examples/Notebooks/flopy3_external_file_handling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FloPy\n",
    "\n",
    "### Quick demo on how FloPy handles external files for arrays"
   ]
  },
  {
   "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",
      "flopy version: 3.3.5\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import shutil\n",
    "import numpy as np\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(\"flopy version: {}\".format(flopy.__version__))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make a model\n",
    "nlay, nrow, ncol = 10, 20, 5\n",
    "model_ws = os.path.join(\"data\", \"external_demo\")\n",
    "\n",
    "if os.path.exists(model_ws):\n",
    "    shutil.rmtree(model_ws)\n",
    "\n",
    "# the place for all of your hand made and costly model inputs\n",
    "array_dir = os.path.join(\"data\", \"array_dir\")\n",
    "if os.path.exists(array_dir):\n",
    "    shutil.rmtree(array_dir)\n",
    "os.mkdir(array_dir)\n",
    "\n",
    "ml = flopy.modflow.Modflow(model_ws=model_ws)\n",
    "dis = flopy.modflow.ModflowDis(\n",
    "    ml, nlay=nlay, nrow=nrow, ncol=ncol, steady=False, nper=2\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "make an ``hk`` and ```vka``` array.  We'll save ```hk``` to files - pretent that you spent months making this important model property.  Then make an ```lpf```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "hk = np.zeros((nlay, nrow, ncol)) + 5.0\n",
    "vka = np.zeros_like(hk)\n",
    "fnames = []\n",
    "for i, h in enumerate(hk):\n",
    "    fname = os.path.join(array_dir, \"hk_{0}.ref\".format(i + 1))\n",
    "    fnames.append(fname)\n",
    "    np.savetxt(fname, h)\n",
    "    vka[i] = i + 1\n",
    "lpf = flopy.modflow.ModflowLpf(ml, hk=fnames, vka=vka)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also have some recharge with mixed args as well.  Pretend the recharge in the second stress period is very important and precise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "warmup_recharge = np.ones((nrow, ncol))\n",
    "important_recharge = np.random.random((nrow, ncol))\n",
    "fname = os.path.join(array_dir, \"important_recharge.ref\")\n",
    "np.savetxt(fname, important_recharge)\n",
    "rch = flopy.modflow.ModflowRch(ml, rech={0: warmup_recharge, 1: fname})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "ml.write_input()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the files that were created"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "model_ws: data/external_demo\n",
      "important_recharge.ref\n",
      "hk_9.ref\n",
      "hk_8.ref\n",
      "hk_6.ref\n",
      "modflowtest.rch\n",
      "hk_7.ref\n",
      "hk_5.ref\n",
      "hk_4.ref\n",
      "hk_1.ref\n",
      "hk_3.ref\n",
      "hk_10.ref\n",
      "hk_2.ref\n",
      "modflowtest.dis\n",
      "modflowtest.nam\n",
      "modflowtest.lpf\n"
     ]
    }
   ],
   "source": [
    "print(\"model_ws:\", ml.model_ws)\n",
    "print(\"\\n\".join(os.listdir(ml.model_ws)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that a copy of the ``hk`` files as well as the important recharge file were made in the ```model_ws```.Let's looks at the ```lpf``` file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['# LPF package for MODFLOW-2005 generated by Flopy 3.3.5\\n',\n",
       " '         0    -1E+30         0  \\n',\n",
       " '         0         0         0         0         0         0         0         0         0         0\\n',\n",
       " '         0         0         0         0         0         0         0         0         0         0\\n',\n",
       " '   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00\\n',\n",
       " '         0         0         0         0         0         0         0         0         0         0\\n',\n",
       " '         0         0         0         0         0         0         0         0         0         0\\n',\n",
       " 'OPEN/CLOSE                        hk_1.ref               1     (FREE) -1 hk layer 1                    \\n',\n",
       " 'INTERNAL               1   (5E15.6) -1 #vka1                          \\n',\n",
       " '   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00\\n',\n",
       " '   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00\\n',\n",
       " '   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00\\n',\n",
       " '   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00\\n',\n",
       " '   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00\\n',\n",
       " '   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00\\n',\n",
       " '   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00\\n',\n",
       " '   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00\\n',\n",
       " '   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00\\n',\n",
       " '   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00\\n',\n",
       " '   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00\\n']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open(os.path.join(ml.model_ws, ml.name + \".lpf\"), \"r\").readlines()[:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the ```open/close``` approach was used - this is because ``ml.array_free_format`` is ``True``.  Notice that ```vka``` is written internally"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml.array_free_format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now change ```model_ws```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/external_demo\n",
