Earth engine Dimensions not working

gistfile1.txt

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate average PCRGlobWB supply using EE\n",
    "\n",
    "* Purpose of script: This script will calculate baseline supply based on runoff for 1960-2014 at 5min resolution\n",
    "* Author: Rutger Hofste\n",
    "* Kernel used: python27\n",
    "* Date created: 20170830"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import ee\n",
    "import folium\n",
    "from folium_gee import *\n",
    "import subprocess\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ee.Initialize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "EE_INPUT_PATH = \"projects/WRI-Aquaduct/PCRGlobWB20V07/\"\n",
    "\n",
    "EE_IC_NAME_ANNUAL =  \"global_historical_reducedmeanrunoff_year_myear_5min_1960_2014\"\n",
    "EE_IC_NAME_MONTH =   \"global_historical_reducedmeanrunoff_month_mmonth_5min_1960_2014\"\n",
    "\n",
    "EE_I_NAME_ANNUAL = EE_IC_NAME_ANNUAL\n",
    "EE_I_NAME_MONTH = EE_IC_NAME_MONTH\n",
    "\n",
    "DIMENSION5MIN = \"4320x2160\"\n",
    "\n",
    "INPUT_FILE_NAME_ANNUAL = \"global_historical_runoff_year_myear_5min_1958_2014\"\n",
    "INPUT_FILE_NAME_MONTH = \"global_historical_runoff_month_mmonth_5min_1958_2014\"\n",
    "\n",
    "YEAR_MIN = 1960\n",
    "YEAR_MAX = 2014\n",
    "ANNUAL_UNITS = \"m/year\"\n",
    "MONTHLY_UNITS = \"m/month\"\n",
    "ANNUAL_EXPORTDESCRIPTION = \"reducedmeanrunoff_year\" #final format reducedmeanrunoff_yearY1960Y2014\n",
    "MONTHLY_EXPORTDESCRIPTION = \"reducedmeanrunoff_month\" #final format reducedmeanrunoff_monthY1960Y2014M01\n",
    "VERSION = \"36\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Standardized format to store assets on Earth Engine is EE_INPUT_PATH / EE_IC_NAME / EE_I_NAME and every image should have the property expertdescription that would allow to export the data to a table header. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def exportToAssetAnnual(ic):\n",
    "    annualExportDescription = ANNUAL_EXPORTDESCRIPTION + \"Y%sY%s\" %(YEAR_MIN,YEAR_MAX)\n",
    "    annualImage = ee.Image(ic.reduce(ee.Reducer.mean()))\n",
    "    properties = {\"rangeMin\":ee.Number(YEAR_MIN),\n",
    "                  \"rangeMax\":ee.Number(YEAR_MAX),\n",
    "                  \"units\":ANNUAL_UNITS,\n",
    "                  \"exportdescription\":annualExportDescription,\n",
    "                  \"creation\":\"RutgerHofste_20170901_Python27\",\n",
    "                  \"nodata_value\":-9999,\n",
    "                  \"reducer\":\"mean\",\n",
    "                  \"time_start\": \"%04d-%0.2d-%0.2d\" %(YEAR_MAX,12,1) \n",
    "                 }\n",
    "    annualImage = annualImage.set(properties)\n",
    "    scale = ee.Image(icAnnual.first()).projection().nominalScale().getInfo()\n",
    "    assetId = EE_INPUT_PATH + EE_IC_NAME_ANNUAL + \"V\" +VERSION+ \"/\" + EE_I_NAME_ANNUAL + \"V\" + VERSION    \n",
    "    task = ee.batch.Export.image.toAsset(\n",
    "        image =  ee.Image(annualImage),\n",
    "        description = EE_I_NAME_ANNUAL + \"V\" + VERSION,\n",
    "        assetId = assetId,\n",
    "        dimensions = DIMENSION5MIN,\n",
    "        region = geometry.bounds().getInfo()['coordinates'][0],\n",
    "        maxPixels = 1e10\n",
    "    )\n",
    "    task.start()\n",
    "    print(assetId)\n",
    "    return annualImage\n",
    "\n",
    "\n",
    "def exportToAssetMonth(month):\n",
    "    monthlyExportDescription = MONTHLY_EXPORTDESCRIPTION + \"Y%sY%sM%0.2d\" %(YEAR_MIN,YEAR_MAX,month)\n",
    "    monthlyImage = filteredMonthlyCollection.filter(ee.Filter.eq(\"month\",ee.Number(month)))\n",
    "    monthlyImage = filteredMonthlyCollection.reduce(ee.Reducer.mean())\n",
    "    properties = {\"month\":month,\n",
    "                  \"reducer\":\"mean\",\n",
    "                  \"rangeMin\":ee.Number(YEAR_MIN),\n",
    "                  \"rangeMax\":ee.Number(YEAR_MAX),\n",
    "                  \"units\":MONTHLY_UNITS,\n",
    "                  \"exportdescription\":monthlyExportDescription,\n",
    "                  \"creation\":\"RutgerHofste_20170901_Python27\",\n",
    "                  \"nodata_value\":-9999,\n",
    "                  \"time_start\": \"%04d-%0.2d-%0.2d\" %(YEAR_MAX,month,1) \n",
    "                 }\n",
    "    monthlyImage  =  monthlyImage.set(properties)\n",
    "\n",
    "    \n",
