\n", + " with initialize(version_base=None, config_path=\"../conf\"):\n", + " cfg = compose(config_name='config.yaml')\n", + "except Exception as e:\n", + " print(f\"Error initializing Hydra: {e}\")\n", + " with initialize(version_base=None, config_path=\"conf\"):\n", + " cfg = compose(config_name='config.yaml')" ] }, { @@ -99,21 +113,21 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "eg_file = here() / \"data/input/nepal_2017_11.nc\"" + "eg_file = here() / \"bld/2009_01_nepal.nc\"" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "#| export \n", - "\n", + "#| export: \n", + "#\n", "def resample_netcdf(\n", " fpath: str, # Path to the netCDF file.\n", " resample: str = \"1D\", # Resampling frequency (e.g., '1H', '1D')\n", @@ -142,9 +156,16 @@ " raise TypeError(\"agg_func must be a callable function like np.mean, np.max, etc.\")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We pull the aggregation function from the config file:" + ] + }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -154,7 +175,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -168,17 +189,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "I'm going to use a dataclass to represent the tiff data. This will allow us to easily pass around the data and metadata associated with the tiff file. Why? Because I can, lol (I've never used dataclasses and I'm curious about them). ChatGPT thinks this will make the code cleaner and easier to read." + "I'm going to use a dataclass to represent the tiff data. This will allow us to easily pass around the data and metadata associated with the tiff file. Why? I've never used dataclasses and I'm curious about them — ChatGPT thinks this will make the code cleaner and easier to read." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "#| export\n", - "\n", + "#| exports:\n", + "#\n", "@dataclass\n", "class RasterFile:\n", " path: str\n", @@ -216,12 +237,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "#| export\n", - "\n", + "#| exports:\n", + "#\n", "def netcdf_to_tiff(\n", " ds: xr.Dataset, # The aggregated xarray dataset to convert. \n", " band: int, # The day to rasterise; 1 indexed just like human english\n", @@ -231,12 +252,6 @@ "\n", " \"\"\"\n", " Convert a netCDF file to a GeoTIFF file.\n", - " \n", - " Args:\n", - " fpath (str): Path to the netCDF file.\n", - " output_path (str): Path to save the output GeoTIFF file.\n", - " variable_name (str): Name of the variable to convert.\n", - " time_index (int): Index of the time dimension to extract.\n", " \"\"\"\n", "\n", " with tempfile.TemporaryDirectory() as tmpdirname:\n", @@ -262,34 +277,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Size: 267kB\n", - "Dimensions: (valid_time: 30, latitude: 20, longitude: 37)\n", - "Coordinates:\n", - " number int64 8B 0\n", - " * latitude (latitude) float64 160B 30.75 30.5 30.25 ... 26.5 26.25 26.0\n", - " * longitude (longitude) float64 296B 79.6 79.85 80.1 ... 88.1 88.35 88.6\n", - " * valid_time (valid_time) datetime64[ns] 240B 2017-11-01 ... 2017-11-30\n", - "Data variables:\n", - " d2m (valid_time, latitude, longitude) float32 89kB 261.6 ... 288.1\n", - " t2m (valid_time, latitude, longitude) float32 89kB 267.3 ... 293.1\n", - " swvl1 (valid_time, latitude, longitude) float32 89kB 0.2773 ... 0.1922\n", - "Attributes:\n", - " GRIB_centre: ecmf\n", - " GRIB_centreDescription: European Centre for Medium-Range Weather Forecasts\n", - " GRIB_subCentre: 0\n", - " Conventions: CF-1.7\n", - " institution: European Centre for Medium-Range Weather Forecasts\n", - " history: 2025-05-14T20:49 GRIB to CDM+CF via cfgrib-0.9.1...\n" - ] - } - ], + "outputs": [], "source": [ "with ClimateDataFileHandler(eg_file) as handler:\n", " ds_path = handler.get_dataset(\"instant\")\n", @@ -306,24 +296,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((20, 37),\n", - " Affine(0.25, 0.0, 79.475,\n", - " 0.0, -0.25, 30.875),\n", - " CRS.from_wkt('GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]'),\n", - " BoundingBox(left=79.475, bottom=25.875, right=88.725, top=30.875))" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "resampled_tiff.data.shape, resampled_tiff.transform, resampled_tiff.crs, resampled_tiff.bounds" ] @@ -345,43 +320,22 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "#| export \n", - "\n", + "#| exports:\n", + "#\n", "def polygon_to_raster_cells(\n", - " vectors,\n", - " raster,\n", - " nodata=None,\n", - " affine=None,\n", - " all_touched=False,\n", - " verbose=False,\n", + " vectors, # list of geometries from a shapefile\n", + " raster, # the raster data as a numpy array\n", + " nodata=None, # the nodata value of the raster\n", + " affine=None, # the affine transform of the raster\n", + " all_touched=False, # whether to include all touched pixels\n", + " verbose=False, \n", " **kwargs,\n", - "):\n", - " \"\"\"Returns an index map for each vector geometry to indices in the raster source.\n", - "\n", - " Parameters\n", - " ----------\n", - " vectors: list of geometries\n", - "\n", - " raster: ndarray\n", - "\n", - " nodata: float\n", - "\n", - " affine: Affine instance\n", - "\n", - " all_touched: bool, optional\n", - " Whether to include every raster cell touched by a geometry, or only\n", - " those having a center point within the polygon.\n", - " defaults to `False`\n", - "\n", - " Returns\n", - " -------\n", - " dict\n", - " A dictionary mapping vector the ids of geometries to locations (indices) in the raster source.\n", - " \"\"\"\n", + ") -> list: # A dictionary mapping vector the ids of geometries to locations (indices) in the raster source.\n", + " \"\"\"Returns an index map for each vector geometry to indices in the raster source.\"\"\"\n", "\n", " cell_map = []\n", "\n", @@ -434,12 +388,17 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "with initialize(version_base=None, config_path=\"../conf\"):\n", - " cfg = compose(config_name='config.yaml')\n", + "try:\n", + " with initialize(version_base=None, config_path=\"../conf\"):\n", + " cfg = compose(config_name='config.yaml')\n", + "except Exception as e:\n", + " print(f\"Error initializing Hydra: {e}\")\n", + " with initialize(version_base=None, config_path=\"conf\"):\n", + " cfg = compose(config_name='config.yaml')\n", "\n", "driver = GoogleDriver(json_key_path=here() / cfg.GOOGLE_DRIVE_AUTH_JSON.path)\n", "drive = driver.get_drive()\n", @@ -448,19 +407,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/777 [00:00\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
healthshedmean_soil_moisturegeometry
010.331351POLYGON ((87.60719 27.37069, 87.60841 27.36969...
170.360747POLYGON ((88.04438 27.4203, 88.04365 27.41925,...
280.332805POLYGON ((88.14528 27.67003, 88.14526 27.66966...
3230.270567POLYGON ((88.0766 27.03545, 88.07695 27.03533,...
4240.212768POLYGON ((87.76435 26.92431, 87.76435 26.924, ...
\n", - "" - ], - "text/plain": [ - " healthshed mean_soil_moisture \\\n", - "0 1 0.331351 \n", - "1 7 0.360747 \n", - "2 8 0.332805 \n", - "3 23 0.270567 \n", - "4 24 0.212768 \n", - "\n", - " geometry \n", - "0 POLYGON ((87.60719 27.37069, 87.60841 27.36969... \n", - "1 POLYGON ((88.04438 27.4203, 88.04365 27.41925,... \n", - "2 POLYGON ((88.14528 27.67003, 88.14526 27.66966... \n", - "3 POLYGON ((88.0766 27.03545, 88.07695 27.03533,... \n", - "4 POLYGON ((87.76435 26.92431, 87.76435 26.924, ... " - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "result = aggregate_to_healthsheds(\n", " res_poly2cell=res_poly2cell,\n", @@ -672,20 +524,9 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "result.plot(column=\"mean_soil_moisture\", legend=True)\n", "plt.title(\"Mean Soil Moisture (m^3 m^-3) by Health Shed Nov 2017 day 1\")\n", @@ -707,340 +548,88 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 2766/2766 [00:01<00:00, 1672.67it/s]\n" - ] - }, - { - "data": { - "image/png": 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", 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "/n/home03/ttapera/.conda/envs/era5_sandbox/lib/python3.11/site-packages/xarray/namedarray/core.py:919: RuntimeWarning: All-NaN slice encountered\n", - " data = func(self.data, axis=axis, **kwargs)\n", - "100%|██████████| 2766/2766 [00:02<00:00, 1179.36it/s]\n" - ] - }, - { - "data": { - "image/png": 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", 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "5.9 s ± 2.07 s per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" - ] - } - ], + "outputs": [], "source": [ - "%%timeit\n", - "\n", - "import random\n", - "\n", - "variables = [\"t2m\", \"d2m\"]\n", - "years = [\"20{:02d}\".format(m) for m in range(9, 24)]\n", - "months = [str(m) for m in range(1, 13)]\n", - "aggregations = [\n", - " (\"Mean\", np.nanmean),\n", - " (\"Max\", np.nanmax),\n", - " (\"Min\", np.nanmin)\n", - "]\n", - "\n", - "exposure_variable = random.choice(variables)\n", - "year = random.choice(years)\n", - "month = random.choice(months)\n", - "aggregation_str, agg_func = random.choice(aggregations)\n", - "input_file = here() / \"data/input/{}_{}.nc\".format(year, month)\n", - "\n", - "with initialize(version_base=None, config_path=\"../conf\"):\n", - " cfg = compose(config_name='config.yaml')\n", - "\n", - "driver = GoogleDriver(json_key_path=here() / cfg.GOOGLE_DRIVE_AUTH_JSON.path)\n", - "drive = driver.get_drive()\n", - "healthsheds = driver.read_healthsheds(cfg.GOOGLE_DRIVE_AUTH_JSON.healthsheds_id)\n", - "\n", - "with ClimateDataFileHandler(input_file) as handler:\n", - " ds_path = handler.get_dataset(\"instant\")\n", - " resampled_nc_file = resample_netcdf(ds_path, agg_func=agg_func)\n", - "\n", - "days = len(resampled_nc_file.valid_time.values)\n", - "day = random.choice(range(1, days + 1))\n", - "\n", - "resampled_tiff = netcdf_to_tiff(\n", - " ds=resampled_nc_file,\n", - " band=day, # the day we're aggregating\n", - " variable=exposure_variable,\n", - " crs=\"EPSG:4326\"\n", - ")\n", + "import random" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| eval: false\n", "\n", - "res_poly2cell=polygon_to_raster_cells(\n", - " vectors = healthsheds.geometry.values, # the geometries of the shapefile of the regions\n", - " raster=resampled_tiff.data, # the raster data above\n", - " nodata=resampled_tiff.nodata, # any intersections with no data, may have to be np.nan\n", - " affine=resampled_tiff.transform, # some math thing need to revise\n", - " all_touched=True, \n", - " verbose=True\n", - ")\n", "\n", - "result = aggregate_to_healthsheds(\n", - " res_poly2cell=res_poly2cell,\n", - " raster=resampled_tiff,\n", - " shapes=healthsheds,\n", - " names_column=\"fs_uid\",\n", - " aggregation_func=agg_func,\n", - " aggregation_name=exposure_variable\n", - ")\n", - "\n", - "result.plot(column=exposure_variable, legend=True)\n", - "plt.title(\"{} {} (K) by Health Shed {}\".format(aggregation_str, exposure_variable, input_file.stem))\n", - "plt.suptitle(\"Aggregation: {}, Day: {}\".format(aggregation_str, str(day)))\n", - "plt.show()" + "# variables = [\"t2m\", \"d2m\"]\n", + "# years = [\"20{:02d}\".format(m) for m in range(9, 24)]\n", + "# months = [str(m) for m in range(1, 13)]\n", + "# aggregations = [\n", + "# (\"Mean\", np.nanmean),\n", + "# (\"Max\", np.nanmax),\n", + "# (\"Min\", np.nanmin)\n", + "# ]\n", + "\n", + "# exposure_variable = random.choice(variables)\n", + "# year = random.choice(years)\n", + "# month = random.choice(months)\n", + "# aggregation_str, agg_func = random.choice(aggregations)\n", + "# input_file = here() / \"data/input/{}_{}.nc\".format(year, month)\n", + "\n", + "# with initialize(version_base=None, config_path=\"../conf\"):\n", + "# cfg = compose(config_name='config.yaml')\n", + "\n", + "# driver = GoogleDriver(json_key_path=here() / cfg.GOOGLE_DRIVE_AUTH_JSON.path)\n", + "# drive = driver.get_drive()\n", + "# healthsheds = driver.read_healthsheds(cfg.GOOGLE_DRIVE_AUTH_JSON.healthsheds_id)\n", + "\n", + "# with ClimateDataFileHandler(input_file) as handler:\n", + "# ds_path = handler.get_dataset(\"instant\")\n", + "# resampled_nc_file = resample_netcdf(ds_path, agg_func=agg_func)\n", + "\n", + "# days = len(resampled_nc_file.valid_time.values)\n", + "# day = random.choice(range(1, days + 1))\n", + "\n", + "# resampled_tiff = netcdf_to_tiff(\n", + "# ds=resampled_nc_file,\n", + "# band=day, # the day we're aggregating\n", + "# variable=exposure_variable,\n", + "# crs=\"EPSG:4326\"\n", + "# )\n", + "\n", + "# res_poly2cell=polygon_to_raster_cells(\n", + "# vectors = healthsheds.geometry.values, # the geometries of the shapefile of the regions\n", + "# raster=resampled_tiff.data, # the raster data above\n", + "# nodata=resampled_tiff.nodata, # any intersections with no data, may have to be np.nan\n", + "# affine=resampled_tiff.transform, # some math thing need to revise\n", + "# all_touched=True, \n", + "# verbose=True\n", + "# )\n", + "\n", + "# result = aggregate_to_healthsheds(\n", + "# res_poly2cell=res_poly2cell,\n", + "# raster=resampled_tiff,\n", + "# shapes=healthsheds,\n", + "# names_column=\"fs_uid\",\n", + "# aggregation_func=agg_func,\n", + "# aggregation_name=exposure_variable\n", + "# )\n", + "\n", + "# result.plot(column=exposure_variable, legend=True)\n", + "# plt.title(\"{} {} (K) by Health Shed {}\".format(aggregation_str, exposure_variable, input_file.stem))\n", + "# plt.suptitle(\"Aggregation: {}, Day: {}\".format(aggregation_str, str(day)))\n", + "# plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ + "::: {.callout-note}\n", + "**Note:** The above code is commented out to prevent execution during documentation generation. You can uncomment and run it in an appropriate environment to test the aggregation process.\n", + ":::\n", + "\n", "3.2 seconds per aggregation is pretty cool!" ] }, @@ -1050,34 +639,23 @@ "metadata": {}, "outputs": [], "source": [ - "\n", + "#| eval: false\n", "result.to_parquet(here() / \"data/testing/test_aggregation.parquet\")" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For QA, we should come up with the following:\n", - "\n", - "- [x] A way to list NAs in the data\n", - "- [ ] A way to visualize the data temporally\n", - "- [ ] A function to convert K to celsius" - ] - }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "#| export\n", - "\n", + "#| exports:\n", + "#\n", "def aggregate_data(\n", - " cfg: DictConfig,\n", - " input_file: str,\n", - " output_file: str,\n", - " exposure_variable: str\n", + " cfg: DictConfig, # the hydra config\n", + " input_file: str, # the input netcdf file\n", + " output_file: str, # the output parquet file\n", + " exposure_variable: str # Which variable in the dataset to aggregate\n", " ) -> None:\n", " '''\n", " Aggregate raster data day-by-day and store all days and statistics as separate columns in a single Parquet file.\n", @@ -1161,1286 +739,18 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing daily aggregation: mean...\n", - "Aggregating to healthshed by: mean...\n", - "Processing day 1...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 777/777 [00:00<00:00, 1320.29it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing day 2...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 777/777 [00:00<00:00, 1335.20it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing day 3...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 777/777 [00:00<00:00, 1340.34it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing day 4...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - 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] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 777/777 [00:00<00:00, 1298.07it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing day 25...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 777/777 [00:00<00:00, 1311.84it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing day 26...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 777/777 [00:00<00:00, 1299.80it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing day 27...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 777/777 [00:00<00:00, 1308.52it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing day 28...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 777/777 [00:00<00:00, 1302.54it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing day 29...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 777/777 [00:00<00:00, 1309.99it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Processing day 30...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 777/777 [00:00<00:00, 1312.36it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Saving final monthly parquet file: /net/rcstorenfs02/ifs/rc_labs/dominici_lab/lab/data_processing/csph-era5_sandbox/data/testing/test_nepal_aggregation.parquet\n" - ] - } - ], + "outputs": [], "source": [ - "with initialize(version_base=None, config_path=\"../conf\"):\n", - " cfg = compose(config_name='config.yaml')\n", + "#| eval: false\n", + "try:\n", + " with initialize(version_base=None, config_path=\"../conf\"):\n", + " cfg = compose(config_name='config.yaml')\n", + "except Exception as e:\n", + " print(f\"Error initializing Hydra: {e}\")\n", + " with initialize(version_base=None, config_path=\"conf\"):\n", + " cfg = compose(config_name='config.yaml')\n", "\n", "cfg.development_mode = False\n", "cfg.query['year'] = 2017\n", @@ -2449,468 +759,36 @@ "\n", "variable = \"swvl1\"\n", "\n", - "aggregate_data(cfg, here() / \"data/input/nepal_2017_11.nc\", here() / \"data/testing/test_nepal_aggregation.parquet\", exposure_variable=variable)" + "aggregate_data(cfg, here() / \"bld/2017_11_nepal.nc\", here() / \"data/testing/test_nepal_aggregation.parquet\", exposure_variable=variable)" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ + "#| eval: false\n", "parquet_file = gpd.read_parquet(here() / \"data/testing/test_nepal_aggregation.parquet\")" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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0.238021 0.268717 0.272461 0.268131 \n", - "4 0.204233 0.212965 0.213644 0.212993 \n", - ".. ... ... ... ... \n", - "772 0.204872 0.209167 0.210570 0.211685 \n", - "773 0.197317 0.202446 0.204245 0.205096 \n", - "774 0.198613 0.200442 0.201081 0.203404 \n", - "775 0.192438 0.193253 0.193823 0.196438 \n", - "776 0.193565 0.194235 0.194550 0.197598 \n", - "\n", - " day_30_daily_max \n", - "0 0.329162 \n", - "1 0.351963 \n", - "2 0.327341 \n", - "3 0.259376 \n", - "4 0.211268 \n", - ".. ... \n", - "772 0.211818 \n", - "773 0.204812 \n", - "774 0.203787 \n", - "775 0.196811 \n", - "776 0.197932 \n", - "\n", - "[777 rows x 92 columns]" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ + "#| eval: false\n", "parquet_file" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ + "#| eval: false\n", "parquet_file.plot(column=\"day_22_daily_mean\", legend=True)" ] }, @@ -2920,7 +798,8 @@ "metadata": {}, "outputs": [], "source": [ - "#| export\n", + "#| exports:\n", + "#\n", "@hydra.main(version_base=None, config_path=\"../../conf\", config_name=\"config\")\n", "def main(cfg: DictConfig) -> None:\n", " # Parse command-line arguments\n", @@ -2947,7 +826,7 @@ "metadata": {}, "outputs": [], "source": [ - "#| export\n", + "#| export:\n", "#| eval: false\n", "try: from nbdev.imports import IN_NOTEBOOK\n", "except: IN_NOTEBOOK=False\n", @@ -2962,30 +841,19 @@ "metadata": {}, "outputs": [], "source": [ - "#| hide\n", + "#| hide: \n", + "#\n", "import nbdev; nbdev.nbdev_export()" ] } ], "metadata": { "kernelspec": { - "display_name": "era5_sandbox", + "display_name": "python3", "language": "python", "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.11" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 5 } diff --git a/notes/03_publish.ipynb b/notes/03_publish.ipynb new file mode 100644 index 0000000..a9d3461 --- /dev/null +++ b/notes/03_publish.ipynb @@ -0,0 +1,719 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "title: \"Publish: Gather the Aggregated Data and Publish to DataVerse\"\n", + "engine: jupyter\n", + "---\n", + "\n", + "## publish \n", + "\n", + "> This is the `publish` module for the ERA5 dataset pipeline. It defines a functions that make use of the `pyDataverse` library and API to publish our outputs to the Harvard Dataverse." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| default_exp publish:\n", + "#" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide:\n", + "#\n", + "from nbdev.showdoc import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, we'll test out the API by pinging the Harvard DataVerse" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| exports:\n", + "#\n", + "import hydra\n", + "import yaml\n", + "import json\n", + "from tqdm import tqdm\n", + "from pyprojroot import here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "api_token_file = here() / \"sandbox/dataverse_api_key.yml\"\n", + "with open(api_token_file, \"r\") as f:\n", + " config = yaml.load(f, Loader=yaml.BaseLoader)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, following the [docs]() for the dataverse tutorial, load a NativeAPI up:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| exports:\n", + "#\n", + "from pyDataverse.api import NativeApi" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The NativeAPI is a catchall API object to be able to do general stuff:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "api = NativeApi(config['base_url'], config['api_token'])\n", + "resp=api.get_info_version()\n", + "#resp.text()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "resp.json()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looks good! Now that we know that it works, we can think more\n", + "about how to publish data there.\n", + "\n", + "## Harvard Dataverse\n", + "\n", + "Let's create a dummy dataset with the components we're\n", + "planning to upload, and then upload and promptly delete it.\n", + "\n", + "To do that, we must import the `models` module and create a Dataset object:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pyDataverse.models import Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ds = Dataset()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This `ds` object is pretty straightforward since it doesn't contain anything yet:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ds.get()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can populate the object from the dummy data on the github repo:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pyDataverse.utils import read_file\n", + "from urllib.request import urlretrieve\n", + "import tempfile" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# url for dummy data\n", + "url = \"https://raw.githubusercontent.com/gdcc/pyDataverse/refs/heads/main/tests/data/user-guide/dataset.json\"\n", + "\n", + "\n", + "with tempfile.NamedTemporaryFile(mode='w+') as tmp:\n", + " urlretrieve(url, tmp.name)\n", + " ds.from_json(read_file(tmp.name))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have to validate the JSON correctly:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ds.validate_json()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Modifying it is easy:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ds.set({\"title\": \"Youth from Austria 2005\"})\n", + "ds.get()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, to create the dataset we use the API:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| eval: false\n", + "# this is only run in interactive sessions for demo purposes\n", + "resp = api.create_dataset(\":root\", ds.json())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you caught the `resp` object, it contains the PID for the newly created dataset.\n", + "\n", + "However, if you didn't you can use the SearchAPI to find it:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| exports:\n", + "#\n", + "from pyDataverse.api import SearchApi" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "search_api = SearchApi(config['base_url'], config['api_token'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| eval: false\n", + "#\n", + "\n", + "resp = search_api.search(\"Youth from Austria\", data_type=\"dataset\")\n", + "results = resp.json()['data']['items']\n", + "result = [x for x in results if \"Youth from Austria\" in x['name']][0]\n", + "result" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| eval: false\n", + "pid = result['global_id']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now to look at the data we created using the NativeAPI again, and delete the dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| eval: false\n", + "\n", + "uploaded_ds = api.get_dataset(pid)\n", + "uploaded_ds.json()['data']\n", + "\n", + "resp = api.delete_dataset(pid)\n", + "resp.json()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With that understanding, we can develop a quick module to do the following:\n", + "\n", + "1. Make the dataset LEGO Compatible\n", + "2. Upload and publish the data to dataverse\n", + "\n", + "## LEGO Compatibility\n", + "\n", + "Let's take an example file to use as a model for LEGO compatibility" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| exports:\n", + "#\n", + "import geopandas as gpd\n", + "import pandas as pd\n", + "import re\n", + "import glob" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ex = gpd.read_parquet(here() / \"bld/2009_06_madagascar_day_swvl1_mean.parquet\")\n", + "ex.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We know that the LEGO data model should look like this:\n", + "\n", + "```\n", + "
/lego\n", + "├── \n", + "│ ├── __\n", + "│ │ ├── __\n", + "│ │ │ ├── _yyyy.parquet\n", + "```\n", + "\n", + "So, for the above file, we'll end up with the LEGO path `data/environmental/exposures_era5/healthshed_monthly/dewpoint_2024.parquet`. In it, we should have the following columns:\n", + "\n", + "\n", + "```\n", + "healthshed_id year month day stat_1 stat_2 ... stat_n \n", + "```\n", + "\n", + "\n", + "This means we should read in all of the exposures for a single timepoint at once. \n", + "I think the smart thing to do is use a glob string to gather all of the pertinent files.\n", + "This will be the first function we export to the library:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| exports:\n", + "# \n", + "\n", + "def gather_exposure_geodataframes(\n", + " glob_string: str, # string for the path to search for the pertinent files\n", + " polygon_id: str, # the string signifying the healthshed ID of the polygon\n", + " exposure: str # the exposure name\n", + " )-> list:\n", + " \"Read in a list of geo dataframes from the same time frame and merge them\"\n", + "\n", + " # first get the initial one so we have the polygon ID and geometry\n", + " frames = glob.glob(str(glob_string))\n", + " initial_gdf=gpd.read_parquet(frames[0])\n", + " merged_df = []\n", + " \n", + " for f in tqdm(frames, desc=\"Processing files\"):\n", + " # read in as a regular dataframe by ignoring geometry\n", + " df = gpd.read_parquet(f).drop([\"geometry\"], axis=1) \n", + " \n", + " # get the year and month\n", + " # Extract year and month\n", + " search_str = rf'_{exposure}_(\\d{{4}})_(\\d{{1,2}})\\.parquet$'\n", + " match = re.search(search_str, f)\n", + "\n", + " if match:\n", + " year = int(match.group(1))\n", + " month = int(match.group(2))\n", + " #print(f\"Year: {year}, Month: {month}\")\n", + " else:\n", + " raise ValueError(f\"Could not extract year and month from filename: {search_str} {f}\")\n", + " \n", + " df['exposure'] = exposure\n", + " df['month'] = month\n", + " df['year'] = year\n", + "\n", + " # Step 1: Melt all day columns (leave 'month' and 'year' as identifiers)\n", + " df_long = df.melt(id_vars=[polygon_id, \"exposure\", \"year\", \"month\"], var_name=\"day_stat\", value_name=\"value\")\n", + "\n", + " # Step 2: Extract day and stat type from column names\n", + " # Example column: \"day_01_daily_mean\"\n", + " df_long[[\"day\", \"stat\"]] = df_long[\"day_stat\"].str.extract(r\"day_(\\d{2})_daily_(mean|max|min|total)\")\n", + "\n", + " # Optional: convert 'day' and month to integer\n", + " df_long[\"day\"] = df_long[\"day\"].astype(int)\n", + " df_long[\"month\"] = df_long[\"month\"].astype(int)\n", + "\n", + " # Drop the original combined column\n", + " df_long = df_long.drop(columns=\"day_stat\")\n", + "\n", + " # Reorder columns\n", + " df_long = df_long[[polygon_id, \"exposure\", \"year\", \"month\", \"day\", \"stat\", \"value\"]]\n", + "\n", + " df_long = df_long.sort_values(by=[\"year\", \"month\", \"day\"])\n", + " df_clean = df_long.pivot(index=[polygon_id, \"exposure\", \"year\", \"month\", \"day\"], columns=\"stat\", values=\"value\").reset_index()\n", + " merged_df.append(df_clean)\n", + "\n", + " return [pd.concat(merged_df).reset_index(drop=True), initial_gdf[[polygon_id, \"geometry\"]]]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "frames = here() / \"data\" / \"testing\" / \"*madagascar*\"\n", + "\n", + "merged = gather_exposure_geodataframes(frames, \"fs_uid\", \"2m_dewpoint_temperature\")\n", + "merged[0].describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This returns one file with all of the geometries and one file\n", + "with the statistics and exposures.\n", + "\n", + "Now, with this, we can move on. The dataset was created in the UI and is available via search and test out how to upload it:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "resp = search_api.search(\"ERA5\", data_type=\"dataset\")\n", + "\n", + "results = resp.json()['data']['items']\n", + "\n", + "result = [x for x in results if \"ERA5\" in x['name']][0]\n", + "era5_pid = result['global_id']\n", + "result" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| exports:\n", + "#\n", + "\n", + "from pyDataverse.models import Datafile\n", + "import os\n", + "import pathlib" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll upload directly from file. In the case of ERA5 vs. LEGO, we\n", + "store the file on disk as LEGO hierarchy, but to upload it to dataverse\n", + "using a flat filename (since creating subdatasets to represent directories might be \n", + "a bit of a hassle)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# assuming the file has a path on disk like:\n", + "f_out = \"environmental/exposures_era5/healthshed_daily/dewpoint_2024.parquet\"\n", + "os.makedirs(here() / \"data\" / \"testing\" / os.path.dirname(f_out), exist_ok=True)\n", + "aggregations, geo = merged\n", + "aggregations.to_parquet(here() / \"data\" / \"testing\" / f_out, index=False)\n", + "\n", + "datafile = Datafile()\n", + "datafile.set({\n", + " # the id of the era5 dataset \n", + " \"pid\": era5_pid,\n", + " # the path to the file on disk goes here\n", + " \"filename\": str(here() / \"data\" / \"testing\" / f_out),\n", + " # use the \"label\" to name the file\n", + " \"label\": f_out.replace(\"/\", \"-\")\n", + "})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| eval: false\n", + "resp = api.upload_datafile(era5_pid, str(here() / \"data\" / \"testing\" / f_out), datafile.json())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pretty simple!\n", + "\n", + "Now, we just need a main function to upload this data. The final upload is one file per\n", + "exposure per year, so these should be the variables we gather data for.\n", + "\n", + "We should get some functionality to gather the groups of these files automatically, based on\n", + "the hydra config:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| exports:\n", + "#\n", + "from hydra import initialize, compose\n", + "from omegaconf import OmegaConf, DictConfig\n", + "from tqdm import tqdm" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "target_dir = here() / \"data\" / \"intermediate\"\n", + "\n", + "try:\n", + " with initialize(version_base=None, config_path=\"../conf\"):\n", + " cfg = compose(config_name='config.yaml')\n", + "except Exception as e:\n", + " print(f\"Error initializing Hydra: {e}\")\n", + " with initialize(version_base=None, config_path=\"conf\"):\n", + " cfg = compose(config_name='config.yaml')\n", + "\n", + "cfg.development_mode = False\n", + "#cfg.query['year'] = 2017\n", + "#cfg.query['month'] = 11\n", + "#cfg.query['geography'] = \"nepal\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| exports:\n", + "#\n", + "\n", + "@hydra.main(version_base=None, config_path=\"../../conf\", config_name=\"config\")\n", + "def main(cfg: DictConfig) -> None:\n", + "\n", + " variables_dict = {\n", + " \"2m_temperature\": \"t2m\",\n", + " \"2m_dewpoint_temperature\": \"d2m\",\n", + " \"volumetric_soil_water_layer_1\": \"swvl1\",\n", + " \"total_precipitation\": \"tp\"\n", + " }\n", + "\n", + " print(OmegaConf.to_yaml(cfg))\n", + "\n", + " #prep dataverse\n", + " api_token_file = here() / \"sandbox/dataverse_api_key.yml\"\n", + " with open(api_token_file, \"r\") as f:\n", + " apiconfig = yaml.load(f, Loader=yaml.BaseLoader)\n", + " api = NativeApi(apiconfig['base_url'], apiconfig['api_token'])\n", + " search_api = SearchApi(apiconfig['base_url'], apiconfig['api_token'])\n", + " resp = search_api.search(\"ERA5\", data_type=\"dataset\")\n", + "\n", + " results = resp.json()['data']['items']\n", + "\n", + " result = [x for x in results if \"ERA5\" in x['name']][0]\n", + " era5_pid = result['global_id']\n", + "\n", + " for geography in cfg.geographies:\n", + " for year in cfg.query['year']:\n", + " for variable, v in variables_dict.items():\n", + " \n", + " print(f\"Processing {geography} for {variable} in {year}\")\n", + " glob_string = here() / \"data\" / \"intermediate\" / f\"*{geography}*{variable}*{year}*\"\n", + " print(f\"Glob: {glob_string}\")\n", + " polygon_id = cfg.geographies[geography]['unique_id']\n", + " print(f\"polygon_id: {polygon_id}\")\n", + " merged = gather_exposure_geodataframes(glob_string, polygon_id, variable)\n", + " print(merged[0].head())\n", + " print(merged[1].head())\n", + "\n", + " output_dir = here() / \"data\" / \"output\" \n", + " \n", + " f_out = f\"environmental/exposures_era5/healthshed_daily/{geography}_{v}_{year}.parquet\"\n", + " os.makedirs(output_dir / os.path.dirname(f_out), exist_ok=True)\n", + " output_path = output_dir / f_out\n", + "\n", + " print(f\"Writing to {output_path}\")\n", + " merged[0].to_parquet(output_path, index=False)\n", + " \n", + "\n", + " print(f\"Uploading {f_out.replace('/', '-')} to Dataverse...\")\n", + " # upload to dataverse\n", + " datafile = Datafile()\n", + " datafile.set({\n", + " \"pid\": era5_pid,\n", + " \"filename\": str(output_path),\n", + " \"label\": f_out.replace(\"/\", \"-\")\n", + " })\n", + "\n", + " resp = api.upload_datafile(era5_pid, output_path, datafile.json())\n", + " assert resp.json()['status'] == \"OK\", f\"Failed to upload datafile: {resp.text}\"\n", + " \n", + " # also save the geometry for the region \n", + " merged[1].to_parquet(output_path.parent / f\"{geography}_geometry.parquet\", index=False)\n", + "\n", + " # and upload it to dataverse\n", + " datafile = Datafile()\n", + " datafile.set({\n", + " \"pid\": era5_pid,\n", + " \"filename\": str(output_path.parent / f\"{geography}_geometry.parquet\"),\n", + " \"label\": f\"{geography}_geometry.parquet\"\n", + " })\n", + "\n", + " resp = api.upload_datafile(era5_pid, output_path.parent / f\"{geography}_geometry.parquet\", datafile.json())\n", + " assert resp.json()['status'] == \"OK\", f\"Failed to upload geometry datafile: {resp.text}\"\n", + "\n", + " print(\"All files processed and uploaded successfully.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| export:\n", + "#| eval: false\n", + "try: from nbdev.imports import IN_NOTEBOOK\n", + "except: IN_NOTEBOOK=False\n", + "\n", + "if __name__ == \"__main__\" and not IN_NOTEBOOK:\n", + " main()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide:\n", + "#\n", + "import nbdev; nbdev.nbdev_export()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notes/10_pytask_demo.ipynb b/notes/10_pytask_demo.ipynb new file mode 100644 index 0000000..cdd3c73 --- /dev/null +++ b/notes/10_pytask_demo.ipynb @@ -0,0 +1,412 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "title: \"Demo: How to Create Pipelines with `pytask`\"\n", + "engine: jupyter\n", + "---\n", + "\n", + "## Data Preparation Demo\n", + "\n", + "> Data preparation task for `pytask` demo\n", + "\n", + "In this notebook, we are demonstrating how to convert our snakemake workflow into a `pytask` workflow. We use the basic tutorial to demonstrate this, but continue\n", + "to use nbdev for development of functions in notebooks.\n", + "\n", + "`pytask` is a task management system that allows you to define tasks and their dependencies, similar to `Snakemake`. It is particularly useful for data science workflows.\n", + "\n", + "There are a number of reasons to use `pytask` over `snakemake`:\n", + "- **Pythonic**: `pytask` is designed to be purely Pythonic by default, allowing you to write tasks and entire pipelines as Python functions.\n", + "- **Flexibility**: `pytask` allows you to define tasks and their dependencies in a more flexible way, using Python functions and decorators, as opposed to orchestrating numerous scripts.\n", + "- **Integration**: `pytask` integrates well with other Python libraries, such as `nbdev` here, or `hydra` configurations if you need, allowing you to use your existing code, notebooks, or configs in your workflows.\n", + "- **Parallelism**: `pytask` supports parallel execution of tasks with `pytask-parallel`, which can speed up your workflows significantly, especially for data processing tasks.\n", + "\n", + "We'll use nbdev to define the task functions, and then export them to the `src` directory. `pytask` is then invoked at the command line to run the tasks." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| default_exp task_data_preparation:\n", + "#" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This demo task is taken from the tutorial at [pytask documentation](https://pytask-dev.readthedocs.io/en/stable/tutorials/write_a_task.html). At minimum, you need your package to contain the following in a config.py file:\n", + "\n", + "```md\n", + "my_project\n", + "│\n", + "├───.pytask\n", + "│\n", + "├───bld\n", + "│ └────...\n", + "│\n", + "├───src\n", + "│ └───my_project\n", + "│ ├────__init__.py\n", + "│ ├────config.py\n", + "│ └────...\n", + "│\n", + "└───pyproject.toml\n", + "```\n", + "\n", + "```python\n", + "#contents of `era5_sandbox.config` module\n", + "from pathlib import Path\n", + "\n", + "\n", + "SRC = Path(__file__).parent.resolve()\n", + "BLD = SRC.joinpath(\"..\", \"..\", \"bld\").resolve()\n", + "```\n", + "\n", + "Additionally, your pyproject.toml file should contain the following at minimum:\n", + "\n", + "```toml\n", + "[tool.pytask.ini_options]\n", + "paths = [\"src/era5_sandbox\"]\n", + "```\n", + "\n", + "The former tells Python where to find the source code and build directory for `pytask` objects and shims, while the latter tells `pytask` where to find the task definitions and dependency DAG." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| exports:\n", + "#\n", + "import os\n", + "from pathlib import Path\n", + "from typing import Annotated\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "from era5_sandbox.config import BLD\n", + "from era5_sandbox.config import data_catalog, demo_catalog\n", + "\n", + "from pytask import PickleNode\n", + "from pytask import Product" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Defining Tasks\n", + "\n", + "To define a task, simply use the `task_` prefix in the function name (or, if you are familiar and comfortable with decorators, use `@pytask.mark.task`). Be verbose and expressive in your use of type hints to specify the input and output data, so that `pytask` can automatically detect and handle the dependencies between tasks. \n", + "\n", + "### Defining Tracked Outputs\n", + "\n", + "To define something as a tracked output, you can annotate the input of the task with `Annotated[Path, Product]`, where `Product` is imported from `pytask`. This tells `pytask` that this is a product of the task and should be saved in the build directory.\n", + "\n", + "In this example, we're generating random data into a data frame and saving the object as a pickle in the `bld` directory (`bld` is the default build directory for `pytask`'s intermediate data). To get that directory, we use the `BLD` variable from the `era5_sandbox.config` module as above. This module itself could also be generated using `nbdev` if you want to keep your configuration in notebooks.\n", + "\n", + "Using `nbdev`, we can also include all of the bells and whistles of function documentation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| exports:\n", + "#\n", + "\n", + "def task_create_random_data(\n", + " seed: Annotated[int, 42], # Default seed for reproducibility\n", + " path_to_data: Annotated[Path, Product] = BLD / \"data.pkl\" # Path to the object in the build directory\n", + " ) -> None:\n", + " \"Create a random data set and save it as a pickle file. Return the path to the saved file.\"\n", + " rng = np.random.default_rng(seed)\n", + " beta = 2\n", + "\n", + " x = rng.normal(loc=5, scale=10, size=1_000)\n", + " epsilon = rng.standard_normal(1_000)\n", + "\n", + " y = beta * x + epsilon\n", + "\n", + " df = pd.DataFrame({\"x\": x, \"y\": y})\n", + "\n", + " # this is a tracked output, so we annotate the return value with `Annotated[Path, Product]`\n", + " df.to_pickle(path_to_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can test the function directly in the notebook:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "task_create_random_data(42)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once this module and function are exported with `nbdev_export`, the functions are in a python package. We can then use the command line to look at the registered tasks:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| eval: false\n", + "\n", + "%%sh\n", + "pytask collect" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's add another task in the same module. This task plots the data we generated. To link the previous task to this one as a dependency, we can list the output of the previous task as an input to this one. This way, `pytask` will know that it needs to run the first task before this one." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| exports:\n", + "#\n", + "\n", + "def task_plot_data(\n", + " path_to_data: Annotated[Path, BLD / \"data.pkl\"], # Path to the data file created by the previous task\n", + " path_to_plot: Annotated[Path, Product] = BLD / \"plot.png\" # Path to the build directory for the plot\n", + ") -> None:\n", + " \"\"\"\n", + " Plot the data from the pickle file and save the plot. Note that this task:\n", + " 1. depends on the data.pkl file created by the previous task,\n", + " 2. does not return any value, but saves a plot to the build directory. So the side effect of the task is what we are interested in here (though this is probably bad practice).\n", + " \"\"\"\n", + "\n", + " df = pd.read_pickle(path_to_data)\n", + " \n", + " _, ax = plt.subplots()\n", + " df.plot(x=\"x\", y=\"y\", ax=ax, kind=\"scatter\")\n", + "\n", + " plt.savefig(path_to_plot)\n", + " plt.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We now have a DAG of tasks that `pytask` can execute. To see the tasks, we can use the command line to create a pygraphviz graph of the tasks:\n", + "\n", + "```bash\n", + "pytask dag\n", + "```\n", + "\n", + "The DAG is saved as a pdf file, and you can view it using any viewer. Now, to run the pipeline, just invoke `pytask` at the command line:\n", + "\n", + "```bash\n", + "pytask\n", + "```\n", + "\n", + "In Jupyter or iPython, you can interact with the task outputs directly:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| eval: false\n", + "\n", + "# list all the files in the build directory\n", + "for file in os.listdir(BLD):\n", + " print(file)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can use these to build subsequent tasks later.\n", + "\n", + "## More Complex Tasks & The Data Catalog\n", + "\n", + "As we define more complex tasks, we can use the `pytask` data catalog to manage the inputs and outputs of our tasks. The data catalog allows us to imperatively name the data and their formats, making it easier to manage the data flow in our tasks. Importantly, we can define the data pythonically, which allows us to use the full power of Python to manipulate and transform our data. This is particularly more useful than snakemake's approach, which requires you to define the data in a more static way using paths and a separate pseudo-language.\n", + "\n", + "The content of the `era5_sandbox.config` module can be extended to include a data catalog:\n", + "\n", + "```python\n", + "from pathlib import Path\n", + "from pytask import DataCatalog, Product\n", + "\n", + "SRC = Path(__file__).parent.resolve()\n", + "BLD = SRC.joinpath(\"..\", \"..\", \"bld\").resolve()\n", + "\n", + "demo_catalog = DataCatalog()\n", + "```\n", + "\n", + "With just this definition, we're now able to refer directly to data by name in our tasks, and `pytask` will handle the paths and formats for us. This allows us to focus on the logic of our tasks rather than the details of data management.\n", + "\n", + ":::{.callout-note}\n", + "This is a major advantage of `pytask` over `snakemake`, as it allows you to define the data in a more flexible and Pythonic way, while still maintaining the benefits of a task management system. It is a similar approach to building pipelines in R with targets, which allows you to define the data in a more flexible way.\n", + ":::\n", + "\n", + "Let's create a task that modifies the data frame by adding a new column. This task will depend on the previous task's output, and we will use the data catalog to define the input and output data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| exports:\n", + "#\n", + "\n", + "def task_add_one(\n", + " path_to_data: Annotated[Path, BLD / \"data.pkl\"], # Path to the data file created by the previous task\n", + " node: Annotated[PickleNode, Product] = demo_catalog[\"mydata\"]\n", + ") -> None:\n", + " \"\"\"\n", + " Add one to the 'y' column of the data frame and save it as a new pickle file.\n", + " \"\"\"\n", + " df = pd.read_pickle(path_to_data)\n", + " df['z'] = df['y'] + 1\n", + " \n", + " node.save(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this function, we've defined that the task relies on the output of the first task being there, the `data.pkl` file. But importantly, we've also defined our product as a `node` from the `PickleNode` module. This will allow `pytask` to handle the serialization and deserialization of the data frame automatically, so we don't have to worry about the details of how the data is stored. We create the datacatalog in our config file, and then tell this task to create a Node in that catalog called `mydata`. Whatever we save with the `node.save()` method will be saved in the build directory, but more importantly _will be indexed and hashed by `pytask`_. This means that if the data changes, `pytask` will know to rerun the task.\n", + "\n", + "To make this even more pythonic, we can modify the format of our task function so that the return type annotator is used as a node in the data catalog. This allows us to define the output of the task as a `PickleNode`, which will automatically handle the serialization and deserialization of the data frame.\n", + "\n", + ":::{.callout-note}\n", + "This is another trick I'm deriving from {targets}. By formatting tasks as pure functions where inputs are parameters and targets are return type annotations, we can define the output of the task as a `PickleNode`, which will automatically handle the serialization and deserialization of the data frame. This again allows us to focus on the logic of our tasks rather than the details of data management.\n", + ":::\n", + "\n", + "So below, we're directly accessing the `data_catalog` to get the `mydata` node, and then modifying it by adding a new column. It _feels_ like we are doing this in place, such as in an iPython session, because we are allowing `pytask` to handle the serialization of the file on disk for us." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| exports:\n", + "#\n", + "\n", + "def task_add_another_column(\n", + " df: Annotated[pd.DataFrame, demo_catalog[\"mydata\"]] # which object in the catalog to fetch from the catalog with node.load()\n", + ") -> Annotated[pd.DataFrame, demo_catalog[\"mydata2\"]]: # which object in the catalog to save the return value to\n", + " \"\"\"\n", + " Add another column to the data frame stored in the PickleNode.\n", + " \"\"\"\n", + "\n", + " # use the datacatalog directly to access the node\n", + " # this is a bit like accessing the node in an iPython session, but pytask\n", + " # will handle the serialization and deserialization for us\n", + " df['w'] = df['z'] * df['y']\n", + " \n", + " return df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To test this interactively, we'd have to import the data catalog's object" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df = demo_catalog[\"mydata\"].load() # load the data frame from the PickleNode\n", + "result = task_add_another_column(df) # call the task function with the loaded data frame" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "result" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we know it will work, we can invoke pytask:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| eval: false\n", + "%%sh\n", + "pytask" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that the outputs are cached and not recomputed unless the inputs change. This is a key feature of `pytask` and other DAGs, allowing you to efficiently manage your data processing tasks without unnecessary recomputation.\n", + "\n", + "## Conclusion\n", + "\n", + "The takeaway here is that with `pytask`, you can define pure functions that take inputs and return outputs, and build a DAG of tasks that can be executed in a flexible and efficient way. This allows you to focus on the logic of your tasks rather than the details of data management, while still maintaining the benefits of a task management system. The key elements are:\n", + "\n", + "- **Task annotation**: You define your tasks by creating pure functions that take inputs and return outputs, and use decorators or naming conventions to mark them as \"tasks\" in a dag\n", + "- **Input and output annotation**: You define the inputs and outputs of your tasksusing type hints, and allow `pytask` to automatically detect and handle the dependencies between tasks.\n", + "- **Data catalog**: You define your data in a Pythonic object in your config called `data_catalog`. As you iteratively develop your DAG, you add objects to the data catalog, which are called nodes. As long as a node is a pythonic object and has a pickle method, `pytask` will handle the serialization and deserialization of the data for you." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notes/20_pytask_config.ipynb b/notes/20_pytask_config.ipynb new file mode 100644 index 0000000..2ea8116 --- /dev/null +++ b/notes/20_pytask_config.ipynb @@ -0,0 +1,530 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "title: \"`pytask` Config: Defining the Pipeline Internals in `pytask`\"\n", + "engine: jupyter\n", + "---\n", + "\n", + "## config\n", + "\n", + "> This is the config module for the `pytask` pipeline. \n", + "This module defines the data catalog(s) and any hard-coded parameters that are used throughout the pipeline." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| default_exp config:\n", + "#" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide:\n", + "#\n", + "from nbdev.showdoc import *" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| exports: \n", + "#\n", + "\n", + "import pandas as pd\n", + "\n", + "from pathlib import Path\n", + "from pyprojroot import here\n", + "from pytask import DataCatalog\n", + "\n", + "\n", + "SRC = here() / \"src\" / \"era5_sandbox\"\n", + "BLD = here() / \"bld\"\n", + "\n", + "demo_catalog = DataCatalog()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## `DEV_MODE`: A Quick Development Flag\n", + "\n", + "I'm adding a flag to the config that can be used for quick development. \n", + "If you import this boolean variable, it can be used to skip tasks,\n", + "setup samples, etc. on the fly by `marking` a task with the `pytask.mark.skipif`\n", + "decorator. Change this to `False` when you're ready to run the full pipeline." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| exports:\n", + "#\n", + "DEV_MODE=True" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The Data Catalog\n", + "\n", + "To manage our pipeline, we're going to use a nested data catalog structure.