{
 "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))"
   ]
  }
 ],
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