aggregate_livneh2016.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import numpy as np\n",
    "import glob as glob\n",
    "from netCDF4 import Dataset\n",
    "import rasterio as rs\n",
    "import progressbar as pb\n",
    "import sys\n",
    "\n",
    "#reg = sys.argv[1]\n",
    "reg = '04'\n",
    "def get_year(index):\n",
    "    return index.year\n",
    "\n",
    "def get_month(index):\n",
    "    return index.month\n",
    "\n",
    "def get_day(index):\n",
    "    return index.day\n",
    "\n",
    "def get_keys(df,idxraster=[]):\n",
    "    '''\n",
    "    Find index raster cell index values.\n",
    "    '''\n",
    "    cells = df.cells\n",
    "    \n",
    "    x = []\n",
    "    y = []\n",
    "    \n",
    "    if len(cells) != 1:\n",
    "        for cell in cells:\n",
    "            xx,yy = np.where(idxraster == cell)\n",
    "            x.append(xx)\n",
    "            y.append(yy)\n",
    "    \n",
    "    else:\n",
    "        xx,yy = np.where(idxraster == cells[0])\n",
    "        x.append(xx)\n",
    "        y.append(yy)\n",
    "    \n",
    "    return x,y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "## generate the time aspect of the data\n",
    "# the Livneh data start at 1915 and end 2015\n",
    "\n",
    "dates = pd.date_range(start = '1915-01-01', end = '2015-12-31', freq = 'D')\n",
    "#months = pd.date_range(start = '1915-01', end = '2015-12', freq = 'M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "with rs.open('./data/livneh_idx.tiff') as ds:\n",
    "    idxRast = np.flipud(ds.read(1)) # flip this to deal with the change from tiff to array\n",
    "\n",
    "dat = pd.read_pickle('./data/livneh_huc_%s_cell_contrib.pcl'%reg)\n",
    "\n",
    "# get the index values of each cell\n",
    "res = dat.apply(get_keys,axis=1,idxraster=idxRast)\n",
    "x,y = zip(*res)\n",
    "dat['x'] = x\n",
    "dat['y'] = y\n",
    "\n",
    "# now compute some min and max index values and subset the index raster\n",
    "xs = []\n",
    "ys = []\n",
    "for x,y in zip(dat.x,dat.y):\n",
    "    xs.extend(x)\n",
    "    ys.extend(y)\n",
    "\n",
    "xs = np.unique(xs)\n",
    "ys = np.unique(ys)\n",
    "\n",
    "# get extents on the local data set needed\n",
    "minX = xs.min()\n",
    "maxX = xs.max()\n",
    "minY = ys.min()\n",
    "maxY = ys.max()\n",
    "\n",
    "extents = [minX,maxX,minY,maxY]\n",
    "\n",
    "idxLocal = idxRast[minX:maxX,minY:maxY] # subset the index raster\n",
    "\n",
    "res = dat.apply(get_keys,axis=1,idxraster = idxLocal) # recompute local indices to subset the data stack.\n",
    "\n",
    "x,y = zip(*res)\n",
    "\n",
    "dat['x_local'] = x\n",
    "dat['y_local'] = y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_idx(df):\n",
    "    if (len(df.x_local) == 0) | (len(df.y_local) == 0):\n",
    "        return 1.\n",
    "    else: \n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "dat['test'] = dat.apply(test_idx,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat.test.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>POI_ID</th>\n",
       "      <th>hru_id_nat</th>\n",
       "      <th>hru_id_reg</th>\n",
       "      <th>region</th>\n",
       "      <th>reg</th>\n",
       "      <th>cells</th>\n",
       "      <th>percents</th>\n",
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       "      <th>x_local</th>\n",
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       "      <th>984</th>\n",
       "      <td>15448640</td>\n",
       "      <td>18173</td>\n",
       "      <td>985</td>\n",
       "      <td>04</td>\n",
       "      <td>04</td>\n",
       "      <td>[448111.0, 448112.0, 448113.0, 449038.0, 44903...</td>\n",
       "      <td>[0.006802721088435374, 0.07482993197278912, 0....</td>\n",
       "      <td>[[482], [482], [482], [483], [483], [483], [48...</td>\n",
       "      <td>[[814], [815], [816], [813], [814], [815], [81...</td>\n",
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       "      <td>987</td>\n",
       "      <td>04</td>\n",
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       "      <td>[[72], [72], [73], [73]]</td>\n",
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       "      <th>987</th>\n",
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       "      <td>0</td>\n",
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       "      <th>989</th>\n",
       "      <td>15458360</td>\n",
       "      <td>18178</td>\n",
       "      <td>990</td>\n",
       "      <td>04</td>\n",
       "      <td>04</td>\n",
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       "      <td>0</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "       POI_ID  hru_id_nat  hru_id_reg region reg  \\\n",
       "980  15448626       18169         981     04  04   \n",
       "981  15450138       18170         982     04  04   \n",
       "982  15448578       18171         983     04  04   \n",
       "983  15448574       18172         984     04  04   \n",
       "984  15448640       18173         985     04  04   \n",
       "985  15448640       18174         986     04  04   \n",
       "986  15456836       18175         987     04  04   \n",
       "987  15450454       18176         988     04  04   \n",
       "988  15458306       18177         989     04  04   \n",