\n", + "This way, we can easily return specific entries to specific tasks\n", + "without having to manage multiple different data catalogs. Specifically,\n", + "we'll have a data catalog for each stage of the pipeline, and each catalog\n", + "will have entries for the inputs, outputs, and any other parameters needed\n", + "for that stage. This is similar to how we used Hydra configs, but\n", + "using the `pytask` data catalog, we can more easily gather the data\n", + "for a specific task in structured manner entirely in Python." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| exports:\n", + "#\n", + "\n", + "stages = [\"mydata\", 'mydata2', # from the demo, ignore\n", + " \"download\", # download task\n", + " \"aggregate\", # aggregation task\n", + " \"publish\", # publishing task\n", + " \"viz\"] # visualization task\n", + "\n", + "buckets = [\n", + " \"inputs\", # any specific inputs, eg for carrying over between tasks\n", + " \"outputs\", # specific output task returns\n", + " \"jobs\", # job parameters as a dataframe\n", + " \"params\" # any lingering hardcoded parameters\n", + " ]\n", + "\n", + "data_catalog = {\n", + "\n", + " stage: {bucket: DataCatalog(name=f\"{stage}_{bucket}\") for bucket in buckets}\n", + " for stage in stages\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The Download Task\n", + "\n", + "A good strategy may be to set pipeline stage parameters in the config file, \n", + "and then use the `pytask` data catalog to manage the data. This way, we can\n", + "easily change the parameters without having to modify the code. This is especially \n", + "useful for the API query, where we need to be able to set the parameter grid for\n", + "the years and data types we want to download data for. So, let's create an entry in the data catalog specifically for the download task.\n", + "\n", + "A good strategy I thought about for grid parameter comprehension is to create a dataframe expands all the combinations of\n", + "parameters, and then uses each combination to create the tasks which are then \n", + "easily added to the data catalog. This way, we can still easily inspect the \n", + "pipeline and see what tasks are being run, while also being able to easily \n", + "change the parameters in the config file without too much hassle.\n", + "\n", + "An important framework decision I'm making here is that each ROW of the dataframe corresponds to a single task, so that we can quickly understand at a glance what the task is doing, and also easily develop the code for the task itself. This is different from the hydra approach where a job is first specified by a default config, and then the parameters are swept over in multiple config files. This is a more flexible approach, IMO, because:\n", + "\n", + "1. each row defines a single task run, so it's easy to understand what the run is doing\n", + "2. it's easy to add or remove runs by simply expanding the list of parameters and using dataframe filters to remove irrelevant parameter combinations\n", + "3. we don't have to independently inspect and manage multiple different/overriding config files\n", + "4. it's all in Python, so we can use the full power of the language to define\n", + " the parameters and the tasks in a single sweep, not through the need of\n", + " hydra+snakemake multi stage/multi-lingual config system\n", + "\n", + "So, to do this, we define one job as a query to the CDS API that must contain:\n", + "- The dataset (re-analysis)\n", + "- The year\n", + "- The month\n", + "- All days in the month\n", + "- All times of day (hour)\n", + "- The geography (region), which will need:\n", + " - The URL to the shapefile to calculate the bounding box\n", + "\n", + "Given one combination of all of these, a single SLURM job can complete the first \"task\" in parallel by having a run assigned to each row of the dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| exports:\n", + "\n", + "# a dataframe for the query parameters, with nested entries for days, times, and variables\n", + "# Dimensions\n", + "years = [str(x) for x in range(2009, 2025)] # 16 years\n", + "months = [str(x).zfill(2) for x in range(1, 13)] # 12 months\n", + "geographies = [\"madagascar\", \"nepal\"] # 2 geographies\n", + "\n", + "# nested values; we want ALL days, times, and variables for each job\n", + "days = [str(x).zfill(2) for x in range(1, 32)]\n", + "times = [f\"{x:02d}:00\" for x in range(24)]\n", + "variables = [\"2m_dewpoint_temperature\", \"2m_temperature\", \"total_precipitation\", \"volumetric_soil_water_layer_1\"]\n", + "\n", + "product_type = \"reanalysis\"\n", + "\n", + "# Map shapefiles to geography\n", + "shapefiles = {\n", + " \"madagascar\": \"https://data.humdata.org/dataset/26fa506b-0727-4d9d-a590-d2abee21ee22/resource/ed94d52e-349e-41be-80cb-62dc0435bd34/download/mdg_adm_bngrc_ocha_20181031_shp.zip\",\n", + " \"nepal\": \"https://data.humdata.org/dataset/07db728a-4f0f-4e98-8eb0-8fa9df61f01c/resource/2eb4c47f-fd6e-425d-b623-d35be1a7640e/download/npl_adm_nd_20240314_ab_shp.zip\"\n", + "}\n", + "\n", + "# Build row-wise combinations of (year, month, geography)\n", + "rows = []\n", + "for year in years:\n", + " for month in months:\n", + " for geo in geographies:\n", + " rows.append({\n", + " \"year\": year,\n", + " \"month\": month,\n", + " \"geography\": geo,\n", + " \"shapefile\": shapefiles[geo],\n", + " \"product_type\": product_type,\n", + " \"day\": days,\n", + " \"time\": times,\n", + " \"variables\": variables,\n", + " \"output\": f\"{year}_{month}_{geo}\"\n", + " })\n", + "\n", + "# Create dataframe\n", + "query_df = pd.DataFrame(rows)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "query_df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(f\"Number of estimated jobs: {query_df.shape[0]}. Examples...\")\n", + "\n", + "for i, row in query_df.sample(3).iterrows():\n", + " print(f\"Year: {row['year']}, Month: {row['month']}, Geography: {row['geography']}, Link: {row['shapefile']}, Variables: {row['variables']}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now add them to the catalog. We're going to use a dictionary to\n", + "nest data catalogs so that we can return specific task products to\n", + "named data catalog nodes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| export:\n", + "# set up catalog\n", + "\n", + "data_catalog['download']['jobs'].add(\"queries_df\", query_df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our data catalog now has a `download|jobs` node with a `queries_df` entry that contains the dataframe of all the jobs to be run in this task." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data_catalog['download']['jobs']['queries_df'].load().head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The Aggregation Task\n", + "\n", + "To carry out the aggregation, we will follow similar logic to the original pipeline and use xarray to aggregate data into spatial and temporal averages. The aggregation task will take the downloaded data and compute the mean over the specified time period and spatial region. However, in this case, we want to aggregate the data diurnally, so we will need to fetch the sundown and sunrise times for the region and use them to compute the diurnal averages.\n", + "\n", + "Once again, we will use a dataframe to define the parameters for the aggregation task.\n", + "\n", + "Here we will use a dataframe with the jobs as rows;\n", + "the first column is \"input\" which is the list of query names from\n", + "the download task, and the last column is the output object name. Columns\n", + "in between can be the parameters needed for the aggregation task, which\n", + "then get expanded to the full list of jobs with `itertools.product`, `explode` or similar,\n", + "and filtered as necessary.\n", + "\n", + "For explanations of the parameters, see the Aggregation Task notebook's final `task_aggregate_data_diurnal` function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| exports:\n", + "\n", + "# aggregate task parameters\n", + "\n", + "inputs = query_df[\"output\"].tolist()\n", + "outputs = [f\"{i}_agg\" for i in inputs]\n", + "\n", + "variable_dict = {\n", + " \"2m_dewpoint_temperature\": \"d2m\",\n", + " \"2m_temperature\": \"t2m\",\n", + " \"total_precipitation\": \"tp\",\n", + " \"volumetric_soil_water_layer_1\": \"swvl1\"\n", + "}\n", + "\n", + "# list of params that get fed into the task functions\n", + "agg_params = {\n", + " \"time\": [\"day\", \"night\"],\n", + " \"solar_classification\": [\"before\"],\n", + " \"variables\": variables,\n", + " \"variables_short\": [variable_dict[x] for x in variables],\n", + " \"aggregation_name\": [\"mean\", \"sum\", \"max\", \"min\"]\n", + "}\n", + "\n", + "from itertools import product\n", + "import pandas as pd\n", + "\n", + "# expand all the params\n", + "agg_params = pd.DataFrame(list(product(*agg_params.values())), columns=agg_params.keys())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Inspecting it:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "agg_params" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's keep only rows where the variables and variables_short match" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| exports:\n", + "# quick filter to keep only matching rows\n", + "\n", + "agg_params = agg_params[agg_params.apply(lambda x: variable_dict[x['variables']] == x['variables_short'], axis=1)]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "agg_params" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Great, and now keeping `sum` only for total precipitation (we don't need mean, max, min for that variable), and removing `sum` for all other variables (we don't need sum for temperature or soil moisture):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| exports:\n", + "# remove rows where tp aggregation is not sum\n", + "mask = (agg_params['variables_short'] == \"tp\") & (agg_params['aggregation_name'] != \"sum\")\n", + "agg_params = agg_params[~mask]\n", + "\n", + "# remove rows where non-tp aggregation is sum\n", + "mask = (agg_params['variables_short'] != \"tp\") & (agg_params['aggregation_name'] == \"sum\")\n", + "agg_params = agg_params[~mask]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "agg_params" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we add the input and output columns by joining:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| exports:\n", + "# set up inputs and parameters\n", + "inputs = pd.DataFrame({\"input\": inputs})\n", + "aggregate_jobs = inputs.merge(agg_params, how=\"cross\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This result gives us the full list of jobs for the aggregation task. 20 rows for the parameters,\n", + "and 384 inputs/outputs, giving a total of 7680 jobs:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "assert aggregate_jobs.shape[0] == 20 * len(inputs)\n", + "aggregate_jobs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A few more configuration items need to be added, like\n", + "the local timezone for each geography, the healthshed filename,\n", + "the healthshed unique ID variable name in the shapefile,\n", + "and whether the variable is instantaneous or accumulated:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| exports:\n", + "# add a few more columns\n", + "aggregate_jobs['local_tz'] = aggregate_jobs['input'].apply(\n", + " lambda x: \"Asia/Kathmandu\" if \"nepal\" in x else \"Indian/Antananarivo\"\n", + ")\n", + "aggregate_jobs['shapefile'] = aggregate_jobs['input'].apply(\n", + " lambda x: \"Nepal_Healthsheds2024.zip\" if \"nepal\" in x else \"healthsheds2022.zip\"\n", + ")\n", + "\n", + "aggregate_jobs['hshd_unique_id'] = aggregate_jobs['input'].apply(\n", + " lambda x: \"fid\" if \"nepal\" in x else \"fs_uid\"\n", + ")\n", + "\n", + "aggregate_jobs['climate_handler_var'] = aggregate_jobs['variables_short'].apply(\n", + " lambda x: \"accum\" if x == \"tp\" else \"instant\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "aggregate_jobs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we add this to the data catalog:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| exports:\n", + "# update catalog\n", + "data_catalog['aggregate']['jobs'].add(\"jobs_df\", aggregate_jobs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our data catalog now has an `aggregate|jobs` node with a `jobs_df` entry that contains the dataframe of all the jobs to be run in this task." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data_catalog['aggregate']['jobs']['jobs_df'].load().head()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notes/20_pytask_logger.ipynb b/notes/20_pytask_logger.ipynb new file mode 100644 index 0000000..e128bc1 --- /dev/null +++ b/notes/20_pytask_logger.ipynb @@ -0,0 +1,95 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "title: \"Logging: A simple logger to inject into `pytask` jobs\"\n", + "engine: jupyter\n", + "---\n", + "\n", + "## logger\n", + "\n", + "> A simple logger module for the pytask tasks" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| default_exp pytask_logger:\n", + "#|" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide:\n", + "# showdoc\n", + "from nbdev.showdoc import *" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| exports:\n", + "# imports \n", + "\n", + "import logging\n", + "from pathlib import Path\n", + "from pyprojroot import here\n", + "from datetime import datetime\n", + "\n", + "LOG_DIR = here(\"logs\")\n", + "# get the date & time for the log file name\n", + "log_date = datetime.now().strftime(\"%Y-%m-%d\")\n", + "log_time = datetime.now().strftime(\"%H-%M-%S\")\n", + "LOG_DIR = here(\"logs\") / log_date / log_time" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| exports:\n", + "# main function to setup a logger\n", + "\n", + "\n", + "\n", + "def setup_logger(name: str, log_path: Path=LOG_DIR, level=logging.INFO) -> logging.Logger:\n", + " log_path.mkdir(parents=True, exist_ok=True)\n", + " formatter = logging.Formatter('%(asctime)s — %(name)s — %(levelname)s — %(message)s')\n", + "\n", + " handler = logging.FileHandler(log_path / f\"{name}.log\", mode='a')\n", + " handler.setFormatter(formatter)\n", + "\n", + " logger = logging.getLogger(name)\n", + " logger.setLevel(level)\n", + " logger.addHandler(handler)\n", + " logger.propagate = False\n", + "\n", + " return logger" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notes/21_pytask_download.ipynb b/notes/21_pytask_download.ipynb new file mode 100644 index 0000000..6fb2d46 --- /dev/null +++ b/notes/21_pytask_download.ipynb @@ -0,0 +1,257 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "title: \"Download: `download` Module as a `pytask` Task\"\n", + "engine: jupyter\n", + "---\n", + "\n", + "## task_download \n", + "\n", + "> This module downloads the raw era5 data from the CDS API. It is similar to the original script, refactored for `pytask`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| default_exp task_download:\n", + "#|" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide:\n", + "# showdoc\n", + "from nbdev.showdoc import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We're going to quickly refactor the pipeline to use pytask instead of hydra and snakemake. This will hopefully demonstrate a simpler and more flexible way to manage data pipelines in Python.\n", + "\n", + "To start off, we need to create a function that queries the CDS API with one job. This function will be used to download the data for each query in the range specified in the data catalog in the config file.\n", + "\n", + "Let's take a look at the data catalog we created in the config module:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| export:\n", + "# necessary imports\n", + "import cdsapi\n", + "import pytask\n", + "import os\n", + "from pytask import task, Product\n", + "from pathlib import Path\n", + "from typing import Annotated\n", + "from pandas import Series\n", + "\n", + "from era5_sandbox.config import data_catalog\n", + "from era5_sandbox.config import BLD\n", + "from era5_sandbox.config import DEV_MODE\n", + "from era5_sandbox.pytask_logger import setup_logger\n", + "from era5_sandbox.download import fetch_GADM, create_bounding_box" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can see the queries entry we created in the data catalog. Each query is a row of a dataframe that contains the parameters for the CDS API query." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "queries = data_catalog['download']['jobs']['queries_df'].load()\n", + "queries" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can test this query like we did in the original work:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "example_query = queries.iloc[0]\n", + "\n", + "create_bounding_box(example_query['shapefile'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this way, we have a similar approach as Hydra configs, but, using the `pytask` data catalog, we can more easily gather the data for a specific task in structured manner entirely in Python." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| eval: false\n", + "\n", + "client = cdsapi.Client()\n", + "\n", + "ex_bounding_box = create_bounding_box(example_query['shapefile'])\n", + "\n", + "request = {\n", + " \"product_type\": example_query['product_type'],\n", + " \"variable\": example_query['variables'], \n", + " \"year\": str(example_query['year']),\n", + " \"month\": str(example_query['month']),\n", + " \"day\": example_query['day'],\n", + " \"time\": example_query['time'],\n", + " \"data_format\": \"netcdf\",\n", + " \"download_format\": \"unarchived\",\n", + " \"area\": ex_bounding_box\n", + " }\n", + "\n", + "target = f\"{example_query['output']}.nc\"\n", + "\n", + "client.retrieve(\"reanalysis-era5-single-levels\", request).download(target)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This works! So now we just need to create a `task_` function that pytask will recognise to parallelise the download of queries over:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| export:\n", + "# define the download task\n", + "\n", + "queries = data_catalog['download']['jobs']['queries_df'].load()\n", + "\n", + "for i, job in queries.iterrows():\n", + "\n", + " @task(id=job['output'], name=f\"Download {job['output']}\")\n", + " def task_download_raw_data(\n", + " _query: Series = job # The query object from the data catalog\n", + " )-> Annotated[Path, data_catalog['download']['outputs'][job['output']]]:\n", + " \n", + " logger = setup_logger(_query['output'])\n", + " output_path = BLD / f\"{_query['output']}.nc\"\n", + " logger.info(f\"Starting download for {_query['output']} to {output_path}\")\n", + "\n", + " # check if string file path exists\n", + " if os.path.exists(output_path):\n", + " logger.info(f\"File {output_path} already exists. Skipping download.\")\n", + " return output_path\n", + "\n", + " client = cdsapi.Client()\n", + " bounding_box = create_bounding_box(_query['shapefile'])\n", + " \n", + " request = {\n", + " \"product_type\": _query['product_type'],\n", + " \"variable\": _query['variables'], \n", + " \"year\": _query['year'],\n", + " \"month\": _query['month'],\n", + " \"day\": _query['day'],\n", + " \"time\": _query['time'],\n", + " \"data_format\": \"netcdf\",\n", + " \"download_format\": \"unarchived\",\n", + " \"area\": bounding_box\n", + " }\n", + " \n", + " client.retrieve(\"reanalysis-era5-land\", request).download(output_path)\n", + " logger.info(f\"Downloaded data for {_query['output']} to {output_path}\")\n", + "\n", + " return output_path" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### How this works (with some help from GPT):\n", + "\n", + "#### 🧠 How pytask Discovers and Executes Tasks\n", + "\n", + "When you run pytask, it automatically scans your project for Python files named `task_*.py`. In these files, it looks for:\n", + "- Functions decorated with `@task`, or\n", + "- Functions prefixed with `task_`\n", + "\n", + "These functions are not executed immediately. Instead, `pytask`:\n", + "1.\tImports each task_*.py module (just like Python would)\n", + "2.\tRegisters any matching task functions as nodes in a directed acyclic graph (DAG)\n", + "3.\tResolves dependencies by analyzing:\n", + " - Input annotations (e.g., `Annotated[x, DependsOn]`)\n", + " - Output declarations (e.g., `return` values or `Product` annotations)\n", + "4.\tBuilds the DAG, where each task function is a node\n", + "5.\tExecutes the tasks, respecting dependency order and skipping up-to-date nodes\n", + "\n", + "So even though the task functions aren’t explicitly “run” in the Python code itself, pytask knows how and when to execute them — based on their position in the DAG.\n", + "\n", + "#### 🔄 How This Differs from Snakemake\n", + "\n", + "In `snakemake`, you’re expected to define a series of explicitly executable rules, often using shell commands or Python scripts. You “stitch together” rules using filenames and wildcard matching.\n", + "\n", + "In contrast:\n", + "- 🐍 pytask is Python-native — tasks are just regular Python functions\n", + "- ⚙️ It builds a DAG from those functions and tracks inputs/outputs automatically\n", + "- 🧱 You are declaring nodes, not scripting execution\n", + "\n", + "Think of your Python files not as scripts to run, but as a way to define and wire together declarative tasks that will be executed by the pytask engine.\n", + "\n", + "---\n", + "\n", + "Because we defined this task in a function and loop, we can easily debug a node in the DAG by simply calling it:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| eval: false\n", + "task_download_raw_data()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notes/22_pytask_aggregate.ipynb b/notes/22_pytask_aggregate.ipynb new file mode 100644 index 0000000..6f0bccc --- /dev/null +++ b/notes/22_pytask_aggregate.ipynb @@ -0,0 +1,906 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "title: \"Aggregation: The `aggregation` Module as a `pytask` Task\"\n", + "format: html\n", + "engine: jupyter\n", + "---\n", + "\n", + "# task_aggregate\n", + "\n", + "> This task aggregates the downloaded data into spatial and temporal averages. It uses xarray to compute summary statistics over the specified time period and spatial region. The aggregation is done diurnally, so we will fetch the sundown and sunrise times for the region and use them to compute the diurnal averages." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| default_exp task_aggregate:\n", + "#" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| hide:\n", + "# showdoc\n", + "\n", + "from nbdev.showdoc import *" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| export:\n", + "#\n", + "\n", + "import os\n", + "import tempfile\n", + "import rasterio\n", + "import yaml\n", + "import xarray as xr\n", + "from pyprojroot import here\n", + "from typing import Literal\n", + "from pytask import task, Product\n", + "from pathlib import Path\n", + "from typing import Annotated\n", + "from rasterstats.io import Raster\n", + "\n", + "from era5_sandbox.config import BLD, data_catalog\n", + "from era5_sandbox.pytask_logger import setup_logger\n", + "\n", + "from era5_sandbox.core import GoogleDriver, _get_callable, describe, ClimateDataFileHandler, kelvin_to_celsius\n", + "\n", + "from era5_sandbox.aggregate import polygon_to_raster_cells, aggregate_to_healthsheds, RasterFile, netcdf_to_tiff" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Diurnal Classification Based on Sun Position\n", + "\n", + "To do diurnal classificaiton, we will need to fetch the sundown and sunrise times for the region and use them to compute the diurnal averages. We will use the [astral library](https://astral.readthedocs.io/en/latest/) to get the sunrise and sunset times for the specified latitude and longitude. The aggregation will be done using xarray, which allows us to compute the mean over the specified time period and spatial region.\n", + "\n", + "Here's our example file:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "eg_file = data_catalog['download']['outputs']['2009_01_nepal'].load()\n", + "with ClimateDataFileHandler(eg_file) as handler:\n", + " \n", + " ds = xr.open_dataset(handler.get_dataset(\"instant\"))\n", + " #ds = xr.open_dataset(handler.get_dataset(\"accum\"))\n", + "\n", + "ds" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see the astral library in action below:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| exports:\n", + "#\n", + "from astral import Observer, sun\n", + "import pandas as pd\n", + "import numpy as np\n", + "from tqdm import tqdm\n", + "import random\n", + "import datetime\n", + "from pytz import UTC" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# get the location of a datapoint in the dataset\n", + "lat, long = ds.coords[\"latitude\"].values[0], ds.coords[\"longitude\"].values[0]\n", + "time = ds['valid_time'].values[0]\n", + "dt = pd.to_datetime(time, utc=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "observer = Observer(latitude=lat, longitude=long, elevation=0)\n", + "sun_info = sun.sun(observer, date=dt)\n", + "sun_info" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Astral is very fast:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%timeit\n", + "\n", + "#fetch a random time from valid_time\n", + "options = ds['valid_time'].values\n", + "\n", + "random_time = random.choice(options)\n", + "dt = pd.to_datetime(random_time, utc=True)\n", + "sun_info = sun.sun(observer, date=dt)\n", + "if dt < sun_info['sunrise']:\n", + " print(f\"Randomly selected time: {dt} is pre_dawn\")\n", + "elif dt >= sun_info['sunrise'] and dt < sun_info['sunset']:\n", + " print(f\"Randomly selected time: {dt} is day\")\n", + "else:\n", + " print(f\"Randomly selected time: {dt} is post_dusk\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This tells us that we can use the valid time for the specific location of each data point in the query and know based on the sun whether it was daytime or nighttime. The runtime will be limited only by the looping.\n", + "Let's put this in a function so that we can use the resampling in `xarray`.\n", + "\n", + "The resampling approach will be a single function that can resample in three ways:\n", + "\n", + "- By calendar date, default (1 value per calendar date)\n", + "- By diurnal class by calendar date (3 values, pre-dawn, day, post-dusk)\n", + "- By solar date (2 values per calendar date, with night classified as \"before\" or \"after\")\n", + "\n", + "Therefore, we'll need 2 internal functions; one to do diurnal, and one to do solar date bins.\n", + "\n", + "Essentially, we are going to create an array-shaped index/mask, (time, latitude, longitude). As a\n", + "demonstration, this loop goes through the first 24 time points in the dataset,\n", + "and calculates the sun info for each latitude and longitude, assigning the values to an array:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time \n", + "times = ds['valid_time'].values[:24]\n", + "lats = ds.coords['latitude'].values\n", + "lons = ds.coords['longitude'].values\n", + "\n", + "result = np.full((len(times), len(lats), len(lons)), \"\", dtype=object)\n", + "\n", + "for i, dt in enumerate(times):\n", + "\n", + " for j, lat in enumerate(lats):\n", + "\n", + " for k, lon in enumerate(lons):\n", + " \n", + " # set the geographical position\n", + " observer = Observer(latitude=lat, longitude=lon, elevation=0)\n", + " \n", + " # use the time\n", + " dt = pd.to_datetime(dt, utc=True)\n", + "\n", + " # where/when is the sun at this time for this position\n", + " sun_info = sun.sun(observer, date=dt)\n", + " result[i, j, k] = sun_info" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So we know that in the first hour, the sun goes up and comes down at slightly different\n", + "times based on latitude and longitude. Take the first hour, for example:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(result.shape)\n", + "hour_1 = 0 # 0th index of the results\n", + "\n", + "min_lat = 0\n", + "min_lon = 0\n", + "max_lat = 48\n", + "max_lon = 90\n", + "print(f\"Even though the reading came from the first HOUR of data UTC, the sun info at the minimum latitude/longitude is: {result[hour_1, min_lat, min_lon]}\")\n", + "\n", + "print(f\"this is different from the sun info at the maximum latitude/longitude is: {result[hour_1, max_lat, max_lon]}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| export:\n", + "# define the basic diurnal classification function\n", + "\n", + "def compute_diurnal_class_bins(\n", + " ds: xr.Dataset\n", + " )-> np.ndarray:\n", + " \"\"\"\n", + " Compute the diurnal value for each data point in the dataset.\n", + " This function iterates over each data point in the dataset,\n", + " calculates the sunrise and sunset times for the given time, latitude and longitude,\n", + " and returns whether or not that data point is before dawn, during the day, or after dusk.\n", + " \"\"\"\n", + "\n", + " times = ds['valid_time'].values\n", + " lats = ds.coords['latitude'].values\n", + " lons = ds.coords['longitude'].values\n", + "\n", + " result = np.full((len(times), len(lats), len(lons)), \"\", dtype=object)\n", + "\n", + " for i, dt in enumerate(tqdm(times, desc=\"Classifying data points by sun position\")):\n", + " # use the time\n", + " dt = pd.to_datetime(dt, utc=True)\n", + "\n", + " for j, lat in enumerate(lats):\n", + "\n", + " for k, lon in enumerate(lons):\n", + " \n", + " # set the geographical position\n", + " observer = Observer(latitude=lat, longitude=lon, elevation=0)\n", + " \n", + " # where/when is the sun at this time for this position\n", + " sun_info = sun.sun(observer, date=dt)\n", + " \n", + " if dt < sun_info['sunrise']:\n", + " result[i, j, k] = \"pre_dawn\"\n", + " elif dt >= sun_info['sunrise'] and dt < sun_info['sunset']:\n", + " result[i, j, k] = \"day\"\n", + " else:\n", + " result[i, j, k] = \"post_dusk\"\n", + "\n", + " return result" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ex=compute_diurnal_class_bins(ds)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So, for our 720 time points, we should find that\n", + "if we take the `set()` of all the classifications within that slice,\n", + "there should be a few of them with 2 classes.\n", + "In other words, at any given hour, almost all of\n", + "the readings are \"day\", because it is daytime across all\n", + "of Madagascar, _but_ at certain timepoints, the sun is rising\n", + "or setting in the northern part of the country and so some\n", + "portion of the slice is classified differently:\n", + "\n", + "![illustrated](./IMG_740012467778-1.jpeg)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for x in range(720):\n", + " print(set(ex[x].flatten()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This works! Now we can do a similar, but slightly more\n", + "complicated function to define \"night\" and \"day\",\n", + "where \"night\" includes all of the values after the sun goes down." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| exports:\n", + "# \n", + "\n", + "def compute_solar_day_night_class_bins(\n", + " ds: xr.Dataset,\n", + " night_direction: Literal[\"before\", \"after\"],\n", + " )-> list:\n", + " \"\"\"\n", + " Compute the diurnal value for each data point in the dataset.\n", + " This function iterates over each data point in the dataset,\n", + " calculates the sunrise and sunset times for the given time, latitude and longitude,\n", + " and returns whether or not that data point is daytime or nighttime.\n", + " The definition of \"nighttime\" can be set to be all the darkness before the sun\n", + " came up (before), or all the darkness after it went down (after).\n", + " \"\"\"\n", + "\n", + " times = ds['valid_time'].values\n", + " lats = ds.coords['latitude'].values\n", + " lons = ds.coords['longitude'].values\n", + "\n", + " result = np.full((len(times), len(lats), len(lons)), \"\", dtype=object)\n", + " datetimes = np.full((len(times), len(lats), len(lons)), \"\", dtype=object)\n", + "\n", + " for i, dt in enumerate(tqdm(times, desc=\"Classifying data points by sun position\")):\n", + " # use the time\n", + " dt = pd.to_datetime(dt, utc=True)\n", + "\n", + " for j, lat in enumerate(lats):\n", + "\n", + " for k, lon in enumerate(lons):\n", + " \n", + " # set the geographical position\n", + " observer = Observer(latitude=lat, longitude=lon, elevation=0)\n", + " if night_direction == \"before\":\n", + " # Night is from previous sunset to today's sunrise\n", + " sun_today = sun.sun(observer, date=dt.date())\n", + " sun_prev = sun.sun(observer, date=(dt - pd.Timedelta(days=1)).date())\n", + " night_start = sun_prev[\"sunset\"].astimezone(pd.Timestamp.utcnow().tz)\n", + " night_end = sun_today[\"sunrise\"].astimezone(pd.Timestamp.utcnow().tz)\n", + " \n", + " # the reading is from yesterday's nighttime\n", + " if night_start <= dt < night_end:\n", + " result[i, j, k] = \"night\"\n", + " # the date counts as today\n", + " datetimes[i, j, k] = dt.date()\n", + "\n", + " # the reading is from daytime\n", + " elif sun_today[\"sunrise\"] <= dt < sun_today[\"sunset\"]:\n", + " result[i, j, k] = \"day\"\n", + " # the date counts as today\n", + " datetimes[i, j, k] = dt.date()\n", + " \n", + " # the reading is from today's nighttime, but counts as tomorrow's night\n", + " else:\n", + " result[i, j, k] = \"night\"\n", + " # the date is tomorrow\n", + " datetimes[i, j, k] = (dt + pd.Timedelta(days=1)).date()\n", + "\n", + " elif night_direction == \"after\":\n", + " # Night is from today's sunset to next sunrise\n", + " sun_today = sun.sun(observer, date=dt.date())\n", + " sun_next = sun.sun(observer, date=(dt + pd.Timedelta(days=1)).date())\n", + " night_start = sun_today[\"sunset\"].astimezone(pd.Timestamp.utcnow().tz)\n", + " night_end = sun_next[\"sunrise\"].astimezone(pd.Timestamp.utcnow().tz)\n", + "\n", + " # the reading is from daytime\n", + " if sun_today[\"sunrise\"] <= dt < sun_today[\"sunset\"]:\n", + " result[i, j, k] = \"day\"\n", + " # the date counts as today\n", + " datetimes[i, j, k] = dt.date()\n", + " # the reading is from tonight\n", + " elif night_start <= dt < night_end:\n", + " result[i, j, k] = \"night\"\n", + " # the date counts as today\n", + " datetimes[i, j, k] = dt.date()\n", + "\n", + " # the reading is from yesterday night\n", + " else:\n", + " # the date counts as yesterday\n", + " result[i, j, k] = \"day\"\n", + " datetimes[i, j, k] = (dt - pd.Timedelta(days=1)).date()\n", + " else:\n", + " raise ValueError(f\"Invalid night_direction: {night_direction}\")\n", + "\n", + " return result, datetimes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "ex_class, ex_dt = compute_solar_day_night_class_bins(ds, \"before\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ex_class" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As before, we should see that most slices are homogenous,\n", + "meaning most of the time, all the readings are from the day,\n", + "but some slices should have day and night values:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for slice_ in range(720):\n", + " print(set(ex_class[slice_].flatten()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The returned array can serve as new \"variable indexes\" for the dataset:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ds_masked = ds.copy()\n", + "ds_masked['solar_class'] = (('valid_time', 'latitude', 'longitude'), ex_class)\n", + "ds_masked[\"solar_date\"] = ((\"valid_time\", \"latitude\", \"longitude\"), ex_dt)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Diurnal Resampling\n", + "\n", + "Now, to see if it will resample by both solar day and diurnal class. Let's try by masking and making copies with NaN in the masked values:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ds_day = ds_masked.where(ds_masked[\"solar_class\"] == \"day\").drop_vars([\"solar_class\", \"solar_date\"])\n", + "ds_night = ds_masked.where(ds_masked[\"solar_class\"] == \"night\").drop_vars([\"solar_class\", \"solar_date\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we set the time zone for Madagascar since, to resample by day and night,\n", + "we should observe the local time:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ds_day = ds_day.assign_coords(valid_time=pd.to_datetime(ds[\"valid_time\"].values).tz_localize(\"UTC\").tz_convert(\"Asia/Kathmandu\"))\n", + "ds_night = ds_night.assign_coords(valid_time=pd.to_datetime(ds[\"valid_time\"].values).tz_localize(\"UTC\").tz_convert(\"Asia/Kathmandu\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now if we can resample by day..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ds_day_rs = ds_day.resample(valid_time=\"1D\").reduce(np.nanmean)\n", + "ds_night_rs = ds_night.resample(valid_time=\"1D\").reduce(np.nanmean)\n", + "ds_day_rs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Can we successfully convert this to a tiff?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from era5_sandbox.aggregate import netcdf_to_tiff" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "raster_day = netcdf_to_tiff(ds_day_rs, band=1, variable=\"d2m\")\n", + "raster_night = netcdf_to_tiff(ds_night_rs, band=1, variable=\"d2m\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looks great! These two rasters represent one calendar day of daytime and nighttime values.\n", + "\n", + "### Testing Polygon to Raster Cells & Healthshed Aggregation\n", + "\n", + "The penultimate step of the aggregate pipeline in the original version is\n", + "assigning each datapoint to the respective healthshed. The `vectors` argument\n", + "comes from the healthshed, and represents each geographic polygon on the ground\n", + "that we want to aggregate data to." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from hydra import initialize, compose" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " with initialize(version_base=None, config_path=\"../conf\"):\n", + " cfg = compose(config_name='config.yaml')\n", + "except Exception as e:\n", + " print(f\"Error initializing Hydra: {e}\")\n", + " with initialize(version_base=None, config_path=\"conf\"):\n", + " cfg = compose(config_name='config.yaml')\n", + "\n", + "driver = GoogleDriver(json_key_path=here() / cfg.GOOGLE_DRIVE_AUTH_JSON.path)\n", + "drive = driver.get_drive()\n", + "healthsheds = driver.read_healthsheds(\"Nepal_Healthsheds2024.zip\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "res_poly2cell=polygon_to_raster_cells(\n", + " vectors = healthsheds.geometry.values, # the geometries of the shapefile of the regions\n", + " raster=raster_day.data, # the raster data above\n", + " nodata=np.nan, # any intersections with no data, may have to be np.nan\n", + " affine=raster_day.transform, # some math thing need to revise\n", + " all_touched=True, \n", + " verbose=True\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This works fine. Finally, we aggregate to healthsheds:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from era5_sandbox.aggregate import aggregate_to_healthsheds" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "result_day = aggregate_to_healthsheds(\n", + " res_poly2cell=res_poly2cell,\n", + " raster=raster_day,\n", + " shapes=healthsheds,\n", + " names_column=\"fid\",\n", + " aggregation_func=np.nanmean,\n", + " aggregation_name=\"mean_dewpoint_day\"\n", + ")\n", + "\n", + "result_night = aggregate_to_healthsheds(\n", + " res_poly2cell=res_poly2cell,\n", + " raster=raster_night,\n", + " shapes=healthsheds,\n", + " names_column=\"fid\",\n", + " aggregation_func=np.nanmean,\n", + " aggregation_name=\"mean_dewpoint_night\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below shows the result of aggregating the daytime dewpoint temperature to the healthshed level:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "result_day" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "result_night" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So from one input, we will have two outputs, one for daytime and one for nighttime, and this will have to loop over the bands (ie each day in the month).\n", + "\n", + "# Putting it all together in a `pytask` task\n", + "\n", + "Below we define our `pytask` task to aggregate data to the healthshed level." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| exports:\n", + "#\n", + "\n", + "job_rows = data_catalog['aggregate']['jobs']['jobs_df'].load()\n", + "\n", + "aggregation_funcs = {\n", + " \"mean\": np.nanmean,\n", + " \"sum\": np.nansum,\n", + " \"max\": np.nanmax,\n", + " \"min\": np.nanmin\n", + "}\n", + "\n", + "for i, job in job_rows.iterrows():\n", + " #print(f\"Job {i+1}: variable={job['variables']}, time={job['time']}, aggregation={job['aggregation_name']}\")\n", + "\n", + " # parse the row into function parameters\n", + " input_file = data_catalog['download']['outputs'][job['input']]\n", + " solar_classification = job['solar_classification']\n", + " variable = job['variables_short']\n", + " time = job['time']\n", + " aggregation_func = aggregation_funcs[job['aggregation_name']]\n", + " aggregation_name = job['aggregation_name']\n", + "\n", + " climate_handler_var = job['climate_handler_var']\n", + " local_tz = job['local_tz']\n", + "\n", + " shapefile = job['shapefile']\n", + " hshd_unique_id = job['hshd_unique_id']\n", + "\n", + " output_file = job['input'] + \"_\" + job['time'] + \"_\" + job['variables_short'] + \"_\" + job['aggregation_name'] + \".parquet\"\n", + "\n", + " @task(id=output_file, name=f\"Aggregate {output_file}\", after=\"task_download_raw_data\")\n", + " def task_aggregate_data_diurnal(\n", + " input_file: Path = data_catalog['download']['outputs'][job['input']], # input data Path from the download task\n", + " aggregation_func: callable = aggregation_func, # the aggregation function\n", + " aggregation_name: str = aggregation_name, # the name of the aggregation function\n", + " time: Literal[\"day\", \"night\"] = time, # whether to aggregate by day or night\n", + " night_direction: Literal[\"before\", \"after\"] = solar_classification, # how to define night\n", + " variable: str = variable, # the variable to aggregate,\n", + " climate_handler_var: Literal[\"instant\", \"accum\"] = climate_handler_var, # whether the variable is instant or accum,\n", + " local_tz: str = local_tz, # the local timezone for resampling\n", + " shapefile: str = shapefile, # the shapefile for the healthsheds,\n", + " hshd_unique_id: str = hshd_unique_id, # the unique id column in the shapefile,\n", + " output_file: str = output_file # the output file name\n", + " ) -> Annotated[Path, data_catalog['aggregate']['outputs'][output_file]]:\n", + " \"\"\"\n", + " Task to aggregate data from a CDSAPI Query to the healthshed\n", + " level. Returns path to parquet file with aggregated data.\n", + " \"\"\"\n", + "\n", + " logger = setup_logger(output_file)\n", + "\n", + " logger.info(f\"Aggregating: {output_file}\")\n", + "\n", + " # check if the string path exists\n", + " # if os.path.exists(output_file):\n", + " # logger.info(f\"File {output_file} already exists. Skipping aggregation.\")\n", + " # return output_file\n", + "\n", + " # get input data\n", + " logger.info(\"Reading input data...\")\n", + " with ClimateDataFileHandler(input_file) as handler:\n", + " ds = xr.open_dataset(handler.get_dataset('instant'))\n", + "\n", + " #get the healthshed shapefile\n", + " logger.info(f\"Reading healthshed shapefile from yaml {here()}...\")\n", + " with open(here() / \"conf\" / \"config.yaml\") as f:\n", + " healthshed_config = yaml.safe_load(f)\n", + "\n", + " key_path = here() / healthshed_config['GOOGLE_DRIVE_AUTH_JSON']['path']\n", + "\n", + " driver = GoogleDriver(json_key_path=key_path)\n", + " drive = driver.get_drive()\n", + " healthsheds = driver.read_healthsheds(shapefile)\n", + "\n", + " # compute the diurnal classification bins\n", + " logger.info(\"Computing diurnal classification bins...\")\n", + " class_bins, class_dts = compute_solar_day_night_class_bins(ds, night_direction)\n", + "\n", + " ds_masked = ds.copy()\n", + "\n", + " # assign classifications\n", + " logger.info(\"Assigning classification bins to dataset...\")\n", + " ds['solar_class'] = (('valid_time', 'latitude', 'longitude'), class_bins)\n", + " ds[\"solar_date\"] = ((\"valid_time\", \"latitude\", \"longitude\"), class_dts)\n", + "\n", + " # mask the dataset to the requested time\n", + " mask = ds[\"solar_class\"] == time\n", + " ds_masked = ds_masked.where(mask)\n", + "\n", + " # set the local timezone\n", + " ds_masked = ds_masked.assign_coords(valid_time=pd.to_datetime(ds[\"valid_time\"].values).tz_localize(\"UTC\").tz_convert(local_tz))\n", + "\n", + " # resample by local date\n", + " logger.info(\"Resampling by local date...\")\n", + " ds_rs = ds_masked.resample(valid_time=\"1D\").reduce(aggregation_func)\n", + "\n", + " # convert to tiff\n", + " logger.info(\"Rasterizing resampled data...\")\n", + " n_bands = ds_rs.dims['valid_time']\n", + "\n", + " # polygon to raster cells for the first band\n", + " logger.info(\"Converting polygons to raster cells...\")\n", + " raster = netcdf_to_tiff(ds_rs, band=1, variable=variable)\n", + " res_poly2cell=polygon_to_raster_cells(\n", + " vectors = healthsheds.geometry.values, # the geometries of the shapefile of the regions\n", + " raster=raster.data, # the raster data above\n", + " nodata=np.nan, # any intersections with no data, may have to be np.nan\n", + " affine=raster.transform, # some math thing need to revise\n", + " all_touched=True, \n", + " verbose=True\n", + " )\n", + "\n", + " result_df = healthsheds[[hshd_unique_id, \"geometry\"]].copy()\n", + "\n", + " # loop over bands and aggregate to healthsheds\n", + " for band in tqdm(range(1, n_bands + 1)):\n", + " logger.info(f\"Processing band {band} of {n_bands}...\")\n", + " \n", + " day = band # band is 1-indexed\n", + "\n", + " day_col = f\"day_{day:02d}\"\n", + "\n", + " # calculate raster for this band\n", + " raster = netcdf_to_tiff(ds_rs, band=band, variable=variable)\n", + "\n", + " # aggregate to healthsheds\n", + " result = aggregate_to_healthsheds(\n", + " res_poly2cell=res_poly2cell,\n", + " raster=raster,\n", + " shapes=healthsheds,\n", + " names_column=hshd_unique_id,\n", + " aggregation_func=aggregation_func,\n", + " aggregation_name=variable\n", + " )\n", + " \n", + " # add band to result dataframe\n", + " result_df[day_col] = result[variable]\n", + "\n", + " # save to parquet\n", + " result_df.to_parquet(f\"{BLD}/{output_file}\")\n", + "\n", + " logger.info(\"Aggregation complete.\")\n", + " \n", + " return Path(f\"{BLD}/{output_file}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That should wrap it up! To test, we can run a single job:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#| eval: false\n", + "# runs the last defined job only\n", + "task_aggregate_data_diurnal()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Or we can run the task in `pytask`:\n", + "\n", + "```bash\n", + "pytask build -k \"nepal and 2009\" --dry-run\n", + "```" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "python3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notes/IMG_740012467778-1.jpeg b/notes/IMG_740012467778-1.jpeg new file mode 100644 index 0000000..52886eb Binary files /dev/null and b/notes/IMG_740012467778-1.jpeg differ diff --git a/notes/prototypes/aggregation_visualizer.Rmd b/notes/_prototypes/_aggregation_visualizer.Rmd similarity index 100% rename from notes/prototypes/aggregation_visualizer.Rmd rename to notes/_prototypes/_aggregation_visualizer.Rmd diff --git a/notes/_prototypes/_download_QA.qmd b/notes/_prototypes/_download_QA.qmd new file mode 100644 index 0000000..d2d492b --- /dev/null +++ b/notes/_prototypes/_download_QA.qmd @@ -0,0 +1,15 @@ +# Investigating The Download Results + +There are a couple of things we should do to QA our data downloads. Specifically, we want to come up with a way of ensuring our aggregations are valid and accurate. This will require some simple EDA. + +```{python} +from pyprojroot import here +import pandas as pd +import os +from hydra import initialize, compose +from omegaconf import OmegaConf, DictConfig +``` + +```{python} +eg_file = here() / "data/input/2010_1.nc" +``` \ No newline at end of file diff --git a/notes/_prototypes/_kenya_demo_01_intro.qmd b/notes/_prototypes/_kenya_demo_01_intro.qmd new file mode 100644 index 0000000..6bbc193 --- /dev/null +++ b/notes/_prototypes/_kenya_demo_01_intro.qmd @@ -0,0 +1,247 @@ +--- +skip_showdoc: true +--- + +# Introduction to the ERA 5 Data + +The ERA5 dataset is the fifth iteration of the ECMWF ReAnalysis dataset, spanning from 1950 to the present. ECMWF is the "European Centre for Medium-Range Weather Forecasts". +The dataset provides comprehensive and high-resolution historical weather and climate data. The source data is from the [Copernicus Climate Data Store (CDS)](https://cds.climate.copernicus.eu/#!