examples/Notebooks/flopy3_shapefile_export.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FloPy shapefile export demo\n",
    "The goal of this notebook is to demonstrate ways to export model information to shapefiles.\n",
    "This example will cover:\n",
    "* basic exporting of information for a model, individual package, or dataset\n",
    "* custom exporting of combined data from different packages\n",
    "* general exporting and importing of geographic data from other sources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "flopy is installed in /Users/jdhughes/Documents/Development/flopy_git/flopy_us/flopy\n",
      "3.7.3 (default, Mar 27 2019, 16:54:48) \n",
      "[Clang 4.0.1 (tags/RELEASE_401/final)]\n",
      "numpy version: 1.16.2\n",
      "matplotlib version: 3.0.3\n",
      "flopy version: 3.2.12\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "# run installed version of flopy or add local path\n",
    "try:\n",
    "    import flopy\n",
    "except:\n",
    "    fpth = os.path.abspath(os.path.join('..', '..'))\n",
    "    sys.path.append(fpth)\n",
    "    import flopy\n",
    "    \n",
    "print(sys.version)\n",
    "print('numpy version: {}'.format(np.__version__))\n",
    "print('matplotlib version: {}'.format(mpl.__version__))\n",
    "print('flopy version: {}'.format(flopy.__version__))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set the output directory\n",
    "outdir = 'tmp'\n",
    "if not os.path.isdir(outdir):\n",
    "    os.makedirs(outdir)\n",
    "\n",
    "# load an existing model\n",
    "model_ws = \"../data/freyberg\"\n",
    "m = flopy.modflow.Modflow.load(\"freyberg.nam\", model_ws=model_ws, verbose=False,\n",
    "                               check=False, exe_name=\"mfnwt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DIS', 'BAS6', 'LPF', 'WEL', 'RIV', 'RCH', 'OC', 'PCG']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.get_package_list()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### set the model coordinate information\n",
    "the coordinate information where the grid is located in a projected coordinate system (e.g. UTM)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = m.modelgrid\n",
    "grid.set_coord_info(xoff=273170, yoff=5088657, epsg=26916)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(273170.0, 278170.0, 5088657.0, 5098657.0)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.extent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Declarative export using attached `.export()` methods\n",
    "#### Export the whole model to a single shapefile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote tmp/model.shp\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'tmp/model.shp'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fname = '{}/model.shp'.format(outdir)\n",
    "m.export(fname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot(1, 1, 1, aspect='equal')\n",
    "extents = grid.extent\n",
    "pc = flopy.plot.plot_shapefile(fname, ax=ax, edgecolor='k', facecolor='none')\n",
    "ax.set_xlim(extents[0], extents[1])\n",
    "ax.set_ylim(extents[2], extents[3])\n",
    "ax.set_title(fname);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote tmp/wel.shp\n"
     ]
    }
   ],
   "source": [
    "fname = '{}/wel.shp'.format(outdir)\n",
    "m.wel.export(fname)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Export a package to a shapefile"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Export a FloPy list or array object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<flopy.utils.util_array.Util3d at 0x11afd5c18>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.lpf.hk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote tmp/hk.shp\n"
     ]
    }
   ],
   "source": [
    "fname = '{}/hk.shp'.format(outdir)\n",
