{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subsetting grids using shapefiles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pygeogrids.grids as grids\n",
    "import pygeogrids.shapefile as shapefile\n",
    "import numpy as np\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "testgrid = grids.genreg_grid(0.1, 0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now subset the 0.1x0.1 degree regular grid with the shapefiles from http://biogeo.ucdavis.edu/data/gadm2.8/gadm28_levels.shp.zip which were downloaded/extracted to `~/Downloads/gadm`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'GetLayer'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-2af9cfcfb60c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m austria = shapefile.get_gad_grid_points(\n\u001b[0m\u001b[1;32m      2\u001b[0m     testgrid, os.path.join('/home', os.environ['USER'], 'Downloads', 'gadm', 'gadm28_levels.shp.zip'), 0, name='Austria')\n",
      "\u001b[0;32m/shares/wpreimes/home/code/pygeogrids/src/pygeogrids/shapefile.py\u001b[0m in \u001b[0;36mget_gad_grid_points\u001b[0;34m(grid, gadm_shp_path, level, name, oid)\u001b[0m\n\u001b[1;32m     73\u001b[0m         \u001b[0mdrv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mogr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGetDriverByName\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'ESRI Shapefile'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m         \u001b[0mds_in\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdrv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgadm_shp_path\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'gadm28_adm{:}.shp'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlevel\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---> 75\u001b[0;31m         \u001b[0mlyr_in\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mds_in\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGetLayer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\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     76\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     77\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlevel\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'GetLayer'"
     ]
    }
   ],
   "source": [
    "austria = shapefile.get_gad_grid_points(\n",
    "    testgrid, os.path.join('/home', os.environ['USER'], 'Downloads', 'gadm', 'gadm28_levels.shp.zip'), 0, name='Austria')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can the plot the resulting grid using a simple scatterplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f654fc03b50>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jpqIqffaMXe9J+zVDb78L+zFjpi5kzPa8LjOx1SNpB7A9Ij4q6RLg4xGxY+zrnweejIjP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\n4hucac3QAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f654fd4fb10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.scatter(austria.arrlon, austria.arrlat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Behind the scenes this functionality uses the `get_shp_grid_points` function of the grid object.\n",
    "\n",
    "We can also use this directly using any `ogr.Geometry` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f654fd4f110>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ps5dZa7xqg/ZrDL39LPX4q8sYusc/SGyv6zyF3+oxszFgHzDl7pvM7MvAqY+2lwPPufu6\nnPs9CfwQeBk4WfT/SKOYr2PbZPc2xkxZMWUZ1UI/Swo/p3r8YdW9P2N/jg1zxH8d8MipDXd/n7uv\n6w37O4A7F7jvZb3bVj70Yf6ObZPd2xgzZcWUZVQL/Swp/Jzq8YdV9/6M/TlWaPCb2WrgcuDWnK8Z\ncBXwxbDRysvrZTfdvY0xU1aZPnuZtcarNmi/xtDbz1KPv7qMoXv8g8T2us5T9Ij/FmArMJ3ztUuA\nZ939sXnu68BXzWy/mU2WyDi0vF520x+ixpgpq0yfvcxa41VvD9qvMfT21eNPt8ffhp7/wFaPmW0C\nJtz9I2Z2KfAxd9/U9/XPA4+7+6fnuf8qd58ysxXAHuCj7n5/zu0mgUmA8fHxi44cOVL2ZxIR6ZzQ\nPf6LgSvMbAI4A1hmZre5+zVmthi4Erhovju7+1Tvv8fM7C5gAzBn8Lv7TmAnzNQ5i4RfSIg++qh9\n3FG74jFmSqHH35aM6vGn0eNPobefNVSPP3vEb2YbgW3u/tZ5bv9qYJG7/7B3eQ/wh+7+lYW+TxU9\n/kHy+uij9HFDdMVjzFQkY2xSzKgef7iMVff4Yzkfp84e//vJfKhrZivNbHdv8xzgG2b2EPAg8LeD\nhn4Iofroo/RxQ3TFY8yUFWOPPyvFjOrxh8tYdY8/hd5+1lBLNrj7XmBv3/aHcm5zFJjoXX4CuGCU\ngGWE7NCOun58FWLMJGGpxx9W1fsztddWK8/cDdmhHXX9+CrEmEnCUo8/rKr3Z2qvrVYO/lB99FH6\nuCG64jFmyoqxx5+VYkb1+MNlrLrHn0JvP2ts+/btTWeYY+fOndsnJ8tX/n/mtctY/ZNLOTT1r/zo\n306yavlSNq9byf/90Yl5t7df8bO8Y+1rZt3nxnevLf2BTZkMKWQqknHUvzP0dooZf3TGWs48+438\n1IlDvMqe59mXV/DtJZez5KXjtW0/du4neHbZRsb+ZX9jGUJk3HPGNu76l0sre+xGeU2GdNNNN31v\n+/btO4vctjXLMufVFEe178gPhqpSVpFhUKYyVTQRKW79687k41vObzpGUK1Ylrmq6mTWQrUtVSWV\ncRSqc1aXcdQ6Zyx1zUE6tyxzVdXJrIVqW6pKxivFjKpzhss4ap0zxbrmIK0Y/HVWqVJYXlnSpzpn\nWKPuz7a9vlsx+OusUqWwvLKkT3XOsEbdn217fbdi8FdVncxaqLalqmS8UsyoOme4jKPWOVOsaw7S\nisEfYpndIssLL/QBTxVL/ca45LEyalnmmLbrWJY5hQ92h9WKVo+ISNeFXpY5CVUsgRzjUr1NZ1JG\nLcvctWWZU1x2eZBWHPHX1eOvW4yZspQxDPX4q8uoHv9crXiPv64ef91izJSljGGox19dRvX452