/home). A comprehensive data documentation guide is available [here](https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation). In total, the entire CDS ERA data is over 10Petabytes. + +Fortunately for us, there are existing [Python](https://github.com/Climate-CAFE/era5-daily-heat-aggregation-python) and [R](https://github.com/Climate-CAFE/era5-daily-heat-aggregation) packages that have gone ahead and demonstrated extracting the data from the API for us, so we are going to use those to develop our workflow. Specifically, we're trying to understand the +following characteristics of the data: + +* size, +* how to download, +* what are the key transformations to map things into the health sheds +* two important variables: + * 2m air temp, and, + * 2m air dew point + +Let's get started + +Important: we need to install the CDS API first, so you'll need to grab an API key. First, you must register for an account and accept the T&Cs, afterwhich the page [here](https://ecmwf-projects.github.io/copernicus-training-c3s/cds-tutorial.html#install-the-cds-api-key) will autopopulate an API key for you. The following code shows a test case to make sure your API key works + +```{python} +import cdsapi + +client = cdsapi.Client() + +dataset = 'reanalysis-era5-pressure-levels' +request = { + 'product_type': ['reanalysis'], + 'variable': ['geopotential'], + 'year': ['2024'], + 'month': ['03'], + 'day': ['01'], + 'time': ['13:00'], + 'pressure_level': ['1000'], + 'data_format': 'grib', +} +target = 'download.grib' + +client.retrieve(dataset, request, target) +``` + +This demonstration is expected to amass 9GB of data for raw raster files (24 years, 12 files per year). The demonstration generates the 24 years of heat measures across Kenya administrative boundaries, in 1-month periods of ERA5-Land data across Kenya with three variables (2-m temp, dew point temp, skin temp) + +```{python} +# imports as recommended by the github repo +import cdsapi +import geopandas as gpd +import os +``` + +I'll use pyprojroot to specify a data path + +```{python} +from pyprojroot.here import here + +ecmw_dir = here("data") +``` + +```{python} +def create_dir(path): + + if not os.path.exists(path): + os.makedirs(path) + + return path +``` + +```{python} +create_dir(ecmw_dir) +``` + +```{python} +# create a directory for the kenya data +create_dir(os.path.join(ecmw_dir, "Kenya_GADM")) +``` + +Next, we need to manually fetch this GADM file for Kenya from here: https://gadm.org/download_country.html + +This is a boundaries geopackage; GeoBoundaries is a global database of administrative boundaries (e.g., countries, states, provinces, districts). Hence, this file provides the +boundaries for Kenyan regions + +```{python} +kenya_shape = gpd.read_file(os.path.join(ecmw_dir, "Kenya_GADM/gadm41_KEN.gpkg"), layer = "ADM_ADM_0") +``` + +```{python} +kenya_shape +``` + +The bounding box represents the coordinates of the shapefile, which is what we'll +use to query Copernicus. Think of it like a mask provided in a file + +```{python} +kenya_bbox = kenya_shape.total_bounds +``` + +```{python} +kenya_bbox +``` + +Technical: Add a small buffer around the bounding box to ensure the whole region +is queried, and round the parameters to a 0.1 resolution. A 0.1 resolution +is applied because the resolution of netCDF ERA5 data is .25x.25 +https://confluence.ecmwf.int/display/CKB/ERA5%3A+What+is+the+spatial+reference + +```{python} +kenya_bbox[0] = round(kenya_bbox[0], 1) - 0.1 +kenya_bbox[1] = round(kenya_bbox[1], 1) - 0.1 +kenya_bbox[2] = round(kenya_bbox[2], 1) + 0.1 +kenya_bbox[3] = round(kenya_bbox[3], 1) + 0.1 +``` + +```{python} +# to build a query, specify [xmin, ymin, xmax, ymax] +query_area = [kenya_bbox[0], kenya_bbox[1], kenya_bbox[2], kenya_bbox[3]] +``` + +```{python} +query_years = list(range(2000, 2024)) +query_years_str = [str(x) for x in query_years] + +query_months = list(range(1, 13)) +query_months_str = [str(x).zfill(2) for x in query_months] +``` + +```{python} +output_dir = create_dir(os.path.join(ecmw_dir, "ERA5_out")) +``` + +```{python} +for year_str in query_years_str: + # Track progress + print("Now processing year ", year_str, "\n") + + # For each year, the query is divided into each month sections. + # If a request is too large, it will not be accepted by the CDS servers, + # so this division of requests is required. + + for month_str in query_months_str: + # Track progress + print("Now processing month ", month_str, "\n") + + # The below is the formatted API request language. All of the inputs + # specified below in proper formatting can be identified by forming a + # request using the Copernicus CDS point-and-click interface for data + # requests. https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=form + # Select the variables, timing, and netcdf as the output format, and then + # select "Show API Request" at the bottom of the screen. + + # Note that the argument in the download() function is the file path and + # file name that data will be exported to and stored at. If using a loop, + # ensure that the unique features of each request are noted in the output. + + # Note: need to create "ERA5_Out" subfolder on your path + + dataset = "reanalysis-era5-land" + request = { + "product_type": "reanalysis", + "variable": ["2m_dewpoint_temperature", + "2m_temperature", + "skin_temperature"], + "year": year_str, + "month": month_str, + "day": [ + "01", "02", "03", + "04", "05", "06", + "07", "08", "09", + "10", "11", "12", + "13", "14", "15", + "16", "17", "18", + "19", "20", "21", + "22", "23", "24", + "25", "26", "27", + "28", "29", "30", + "31"], + "time": [ + "00:00", "01:00", "02:00", + "03:00", "04:00", "05:00", + "06:00", "07:00", "08:00", + "09:00", "10:00", "11:00", + "12:00", "13:00", "14:00", + "15:00", "16:00", "17:00", + "18:00", "19:00", "20:00", + "21:00", "22:00", "23:00"], + "data_format": "netcdf", + "download_format": "unarchived", + "area": query_area + } + + client = cdsapi.Client() + client.retrieve(dataset, request).download(os.path.join(output_dir, + "{}_{}.nc".format(year_str, month_str))) +``` + +From the logs, it looks like one month of data takes approximately 10 minutes. In this query, we might end up with 48 hours of downloading for the full 24 years. Clearly this will need to be multithreaded/paralleled to be efficient. + +## Testing Soil Moisture Downloads + +```{python} +import cdsapi + +dataset = "reanalysis-era5-land" +request = { + "variable": ["volumetric_soil_water_layer_1"], + "year": "2009", + "month": "01", + "day": ["01"], + "time": ["01:00"], + "data_format": "netcdf", + "download_format": "unarchived" +} + +client = cdsapi.Client() +client.retrieve(dataset, request).download() +``` + +```{python} +from pyprojroot.here import here + +ecmw_dir = here("notes/prototypes/") +``` + +```{python} +ecmw_dir / "soil.nc" +``` + +```{python} +import xarray +import os + +temp_file = xarray.open_dataset(os.path.join(ecmw_dir / "soil.nc"), decode_coords="all") +``` + +```{python} +temp_file +``` + +```{python} +temp_file['swvl1'].plot() +``` + +```{python} + +``` \ No newline at end of file diff --git a/notes/_prototypes/_kenya_demo_02_fishnet.qmd b/notes/_prototypes/_kenya_demo_02_fishnet.qmd new file mode 100644 index 0000000..120d456 --- /dev/null +++ b/notes/_prototypes/_kenya_demo_02_fishnet.qmd @@ -0,0 +1,228 @@ +--- +skip_showdoc: true +--- + +# Part 2: Aggregation via Fishnet + +This script is the first in a two-step raster processing process. In this script a grid-based polygon will be derived from the raster grid of ERA5 data. The goal +is to create a fishnet that can be used to extract ERA5 data from raster stack including ERA5 hourly data (this file). This will allow for extraction from raster stack +without the large computational burden of a loop (as below) + +```{python} +import geopandas as gpd +import os +import glob +# xarray makes working with labelled multi-dimensional arrays in Python simple, efficient, and fun! +import xarray +# The rioxarray package is an extension of xarray designed +# for working with raster (geospatial) data in Python. +# It provides an easy way to read, write, and manipulate GeoTIFF and other raster formats while maintaining spatial metadata. +import rioxarray +# for geometric operations on vector data (points, lines, polygons). It allows users to create, manipulate, and analyze geometric shapes in 2D space +import shapely +from shapely.geometry import Polygon +import numpy +# you need to install gdal here, not osgeo +# gdal is generally a translator library for raster and vector geospatial data formats +from osgeo import gdal, ogr +``` + +```{python} +# create a fishnet grid using GDAL +def make_fishnet(outputGridfn,xmin,xmax,ymin,ymax,rows,cols): + # Calculate grid parameters + xmin = float(xmin) + xmax = float(xmax) + ymin = float(ymin) + ymax = float(ymax) + gridWidth = float((xmax-xmin) / cols) + gridHeight = float((ymax-ymin) / rows) + + # Start grid cell envelope + ringXleftOrigin = xmin + ringXrightOrigin = xmin + gridWidth + ringYtopOrigin = ymax + ringYbottomOrigin = ymax-gridHeight + + # Create the output shapefile + outDriver = ogr.GetDriverByName('ESRI Shapefile') + if os.path.exists(outputGridfn): + os.remove(outputGridfn) + outDataSource = outDriver.CreateDataSource(outputGridfn) + outLayer = outDataSource.CreateLayer(outputGridfn, geom_type=ogr.wkbPolygon) + # Add fields to the layer + featureDefn = outLayer.GetLayerDefn() + + # Create grid cells + countcols = 0 + while countcols < cols: + countcols += 1 + + # Reset envelope for rows + ringYtop = ringYtopOrigin + ringYbottom =ringYbottomOrigin + countrows = 0 + + while countrows < rows: + countrows += 1 + ring = ogr.Geometry(ogr.wkbLinearRing) + ring.AddPoint(ringXleftOrigin, ringYtop) + ring.AddPoint(ringXrightOrigin, ringYtop) + ring.AddPoint(ringXrightOrigin, ringYbottom) + ring.AddPoint(ringXleftOrigin, ringYbottom) + ring.AddPoint(ringXleftOrigin, ringYtop) + poly = ogr.Geometry(ogr.wkbPolygon) + poly.AddGeometry(ring) + + # Add new geom to layer + outFeature = ogr.Feature(featureDefn) + outFeature.SetGeometry(poly) + outLayer.CreateFeature(outFeature) + outFeature = None + + # New envelope for next poly + ringYtop = ringYtop - gridHeight + ringYbottom = ringYbottom - gridHeight + + # New envelope for next poly + ringXleftOrigin = ringXleftOrigin + gridWidth + ringXrightOrigin = ringXrightOrigin + gridWidth + + # Save and close DataSources + outDataSource = None +``` + +```{python} +from pyprojroot import here +``` + +```{python} +era_dir = here("data/ERA5_out") +``` + +```{python} +temp_file = xarray.open_dataset(os.path.join(era_dir, "2000_01.nc"), decode_coords="all") +``` + +```{python} + +``` + +```{python} +t0='2000-01-01T00:00:00.000000000' +temp_file['t2m']['valid_time'][0] +``` + +```{python} +list(temp_file['t2m']['valid_time'].data) +``` + +```{python} +temp_file['t2m'][0].shape +``` + +```{python} +import matplotlib.pyplot as plt +from matplotlib.pyplot import figure +#from matplotlib.pyplot +import cartopy.feature as cfeature +import cartopy.crs as ccrs +# Ensure lat/lon are the correct names in your dataset + +var=temp_file['t2m'][0] + +lon = temp_file.coords.get("longitude") +lat = temp_file.coords.get("latitude") + +plt.figure(figsize=(12, 6)) +ax = plt.axes(projection=ccrs.PlateCarree()) # Set projection for geographic map + +# Add map features +ax.add_feature(cfeature.BORDERS, linestyle=":") +ax.add_feature(cfeature.COASTLINE) + +ax.set_extent([lon.min() - 3, lon.max() + 3, lat.min() - 3, lat.max() + 3], crs=ccrs.PlateCarree()) + +# Plot raster using lat/lon +im = ax.pcolormesh(lon, lat, var, transform=ccrs.PlateCarree()) + +# Add colorbar +plt.colorbar(im, label=var.name) +plt.title(f"{var.name} Spatial Distribution") + +plt.show() +``` + +```{python} +lat +``` + +```{python} +lon +``` + +```{python} +temp_file['t2m'] +``` + +```{python} +era_files = glob.glob(os.path.join(era_dir, '*.nc')) +``` + +We read in the netcdf files and stack them in the 4th dimension by year + +```{python} +era_stack = xarray.open_mfdataset(era_files, decode_coords="all") +``` + +```{python} +era_stack +``` + +The data above does not have a [coordinate reference system](https://en.wikipedia.org/wiki/Spatial_reference_system), needed to interpret, transform, or align datasets. Hence, we assign the WGS84 standard + +```{python} +era_stack.rio.write_crs("WGS 84", inplace=True) +``` + +Here, we are making a shapefile that is a fishnet grid of the raster extent. +It will essentially be a polygon of lines surrounding each ERA5 cell + +```{python} +era_extent = era_stack.rio.bounds() +``` + +```{python} +xmin = era_extent[0] +xmax = era_extent[2] +ymin = era_extent[1] +ymax = era_extent[3] + +height = era_stack.rio.height +width = era_stack.rio.width + +era_coords = [(xmin, ymin), (xmin, ymax), (xmax, ymax), (xmax, ymin), (xmin, ymin)] +era_polygon = Polygon(era_coords) +``` + +```{python} +# implement the fishnet function +make_fishnet(os.path.join(era_dir, 'era_fishnet.shp'), xmin,xmax,ymin,ymax,height,width) +``` + +```{python} +ogr_shp = gpd.read_file(os.path.join(era_dir, 'era_fishnet.shp')) +print(ogr_shp.crs) # this is none, so we have to set it + +# Set the CRS for the created fishnet shape file as the same one from the stacked raster files. +new_crs = era_stack.rio.crs.data # Replace with the desired CRS +ogr_shp = ogr_shp.set_crs(new_crs) +``` + +```{python} +ogr_shp.to_file(os.path.join(era_dir, 'era_fishnet.shp')) +``` + +```{python} + +``` \ No newline at end of file diff --git a/notes/_prototypes/_kenya_demo_03_aggregate.qmd b/notes/_prototypes/_kenya_demo_03_aggregate.qmd new file mode 100644 index 0000000..2d90f2f --- /dev/null +++ b/notes/_prototypes/_kenya_demo_03_aggregate.qmd @@ -0,0 +1,19 @@ +--- +skip_showdoc: true +--- + +# Part 3: Joining Fishnet to the Ward geometries + +In this file, we will join the fishnet, which is a polygon grid with lines +surrounding the grid of the ERA5 raster with the Ward geometries that have +been queried from the Database of Global Administrative Boundaries +(gadm.org). In merging the polygon grid with the ward polygon data, +we ensure that every ward will be aligned with the relevant +ERA5 temperature metrics for the area. + +Next, we Create extraction points from the union of the wards and fishnet. These +are what we can use to extract values from the raster that overlaps with +with the points aligning to each wards (this file). + +Lastly, we Estimate the ward-level exposure to ERA5, accounting for the availability +of data within the wards (this file) \ No newline at end of file diff --git a/notes/_prototypes/_learning_aggregations_w_michelle_20250328.qmd b/notes/_prototypes/_learning_aggregations_w_michelle_20250328.qmd new file mode 100644 index 0000000..0b81808 --- /dev/null +++ b/notes/_prototypes/_learning_aggregations_w_michelle_20250328.qmd @@ -0,0 +1,719 @@ +--- +skip_showdoc: true +--- + +## Prototyping Spatial Aggregations + +We're going to learn how to aggregate the exposure data into daily values. This is useful for analyzing the data over a longer period of time, such as a week or a month, and is part of the larger goal of this project to aggregate the ERA5 dataset for Madagascar. + + +Doing an aggregation of a netcdf file is relatively simple. What we need to do is read in the data, and then use the `xarray` library to group the data by time using a resampler method. We can then use the `mean` function to calculate the average value for each day. + +```{python} +import xarray as xr +import matplotlib.pyplot as plt +import cartopy.crs as ccrs +import cartopy.feature as cfeature +from pyprojroot import here +from hydra import initialize, compose +from omegaconf import OmegaConf +``` + +Let's look at the data that we've already downloaded. In the pipeline. We'll use the xarray library to open it up and inspect it. + +```{python} +# Load the NetCDF file +fpath = here() / "data/input/2010_1.nc" +ds = xr.open_dataset(fpath) +``` + +This is a netcdf file. It has the following dimensions representing time series, as well as the variables we downloaded at specific locations: + +```{python} +ds +``` + +We can see that our variables are accessible with indeces. The reason we have 744 time points is because it is hourly data for the entire month. + +Interestingly, we can simply use the `.resample()` method to create mathematically aggregated data. + +```{python} +# Perform multiple aggregations +daily_mean = ds.resample(valid_time="1D").mean() # Daily mean +daily_max = ds.resample(valid_time="1D").max() # Daily max +daily_min = ds.resample(valid_time="1D").min() # Daily min + +# Combine the results into a new dataset +daily_aggregated = xr.Dataset({ + "t2m_mean": daily_mean["t2m"], + "t2m_max": daily_max["t2m"], + "t2m_min": daily_min["t2m"], + "d2m_mean": daily_mean["d2m"], + "d2m_max": daily_max["d2m"], + "d2m_min": daily_min["d2m"] +}) + +daily_aggregated +``` + +Look at how this compares to the original data: + +```{python} +ds +``` + +With data of this shape, we can plot the mean temperature over months + +```{python} +# Select a specific grid point (e.g., latitude=-1, longitude=0) +variable='mean' + +# note: we can use the isel method to select the grid point. In this case, +# we are selecting the bottom-left grid point (latitude=-1, longitude=0) because we're selecting +# the smallest value for latitude: +# time=0: Selects the first time point. +# latitude=-1: Selects the last latitude (bottom-most, as latitude is usually ordered from north to south). +# longitude=0: Selects the first longitude (left-most). +t2m_mean_point = daily_aggregated["t2m_" + variable].isel(latitude=-1, longitude=0) + +# Plot the time series +plt.figure(figsize=(10, 6)) +t2m_mean_point.plot(label="Daily Mean t2m") +plt.title("Daily Aggregated ({}) Temperature at Bottom-Left Grid Point".format(variable)) +plt.xlabel("Time") +plt.ylabel("Temperature (K)") +plt.legend() +plt.grid() +plt.show() +``` + +How does this compared to the disaggregated data? + +```{python} +t2m_point = ds["t2m"].isel(latitude=-1, longitude=0) + +# Plot the time series +plt.figure(figsize=(10, 6)) +t2m_point.plot(label="Daily Mean t2m") +plt.title("Daily Disaggregated Temperature at Bottom-Left Grid Point") +plt.xlabel("Time") +plt.ylabel("Temperature (K)") +plt.legend() +plt.grid() +plt.show() +``` + +These temperature plots match beautifully! This means our aggregation over the 31 days works! + +Let's look at the aggregation over a map: + +```{python} +# Select the first day of t2m_mean +variable="mean" +t2m_mean_day1 = daily_aggregated["t2m_" + variable].isel(valid_time=0) + +# Set the absolute min and max for the color bar +vmin = 270 # Minimum value (e.g., 270 K) +vmax = 310 # Maximum value (e.g., 310 K) + +# Create a plot with Cartopy +plt.figure(figsize=(10, 6)) +ax = plt.axes(projection=ccrs.PlateCarree()) # Use PlateCarree projection for latitude/longitude data + +# Plot the data +t2m_mean_day1.plot(ax=ax, cmap="coolwarm", transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax, cbar_kwargs={"label": "Temperature (K)"}) + +# Add Madagascar's border using Cartopy's built-in features +ax.add_feature(cfeature.BORDERS, edgecolor="black", linewidth=1) # Add country borders +ax.add_feature(cfeature.COASTLINE, edgecolor="black", linewidth=0.8) # Add coastlines + +# Optionally, zoom in on Madagascar +ax.set_extent([43, 51, -26, -11], crs=ccrs.PlateCarree()) # Longitude and latitude bounds for Madagascar + +# Add gridlines +ax.gridlines(draw_labels=True, linewidth=0.5, color="gray", alpha=0.5, linestyle="--") + +# Add a title +plt.title("Mean Daily {} Temperature (Day 1)".format(variable)) +plt.show() +``` + +Looks great. Now, we need to see if we can do a spatial aggregation: + +>A mathematical aggregation like mean() involves summarizing data values (e.g., averaging) across a specific dimension, such as time, without considering spatial relationships. For example, calculating the daily mean temperature from hourly data is purely numerical. +In contrast, a spatial aggregation using rasters and polygons involves summarizing data based on spatial boundaries. For example, when aggregating raster data (e.g., temperature) over a polygon (e.g., a country's boundary), the process involves selecting raster cells that fall within the polygon and computing a summary statistic (e.g., mean, sum) for those spatially defined areas. This type of aggregation accounts for geographic context and spatial relationships. + +To do this, we'll need to read in the shapefile that defines the shape of the polygon (ie the physical ground) and find the pixels of data that fall within the polygon. We can then use the `xarray` library to group the data by time using a resampler method. We can then use the `mean` function to calculate the average value for each day. + +```{python} +import geopandas as gpd + +# we learned how to read in shapefiles in the kenya demo notebook +zip_url_or_path = here() / "data/testing/gadm41_MDG.gpkg" + +shape = gpd.read_file(zip_url_or_path, layer = "ADM_ADM_1") +``` + +We are using the layer 1 of this shapefile from GADM.org. This refers to the states in red: + +![image.png](attachment:image.png) + +When we read in the shapefile, the data in the `geometry` column is a specification of the polygons that represent geographic boundaries. + +```{python} +shape +``` + +In a vector image such as a shapefile, the steps between each value are not guaranteed to be equal (unlike on a cartesian plane), so we need to think about how those values "project" onto a known Coordinate Reference System (CRS) that has equal steps. + +A quick note about CRS: + +> The WGS 84 (World Geodetic System 1984) is a widely used global Coordinate Reference System (CRS). It is the standard CRS for GPS (Global Positioning System) and is commonly used in geospatial applications. WGS 84 defines a geographic coordinate system based on a specific ellipsoid model of the Earth. + +> Key Features of WGS 84 +Type: Geographic Coordinate System (GCS). +Coordinates are represented in latitude, longitude, and optionally altitude. +Units: Degrees (for latitude and longitude). +Ellipsoid: WGS 84 uses a reference ellipsoid with: +Semi-major axis: 6,378,137 meters. +Flattening: 1 / 298.257223563. +Datum: The WGS 84 datum defines the origin and orientation of the coordinate system. +EPSG Code: The EPSG code for WGS 84 is 4326. + +Spatial geometry is complicated and silly, hence [all maps are wrong](https://youtu.be/kIID5FDi2JQ?si=OZASX3i6Aglqwa4u). + +Nevertheless, we can see that the shapefile has a CRS of EPSG:4326, which is what we want: + +```{python} +shape.crs +``` + +Were this different, we'd have to find some way to adjust these projections. For our netCDF file, however, we don't need to worry about this because the data themselves are created using a rasterized netCDF file, which is a standard format for storing gridded data. The data is already in a grid format, and the pixel values are already aligned with the geographic coordinates of the raster. In spatial geometry, we use degrees to represent the latitude and longitude of the corners of each pixel. This means that the data is already in a format that can be easily manipulated and analyzed using xarray and geopandas, because we refer to where the pixel is located in the world using degrees. It is essentially an absolute reference system. + +In the ERA5 dataset, the resolution is said to be 0.25 degrees, which means that each pixel represents a square area of approximately 25 km x 25 km at the equator. So at every unit of 0.25 degrees north-south or east-west, we have a new pixel of data, with a value for temperature or dewpoint or whatever. You can physically see each of these on the plot. + +Learn more about ERA5's resolution [here](https://confluence.ecmwf.int/display/CKB/ERA5%3A+What+is+the+spatial+reference). + +Now, in order to aggregate data spatially, we're pasting in a utility here for finding the intersecting values between our netcdf data and the polygons represented in our shapefile (ie the states, regions, etc.). + +Source: https://github.com/NSAPH-Data-Processing/air_pollution__aqdh/blob/main/utils/faster_zonal_stats.py + +```{python} +import numpy as np +from tqdm import tqdm +from math import ceil, floor + +from rasterstats.io import Raster +from rasterstats.utils import boxify_points, rasterize_geom +``` + +This function indexes each pixel and maps it to the polygon it falls within. A few notes about this function: + +- It uses the `rasterstats.io` library to read in a raster tiff file +- It uses affine transformations to convert the pixel coordinates to geographic coordinates +- It needs to know where there is no data in the raster file, so we need to set a `nodata` value +- `all_touched` is a boolean that determines whether to include all pixels that touch the polygon or just the ones that are fully contained within it; this is a domain specific choice + +```{python} +def polygon_to_raster_cells( + vectors, + raster, + band=1, + nodata=None, + affine=None, + all_touched=False, + verbose=False, + **kwargs, +): + """Returns an index map for each vector geometry to indices in the raster source. + + Parameters + ---------- + vectors: list of geometries + + raster: ndarray + + nodata: float + + affine: Affine instance + + all_touched: bool, optional + Whether to include every raster cell touched by a geometry, or only + those having a center point within the polygon. + defaults to `False` + + Returns + ------- + dict + A dictionary mapping vector the ids of geometries to locations (indices) in the raster source. + """ + + cell_map = [] + + with Raster(raster, affine, nodata, band) as rast: + # used later to crop raster and find start row and col + min_lon, dlon = affine.c, affine.a + max_lat, dlat = affine.f, -affine.e + H, W = rast.shape + + for geom in tqdm(vectors, disable=(not verbose)): + if "Point" in geom.geom_type: + geom = boxify_points(geom, rast) + + # find geometry bounds to crop raster + # the raster and geometry must be in the same lon/lat coordinate system + start_row = max(0, min(H - 1, floor((max_lat - geom.bounds[3]) / dlat))) + start_col = min(W - 1, max(0, floor((geom.bounds[0] - min_lon) / dlon))) + end_col = max(0, min(W - 1, ceil((geom.bounds[2] - min_lon) / dlon))) + end_row = min(H - 1, max(0, ceil((max_lat - geom.bounds[1]) / dlat))) + geom_bounds = ( + min_lon + dlon * start_col, # left + max_lat - dlat * end_row - 1e-12, # bottom + min_lon + dlon * end_col + 1e-12, # right + max_lat - dlat * start_row, # top + ) + + # crop raster to area of interest and rasterize + fsrc = rast.read(bounds=geom_bounds) + rv_array = rasterize_geom(geom, like=fsrc, all_touched=all_touched) + indices = np.nonzero(rv_array) + + if len(indices[0]) > 0: + indices = (indices[0] + start_row, indices[1] + start_col) + assert 0 <= indices[0].min() < rast.shape[0] + assert 0 <= indices[1].min() < rast.shape[1] + else: + pass # stop here for debug + + cell_map.append(indices) + + return cell_map +``` + +So to implement this we need to first convert the netcdf to a tiff so that we can rasterize it to each of the polygons in the shapefile. We do this with `rioxarray` + +```{python} +import rioxarray as rxr +``` + +First, we pick our variable of interest, then we set the spatial properties to make sure it conforms to the CRS we wanted + +```{python} +temperature = daily_aggregated['t2m_mean'] +``` + +```{python} +temp_set = temperature.rio.set_spatial_dims(x_dim="longitude", y_dim="latitude") +temp_set = temp_set.rio.write_crs("EPSG:4326") +``` + +Write it out to tiff and read it back in (there's no way to do this in memory) + +```{python} +temp_set.rio.to_raster("temp.tif") +``` + +Now we can investigate the tiff and see that it has all the properties necessary for the function + +```{python} +import rasterio + +src = rasterio.open("temp.tif") +raster = src.read(1) # Numpy array +profile = src.profile # Metadata +transform = src.transform +``` + +```{python} +# the number of data points +src.count +``` + +```{python} +# the affine transformation matrix: +# Pixel size (resolution in x and y). +# Origin (top-left corner in spatial coordinates). +# Rotation (if the raster is not north-up). +src.transform +``` + +```{python} +# any missing data locations +src.nodata +``` + +```{python} +# the number of rows and columns +print(src.width, src.height) +``` + +Fetch the array of data + +```{python} +raster_array = src.read(1) +``` + +Function go brrrr + +```{python} +res_poly2cell=polygon_to_raster_cells( + vectors = shape.geometry.values, # the geometries of the shapefile of the regions + raster=raster_array, # the raster data above + band=1, # the value of the day that we're using + nodata=src.nodata, # any intersections with no data, may have to be np.nan + affine=src.transform, # some math thing need to revise + all_touched=True, + verbose=True +) +``` + +The data below maps which grid entries fall into each of the regions in the shapefile (e.g. which pixel is in which state) + +```{python} +res_poly2cell +``` + +```{python} +len(res_poly2cell) +``` + +Look familiar? + +These are the 6 states in the shapefile. The values in the array are the indexes of the pixels in the netcdf file that fall within the polygon. +Now, within each of these we can aggregate mathematically eg min max mean etc. + +```{python} +# the values themselves +raster_array +``` + +```{python} +stats = [] +for indices in res_poly2cell: + if len(indices[0]) == 0: + # no cells found for this polygon + stats.append(np.nan) + else: + cells = raster[indices] + if sum(~np.isnan(cells)) == 0: + # no valid cells found for this polygon + stats.append(np.nan) + continue + else: + # compute MEAN of valid cells + # but this stat can be ANYTHING + stats.append(np.nanmean(cells)) +``` + +```{python} +stats +``` + +Looks like it worked! + +```{python} +import pandas as pd + +pd.DataFrame({"l1_region": shape.NAME_1, "mean_31_day_temp": stats}) +``` + +### Let's try it with Level 3 data + +```{python} +# first get the shape of the polygons + +shape = gpd.read_file(zip_url_or_path, layer = "ADM_ADM_3") + +# get the new mapping of the pixels to the shapes in the region + +res_poly2cell = polygon_to_raster_cells( + vectors = shape.geometry.values, # the geometries of the shapefile of the regions + raster=raster_array, # the raster data above + band=1, # the value of the day that we're using + nodata=src.nodata, # any intersections with no data, may have to be np.nan + affine=src.transform, # some math thing need to revise + all_touched=True, + verbose=True +) +``` + +```{python} +len(res_poly2cell) +``` + +```{python} +# demonsttrate that because this is a "denser" set of polygons +# this iwll take longer +stats = [] + +for indices in res_poly2cell: + if len(indices[0]) == 0: + # no cells found for this polygon + stats.append(np.nan) + else: + cells = raster[indices] + if sum(~np.isnan(cells)) == 0: + # no valid cells found for this polygon + stats.append(np.nan) + continue + else: + # compute mean of valid cells + stats.append(np.nanmean(cells)) +``` + +```{python} +stats +``` + +Now we have 110 mean temperatuers for each of the shapefile's regions. + +```{python} + +df = pd.DataFrame( + {"l3_territory": shape.NAME_3, "dummy_date_in_future": 1, "temp_vals": stats} + ) +``` + +```{python} +df +``` + +```{python} +# now we plot it using the shape.geometry to get the shapefile's location for each region +gdf = gpd.GeoDataFrame(df, geometry=shape.geometry.values, crs=shape.crs) +gdf.plot(column="temp_vals", legend=True) +plt.show() +``` + +We can test this out with our healthsheds file + +```{python} +healthsheds = gpd.read_file(here() / "data/testing/mdg_healthsheds2022") +``` + +```{python} +healthsheds +``` + +```{python} +# there are NAs to remove +healthsheds.dropna(subset = ['geometry'], inplace=True) +``` + +```{python} +len(set(healthsheds.fs_uid)) +``` + +```{python} +# get the new mapping of the pixels to the shapes in the region + +res_poly2cell = polygon_to_raster_cells( + vectors = healthsheds.geometry.values, # the geometries of the shapefile of the regions + raster=raster_array, # the raster data above + band=1, # the value of the day that we're using + nodata=src.nodata, # any intersections with no data, may have to be np.nan + affine=src.transform, # some math thing need to revise + all_touched=True, + verbose=True +) + +stats = [] + +for indices in res_poly2cell: + if len(indices[0]) == 0: + # no cells found for this polygon + stats.append(np.nan) + else: + cells = raster[indices] + if sum(~np.isnan(cells)) == 0: + # no valid cells found for this polygon + stats.append(np.nan) + continue + else: + # compute mean of valid cells + stats.append(np.nanmean(cells)) +``` + +```{python} +df = pd.DataFrame( + {"healthshed": healthsheds.fs_uid, "dummy_date_in_future": 1, "temp_vals": stats} + ) +``` + +```{python} +# now we plot it using the shape.geometry to get the shapefile's location for each region +gdf = gpd.GeoDataFrame(df, geometry=healthsheds.geometry.values, crs=shape.crs) +gdf.plot(column="temp_vals", legend=True) +plt.show() +``` + +Now that we've demonstrated how this could work, we can substitute the GADM shapefiles for our healthsheds, and put it in a pipeline!!! + +## Nepal + +We've modified the pipeline to now download Nepal as well. We'll test out an aggregation using the aggregation shapefiles we were provided by Dimeji. We probably want to decide on where to centralize data storage for files like this + +```{python} +try: from era5_sandbox.core import GoogleDriver, _get_callable, describe +except: from core import GoogleDriver, _get_callable, describe + +try: from era5_sandbox.download import download_raw_era5 +except: from download import download_raw_era5 + +try: from era5_sandbox.aggregate import resample_netcdf, netcdf_to_tiff, polygon_to_raster_cells, aggregate_to_healthsheds +except: from aggregate import resample_netcdf, netcdf_to_tiff, polygon_to_raster_cells, aggregate_to_healthsheds +``` + +```{python} +from hydra import initialize, compose +from omegaconf import OmegaConf + +# unfortunately, we have to use the initialize function to load the config file +# this is because the @hydra decorator does not work with Notebooks very well +# this is a known issue with Hydra: https://gist.github.com/bdsaglam/586704a98336a0cf0a65a6e7c247d248 +# +# just use the relative path from the notebook to the config dir +with initialize(version_base=None, config_path="../../conf"): + cfg = compose(config_name='config.yaml') + +cfg.development_mode = False +cfg.query['year'] = 2023 +cfg.query['month'] = 10 +cfg.query['day'] = 1 +cfg.query['time'] = "00:00" +cfg.query['geography'] = "nepal" +download_raw_era5(cfg) +``` + +Now let's read it in and run the aggregation: + +```{python} +# Load the NetCDF file +fpath = here() / "data/input/nepal_2023_10.nc" +ds = xr.open_dataset(fpath) +``` + +```{python} +ds +``` + +```{python} +# Perform multiple aggregations +daily_mean = ds.resample(valid_time="1D").mean() # Daily mean +daily_max = ds.resample(valid_time="1D").max() # Daily max +daily_min = ds.resample(valid_time="1D").min() # Daily min + +# Combine the results into a new dataset +daily_aggregated = xr.Dataset({ + "t2m_mean": daily_mean["t2m"], + "t2m_max": daily_max["t2m"], + "t2m_min": daily_min["t2m"], + "d2m_mean": daily_mean["d2m"], + "d2m_max": daily_max["d2m"], + "d2m_min": daily_min["d2m"] +}) + +daily_aggregated +``` + +```{python} +# Select the first day of t2m_mean +variable="mean" +t2m_mean_day1 = daily_aggregated["t2m_" + variable].isel(valid_time=0) + +# Set the absolute min and max for the color bar +vmin = 270 # Minimum value (e.g., 270 K) +vmax = 310 # Maximum value (e.g., 310 K) + +# Create a plot with Cartopy +plt.figure(figsize=(10, 6)) +ax = plt.axes(projection=ccrs.PlateCarree()) # Use PlateCarree projection for latitude/longitude data + +# Plot the data +t2m_mean_day1.plot(ax=ax, cmap="coolwarm", transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax, cbar_kwargs={"label": "Temperature (K)"}) + +# Add Madagascar's border using Cartopy's built-in features +ax.add_feature(cfeature.BORDERS, edgecolor="black", linewidth=1) # Add country borders +ax.add_feature(cfeature.COASTLINE, edgecolor="black", linewidth=0.8) # Add coastlines + +# Optionally, zoom in on Madagascar +#ax.set_extent([43, 51, -26, -11], crs=ccrs.PlateCarree()) # Longitude and latitude bounds for Madagascar + +# Add gridlines +ax.gridlines(draw_labels=True, linewidth=0.5, color="gray", alpha=0.5, linestyle="--") + +# Add a title +plt.title("Mean Daily {} Temperature (Day 1)".format(variable)) +plt.show() +``` + +We're going to create the aggregations using the function we defined in the aggregate module + +```{python} +resampled_nc = resample_netcdf(fpath) + +resampled_tiff = netcdf_to_tiff( + ds=resampled_nc, + variable="t2m", + crs="EPSG:4326" +) +``` + +Now we fetch the shapefile for administrative aggregations using our googledriver class: + +```{python} +driver = GoogleDriver(json_key_path=here() / cfg.GOOGLE_DRIVE_AUTH_JSON.path) +drive = driver.get_drive() +``` + +```{python} +shape = "Nepal_Healthsheds2024.zip" +``` + +```{python} +healthsheds = driver.read_healthsheds(shape) +``` + +```{python} +healthsheds.columns +``` + +```{python} +healthsheds.describe() +``` + +```{python} +len(set(healthsheds['fid'].values)) +``` + +```{python} +res_poly2cell=polygon_to_raster_cells( + vectors = healthsheds.geometry.values, # the geometries of the shapefile of the regions + raster=resampled_tiff.data, # the raster data above + band=1, # the value of the day that we're using + nodata=resampled_tiff.nodata, # any intersections with no data, may have to be np.nan + affine=resampled_tiff.transform, # some math thing need to revise + all_touched=True, + verbose=True +) +``` + +```{python} +result = aggregate_to_healthsheds( + res_poly2cell=res_poly2cell, + raster=resampled_tiff, + shapes=healthsheds, + names_column="fid", + aggregation_func=np.nanmean, + aggregation_name="mean_temperature" +) +result.head() +``` + +```{python} +result.plot(column="mean_temperature", legend=True) +plt.title("Mean Temperature (K) by Health Shed October 2023") +plt.show() +``` + +This should work by slotting right into the pipeline, only changing the function for the names column \ No newline at end of file diff --git a/notes/prototypes/image.png b/notes/_prototypes/image.png similarity index 100% rename from notes/prototypes/image.png rename to notes/_prototypes/image.png diff --git a/notes/index.ipynb b/notes/index.ipynb index 58de015..83e40d9 100644 --- a/notes/index.ipynb +++ b/notes/index.ipynb @@ -1,29 +1,34 @@ { "cells": [ { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "#| hide\n", - "from era5_sandbox.core import *" + "---\n", + "title: \"The ERA5 Spatial Aggregation Pipeline\"\n", + "exec_all: true\n", + "---" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ - "# era5_sandbox\n", - "\n", - "> Sandbox environment for era5 development" + "#| hide: null\n", + "from era5_sandbox.core import *" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Here we are developing functions and code for the Madagascar ERA5 dataset project. The goal is for exposure data to be made available at the daily resolution when possible. Finer resolutions shouldn’t ever be needed for our purposes, and it should then be relatively easy to aggregate at coarser resolutions, such as weekly or monthly.\n", + "## era5_sandbox\n", + "\n", + "> Sandbox environment for era5 development\n", + "\n", + "This package documents the development and implementation of functions and code for the Madagascar ERA5 dataset project. The goal is for exposure data to be made available at the daily resolution when possible. Finer resolutions shouldn’t ever be needed for our purposes, and it should then be relatively easy to aggregate at coarser resolutions, such as weekly or monthly. Additionally, we've extended this work to Nepal as well.\n", "\n", "Variables should generally be made available from 2010 onward, as that’s where our clinic data starts.\n", "\n", @@ -31,11 +36,15 @@ "\n", "Preliminary list of environmental variables\n", "\n", - "- [ ] 2-m air temperature from ERA5: daily min, max, mean\n", + "- [x] 2-m air temperature from ERA5: daily min, max, mean\n", " \n", - "- [ ] 2-m air dew point temperature from ERA5: daily min, max, mean\n", + "- [x] 2-m air dew point temperature from ERA5: daily min, max, mean\n", + "\n", + "- [x] Precipitation: daily total (ERA5)\n", "\n", - "- [ ] Precipitation: daily total (ERA5)\n", + "- [x] Soil moisture: daily average (ERA5)\n", + "\n", + "Variables from other sources:\n", "\n", "- [ ] Sea surface temperature: daily average and maximum in the nearest neighbor for each healthshed.\n", "\n", @@ -55,132 +64,102 @@ "\n", "- [ ] Linking/segmenting healthsheds into climate zones and other \n", "\n", - "- [ ] Relative humidity: daily average (lower priority)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Developer Guide" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you are new to using `nbdev` here are some useful pointers to get you started." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Install era5_sandbox in Development mode" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ + "- [ ] Relative humidity: daily average (lower priority)\n", + "\n", + "Those from the ERA5 dataset will be housed here, but we may likely develop a separate repository for the other datasets.\n", + "\n", + "## Developer Guide\n", + "\n", + "This package is built and maintained with `nbdev`. If you are new to using `nbdev` here are some useful pointers to get you started.\n", + "\n", + "### Install era5_sandbox in Development mode\n", + "\n", "```sh\n", "# make sure era5_sandbox package is installed in development mode\n", "$ pip install -e .\n", + "```\n", "\n", - "# To make changes, go to the \"notes\" directory and edit the notebooks as necessary.\n", - "# Each notebook refers to a module in the era5_sandbox package. Cells are exported to the module\n", - "# when the notebook is saved and you run the following command:\n", + "To make changes, go to the \"notes\" directory and edit the notebooks as necessary.\n", + "Each notebook refers to a module in the era5_sandbox package. Cells are exported to the module\n", + "when the notebook is saved and you run the following command:\n", "\n", + "```sh\n", "$ nbdev_export\n", "```\n", "\n", - "For e.g., to change functionality of the `testAPI()` function in the testAPI Hydra rule, you would edit the `testAPI` notebook in the `notes` directory `notes/testAPI.ipynb`, and then save that notebook and run `nbdev_export` to update the `core` module in the package." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Usage" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Installation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Install latest from the GitHub [repository][repo]:\n", + "For e.g., to change functionality of the `testAPI()` function in the testAPI Hydra rule, you would edit the `testAPI` notebook in the `notes` directory `notes/testAPI.ipynb`, and then save that notebook and run `nbdev_export` to update the `core` module in the package.\n", + "\n", + "### How to Run the Pipeline\n", + "\n", + "The pipeline downloads ERA5 variables for a given date range and geographical bounding box. You can learn how each of these steps was by following the notebooks in `notes` in numerical order.\n", + "\n", + "::: {.callout-important}\n", + "The pipeline has two implementations: one using `snakemake` and `hydra`, and another using `pytask`. The `pytask` implementation is the more recent one, and is recommended for future use. The `snakemake` implementation is left here for reference to legacy code.\n", + ":::\n", + "\n", + "#### Using `pytask`\n", + "\n", + "To run the pipeline, the `pytask` config at `note/20_pytask_config.qmd` should be reviewed\n", + "and updated if necessary. The pipeline can then be run with the following command:\n", "\n", "```sh\n", - "$ pip install git+https://github.com/NSAPH-Data-Processing/era5_sandbox\n", + "$ sbatch pytask.sbatch\n", "```\n", "\n", - "or clone and install in development mode:\n", + "#### Using `snakemake` and `hydra`\n", + "\n", + "To run the pipeline, the config at `config/config.yaml` should be updated with the desired date range and geographical bounding box. The pipeline can then be run with the following command:\n", "\n", "```sh\n", - "$ git clone https://github.com/NSAPH-Data-Processing/era5_sandbox\n", - "$ pip install -e .\n", + "sbatch snakemake.sbatch\n", "```\n", "\n", + "### What Does the Pipeline Produce?\n", "\n", - "[repo]: https://github.com/NSAPH-Data-Processing/era5_sandbox" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Documentation" + "Using `pytask`'s data catalog, you can investigate the downloaded raw data with python, eg.:" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ - "🚧Documentation is in development 🚧" + "#| exec_doc:\n", + "#\n", + "import xarray as xr\n", + "from era5_sandbox.config import data_catalog\n", + "from era5_sandbox.core import ClimateDataFileHandler\n", + "\n", + "ex_nc = list(data_catalog['download']['outputs']._entries).pop()\n", + "ex_nc_path = data_catalog['download']['outputs'][ex_nc].load()\n", + "\n", + "with ClimateDataFileHandler(ex_nc_path) as handler:\n", + " ds = xr.open_dataset(handler.get_dataset(\"instant\"))\n", + "\n", + "ds" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## How to use" + "And plot it with cartopy, eg.:" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ - "The pipeline currently downloads ERA5 temperature and dew point temperature data for a given date range and geographical bounding box. You can learn each of these steps by following the notebooks in `notes` in numerical order.\n", - "\n", - "To run the pipeline, the config at `config/config.yaml` should be updated with the desired date range and geographical bounding box. The pipeline can then be run with the following command:\n", - "\n", - "```sh\n", - "sbatch snakemake.sbatch\n", - "```\n", - "\n", - "You can investigate the downloaded raw data with python, eg.:\n", - "\n", - "```python\n", - "import xarray as xr\n", + "#| exec_doc:\n", + "#\n", "import matplotlib.pyplot as plt\n", "import cartopy.crs as ccrs\n", "import cartopy.feature as cfeature\n", "\n", - "### the path to any of the downloaded files\n", - "file_path = \"/n/dominici_lab/lab/data_processing/csph-era5_sandbox/data/input/2010_01.nc\"\n", - "data = xr.open_dataset(file_path)\n", - "\n", - "\n", - "temperature = data[\"t2m\"]\n", - "\n", - "\n", + "temperature = ds[\"t2m\"]\n", "\n", "# Select a specific time step\n", "temperature_at_time = temperature.isel(valid_time=0)\n", @@ -192,28 +171,14 @@ "ax.coastlines()\n", "ax.add_feature(cfeature.BORDERS, linestyle=\":\")\n", "ax.set_title(\"Temperature at Time Step 0\")\n", - "plt.show()\n", - "```" + "plt.show()" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "1+1" + "You can also load the aggregated data:" ] }, { @@ -221,7 +186,17 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "#| exec_doc:\n", + "#\n", + "import pandas as pd\n", + "import geopandas as gpd\n", + "from era5_sandbox.config import data_catalog\n", + "\n", + "ex_agg_path = data_catalog['aggregate']['outputs']['2019_08_madagascar_night_d2m_max.parquet'].load()\n", + "\n", + "gpd.read_parquet(ex_agg_path).describe()" + ] } ], "metadata": { @@ -232,5 +207,5 @@ } }, "nbformat": 4, - "nbformat_minor": 4 + "nbformat_minor": 5 } diff --git a/notes/logs/2025-03-17/12-59-36/.hydra/config.yaml b/notes/logs/2025-03-17/12-59-36/.hydra/config.yaml deleted file mode 100644 index e675fff..0000000 --- a/notes/logs/2025-03-17/12-59-36/.hydra/config.yaml +++ /dev/null @@ -1,34 +0,0 @@ -query: - product_type: reanalysis - variable: - - 2m_dewpoint_temperature - - 2m_temperature - - skin_temperature - year: - - 2010 - - 2011 - month: - - 1 - - 2 - - 3 - day: - - 1 - - 2 - - 3 - - 4 - - 5 - time: - - 0 - - 6 - - 12 - - 18 - area: - - 0 - - 360 - - -90 - - 90 - data_format: netcdf - download_format: unarchived -datapaths: - input: null - output: null diff --git a/notes/logs/2025-03-17/12-59-36/.hydra/hydra.yaml b/notes/logs/2025-03-17/12-59-36/.hydra/hydra.yaml deleted file mode 100644 index b04b55c..0000000 --- a/notes/logs/2025-03-17/12-59-36/.hydra/hydra.yaml +++ /dev/null @@ -1,155 +0,0 @@ -hydra: - run: - dir: logs/${now:%Y-%m-%d}/${now:%H-%M-%S} - sweep: - dir: multirun/${now:%Y-%m-%d}/${now:%H-%M-%S} - subdir: ${hydra.job.num} - launcher: - _target_: hydra._internal.core_plugins.basic_launcher.BasicLauncher - sweeper: - _target_: hydra._internal.core_plugins.basic_sweeper.BasicSweeper - max_batch_size: null - params: null - help: - app_name: ${hydra.job.name} - header: '${hydra.help.app_name} is powered by Hydra. - - ' - footer: 'Powered by Hydra (https://hydra.cc) - - Use --hydra-help to view Hydra specific help - - ' - template: '${hydra.help.header} - - == Configuration groups == - - Compose your configuration from those groups (group=option) - - - $APP_CONFIG_GROUPS - - - == Config == - - Override anything in the config (foo.bar=value) - - - $CONFIG - - - ${hydra.help.footer} - - ' - hydra_help: - template: 'Hydra (${hydra.runtime.version}) - - See https://hydra.cc for more info. - - - == Flags == - - $FLAGS_HELP - - - == Configuration groups == - - Compose your configuration from those groups (For example, append hydra/job_logging=disabled - to command line) - - - $HYDRA_CONFIG_GROUPS - - - Use ''--cfg hydra'' to Show the Hydra config. - - ' - hydra_help: ??? - hydra_logging: - version: 1 - formatters: - simple: - format: '[%(asctime)s][HYDRA] %(message)s' - handlers: - console: - class: logging.StreamHandler - formatter: simple - stream: ext://sys.stdout - root: - level: INFO - handlers: - - console - loggers: - logging_example: - level: DEBUG - disable_existing_loggers: false - job_logging: - version: 1 - formatters: - simple: - format: '[%(asctime)s][%(name)s][%(levelname)s] - %(message)s' - handlers: - console: - class: logging.StreamHandler - formatter: simple - stream: ext://sys.stdout - file: - class: logging.FileHandler - formatter: simple - filename: ${hydra.runtime.output_dir}/${hydra.job.name}.log - root: - level: INFO - handlers: - - console - - file - disable_existing_loggers: false - env: {} - mode: RUN - searchpath: [] - callbacks: {} - output_subdir: .hydra - overrides: - hydra: - - hydra.mode=RUN - task: [] - job: - name: ipython-input-1-3f35d1394572 - chdir: null - override_dirname: '' - id: ??? - num: ??? - config_name: config - env_set: {} - env_copy: [] - config: - override_dirname: - kv_sep: '=' - item_sep: ',' - exclude_keys: [] - runtime: - version: 1.3.2 - version_base: '1.3' - cwd: /Users/tit420/projects/era5_sandbox/notes - config_sources: - - path: hydra.conf - schema: pkg - provider: hydra - - path: /Users/tit420/projects/era5_sandbox/conf - schema: file - provider: main - - path: '' - schema: structured - provider: schema - output_dir: /Users/tit420/projects/era5_sandbox/notes/logs/2025-03-17/12-59-36 - choices: - datapaths: datapaths - hydra/env: default - hydra/callbacks: null - hydra/job_logging: default - hydra/hydra_logging: default - hydra/hydra_help: default - hydra/help: default - hydra/sweeper: basic - hydra/launcher: basic - hydra/output: default - verbose: false diff --git a/notes/logs/2025-03-17/12-59-36/.hydra/overrides.yaml b/notes/logs/2025-03-17/12-59-36/.hydra/overrides.yaml deleted file mode 100644 index fe51488..0000000 --- a/notes/logs/2025-03-17/12-59-36/.hydra/overrides.yaml +++ /dev/null @@ -1 +0,0 @@ -[] diff --git a/notes/logs/2025-03-17/12-59-36/ipython-input-1-3f35d1394572.log b/notes/logs/2025-03-17/12-59-36/ipython-input-1-3f35d1394572.log deleted file mode 100644 index 10854a9..0000000 --- a/notes/logs/2025-03-17/12-59-36/ipython-input-1-3f35d1394572.log +++ /dev/null @@ -1,4 +0,0 @@ -[2025-03-17 12:59:37,230][datapi.legacy_api_client][INFO] - [2024-09-26T00:00:00] Watch our [Forum](https://forum.ecmwf.int/) for Announcements, news and other discussed topics. -[2025-03-17 12:59:37,232][datapi.legacy_api_client][WARNING] - [2024-06-16T00:00:00] CDS API syntax is changed and some keys or parameter names may have also changed. To avoid requests failing, please use the "Show API request code" tool on the dataset Download Form to check you are using the correct syntax for your API request. -[2025-03-17 12:59:37,541][datapi.legacy_api_client][INFO] - Request ID is 94401c1f-cc22-4d58-acea-0cca463df9ab -[2025-03-17 12:59:37,676][datapi.legacy_api_client][INFO] - status has been updated to accepted diff --git a/notes/prototypes/download_QA.ipynb b/notes/prototypes/download_QA.ipynb deleted file mode 100644 index 96e6b5a..0000000 --- a/notes/prototypes/download_QA.ipynb +++ /dev/null @@ -1,47 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "036be852", - "metadata": {}, - "source": [ - "# Investigating The Download Results\n", - "\n", - "There are a couple of things we should do to QA our data downloads. Specifically, we want to come up with a way of ensuring our aggregations are valid and accurate. This will require some simple EDA." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "08094a12", - "metadata": {}, - "outputs": [], - "source": [ - "from pyprojroot import here\n", - "import pandas as pd\n", - "import os\n", - "from hydra import initialize, compose\n", - "from omegaconf import OmegaConf, DictConfig" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c3e18bb3", - "metadata": {}, - "outputs": [], - "source": [ - "eg_file = here() / \"data/input/2010_1.nc\"" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notes/prototypes/kenya_demo_01_intro.ipynb b/notes/prototypes/kenya_demo_01_intro.ipynb deleted file mode 100644 index 8fdb30b..0000000 --- a/notes/prototypes/kenya_demo_01_intro.ipynb +++ /dev/null @@ -1,6142 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "skip_showdoc: true\n", - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Introduction to the ERA 5 Data\n", - "\n", - "The ERA5 dataset is the fifth iteration of the ECMWF ReAnalysis dataset, spanning from 1950 to the present. ECMWF is the \"European Centre for Medium-Range Weather Forecasts\".\n", - "The dataset provides comprehensive and high-resolution historical weather and climate data. The source data is from the [Copernicus Climate Data Store (CDS)](https://cds.climate.copernicus.eu/#!/home). A comprehensive data documentation guide is available [here](https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation). In total, the entire CDS ERA data is over 10Petabytes.\n", - "\n", - "Fortunately for us, there are existing [Python](https://github.com/Climate-CAFE/era5-daily-heat-aggregation-python) and [R](https://github.com/Climate-CAFE/era5-daily-heat-aggregation) packages that have gone ahead and demonstrated extracting the data from the API for us, so we are going to use those to develop our workflow. Specifically, we're trying to understand the\n", - "following characteristics of the data:\n", - "\n", - "* size, \n", - "* how to download, \n", - "* what are the key transformations to map things into the health sheds\n", - "* two important variables: \n", - " * 2m air temp, and, \n", - " * 2m air dew point\n", - "\n", - "Let's get started\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Important: we need to install the CDS API first, so you'll need to grab an API key. First, you must register for an account and accept the T&Cs, afterwhich the page [here](https://ecmwf-projects.github.io/copernicus-training-c3s/cds-tutorial.html#install-the-cds-api-key) will autopopulate an API key for you. The following code shows a test case to make sure your API key works" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-03-03 13:31:07,682 INFO [2024-09-26T00:00:00] Watch our [Forum](https://forum.ecmwf.int/) for Announcements, news and other discussed topics.\n", - "2025-03-03 13:31:07,683 WARNING [2024-06-16T00:00:00] CDS API syntax is changed and some keys or parameter names may have also changed. To avoid requests failing, please use the \"Show API request code\" tool on the dataset Download Form to check you are using the correct syntax for your API request.\n", - "2025-03-03 13:31:07,959 INFO Request ID is 07de689d-b7df-439b-b303-2214b8f3eec0\n", - "2025-03-03 13:31:08,091 INFO status has been updated to accepted\n", - "2025-03-03 13:34:00,781 INFO status has been updated to successful\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "fb379a3afd064123b72fc016bd7ea267", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "1fd5a2b7ad40b8c614c78061a75d30d0.grib: 0%| | 0.00/1.98M [00:00\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
GID_0COUNTRYgeometry
0KENKenyaMULTIPOLYGON (((39.38014 -4.71792, 39.37986 -4...
