9_Evapotranspiration_Parallel.ipynb

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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Use QGIS-Processing to create Evapotranspiration Images"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Parallel set up (12 engines)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.parallel import Client\n",
      "import os\n",
      "rc=Client()\n",
      "rc.ids"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dview=rc[:]\n",
      "dview.scatter('a' , range(12))\n",
      "dview.get('a')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Parallel per-engine imports and set up"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%px\n",
      "from __future__ import print_function\n",
      "\n",
      "from rpy2.robjects.packages import importr\n",
      "from rpy2.robjects import r\n",
      "import rpy2.robjects.numpy2ri as rpyn\n",
      "import datetime\n",
      "import ee\n",
      "import numpy as np\n",
      "import os\n",
      "import matplotlib.pyplot as plt\n",
      "from pylab import figure\n",
      "import matplotlib.dates as ndates\n",
      "from pandas import *\n",
      "\n",
      "from IPython.html import widgets\n",
      "from IPython.display import display\n",
      "from IPython.utils import traitlets\n",
      "from IPython.core.display import Javascript\n",
      "\n",
      "% reload_ext rpy2.ipython\n",
      "% matplotlib inline\n",
      "wd = 'C:/Alex/script/evapo/'\n",
      "os.chdir(wd)\n",
      "monthList = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
      "month = a[0]\n",
      "monthName = monthList[month]\n",
      "try:\n",
      "    os.mkdir(wd+monthName)\n",
      "except WindowsError:\n",
      "    print ('Directory already created, it will be deleted at the end of the script if delete_temp is set to True!')\n",
      "    \n",
      "os.chdir(wd+monthName)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Monthly extraction from ncdf"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%px\n",
      "%%R -i month,monthName\n",
      "library(raster)\n",
      "library(rgdal)\n",
      "path_evap <- \"P:/3_Malaria_Risk/1_Data/9_Evapotranspiration\"\n",
      "\n",
      "setwd (path_evap)\n",
      "myshp <- readOGR(\"C:/Alex/script/MDG_adm0.shp\", layer=basename(\"MDG_adm0\") )\n",
      "e <- extent(myshp)\n",
      "fileList <- list.files(path=path_evap, full.names = FALSE, pattern = \"*.nc\")\n",
      "\n",
      "for (i in fileList){\n",
      "myraster <- raster(i, band = month+1)\n",
      "\n",
      "# Cropping the raster to the shapefile spatial extent\n",
      "myraster.crop <- crop(myraster, e, snap=\"out\")\n",
      "\n",
      "# Dummy raster with a spatial extension equal to the cropped raster,\n",
      "# but full of NA values\n",
      "crop          <- setValues(myraster.crop, NA)\n",
      "\n",
      "#  Rasterize the catchment boundaries, with NA outside the catchment \n",
      "myshp.r <- rasterize(myshp, crop)\n",
      "\n",
      "# Putting NA values in all the raster cells outside the shapefile boundaries\n",
      "myraster.masked <- mask(x=myraster.crop, mask=myshp.r)\n",
      "\n",
      "#plot(myraster)\n",
      "#print(myraster.masked)\n",
      "outputname <- i\n",
      "\n",
      "outputname <- sub(\".nc\" , monthName, outputname)\n",
      "outfile <- paste(\"C:/Alex/script/evapo/\",monthName, sep=\"\")\n",
      "outfile2 <- paste(outfile, \"/\", sep=\"\")\n",
      "outfile3 <- paste(outfile2, outputname, sep=\"\")\n",
      "outfile4 <- paste(outfile3, \".tif\", sep=\"\")\n",
      "writeRaster(myraster.masked, outfile4, format = \"GTiff\")\n",