      "\n",
      "creating model workspace...\n",
      "   data/new_external_demo_dir\n"
     ]
    }
   ],
   "source": [
    "print(ml.model_ws)\n",
    "ml.model_ws = os.path.join(\"data\", \"new_external_demo_dir\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now when we call ``write_input()``, a copy of external files are made in the current ```model_ws```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "ml.write_input()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "important_recharge.ref\n",
      "hk_9.ref\n",
      "hk_8.ref\n",
      "hk_6.ref\n",
      "hk_7.ref\n",
      "hk_5.ref\n",
      "hk_4.ref\n",
      "hk_1.ref\n",
      "hk_3.ref\n",
      "hk_10.ref\n",
      "hk_2.ref\n"
     ]
    }
   ],
   "source": [
    "# list the files in model_ws that have 'hk' in the name\n",
    "print(\n",
    "    \"\\n\".join(\n",
    "        [\n",
    "            name\n",
    "            for name in os.listdir(ml.model_ws)\n",
    "            if \"hk\" in name or \"impor\" in name\n",
    "        ]\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we see that the external files were copied to the new ```model_ws```\n",
    "\n",
    "### Using ```external_path```\n",
    "\n",
    "It is sometimes useful when first building a model to write the model arrays as external files for processing and parameter estimation.  The ```model``` attribute ```external_path``` triggers this behavior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make a model - same code as before except for the model constructor\n",
    "nlay, nrow, ncol = 10, 20, 5\n",
    "model_ws = os.path.join(\"data\", \"external_demo\")\n",
    "\n",
    "if os.path.exists(model_ws):\n",
    "    shutil.rmtree(model_ws)\n",
    "\n",
    "# the place for all of your hand made and costly model inputs\n",
    "array_dir = os.path.join(\"data\", \"array_dir\")\n",
    "if os.path.exists(array_dir):\n",
    "    shutil.rmtree(array_dir)\n",
    "os.mkdir(array_dir)\n",
    "\n",
    "# lets make an external path relative to the model_ws\n",
    "ml = flopy.modflow.Modflow(model_ws=model_ws, external_path=\"ref\")\n",
    "dis = flopy.modflow.ModflowDis(\n",
    "    ml, nlay=nlay, nrow=nrow, ncol=ncol, steady=False, nper=2\n",
    ")\n",
    "\n",
    "hk = np.zeros((nlay, nrow, ncol)) + 5.0\n",
    "vka = np.zeros_like(hk)\n",
    "fnames = []\n",
    "for i, h in enumerate(hk):\n",
    "    fname = os.path.join(array_dir, \"hk_{0}.ref\".format(i + 1))\n",
    "    fnames.append(fname)\n",
    "    np.savetxt(fname, h)\n",
    "    vka[i] = i + 1\n",
    "lpf = flopy.modflow.ModflowLpf(ml, hk=fnames, vka=vka)\n",
    "\n",
    "warmup_recharge = np.ones((nrow, ncol))\n",
    "important_recharge = np.random.random((nrow, ncol))\n",
    "fname = os.path.join(array_dir, \"important_recharge.ref\")\n",
    "np.savetxt(fname, important_recharge)\n",
    "rch = flopy.modflow.ModflowRch(ml, rech={0: warmup_recharge, 1: fname})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the model constructor created both ```model_ws``` and ```external_path``` which is _relative to the model_ws_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ref']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(ml.model_ws)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, when we call ```write_input()```, any array properties that were specified as ```np.ndarray``` will be written externally.  If a scalar was passed as the argument, the value remains internal to the model input files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Util2d:delr: resetting 'how' to external\n",
      "Util2d:delc: resetting 'how' to external\n",
      "Util2d:model_top: resetting 'how' to external\n",
      "Util2d:botm layer 1: resetting 'how' to external\n",
      "Util2d:botm layer 2: resetting 'how' to external\n",
      "Util2d:botm layer 3: resetting 'how' to external\n",
      "Util2d:botm layer 4: resetting 'how' to external\n",
      "Util2d:botm layer 5: resetting 'how' to external\n",
      "Util2d:botm layer 6: resetting 'how' to external\n",
      "Util2d:botm layer 7: resetting 'how' to external\n",
      "Util2d:botm layer 8: resetting 'how' to external\n",
      "Util2d:botm layer 9: resetting 'how' to external\n",
      "Util2d:botm layer 10: resetting 'how' to external\n",
      "Util2d:ss layer 1: resetting 'how' to external\n",
      "Util2d:ss layer 2: resetting 'how' to external\n",
      "Util2d:ss layer 3: resetting 'how' to external\n",
      "Util2d:ss layer 4: resetting 'how' to external\n",
      "Util2d:ss layer 5: resetting 'how' to external\n",
      "Util2d:ss layer 6: resetting 'how' to external\n",
      "Util2d:ss layer 7: resetting 'how' to external\n",
      "Util2d:ss layer 8: resetting 'how' to external\n",
      "Util2d:ss layer 9: resetting 'how' to external\n",
      "Util2d:ss layer 10: resetting 'how' to external\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['# LPF package for MODFLOW-2005 generated by Flopy 3.3.5\\n',\n",