\n", - "" - ], - "text/plain": [ - " GID_0 COUNTRY geometry\n", - "0 KEN Kenya MULTIPOLYGON (((39.38014 -4.71792, 39.37986 -4..." - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "kenya_shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The bounding box represents the coordinates of the shapefile, which is what we'll\n", - "use to query Copernicus. Think of it like a mask provided in a file" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "kenya_bbox = kenya_shape.total_bounds" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([33.909588 , -4.720417 , 41.92621613, 5.06116581])" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "kenya_bbox" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Technical: Add a small buffer around the bounding box to ensure the whole region \n", - "is queried, and round the parameters to a 0.1 resolution. A 0.1 resolution\n", - "is applied because the resolution of netCDF ERA5 data is .25x.25\n", - "https://confluence.ecmwf.int/display/CKB/ERA5%3A+What+is+the+spatial+reference\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "kenya_bbox[0] = round(kenya_bbox[0], 1) - 0.1\n", - "kenya_bbox[1] = round(kenya_bbox[1], 1) - 0.1\n", - "kenya_bbox[2] = round(kenya_bbox[2], 1) + 0.1\n", - "kenya_bbox[3] = round(kenya_bbox[3], 1) + 0.1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# to build a query, specify [xmin, ymin, xmax, ymax]\n", - "query_area = [kenya_bbox[0], kenya_bbox[1], kenya_bbox[2], kenya_bbox[3]]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "query_years = list(range(2000, 2024))\n", - "query_years_str = [str(x) for x in query_years]\n", - "\n", - "query_months = list(range(1, 13))\n", - "query_months_str = [str(x).zfill(2) for x in query_months]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "output_dir = create_dir(os.path.join(ecmw_dir, \"ERA5_out\"))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Now processing year 2000 \n", - "\n", - "Now processing month 01 \n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-03-03 13:52:06,896 INFO [2024-09-26T00:00:00] Watch our [Forum](https://forum.ecmwf.int/) for Announcements, news and other discussed topics.\n", - "2025-03-03 13:52:06,897 WARNING [2024-06-16T00:00:00] CDS API syntax is changed and some keys or parameter names may have also changed. To avoid requests failing, please use the \"Show API request code\" tool on the dataset Download Form to check you are using the correct syntax for your API request.\n", - "2025-03-03 13:52:07,181 INFO Request ID is 56d97887-22e9-441c-b33c-2236e5feaa87\n", - "2025-03-03 13:52:07,308 INFO status has been updated to accepted\n", - "2025-03-03 13:52:15,943 INFO status has been updated to successful\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "5e8426ff1c4d4f7c9c4bf7fe34369109", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "3f0a8829f1720f8fa1289e11eedada58.nc: 0%| | 0.00/23.4M [00:00: Failed to resolve 'cds.climate.copernicus.eu' ([Errno 8] nodename nor servname provided, or not known)\"))], attemps 1 of 500\n", - "Retrying in 120 seconds\n", - "2025-03-03 18:40:35,507 INFO status has been updated to successful\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "aeccd1069d2d4f80a3b569d25da240ff", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "9cbcabcbc8259ee36e1d9c27c7bcd2e5.nc: 0%| | 0.00/22.9M [00:00: Failed to resolve 'cds.climate.copernicus.eu' ([Errno 8] nodename nor servname provided, or not known)\"))], attemps 1 of 500\n", - "Retrying in 120 seconds\n", - "Recovering from connection error [HTTPSConnectionPool(host='cds.climate.copernicus.eu', port=443): Max retries exceeded with url: /api/retrieve/v1/jobs/11dac302-36ed-485c-acdb-663ecdfb3b2e?log=True&request=True (Caused by NameResolutionError(\": Failed to resolve 'cds.climate.copernicus.eu' ([Errno 8] nodename nor servname provided, or not known)\"))], attemps 2 of 500\n", - "Retrying in 120 seconds\n", - "Recovering from connection error [HTTPSConnectionPool(host='cds.climate.copernicus.eu', port=443): Max retries exceeded with url: /api/retrieve/v1/jobs/11dac302-36ed-485c-acdb-663ecdfb3b2e?log=True&request=True (Caused by NameResolutionError(\": Failed to resolve 'cds.climate.copernicus.eu' ([Errno 8] nodename nor servname provided, or not known)\"))], attemps 3 of 500\n", - "Retrying in 120 seconds\n", - "Recovering from connection error [HTTPSConnectionPool(host='cds.climate.copernicus.eu', port=443): Max retries exceeded with url: /api/retrieve/v1/jobs/11dac302-36ed-485c-acdb-663ecdfb3b2e?log=True&request=True (Caused by NameResolutionError(\": Failed to resolve 'cds.climate.copernicus.eu' ([Errno 8] nodename nor servname provided, or not known)\"))], attemps 4 of 500\n", - "Retrying in 120 seconds\n", - "2025-03-04 22:18:35,613 INFO status has been updated to successful\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "d2701bf43b734520bd134beaba73eed8", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "bf5ea2b1c1907fc99fe424f97a71a8c1.nc: 0%| | 0.00/23.5M [00:00: Failed to resolve 'cds.climate.copernicus.eu' ([Errno 8] nodename nor servname provided, or not known)\"))], attemps 2 of 500\n", - "Retrying in 120 seconds\n", - "2025-03-05 11:17:00,960 INFO status has been updated to successful\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "6f3645a9aec942aca89ca91059a818b9", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "6305750f2c651fc9ef6aac03b3db5eef.nc: 0%| | 0.00/20.1M [00:00: Failed to resolve 'cds.climate.copernicus.eu' ([Errno 8] nodename nor servname provided, or not known)\"))], attemps 2 of 500\n", - "Retrying in 120 seconds\n", - "2025-03-05 14:21:52,638 INFO status has been updated to successful\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "a76dbe6c671f400a83e785e9d2bde4dc", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "a47e7af5675feb4a154920e3d062d2cb.nc: 0%| | 0.00/20.1M [00:00: Failed to resolve 'cds.climate.copernicus.eu' ([Errno 8] nodename nor servname provided, or not known)\"))], attemps 2 of 500\n", - "Retrying in 120 seconds\n", - "2025-03-06 10:10:27,455 INFO status has been updated to successful\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "ea9da59b2aa046b79e659bf361a87c96", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "1a4f913cba0a5f56439a442d3fc437f7.nc: 0%| | 0.00/22.8M [00:00\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
<xarray.Dataset> Size: 26MB\n",
-       "Dimensions:     (valid_time: 1, latitude: 1801, longitude: 3600)\n",
-       "Coordinates:\n",
-       "    number      int64 8B ...\n",
-       "  * valid_time  (valid_time) datetime64[ns] 8B 2009-01-01T01:00:00\n",
-       "  * latitude    (latitude) float64 14kB 90.0 89.9 89.8 ... -89.8 -89.9 -90.0\n",
-       "  * longitude   (longitude) float64 29kB 0.0 0.1 0.2 0.3 ... 359.7 359.8 359.9\n",
-       "    expver      <U4 16B ...\n",
-       "Data variables:\n",
-       "    swvl1       (valid_time, latitude, longitude) float32 26MB ...\n",
-       "Attributes:\n",
-       "    GRIB_centre:             ecmf\n",
-       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
-       "    GRIB_subCentre:          0\n",
-       "    Conventions:             CF-1.7\n",
-       "    institution:             European Centre for Medium-Range Weather Forecasts\n",
-       "    history:                 2025-05-13T16:55 GRIB to CDM+CF via cfgrib-0.9.1...
" - ], - "text/plain": [ - " Size: 26MB\n", - "Dimensions: (valid_time: 1, latitude: 1801, longitude: 3600)\n", - "Coordinates:\n", - " number int64 8B ...\n", - " * valid_time (valid_time) datetime64[ns] 8B 2009-01-01T01:00:00\n", - " * latitude (latitude) float64 14kB 90.0 89.9 89.8 ... -89.8 -89.9 -90.0\n", - " * longitude (longitude) float64 29kB 0.0 0.1 0.2 0.3 ... 359.7 359.8 359.9\n", - " expver " - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "temp_file['swvl1'].plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "era5_sandbox", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.11" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notes/prototypes/kenya_demo_02_fishnet.ipynb b/notes/prototypes/kenya_demo_02_fishnet.ipynb deleted file mode 100644 index 1ed7959..0000000 --- a/notes/prototypes/kenya_demo_02_fishnet.ipynb +++ /dev/null @@ -1,5000 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "skip_showdoc: true\n", - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Part 2: Aggregation via Fishnet\n", - "\n", - "This script is the first in a two-step raster processing process. In this script a grid-based polygon will be derived from the raster grid of ERA5 data. The goal\n", - "is to create a fishnet that can be used to extract ERA5 data from raster stack including ERA5 hourly data (this file). This will allow for extraction from raster stack \n", - "without the large computational burden of a loop (as below)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import geopandas as gpd\n", - "import os \n", - "import glob\n", - "# xarray makes working with labelled multi-dimensional arrays in Python simple, efficient, and fun!\n", - "import xarray\n", - "# The rioxarray package is an extension of xarray designed\n", - "# for working with raster (geospatial) data in Python. \n", - "# It provides an easy way to read, write, and manipulate GeoTIFF and other raster formats while maintaining spatial metadata.\n", - "import rioxarray\n", - "# for geometric operations on vector data (points, lines, polygons). It allows users to create, manipulate, and analyze geometric shapes in 2D space\n", - "import shapely\n", - "from shapely.geometry import Polygon\n", - "import numpy\n", - "# you need to install gdal here, not osgeo\n", - "# gdal is generally a translator library for raster and vector geospatial data formats\n", - "from osgeo import gdal, ogr" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# create a fishnet grid using GDAL\n", - "def make_fishnet(outputGridfn,xmin,xmax,ymin,ymax,rows,cols):\n", - " # Calculate grid parameters\n", - " xmin = float(xmin)\n", - " xmax = float(xmax)\n", - " ymin = float(ymin)\n", - " ymax = float(ymax)\n", - " gridWidth = float((xmax-xmin) / cols)\n", - " gridHeight = float((ymax-ymin) / rows)\n", - "\n", - " # Start grid cell envelope\n", - " ringXleftOrigin = xmin\n", - " ringXrightOrigin = xmin + gridWidth\n", - " ringYtopOrigin = ymax\n", - " ringYbottomOrigin = ymax-gridHeight\n", - "\n", - " # Create the output shapefile\n", - " outDriver = ogr.GetDriverByName('ESRI Shapefile')\n", - " if os.path.exists(outputGridfn):\n", - " os.remove(outputGridfn)\n", - " outDataSource = outDriver.CreateDataSource(outputGridfn)\n", - " outLayer = outDataSource.CreateLayer(outputGridfn, geom_type=ogr.wkbPolygon)\n", - " # Add fields to the layer\n", - " featureDefn = outLayer.GetLayerDefn()\n", - "\n", - " # Create grid cells\n", - " countcols = 0\n", - " while countcols < cols:\n", - " countcols += 1\n", - "\n", - " # Reset envelope for rows\n", - " ringYtop = ringYtopOrigin\n", - " ringYbottom =ringYbottomOrigin\n", - " countrows = 0\n", - "\n", - " while countrows < rows:\n", - " countrows += 1\n", - " ring = ogr.Geometry(ogr.wkbLinearRing)\n", - " ring.AddPoint(ringXleftOrigin, ringYtop)\n", - " ring.AddPoint(ringXrightOrigin, ringYtop)\n", - " ring.AddPoint(ringXrightOrigin, ringYbottom)\n", - " ring.AddPoint(ringXleftOrigin, ringYbottom)\n", - " ring.AddPoint(ringXleftOrigin, ringYtop)\n", - " poly = ogr.Geometry(ogr.wkbPolygon)\n", - " poly.AddGeometry(ring)\n", - "\n", - " # Add new geom to layer\n", - " outFeature = ogr.Feature(featureDefn)\n", - " outFeature.SetGeometry(poly)\n", - " outLayer.CreateFeature(outFeature)\n", - " outFeature = None\n", - "\n", - " # New envelope for next poly\n", - " ringYtop = ringYtop - gridHeight\n", - " ringYbottom = ringYbottom - gridHeight\n", - "\n", - " # New envelope for next poly\n", - " ringXleftOrigin = ringXleftOrigin + gridWidth\n", - " ringXrightOrigin = ringXrightOrigin + gridWidth\n", - "\n", - " # Save and close DataSources\n", - " outDataSource = None" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from pyprojroot import here" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "era_dir = here(\"data/ERA5_out\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "temp_file = xarray.open_dataset(os.path.join(era_dir, \"2000_01.nc\"), decode_coords=\"all\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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-       "    t2m         (valid_time, latitude, longitude) float32 2GB dask.array<chunksize=(372, 42, 51), meta=np.ndarray>\n",
-       "    skt         (valid_time, latitude, longitude) float32 2GB dask.array<chunksize=(372, 42, 51), meta=np.ndarray>\n",
-       "Attributes:\n",
-       "    GRIB_centre:             ecmf\n",
-       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
-       "    GRIB_subCentre:          0\n",
-       "    Conventions:             CF-1.7\n",
-       "    institution:             European Centre for Medium-Range Weather Forecasts\n",
-       "    history:                 2025-03-03T18:32 GRIB to CDM+CF via cfgrib-0.9.1...
" - ], - "text/plain": [ - " Size: 5GB\n", - "Dimensions: (valid_time: 50400, latitude: 83, longitude: 101)\n", - "Coordinates:\n", - " number int64 8B 0\n", - " * valid_time (valid_time) datetime64[ns] 403kB 2000-01-01 ... 2005-09-30T2...\n", - " * latitude (latitude) float64 664B 42.0 41.9 41.8 41.7 ... 34.0 33.9 33.8\n", - " * longitude (longitude) float64 808B -4.8 -4.7 -4.6 -4.5 ... 4.9 5.0 5.1 5.2\n", - " expver (valid_time) \n", - "Data variables:\n", - " d2m (valid_time, latitude, longitude) float32 2GB dask.array\n", - " t2m (valid_time, latitude, longitude) float32 2GB dask.array\n", - " skt (valid_time, latitude, longitude) float32 2GB dask.array\n", - "Attributes:\n", - " GRIB_centre: ecmf\n", - " GRIB_centreDescription: European Centre for Medium-Range Weather Forecasts\n", - " GRIB_subCentre: 0\n", - " Conventions: CF-1.7\n", - " institution: European Centre for Medium-Range Weather Forecasts\n", - " history: 2025-03-03T18:32 GRIB to CDM+CF via cfgrib-0.9.1..." - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "era_stack" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The data above does not have a [coordinate reference system](https://en.wikipedia.org/wiki/Spatial_reference_system), needed to interpret, transform, or align datasets. Hence, we assign the WGS84 standard" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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-       "Coordinates:\n",
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-       "  * valid_time   (valid_time) datetime64[ns] 403kB 2000-01-01 ... 2005-09-30T...\n",
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-       "  * longitude    (longitude) float64 808B -4.8 -4.7 -4.6 -4.5 ... 5.0 5.1 5.2\n",
-       "    expver       (valid_time) <U4 806kB dask.array<chunksize=(744,), meta=np.ndarray>\n",
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-       "Data variables:\n",
-       "    d2m          (valid_time, latitude, longitude) float32 2GB dask.array<chunksize=(372, 42, 51), meta=np.ndarray>\n",
-       "    t2m          (valid_time, latitude, longitude) float32 2GB dask.array<chunksize=(372, 42, 51), meta=np.ndarray>\n",
-       "    skt          (valid_time, latitude, longitude) float32 2GB dask.array<chunksize=(372, 42, 51), meta=np.ndarray>\n",
-       "Attributes:\n",
-       "    GRIB_centre:             ecmf\n",
-       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
-       "    GRIB_subCentre:          0\n",
-       "    Conventions:             CF-1.7\n",
-       "    institution:             European Centre for Medium-Range Weather Forecasts\n",
-       "    history:                 2025-03-03T18:32 GRIB to CDM+CF via cfgrib-0.9.1...
" - ], - "text/plain": [ - " Size: 5GB\n", - "Dimensions: (valid_time: 50400, latitude: 83, longitude: 101)\n", - "Coordinates:\n", - " number int64 8B 0\n", - " * valid_time (valid_time) datetime64[ns] 403kB 2000-01-01 ... 2005-09-30T...\n", - " * latitude (latitude) float64 664B 42.0 41.9 41.8 41.7 ... 34.0 33.9 33.8\n", - " * longitude (longitude) float64 808B -4.8 -4.7 -4.6 -4.5 ... 5.0 5.1 5.2\n", - " expver (valid_time) \n", - " spatial_ref int64 8B 0\n", - "Data variables:\n", - " d2m (valid_time, latitude, longitude) float32 2GB dask.array\n", - " t2m (valid_time, latitude, longitude) float32 2GB dask.array\n", - " skt (valid_time, latitude, longitude) float32 2GB dask.array\n", - "Attributes:\n", - " GRIB_centre: ecmf\n", - " GRIB_centreDescription: European Centre for Medium-Range Weather Forecasts\n", - " GRIB_subCentre: 0\n", - " Conventions: CF-1.7\n", - " institution: European Centre for Medium-Range Weather Forecasts\n", - " history: 2025-03-03T18:32 GRIB to CDM+CF via cfgrib-0.9.1..." - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "era_stack.rio.write_crs(\"WGS 84\", inplace=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here, we are making a shapefile that is a fishnet grid of the raster extent.\n", - "It will essentially be a polygon of lines surrounding each ERA5 cell" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "era_extent = era_stack.rio.bounds()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "xmin = era_extent[0]\n", - "xmax = era_extent[2]\n", - "ymin = era_extent[1]\n", - "ymax = era_extent[3]\n", - "\n", - "height = era_stack.rio.height\n", - "width = era_stack.rio.width\n", - "\n", - "era_coords = [(xmin, ymin), (xmin, ymax), (xmax, ymax), (xmax, ymin), (xmin, ymin)]\n", - "era_polygon = Polygon(era_coords)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/tit420/mamba/envs/era5_sandbox/lib/python3.13/site-packages/osgeo/gdal.py:311: FutureWarning: Neither gdal.UseExceptions() nor gdal.DontUseExceptions() has been explicitly called. In GDAL 4.0, exceptions will be enabled by default.\n", - " warnings.warn(\n" - ] - } - ], - "source": [ - "# implement the fishnet function\n", - "make_fishnet(os.path.join(era_dir, 'era_fishnet.shp'), xmin,xmax,ymin,ymax,height,width)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "None\n" - ] - } - ], - "source": [ - "ogr_shp = gpd.read_file(os.path.join(era_dir, 'era_fishnet.shp'))\n", - "print(ogr_shp.crs) # this is none, so we have to set it\n", - "\n", - "# Set the CRS for the created fishnet shape file as the same one from the stacked raster files. \n", - "new_crs = era_stack.rio.crs.data # Replace with the desired CRS\n", - "ogr_shp = ogr_shp.set_crs(new_crs)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ogr_shp.to_file(os.path.join(era_dir, 'era_fishnet.shp'))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notes/prototypes/kenya_demo_03_aggregate.ipynb b/notes/prototypes/kenya_demo_03_aggregate.ipynb deleted file mode 100644 index a05f545..0000000 --- a/notes/prototypes/kenya_demo_03_aggregate.ipynb +++ /dev/null @@ -1,37 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "skip_showdoc: true\n", - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Part 3: Joining Fishnet to the Ward geometries\n", - "\n", - "In this file, we will join the fishnet, which is a polygon grid with lines \n", - "surrounding the grid of the ERA5 raster with the Ward geometries that have\n", - "been queried from the Database of Global Administrative Boundaries\n", - "(gadm.org). In merging the polygon grid with the ward polygon data, \n", - "we ensure that every ward will be aligned with the relevant\n", - "ERA5 temperature metrics for the area.\n", - "\n", - "Next, we Create extraction points from the union of the wards and fishnet. These \n", - "are what we can use to extract values from the raster that overlaps with\n", - "with the points aligning to each wards (this file).\n", - "\n", - "Lastly, we Estimate the ward-level exposure to ERA5, accounting for the availability\n", - "of data within the wards (this file)" - ] - } - ], - "metadata": {}, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notes/prototypes/learning_aggregations_w_michelle_20250328.ipynb b/notes/prototypes/learning_aggregations_w_michelle_20250328.ipynb deleted file mode 100644 index bd23887..0000000 --- a/notes/prototypes/learning_aggregations_w_michelle_20250328.ipynb +++ /dev/null @@ -1,5292 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "skip_showdoc: true\n", - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prototyping Spatial Aggregations\n", - "\n", - "We're going to learn how to aggregate the exposure data into daily values. This is useful for analyzing the data over a longer period of time, such as a week or a month, and is part of the larger goal of this project to aggregate the ERA5 dataset for Madagascar.\n", - "\n", - "\n", - "Doing an aggregation of a netcdf file is relatively simple. What we need to do is read in the data, and then use the `xarray` library to group the data by time using a resampler method. We can then use the `mean` function to calculate the average value for each day.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import xarray as xr\n", - "import matplotlib.pyplot as plt\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature\n", - "from pyprojroot import here\n", - "from hydra import initialize, compose\n", - "from omegaconf import OmegaConf" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's look at the data that we've already downloaded. In the pipeline. We'll use the xarray library to open it up and inspect it." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Load the NetCDF file\n", - "fpath = here() / \"data/input/2010_1.nc\"\n", - "ds = xr.open_dataset(fpath)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is a netcdf file. It has the following dimensions representing time series, as well as the variables we downloaded at specific locations:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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<xarray.Dataset> Size: 12MB\n",
-       "Dimensions:     (valid_time: 744, latitude: 59, longitude: 33)\n",
-       "Coordinates:\n",
-       "    number      int64 8B ...\n",
-       "  * valid_time  (valid_time) datetime64[ns] 6kB 2010-01-01 ... 2010-01-31T23:...\n",
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-       "    expver      (valid_time) <U4 12kB ...\n",
-       "Data variables:\n",
-       "    d2m         (valid_time, latitude, longitude) float32 6MB ...\n",
-       "    t2m         (valid_time, latitude, longitude) float32 6MB ...\n",
-       "Attributes:\n",
-       "    GRIB_centre:             ecmf\n",
-       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
-       "    GRIB_subCentre:          0\n",
-       "    Conventions:             CF-1.7\n",
-       "    institution:             European Centre for Medium-Range Weather Forecasts\n",
-       "    history:                 2025-03-27T17:15 GRIB to CDM+CF via cfgrib-0.9.1...
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<xarray.Dataset> Size: 1MB\n",
-       "Dimensions:     (latitude: 59, longitude: 33, valid_time: 31)\n",
-       "Coordinates:\n",
-       "    number      int64 8B 0\n",
-       "  * latitude    (latitude) float64 472B -11.6 -11.85 -12.1 ... -25.85 -26.1\n",
-       "  * longitude   (longitude) float64 264B 42.7 42.95 43.2 ... 50.2 50.45 50.7\n",
-       "  * valid_time  (valid_time) datetime64[ns] 248B 2010-01-01 ... 2010-01-31\n",
-       "Data variables:\n",
-       "    t2m_mean    (valid_time, latitude, longitude) float32 241kB 300.9 ... 297.9\n",
-       "    t2m_max     (valid_time, latitude, longitude) float32 241kB 301.2 ... 299.1\n",
-       "    t2m_min     (valid_time, latitude, longitude) float32 241kB 300.3 ... 296.8\n",
-       "    d2m_mean    (valid_time, latitude, longitude) float32 241kB 297.7 ... 293.1\n",
-       "    d2m_max     (valid_time, latitude, longitude) float32 241kB 298.0 ... 295.8\n",
-       "    d2m_min     (valid_time, latitude, longitude) float32 241kB 297.3 ... 290.2
" - ], - "text/plain": [ - " Size: 1MB\n", - "Dimensions: (latitude: 59, longitude: 33, valid_time: 31)\n", - "Coordinates:\n", - " number int64 8B 0\n", - " * latitude (latitude) float64 472B -11.6 -11.85 -12.1 ... -25.85 -26.1\n", - " * longitude (longitude) float64 264B 42.7 42.95 43.2 ... 50.2 50.45 50.7\n", - " * valid_time (valid_time) datetime64[ns] 248B 2010-01-01 ... 2010-01-31\n", - "Data variables:\n", - " t2m_mean (valid_time, latitude, longitude) float32 241kB 300.9 ... 297.9\n", - " t2m_max (valid_time, latitude, longitude) float32 241kB 301.2 ... 299.1\n", - " t2m_min (valid_time, latitude, longitude) float32 241kB 300.3 ... 296.8\n", - " d2m_mean (valid_time, latitude, longitude) float32 241kB 297.7 ... 293.1\n", - " d2m_max (valid_time, latitude, longitude) float32 241kB 298.0 ... 295.8\n", - " d2m_min (valid_time, latitude, longitude) float32 241kB 297.3 ... 290.2" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Perform multiple aggregations\n", - "daily_mean = ds.resample(valid_time=\"1D\").mean() # Daily mean\n", - "daily_max = ds.resample(valid_time=\"1D\").max() # Daily max\n", - "daily_min = ds.resample(valid_time=\"1D\").min() # Daily min\n", - "\n", - "# Combine the results into a new dataset\n", - "daily_aggregated = xr.Dataset({\n", - " \"t2m_mean\": daily_mean[\"t2m\"],\n", - " \"t2m_max\": daily_max[\"t2m\"],\n", - " \"t2m_min\": daily_min[\"t2m\"],\n", - " \"d2m_mean\": daily_mean[\"d2m\"],\n", - " \"d2m_max\": daily_max[\"d2m\"],\n", - " \"d2m_min\": daily_min[\"d2m\"]\n", - "})\n", - "\n", - "daily_aggregated" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Look at how this compares to the original data:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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<xarray.Dataset> Size: 12MB\n",
-       "Dimensions:     (valid_time: 744, latitude: 59, longitude: 33)\n",
-       "Coordinates:\n",
-       "    number      int64 8B ...\n",
-       "  * valid_time  (valid_time) datetime64[ns] 6kB 2010-01-01 ... 2010-01-31T23:...\n",
-       "  * latitude    (latitude) float64 472B -11.6 -11.85 -12.1 ... -25.85 -26.1\n",
-       "  * longitude   (longitude) float64 264B 42.7 42.95 43.2 ... 50.2 50.45 50.7\n",
-       "    expver      (valid_time) <U4 12kB ...\n",
-       "Data variables:\n",
-       "    d2m         (valid_time, latitude, longitude) float32 6MB ...\n",
-       "    t2m         (valid_time, latitude, longitude) float32 6MB ...\n",
-       "Attributes:\n",
-       "    GRIB_centre:             ecmf\n",
-       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
-       "    GRIB_subCentre:          0\n",
-       "    Conventions:             CF-1.7\n",
-       "    institution:             European Centre for Medium-Range Weather Forecasts\n",
-       "    history:                 2025-03-27T17:15 GRIB to CDM+CF via cfgrib-0.9.1...
" - ], - "text/plain": [ - " Size: 12MB\n", - "Dimensions: (valid_time: 744, latitude: 59, longitude: 33)\n", - "Coordinates:\n", - " number int64 8B ...\n", - " * valid_time (valid_time) datetime64[ns] 6kB 2010-01-01 ... 2010-01-31T23:...\n", - " * latitude (latitude) float64 472B -11.6 -11.85 -12.1 ... -25.85 -26.1\n", - " * longitude (longitude) float64 264B 42.7 42.95 43.2 ... 50.2 50.45 50.7\n", - " expver (valid_time) " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Select a specific grid point (e.g., latitude=-1, longitude=0)\n", - "variable='mean'\n", - "\n", - "# note: we can use the isel method to select the grid point. In this case,\n", - "# we are selecting the bottom-left grid point (latitude=-1, longitude=0) because we're selecting\n", - "# the smallest value for latitude:\n", - "# time=0: Selects the first time point.\n", - "# latitude=-1: Selects the last latitude (bottom-most, as latitude is usually ordered from north to south).\n", - "# longitude=0: Selects the first longitude (left-most).\n", - "t2m_mean_point = daily_aggregated[\"t2m_\" + variable].isel(latitude=-1, longitude=0)\n", - "\n", - "# Plot the time series\n", - "plt.figure(figsize=(10, 6))\n", - "t2m_mean_point.plot(label=\"Daily Mean t2m\")\n", - "plt.title(\"Daily Aggregated ({}) Temperature at Bottom-Left Grid Point\".format(variable))\n", - "plt.xlabel(\"Time\")\n", - "plt.ylabel(\"Temperature (K)\")\n", - "plt.legend()\n", - "plt.grid()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "How does this compared to the disaggregated data?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "t2m_point = ds[\"t2m\"].isel(latitude=-1, longitude=0)\n", - "\n", - "# Plot the time series\n", - "plt.figure(figsize=(10, 6))\n", - "t2m_point.plot(label=\"Daily Mean t2m\")\n", - "plt.title(\"Daily Disaggregated Temperature at Bottom-Left Grid Point\")\n", - "plt.xlabel(\"Time\")\n", - "plt.ylabel(\"Temperature (K)\")\n", - "plt.legend()\n", - "plt.grid()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "These temperature plots match beautifully! This means our aggregation over the 31 days works!\n", - "\n", - "Let's look at the aggregation over a map:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Select the first day of t2m_mean\n", - "variable=\"mean\"\n", - "t2m_mean_day1 = daily_aggregated[\"t2m_\" + variable].isel(valid_time=0)\n", - "\n", - "# Set the absolute min and max for the color bar\n", - "vmin = 270 # Minimum value (e.g., 270 K)\n", - "vmax = 310 # Maximum value (e.g., 310 K)\n", - "\n", - "# Create a plot with Cartopy\n", - "plt.figure(figsize=(10, 6))\n", - "ax = plt.axes(projection=ccrs.PlateCarree()) # Use PlateCarree projection for latitude/longitude data\n", - "\n", - "# Plot the data\n", - "t2m_mean_day1.plot(ax=ax, cmap=\"coolwarm\", transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax, cbar_kwargs={\"label\": \"Temperature (K)\"})\n", - "\n", - "# Add Madagascar's border using Cartopy's built-in features\n", - "ax.add_feature(cfeature.BORDERS, edgecolor=\"black\", linewidth=1) # Add country borders\n", - "ax.add_feature(cfeature.COASTLINE, edgecolor=\"black\", linewidth=0.8) # Add coastlines\n", - "\n", - "# Optionally, zoom in on Madagascar\n", - "ax.set_extent([43, 51, -26, -11], crs=ccrs.PlateCarree()) # Longitude and latitude bounds for Madagascar\n", - "\n", - "# Add gridlines\n", - "ax.gridlines(draw_labels=True, linewidth=0.5, color=\"gray\", alpha=0.5, linestyle=\"--\")\n", - "\n", - "# Add a title\n", - "plt.title(\"Mean Daily {} Temperature (Day 1)\".format(variable))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Looks great. Now, we need to see if we can do a spatial aggregation:\n", - "\n", - ">A mathematical aggregation like mean() involves summarizing data values (e.g., averaging) across a specific dimension, such as time, without considering spatial relationships. For example, calculating the daily mean temperature from hourly data is purely numerical. \n", - "In contrast, a spatial aggregation using rasters and polygons involves summarizing data based on spatial boundaries. For example, when aggregating raster data (e.g., temperature) over a polygon (e.g., a country's boundary), the process involves selecting raster cells that fall within the polygon and computing a summary statistic (e.g., mean, sum) for those spatially defined areas. This type of aggregation accounts for geographic context and spatial relationships." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To do this, we'll need to read in the shapefile that defines the shape of the polygon (ie the physical ground) and find the pixels of data that fall within the polygon. We can then use the `xarray` library to group the data by time using a resampler method. We can then use the `mean` function to calculate the average value for each day." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import geopandas as gpd\n", - "\n", - "# we learned how to read in shapefiles in the kenya demo notebook\n", - "zip_url_or_path = here() / \"data/testing/gadm41_MDG.gpkg\"\n", - "\n", - "shape = gpd.read_file(zip_url_or_path, layer = \"ADM_ADM_1\")\n" - ] - }, - { - "attachments": { - "image.png": { - "image/png": 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- } - }, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We are using the layer 1 of this shapefile from GADM.org. This refers to the states in red:\n", - "\n", - "![image.png](attachment:image.png)\n", - "\n", - "When we read in the shapefile, the data in the `geometry` column is a specification of the polygons that represent geographic boundaries." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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GID_1GID_0COUNTRYNAME_1VARNAME_1NL_NAME_1TYPE_1ENGTYPE_1CC_1HASC_1ISO_1geometry
0MDG.1_1MDGMadagascarAntananarivoNANANANANANAMG-TMULTIPOLYGON (((46.93914 -20.17013, 46.9386 -2...
1MDG.2_1MDGMadagascarAntsirananaNANANANANANAMG-DMULTIPOLYGON (((50.15347 -16.0032, 50.15347 -1...
2MDG.3_1MDGMadagascarFianarantsoaNANANANANANAMG-FMULTIPOLYGON (((47.04934 -24.08504, 47.04925 -...
3MDG.4_1MDGMadagascarMahajangaNANANANANANAMG-MMULTIPOLYGON (((44.23931 -18.96421, 44.23931 -...
4MDG.5_1MDGMadagascarToamasinaNANANANANANAMG-AMULTIPOLYGON (((47.67118 -20.36464, 47.6713 -2...
5MDG.6_1MDGMadagascarToliaryNANANANANANANAMULTIPOLYGON (((44.33236 -25.26931, 44.33236 -...
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" - ], - "text/plain": [ - " GID_1 GID_0 COUNTRY NAME_1 VARNAME_1 NL_NAME_1 TYPE_1 \\\n", - "0 MDG.1_1 MDG Madagascar Antananarivo NA NA NA \n", - "1 MDG.2_1 MDG Madagascar Antsiranana NA NA NA \n", - "2 MDG.3_1 MDG Madagascar Fianarantsoa NA NA NA \n", - "3 MDG.4_1 MDG Madagascar Mahajanga NA NA NA \n", - "4 MDG.5_1 MDG Madagascar Toamasina NA NA NA \n", - "5 MDG.6_1 MDG Madagascar Toliary NA NA NA \n", - "\n", - " ENGTYPE_1 CC_1 HASC_1 ISO_1 \\\n", - "0 NA NA NA MG-T \n", - "1 NA NA NA MG-D \n", - "2 NA NA NA MG-F \n", - "3 NA NA NA MG-M \n", - "4 NA NA NA MG-A \n", - "5 NA NA NA NA \n", - "\n", - " geometry \n", - "0 MULTIPOLYGON (((46.93914 -20.17013, 46.9386 -2... \n", - "1 MULTIPOLYGON (((50.15347 -16.0032, 50.15347 -1... \n", - "2 MULTIPOLYGON (((47.04934 -24.08504, 47.04925 -... \n", - "3 MULTIPOLYGON (((44.23931 -18.96421, 44.23931 -... \n", - "4 MULTIPOLYGON (((47.67118 -20.36464, 47.6713 -2... \n", - "5 MULTIPOLYGON (((44.33236 -25.26931, 44.33236 -... " - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In a vector image such as a shapefile, the steps between each value are not guaranteed to be equal (unlike on a cartesian plane), so we need to think about how those values \"project\" onto a known Coordinate Reference System (CRS) that has equal steps.\n", - "\n", - "A quick note about CRS:\n", - "\n", - "> The WGS 84 (World Geodetic System 1984) is a widely used global Coordinate Reference System (CRS). It is the standard CRS for GPS (Global Positioning System) and is commonly used in geospatial applications. WGS 84 defines a geographic coordinate system based on a specific ellipsoid model of the Earth. \n", - "\n", - "> Key Features of WGS 84 \n", - "Type: Geographic Coordinate System (GCS). \n", - "Coordinates are represented in latitude, longitude, and optionally altitude. \n", - "Units: Degrees (for latitude and longitude). \n", - "Ellipsoid: WGS 84 uses a reference ellipsoid with: \n", - "Semi-major axis: 6,378,137 meters. \n", - "Flattening: 1 / 298.257223563. \n", - "Datum: The WGS 84 datum defines the origin and orientation of the coordinate system. \n", - "EPSG Code: The EPSG code for WGS 84 is 4326. \n", - "\n", - "Spatial geometry is complicated and silly, hence [all maps are wrong](https://youtu.be/kIID5FDi2JQ?si=OZASX3i6Aglqwa4u).\n", - "\n", - "Nevertheless, we can see that the shapefile has a CRS of EPSG:4326, which is what we want:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\n", - "Name: WGS 84\n", - "Axis Info [ellipsoidal]:\n", - "- Lat[north]: Geodetic latitude (degree)\n", - "- Lon[east]: Geodetic longitude (degree)\n", - "Area of Use:\n", - "- name: World.\n", - "- bounds: (-180.0, -90.0, 180.0, 90.0)\n", - "Datum: World Geodetic System 1984 ensemble\n", - "- Ellipsoid: WGS 84\n", - "- Prime Meridian: Greenwich" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "shape.crs" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Were this different, we'd have to find some way to adjust these projections. For our netCDF file, however, we don't need to worry about this because the data themselves are created using a rasterized netCDF file, which is a standard format for storing gridded data. The data is already in a grid format, and the pixel values are already aligned with the geographic coordinates of the raster. In spatial geometry, we use degrees to represent the latitude and longitude of the corners of each pixel. This means that the data is already in a format that can be easily manipulated and analyzed using xarray and geopandas, because we refer to where the pixel is located in the world using degrees. It is essentially an absolute reference system.\n", - "\n", - "In the ERA5 dataset, the resolution is said to be 0.25 degrees, which means that each pixel represents a square area of approximately 25 km x 25 km at the equator. So at every unit of 0.25 degrees north-south or east-west, we have a new pixel of data, with a value for temperature or dewpoint or whatever. You can physically see each of these on the plot.\n", - "\n", - "Learn more about ERA5's resolution [here](https://confluence.ecmwf.int/display/CKB/ERA5%3A+What+is+the+spatial+reference)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, in order to aggregate data spatially, we're pasting in a utility here for finding the intersecting values between our netcdf data and the polygons represented in our shapefile (ie the states, regions, etc.).\n", - "\n", - "Source: https://github.com/NSAPH-Data-Processing/air_pollution__aqdh/blob/main/utils/faster_zonal_stats.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "from tqdm import tqdm\n", - "from math import ceil, floor\n", - "\n", - "from rasterstats.io import Raster\n", - "from rasterstats.utils import boxify_points, rasterize_geom" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This function indexes each pixel and maps it to the polygon it falls within. A few notes about this function:\n", - "\n", - "- It uses the `rasterstats.io` library to read in a raster tiff file\n", - "- It uses affine transformations to convert the pixel coordinates to geographic coordinates\n", - "- It needs to know where there is no data in the raster file, so we need to set a `nodata` value\n", - "- `all_touched` is a boolean that determines whether to include all pixels that touch the polygon or just the ones that are fully contained within it; this is a domain specific choice" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def polygon_to_raster_cells(\n", - " vectors,\n", - " raster,\n", - " band=1,\n", - " nodata=None,\n", - " affine=None,\n", - " all_touched=False,\n", - " verbose=False,\n", - " **kwargs,\n", - "):\n", - " \"\"\"Returns an index map for each vector geometry to indices in the raster source.\n", - "\n", - " Parameters\n", - " ----------\n", - " vectors: list of geometries\n", - "\n", - " raster: ndarray\n", - "\n", - " nodata: float\n", - "\n", - " affine: Affine instance\n", - "\n", - " all_touched: bool, optional\n", - " Whether to include every raster cell touched by a geometry, or only\n", - " those having a center point within the polygon.\n", - " defaults to `False`\n", - "\n", - " Returns\n", - " -------\n", - " dict\n", - " A dictionary mapping vector the ids of geometries to locations (indices) in the raster source.\n", - " \"\"\"\n", - "\n", - " cell_map = []\n", - "\n", - " with Raster(raster, affine, nodata, band) as rast:\n", - " # used later to crop raster and find start row and col\n", - " min_lon, dlon = affine.c, affine.a\n", - " max_lat, dlat = affine.f, -affine.e\n", - " H, W = rast.shape\n", - "\n", - " for geom in tqdm(vectors, disable=(not verbose)):\n", - " if \"Point\" in geom.geom_type:\n", - " geom = boxify_points(geom, rast)\n", - "\n", - " # find geometry bounds to crop raster\n", - " # the raster and geometry must be in the same lon/lat coordinate system\n", - " start_row = max(0, min(H - 1, floor((max_lat - geom.bounds[3]) / dlat)))\n", - " start_col = min(W - 1, max(0, floor((geom.bounds[0] - min_lon) / dlon)))\n", - " end_col = max(0, min(W - 1, ceil((geom.bounds[2] - min_lon) / dlon)))\n", - " end_row = min(H - 1, max(0, ceil((max_lat - geom.bounds[1]) / dlat)))\n", - " geom_bounds = (\n", - " min_lon + dlon * start_col, # left\n", - " max_lat - dlat * end_row - 1e-12, # bottom\n", - " min_lon + dlon * end_col + 1e-12, # right\n", - " max_lat - dlat * start_row, # top\n", - " )\n", - "\n", - " # crop raster to area of interest and rasterize\n", - " fsrc = rast.read(bounds=geom_bounds)\n", - " rv_array = rasterize_geom(geom, like=fsrc, all_touched=all_touched)\n", - " indices = np.nonzero(rv_array)\n", - "\n", - " if len(indices[0]) > 0:\n", - " indices = (indices[0] + start_row, indices[1] + start_col)\n", - " assert 0 <= indices[0].min() < rast.shape[0]\n", - " assert 0 <= indices[1].min() < rast.shape[1]\n", - " else:\n", - " pass # stop here for debug\n", - "\n", - " cell_map.append(indices)\n", - "\n", - " return cell_map" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "So to implement this we need to first convert the netcdf to a tiff so that we can rasterize it to each of the polygons in the shapefile. We do this with `rioxarray`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import rioxarray as rxr" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, we pick our variable of interest, then we set the spatial properties to make sure it conforms to the CRS we wanted" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "temperature = daily_aggregated['t2m_mean']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "temp_set = temperature.rio.set_spatial_dims(x_dim=\"longitude\", y_dim=\"latitude\")\n", - "temp_set = temp_set.rio.write_crs(\"EPSG:4326\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Write it out to tiff and read it back in (there's no way to do this in memory)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "temp_set.rio.to_raster(\"temp.tif\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can investigate the tiff and see that it has all the properties necessary for the function" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import rasterio\n", - "\n", - "src = rasterio.open(\"temp.tif\")\n", - "raster = src.read(1) # Numpy array\n", - "profile = src.profile # Metadata\n", - "transform = src.transform" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "31" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# the number of data points\n", - "src.count\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Affine(0.25, 0.0, 42.575,\n", - " 0.0, -0.25000000000000006, -11.475)" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# the affine transformation matrix:\n", - "# Pixel size (resolution in x and y).\n", - "# Origin (top-left corner in spatial coordinates).\n", - "# Rotation (if the raster is not north-up). \n", - "src.transform" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# any missing data locations\n", - "src.nodata" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "33 59\n" - ] - } - ], - "source": [ - "# the number of rows and columns\n", - "print(src.width, src.height)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fetch the array of data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "raster_array = src.read(1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Function go brrrr" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/6 [00:00\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
l1_regionmean_31_day_temp
0Antananarivo296.206116
1Antsiranana298.529266
2Fianarantsoa298.513550
3Mahajanga299.249207
4Toamasina296.939911
5Toliary301.936096
\n", - "" - ], - "text/plain": [ - " l1_region mean_31_day_temp\n", - "0 Antananarivo 296.206116\n", - "1 Antsiranana 298.529266\n", - "2 Fianarantsoa 298.513550\n", - "3 Mahajanga 299.249207\n", - "4 Toamasina 296.939911\n", - "5 Toliary 301.936096" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "pd.DataFrame({\"l1_region\": shape.NAME_1, \"mean_31_day_temp\": stats})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Let's try it with Level 3 data\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 110/110 [00:00<00:00, 264.02it/s]\n" - ] - } - ], - "source": [ - "# first get the shape of the polygons\n", - "\n", - "shape = gpd.read_file(zip_url_or_path, layer = \"ADM_ADM_3\")\n", - "\n", - "# get the new mapping of the pixels to the shapes in the region\n", - "\n", - "res_poly2cell = polygon_to_raster_cells(\n", - " vectors = shape.geometry.values, # the geometries of the shapefile of the regions\n", - " raster=raster_array, # the raster data above\n", - " band=1, # the value of the day that we're using\n", - " nodata=src.nodata, # any intersections with no data, may have to be np.nan\n", - " affine=src.transform, # some math thing need to revise\n", - " all_touched=True, \n", - " verbose=True\n", - ")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "110" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(res_poly2cell)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# demonsttrate that because this is a \"denser\" set of polygons\n", - "# this iwll take longer\n", - "stats = []\n", - "\n", - "for indices in res_poly2cell:\n", - " if len(indices[0]) == 0:\n", - " # no cells found for this polygon\n", - " stats.append(np.nan)\n", - " else:\n", - " cells = raster[indices]\n", - " if sum(~np.isnan(cells)) == 0:\n", - " # no valid cells found for this polygon\n", - " stats.append(np.nan)\n", - " continue\n", - " else:\n", - " # compute mean of valid cells\n", - " stats.append(np.nanmean(cells))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[np.float32(295.27213),\n", - " np.float32(293.5218),\n", - " np.float32(295.74274),\n", - " np.float32(296.6221),\n", - " np.float32(294.4437),\n", - " np.float32(294.15137),\n", - " np.float32(294.45166),\n", - " np.float32(294.1732),\n", - " np.float32(297.0975),\n", - " np.float32(297.86023),\n", - " np.float32(294.1745),\n", - " np.float32(296.0813),\n", - " np.float32(296.35345),\n", - " np.float32(293.1277),\n", - " np.float32(293.0299),\n", - " np.float32(293.55472),\n", - " np.float32(292.9831),\n", - 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"source": [ - "Now we have 110 mean temperatuers for each of the shapefile's regions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "df = pd.DataFrame(\n", - " {\"l3_territory\": shape.NAME_3, \"dummy_date_in_future\": 1, \"temp_vals\": stats}\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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l3_territorydummy_date_in_futuretemp_vals
0Ambohidratrimo1295.272125
1Andramasina1293.521790
2Anjozorobe1295.742737
3Ankazobe1296.622101
4Antananarivo-Nord1294.443695
............