       "989  15458360       18178         990     04  04   \n",
       "\n",
       "                                                 cells  \\\n",
       "980                                         [450888.0]   \n",
       "981                                         [450889.0]   \n",
       "982                               [450889.0, 450890.0]   \n",
       "983  [448108.0, 448109.0, 448110.0, 449036.0, 44903...   \n",
       "984  [448111.0, 448112.0, 448113.0, 449038.0, 44903...   \n",
       "985  [448112.0, 448113.0, 449040.0, 449041.0, 44996...   \n",
       "986           [449958.0, 449959.0, 450886.0, 450887.0]   \n",
       "987  [445327.0, 445328.0, 446255.0, 446256.0, 44718...   \n",
       "988  [442539.0, 442540.0, 442541.0, 443467.0, 44346...   \n",
       "989  [441611.0, 441612.0, 442538.0, 442539.0, 44254...   \n",
       "\n",
       "                                              percents  \\\n",
       "980                                              [1.0]   \n",
       "981                                              [1.0]   \n",
       "982           [0.7777777777777778, 0.2222222222222222]   \n",
       "983  [0.0033707865168539327, 0.08314606741573034, 0...   \n",
       "984  [0.006802721088435374, 0.07482993197278912, 0....   \n",
       "985  [0.003171247357293869, 0.039112050739957716, 0...   \n",
       "986  [0.26063829787234044, 0.06914893617021277, 0.2...   \n",
       "987  [0.024793388429752067, 0.045454545454545456, 0...   \n",
       "988  [0.041666666666666664, 0.4097222222222222, 0.1...   \n",
       "989  [0.01694915254237288, 0.01694915254237288, 0.3...   \n",
       "\n",
       "                                                     x  \\\n",
       "980                                            [[485]]   \n",
       "981                                            [[485]]   \n",
       "982                                     [[485], [485]]   \n",
       "983  [[482], [482], [482], [483], [483], [483], [48...   \n",
       "984  [[482], [482], [482], [483], [483], [483], [48...   \n",
       "985  [[482], [482], [483], [483], [484], [484], [48...   \n",
       "986                       [[484], [484], [485], [485]]   \n",
       "987  [[479], [479], [480], [480], [481], [481], [48...   \n",
       "988         [[476], [476], [476], [477], [477], [477]]   \n",
       "989                [[475], [475], [476], [476], [476]]   \n",
       "\n",
       "                                                     y  \\\n",
       "980                                            [[807]]   \n",
       "981                                            [[808]]   \n",
       "982                                     [[808], [809]]   \n",
       "983  [[811], [812], [813], [811], [812], [813], [81...   \n",
       "984  [[814], [815], [816], [813], [814], [815], [81...   \n",
       "985  [[815], [816], [815], [816], [814], [815], [81...   \n",
       "986                       [[805], [806], [805], [806]]   \n",
       "987  [[814], [815], [814], [815], [814], [815], [81...   \n",
       "988         [[810], [811], [812], [810], [811], [812]]   \n",
       "989                [[810], [811], [809], [810], [811]]   \n",
       "\n",
       "                                               x_local  \\\n",
       "980                                             [[73]]   \n",
       "981                                             [[73]]   \n",
       "982                                       [[73], [73]]   \n",
       "983  [[70], [70], [70], [71], [71], [71], [71], [72...   \n",
       "984  [[70], [70], [70], [71], [71], [71], [72], [72...   \n",
       "985  [[70], [70], [71], [71], [72], [72], [72], [72...   \n",
       "986                           [[72], [72], [73], [73]]   \n",
       "987  [[67], [67], [68], [68], [69], [69], [70], [70...   \n",
       "988               [[64], [64], [64], [65], [65], [65]]   \n",
       "989                     [[63], [63], [64], [64], [64]]   \n",
       "\n",
       "                                               y_local  test  \n",
       "980                                            [[299]]     0  \n",
       "981                                            [[300]]     0  \n",
       "982                                     [[300], [301]]     0  \n",
       "983  [[303], [304], [305], [303], [304], [305], [30...     0  \n",
       "984  [[306], [307], [308], [305], [306], [307], [30...     0  \n",
       "985  [[307], [308], [307], [308], [306], [307], [30...     0  \n",
       "986                       [[297], [298], [297], [298]]     0  \n",
       "987  [[306], [307], [306], [307], [306], [307], [30...     0  \n",
       "988         [[302], [303], [304], [302], [303], [304]]     0  \n",
       "989                [[302], [303], [301], [302], [303]]     0  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat[980:990]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5936"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_fractional_date(fl):\n",
    "    yearMonth = fl.split('.')[-2]\n",
    "    year = float(yearMonth[0:4])\n",
    "    month = float(yearMonth[4:6])-0.5\n",
    "    \n",
    "    return year + (month/12.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create and sort a data frame to ensure that files are read in the correct order.\n",
    "livneh = pd.DataFrame()\n",