       " '         0    -1E+30         0  \\n',\n",
       " '         0         0         0         0         0         0         0         0         0         0\\n',\n",
       " '         0         0         0         0         0         0         0         0         0         0\\n',\n",
       " '   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00\\n',\n",
       " '         0         0         0         0         0         0         0         0         0         0\\n',\n",
       " '         0         0         0         0         0         0         0         0         0         0\\n',\n",
       " 'OPEN/CLOSE                    ref/hk_1.ref               1     (FREE) -1 hk layer 1                    \\n',\n",
       " 'OPEN/CLOSE                    ref/vka1.ref               1   (5E15.6) -1 vka1                          \\n',\n",
       " 'OPEN/CLOSE              ref/ss_layer_1.ref               1   (5E15.6) -1 ss layer 1                    \\n',\n",
       " 'OPEN/CLOSE                    ref/hk_2.ref               1     (FREE) -1 hk layer 2                    \\n',\n",
       " 'OPEN/CLOSE                    ref/vka2.ref               1   (5E15.6) -1 vka2                          \\n',\n",
       " 'OPEN/CLOSE              ref/ss_layer_2.ref               1   (5E15.6) -1 ss layer 2                    \\n',\n",
       " 'OPEN/CLOSE                    ref/hk_3.ref               1     (FREE) -1 hk layer 3                    \\n',\n",
       " 'OPEN/CLOSE                    ref/vka3.ref               1   (5E15.6) -1 vka3                          \\n',\n",
       " 'OPEN/CLOSE              ref/ss_layer_3.ref               1   (5E15.6) -1 ss layer 3                    \\n',\n",
       " 'OPEN/CLOSE                    ref/hk_4.ref               1     (FREE) -1 hk layer 4                    \\n',\n",
       " 'OPEN/CLOSE                    ref/vka4.ref               1   (5E15.6) -1 vka4                          \\n',\n",
       " 'OPEN/CLOSE              ref/ss_layer_4.ref               1   (5E15.6) -1 ss layer 4                    \\n',\n",
       " 'OPEN/CLOSE                    ref/hk_5.ref               1     (FREE) -1 hk layer 5                    \\n']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml.write_input()\n",
    "open(os.path.join(ml.model_ws, ml.name + \".lpf\"), \"r\").readlines()[:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, ```vka``` was also written externally, but not the storage properties.Let's verify the contents of the external path directory. We see our hard-fought ```hk``` and ```important_recharge``` arrays, as well as the ``vka`` arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Util2d:delr: resetting 'how' to external\n",
      "Util2d:delc: resetting 'how' to external\n",
      "Util2d:model_top: resetting 'how' to external\n",
      "Util2d:botm layer 1: resetting 'how' to external\n",
      "Util2d:botm layer 2: resetting 'how' to external\n",
      "Util2d:botm layer 3: resetting 'how' to external\n",
      "Util2d:botm layer 4: resetting 'how' to external\n",
      "Util2d:botm layer 5: resetting 'how' to external\n",
      "Util2d:botm layer 6: resetting 'how' to external\n",
      "Util2d:botm layer 7: resetting 'how' to external\n",
      "Util2d:botm layer 8: resetting 'how' to external\n",
      "Util2d:botm layer 9: resetting 'how' to external\n",
      "Util2d:botm layer 10: resetting 'how' to external\n",
      "Util2d:ss layer 1: resetting 'how' to external\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Util2d:ss layer 2: resetting 'how' to external\n",
      "Util2d:ss layer 3: resetting 'how' to external\n",
      "Util2d:ss layer 4: resetting 'how' to external\n",
      "Util2d:ss layer 5: resetting 'how' to external\n",
      "Util2d:ss layer 6: resetting 'how' to external\n",
      "Util2d:ss layer 7: resetting 'how' to external\n",
      "Util2d:ss layer 8: resetting 'how' to external\n",
      "Util2d:ss layer 9: resetting 'how' to external\n",
      "Util2d:ss layer 10: resetting 'how' to external\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['# LPF package for MODFLOW-2005 generated by Flopy 3.3.5\\n',\n",
       " '         0    -1E+30         0  \\n',\n",
       " '         0         0         0         0         0         0         0         0         0         0\\n',\n",
       " '         0         0         0         0         0         0         0         0         0         0\\n',\n",
       " '   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00\\n',\n",
       " '         0         0         0         0         0         0         0         0         0         0\\n',\n",
       " '         0         0         0         0         0         0         0         0         0         0\\n',\n",
       " 'OPEN/CLOSE                    ref/hk_1.ref               1     (FREE) -1 hk layer 1                    \\n',\n",
       " 'OPEN/CLOSE                    ref/vka1.ref               1   (5E15.6) -1 vka1                          \\n',\n",
       " 'OPEN/CLOSE              ref/ss_layer_1.ref               1   (5E15.6) -1 ss layer 1                    \\n',\n",
       " 'OPEN/CLOSE                    ref/hk_2.ref               1     (FREE) -1 hk layer 2                    \\n',\n",
