GEE_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Google Earth Engine Python examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First of all, let's set the authentication info for\n",
    "# API Highways.\n",
    "api_base = \"https://api.apihighways.org/v1\"\n",
    "credentials = input()\n",
    "api_headers = {\n",
    "    \"Content-Type\": \"application/json\",\n",
    "    \"User-Agent\": \"Python3.6 requests\",\n",
    "    \"Authorization\": f\"Bearer: {credentials}\"\n",
    "}\n",
    "\n",
    "# 👇 Remove the output below! DON'T COMMIT!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Making request to https://api.apihighways.org/v1/dataset?page[size]=1000&provider=gee\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'id': 'bada9e1b-321c-4db2-a44f-4d56cc7a55e2',\n",
       "  'type': 'dataset',\n",
       "  'attributes': {'name': 'GPWv4: Gridded Population of the World Version 4, Ancillary Data Grids',\n",
       "   'slug': 'GPWv4-Gridded-Population-of-the-World-Version-4-Ancillary-Data-Grids',\n",
       "   'type': None,\n",
       "   'dataPath': None,\n",
       "   'attributesPath': None,\n",
       "   'connectorType': 'rest',\n",
       "   'provider': 'gee',\n",
       "   'userId': '5aad5c86a61d3ddd586e5861',\n",
       "   'connectorUrl': None,\n",
       "   'tableName': 'CIESIN/GPWv4/ancillary-data-grids',\n",
       "   'status': 'saved',\n",
       "   'published': True,\n",
       "   'sandbox': False,\n",
       "   'overwrite': False,\n",
       "   'verified': False,\n",
       "   'blockchain': {},\n",
       "   'subscribable': {},\n",
       "   'env': 'production',\n",
       "   'geoInfo': False,\n",
       "   'protected': False,\n",
       "   'legend': {'date': [], 'region': [], 'country': [], 'nested': []},\n",
       "   'clonedHost': {},\n",
       "   'errorMessage': '',\n",
       "   'taskId': None,\n",
       "   'createdAt': '2018-06-08T14:04:11.981Z',\n",
       "   'updatedAt': '2018-06-08T14:04:13.209Z'}},\n",
       " {'id': 'f527854a-e6bd-4147-a2ea-ca7c530beec2',\n",
       "  'type': 'dataset',\n",
       "  'attributes': {'name': 'JRC Global Surface Water Mapping Layers, v1.0',\n",
       "   'slug': 'JRC-Global-Surface-Water-Mapping-Layers-v10',\n",
       "   'type': None,\n",
       "   'dataPath': None,\n",
       "   'attributesPath': None,\n",
       "   'connectorType': 'rest',\n",
       "   'provider': 'gee',\n",
       "   'userId': '5aad5c86a61d3ddd586e5861',\n",
       "   'connectorUrl': None,\n",
       "   'tableName': 'JRC/GSW1_0/GlobalSurfaceWater',\n",
       "   'status': 'saved',\n",
       "   'published': True,\n",
       "   'sandbox': False,\n",
       "   'overwrite': False,\n",
       "   'verified': False,\n",
       "   'blockchain': {},\n",
       "   'subscribable': {},\n",
       "   'env': 'production',\n",
       "   'geoInfo': False,\n",
       "   'protected': False,\n",
       "   'legend': {'date': [], 'region': [], 'country': [], 'nested': []},\n",
       "   'clonedHost': {},\n",
       "   'errorMessage': '',\n",
       "   'taskId': None,\n",
       "   'createdAt': '2018-06-08T14:04:20.062Z',\n",
       "   'updatedAt': '2018-06-08T14:04:20.852Z'}},\n",
       " {'id': '3187281b-3ad3-4720-ada2-5151a4fa0cda',\n",
       "  'type': 'dataset',\n",
       "  'attributes': {'name': 'GlobCover: Global Land Cover Map',\n",
       "   'slug': 'GlobCover-Global-Land-Cover-Map',\n",
       "   'type': None,\n",
       "   'dataPath': None,\n",
       "   'attributesPath': None,\n",
       "   'connectorType': 'rest',\n",
       "   'provider': 'gee',\n",
       "   'userId': '5aad5c86a61d3ddd586e5861',\n",
       "   'connectorUrl': None,\n",
       "   'tableName': 'ESA/GLOBCOVER_L4_200901_200912_V2_3',\n",
       "   'status': 'saved',\n",
       "   'published': True,\n",
       "   'sandbox': False,\n",
       "   'overwrite': False,\n",
       "   'verified': False,\n",
       "   'blockchain': {},\n",
       "   'subscribable': {},\n",
       "   'env': 'production',\n",
       "   'geoInfo': False,\n",
       "   'protected': False,\n",
       "   'legend': {'date': [], 'region': [], 'country': [], 'nested': []},\n",
       "   'clonedHost': {},\n",
       "   'errorMessage': '',\n",
       "   'taskId': None,\n",
       "   'createdAt': '2018-06-08T14:04:27.905Z',\n",
       "   'updatedAt': '2018-06-08T14:04:28.513Z'}},\n",
       " {'id': 'e4c43dc4-52d8-4f98-bed1-ccb4779601b8',\n",
       "  'type': 'dataset',\n",
       "  'attributes': {'name': 'GFSAD1000: Cropland Extent 1km Crop Dominance, Global Food-Support Analysis Data',\n",
       "   'slug': 'GFSAD1000-Cropland-Extent-1km-Crop-Dominance-Global-Food-Support-Analysis-Data',\n",
       "   'type': None,\n",
       "   'dataPath': None,\n",
       "   'attributesPath': None,\n",
       "   'connectorType': 'rest',\n",
       "   'provider': 'gee',\n",
       "   'userId': '5aad5c86a61d3ddd586e5861',\n",
       "   'connectorUrl': None,\n",
       "   'tableName': 'USGS/GFSAD1000_V0',\n",
       "   'status': 'saved',\n",
       "   'published': True,\n",
       "   'sandbox': False,\n",
       "   'overwrite': False,\n",
       "   'verified': False,\n",
       "   'blockchain': {},\n",
       "   'subscribable': {},\n",
       "   'env': 'production',\n",
       "   'geoInfo': False,\n",
       "   'protected': False,\n",
       "   'legend': {'date': [], 'region': [], 'country': [], 'nested': []},\n",
       "   'clonedHost': {},\n",
       "   'errorMessage': '',\n",
       "   'taskId': None,\n",
       "   'createdAt': '2018-06-08T14:04:35.644Z',\n",
       "   'updatedAt': '2018-06-08T14:04:36.409Z'}},\n",
       " {'id': 'de6118de-f898-45b1-a3c2-9ad2295cd193',\n",
       "  'type': 'dataset',\n",
       "  'attributes': {'name': 'GFSAD1000: Cropland Extent 1km Multi-Study Crop Mask, Global Food-Support Analysis Data',\n",
       "   'slug': 'GFSAD1000-Cropland-Extent-1km-Multi-Study-Crop-Mask-Global-Food-Support-Analysis-Data',\n",
       "   'type': None,\n",
       "   'dataPath': None,\n",
       "   'attributesPath': None,\n",
       "   'connectorType': 'rest',\n",
       "   'provider': 'gee',\n",
       "   'userId': '5aad5c86a61d3ddd586e5861',\n",
       "   'connectorUrl': None,\n",
       "   'tableName': 'USGS/GFSAD1000_V1',\n",
       "   'status': 'saved',\n",
       "   'published': True,\n",
       "   'sandbox': False,\n",
       "   'overwrite': False,\n",
       "   'verified': False,\n",
       "   'blockchain': {},\n",
       "   'subscribable': {},\n",
       "   'env': 'production',\n",
       "   'geoInfo': False,\n",
       "   'protected': False,\n",
       "   'legend': {'date': [], 'region': [], 'country': [], 'nested': []},\n",
       "   'clonedHost': {},\n",
       "   'errorMessage': '',\n",
       "   'taskId': None,\n",
       "   'createdAt': '2018-06-08T14:04:43.369Z',\n",
       "   'updatedAt': '2018-06-08T14:04:44.096Z'}},\n",
       " {'id': 'a60ade48-ab95-4612-9bfa-8bd43802fe2a',\n",
       "  'type': 'dataset',\n",
       "  'attributes': {'name': 'FORMA Global Forest Watch Deforestation Alerts, 500m',\n",
       "   'slug': 'FORMA-Global-Forest-Watch-Deforestation-Alerts-500m',\n",
       "   'type': None,\n",
       "   'dataPath': None,\n",
       "   'attributesPath': None,\n",
       "   'connectorType': 'rest',\n",
       "   'provider': 'gee',\n",
       "   'userId': '5aad5c86a61d3ddd586e5861',\n",
       "   'connectorUrl': None,\n",
       "   'tableName': 'FORMA/FORMA_500m',\n",
       "   'status': 'saved',\n",
       "   'published': True,\n",
       "   'sandbox': False,\n",
       "   'overwrite': False,\n",
       "   'verified': False,\n",
       "   'blockchain': {},\n",
       "   'subscribable': {},\n",
       "   'env': 'production',\n",
       "   'geoInfo': False,\n",
       "   'protected': False,\n",
       "   'legend': {'date': [], 'region': [], 'country': [], 'nested': []},\n",
       "   'clonedHost': {},\n",
       "   'errorMessage': '',\n",
       "   'taskId': None,\n",
       "   'createdAt': '2018-06-08T14:04:51.180Z',\n",
       "   'updatedAt': '2018-06-08T14:04:51.842Z'}},\n",
       " {'id': '37da3590-49af-41b3-bcb4-1e67c6641ad3',\n",
       "  'type': 'dataset',\n",
       "  'attributes': {'name': 'Hansen Global Forest Change v1.0 (2000-2012)',\n",