    "m.lpf.hk.export('{}/hk.shp'.format(outdir))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAL0AAAEICAYAAAAKp/VCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAGfFJREFUeJztnXuUXVWd5z9fCRDBQB5KJICEV7dEV0AoIg4+eHXk4eq4aNDAkjeT6RlatB1BoG1BHjNBe8D0ANoZoIeoIdC0abCleQwQaXulIQEDJBA0QoQQJEICCdIRKn7nj70ruVSdU3VvblXlsX+fte665/zOPnv/7qnv2bX3Pvvsn2wTBCXxrk3tQBAMNiH6oDhC9EFxhOiD4gjRB8URog+KI0S/BSFpjqRzao6NlWRJQ9osw5L2bSePzZ0QfZtIWirp6AHMfztJr0h6z0CVURoh+s2fTwILbL+xqR3ZWgjRt4Gk7wMfAH4s6Q1JF+TmwZmSXpC0StKfSzpE0hOSXpN0bcP5Z0j6N0n/W9LrkhZLOqpbMccBdzXs75nPWSPpXknvrfHtz/J/oQ9XHNtX0k9zma9IurVbkqMl/TL7f50kteDv5o/t+LTxAZYCR+ftsYCB7wFDgYnAWuCfgF2A3YAVwKdy+jOATuAvgW2BzwOvAyMb8l8M/HHengP8Cvgj4N15f2q3socAZwJLgH1rfL4F+CtSpTcU+HjDMQP/DAwn3dC/BY5p1t8t4RM1/cBwue21tu8FfgfcYnuF7ReBfwU+0pB2BfAd22/bvhV4BjgeQNLewLa2n2lI//e2f2H7P4DbgAO7lf1l4HzgcNtLavx7G9gTGJP9/Fm341Ntv2b7eeDBbmXU+rulEKIfGF5u2P6Piv3GTumLztVo5tfAmLx9PO9s2gD8pmH7zW55QRL8dbaX9eLfBYCARyQtknRWC2X05u8WQYi+fdqdprpbV5s58wFged4+DvhJi/lNBL4u6c/qEtj+je3/bHsM8F+A61sYpuzN3y2CEH37vAzs3cb5uwDnSdpW0knA/sBdkt4NTCC121thEXAMcJ2kP61KIOkkSbvn3VWkG3ddO/626OMmJUTfPv+TVLO+Bpy4Eec/DOwHvAJcCZxo+1XgKGCu7bWtZmj7ceAzwP+RdCxAHl36RE5yCPCwpDeAO4Ev2X6uTX+3GPTO5lkwmEg6AzjH9scrjl0PLLR9/aA7VkNv/m5JtPXIOhhQFgA/3tRObI2E6DdTbE/f1D5srUTzJiiO6MgGxVFM82bo0KEeNmxYW3mM2emVfvKmb9b8btsetmE7vl2Zdrm3a6usMXqrrfMHiiee5RXb7+vvfIsR/bBhwzjhhBPayuPSP7mhn7zpmznz3t/DdvghL1amvfTtnmlb4dJte3t4u+kYc9Iffj0Q+UbzJiiOEH1QHCH6oDhC9EFxFNORbYXB7LDWUdVpvfTt3StSVlPXOW0lj6bLqslzc+0gR00fFEeIPiiOEH1QHCH6oDia6shKWgqsIb1d02m7Q9JI4FbSW/hLgc/ZXiVpBHATsA9pJYCzbC/M+fwlcA7pTZ0ngTNtr5W0FzALGAk8Bpxq+y1J2wMzgIOBV4HP216a87oIODv7dJ7te9q7FFsXA9Fh3VpopaY/wvaBtjvy/oXA/bb3A+7P+wAXkxYnGg+cBkwDkLQbcB7QYfvDwDbA5HzOVcA1Oa9VJDGTv1fZ3he4JqdD0rh87odIr8ZdL2mbln55UCztNG8mATfn7ZuBz+btcaSbANuLgbGSRudjQ4B35/UWdwCW55eMjwRur8irsYzbgaNy+knALNu/z6+5LSG9TxoEfdKs6A3cK+lRSVOybbTtlwDy9y7Z/jhwAoCkCaT1VXbPa778DfA88BLwel4XZhTwmu3OfP4y0qJI5O8XchmdpIWFRjXaK84Jgl5pVvSH2T4IOBY4V9Ine0k7FRghaQHwReDnQGdu608C9iKtk7KjpC+Q1l/pTtebLXXHejtnPZKmSJovaf7atS2/Xx1spTQletvL8/cKYDapKfGypF0B8veKnGa17TNtH0hq078PeA44GnjO9m9tvw38CPhPpLfqhzcsMb07G9ZRWQbskcsYAuwMrGy0V5zT6Pd02x22O4YOHdrMTw0KoM/RG0k7Au+yvSZvTwQuIy0dcTqpZj8duCOnHw68afst0kjNQ7ZXS3oeOFTSDqRVvo4C5tu2pAdJy2fMasyroYy5+fgDOf2dwExJV5P+a+wHPNLb7xiz0yuV0wtilKOarfm6NDNkORqYnRe1GgLMtH23pHnAbZLOJrXTT8rp9wdmSFoHPEUeibH9sKTbSUOSnaRmT9fLz18DZkm6IttvzPYbge9LWkKq4SfnvBZJui3n3wmca7vZxYqCwulT9LafBQ6osHctSNTdPpdU81bldQlwSU0ZPUZf8kJHJ3W352NXkhYbCoKWiCeyQXGE6IPiiPn0bTJnXvXjgXbnw29q6ubC6xtTe9gu+etrK1JuvkRNHxRHiD4ojhB9UBwh+qA4QvRBccToTQUtjbJ0j+2XmbMFjdRUUXsNtrCRmiqipg+KI0QfFEeIPiiOEH1QHNGRbZNvXv4XlfbBfDRf5UMr5Q/U9Ij+WIZwIIiaPiiOEH1QHCH6oDhC9EFxREe2Bao6W77swoqUcGmzeW5hT26rrsGW9huipg+KI0QfFEeIPiiOEH1QHCH6oDhi9KZNDllzflvnzxv27Ur75joisrn61QpR0wfFEaIPiiNEHxRHiD4ojujIbmLqOsJVHdzNtRNZNxd+c/U3avqgOEL0QXGE6IPiCNEHxRGiD4qjqdEbSUuBNcA6oNN2h6SRwK3AWGAp8Dnbq3K82JuAfYC1wFm2F+Z8hgM3AB8mxX09y/bcXvISMA04DngTOMP2Yzmv04GvZxevsN0VWTygfpWGKtpduaFulKaVVRqq83i+HbdqaaWmP8L2gbY78v6FwP229wPuz/sAFwMLbI8nxZGd1pDHNOBu2x8kBW97uo+8jiUFbdsPmAJ8FyDfJJcAHyUFaLsk32xB0CftNG8mAV21683AZ/P2OJJwsb0YGCtptKSdgE+Sw2Xafsv2a33kNQmY4cS/k4Is7wp8GrjP9krbq4D7gGPa+C1BQTQregP3SnpU0pRsG237JYD8vUu2Pw6cACBpArAnKaL33sBvgb+X9HNJN+RgzL3ltRvwQoMfy7Ktzv4OJE2RNF/S/FdXN/lLg62eZkV/mO2DSM2NcyV9spe0U4ERkhYAXyQFQ+4k9R8OAr5r+yPA79jQjKlDFTb3Yn+nwZ5uu8N2x6id+igpKIamOrK2l+fvFZJmk9rRL0va1fZLucmxIqdZDZwJkDuiz+XPDsAy2w/nbG9ng+gr8yLV4Hs0uLI7sDzbD+9mn9Psjw42nqrOad2KEC0tLVgxlWF6Rbr+oM+aXtKOkoZ1bQMTgYXAncDpOdnpwB05zXBJ22X7OcBDtlfb/g3wgqQ/zseOIoW5py6vbD9NiUOB13Pz5x5goqQRuQM7MduCoE+aqelHA7NTpc0QYKbtuyXNA26TdDZpbKkrnP3+wAxJ60iiPrshry8CP8w3xbPk/wikJlFVXneRhiuXkIYszwSwvVLS5cC8nO4y2ytb+uVBsfQpetvPkoYXu9tfJdXW3e1zSUOMVXktADoq7HV5GTi3Jq+bSM8DgqAl4olsUBwh+qA44iWSrZSBCgpRmW8/vCyyuU5DCIKtghB9UBwh+qA4QvRBcURHdiul3fn0dSsc6BtTmzq/js1h5YSo6YPiCNEHxRGiD4ojRB8Uh9Kcrq2fA/aR776q5z3eSgfq8AU9r9X5HzylLb/qOH7oLU2n3fmR63rYvnLP0xUpB5d2O7hjTvrDow3vZPcbUdMHxRGiD4ojRB8UR4g+KI4QfVAcMQ2hBQ4/5MWexjWDV37d1IKrPz14PmwNRE0fFEeIPiiOEH1QHCH6oDhC9EFxhOiD4gjRB8URog+KI0QfFEeIPiiOmIawlXL1p/evtG8OL5dsaqKmD4ojRB8UR4g+KI4QfVAcTXVkJS0lzRxfB3Ta7ugltP0IUlicfYC1wFm2FzbktQ0wH3jR9meybS9gFjASeAw41fZbkrYHZgAHA68Cn7e9NJ9zESme1TrgPNsRaG0LoG5Zv8GklZr+CNsHNizJUBfa/mJgge3xwGnAtG75fAnoPoRwFXBNzmsVG4KznQ2ssr0vcE1Oh6RxwGTgQ6RI4dfnmykI+qSd5k1daPtxpJsA24uBsZJGA0jaHTgeuKErkxxr9khSXNnueTWWcTtwVE4/CZhl+/e2nyNFH5zQxm8JCqJZ0Ru4V9KjkqZkW11o+8eBEwAkTQD2JAU3BvgOcAHwh4a8RwGv2e7M+40h73cDXshldAKv5/Tr7RXnBEGvNPtw6jDbyyXtAtwnaXEvaacC0yQtAJ4Efg50SvoMsML2o5IOb0jfW8j7umO9nbMh43SDTgHY7b29eBwURVOit708f6+QNJvUlKgMbW97NTnIcW6KPJc/k4E/lXQcMBTYSdIPgFOB4ZKG5Np8d2B5LnoZsAewTNIQYGdgZYO9i8ZzGv2eTo62fsA+KmP9wqBP+hS9pB2Bd9lek7cnApexIbT9VBpC20saDrxp+y3gHOChfCNclD/kmv6rtr+Q9x8ETiSN4KzPq6GMufn4A7Yt6U5gpqSrgTGkYM2PtHcpyqBqesJgTk2oWzt0MEd1mqnpRwOzU6XNEGCm7bslzaM6tP3+wAxJ64Cn2DAS0xtfA2ZJuoLUHLox228Evi9pCamGnwxge5Gk23L+ncC5ttc1UU4Q9C16288CB1TY60LbzyXVvL3lOQeY062MHqMvttey4WbqfuxK4MpenQ+CCuKJbFAcIfqgOGI+fQvMmVfxKOCDg+9HO5yzalKFtWfEwK2ZqOmD4gjRB8URog+KI0QfFEeIPiiOGL1pgXaDMswb9u2m07YS6rMVbhhxRw/bV3q83rB1EzV9UBwh+qA4QvRBcYTog+KIjmyb1HVOD1lzflPnD1SHtY6BmIZQt4RgVTTES/762rbK6g+ipg+KI0QfFEeIPiiOEH1QHNGRbYGqTmfdC81VHdzB7rRW0e4T2YF6sbz62jzfdr5VRE0fFEeIPiiOEH1QHCH6oDhC9EFxxOhNmwzUiEzVI/ygf4iaPiiOEH1QHCH6oDhC9EFxREe2BTbX+eFBa0RNHxRHiD4ojhB9UBwh+qA4QvRBcTQ1eiNpKWkBu3VAp+0OSSOBW4GxwFLgc7ZXSRoB3ATsA6wFzrK9UNIewAzg/aTgydNtT8v51+UlYBpwHPAmcIbtx/I5pwNfzy5eYbsrsviAUTVSUzddoNlRnVamG9StOjBz/M962E554uNt5zsQtHu9+oNWavojbB9ouyPvXwjcb3s/4P68D3AxsMD2eOA0kmghRQH877b3Bw4FzpU0ro+8jiUFbduPFAT5u7D+JrkE+CgpQNsl+WYLgj5pp3kzCeiqXW8GPpu3x5GEi+3FwFhJo22/1FVL214DPM2G0PZ1eU0CZjjx76Qgy7sCnwbus73S9irgPuCYNn5LUBDNit7AvZIezaHnAUbbfgkgf++S7Y8DJwBImgDsSYrovR5JY4GPAA/3kdduwAsNpy7Ltjr7O5A0RdJ8SfNfXd3kLw22epp9InuY7eWSdgHuk7S4l7RTgWmSFgBPkoIhd3YdlPQe4B+BL+dI4r2hCpt7sb/TYE8HpgMcsI96HA/KpCnR216ev1dImk1qR78saVfbL+Umx4qcZjVwJkDuiD6XP0jaliT4H9r+UUMRlXmRavA9GtLtDizP9sO72ec0+6MbqVrNoJU58q10wKo6cXWdyKoVBqqX5AOe6LnCwWDSym+oo+raTOeCjfapN/ps3kjaUdKwrm1gIrAQuBM4PSc7HbgjpxkuabtsPwd4yPbqfAPcCDxt++puxVTmle2nKXEo8Hpu/twDTJQ0IndgJ2ZbEPRJMzX9aGB20ixDgJm275Y0D7hN0tmkBUq6wtnvD8yQtA54Cjg72w8DTgWezE0fgItt30VqElXldRdpuHIJacjyTADbKyVdDszL6S6zvbLlXx8USZ+it/0scECF/VXgqAr7XNIQY3f7z6hui/eWl4Fza865ifQ8IAhaIp7IBsURog+KI14iGSCaHampmkIA8JV7RvWw7fRIdfCEcyb0HNWpWrOyP6gaQaora6DWvWyXqOmD4gjRB8URog+KI0QfFEd0ZNukLihDFXWd1io6zn+1h+2Bn1ZHMtzU1E2PqOrg9seUhXaJmj4ojhB9UBwh+qA4QvRBcRTfkW13fXl9o/op6eoJF/awPfDTnumO/NT5ledv6k5rXee0yt86X9t9ettC0MOWiJo+KI4QfVAcIfqgOEL0QXGE6IPiKH70pl3qVkP45uU9R3UGc2pBK1MDqqgbVdoaiJo+KI4QfVAcIfqgOEL0QXEU05F9et37OWTN6T3sxw+9ZUDKq+rgzq98Wby6Y1k1974/Or1V+c7/ds+X0KH59e2h2q9WXk6vmk8/UBMxoqYPiiNEHxRHiD4ojhB9UBwh+qA4ihm9qeMna0/uYeuPEZ1WogZWMZgvkbQbXbDdURqomc5xysZ61DtR0wfFEaIPiiNEHxRHiD4ojqY6spKWAmuAdUCn7Y5eQtuPIIXF2QdYC5xle2HO5xhSBPFtgBtsT832vYBZwEjgMeBU229J2h6YARwMvAp83vbSfM5FpHhW64DzbPdboLWqzi3UPa5vj7qOXdX0hNroghW0ssrCDSNamXKw5dNKTX+E7QNtd+T9utD2FwMLbI8HTiOJHEnbANcBx5Kiip8saVw+5yrgmpzXKjYEZzsbWGV7X+CanI583mTgQ6RI4dfn/IOgT9pp3tSFth9HugmwvRgYK2k0KfbsEtvP2n6LVLNPyqE2jwRur8irsYzbgaNy+knALNu/t/0cKfrghDZ+S1AQzYrewL2SHpU0JdvqQts/DpwAIGkCsCcpuHFdaPtRwGu2O7vZaTwnH389p6/LKwj6pNmHU4fZXi5pF+A+SYt7STsVmJZjxT4J/BzoZONC3m/MOevJN+gUgG1G7dSLy0FJNCV628vz9wpJs0lNicrQ9rZXk4Mc56bIc/mzA9Uh718BhksakmvzLjukGnwPYJmkIcDOwMoGe/e8uvs9HZgOsN3YXXvcFEGZ9Cl6STsC77K9Jm9PBC5jQ2j7qTSEtpc0HHgzt9vPAR6yvTpHGN8vj9S8SOqInmLbkh4ETiS189fn1VDG3Hz8gZz+TmCmpKuBMaRgzY+0fzl6p2o1g3ZHdFqZAtDKiMymXgsTqkemfFnPNT4BfrKm6rdd1c8eJZqp6UcDs1OlzRBgpu27s4irQtvvD8yQtA54ijwSY7tT0l8A95CGLG+yvSif8zVglqQrSM2hG7P9RuD7kpaQavjJOa9Fkm7L+XcC59pet7EXISiLPkVv+1nggAp7XWj7uaSatyqvu4C7asroMfpiey0bbqbux64EruzD/SDoQTyRDYojRB8UR/Hz6dulflm/9ubTV7E5dE6raCUyYLtBMPqDqOmD4gjRB8URog+KI0QfFEd0ZNukbu79N9vMt6pz2HF++5EIq6MDNj9Pv+ol8LqnylW/oe56DSZR0wfFEaIPiiNEHxRHiD4ojhB9UBwxerOJqV8NoeeISCujLHVUjfRUj+g0PyJU9xuq3j/YHIiaPiiOEH1QHCH6oDhC9EFxREd2gKiaZ7/zI9c1fX4rS/hV0W6Y+7o156uiE84c31ZRg07U9EFxhOiD4gjRB8URog+KI0QfFIfsMpZ4lPRb4Nf9kNV7SetvbilsSf5293VP2+/r70KKEX1/IWl+Q2CKzZ4tyd/B8jWaN0FxhOiD4gjRt870Te1Ai2xJ/g6Kr9GmD4ojavqgOEL0QXEUI3pJe0h6UNLTkhZJ+lK23yppQf4szQHiGs/7gKQ3JH21wXaMpGckLZF0YYN9L0kPS/plzne7bN8+7y/Jx8cOhL+Sxkuam9M/KWloth+c95dI+tscCwxJIyXdl/29Lwe+Rom/zemfkHRQf/oqaVtJN2efns6BsAft2mK7iA+wK3BQ3h4G/AIY1y3N/wK+0c32j8A/AF/N+9sAvwL2BrYjhRAdl4/dBkzO298D/mve/m/A9/L2ZODW/vaXNE38CeCAvD8K2CZvPwJ8jBSV8V+AY7P9W8CFeftC4Kq8fVxOJ+BQ4OF+9vUUUhxgSAH4lpIizw/Otd3UYtyEN8EdwJ807IsUm3a/BttngW8DlzaI/mPAPQ1pLsofkZ4mDumejhRn62MN4nyFPIjQX/5mof6gRpCLG/ZPBv4ubz8D7NqQ7pm8/XfAyQ3nrE/XT76eDPw4X4tR+SYZOVjXtpjmTSP5X+BHgIcbzJ8AXrb9y5xmR1IAuO7LUvZnEOh+8xf4I8CS7pH0mKQLGspeVuEv1AfA3ujg1E36ejvwO+AlUpC+v7G9spdy+/XaFvfmlKT3kJosX3aKedvFycAtDfvfBK6x/UZuAq/PoiLbjQ0C3Z/+DgE+DhwCvAncL+lRoPGcZsveKH9b8HUCsI4UDnUE8K+S/l8v5fbrtS1K9JK2Jf1Rfmj7Rw32IcAJwMENyT8KnCjpW8Bw4A+S1gKP0n9BoPvT32XAT22/ktPcBRwE/CD70t1fqAmATZPBqdvw9RTgbttvAysk/RvQQaqxB/zaFtO8ySMWNwJP27662+GjSe3e9c0A25+wPdb2WOA7wP+wfS2wPgh0HkGYDNzp1KjsCgIN1UGgoSEIdH/6S2rbjpe0Q/7jfwp4Kjdb1kg6NOd5Wo1f3f09LY/iHAq83tUM6idfnweOzPnvSOosL2aQru0m71AO1of0r9+kEY4F+XNcPvZ/gT/v5dxLyR1Zb+g0/oI00vBXDfa9SSMlS0gjPttn+9C8vyQf33sg/AW+ACwCFgLfarB3ZNuvgGvZ8CR+FHA/8Mv8PdIbOp7X5fRPAh396Svwnnw9FpECYJ8/mNc2piEExVFM8yYIugjRB8URog+KI0QfFEeIPiiOEH1QHCH6oDj+P4TQnUyawbqqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot(1, 1, 1, aspect='equal')\n",
    "extents = grid.extent\n",
    "a = m.lpf.hk.array.ravel()\n",
    "pc = flopy.plot.plot_shapefile(fname, ax=ax, a=a)\n",
    "ax.set_xlim(extents[0], extents[1])\n",
    "ax.set_ylim(extents[2], extents[3])\n",
    "ax.set_title(fname);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<flopy.utils.util_list.MfList at 0x11afe1208>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.riv.stress_period_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote tmp/riv_spd.shp\n"
     ]
    }
   ],
   "source": [
    "m.riv.stress_period_data.export('{}/riv_spd.shp'.format(outdir))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MfList.export() exports the whole grid by default, regardless of the locations of the boundary cells\n",
    "`sparse=True` only exports the boundary cells in the MfList"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote tmp/riv_spd.shp\n"
     ]
    }
   ],
   "source": [
    "m.riv.stress_period_data.export('{}/riv_spd.shp'.format(outdir), sparse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote tmp/wel_spd.shp\n"
     ]
    }
   ],
   "source": [
    "m.wel.stress_period_data.export('{}/wel_spd.shp'.format(outdir), sparse=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ad-hoc exporting using `recarray2shp`\n",
    "* The main idea is to create a recarray with all of the attribute information, and a list of geometry features (one feature per row in the recarray)\n",
    "* each geometry feature is an instance of the `Point`, `LineString` or `Polygon` classes in `flopy.utils.geometry`. The shapefile format requires all the features to be of the same type.\n",
    "* We will use pandas dataframes for these examples because they are easy to work with, and then convert them to recarrays prior to exporting.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from flopy.export.shapefile_utils import recarray2shp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### combining data from different packages\n",
    "write a shapefile of RIV and WEL package cells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py:6692: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  sort=sort)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cond</th>\n",
       "      <th>flux</th>\n",
       "      <th>i</th>\n",
       "      <th>iface</th>\n",
       "      <th>j</th>\n",
       "      <th>k</th>\n",
       "      <th>rbot</th>\n",
       "      <th>stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.00820</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.00410</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.00390</td>\n",
       "      <td>19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.00083</td>\n",
       "      <td>25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.00072</td>\n",
       "      <td>28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cond     flux   i  iface   j  k  rbot  stage\n",
       "0   NaN -0.00820   8    0.0  15  0   NaN    NaN\n",
       "1   NaN -0.00410  10    0.0  12  0   NaN    NaN\n",
       "2   NaN -0.00390  19    0.0  13  0   NaN    NaN\n",
       "3   NaN -0.00083  25    0.0   9  0   NaN    NaN\n",
       "4   NaN -0.00072  28    0.0   5  0   NaN    NaN"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wellspd = pd.DataFrame(m.wel.stress_period_data[0])\n",
    "rivspd = pd.DataFrame(m.riv.stress_period_data[0])\n",
    "spd = wellspd.append(rivspd)\n",