    "livneh['files'] = glob.glob('/home/tbarnhart/projects/NHM_precipitation/data/livneh2016/*.nc')\n",
    "livneh['date'] = livneh.files.map(get_fractional_date)\n",
    "livneh.sort_values('date',inplace=True,ascending=True)\n",
    "\n",
    "livneh.reset_index()\n",
    "fl = livneh.files[0]\n",
    "liv = Dataset(fl)\n",
    "Tmin = np.array(liv.variables['Tmin'][:,minX:maxX,minY:maxY],dtype=np.float64)\n",
    "Tmax = np.array(liv.variables['Tmax'][:,minX:maxX,minY:maxY],dtype=np.float64)\n",
    "Prec = np.array(liv.variables['Prec'][:,minX:maxX,minY:maxY],dtype=np.float64)\n",
    "\n",
    "for fl in livneh.files[1:]:\n",
    "    liv = Dataset(fl)\n",
    "    Tmin = np.concatenate((Tmin,np.array(liv.variables['Tmin'][:,minX:maxX,minY:maxY],dtype=np.float64)),axis=0)\n",
    "    Tmax = np.concatenate((Tmax,np.array(liv.variables['Tmax'][:,minX:maxX,minY:maxY],dtype=np.float64)),axis=0)\n",
    "    Prec = np.concatenate((Prec,np.array(liv.variables['Prec'][:,minX:maxX,minY:maxY],dtype=np.float64)),axis=0)\n",
    "    \n",
    "noData = 1e+20\n",
    "\n",
    "# handle no data values:\n",
    "Tmin[Tmin == noData] = np.NaN\n",
    "Tmax[Tmax == noData] = np.NaN\n",
    "Prec[Prec == noData] = np.NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/miniconda3/envs/py36/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2524\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2525\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2526\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'hru_1545'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/miniconda3/envs/py36/lib/python3.6/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mset\u001b[0;34m(self, item, value, check)\u001b[0m\n\u001b[1;32m   3967\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3968\u001b[0;31m             \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3969\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/py36/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2526\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2527\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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   2528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'hru_1545'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-72-8f58bacdffc9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mhru\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhru_id_reg\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# create space for each HRU\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m     \u001b[0mout\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'hru_%s'\u001b[0m\u001b[0;34m%\u001b[0m\u001b[0mhru\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m999\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'datetime'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m# clean up\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/py36/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   2517\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2518\u001b[0m             \u001b[0;31m# set column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2519\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2520\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2521\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_setitem_slice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/py36/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_set_item\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   2584\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ensure_valid_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2585\u001b[0m         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sanitize_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2586\u001b[0;31m         \u001b[0mNDFrame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2587\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2588\u001b[0m         \u001b[0;31m# check if we are modifying a copy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/py36/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_set_item\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   1952\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1953\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_set_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1954\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1955\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_clear_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1956\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/py36/lib/python3.6/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mset\u001b[0;34m(self, item, value, check)\u001b[0m\n\u001b[1;32m   3969\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3970\u001b[0m             \u001b[0;31m# This item wasn't present, just insert at end\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3971\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3972\u001b[0m             \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3973\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/py36/lib/python3.6/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36minsert\u001b[0;34m(self, loc, item, value, allow_duplicates)\u001b[0m\n\u001b[1;32m   4067\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4068\u001b[0m         \u001b[0;31m# insert to the axis; this could possibly raise a TypeError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4069\u001b[0;31m         \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4070\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4071\u001b[0m         block = make_block(values=value, ndim=self.ndim,\n",