       "   'slug': 'Hansen-Global-Forest-Change-v10-2000-2012',\n",
       "   'type': None,\n",
       "   'dataPath': None,\n",
       "   'attributesPath': None,\n",
       "   'connectorType': 'rest',\n",
       "   'provider': 'gee',\n",
       "   'userId': '5aad5c86a61d3ddd586e5861',\n",
       "   'connectorUrl': None,\n",
       "   'tableName': 'UMD/hansen/global_forest_change_2013',\n",
       "   'status': 'saved',\n",
       "   'published': True,\n",
       "   'sandbox': False,\n",
       "   'overwrite': False,\n",
       "   'verified': False,\n",
       "   'blockchain': {},\n",
       "   'subscribable': {},\n",
       "   'env': 'production',\n",
       "   'geoInfo': False,\n",
       "   'protected': False,\n",
       "   'legend': {'date': [], 'region': [], 'country': [], 'nested': []},\n",
       "   'clonedHost': {},\n",
       "   'errorMessage': '',\n",
       "   'taskId': None,\n",
       "   'createdAt': '2018-06-08T14:04:59.019Z',\n",
       "   'updatedAt': '2018-06-08T14:04:59.746Z'}},\n",
       " {'id': '6ad23a9d-e967-41b4-82c2-e2e9b61c0bf5',\n",
       "  'type': 'dataset',\n",
       "  'attributes': {'name': 'Hansen Global Forest Change v1.1 (2000-2013)',\n",
       "   'slug': 'Hansen-Global-Forest-Change-v11-2000-2013',\n",
       "   'type': None,\n",
       "   'dataPath': None,\n",
       "   'attributesPath': None,\n",
       "   'connectorType': 'rest',\n",
       "   'provider': 'gee',\n",
       "   'userId': '5aad5c86a61d3ddd586e5861',\n",
       "   'connectorUrl': None,\n",
       "   'tableName': 'UMD/hansen/global_forest_change_2014',\n",
       "   'status': 'saved',\n",
       "   'published': True,\n",
       "   'sandbox': False,\n",
       "   'overwrite': False,\n",
       "   'verified': False,\n",
       "   'blockchain': {},\n",
       "   'subscribable': {},\n",
       "   'env': 'production',\n",
       "   'geoInfo': False,\n",
       "   'protected': False,\n",
       "   'legend': {'date': [], 'region': [], 'country': [], 'nested': []},\n",
       "   'clonedHost': {},\n",
       "   'errorMessage': '',\n",
       "   'taskId': None,\n",
       "   'createdAt': '2018-06-08T14:05:06.854Z',\n",
       "   'updatedAt': '2018-06-08T14:05:07.492Z'}},\n",
       " {'id': 'fd2e6a2e-82cd-46c9-a6c4-99a59797e960',\n",
       "  'type': 'dataset',\n",
       "  'attributes': {'name': 'Hansen Global Forest Change v1.2 (2000-2014)',\n",
       "   'slug': 'Hansen-Global-Forest-Change-v12-2000-2014',\n",
       "   'type': None,\n",
       "   'dataPath': None,\n",
       "   'attributesPath': None,\n",
       "   'connectorType': 'rest',\n",
       "   'provider': 'gee',\n",
       "   'userId': '5aad5c86a61d3ddd586e5861',\n",
       "   'connectorUrl': None,\n",
       "   'tableName': 'UMD/hansen/global_forest_change_2015',\n",
       "   'status': 'saved',\n",
       "   'published': True,\n",
       "   'sandbox': False,\n",
       "   'overwrite': False,\n",
       "   'verified': False,\n",
       "   'blockchain': {},\n",
       "   'subscribable': {},\n",
       "   'env': 'production',\n",
       "   'geoInfo': False,\n",
       "   'protected': False,\n",
       "   'legend': {'date': [], 'region': [], 'country': [], 'nested': []},\n",
       "   'clonedHost': {},\n",
       "   'errorMessage': '',\n",
       "   'taskId': None,\n",
       "   'createdAt': '2018-06-08T14:05:14.702Z',\n",
       "   'updatedAt': '2018-06-08T14:05:15.651Z'}},\n",
       " {'id': '5222b3b5-4564-4133-8b2a-7bc8380194bb',\n",
       "  'type': 'dataset',\n",
       "  'attributes': {'name': 'Hansen Global Forest Change v1.3 (2000-2015)',\n",
       "   'slug': 'Hansen-Global-Forest-Change-v13-2000-2015',\n",
       "   'type': None,\n",
       "   'dataPath': None,\n",
       "   'attributesPath': None,\n",
       "   'connectorType': 'rest',\n",
       "   'provider': 'gee',\n",
       "   'userId': '5aad5c86a61d3ddd586e5861',\n",
       "   'connectorUrl': None,\n",
       "   'tableName': 'UMD/hansen/global_forest_change_2015_v1_3',\n",
       "   'status': 'saved',\n",
       "   'published': True,\n",
       "   'sandbox': False,\n",
       "   'overwrite': False,\n",
       "   'verified': False,\n",
       "   'blockchain': {},\n",
       "   'subscribable': {},\n",
       "   'env': 'production',\n",
       "   'geoInfo': False,\n",
       "   'protected': False,\n",
       "   'legend': {'date': [], 'region': [], 'country': [], 'nested': []},\n",
       "   'clonedHost': {},\n",
       "   'errorMessage': '',\n",
       "   'taskId': None,\n",
       "   'createdAt': '2018-06-08T14:05:22.689Z',\n",
       "   'updatedAt': '2018-06-08T14:05:23.484Z'}},\n",
       " {'id': '54aba790-03df-4f3d-90e3-9bca0bde0c18',\n",
       "  'type': 'dataset',\n",
       "  'attributes': {'name': 'Hansen Global Forest Change v1.4 (2000-2016)',\n",
       "   'slug': 'Hansen-Global-Forest-Change-v14-2000-2016',\n",
       "   'type': None,\n",
       "   'dataPath': None,\n",
       "   'attributesPath': None,\n",
       "   'connectorType': 'rest',\n",
       "   'provider': 'gee',\n",
       "   'userId': '5aad5c86a61d3ddd586e5861',\n",
       "   'connectorUrl': None,\n",
       "   'tableName': 'UMD/hansen/global_forest_change_2016_v1_4',\n",
       "   'status': 'saved',\n",
       "   'published': True,\n",
       "   'sandbox': False,\n",
       "   'overwrite': False,\n",
       "   'verified': False,\n",
       "   'blockchain': {},\n",
       "   'subscribable': {},\n",
       "   'env': 'production',\n",
       "   'geoInfo': False,\n",
       "   'protected': False,\n",
       "   'legend': {'date': [], 'region': [], 'country': [], 'nested': []},\n",
       "   'clonedHost': {},\n",
       "   'errorMessage': '',\n",
       "   'taskId': None,\n",
       "   'createdAt': '2018-06-08T14:05:30.579Z',\n",
       "   'updatedAt': '2018-06-08T14:05:31.164Z'}},\n",
       " {'id': '62ab4595-4877-4725-a167-aa7072a97a82',\n",
       "  'type': 'dataset',\n",
       "  'attributes': {'name': 'Accessibility to Cities 2015',\n",
       "   'slug': 'Accessibility-to-Cities-2015',\n",
       "   'type': None,\n",
       "   'dataPath': None,\n",
       "   'attributesPath': None,\n",
       "   'connectorType': 'rest',\n",
       "   'provider': 'gee',\n",
       "   'userId': '5aad5c86a61d3ddd586e5861',\n",
       "   'connectorUrl': None,\n",
       "   'tableName': 'Oxford/MAP/accessibility_to_cities_2015_v1_0',\n",
       "   'status': 'saved',\n",
       "   'published': True,\n",
       "   'sandbox': False,\n",
       "   'overwrite': False,\n",
       "   'verified': False,\n",
       "   'blockchain': {},\n",
       "   'subscribable': {},\n",
       "   'env': 'production',\n",
       "   'geoInfo': False,\n",
       "   'protected': False,\n",
       "   'legend': {'date': [], 'region': [], 'country': [], 'nested': []},\n",
       "   'clonedHost': {},\n",
       "   'errorMessage': '',\n",
       "   'taskId': None,\n",
       "   'createdAt': '2018-06-08T14:05:38.266Z',\n",
       "   'updatedAt': '2018-06-08T14:05:38.822Z'}},\n",
       " {'id': '77a03a98-be7f-4409-823d-1a1442c3653a',\n",
       "  'type': 'dataset',\n",
       "  'attributes': {'name': 'Global Friction Surface 2015',\n",
       "   'slug': 'Global-Friction-Surface-2015',\n",
       "   'type': None,\n",
       "   'dataPath': None,\n",
       "   'attributesPath': None,\n",
       "   'connectorType': 'rest',\n",
       "   'provider': 'gee',\n",
       "   'userId': '5aad5c86a61d3ddd586e5861',\n",
       "   'connectorUrl': None,\n",
       "   'tableName': 'Oxford/MAP/friction_surface_2015_v1_0',\n",
       "   'status': 'saved',\n",
       "   'published': True,\n",
       "   'sandbox': False,\n",
       "   'overwrite': False,\n",
       "   'verified': False,\n",
       "   'blockchain': {},\n",
       "   'subscribable': {},\n",
       "   'env': 'production',\n",
       "   'geoInfo': False,\n",
       "   'protected': False,\n",
       "   'legend': {'date': [], 'region': [], 'country': [], 'nested': []},\n",
       "   'clonedHost': {},\n",
       "   'errorMessage': '',\n",
       "   'taskId': None,\n",
       "   'createdAt': '2018-06-08T14:05:46.145Z',\n",
       "   'updatedAt': '2018-06-08T14:05:46.683Z'}},\n",
       " {'id': '9afe902b-a456-4557-9266-9b2b873804fe',\n",
       "  'type': 'dataset',\n",
