plotting with contextily and geoviews

uber · Nov 12, 2023 · d52679a · d52679a
1 parent 1fea86c
commit d52679a
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diff --git a/poly/malaria.ipynb b/poly/malaria.ipynb
@@ -54,25 +54,25 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "497350a3-fce8-420f-8a73-304c088dcd9d",
+   "id": "7db24528-5622-4c85-8c8c-2bba1cb28459",
    "metadata": {},
    "outputs": [],
    "source": [
-    "def show_gj_str(gj_str):\n",
-    "    import json\n",
-    "    from IPython.display import GeoJSON\n",
-    "    \n",
-    "    return GeoJSON(json.loads(gj_str))"
+    "colombia.crs"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e7fed94f-6ab0-43b8-97eb-f7a0a7483c41",
+   "id": "497350a3-fce8-420f-8a73-304c088dcd9d",
    "metadata": {},
    "outputs": [],
    "source": [
-    "colombia["
+    "def show_gj_str(gj_str):\n",
+    "    import json\n",
+    "    from IPython.display import GeoJSON\n",
+    "    \n",
+    "    return GeoJSON(json.loads(gj_str))"
    ]
   },
   {

diff --git a/poly/plotting.ipynb b/poly/plotting.ipynb
@@ -0,0 +1,180 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "595a3eea-392d-44e1-a657-1a2771b08947",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import h3\n",
+    "import geopandas\n",
+    "import geodatasets\n",
+    "import contextily as cx\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9e971ca2-24a4-43ba-8a90-83f6091b462b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "2263 # new york\n",
+    "4326 # wgs84\n",
+    "3857 # web mercator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "92bb0020-9c94-474f-90c1-96faa8744759",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = geopandas.read_file(geodatasets.get_path('nybb'))\n",
+    "df.crs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9df12bd5-b36e-413c-a18a-67ae857f9daf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1b12a4bc-196c-4acb-931e-8cbe76a8d7c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def foo(df, epsg):\n",
+    "    df = df.to_crs(epsg=epsg)\n",
+    "    \n",
+    "    ax = df.plot(figsize=(10, 10), alpha=0.5, edgecolor='k', column='BoroName', categorical=True, legend=True, legend_kwds={'loc': 'upper left'})\n",
+    "    \n",
+    "    ax.get_xaxis().set_visible(False)\n",
+    "    ax.get_yaxis().set_visible(False)\n",
+    "    \n",
+    "    cx.add_basemap(ax, crs=df.crs, source=cx.providers.CartoDB.Positron)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d76ec57d-83f0-4c69-8c67-6536ca881f65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "foo(df, 2263)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cfd6380d-8b2c-4176-8c50-48bea9bd04c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "foo(df, 4326)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8e948d0d-f6a0-4306-a23e-4feb99de0e4e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "foo(df, 3857)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee42f565-4b74-469a-9adb-0a4b448ce685",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2ad7aa3c-e249-4ef7-b760-d25794e405df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import geodatasets as gds\n",
+    "import geopandas as gpd\n",
+    "import geoviews as gv\n",
+    "import cartopy.crs as ccrs\n",
+    "\n",
+    "gv.extension('bokeh');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0c9964ae-c52f-464c-ab7c-965794fe918a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def bar(df, epsg):\n",
+    "    crs = ccrs.epsg(epsg)\n",
+    "    df = df.to_crs(crs)\n",
+    "    \n",
+    "    tiles = gv.tile_sources.CartoLight\n",
+    "    polys = gv.Polygons(df, vdims=['BoroName'], crs=df.crs)\n",
+    "\n",
+    "    out = tiles * polys.opts(color='BoroName', cmap='Pastel1', tools=['hover'], width=1200, height=800, alpha=0.5, active_tools=['wheel_zoom'], xaxis=None, yaxis=None)\n",
+    "\n",
+    "    return out"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8a3d3d09-e57c-4985-a246-e2e27ff85157",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bar(df, 3857)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "831de887-a746-430b-a586-5e7069581d95",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.11.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}