3. Mean areal values for Shang.ipynb

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    "################################################################\n",
    "# Scirpt_Name: Mean_Areal_Value.py                             #\n",
    "# Purpose(s): 1. Looping through all selected watersheds       #\n",
    "#                Shp file                                      #\n",
    "#             2. Looping through all GeoTIFF PET data-sets     # \n",
    "#             2. Masking all GeoTIFF PET data-sets with Shp    #\n",
    "#                files, and concatenating into one inified     #\n",
    "#                .NC file                                      #\n",
    "#             3.Calculating the mean areal value time series   #\n",
    "#               for each selected watersheds and save the file #\n",
    "#               at local disk in .CSV format                   #\n",
    "################################################################\n",
    "# Written by Lujun Zhang @ U of Oklahoma 03/31/2020            #\n",
    "# REVISION HISTORY                                             #\n",
    "# 20200331 -- Initial version completed by Lujun @ OU          #\n",
    "################################################################\n",
    "# Requie packages: Numpy, Pandas, necCDF4, GDAL                #\n",
    "################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
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   "source": [
    "import numpy as np\n",
    "import numpy.ma as ma\n",
    "import pandas as pd\n",
    "from datetime import timedelta\n",
    "import sys\n",
    "import os\n",
    "import time\n",
    "import datetime\n",
    "from netCDF4 import Dataset  \n",
    "from osgeo import gdal, ogr\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl \n",
    "from mpl_toolkits.basemap import Basemap\n",
    "from mpl_toolkits.basemap import interp\n",
    "from multiprocessing import Pool\n",
    "from ftplib import FTP\n",
    "import gdal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clipping NMME Members Using shapefile\n",
    "def GeoTIFF_Mask(GeoInput,ShpFileDirec):\n",
    "    '''\n",
    "    1) This function:\n",
    "    - clipping & Masking the origional GeoTIFF dataset accorindg to \n",
    "      a certain given input shapefile;\n",
    "    - returns a masked Numpy Array over desired region\n",
    "    2) The input elements are explainted below:\n",
    "    - GeoInput: a GeoTIFF Dataset directory\n",
    "    - ShpFileDirec: Shape file's directory in str format\n",
    "    '''\n",
    "    #########################################################################################################\n",
    "    ## Before anything Remerber to make sure the long & latitude between input shapefile and PET dataset   ##\n",
    "    #  are consistent with each other                                                                       #\n",
    "    ##-----------------------------------------------------------------------------------------------------##\n",
    "    #  Here we do not modify either GeoTiff data-sets' longitude or shpfiles' Longitude since they all have #\n",
    "    #  the same range of [-180,180] which are consistent with each other already                            #\n",
    "    ##-----------------------------------------------------------------------------------------------------##\n",
    "    # However such shifting processes could be done using the 'ogr2ogr' package within the command line     #\n",