rF\n4G9bx1ZEPf6w1OOfrRWDv20dWxH1+MNSj3+2Vgz+unr8dYsxU5YyhqEef3UZ1eOfqxWDv64ef9Pd\n7hgyKaN6/DFtq8dfTitaPSIiXacef0u63jFmUkb1+LvW429jr78VR/zq8TdHGcNQj7+6jKP2+LNi\n7fWrx19CjF3vGDNlKWMY6vFXl3HUHn9WG3r9rRj8bevYiqjHH1bo/Zn6zGnF4G9bx1ZEPf6wQu/P\n1GdOKwa/evzNUcYw1OOvLuOoPf6sNvT6WzH41eNXxrZlVI8/nh5/G3v9rWj1iIh0XSd7/FnD9vpj\n7HrHmKlIxkE951HPuejKflSPXz3+qrTyiL9Mrz/GrneMmbLyMi7Ucw5xzkWIjLFRj7+6jOrxz9WK\n9/izyvT6Y+x6x5gpKy/jQj3nEOdcDCvF/agef7iM6vHPVXjwm9mYmR0ws3t72182s4O9P0+a2cF5\n7rfRzB41s8fN7IZQwReSese2DeZ7DPTYFKMef1jq8c82zBH/dcAjpzbc/X3uvs7d1wF3AHdm72Bm\nY8DngHcBa4GrzWztaJEHS71j2wbzPQZ6bIpRjz8s9fhnKzT4zWw1cDlwa87XDLgK+GLOXTcAj7v7\nE+5+AvgSsLl83GLK9Ppj7HrHmCkrL+NCPecQ51wMK8X9qB5/uIzq8c9V9Ij/FmArMJ3ztUuAZ939\nsZyvrQKe6tt+unddpcr0+mPseseYqUjGhT74CnHORRf2o3r86vFXaWCrx8w2ARPu/hEzuxT4mLtv\n6vv655k5qv90zn3fA2x099/qbX8A+AV3vzbntpPAJMD4+PhFR44cKf9TiYh0TOge/8XAFWY2AZwB\nLDOz29z9GjNbDFwJXDTPfaeAc/u2V/eum8PddwI7YabOWST8KLJd8jJd76r76lVkakLdvf229PiV\nqZ6MMb5mqjZUjz97xG9mG4Ft7v7WeW6/GPgO8DZmBv43gV9194cX+j5Vn7mb1yUv0/Wuuq8eOlMT\nmujtZ6XY449BjJmyQmSM7TVTVp09/veT+VDXzFaa2W4Adz8JXAvcx0wj6PZBQ78OeV3yMl3vqvvq\noTM1oYneflaKPf4YxJgpK0TG2F4zdRhqyQZ33wvs7dv+UM5tjgITfdu7gd1lA1YhZAc3xr56TB3j\nmLKIzKdrz9NWnrk7SMgObox99Zg6xjFlEZlP156nnRz8eV3yMl3vqvvqoTM1oYneflaKPf4YxJgp\nK0TG2F4zdejk4M/rkpfpelfdVw+dqQlN9Pbb0ONXpvoyxvaaqUMrV+cUEekarcefiEH99iId5TZ0\nkKvu+Xelj96FTG0916VuOuJvSJF+e5GOcuod5Dp6/l3po4cWY6asNpzrEkrn1+NPQZF+e5GOcuod\n5Dp6/l3po4cWY6asNpzr0gQN/obUcS5BClLOLunq+vNOg78hdZxLkIKUs0u6uv680+BvSJF+e5GO\ncuod5Dp6/l3po4cWY6asNpzr0gQN/oYU6bcX6Sin/iFVHT3/rvTRu5Cpjee6NEF1zgZtuXBVoSdg\n25+k2f1w94Epvnb4eIOJpE3Wv+5MPr7l/KZjREV1TolKFfXOttYSqxZjpqwuVJ6LUp1TklVFvbOt\ntcSqxZgpqwuV5ypo8EtUul6zk2roeTWbBr9Epes1O6mGnlezafBLVKqod7a1lli1GDNldaHyXAUN\nfolKFfXOttYSu5ipi5XnKqjVIyLSAlqWWTolxPLWTW/HmDHEsuDZxyZ7+yKPnY7Ww9MRvyQt1PLW\nTYsx46gd+bzHpv/2RR67rnTwQ1CPXzoj1PLWTYsx46gd+bzHpv/2RR47dfCrocEvSVM/u3nzPQaD\nri/62OkxDk+DX5Kmfnbz5nsMBl1f9LHTYxyeBr8kLdTy1k2LMeOoHfm8x6b/9kUeO3Xwq6HBL0kL\ntbx109sxZhy1I5/32PTfvshjpw92q6FWj4hIC6jHL9KwQf30VHr8OtpuJx3xiwRW5ncKxNjjV4c+\nLerxizSozO8UiLHHrw59exUe/GY2ZmYHzOzevus+amaHzexhM7t5nvs9aWaHzOygmekwXlqvTb3z\nNv0s8oph3uO/DngEWAZgZpcBm4EL3P1FM1uxwH0vc/fvl48pko6Vy5cy1ZKBqQ59OxU64jez1cDl\nwK19V38Y+KS7vwjg7sfCxxNJT5nfKRBjj18d+vYq+lbPLcBWYLrvuvOAS8zsn8zs62b25nnu68BX\nzWy/mU2OkFUkCWV+p0CMPX59sNteA9/qMbNNwDF3329ml2bueybwFuDNwO1m9gafWxP6RXef6r0V\ntMfMDrv7/TnfZxKYBBgfHy/304hEYsuFq0oNTQ1aqUOR9/