105Belon-i Tsiribihina1302.330322
106Mahabo1302.211365
107Manja1303.465607
108Miandrivazo1300.230927
109Morondava1302.996185
\n", - "

110 rows × 3 columns

\n", - "
" - ], - "text/plain": [ - " l3_territory dummy_date_in_future temp_vals\n", - "0 Ambohidratrimo 1 295.272125\n", - "1 Andramasina 1 293.521790\n", - "2 Anjozorobe 1 295.742737\n", - "3 Ankazobe 1 296.622101\n", - "4 Antananarivo-Nord 1 294.443695\n", - ".. ... ... ...\n", - "105 Belon-i Tsiribihina 1 302.330322\n", - "106 Mahabo 1 302.211365\n", - "107 Manja 1 303.465607\n", - "108 Miandrivazo 1 300.230927\n", - "109 Morondava 1 302.996185\n", - "\n", - "[110 rows x 3 columns]" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# now we plot it using the shape.geometry to get the shapefile's location for each region\n", - "gdf = gpd.GeoDataFrame(df, geometry=shape.geometry.values, crs=shape.crs)\n", - "gdf.plot(column=\"temp_vals\", legend=True)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can test this out with our healthsheds file" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "healthsheds = gpd.read_file(here() / \"data/testing/mdg_healthsheds2022\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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fs_uidfs_popn_uidn_instatreg_uidreg_namedist_uiddist_namefs_typefs_namefs_lln_compn_shapegeometry
0A0YU5ksfXZS1301466O0yrAFTjghGVatovavyhBOXdumAvNcMananjaryCSB2CSB2 MorafenoPOINT (48.180332 -21.097472)1.01POLYGON ((48.21537 -20.95662, 48.21593 -20.956...
1A1SY9AiVPYF310344I9lEj4mALlsAnalamangavHRv6NgA70xManjakandrianaCSB1CSB1 AmbohidraisoloPOINT (47.879317 -19.161348)1.01POLYGON ((47.85923 -19.09954, 47.8601 -19.0998...
2A38WhL0NPsX234433O0yrAFTjghGVatovavyhBOXdumAvNcMananjaryCSB2CSB2 Mahatsara IefakaPOINT (48.326937 -21.11468)1.01POLYGON ((48.35588 -21.05353, 48.35801 -21.057...
3A6fVNQgqqJg749455kgGIXgdG56rHaute MatsiatraBU35owjfn8GVohibatoCSB2CSB2 AnkaromalazaNone1.01POLYGON ((47.18552 -21.70624, 47.19275 -21.711...
4A77QRkmKUul838754I9lEj4mALlsAnalamangadsDbxSkO1STAndramasinaCSB1CSB1 MangabePOINT (47.716094 -19.176215)1.01POLYGON ((47.73249 -19.15372, 47.73267 -19.153...
.............................................
2768zw74in2A9Vn458055A8UMJuP8iI3BoenyffiVmdBUwzIMarovoayCSB2CSB2 Ampijoroa NordNone1.01POLYGON ((46.73145 -16.12848, 46.73365 -16.131...
2769zwhfpU5j9aV744055PTqLWwjcAoxAnosyKBe7h4EfJDfTaolagnaroCSB2CSB2 TanandavaNone1.01POLYGON ((47.00558 -24.44654, 47.00489 -24.446...
2770zwyM0iDw9X7570533wR0PL2iap0sAtsinananaxgvRu8zZAZKMarolamboCSB1CSB1 Maroariana IPOINT (47.948408 -20.025144)1.01POLYGON ((47.95335 -19.9937, 47.95573 -20.0087...
2771zxQu4lRMMP9764765zJ9UJ7RhCwVDianas3HejcPkUeJAntsiranana IICSB2CSB2 AntsalakaPOINT (49.251201 -12.640404)1.01POLYGON ((49.27604 -12.6479, 49.27603 -12.6479...
2772zywLk4jVk8O871499gHNBsfuG0CzBetsibokaSrAluezWP64MaevatananaCSB2CSB2 BemokotraNone1.01POLYGON ((46.78569 -17.06743, 46.78644 -17.069...
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" - ], - "text/plain": [ - " fs_uid fs_pop n_uid n_instat reg_uid reg_name \\\n", - "0 A0YU5ksfXZS 13014 6 6 O0yrAFTjghG Vatovavy \n", - "1 A1SY9AiVPYF 3103 4 4 I9lEj4mALls Analamanga \n", - "2 A38WhL0NPsX 2344 3 3 O0yrAFTjghG Vatovavy \n", - "3 A6fVNQgqqJg 7494 5 5 kgGIXgdG56r Haute Matsiatra \n", - "4 A77QRkmKUul 8387 5 4 I9lEj4mALls Analamanga \n", - "... ... ... ... ... ... ... \n", - "2768 zw74in2A9Vn 4580 5 5 A8UMJuP8iI3 Boeny \n", - "2769 zwhfpU5j9aV 7440 5 5 PTqLWwjcAox Anosy \n", - "2770 zwyM0iDw9X7 5705 3 3 wR0PL2iap0s Atsinanana \n", - "2771 zxQu4lRMMP9 7647 6 5 zJ9UJ7RhCwV Diana \n", - "2772 zywLk4jVk8O 8714 9 9 gHNBsfuG0Cz Betsiboka \n", - "\n", - " dist_uid dist_name fs_type fs_name \\\n", - "0 hBOXdumAvNc Mananjary CSB2 CSB2 Morafeno \n", - "1 vHRv6NgA70x Manjakandriana CSB1 CSB1 Ambohidraisolo \n", - "2 hBOXdumAvNc Mananjary CSB2 CSB2 Mahatsara Iefaka \n", - "3 BU35owjfn8G Vohibato CSB2 CSB2 Ankaromalaza \n", - "4 dsDbxSkO1ST Andramasina CSB1 CSB1 Mangabe \n", - "... ... ... ... ... \n", - "2768 ffiVmdBUwzI Marovoay CSB2 CSB2 Ampijoroa Nord \n", - "2769 KBe7h4EfJDf Taolagnaro CSB2 CSB2 Tanandava \n", - "2770 xgvRu8zZAZK Marolambo CSB1 CSB1 Maroariana I \n", - "2771 s3HejcPkUeJ Antsiranana II CSB2 CSB2 Antsalaka \n", - "2772 SrAluezWP64 Maevatanana CSB2 CSB2 Bemokotra \n", - "\n", - " fs_ll n_comp n_shape \\\n", - "0 POINT (48.180332 -21.097472) 1.0 1 \n", - "1 POINT (47.879317 -19.161348) 1.0 1 \n", - "2 POINT (48.326937 -21.11468) 1.0 1 \n", - "3 None 1.0 1 \n", - "4 POINT (47.716094 -19.176215) 1.0 1 \n", - "... ... ... ... \n", - "2768 None 1.0 1 \n", - "2769 None 1.0 1 \n", - "2770 POINT (47.948408 -20.025144) 1.0 1 \n", - "2771 POINT (49.251201 -12.640404) 1.0 1 \n", - "2772 None 1.0 1 \n", - "\n", - " geometry \n", - "0 POLYGON ((48.21537 -20.95662, 48.21593 -20.956... \n", - "1 POLYGON ((47.85923 -19.09954, 47.8601 -19.0998... \n", - "2 POLYGON ((48.35588 -21.05353, 48.35801 -21.057... \n", - "3 POLYGON ((47.18552 -21.70624, 47.19275 -21.711... \n", - "4 POLYGON ((47.73249 -19.15372, 47.73267 -19.153... \n", - "... ... \n", - "2768 POLYGON ((46.73145 -16.12848, 46.73365 -16.131... \n", - "2769 POLYGON ((47.00558 -24.44654, 47.00489 -24.446... \n", - "2770 POLYGON ((47.95335 -19.9937, 47.95573 -20.0087... \n", - "2771 POLYGON ((49.27604 -12.6479, 49.27603 -12.6479... \n", - "2772 POLYGON ((46.78569 -17.06743, 46.78644 -17.069... \n", - "\n", - "[2773 rows x 14 columns]" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "healthsheds" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# there are NAs to remove\n", - "healthsheds.dropna(subset = ['geometry'], inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2766" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(set(healthsheds.fs_uid))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/2766 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# now we plot it using the shape.geometry to get the shapefile's location for each region\n", - "gdf = gpd.GeoDataFrame(df, geometry=healthsheds.geometry.values, crs=shape.crs)\n", - "gdf.plot(column=\"temp_vals\", legend=True)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we've demonstrated how this could work, we can substitute the GADM shapefiles for our healthsheds, and put it in a pipeline!!!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Nepal\n", - "\n", - "We've modified the pipeline to now download Nepal as well. We'll test out an aggregation using the aggregation shapefiles we were provided by Dimeji. We probably want to decide on where to centralize data storage for files like this" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "try: from era5_sandbox.core import GoogleDriver, _get_callable, describe\n", - "except: from core import GoogleDriver, _get_callable, describe\n", - "\n", - "try: from era5_sandbox.download import download_raw_era5\n", - "except: from download import download_raw_era5\n", - "\n", - "try: from era5_sandbox.aggregate import resample_netcdf, netcdf_to_tiff, polygon_to_raster_cells, aggregate_to_healthsheds\n", - "except: from aggregate import resample_netcdf, netcdf_to_tiff, polygon_to_raster_cells, aggregate_to_healthsheds" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2025-04-22 15:40:17,129 INFO [2024-09-26T00:00:00] Watch our [Forum](https://forum.ecmwf.int/) for Announcements, news and other discussed topics.\n", - "2025-04-22 15:40:17,130 WARNING [2024-06-16T00:00:00] CDS API syntax is changed and some keys or parameter names may have also changed. To avoid requests failing, please use the \"Show API request code\" tool on the dataset Download Form to check you are using the correct syntax for your API request.\n", - "2025-04-22 15:40:21,113 INFO Request ID is ef1937bd-855a-4651-af70-550b72415172\n", - "2025-04-22 15:40:21,266 INFO status has been updated to accepted\n", - "2025-04-22 15:40:35,703 INFO status has been updated to successful\n", - " " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Done\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r" - ] - } - ], - "source": [ - "from hydra import initialize, compose\n", - "from omegaconf import OmegaConf\n", - "\n", - "# unfortunately, we have to use the initialize function to load the config file\n", - "# this is because the @hydra decorator does not work with Notebooks very well\n", - "# this is a known issue with Hydra: https://gist.github.com/bdsaglam/586704a98336a0cf0a65a6e7c247d248\n", - "# \n", - "# just use the relative path from the notebook to the config dir\n", - "with initialize(version_base=None, config_path=\"../../conf\"):\n", - " cfg = compose(config_name='config.yaml')\n", - "\n", - "cfg.development_mode = False\n", - "cfg.query['year'] = 2023\n", - "cfg.query['month'] = 10\n", - "cfg.query['day'] = 1\n", - "cfg.query['time'] = \"00:00\"\n", - "cfg.query['geography'] = \"nepal\"\n", - "download_raw_era5(cfg)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's read it in and run the aggregation:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Load the NetCDF file\n", - "fpath = here() / \"data/input/nepal_2023_10.nc\"\n", - "ds = xr.open_dataset(fpath)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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<xarray.Dataset> Size: 6kB\n",
-       "Dimensions:     (valid_time: 1, latitude: 20, longitude: 37)\n",
-       "Coordinates:\n",
-       "    number      int64 8B ...\n",
-       "  * valid_time  (valid_time) datetime64[ns] 8B 2023-10-01\n",
-       "  * latitude    (latitude) float64 160B 30.75 30.5 30.25 ... 26.5 26.25 26.0\n",
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-       "    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts\n",
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-       "    institution:             European Centre for Medium-Range Weather Forecasts\n",
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<xarray.Dataset> Size: 18kB\n",
-       "Dimensions:     (latitude: 20, longitude: 37, valid_time: 1)\n",
-       "Coordinates:\n",
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-       "  * latitude    (latitude) float64 160B 30.75 30.5 30.25 ... 26.5 26.25 26.0\n",
-       "  * longitude   (longitude) float64 296B 79.6 79.85 80.1 ... 88.1 88.35 88.6\n",
-       "    expver      <U4 16B '0001'\n",
-       "  * valid_time  (valid_time) datetime64[ns] 8B 2023-10-01\n",
-       "Data variables:\n",
-       "    t2m_mean    (valid_time, latitude, longitude) float32 3kB 271.0 ... 300.9\n",
-       "    t2m_max     (valid_time, latitude, longitude) float32 3kB 271.0 ... 300.9\n",
-       "    t2m_min     (valid_time, latitude, longitude) float32 3kB 271.0 ... 300.9\n",
-       "    d2m_mean    (valid_time, latitude, longitude) float32 3kB 269.4 ... 299.8\n",
-       "    d2m_max     (valid_time, latitude, longitude) float32 3kB 269.4 ... 299.8\n",
-       "    d2m_min     (valid_time, latitude, longitude) float32 3kB 269.4 ... 299.8
" - ], - "text/plain": [ - " Size: 18kB\n", - "Dimensions: (latitude: 20, longitude: 37, valid_time: 1)\n", - "Coordinates:\n", - " number int64 8B 0\n", - " * latitude (latitude) float64 160B 30.75 30.5 30.25 ... 26.5 26.25 26.0\n", - " * longitude (longitude) float64 296B 79.6 79.85 80.1 ... 88.1 88.35 88.6\n", - " expver " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Select the first day of t2m_mean\n", - "variable=\"mean\"\n", - "t2m_mean_day1 = daily_aggregated[\"t2m_\" + variable].isel(valid_time=0)\n", - "\n", - "# Set the absolute min and max for the color bar\n", - "vmin = 270 # Minimum value (e.g., 270 K)\n", - "vmax = 310 # Maximum value (e.g., 310 K)\n", - "\n", - "# Create a plot with Cartopy\n", - "plt.figure(figsize=(10, 6))\n", - "ax = plt.axes(projection=ccrs.PlateCarree()) # Use PlateCarree projection for latitude/longitude data\n", - "\n", - "# Plot the data\n", - "t2m_mean_day1.plot(ax=ax, cmap=\"coolwarm\", transform=ccrs.PlateCarree(), vmin=vmin, vmax=vmax, cbar_kwargs={\"label\": \"Temperature (K)\"})\n", - "\n", - "# Add Madagascar's border using Cartopy's built-in features\n", - "ax.add_feature(cfeature.BORDERS, edgecolor=\"black\", linewidth=1) # Add country borders\n", - "ax.add_feature(cfeature.COASTLINE, edgecolor=\"black\", linewidth=0.8) # Add coastlines\n", - "\n", - "# Optionally, zoom in on Madagascar\n", - "#ax.set_extent([43, 51, -26, -11], crs=ccrs.PlateCarree()) # Longitude and latitude bounds for Madagascar\n", - "\n", - "# Add gridlines\n", - "ax.gridlines(draw_labels=True, linewidth=0.5, color=\"gray\", alpha=0.5, linestyle=\"--\")\n", - "\n", - "# Add a title\n", - "plt.title(\"Mean Daily {} Temperature (Day 1)\".format(variable))\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We're going to create the aggregations using the function we defined in the aggregate module" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "resampled_nc = resample_netcdf(fpath)\n", - "\n", - "resampled_tiff = netcdf_to_tiff(\n", - " ds=resampled_nc,\n", - " variable=\"t2m\",\n", - " crs=\"EPSG:4326\"\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we fetch the shapefile for administrative aggregations using our googledriver class:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "driver = GoogleDriver(json_key_path=here() / cfg.GOOGLE_DRIVE_AUTH_JSON.path)\n", - "drive = driver.get_drive()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "shape = \"Nepal_Healthsheds2024.zip\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "healthsheds = driver.read_healthsheds(shape)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['fid', 'STATE_CODE', 'DISTRICT', 'GaPa_NaPa', 'Type_GN', 'Province',\n", - " 'geometry', 'mean_temperature'],\n", - " dtype='object')" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "healthsheds.columns" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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75%583.0000005.000000298.800537
max777.0000007.000000300.483398
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" - ], - "text/plain": [ - " fid STATE_CODE mean_temperature\n", - "count 777.000000 777.000000 777.000000\n", - "mean 389.000000 3.651223 293.174866\n", - "std 224.444871 2.000799 6.550984\n", - "min 1.000000 1.000000 270.501404\n", - "25% 195.000000 2.000000 290.117432\n", - "50% 389.000000 3.000000 294.104492\n", - "75% 583.000000 5.000000 298.800537\n", - "max 777.000000 7.000000 300.483398" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "healthsheds.describe()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "777" - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(set(healthsheds['fid'].values))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 777/777 [00:00<00:00, 1125.83it/s]\n" - ] - } - ], - "source": [ - "res_poly2cell=polygon_to_raster_cells(\n", - " vectors = healthsheds.geometry.values, # the geometries of the shapefile of the regions\n", - " raster=resampled_tiff.data, # the raster data above\n", - " band=1, # the value of the day that we're using\n", - " nodata=resampled_tiff.nodata, # any intersections with no data, may have to be np.nan\n", - " affine=resampled_tiff.transform, # some math thing need to revise\n", - " all_touched=True, \n", - " verbose=True\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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healthshedmean_temperaturegeometry
01292.330078POLYGON ((87.60719 27.37069, 87.60841 27.36969...
17286.162109POLYGON ((88.04438 27.4203, 88.04365 27.41925,...
28282.460938POLYGON ((88.14528 27.67003, 88.14526 27.66966...
323293.310547POLYGON ((88.0766 27.03545, 88.07695 27.03533,...
424295.396484POLYGON ((87.76435 26.92431, 87.76435 26.924, ...
\n", - "
" - ], - "text/plain": [ - " healthshed mean_temperature \\\n", - "0 1 292.330078 \n", - "1 7 286.162109 \n", - "2 8 282.460938 \n", - "3 23 293.310547 \n", - "4 24 295.396484 \n", - "\n", - " geometry \n", - "0 POLYGON ((87.60719 27.37069, 87.60841 27.36969... \n", - "1 POLYGON ((88.04438 27.4203, 88.04365 27.41925,... \n", - "2 POLYGON ((88.14528 27.67003, 88.14526 27.66966... \n", - "3 POLYGON ((88.0766 27.03545, 88.07695 27.03533,... \n", - "4 POLYGON ((87.76435 26.92431, 87.76435 26.924, ... " - ] - }, - "execution_count": null, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "result = aggregate_to_healthsheds(\n", - " res_poly2cell=res_poly2cell,\n", - " raster=resampled_tiff,\n", - " shapes=healthsheds,\n", - " names_column=\"fid\",\n", - " aggregation_func=np.nanmean,\n", - " aggregation_name=\"mean_temperature\"\n", - ")\n", - "result.head()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "result.plot(column=\"mean_temperature\", legend=True)\n", - "plt.title(\"Mean Temperature (K) by Health Shed October 2023\")\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This should work by slotting right into the pipeline, only changing the function for the names column" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "python3", - "language": "python", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notes/sidebar.yml b/notes/sidebar.yml index c80eda4..caf3166 100644 --- a/notes/sidebar.yml +++ b/notes/sidebar.yml @@ -2,5 +2,15 @@ website: sidebar: contents: - index.ipynb + - section: "Snakemake Modules" - 00_core.ipynb - 01_download_raw_data.ipynb + - 02_aggregate.ipynb + - 03_publish.ipynb + - section: "PyTask Modules" + - 20_pytask_config.ipynb + - 20_pytask_logger.ipynb + - 21_pytask_download.ipynb + - 22_pytask_aggregate.ipynb + - section: "PyTask Demo" + - 10_pytask_demo.ipynb diff --git a/notes_qmd/00_core.qmd b/notes_qmd/00_core.qmd new file mode 100644 index 0000000..8159d54 --- /dev/null +++ b/notes_qmd/00_core.qmd @@ -0,0 +1,574 @@ +--- +title: "Core Module: Internal functions and testing" +exec_all: true +--- + +## core + +> This is a core library for the ERA5 dataset pipeline. It defines a few helpful functions such as an API tester to test your API key and connection. + + +```{python} +#| default_exp core: +# +``` + +```{python} +#| hide: +# +from nbdev.showdoc import * +``` + +```{python} +#| exports: +# +import os +import cdsapi +import hydra +import json +import tempfile +import argparse +import zipfile +import shutil +import geopandas as gpd +from pathlib import Path +from pydrive2.auth import GoogleAuth +from pydrive2.drive import GoogleDrive +from omegaconf import DictConfig, OmegaConf +from pyprojroot import here +from importlib import import_module +``` + +## Utilities + +Some utilities are provided to help you with the ERA5 dataset. + +```{python} +#| exports: +# +def describe( + cfg: DictConfig=None, # Configuration file + )-> None: + "Describe the configuration file used by Hydra for the pipeline" + + if cfg is None: + print("No configuration file provided. Generating default configuration file.") + cfg = OmegaConf.create() + + print("This package fetches ERA5 data. The following is the config file used by Hydra for the pipeline:\n") + print(OmegaConf.to_yaml(cfg)) +``` + +```{python} +#| exporti: +# +def _expand_path( + path: str # Path on user's machine + )-> str: # Expanded path + "Expand the path on the user's machine for cross compatibility" + + # Expand ~ to the user's home directory + path = os.path.expanduser(path) + # Expand environment variables + path = os.path.expandvars(path) + # Convert to absolute path + path = os.path.abspath(path) + return path +``` + +```{python} +#| exporti: +# +def _get_callable(func_path): + """Dynamically import a callable from a string path.""" + module_name, func_name = func_path.rsplit(".", 1) + module = import_module(module_name) + return getattr(module, func_name) +``` + +```{python} +#| exporti: +# a directory structure creator +def _create_directory_structure( + base_path: str, # The base directory where the structure will be created + structure: dict # A dictionary representing the directory structure + )->None: + """ + Recursively creates a directory structure from a dictionary. + """ + for folder, substructure in structure.items(): + # Create the current directory + current_path = os.path.join(base_path, folder) + os.makedirs(current_path, exist_ok=True) + + # Recursively create subdirectories if substructure is a dictionary + if isinstance(substructure, dict): + _create_directory_structure(current_path, substructure) +``` + +In addition, we've defined 3 private functions to help with path expansion `_expand_path`, dynamic function importing `_get_callable`, and directory structure creation `_create_directory_structure`. + +### A Simple Temperature Conversion Function + +```{python} +#| export: +# +def kelvin_to_celsius( + kelvin: float # Temperature in Kelvin + ) -> float: # Temperature in Celsius + """ + Convert temperature from Kelvin to Celsius. + """ + return kelvin - 273.15 +``` + +### A Class for Authenticating Google Drive + +We're going to use a class to authenticate and interact with google drive. The goal is to have a simple interface to fetch the healthshed files dynamically from google drive in the pipeline. + +::: {.callout-important} +This class was implemented when all of our data +was stored on a private Google Drive. Since we +have moved all of our data to FASRC, this will +likely be deprecated in the near future. +::: + +```{python} +#| export: +# +class GoogleDriver: + """ + A class to handle Google Drive authentication and file management. + This class uses the PyDrive2 library to authenticate with Google Drive using a service account. + + It provides three methods: authenticating the account, getting the drive object, and downloading the healthshed files for madagascar. + """ + def __init__(self, json_key_path=None): + self.json_key_path = json_key_path or os.getenv("GOOGLE_DRIVE_AUTH_JSON") + if not self.json_key_path or not os.path.isfile(self.json_key_path): + raise FileNotFoundError(f"Service account key file not found: {self.json_key_path}") + self.drive = self._authenticate() + + def _authenticate(self): + + settings = { + "client_config_backend": "service", + "service_config": { + "client_json_file_path": self.json_key_path + } + } + gauth = GoogleAuth(settings=settings) + + gauth.ServiceAuth() + + return GoogleDrive(gauth) + + def get_drive(self): + return self.drive +``` + +Here's how we use it. The credentials for the data-pipeline service account are +available in the sandbox folder, and the path to said folder is set in the config: + +```{python} +from hydra import initialize, compose +from omegaconf import OmegaConf +``` + +```{python} +# unfortunately, we have to use the initialize function to load the config file +# this is because the @hydra decorator does not work with Notebooks very well +# this is a known issue with Hydra: https://gist.github.com/bdsaglam/586704a98336a0cf0a65a6e7c247d248 +# +# just use the relative path from the notebook to the config dir +try: + with initialize(version_base=None, config_path="../conf"): + cfg = compose(config_name='config.yaml') +except Exception as e: + print(f"Error initializing Hydra: {e}") + with initialize(version_base=None, config_path="conf"): + cfg = compose(config_name='config.yaml') +``` + +::: {.callout-important} +If we continue with `pytask`, we will not need to +use hydra at all, and so the above strategy +may get deprecated. +::: + +```{python} +auth = GoogleDriver(json_key_path=here() / cfg.GOOGLE_DRIVE_AUTH_JSON.path) +drive = auth.get_drive() +``` + +Here's how we might check that the healthsheds are accessible in the drive: + +```{python} +# we're using the madagascar healthshed folder as an example +folder_id = cfg.geographies.madagascar.healthsheds +folder_name = "healthsheds2022.zip" +file_list = drive.ListFile({'q': f" title='{folder_name}' and trashed = false "}).GetList() + +for file in file_list: + print(f"{file['title']} - {file['mimeType']}") +``` + +That being said, we can read in the healthsheds into geopandas by downloading them to a temp directory. The healthsheds must be a zipped shapefiles package with the files at the root of the zip directory. + +```{python} +with tempfile.TemporaryDirectory() as temp_dir: + # Create a temporary directory to store the downloaded file + zip_path = os.path.join(temp_dir, folder_name) + + # Download file from Google Drive + file_obj = drive.CreateFile({'id': file_list[0]['id']}) + file_obj.GetContentFile(zip_path) + + # Read shapefile directly from ZIP + gdf = gpd.read_file(f"zip://{zip_path}") +``` + +That works! So now we can patch the class to include this workflow: + +```{python} +#| export: +# +from fastcore.basics import patch +``` + +```{python} +#| export: +# +@patch +def read_healthsheds(self:GoogleDriver, healthshed_zip_name): + + file_list = self.drive.ListFile({'q': f" title='{healthshed_zip_name}' and trashed = false "}).GetList() + + with tempfile.TemporaryDirectory() as temp_dir: + # Create a temporary directory to store the downloaded file + zip_path = os.path.join(temp_dir, healthshed_zip_name) + + # Download file from Google Drive + file_obj = self.drive.CreateFile({'id': file_list[0]['id']}) + file_obj.GetContentFile(zip_path) + + # Read shapefile directly from ZIP + gdf = gpd.read_file(f"zip://{zip_path}") + + # we need to ensure that the healthsheds only contain valid polygons + gdf = gdf[gdf.geometry.notnull()] + gdf.reset_index(drop=True, inplace=True) + + return gdf +``` + +And to check that it works: + +```{python} +driver = GoogleDriver(json_key_path=here() / cfg.GOOGLE_DRIVE_AUTH_JSON.path) +drive = driver.get_drive() +healthsheds = driver.read_healthsheds("healthsheds2022.zip") + +healthsheds.describe() +``` + +## CDS File Handler Type + +::: {.callout-important} +This section may also be deprecated. Since adding `swvl1` to the pipeline, we have not needed to use this class. We leave it here for now for reference. +::: + +We're going to make a file handler type to help deal with CDS files. This is to fix [NSAPH-Data-Processing/era5_sandbox#13](https://github.com/NSAPH-Data-Processing/era5_sandbox/issues/13). + +Usually, when you download data, it comes out as a simple .nc file that can be opened with xarray. However, the CDS API has a few different file types that are not .nc files. For example, the ERA5 data is stored in a .grib file format. This is a common format for meteorological data, and it is used by the ECMWF. When a query has multiple variables, sometimes they are downloaded as a .zip file to separat the grib from the netcdf. + +So, below, we define a class that can handle the file no matter what the type is. It will check the file type and then use the appropriate method to open it. The class will also have a method to check if the file is a .zip file, and if so, it will unzip it and return the path to the unzipped file. + +```{python} +#| export: +# +class ClimateDataFileHandler: + """ + A class to handle file operations for the Climate Data Store (CDS). + This class provides unpack files downloaded from the CDS API. It must be able to + handle the unpacking of files downloaded from the CDS API. This means that + if the file is a basic netcdf, it should be passed to the netcdf handler. If + the file is a zip, it should be handled by the zip handler in temp and the + data returned as required. + """ + + def __init__(self, input_path: str): + """ + Core initialization. It requires only the path. The flags are + then used to do the handling logic. + """ + self.original_path = Path(input_path) + + # major flag here for logic + self.is_zip = False + + # the unzipping directory + self.unzipped_dir = None + + # the instant data, such as temperature + self.instant_file = None + + # the cumulative data, such as precipitation + self.accum_file = None + + # any extraneous data + self.other_files = [] + + # ready to be used + self._prepared = False + + def prepare(self): + """ + Inspect the file and prepare the appropriate NetCDF paths. + """ + if self._prepared: + return + + if not self.original_path.exists(): + raise FileNotFoundError(f"{self.original_path} does not exist") + + # Detect ZIP by magic number + # chatgpt implementation here; this is a common way to check for zip files + with open(self.original_path, "rb") as f: + sig = f.read(4) + self.is_zip = sig == b'PK\x03\x04' + + if self.is_zip: + self._unzip_and_scan() + else: + self.instant_file = str(self.original_path) + + self._prepared = True + + def _unzip_and_scan(self): + """Extract and identify stepType-specific NetCDFs from ZIP.""" + self.unzipped_dir = tempfile.TemporaryDirectory() + with zipfile.ZipFile(self.original_path, 'r') as zip_ref: + zip_ref.extractall(self.unzipped_dir.name) + + for f in Path(self.unzipped_dir.name).glob("*.nc"): + if "stepType-instant" in f.name: + self.instant_file = str(f) + elif "stepType-accum" in f.name: + self.accum_file = str(f) + else: + self.other_files.append(str(f)) + + def get_dataset(self, type: str = "instant") -> str: + """Get the appropriate dataset path ('instant' or 'accum').""" + self.prepare() + + if type == "instant" and self.instant_file: + return self.instant_file + elif type == "accum" and self.accum_file: + return self.accum_file + elif type == "any": + return self.instant_file or self.accum_file or (self.other_files[0] if self.other_files else None) + else: + raise ValueError(f"No file found for requested type '{type}'") + + def cleanup(self): + """Clean up any temporary directories created during unzip.""" + if self.unzipped_dir is not None: + self.unzipped_dir.cleanup() +``` + +```{python} +import xarray as xr +from fastcore.test import test_fail +``` + +```{python} + +eg_file = here() / "bld/2019_5_madagascar.nc" + +# this fails because the nc file downloaded has grib and netcdf in it, so +# xr cannot handle it. +def wont_work(multilayer_file): + + ds = xr.open_dataset(multilayer_file) + +test_fail( + wont_work, + args=(eg_file) +) + +# equivalent to saying try: wont_work(eg_file) Except: some error handling +``` + +The above fails because the download contains temperature and precipitation data, which get encoded silently as different formats. Even though it is one file, it contains both grib and netcdf data and is encoded as a .zip file. So we use the class to read it instead: + +```{python} +handler = ClimateDataFileHandler(eg_file) +handler.prepare() +ds1 = xr.open_dataset(handler.get_dataset("instant")) +#ds2 = xr.open_dataset(handler.get_dataset("accum")) +``` + +::: {.callout-important} +The above line for `ds2` is commented out because the example file does not separate accumulation data. +::: + +```{python} +ds1 +``` + +```{python} +#ds2 +``` + +```{python} +handler.cleanup() +``` + +Great! Let's add a context handler and this can be added to the pipeline, +so that with the entry and exit methods, we can now use the class in a `with` statement: + + +```{python} +#| exporti: +# +@patch +def __enter__(self:ClimateDataFileHandler): + self.prepare() + return self + +@patch +def __exit__(self:ClimateDataFileHandler, exc_type, exc_val, exc_tb): + self.cleanup() +``` + + +```{python} +with ClimateDataFileHandler(eg_file) as handler: + ds1 = xr.open_dataset(handler.get_dataset("instant")) + #ds2 = xr.open_dataset(handler.get_dataset("accum")) + + print(ds1) + #print(ds2) +``` + +## Tests and Main + +In `nbdev`, our tests are embedded in the notebook. Whenever you export the notebook, all the cells that are specified to run are run, and hence, the tests are executed. The tests are also exported. This is a great way to ensure that your documentation is always up-to-date. For this module, we're using the `testAPI()` function as our main test. + +```{python} +#| exports: +# +def testAPI( + cfg: DictConfig=None, + dataset:str="reanalysis-era5-pressure-levels" + )-> bool: + + # parse config + testing=cfg.development_mode + output_path=here("data") / "testing" + + print(OmegaConf.to_yaml(cfg)) + + try: + client = cdsapi.Client() + + # build request + request = { + 'product_type': ['reanalysis'], + 'variable': ['geopotential'], + 'year': ['2024'], + 'month': ['03'], + 'day': ['01'], + 'time': ['13:00'], + 'pressure_level': ['1000'], + 'data_format': 'grib', + } + + target = output_path / 'test_download.grib' + + print("Testing API connection by downloading a dummy dataset to {}...".format(output_path)) + + client.retrieve(dataset, request, target) + + if not testing: + os.remove(target) + + print("API connection test successful.") + return True + + except Exception as e: + print("API connection test failed.") + print("Did you set up your API key with CDS? If not, please visit https://cds.climate.copernicus.eu/how-to-api#install-the-cds-api-client") + print("Error: {}".format(e)) + return False +``` + +We can see that this API tester tool works with Hydra configuration: + +```{python} +from hydra import initialize, compose +from omegaconf import OmegaConf +``` + +```{python} +# unfortunately, we have to use the initialize function to load the config file +# this is because the @hydra decorator does not work with Notebooks very well +# this is a known issue with Hydra: https://gist.github.com/bdsaglam/586704a98336a0cf0a65a6e7c247d248 +# +# just use the relative path from the notebook to the config dir +try: + with initialize(version_base=None, config_path="../conf"): + cfg = compose(config_name='config.yaml') +except Exception as e: + print(f"Error initializing Hydra: {e}") + with initialize(version_base=None, config_path="conf"): + cfg = compose(config_name='config.yaml') + +describe(cfg) +``` + +### Importing the Main Function + +::: {.callout-important} +As mentioned, if we continue with `pytask`, we will not need to use hydra at all, and so the main function +may get deprecated as `pytask` will handle the pipeline execution without `__main__` scripts. +::: + +Important: using `__main__` in nbdev and Hydra is a little bit tricky. We need to define the main function in the module ONLY ONCE and then when we export the notebook to script, we need to add the `nbdev.imports.IN_NOTEBOOK` variable. This way, the main function will only be executed when we run the notebook and not when we import the module. + +```python +from nbdev.imports import IN_NOTEBOOK +``` + +You'll see this listed throughout the notebooks. + +```{python} +#| exports: +# +@hydra.main(version_base=None, config_path="../../conf", config_name="config") +def main(cfg: DictConfig) -> None: + + # Create the directory structure + _create_directory_structure(here() / "data", cfg.datapaths) + + # test the api + testAPI(cfg=cfg) +``` + +```{python} +#| export: null +#| eval: false +try: from nbdev.imports import IN_NOTEBOOK +except: IN_NOTEBOOK=False + +if __name__ == "__main__" and not IN_NOTEBOOK: + main() +``` + +```{python} +#| hide: +# +import nbdev; nbdev.nbdev_export() +``` \ No newline at end of file diff --git a/notes_qmd/01_download_raw_data.qmd b/notes_qmd/01_download_raw_data.qmd new file mode 100644 index 0000000..f55e2ab --- /dev/null +++ b/notes_qmd/01_download_raw_data.qmd @@ -0,0 +1,259 @@ +--- +title: "Download Module: Downloading Raw Data from CDSAPI" +engine: jupyter +--- + +## download + +> This module downloads the raw data from CDS and saves it in the local directory + +```{python} +#| default_exp download: +# +``` + +```{python} +#| hide: +# +from nbdev.showdoc import * +``` + +We use a similar approach to the one in the tutorial to download the data +to local storage. + +```{python} +#| export: +# +import os +import hydra +import cdsapi +import tempfile +import zipfile +import requests +import geopandas as gpd +from pathlib import Path +from pyprojroot import here +from shapely.geometry import box +from omegaconf import DictConfig, ListConfig, OmegaConf + +try: from era5_sandbox.core import _expand_path +except: from core import _expand_path +``` + +```{python} +#| exporti: +# +def _validate_query( + query_body: DictConfig + )->bool: + ''' + Check that the query is valid + ###TODO Not a good idea to overwrite components of the query body because the user may believe something and the function may give somehting else back + Better to just tell them something is wrong + ''' + + required_keys = ['product_type', 'variable', 'year', 'month', 'day', 'time', 'area', 'data_format', 'download_format'] + if not all([key in query_body.keys() for key in required_keys]): + print(f"Missing required key in query. Required keys are {required_keys}") + print("Query validation failed") + raise ValueError("Invalid query") + + if isinstance(query_body['year'], ListConfig): + query_body['year'] = [str(x).zfill(2) for x in query_body['year']] + else: + query_body['year'] = str(query_body['year']) + if isinstance(query_body['month'], ListConfig): + query_body['month'] = [str(x).zfill(2) for x in query_body['month']] + else: + query_body['month'] = str(query_body['month']).zfill(2) + + if isinstance(query_body['day'], ListConfig): + query_body['day'] = [str(x).zfill(2) for x in query_body['day']] + else: + query_body['day'] = str(query_body['day']).zfill(2) + + return OmegaConf.to_container(query_body, resolve=True) +``` + +The background functionality in this module involves downloading the +bounding box of a region of interest, and sending that to the +CDS API query. As such, we define two helper functions to +fetch the OCHA/HDX shapefiles for a geographic region, and +another to create the bounding box from the files. + +```{python} +#| export: +# +def fetch_GADM( + url: str="https://geodata.ucdavis.edu/gadm/gadm4.1/gpkg/gadm41_MDG.gpkg", # URL to fetch the GADM data for Madagascar + output_file: str="gadm41_MDG.gpkg" # file path to save the GADM data + )-> str: + ''' + Fetch the GADM bounding box for geographic region + ''' + + output_file_path = _expand_path(output_file) + if os.path.exists(output_file_path): + print("GADM data already exists") + return output_file_path + + print("Fetching GADM bounding box data for region") + os.system("curl --output {} {}".format(output_file, url)) + print("GADM data fetched") + + return output_file_path +``` + +```{python} +#| exports: +# +def create_bounding_box( + zip_url_or_path: str, # URL or local path to the zipped shapefile. + buffer_km: float = 50, # Buffer distance in kilometers to expand the bounding box. + round_to: int = 1 # Number of decimal places to round the bounding box coordinates. +) -> list: # Bounding box in the CDS API area format [North, West, South, East] + ''' + Create a bounding box from OCHA/HDX shapefile data with a buffer. + ''' + with tempfile.TemporaryDirectory() as tmpdir: + # Download if it's a URL + if zip_url_or_path.startswith("http"): + response = requests.get(zip_url_or_path) + zip_path = os.path.join(tmpdir, "ocha_data.zip") + with open(zip_path, "wb") as f: + f.write(response.content) + else: + zip_path = zip_url_or_path + + # Unzip + with zipfile.ZipFile(zip_path, 'r') as zip_ref: + zip_ref.extractall(tmpdir) + + # Find the .shp file + shp_files = list(Path(tmpdir).rglob("*.shp")) + if not shp_files: + raise FileNotFoundError("No shapefile (.shp) found in the extracted archive.") + shp_path = str(shp_files[0]) # Use first found .shp + + # Read shapefile + shape = gpd.read_file(shp_path) + + # Reproject to projected CRS (you may want to detect the correct UTM zone) + shape_proj = shape.to_crs(epsg=32738) + + # Apply buffer + buffered = shape_proj.geometry.buffer(buffer_km * 1000) + + # Convert back to geographic coordinates + buffered_geo = gpd.GeoSeries(buffered, crs=shape_proj.crs).to_crs(epsg=4326) + + # Get bounding box + bounds = buffered_geo.total_bounds # [min_x, min_y, max_x, max_y] + bbox = [ + round(bounds[3], round_to), # North + round(bounds[0], round_to), # West + round(bounds[1], round_to), # South + round(bounds[2], round_to) # East + ] + + return bbox +``` + +The primary function to download the data from CDSAPI is defined below. + +```{python} +#| exports: +# +def download_raw_era5( + cfg: DictConfig # hydra configuration file + )->None: + ''' + Send the query to the API and download the data + ''' + + # parse the cfg + testing = cfg.development_mode # for testing + output_dir = here("data/input") # output directory + + geography = cfg.query.geography + + target = os.path.join(_expand_path(output_dir), "{}_{}_{}.nc".format(geography, cfg.query['year'], cfg.query['month'])) + + client = cdsapi.Client() + + query = _validate_query(cfg.query) + + dataset = cfg.dataset + # to make sure the query is valid at the end + del query['geography'] + + # Send the query to the client + if not testing: + bounds = create_bounding_box(cfg.geographies[geography]['shapefile']) + query['area'] = bounds + client.retrieve(dataset, query).download(target) + + print("Downloaded file to: {}".format(target)) + else: + print(f"Testing mode. Not downloading data. Query is {query}") + + print("Done") +``` + +## Tests and Main + +Here we define some tests and the main function that will be used to download the data. + +```{python} +#| eval: false +from hydra import initialize, compose +from omegaconf import OmegaConf + +# unfortunately, we have to use the initialize function to load the config file +# this is because the @hydra decorator does not work with Notebooks very well +# this is a known issue with Hydra: https://gist.github.com/bdsaglam/586704a98336a0cf0a65a6e7c247d248 +# +# just use the relative path from the notebook to the config dir +try: + with initialize(version_base=None, config_path="../conf"): + cfg = compose(config_name='config.yaml') +except Exception as e: + print(f"Error initializing Hydra: {e}") + with initialize(version_base=None, config_path="conf"): + cfg = compose(config_name='config.yaml') + +cfg.development_mode = False +cfg.query['year'] = 2017 +cfg.query['month'] = 11 +#cfg.query['day'] = 1 +#cfg.query['time'] = "00:00" +cfg.query['geography'] = "nepal" +download_raw_era5(cfg) +``` + +```{python} +#| exports: +# +@hydra.main(config_path="../../conf", config_name="config", version_base=None) +def main(cfg: DictConfig) -> None: + download_raw_era5(cfg=cfg) + # better approach would be to have the function only use the specific arguments of the config +``` + +```{python} +#| export: +#| eval: false +try: from nbdev.imports import IN_NOTEBOOK +except: IN_NOTEBOOK=False + +if __name__ == "__main__" and not IN_NOTEBOOK: + print('Running from __main__ ...') + + main() +``` + +```{python} +#| hide: +# +import nbdev; nbdev.nbdev_export() +``` \ No newline at end of file diff --git a/notes_qmd/02_aggregate.qmd b/notes_qmd/02_aggregate.qmd new file mode 100644 index 0000000..29f43fe --- /dev/null +++ b/notes_qmd/02_aggregate.qmd @@ -0,0 +1,621 @@ +--- +title: "Aggregate Module: Spatial Aggregation to Healthsheds" +execute: + freeze: auto +engine: jupyter +--- + +## aggregate + +> This module aggregates the downloaded data into the respective output dataframes. + +```{python} +#| default_exp aggregate: +# +``` + +```{python} +#| hide: +# +from nbdev.showdoc import * +``` + +We prototyped the code in this module using a Jupyter notebook. The notebook is available in `notes/prototypes/learning_aggregations_w_michelle_20250328.ipynb`. The code in this module is a cleaned-up version of the code in that notebook. The notebook contains additional comments and explanations of the code, which may be helpful for understanding the code in this module. + +The basic process is as follows: + +1. Load the netCDF data in memory +2. Statistically aggregate the hourly data to daily data per exposure using resample() +3. Write out the data to tiff +4. Read the tiff data back in +5. Read in the shapefile that defines the healthsheds +6. Spatially aggregate the exposure data to the healthsheds +7. Quality check the aggregations +8. Write out final aggregations to tiff + +```{python} +#| exports: +# +import tempfile +import rasterio +import hydra +import argparse +import os + +import pandas as pd +import geopandas as gpd +import numpy as np +import xarray as xr +import matplotlib.pyplot as plt + +from dataclasses import dataclass, field +from typing import Optional, Tuple +from pyprojroot import here +from hydra import initialize, compose +from omegaconf import OmegaConf, DictConfig +from tqdm import tqdm +from math import ceil, floor +from rasterstats.io import Raster +from rasterstats.utils import boxify_points, rasterize_geom + +try: from era5_sandbox.core import GoogleDriver, _get_callable, describe, ClimateDataFileHandler, kelvin_to_celsius +except: from core import GoogleDriver, _get_callable, describe, ClimateDataFileHandler, kelvin_to_celsius +``` + +```{python} +try: + with initialize(version_base=None, config_path="../conf"): + cfg = compose(config_name='config.yaml') +except Exception as e: + print(f"Error initializing Hydra: {e}") + with initialize(version_base=None, config_path="conf"): + cfg = compose(config_name='config.yaml') +``` + +We're going to write a function that aggregates the data for a single exposure from a file. This file should be the single month data we got from the previous step in the pipeline. + +```{python} +eg_file = here() / "bld/2009_01_nepal.nc" +``` + +```{python} +#| export: +# +def resample_netcdf( + fpath: str, # Path to the netCDF file. + resample: str = "1D", # Resampling frequency (e.g., '1H', '1D') + agg_func: callable = np.mean, # Aggregation function (e.g., np.mean, np.sum). + time_dim: str = "valid_time", # Name of the time dimension in the dataset. + **xr_open_kwargs # keywords for python's xarray module + ) -> xr.Dataset: + """ + Resample a netCDF file to a specified frequency and aggregation method. + + Args: + fpath (str): Path to the netCDF file. + resample (str): Resampling frequency (e.g., '1H', '1D'). + agg_func (callable): Aggregation function (e.g., np.mean, np.sum). + + Returns: + xarray.Dataset: Resampled dataset. + """ + + ds = xr.open_dataset(fpath, **xr_open_kwargs) + + if callable(agg_func): + # Use xarray's reduce method with the callable + return ds.resample({time_dim: resample}).reduce(agg_func) + else: + raise TypeError("agg_func must be a callable function like np.mean, np.max, etc.") +``` + +We pull the aggregation function from the config file: + +```{python} +var = 'swvl1' +agg_func = _get_callable(cfg['aggregation']['aggregation'][var]['hourly_to_daily'][0]['function']) +``` + +```{python} +with ClimateDataFileHandler(eg_file) as handler: + + ds_path = handler.get_dataset("instant") + resampled_data = resample_netcdf(ds_path, agg_func=agg_func) +``` + +I'm going to use a dataclass to represent the tiff data. This will allow us to easily pass around the data and metadata associated with the tiff file. Why? I've never used dataclasses and I'm curious about them — ChatGPT thinks this will make the code cleaner and easier to read. + +```{python} +#| exports: +# +@dataclass +class RasterFile: + path: str + band: int # note that this is 1-indexed + data: Optional[np.ndarray] = field(default=None, init=False) + transform: Optional[rasterio.Affine] = field(default=None, init=False) + crs: Optional[str] = field(default=None, init=False) + nodata: Optional[float] = field(default=None, init=False) + bounds: Optional[Tuple[float, float, float, float]] = field(default=None, init=False) + + def load(self): + """Load raster data and basic metadata.""" + with rasterio.open(self.path) as src: + self.data = src.read(self.band) # each day gets one rasterfile + self.transform = src.transform + self.crs = src.crs + self.nodata = src.nodata + self.bounds = src.bounds + return self + + def shape(self) -> Optional[Tuple[int, int]]: + """Return the shape of the raster data.""" + return self.data.shape if self.data is not None else None + + def __str__(self): + return f"RasterFile(path='{self.path}', shape={self.shape()}, crs='{self.crs}')" +``` + +Next, a function to write and read the netCDF to tiff: + +```{python} +#| exports: +# +def netcdf_to_tiff( + ds: xr.Dataset, # The aggregated xarray dataset to convert. + band: int, # The day to rasterise; 1 indexed just like human english + variable: str, # The variable name to convert. + crs: str = "EPSG:4326", # Coordinate reference system (default is WGS84). + ): + + """ + Convert a netCDF file to a GeoTIFF file. + """ + + with tempfile.TemporaryDirectory() as tmpdirname: + + # Select the variable and time index + variable = ds[variable] + ds_ = variable.rio.set_spatial_dims(x_dim="longitude", y_dim="latitude") + ds_ = ds_.rio.write_crs(crs) + # Save as GeoTIFF + ds_.rio.to_raster(f"{tmpdirname}/output.tif") + # Load the raster file + raster_file = RasterFile(path=f"{tmpdirname}/output.tif", band=band).load() + + return raster_file +``` + +Now to test it: + +```{python} +with ClimateDataFileHandler(eg_file) as handler: + ds_path = handler.get_dataset("instant") + resampled_nc = resample_netcdf(ds_path) + +print(resampled_nc) +resampled_tiff = netcdf_to_tiff( + ds=resampled_nc, + band=28, + variable="swvl1", + crs="EPSG:4326" +) +``` + +```{python} +resampled_tiff.data.shape, resampled_tiff.transform, resampled_tiff.crs, resampled_tiff.bounds +``` + +Super cool! The tiff file is created and the data is read back in correctly. Now we can move on to the next