clip_hrus.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Script to clip the index raster to each NHM HRU\n",
    "\n",
    "Theodore Barnhart | tbarnhart@usgs.gov"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "import subprocess\n",
    "import os\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "proj = 'GEOGCS[\\\"unnamed ellipse\\\",DATUM[\\\"unknown\\\",SPHEROID[\\\"unnamed\\\",6371200,0]],PRIMEM[\\\"Greenwich\\\",0],UNIT[\\\"degree\\\",0.0174532925199433]]'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_outpath(df):\n",
    "    outpath = './data/nhrus/spherical_tiffs/HUC_%s_nhruID_%s.tiff'%(df.region,df.nhruID)\n",
    "    return outpath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clip_raster(df):\n",
    "    \n",
    "    cutline = df.cutline\n",
    "    feature = df.nhruID\n",
    "    inpath = df.inpath\n",
    "    outpath = df.outpath\n",
    "    \n",
    "    cmd = \"gdalwarp -t_srs \\'%s\\' -cutline %s -cwhere hru_id_nat=%s -crop_to_cutline -dstnodata -9999 -ot Int32 -overwrite %s %s\"%(proj,cutline,\n",
    "        feature,inpath,outpath)\n",
    "    \n",
    "    subprocess.call(cmd,shell=True)\n",
    "    \n",
    "    return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def runClip(fl,inpath='tmp'):\n",
    "    tmp = gpd.read_file(fl)\n",
    "    dat = pd.DataFrame({'nhruID':tmp.hru_id_nat.unique()}) #dataframe of the unique NHM HRU identifiers\n",
    "    del tmp # close tmp file\n",
    "    \n",
    "    reg = fl.split('_')[-2] # extract the region\n",
    "    \n",
    "    dat['cutline'] = fl # insert the shapefile as the cutline\n",
    "    dat['region'] = reg\n",
    "    dat['inpath'] = inpath # specified\n",
    "    dat['outpath'] = dat.apply(make_outpath,axis=1)\n",
    "    \n",
    "    dat.apply(clip_raster,axis=1) # run the clip code\n",
    "    print('HUC%s Done!'%reg)\n",
    "    # test that the number of files created is the same as the number of features\n",
    "    #files = glob.glob('./data/nhrus/AEA_tiffs/HUC_%s_*.tiff'%(reg))\n",
    "    #if (len(files)-len(dat)) != 0:\n",
    "    #    return 'Error!'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_file(df):\n",
    "    hru = df.nhruID\n",
    "    reg = df.region\n",
    "    return os.path.isfile('./data/nhrus/spherical_tiffs/HUC_%s_nhruID_%s.tiff'%(reg,hru))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_out(out):\n",
    "    out = str(out.stdout)\n",
    "    if len(out.spit) == 1:\n",
    "        return None,None\n",
    "    else:\n",
    "        x = int(out.split()[-1].split('L')[0])\n",
    "        y = int(out.split()[-3].split('P')[0])\n",
    "        return x,y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clip_raster_output(df):\n",
    "    \n",
    "    cutline = df.cutline\n",
    "    feature = df.nhruID\n",
    "    inpath = df.inpath\n",
    "    outpath = df.outpath\n",
    "    \n",
    "    cmd = \"gdalwarp -t_srs \\'%s\\' -cutline %s -cwhere hru_id_nat=%s -crop_to_cutline -dstnodata -9999 -ot Int32 -overwrite %s %s\"%(proj,cutline,\n",
    "        feature,inpath,outpath)\n",
    "    \n",
    "    out = subprocess.run(cmd,shell=True,stdout=subprocess.PIPE)\n",
    "    \n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clip_raster_output_12(df):\n",
    "    \n",
    "    cutline = df.cutline\n",
    "    feature = df.nhruID\n",
    "    inpath = df.inpath\n",
    "    outpath = df.outpath\n",
    "    \n",
    "    cmd = \"gdalwarp -cutline %s -cwhere hru_id=%s -crop_to_cutline -overwrite %s %s\"%(cutline,\n",
    "        feature,inpath,outpath)\n",
    "    \n",
    "    out = subprocess.run(cmd,shell=True,stdout=subprocess.PIPE)\n",
    "    \n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check(fl,inpath='tmp'):\n",
    "    '''Figure out which HRUS are missing'''\n",
    "    reg = fl.split('_')[-2] # extract the region\n",
    "    tmp = gpd.read_file(fl)\n",
    "    dat = pd.DataFrame({'nhruID':tmp.hru_id_nat.unique()}) #dataframe of the unique NHM HRU identifiers\n",
    "    del tmp # close tmp file\n",
    "    \n",
    "    dat['cutline'] = fl # insert the shapefile as the cutline\n",
    "    dat['region'] = reg\n",
    "    dat['inpath'] = inpath # specified\n",
    "    dat['outpath'] = dat.apply(make_outpath,axis=1)\n",
    "    dat['region'] = reg # insert the region\n",
    "    dat['isfile'] = dat.apply(is_file,axis=1)\n",
    "    \n",
    "    dat2 = dat.loc[dat.isfile==False].copy() # select only the missing files\n",
    "    \n",
    "    if len(dat2) > 0:\n",
    "        dat2['out'] = dat2.apply(clip_raster_output,axis=1) # run the clip routine again, but save the output\n",
    "        res = dat2.out.map(parse_out) # parse the output into the raster size trying to be created\n",
    "        x,y = zip(*res) # unpack the results and put into the dataframe \n",
    "        dat2['cols'] = x\n",
    "        dat2['rows'] = y\n",
    "        dat2['cells'] = dat2.rows*dat2.cols\n",
    "    \n",
    "        if dat2.cells.sum()>0:\n",
    "            print('non-zero raster!')\n",
    "    \n",
    "        return dat2\n",
    "    \n",
    "    elif len(dat) - dat.isfile.sum() == 0:\n",
    "        print('%s Complete!'%reg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "newRegions = ['01','02','03','04','05','06','07','08','09','10U','10L','11','12','13','14','15','16','17','18']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make new files\n",
    "files = []\n",
    "for reg in newRegions:\n",
    "    files.append('./data/nhrus/clean_AEA/nhru_%s_clean.shp'%reg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['./data/nhrus/clean_AEA/nhru_01_clean.shp',\n",
       " './data/nhrus/clean_AEA/nhru_02_clean.shp',\n",
       " './data/nhrus/clean_AEA/nhru_03_clean.shp',\n",
       " './data/nhrus/clean_AEA/nhru_04_clean.shp',\n",
       " './data/nhrus/clean_AEA/nhru_05_clean.shp',\n",
       " './data/nhrus/clean_AEA/nhru_06_clean.shp',\n",
       " './data/nhrus/clean_AEA/nhru_07_clean.shp',\n",
       " './data/nhrus/clean_AEA/nhru_08_clean.shp',\n",
       " './data/nhrus/clean_AEA/nhru_09_clean.shp',\n",
       " './data/nhrus/clean_AEA/nhru_10U_clean.shp',\n",
       " './data/nhrus/clean_AEA/nhru_10L_clean.shp',\n",
       " './data/nhrus/clean_AEA/nhru_11_clean.shp',\n",
       " './data/nhrus/clean_AEA/nhru_12_clean.shp',\n",
       " './data/nhrus/clean_AEA/nhru_13_clean.shp',\n",
       " './data/nhrus/clean_AEA/nhru_14_clean.shp',\n",
       " './data/nhrus/clean_AEA/nhru_15_clean.shp',\n",
       " './data/nhrus/clean_AEA/nhru_16_clean.shp',\n",
       " './data/nhrus/clean_AEA/nhru_17_clean.shp',\n",
       " './data/nhrus/clean_AEA/nhru_18_clean.shp']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "files = pd.DataFrame({'file':files})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HUC01 Done!\n",
      "HUC02 Done!\n",
      "HUC03 Done!\n",
      "HUC04 Done!\n",
      "HUC05 Done!\n",
      "HUC06 Done!\n",
      "HUC07 Done!\n",
      "HUC08 Done!\n",
      "HUC09 Done!\n",
      "HUC10U Done!\n",
      "HUC10L Done!\n",
      "HUC11 Done!\n",
      "HUC12 Done!\n",
      "HUC13 Done!\n",
      "HUC14 Done!\n",
      "HUC15 Done!\n",
      "HUC16 Done!\n",
      "HUC17 Done!\n",
      "HUC18 Done!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0     None\n",
       "1     None\n",
       "2     None\n",
       "3     None\n",
       "4     None\n",
       "5     None\n",
       "6     None\n",
       "7     None\n",
       "8     None\n",
       "9     None\n",
       "10    None\n",
       "11    None\n",
       "12    None\n",
       "13    None\n",
       "14    None\n",
       "15    None\n",
       "16    None\n",
       "17    None\n",
       "18    None\n",
       "Name: file, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files.file.apply(runClip,inpath='./data/NLDASv2_idx_0125.tiff')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'files' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-34a59ceb5a5f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmissing\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfiles\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcheck\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0minpath\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'./data/NLDASv2_idx_0125.tiff'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'files' is not defined"
     ]
    }
   ],
   "source": [
    "missing = files.file.apply(check,inpath='./data/NLDASv2_idx_0125.tiff')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                                  None\n",
       "1                                                  None\n",
       "2                                                  None\n",
       "3           nhruID                                  ...\n",
       "4                                                  None\n",
       "5                                                  None\n",
       "6                                                  None\n",
       "7                                                  None\n",
       "8                                                  None\n",
       "9                                                  None\n",
       "10                                                 None\n",
       "11                                                 None\n",
       "12                                                 None\n",
       "13                                                 None\n",
       "Name: file, dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nhruID</th>\n",
       "      <th>cutline</th>\n",
       "      <th>region</th>\n",
       "      <th>inpath</th>\n",
       "      <th>outpath</th>\n",
       "      <th>isfile</th>\n",
       "      <th>out</th>\n",
       "      <th>cols</th>\n",
       "      <th>rows</th>\n",
       "      <th>cells</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>17305</td>\n",
       "      <td>./data/nhrus/clean_AEA/nhru_04_clean.shp</td>\n",
       "      <td>04</td>\n",
       "      <td>./data/hrap_grid_AEA_idx_sm.tiff</td>\n",
       "      <td>./data/nhrus/AEA_tiffs/HUC_04_nhruID_17305.tiff</td>\n",
       "      <td>False</td>\n",
       "      <td>CompletedProcess(args='gdalwarp -cutline ./dat...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3746</th>\n",
       "      <td>20935</td>\n",
       "      <td>./data/nhrus/clean_AEA/nhru_04_clean.shp</td>\n",
       "      <td>04</td>\n",
       "      <td>./data/hrap_grid_AEA_idx_sm.tiff</td>\n",
       "      <td>./data/nhrus/AEA_tiffs/HUC_04_nhruID_20935.tiff</td>\n",
       "      <td>False</td>\n",
       "      <td>CompletedProcess(args='gdalwarp -cutline ./dat...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3835</th>\n",
       "      <td>21024</td>\n",
       "      <td>./data/nhrus/clean_AEA/nhru_04_clean.shp</td>\n",
       "      <td>04</td>\n",
       "      <td>./data/hrap_grid_AEA_idx_sm.tiff</td>\n",
       "      <td>./data/nhrus/AEA_tiffs/HUC_04_nhruID_21024.tiff</td>\n",
       "      <td>False</td>\n",
       "      <td>CompletedProcess(args='gdalwarp -cutline ./dat...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4538</th>\n",
       "      <td>21727</td>\n",
       "      <td>./data/nhrus/clean_AEA/nhru_04_clean.shp</td>\n",
       "      <td>04</td>\n",
       "      <td>./data/hrap_grid_AEA_idx_sm.tiff</td>\n",
       "      <td>./data/nhrus/AEA_tiffs/HUC_04_nhruID_21727.tiff</td>\n",
       "      <td>False</td>\n",
       "      <td>CompletedProcess(args='gdalwarp -cutline ./dat...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5067</th>\n",
       "      <td>22256</td>\n",
       "      <td>./data/nhrus/clean_AEA/nhru_04_clean.shp</td>\n",
       "      <td>04</td>\n",
       "      <td>./data/hrap_grid_AEA_idx_sm.tiff</td>\n",
       "      <td>./data/nhrus/AEA_tiffs/HUC_04_nhruID_22256.tiff</td>\n",
       "      <td>False</td>\n",
       "      <td>CompletedProcess(args='gdalwarp -cutline ./dat...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      nhruID                                   cutline region  \\\n",
       "116    17305  ./data/nhrus/clean_AEA/nhru_04_clean.shp     04   \n",
       "3746   20935  ./data/nhrus/clean_AEA/nhru_04_clean.shp     04   \n",
       "3835   21024  ./data/nhrus/clean_AEA/nhru_04_clean.shp     04   \n",
       "4538   21727  ./data/nhrus/clean_AEA/nhru_04_clean.shp     04   \n",
       "5067   22256  ./data/nhrus/clean_AEA/nhru_04_clean.shp     04   \n",
       "\n",
       "                                inpath  \\\n",
       "116   ./data/hrap_grid_AEA_idx_sm.tiff   \n",
       "3746  ./data/hrap_grid_AEA_idx_sm.tiff   \n",
       "3835  ./data/hrap_grid_AEA_idx_sm.tiff   \n",
       "4538  ./data/hrap_grid_AEA_idx_sm.tiff   \n",
       "5067  ./data/hrap_grid_AEA_idx_sm.tiff   \n",
       "\n",
       "                                              outpath  isfile  \\\n",
       "116   ./data/nhrus/AEA_tiffs/HUC_04_nhruID_17305.tiff   False   \n",
       "3746  ./data/nhrus/AEA_tiffs/HUC_04_nhruID_20935.tiff   False   \n",
       "3835  ./data/nhrus/AEA_tiffs/HUC_04_nhruID_21024.tiff   False   \n",
       "4538  ./data/nhrus/AEA_tiffs/HUC_04_nhruID_21727.tiff   False   \n",
       "5067  ./data/nhrus/AEA_tiffs/HUC_04_nhruID_22256.tiff   False   \n",
       "\n",
       "                                                    out  cols  rows  cells  \n",
       "116   CompletedProcess(args='gdalwarp -cutline ./dat...     0     1      0  \n",
       "3746  CompletedProcess(args='gdalwarp -cutline ./dat...     0     0      0  \n",
       "3835  CompletedProcess(args='gdalwarp -cutline ./dat...     1     0      0  \n",
       "4538  CompletedProcess(args='gdalwarp -cutline ./dat...     0     0      0  \n",
       "5067  CompletedProcess(args='gdalwarp -cutline ./dat...     1     0      0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Code the few remaining cells by hand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "reg04_NHRU = [17305,20935,21024,21727,22256]\n",
    "reg04_cells = [[296823],[354989],[356077],[306739],[260807]]\n",
    "reg04_percents = [[1],[1],[1],[1],[1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "reg08 = pd.DataFrame()\n",
    "reg08['nhruID'] = reg04_NHRU\n",
    "reg08['reg_hruID'] = [117,3747,3836,4539,5068]\n",
    "reg08['cells'] = reg04_cells\n",
    "reg08['percents'] = reg04_percents\n",
    "reg08['reg'] = '04'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "reg08.to_pickle('./data/reg04_unclipped.pcl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "      <th>nhruID</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40817</td>\n",
       "      <td>./data/nhrus/clean_AEA/nhru_08_clean.shp</td>\n",
       "      <td>08</td>\n",
       "      <td>./data/hrap_grid_AEA_idx_sm.tiff</td>\n",
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       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>40910</td>\n",
       "      <td>./data/nhrus/clean_AEA/nhru_08_clean.shp</td>\n",
       "      <td>08</td>\n",
       "      <td>./data/hrap_grid_AEA_idx_sm.tiff</td>\n",
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       "      <td>./data/hrap_grid_AEA_idx_sm.tiff</td>\n",
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      "text/plain": [
       "     nhruID                                   cutline region  \\\n",
       "2     40817  ./data/nhrus/clean_AEA/nhru_08_clean.shp     08   \n",
       "95    40910  ./data/nhrus/clean_AEA/nhru_08_clean.shp     08   \n",
       "111   40926  ./data/nhrus/clean_AEA/nhru_08_clean.shp     08   \n",
       "\n",
       "                               inpath  \\\n",
       "2    ./data/hrap_grid_AEA_idx_sm.tiff   \n",
       "95   ./data/hrap_grid_AEA_idx_sm.tiff   \n",
       "111  ./data/hrap_grid_AEA_idx_sm.tiff   \n",
       "\n",
       "                                             outpath  isfile  \\\n",
