emileten · March 10, 2023 00:38
diff --git a/some_notebook.ipynb b/some_notebook.ipynb
 {
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "054f49cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from folium import Map, TileLayer\n",
    "import json\n",
    "from matplotlib import cm\n",
    "import requests"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c1df385",
   "metadata": {},
   "source": [
    "# Discover data from STAC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e45da7c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'s3://nasa-maap-data-store/file-staging/nasa-map/AfriSAR_AGB_Maps_1681___1/Rabi_AGB_50m.tif'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stac_endpoint = \"https://stac.maap-project.org\"\n",
    "titiler_endpoint = \"https://titiler.dit.maap-project.org\"\n",
    "collection = \"AfriSAR_AGB_Maps_1681\"\n",
    "r = requests.get(f\"{stac_endpoint}/collections/{collection}/items\").json()\n",
    "url = r['features'][0]['assets']['data']['href']\n",
    "url"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e9883c3",
   "metadata": {},
   "source": [
    "# Get data values from the `/statistics` endpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2787cadf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# You can use gdalinfo /vsis3/nasa-maap-data-store/file-staging/nasa-map/SRTMGL1_COD___001/N21W089.SRTMGL1.tif -stats\n",
    "# or you can get metadata from titiler.\n",
    "r = requests.get(\n",
    "    f\"{titiler_endpoint}/cog/statistics\",\n",
    "    params = {\n",
    "        \"url\": url\n",
    "    }\n",
    ").json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "868d860c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'b1': {'min': 0.08733899891376495,\n",
       "  'max': 516.4199829101562,\n",
       "  'mean': 275.47825792468166,\n",
       "  'count': 3691.0,\n",
       "  'sum': 1016790.25,\n",
       "  'std': 77.72327324432969,\n",
       "  'median': 279.57000732421875,\n",
       "  'majority': 245.52000427246094,\n",
       "  'minority': 0.08733899891376495,\n",
       "  'unique': 3463.0,\n",
       "  'histogram': [[40.0,\n",
       "    47.0,\n",
       "    142.0,\n",
       "    386.0,\n",
       "    814.0,\n",
       "    981.0,\n",
       "    831.0,\n",
       "    347.0,\n",
       "    92.0,\n",
       "    11.0],\n",
       "   [0.08733899891376495,\n",
       "    51.720603942871094,\n",
       "    103.35386657714844,\n",
       "    154.9871368408203,\n",
       "    206.62039184570312,\n",
       "    258.253662109375,\n",
       "    309.8869323730469,\n",
       "    361.52020263671875,\n",
       "    413.1534423828125,\n",
       "    464.7867126464844,\n",
       "    516.4199829101562]],\n",
       "  'valid_percent': 34.8,\n",
       "  'masked_pixels': 6914.0,\n",
       "  'valid_pixels': 3691.0,\n",
       "  'percentile_2': 95.27279968261719,\n",
       "  'percentile_98': 421.5600097656249}}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0a086740",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0.08733899891376495, 51.720603942871094],\n",
       " [51.720603942871094, 103.35386657714844],\n",
       " [103.35386657714844, 154.9871368408203],\n",
       " [154.9871368408203, 206.62039184570312],\n",
       " [206.62039184570312, 258.253662109375],\n",
       " [258.253662109375, 309.8869323730469],\n",