    "spd.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### create a list of Polygon features from the cell vertices stored in the SpatialReference object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<flopy.utils.geometry.Polygon at 0x11e535320>,\n",
       " <flopy.utils.geometry.Polygon at 0x11e535208>,\n",
       " <flopy.utils.geometry.Polygon at 0x11e5351d0>,\n",
       " <flopy.utils.geometry.Polygon at 0x11e5354e0>,\n",
       " <flopy.utils.geometry.Polygon at 0x11e5352b0>,\n",
       " <flopy.utils.geometry.Polygon at 0x11e535358>,\n",
       " <flopy.utils.geometry.Polygon at 0x11e535630>,\n",
       " <flopy.utils.geometry.Polygon at 0x11e5353c8>,\n",
       " <flopy.utils.geometry.Polygon at 0x11e535470>,\n",
       " <flopy.utils.geometry.Polygon at 0x11e535438>,\n",
       " <flopy.utils.geometry.Polygon at 0x11e535400>,\n",
       " <flopy.utils.geometry.Polygon at 0x11e535710>,\n",
       " <flopy.utils.geometry.Polygon at 0x11e5357b8>,\n",
       " <flopy.utils.geometry.Polygon at 0x11e535780>,\n",
       " <flopy.utils.geometry.Polygon at 0x11e535748>,\n",
       " <flopy.utils.geometry.Polygon at 0x11e535828>,\n",
       " <flopy.utils.geometry.Polygon at 0x11e5358d0>,\n",
       " <flopy.utils.geometry.Polygon at 0x11e535898>,\n",
       " <flopy.utils.geometry.Polygon at 0x11e535860>,\n",
       " <flopy.utils.geometry.Polygon at 0x11e535940>,\n",
       " <flopy.utils.geometry.Polygon at 0x11e5359e8>,\n",
       " <flopy.utils.geometry.Polygon at 0x11e5359b0>,\n",
       " <flopy.utils.geometry.Polygon at 0x11e535978>,\n",
       " <flopy.utils.geometry.Polygon at 0x11e535a58>,\n",
       " <flopy.utils.geometry.Polygon at 0x11e535b00>,\n",
       " <flopy.utils.geometry.Polygon at 0x11e535ac8>,\n",
       " <flopy.utils.geometry.Polygon at 0x11e535a90>,\n",
       " <flopy.utils.geometry.Polygon at 0x11e535b70>,\n",
       " <flopy.utils.geometry.Polygon at 0x11e535c18>,\n",
       " <flopy.utils.geometry.Polygon at 0x11e535be0>,\n",
       " <flopy.utils.geometry.Polygon at 0x11e535ba8>,\n",
       " <flopy.utils.geometry.Polygon at 0x11e535c88>,\n",
       " <flopy.utils.geometry.Polygon at 0x11e535d30>,\n",
       " <flopy.utils.geometry.Polygon at 0x11e535cf8>,\n",
       " <flopy.utils.geometry.Polygon at 0x11e535cc0>,\n",
       " <flopy.utils.geometry.Polygon at 0x11e535da0>,\n",
       " <flopy.utils.geometry.Polygon at 0x11e535e48>,\n",
       " <flopy.utils.geometry.Polygon at 0x11e535e10>,\n",
       " <flopy.utils.geometry.Polygon at 0x11e535dd8>,\n",
       " <flopy.utils.geometry.Polygon at 0x11e535eb8>,\n",
       " <flopy.utils.geometry.Polygon at 0x11e535f60>,\n",
       " <flopy.utils.geometry.Polygon at 0x11e535f28>,\n",
       " <flopy.utils.geometry.Polygon at 0x11e535ef0>,\n",
       " <flopy.utils.geometry.Polygon at 0x11e535fd0>,\n",
       " <flopy.utils.geometry.Polygon at 0x11e535668>,\n",
       " <flopy.utils.geometry.Polygon at 0x11e539080>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from flopy.utils.geometry import Polygon\n",
    "\n",
    "vertices = []\n",
    "for row, col in zip(spd.i, spd.j):\n",
    "    vertices.append(grid.get_cell_vertices(row, col))\n",
    "polygons = [Polygon(vrt) for vrt in vertices]\n",
    "polygons"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### write the shapefile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote tmp/bcs.shp\n"
     ]
    }
   ],
   "source": [
    "fname = '{}/bcs.shp'.format(outdir)\n",
    "recarray2shp(spd.to_records(), geoms=polygons,\n",
    "             shpname=fname,\n",
    "             epsg=grid.epsg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot(1, 1, 1, aspect='equal')\n",
    "extents = grid.extent\n",
    "pc = flopy.plot.plot_shapefile(fname, ax=ax)\n",
    "ax.set_xlim(extents[0], extents[1])\n",
    "ax.set_ylim(extents[2], extents[3])\n",
    "ax.set_title(fname);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### exporting other data\n",
    "Suppose we have some well data with actual locations that we want to export to a shapefile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wellID</th>\n",
