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": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.6.0 |Anaconda 4.3.0 (x86_64)| (default, Dec 23 2016, 13:19:00) \n",
      "[GCC 4.2.1 Compatible Apple LLVM 6.0 (clang-600.0.57)]\n",
      "numpy version: 1.11.3\n",
      "matplotlib version: 2.0.0\n",
      "flopy version: 3.2.6\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import os \n",
    "import sys\n",
    "import shutil\n",
    "import flopy\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\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": {
    "collapsed": false
   },
   "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(mf, nlay=nlay, nrow=nrow, ncol=ncol, delr=20, delc=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a linear data array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "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": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x10f225e10>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cbef550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mm = flopy.plot.ModelMap(model=mf)\n",
    "v = mm.plot_array(b)\n",
    "mm.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": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "text = 'head'\n",
    "\n",
    "# write a binary data file\n",
    "pertim = dtype(1.0)\n",
    "header = flopy.utils.BinaryHeader.create(bintype=text, precision=precision,\n",
    "                                         text=text, nrow=nrow, ncol=ncol,\n",
    "                                         ilay=1, pertim=pertim,\n",
    "                                         totim=pertim, kstp=1, kper=1)\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": {
    "collapsed": false
   },
   "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": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x10f3e0b00>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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op7PbMn1uIBSX5u+VtHaKz6eVpy5ERC2TtKyqzBv4HtubJB0LrJH0hO0H6hw3LauIqK7H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\nq4hohf8PznsLnUt5jAwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f2509b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mm = flopy.plot.ModelMap(model=mf)\n",
    "v = mm.plot_array(br)\n",
    "mm.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": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x10f538cf8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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k/WSdLn7W6jNcuufysqrFrdJc83F7oYtD5jPdbgAk1brd5Nt/nel2A2yRdGGqRJndYt1m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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f3e8fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mm = flopy.plot.ModelMap(model=mf)\n",
    "v = mm.plot_array(b-br)\n",
    "mm.plot_grid()\n",
    "plt.colorbar(v, shrink=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}