       " [309.8869323730469, 361.52020263671875],\n",
       " [361.52020263671875, 413.1534423828125],\n",
       " [413.1534423828125, 464.7867126464844],\n",
       " [464.7867126464844, 516.4199829101562]]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "buckets = r['b1']['histogram'][1]\n",
    "bucket_ranges = [[buckets[i],buckets[i+1]] for i in range(len(buckets)-1)]\n",
    "bucket_ranges"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce6b5b33",
   "metadata": {},
   "source": [
    "# Pick a color map and create a linear mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "78a9cf0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# All colormaps can be found here: https://matplotlib.org/3.1.0/tutorials/colors/colormaps.html\n",
    "sequential_cmaps = [\n",
    "    'Greys', 'Purples', 'Blues', 'Greens', 'Oranges', 'Reds',\n",
    "    'YlOrBr', 'YlOrRd', 'OrRd', 'PuRd', 'RdPu', 'BuPu',\n",
    "    'GnBu', 'PuBu', 'YlGnBu', 'PuBuGn', 'BuGn', 'YlGn'\n",
    "]\n",
    "\n",
    "sequential_cmaps2 = [\n",
    "    'binary', 'gist_yarg', 'gist_gray', 'gray', 'bone', 'pink',\n",
    "    'spring', 'summer', 'autumn', 'winter', 'cool', 'Wistia',\n",
    "    'hot', 'afmhot', 'gist_heat', 'copper']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "daa67372",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[([0.08733899891376495, 51.720603942871094], [0, 0, 0, 255]),\n",
       " ([51.720603942871094, 103.35386657714844], [18, 48, 119, 255]),\n",
       " ([103.35386657714844, 154.9871368408203], [37, 102, 124, 255]),\n",
       " ([154.9871368408203, 206.62039184570312], [54, 135, 111, 255]),\n",
       " ([206.62039184570312, 258.253662109375], [67, 151, 77, 255]),\n",
       " ([258.253662109375, 309.8869323730469], [123, 167, 82, 255]),\n",
       " ([309.8869323730469, 361.52020263671875], [169, 179, 91, 255]),\n",
       " ([361.52020263671875, 413.1534423828125], [191, 164, 100, 255]),\n",
       " ([413.1534423828125, 464.7867126464844], [221, 186, 167, 255]),\n",
       " ([464.7867126464844, 516.4199829101562], [253, 250, 250, 255])]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rgbs = [[int(value) for value in rgb] for rgb in cm.ScalarMappable(cmap='gist_earth').to_rgba(x=buckets[:-1], bytes=True)]\n",
    "color_map = list(zip(bucket_ranges, rgbs))\n",
    "# some tweaking may be necessary\n",
    "color_map"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4c8124d",
   "metadata": {},
   "source": [
    "# Preview the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f77dc66d",
   "metadata": {},
   "outputs": [],
   "source": [
    "cmap = json.dumps(color_map)\n",
    "\n",
    "r = requests.get(\n",
    "    f\"{titiler_endpoint}/cog/tilejson.json\",\n",
    "    params = {\n",
    "        \"url\": url,\n",
    "        \"colormap\": cmap\n",
    "    }\n",
    ").json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f0c7eaac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'tilejson': '2.2.0',\n",
       " 'version': '1.0.0',\n",
       " 'scheme': 'xyz',\n",