       "      <th>q</th>\n",
       "      <th>x_utm</th>\n",
       "      <th>y_utm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-940.961656</td>\n",
       "      <td>5.088748e+06</td>\n",
       "      <td>271358.937474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-1203.628449</td>\n",
       "      <td>5.088767e+06</td>\n",
       "      <td>272186.442759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>-856.466868</td>\n",
       "      <td>5.089298e+06</td>\n",
       "      <td>270558.085055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-1213.623077</td>\n",
       "      <td>5.089193e+06</td>\n",
       "      <td>272021.172927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>-1047.893293</td>\n",
       "      <td>5.088885e+06</td>\n",
       "      <td>272616.283311</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   wellID            q         x_utm          y_utm\n",
       "0       0  -940.961656  5.088748e+06  271358.937474\n",
       "1       1 -1203.628449  5.088767e+06  272186.442759\n",
       "2       2  -856.466868  5.089298e+06  270558.085055\n",
       "3       3 -1213.623077  5.089193e+06  272021.172927\n",
       "4       4 -1047.893293  5.088885e+06  272616.283311"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "welldata = pd.DataFrame({'wellID': np.arange(0, 10),\n",
    "                         'q': np.random.randn(10)*100 - 1000,\n",
    "                         'x_utm': np.random.rand(10)*1000 + grid.yoffset,\n",
    "                         'y_utm': grid.xoffset - np.random.rand(10)*3000})\n",
    "welldata.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### convert the x, y coorindates to point features and then export"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote tmp/wel_data.shp\n"
     ]
    }
   ],
   "source": [
    "from flopy.utils.geometry import Point\n",
    "geoms = [Point(x, y) for x, y in zip(welldata.x_utm, welldata.y_utm)]\n",
    "\n",
    "fname = '{}/wel_data.shp'.format(outdir)\n",
    "recarray2shp(welldata.to_records(), geoms=geoms,\n",
    "             shpname=fname,\n",
    "             epsg=grid.epsg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot(1, 1, 1, aspect='equal')\n",
    "extents = grid.extent\n",
    "pc = flopy.plot.plot_shapefile(fname, ax=ax, radius=25)\n",
    "ax.set_xlim(extents[0], extents[1])\n",
    "ax.set_ylim(extents[2], extents[3])\n",
    "ax.set_title(fname);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding attribute data to an existing shapefile\n",
    "Suppose we have a GIS coverage representing the river in the riv package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote tmp/riv_reaches.shp\n"
     ]
    }
   ],
   "source": [
    "from flopy.utils.geometry import LineString \n",
    "\n",
    "### make up a linestring shapefile of the river reaches\n",
    "i, j = m.riv.stress_period_data[0].i, m.riv.stress_period_data[0].j\n",
    "x0 = grid.xyzcellcenters[0][i[0], j[0]]\n",
    "x1 = grid.xyzcellcenters[0][i[-1], j[-1]]\n",
    "y0 = grid.xyzcellcenters[1][i[0], j[0]]\n",
    "y1 = grid.xyzcellcenters[1][i[-1], j[-1]]\n",
    "x = np.linspace(x0, x1, m.nrow+1)\n",
    "y = np.linspace(y0, y1, m.nrow+1)\n",
    "l0 = zip(list(zip(x[:-1], y[:-1])), list(zip(x[1:], y[1:])))\n",
    "lines = [LineString(l) for l in l0]\n",
    "\n",
    "rivdata = pd.DataFrame(m.riv.stress_period_data[0])\n",
    "rivdata['reach'] = np.arange(len(lines))\n",
    "lines_shapefile = '{}/riv_reaches.shp'.format(outdir)\n",
    "recarray2shp(rivdata.to_records(index=False), geoms=lines,\n",
    "             shpname=lines_shapefile,\n",