    "    scale = ee.Image(icMonthly.first()).projection().nominalScale().getInfo()\n",
    "\n",
    "    assetId = EE_INPUT_PATH + EE_IC_NAME_MONTH + \"V\" +VERSION+ \"/\" + EE_I_NAME_MONTH + \"M%0.2dV%s\" %(month,VERSION)  \n",
    "    task = ee.batch.Export.image.toAsset(\n",
    "        image =  ee.Image(monthlyImage),\n",
    "        description = EE_I_NAME_MONTH + \"M%0.2dV%s\" %(month,VERSION) ,\n",
    "        assetId = assetId,\n",
    "        dimensions = DIMENSION5MIN,\n",
    "        region = geometry.bounds().getInfo()['coordinates'][0],\n",
    "        maxPixels = 1e10\n",
    "    )\n",
    "    task.start()\n",
    "    print(month)\n",
    "    return 1\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "icAnnual = ee.ImageCollection(os.path.join(EE_INPUT_PATH,INPUT_FILE_NAME_ANNUAL))\n",
    "icMonthly = ee.ImageCollection(os.path.join(EE_INPUT_PATH,INPUT_FILE_NAME_MONTH))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dateFilterMin = ee.Filter.gte(\"year\",YEAR_MIN)\n",
    "dateFilterMax = ee.Filter.lte(\"year\",YEAR_MAX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "geometry = ee.Geometry.Polygon(coords=[[-180.0, -90.0], [180,  -90.0], [180, 89], [180,90]], proj= ee.Projection('EPSG:4326'),geodesic=False )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "filteredAnnualCollection = ee.ImageCollection(icAnnual.filter(dateFilterMin).filter(dateFilterMax))\n",
    "filteredMonthlyCollection = ee.ImageCollection(icMonthly.filter(dateFilterMin).filter(dateFilterMax))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sampleImage = ee.Image(filteredAnnualCollection.first())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "earthengine create collection projects/WRI-Aquaduct/PCRGlobWB20V07/global_historical_reducedmeanrunoff_year_myear_5min_1960_2014V36\n"
     ]
    }
   ],
   "source": [
    "icAnnualPath = os.path.join(EE_INPUT_PATH,EE_IC_NAME_ANNUAL+ \"V\" + VERSION)\n",
    "command = (\"earthengine create collection %s\") %icAnnualPath\n",
    "print(command)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Asset projects/WRI-Aquaduct/PCRGlobWB20V07/global_historical_reducedmeanrunoff_year_myear_5min_1960_2014V36 already exists\\n'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subprocess.check_output(command,shell=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "earthengine create collection projects/WRI-Aquaduct/PCRGlobWB20V07/global_historical_reducedmeanrunoff_month_mmonth_5min_1960_2014V36\n"
     ]
    }
   ],
   "source": [
    "icMonthPath = os.path.join(EE_INPUT_PATH,EE_IC_NAME_MONTH+ \"V\" + VERSION)\n",
    "command = (\"earthengine create collection %s\") %icMonthPath\n",
    "print(command)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Asset projects/WRI-Aquaduct/PCRGlobWB20V07/global_historical_reducedmeanrunoff_month_mmonth_5min_1960_2014V36 already exists\\n'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subprocess.check_output(command,shell=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Run the functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "projects/WRI-Aquaduct/PCRGlobWB20V07/global_historical_reducedmeanrunoff_year_myear_5min_1960_2014V36/global_historical_reducedmeanrunoff_year_myear_5min_1960_2014V36\n"
     ]
    }
   ],
   "source": [
    "annualImage = exportToAssetAnnual(filteredAnnualCollection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "months = list(range(1,13))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "map(exportToAssetMonth,months)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## check results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "annualImage = annualImage.reproject('EPSG:4326',sampleImage.projection().getInfo())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lat = 39.495159\n",
    "lon = -107.3689237\n",
    "zoom_start=5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "m = folium.Map(location=[lat, lon], tiles=\"OpenStreetMap\", zoom_start=zoom_start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vis_params = {'min':0.0, 'max':100, 'palette':'00FFFF,0000FF'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "folium_gee_layer(m,annualImage,vis_params=vis_params,folium_kwargs={'overlay':True,'name':'reducedRunoff'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "m.add_child(folium.LayerControl())\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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