      "#plot()\n",
      "}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Parallel Processing set up"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%px\n",
      "#STEP 0: Set up paths, working directory inside IPython\n",
      "\n",
      "import sys\n",
      "import os\n",
      "import shutil\n",
      "\n",
      "#QGIS & executables paths(required)\n",
      "sys.path.append('C:\\Program Files (x86)\\QGIS Wien\\lib')\n",
      "sys.path.append('C:\\Program Files (x86)\\QGIS Wien\\apps')\n",
      "sys.path.append('C:\\Program Files (x86)\\QGIS Wien\\apps\\qgis\\python')\n",
      "sys.path.append('C:\\Program Files (x86)\\QGIS Wien\\apps\\qgis\\python\\plugins')\n",
      "sys.path.append('C:\\Program Files (x86)\\QGIS Wien\\apps\\qgis\\python\\plugins\\processing')\n",
      "\n",
      "delete_temp = True\n",
      "\n",
      "#Set temporary folder\n",
      "os.chdir(wd+monthName)\n",
      "print(os.getcwd())\n",
      "os.environ['TMP']=os.getcwd()\n",
      "print(os.environ['TMP'])\n",
      "\n",
      "import qgis\n",
      "import gdal\n",
      "from PyQt4.QtCore import QFileInfo,QSettings\n",
      "from qgis.analysis import QgsRasterCalculator, QgsRasterCalculatorEntry\n",
      "from qgis.core import *\n",
      "import qgis.utils\n",
      "\n",
      "\n",
      "#Country name(for output filenames)\n",
      "country = 'MDG'\n",
      "\n",
      "#QGIS path(required)\n",
      "#QgsApplication.setPrefixPath('/Applications/QGIS.app/Contents/MacOS',True)\n",
      "QgsApplication.setPrefixPath('C:/PROGRA~2/QGISWI~1/apps/qgis', True)\n",
      "QgsApplication.initQgis()\n",
      "\n",
      "import sys\n",
      "import os, sys, glob\n",
      "from qgis.core import *\n",
      "from PyQt4.QtGui import *\n",
      "app = QApplication([])\n",
      "\n",
      "# Prepare processing framework \n",
      "import processing\n",
      "from processing.core.Processing import Processing\n",
      "from processing.core.ProcessingConfig import ProcessingConfig\n",
      "ProcessingConfig.setSettingValue('OUTPUT_FOLDER',os.getcwd())\n",
      "Processing.initialize()\n",
      "from processing.tools import *\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Monthly processing via Processing-GRASS r:series"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%px\n",
      "\n",
      "fileList = os.listdir(os.getcwd())\n",
      "print(fileList)\n",
      "tiflist= '' \n",
      "for file in os.listdir(os.getcwd()):\n",
      "    if file.endswith(\".tif\"):\n",
      "        tiflist=tiflist+os.getcwd()+'/'+file+\";\"\n",
      "tiflist=tiflist[:-1]\n",
      "print(tiflist)\n",
      "\n",
      "templayer=QgsRasterLayer(os.listdir(os.getcwd())[0])\n",
      "temp=templayer.extent()\n",
      "extent=str(temp.xMinimum())+','+str(temp.xMaximum())+','+str(temp.yMinimum())+','+str(temp.yMaximum())\n",
      "\n",
      "processing.runalg('grass:r.series',tiflist,True, 4, '-10000000000,10000000000',extent,0, wd+country+'_Evapo_'+monthName+'_Minimum.tif')\n",
      "processing.runalg('grass:r.series',tiflist,True, 6, '-10000000000,10000000000',extent,0, wd+country+'_Evapo_'+monthName+'_Maximum.tif')\n",
      "processing.runalg('grass:r.series',tiflist,True, 0, '-10000000000,10000000000',extent,0, wd+country+'_Evapo_'+monthName+'_Average.tif')\n",
      "#processing.runalg('grass:r.series',tiflist,True, 10, '-10000000000,10000000000',extent,0, wd+country+'_Evapo_'+monthName+'_Sum.tif')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "2011/2013 script set up (core engine)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#STEP 0: Set up paths, working directory inside IPython\n",