step, which is to aggregate the data by healthshed. + +## Polygon to Raster Cells + +This function was initially shared from a previous NSAPH aggregation pipeline [here](https://github.com/NSAPH-Data-Processing/air_pollution__aqdh/blob/2a8109075fe7a8fbf7c435cc34ffa97b63f5e133/utils/faster_zonal_stats.py#L17). To better understand this, here is a ChatGPT explanation of the code: + +> This function, `polygon_to_raster_cells`, is doing a crucial first step in spatial alignment: it determines which raster cells are “touched” by each polygon geometry (e.g., administrative areas, watersheds, etc.). +Essentially, this function helps figure out which pixels from a raster image fall inside each polygon (like a district, region, or shape). It does this by looking at each polygon one by one, zooming in on just the part of the raster that overlaps with that shape, and marking the pixels that are inside. This is kind of like placing a cookie cutter (the polygon) on a pixelated map (the raster) and seeing which pixels get cut. +The result is a list where each item tells you the pixel locations that match a specific polygon. You can then use those pixel locations to pull out data from the raster, like temperatures or rainfall, and calculate statistics (like the average) for each shape. This is a key step when you want to summarize raster data within specific regions, like figuring out the average temperature in each county or how much vegetation is in each park. + +```{python} +#| exports: +# +def polygon_to_raster_cells( + vectors, # list of geometries from a shapefile + raster, # the raster data as a numpy array + nodata=None, # the nodata value of the raster + affine=None, # the affine transform of the raster + all_touched=False, # whether to include all touched pixels + verbose=False, + **kwargs, +) -> list: # A dictionary mapping vector the ids of geometries to locations (indices) in the raster source. + """Returns an index map for each vector geometry to indices in the raster source.""" + + cell_map = [] + + with Raster(raster, affine, nodata) as rast: + # used later to crop raster and find start row and col + min_lon, dlon = affine.c, affine.a + max_lat, dlat = affine.f, -affine.e + H, W = rast.shape + + for geom in tqdm(vectors, disable=(not verbose)): + if "Point" in geom.geom_type: + geom = boxify_points(geom, rast) + + # find geometry bounds to crop raster + # the raster and geometry must be in the same lon/lat coordinate system + start_row = max(0, min(H - 1, floor((max_lat - geom.bounds[3]) / dlat))) + start_col = min(W - 1, max(0, floor((geom.bounds[0] - min_lon) / dlon))) + end_col = max(0, min(W - 1, ceil((geom.bounds[2] - min_lon) / dlon))) + end_row = min(H - 1, max(0, ceil((max_lat - geom.bounds[1]) / dlat))) + geom_bounds = ( + min_lon + dlon * start_col, # left + max_lat - dlat * end_row - 1e-12, # bottom + min_lon + dlon * end_col + 1e-12, # right + max_lat - dlat * start_row, # top + ) + + # crop raster to area of interest and rasterize + fsrc = rast.read(bounds=geom_bounds) + rv_array = rasterize_geom(geom, like=fsrc, all_touched=all_touched) + indices = np.nonzero(rv_array) + + if len(indices[0]) > 0: + indices = (indices[0] + start_row, indices[1] + start_col) + assert 0 <= indices[0].min() < rast.shape[0] + assert 0 <= indices[1].min() < rast.shape[1] + else: + pass # stop here for debug + + cell_map.append(indices) + + return cell_map +``` + +To use this, we must define the polygon and raster data. The polygon data is the healthshed shapefile, and the raster data is the tiff file we created earlier. We can use the `GoogleDriver` class we defined in `core` to read in the shapefile. + +```{python} +try: + with initialize(version_base=None, config_path="../conf"): + cfg = compose(config_name='config.yaml') +except Exception as e: + print(f"Error initializing Hydra: {e}") + with initialize(version_base=None, config_path="conf"): + cfg = compose(config_name='config.yaml') + +driver = GoogleDriver(json_key_path=here() / cfg.GOOGLE_DRIVE_AUTH_JSON.path) +drive = driver.get_drive() +healthsheds = driver.read_healthsheds("Nepal_Healthsheds2024.zip") +``` + +```{python} +res_poly2cell=polygon_to_raster_cells( + vectors = healthsheds.geometry.values, # the geometries of the shapefile of the regions + raster=resampled_tiff.data, # the raster data above + nodata=resampled_tiff.nodata, # any intersections with no data, may have to be np.nan + affine=resampled_tiff.transform, # some math thing need to revise + all_touched=True, + verbose=True +) +``` + +The data below maps which grid entries fall into each of the regions in the shapefile (e.g. which pixel is in which state) + +```{python} +res_poly2cell[:5] +``` + +Last but not least, we aggregate these data to the healthshed level. We can use the `rasterstats` package to do this. + +```{python} +#| exports: +# +def aggregate_to_healthsheds( + res_poly2cell: list, # the result of polygon_to_raster_cells + raster: RasterFile, # the raster data + shapes: gpd.GeoDataFrame, # the shapes of the health sheds + names_column: str = "fs_uid", # the unique identifier column name of the health sheds + aggregation_func: callable = np.nanmean, # the aggregation function + aggregation_name: str = "mean" # the name of the aggregation function + ) -> gpd.GeoDataFrame: + """ + Aggregate the raster data to the health sheds. + """ + + stats = [] + + for indices in res_poly2cell: + if len(indices[0]) == 0: + # no cells found for this polygon + stats.append(np.nan) + else: + cells = raster.data[indices] + if sum(~np.isnan(cells)) == 0: + # no valid cells found for this polygon + stats.append(np.nan) + continue + else: + # compute MEAN of valid cells + # but this stat can be ANYTHING + stats.append(aggregation_func(cells)) + + # clean up the result into a dataframe + stats = pd.Series(stats) + shapes[aggregation_name] = stats + df = pd.DataFrame( + {"healthshed": shapes[names_column], aggregation_name: stats} + ) + gdf = gpd.GeoDataFrame(df, geometry=shapes.geometry.values, crs=shapes.crs) + return gdf +``` + +And now we apply it: + +```{python} +result = aggregate_to_healthsheds( + res_poly2cell=res_poly2cell, + raster=resampled_tiff, + shapes=healthsheds, + names_column="fid", + aggregation_func=np.nanmean, + aggregation_name="mean_soil_moisture" +) +result.head() +``` + +And plot for QA: + +```{python} +result.plot(column="mean_soil_moisture", legend=True) +plt.title("Mean Soil Moisture (m^3 m^-3) by Health Shed Nov 2017 day 1") +plt.show() +``` + +That looks great! The data is aggregated to the healthshed level, and we can see the differences in exposure across the healthsheds. We can also see that the data is not uniform across the healthsheds, which is what we expect. + +## Tests and Main + +Now we can wrap this up in a main function that will simply take in the input file and generate this output. We can also add some tests to make sure the data is aggregated correctly; tests will run automatically in this notebook. + +```{python} +import random +``` + + +```{python} +#| eval: false + + +# variables = ["t2m", "d2m"] +# years = ["20{:02d}".format(m) for m in range(9, 24)] +# months = [str(m) for m in range(1, 13)] +# aggregations = [ +# ("Mean", np.nanmean), +# ("Max", np.nanmax), +# ("Min", np.nanmin) +# ] + +# exposure_variable = random.choice(variables) +# year = random.choice(years) +# month = random.choice(months) +# aggregation_str, agg_func = random.choice(aggregations) +# input_file = here() / "data/input/{}_{}.nc".format(year, month) + +# with initialize(version_base=None, config_path="../conf"): +# cfg = compose(config_name='config.yaml') + +# driver = GoogleDriver(json_key_path=here() / cfg.GOOGLE_DRIVE_AUTH_JSON.path) +# drive = driver.get_drive() +# healthsheds = driver.read_healthsheds(cfg.GOOGLE_DRIVE_AUTH_JSON.healthsheds_id) + +# with ClimateDataFileHandler(input_file) as handler: +# ds_path = handler.get_dataset("instant") +# resampled_nc_file = resample_netcdf(ds_path, agg_func=agg_func) + +# days = len(resampled_nc_file.valid_time.values) +# day = random.choice(range(1, days + 1)) + +# resampled_tiff = netcdf_to_tiff( +# ds=resampled_nc_file, +# band=day, # the day we're aggregating +# variable=exposure_variable, +# crs="EPSG:4326" +# ) + +# res_poly2cell=polygon_to_raster_cells( +# vectors = healthsheds.geometry.values, # the geometries of the shapefile of the regions +# raster=resampled_tiff.data, # the raster data above +# nodata=resampled_tiff.nodata, # any intersections with no data, may have to be np.nan +# affine=resampled_tiff.transform, # some math thing need to revise +# all_touched=True, +# verbose=True +# ) + +# result = aggregate_to_healthsheds( +# res_poly2cell=res_poly2cell, +# raster=resampled_tiff, +# shapes=healthsheds, +# names_column="fs_uid", +# aggregation_func=agg_func, +# aggregation_name=exposure_variable +# ) + +# result.plot(column=exposure_variable, legend=True) +# plt.title("{} {} (K) by Health Shed {}".format(aggregation_str, exposure_variable, input_file.stem)) +# plt.suptitle("Aggregation: {}, Day: {}".format(aggregation_str, str(day))) +# plt.show() +``` + +::: {.callout-note} +**Note:** The above code is commented out to prevent execution during documentation generation. You can uncomment and run it in an appropriate environment to test the aggregation process. +::: + +3.2 seconds per aggregation is pretty cool! + +```{python} +#| eval: false +result.to_parquet(here() / "data/testing/test_aggregation.parquet") +``` + +```{python} +#| exports: +# +def aggregate_data( + cfg: DictConfig, # the hydra config + input_file: str, # the input netcdf file + output_file: str, # the output parquet file + exposure_variable: str # Which variable in the dataset to aggregate + ) -> None: + ''' + Aggregate raster data day-by-day and store all days and statistics as separate columns in a single Parquet file. + ''' + + if cfg.development_mode: + describe(cfg) + return None + + geography = cfg['query'].geography + year = cfg['query']['year'] + month = cfg['query']['month'] + daily_aggs = cfg['aggregation']['aggregation'][exposure_variable]['hourly_to_daily'] + healthshed_aggs = cfg['aggregation']['aggregation'][exposure_variable]['daily_to_healthshed'] + + # Load healthsheds + driver = GoogleDriver(json_key_path=here() / cfg.GOOGLE_DRIVE_AUTH_JSON.path) + drive = driver.get_drive() + healthsheds = driver.read_healthsheds(cfg.geographies[geography].healthsheds) + + # Initialize output DataFrame + result_df = healthsheds[[cfg.geographies[geography].unique_id, "geometry"]].copy() + + for daily_agg in daily_aggs: + print(f"Processing daily aggregation: {daily_agg['name']}...") + + daily_agg_func = _get_callable(daily_agg['function']) + + with ClimateDataFileHandler(input_file) as handler: + if exposure_variable in ["t2m", "d2m", "swvl1"]: + ds_path = handler.get_dataset("instant") + else: + ds_path = handler.get_dataset("accum") + resampled_nc_file = resample_netcdf(ds_path, agg_func=daily_agg_func) + + for healthshed_agg in healthshed_aggs: + print(f"Aggregating to healthshed by: {healthshed_agg['name']}...") + + # Get the number of days in the dataset + days = len(resampled_nc_file.valid_time.values) + + # Get the aggregation function for healthshed + healthshed_agg_func = _get_callable(healthshed_agg['function']) + days = len(resampled_nc_file.valid_time.values) + + for day in range(1, days + 1): + print(f"Processing day {day}...") + + day_col = f"day_{day:02d}_daily_{daily_agg['name']}" + resampled_tiff = netcdf_to_tiff( + ds=resampled_nc_file, + band=day, + variable=exposure_variable, + crs="EPSG:4326" + ) + + result_poly2cell = polygon_to_raster_cells( + vectors=healthsheds.geometry.values, + raster=resampled_tiff.data, + nodata=resampled_tiff.nodata, + affine=resampled_tiff.transform, + all_touched=True, + verbose=True + ) + + res = aggregate_to_healthsheds( + res_poly2cell=result_poly2cell, + raster=resampled_tiff, + shapes=healthsheds, + names_column=cfg.geographies[geography].unique_id, + aggregation_func=healthshed_agg_func, + aggregation_name=exposure_variable + ) + + result_df[day_col] = res[exposure_variable] + + print(f"Saving final monthly parquet file: {output_file}") + result_df.to_parquet(output_file, compression="snappy") + # return(result_df) +``` + +```{python} +#| eval: false +try: + with initialize(version_base=None, config_path="../conf"): + cfg = compose(config_name='config.yaml') +except Exception as e: + print(f"Error initializing Hydra: {e}") + with initialize(version_base=None, config_path="conf"): + cfg = compose(config_name='config.yaml') + +cfg.development_mode = False +cfg.query['year'] = 2017 +cfg.query['month'] = 11 +cfg.query['geography'] = "nepal" + +variable = "swvl1" + +aggregate_data(cfg, here() / "bld/2017_11_nepal.nc", here() / "data/testing/test_nepal_aggregation.parquet", exposure_variable=variable) +``` + +```{python} +#| eval: false +parquet_file = gpd.read_parquet(here() / "data/testing/test_nepal_aggregation.parquet") +``` + +```{python} +#| eval: false +parquet_file +``` + +```{python} +#| eval: false +parquet_file.plot(column="day_22_daily_mean", legend=True) +``` + +```{python} +#| exports: +# +@hydra.main(version_base=None, config_path="../../conf", config_name="config") +def main(cfg: DictConfig) -> None: + # Parse command-line arguments + input_file = str(snakemake.input[0]) # First input file + output_file = str(snakemake.output[0]) + geography = str(snakemake.params.geography) + aggregation_variable = str(snakemake.params.variable) + + variables_dict = { + "2m_temperature": "t2m", + "2m_dewpoint_temperature": "d2m", + "volumetric_soil_water_layer_1": "swvl1", + "total_precipitation": "tp" + } + + cfg['query']['geography'] = geography + + aggregate_data(cfg, input_file=input_file, output_file=output_file, exposure_variable=variables_dict[aggregation_variable]) +``` + +```{python} +#| export: +#| eval: false +try: from nbdev.imports import IN_NOTEBOOK +except: IN_NOTEBOOK=False + +if __name__ == "__main__" and not IN_NOTEBOOK: + main() +``` + +```{python} +#| hide: +# +import nbdev; nbdev.nbdev_export() +``` \ No newline at end of file diff --git a/notes_qmd/03_publish.qmd b/notes_qmd/03_publish.qmd new file mode 100644 index 0000000..5fb502c --- /dev/null +++ b/notes_qmd/03_publish.qmd @@ -0,0 +1,456 @@ +--- +title: "Publish: Gather the Aggregated Data and Publish to DataVerse" +engine: jupyter +--- + +## publish + +> This is the `publish` module for the ERA5 dataset pipeline. It defines a functions that make use of the `pyDataverse` library and API to publish our outputs to the Harvard Dataverse. + +```{python} +#| default_exp publish: +# +``` + +```{python} +#| hide: +# +from nbdev.showdoc import * +``` + +First, we'll test out the API by pinging the Harvard DataVerse + +```{python} +#| exports: +# +import hydra +import yaml +import json +from tqdm import tqdm +from pyprojroot import here +``` + +```{python} +api_token_file = here() / "sandbox/dataverse_api_key.yml" +with open(api_token_file, "r") as f: + config = yaml.load(f, Loader=yaml.BaseLoader) +``` + +Now, following the [docs]() for the dataverse tutorial, load a NativeAPI up: + +```{python} +#| exports: +# +from pyDataverse.api import NativeApi +``` + +The NativeAPI is a catchall API object to be able to do general stuff: + +```{python} +api = NativeApi(config['base_url'], config['api_token']) +resp=api.get_info_version() +#resp.text() +``` + +```{python} +resp.json() +``` + +Looks good! Now that we know that it works, we can think more +about how to publish data there. + +## Harvard Dataverse + +Let's create a dummy dataset with the components we're +planning to upload, and then upload and promptly delete it. + +To do that, we must import the `models` module and create a Dataset object: + +```{python} +from pyDataverse.models import Dataset +``` + +```{python} +ds = Dataset() +``` + +This `ds` object is pretty straightforward since it doesn't contain anything yet: + +```{python} +ds.get() +``` + +We can populate the object from the dummy data on the github repo: + +```{python} +from pyDataverse.utils import read_file +from urllib.request import urlretrieve +import tempfile +``` + +```{python} +# url for dummy data +url = "https://raw.githubusercontent.com/gdcc/pyDataverse/refs/heads/main/tests/data/user-guide/dataset.json" + + +with tempfile.NamedTemporaryFile(mode='w+') as tmp: + urlretrieve(url, tmp.name) + ds.from_json(read_file(tmp.name)) +``` + +We have to validate the JSON correctly: + +```{python} +ds.validate_json() +``` + +Modifying it is easy: + +```{python} +ds.set({"title": "Youth from Austria 2005"}) +ds.get() +``` + +Now, to create the dataset we use the API: + +```{python} +#| eval: false +# this is only run in interactive sessions for demo purposes +resp = api.create_dataset(":root", ds.json()) +``` + +If you caught the `resp` object, it contains the PID for the newly created dataset. + +However, if you didn't you can use the SearchAPI to find it: + +```{python} +#| exports: +# +from pyDataverse.api import SearchApi +``` + +```{python} +search_api = SearchApi(config['base_url'], config['api_token']) +``` + +```{python} +#| eval: false +# + +resp = search_api.search("Youth from Austria", data_type="dataset") +results = resp.json()['data']['items'] +result = [x for x in results if "Youth from Austria" in x['name']][0] +result +``` + +```{python} +#| eval: false +pid = result['global_id'] +``` + +Now to look at the data we created using the NativeAPI again, and delete the dataset: + +```{python} +#| eval: false + +uploaded_ds = api.get_dataset(pid) +uploaded_ds.json()['data'] + +resp = api.delete_dataset(pid) +resp.json() +``` + +With that understanding, we can develop a quick module to do the following: + +1. Make the dataset LEGO Compatible +2. Upload and publish the data to dataverse + +## LEGO Compatibility + +Let's take an example file to use as a model for LEGO compatibility + +```{python} +#| exports: +# +import geopandas as gpd +import pandas as pd +import re +import glob +``` + +```{python} +ex = gpd.read_parquet(here() / "bld/2009_06_madagascar_day_swvl1_mean.parquet") +ex.describe() +``` + +We know that the LEGO data model should look like this: + +``` +
/lego +├── +│ ├── __ +│ │ ├── __ +│ │ │ ├── _yyyy.parquet +``` + +So, for the above file, we'll end up with the LEGO path `data/environmental/exposures_era5/healthshed_monthly/dewpoint_2024.parquet`. In it, we should have the following columns: + + +``` +healthshed_id year month day stat_1 stat_2 ... stat_n +``` + + +This means we should read in all of the exposures for a single timepoint at once. +I think the smart thing to do is use a glob string to gather all of the pertinent files. +This will be the first function we export to the library: + +```{python} +#| exports: +# + +def gather_exposure_geodataframes( + glob_string: str, # string for the path to search for the pertinent files + polygon_id: str, # the string signifying the healthshed ID of the polygon + exposure: str # the exposure name + )-> list: + "Read in a list of geo dataframes from the same time frame and merge them" + + # first get the initial one so we have the polygon ID and geometry + frames = glob.glob(str(glob_string)) + initial_gdf=gpd.read_parquet(frames[0]) + merged_df = [] + + for f in tqdm(frames, desc="Processing files"): + # read in as a regular dataframe by ignoring geometry + df = gpd.read_parquet(f).drop(["geometry"], axis=1) + + # get the year and month + # Extract year and month + search_str = rf'_{exposure}_(\d{{4}})_(\d{{1,2}})\.parquet$' + match = re.search(search_str, f) + + if match: + year = int(match.group(1)) + month = int(match.group(2)) + #print(f"Year: {year}, Month: {month}") + else: + raise ValueError(f"Could not extract year and month from filename: {search_str} {f}") + + df['exposure'] = exposure + df['month'] = month + df['year'] = year + + # Step 1: Melt all day columns (leave 'month' and 'year' as identifiers) + df_long = df.melt(id_vars=[polygon_id, "exposure", "year", "month"], var_name="day_stat", value_name="value") + + # Step 2: Extract day and stat type from column names + # Example column: "day_01_daily_mean" + df_long[["day", "stat"]] = df_long["day_stat"].str.extract(r"day_(\d{2})_daily_(mean|max|min|total)") + + # Optional: convert 'day' and month to integer + df_long["day"] = df_long["day"].astype(int) + df_long["month"] = df_long["month"].astype(int) + + # Drop the original combined column + df_long = df_long.drop(columns="day_stat") + + # Reorder columns + df_long = df_long[[polygon_id, "exposure", "year", "month", "day", "stat", "value"]] + + df_long = df_long.sort_values(by=["year", "month", "day"]) + df_clean = df_long.pivot(index=[polygon_id, "exposure", "year", "month", "day"], columns="stat", values="value").reset_index() + merged_df.append(df_clean) + + return [pd.concat(merged_df).reset_index(drop=True), initial_gdf[[polygon_id, "geometry"]]] +``` + +```{python} +frames = here() / "data" / "testing" / "*madagascar*" + +merged = gather_exposure_geodataframes(frames, "fs_uid", "2m_dewpoint_temperature") +merged[0].describe() +``` + +This returns one file with all of the geometries and one file +with the statistics and exposures. + +Now, with this, we can move on. The dataset was created in the UI and is available via search and test out how to upload it: + +```{python} +resp = search_api.search("ERA5", data_type="dataset") + +results = resp.json()['data']['items'] + +result = [x for x in results if "ERA5" in x['name']][0] +era5_pid = result['global_id'] +result +``` + +```{python} +#| exports: +# + +from pyDataverse.models import Datafile +import os +import pathlib +``` + +We'll upload directly from file. In the case of ERA5 vs. LEGO, we +store the file on disk as LEGO hierarchy, but to upload it to dataverse +using a flat filename (since creating subdatasets to represent directories might be +a bit of a hassle) + +```{python} +# assuming the file has a path on disk like: +f_out = "environmental/exposures_era5/healthshed_daily/dewpoint_2024.parquet" +os.makedirs(here() / "data" / "testing" / os.path.dirname(f_out), exist_ok=True) +aggregations, geo = merged +aggregations.to_parquet(here() / "data" / "testing" / f_out, index=False) + +datafile = Datafile() +datafile.set({ + # the id of the era5 dataset + "pid": era5_pid, + # the path to the file on disk goes here + "filename": str(here() / "data" / "testing" / f_out), + # use the "label" to name the file + "label": f_out.replace("/", "-") +}) +``` + +```{python} +#| eval: false +resp = api.upload_datafile(era5_pid, str(here() / "data" / "testing" / f_out), datafile.json()) +``` + +Pretty simple! + +Now, we just need a main function to upload this data. The final upload is one file per +exposure per year, so these should be the variables we gather data for. + +We should get some functionality to gather the groups of these files automatically, based on +the hydra config: + +```{python} +#| exports: +# +from hydra import initialize, compose +from omegaconf import OmegaConf, DictConfig +from tqdm import tqdm +``` + +```{python} +target_dir = here() / "data" / "intermediate" + +try: + with initialize(version_base=None, config_path="../conf"): + cfg = compose(config_name='config.yaml') +except Exception as e: + print(f"Error initializing Hydra: {e}") + with initialize(version_base=None, config_path="conf"): + cfg = compose(config_name='config.yaml') + +cfg.development_mode = False +#cfg.query['year'] = 2017 +#cfg.query['month'] = 11 +#cfg.query['geography'] = "nepal" +``` + +```{python} +#| exports: +# + +@hydra.main(version_base=None, config_path="../../conf", config_name="config") +def main(cfg: DictConfig) -> None: + + variables_dict = { + "2m_temperature": "t2m", + "2m_dewpoint_temperature": "d2m", + "volumetric_soil_water_layer_1": "swvl1", + "total_precipitation": "tp" + } + + print(OmegaConf.to_yaml(cfg)) + + #prep dataverse + api_token_file = here() / "sandbox/dataverse_api_key.yml" + with open(api_token_file, "r") as f: + apiconfig = yaml.load(f, Loader=yaml.BaseLoader) + api = NativeApi(apiconfig['base_url'], apiconfig['api_token']) + search_api = SearchApi(apiconfig['base_url'], apiconfig['api_token']) + resp = search_api.search("ERA5", data_type="dataset") + + results = resp.json()['data']['items'] + + result = [x for x in results if "ERA5" in x['name']][0] + era5_pid = result['global_id'] + + for geography in cfg.geographies: + for year in cfg.query['year']: + for variable, v in variables_dict.items(): + + print(f"Processing {geography} for {variable} in {year}") + glob_string = here() / "data" / "intermediate" / f"*{geography}*{variable}*{year}*" + print(f"Glob: {glob_string}") + polygon_id = cfg.geographies[geography]['unique_id'] + print(f"polygon_id: {polygon_id}") + merged = gather_exposure_geodataframes(glob_string, polygon_id, variable) + print(merged[0].head()) + print(merged[1].head()) + + output_dir = here() / "data" / "output" + + f_out = f"environmental/exposures_era5/healthshed_daily/{geography}_{v}_{year}.parquet" + os.makedirs(output_dir / os.path.dirname(f_out), exist_ok=True) + output_path = output_dir / f_out + + print(f"Writing to {output_path}") + merged[0].to_parquet(output_path, index=False) + + + print(f"Uploading {f_out.replace('/', '-')} to Dataverse...") + # upload to dataverse + datafile = Datafile() + datafile.set({ + "pid": era5_pid, + "filename": str(output_path), + "label": f_out.replace("/", "-") + }) + + resp = api.upload_datafile(era5_pid, output_path, datafile.json()) + assert resp.json()['status'] == "OK", f"Failed to upload datafile: {resp.text}" + + # also save the geometry for the region + merged[1].to_parquet(output_path.parent / f"{geography}_geometry.parquet", index=False) + + # and upload it to dataverse + datafile = Datafile() + datafile.set({ + "pid": era5_pid, + "filename": str(output_path.parent / f"{geography}_geometry.parquet"), + "label": f"{geography}_geometry.parquet" + }) + + resp = api.upload_datafile(era5_pid, output_path.parent / f"{geography}_geometry.parquet", datafile.json()) + assert resp.json()['status'] == "OK", f"Failed to upload geometry datafile: {resp.text}" + + print("All files processed and uploaded successfully.") + +``` + +```{python} +#| export: +#| eval: false +try: from nbdev.imports import IN_NOTEBOOK +except: IN_NOTEBOOK=False + +if __name__ == "__main__" and not IN_NOTEBOOK: + main() +``` + +```{python} +#| hide: +# +import nbdev; nbdev.nbdev_export() +``` \ No newline at end of file diff --git a/notes_qmd/10_pytask_demo.qmd b/notes_qmd/10_pytask_demo.qmd new file mode 100644 index 0000000..1757558 --- /dev/null +++ b/notes_qmd/10_pytask_demo.qmd @@ -0,0 +1,279 @@ +--- +title: "Demo: How to Create Pipelines with `pytask`" +engine: jupyter +--- + +## Data Preparation Demo + +> Data preparation task for `pytask` demo + +In this notebook, we are demonstrating how to convert our snakemake workflow into a `pytask` workflow. We use the basic tutorial to demonstrate this, but continue +to use nbdev for development of functions in notebooks. + +`pytask` is a task management system that allows you to define tasks and their dependencies, similar to `Snakemake`. It is particularly useful for data science workflows. + +There are a number of reasons to use `pytask` over `snakemake`: +- **Pythonic**: `pytask` is designed to be purely Pythonic by default, allowing you to write tasks and entire pipelines as Python functions. +- **Flexibility**: `pytask` allows you to define tasks and their dependencies in a more flexible way, using Python functions and decorators, as opposed to orchestrating numerous scripts. +- **Integration**: `pytask` integrates well with other Python libraries, such as `nbdev` here, or `hydra` configurations if you need, allowing you to use your existing code, notebooks, or configs in your workflows. +- **Parallelism**: `pytask` supports parallel execution of tasks with `pytask-parallel`, which can speed up your workflows significantly, especially for data processing tasks. + +We'll use nbdev to define the task functions, and then export them to the `src` directory. `pytask` is then invoked at the command line to run the tasks. + +```{python} +#| default_exp task_data_preparation: +# +``` + +This demo task is taken from the tutorial at [pytask documentation](https://pytask-dev.readthedocs.io/en/stable/tutorials/write_a_task.html). At minimum, you need your package to contain the following in a config.py file: + +```md +my_project +│ +├───.pytask +│ +├───bld +│ └────... +│ +├───src +│ └───my_project +│ ├────__init__.py +│ ├────config.py +│ └────... +│ +└───pyproject.toml +``` + +```python +#contents of `era5_sandbox.config` module +from pathlib import Path + + +SRC = Path(__file__).parent.resolve() +BLD = SRC.joinpath("..", "..", "bld").resolve() +``` + +Additionally, your pyproject.toml file should contain the following at minimum: + +```toml +[tool.pytask.ini_options] +paths = ["src/era5_sandbox"] +``` + +The former tells Python where to find the source code and build directory for `pytask` objects and shims, while the latter tells `pytask` where to find the task definitions and dependency DAG. + +```{python} +#| exports: +# +import os +from pathlib import Path +from typing import Annotated + +import numpy as np +import matplotlib.pyplot as plt +import pandas as pd +from era5_sandbox.config import BLD +from era5_sandbox.config import data_catalog, demo_catalog + +from pytask import PickleNode +from pytask import Product +``` + + +### Defining Tasks + +To define a task, simply use the `task_` prefix in the function name (or, if you are familiar and comfortable with decorators, use `@pytask.mark.task`). Be verbose and expressive in your use of type hints to specify the input and output data, so that `pytask` can automatically detect and handle the dependencies between tasks. + +### Defining Tracked Outputs + +To define something as a tracked output, you can annotate the input of the task with `Annotated[Path, Product]`, where `Product` is imported from `pytask`. This tells `pytask` that this is a product of the task and should be saved in the build directory. + +In this example, we're generating random data into a data frame and saving the object as a pickle in the `bld` directory (`bld` is the default build directory for `pytask`'s intermediate data). To get that directory, we use the `BLD` variable from the `era5_sandbox.config` module as above. This module itself could also be generated using `nbdev` if you want to keep your configuration in notebooks. + +Using `nbdev`, we can also include all of the bells and whistles of function documentation. + +```{python} +#| exports: +# + +def task_create_random_data( + seed: Annotated[int, 42], # Default seed for reproducibility + path_to_data: Annotated[Path, Product] = BLD / "data.pkl" # Path to the object in the build directory + ) -> None: + "Create a random data set and save it as a pickle file. Return the path to the saved file." + rng = np.random.default_rng(seed) + beta = 2 + + x = rng.normal(loc=5, scale=10, size=1_000) + epsilon = rng.standard_normal(1_000) + + y = beta * x + epsilon + + df = pd.DataFrame({"x": x, "y": y}) + + # this is a tracked output, so we annotate the return value with `Annotated[Path, Product]` + df.to_pickle(path_to_data) +``` + +We can test the function directly in the notebook: + +```{python} +task_create_random_data(42) +``` + +Once this module and function are exported with `nbdev_export`, the functions are in a python package. We can then use the command line to look at the registered tasks: + +```{python} +#| eval: false + +%%sh +pytask collect +``` + +Let's add another task in the same module. This task plots the data we generated. To link the previous task to this one as a dependency, we can list the output of the previous task as an input to this one. This way, `pytask` will know that it needs to run the first task before this one. + +```{python} +#| exports: +# + +def task_plot_data( + path_to_data: Annotated[Path, BLD / "data.pkl"], # Path to the data file created by the previous task + path_to_plot: Annotated[Path, Product] = BLD / "plot.png" # Path to the build directory for the plot +) -> None: + """ + Plot the data from the pickle file and save the plot. Note that this task: + 1. depends on the data.pkl file created by the previous task, + 2. does not return any value, but saves a plot to the build directory. So the side effect of the task is what we are interested in here (though this is probably bad practice). + """ + + df = pd.read_pickle(path_to_data) + + _, ax = plt.subplots() + df.plot(x="x", y="y", ax=ax, kind="scatter") + + plt.savefig(path_to_plot) + plt.close() +``` + +We now have a DAG of tasks that `pytask` can execute. To see the tasks, we can use the command line to create a pygraphviz graph of the tasks: + +```bash +pytask dag +``` + +The DAG is saved as a pdf file, and you can view it using any viewer. Now, to run the pipeline, just invoke `pytask` at the command line: + +```bash +pytask +``` + +In Jupyter or iPython, you can interact with the task outputs directly: + +```{python} +#| eval: false + +# list all the files in the build directory +for file in os.listdir(BLD): + print(file) +``` + +We can use these to build subsequent tasks later. + +## More Complex Tasks & The Data Catalog + +As we define more complex tasks, we can use the `pytask` data catalog to manage the inputs and outputs of our tasks. The data catalog allows us to imperatively name the data and their formats, making it easier to manage the data flow in our tasks. Importantly, we can define the data pythonically, which allows us to use the full power of Python to manipulate and transform our data. This is particularly more useful than snakemake's approach, which requires you to define the data in a more static way using paths and a separate pseudo-language. + +The content of the `era5_sandbox.config` module can be extended to include a data catalog: + +```python +from pathlib import Path +from pytask import DataCatalog, Product + +SRC = Path(__file__).parent.resolve() +BLD = SRC.joinpath("..", "..", "bld").resolve() + +demo_catalog = DataCatalog() +``` + +With just this definition, we're now able to refer directly to data by name in our tasks, and `pytask` will handle the paths and formats for us. This allows us to focus on the logic of our tasks rather than the details of data management. + +:::{.callout-note} +This is a major advantage of `pytask` over `snakemake`, as it allows you to define the data in a more flexible and Pythonic way, while still maintaining the benefits of a task management system. It is a similar approach to building pipelines in R with targets, which allows you to define the data in a more flexible way. +::: + +Let's create a task that modifies the data frame by adding a new column. This task will depend on the previous task's output, and we will use the data catalog to define the input and output data. + +```{python} +#| exports: +# + +def task_add_one( + path_to_data: Annotated[Path, BLD / "data.pkl"], # Path to the data file created by the previous task + node: Annotated[PickleNode, Product] = demo_catalog["mydata"] +) -> None: + """ + Add one to the 'y' column of the data frame and save it as a new pickle file. + """ + df = pd.read_pickle(path_to_data) + df['z'] = df['y'] + 1 + + node.save(df) +``` + +In this function, we've defined that the task relies on the output of the first task being there, the `data.pkl` file. But importantly, we've also defined our product as a `node` from the `PickleNode` module. This will allow `pytask` to handle the serialization and deserialization of the data frame automatically, so we don't have to worry about the details of how the data is stored. We create the datacatalog in our config file, and then tell this task to create a Node in that catalog called `mydata`. Whatever we save with the `node.save()` method will be saved in the build directory, but more importantly _will be indexed and hashed by `pytask`_. This means that if the data changes, `pytask` will know to rerun the task. + +To make this even more pythonic, we can modify the format of our task function so that the return type annotator is used as a node in the data catalog. This allows us to define the output of the task as a `PickleNode`, which will automatically handle the serialization and deserialization of the data frame. + +:::{.callout-note} +This is another trick I'm deriving from {targets}. By formatting tasks as pure functions where inputs are parameters and targets are return type annotations, we can define the output of the task as a `PickleNode`, which will automatically handle the serialization and deserialization of the data frame. This again allows us to focus on the logic of our tasks rather than the details of data management. +::: + +So below, we're directly accessing the `data_catalog` to get the `mydata` node, and then modifying it by adding a new column. It _feels_ like we are doing this in place, such as in an iPython session, because we are allowing `pytask` to handle the serialization of the file on disk for us. + +```{python} +#| exports: +# + +def task_add_another_column( + df: Annotated[pd.DataFrame, demo_catalog["mydata"]] # which object in the catalog to fetch from the catalog with node.load() +) -> Annotated[pd.DataFrame, demo_catalog["mydata2"]]: # which object in the catalog to save the return value to + """ + Add another column to the data frame stored in the PickleNode. + """ + + # use the datacatalog directly to access the node + # this is a bit like accessing the node in an iPython session, but pytask + # will handle the serialization and deserialization for us + df['w'] = df['z'] * df['y'] + + return df +``` + +To test this interactively, we'd have to import the data catalog's object + +```{python} +df = demo_catalog["mydata"].load() # load the data frame from the PickleNode +result = task_add_another_column(df) # call the task function with the loaded data frame +``` + +```{python} +result +``` + +Now that we know it will work, we can invoke pytask: + +```{python} +#| eval: false +%%sh +pytask +``` + +Notice that the outputs are cached and not recomputed unless the inputs change. This is a key feature of `pytask` and other DAGs, allowing you to efficiently manage your data processing tasks without unnecessary recomputation. + +## Conclusion + +The takeaway here is that with `pytask`, you can define pure functions that take inputs and return outputs, and build a DAG of tasks that can be executed in a flexible and efficient way. This allows you to focus on the logic of your tasks rather than the details of data management, while still maintaining the benefits of a task management system. The key elements are: + +- **Task annotation**: You define your tasks by creating pure functions that take inputs and return outputs, and use decorators or naming conventions to mark them as "tasks" in a dag +- **Input and output annotation**: You define the inputs and outputs of your tasksusing type hints, and allow `pytask` to automatically detect and handle the dependencies between tasks. +- **Data catalog**: You define your data in a Pythonic object in your config called `data_catalog`. As you iteratively develop your DAG, you add objects to the data catalog, which are called nodes. As long as a node is a pythonic object and has a pickle method, `pytask` will handle the serialization and deserialization of the data for you. \ No newline at end of file diff --git a/notes_qmd/20_pytask_config.qmd b/notes_qmd/20_pytask_config.qmd new file mode 100644 index 0000000..0aff0fb --- /dev/null +++ b/notes_qmd/20_pytask_config.qmd @@ -0,0 +1,332 @@ +--- +title: "`pytask` Config: Defining the Pipeline Internals in `pytask`" +engine: jupyter +--- + +## config + +> This is the config module for the `pytask` pipeline. +This module defines the data catalog(s) and any hard-coded parameters that are used throughout the pipeline. + +```{python} +#| default_exp config: +# +``` + +```{python} +#| hide: +# +from nbdev.showdoc import * +``` + +```{python} +#| exports: +# + +import pandas as pd + +from pathlib import Path +from pyprojroot import here +from pytask import DataCatalog + + +SRC = here() / "src" / "era5_sandbox" +BLD = here() / "bld" + +demo_catalog = DataCatalog() +``` + +## `DEV_MODE`: A Quick Development Flag + +I'm adding a flag to the config that can be used for quick development. +If you import this boolean variable, it can be used to skip tasks, +setup samples, etc. on the fly by `marking` a task with the `pytask.mark.skipif` +decorator. Change this to `False` when you're ready to run the full pipeline. + +```{python} +#| exports: +# +DEV_MODE=True +``` + +## The Data Catalog + +To manage our pipeline, we're going to use a nested data catalog structure. +This way, we can easily return specific entries to specific tasks +without having to manage multiple different data catalogs. Specifically, +we'll have a data catalog for each stage of the pipeline, and each catalog +will have entries for the inputs, outputs, and any other parameters needed +for that stage. This is similar to how we used Hydra configs, but +using the `pytask` data catalog, we can more easily gather the data +for a specific task in structured manner entirely in Python. + +```{python} +#| exports: +# + +stages = ["mydata", 'mydata2', # from the demo, ignore + "download", # download task + "aggregate", # aggregation task + "publish", # publishing task + "viz"] # visualization task + +buckets = [ + "inputs", # any specific inputs, eg for carrying over between tasks + "outputs", # specific output task returns + "jobs", # job parameters as a dataframe + "params" # any lingering hardcoded parameters + ] + +data_catalog = { + + stage: {bucket: DataCatalog(name=f"{stage}_{bucket}") for bucket in buckets} + for stage in stages +} +``` + + +## The Download Task + +A good strategy may be to set pipeline stage parameters in the config file, +and then use the `pytask` data catalog to manage the data. This way, we can +easily change the parameters without having to modify the code. This is especially +useful for the API query, where we need to be able to set the parameter grid for +the years and data types we want to download data for. So, let's create an entry in the data catalog specifically for the download task. + +A good strategy I thought about for grid parameter comprehension is to create a dataframe expands all the combinations of +parameters, and then uses each combination to create the tasks which are then +easily added to the data catalog. This way, we can still easily inspect the +pipeline and see what tasks are being run, while also being able to easily +change the parameters in the config file without too much hassle. + +An important framework decision I'm making here is that each ROW of the dataframe corresponds to a single task, so that we can quickly understand at a glance what the task is doing, and also easily develop the code for the task itself. This is different from the hydra approach where a job is first specified by a default config, and then the parameters are swept over in multiple config files. This is a more flexible approach, IMO, because: + +1. each row defines a single task run, so it's easy to understand what the run is doing +2. it's easy to add or remove runs by simply expanding the list of parameters and using dataframe filters to remove irrelevant parameter combinations +3. we don't have to independently inspect and manage multiple different/overriding config files +4. it's all in Python, so we can use the full power of the language to define + the parameters and the tasks in a single sweep, not through the need of + hydra+snakemake multi stage/multi-lingual config system + +So, to do this, we define one job as a query to the CDS API that must contain: +- The dataset (re-analysis) +- The year +- The month +- All days in the month +- All times of day (hour) +- The geography (region), which will need: + - The URL to the shapefile to calculate the bounding box + +Given one combination of all of these, a single SLURM job can complete the first "task" in parallel by having a run assigned to each row of the dataframe. + +```{python} +#| exports: + +# a dataframe for the query parameters, with nested entries for days, times, and variables +# Dimensions +years = [str(x) for x in range(2009, 2025)] # 16 years +months = [str(x).zfill(2) for x in range(1, 13)] # 12 months +geographies = ["madagascar", "nepal"] # 2 geographies + +# nested values; we want ALL days, times, and variables for each job +days = [str(x).zfill(2) for x in range(1, 32)] +times = [f"{x:02d}:00" for x in range(24)] +variables = ["2m_dewpoint_temperature", "2m_temperature", "total_precipitation", "volumetric_soil_water_layer_1"] + +product_type = "reanalysis" + +# Map shapefiles to geography +shapefiles = { + "madagascar": "https://data.humdata.org/dataset/26fa506b-0727-4d9d-a590-d2abee21ee22/resource/ed94d52e-349e-41be-80cb-62dc0435bd34/download/mdg_adm_bngrc_ocha_20181031_shp.zip", + "nepal": "https://data.humdata.org/dataset/07db728a-4f0f-4e98-8eb0-8fa9df61f01c/resource/2eb4c47f-fd6e-425d-b623-d35be1a7640e/download/npl_adm_nd_20240314_ab_shp.zip" +} + +# Build row-wise combinations of (year, month, geography) +rows = [] +for year in years: + for month in months: + for geo in geographies: + rows.append({ + "year": year, + "month": month, + "geography": geo, + "shapefile": shapefiles[geo], + "product_type": product_type, + "day": days, + "time": times, + "variables": variables, + "output": f"{year}_{month}_{geo}" + }) + +# Create dataframe +query_df = pd.DataFrame(rows) +``` + +```{python} +query_df +``` + +```{python} +print(f"Number of estimated jobs: {query_df.shape[0]}. Examples...") + +for i, row in query_df.sample(3).iterrows(): + print(f"Year: {row['year']}, Month: {row['month']}, Geography: {row['geography']}, Link: {row['shapefile']}, Variables: {row['variables']}") +``` + +Now add them to the catalog. We're going to use a dictionary to +nest data catalogs so that we can return specific task products to +named data catalog nodes. + +```{python} +#| export: +# set up catalog + +data_catalog['download']['jobs'].add("queries_df", query_df) +``` + +Our data catalog now has a `download|jobs` node with a `queries_df` entry that contains the dataframe of all the jobs to be run in this task. + +```{python} +data_catalog['download']['jobs']['queries_df'].load().head() +``` + +## The Aggregation Task + +To carry out the aggregation, we will follow similar logic to the original pipeline and use xarray to aggregate data into spatial and temporal averages. The aggregation task will take the downloaded data and compute the mean over the specified time period and spatial region. However, in this case, we want to aggregate the data diurnally, so we will need to fetch the sundown and sunrise times for the region and use them to compute the diurnal averages. + +Once again, we will use a dataframe to define the parameters for the aggregation task. + +Here we will use a dataframe with the jobs as rows; +the first column is "input" which is the list of query names from +the download task, and the last column is the output object name. Columns +in between can be the parameters needed for the aggregation task, which +then get expanded to the full list of jobs with `itertools.product`, `explode` or similar, +and filtered as necessary. + +For explanations of the parameters, see the Aggregation Task notebook's final `task_aggregate_data_diurnal` function. + +```{python} +#| exports: + +# aggregate task parameters + +inputs = query_df["output"].tolist() +outputs = [f"{i}_agg" for i in inputs] + +variable_dict = { + "2m_dewpoint_temperature": "d2m", + "2m_temperature": "t2m", + "total_precipitation": "tp", + "volumetric_soil_water_layer_1": "swvl1" +} + +# list of params that get fed into the task functions +agg_params = { + "time": ["day", "night"], + "solar_classification": ["before"], + "variables": variables, + "variables_short": [variable_dict[x] for x in variables], + "aggregation_name": ["mean", "sum", "max", "min"] +} + +from itertools import product +import pandas as pd + +# expand all the params +agg_params = pd.DataFrame(list(product(*agg_params.values())), columns=agg_params.keys()) +``` + +Inspecting it: + +```{python} +agg_params +``` + +Let's keep only rows where the variables and variables_short match + +```{python} +#| exports: +# quick filter to keep only matching rows + +agg_params = agg_params[agg_params.apply(lambda x: variable_dict[x['variables']] == x['variables_short'], axis=1)] +``` + +```{python} +agg_params +``` + +Great, and now keeping `sum` only for total precipitation (we don't need mean, max, min for that variable), and removing `sum` for all other variables (we don't need sum for temperature or soil