    "    # window. The example code with the purpose of shifing 'Mill.shp' is shown below:                       #\n",
    "    # ogr2ogr Mill_Shifted.shp Mill.shp -dialect sqlite -sql \"SELECT ShiftCoords(geometry,360,0) FROM Mill\" #\n",
    "    #########################################################################################################\n",
    "    ##read shapefile   \n",
    "    shpDS = ogr.Open(ShpFileDirec)\n",
    "    shpLyr = shpDS.GetLayer()\n",
    "    Envelop = shpLyr.GetExtent() \n",
    "    xmin,xmax,ymin,ymax = [Envelop[0],Envelop[1],Envelop[2],Envelop[3]]    #Your extents as given above\n",
    "   #xmin = xmin-0.04168701\n",
    "   #xmax = xmax+0.04168701\n",
    "   #ymin = ymin-0.041664124\n",
    "   #ymax = ymax+0.041664124\n",
    "    mask_RES = []\n",
    "    \n",
    "    ## Extract Origin GeoTIFF Data \n",
    "    gdal.GetDriverByName('GTiff').Register()\n",
    "    img = gdal.Open(GeoInput)\n",
    "    band = img.GetRasterBand(1)\n",
    "    geoinfo = img.GetGeoTransform()\n",
    "    lon_res = geoinfo[1]; originX = geoinfo[0];\n",
    "    lat_res = -geoinfo[5]; originY = geoinfo[3]; \n",
    "    \n",
    "    varData_Ori = band.ReadAsArray()\n",
    "    varData_Ori = np.ma.array(varData_Ori)\n",
    "    lon_Ori = sorted(np.arange(originX, originX+lon_res*(np.shape(varData_Ori)[1])+0.001, lon_res))\n",
    "    lon_Ori = np.array(lon_Ori)\n",
    "    lat_Ori = sorted(np.arange(originY-lat_res*(np.shape(varData_Ori)[0]), originY, lat_res))\n",
    "    lat_Ori = np.array(lat_Ori)\n",
    "    \n",
    "    ##Create mask\n",
    "    if len(mask_RES) == 0 :\n",
    "        #get boundary and xs ys\n",
    "        lat_bnds, lon_bnds = [ymin-lat_res, ymax+lat_res], [xmin-lon_res,xmax+lon_res]\n",
    "        lat_inds = np.where((lat_Ori >= (lat_bnds[0])) & (lat_Ori <= lat_bnds[1]))\n",
    "        lon_inds = np.where((lon_Ori >= (lon_bnds[0])) & (lon_Ori <= lon_bnds[1]))\n",
    "        ncols = len(lon_inds[0])\n",
    "        nrows = len(lat_inds[0])\n",
    "\n",
    "        #nreftime = len(ref_Ori)\n",
    "        #create geotransform\n",
    "        xres = (xmax - xmin) / float(ncols)\n",
    "        yres = (ymax - ymin) / float(nrows)\n",
    "        geotransform = (xmin,xres,0,ymax,0,-yres)\n",
    "        #create mask\n",
    "        mask_DS = gdal.GetDriverByName('MEM').Create('', ncols, nrows, 1 ,gdal.GDT_Int32)\n",
    "        mask_RB = mask_DS.GetRasterBand(1)\n",
    "        mask_RB.Fill(0) #initialise raster with zeros\n",
    "        mask_RB.SetNoDataValue(-32767)\n",
    "        mask_DS.SetGeoTransform(geotransform)\n",
    "        maskvalue = 1\n",
    "        err = gdal.RasterizeLayer(mask_DS, [maskvalue], shpLyr)\n",
    "        mask_DS.FlushCache()\n",
    "        mask_array = mask_DS.GetRasterBand(1).ReadAsArray()    \n",
    "        mask_RES = ma.masked_equal(mask_array, 255)          \n",
    "        ma.set_fill_value(mask_RES, -32767)\n",
    "        mask = np.expand_dims(np.logical_not(np.flipud(mask_RES.mask)),0)\n",
    "    ##Set Mask\n",
    "    var_subset = varData_Ori[min(lon_inds[0]):max(lon_inds[0])+1,min(lat_inds[0]):max(lat_inds[0])+1]\n",
    "    #var_subset.__setmask__(mask) # update mask (flipud is reverse 180)\n",
    "    var_subset = np.transpose(var_subset)\n",
    "    lat_subset = lat_Ori[min(lat_inds[0]):max(lat_inds[0])+1] \n",
    "    lon_subset = lon_Ori[min(lon_inds[0]):max(lon_inds[0])]+360\n",
    "    #print(np.shape(var_subset))\n",
    "    #print(varData_Ori)\n",
    "    #print(np.shape(lon_subset))\n",
    "    #print(np.shape(lat_subset))\n",
    "    \n",