       " 'tiles': ['https://titiler.dit.maap-project.org/cog/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?url=s3%3A%2F%2Fnasa-maap-data-store%2Ffile-staging%2Fnasa-map%2FAfriSAR_AGB_Maps_1681___1%2FRabi_AGB_50m.tif&colormap=%5B%5B%5B0.08733899891376495%2C+51.720603942871094%5D%2C+%5B0%2C+0%2C+0%2C+255%5D%5D%2C+%5B%5B51.720603942871094%2C+103.35386657714844%5D%2C+%5B18%2C+48%2C+119%2C+255%5D%5D%2C+%5B%5B103.35386657714844%2C+154.9871368408203%5D%2C+%5B37%2C+102%2C+124%2C+255%5D%5D%2C+%5B%5B154.9871368408203%2C+206.62039184570312%5D%2C+%5B54%2C+135%2C+111%2C+255%5D%5D%2C+%5B%5B206.62039184570312%2C+258.253662109375%5D%2C+%5B67%2C+151%2C+77%2C+255%5D%5D%2C+%5B%5B258.253662109375%2C+309.8869323730469%5D%2C+%5B123%2C+167%2C+82%2C+255%5D%5D%2C+%5B%5B309.8869323730469%2C+361.52020263671875%5D%2C+%5B169%2C+179%2C+91%2C+255%5D%5D%2C+%5B%5B361.52020263671875%2C+413.1534423828125%5D%2C+%5B191%2C+164%2C+100%2C+255%5D%5D%2C+%5B%5B413.1534423828125%2C+464.7867126464844%5D%2C+%5B221%2C+186%2C+167%2C+255%5D%5D%2C+%5B%5B464.7867126464844%2C+516.4199829101562%5D%2C+%5B253%2C+250%2C+250%2C+255%5D%5D%5D'],\n",
       " 'minzoom': 12,\n",
       " 'maxzoom': 12,\n",
       " 'bounds': [9.859138724101811,\n",
       "  -1.9460214484122889,\n",
       "  9.906363400458433,\n",
       "  -1.9003139362380275],\n",
       " 'center': [9.882751062280121, -1.9231676923251582, 12]}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "60213a44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe srcdoc=\"&lt;!DOCTYPE html&gt;\n",
       "&lt;html&gt;\n",
       "&lt;head&gt;\n",
       "    \n",
       "    &lt;meta http-equiv=&quot;content-type&quot; content=&quot;text/html; charset=UTF-8&quot; /&gt;\n",
       "    \n",
       "        &lt;script&gt;\n",
       "            L_NO_TOUCH = false;\n",
       "            L_DISABLE_3D = false;\n",
       "        &lt;/script&gt;\n",
       "    \n",
       "    &lt;style&gt;html, body {width: 100%;height: 100%;margin: 0;padding: 0;}&lt;/style&gt;\n",
       "    &lt;style&gt;#map {position:absolute;top:0;bottom:0;right:0;left:0;}&lt;/style&gt;\n",
       "    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/[email protected]/dist/leaflet.js&quot;&gt;&lt;/script&gt;\n",
       "    &lt;script src=&quot;https://code.jquery.com/jquery-1.12.4.min.js&quot;&gt;&lt;/script&gt;\n",
       "    &lt;script src=&quot;https://cdn.jsdelivr.net/npm/[email protected]/dist/js/bootstrap.bundle.min.js&quot;&gt;&lt;/script&gt;\n",
       "    &lt;script src=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js&quot;&gt;&lt;/script&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/[email protected]/dist/leaflet.css&quot;/&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/[email protected]/dist/css/bootstrap.min.css&quot;/&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://netdna.bootstrapcdn.com/bootstrap/3.0.0/css/bootstrap.min.css&quot;/&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/npm/@fortawesome/[email protected]/css/all.min.css&quot;/&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css&quot;/&gt;\n",
       "    &lt;link rel=&quot;stylesheet&quot; href=&quot;https://cdn.jsdelivr.net/gh/python-visualization/folium/folium/templates/leaflet.awesome.rotate.min.css&quot;/&gt;\n",
       "    \n",
       "            &lt;meta name=&quot;viewport&quot; content=&quot;width=device-width,\n",
       "                initial-scale=1.0, maximum-scale=1.0, user-scalable=no&quot; /&gt;\n",
       "            &lt;style&gt;\n",
       "                #map_492d60febf2415a1f631a74a55f2bf2d {\n",
       "                    position: relative;\n",
       "                    width: 100.0%;\n",