moisture): + +```{python} +#| exports: +# remove rows where tp aggregation is not sum +mask = (agg_params['variables_short'] == "tp") & (agg_params['aggregation_name'] != "sum") +agg_params = agg_params[~mask] + +# remove rows where non-tp aggregation is sum +mask = (agg_params['variables_short'] != "tp") & (agg_params['aggregation_name'] == "sum") +agg_params = agg_params[~mask] +``` + +```{python} +agg_params +``` + +Now we add the input and output columns by joining: + +```{python} +#| exports: +# set up inputs and parameters +inputs = pd.DataFrame({"input": inputs}) +aggregate_jobs = inputs.merge(agg_params, how="cross") +``` + +This result gives us the full list of jobs for the aggregation task. 20 rows for the parameters, +and 384 inputs/outputs, giving a total of 7680 jobs: + +```{python} +assert aggregate_jobs.shape[0] == 20 * len(inputs) +aggregate_jobs +``` + +A few more configuration items need to be added, like +the local timezone for each geography, the healthshed filename, +the healthshed unique ID variable name in the shapefile, +and whether the variable is instantaneous or accumulated: + +```{python} +#| exports: +# add a few more columns +aggregate_jobs['local_tz'] = aggregate_jobs['input'].apply( + lambda x: "Asia/Kathmandu" if "nepal" in x else "Indian/Antananarivo" +) +aggregate_jobs['shapefile'] = aggregate_jobs['input'].apply( + lambda x: "Nepal_Healthsheds2024.zip" if "nepal" in x else "healthsheds2022.zip" +) + +aggregate_jobs['hshd_unique_id'] = aggregate_jobs['input'].apply( + lambda x: "fid" if "nepal" in x else "fs_uid" +) + +aggregate_jobs['climate_handler_var'] = aggregate_jobs['variables_short'].apply( + lambda x: "accum" if x == "tp" else "instant" +) +``` + +```{python} +aggregate_jobs +``` + +Now we add this to the data catalog: + +```{python} +#| exports: +# update catalog +data_catalog['aggregate']['jobs'].add("jobs_df", aggregate_jobs) +``` + +Our data catalog now has an `aggregate|jobs` node with a `jobs_df` entry that contains the dataframe of all the jobs to be run in this task. + +```{python} +data_catalog['aggregate']['jobs']['jobs_df'].load().head() +``` \ No newline at end of file diff --git a/notes_qmd/20_pytask_logger.qmd b/notes_qmd/20_pytask_logger.qmd new file mode 100644 index 0000000..430715f --- /dev/null +++ b/notes_qmd/20_pytask_logger.qmd @@ -0,0 +1,57 @@ +--- +title: "Logging: A simple logger to inject into `pytask` jobs" +engine: jupyter +--- + +## logger + +> A simple logger module for the pytask tasks + +```{python} +#| default_exp pytask_logger: +#| +``` + +```{python} +#| hide: +# showdoc +from nbdev.showdoc import * +``` + +```{python} +#| exports: +# imports + +import logging +from pathlib import Path +from pyprojroot import here +from datetime import datetime + +LOG_DIR = here("logs") +# get the date & time for the log file name +log_date = datetime.now().strftime("%Y-%m-%d") +log_time = datetime.now().strftime("%H-%M-%S") +LOG_DIR = here("logs") / log_date / log_time + +``` + +```{python} +#| exports: +# main function to setup a logger + + + +def setup_logger(name: str, log_path: Path=LOG_DIR, level=logging.INFO) -> logging.Logger: + log_path.mkdir(parents=True, exist_ok=True) + formatter = logging.Formatter('%(asctime)s — %(name)s — %(levelname)s — %(message)s') + + handler = logging.FileHandler(log_path / f"{name}.log", mode='a') + handler.setFormatter(formatter) + + logger = logging.getLogger(name) + logger.setLevel(level) + logger.addHandler(handler) + logger.propagate = False + + return logger +``` \ No newline at end of file diff --git a/notes_qmd/21_pytask_download.qmd b/notes_qmd/21_pytask_download.qmd new file mode 100644 index 0000000..85f2e20 --- /dev/null +++ b/notes_qmd/21_pytask_download.qmd @@ -0,0 +1,170 @@ +--- +title: "Download: `download` Module as a `pytask` Task" +engine: jupyter +--- + +## task_download + +> This module downloads the raw era5 data from the CDS API. It is similar to the original script, refactored for `pytask`. + +```{python} +#| default_exp task_download: +#| +``` + +```{python} +#| hide: +# showdoc +from nbdev.showdoc import * +``` + +We're going to quickly refactor the pipeline to use pytask instead of hydra and snakemake. This will hopefully demonstrate a simpler and more flexible way to manage data pipelines in Python. + +To start off, we need to create a function that queries the CDS API with one job. This function will be used to download the data for each query in the range specified in the data catalog in the config file. + +Let's take a look at the data catalog we created in the config module: + +```{python} +#| export: +# necessary imports +import cdsapi +import pytask +import os +from pytask import task, Product +from pathlib import Path +from typing import Annotated +from pandas import Series + +from era5_sandbox.config import data_catalog +from era5_sandbox.config import BLD +from era5_sandbox.config import DEV_MODE +from era5_sandbox.pytask_logger import setup_logger +from era5_sandbox.download import fetch_GADM, create_bounding_box + +``` + +You can see the queries entry we created in the data catalog. Each query is a row of a dataframe that contains the parameters for the CDS API query. + +```{python} +queries = data_catalog['download']['jobs']['queries_df'].load() +queries +``` + +We can test this query like we did in the original work: + + +```{python} +example_query = queries.iloc[0] + +create_bounding_box(example_query['shapefile']) +``` + +In this way, we have a similar approach as Hydra configs, but, using the `pytask` data catalog, we can more easily gather the data for a specific task in structured manner entirely in Python. + +```{python} +#| eval: false + +client = cdsapi.Client() + +ex_bounding_box = create_bounding_box(example_query['shapefile']) + +request = { + "product_type": example_query['product_type'], + "variable": example_query['variables'], + "year": str(example_query['year']), + "month": str(example_query['month']), + "day": example_query['day'], + "time": example_query['time'], + "data_format": "netcdf", + "download_format": "unarchived", + "area": ex_bounding_box + } + +target = f"{example_query['output']}.nc" + +client.retrieve("reanalysis-era5-single-levels", request).download(target) +``` + +This works! So now we just need to create a `task_` function that pytask will recognise to parallelise the download of queries over: + +```{python} +#| export: +# define the download task + +queries = data_catalog['download']['jobs']['queries_df'].load() + +for i, job in queries.iterrows(): + + @task(id=job['output'], name=f"Download {job['output']}") + def task_download_raw_data( + _query: Series = job # The query object from the data catalog + )-> Annotated[Path, data_catalog['download']['outputs'][job['output']]]: + + logger = setup_logger(_query['output']) + output_path = BLD / f"{_query['output']}.nc" + logger.info(f"Starting download for {_query['output']} to {output_path}") + + # check if string file path exists + if os.path.exists(output_path): + logger.info(f"File {output_path} already exists. Skipping download.") + return output_path + + client = cdsapi.Client() + bounding_box = create_bounding_box(_query['shapefile']) + + request = { + "product_type": _query['product_type'], + "variable": _query['variables'], + "year": _query['year'], + "month": _query['month'], + "day": _query['day'], + "time": _query['time'], + "data_format": "netcdf", + "download_format": "unarchived", + "area": bounding_box + } + + client.retrieve("reanalysis-era5-land", request).download(output_path) + logger.info(f"Downloaded data for {_query['output']} to {output_path}") + + return output_path +``` + +### How this works (with some help from GPT): + +#### 🧠 How pytask Discovers and Executes Tasks + +When you run pytask, it automatically scans your project for Python files named `task_*.py`. In these files, it looks for: +- Functions decorated with `@task`, or +- Functions prefixed with `task_` + +These functions are not executed immediately. Instead, `pytask`: +1. Imports each task_*.py module (just like Python would) +2. Registers any matching task functions as nodes in a directed acyclic graph (DAG) +3. Resolves dependencies by analyzing: + - Input annotations (e.g., `Annotated[x, DependsOn]`) + - Output declarations (e.g., `return` values or `Product` annotations) +4. Builds the DAG, where each task function is a node +5. Executes the tasks, respecting dependency order and skipping up-to-date nodes + +So even though the task functions aren’t explicitly “run” in the Python code itself, pytask knows how and when to execute them — based on their position in the DAG. + +#### 🔄 How This Differs from Snakemake + +In `snakemake`, you’re expected to define a series of explicitly executable rules, often using shell commands or Python scripts. You “stitch together” rules using filenames and wildcard matching. + +In contrast: +- 🐍 pytask is Python-native — tasks are just regular Python functions +- ⚙️ It builds a DAG from those functions and tracks inputs/outputs automatically +- 🧱 You are declaring nodes, not scripting execution + +Think of your Python files not as scripts to run, but as a way to define and wire together declarative tasks that will be executed by the pytask engine. + +--- + +Because we defined this task in a function and loop, we can easily debug a node in the DAG by simply calling it: + +```{python} +#| eval: false +task_download_raw_data() +``` diff --git a/notes_qmd/22_pytask_aggregate.qmd b/notes_qmd/22_pytask_aggregate.qmd new file mode 100644 index 0000000..3868c8e --- /dev/null +++ b/notes_qmd/22_pytask_aggregate.qmd @@ -0,0 +1,638 @@ +--- +title: "Aggregation: The `aggregation` Module as a `pytask` Task" +format: html +engine: jupyter +--- + +# task_aggregate + +> This task aggregates the downloaded data into spatial and temporal averages. It uses xarray to compute summary statistics over the specified time period and spatial region. The aggregation is done diurnally, so we will fetch the sundown and sunrise times for the region and use them to compute the diurnal averages. + +```{python} +#| default_exp task_aggregate: +# +``` + +```{python} +#| hide: +# showdoc + +from nbdev.showdoc import * + +``` + +```{python} +#| export: +# + +import os +import tempfile +import rasterio +import yaml +import xarray as xr +from pyprojroot import here +from typing import Literal +from pytask import task, Product +from pathlib import Path +from typing import Annotated +from rasterstats.io import Raster + +from era5_sandbox.config import BLD, data_catalog +from era5_sandbox.pytask_logger import setup_logger + +from era5_sandbox.core import GoogleDriver, _get_callable, describe, ClimateDataFileHandler, kelvin_to_celsius + +from era5_sandbox.aggregate import polygon_to_raster_cells, aggregate_to_healthsheds, RasterFile, netcdf_to_tiff + +``` + +## Diurnal Classification Based on Sun Position + +To do diurnal classificaiton, we will need to fetch the sundown and sunrise times for the region and use them to compute the diurnal averages. We will use the [astral library](https://astral.readthedocs.io/en/latest/) to get the sunrise and sunset times for the specified latitude and longitude. The aggregation will be done using xarray, which allows us to compute the mean over the specified time period and spatial region. + +Here's our example file: + +```{python} + +eg_file = data_catalog['download']['outputs']['2009_01_nepal'].load() +with ClimateDataFileHandler(eg_file) as handler: + + ds = xr.open_dataset(handler.get_dataset("instant")) + #ds = xr.open_dataset(handler.get_dataset("accum")) + +ds + +``` + +We can see the astral library in action below: + +```{python} +#| exports: +# +from astral import Observer, sun +import pandas as pd +import numpy as np +from tqdm import tqdm +import random +import datetime +from pytz import UTC + +``` + +```{python} +# get the location of a datapoint in the dataset +lat, long = ds.coords["latitude"].values[0], ds.coords["longitude"].values[0] +time = ds['valid_time'].values[0] +dt = pd.to_datetime(time, utc=True) + +``` + +```{python} +dt +``` + +```{python} +observer = Observer(latitude=lat, longitude=long, elevation=0) +sun_info = sun.sun(observer, date=dt) +sun_info + +``` + +Astral is very fast: +```{python} +%%timeit + +#fetch a random time from valid_time +options = ds['valid_time'].values + +random_time = random.choice(options) +dt = pd.to_datetime(random_time, utc=True) +sun_info = sun.sun(observer, date=dt) +if dt < sun_info['sunrise']: + print(f"Randomly selected time: {dt} is pre_dawn") +elif dt >= sun_info['sunrise'] and dt < sun_info['sunset']: + print(f"Randomly selected time: {dt} is day") +else: + print(f"Randomly selected time: {dt} is post_dusk") + + +``` + +This tells us that we can use the valid time for the specific location of each data point in the query and know based on the sun whether it was daytime or nighttime. The runtime will be limited only by the looping. +Let's put this in a function so that we can use the resampling in `xarray`. + +The resampling approach will be a single function that can resample in three ways: + +- By calendar date, default (1 value per calendar date) +- By diurnal class by calendar date (3 values, pre-dawn, day, post-dusk) +- By solar date (2 values per calendar date, with night classified as "before" or "after") + +Therefore, we'll need 2 internal functions; one to do diurnal, and one to do solar date bins. + +Essentially, we are going to create an array-shaped index/mask, (time, latitude, longitude). As a +demonstration, this loop goes through the first 24 time points in the dataset, +and calculates the sun info for each latitude and longitude, assigning the values to an array: + +```{python} +%%time +times = ds['valid_time'].values[:24] +lats = ds.coords['latitude'].values +lons = ds.coords['longitude'].values + +result = np.full((len(times), len(lats), len(lons)), "", dtype=object) + +for i, dt in enumerate(times): + + for j, lat in enumerate(lats): + + for k, lon in enumerate(lons): + + # set the geographical position + observer = Observer(latitude=lat, longitude=lon, elevation=0) + + # use the time + dt = pd.to_datetime(dt, utc=True) + + # where/when is the sun at this time for this position + sun_info = sun.sun(observer, date=dt) + result[i, j, k] = sun_info +``` + +So we know that in the first hour, the sun goes up and comes down at slightly different +times based on latitude and longitude. Take the first hour, for example: + +```{python} +print(result.shape) +hour_1 = 0 # 0th index of the results + +min_lat = 0 +min_lon = 0 +max_lat = 48 +max_lon = 90 +print(f"Even though the reading came from the first HOUR of data UTC, the sun info at the minimum latitude/longitude is: {result[hour_1, min_lat, min_lon]}") + +print(f"this is different from the sun info at the maximum latitude/longitude is: {result[hour_1, max_lat, max_lon]}") +``` + +```{python} +#| export: +# define the basic diurnal classification function + +def compute_diurnal_class_bins( + ds: xr.Dataset + )-> np.ndarray: + """ + Compute the diurnal value for each data point in the dataset. + This function iterates over each data point in the dataset, + calculates the sunrise and sunset times for the given time, latitude and longitude, + and returns whether or not that data point is before dawn, during the day, or after dusk. + """ + + times = ds['valid_time'].values + lats = ds.coords['latitude'].values + lons = ds.coords['longitude'].values + + result = np.full((len(times), len(lats), len(lons)), "", dtype=object) + + for i, dt in enumerate(tqdm(times, desc="Classifying data points by sun position")): + # use the time + dt = pd.to_datetime(dt, utc=True) + + for j, lat in enumerate(lats): + + for k, lon in enumerate(lons): + + # set the geographical position + observer = Observer(latitude=lat, longitude=lon, elevation=0) + + # where/when is the sun at this time for this position + sun_info = sun.sun(observer, date=dt) + + if dt < sun_info['sunrise']: + result[i, j, k] = "pre_dawn" + elif dt >= sun_info['sunrise'] and dt < sun_info['sunset']: + result[i, j, k] = "day" + else: + result[i, j, k] = "post_dusk" + + return result +``` + +```{python} +ex=compute_diurnal_class_bins(ds) +``` + +So, for our 720 time points, we should find that +if we take the `set()` of all the classifications within that slice, +there should be a few of them with 2 classes. +In other words, at any given hour, almost all of +the readings are "day", because it is daytime across all +of Madagascar, _but_ at certain timepoints, the sun is rising +or setting in the northern part of the country and so some +portion of the slice is classified differently: + +![illustrated](./IMG_740012467778-1.jpeg) + +```{python} +for x in range(720): + print(set(ex[x].flatten())) +``` + +This works! Now we can do a similar, but slightly more +complicated function to define "night" and "day", +where "night" includes all of the values after the sun goes down. + +```{python} +#| exports: +# + +def compute_solar_day_night_class_bins( + ds: xr.Dataset, + night_direction: Literal["before", "after"], + )-> list: + """ + Compute the diurnal value for each data point in the dataset. + This function iterates over each data point in the dataset, + calculates the sunrise and sunset times for the given time, latitude and longitude, + and returns whether or not that data point is daytime or nighttime. + The definition of "nighttime" can be set to be all the darkness before the sun + came up (before), or all the darkness after it went down (after). + """ + + times = ds['valid_time'].values + lats = ds.coords['latitude'].values + lons = ds.coords['longitude'].values + + result = np.full((len(times), len(lats), len(lons)), "", dtype=object) + datetimes = np.full((len(times), len(lats), len(lons)), "", dtype=object) + + for i, dt in enumerate(tqdm(times, desc="Classifying data points by sun position")): + # use the time + dt = pd.to_datetime(dt, utc=True) + + for j, lat in enumerate(lats): + + for k, lon in enumerate(lons): + + # set the geographical position + observer = Observer(latitude=lat, longitude=lon, elevation=0) + if night_direction == "before": + # Night is from previous sunset to today's sunrise + sun_today = sun.sun(observer, date=dt.date()) + sun_prev = sun.sun(observer, date=(dt - pd.Timedelta(days=1)).date()) + night_start = sun_prev["sunset"].astimezone(pd.Timestamp.utcnow().tz) + night_end = sun_today["sunrise"].astimezone(pd.Timestamp.utcnow().tz) + + # the reading is from yesterday's nighttime + if night_start <= dt < night_end: + result[i, j, k] = "night" + # the date counts as today + datetimes[i, j, k] = dt.date() + + # the reading is from daytime + elif sun_today["sunrise"] <= dt < sun_today["sunset"]: + result[i, j, k] = "day" + # the date counts as today + datetimes[i, j, k] = dt.date() + + # the reading is from today's nighttime, but counts as tomorrow's night + else: + result[i, j, k] = "night" + # the date is tomorrow + datetimes[i, j, k] = (dt + pd.Timedelta(days=1)).date() + + elif night_direction == "after": + # Night is from today's sunset to next sunrise + sun_today = sun.sun(observer, date=dt.date()) + sun_next = sun.sun(observer, date=(dt + pd.Timedelta(days=1)).date()) + night_start = sun_today["sunset"].astimezone(pd.Timestamp.utcnow().tz) + night_end = sun_next["sunrise"].astimezone(pd.Timestamp.utcnow().tz) + + # the reading is from daytime + if sun_today["sunrise"] <= dt < sun_today["sunset"]: + result[i, j, k] = "day" + # the date counts as today + datetimes[i, j, k] = dt.date() + # the reading is from tonight + elif night_start <= dt < night_end: + result[i, j, k] = "night" + # the date counts as today + datetimes[i, j, k] = dt.date() + + # the reading is from yesterday night + else: + # the date counts as yesterday + result[i, j, k] = "day" + datetimes[i, j, k] = (dt - pd.Timedelta(days=1)).date() + else: + raise ValueError(f"Invalid night_direction: {night_direction}") + + return result, datetimes + +``` + +```{python} +%%time +ex_class, ex_dt = compute_solar_day_night_class_bins(ds, "before") +``` + +```{python} +ex_class +``` + +As before, we should see that most slices are homogenous, +meaning most of the time, all the readings are from the day, +but some slices should have day and night values: + +```{python} +for slice_ in range(720): + print(set(ex_class[slice_].flatten())) +``` + +The returned array can serve as new "variable indexes" for the dataset: + +```{python} +ds_masked = ds.copy() +ds_masked['solar_class'] = (('valid_time', 'latitude', 'longitude'), ex_class) +ds_masked["solar_date"] = (("valid_time", "latitude", "longitude"), ex_dt) +``` + +## Diurnal Resampling + +Now, to see if it will resample by both solar day and diurnal class. Let's try by masking and making copies with NaN in the masked values: + +```{python} +ds_day = ds_masked.where(ds_masked["solar_class"] == "day").drop_vars(["solar_class", "solar_date"]) +ds_night = ds_masked.where(ds_masked["solar_class"] == "night").drop_vars(["solar_class", "solar_date"]) +``` + +Next, we set the time zone for Madagascar since, to resample by day and night, +we should observe the local time: + +```{python} +ds_day = ds_day.assign_coords(valid_time=pd.to_datetime(ds["valid_time"].values).tz_localize("UTC").tz_convert("Asia/Kathmandu")) +ds_night = ds_night.assign_coords(valid_time=pd.to_datetime(ds["valid_time"].values).tz_localize("UTC").tz_convert("Asia/Kathmandu")) +``` + +Now if we can resample by day... + +```{python} +ds_day_rs = ds_day.resample(valid_time="1D").reduce(np.nanmean) +ds_night_rs = ds_night.resample(valid_time="1D").reduce(np.nanmean) +ds_day_rs +``` + +Can we successfully convert this to a tiff? + +```{python} +from era5_sandbox.aggregate import netcdf_to_tiff +``` + +```{python} +raster_day = netcdf_to_tiff(ds_day_rs, band=1, variable="d2m") +raster_night = netcdf_to_tiff(ds_night_rs, band=1, variable="d2m") +``` + +Looks great! These two rasters represent one calendar day of daytime and nighttime values. + +### Testing Polygon to Raster Cells & Healthshed Aggregation + +The penultimate step of the aggregate pipeline in the original version is +assigning each datapoint to the respective healthshed. The `vectors` argument +comes from the healthshed, and represents each geographic polygon on the ground +that we want to aggregate data to. + +```{python} +from hydra import initialize, compose +``` + +```{python} +try: + with initialize(version_base=None, config_path="../conf"): + cfg = compose(config_name='config.yaml') +except Exception as e: + print(f"Error initializing Hydra: {e}") + with initialize(version_base=None, config_path="conf"): + cfg = compose(config_name='config.yaml') + +driver = GoogleDriver(json_key_path=here() / cfg.GOOGLE_DRIVE_AUTH_JSON.path) +drive = driver.get_drive() +healthsheds = driver.read_healthsheds("Nepal_Healthsheds2024.zip") +``` + +```{python} +res_poly2cell=polygon_to_raster_cells( + vectors = healthsheds.geometry.values, # the geometries of the shapefile of the regions + raster=raster_day.data, # the raster data above + nodata=np.nan, # any intersections with no data, may have to be np.nan + affine=raster_day.transform, # some math thing need to revise + all_touched=True, + verbose=True +) +``` + +This works fine. Finally, we aggregate to healthsheds: + +```{python} +from era5_sandbox.aggregate import aggregate_to_healthsheds + +``` + +```{python} +result_day = aggregate_to_healthsheds( + res_poly2cell=res_poly2cell, + raster=raster_day, + shapes=healthsheds, + names_column="fid", + aggregation_func=np.nanmean, + aggregation_name="mean_dewpoint_day" +) + +result_night = aggregate_to_healthsheds( + res_poly2cell=res_poly2cell, + raster=raster_night, + shapes=healthsheds, + names_column="fid", + aggregation_func=np.nanmean, + aggregation_name="mean_dewpoint_night" +) +``` + +Below shows the result of aggregating the daytime dewpoint temperature to the healthshed level: + +```{python} +result_day +``` + +```{python} +result_night +``` + +So from one input, we will have two outputs, one for daytime and one for nighttime, and this will have to loop over the bands (ie each day in the month). + +# Putting it all together in a `pytask` task + +Below we define our `pytask` task to aggregate data to the healthshed level. + +```{python} +#| exports: +# + +job_rows = data_catalog['aggregate']['jobs']['jobs_df'].load() + +aggregation_funcs = { + "mean": np.nanmean, + "sum": np.nansum, + "max": np.nanmax, + "min": np.nanmin +} + +for i, job in job_rows.iterrows(): + #print(f"Job {i+1}: variable={job['variables']}, time={job['time']}, aggregation={job['aggregation_name']}") + + # parse the row into function parameters + input_file = data_catalog['download']['outputs'][job['input']] + solar_classification = job['solar_classification'] + variable = job['variables_short'] + time = job['time'] + aggregation_func = aggregation_funcs[job['aggregation_name']] + aggregation_name = job['aggregation_name'] + + climate_handler_var = job['climate_handler_var'] + local_tz = job['local_tz'] + + shapefile = job['shapefile'] + hshd_unique_id = job['hshd_unique_id'] + + output_file = job['input'] + "_" + job['time'] + "_" + job['variables_short'] + "_" + job['aggregation_name'] + ".parquet" + + @task(id=output_file, name=f"Aggregate {output_file}", after="task_download_raw_data") + def task_aggregate_data_diurnal( + input_file: Path = data_catalog['download']['outputs'][job['input']], # input data Path from the download task + aggregation_func: callable = aggregation_func, # the aggregation function + aggregation_name: str = aggregation_name, # the name of the aggregation function + time: Literal["day", "night"] = time, # whether to aggregate by day or night + night_direction: Literal["before", "after"] = solar_classification, # how to define night + variable: str = variable, # the variable to aggregate, + climate_handler_var: Literal["instant", "accum"] = climate_handler_var, # whether the variable is instant or accum, + local_tz: str = local_tz, # the local timezone for resampling + shapefile: str = shapefile, # the shapefile for the healthsheds, + hshd_unique_id: str = hshd_unique_id, # the unique id column in the shapefile, + output_file: str = output_file # the output file name + ) -> Annotated[Path, data_catalog['aggregate']['outputs'][output_file]]: + """ + Task to aggregate data from a CDSAPI Query to the healthshed + level. Returns path to parquet file with aggregated data. + """ + + logger = setup_logger(output_file) + + logger.info(f"Aggregating: {output_file}") + + # check if the string path exists + # if os.path.exists(output_file): + # logger.info(f"File {output_file} already exists. Skipping aggregation.") + # return output_file + + # get input data + logger.info("Reading input data...") + with ClimateDataFileHandler(input_file) as handler: + ds = xr.open_dataset(handler.get_dataset('instant')) + + #get the healthshed shapefile + logger.info(f"Reading healthshed shapefile from yaml {here()}...") + with open(here() / "conf" / "config.yaml") as f: + healthshed_config = yaml.safe_load(f) + + key_path = here() / healthshed_config['GOOGLE_DRIVE_AUTH_JSON']['path'] + + driver = GoogleDriver(json_key_path=key_path) + drive = driver.get_drive() + healthsheds = driver.read_healthsheds(shapefile) + + # compute the diurnal classification bins + logger.info("Computing diurnal classification bins...") + class_bins, class_dts = compute_solar_day_night_class_bins(ds, night_direction) + + ds_masked = ds.copy() + + # assign classifications + logger.info("Assigning classification bins to dataset...") + ds['solar_class'] = (('valid_time', 'latitude', 'longitude'), class_bins) + ds["solar_date"] = (("valid_time", "latitude", "longitude"), class_dts) + + # mask the dataset to the requested time + mask = ds["solar_class"] == time + ds_masked = ds_masked.where(mask) + + # set the local timezone + ds_masked = ds_masked.assign_coords(valid_time=pd.to_datetime(ds["valid_time"].values).tz_localize("UTC").tz_convert(local_tz)) + + # resample by local date + logger.info("Resampling by local date...") + ds_rs = ds_masked.resample(valid_time="1D").reduce(aggregation_func) + + # convert to tiff + logger.info("Rasterizing resampled data...") + n_bands = ds_rs.dims['valid_time'] + + # polygon to raster cells for the first band + logger.info("Converting polygons to raster cells...") + raster = netcdf_to_tiff(ds_rs, band=1, variable=variable) + res_poly2cell=polygon_to_raster_cells( + vectors = healthsheds.geometry.values, # the geometries of the shapefile of the regions + raster=raster.data, # the raster data above + nodata=np.nan, # any intersections with no data, may have to be np.nan + affine=raster.transform, # some math thing need to revise + all_touched=True, + verbose=True + ) + + result_df = healthsheds[[hshd_unique_id, "geometry"]].copy() + + # loop over bands and aggregate to healthsheds + for band in tqdm(range(1, n_bands + 1)): + logger.info(f"Processing band {band} of {n_bands}...") + + day = band # band is 1-indexed + + day_col = f"day_{day:02d}" + + # calculate raster for this band + raster = netcdf_to_tiff(ds_rs, band=band, variable=variable) + + # aggregate to healthsheds + result = aggregate_to_healthsheds( + res_poly2cell=res_poly2cell, + raster=raster, + shapes=healthsheds, + names_column=hshd_unique_id, + aggregation_func=aggregation_func, + aggregation_name=variable + ) + + # add band to result dataframe + result_df[day_col] = result[variable] + + # save to parquet + result_df.to_parquet(f"{BLD}/{output_file}") + + logger.info("Aggregation complete.") + + return Path(f"{BLD}/{output_file}") + +``` + +That should wrap it up! To test, we can run a single job: + +```{python} +#| eval: false +# runs the last defined job only +task_aggregate_data_diurnal() +``` + +Or we can run the task in `pytask`: + +```bash +pytask build -k "nepal and 2009" --dry-run +``` \ No newline at end of file diff --git a/notes_qmd/IMG_740012467778-1.jpeg b/notes_qmd/IMG_740012467778-1.jpeg new file mode 100644 index 0000000..52886eb Binary files /dev/null and b/notes_qmd/IMG_740012467778-1.jpeg differ diff --git a/notes_qmd/index.qmd b/notes_qmd/index.qmd new file mode 100644 index 0000000..bc971cc --- /dev/null +++ b/notes_qmd/index.qmd @@ -0,0 +1,157 @@ +--- +title: "The ERA5 Spatial Aggregation Pipeline" +exec_all: true +--- + +```{python} +#| hide: null +from era5_sandbox.core import * +``` + +## era5_sandbox + +> Sandbox environment for era5 development + +This package documents the development and implementation of functions and code for the Madagascar ERA5 dataset project. The goal is for exposure data to be made available at the daily resolution when possible. Finer resolutions shouldn’t ever be needed for our purposes, and it should then be relatively easy to aggregate at coarser resolutions, such as weekly or monthly. Additionally, we've extended this work to Nepal as well. + +Variables should generally be made available from 2010 onward, as that’s where our clinic data starts. + +All data are ideally made available at the “healthshed” geographical level. Healthsheds are defined as geographical areas where people who live all go to the same clinic. There are a total of ~2700 public clinics in Madagascar, hence ~2700 healthsheds, with each healthshed containing ~10000 people on average. + +Preliminary list of environmental variables + +- [x] 2-m air temperature from ERA5: daily min, max, mean + +- [x] 2-m air dew point temperature from ERA5: daily min, max, mean + +- [x] Precipitation: daily total (ERA5) + +- [x] Soil moisture: daily average (ERA5) + +Variables from other sources: + +- [ ] Sea surface temperature: daily average and maximum in the nearest neighbor for each healthshed. + +- [ ] Precipitation: daily total (CHIRPS) + +- [ ] Chlorophyll-A (Giacomo) + +- [ ] Wealth index: Available from Giacomo + +- [ ] NDVI + +- [ ] Tropical storm + +- [ ] Flooding + +- [ ] Deforestation + +- [ ] Linking/segmenting healthsheds into climate zones and other + +- [ ] Relative humidity: daily average (lower priority) + +Those from the ERA5 dataset will be housed here, but we may likely develop a separate repository for the other datasets. + +## Developer Guide + +This package is built and maintained with `nbdev`. If you are new to using `nbdev` here are some useful pointers to get you started. + +### Install era5_sandbox in Development mode + +```sh +# make sure era5_sandbox package is installed in development mode +$ pip install -e . +``` + +To make changes, go to the "notes" directory and edit the notebooks as necessary. +Each notebook refers to a module in the era5_sandbox package. Cells are exported to the module +when the notebook is saved and you run the following command: + +```sh +$ nbdev_export +``` + +For e.g., to change functionality of the `testAPI()` function in the testAPI Hydra rule, you would edit the `testAPI` notebook in the `notes` directory `notes/testAPI.ipynb`, and then save that notebook and run `nbdev_export` to update the `core` module in the package. + +### How to Run the Pipeline + +The pipeline downloads ERA5 variables for a given date range and geographical bounding box. You can learn how each of these steps was by following the notebooks in `notes` in numerical order. + +::: {.callout-important} +The pipeline has two implementations: one using `snakemake` and `hydra`, and another using `pytask`. The `pytask` implementation is the more recent one, and is recommended for future use. The `snakemake` implementation is left here for reference to legacy code. +::: + +#### Using `pytask` + +To run the pipeline, the `pytask` config at `note/20_pytask_config.qmd` should be reviewed +and updated if necessary. The pipeline can then be run with the following command: + +```sh +$ sbatch pytask.sbatch +``` + +#### Using `snakemake` and `hydra` + +To run the pipeline, the config at `config/config.yaml` should be updated with the desired date range and geographical bounding box. The pipeline can then be run with the following command: + +```sh +sbatch snakemake.sbatch +``` + +### What Does the Pipeline Produce? + +Using `pytask`'s data catalog, you can investigate the downloaded raw data with python, eg.: + +```{python} +#| exec_doc: +# +import xarray as xr +from era5_sandbox.config import data_catalog +from era5_sandbox.core import ClimateDataFileHandler + +ex_nc = list(data_catalog['download']['outputs']._entries).pop() +ex_nc_path = data_catalog['download']['outputs'][ex_nc].load() + +with ClimateDataFileHandler(ex_nc_path) as handler: + ds = xr.open_dataset(handler.get_dataset("instant")) + +ds +``` + +And plot it with cartopy, eg.: + +```{python} +#| exec_doc: +# +import matplotlib.pyplot as plt +import cartopy.crs as ccrs +import cartopy.feature as cfeature + +temperature = ds["t2m"] + +# Select a specific time step +temperature_at_time = temperature.isel(valid_time=0) + +# Plot the data on a map +plt.figure(figsize=(12, 8)) +ax = plt.axes(projection=ccrs.PlateCarree()) +temperature_at_time.plot(ax=ax, cmap="coolwarm", transform=ccrs.PlateCarree(), cbar_kwargs={"label": "Temperature (K)"}) +ax.coastlines() +ax.add_feature(cfeature.BORDERS, linestyle=":") +ax.set_title("Temperature at Time Step 0") +plt.show() +``` + +You can also load the aggregated data: + +```{python} +#| exec_doc: +# +import pandas as pd +import geopandas as gpd +from era5_sandbox.config import data_catalog + +ex_agg_path = data_catalog['aggregate']['outputs']['2019_08_madagascar_night_d2m_max.parquet'].load() + +gpd.read_parquet(ex_agg_path).describe() +``` \ No newline at end of file diff --git a/notes_qmd/nbdev.yml b/notes_qmd/nbdev.yml new file mode 100644 index 0000000..d8c5049 --- /dev/null +++ b/notes_qmd/nbdev.yml @@ -0,0 +1,9 @@ +project: + output-dir: _docs + +website: + title: "era5_sandbox" + site-url: "https://TinasheMTapera.github.io/era5_sandbox" + description: "Sandbox environment for era5 development" + repo-branch: main + repo-url: "https://github.com/TinasheMTapera/era5_sandbox" diff --git a/notes_qmd/sidebar.yml b/notes_qmd/sidebar.yml new file mode 100644 index 0000000..caf3166 --- /dev/null +++ b/notes_qmd/sidebar.yml @@ -0,0 +1,16 @@ +website: + sidebar: + contents: + - index.ipynb + - section: "Snakemake Modules" + - 00_core.ipynb + - 01_download_raw_data.ipynb + - 02_aggregate.ipynb + - 03_publish.ipynb + - section: "PyTask Modules" + - 20_pytask_config.ipynb + - 20_pytask_logger.ipynb + - 21_pytask_download.ipynb + - 22_pytask_aggregate.ipynb + - section: "PyTask Demo" + - 10_pytask_demo.ipynb diff --git a/notes_qmd/styles.css b/notes_qmd/styles.css new file mode 100644 index 0000000..66ccc49 --- /dev/null +++ b/notes_qmd/styles.css @@ -0,0 +1,37 @@ +.cell { + margin-bottom: 1rem; +} + +.cell > .sourceCode { + margin-bottom: 0; +} + +.cell-output > pre { + margin-bottom: 0; +} + +.cell-output > pre, .cell-output > .sourceCode > pre, .cell-output-stdout > pre { + margin-left: 0.8rem; + margin-top: 0; + background: none; + border-left: 2px solid lightsalmon; + border-top-left-radius: 0; + border-top-right-radius: 0; +} + +.cell-output > .sourceCode { + border: none; +} + +.cell-output > .sourceCode { + background: none; + margin-top: 0; +} + +div.description { + padding-left: 2px; + padding-top: 5px; + font-style: italic; + font-size: 135%; + opacity: 70%; +} diff --git a/pyproject.toml b/pyproject.toml index f2c07bf..c9d6b9e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,3 +1,7 @@ [build-system] requires = ["setuptools>=64.0"] build-backend = "setuptools.build_meta" + +[tool.pytask.ini_options] +paths = ["src/era5_sandbox"] +editor_url_scheme = "vscode" \ No newline at end of file diff --git a/pytask.sbatch b/pytask.sbatch new file mode 100644 index 0000000..3063b72 --- /dev/null +++ b/pytask.sbatch @@ -0,0 +1,15 @@ +#!/bin/bash +# +#SBATCH -p intermediate # partition (queue) +#SBATCH -c 6 # number of cores +#SBATCH --cpus-per-task=36 # request >= number of workers you want +#SBATCH --mem 100GB # memory +#SBATCH -t 1-12:00 # time (D-HH:MM) + +#SBATCH --mail-type=BEGIN,END,TIME_LIMIT_80, +#SBATCH --mail-user=ttapera@hsph.harvard.edu + +# This is a test to see if we can use pytask to download ERA5 data in parallel + +#pytask --dry-run +pytask --parallel-backend loky -n 36 \ No newline at end of file diff --git a/pytask_collect.txt b/pytask_collect.txt new file mode 100644 index 0000000..efaaea3 --- /dev/null +++ b/pytask_collect.txt @@ -0,0 +1,135193 @@ +─────────────────────────────────────────────────────────────────────────────────────────────────────── Start pytask session ──────────────────────────────────────────────────────────────────────────────────────────────────────── +Platform: linux -- Python 3.11.11, pytask 0.5.5, pluggy 1.5.0 +Root: /net/rcstorenfs02/ifs/rc_labs/dominici_lab/lab/data_processing/csph-era5_sandbox +Configuration: /net/rcstorenfs02/ifs/rc_labs/dominici_lab/lab/data_processing/csph-era5_sandbox/pyproject.toml +Plugins: pytask_parallel-0.5.1, vscode-0.0.2 +Collected 8068 tasks. + +Collected tasks: +├── 🐍 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 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│ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 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├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ ├── 📄 +│ │ └── 📄 +│ └── 📝 +│ ├── 📄 +│ ├── 📄 +│ ├── 📄 +│ ├── 📄 +│ ├── 📄 +│ ├── 📄 +│ ├── 📄 +│ ├── 📄 +│ ├── 📄 +│ ├── 📄 +│ ├── 📄 +│ ├── 📄 +│ ├── 📄 +│ ├── 📄 +│ ├── 📄 +│ └── 📄 +├── 🐍 +│ ├── 📝 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ └── 📄 +│ ├── 📝 +│ │ ├── 📄 +│ │ └── 📄 +│ └── 📝 +│ ├── 📄 +│ └── 📄 +└── 🐍 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ 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└── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + 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📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 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├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 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├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + 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├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + ├── 📝 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ ├── 📄 + │ └── 📄 + └── 📝 + ├── 📄 + ├── 📄 + ├── 📄 + ├── 📄 + ├── 📄 + ├── 📄 + ├── 📄 + ├── 📄 + ├── 📄 + ├── 📄 + └── 📄 + +───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── diff --git a/settings.ini b/settings.ini index c563df1..6294d72 100644 --- a/settings.ini +++ b/settings.ini @@ -7,7 +7,7 @@ repo = era5_sandbox lib_name = %(repo)s version = 0.0.1 min_python = 3.7 -license = apache2 +license = CC-BY-4.0 black_formatting = False ### nbdev ### @@ -20,7 +20,7 @@ put_version_in_init = True ### Docs ### branch = main -custom_sidebar = False +custom_sidebar = True doc_host = https://%(user)s.github.io doc_baseurl = /%(repo)s git_url = https://github.com/%(user)s/%(repo)s @@ -38,7 +38,7 @@ status = 3 user = TinasheMTapera ### Optional ### -requirements = fastcore pyprojroot ipykernel nbdev cdsapi hydra-core omegaconf ipykernel snakemake geopandas xarray shapely matplotlib cartopy rasterstats rioxarray pydrive2 google-auth numpy tqdm +requirements = fastcore pyprojroot ipykernel nbdev cdsapi hydra-core omegaconf ipykernel snakemake geopandas xarray shapely matplotlib cartopy rasterstats rioxarray pydrive2 google-auth numpy tqdm pytask pygraphviz pytask-parallel # dev_requirements = # console_scripts = src # conda_user = diff --git a/snakefile b/snakefile index eb39f48..14d7dd4 100644 --- a/snakefile +++ b/snakefile @@ -20,10 +20,36 @@ months_cfg = OmegaConf.to_container(cfg.query.month, resolve=True) variable_cfg = OmegaConf.to_container(cfg.query.variable, resolve=True) geographies_cfg = OmegaConf.to_container(cfg.query.geography, resolve=True) +variables_dict = { + "2m_temperature": "t2m", + "2m_dewpoint_temperature": "d2m", + "volumetric_soil_water_layer_1": "swvl1", + "total_precipitation": "tp" +} + +# Map internal variable names to ERA5 codes +variable_codes = [variables_dict[v] for v in variable_cfg] + +intermediate_targets = expand( + data_dir / "intermediate/{geography}_environmental_exposure-era5_healthshed_{variable}_{year}_{month}.parquet", + geography=geographies_cfg, + variable=variable_cfg, + year=years_cfg, + month=months_cfg +) + +#convert this to a directory based rule +upload_targets = expand( + data_dir / "output/environmental/exposures_era5/healthshed_daily/{geography}_{variable}_{year}.parquet", + geography=geographies_cfg, + variable=variable_codes, + year=years_cfg +) + rule all: input: - expand(data_dir / "intermediate/{geography}_environmental_exposure-era5_healthshed_{variable}_{year}_{month}.parquet", - geography=geographies_cfg, variable=variable_cfg, year=years_cfg, month=months_cfg) + # intermediate_targets, + upload_targets rule test_api: output: @@ -53,31 +79,42 @@ rule spatial_aggregate_raw_era5: script: "src/era5_sandbox/aggregate.py" -rule summarize_na_dashboard: +rule compile_results_and_upload: input: - rmd = "notes/prototypes/aggregation_visualizer.Rmd", + intermediate_targets output: - data_dir / "notes/prototypes/figures/{geography}_mean_temperature_{year}.png}", - data_dir / "notes/prototypes/figures/{geography}_max_temperature_{year}.png}", - data_dir / "notes/prototypes/figures/{geography}_min_temperature_{year}.png}", - data_dir / "notes/prototypes/figures/{geography}_mean_dewpoint_{year}.png}", - data_dir / "notes/prototypes/figures/{geography}_max_dewpoint_{year}.png}", - data_dir / "notes/prototypes/figures/{geography}_min_dewpoint_{year}.png}", - data_dir / "notes/prototypes/figures/{geography}_total_precipitation_{year}.png}" - shell: - """ - Rscript -e "rmarkdown::render( - input = './notes/prototypes/aggregation_visualizer.Rmd', - params = list( - in_pipeline = TRUE, - output_files = list( - raw_na_summary = 'data/testing/raw_na_summary.csv', - temp_agg = 'data/testing/temperature_agg_long.csv', - precip_agg = 'data/testing/precipitation_agg_long.csv', - dewpoint_agg = 'data/testing/dewpoint_agg_long.csv' - ) - ), - output_file = tempfile(), - quiet = FALSE - )" - """ \ No newline at end of file + upload_targets + message: + "[UPLOAD] Combining and uploading results to dataverse" + script: + "src/era5_sandbox/publish.py" + + +# rule summarize_na_dashboard: +# input: +# rmd = "notes/prototypes/aggregation_visualizer.Rmd", +# output: +# data_dir / "notes/prototypes/figures/{geography}_mean_temperature_{year}.png}", +# data_dir / "notes/prototypes/figures/{geography}_max_temperature_{year}.png}", +# data_dir / "notes/prototypes/figures/{geography}_min_temperature_{year}.png}", +# data_dir / "notes/prototypes/figures/{geography}_mean_dewpoint_{year}.png}", +# data_dir / "notes/prototypes/figures/{geography}_max_dewpoint_{year}.png}", +# data_dir / "notes/prototypes/figures/{geography}_min_dewpoint_{year}.png}", +# data_dir / "notes/prototypes/figures/{geography}_total_precipitation_{year}.png}" +# shell: +# """ +# Rscript -e "rmarkdown::render( +# input = './notes/prototypes/aggregation_visualizer.Rmd', +# params = list( +# in_pipeline = TRUE, +# output_files = list( +# raw_na_summary = 'data/testing/raw_na_summary.csv', +# temp_agg = 'data/testing/temperature_agg_long.csv', +# precip_agg = 'data/testing/precipitation_agg_long.csv', +# dewpoint_agg = 'data/testing/dewpoint_agg_long.csv' +# ) +# ), +# output_file = tempfile(), +# quiet = FALSE +# )" +# """ \ No newline at end of file diff --git a/snakemake.sbatch b/snakemake.sbatch index 97652df..206facc 100644 --- a/snakemake.sbatch +++ b/snakemake.sbatch @@ -1,11 +1,13 @@ #!/bin/bash # #SBATCH -p intermediate # partition (queue) -#SBATCH -c 12 # number of cores +#SBATCH -c 20 # number of cores #SBATCH --mem 250GB # memory -#SBATCH -t 0-20:00 # time (D-HH:MM) +#SBATCH -t 1-12:00 # time (D-HH:MM) -snakemake --cores 12 --rerun-incomplete +snakemake --cores 20 + +# python src/era5_sandbox/publish.py # Rscript -e "rmarkdown::render( # input = './notes/prototypes/aggregation_visualizer.Rmd', diff --git a/src/era5_sandbox.egg-info/PKG-INFO b/src/era5_sandbox.egg-info/PKG-INFO index c0ee372..227e10c 100644 --- a/src/era5_sandbox.egg-info/PKG-INFO +++ b/src/era5_sandbox.egg-info/PKG-INFO @@ -5,7 +5,7 @@ Summary: Sandbox environment for era5 development Home-page: https://github.com/TinasheMTapera/era5_sandbox Author: Tinashe M. Tapera Author-email: tinashemtapera@gmail.com -License: Apache Software License 2.0 +License: CC-BY-4.0 Keywords: nbdev jupyter notebook python Classifier: Development Status :: 4 - Beta Classifier: Intended Audience :: Developers @@ -16,7 +16,6 @@ Classifier: Programming Language :: Python :: 3.9 Classifier: Programming Language :: Python :: 3.10 Classifier: Programming Language :: Python :: 3.11 Classifier: Programming Language :: Python :: 3.12 -Classifier: License :: OSI Approved :: Apache Software License Requires-Python: >=3.7 Description-Content-Type: text/markdown License-File: LICENSE @@ -26,12 +25,23 @@ Requires-Dist: ipykernel Requires-Dist: nbdev Requires-Dist: cdsapi Requires-Dist: hydra-core +Requires-Dist: omegaconf Requires-Dist: ipykernel Requires-Dist: snakemake Requires-Dist: geopandas Requires-Dist: xarray +Requires-Dist: shapely Requires-Dist: matplotlib Requires-Dist: cartopy +Requires-Dist: rasterstats +Requires-Dist: rioxarray +Requires-Dist: pydrive2 +Requires-Dist: google-auth +Requires-Dist: numpy +Requires-Dist: tqdm +Requires-Dist: pytask +Requires-Dist: pygraphviz +Requires-Dist: pytask-parallel Provides-Extra: dev Dynamic: author Dynamic: author-email @@ -47,4 +57,31 @@ Dynamic: requires-dist Dynamic: requires-python Dynamic: summary -This file will be overwritten by `index.ipynb` +# ERA5 Exposure Aggregation Pipeline + +This repository contains a pipeline for aggregating ERA5 environmental exposures data to a 0.1 degree grid. The pipeline is designed to be run on FASRC. We developed +this pipeline using `nbdev`, which means that we can create modules and scripts from notebooks. +Hence, all of the documentation for how the pipeline was developed and validated is +available in `notes/index.ipynb` and the associated notebooks. + +## How to Review a PR + +To review a PR on this repository, follow these steps: + +0. Obtain an API key for the ERA5 datastore from [here](https://cds.climate.copernicus.eu/how-to-api), and ask Tinashe for access to the Golden Lab `googledriver` API key + +1. Clone this repository to your workspace on FASRC + +2. Create a conda environment with `conda create -n era5_sandbox python=3.10` and install all of the necessary dependencies for the package with `pip install -e .