gvBq4wswngDGCZmd0GPA3c2Rv0D5rZ\nNHAWcLz/zu4+1fvvMTO7C9gAzBn87r4T2Akzdc7yP5JIOw17boB6+zKfoXr8vSP+j7n7JjP7HWCl\nu99oZucB/wCM9x/xm9mrgUXu/sPe5T3AH7r7Vxb6Purxi8xW5tyAYam3n7a6evxfAN5gZt8GvgR8\n0N3dzFaa2e7ebc4BvmFmDwEPAn87aOiLyFxlzg0Ylnr73THUkg3uvhfY27t8Argm5zZHgYne5SeA\nC0YNKdJ1dfXp1dvvBp25K5KAuvr06u13gwa/SALKnBswLPX2u0ODXyQBZc4NUG9f5qNlmUUSUfbc\nAJEsHfGLiHSMBr+ISMdo8IuIdIwGv4hIx2jwi4h0jAa/iEjHRPnL1s3sOHCkgW99FhD7bwpTxjCU\nMQxlDCNExte5+9lFbhjl4G+Kme0rurpdU5QxDGUMQxnDqDuj3uoREekYDX4RkY7R4J9tZ9MBClDG\nMJQxDGUMo9aMeo9fRKRjdMQvItIxnRz8ZvYFMzvW+7WRp64708z2mNljvf/+ZIQZ32tmD5vZtJk1\n3lKYJ+MOMztsZt8ys7vMbHmEGf+ol++gmf29ma2MLWPf1/6jmbmZndVEtr4ceftxu5lN9fbjQTOb\niC1j7/qP9p6TD5vZzU3l62XJ249f7tuHT5rZwapzdHLwA7uAjZnrbgD+wd3fyMwvjr+h7lAZu5ib\n8dvAlcD9tafJt4u5GfcA/97dfw74DrCt7lAZu5ibcYe7/5y7rwPuBW6sPdVsu5ibETM7F3gH8N26\nA+XYRU5G4DPuvq73Z3fO1+u0i0xGM7sM2Axc4O4/C3yqgVz9dpHJ6O7vO7UPgTuAO6sO0cnB7+73\nAz/IXL0Z+LPe5T8DttQaKiMvo7s/4u7R/DbseTL+vbuf7G0+AKyuPdjsPHkZ/1/f5quBRj/omuf5\nCPAZYCsN54MFM0ZjnowfBj7p7i/2bnOs9mB9FtqPZmbAVcAXq87RycE/j3Pc/Xu9y88A5zQZpiV+\nA/i7pkPkMbP/YmZPAb9G80f8c5jZZmDK3R9qOssAH+29bfaFpt8encd5wCVm9k9m9nUze3PTgRZw\nCfCsuz9W9TfS4M/hM1Wnxo+yUmZmvw+cBP6i6Sx53P333f1cZvJd23Sefmb2KuA/EeH/kDI+D7wB\nWAd8D/h0s3FyLQbOBN4CXA/c3juyjtHV1HC0Dxr8/Z41s9cC9P7b6D8JU2ZmHwI2Ab/m8feF/wL4\nlaZDZPw08HrgITN7kpm3y/7ZzF7TaKoMd3/W3V9292ngvwEbms6U42ngTp/xIDDNzLo4UTGzxcx8\nfvflOr6fBv8r7gE+2Lv8QeCvG8ySLDPbyMz70le4+/NN58ljZm/s29wMHG4qSx53P+TuK9x9jbuv\nYWZ4/by7P9NwtFlOHSj1/DIz5YPY3A1cBmBm5wGnEeeCbW8HDrv707V8N3fv3B9m/jn1PeAlZl5U\nvwn8FDNtnseArwJnRpjxl3uXXwSeBe6LMOPjwFPAwd6fP40w4x3MDKlvAX8DrIotY+brTwJnxZYR\n+J/Aod5+vAd4bYQZTwNu6z3e/wz8UmwZe9fvAn6nrhw6c1dEpGP0Vo+ISMdo8IuIdIwGv4hIx2jw\ni4h0jAa/iEjHaPCLiHSMBr+ISMdo8IuIdMz/B/OVLPE6NbFfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f65541c8fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ring = ogr.Geometry(ogr.wkbLinearRing)\n",
    "ring.AddPoint(14, 47)\n",
    "ring.AddPoint(14, 48)\n",
    "ring.AddPoint(16, 48)\n",
    "ring.AddPoint(16, 47)\n",
    "ring.AddPoint(14, 47)\n",
    "\n",
    "poly = ogr.Geometry(ogr.wkbPolygon)\n",
    "poly.AddGeometry(ring)\n",
    "subgrid = austria.get_shp_grid_points(poly)\n",
    "plt.scatter(austria.arrlon, austria.arrlat)\n",
    "plt.scatter(subgrid.arrlon, subgrid.arrlat, c='orange')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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