    "             epsg=grid.epsg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mS/oSaTh0g9tvAG6W1E16wk8FMLNlkm5z/zuAS80G2Vf2/x8x2FZvlB6cg5+Ojg7r7OxsdRgDkifH/y1vWP7n/d6upCWFJfKmEZ/IBtkRog+yI0QflPI/n37VwtiAJkQflLLggB+2OoSmEqIPsiNEH2RHiD7IjhB9UMq4thNbHUJTCdEHpazetKjVITSVEH2QHSH6IDtC9EF2hOiD7AjRB6XE6k2QHbF6EwQDnBB9kB0h+iA7QvRBKTGRDbIjJrJBMMAJ0QfZEaIPsiNEH5QSE9kgO2IiGwQDnBB9kB0h+iA7QvRBdoTog1JmvOHYVofQVEL0QSmHPrOg1SE0lRB9kB0h+iA7QvRBdoTog1L2OfidrQ6hqTQkekmPSVoqqUtSp9tGSlogabVvR7h9hKS5kh6StFjS8W4/QtLdklZIWibpkwX/tXxJ0tcldbu/iYU6F3r51ZIuJNhrvPz0va0Ooan05kl/iplNKCS+uhxYaGbjgIV+DHAl0GVmJ5DyyM50+w7g02Z2LHAScKmk8SW+ziSlyxxHSoJ8PaSbBPgccCIpp+3nem6UICijL8ObycCNvn8j8AHfH08SLma2EmiXNMrMNpjZz93+LLCC3anta/maDNxkiZ+RkiyPBs4AFpjZZjPbAiwA3tuHawkyolHRG3CXpCWeeh5glJltAPDtIW5/EDgHQNIk4EhSRu9dSGoH3gwsKvF1GLC2UHWd22rZX4GkGZI6JXVu2rSpwUsNBjuNJE8GONnM1ks6BFggaWWdslcDMyV1AUtJyZB39JyU9DrgB8CnzGx7SbuqYrM69lcazGYBsyDlkS1pK6hBlhNZM1vv243AXNI4+ikfauDbjV5mu5lNM7MJpDF9G7DGy+1DEvz3zKyYvauqL9IT/IhCucOB9XXswV4gu4mspGGSDuzZB04HHgbuAHpWTS4EbvcywyXt6/aLgXvNbLskkdLerzCzv6topqovt1/gqzgnAdt8+DMfON1XikZ4TPN7ee1BpjQyvBkFzE2aZShwi5ndKel+4DZJ04HHgXO9/LHATZJ2AsuB6W4/GfhjYKkPfQCuNLN5pCFRNV/zgLOAbuAFYBqAmW2W9EXgfi/3BTPb3OurD7JEZnkMdTs6Oqyzs7PVYQxINswZwugpO/u9XUlLCkvkTSM+kQ1KyXIiG+RNdhPZIBhshOiD7AjRB9kRog+yI0QflBKrN0F2xOpNEAxwQvRBdoTog+wI0QelPPHS2a0OoamE6INSVj/0QKtDaCoh+iA7QvRBdoTog+wI0QeltI2NRGtBZmxaE4nWgmBAE6IPsiNEH2RHiD7IjhB9UEqs3gTZEas3QTDACdEH2RGiD7IjRB+UEhPZIDtiIhsEA5wQfZAdIfogO0L0QSkxkQ2yI8uJrKTHJC2V1CWp0221UtuPkDTX09ovlnR8wc97Ja3ytPeXF+xjJS1yX7f2JGqTtJ8fd/v59kKdK9y+StIZzemOIAd686Q/xcwmFHIA1UptfyXQZWYnkFJqzgSQNAT4R+BMUlbx8ySN9zrXANe6ry3sTs42HdhiZkcD13o5vN5U4DhSpvDr3H8QlNKX4U2t1PbjSTcBZrYSaJc0ipR7ttvMHjWzXwOzgcmeavNUYE4VX8U25gCnefnJwGwze8nM1pCyD07qw7UEGdGo6A24S9ISSTPcViu1/YPAOQCSJgFHkpIb10ptfxCw1cx2VNgp1vHz27x8LV9BUEojeWQBTjaz9ZIOARZIWlmn7NXATM8VuxR4ANjBnqW835M6u/AbdAbAmDFj6oQc1OP50ee3OoSm0tCT3szW+3YjMJc0lKia2t7MtpvZNDObQBrTtwFrqJ3a/mlguKShFXaKdfz864HNdXxVxj3LzDrMrKOtra2RSw2qcP+KZ1odQlMpFb2kYZIO7NknpaR/mBqp7SUN71l9AS4G7jWz7aTs3uN8pWZf0kT0DkvZm+8GplT6qmhjCvATL38HMNVXd8YC44DFe9IBQX40MrwZBcxN80eGAreY2Z2S7qd6avtjgZsk7QSW4ysxZrZD0seA+cAQ4DtmtszrXAbMlvQl0nDoBrffANwsqZv0hJ/qvpZJus397wAuNbP+T2kdDEhKRW9mjwJvqmJ/Bjitiv0+0pO3mq95wLwabbxq9cXMXmT3zVR57irgqpLwg+BVxCeyQSnjxlV9hg1YQvRBKatXr251CE0lRB9kR4g+yI4QfZAdIfqglJjIBtkRE9kgGOCE6IPsCNEH2RGiD0qJiWyQHTGRDYIBTog+yI4QfZAdIfogO0L0QSmxehNkR6zeBMEAJ0QfZEeIPsiOEH1Qytlnn93qEJpKiD4o5f3vf3+rQ2gqIfogO5Tekjf4kbQJ+GUTXB1Mev/mQGEgxVsZ65Fm1vSXkGYj+mYhqbOQmOL/PQMp3v6KNYY3QXaE6IPsCNH3nlmtDqCXDKR4+yXWGNMH2RFP+iA7QvRBdmQjeklHSLpb0gpJyyR90u23elLoLk8S3VVRb4yk5yR9pmBrWhLoZsYr6QRJ93n5pZL2d/tb/Lhb0tc9LWm9BNjyct2eBHtiM2OVtI+kGz2mFZKu6M++xcyy+AFGAxN9/0DgF8D4ijJfA/6qwvYD4F+Bz/jxEOAR4ChgX1IK0fF+7jZgqu9/E/iI738U+KbvTwVubXa8pKwyDwFv8uODgCG+vxh4Gykr44+BM93+FeBy378cuMb3z/JyAk4CFjU51g+R8gADHAA8BrT3W9+2WowtvAluB36/cCxSbtpxBdsHgL8FPl8Q/duA+YUyV/iPSJ8mDq0sR8qz9baCOJ/GFxGaFa8L9bs1BLmycHwe8C3fXwWMLpRb5fvfAs4r1NlVrkmxngf8yPviIL9JRvZX32YzvCnifwLfDCwqmN8BPGVmq73MMFICuL+uqN7MJNBNixf4bcAkzZf0c0mfLbS9rkq8UDsB9h4np24w1jnA88AGUpK+r5rZ5jrtNrVvG02ePGiQ9DrSkOVTllJ99nAe8P3C8V8D15rZcz4E3uWiits9TQLdzHiHAm8H3gq8ACyUtAQo1mm07T2KtxexTgJ2AocCI4D/lvSfddptat9mJXpJ+5B+Kd8zsx8W7EOBc4C3FIqfCEyR9BVgOPAbSS8CSyhJAu1PnGpJoNfplUmgmxnvOuC/zOxpLzMPmAh812OpjBc8AbaZbVAhATYNJqfuQ6wfAu40s5eBjZJ+CnSQnth7vW+zGd74isUNwAoz+7uK0+8hjXt3DQPM7B1m1m5m7cDfA39jZt+guUmgmxYvaWx7gqQD/Jf/LmC5D1uelXSS+7ygRlyV8V7gqzgnAdt6hkFNivVx4FT3P4w0WV5JP/VtyyeU/fVD+tNvpBWOLv85y8/9C3BJnbqfxyeytnvS+AvSSsNfFOxHkVZKukkrPvu5fX8/7vbzR+2NeIHzgWWkjO5fKdg73PYI8A12fxJ/ELAQWO3bkbZ74vmPXn4p0NHMWIHXeX8sIyXA/vP+7Nv4N4QgO7IZ3gRBDyH6IDtC9EF2hOiD7AjRB9kRog+yI0QfZMf/AXU/aVKEEnQzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot(1, 1, 1, aspect='equal')\n",
    "extents = grid.extent\n",
    "pc = flopy.plot.plot_shapefile(lines_shapefile, ax=ax, radius=25)\n",
    "ax.set_xlim(extents[0], extents[1])\n",
    "ax.set_ylim(extents[2], extents[3])\n",
    "ax.set_title(lines_shapefile);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### read in the GIS coverage using `shp2recarray`\n",
    "`shp2recarray` reads a shapefile into a numpy record array, which can easily be converted to a DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from flopy.export.shapefile_utils import shp2recarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>k</th>\n",
       "      <th>i</th>\n",
       "      <th>j</th>\n",
       "      <th>stage</th>\n",
       "      <th>cond</th>\n",
       "      <th>rbot</th>\n",
       "      <th>iface</th>\n",