       " 'OPEN/CLOSE                    ref/vka2.ref               1   (5E15.6) -1 vka2                          \\n',\n",
       " 'OPEN/CLOSE              ref/ss_layer_2.ref               1   (5E15.6) -1 ss layer 2                    \\n',\n",
       " 'OPEN/CLOSE                    ref/hk_3.ref               1     (FREE) -1 hk layer 3                    \\n',\n",
       " 'OPEN/CLOSE                    ref/vka3.ref               1   (5E15.6) -1 vka3                          \\n',\n",
       " 'OPEN/CLOSE              ref/ss_layer_3.ref               1   (5E15.6) -1 ss layer 3                    \\n',\n",
       " 'OPEN/CLOSE                    ref/hk_4.ref               1     (FREE) -1 hk layer 4                    \\n',\n",
       " 'OPEN/CLOSE                    ref/vka4.ref               1   (5E15.6) -1 vka4                          \\n',\n",
       " 'OPEN/CLOSE              ref/ss_layer_4.ref               1   (5E15.6) -1 ss layer 4                    \\n',\n",
       " 'OPEN/CLOSE                    ref/hk_5.ref               1     (FREE) -1 hk layer 5                    \\n']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml.lpf.ss.how = \"internal\"\n",
    "ml.write_input()\n",
    "open(os.path.join(ml.model_ws, ml.name + \".lpf\"), \"r\").readlines()[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ss_layer_1.ref\n",
      "ss_layer_3.ref\n",
      "ss_layer_2.ref\n",
      "ss_layer_6.ref\n",
      "ss_layer_10.ref\n",
      "ss_layer_7.ref\n",
      "ss_layer_5.ref\n",
      "important_recharge.ref\n",
      "ss_layer_4.ref\n",
      "botm_layer_8.ref\n",
      "vka7.ref\n",
      "vka6.ref\n",
      "botm_layer_9.ref\n",
      "vka4.ref\n",
      "vka5.ref\n",
      "hk_9.ref\n",
      "vka1.ref\n",
      "hk_8.ref\n",
      "vka2.ref\n",
      "vka3.ref\n",
      "model_top.ref\n",
      "hk_6.ref\n",
      "botm_layer_1.ref\n",
      "hk_7.ref\n",
      "hk_5.ref\n",
      "botm_layer_2.ref\n",
      "botm_layer_3.ref\n",
      "hk_4.ref\n",
      "botm_layer_7.ref\n",
      "vka8.ref\n",
      "vka9.ref\n",
      "hk_1.ref\n",
      "botm_layer_6.ref\n",
      "vka10.ref\n",
      "botm_layer_4.ref\n",
      "hk_3.ref\n",
      "hk_10.ref\n",
      "hk_2.ref\n",
      "botm_layer_5.ref\n",
      "ss_layer_9.ref\n",
      "rech_0.ref\n",
      "botm_layer_10.ref\n",
      "delc.ref\n",
      "ss_layer_8.ref\n",
      "delr.ref\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n\".join(os.listdir(os.path.join(ml.model_ws, ml.external_path))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fixed format\n",
    "\n",
    "All of this behavior also works for fixed-format type models (really, really old models - I mean OLD!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Util2d:delr: resetting 'how' to external\n",
      "Util2d:delc: resetting 'how' to external\n",
      "Util2d:model_top: resetting 'how' to external\n",
      "Util2d:botm layer 1: resetting 'how' to external\n",
      "Util2d:botm layer 2: resetting 'how' to external\n",
      "Util2d:botm layer 3: resetting 'how' to external\n",
      "Util2d:botm layer 4: resetting 'how' to external\n",
      "Util2d:botm layer 5: resetting 'how' to external\n",
      "Util2d:botm layer 6: resetting 'how' to external\n",
      "Util2d:botm layer 7: resetting 'how' to external\n",
      "Util2d:botm layer 8: resetting 'how' to external\n",
      "Util2d:botm layer 9: resetting 'how' to external\n",
      "Util2d:botm layer 10: resetting 'how' to external\n",
      "Util2d hk layer 1: can't be free format...resetting\n",
      "Util2d:ss layer 1: resetting 'how' to external\n",
      "Util2d hk layer 2: can't be free format...resetting\n",
      "Util2d:ss layer 2: resetting 'how' to external\n",
      "Util2d hk layer 3: can't be free format...resetting\n",
      "Util2d:ss layer 3: resetting 'how' to external\n",
      "Util2d hk layer 4: can't be free format...resetting\n",
      "Util2d:ss layer 4: resetting 'how' to external\n",
      "Util2d hk layer 5: can't be free format...resetting\n",
      "Util2d:ss layer 5: resetting 'how' to external\n",
      "Util2d hk layer 6: can't be free format...resetting\n",
      "Util2d:ss layer 6: resetting 'how' to external\n",
      "Util2d hk layer 7: can't be free format...resetting\n",
      "Util2d:ss layer 7: resetting 'how' to external\n",
      "Util2d hk layer 8: can't be free format...resetting\n",
      "Util2d:ss layer 8: resetting 'how' to external\n",
      "Util2d hk layer 9: can't be free format...resetting\n",
      "Util2d:ss layer 9: resetting 'how' to external\n",
      "Util2d hk layer 10: can't be free format...resetting\n",
      "Util2d:ss layer 10: resetting 'how' to external\n",
      "Util2d rech_2: can't be free format...resetting\n"
     ]
    }
   ],
   "source": [
    "# make a model - same code as before except for the model constructor\n",
    "nlay, nrow, ncol = 10, 20, 5\n",
    "model_ws = os.path.join(\"data\", \"external_demo\")\n",
    "\n",
    "if os.path.exists(model_ws):\n",
    "    shutil.rmtree(model_ws)\n",
    "\n",
    "# the place for all of your hand made and costly model inputs\n",
    "array_dir = os.path.join(\"data\", \"array_dir\")\n",
    "if os.path.exists(array_dir):\n",
    "    shutil.rmtree(array_dir)\n",
    "os.mkdir(array_dir)\n",
    "\n",
    "# lets make an external path relative to the model_ws\n",
    "ml = flopy.modflow.Modflow(model_ws=model_ws, external_path=\"ref\")\n",
    "\n",
    "# explicitly reset the free_format flag BEFORE ANY PACKAGES ARE MADE!!!\n",
    "ml.array_free_format = False\n",
    "\n",
    "dis = flopy.modflow.ModflowDis(\n",
    "    ml, nlay=nlay, nrow=nrow, ncol=ncol, steady=False, nper=2\n",
    ")\n",
    "\n",
    "hk = np.zeros((nlay, nrow, ncol)) + 5.0\n",
    "vka = np.zeros_like(hk)\n",
    "fnames = []\n",
    "for i, h in enumerate(hk):\n",
    "    fname = os.path.join(array_dir, \"hk_{0}.ref\".format(i + 1))\n",
    "    fnames.append(fname)\n",
    "    np.savetxt(fname, h)\n",
    "    vka[i] = i + 1\n",
    "lpf = flopy.modflow.ModflowLpf(ml, hk=fnames, vka=vka)\n",
    "ml.lpf.ss.how = \"internal\"\n",
    "warmup_recharge = np.ones((nrow, ncol))\n",
    "important_recharge = np.random.random((nrow, ncol))\n",
    "fname = os.path.join(array_dir, \"important_recharge.ref\")\n",
    "np.savetxt(fname, important_recharge)\n",
    "rch = flopy.modflow.ModflowRch(ml, rech={0: warmup_recharge, 1: fname})\n",
    "\n",
    "ml.write_input()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that now the external arrays are being handled through the name file.  Let's look at the name file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['# Name file for MODFLOW-2005, generated by Flopy version 3.3.5.\\n',\n",
       " '#xll:0.0; yll:0.0; rotation:0.0; units:meters; lenuni:2; start_datetime:1-1-1970\\n',\n",
       " 'LIST               2  modflowtest.list\\n',\n",
       " 'DIS               11  modflowtest.dis\\n',\n",
       " 'LPF               15  modflowtest.lpf\\n',\n",
       " 'RCH               19  modflowtest.rch\\n',\n",
       " 'DATA            1001  ref/delr.ref\\n',\n",
       " 'DATA            1002  ref/delc.ref\\n',\n",
       " 'DATA            1003  ref/model_top.ref\\n',\n",
       " 'DATA            1004  ref/botm_layer_1.ref\\n',\n",
       " 'DATA            1005  ref/botm_layer_2.ref\\n',\n",
       " 'DATA            1006  ref/botm_layer_3.ref\\n',\n",
       " 'DATA            1007  ref/botm_layer_4.ref\\n',\n",
       " 'DATA            1008  ref/botm_layer_5.ref\\n',\n",
       " 'DATA            1009  ref/botm_layer_6.ref\\n',\n",
       " 'DATA            1010  ref/botm_layer_7.ref\\n',\n",
       " 'DATA            1011  ref/botm_layer_8.ref\\n',\n",
       " 'DATA            1012  ref/botm_layer_9.ref\\n',\n",
       " 'DATA            1013  ref/botm_layer_10.ref\\n',\n",
       " 'DATA            1014  ref/hk_1.ref\\n',\n",
       " 'DATA            1015  ref/vka1.ref\\n',\n",
       " 'DATA            1016  ref/ss_layer_1.ref\\n',\n",
       " 'DATA            1017  ref/hk_2.ref\\n',\n",
       " 'DATA            1018  ref/vka2.ref\\n',\n",
       " 'DATA            1019  ref/ss_layer_2.ref\\n',\n",
       " 'DATA            1020  ref/hk_3.ref\\n',\n",
       " 'DATA            1021  ref/vka3.ref\\n',\n",
       " 'DATA            1022  ref/ss_layer_3.ref\\n',\n",
       " 'DATA            1023  ref/hk_4.ref\\n',\n",
       " 'DATA            1024  ref/vka4.ref\\n',\n",
       " 'DATA            1025  ref/ss_layer_4.ref\\n',\n",
       " 'DATA            1026  ref/hk_5.ref\\n',\n",
       " 'DATA            1027  ref/vka5.ref\\n',\n",
       " 'DATA            1028  ref/ss_layer_5.ref\\n',\n",
       " 'DATA            1029  ref/hk_6.ref\\n',\n",
       " 'DATA            1030  ref/vka6.ref\\n',\n",
       " 'DATA            1031  ref/ss_layer_6.ref\\n',\n",
       " 'DATA            1032  ref/hk_7.ref\\n',\n",
       " 'DATA            1033  ref/vka7.ref\\n',\n",
       " 'DATA            1034  ref/ss_layer_7.ref\\n',\n",
       " 'DATA            1035  ref/hk_8.ref\\n',\n",
       " 'DATA            1036  ref/vka8.ref\\n',\n",
       " 'DATA            1037  ref/ss_layer_8.ref\\n',\n",
       " 'DATA            1038  ref/hk_9.ref\\n',\n",
       " 'DATA            1039  ref/vka9.ref\\n',\n",
       " 'DATA            1040  ref/ss_layer_9.ref\\n',\n",
       " 'DATA            1041  ref/hk_10.ref\\n',\n",
       " 'DATA            1042  ref/vka10.ref\\n',\n",
       " 'DATA            1043  ref/ss_layer_10.ref\\n',\n",
       " 'DATA            1044  ref/rech_0.ref\\n',\n",
       " 'DATA            1045  ref/important_recharge.ref\\n']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open(os.path.join(ml.model_ws, ml.name + \".nam\"), \"r\").readlines()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### \"free\" and \"binary\" format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Util2d:delr: resetting 'how' to external\n",
      "Util2d:delc: resetting 'how' to external\n",
      "Util2d:model_top: resetting 'how' to external\n",
      "Util2d:botm layer 1: resetting 'how' to external\n",
      "Util2d:botm layer 2: resetting 'how' to external\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Util2d:botm layer 3: resetting 'how' to external\n",
      "Util2d:botm layer 4: resetting 'how' to external\n",
      "Util2d:botm layer 5: resetting 'how' to external\n",
      "Util2d:botm layer 6: resetting 'how' to external\n",
      "Util2d:botm layer 7: resetting 'how' to external\n",
      "Util2d:botm layer 8: resetting 'how' to external\n",
      "Util2d:botm layer 9: resetting 'how' to external\n",
      "Util2d:botm layer 10: resetting 'how' to external\n",
      "Util2d:ss layer 1: resetting 'how' to external\n",
      "Util2d:ss layer 2: resetting 'how' to external\n",
      "Util2d:ss layer 3: resetting 'how' to external\n",
      "Util2d:ss layer 4: resetting 'how' to external\n",
      "Util2d:ss layer 5: resetting 'how' to external\n",
      "Util2d:ss layer 6: resetting 'how' to external\n",
      "Util2d:ss layer 7: resetting 'how' to external\n",
      "Util2d:ss layer 8: resetting 'how' to external\n",
      "Util2d:ss layer 9: resetting 'how' to external\n",
      "Util2d:ss layer 10: resetting 'how' to external\n"
     ]
    }
   ],
   "source": [
    "ml.dis.botm[0].format.binary = True\n",
    "ml.write_input()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['# Name file for MODFLOW-2005, generated by Flopy version 3.3.5.\\n',\n",
       " '#xll:0.0; yll:0.0; rotation:0.0; units:meters; lenuni:2; start_datetime:1-1-1970\\n',\n",
       " 'LIST               2  modflowtest.list\\n',\n",
       " 'DIS               11  modflowtest.dis\\n',\n",
       " 'LPF               15  modflowtest.lpf\\n',\n",
       " 'RCH               19  modflowtest.rch\\n',\n",
       " 'DATA            1046  ref/delr.ref\\n',\n",
       " 'DATA            1047  ref/delc.ref\\n',\n",
       " 'DATA            1048  ref/model_top.ref\\n',\n",
       " 'DATA(BINARY)    1049  ref/botm_layer_1.ref\\n',\n",
       " 'DATA            1050  ref/botm_layer_2.ref\\n',\n",
       " 'DATA            1051  ref/botm_layer_3.ref\\n',\n",
       " 'DATA            1052  ref/botm_layer_4.ref\\n',\n",
       " 'DATA            1053  ref/botm_layer_5.ref\\n',\n",
       " 'DATA            1054  ref/botm_layer_6.ref\\n',\n",
       " 'DATA            1055  ref/botm_layer_7.ref\\n',\n",
       " 'DATA            1056  ref/botm_layer_8.ref\\n',\n",
       " 'DATA            1057  ref/botm_layer_9.ref\\n',\n",
       " 'DATA            1058  ref/botm_layer_10.ref\\n',\n",
       " 'DATA            1059  ref/hk_1.ref\\n',\n",
       " 'DATA            1060  ref/vka1.ref\\n',\n",
       " 'DATA            1061  ref/ss_layer_1.ref\\n',\n",
       " 'DATA            1062  ref/hk_2.ref\\n',\n",
       " 'DATA            1063  ref/vka2.ref\\n',\n",
       " 'DATA            1064  ref/ss_layer_2.ref\\n',\n",
       " 'DATA            1065  ref/hk_3.ref\\n',\n",
       " 'DATA            1066  ref/vka3.ref\\n',\n",
       " 'DATA            1067  ref/ss_layer_3.ref\\n',\n",
       " 'DATA            1068  ref/hk_4.ref\\n',\n",
       " 'DATA            1069  ref/vka4.ref\\n',\n",
       " 'DATA            1070  ref/ss_layer_4.ref\\n',\n",
       " 'DATA            1071  ref/hk_5.ref\\n',\n",
       " 'DATA            1072  ref/vka5.ref\\n',\n",
       " 'DATA            1073  ref/ss_layer_5.ref\\n',\n",
       " 'DATA            1074  ref/hk_6.ref\\n',\n",
       " 'DATA            1075  ref/vka6.ref\\n',\n",
       " 'DATA            1076  ref/ss_layer_6.ref\\n',\n",
       " 'DATA            1077  ref/hk_7.ref\\n',\n",
       " 'DATA            1078  ref/vka7.ref\\n',\n",
       " 'DATA            1079  ref/ss_layer_7.ref\\n',\n",
       " 'DATA            1080  ref/hk_8.ref\\n',\n",
       " 'DATA            1081  ref/vka8.ref\\n',\n",
       " 'DATA            1082  ref/ss_layer_8.ref\\n',\n",
       " 'DATA            1083  ref/hk_9.ref\\n',\n",
       " 'DATA            1084  ref/vka9.ref\\n',\n",
       " 'DATA            1085  ref/ss_layer_9.ref\\n',\n",
       " 'DATA            1086  ref/hk_10.ref\\n',\n",
       " 'DATA            1087  ref/vka10.ref\\n',\n",
       " 'DATA            1088  ref/ss_layer_10.ref\\n',\n",
       " 'DATA            1089  ref/rech_0.ref\\n',\n",
       " 'DATA            1090  ref/important_recharge.ref\\n']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open(os.path.join(ml.model_ws, ml.name + \".nam\"), \"r\").readlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['# DIS package for MODFLOW-2005 generated by Flopy 3.3.5\\n',\n",
       " '        10        20         5         2         4         2\\n',\n",
       " '  0  0  0  0  0  0  0  0  0  0\\n',\n",
       " '      1046         1           (5E15.6)        -1 #delr\\n',\n",
       " '      1047         1          (20E15.6)        -1 #delc\\n',\n",
       " '      1048         1           (5E15.6)        -1 #model_top\\n',\n",
       " '     -1049         1           (BINARY)        -1 #botm layer 1\\n',\n",
       " '      1050         1           (5E15.6)        -1 #botm layer 2\\n',\n",
       " '      1051         1           (5E15.6)        -1 #botm layer 3\\n',\n",
       " '      1052         1           (5E15.6)        -1 #botm layer 4\\n',\n",
       " '      1053         1           (5E15.6)        -1 #botm layer 5\\n',\n",
       " '      1054         1           (5E15.6)        -1 #botm layer 6\\n',\n",
       " '      1055         1           (5E15.6)        -1 #botm layer 7\\n',\n",
       " '      1056         1           (5E15.6)        -1 #botm layer 8\\n',\n",
       " '      1057         1           (5E15.6)        -1 #botm layer 9\\n',\n",
       " '      1058         1           (5E15.6)        -1 #botm layer 10\\n',\n",
       " '      1.000000             1  1.000000  TR \\n',\n",
       " '      1.000000             1  1.000000  TR \\n']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open(os.path.join(ml.model_ws, ml.name + \".dis\"), \"r\").readlines()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The ```.how``` attribute\n",
    "```Util2d``` includes a ```.how``` attribute that gives finer grained control of how arrays will written"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'external'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml.lpf.hk[0].how"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This will raise an error since our model does not support free format..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Util2d:delr: resetting 'how' to external\n",
      "Util2d:delc: resetting 'how' to external\n",
      "Util2d:model_top: resetting 'how' to external\n",
      "Util2d:botm layer 1: resetting 'how' to external\n",
      "Util2d:botm layer 2: resetting 'how' to external\n",
      "Util2d:botm layer 3: resetting 'how' to external\n",
      "Util2d:botm layer 4: resetting 'how' to external\n",
      "Util2d:botm layer 5: resetting 'how' to external\n",
      "Util2d:botm layer 6: resetting 'how' to external\n",
      "Util2d:botm layer 7: resetting 'how' to external\n",
      "Util2d:botm layer 8: resetting 'how' to external\n",
      "Util2d:botm layer 9: resetting 'how' to external\n",
      "Util2d:botm layer 10: resetting 'how' to external\n",
      "\n",
      " Util2d error: 'how' is openclose, but model doesn't support free fmt \n",
      "\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    ml.lpf.hk[0].how = \"openclose\"\n",
    "    ml.lpf.hk[0].how\n",
    "    ml.write_input()\n",
    "except Exception as e:\n",
    "    print(\"\\n\", e, \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So let's reset hk layer 1 back to external..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'external'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml.lpf.hk[0].how = \"external\"\n",
    "ml.lpf.hk[0].how"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "ml.dis.top.how = \"external\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Util2d:delr: resetting 'how' to external\n",
      "Util2d:delc: resetting 'how' to external\n",
      "Util2d:botm layer 1: resetting 'how' to external\n",
      "Util2d:botm layer 2: resetting 'how' to external\n",
      "Util2d:botm layer 3: resetting 'how' to external\n",
      "Util2d:botm layer 4: resetting 'how' to external\n",
      "Util2d:botm layer 5: resetting 'how' to external\n",
      "Util2d:botm layer 6: resetting 'how' to external\n",
      "Util2d:botm layer 7: resetting 'how' to external\n",
      "Util2d:botm layer 8: resetting 'how' to external\n",
      "Util2d:botm layer 9: resetting 'how' to external\n",
      "Util2d:botm layer 10: resetting 'how' to external\n",
      "Util2d:ss layer 1: resetting 'how' to external\n",
      "Util2d:ss layer 2: resetting 'how' to external\n",
      "Util2d:ss layer 3: resetting 'how' to external\n",
      "Util2d:ss layer 4: resetting 'how' to external\n",
      "Util2d:ss layer 5: resetting 'how' to external\n",
      "Util2d:ss layer 6: resetting 'how' to external\n",
      "Util2d:ss layer 7: resetting 'how' to external\n",
      "Util2d:ss layer 8: resetting 'how' to external\n",
      "Util2d:ss layer 9: resetting 'how' to external\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Util2d:ss layer 10: resetting 'how' to external\n"
     ]
    }
   ],
   "source": [
    "ml.write_input()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['# DIS package for MODFLOW-2005 generated by Flopy 3.3.5\\n',\n",
       " '        10        20         5         2         4         2\\n',\n",
       " '  0  0  0  0  0  0  0  0  0  0\\n',\n",
       " '      1104         1           (5E15.6)        -1 #delr\\n',\n",
       " '      1105         1          (20E15.6)        -1 #delc\\n',\n",
       " '      1106         1           (5E15.6)        -1 #model_top\\n',\n",
       " '     -1107         1           (BINARY)        -1 #botm layer 1\\n',\n",
       " '      1108         1           (5E15.6)        -1 #botm layer 2\\n',\n",
       " '      1109         1           (5E15.6)        -1 #botm layer 3\\n',\n",
       " '      1110         1           (5E15.6)        -1 #botm layer 4\\n',\n",
       " '      1111         1           (5E15.6)        -1 #botm layer 5\\n',\n",
       " '      1112         1           (5E15.6)        -1 #botm layer 6\\n',\n",
       " '      1113         1           (5E15.6)        -1 #botm layer 7\\n',\n",
       " '      1114         1           (5E15.6)        -1 #botm layer 8\\n',\n",
       " '      1115         1           (5E15.6)        -1 #botm layer 9\\n',\n",
       " '      1116         1           (5E15.6)        -1 #botm layer 10\\n',\n",
       " '      1.000000             1  1.000000  TR \\n',\n",
       " '      1.000000             1  1.000000  TR \\n']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open(os.path.join(ml.model_ws, ml.name + \".dis\"), \"r\").readlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['# LPF package for MODFLOW-2005 generated by Flopy 3.3.5\\n',\n",
       " '         0    -1E+30         0  \\n',\n",