       "  'attributes': {'name': 'SRTM Digital Elevation Data Version 4',\n",
       "   'slug': 'SRTM-Digital-Elevation-Data-Version-4',\n",
       "   'type': None,\n",
       "   'dataPath': None,\n",
       "   'attributesPath': None,\n",
       "   'connectorType': 'rest',\n",
       "   'provider': 'gee',\n",
       "   'userId': '5aad5c86a61d3ddd586e5861',\n",
       "   'connectorUrl': None,\n",
       "   'tableName': 'CGIAR/SRTM90_V4',\n",
       "   'status': 'saved',\n",
       "   'published': True,\n",
       "   'sandbox': False,\n",
       "   'overwrite': False,\n",
       "   'verified': False,\n",
       "   'blockchain': {},\n",
       "   'subscribable': {},\n",
       "   'env': 'production',\n",
       "   'geoInfo': False,\n",
       "   'protected': False,\n",
       "   'legend': {'date': [], 'region': [], 'country': [], 'nested': []},\n",
       "   'clonedHost': {},\n",
       "   'errorMessage': '',\n",
       "   'taskId': None,\n",
       "   'createdAt': '2018-06-08T14:05:53.935Z',\n",
       "   'updatedAt': '2018-06-08T14:05:54.504Z'}},\n",
       " {'id': '945f25b7-925e-4f6b-a0fb-77a15ab3b4cb',\n",
       "  'type': 'dataset',\n",
       "  'attributes': {'name': 'Global Forest Canopy Height, 2005',\n",
       "   'slug': 'Global-Forest-Canopy-Height-2005',\n",
       "   'type': None,\n",
       "   'dataPath': None,\n",
       "   'attributesPath': None,\n",
       "   'connectorType': 'rest',\n",
       "   'provider': 'gee',\n",
       "   'userId': '5aad5c86a61d3ddd586e5861',\n",
       "   'connectorUrl': None,\n",
       "   'tableName': 'NASA/JPL/global_forest_canopy_height_2005',\n",
       "   'status': 'saved',\n",
       "   'published': True,\n",
       "   'sandbox': False,\n",
       "   'overwrite': False,\n",
       "   'verified': False,\n",
       "   'blockchain': {},\n",
       "   'subscribable': {},\n",
       "   'env': 'production',\n",
       "   'geoInfo': False,\n",
       "   'protected': False,\n",
       "   'legend': {'date': [], 'region': [], 'country': [], 'nested': []},\n",
       "   'clonedHost': {},\n",
       "   'errorMessage': '',\n",
       "   'taskId': None,\n",
       "   'createdAt': '2018-06-08T14:06:01.682Z',\n",
       "   'updatedAt': '2018-06-08T14:06:02.270Z'}},\n",
       " {'id': '4e0ca8d0-5eb7-4949-9da2-e1211adb73b0',\n",
       "  'type': 'dataset',\n",
       "  'attributes': {'name': 'Canada AAFC Annual Crop Inventory',\n",
       "   'slug': 'Canada-AAFC-Annual-Crop-Inventory',\n",
       "   'type': None,\n",
       "   'dataPath': None,\n",
       "   'attributesPath': None,\n",
       "   'connectorType': 'rest',\n",
       "   'provider': 'gee',\n",
       "   'userId': '5aad5c86a61d3ddd586e5861',\n",
       "   'connectorUrl': None,\n",
       "   'tableName': 'AAFC/ACI',\n",
       "   'status': 'saved',\n",
       "   'published': True,\n",
       "   'sandbox': False,\n",
       "   'overwrite': False,\n",
       "   'verified': False,\n",
       "   'blockchain': {},\n",
       "   'subscribable': {},\n",
       "   'env': 'production',\n",
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       "   'overwrite': False,\n",
       "   'verified': False,\n",
       "   'blockchain': {},\n",
       "   'subscribable': {},\n",
       "   'env': 'production',\n",
       "   'geoInfo': False,\n",
       "   'protected': False,\n",
       "   'legend': {'date': [], 'region': [], 'country': [], 'nested': []},\n",
       "   'clonedHost': {},\n",
       "   'errorMessage': '',\n",
       "   'taskId': None,\n",
       "   'createdAt': '2018-06-08T14:25:17.296Z',\n",
       "   'updatedAt': '2018-06-08T14:25:18.142Z'}}]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This way we can get _all_ gee datasets\n",
    "import requests\n",
    "import json\n",
    "\n",
    "def get_from_api(endpoint, attr_dict, api_base = api_base, api_headers = api_headers):\n",
    "    attrs = '&'.join([f\"{k}={v}\" for k, v in attr_dict.items()])\n",
    "    attrs_str = '?' + attrs if attrs is not \"\" else \"\"\n",
    "    final_endpoint = f\"{api_base}{endpoint}{attrs_str}\"\n",
    "    print(f\"Making request to {final_endpoint}\")\n",
    "    result = json.loads(\n",
    "        requests.get(\n",
    "            final_endpoint,\n",
    "            headers = api_headers\n",
    "        ).text\n",
    "    )\n",
    "    try:\n",
    "        return result['data']\n",
    "    except KeyError: # No data\n",
    "        return []\n",
    "\n",
    "# To get the basic, paginated index\n",
    "# get_from_api('/dataset', {})\n",
    "\n",
    "# All GEE datasets\n",
    "get_from_api('/dataset', {\"page[size]\": 1000, \"provider\": \"gee\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Making request to https://api.apihighways.org/v1/dataset?page[size]=1000&provider=gee&name=nex&includes=metadata\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'id': '70d6483b-0f22-4c45-8e7f-b2c029b7f534',\n",
       "  'type': 'dataset',\n",
       "  'attributes': {'name': 'NEX-DCP30: Ensemble Stats for NASA Earth Exchange Downscaled Climate Projections',\n",
       "   'slug': 'NEX-DCP30-Ensemble-Stats-for-NASA-Earth-Exchange-Downscaled-Climate-Projections',\n",
       "   'type': None,\n",
       "   'dataPath': None,\n",
       "   'attributesPath': None,\n",
       "   'connectorType': 'rest',\n",
       "   'provider': 'gee',\n",
       "   'userId': '5aad5c86a61d3ddd586e5861',\n",
       "   'connectorUrl': None,\n",
       "   'tableName': 'NASA/NEX-DCP30_ENSEMBLE_STATS',\n",
       "   'status': 'saved',\n",
       "   'published': True,\n",
       "   'sandbox': False,\n",
       "   'overwrite': False,\n",
       "   'verified': False,\n",
       "   'blockchain': {},\n",
       "   'subscribable': {},\n",
       "   'env': 'production',\n",
       "   'geoInfo': False,\n",
       "   'protected': False,\n",
       "   'legend': {'date': [], 'region': [], 'country': [], 'nested': []},\n",
       "   'clonedHost': {},\n",
       "   'errorMessage': '',\n",
       "   'taskId': None,\n",
       "   'createdAt': '2018-06-08T14:14:34.841Z',\n",
       "   'updatedAt': '2018-06-08T14:14:36.080Z',\n",
       "   'metadata': [{'id': '5b1a8f51dfeb1c0011f1b4b1',\n",
       "     'type': 'metadata',\n",
       "     'attributes': {'dataset': '70d6483b-0f22-4c45-8e7f-b2c029b7f534',\n",
       "      'language': 'en',\n",
       "      'name': 'NEX-DCP30: Ensemble Stats for NASA Earth Exchange Downscaled Climate Projections',\n",
       "      'description': \"The NASA NEX-DCP30 dataset is comprised of downscaled climate scenarios for the conterminous United States that are derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5, see Taylor et al. 2012) and across the four greenhouse gas emissions scenarios known as Representative Concentration Pathways (RCPs, see Meinshausen et al. 2011) developed for the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). The purpose of these datasets is to provide a set of high resolution, bias-corrected climate change projections that can be used to evaluate climate change impacts on processes that are sensitive to finer-scale climate gradients and the effects of local topography on climate conditions.The dataset contains monthly projections covering the periods from 1950 through 2005 (Retrospective Run) and from 2006 to 2099 (Prospective Run). It includes ensemble statistics calculated for each RCP from all model runs available for the pr, tasmin, and tasmax bands.NEX-DCP30 was prepared by the Climate Analytics Group and NASA Ames Research Center using the NASA Earth Exchange, and distributed by the NASA Center for Climate Simulation (NCCS).Resolution30 ARC_SECONDS BandsNameUnitsDescriptionpr_meankg/(m^2*s)Monthly mean of the daily precipitation rate at surface; includes both liquid and solid phases from all types of clouds (both large-scale and convective)pr_quartile25kg/(m^2*s)25th quartile of the precipitation at surface; includes both liquid and solid phases from all types of clouds (both large-scale and convective)pr_mediankg/(m^2*s)Median of precipitation at surface; includes both liquid and solid phases from all types of clouds (both large-scale and convective)pr_quartile75kg/(m^2*s)75th quartile of the precipitation at surface; includes both liquid and solid phases from all types of clouds (both large-scale and convective)tasmin_meanKMonthly mean of the daily-minimum near-surface air temperaturetasmin_quartile25K25th quartile the daily-minimum near-surface air temperaturetasmin_medianKMedian of the daily-minimum near-surface air temperaturetasmin_quartile75K75th quartile of the daily-minimum near-surface air temperaturetasmax_meanKMonthly mean of the daily-maximum near-surface air temperaturetasmax_quartile25K25th quartile of the daily-maximum near-surface air temperaturetasmax_medianKMedian of the daily-maximum near-surface air temperaturetasmax_quartile75K75th quartile of the daily-maximum near-surface air temperatureImage PropertiesNameTypeDescriptionscenarioSTRINGName of the CMIP5 scenario. It is one of: 'historical', 'rcp26', 'rcp45', 'rcp60', 'rcp85', where 'historical' designates retrospective model runs (pre-2006).monthDOUBLECalendar monthTerms of UseThis dataset is in the public domain and is available without restriction on use and distribution. See NASA's Earth Science Data & Information Policy for additional information.Suggested citation(s)Thrasher, B., J. Xiong, W. Wang, F. Melton, A. Michaelis and R. Nemani (2013), Downscaled Climate Projections Suitable for Resource Management, Eos Trans. AGU, 94(37), 321.\",\n",