      "\n",
      "import sys\n",
      "import os\n",
      "import shutil\n",
      "\n",
      "#QGIS & executables paths(required)\n",
      "sys.path.append('C:\\Program Files (x86)\\QGIS Wien\\lib')\n",
      "sys.path.append('C:\\Program Files (x86)\\QGIS Wien\\apps')\n",
      "sys.path.append('C:\\Program Files (x86)\\QGIS Wien\\apps\\qgis\\python')\n",
      "sys.path.append('C:\\Program Files (x86)\\QGIS Wien\\apps\\qgis\\python\\plugins')\n",
      "sys.path.append('C:\\Program Files (x86)\\QGIS Wien\\apps\\qgis\\python\\plugins\\processing')\n",
      "\n",
      "delete_temp = True\n",
      "\n",
      "#Set temporary folder\n",
      "wd = 'C:/Alex/script/evapo/'\n",
      "\n",
      "print(os.getcwd())\n",
      "os.environ['TMP']=os.getcwd()\n",
      "print(os.environ['TMP'])\n",
      "\n",
      "import qgis\n",
      "import gdal\n",
      "from PyQt4.QtCore import QFileInfo,QSettings\n",
      "from qgis.analysis import QgsRasterCalculator, QgsRasterCalculatorEntry\n",
      "from qgis.core import *\n",
      "import qgis.utils\n",
      "\n",
      "\n",
      "#Country name(for output filenames)\n",
      "country = 'MDG'\n",
      "\n",
      "#QGIS path(required)\n",
      "#QgsApplication.setPrefixPath('/Applications/QGIS.app/Contents/MacOS',True)\n",
      "QgsApplication.setPrefixPath('C:/PROGRA~2/QGISWI~1/apps/qgis', True)\n",
      "QgsApplication.initQgis()\n",
      "\n",
      "import sys\n",
      "import os, sys, glob\n",
      "from qgis.core import *\n",
      "from PyQt4.QtGui import *\n",
      "app = QApplication([])\n",
      "\n",
      "# Prepare processing framework \n",
      "import processing\n",
      "from processing.core.Processing import Processing\n",
      "from processing.core.ProcessingConfig import ProcessingConfig\n",
      "ProcessingConfig.setSettingValue('OUTPUT_FOLDER',os.getcwd())\n",
      "Processing.initialize()\n",
      "from processing.tools import *\n",
      "\n",
      "\n",
      "from __future__ import print_function\n",
      "\n",
      "from rpy2.robjects.packages import importr\n",
      "from rpy2.robjects import r\n",
      "import rpy2.robjects.numpy2ri as rpyn\n",
      "import datetime\n",
      "import ee\n",
      "import numpy as np\n",
      "import os\n",
      "import matplotlib.pyplot as plt\n",
      "from pylab import figure\n",
      "import matplotlib.dates as ndates\n",
      "from pandas import *\n",
      "\n",
      "from IPython.html import widgets\n",
      "from IPython.display import display\n",
      "from IPython.utils import traitlets\n",
      "from IPython.core.display import Javascript\n",
      "\n",
      "% reload_ext rpy2.ipython\n",
      "% matplotlib inline\n",
      "\n",
      "try:\n",
      "    os.mkdir(wd+\"2011\")\n",
      "    os.mkdir(wd+\"2013\")\n",
      "except WindowsError:\n",
      "    print ('Directory already created, it will be deleted at the end of the script if delete_temp is set to True!')\n",
      "    \n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Load already processed 2011 and 2013 images and process them via Processing-GRASS r:series"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "tiflist2011= '' \n",
      "tiflist2013= ''\n",
      "for path, subdirs, files in os.walk(wd):\n",
      "    for name in files:\n",
      "        filename=os.path.join(path, name)\n",
      "        #print (filename)\n",
      "        if(filename.count(\"2011\")&filename.endswith(\".tif\")):\n",
      "            tiflist2011=tiflist2011+filename+\";\"\n",
      "        if(filename.count(\"2013\")&filename.endswith(\".tif\")):\n",
      "            tiflist2013=tiflist2013+filename+\";\"\n",
