{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "large-cassette", "metadata": {}, "outputs": [], "source": [ "import geemap\n", "import ee\n", "import os" ] }, { "cell_type": "code", "execution_count": 2, "id": "welsh-commissioner", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "08bff955386d4ebf9fc1b849e960d3d9", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Map(center=[40, -100], controls=(WidgetControl(options=['position', 'transparent_bg'], widget=HBox(children=(T…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Map = geemap.Map(center=[40,-100], zoom=4)\n", "Map" ] }, { "cell_type": "code", "execution_count": 3, "id": "fixed-numbers", "metadata": {}, "outputs": [], "source": [ "dataset_ulh = ee.ImageCollection('CIESIN/GPWv411/GPW_Population_Count')\n", "raster_vis = {\n", " \"max\": 1000.0,\n", " \"palette\": [\n", " \"ffffe7\",\n", " \"86a192\",\n", " \"509791\",\n", " \"307296\",\n", " \"2c4484\",\n", " \"000066\"\n", " ],\n", " \"min\": 0.0\n", "};\n", "Map.addLayer(dataset_ulh, {}, \"CIESIN/GPWv411/GPW_Population_Count\")" ] }, { "cell_type": "code", "execution_count": 47, "id": "boolean-section", "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "''", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mdataset_qhk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mee\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mImageCollection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'WorldPop/GP/100m/pop'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mMap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maddLayer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset_qhk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m~/miniconda3/envs/gee/lib/python3.9/site-packages/geemap/geemap.py\u001b[0m in \u001b[0;36madd_ee_layer\u001b[0;34m(self, ee_object, vis_params, name, shown, opacity)\u001b[0m\n\u001b[1;32m 1303\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlayer\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1304\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1305\u001b[0;31m \u001b[0mexisting_object\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mee_layer_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"ee_object\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1306\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1307\u001b[0m if isinstance(existing_object, ee.Image) or isinstance(\n", "\u001b[0;31mKeyError\u001b[0m: ''" ] } ], "source": [ "dataset_qhk = ee.ImageCollection('WorldPop/GP/100m/pop')\n", "Map.addLayer(dataset_qhk, raster_vis, \"\")" ] }, { "cell_type": "code", "execution_count": 4, "id": "unlikely-spirituality", "metadata": {}, "outputs": [], "source": [ "Map.add_minimap()" ] }, { "cell_type": "code", "execution_count": 20, "id": "ordered-length", "metadata": {}, "outputs": [], "source": [ "# Add Earth Engine dataset\n", "dem = ee.Image('USGS/SRTMGL1_003')\n", "\n", "# Set visualization parameters.\n", "dem_vis = {\n", " 'min': 0,\n", " 'max': 4000,\n", " 'palette': ['006633', 'E5FFCC', '662A00', 'D8D8D8', 'F5F5F5']}\n", "\n", "# Add Earth Engine DEM to map\n", "Map.addLayer(dem, dem_vis, 'SRTM DEM')\n", "\n", "# Add Landsat data to map\n", "landsat = ee.Image('LE7_TOA_5YEAR/1999_2003')\n", "\n", "landsat_vis = {\n", " 'bands': ['B4', 'B3', 'B2'], \n", " 'gamma': 1.4\n", "}\n", "Map.addLayer(landsat, landsat_vis, \"LE7_TOA_5YEAR/1999_2003\")\n", "\n", "states = ee.FeatureCollection(\"TIGER/2018/States\")\n", "Map.addLayer(states, {}, 'US States')" ] }, { "cell_type": "code", "execution_count": 41, "id": "corrected-address", "metadata": {}, "outputs": [], "source": [ "Map.search_locations" ] }, { "cell_type": "code", "execution_count": 21, "id": "circular-johns", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Computing statistics ...\n", "Generating URL ...\n", "Downloading data from https://earthengine.googleapis.com/v1alpha/projects/earthengine-legacy/tables/12c2b4c69f8909de6757f0ea4a348aa8-bba8b67770dc2ef4463b7d757353305e:getFeatures\n", "Please wait ...\n", "Data downloaded to /home/nicholasjones/Downloads/dem_stats.csv\n" ] } ], "source": [ "out_dir = os.path.join(os.path.expanduser('~'), 'Downloads')\n", "out_dem_stats = os.path.join(out_dir, 'dem_stats.csv') \n", "\n", "if not os.path.exists(out_dir):\n", " os.makedirs(out_dir)\n", "\n", "# Allowed output formats: csv, shp, json, kml, kmz\n", "# Allowed statistics type: MEAN, MAXIMUM, MINIMUM, MEDIAN, STD, MIN_MAX, VARIANCE, SUM\n", "geemap.zonal_statistics(dem, states, out_dem_stats, statistics_type='MEAN', scale=1000)" ] }, { "cell_type": "code", "execution_count": 34, "id": "olympic-validity", "metadata": {}, "outputs": [], "source": [ "pop = ee.ImageCollection(\"WorldPop/GP/100m/pop\").first()" ] }, { "cell_type": "code", "execution_count": 35, "id": "sudden-repeat", "metadata": {}, "outputs": [], "source": [ "pop_img = pop.select('population_count')" ] }, { "cell_type": "code", "execution_count": 38, "id": "arranged-station", "metadata": {}, "outputs": [], "source": [ "raster_vis = {\n", " \"max\": 1000.0,\n", " \"palette\": [\n", " \"ffffe7\",\n", " \"86a192\",\n", " \"509791\",\n", " \"307296\",\n", " \"2c4484\",\n", " \"000066\"\n", " ],\n", " \"min\": 0.0\n", "};" ] }, { "cell_type": "code", "execution_count": 40, "id": "mechanical-cisco", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "add_layer() takes 2 positional arguments but 4 were given", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mMap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_layer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpop_img\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mraster_vis\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'img'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mTypeError\u001b[0m: add_layer() takes 2 positional arguments but 4 were given" ] } ], "source": [ "Map.add_layer(pop_img,raster_vis,'img')" ] }, { "cell_type": "code", "execution_count": null, "id": "removed-disclosure", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "gee", "language": "python", "name": "gee" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.1" } }, "nbformat": 4, "nbformat_minor": 5 }