       "      'sourceOrganization': 'NASA / CSU',\n",
       "      'dataSourceUrl': 'https://cds.nccs.nasa.gov/nex/',\n",
       "      'dataDownloadUrl': {'csv': 'https://api.apihighways.org/v1/download/70d6483b-0f22-4c45-8e7f-b2c029b7f534?sql=select * from NASA/NEX-DCP30_ENSEMBLE_STATS&format=csv',\n",
       "       'json': 'https://api.apihighways.org/v1/download/70d6483b-0f22-4c45-8e7f-b2c029b7f534?sql=select * from NASA/NEX-DCP30_ENSEMBLE_STATS&format=json'},\n",
       "      'info': {},\n",
       "      'units': {},\n",
       "      'columns': {},\n",
       "      'userId': '5aad5c86a61d3ddd586e5861',\n",
       "      'createdAt': '2018-06-08T14:14:41.771Z',\n",
       "      'updatedAt': '2018-06-08T14:14:41.771Z',\n",
       "      'status': 'published'}}]}},\n",
       " {'id': 'e83d31f4-80c4-4b71-9762-00af4637c530',\n",
       "  'type': 'dataset',\n",
       "  'attributes': {'name': 'NEX-DCP30: NASA Earth Exchange Downscaled Climate Projections',\n",
       "   'slug': 'NEX-DCP30-NASA-Earth-Exchange-Downscaled-Climate-Projections',\n",
       "   'type': None,\n",
       "   'dataPath': None,\n",
       "   'attributesPath': None,\n",
       "   'connectorType': 'rest',\n",
       "   'provider': 'gee',\n",
       "   'userId': '5aad5c86a61d3ddd586e5861',\n",
       "   'connectorUrl': None,\n",
       "   'tableName': 'NASA/NEX-DCP30',\n",
       "   'status': 'saved',\n",
       "   'published': True,\n",
       "   'sandbox': False,\n",
       "   'overwrite': False,\n",
       "   'verified': False,\n",
       "   'blockchain': {},\n",
       "   'subscribable': {},\n",
       "   'env': 'production',\n",
       "   'geoInfo': False,\n",
       "   'protected': False,\n",
       "   'legend': {'date': [], 'region': [], 'country': [], 'nested': []},\n",
       "   'clonedHost': {},\n",
       "   'errorMessage': '',\n",
       "   'taskId': None,\n",
       "   'createdAt': '2018-06-08T14:14:42.745Z',\n",
       "   'updatedAt': '2018-06-08T14:14:44.432Z',\n",
       "   'metadata': [{'id': '5b1a8f5903ebe20011651e24',\n",
       "     'type': 'metadata',\n",
       "     'attributes': {'dataset': 'e83d31f4-80c4-4b71-9762-00af4637c530',\n",
       "      'language': 'en',\n",
       "      'name': 'NEX-DCP30: NASA Earth Exchange Downscaled Climate Projections',\n",
       "      'description': \"The NASA NEX-DCP30 dataset is comprised of downscaled climate scenarios for the conterminous United States that are derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5, see Taylor et al. 2012) and across the four greenhouse gas emissions scenarios known as Representative Concentration Pathways (RCPs, see Meinshausen et al. 2011) developed for the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). The purpose of these datasets is to provide a set of high resolution, bias-corrected climate change projections that can be used to evaluate climate change impacts on processes that are sensitive to finer-scale climate gradients and the effects of local topography on climate conditions.The dataset contains monthly projections covering the periods from 1950 through 2005 (Retrospective Run) and from 2006 to 2099 (Prospective Run). It includes downscaled projections from 33 models. Not every scenario contains projections from every model.NEX-DCP30 was prepared by the Climate Analytics Group and NASA Ames Research Center using the NASA Earth Exchange, and distributed by the NASA Center for Climate Simulation (NCCS).Resolution30 ARC_SECONDS BandsNameUnitsDescriptionprkg/(m^2*s)Monthly mean of the daily precipitation rate at surface; includes both liquid and solid phases from all types of clouds (both large-scale and convective)tasminKMonthly mean of the daily-minimum near-surface air temperaturetasmaxKMonthly mean of the daily-maximum near-surface air temperatureImage PropertiesNameTypeDescriptionscenarioSTRINGName of the CMIP5 scenario. It is one of: 'historical', 'rcp26', 'rcp45', 'rcp60', 'rcp85', where 'historical' designates retrospective model runs (pre-2006).modelSTRINGName of the CMIP5 model. It is one of 'ACCESS1-0', 'bcc-csm1-1', 'bcc-csm1-1-m', 'BNU-ESM', 'CanESM2', 'CCSM4', 'CESM1-BGC', 'CESM1-CAM5', 'CMCC-CM', 'CNRM-CM5', 'CSIRO-Mk3-6-0', 'FGOALS-g2', 'FIO-ESM', 'GFDL-CM3', 'GFDL-ESM2G', 'GFDL-ESM2M', 'GISS-E2-H-CC', 'GISS-E2-R', 'GISS-E2-R-CC', 'HadGEM2-AO', 'HadGEM2-CC', 'HadGEM2-ES', 'inmcm4', 'IPSL-CM5A-LR', 'IPSL-CM5A-MR', 'IPSL-CM5B-LR', 'MIROC5', 'MIROC-ESM', 'MIROC-ESM-CHEM', 'MPI-ESM-LR', 'MPI-ESM-MR', 'MRI-CGCM3', 'NorESM1-M'.metricDOUBLECalendar monthTerms of UseThis dataset is in the public domain and is available without restriction on use and distribution. See NASA's Earth Science Data & Information Policy for additional information.Suggested citation(s)Thrasher, B., J. Xiong, W. Wang, F. Melton, A. Michaelis and R. Nemani (2013), Downscaled Climate Projections Suitable for Resource Management, Eos Trans. AGU, 94(37), 321.\",\n",
       "      'sourceOrganization': 'NASA / CSU',\n",
       "      'dataSourceUrl': 'https://cds.nccs.nasa.gov/nex/',\n",
       "      'dataDownloadUrl': {'csv': 'https://api.apihighways.org/v1/download/e83d31f4-80c4-4b71-9762-00af4637c530?sql=select * from NASA/NEX-DCP30&format=csv',\n",
       "       'json': 'https://api.apihighways.org/v1/download/e83d31f4-80c4-4b71-9762-00af4637c530?sql=select * from NASA/NEX-DCP30&format=json'},\n",
       "      'info': {},\n",
       "      'units': {},\n",
       "      'columns': {},\n",
       "      'userId': '5aad5c86a61d3ddd586e5861',\n",
       "      'createdAt': '2018-06-08T14:14:49.614Z',\n",
       "      'updatedAt': '2018-06-08T14:14:49.614Z',\n",
       "      'status': 'published'}}]}},\n",
       " {'id': 'f53a4540-4019-47d0-9c53-7468d61fbddb',\n",
       "  'type': 'dataset',\n",
       "  'attributes': {'name': 'NEX-GDDP: NASA Earth Exchange Global Daily Downscaled Climate Projections',\n",
       "   'slug': 'NEX-GDDP-NASA-Earth-Exchange-Global-Daily-Downscaled-Climate-Projections',\n",
       "   'type': None,\n",
       "   'dataPath': None,\n",
       "   'attributesPath': None,\n",
       "   'connectorType': 'rest',\n",
       "   'provider': 'gee',\n",
       "   'userId': '5aad5c86a61d3ddd586e5861',\n",
       "   'connectorUrl': None,\n",
       "   'tableName': 'NASA/NEX-GDDP',\n",
       "   'status': 'saved',\n",
       "   'published': True,\n",
       "   'sandbox': False,\n",
       "   'overwrite': False,\n",
       "   'verified': False,\n",
       "   'blockchain': {},\n",
       "   'subscribable': {},\n",
       "   'env': 'production',\n",
       "   'geoInfo': False,\n",
       "   'protected': False,\n",
       "   'legend': {'date': [], 'region': [], 'country': [], 'nested': []},\n",
       "   'clonedHost': {},\n",
       "   'errorMessage': '',\n",
       "   'taskId': None,\n",
       "   'createdAt': '2018-06-08T14:14:50.654Z',\n",
       "   'updatedAt': '2018-06-08T14:14:53.386Z',\n",
       "   'metadata': [{'id': '5b1a8f6103ebe20011651e25',\n",
       "     'type': 'metadata',\n",
       "     'attributes': {'dataset': 'f53a4540-4019-47d0-9c53-7468d61fbddb',\n",
       "      'language': 'en',\n",