    "    return var_subset, lon_subset, lat_subset"
   ]
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   "execution_count": 4,
   "metadata": {
    "scrolled": true
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:/users/zhan0101/Desktop/20200329 Selected_watersheds/Wimberly.shp\n",
      "['20010416', '20010418']\n"
     ]
    }
   ],
   "source": [
    "# Shapefile information specification\n",
    "#gtiff = 'C:/users/zhan0101/desktop/20200331 PET/Wimberly/ra20010201.tif'\n",
    "#shp = 'C:/users/zhan0101/desktop/20200329 Selected_watersheds/Wimberly.shp'\n",
    "\n",
    "#SHP_Names = ['Delaware_Shifted.shp','Mill_Shifted.shp','Sycamore_Shifted.shp','Wimberly_Shifted.shp']\n",
    "Names = ['Delaware/','Mill/','Sycamore/','Wimberly/']\n",
    "Add_1 = 'C:/users/zhan0101/Desktop/20200329 Selected_watersheds/'\n",
    "Add_2 = 'C:/users/zhan0101/Desktop/20200331 PET/'\n",
    "idx = pd.date_range(start='2001/02/01', end='2019/12/31', freq=\"d\")\n",
    "\n",
    "for i in Names[3:]:\n",
    "    missing_counter = 0\n",
    "    missing_dates = []\n",
    "    SHPInput = Add_1+i[0:-1]+'.shp'\n",
    "    print\n",
    "    print(SHPInput)\n",
    "    path = Add_2+i\n",
    "    path_list = os.listdir(path)\n",
    "    data = np.zeros(np.shape(path_list)[0])\n",
    "    for j in range(np.size(path_list)):\n",
    "        GeoInput = path+path_list[j]\n",
    "        if j == 0:\n",
    "            data_tab, Lon, Lat = GeoTIFF_Mask(GeoInput, SHPInput)\n",
    "            data_tab = np.expand_dims(data_tab, 0)\n",
    "            Data = data_tab\n",
    "        elif j == range(np.size(path_list))[-1]:\n",
    "            data_tab, Lon, Lat = GeoTIFF_Mask(GeoInput, SHPInput)\n",
    "            data_tab = np.expand_dims(data_tab, 0)\n",
    "            Data = np.concatenate((Data,data_tab),axis=0)  \n",
    "            # Saving as NC file\n",
    "            Out_Direc = 'F:/PET/'+i[0:-1]+'_PET.nc'\n",
    "            ncOutput = Dataset(Out_Direc, 'w', format='NETCDF4')\n",
    "            ncOutput.createDimension('Lon', np.size(Lon))\n",
    "            ncOutput.createDimension('Lat', np.size(Lat))\n",
    "            ncOutput.createDimension('T', np.shape(Data)[0])\n",
    "            # Add lat Variable\n",
    "            var_out_Y = ncOutput.createVariable('Lat','f',(\"Lat\"))\n",
    "            ncOutput.variables['Lat'][:] = Lat[:]\n",
    "            # Add lon Variable\n",
    "            var_out_X = ncOutput.createVariable('Lon','f',(\"Lon\"))\n",
    "            ncOutput.variables['Lon'][:] = Lon[:]\n",
    "            # Add time Variable\n",
    "            var_out_S = ncOutput.createVariable('T','f',(\"T\"))\n",
    "            ncOutput.variables['T'][:] = np.arange(np.shape(Data)[0])[:]\n",
    "            # Add data Variable\n",
    "            var_out_data = ncOutput.createVariable('Prec', 'f',(\"T\",\"Lat\",\"Lon\"))\n",
    "            ncOutput.variables['Prec'][:,:,:] = Data[:,:,:]\n",
    "            # attr\n",
    "            ncOutput.history = \"Created datatime \" + datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\") + \" by LujunZ at OU\"\n",
    "            ncOutput.source  = \"netCDF4 under python 3.6.5\"\n",
    "            ncOutput.close()  # close the new file  \n",
    "            \n",
    "            #if i == 'Wimberly/':\n",
    "            #    mean_areal_value = Data\n",
    "            #else:\n",
    "            #    mean_areal_value = np.mean(Data,axis=(1,2))\n",
    "            mean_areal_value = np.mean(Data,axis=(1,2))\n",
    "            # Svaing mean areal value into .CSV format at local disk\n",
    "            \n",
    "            mean_areal_value = pd.DataFrame(data=mean_areal_value,index=idx,columns=None)\n",
    "            mean_areal_value.to_csv('F:/PET/'+i[:-1]+'_PET.csv')\n",
    "        else:\n",
    "            #print(path_list[j][2:-4] == idx[j+missing_counter].strftime('%Y%m%d'))\n",
    "            if path_list[j][2:-4] == idx[j+missing_counter].strftime('%Y%m%d'):\n",
    "                data_tab, Lon, Lat = GeoTIFF_Mask(GeoInput, SHPInput)\n",
    "                data_tab = np.expand_dims(data_tab, 0)\n",
    "                Data = np.concatenate((Data,data_tab),axis=0)  \n",
    "            else:\n",
    "                missing_counter = missing_counter+1\n",
    "                missing_dates.append(idx[j+missing_counter].strftime('%Y%m%d'))\n",
    "                data_tab = np.zeros(np.shape(data_tab))\n",
    "                Data = np.concatenate((Data,data_tab),axis=0)\n",
    "                \n",
    "                data_tab, Lon, Lat = GeoTIFF_Mask(GeoInput, SHPInput)\n",
    "                data_tab = np.expand_dims(data_tab, 0)\n",
    "                Data = np.concatenate((Data,data_tab),axis=0)\n",
    "    print(missing_dates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zhan0101\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:12: MatplotlibDeprecationWarning: \n",
      "The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.\n",
      "  if sys.path[0] == '':\n",
      "C:\\Users\\zhan0101\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:13: MatplotlibDeprecationWarning: \n",
      "The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.\n",
      "  del sys.path[0]\n",
      "C:\\Users\\zhan0101\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:18: MatplotlibDeprecationWarning: \n",
      "The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.\n",
      "C:\\Users\\zhan0101\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:19: MatplotlibDeprecationWarning: \n",
      "The dedent function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use inspect.cleandoc instead.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Data = Dataset('F:/PET/Mill_PET.nc')\n",
    "\n",
    "Lat = Data['Lat']\n",
    "Long = Data['Lon']\n",
    "Prec = Data['Prec'][0,:,:]\n",
    "\n",
    "lon_min=np.min(Long)-10; lon_max=np.max(Long)+10\n",
    "lat_min=np.min(Lat)-10; lat_max=np.max(Lat)+10\n",
    "\n",
    "plt.figure(figsize=(16,9))\n",
    "plt.title('PRISM Precipitation Test Plot',fontsize=22)\n",
    "m=Basemap(projection='mill',llcrnrlon=lon_min,urcrnrlon=lon_max,llcrnrlat=lat_min,urcrnrlat=lat_max,resolution='l')\n",
    "shp_info = m.readshapefile(\"C:/users/Zhan0101/desktop/20200329 Selected_watersheds/Mill_Shifted\",'MILL')\n",
    "\n",
    "lon,lat=np.meshgrid(Long,Lat)\n",
    "lon,lat=m(lon,lat)\n",
    "m.drawcoastlines(linewidth=1)\n",
    "m.drawstates(linewidth=1.5)\n",
    "m.drawcountries(linewidth=2)\n",
    "m.drawparallels(np.arange(-90, 90,5), labels=[1,0,0,0], fontsize=13,linewidth=0.8)\n",
    "m.drawmeridians(np.arange(-180, 180, 5), labels=[0,0,0,1], fontsize=13,linewidth=0.8)\n",
    "cmap = plt.cm.jet_r\n",
    "norm = mpl.colors.Normalize(vmin=0, vmax=np.max(Prec))\n",
    "#norm = mpl.colors.Normalize(vmin=0, vmax=0.01)\n",
    "cf = plt.pcolormesh(lon,lat,Prec,cmap=cmap,norm=norm)\n",
    "#cax = ax[1].pcolor(lon,lat,Prec[450,0,3],camp=camp)\n",
    "cax=plt.axes([0.93, 0.15, 0.01,0.7])\n",
    "cbar = plt.colorbar(cf,cax=cax)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}