` + +3. Run the `core` module to test your API key and setup the data +directory structure + +`python src/era5_sandbox/core.py` + +4. Symlink your local data directory to the original work +`ln -s [YOUR WORKING DIRECTORY]/data /n/dominici_lab/lab/data_processing/csph-era5_sandbox/data` + +5. Dry run by removing a file from data `snakemake --dry-run` + +6. Run the pipeline `sbatch snakemake.sbatch` diff --git a/src/era5_sandbox.egg-info/SOURCES.txt b/src/era5_sandbox.egg-info/SOURCES.txt index 016a003..19fda53 100644 --- a/src/era5_sandbox.egg-info/SOURCES.txt +++ b/src/era5_sandbox.egg-info/SOURCES.txt @@ -6,8 +6,15 @@ settings.ini setup.py src/era5_sandbox/__init__.py src/era5_sandbox/_modidx.py +src/era5_sandbox/aggregate.py +src/era5_sandbox/config.py src/era5_sandbox/core.py src/era5_sandbox/download.py +src/era5_sandbox/publish.py +src/era5_sandbox/pytask_logger.py +src/era5_sandbox/task_aggregate.py +src/era5_sandbox/task_data_preparation.py +src/era5_sandbox/task_download.py src/era5_sandbox.egg-info/PKG-INFO src/era5_sandbox.egg-info/SOURCES.txt src/era5_sandbox.egg-info/dependency_links.txt diff --git a/src/era5_sandbox.egg-info/requires.txt b/src/era5_sandbox.egg-info/requires.txt index e73bcd8..3f30fe5 100644 --- a/src/era5_sandbox.egg-info/requires.txt +++ b/src/era5_sandbox.egg-info/requires.txt @@ -4,11 +4,22 @@ ipykernel nbdev cdsapi hydra-core +omegaconf ipykernel snakemake geopandas xarray +shapely matplotlib cartopy +rasterstats +rioxarray +pydrive2 +google-auth +numpy +tqdm +pytask +pygraphviz +pytask-parallel [dev] diff --git a/src/era5_sandbox/__pycache__/__init__.cpython-311.pyc b/src/era5_sandbox/__pycache__/__init__.cpython-311.pyc index 9a80455..5281e28 100644 Binary files a/src/era5_sandbox/__pycache__/__init__.cpython-311.pyc and b/src/era5_sandbox/__pycache__/__init__.cpython-311.pyc differ diff --git a/src/era5_sandbox/__pycache__/_modidx.cpython-311.pyc b/src/era5_sandbox/__pycache__/_modidx.cpython-311.pyc index 5911cd5..5296e6f 100644 Binary files a/src/era5_sandbox/__pycache__/_modidx.cpython-311.pyc and b/src/era5_sandbox/__pycache__/_modidx.cpython-311.pyc differ diff --git a/src/era5_sandbox/__pycache__/aggregate.cpython-311.pyc b/src/era5_sandbox/__pycache__/aggregate.cpython-311.pyc index 9b6a214..a718bc9 100644 Binary files a/src/era5_sandbox/__pycache__/aggregate.cpython-311.pyc and b/src/era5_sandbox/__pycache__/aggregate.cpython-311.pyc differ diff --git a/src/era5_sandbox/__pycache__/config.cpython-311.pyc b/src/era5_sandbox/__pycache__/config.cpython-311.pyc new file mode 100644 index 0000000..c519fdd Binary files /dev/null and b/src/era5_sandbox/__pycache__/config.cpython-311.pyc differ diff --git a/src/era5_sandbox/__pycache__/core.cpython-311.pyc b/src/era5_sandbox/__pycache__/core.cpython-311.pyc index e83fe69..381b04f 100644 Binary files a/src/era5_sandbox/__pycache__/core.cpython-311.pyc and b/src/era5_sandbox/__pycache__/core.cpython-311.pyc differ diff --git a/src/era5_sandbox/__pycache__/download.cpython-311.pyc b/src/era5_sandbox/__pycache__/download.cpython-311.pyc index d316112..77bdc73 100644 Binary files a/src/era5_sandbox/__pycache__/download.cpython-311.pyc and b/src/era5_sandbox/__pycache__/download.cpython-311.pyc differ diff --git a/src/era5_sandbox/__pycache__/pytask_logger.cpython-311.pyc b/src/era5_sandbox/__pycache__/pytask_logger.cpython-311.pyc new file mode 100644 index 0000000..adfddf5 Binary files /dev/null and b/src/era5_sandbox/__pycache__/pytask_logger.cpython-311.pyc differ diff --git a/src/era5_sandbox/__pycache__/task_aggregate.cpython-311.pyc b/src/era5_sandbox/__pycache__/task_aggregate.cpython-311.pyc new file mode 100644 index 0000000..f56c980 Binary files /dev/null and b/src/era5_sandbox/__pycache__/task_aggregate.cpython-311.pyc differ diff --git a/src/era5_sandbox/__pycache__/task_data_preparation.cpython-311.pyc b/src/era5_sandbox/__pycache__/task_data_preparation.cpython-311.pyc new file mode 100644 index 0000000..3a6f026 Binary files /dev/null and b/src/era5_sandbox/__pycache__/task_data_preparation.cpython-311.pyc differ diff --git a/src/era5_sandbox/__pycache__/task_download.cpython-311.pyc b/src/era5_sandbox/__pycache__/task_download.cpython-311.pyc new file mode 100644 index 0000000..2489fb9 Binary files /dev/null and b/src/era5_sandbox/__pycache__/task_download.cpython-311.pyc differ diff --git a/src/era5_sandbox/_modidx.py b/src/era5_sandbox/_modidx.py index 7d2fdae..00aef7d 100644 --- a/src/era5_sandbox/_modidx.py +++ b/src/era5_sandbox/_modidx.py @@ -23,6 +23,7 @@ 'era5_sandbox/aggregate.py'), 'era5_sandbox.aggregate.resample_netcdf': ( 'aggregate.html#resample_netcdf', 'era5_sandbox/aggregate.py')}, + 'era5_sandbox.config': {}, 'era5_sandbox.core': { 'era5_sandbox.core.ClimateDataFileHandler': ('core.html#climatedatafilehandler', 'era5_sandbox/core.py'), 'era5_sandbox.core.ClimateDataFileHandler.__enter__': ( 'core.html#climatedatafilehandler.__enter__', 'era5_sandbox/core.py'), @@ -61,4 +62,22 @@ 'era5_sandbox/download.py'), 'era5_sandbox.download.fetch_GADM': ( 'download_raw_data.html#fetch_gadm', 'era5_sandbox/download.py'), - 'era5_sandbox.download.main': ('download_raw_data.html#main', 'era5_sandbox/download.py')}}} + 'era5_sandbox.download.main': ('download_raw_data.html#main', 'era5_sandbox/download.py')}, + 'era5_sandbox.publish': { 'era5_sandbox.publish.gather_exposure_geodataframes': ( 'publish.html#gather_exposure_geodataframes', + 'era5_sandbox/publish.py'), + 'era5_sandbox.publish.main': ('publish.html#main', 'era5_sandbox/publish.py')}, + 'era5_sandbox.pytask_logger': { 'era5_sandbox.pytask_logger.setup_logger': ( 'pytask_logger.html#setup_logger', + 'era5_sandbox/pytask_logger.py')}, + 'era5_sandbox.task_aggregate': { 'era5_sandbox.task_aggregate.compute_diurnal_class_bins': ( 'pytask_aggregate.html#compute_diurnal_class_bins', + 'era5_sandbox/task_aggregate.py'), + 'era5_sandbox.task_aggregate.compute_solar_day_night_class_bins': ( 'pytask_aggregate.html#compute_solar_day_night_class_bins', + 'era5_sandbox/task_aggregate.py')}, + 'era5_sandbox.task_data_preparation': { 'era5_sandbox.task_data_preparation.task_add_another_column': ( 'pytask_demo.html#task_add_another_column', + 'era5_sandbox/task_data_preparation.py'), + 'era5_sandbox.task_data_preparation.task_add_one': ( 'pytask_demo.html#task_add_one', + 'era5_sandbox/task_data_preparation.py'), + 'era5_sandbox.task_data_preparation.task_create_random_data': ( 'pytask_demo.html#task_create_random_data', + 'era5_sandbox/task_data_preparation.py'), + 'era5_sandbox.task_data_preparation.task_plot_data': ( 'pytask_demo.html#task_plot_data', + 'era5_sandbox/task_data_preparation.py')}, + 'era5_sandbox.task_download': {}}} diff --git a/src/era5_sandbox/aggregate.py b/src/era5_sandbox/aggregate.py index a98390f..e099314 100644 --- a/src/era5_sandbox/aggregate.py +++ b/src/era5_sandbox/aggregate.py @@ -5,6 +5,7 @@ 'aggregate_data', 'main'] # %% ../../notes/02_aggregate.ipynb 4 +#| exports: # import tempfile import rasterio import hydra @@ -31,6 +32,7 @@ except: from core import GoogleDriver, _get_callable, describe, ClimateDataFileHandler, kelvin_to_celsius # %% ../../notes/02_aggregate.ipynb 8 +#| export: # def resample_netcdf( fpath: str, # Path to the netCDF file. resample: str = "1D", # Resampling frequency (e.g., '1H', '1D') @@ -58,7 +60,8 @@ def resample_netcdf( else: raise TypeError("agg_func must be a callable function like np.mean, np.max, etc.") -# %% ../../notes/02_aggregate.ipynb 12 +# %% ../../notes/02_aggregate.ipynb 13 +#| exports: # @dataclass class RasterFile: path: str @@ -86,7 +89,8 @@ def shape(self) -> Optional[Tuple[int, int]]: def __str__(self): return f"RasterFile(path='{self.path}', shape={self.shape()}, crs='{self.crs}')" -# %% ../../notes/02_aggregate.ipynb 14 +# %% ../../notes/02_aggregate.ipynb 15 +#| exports: # def netcdf_to_tiff( ds: xr.Dataset, # The aggregated xarray dataset to convert. band: int, # The day to rasterise; 1 indexed just like human english @@ -96,12 +100,6 @@ def netcdf_to_tiff( """ Convert a netCDF file to a GeoTIFF file. - - Args: - fpath (str): Path to the netCDF file. - output_path (str): Path to save the output GeoTIFF file. - variable_name (str): Name of the variable to convert. - time_index (int): Index of the time dimension to extract. """ with tempfile.TemporaryDirectory() as tmpdirname: @@ -117,38 +115,18 @@ def netcdf_to_tiff( return raster_file -# %% ../../notes/02_aggregate.ipynb 19 +# %% ../../notes/02_aggregate.ipynb 20 +#| exports: # def polygon_to_raster_cells( - vectors, - raster, - nodata=None, - affine=None, - all_touched=False, - verbose=False, + vectors, # list of geometries from a shapefile + raster, # the raster data as a numpy array + nodata=None, # the nodata value of the raster + affine=None, # the affine transform of the raster + all_touched=False, # whether to include all touched pixels + verbose=False, **kwargs, -): - """Returns an index map for each vector geometry to indices in the raster source. - - Parameters - ---------- - vectors: list of geometries - - raster: ndarray - - nodata: float - - affine: Affine instance - - all_touched: bool, optional - Whether to include every raster cell touched by a geometry, or only - those having a center point within the polygon. - defaults to `False` - - Returns - ------- - dict - A dictionary mapping vector the ids of geometries to locations (indices) in the raster source. - """ +) -> list: # A dictionary mapping vector the ids of geometries to locations (indices) in the raster source. + """Returns an index map for each vector geometry to indices in the raster source.""" cell_map = [] @@ -191,7 +169,8 @@ def polygon_to_raster_cells( return cell_map -# %% ../../notes/02_aggregate.ipynb 26 +# %% ../../notes/02_aggregate.ipynb 27 +#| exports: # def aggregate_to_healthsheds( res_poly2cell: list, # the result of polygon_to_raster_cells raster: RasterFile, # the raster data @@ -230,13 +209,13 @@ def aggregate_to_healthsheds( gdf = gpd.GeoDataFrame(df, geometry=shapes.geometry.values, crs=shapes.crs) return gdf - -# %% ../../notes/02_aggregate.ipynb 36 +# %% ../../notes/02_aggregate.ipynb 37 +#| exports: # def aggregate_data( - cfg: DictConfig, - input_file: str, - output_file: str, - exposure_variable: str + cfg: DictConfig, # the hydra config + input_file: str, # the input netcdf file + output_file: str, # the output parquet file + exposure_variable: str # Which variable in the dataset to aggregate ) -> None: ''' Aggregate raster data day-by-day and store all days and statistics as separate columns in a single Parquet file. @@ -317,7 +296,8 @@ def aggregate_data( result_df.to_parquet(output_file, compression="snappy") # return(result_df) -# %% ../../notes/02_aggregate.ipynb 41 +# %% ../../notes/02_aggregate.ipynb 42 +#| exports: # @hydra.main(version_base=None, config_path="../../conf", config_name="config") def main(cfg: DictConfig) -> None: # Parse command-line arguments @@ -337,8 +317,8 @@ def main(cfg: DictConfig) -> None: aggregate_data(cfg, input_file=input_file, output_file=output_file, exposure_variable=variables_dict[aggregation_variable]) -# %% ../../notes/02_aggregate.ipynb 42 -#| eval: false +# %% ../../notes/02_aggregate.ipynb 43 +#| export: #| eval: false try: from nbdev.imports import IN_NOTEBOOK except: IN_NOTEBOOK=False diff --git a/src/era5_sandbox/config.py b/src/era5_sandbox/config.py new file mode 100644 index 0000000..ff1359c --- /dev/null +++ b/src/era5_sandbox/config.py @@ -0,0 +1,160 @@ +# AUTOGENERATED! DO NOT EDIT! File to edit: ../../notes/20_pytask_config.ipynb. + +# %% auto 0 +__all__ = ['SRC', 'BLD', 'demo_catalog', 'DEV_MODE', 'stages', 'buckets', 'data_catalog', 'years', 'months', 'geographies', + 'days', 'times', 'variables', 'product_type', 'shapefiles', 'rows', 'query_df', 'inputs', 'outputs', + 'variable_dict', 'agg_params', 'mask', 'aggregate_jobs'] + +# %% ../../notes/20_pytask_config.ipynb 3 +#| exports: # + +import pandas as pd + +from pathlib import Path +from pyprojroot import here +from pytask import DataCatalog + + +SRC = here() / "src" / "era5_sandbox" +BLD = here() / "bld" + +demo_catalog = DataCatalog() + +# %% ../../notes/20_pytask_config.ipynb 5 +#| exports: # +DEV_MODE=True + +# %% ../../notes/20_pytask_config.ipynb 7 +#| exports: # + +stages = ["mydata", 'mydata2', # from the demo, ignore + "download", # download task + "aggregate", # aggregation task + "publish", # publishing task + "viz"] # visualization task + +buckets = [ + "inputs", # any specific inputs, eg for carrying over between tasks + "outputs", # specific output task returns + "jobs", # job parameters as a dataframe + "params" # any lingering hardcoded parameters + ] + +data_catalog = { + + stage: {bucket: DataCatalog(name=f"{stage}_{bucket}") for bucket in buckets} + for stage in stages +} + +# %% ../../notes/20_pytask_config.ipynb 9 +#| exports: # a dataframe for the query parameters, with nested entries for days, times, and variables +# Dimensions +years = [str(x) for x in range(2009, 2025)] # 16 years +months = [str(x).zfill(2) for x in range(1, 13)] # 12 months +geographies = ["madagascar", "nepal"] # 2 geographies + +# nested values; we want ALL days, times, and variables for each job +days = [str(x).zfill(2) for x in range(1, 32)] +times = [f"{x:02d}:00" for x in range(24)] +variables = ["2m_dewpoint_temperature", "2m_temperature", "total_precipitation", "volumetric_soil_water_layer_1"] + +product_type = "reanalysis" + +# Map shapefiles to geography +shapefiles = { + "madagascar": "https://data.humdata.org/dataset/26fa506b-0727-4d9d-a590-d2abee21ee22/resource/ed94d52e-349e-41be-80cb-62dc0435bd34/download/mdg_adm_bngrc_ocha_20181031_shp.zip", + "nepal": "https://data.humdata.org/dataset/07db728a-4f0f-4e98-8eb0-8fa9df61f01c/resource/2eb4c47f-fd6e-425d-b623-d35be1a7640e/download/npl_adm_nd_20240314_ab_shp.zip" +} + +# Build row-wise combinations of (year, month, geography) +rows = [] +for year in years: + for month in months: + for geo in geographies: + rows.append({ + "year": year, + "month": month, + "geography": geo, + "shapefile": shapefiles[geo], + "product_type": product_type, + "day": days, + "time": times, + "variables": variables, + "output": f"{year}_{month}_{geo}" + }) + +# Create dataframe +query_df = pd.DataFrame(rows) + +# %% ../../notes/20_pytask_config.ipynb 13 +#| export: # set up catalog + +data_catalog['download']['jobs'].add("queries_df", query_df) + +# %% ../../notes/20_pytask_config.ipynb 17 +#| exports: # aggregate task parameters + +inputs = query_df["output"].tolist() +outputs = [f"{i}_agg" for i in inputs] + +variable_dict = { + "2m_dewpoint_temperature": "d2m", + "2m_temperature": "t2m", + "total_precipitation": "tp", + "volumetric_soil_water_layer_1": "swvl1" +} + +# list of params that get fed into the task functions +agg_params = { + "time": ["day", "night"], + "solar_classification": ["before"], + "variables": variables, + "variables_short": [variable_dict[x] for x in variables], + "aggregation_name": ["mean", "sum", "max", "min"] +} + +from itertools import product +import pandas as pd + +# expand all the params +agg_params = pd.DataFrame(list(product(*agg_params.values())), columns=agg_params.keys()) + +# %% ../../notes/20_pytask_config.ipynb 21 +#| exports: # quick filter to keep only matching rows + +agg_params = agg_params[agg_params.apply(lambda x: variable_dict[x['variables']] == x['variables_short'], axis=1)] + +# %% ../../notes/20_pytask_config.ipynb 24 +#| exports: # remove rows where tp aggregation is not sum +mask = (agg_params['variables_short'] == "tp") & (agg_params['aggregation_name'] != "sum") +agg_params = agg_params[~mask] + +# remove rows where non-tp aggregation is sum +mask = (agg_params['variables_short'] != "tp") & (agg_params['aggregation_name'] == "sum") +agg_params = agg_params[~mask] + +# %% ../../notes/20_pytask_config.ipynb 27 +#| exports: # set up inputs and parameters +inputs = pd.DataFrame({"input": inputs}) +aggregate_jobs = inputs.merge(agg_params, how="cross") + +# %% ../../notes/20_pytask_config.ipynb 31 +#| exports: # add a few more columns +aggregate_jobs['local_tz'] = aggregate_jobs['input'].apply( + lambda x: "Asia/Kathmandu" if "nepal" in x else "Indian/Antananarivo" +) +aggregate_jobs['shapefile'] = aggregate_jobs['input'].apply( + lambda x: "Nepal_Healthsheds2024.zip" if "nepal" in x else "healthsheds2022.zip" +) + +aggregate_jobs['hshd_unique_id'] = aggregate_jobs['input'].apply( + lambda x: "fid" if "nepal" in x else "fs_uid" +) + +aggregate_jobs['climate_handler_var'] = aggregate_jobs['variables_short'].apply( + lambda x: "accum" if x == "tp" else "instant" +) + +# %% ../../notes/20_pytask_config.ipynb 34 +#| exports: # update catalog +data_catalog['aggregate']['jobs'].add("jobs_df", aggregate_jobs) diff --git a/src/era5_sandbox/core.py b/src/era5_sandbox/core.py index f65965d..c69c62a 100644 --- a/src/era5_sandbox/core.py +++ b/src/era5_sandbox/core.py @@ -1,11 +1,10 @@ -"""This is a core library for the ERA5 dataset pipeline. It defines""" - # AUTOGENERATED! DO NOT EDIT! File to edit: ../../notes/00_core.ipynb. # %% auto 0 __all__ = ['describe', 'kelvin_to_celsius', 'GoogleDriver', 'ClimateDataFileHandler', 'testAPI', 'main'] # %% ../../notes/00_core.ipynb 3 +#| exports: # import os import cdsapi import hydra @@ -22,8 +21,8 @@ from pyprojroot import here from importlib import import_module - # %% ../../notes/00_core.ipynb 5 +#| exports: # def describe( cfg: DictConfig=None, # Configuration file )-> None: @@ -37,6 +36,7 @@ def describe( print(OmegaConf.to_yaml(cfg)) # %% ../../notes/00_core.ipynb 6 +#| exporti: # def _expand_path( path: str # Path on user's machine )-> str: # Expanded path @@ -51,6 +51,7 @@ def _expand_path( return path # %% ../../notes/00_core.ipynb 7 +#| exporti: # def _get_callable(func_path): """Dynamically import a callable from a string path.""" module_name, func_name = func_path.rsplit(".", 1) @@ -58,16 +59,13 @@ def _get_callable(func_path): return getattr(module, func_name) # %% ../../notes/00_core.ipynb 8 +#| exporti: # a directory structure creator def _create_directory_structure( base_path: str, # The base directory where the structure will be created structure: dict # A dictionary representing the directory structure )->None: """ Recursively creates a directory structure from a dictionary. - - Args: - base_path (str): The base directory where the structure will be created. - structure (dict): A dictionary representing the directory structure. """ for folder, substructure in structure.items(): # Create the current directory @@ -78,20 +76,18 @@ def _create_directory_structure( if isinstance(substructure, dict): _create_directory_structure(current_path, substructure) -# %% ../../notes/00_core.ipynb 9 -def kelvin_to_celsius(kelvin): +# %% ../../notes/00_core.ipynb 10 +#| export: # +def kelvin_to_celsius( + kelvin: float # Temperature in Kelvin + ) -> float: # Temperature in Celsius """ Convert temperature from Kelvin to Celsius. - - Args: - kelvin (float): Temperature in Kelvin. - - Returns: - float: Temperature in Celsius. """ return kelvin - 273.15 -# %% ../../notes/00_core.ipynb 11 +# %% ../../notes/00_core.ipynb 12 +#| export: # class GoogleDriver: """ A class to handle Google Drive authentication and file management. @@ -122,10 +118,12 @@ def _authenticate(self): def get_drive(self): return self.drive -# %% ../../notes/00_core.ipynb 20 +# %% ../../notes/00_core.ipynb 23 +#| export: # from fastcore.basics import patch -# %% ../../notes/00_core.ipynb 21 +# %% ../../notes/00_core.ipynb 24 +#| export: # @patch def read_healthsheds(self:GoogleDriver, healthshed_zip_name): @@ -148,7 +146,8 @@ def read_healthsheds(self:GoogleDriver, healthshed_zip_name): return gdf -# %% ../../notes/00_core.ipynb 25 +# %% ../../notes/00_core.ipynb 28 +#| export: # class ClimateDataFileHandler: """ A class to handle file operations for the Climate Data Store (CDS). @@ -239,7 +238,8 @@ def cleanup(self): if self.unzipped_dir is not None: self.unzipped_dir.cleanup() -# %% ../../notes/00_core.ipynb 33 +# %% ../../notes/00_core.ipynb 38 +#| exporti: # @patch def __enter__(self:ClimateDataFileHandler): self.prepare() @@ -249,7 +249,8 @@ def __enter__(self:ClimateDataFileHandler): def __exit__(self:ClimateDataFileHandler, exc_type, exc_val, exc_tb): self.cleanup() -# %% ../../notes/00_core.ipynb 36 +# %% ../../notes/00_core.ipynb 41 +#| exports: # def testAPI( cfg: DictConfig=None, dataset:str="reanalysis-era5-pressure-levels" @@ -294,7 +295,8 @@ def testAPI( print("Error: {}".format(e)) return False -# %% ../../notes/00_core.ipynb 40 +# %% ../../notes/00_core.ipynb 46 +#| exports: # @hydra.main(version_base=None, config_path="../../conf", config_name="config") def main(cfg: DictConfig) -> None: @@ -303,11 +305,3 @@ def main(cfg: DictConfig) -> None: # test the api testAPI(cfg=cfg) - -# %% ../../notes/00_core.ipynb 41 -#| eval: false -try: from nbdev.imports import IN_NOTEBOOK -except: IN_NOTEBOOK=False - -if __name__ == "__main__" and not IN_NOTEBOOK: - main() diff --git a/src/era5_sandbox/download.py b/src/era5_sandbox/download.py index 96f4d69..d38d525 100644 --- a/src/era5_sandbox/download.py +++ b/src/era5_sandbox/download.py @@ -1,11 +1,10 @@ -"""This module downloads the raw data from CDS and saves it in the local directory""" - # AUTOGENERATED! DO NOT EDIT! File to edit: ../../notes/01_download_raw_data.ipynb. # %% auto 0 __all__ = ['fetch_GADM', 'create_bounding_box', 'download_raw_era5', 'main'] # %% ../../notes/01_download_raw_data.ipynb 4 +#| export: # import os import hydra import cdsapi @@ -22,11 +21,14 @@ except: from core import _expand_path # %% ../../notes/01_download_raw_data.ipynb 5 +#| exporti: # def _validate_query( query_body: DictConfig )->bool: ''' Check that the query is valid + ###TODO Not a good idea to overwrite components of the query body because the user may believe something and the function may give somehting else back + Better to just tell them something is wrong ''' required_keys = ['product_type', 'variable', 'year', 'month', 'day', 'time', 'area', 'data_format', 'download_format'] @@ -51,14 +53,14 @@ def _validate_query( return OmegaConf.to_container(query_body, resolve=True) -# %% ../../notes/01_download_raw_data.ipynb 6 +# %% ../../notes/01_download_raw_data.ipynb 7 +#| export: # def fetch_GADM( - url: str="https://geodata.ucdavis.edu/gadm/gadm4.1/gpkg/gadm41_MDG.gpkg", + url: str="https://geodata.ucdavis.edu/gadm/gadm4.1/gpkg/gadm41_MDG.gpkg", # URL to fetch the GADM data for Madagascar output_file: str="gadm41_MDG.gpkg" # file path to save the GADM data )-> str: ''' - Fetch the GADM data for Madagascar - https://geodata.ucdavis.edu/gadm/gadm4.1/gpkg/gadm41_MDG.gpkg + Fetch the GADM bounding box for geographic region ''' output_file_path = _expand_path(output_file) @@ -72,22 +74,15 @@ def fetch_GADM( return output_file_path -# %% ../../notes/01_download_raw_data.ipynb 7 +# %% ../../notes/01_download_raw_data.ipynb 8 +#| exports: # def create_bounding_box( - zip_url_or_path: str, - buffer_km: float = 50, - round_to: int = 1 -) -> list: + zip_url_or_path: str, # URL or local path to the zipped shapefile. + buffer_km: float = 50, # Buffer distance in kilometers to expand the bounding box. + round_to: int = 1 # Number of decimal places to round the bounding box coordinates. +) -> list: # Bounding box in the CDS API area format [North, West, South, East] ''' Create a bounding box from OCHA/HDX shapefile data with a buffer. - - Parameters: - zip_url_or_path (str): URL or local path to the zipped shapefile. - buffer_km (float): Buffer distance in kilometers to expand the bounding box. - round_to (int): Number of decimal places to round the bounding box coordinates. - - Returns: - list: Bounding box in the CDS API area format [North, West, South, East]. ''' with tempfile.TemporaryDirectory() as tmpdir: # Download if it's a URL @@ -132,8 +127,8 @@ def create_bounding_box( return bbox - -# %% ../../notes/01_download_raw_data.ipynb 8 +# %% ../../notes/01_download_raw_data.ipynb 10 +#| exports: # def download_raw_era5( cfg: DictConfig # hydra configuration file )->None: @@ -169,13 +164,15 @@ def download_raw_era5( print("Done") -# %% ../../notes/01_download_raw_data.ipynb 11 +# %% ../../notes/01_download_raw_data.ipynb 13 +#| exports: # @hydra.main(config_path="../../conf", config_name="config", version_base=None) def main(cfg: DictConfig) -> None: download_raw_era5(cfg=cfg) + # better approach would be to have the function only use the specific arguments of the config -# %% ../../notes/01_download_raw_data.ipynb 12 -#| eval: false +# %% ../../notes/01_download_raw_data.ipynb 14 +#| export: #| eval: false try: from nbdev.imports import IN_NOTEBOOK except: IN_NOTEBOOK=False diff --git a/src/era5_sandbox/publish.py b/src/era5_sandbox/publish.py new file mode 100644 index 0000000..6a959db --- /dev/null +++ b/src/era5_sandbox/publish.py @@ -0,0 +1,185 @@ +# AUTOGENERATED! DO NOT EDIT! File to edit: ../../notes/03_publish.ipynb. + +# %% auto 0 +__all__ = ['gather_exposure_geodataframes', 'main'] + +# %% ../../notes/03_publish.ipynb 4 +#| exports: # +import hydra +import yaml +import json +from tqdm import tqdm +from pyprojroot import here + +# %% ../../notes/03_publish.ipynb 7 +#| exports: # +from pyDataverse.api import NativeApi + +# %% ../../notes/03_publish.ipynb 26 +#| exports: # +from pyDataverse.api import SearchApi + +# %% ../../notes/03_publish.ipynb 33 +#| exports: # +import geopandas as gpd +import pandas as pd +import re +import glob + +# %% ../../notes/03_publish.ipynb 36 +#| exports: # + +def gather_exposure_geodataframes( + glob_string: str, # string for the path to search for the pertinent files + polygon_id: str, # the string signifying the healthshed ID of the polygon + exposure: str # the exposure name + )-> list: + "Read in a list of geo dataframes from the same time frame and merge them" + + # first get the initial one so we have the polygon ID and geometry + frames = glob.glob(str(glob_string)) + initial_gdf=gpd.read_parquet(frames[0]) + merged_df = [] + + for f in tqdm(frames, desc="Processing files"): + # read in as a regular dataframe by ignoring geometry + df = gpd.read_parquet(f).drop(["geometry"], axis=1) + + # get the year and month + # Extract year and month + search_str = rf'_{exposure}_(\d{{4}})_(\d{{1,2}})\.parquet$' + match = re.search(search_str, f) + + if match: + year = int(match.group(1)) + month = int(match.group(2)) + #print(f"Year: {year}, Month: {month}") + else: + raise ValueError(f"Could not extract year and month from filename: {search_str} {f}") + + df['exposure'] = exposure + df['month'] = month + df['year'] = year + + # Step 1: Melt all day columns (leave 'month' and 'year' as identifiers) + df_long = df.melt(id_vars=[polygon_id, "exposure", "year", "month"], var_name="day_stat", value_name="value") + + # Step 2: Extract day and stat type from column names + # Example column: "day_01_daily_mean" + df_long[["day", "stat"]] = df_long["day_stat"].str.extract(r"day_(\d{2})_daily_(mean|max|min|total)") + + # Optional: convert 'day' and month to integer + df_long["day"] = df_long["day"].astype(int) + df_long["month"] = df_long["month"].astype(int) + + # Drop the original combined column + df_long = df_long.drop(columns="day_stat") + + # Reorder columns + df_long = df_long[[polygon_id, "exposure", "year", "month", "day", "stat", "value"]] + + df_long = df_long.sort_values(by=["year", "month", "day"]) + df_clean = df_long.pivot(index=[polygon_id, "exposure", "year", "month", "day"], columns="stat", values="value").reset_index() + merged_df.append(df_clean) + + return [pd.concat(merged_df).reset_index(drop=True), initial_gdf[[polygon_id, "geometry"]]] + +# %% ../../notes/03_publish.ipynb 40 +#| exports: # + +from pyDataverse.models import Datafile +import os +import pathlib + +# %% ../../notes/03_publish.ipynb 45 +#| exports: # +from hydra import initialize, compose +from omegaconf import OmegaConf, DictConfig +from tqdm import tqdm + +# %% ../../notes/03_publish.ipynb 47 +#| exports: # + +@hydra.main(version_base=None, config_path="../../conf", config_name="config") +def main(cfg: DictConfig) -> None: + + variables_dict = { + "2m_temperature": "t2m", + "2m_dewpoint_temperature": "d2m", + "volumetric_soil_water_layer_1": "swvl1", + "total_precipitation": "tp" + } + + print(OmegaConf.to_yaml(cfg)) + + #prep dataverse + api_token_file = here() / "sandbox/dataverse_api_key.yml" + with open(api_token_file, "r") as f: + apiconfig = yaml.load(f, Loader=yaml.BaseLoader) + api = NativeApi(apiconfig['base_url'], apiconfig['api_token']) + search_api = SearchApi(apiconfig['base_url'], apiconfig['api_token']) + resp = search_api.search("ERA5", data_type="dataset") + + results = resp.json()['data']['items'] + + result = [x for x in results if "ERA5" in x['name']][0] + era5_pid = result['global_id'] + + for geography in cfg.geographies: + for year in cfg.query['year']: + for variable, v in variables_dict.items(): + + print(f"Processing {geography} for {variable} in {year}") + glob_string = here() / "data" / "intermediate" / f"*{geography}*{variable}*{year}*" + print(f"Glob: {glob_string}") + polygon_id = cfg.geographies[geography]['unique_id'] + print(f"polygon_id: {polygon_id}") + merged = gather_exposure_geodataframes(glob_string, polygon_id, variable) + print(merged[0].head()) + print(merged[1].head()) + + output_dir = here() / "data" / "output" + + f_out = f"environmental/exposures_era5/healthshed_daily/{geography}_{v}_{year}.parquet" + os.makedirs(output_dir / os.path.dirname(f_out), exist_ok=True) + output_path = output_dir / f_out + + print(f"Writing to {output_path}") + merged[0].to_parquet(output_path, index=False) + + + print(f"Uploading {f_out.replace('/', '-')} to Dataverse...") + # upload to dataverse + datafile = Datafile() + datafile.set({ + "pid": era5_pid, + "filename": str(output_path), + "label": f_out.replace("/", "-") + }) + + resp = api.upload_datafile(era5_pid, output_path, datafile.json()) + assert resp.json()['status'] == "OK", f"Failed to upload datafile: {resp.text}" + + # also save the geometry for the region + merged[1].to_parquet(output_path.parent / f"{geography}_geometry.parquet", index=False) + + # and upload it to dataverse + datafile = Datafile() + datafile.set({ + "pid": era5_pid, + "filename": str(output_path.parent / f"{geography}_geometry.parquet"), + "label": f"{geography}_geometry.parquet" + }) + + resp = api.upload_datafile(era5_pid, output_path.parent / f"{geography}_geometry.parquet", datafile.json()) + assert resp.json()['status'] == "OK", f"Failed to upload geometry datafile: {resp.text}" + + print("All files processed and uploaded successfully.") + +# %% ../../notes/03_publish.ipynb 48 +#| export: #| eval: false +try: from nbdev.imports import IN_NOTEBOOK +except: IN_NOTEBOOK=False + +if __name__ == "__main__" and not IN_NOTEBOOK: + main() diff --git a/src/era5_sandbox/pytask_logger.py b/src/era5_sandbox/pytask_logger.py new file mode 100644 index 0000000..a81fba3 --- /dev/null +++ b/src/era5_sandbox/pytask_logger.py @@ -0,0 +1,37 @@ +# AUTOGENERATED! DO NOT EDIT! File to edit: ../../notes/20_pytask_logger.ipynb. + +# %% auto 0 +__all__ = ['LOG_DIR', 'log_date', 'log_time', 'setup_logger'] + +# %% ../../notes/20_pytask_logger.ipynb 3 +#| exports: # imports + +import logging +from pathlib import Path +from pyprojroot import here +from datetime import datetime + +LOG_DIR = here("logs") +# get the date & time for the log file name +log_date = datetime.now().strftime("%Y-%m-%d") +log_time = datetime.now().strftime("%H-%M-%S") +LOG_DIR = here("logs") / log_date / log_time + +# %% ../../notes/20_pytask_logger.ipynb 4 +#| exports: # main function to setup a logger + + + +def setup_logger(name: str, log_path: Path=LOG_DIR, level=logging.INFO) -> logging.Logger: + log_path.mkdir(parents=True, exist_ok=True) + formatter = logging.Formatter('%(asctime)s — %(name)s — %(levelname)s — %(message)s') + + handler = logging.FileHandler(log_path / f"{name}.log", mode='a') + handler.setFormatter(formatter) + + logger = logging.getLogger(name) + logger.setLevel(level) + logger.addHandler(handler) + logger.propagate = False + + return logger diff --git a/src/era5_sandbox/task_aggregate.py b/src/era5_sandbox/task_aggregate.py new file mode 100644 index 0000000..447dada --- /dev/null +++ b/src/era5_sandbox/task_aggregate.py @@ -0,0 +1,310 @@ +# AUTOGENERATED! DO NOT EDIT! File to edit: ../../notes/22_pytask_aggregate.ipynb. + +# %% auto 0 +__all__ = ['job_rows', 'aggregation_funcs', 'compute_diurnal_class_bins', 'compute_solar_day_night_class_bins'] + +# %% ../../notes/22_pytask_aggregate.ipynb 3 +#| export: # + +import os +import tempfile +import rasterio +import yaml +import xarray as xr +from pyprojroot import here +from typing import Literal +from pytask import task, Product +from pathlib import Path +from typing import Annotated +from rasterstats.io import Raster + +from .config import BLD, data_catalog +from .pytask_logger import setup_logger + +from .core import GoogleDriver, _get_callable, describe, ClimateDataFileHandler, kelvin_to_celsius + +from .aggregate import polygon_to_raster_cells, aggregate_to_healthsheds, RasterFile, netcdf_to_tiff + +# %% ../../notes/22_pytask_aggregate.ipynb 7 +#| exports: # +from astral import Observer, sun +import pandas as pd +import numpy as np +from tqdm import tqdm +import random +import datetime +from pytz import UTC + +# %% ../../notes/22_pytask_aggregate.ipynb 17 +#| export: # define the basic diurnal classification function + +def compute_diurnal_class_bins( + ds: xr.Dataset + )-> np.ndarray: + """ + Compute the diurnal value for each data point in the dataset. + This function iterates over each data point in the dataset, + calculates the sunrise and sunset times for the given time, latitude and longitude, + and returns whether or not that data point is before dawn, during the day, or after dusk. + """ + + times = ds['valid_time'].values + lats = ds.coords['latitude'].values + lons = ds.coords['longitude'].values + + result = np.full((len(times), len(lats), len(lons)), "", dtype=object) + + for i, dt in enumerate(tqdm(times, desc="Classifying data points by sun position")): + # use the time + dt = pd.to_datetime(dt, utc=True) + + for j, lat in enumerate(lats): + + for k, lon in enumerate(lons): + + # set the geographical position + observer = Observer(latitude=lat, longitude=lon, elevation=0) + + # where/when is the sun at this time for this position + sun_info = sun.sun(observer, date=dt) + + if dt < sun_info['sunrise']: + result[i, j, k] = "pre_dawn" + elif dt >= sun_info['sunrise'] and dt < sun_info['sunset']: + result[i, j, k] = "day" + else: + result[i, j, k] = "post_dusk" + + return result + +# %% ../../notes/22_pytask_aggregate.ipynb 22 +#| exports: # + +def compute_solar_day_night_class_bins( + ds: xr.Dataset, + night_direction: Literal["before", "after"], + )-> list: + """ + Compute the diurnal value for each data point in the dataset. + This function iterates over each data point in the dataset, + calculates the sunrise and sunset times for the given time, latitude and longitude, + and returns whether or not that data point is daytime or nighttime. + The definition of "nighttime" can be set to be all the darkness before the sun + came up (before), or all the darkness after it went down (after). + """ + + times = ds['valid_time'].values + lats = ds.coords['latitude'].values + lons = ds.coords['longitude'].values + + result = np.full((len(times), len(lats), len(lons)), "", dtype=object) + datetimes = np.full((len(times), len(lats), len(lons)), "", dtype=object) + + for i, dt in enumerate(tqdm(times, desc="Classifying data points by sun position")): + # use the time + dt = pd.to_datetime(dt, utc=True) + + for j, lat in enumerate(lats): + + for k, lon in enumerate(lons): + + # set the geographical position + observer = Observer(latitude=lat, longitude=lon, elevation=0) + if night_direction == "before": + # Night is from previous sunset to today's sunrise + sun_today = sun.sun(observer, date=dt.date()) + sun_prev = sun.sun(observer, date=(dt - pd.Timedelta(days=1)).date()) + night_start = sun_prev["sunset"].astimezone(pd.Timestamp.utcnow().tz) + night_end = sun_today["sunrise"].astimezone(pd.Timestamp.utcnow().tz) + + # the reading is from yesterday's nighttime + if night_start <= dt < night_end: + result[i, j, k] = "night" + # the date counts as today + datetimes[i, j, k] = dt.date() + + # the reading is from daytime + elif sun_today["sunrise"] <= dt < sun_today["sunset"]: + result[i, j, k] = "day" + # the date counts as today + datetimes[i, j, k] = dt.date() + + # the reading is from today's nighttime, but counts as tomorrow's night + else: + result[i, j, k] = "night" + # the date is tomorrow + datetimes[i, j, k] = (dt + pd.Timedelta(days=1)).date() + + elif night_direction == "after": + # Night is from today's sunset to next sunrise + sun_today = sun.sun(observer, date=dt.date()) + sun_next = sun.sun(observer, date=(dt + pd.Timedelta(days=1)).date()) + night_start = sun_today["sunset"].astimezone(pd.Timestamp.utcnow().tz) + night_end = sun_next["sunrise"].astimezone(pd.Timestamp.utcnow().tz) + + # the reading is from daytime + if sun_today["sunrise"] <= dt < sun_today["sunset"]: + result[i, j, k] = "day" + # the date counts as today + datetimes[i, j, k] = dt.date() + # the reading is from tonight + elif night_start <= dt < night_end: + result[i, j, k] = "night" + # the date counts as today + datetimes[i, j, k] = dt.date() + + # the reading is from yesterday night + else: + # the date counts as yesterday + result[i, j, k] = "day" + datetimes[i, j, k] = (dt - pd.Timedelta(days=1)).date() + else: + raise ValueError(f"Invalid night_direction: {night_direction}") + + return result, datetimes + +# %% ../../notes/22_pytask_aggregate.ipynb 49 +#| exports: # + +job_rows = data_catalog['aggregate']['jobs']['jobs_df'].load() + +aggregation_funcs = { + "mean": np.nanmean, + "sum": np.nansum, + "max": np.nanmax, + "min": np.nanmin +} + +for i, job in job_rows.iterrows(): + #print(f"Job {i+1}: variable={job['variables']}, time={job['time']}, aggregation={job['aggregation_name']}") + + # parse the row into function parameters + input_file = data_catalog['download']['outputs'][job['input']] + solar_classification = job['solar_classification'] + variable = job['variables_short'] + time = job['time'] + aggregation_func = aggregation_funcs[job['aggregation_name']] + aggregation_name = job['aggregation_name'] + + climate_handler_var = job['climate_handler_var'] + local_tz = job['local_tz'] + + shapefile = job['shapefile'] + hshd_unique_id = job['hshd_unique_id'] + + output_file = job['input'] + "_" + job['time'] + "_" + job['variables_short'] + "_" + job['aggregation_name'] + ".parquet" + + @task(id=output_file, name=f"Aggregate {output_file}", after="task_download_raw_data") + def task_aggregate_data_diurnal( + input_file: Path = data_catalog['download']['outputs'][job['input']], # input data Path from the download task + aggregation_func: callable = aggregation_func, # the aggregation function + aggregation_name: str = aggregation_name, # the name of the aggregation function + time: Literal["day", "night"] = time, # whether to aggregate by day or night + night_direction: Literal["before", "after"] = solar_classification, # how to define night + variable: str = variable, # the variable to aggregate, + climate_handler_var: Literal["instant", "accum"] = climate_handler_var, # whether the variable is instant or accum, + local_tz: str = local_tz, # the local timezone for resampling + shapefile: str = shapefile, # the shapefile for the healthsheds, + hshd_unique_id: str = hshd_unique_id, # the unique id column in the shapefile, + output_file: str = output_file # the output file name + ) -> Annotated[Path, data_catalog['aggregate']['outputs'][output_file]]: + """ + Task to aggregate data from a CDSAPI Query to the healthshed + level. Returns path to parquet file with aggregated data. + """ + + logger = setup_logger(output_file) + + logger.info(f"Aggregating: {output_file}") + + # check if the string path exists + # if os.path.exists(output_file): + # logger.info(f"File {output_file} already exists. Skipping aggregation.") + # return output_file + + # get input data + logger.info("Reading input data...") + with ClimateDataFileHandler(input_file) as handler: + ds = xr.open_dataset(handler.get_dataset('instant')) + + #get the healthshed shapefile + logger.info(f"Reading healthshed shapefile from yaml {here()}...") + with open(here() / "conf" / "config.yaml") as f: + healthshed_config = yaml.safe_load(f) + + key_path = here() / healthshed_config['GOOGLE_DRIVE_AUTH_JSON']['path'] + + driver = GoogleDriver(json_key_path=key_path) + drive = driver.get_drive() + healthsheds = driver.read_healthsheds(shapefile) + + # compute the diurnal classification bins + logger.info("Computing diurnal classification bins...") + class_bins, class_dts = compute_solar_day_night_class_bins(ds, night_direction) + + ds_masked = ds.copy() + + # assign classifications + logger.info("Assigning classification bins to dataset...") + ds['solar_class'] = (('valid_time', 'latitude', 'longitude'), class_bins) + ds["solar_date"] = (("valid_time", "latitude", "longitude"), class_dts) + + # mask the dataset to the requested time + mask = ds["solar_class"] == time + ds_masked = ds_masked.where(mask) + + # set the local timezone + ds_masked = ds_masked.assign_coords(valid_time=pd.to_datetime(ds["valid_time"].values).tz_localize("UTC").tz_convert(local_tz)) + + # resample by local date + logger.info("Resampling by local date...") + ds_rs = ds_masked.resample(valid_time="1D").reduce(aggregation_func) + + # convert to tiff + logger.info("Rasterizing resampled data...") + n_bands = ds_rs.dims['valid_time'] + + # polygon to raster cells for the first band + logger.info("Converting polygons to raster cells...") + raster = netcdf_to_tiff(ds_rs, band=1, variable=variable) + res_poly2cell=polygon_to_raster_cells( + vectors = healthsheds.geometry.values, # the geometries of the shapefile of the regions + raster=raster.data, # the raster data above + nodata=np.nan, # any intersections with no data, may have to be np.nan + affine=raster.transform, # some math thing need to revise + all_touched=True, + verbose=True + ) + + result_df = healthsheds[[hshd_unique_id, "geometry"]].copy() + + # loop over bands and aggregate to healthsheds + for band in tqdm(range(1, n_bands + 1)): + logger.info(f"Processing band {band} of {n_bands}...") + + day = band # band is 1-indexed + + day_col = f"day_{day:02d}" + + # calculate raster for this band + raster = netcdf_to_tiff(ds_rs, band=band, variable=variable) + + # aggregate to healthsheds + result = aggregate_to_healthsheds( + res_poly2cell=res_poly2cell, + raster=raster, + shapes=healthsheds, + names_column=hshd_unique_id, + aggregation_func=aggregation_func, + aggregation_name=variable + ) + + # add band to result dataframe + result_df[day_col] = result[variable] + + # save to parquet + result_df.to_parquet(f"{BLD}/{output_file}") + + logger.info("Aggregation complete.") + + return Path(f"{BLD}/{output_file}") diff --git a/src/era5_sandbox/task_data_preparation.py b/src/era5_sandbox/task_data_preparation.py new file mode 100644 index 0000000..88a221b --- /dev/null +++ b/src/era5_sandbox/task_data_preparation.py @@ -0,0 +1,93 @@ +# AUTOGENERATED! DO NOT EDIT! File to edit: ../../notes/10_pytask_demo.ipynb. + +# %% auto 0 +__all__ = ['task_create_random_data', 'task_plot_data', 'task_add_one', 'task_add_another_column'] + +# %% ../../notes/10_pytask_demo.ipynb 3 +#| exports: # +import os +from pathlib import Path +from typing import Annotated + +import numpy as np +import matplotlib.pyplot as plt +import pandas as pd +from .config import BLD +from .config import data_catalog, demo_catalog + +from pytask import PickleNode +from pytask import Product + +# %% ../../notes/10_pytask_demo.ipynb 5 +#| exports: # + +def task_create_random_data( + seed: Annotated[int, 42], # Default seed for reproducibility + path_to_data: Annotated[Path, Product] = BLD / "data.pkl" # Path to the object in the build directory + ) -> None: + "Create a random data set and save it as a pickle file. Return the path to the saved file." + rng = np.random.default_rng(seed) + beta = 2 + + x = rng.normal(loc=5, scale=10, size=1_000) + epsilon = rng.standard_normal(1_000) + + y = beta * x + epsilon + + df = pd.DataFrame({"x": x, "y": y}) + + # this is a tracked output, so we annotate the return value with `Annotated[Path, Product]` + df.to_pickle(path_to_data) + +# %% ../../notes/10_pytask_demo.ipynb 11 +#| exports: # + +def task_plot_data( + path_to_data: Annotated[Path, BLD / "data.pkl"], # Path to the data file created by the previous task + path_to_plot: Annotated[Path, Product] = BLD / "plot.png" # Path to the build directory for the plot +) -> None: + """ + Plot the data from the pickle file and save the plot. Note that this task: + 1. depends on the data.pkl file created by the previous task, + 2. does not return any value, but saves a plot to the build directory. So the side effect of the task is what we are interested in here (though this is probably bad practice). + """ + + df = pd.read_pickle(path_to_data) + + _, ax = plt.subplots() + df.plot(x="x", y="y", ax=ax, kind="scatter") + + plt.savefig(path_to_plot) + plt.close() + +# %% ../../notes/10_pytask_demo.ipynb 15 +#| exports: # + +def task_add_one( + path_to_data: Annotated[Path, BLD / "data.pkl"], # Path to the data file created by the previous task + node: Annotated[PickleNode, Product] = demo_catalog["mydata"] +) -> None: + """ + Add one to the 'y' column of the data frame and save it as a new pickle file. + """ + df = pd.read_pickle(path_to_data) + df['z'] = df['y'] + 1 + + node.save(df) + +# %% ../../notes/10_pytask_demo.ipynb 17 +#| exports: # + +def task_add_another_column( + df: Annotated[pd.DataFrame, demo_catalog["mydata"]] # which object in the catalog to fetch from the catalog with node.load() +) -> Annotated[pd.DataFrame, demo_catalog["mydata2"]]: # which object in the catalog to save the return value to + """ + Add another column to the data frame stored in the PickleNode. + """ + + # use the datacatalog directly to access the node + # this is a bit like accessing the node in an iPython session, but pytask + # will handle the serialization and deserialization for us + df['w'] = df['z'] * df['y'] + + return df diff --git a/src/era5_sandbox/task_download.py b/src/era5_sandbox/task_download.py new file mode 100644 index 0000000..b7be9b2 --- /dev/null +++ b/src/era5_sandbox/task_download.py @@ -0,0 +1,61 @@ +# AUTOGENERATED! DO NOT EDIT! File to edit: ../../notes/21_pytask_download.ipynb. + +# %% auto 0 +__all__ = ['queries'] + +# %% ../../notes/21_pytask_download.ipynb 4 +#| export: # necessary imports +import cdsapi +import pytask +import os +from pytask import task, Product +from pathlib import Path +from typing import Annotated +from pandas import Series + +from .config import data_catalog +from .config import BLD +from .config import DEV_MODE +from .pytask_logger import setup_logger +from .download import fetch_GADM, create_bounding_box + +# %% ../../notes/21_pytask_download.ipynb 12 +#| export: # define the download task + +queries = data_catalog['download']['jobs']['queries_df'].load() + +for i, job in queries.iterrows(): + + @task(id=job['output'], name=f"Download {job['output']}") + def task_download_raw_data( + _query: Series = job # The query object from the data catalog + )-> Annotated[Path, data_catalog['download']['outputs'][job['output']]]: + + logger = setup_logger(_query['output']) + output_path = BLD / f"{_query['output']}.nc" + logger.info(f"Starting download for {_query['output']} to {output_path}") + + # check if string file path exists + if os.path.exists(output_path): + logger.info(f"File {output_path} already exists. Skipping download.") + return output_path + + client = cdsapi.Client() + bounding_box = create_bounding_box(_query['shapefile']) + + request = { + "product_type": _query['product_type'], + "variable": _query['variables'], + "year": _query['year'], + "month": _query['month'], + "day": _query['day'], + "time": _query['time'], + "data_format": "netcdf", + "download_format": "unarchived", + "area": bounding_box + } + + client.retrieve("reanalysis-era5-land", request).download(output_path) + logger.info(f"Downloaded data for {_query['output']} to {output_path}") + + return output_path diff --git a/src/pyproject.toml b/src/pyproject.toml new file mode 100644 index 0000000..89ce145 --- /dev/null +++ b/src/pyproject.toml @@ -0,0 +1,11 @@ +[build-system] +requires = ["setuptools>=64.0"] +build-backend = "setuptools.build_meta" + +[project] +name="era5_sandbox" +requires-python=">=3.7" +dynamic = [ "keywords", "description", "version", "dependencies", "optional-dependencies", "readme", "license", "authors", "classifiers", "entry-points", "scripts", "urls"] + +[tool.uv] +cache-keys = [{ file = "pyproject.toml" }, { file = "settings.ini" }, { file = "setup.py" }]