examples/Notebooks/flopy3_save_binary_data_file.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FloPy\n",
    "\n",
    "### Quick demo on how to use FloPy to save array data as a binary file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.8.11 (default, Aug  6 2021, 08:56:27) \n",
      "[Clang 10.0.0 ]\n",
      "numpy version: 1.19.2\n",
      "matplotlib version: 3.4.2\n",
      "flopy version: 3.3.5\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import shutil\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\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": [
    "nlay, nrow, ncol = 1, 20, 10\n",
    "model_ws = os.path.join(\"data\", \"binary_data\")\n",
    "\n",
    "if os.path.exists(model_ws):\n",
    "    shutil.rmtree(model_ws)\n",
    "\n",
    "precision = \"single\"  # or 'double'\n",
    "dtype = np.float32  # or np.float64\n",
    "\n",
    "mf = flopy.modflow.Modflow(model_ws=model_ws)\n",
    "dis = flopy.modflow.ModflowDis(\n",
    "    mf, nlay=nlay, nrow=nrow, ncol=ncol, delr=20, delc=10\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a linear data array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20, 10)\n"
     ]
    }
   ],
   "source": [
    "# create the first row of data\n",
    "b = np.linspace(10, 1, num=ncol, dtype=dtype).reshape(1, ncol)\n",
    "\n",
    "# extend data to every row\n",
    "b = np.repeat(b, nrow, axis=0)\n",
    "\n",
    "# print the shape and type of the data\n",
    "print(b.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the data array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pmv = flopy.plot.PlotMapView(model=mf)\n",
    "v = pmv.plot_array(b)\n",
    "pmv.plot_grid()\n",
    "plt.colorbar(v, shrink=0.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write the linear data array to a binary file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = \"head\"\n",
    "\n",
    "# write a binary data file\n",
    "pertim = dtype(1.0)\n",
    "header = flopy.utils.BinaryHeader.create(\n",
    "    bintype=text,\n",
    "    precision=precision,\n",
    "    text=text,\n",
    "    nrow=nrow,\n",
    "    ncol=ncol,\n",
    "    ilay=1,\n",
    "    pertim=pertim,\n",
    "    totim=pertim,\n",
    "    kstp=1,\n",
    "    kper=1,\n",
    ")\n",
    "pth = os.path.join(model_ws, \"bottom.bin\")\n",
    "flopy.utils.Util2d.write_bin(b.shape, pth, b, header_data=header)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read the binary data file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "bo = flopy.utils.HeadFile(pth, precision=precision)\n",
    "br = bo.get_data(idx=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the data that was read from the binary file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pmv = flopy.plot.PlotMapView(model=mf)\n",
    "v = pmv.plot_array(br)\n",
    "pmv.plot_grid()\n",
    "plt.colorbar(v, shrink=0.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the difference in the two values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pmv = flopy.plot.PlotMapView(model=mf)\n",
    "v = pmv.plot_array(b - br)\n",
    "pmv.plot_grid()\n",
    "plt.colorbar(v, shrink=0.5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}