      "\u001b[0;32m~/miniconda3/envs/py36/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36minsert\u001b[0;34m(self, loc, item)\u001b[0m\n\u001b[1;32m   3718\u001b[0m         \"\"\"\n\u001b[1;32m   3719\u001b[0m         \u001b[0m_self\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3720\u001b[0;31m         \u001b[0mitem\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_coerce_scalar_to_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3721\u001b[0m         \u001b[0midx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_self\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_self\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mloc\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[1;32m   3722\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_shallow_copy_with_infer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/py36/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36m_coerce_scalar_to_index\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m    762\u001b[0m             \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    763\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 764\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_attributes_dict\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[1;32m    765\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    766\u001b[0m     _index_shared_docs['copy'] = \"\"\"\n",
      "\u001b[0;32m~/miniconda3/envs/py36/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36m__new__\u001b[0;34m(cls, data, dtype, copy, name, fastpath, tupleize_cols, **kwargs)\u001b[0m\n\u001b[1;32m    372\u001b[0m                         \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    373\u001b[0m             \u001b[0;31m# other iterable of some kind\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 374\u001b[0;31m             \u001b[0msubarr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_asarray_tuplesafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobject\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    375\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    376\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/py36/lib/python3.6/site-packages/pandas/core/common.py\u001b[0m in \u001b[0;36m_asarray_tuplesafe\u001b[0;34m(values, dtype)\u001b[0m\n\u001b[1;32m    374\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    375\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_asarray_tuplesafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\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[0;32m--> 376\u001b[0;31m     \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mIndex\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    377\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    378\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__array__'\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[0;32m~/miniconda3/envs/py36/lib/python3.6/importlib/_bootstrap.py\u001b[0m in \u001b[0;36m_handle_fromlist\u001b[0;34m(module, fromlist, import_, recursive)\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# now loop through each nhru in the region\n",
    "# prepair the output data frame\n",
    "out = pd.DataFrame()\n",
    "out['datetime'] = dates\n",
    "out.index = pd.DatetimeIndex(out.datetime)\n",
    "out['year'] = out.index.map(get_year)\n",
    "out['month'] = out.index.map(get_month)\n",
    "out['day'] = out.index.map(get_day)\n",
    "out['hour'] = 0\n",
    "out['minute'] = 0\n",
    "out['second'] = 0\n",
    "\n",
    "for hru in dat.hru_id_reg: # create space for each HRU\n",
    "    out['hru_%s'%hru] = -999\n",
    "    \n",
    "del out['datetime'] # clean up\n",
    "\n",
    "Pout = out.copy()\n",
    "Tminout = out.copy()\n",
    "Tmaxout = out.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "for hru,x,y,percents in zip(dat.hru_id_reg,dat.x_local,dat.y_local,dat.percents):\n",
    "    PrecTmp = Prec[:,x,y]\n",
    "    TminTmp = Tmin[:,x,y]\n",
    "    TmaxTmp = Tmax[:,x,y]\n",
    "    \n",
    "    n,m,k = PrecTmp.shape\n",
    "    percents = np.reshape(percents,(1,m,k))\n",
    "    percents = np.repeat(percents,n,axis=0)\n",
    "    \n",
    "    PrecTmp = np.nansum(PrecTmp * percents,axis=0)\n",
    "    TminTmp = np.nansum(TminTmp * percents,axis=0)\n",
    "    TmaxTmp = np.nansum(TmaxTmp * percents,axis=0)\n",
    "    \n",
    "    #convert units\n",
    "    Pout['hru_%s'%hru] = PrecTmp * 0.0393701 # mm >> inches\n",
    "    Tminout['hru_%s'%hru] = (TminTmp * (9./5.)) + 32 # deg C >> Deg F\n",
    "    Tmaxout['hru_%s'%hru] = (TmaxTmp * (9./5.)) + 32. # deg C >> Deg F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the data\n",
    "Pout.to_pickle('/home/tbarnhart/projects/NHM_precipitation/data/livneh_Prec_reg_%s.pcl'%reg)\n",
    "Tminout.to_pickle('/home/tbarnhart/projects/NHM_precipitation/data/livneh_Tmin_reg_%s.pcl'%reg)\n",
    "Tmaxout.to_pickle('/home/tbarnhart/projects/NHM_precipitation/data/livneh_Tmax_reg_%s.pcl'%reg)"
   ]
  }
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