       "                    height: 100.0%;\n",
       "                    left: 0.0%;\n",
       "                    top: 0.0%;\n",
       "                }\n",
       "                .leaflet-container { font-size: 1rem; }\n",
       "            &lt;/style&gt;\n",
       "        \n",
       "&lt;/head&gt;\n",
       "&lt;body&gt;\n",
       "    \n",
       "    \n",
       "            &lt;div class=&quot;folium-map&quot; id=&quot;map_492d60febf2415a1f631a74a55f2bf2d&quot; &gt;&lt;/div&gt;\n",
       "        \n",
       "&lt;/body&gt;\n",
       "&lt;script&gt;\n",
       "    \n",
       "    \n",
       "            var map_492d60febf2415a1f631a74a55f2bf2d = L.map(\n",
       "                &quot;map_492d60febf2415a1f631a74a55f2bf2d&quot;,\n",
       "                {\n",
       "                    center: [-1.9231676923251582, 9.882751062280121],\n",
       "                    crs: L.CRS.EPSG3857,\n",
       "                    zoom: 13,\n",
       "                    zoomControl: true,\n",
       "                    preferCanvas: false,\n",
       "                }\n",
       "            );\n",
       "\n",
       "            \n",
       "\n",
       "        \n",
       "    \n",
       "            var tile_layer_f2c503109a3887e3fb018367d04feeb9 = L.tileLayer(\n",
       "                &quot;https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png&quot;,\n",
       "                {&quot;attribution&quot;: &quot;Data by \\u0026copy; \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://openstreetmap.org\\&quot;\\u003eOpenStreetMap\\u003c/a\\u003e, under \\u003ca target=\\&quot;_blank\\&quot; href=\\&quot;http://www.openstreetmap.org/copyright\\&quot;\\u003eODbL\\u003c/a\\u003e.&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 18, &quot;maxZoom&quot;: 18, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n",
       "            ).addTo(map_492d60febf2415a1f631a74a55f2bf2d);\n",
       "        \n",
       "    \n",
       "            var tile_layer_4d8720612c19f8fd43db2cbfe42122ac = L.tileLayer(\n",
       "                &quot;https://titiler.dit.maap-project.org/cog/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?url=s3%3A%2F%2Fnasa-maap-data-store%2Ffile-staging%2Fnasa-map%2FAfriSAR_AGB_Maps_1681___1%2FRabi_AGB_50m.tif\\u0026colormap=%5B%5B%5B0.08733899891376495%2C+51.720603942871094%5D%2C+%5B0%2C+0%2C+0%2C+255%5D%5D%2C+%5B%5B51.720603942871094%2C+103.35386657714844%5D%2C+%5B18%2C+48%2C+119%2C+255%5D%5D%2C+%5B%5B103.35386657714844%2C+154.9871368408203%5D%2C+%5B37%2C+102%2C+124%2C+255%5D%5D%2C+%5B%5B154.9871368408203%2C+206.62039184570312%5D%2C+%5B54%2C+135%2C+111%2C+255%5D%5D%2C+%5B%5B206.62039184570312%2C+258.253662109375%5D%2C+%5B67%2C+151%2C+77%2C+255%5D%5D%2C+%5B%5B258.253662109375%2C+309.8869323730469%5D%2C+%5B123%2C+167%2C+82%2C+255%5D%5D%2C+%5B%5B309.8869323730469%2C+361.52020263671875%5D%2C+%5B169%2C+179%2C+91%2C+255%5D%5D%2C+%5B%5B361.52020263671875%2C+413.1534423828125%5D%2C+%5B191%2C+164%2C+100%2C+255%5D%5D%2C+%5B%5B413.1534423828125%2C+464.7867126464844%5D%2C+%5B221%2C+186%2C+167%2C+255%5D%5D%2C+%5B%5B464.7867126464844%2C+516.4199829101562%5D%2C+%5B253%2C+250%2C+250%2C+255%5D%5D%5D&quot;,\n",
       "                {&quot;attribution&quot;: &quot;Swisstopo&quot;, &quot;detectRetina&quot;: false, &quot;maxNativeZoom&quot;: 18, &quot;maxZoom&quot;: 18, &quot;minZoom&quot;: 0, &quot;noWrap&quot;: false, &quot;opacity&quot;: 1, &quot;subdomains&quot;: &quot;abc&quot;, &quot;tms&quot;: false}\n",