       "      'name': 'NEX-GDDP: NASA Earth Exchange Global Daily Downscaled Climate Projections',\n",
       "      'description': \"The NASA NEX-GDDP dataset is comprised of downscaled climate scenarios for the globe that are derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5, see Taylor et al. 2012) and across two of the four greenhouse gas emissions scenarios known as Representative Concentration Pathways (RCPs, see [Meinshausen et al. 2011] (https://rd.springer.com/article/10.1007%2Fs10584-011-0156-z#page-1)). The CMIP5 GCM runs were developed in support of the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5).This dataset was prepared by the Climate Analytics Group and NASA Ames Research Center using the NASA Earth Exchange, and distributed by the NASA Center for Climate Simulation (NCCS).Resolution0.25 ARC_DEGREES BandsNameUnitsDescriptionprkg/(m^2*s)Daily mean of precipitation at surface; includes both liquid and solid phases from all types of clouds (both large-scale and convective)tasminKDaily mean of the daily-minimum near-surface air temperaturetasmaxKDaily mean of the daily-maximum near-surface air temperatureImage PropertiesNameTypeDescriptionmodelSTRINGName of the CMIP5 model. It is one of 'ACCESS1-0', 'bcc-csm1-1', 'BNU-ESM', 'CanESM2', 'CCSM4', 'CESM1-BGC', 'CNRM-CM5', 'CSIRO-Mk3-6-0', 'GFDL-CM3', 'GFDL-ESM2G', 'GFDL-ESM2M', 'inmcm4', 'IPSL-CM5A-LR', 'IPSL-CM5A-MR', 'MIROC-ESM', 'MIROC-ESM-CHEM', 'MIROC5', 'MPI-ESM-LR', 'MPI-ESM-MR', 'MRI-CGCM3', 'NorESM1-M'.scenarioSTRINGName of the CMIP5 scenario. It is one of: 'historical', 'rcp45', 'rcp85', where 'historical' designates retrospective model runs (pre-2006).yearDOUBLECalendar yearmonthDOUBLECalendar monthdayDOUBLECalendar dayTerms of UseThis dataset is in the public domain and is available without restriction on use and distribution. See NASA's Earth Science Data & Information Policy for additional information.Suggested citation(s)Thrasher, B., Maurer, E. P., McKellar, C., & Duffy, P. B., 2012: Technical Note: Bias correcting climate model simulated daily temperature extremes with quantile mapping. Hydrology and Earth System Sciences, 16(9), 3309-3314.\",\n",
       "      'sourceOrganization': 'NASA / CSU',\n",
       "      'dataSourceUrl': 'https://cds.nccs.nasa.gov/nex-gddp/',\n",
       "      'dataDownloadUrl': {'csv': 'https://api.apihighways.org/v1/download/f53a4540-4019-47d0-9c53-7468d61fbddb?sql=select * from NASA/NEX-GDDP&format=csv',\n",
       "       'json': 'https://api.apihighways.org/v1/download/f53a4540-4019-47d0-9c53-7468d61fbddb?sql=select * from NASA/NEX-GDDP&format=json'},\n",
       "      'info': {},\n",
       "      'units': {},\n",
       "      'columns': {},\n",
       "      'userId': '5aad5c86a61d3ddd586e5861',\n",
       "      'createdAt': '2018-06-08T14:14:57.604Z',\n",
       "      'updatedAt': '2018-06-08T14:14:57.604Z',\n",
       "      'status': 'published'}}]}}]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can search by name too\n",
    "nex_datasets = get_from_api('/dataset', {\"page[size]\": 1000, \"provider\": \"gee\", \"name\": \"nex\", \"includes\": \"metadata\"})\n",
    "nex_datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>description</th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>tableName</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The NASA NEX-DCP30 dataset is comprised of downscaled climate scenarios for the conterminous United States that are derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5, see Taylor et al. 2012) and across the four greenhouse gas emissions scenarios known as Representative Concentration Pathways (RCPs, see Meinshausen et al. 2011) developed for the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). The purpose of these datasets is to provide a set of high resolution, bias-corrected climate change projections that can be used to evaluate climate change impacts on processes that are sensitive to finer-scale climate gradients and the effects of local topography on climate conditions.The dataset contains monthly projections covering the periods from 1950 through 2005 (Retrospective Run) and from 2006 to 2099 (Prospective Run). It includes ensemble statistics calculated for each RCP from all model runs available for the pr, tasmin, and tasmax bands.NEX-DCP30 was prepared by the Climate Analytics Group and NASA Ames Research Center using the NASA Earth Exchange, and distributed by the NASA Center for Climate Simulation (NCCS).Resolution30 ARC_SECONDS BandsNameUnitsDescriptionpr_meankg/(m^2*s)Monthly mean of the daily precipitation rate at surface; includes both liquid and solid phases from all types of clouds (both large-scale and convective)pr_quartile25kg/(m^2*s)25th quartile of the precipitation at surface; includes both liquid and solid phases from all types of clouds (both large-scale and convective)pr_mediankg/(m^2*s)Median of precipitation at surface; includes both liquid and solid phases from all types of clouds (both large-scale and convective)pr_quartile75kg/(m^2*s)75th quartile of the precipitation at surface; includes both liquid and solid phases from all types of clouds (both large-scale and convective)tasmin_meanKMonthly mean of the daily-minimum near-surface air temperaturetasmin_quartile25K25th quartile the daily-minimum near-surface air temperaturetasmin_medianKMedian of the daily-minimum near-surface air temperaturetasmin_quartile75K75th quartile of the daily-minimum near-surface air temperaturetasmax_meanKMonthly mean of the daily-maximum near-surface air temperaturetasmax_quartile25K25th quartile of the daily-maximum near-surface air temperaturetasmax_medianKMedian of the daily-maximum near-surface air temperaturetasmax_quartile75K75th quartile of the daily-maximum near-surface air temperatureImage PropertiesNameTypeDescriptionscenarioSTRINGName of the CMIP5 scenario. It is one of: 'historical', 'rcp26', 'rcp45', 'rcp60', 'rcp85', where 'historical' designates retrospective model runs (pre-2006).monthDOUBLECalendar monthTerms of UseThis dataset is in the public domain and is available without restriction on use and distribution. See NASA's Earth Science Data &amp; Information Policy for additional information.Suggested citation(s)Thrasher, B., J. Xiong, W. Wang, F. Melton, A. Michaelis and R. Nemani (2013), Downscaled Climate Projections Suitable for Resource Management, Eos Trans. AGU, 94(37), 321.</td>\n",
       "      <td>70d6483b-0f22-4c45-8e7f-b2c029b7f534</td>\n",
       "      <td>NEX-DCP30: Ensemble Stats for NASA Earth Exchange Downscaled Climate Projections</td>\n",
       "      <td>NASA/NEX-DCP30_ENSEMBLE_STATS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The NASA NEX-DCP30 dataset is comprised of downscaled climate scenarios for the conterminous United States that are derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5, see Taylor et al. 2012) and across the four greenhouse gas emissions scenarios known as Representative Concentration Pathways (RCPs, see Meinshausen et al. 2011) developed for the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). The purpose of these datasets is to provide a set of high resolution, bias-corrected climate change projections that can be used to evaluate climate change impacts on processes that are sensitive to finer-scale climate gradients and the effects of local topography on climate conditions.The dataset contains monthly projections covering the periods from 1950 through 2005 (Retrospective Run) and from 2006 to 2099 (Prospective Run). It includes downscaled projections from 33 models. Not every scenario contains projections from every model.NEX-DCP30 was prepared by the Climate Analytics Group and NASA Ames Research Center using the NASA Earth Exchange, and distributed by the NASA Center for Climate Simulation (NCCS).Resolution30 ARC_SECONDS BandsNameUnitsDescriptionprkg/(m^2*s)Monthly mean of the daily precipitation rate at surface; includes both liquid and solid phases from all types of clouds (both large-scale and convective)tasminKMonthly mean of the daily-minimum near-surface air temperaturetasmaxKMonthly mean of the daily-maximum near-surface air temperatureImage PropertiesNameTypeDescriptionscenarioSTRINGName of the CMIP5 scenario. It is one of: 'historical', 'rcp26', 'rcp45', 'rcp60', 'rcp85', where 'historical' designates retrospective model runs (pre-2006).modelSTRINGName of the CMIP5 model. It is one of 'ACCESS1-0', 'bcc-csm1-1', 'bcc-csm1-1-m', 'BNU-ESM', 'CanESM2', 'CCSM4', 'CESM1-BGC', 'CESM1-CAM5', 'CMCC-CM', 'CNRM-CM5', 'CSIRO-Mk3-6-0', 'FGOALS-g2', 'FIO-ESM', 'GFDL-CM3', 'GFDL-ESM2G', 'GFDL-ESM2M', 'GISS-E2-H-CC', 'GISS-E2-R', 'GISS-E2-R-CC', 'HadGEM2-AO', 'HadGEM2-CC', 'HadGEM2-ES', 'inmcm4', 'IPSL-CM5A-LR', 'IPSL-CM5A-MR', 'IPSL-CM5B-LR', 'MIROC5', 'MIROC-ESM', 'MIROC-ESM-CHEM', 'MPI-ESM-LR', 'MPI-ESM-MR', 'MRI-CGCM3', 'NorESM1-M'.metricDOUBLECalendar monthTerms of UseThis dataset is in the public domain and is available without restriction on use and distribution. See NASA's Earth Science Data &amp; Information Policy for additional information.Suggested citation(s)Thrasher, B., J. Xiong, W. Wang, F. Melton, A. Michaelis and R. Nemani (2013), Downscaled Climate Projections Suitable for Resource Management, Eos Trans. AGU, 94(37), 321.</td>\n",
