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Cog colormap example notebook #73

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247 changes: 247 additions & 0 deletions notebooks/tutorial/cog colormap.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Setup"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Setup\n",
"import os\n",
"\n",
"## Data folder\n",
"def file_path_prefix(file_prefix):\n",
" folder = f'./data/{file_prefix}'\n",
" os.makedirs(folder, exist_ok=True)\n",
" return f'{folder}/{file_prefix}'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Inputs"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Inputs\n",
"## Name of the area of interest\n",
"aoi_name='Salvador'\n",
"\n",
"## Path to polygon file the area you want data for\n",
"aoi_url = 'https://cities-indicators.s3.eu-west-3.amazonaws.com/data/boundaries/boundary-BRA-Salvador-ADM4union.geojson'\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Get Polygon for AOI"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# load boundary\n",
"import geopandas as gpd\n",
"\n",
"# If you are using an SSO accout, you need to be authenticated first\n",
"# !aws sso login\n",
"aoi_gdf = gpd.read_file(aoi_url, driver='GeoJSON')\n",
"\n",
"aoi_gdf = aoi_gdf.to_crs(epsg=4326)\n",
"\n",
"## Write to file\n",
"\n",
"file_path = f'{file_path_prefix(aoi_name)}-boundary.geojson'\n",
"aoi_gdf.to_file(file_path, driver='GeoJSON')\n",
"print(f'File saved to {file_path}')\n",
"\n",
"\n",
"\n",
"## Get area in km2 of the city rounded to the nearest integer\n",
"aoi_gdf_area = aoi_gdf['geometry'].to_crs(epsg=3857).area/ 10**6 # in km2\n",
"aoi_gdf_area = round(aoi_gdf_area.values[0])\n",
"print(f'Area: {aoi_gdf_area} sqkm')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# LULCv2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from city_metrix.layers import SmartSurfaceLULC\n",
"\n",
"aoi_smart_surface_lulc = SmartSurfaceLULC().get_data(aoi_gdf.total_bounds)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from matplotlib.colors import ListedColormap\n",
"from enum import Enum\n",
"\n",
"# Define the lulcPaletteV3\n",
"lulcPaletteV3 = [\n",
" 'b2df8a',\n",
" 'cccccc',\n",
" '808080',\n",
" '33a02c',\n",
" 'a6cee3',\n",
" '000000',\n",
" '493910',\n",
" '5e4915',\n",
" '735a19',\n",
" '886a1e',\n",
" '9d7a23',\n",
" 'b28b27',\n",
" 'f5efba',\n",
" 'c9beb6'\n",
"]\n",
"\n",
"# Define the SsLulcClass Enum\n",
"class SsLulcClass(Enum):\n",
" GREEN_SPACE_OTHER = 1\n",
" BUILT_UP_OTHER = 2\n",
" BARREN = 3\n",
" OPEN_SPACE = 10\n",
" WATER = 20\n",
" ROADS = 30\n",
" NON_CLASSIFIED_BUILDINGS = 40\n",
" RESIDENTIAL_HIGH_SLOPE = 41\n",
" NON_RESIDENTIAL_HIGH_SLOPE = 42\n",
" UNCLASSIFIED_HIGH_SLOPE = 43\n",
" RESIDENTIAL_LOW_SLOPE = 44\n",
" NON_RESIDENTIAL_LOW_SLOPE = 45\n",
" UNCLASSIFIED_LOW_SLOPE = 46\n",
" PARKING = 50\n",
"\n",
"# Create a colormap dictionary\n",
"colormap = {cls.value: f'#{color}' for cls, color in zip(SsLulcClass, lulcPaletteV3)}\n",
"\n",
"# Create a ListedColormap for matplotlib\n",
"cmap = ListedColormap([colormap[key] for key in colormap.keys()])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cmap"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Convert hex color codes to RGBA tuples\n",
"def hex_to_rgba(hex_color):\n",
" hex_color = hex_color.lstrip('#')\n",
" return tuple(int(hex_color[i:i+2], 16) for i in (0, 2, 4)) + (255,)\n",
"\n",
"rgba_colormap = {key: hex_to_rgba(value) for key, value in colormap.items()}\n",
"\n",
"rgba_colormap"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"rgba_colormap"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Save the DataArray with the colormap as a COG\n",
"file_path = f'{file_path_prefix(aoi_name)}-smart_surface_lulc_styled.tif'\n",
"\n",
"aoi_smart_surface_lulc.rio.to_raster(\n",
" file_path,\n",
" driver=\"COG\",\n",
" dtype=\"uint8\",\n",
" compress=\"deflate\")\n",
"\n",
"print(f'File saved to {file_path}')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import rasterio\n",
"\n",
"with rasterio.Env():\n",
"\n",
" with rasterio.open(file_path) as src:\n",
" shade = src.read(1)\n",
" meta = src.meta\n",
"\n",
" with rasterio.open(file_path, 'w', **meta) as dst:\n",
" dst.write(shade, indexes=1)\n",
" dst.write_colormap(\n",
" 1, rgba_colormap)\n",
" cmap = dst.colormap(1)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "cities-cif",
"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": "3.10.14"
}
},
"nbformat": 4,
"nbformat_minor": 2
}