       "            ).addTo(map_492d60febf2415a1f631a74a55f2bf2d);\n",
       "        \n",
       "&lt;/script&gt;\n",
       "&lt;/html&gt;\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x7f2524e9b310>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "bounds = r[\"bounds\"]\n",
    "m = Map(\n",
    "    location=((bounds[1] + bounds[3]) / 2,(bounds[0] + bounds[2]) / 2),\n",
    "    zoom_start=r[\"minzoom\"] + 1\n",
    ")\n",
    "\n",
    "aod_layer = TileLayer(\n",
    "    tiles=r[\"tiles\"][0],\n",
    "    opacity=1,\n",
    "    attr=\"Swisstopo\"\n",
    ")\n",
    "aod_layer.add_to(m)\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c19261f",
   "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": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
 }
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 31,
	"id": "054f49cc",
	"metadata": {},
	"outputs": [],
	"source": [
	"from folium import Map, TileLayer\n",
	"import json\n",
	"from matplotlib import cm\n",
	"import requests"
	]
	},
	{
	"cell_type": "markdown",
	"id": "1c1df385",
	"metadata": {},
	"source": [
	"# Discover data from STAC"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 32,
	"id": "e45da7c7",
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"'s3://nasa-maap-data-store/file-staging/nasa-map/AfriSAR_AGB_Maps_1681___1/Rabi_AGB_50m.tif'"
	]
	},
	"execution_count": 32,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"stac_endpoint = \"https://stac.maap-project.org\"\n",
	"titiler_endpoint = \"https://titiler.dit.maap-project.org\"\n",
	"collection = \"AfriSAR_AGB_Maps_1681\"\n",
	"r = requests.get(f\"{stac_endpoint}/collections/{collection}/items\").json()\n",
	"url = r['features'][0]['assets']['data']['href']\n",
	"url"
	]
	},
	{
	"cell_type": "markdown",
	"id": "8e9883c3",
	"metadata": {},
	"source": [
	"# Get data values from the `/statistics` endpoint"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 33,
	"id": "2787cadf",
	"metadata": {},
	"outputs": [],
	"source": [
	"# You can use gdalinfo /vsis3/nasa-maap-data-store/file-staging/nasa-map/SRTMGL1_COD___001/N21W089.SRTMGL1.tif -stats\n",
	"# or you can get metadata from titiler.\n",
	"r = requests.get(\n",
	" f\"{titiler_endpoint}/cog/statistics\",\n",
	" params = {\n",
	" \"url\": url\n",
	" }\n",
	").json()"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 34,
	"id": "868d860c",
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"{'b1': {'min': 0.08733899891376495,\n",
	" 'max': 516.4199829101562,\n",
	" 'mean': 275.47825792468166,\n",
	" 'count': 3691.0,\n",
	" 'sum': 1016790.25,\n",
	" 'std': 77.72327324432969,\n",
	" 'median': 279.57000732421875,\n",
	" 'majority': 245.52000427246094,\n",
	" 'minority': 0.08733899891376495,\n",
	" 'unique': 3463.0,\n",
	" 'histogram': [[40.0,\n",
	" 47.0,\n",
	" 142.0,\n",
	" 386.0,\n",
	" 814.0,\n",
	" 981.0,\n",
	" 831.0,\n",
	" 347.0,\n",
	" 92.0,\n",
	" 11.0],\n",
	" [0.08733899891376495,\n",
	" 51.720603942871094,\n",
	" 103.35386657714844,\n",
	" 154.9871368408203,\n",
	" 206.62039184570312,\n",
	" 258.253662109375,\n",
	" 309.8869323730469,\n",
	" 361.52020263671875,\n",
	" 413.1534423828125,\n",
	" 464.7867126464844,\n",
	" 516.4199829101562]],\n",
	" 'valid_percent': 34.8,\n",
	" 'masked_pixels': 6914.0,\n",
	" 'valid_pixels': 3691.0,\n",
	" 'percentile_2': 95.27279968261719,\n",
	" 'percentile_98': 421.5600097656249}}"
	]
	},
	"execution_count": 34,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"r"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 35,
	"id": "0a086740",
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"[[0.08733899891376495, 51.720603942871094],\n",
	" [51.720603942871094, 103.35386657714844],\n",
	" [103.35386657714844, 154.9871368408203],\n",
	" [154.9871368408203, 206.62039184570312],\n",
	" [206.62039184570312, 258.253662109375],\n",
	" [258.253662109375, 309.8869323730469],\n",
	" [309.8869323730469, 361.52020263671875],\n",
	" [361.52020263671875, 413.1534423828125],\n",
	" [413.1534423828125, 464.7867126464844],\n",
	" [464.7867126464844, 516.4199829101562]]"
	]
	},
	"execution_count": 35,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"buckets = r['b1']['histogram'][1]\n",
	"bucket_ranges = [[buckets[i],buckets[i+1]] for i in range(len(buckets)-1)]\n",
	"bucket_ranges"
	]
	},
	{
	"cell_type": "markdown",
	"id": "ce6b5b33",
	"metadata": {},
	"source": [
	"# Pick a color map and create a linear mapping"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 36,
	"id": "78a9cf0d",
	"metadata": {},
	"outputs": [],
	"source": [
	"# All colormaps can be found here: https://matplotlib.org/3.1.0/tutorials/colors/colormaps.html\n",
	"sequential_cmaps = [\n",
	" 'Greys', 'Purples', 'Blues', 'Greens', 'Oranges', 'Reds',\n",
	" 'YlOrBr', 'YlOrRd', 'OrRd', 'PuRd', 'RdPu', 'BuPu',\n",
	" 'GnBu', 'PuBu', 'YlGnBu', 'PuBuGn', 'BuGn', 'YlGn'\n",
	"]\n",
	"\n",
	"sequential_cmaps2 = [\n",
	" 'binary', 'gist_yarg', 'gist_gray', 'gray', 'bone', 'pink',\n",
	" 'spring', 'summer', 'autumn', 'winter', 'cool', 'Wistia',\n",
	" 'hot', 'afmhot', 'gist_heat', 'copper']"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 37,
	"id": "daa67372",
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"[([0.08733899891376495, 51.720603942871094], [0, 0, 0, 255]),\n",
	" ([51.720603942871094, 103.35386657714844], [18, 48, 119, 255]),\n",
	" ([103.35386657714844, 154.9871368408203], [37, 102, 124, 255]),\n",
	" ([154.9871368408203, 206.62039184570312], [54, 135, 111, 255]),\n",
	" ([206.62039184570312, 258.253662109375], [67, 151, 77, 255]),\n",
	" ([258.253662109375, 309.8869323730469], [123, 167, 82, 255]),\n",
	" ([309.8869323730469, 361.52020263671875], [169, 179, 91, 255]),\n",
	" ([361.52020263671875, 413.1534423828125], [191, 164, 100, 255]),\n",
	" ([413.1534423828125, 464.7867126464844], [221, 186, 167, 255]),\n",
	" ([464.7867126464844, 516.4199829101562], [253, 250, 250, 255])]"
	]
	},
	"execution_count": 37,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"rgbs = [[int(value) for value in rgb] for rgb in cm.ScalarMappable(cmap='gist_earth').to_rgba(x=buckets[:-1], bytes=True)]\n",
	"color_map = list(zip(bucket_ranges, rgbs))\n",
	"# some tweaking may be necessary\n",
	"color_map"
	]
	},
	{
	"cell_type": "markdown",
	"id": "c4c8124d",
	"metadata": {},
	"source": [
	"# Preview the data"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 38,
	"id": "f77dc66d",
	"metadata": {},
	"outputs": [],
	"source": [
	"cmap = json.dumps(color_map)\n",
	"\n",
	"r = requests.get(\n",
	" f\"{titiler_endpoint}/cog/tilejson.json\",\n",
	" params = {\n",
	" \"url\": url,\n",
	" \"colormap\": cmap\n",
	" }\n",
