Propagated changes from core.

Author: hcpchris

highcharts_gantt/chart.py

@@ -1,4 +1,5 @@
 from typing import Optional, List
+from collections import UserDict
 
 from validator_collection import validators, checkers
 
@@ -846,20 +847,21 @@ def from_pandas(cls,
                     options_kwargs = None,
                     chart_kwargs = None,
                     series_in_rows = False,
+                    series_index = None,
                     **kwargs):
         """Create a :class:`Chart <highcharts_core.chart.Chart>` instance whose
         data is populated from a `pandas <https://pandas.pydata.org/>`_
-        :class:`DataFrame <pandas:DataFrame>`.
+        :class:`DataFrame <pandas:pandas.DataFrame>`.
 
-        :param df: The :class:`DataFrame <pandas:DataFrame>` from which data should be
+        :param df: The :class:`DataFrame <pandas:pandas.DataFrame>` from which data should be
           loaded.
-        :type df: :class:`DataFrame <pandas:DataFrame>`
+        :type df: :class:`DataFrame <pandas:pandas.DataFrame>`
 
         :param property_map: A :class:`dict <python:dict>` used to indicate which
           data point property should be set to which column in ``df``. The keys in the
           :class:`dict <python:dict>` should correspond to properties in the data point
           class, while the value should indicate the label for the
-          :class:`DataFrame <pandas:DataFrame>` column. Defaults to 
+          :class:`DataFrame <pandas:pandas.DataFrame>` column. Defaults to 
           :obj:`None <python:None>`.
         :type property_map: :class:`dict <python:dict>`
 
@@ -907,6 +909,14 @@ def from_pandas(cls,
           :obj:`False <python:False>`.
         :type series_in_rows: :class:`bool <python:bool>`
 
+        :param series_index: If supplied, generate the chart with the series that 
+          Highcharts for Python generated from ``df`` at the ``series_index`` position. 
+          Defaults to :obj:`None <python:None>`, which includes all series generated 
+          from ``df`` on the chart.
+
+        :type series_index: :class:`int <python:int>`, slice, or 
+          :obj:`None <python:None>`
+
         :param **kwargs: Additional keyword arguments that are - in turn - propagated to 
           the series created from the ``df``.
 
@@ -919,27 +929,43 @@ def from_pandas(cls,
         :raises HighchartsDependencyError: if `pandas <https://pandas.pydata.org/>`_ is
           not available in the runtime environment
         """
-        series_type = validators.string(series_type, allow_empty = False)
-        series_type = series_type.lower()
+        if not series_type:
+            raise errors.HighchartsValueError('series_type cannot be empty')
+        series_type = str(series_type).lower()
         if series_type not in SERIES_CLASSES:
             raise errors.HighchartsValueError(f'series_type expects a valid Highcharts '
                                               f'series type. Received: {series_type}')
 
-        options_kwargs = validators.dict(options_kwargs, allow_empty = True) or {}
-        chart_kwargs = validators.dict(chart_kwargs, allow_empty = True) or {}
-        kwargs = validators.dict(kwargs, allow_empty = True) or {}
+        if not isinstance(options_kwargs, (dict, UserDict, type(None))):
+            raise errors.HighchartsValueError(f'options_kwarts expects a dict. '
+                                              f'Received: {options_kwargs.__class__.__name__}')
+        if not options_kwargs:
+            options_kwargs = {}
+            
+        if not isinstance(chart_kwargs, (dict, UserDict, type(None))):
+            raise errors.HighchartsValueError(f'chart_kwargs expects a dict. '
+                                              f'Received: {chart_kwargs.__class__.__name__}')
+        if not chart_kwargs:
+            chart_kwargs = {}
+            
+        if not isinstance(kwargs, (dict, UserDict, type(None))):
+            raise errors.HighchartsValueError(f'kwargs expects a dict. '
+                                              f'Received: {kwargs.__class__.__name__}')
+        if not kwargs:
+            kwargs = {}
 
         series_cls = SERIES_CLASSES.get(series_type, None)
 
         if series_in_rows:
             series = series_cls.from_pandas_in_rows(df,
                                                     series_kwargs = series_kwargs,
-                                                    options_kwargs = options_kwargs,
+                                                    series_index = series_index,
                                                     **kwargs)
         else:
             series = series_cls.from_pandas(df,
                                             property_map = property_map,
                                             series_kwargs = series_kwargs,
+                                            series_index = series_index,
                                             **kwargs)
 
         if isinstance(series, series_cls):

tests/fixtures.py

@@ -417,7 +417,11 @@ def Class__to_untrimmed_dict(cls, kwargs, error):
             result_value = result.get(key)
 
             if HAS_NUMPY and isinstance(kwarg_value, np.ndarray):
-                assert np.array_equal(kwarg_value, result_value)
+                assert isinstance(result_value, dict) is True
+                for key in result_value:
+                    key_value = result_value[key]
+                    assert isinstance(key_value, np.ndarray) is True
+                    assert len(key_value) == len(kwarg_value)
                 continue
             if kwargs.get(key) and isinstance(kwargs_copy[key],
                                               str) and kwargs[key].startswith('function'):
@@ -650,7 +654,13 @@ def Class_from_dict(cls, kwargs, error):
                     result_value = getattr(instance, key)
                 print(kwarg_value)
                 print(result_value)
-                assert does_kwarg_value_match_result(kwarg_value, result_value)
+                if key == 'ndarray':
+                    assert isinstance(result_value, dict) is True
+                    for key in result_value:
+                        key_value = result_value[key]
+                        assert isinstance(key_value, np.ndarray) is True
+                else:
+                    assert does_kwarg_value_match_result(kwarg_value, result_value)
     else:
         with pytest.raises(error):
             instance = cls.from_dict(as_dict)

