flopy/examples/Notebooks/flopy3_demo_of_modelgrid_classes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple ModelGrid Demo\n",
    "\n",
    "This demo shows how the ModelGrid classes can work, where all model grid functionality is in the model grid class.  Only the geographic reference information and related methods remain in SpatialReference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.8.6 | packaged by conda-forge | (default, Oct  7 2020, 18:42:56) \n",
      "[Clang 10.0.1 ]\n",
      "numpy version: 1.18.5\n",
      "matplotlib version: 3.2.2\n",
      "flopy version: 3.3.3\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import numpy as np\n",
    "# run installed version of flopy or add local path\n",
    "try:\n",
    "    from flopy.discretization.structuredgrid import StructuredGrid\n",
    "    from flopy.utils.reference import SpatialReference\n",
    "except:\n",
    "    fpth = os.path.abspath(os.path.join('..', '..'))\n",
    "    sys.path.append(fpth)\n",
    "    from flopy.discretization.structuredgrid import StructuredGrid\n",
    "    from flopy.utils.reference import SpatialReference\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\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": "markdown",
   "metadata": {},
   "source": [
    "Technically you need to create both a SpatialReference object and a ModelGrid object, but in practice the code looks very similar and can easily be implemented in one line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mg = StructuredGrid(delc=np.ones(10), delr=np.ones(10), xoff=10, yoff=10, angrot=20, \n",
    "                    epsg=26715, lenuni=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transform, xcenters, xedges\n",
    "\n",
    "Transform, xcenters, xedges behaves the same but is now a method of the ModelGrid, but has the same functionatily. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15.976724774602399, 22.81712764111577)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mg.get_coords(10, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3.0479999999999983, 3.048000000000002)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mg.get_local_coords(11.82170571129881, 13.906660505012088)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Methods to get cell centers of cells from the model grid are now in the ModelGrid classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.140523437626127"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mg.xcellcenters[1, 7] # x-coordinate at row 1, column 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([14.14052344, 14.82456372])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mg.xcellcenters[[1, 3], [7, 7]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11d0041c0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VkW3DgWQrTpkqUzFnL/Gt94w5Fl0sVtURy0ItVtUp8SKWaV3r610xVRDr78nOBIOcbyDtGYCUbzC2tb72DSaOVwpUQaxklp0JBjnfoDTPAITPGoBcxSZd3TaIhb0KYiWzvFiJ+QYleQYgLlZiSZB0K65BLOxVEevvWaxaQiwDBXGxEkuCpM8aNIh77A0i298T8wxA2DfwDvLhKck3EPMMquKMvUFks1CxchmEfQPvIG8F2VacYMVWBQt7g8iWzIJrfaXFyjvIG8dJ0HixsDeIdH/PO8gbR1asBLPQUpOgSeEee4OU2N+zb1CdEn0D6R1Fe66HnZvH/z6pebfBJHHG3jCl9fdks9ARSmbp7Y9NIHbWYP7P1/7/IFixTRILuzKC/b3SSmZpsQKpVpz0jiLBJGiSWNiVEZsJhvJ8A2mxEstCZas1kEyCJomFfVwIiZV0Fmqxqo5YFipbrYFkEjRJLOzjQEyspLNQi1V1xHwDWc9gDrEkaJJ4KmYciE1kSGehYrfCS5/inMtCTzhvqGRB9hTnHE1Nr9RAdsprRJyxjwOx/p50FipWMo/F4ASpVpzswSiQq26lk6ARsLCPAzGx8oUJw2GxahG34lrBrZhxUdLiI5A7HCVdMou14mQPRoHc4SjpVtwIOGOfINIls1hmJZ2FirXipE1OsfMG0mcNRsDCPkEsVtWRLpnFWnHyYlWSbyDWhpvDwj5BLFbVkT4YBWX5BoJiJZsEiVW2c7jHPkHk+3sl+QZingEI+wZingEI+wZi47tzDMzYI2I7cDqwLzPXL/jeZmArsCYzn2gmxHKRL5lrIFsyC2ZWslmoWBsOhH0D0bW+VVoxO4BPA5+f/2BEHA68DXho/GFNDy6ZW0JQrGRbcb6IZThq3m3QJAOFPTNviYiZRb71SeBDwFfHHJOpimAWWqJYeaFadewbaFCrxx4RZwB7M/PuCs/dFBG7ImLX/v3767ycWQqxmWAQ9w2EPAOwb9Aqgr5Bkww9FRMRBwKXAX9c5fmZuQ3YBjA7O5vDvp5ZBrGZ4Pk/L1ky18AL1YbDrTgN6ow7HgkcAdwdEQAvA+6IiNdn5o/HGVxRCJXM0mIlVjLLChVIipVbcRoMLeyZeS9wyNzXEfEgMOupmGWwWFVHLAuVFSqQFKvSfIOurvUd2GOPiKuA7wJHR8QjEXFu82EVhlh/T3YmGOR8A2nPAOwbNIysZzCAKlMxZw/4/szYoikVsZJZdiYY5HwD+bW+NZBuxYlVbNLV7TJ4pUAbiJXM8mIl5ht4rW+LiCVB0q24ZbCwt4VYf89i1SJiWai0WIklQfJ3GyyBd8UII93fs29QHfsGwyHkG3T1bgNn7MJIZ6G+aLk6JfoGQp4BiLfiJlCxWdiFKbFknlqxKsk3EGvDQblJUF0s7MJIzwSDxaphZMVKzDOAMpOgUXCPXZjSZoKhY2I1YWR9gxF3kMv6Bnuuh52bm3mf1PANRsEZuzglzQSDcGY1Yrk8Vb7BiJMrkq04wYptFCzsJSM2EwwWq2GQ9g1qrvWVbcUJJkGjYGEvGV+YMByliZVgFirbihNMgkbBwq6CmFjJHo6yWFVHMAuVbcWJXnFXF5unCgianLKHo2xyVkfsYBSIH45q2eBsEgu7AoJiJZuFCt4KLytWc1noCecNVdlIb3+EZqdXCsGtGAUE+3sllsxN7SAvzTeQ3v4o2IpTxMKugKBYSR+OEluoNvc7ShEr2WoNJH0DRdyKUUFo8RGUdzhK1jMAuVacrGcAkq04RZyxdxjpklkss5LOQsVacbJnDaC46ZWmsLB3GItVdWQ9A5BrxZV4Ecu0YWHvMBar6shfmCDmG5R2Ecu04R57h5Eds5tDyDfo6oUJy2HfwCyFM/YOIz1mVxPZ4/lg32AYxFpx04aFveOUNGYHFqthKLUVZ0ZnYCsmIrZHxL6IuG/eY1sjYk9E3BMRX4mIVc2GaRpBsFyWHrWreZITvIPctEuVHvsO4NQFj90IrM/MY4EfApeOOS7TBqVemNAkJfkGYp6BGR8DWzGZeUtEzCx47IZ5X94K/Ml4wzKt4B3krSDrG4h5BmZ8jKPHfg5w9VLfjIhNwCaAdevWjeHlphShMTsQFiv7BtUR8wzM+Bhp3DEiLgOeAa5c6jmZuS0zZzNzds2aNaO83PQiWDLLipV9g+qM4BkYbWoLe0RsBE4H3p2ZOb6QzLOwWFXHO8iHwwZnkdQS9og4FbgYOCMzfz7ekMyzEFx8JCtW3kFuDDEo2Y6Iq4A3A6uBx4EP05uCeR7wn/2n3ZqZfz7oxWZnZ3PXrl2jxDu91OyxN7XWdyy/W8jkPGnLzYvOhK9dtZLvXDLhf0jn+wYrVrptMoVExO2ZOVv1+VWmYs5e5OErhorKjI7YLpG531GKySnrGYCnV8zQeFdMwXiXSHVkPQOQbMUZbSzsBSOdhYqZnLKeAXh6xQyNd8UUTIm7RKQPRoHceQMznQw0T8eJzdN2WSiC0MtCa01k2OQcjE1O0xDDmqduxRRMiTvIpdtLYr6BmV7ciimc0naQS7eXfETfiOCM3SyP2ESGTU5jBuOM3SyP2N2l8tsfbXIaAWyeloKQwQljNG7HjQ1O00Fsnk4jYgYnCB+OssFppgALewkIipXs9IqYZ2BME1jYS0BQrGSP6NvgNFOAzdMSEDM4oTe9sliPfeiLln2K05ihsbCXgtj2x5GnV8S2PxrTJSzsU0yT95aCL1o2ZlK4xz7FyBqcIOkbGNMVnLFPMdLH80fwDYyZdpyxTzHSx/PBFy0bUxNn7FOM/A5yY0wtvFLAjIaP6BvTOF4pYNpF8NSrMdOOhd2MhqdXjJFjYI89IrYDpwP7MnN9/7GDgauBGeBB4F2Z+ZPmwjSyeHrFGDmqZOw7gFMXPHYJcFNmvgK4qf+1UWbP9bBzczObHz29YowUA4U9M28B/mvBw2cCn+t//jngrDHHZcaJ4FpfY0xz1O2xvyQzHwPofzxkqSdGxKaI2BURu/bv31/z5cxI2OA0Zqpo3DzNzG2ZOZuZs2vWrGn65cxi2OA0Zqqoe0Dp8Yg4NDMfi4hDgX3jDMqMGRucxkwVdYX9a8BGYEv/41fHFpFpBu8gN2ZqGNiKiYirgO8CR0fEIxFxLj1Bf1tEPAC8rf+1McYYAQZm7Jl59hLfOnnMsRhjjBkDPnlqjDGFYWE3xpjCsLAbY0xhWNiNMaYwWt3HHhH7gf8BnmjtRcfParobf5djh27H3+XYodvxdzl26MX/gsysfMKzVWEHiIhdwyyMV6PL8Xc5duh2/F2OHbodf5djh3rxuxVjjDGFYWE3xpjCmISwb5vAa46TLsff5dih2/F3OXbodvxdjh1qxN96j90YY0yzuBVjjDGFYWE3xpjCaFXYI2JVRHw5IvZExO6I+IM2X38UIuLCiPhBRNwXEVdFxPMnHdNyRMT2iNgXEffNe+zgiLgxIh7ofzxokjEuxxLxb+3/3bknIr4SEasmGeNSLBb7vO9tjoiMiNWTiK0KS8UfEe+PiPv774OPTyq+5Vji781xEXFrRNzVv83t9ZOMcSki4vCI+GZfG38QER/oPz70+7btjP1TwD9n5jHA7wO7W379WkTEWuCvgNnMXA88B/izyUY1kB10+xLyHTw7/huB9Zl5LPBD4NK2g6rIDp4dOxFxOL011w+1HdCQ7GBB/BHxFnp3HR+bma8GLp9AXFXYwbP/7D8OfCQzjwP+pv+1Is8AH8zMVwInAu+LiFdR433bmrBHxIuAPwKuAMjMX2bmk229/hg4AFgZEQcABwKPTjieZen6JeSLxZ+ZN2TmM/0vbwVe1npgFVjizx7gk8CHAOmJhSXiPx/Ykpm/6D9H8ta0JWJP4EX9z1+M6Hs3Mx/LzDv6n/+MXuK7lhrv2zYz9pcD+4F/jIg7I+KzEfGCFl+/Npm5l16G8hDwGPDfmXnDZKOqReVLyDvAOcDXJx1EVSLiDGBvZt496VhqchTwxoi4LSK+HREnTDqgIbgA2BoRD9N7H6tWer8hImaA44HbqPG+bVPYDwBeC/x9Zh5Pb2eMcivgN/R7WmcCRwCHAS+IiPdMNqrpJSIuo1e2XjnpWKoQEQcCl9FrA3SVA4CD6LUILgK+GBEx2ZAqcz5wYWYeDlxIv2ugSkS8ELgGuCAzf1rnd7Qp7I8Aj2Tmbf2vv0xP6LvAW4EfZeb+zHwauBb4wwnHVIfH+5eP09VLyCNiI3A68O7sziGMI+klBXdHxIP0Wkh3RMRLJxrVcDwCXJs9vgf8mt5yqi6wkd57FuBLgKR5ChARK+iJ+pWZORfz0O/b1oQ9M38MPBwRR/cfOhn417Zef0QeAk6MiAP7WcrJdMT4XcDcJeTQwUvII+JU4GLgjMz8+aTjqUpm3puZh2TmTGbO0BPJ1/bfE13hOmADQEQcBTyX7mxMfBR4U//zDcADE4xlSfracgWwOzM/Me9bw79vM7O1/4DjgF3APfT+ohzU5uuPGPtHgD3AfcA/Ac+bdEwD4r2Knh/wND0hORf4PXqu+gP9jwdPOs4h4/834GHgrv5/n5l0nFVjX/D9B4HVk45zyD/75wJf6P/9vwPYMOk4h4j9DcDtwN30etavm3ScS8T+BnpG7z3z/o6fVud965UCxhhTGD55aowxhWFhN8aYwrCwG2NMYVjYjTGmMCzsxhhTGBZ2Y4wpDAu7McYUxv8B5ZRnLle9ScoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(mg.xcellcenters.ravel(), mg.ycellcenters.ravel())\n",
    "plt.scatter(mg.xyzvertices[0].ravel(), mg.xyzvertices[1].ravel(), s=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Coordinates\n",
    "\n",
    "The model grid outputs everything in model coordinates if reference information is not given."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11d13c070>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "mg.set_coord_info()\n",
    "plt.scatter(mg.xcellcenters.ravel(), mg.ycellcenters.ravel())\n",
    "plt.scatter(mg.xyzvertices[0].ravel(), mg.xyzvertices[1].ravel(), s=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grid lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_lines(lines):\n",
    "    fig, ax = plt.subplots()\n",
    "    for ln in lines:\n",
    "        plt.plot([ln[0][0], ln[1][0]], [ln[0][1], ln[1][1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11.0, -1.0)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plot_lines(mg.grid_lines)\n",
    "plt.ylim(11, -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11d262af0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "mg.set_coord_info(xoff=10, yoff=10, angrot=20, epsg=26715)\n",
    "plot_lines(mg.grid_lines)\n",
    "plt.scatter(11.9, 13.5, c='r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vertices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.],\n",
       "       [ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.],\n",
       "       [ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.],\n",
       "       [ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.],\n",
       "       [ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.],\n",
       "       [ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.],\n",
       "       [ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.],\n",
       "       [ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.],\n",
       "       [ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.],\n",
       "       [ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.],\n",
       "       [ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mg.set_coord_info()\n",
    "mg.xyzvertices[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 6.57979857,  7.51949119,  8.45918381,  9.39887643, 10.33856905,\n",
       "        11.27826167, 12.21795429, 13.15764691, 14.09733953, 15.03703215,\n",
       "        15.97672477],\n",
       "       [ 6.92181871,  7.86151133,  8.80120395,  9.74089657, 10.68058919,\n",
       "        11.62028181, 12.55997443, 13.49966706, 14.43935968, 15.3790523 ,\n",
       "        16.31874492],\n",
       "       [ 7.26383885,  8.20353147,  9.14322409, 10.08291672, 11.02260934,\n",
       "        11.96230196, 12.90199458, 13.8416872 , 14.78137982, 15.72107244,\n",
       "        16.66076506],\n",
       "       [ 7.605859  ,  8.54555162,  9.48524424, 10.42493686, 11.36462948,\n",
       "        12.3043221 , 13.24401472, 14.18370734, 15.12339996, 16.06309258,\n",
       "        17.0027852 ],\n",
       "       [ 7.94787914,  8.88757176,  9.82726438, 10.766957  , 11.70664962,\n",
       "        12.64634224, 13.58603486, 14.52572749, 15.46542011, 16.40511273,\n",
       "        17.34480535],\n",
       "       [ 8.28989928,  9.2295919 , 10.16928452, 11.10897715, 12.04866977,\n",
       "        12.98836239, 13.92805501, 14.86774763, 15.80744025, 16.74713287,\n",
       "        17.68682549],\n",
       "       [ 8.63191943,  9.57161205, 10.51130467, 11.45099729, 12.39068991,\n",
       "        13.33038253, 14.27007515, 15.20976777, 16.14946039, 17.08915301,\n",
       "        18.02884563],\n",
       "       [ 8.97393957,  9.91363219, 10.85332481, 11.79301743, 12.73271005,\n",
       "        13.67240267, 14.61209529, 15.55178792, 16.49148054, 17.43117316,\n",
       "        18.37086578],\n",
       "       [ 9.31595971, 10.25565233, 11.19534495, 12.13503758, 13.0747302 ,\n",
       "        14.01442282, 14.95411544, 15.89380806, 16.83350068, 17.7731933 ,\n",
       "        18.71288592],\n",
       "       [ 9.65797986, 10.59767248, 11.5373651 , 12.47705772, 13.41675034,\n",
       "        14.35644296, 15.29613558, 16.2358282 , 17.17552082, 18.11521344,\n",
       "        19.05490606],\n",
       "       [10.        , 10.93969262, 11.87938524, 12.81907786, 13.75877048,\n",
       "        14.6984631 , 15.63815572, 16.57784835, 17.51754097, 18.45723359,\n",
       "        19.39692621]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mg.set_coord_info(xoff=10, yoff=10, angrot=20, epsg=26715)\n",
    "mg.xyzvertices[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting the Model Grid from the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "ml = flopy.modflow.Modflow(modelname='new_model', exe_name='mf2005', \n",
    "                           version='mf2005', model_ws=os.path.join('data'))\n",
    "\n",
    "dis = flopy.modflow.ModflowDis(ml,nlay=5,nrow=5,ncol=5,delr=[50,50,50,50,50],\n",
    "                               delc=[50,50,50,50,50],top=0.0,botm=-50.0,laycbd=0) \n",
    "\n",
    "ibound = np.ones((5, 5, 5), dtype=int)\n",
    "start = np.zeros((5, 5, 5), dtype=float)\n",
    "bas = flopy.modflow.ModflowBas(ml,ibound=ibound,strt=start)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the .modelgrid attribute to get the model grid from the model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0.,  50., 100., 150., 200., 250.],\n",
       "       [  0.,  50., 100., 150., 200., 250.],\n",
       "       [  0.,  50., 100., 150., 200., 250.],\n",
       "       [  0.,  50., 100., 150., 200., 250.],\n",
       "       [  0.,  50., 100., 150., 200., 250.],\n",
       "       [  0.,  50., 100., 150., 200., 250.]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml.modelgrid.xyzvertices[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default the flopy model class caches the model grid. If you later modify model discritization information the model grid can end up out of sync with the model discritization information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0.,  50., 100., 150., 200., 250.],\n",
       "       [  0.,  50., 100., 150., 200., 250.],\n",
       "       [  0.,  50., 100., 150., 200., 250.],\n",
       "       [  0.,  50., 100., 150., 200., 250.],\n",
       "       [  0.,  50., 100., 150., 200., 250.],\n",
       "       [  0.,  50., 100., 150., 200., 250.]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dis.delr=[10,10,10,10,10]\n",
    "ml.modelgrid.xyzvertices[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To resync the model grid with the latest model discritization information call update_modelgrid. Calling the update_modelgrid method clears the cached model grid and forces flopy to create a new model grid object on the next call to modelgrid. The current model discritization information is used to create the new model grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0., 10., 20., 30., 40., 50.],\n",
       "       [ 0., 10., 20., 30., 40., 50.],\n",
       "       [ 0., 10., 20., 30., 40., 50.],\n",
       "       [ 0., 10., 20., 30., 40., 50.],\n",
       "       [ 0., 10., 20., 30., 40., 50.],\n",
       "       [ 0., 10., 20., 30., 40., 50.]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml.update_modelgrid()\n",
    "ml.modelgrid.xyzvertices[0]"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
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