	").json()"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 39,
	"id": "f0c7eaac",
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"{'tilejson': '2.2.0',\n",
	" 'version': '1.0.0',\n",
	" 'scheme': 'xyz',\n",
	" 'tiles': ['https://titiler.dit.maap-project.org/cog/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?url=s3%3A%2F%2Fnasa-maap-data-store%2Ffile-staging%2Fnasa-map%2FAfriSAR_AGB_Maps_1681___1%2FRabi_AGB_50m.tif&colormap=%5B%5B%5B0.08733899891376495%2C+51.720603942871094%5D%2C+%5B0%2C+0%2C+0%2C+255%5D%5D%2C+%5B%5B51.720603942871094%2C+103.35386657714844%5D%2C+%5B18%2C+48%2C+119%2C+255%5D%5D%2C+%5B%5B103.35386657714844%2C+154.9871368408203%5D%2C+%5B37%2C+102%2C+124%2C+255%5D%5D%2C+%5B%5B154.9871368408203%2C+206.62039184570312%5D%2C+%5B54%2C+135%2C+111%2C+255%5D%5D%2C+%5B%5B206.62039184570312%2C+258.253662109375%5D%2C+%5B67%2C+151%2C+77%2C+255%5D%5D%2C+%5B%5B258.253662109375%2C+309.8869323730469%5D%2C+%5B123%2C+167%2C+82%2C+255%5D%5D%2C+%5B%5B309.8869323730469%2C+361.52020263671875%5D%2C+%5B169%2C+179%2C+91%2C+255%5D%5D%2C+%5B%5B361.52020263671875%2C+413.1534423828125%5D%2C+%5B191%2C+164%2C+100%2C+255%5D%5D%2C+%5B%5B413.1534423828125%2C+464.7867126464844%5D%2C+%5B221%2C+186%2C+167%2C+255%5D%5D%2C+%5B%5B464.7867126464844%2C+516.4199829101562%5D%2C+%5B253%2C+250%2C+250%2C+255%5D%5D%5D'],\n",
	" 'minzoom': 12,\n",
	" 'maxzoom': 12,\n",
	" 'bounds': [9.859138724101811,\n",
	" -1.9460214484122889,\n",
	" 9.906363400458433,\n",
	" -1.9003139362380275],\n",
	" 'center': [9.882751062280121, -1.9231676923251582, 12]}"
	]
	},
	"execution_count": 39,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"r"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 40,
	"id": "60213a44",
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/html": [
	"<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe srcdoc=\"<!DOCTYPE html>\n",
	"<html>\n",
	"<head>\n",
	" \n",
	" <meta http-equiv="content-type" content="text/html; charset=UTF-8" />\n",
	" \n",
	" <script>\n",
	" L_NO_TOUCH = false;\n",
	" L_DISABLE_3D = false;\n",
	" </script>\n",
	" \n",
	" <style>html, body {width: 100%;height: 100%;margin: 0;padding: 0;}</style>\n",
	" <style>#map {position:absolute;top:0;bottom:0;right:0;left:0;}</style>\n",
	" <script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/leaflet.js"></script>\n",
	" <script src="https://code.jquery.com/jquery-1.12.4.min.js"></script>\n",
	" <script src="https://cdn.jsdelivr.net/npm/[email protected]/dist/js/bootstrap.bundle.min.js"></script>\n",
	" <script src="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.js"></script>\n",
	" <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/leaflet.css"/>\n",
	" <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/css/bootstrap.min.css"/>\n",
	" <link rel="stylesheet" href="https://netdna.bootstrapcdn.com/bootstrap/3.0.0/css/bootstrap.min.css"/>\n",
	" <link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@fortawesome/[email protected]/css/all.min.css"/>\n",
	" <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/Leaflet.awesome-markers/2.0.2/leaflet.awesome-markers.css"/>\n",
	" <link rel="stylesheet" href="https://cdn.jsdelivr.net/gh/python-visualization/folium/folium/templates/leaflet.awesome.rotate.min.css"/>\n",
	" \n",
	" <meta name="viewport" content="width=device-width,\n",
	" initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n",
	" <style>\n",