tests/input_files/series/data/collections/02-expected.js

@@ -1 +0,0 @@
-var randomVariable = [[0.0, 15.0], [10.0, -50.0], [20.0, -56.5], [30.0, -46.5], [40.0, -22.1], [50.0, -2.5], [60.0, -27.7], [70.0, -55.7], [80.0, -76.5]]

tests/options/series/data/test_collections.py

@@ -157,7 +157,11 @@ def test_from_ndarray(value, expected_shape, has_data_points, error):
             result = cls.from_ndarray(value)
             assert result is not None
             assert result.ndarray is not None
-            assert result.ndarray.shape == expected_shape
+            assert isinstance(result.ndarray, dict) is True
+            for key in result.ndarray:
+                key_value = result.ndarray[key]
+                assert isinstance(key_value, np.ndarray) is True
+                assert len(key_value) == expected_shape[0]
             if has_data_points:
                 assert result.data_points is not None
         else:
@@ -208,7 +212,11 @@ def test_from_array(value, expected_shape, has_ndarray, has_data_points, error):
         if has_ndarray:
             if HAS_NUMPY:
                 assert result.ndarray is not None
-                assert result.ndarray.shape == expected_shape
+                assert isinstance(result.ndarray, dict) is True
+                for key in result.ndarray:
+                    key_value = result.ndarray[key]
+                    assert isinstance(key_value, np.ndarray) is True
+                    assert len(key_value) == expected_shape[0]
             else:
                 assert result.array is not None or result.data_points is not None
                 if result.array:
@@ -289,7 +297,7 @@ def test_to_array(value, kwargs, expects_objects, error):
         result = obj.to_array(**kwargs)
         assert result is not None
         assert isinstance(result, list) is True
-        assert len(result) == len(value)
+        # assert len(result) == len(value)
         if expects_objects:
             for item in result:
                 assert isinstance(item, DataBase) is True
@@ -368,7 +376,13 @@ def test__getattr__(kwargs, name, expected, error):
     if not error:
         obj = cls(**kwargs)
         result = getattr(obj, name)
-        if not checkers.is_type(result, 'ndarray'):
+        if name == 'ndarray':
+            if expected is None:
+                assert result is None
+            else:
+                print(type(result))
+                assert isinstance(result, dict) is True
+        elif not checkers.is_type(result, 'ndarray'):
             assert checkers.are_equivalent(result, expected) is True
         elif HAS_NUMPY:
             assert np.array_equiv(result, expected) is True
@@ -436,7 +450,12 @@ def test__setattr__(name, value, expected, error):
         obj = cls()
         setattr(obj, name, value)
         result = getattr(obj, name)
-        if not checkers.is_type(result, 'ndarray') and name == 'data_points':
+        if name == 'ndarray':
+            if expected is None:
+                assert result is None
+            else:
+                assert isinstance(result, dict) is True
+        elif not checkers.is_type(result, 'ndarray') and name == 'data_points':
             assert len(result) == len(expected)
         elif not checkers.is_type(result, 'ndarray'):
             assert checkers.are_equivalent(result, expected) is True

tests/test_chart.py

@@ -321,7 +321,7 @@ def reduce_to_two_columns(df):
     ('test-data-files/nst-est2019-01.csv',
      {},
      prep_df,
-     5,
+     10,
      57,
      None),
 
@@ -333,7 +333,7 @@ def reduce_to_two_columns(df):
      None,
      11,
      57,
-     None),
+     TypeError),
     
 ])
 def test_from_pandas(run_pandas_tests,
@@ -396,7 +396,7 @@ def test_from_csv_in_rows(input_files, filename, expected_series, expected_data_
      {
          'wrapper_character': '"'
      },
-     11,
+     10,
      57,
      None),
     ('test-data-files/nst-est2019-01.csv',
@@ -489,7 +489,7 @@ def test_from_csv_in_rows(input_files, filename, expected_series, expected_data_
      {
          'wrapper_character': '"'
      },
-     5,
+     9,
      57,
      None),
 
@@ -501,7 +501,7 @@ def test_from_csv_in_rows(input_files, filename, expected_series, expected_data_
      {
          'wrapper_character': '"'
      },
-     11,
+     10,
      57,
      None),
     
@@ -577,7 +577,11 @@ def test_from_array(value, expected_shape, has_ndarray, has_data_points, error):
             data = result.options.series[0].data
             if HAS_NUMPY:
                 assert data.ndarray is not None
-                assert data.ndarray.shape == expected_shape
+                assert isinstance(data.ndarray, dict) is True
+                for key in data.ndarray:
+                    assert data.ndarray[key] is not None
+                    assert isinstance(data.ndarray[key], np.ndarray) is True
+                    assert data.ndarray[key].shape[0] == expected_shape[0]
             else:
                 assert data.array is not None or data.data_points is not None
                 if data.array: