Figure 6: Data processing / preparation

Fig6_PyGMT_dataprocessing.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "825fcbfa-7ade-4eab-9a85-b7505013befd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Copy finale version of script from normal Python file"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Fig6_PyGMT_dataprocessing.py

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+import pygmt
+
+df_bath = pygmt.datasets.load_sample_data("bathymetry")
+region_study = [
+    min(df_bath.longitude),
+    max(df_bath.longitude),
+    min(df_bath.latitude),
+    max(df_bath.latitude),
+]
+
+region_sel = [360 - 114, 360 - 111, 22, 26]
+df_bath_sel = pygmt.select(data=df_bath, region=region_sel)
+
+block_size = "10m"  # arc-minutes
+# Count data points within each block
+df_bath_sel_count = pygmt.blockmean(
+    data=df_bath_sel, spacing=block_size, region=region_study, summary="n"
+)
+# Calculate mean bathymetry within each block
+df_bath_sel_mean = pygmt.blockmean(
+    data=df_bath_sel, spacing=block_size, region=region_study
+)
+
+# Convert tabular data to GMT-ready grid
+grd_bath_sel_count = pygmt.xyz2grd(
+    data=df_bath_sel_count, region=region_study, spacing=block_size
+)
+grd_bath_sel_mean = pygmt.xyz2grd(
+    data=df_bath_sel_mean, region=region_study, spacing=block_size
+)
+
+# -----------------------------------------------------------------------------
+fig = pygmt.Figure()
+
+for block_value in ["counts", "mean bathymetry in meters"]:
+    match block_value:
+        case "counts":
+            grd_block = grd_bath_sel_count
+        case "mean bathymetry in meters":
+            grd_block = grd_bath_sel_mean
+
+    fig.coast(region=region_study, projection="M12c", land="gray", frame=True)
+
+    # Plot all data points in black
+    fig.plot(data=df_bath, style="p0.4p", fill="black")
+
+    # Plot grid color-coded by number of data points within each block
+    fig.grdimage(grid=grd_block, cmap="SCM/batlow", nan_transparent=True)
+    fig.colorbar(frame=f"x+l{block_value} per block")
+
+    # Plot data points within subregion in white
+    fig.plot(data=df_bath_sel, style="p0.4p", fill="white")
+
+    fig.shift_origin(xshift="w+2c")
+
+fig.show()
+fig.savefig(fname="Fig6_PyGMT_dataprocessing.png")