       "      <td>e83d31f4-80c4-4b71-9762-00af4637c530</td>\n",
       "      <td>NEX-DCP30: NASA Earth Exchange Downscaled Climate Projections</td>\n",
       "      <td>NASA/NEX-DCP30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>The NASA NEX-GDDP dataset is comprised of downscaled climate scenarios for the globe that are derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5, see Taylor et al. 2012) and across two of the four greenhouse gas emissions scenarios known as Representative Concentration Pathways (RCPs, see [Meinshausen et al. 2011] (https://rd.springer.com/article/10.1007%2Fs10584-011-0156-z#page-1)). The CMIP5 GCM runs were developed in support of the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5).This dataset was prepared by the Climate Analytics Group and NASA Ames Research Center using the NASA Earth Exchange, and distributed by the NASA Center for Climate Simulation (NCCS).Resolution0.25 ARC_DEGREES BandsNameUnitsDescriptionprkg/(m^2*s)Daily mean of precipitation at surface; includes both liquid and solid phases from all types of clouds (both large-scale and convective)tasminKDaily mean of the daily-minimum near-surface air temperaturetasmaxKDaily mean of the daily-maximum near-surface air temperatureImage PropertiesNameTypeDescriptionmodelSTRINGName of the CMIP5 model. It is one of 'ACCESS1-0', 'bcc-csm1-1', 'BNU-ESM', 'CanESM2', 'CCSM4', 'CESM1-BGC', 'CNRM-CM5', 'CSIRO-Mk3-6-0', 'GFDL-CM3', 'GFDL-ESM2G', 'GFDL-ESM2M', 'inmcm4', 'IPSL-CM5A-LR', 'IPSL-CM5A-MR', 'MIROC-ESM', 'MIROC-ESM-CHEM', 'MIROC5', 'MPI-ESM-LR', 'MPI-ESM-MR', 'MRI-CGCM3', 'NorESM1-M'.scenarioSTRINGName of the CMIP5 scenario. It is one of: 'historical', 'rcp45', 'rcp85', where 'historical' designates retrospective model runs (pre-2006).yearDOUBLECalendar yearmonthDOUBLECalendar monthdayDOUBLECalendar dayTerms of UseThis dataset is in the public domain and is available without restriction on use and distribution. See NASA's Earth Science Data &amp; Information Policy for additional information.Suggested citation(s)Thrasher, B., Maurer, E. P., McKellar, C., &amp; Duffy, P. B., 2012: Technical Note: Bias correcting climate model simulated daily temperature extremes with quantile mapping. Hydrology and Earth System Sciences, 16(9), 3309-3314.</td>\n",
       "      <td>f53a4540-4019-47d0-9c53-7468d61fbddb</td>\n",
       "      <td>NEX-GDDP: NASA Earth Exchange Global Daily Downscaled Climate Projections</td>\n",
       "      <td>NASA/NEX-GDDP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           description  \\\n",
       "0  The NASA NEX-DCP30 dataset is comprised of downscaled climate scenarios for the conterminous United States that are derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5, see Taylor et al. 2012) and across the four greenhouse gas emissions scenarios known as Representative Concentration Pathways (RCPs, see Meinshausen et al. 2011) developed for the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). The purpose of these datasets is to provide a set of high resolution, bias-corrected climate change projections that can be used to evaluate climate change impacts on processes that are sensitive to finer-scale climate gradients and the effects of local topography on climate conditions.The dataset contains monthly projections covering the periods from 1950 through 2005 (Retrospective Run) and from 2006 to 2099 (Prospective Run). It includes ensemble statistics calculated for each RCP from all model runs available for the pr, tasmin, and tasmax bands.NEX-DCP30 was prepared by the Climate Analytics Group and NASA Ames Research Center using the NASA Earth Exchange, and distributed by the NASA Center for Climate Simulation (NCCS).Resolution30 ARC_SECONDS BandsNameUnitsDescriptionpr_meankg/(m^2*s)Monthly mean of the daily precipitation rate at surface; includes both liquid and solid phases from all types of clouds (both large-scale and convective)pr_quartile25kg/(m^2*s)25th quartile of the precipitation at surface; includes both liquid and solid phases from all types of clouds (both large-scale and convective)pr_mediankg/(m^2*s)Median of precipitation at surface; includes both liquid and solid phases from all types of clouds (both large-scale and convective)pr_quartile75kg/(m^2*s)75th quartile of the precipitation at surface; includes both liquid and solid phases from all types of clouds (both large-scale and convective)tasmin_meanKMonthly mean of the daily-minimum near-surface air temperaturetasmin_quartile25K25th quartile the daily-minimum near-surface air temperaturetasmin_medianKMedian of the daily-minimum near-surface air temperaturetasmin_quartile75K75th quartile of the daily-minimum near-surface air temperaturetasmax_meanKMonthly mean of the daily-maximum near-surface air temperaturetasmax_quartile25K25th quartile of the daily-maximum near-surface air temperaturetasmax_medianKMedian of the daily-maximum near-surface air temperaturetasmax_quartile75K75th quartile of the daily-maximum near-surface air temperatureImage PropertiesNameTypeDescriptionscenarioSTRINGName of the CMIP5 scenario. It is one of: 'historical', 'rcp26', 'rcp45', 'rcp60', 'rcp85', where 'historical' designates retrospective model runs (pre-2006).monthDOUBLECalendar monthTerms of UseThis dataset is in the public domain and is available without restriction on use and distribution. See NASA's Earth Science Data & Information Policy for additional information.Suggested citation(s)Thrasher, B., J. Xiong, W. Wang, F. Melton, A. Michaelis and R. Nemani (2013), Downscaled Climate Projections Suitable for Resource Management, Eos Trans. AGU, 94(37), 321.   \n",
       "1  The NASA NEX-DCP30 dataset is comprised of downscaled climate scenarios for the conterminous United States that are derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5, see Taylor et al. 2012) and across the four greenhouse gas emissions scenarios known as Representative Concentration Pathways (RCPs, see Meinshausen et al. 2011) developed for the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). The purpose of these datasets is to provide a set of high resolution, bias-corrected climate change projections that can be used to evaluate climate change impacts on processes that are sensitive to finer-scale climate gradients and the effects of local topography on climate conditions.The dataset contains monthly projections covering the periods from 1950 through 2005 (Retrospective Run) and from 2006 to 2099 (Prospective Run). It includes downscaled projections from 33 models. Not every scenario contains projections from every model.NEX-DCP30 was prepared by the Climate Analytics Group and NASA Ames Research Center using the NASA Earth Exchange, and distributed by the NASA Center for Climate Simulation (NCCS).Resolution30 ARC_SECONDS BandsNameUnitsDescriptionprkg/(m^2*s)Monthly mean of the daily precipitation rate at surface; includes both liquid and solid phases from all types of clouds (both large-scale and convective)tasminKMonthly mean of the daily-minimum near-surface air temperaturetasmaxKMonthly mean of the daily-maximum near-surface air temperatureImage PropertiesNameTypeDescriptionscenarioSTRINGName of the CMIP5 scenario. It is one of: 'historical', 'rcp26', 'rcp45', 'rcp60', 'rcp85', where 'historical' designates retrospective model runs (pre-2006).modelSTRINGName of the CMIP5 model. It is one of 'ACCESS1-0', 'bcc-csm1-1', 'bcc-csm1-1-m', 'BNU-ESM', 'CanESM2', 'CCSM4', 'CESM1-BGC', 'CESM1-CAM5', 'CMCC-CM', 'CNRM-CM5', 'CSIRO-Mk3-6-0', 'FGOALS-g2', 'FIO-ESM', 'GFDL-CM3', 'GFDL-ESM2G', 'GFDL-ESM2M', 'GISS-E2-H-CC', 'GISS-E2-R', 'GISS-E2-R-CC', 'HadGEM2-AO', 'HadGEM2-CC', 'HadGEM2-ES', 'inmcm4', 'IPSL-CM5A-LR', 'IPSL-CM5A-MR', 'IPSL-CM5B-LR', 'MIROC5', 'MIROC-ESM', 'MIROC-ESM-CHEM', 'MPI-ESM-LR', 'MPI-ESM-MR', 'MRI-CGCM3', 'NorESM1-M'.metricDOUBLECalendar monthTerms of UseThis dataset is in the public domain and is available without restriction on use and distribution. See NASA's Earth Science Data & Information Policy for additional information.Suggested citation(s)Thrasher, B., J. Xiong, W. Wang, F. Melton, A. Michaelis and R. Nemani (2013), Downscaled Climate Projections Suitable for Resource Management, Eos Trans. AGU, 94(37), 321.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   \n",
       "2  The NASA NEX-GDDP dataset is comprised of downscaled climate scenarios for the globe that are derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5, see Taylor et al. 2012) and across two of the four greenhouse gas emissions scenarios known as Representative Concentration Pathways (RCPs, see [Meinshausen et al. 2011] (https://rd.springer.com/article/10.1007%2Fs10584-011-0156-z#page-1)). The CMIP5 GCM runs were developed in support of the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5).This dataset was prepared by the Climate Analytics Group and NASA Ames Research Center using the NASA Earth Exchange, and distributed by the NASA Center for Climate Simulation (NCCS).Resolution0.25 ARC_DEGREES BandsNameUnitsDescriptionprkg/(m^2*s)Daily mean of precipitation at surface; includes both liquid and solid phases from all types of clouds (both large-scale and convective)tasminKDaily mean of the daily-minimum near-surface air temperaturetasmaxKDaily mean of the daily-maximum near-surface air temperatureImage PropertiesNameTypeDescriptionmodelSTRINGName of the CMIP5 model. It is one of 'ACCESS1-0', 'bcc-csm1-1', 'BNU-ESM', 'CanESM2', 'CCSM4', 'CESM1-BGC', 'CNRM-CM5', 'CSIRO-Mk3-6-0', 'GFDL-CM3', 'GFDL-ESM2G', 'GFDL-ESM2M', 'inmcm4', 'IPSL-CM5A-LR', 'IPSL-CM5A-MR', 'MIROC-ESM', 'MIROC-ESM-CHEM', 'MIROC5', 'MPI-ESM-LR', 'MPI-ESM-MR', 'MRI-CGCM3', 'NorESM1-M'.scenarioSTRINGName of the CMIP5 scenario. It is one of: 'historical', 'rcp45', 'rcp85', where 'historical' designates retrospective model runs (pre-2006).yearDOUBLECalendar yearmonthDOUBLECalendar monthdayDOUBLECalendar dayTerms of UseThis dataset is in the public domain and is available without restriction on use and distribution. See NASA's Earth Science Data & Information Policy for additional information.Suggested citation(s)Thrasher, B., Maurer, E. P., McKellar, C., & Duffy, P. B., 2012: Technical Note: Bias correcting climate model simulated daily temperature extremes with quantile mapping. Hydrology and Earth System Sciences, 16(9), 3309-3314.                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                \n",
       "\n",
       "                                     id  \\\n",
       "0  70d6483b-0f22-4c45-8e7f-b2c029b7f534   \n",
       "1  e83d31f4-80c4-4b71-9762-00af4637c530   \n",
       "2  f53a4540-4019-47d0-9c53-7468d61fbddb   \n",
       "\n",
       "                                                                               name  \\\n",
       "0  NEX-DCP30: Ensemble Stats for NASA Earth Exchange Downscaled Climate Projections   \n",
       "1  NEX-DCP30: NASA Earth Exchange Downscaled Climate Projections                      \n",
       "2  NEX-GDDP: NASA Earth Exchange Global Daily Downscaled Climate Projections          \n",
       "\n",
       "                       tableName  \n",
       "0  NASA/NEX-DCP30_ENSEMBLE_STATS  \n",
       "1  NASA/NEX-DCP30                 \n",
       "2  NASA/NEX-GDDP                  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Let's get some information prior to the analysis\n",
    "nex_datasets_info = [\n",
    "    {\n",
    "        'id':          dset['id'],\n",
    "        'name':        dset['attributes']['name'],\n",
    "        'tableName':   dset['attributes']['tableName'],\n",
    "        'description': dset['attributes']['metadata'][0]['attributes']['description']\n",
    "    } for dset in nex_datasets\n",
    "]\n",
    "\n",
    "# We'll use pandas for display\n",
    "import pandas as pd\n",
    "pd.set_option('display.max_colwidth', -1)\n",
    "pd.DataFrame(nex_datasets_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's move to GEE for the rest of the analysis\n",
    "import ee\n",
    "# Earth Engine is managed by Google and currently\n",
    "# by invitation only. You'll have to apply to use\n",
    "# the service and authorize the library prior to\n",
    "# using it. Instructions for doing so can be found\n",
    "# at https://developers.google.com/earth-engine/python_install\n",
    "ee.Initialize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We are interested in the data from the first dataset, the\n",
    "# NASA DCP30 ensemble stats. For more information on this dataset\n",
    "# you can check out https://cds.nccs.nasa.gov/nex/\n",
    "# Two important academic papers about this topic:\n",
    "# The Representative Concentration Pathways:\n",
    "# https://rd.springer.com/article/10.1007%2Fs10584-011-0156-z#page-1\n",
    "# And an overview of the raw models from which these datasets are composed\n",
    "# https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1\n",
    "\n",
    "dcp30_ensemble_stats = ee.ImageCollection(\"NASA/NEX-DCP30_ENSEMBLE_STATS\")\n",
    "# GEE works similarly to other 'big data' environments, where \n",
    "# local objects are proxies to data 'living' in a remote server.\n",
    "# When we call the `.getInfo()` function we are requesting for the\n",
    "# calculations to be actually made, and the result be transmitted\n",
    "# to this python environment.\n",
    "dcp30_ensemble_stats.getInfo()\n",
    "# This call will fail! There are _many_ images on this ImageCollection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'type': 'ImageCollection',\n",
       " 'bands': [],\n",
       " 'id': 'NASA/NEX-DCP30_ENSEMBLE_STATS',\n",
       " 'version': 1526494494374679,\n",
       " 'properties': {'date_range': [-631152000000.0, 4102358400000.0],\n",
       "  'period': 0.0,\n",
       "  'system:visualization_0_min': '265.0',\n",
       "  'system:visualization_0_bands': 'tasmin',\n",
       "  'thumb': 'https://mw1.google.com/ges/dd/images/NASA_NEX-DCP30_thumb.png',\n",
       "  'description': '<p>The NASA NEX-DCP30 dataset is comprised of downscaled climate scenarios\\nfor the conterminous United States that are derived from the General\\nCirculation Model (GCM) runs conducted under the Coupled Model\\nIntercomparison Project Phase 5 (CMIP5, see <a href=\"https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1\">Taylor et al. 2012</a>)\\nand across the four greenhouse gas emissions scenarios known as\\nRepresentative Concentration Pathways (RCPs, see <a href=\"https://rd.springer.com/article/10.1007%2Fs10584-011-0156-z#page-1\">Meinshausen et al. 2011</a>)\\ndeveloped for the Fifth Assessment Report of the Intergovernmental Panel\\non Climate Change (IPCC AR5). The purpose of these datasets is to provide\\na set of high resolution, bias-corrected climate change projections that\\ncan be used to evaluate climate change impacts on processes that are\\nsensitive to finer-scale climate gradients and the effects of local\\ntopography on climate conditions.</p><p>The dataset contains monthly projections covering the periods from 1950\\nthrough 2005 (Retrospective Run) and from 2006 to 2099 (Prospective Run).\\nIt includes ensemble statistics calculated for each RCP from all model\\nruns available for the pr, tasmin, and tasmax bands.</p><p>NEX-DCP30 was prepared by the Climate Analytics Group and NASA Ames\\nResearch Center using the NASA Earth Exchange, and distributed by the NASA\\nCenter for Climate Simulation (NCCS).