	" #map_492d60febf2415a1f631a74a55f2bf2d {\n",
	" position: relative;\n",
	" width: 100.0%;\n",
	" height: 100.0%;\n",
	" left: 0.0%;\n",
	" top: 0.0%;\n",
	" }\n",
	" .leaflet-container { font-size: 1rem; }\n",
	" </style>\n",
	" \n",
	"</head>\n",
	"<body>\n",
	" \n",
	" \n",
	" <div class="folium-map" id="map_492d60febf2415a1f631a74a55f2bf2d" ></div>\n",
	" \n",
	"</body>\n",
	"<script>\n",
	" \n",
	" \n",
	" var map_492d60febf2415a1f631a74a55f2bf2d = L.map(\n",
	" "map_492d60febf2415a1f631a74a55f2bf2d",\n",
	" {\n",
	" center: [-1.9231676923251582, 9.882751062280121],\n",
	" crs: L.CRS.EPSG3857,\n",
	" zoom: 13,\n",
	" zoomControl: true,\n",
	" preferCanvas: false,\n",
	" }\n",
	" );\n",
	"\n",
	" \n",
	"\n",
	" \n",
	" \n",
	" var tile_layer_f2c503109a3887e3fb018367d04feeb9 = L.tileLayer(\n",
	" "https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",\n",
	" {"attribution": "Data by \\u0026copy; \\u003ca target=\\"_blank\\" href=\\"http://openstreetmap.org\\"\\u003eOpenStreetMap\\u003c/a\\u003e, under \\u003ca target=\\"_blank\\" href=\\"http://www.openstreetmap.org/copyright\\"\\u003eODbL\\u003c/a\\u003e.", "detectRetina": false, "maxNativeZoom": 18, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}\n",
	" ).addTo(map_492d60febf2415a1f631a74a55f2bf2d);\n",
	" \n",
	" \n",
	" var tile_layer_4d8720612c19f8fd43db2cbfe42122ac = L.tileLayer(\n",
	" "https://titiler.dit.maap-project.org/cog/tiles/WebMercatorQuad/{z}/{x}/{y}@1x?url=s3%3A%2F%2Fnasa-maap-data-store%2Ffile-staging%2Fnasa-map%2FAfriSAR_AGB_Maps_1681___1%2FRabi_AGB_50m.tif\\u0026colormap=%5B%5B%5B0.08733899891376495%2C+51.720603942871094%5D%2C+%5B0%2C+0%2C+0%2C+255%5D%5D%2C+%5B%5B51.720603942871094%2C+103.35386657714844%5D%2C+%5B18%2C+48%2C+119%2C+255%5D%5D%2C+%5B%5B103.35386657714844%2C+154.9871368408203%5D%2C+%5B37%2C+102%2C+124%2C+255%5D%5D%2C+%5B%5B154.9871368408203%2C+206.62039184570312%5D%2C+%5B54%2C+135%2C+111%2C+255%5D%5D%2C+%5B%5B206.62039184570312%2C+258.253662109375%5D%2C+%5B67%2C+151%2C+77%2C+255%5D%5D%2C+%5B%5B258.253662109375%2C+309.8869323730469%5D%2C+%5B123%2C+167%2C+82%2C+255%5D%5D%2C+%5B%5B309.8869323730469%2C+361.52020263671875%5D%2C+%5B169%2C+179%2C+91%2C+255%5D%5D%2C+%5B%5B361.52020263671875%2C+413.1534423828125%5D%2C+%5B191%2C+164%2C+100%2C+255%5D%5D%2C+%5B%5B413.1534423828125%2C+464.7867126464844%5D%2C+%5B221%2C+186%2C+167%2C+255%5D%5D%2C+%5B%5B464.7867126464844%2C+516.4199829101562%5D%2C+%5B253%2C+250%2C+250%2C+255%5D%5D%5D",\n",
	" {"attribution": "Swisstopo", "detectRetina": false, "maxNativeZoom": 18, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}\n",
	" ).addTo(map_492d60febf2415a1f631a74a55f2bf2d);\n",
	" \n",
	"</script>\n",
	"</html>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
	],
	"text/plain": [
	"<folium.folium.Map at 0x7f2524e9b310>"
	]
	},
	"execution_count": 40,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"\n",
	"bounds = r[\"bounds\"]\n",
	"m = Map(\n",
	" location=((bounds[1] + bounds[3]) / 2,(bounds[0] + bounds[2]) / 2),\n",
	" zoom_start=r[\"minzoom\"] + 1\n",
	")\n",
	"\n",
	"aod_layer = TileLayer(\n",
	" tiles=r[\"tiles\"][0],\n",
	" opacity=1,\n",
	" attr=\"Swisstopo\"\n",
	")\n",
	"aod_layer.add_to(m)\n",
	"m"
	]
	},
	{
	"cell_type": "code",
	"execution_count": null,
	"id": "9c19261f",
	"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": "3.7.8"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 5
	}