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{
"cells": [
{
"cell_type": "code",
"execution_count": 9,
"id": "81ec2c58",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"65673.0\n"
]
}
],
"source": [
"from shapely.geometry import shape\n",
"from shapely.geometry import mapping\n",
"import json \n",
"\n",
"with open('data/band_1.geojson') as a: \n",
" geojson_data = json.load(a)\n",
"\n",
"\n",
"num_points = len(geojson_data['features'])/2\n",
"print(num_points)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "5720f337",
"metadata": {},
"outputs": [],
"source": [
"# Simplify a geometry\n",
"from shapely.geometry import shape, Point\n",
"import random \n",
"def simplify_geometry_to_centroid(geometry):\n",
" geom = shape(geometry)\n",
" centroid = geom.centroid # Calculate the centroid of the polygon\n",
" return mapping(Point(centroid.x, centroid.y)) # Convert centroid to Point geometry\n",
"\n",
"# Apply simplification to each feature in your GeoJSON\n",
"for feature in geojson_data['features']:\n",
" feature['geometry'] = simplify_geometry_to_centroid(feature['geometry'])\n",
"\n",
"#shuffle data \n",
"random.shuffle(geojson_data['features'])\n",
"#only sample half of the data \n",
"geojson_data['features'] = geojson_data['features'][:int(num_points)]\n",
"\n",
"with open('data/simplified_band_1.geojson', 'w') as w:\n",
" json.dump(geojson_data, w, indent=4)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "402ae676",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"#0d0887\n",
"#7d03a8\n",
"#cb4679\n",
"#f89441\n",
"#f0f921\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/fd/f53d2xmj7rv2ks6v0c5q59880000gn/T/ipykernel_27622/1044773481.py:15: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
" colormap = cm.get_cmap(\"plasma\") # Choose your colormap\n"
]
}
],
"source": [
"import matplotlib.cm as cm\n",
"from matplotlib.colors import BoundaryNorm\n",
"import matplotlib.colors as colors\n",
"\n",
"iwi_bins = [0.052, 0.140, 0.161, 0.187, 0.240, 0.696] # Custom bin values\n",
"iwi_labels = [\n",
" \"0.052 – 0.140\",\n",
" \"0.140 – 0.161\",\n",
" \"0.161 – 0.187\",\n",
" \"0.187 – 0.240\",\n",
" \"0.240 – 0.696\"\n",
"]\n",
"\n",
"# Generate a colormap and norm based on the custom bins\n",
"colormap = cm.get_cmap(\"plasma\") # Choose your colormap\n",
"norm = BoundaryNorm(iwi_bins, colormap.N)\n",
"\n",
"# Function to get color based on IWI value\n",
"def get_color(iwi):\n",
" # Find the color for the given IWI value based on the colormap and norm\n",
" rgba = colormap(norm(iwi)) # Convert to RGBA\n",
" return colors.to_hex(rgba) # Convert to HEX\n",
"\n",
"\n",
"print(get_color(0.1))\n",
"print(get_color(0.15))\n",
"print(get_color(0.17))\n",
"print(get_color(0.22))\n",
"print(get_color(0.5))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "myenv",
"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": "3.10.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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