       "      <th>reach</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>20.100000</td>\n",
       "      <td>0.05</td>\n",
       "      <td>20.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>&lt;flopy.utils.geometry.LineString object at 0x1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>19.870001</td>\n",
       "      <td>0.05</td>\n",
       "      <td>19.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>&lt;flopy.utils.geometry.LineString object at 0x1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>19.650000</td>\n",
       "      <td>0.05</td>\n",
       "      <td>19.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>&lt;flopy.utils.geometry.LineString object at 0x1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>19.420000</td>\n",
       "      <td>0.05</td>\n",
       "      <td>19.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>&lt;flopy.utils.geometry.LineString object at 0x1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>19.190001</td>\n",
       "      <td>0.05</td>\n",
       "      <td>19.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>&lt;flopy.utils.geometry.LineString object at 0x1...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   k  i   j      stage  cond   rbot  iface  reach  \\\n",
       "0  0  0  14  20.100000  0.05  20.00    0.0      0   \n",
       "1  0  1  14  19.870001  0.05  19.75    0.0      1   \n",
       "2  0  2  14  19.650000  0.05  19.50    0.0      2   \n",
       "3  0  3  14  19.420000  0.05  19.25    0.0      3   \n",
       "4  0  4  14  19.190001  0.05  19.00    0.0      4   \n",
       "\n",
       "                                            geometry  \n",
       "0  <flopy.utils.geometry.LineString object at 0x1...  \n",
       "1  <flopy.utils.geometry.LineString object at 0x1...  \n",
       "2  <flopy.utils.geometry.LineString object at 0x1...  \n",
       "3  <flopy.utils.geometry.LineString object at 0x1...  \n",
       "4  <flopy.utils.geometry.LineString object at 0x1...  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linesdata = shp2recarray(lines_shapefile)\n",
    "linesdata = pd.DataFrame(linesdata)\n",
    "linesdata.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Suppose we have some flow information that we read in from the cell budget file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.11485855,  1.94620197,  1.08441109,  1.07929936,  1.17440057,\n",
       "        1.64209938,  0.72252696,  0.8407158 , -0.21046057,  0.13167555,\n",
       "        2.68301912,  0.08972706,  1.88057607,  1.88494599,  1.16177796,\n",
       "        0.42374506,  1.8070325 ,  0.81619584,  0.84813794,  1.11828864,\n",
       "        3.05537359,  1.42343481,  0.94751769,  2.22569849,  1.03833913,\n",
       "        1.77146735,  0.95787635,  1.23930439,  1.4344493 ,  0.88484271,\n",
       "        0.54833551,  0.94210129,  0.96729489,  0.04853829, -0.75142614,\n",
       "        1.87275731,  1.79506906,  1.34570328,  0.00525776,  0.12108178])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make up some fluxes between the river and aquifer at each reach\n",
    "q = np.random.randn(len(linesdata))+1\n",
    "q"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Add reachs fluxes and cumulative flow to lines DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "linesdata['qreach'] = q\n",
    "linesdata['qstream'] = np.cumsum(q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote tmp/riv_reaches.shp\n"
     ]
    }
   ],
   "source": [
    "recarray2shp(linesdata.drop('geometry', axis=1).to_records(), \n",
    "             geoms=linesdata.geometry,\n",
    "             shpname=lines_shapefile,\n",
    "             epsg=grid.epsg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot(1, 1, 1, aspect='equal')\n",
    "extents = grid.extent\n",
    "pc = flopy.plot.plot_shapefile(lines_shapefile, ax=ax, radius=25)\n",
    "ax.set_xlim(extents[0], extents[1])\n",
    "ax.set_ylim(extents[2], extents[3])\n",
    "ax.set_title(lines_shapefile);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
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