       "2    ./data/nhrus/AEA_tiffs/HUC_08_nhruID_40817.tiff   False   \n",
       "95   ./data/nhrus/AEA_tiffs/HUC_08_nhruID_40910.tiff   False   \n",
       "111  ./data/nhrus/AEA_tiffs/HUC_08_nhruID_40926.tiff   False   \n",
       "\n",
       "                                                   out  cols  rows  cells  \n",
       "2    CompletedProcess(args='gdalwarp -cutline ./dat...     1     0      0  \n",
       "95   CompletedProcess(args='gdalwarp -cutline ./dat...     0     1      0  \n",
       "111  CompletedProcess(args='gdalwarp -cutline ./dat...     0     0      0  "
      ]
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     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
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    "missing[1]"
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  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "reg08_NHRU = [40817,40910,40926,40826,40827,40891,40908,41064]\n",
    "reg08_cells = [[788822],[780963],[772030],[778776],[778776],[776509],[780963],[760827]]\n",
    "reg08_percents = [[1],[1],[1],[1],[1],[1],[1],[1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "reg08 = pd.DataFrame()\n",
    "reg08['nhruID'] = reg08_NHRU\n",
    "reg08['reg_hruID'] = [3,96,112,12,13,77,94,250]\n",
    "reg08['cells'] = reg08_cells\n",
    "reg08['percents'] = reg08_percents\n",
    "reg08['reg'] = '08'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "reg08.to_pickle('./data/reg08_unclipped.pcl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nhruID</th>\n",
       "      <th>cutline</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8081</th>\n",
       "      <td>63665</td>\n",
       "      <td>./data/nhrus/clean_AEA/nhru_10U_clean.shp</td>\n",
       "      <td>10U</td>\n",
       "      <td>./data/hrap_grid_AEA_idx_sm.tiff</td>\n",
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       "      <td>False</td>\n",
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       "      <th>8097</th>\n",
       "      <td>63681</td>\n",
       "      <td>./data/nhrus/clean_AEA/nhru_10U_clean.shp</td>\n",
       "      <td>10U</td>\n",
       "      <td>./data/hrap_grid_AEA_idx_sm.tiff</td>\n",
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       "      nhruID                                    cutline region  \\\n",
       "8081   63665  ./data/nhrus/clean_AEA/nhru_10U_clean.shp    10U   \n",
       "8097   63681  ./data/nhrus/clean_AEA/nhru_10U_clean.shp    10U   \n",
       "\n",
       "                                inpath  \\\n",
       "8081  ./data/hrap_grid_AEA_idx_sm.tiff   \n",
       "8097  ./data/hrap_grid_AEA_idx_sm.tiff   \n",
       "\n",
       "                                               outpath  isfile  \\\n",
       "8081  ./data/nhrus/AEA_tiffs/HUC_10U_nhruID_63665.tiff   False   \n",
       "8097  ./data/nhrus/AEA_tiffs/HUC_10U_nhruID_63681.tiff   False   \n",
       "\n",
       "                                                    out  cols  rows  cells  \n",
       "8081  CompletedProcess(args='gdalwarp -cutline ./dat...     0     1      0  \n",
       "8097  CompletedProcess(args='gdalwarp -cutline ./dat...     0     1      0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "missing[4]"
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  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "reg10U_NHRU = [63665,63681]\n",
    "reg10U_cells = [[354526],[354513]]\n",
    "reg10U_percents = [[1],[1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "reg10U = pd.DataFrame()\n",
    "reg10U['nhruID'] = reg10U_NHRU\n",
    "reg10U['reg_hruID'] = [8082,8098]\n",
    "reg10U['cells'] = reg10U_cells\n",
    "reg10U['percents'] = reg10U_percents\n",
    "reg10U['reg'] = '10U'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "reg10U.to_pickle('./data/reg10U_unclipped.pcl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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