      "tiflist2011=tiflist2011[:-1]\n",
      "tiflist2013=tiflist2013[:-1]\n",
      "print(tiflist2011)\n",
      "print(tiflist2013)\n",
      "templayer=QgsRasterLayer(tiflist2011.split(\";\")[0])\n",
      "temp=templayer.extent()\n",
      "extent=str(temp.xMinimum())+','+str(temp.xMaximum())+','+str(temp.yMinimum())+','+str(temp.yMaximum())\n",
      "\n",
      "processing.runalg('grass:r.series',tiflist2011,True, 4, '-10000000000,10000000000',extent,0, wd+'2011/'+country+'_Evapo_2011_Minimum.tif')\n",
      "processing.runalg('grass:r.series',tiflist2011,True, 6, '-10000000000,10000000000',extent,0, wd+'2011/'+country+'_Evapo_2011_Maximum.tif')\n",
      "processing.runalg('grass:r.series',tiflist2011,True, 0, '-10000000000,10000000000',extent,0, wd+'2011/'+country+'_Evapo_2011_Average.tif')\n",
      "#processing.runalg('grass:r.series',tiflist2011,True, 10, '-10000000000,10000000000',extent,0, wd+'2011/'+country+'_Evapo_2011_Sum.tif')\n",
      "\n",
      "processing.runalg('grass:r.series',tiflist2013,True, 4, '-10000000000,10000000000',extent,0, wd+'2013/'+country+'_Evapo_2013_Minimum.tif')\n",
      "processing.runalg('grass:r.series',tiflist2013,True, 6, '-10000000000,10000000000',extent,0, wd+'2013/'+country+'_Evapo_2013_Maximum.tif')\n",
      "processing.runalg('grass:r.series',tiflist2013,True, 0, '-10000000000,10000000000',extent,0, wd+'2013/'+country+'_Evapo_2013_Average.tif')\n",
      "#processing.runalg('grass:r.series',tiflist2013,True, 10, '-10000000000,10000000000',extent,0, wd+'2013/'+country+'_Evapo_2013_Sum.tif')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Exit QGIS and cleanup"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%px\n",
      "\n",
      "#STEP 6 exit QGIS and remove temporary files\n",
      "\n",
      "\n",
      "QgsApplication.exitQgis()\n",
      "#QgsApplication.quit()\n",
      "app.quit()\n",
      "#print(os.getcwd())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#STEP 6 exit QGIS and remove temporary files\n",
      "\n",
      "\n",
      "QgsApplication.exitQgis()\n",
      "#QgsApplication.quit()\n",
      "app.quit()\n",
      "#print(os.getcwd())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "% reload_ext rpy2.ipython"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R\n",
      "wd <- \"P:/3_Malaria_Risk/3_Variables/9_Evapotranspiration/\"\n",
      "library('sp')\n",
      "library('maps')\n",
      "library('spam')\n",
      "library('XML')\n",
      "library('raster')\n",
      "library('rgdal')\n",
      "library('ggplot2')\n",
      "library('fields')\n",
      "library('spgrass6')\n",
      "library('prevR')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R -w 43 -h 20 -u cm\n",
      "fn2011 <- paste(wd,\"MDG_Evapo_2011_Average.tif\",sep=\"\")\n",
      "r2011 <- raster(fn2011)\n",
      "fn2013 <- paste(wd,\"MDG_Evapo_2013_Average.tif\",sep=\"\")\n",
      "r2013 <- raster(fn2013)\n",
      "\n",
      "par(mfrow=c(1,2), mar=c(5,5,2,4),oma=c(1,1,1,5),cex.axis=1.3,cex.lab=1.3,cex.main=1.6)\n",
      "plot(r2011, main='Evapotranspiration 2011', xlab='lon',ylab='lat', col=prevR.colors.green.inverse(50))\n",
      "plot(r2013, main='Evapotranspiration 2013', xlab='lon',ylab='lat', col=prevR.colors.green.inverse(50))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Q/iN4gQUvI/EMIrtjQpI3mI7ya7ZolWy/30KIvYYslBA7RSXLE8WjKALq1H2x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\nmyAzUBFTgpq8hGx6CXuV+e8z9rYy/zkAgD7611vXAaRI3Qglt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      }
     ],
     "prompt_number": 14
    },
    {
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 ]
}