</p><p><b>Resolution</b><br>30 ARC_SECONDS\\n</p><p><b>Bands</b><table class=\"eecat\"><tr><th scope=\"col\">Name</th><th scope=\"col\">Units</th><th scope=\"col\">Description</th></tr><tr><td>pr_mean</td><td>kg/(m^2*s)</td><td><p>Monthly mean of the daily precipitation rate at surface; includes both liquid and solid phases from all types of clouds (both large-scale and convective)</p></td></tr><tr><td>pr_quartile25</td><td>kg/(m^2*s)</td><td><p>25th quartile of the precipitation at surface; includes both liquid and solid phases from all types of clouds (both large-scale and convective)</p></td></tr><tr><td>pr_median</td><td>kg/(m^2*s)</td><td><p>Median of precipitation at surface; includes both liquid and solid phases from all types of clouds (both large-scale and convective)</p></td></tr><tr><td>pr_quartile75</td><td>kg/(m^2*s)</td><td><p>75th quartile of the precipitation at surface; includes both liquid and solid phases from all types of clouds (both large-scale and convective)</p></td></tr><tr><td>tasmin_mean</td><td>K</td><td><p>Monthly mean of the daily-minimum near-surface air temperature</p></td></tr><tr><td>tasmin_quartile25</td><td>K</td><td><p>25th quartile the daily-minimum near-surface air temperature</p></td></tr><tr><td>tasmin_median</td><td>K</td><td><p>Median of the daily-minimum near-surface air temperature</p></td></tr><tr><td>tasmin_quartile75</td><td>K</td><td><p>75th quartile of the daily-minimum near-surface air temperature</p></td></tr><tr><td>tasmax_mean</td><td>K</td><td><p>Monthly mean of the daily-maximum near-surface air temperature</p></td></tr><tr><td>tasmax_quartile25</td><td>K</td><td><p>25th quartile of the daily-maximum near-surface air temperature</p></td></tr><tr><td>tasmax_median</td><td>K</td><td><p>Median of the daily-maximum near-surface air temperature</p></td></tr><tr><td>tasmax_quartile75</td><td>K</td><td><p>75th quartile of the daily-maximum near-surface air temperature</p></td></tr></table><p><b>Image Properties</b><table class=\"eecat\"><tr><th scope=\"col\">Name</th><th scope=\"col\">Type</th><th scope=\"col\">Description</th></tr><tr><td>scenario</td><td>STRING</td><td><p>Name of the CMIP5 scenario. It is one of: \\'historical\\', \\'rcp26\\', \\'rcp45\\', \\'rcp60\\', \\'rcp85\\', where \\'historical\\' designates retrospective model runs (pre-2006).</p></td></tr><tr><td>month</td><td>DOUBLE</td><td><p>Calendar month</p></td></tr></table><p><b>Terms of Use</b><br><p>This dataset is in the public domain and is available\\nwithout restriction on use and distribution. See <a href=\"https://science.nasa.gov/earth-science/earth-science-data/data-information-policy\">NASA\\'s\\nEarth Science Data &amp; Information Policy</a>\\nfor additional information.</p><p><b>Suggested citation(s)</b><ul><li><p>Thrasher, B., J. Xiong, W. Wang, F. Melton, A. Michaelis and R. Nemani (2013), Downscaled Climate Projections Suitable for Resource Management, Eos Trans. AGU, 94(37), 321.</p></li></ul><style>\\n  table.eecat {\\n  border: 1px solid black;\\n  border-collapse: collapse;\\n  font-size: 13px;\\n  }\\n  table.eecat td, tr, th {\\n  text-align: left; vertical-align: top;\\n  border: 1px solid gray; padding: 3px;\\n  }\\n  td.nobreak { white-space: nowrap; }\\n</style>',\n",
       "  'source_tags': ['nasa', 'csu', 'nex'],\n",
       "  'provider_url': 'https://cds.nccs.nasa.gov/nex/',\n",
       "  'title': 'NEX-DCP30: Ensemble Stats for NASA Earth Exchange Downscaled Climate Projections',\n",
       "  'sample': 'https://mw1.google.com/ges/dd/images/NASA_NEX-DCP30_sample.png',\n",
       "  'tags': ['nasa',\n",
       "   'csu',\n",
       "   'nex',\n",
       "   'precipitation',\n",
       "   'temperature',\n",
       "   'cmip5',\n",
       "   'climate',\n",
       "   'geophysical'],\n",
       "  'system:visualization_0_max': '285.0',\n",
       "  'product_tags': ['precipitation',\n",
       "   'temperature',\n",
       "   'cmip5',\n",
       "   'climate',\n",
       "   'geophysical'],\n",
       "  'provider': 'NASA / CSU',\n",
       "  'system:visualization_0_palette': ['blue',\n",
       "   'purple',\n",
       "   'cyan',\n",
       "   'green',\n",
       "   'yellow',\n",
       "   'red'],\n",
       "  'system:visualization_0_name': 'Minimum Air Temperature'},\n",
       " 'features': []}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can, however, filter this collection. Some ways\n",
    "# to do it. Notice that we can chain the calls!\n",
    "dcp30_ensemble_stats.filter(ee.Filter.eq('scenario', 'rcp85')).filterDate(1995).limit(1).getInfo()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "<folium.folium.Map at 0x7f9a12705b00>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First of all, let's declare the locations of Accra and Freetown\n",
    "# as points of interest (POIs) so we can extract the data we are\n",
    "# interested in\n",
    "\n",
    "accra_lat, accra_lon = (5.6037, -0.1870)\n",
    "freetown_lat, freetown_lon = (8.484, -13.22994)\n",
    "\n",
    "#                                           ⬇️ lat lon order for GEE\n",
    "accra_loc    = ee.Feature(ee.Geometry.Point(accra_lat, accra_lon)).buffer(5000)\n",
    "freetown_loc = ee.Feature(ee.Geometry.Point(freetown_lat, freetown_lon))\n",
    "\n",
    "# Let's check the coordinates are correct in a folium map (not in GEE)\n",
    "import folium\n",
    "accra_map = folium.Map(location=[accra_lat, accra_lon], tiles=\"OpenStreetMap\", zoom_start=8)\n",
    "freetown_map = folium.Map(location=[freetown_lat, freetown_lon], tiles=\"OpenStreetMap\", zoom_start=8)\n",
    "accra_map\n",
    "#freetown_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ee.imagecollection.ImageCollection at 0x7f9a119360b8>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We'll rescale the datetime, which is in miliseconds,\n",
    "# so we can work more easily with it\n",
    "def rescale_time(image):\n",
    "    return image.addBands(image.metadata('system:time_start').divide(1e10))\n",
    "\n",
    "rescaled_dcp30 = dcp30_ensemble_stats.map(rescale_time)\n",
    "# Notice we're not calling the getInfo() function\n",
    "rescaled_dcp30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'type': 'Image',\n",
       " 'bands': [{'id': 'scale',\n",
       "   'data_type': {'type': 'PixelType', 'precision': 'double'},\n",
       "   'crs': 'EPSG:4326',\n",
       "   'crs_transform': [1.0, 0.0, 0.0, 0.0, 1.0, 0.0]},\n",
       "  {'id': 'offset',\n",
       "   'data_type': {'type': 'PixelType', 'precision': 'double'},\n",
       "   'crs': 'EPSG:4326',\n",
       "   'crs_transform': [1.0, 0.0, 0.0, 0.0, 1.0, 0.0]}]}"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# And we'll fit a linear regresion through the datacube:\n",
    "linearFit = rescaled_dcp30.select(\n",
    "    ['system:time_start', 'tasmax_mean']\n",
    ").reduce(\n",
    "    ee.Reducer.linearFit()\n",
    ")\n",
    "\n",
    "linearFit.getInfo()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We can export this image to Google Drive so we can inspect it\n",
    "# in a GIS package. We'll have to define a geometry first\n",
    "\n",
    "llx = -180\n",
    "lly = -90\n",
    "urx = 180\n",
    "ury = 90\n",
    "geometry = [[llx,lly], [llx,ury], [urx,ury], [urx,lly]]\n",
    "task_config = {\n",
    "    'description': 'linear_fit_example',\n",
    "    'scale': 30, \n",
    "    'region': geometry,\n",
    "    'maxPixels': 1e13\n",
    "    }\n",
    "\n",
    "task = ee.batch.Export.image(linearFit, 'linear_fit_example', task_config)\n",
    "task.start()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': 'TXDQS7I76YH5QHOUBMPKNAA4',\n",
       " 'state': 'RUNNING',\n",
       " 'creation_timestamp_ms': 1529512671729,\n",
       " 'update_timestamp_ms': 1529512677245,\n",
       " 'description': 'linear_fit_example',\n",
       " 'task_type': 'EXPORT_IMAGE',\n",
       " 'start_timestamp_ms': 1529512676894,\n",
       " 'progress': 0.0}"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "task.status()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# When fitting a linear regression with the time as the dependent variable and\n",
    "# monthly average max temperature as an independent variable, the slope of the\n",
    "# regression is a measure of expected change in max temperatures in the future.\n",
    "# Let's put it in a map. The basic mechanism in GEE with Jupyter is to obtain a\n",
    "# thumbnail and display it directly. Beware: these thumbnails remain cached for\n",
    "# a couple of days, so the URL is not reusable in other contexts.\n",
    "\n",
    "from IPython.display import Image\n"
   ]
  }
 ],
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