       " '         0         0         0         0         0         0         0         0         0         0\\n',\n",
       " '         0         0         0         0         0         0         0         0         0         0\\n',\n",
       " '   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00   1.000000E+00\\n',\n",
       " '         0         0         0         0         0         0         0         0         0         0\\n',\n",
       " '         0         0         0         0         0         0         0         0         0         0\\n',\n",
       " '      1117         1           (5E15.6)        -1 #hk layer 1\\n',\n",
       " '      1118         1           (5E15.6)        -1 #vka1\\n',\n",
       " '      1119         1           (5E15.6)        -1 #ss layer 1\\n',\n",
       " '      1120         1           (5E15.6)        -1 #hk layer 2\\n',\n",
       " '      1121         1           (5E15.6)        -1 #vka2\\n',\n",
       " '      1122         1           (5E15.6)        -1 #ss layer 2\\n',\n",
       " '      1123         1           (5E15.6)        -1 #hk layer 3\\n',\n",
       " '      1124         1           (5E15.6)        -1 #vka3\\n',\n",
       " '      1125         1           (5E15.6)        -1 #ss layer 3\\n',\n",
       " '      1126         1           (5E15.6)        -1 #hk layer 4\\n',\n",
       " '      1127         1           (5E15.6)        -1 #vka4\\n',\n",
       " '      1128         1           (5E15.6)        -1 #ss layer 4\\n',\n",
       " '      1129         1           (5E15.6)        -1 #hk layer 5\\n',\n",
       " '      1130         1           (5E15.6)        -1 #vka5\\n',\n",
       " '      1131         1           (5E15.6)        -1 #ss layer 5\\n',\n",
       " '      1132         1           (5E15.6)        -1 #hk layer 6\\n',\n",
       " '      1133         1           (5E15.6)        -1 #vka6\\n',\n",
       " '      1134         1           (5E15.6)        -1 #ss layer 6\\n',\n",
       " '      1135         1           (5E15.6)        -1 #hk layer 7\\n',\n",
       " '      1136         1           (5E15.6)        -1 #vka7\\n',\n",
       " '      1137         1           (5E15.6)        -1 #ss layer 7\\n',\n",
       " '      1138         1           (5E15.6)        -1 #hk layer 8\\n',\n",
       " '      1139         1           (5E15.6)        -1 #vka8\\n',\n",
       " '      1140         1           (5E15.6)        -1 #ss layer 8\\n',\n",
       " '      1141         1           (5E15.6)        -1 #hk layer 9\\n',\n",
       " '      1142         1           (5E15.6)        -1 #vka9\\n',\n",
       " '      1143         1           (5E15.6)        -1 #ss layer 9\\n',\n",
       " '      1144         1           (5E15.6)        -1 #hk layer 10\\n',\n",
       " '      1145         1           (5E15.6)        -1 #vka10\\n',\n",
       " '      1146         1           (5E15.6)        -1 #ss layer 10\\n']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open(os.path.join(ml.model_ws, ml.name + \".lpf\"), \"r\").readlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['# Name file for MODFLOW-2005, generated by Flopy version 3.3.5.\\n',\n",
       " '#xll:0.0; yll:0.0; rotation:0.0; units:meters; lenuni:2; start_datetime:1-1-1970\\n',\n",
       " 'LIST               2  modflowtest.list\\n',\n",
       " 'DIS               11  modflowtest.dis\\n',\n",
       " 'LPF               15  modflowtest.lpf\\n',\n",
       " 'RCH               19  modflowtest.rch\\n',\n",
       " 'DATA            1104  ref/delr.ref\\n',\n",
       " 'DATA            1105  ref/delc.ref\\n',\n",
       " 'DATA            1106  ref/model_top.ref\\n',\n",
       " 'DATA(BINARY)    1107  ref/botm_layer_1.ref\\n',\n",
       " 'DATA            1108  ref/botm_layer_2.ref\\n',\n",
       " 'DATA            1109  ref/botm_layer_3.ref\\n',\n",
       " 'DATA            1110  ref/botm_layer_4.ref\\n',\n",
       " 'DATA            1111  ref/botm_layer_5.ref\\n',\n",
       " 'DATA            1112  ref/botm_layer_6.ref\\n',\n",
       " 'DATA            1113  ref/botm_layer_7.ref\\n',\n",
       " 'DATA            1114  ref/botm_layer_8.ref\\n',\n",
       " 'DATA            1115  ref/botm_layer_9.ref\\n',\n",
       " 'DATA            1116  ref/botm_layer_10.ref\\n',\n",
       " 'DATA            1117  ref/hk_1.ref\\n',\n",
       " 'DATA            1118  ref/vka1.ref\\n',\n",
       " 'DATA            1119  ref/ss_layer_1.ref\\n',\n",
       " 'DATA            1120  ref/hk_2.ref\\n',\n",
       " 'DATA            1121  ref/vka2.ref\\n',\n",
       " 'DATA            1122  ref/ss_layer_2.ref\\n',\n",
       " 'DATA            1123  ref/hk_3.ref\\n',\n",
       " 'DATA            1124  ref/vka3.ref\\n',\n",
       " 'DATA            1125  ref/ss_layer_3.ref\\n',\n",
       " 'DATA            1126  ref/hk_4.ref\\n',\n",
       " 'DATA            1127  ref/vka4.ref\\n',\n",
       " 'DATA            1128  ref/ss_layer_4.ref\\n',\n",
       " 'DATA            1129  ref/hk_5.ref\\n',\n",
       " 'DATA            1130  ref/vka5.ref\\n',\n",
       " 'DATA            1131  ref/ss_layer_5.ref\\n',\n",
       " 'DATA            1132  ref/hk_6.ref\\n',\n",
       " 'DATA            1133  ref/vka6.ref\\n',\n",
       " 'DATA            1134  ref/ss_layer_6.ref\\n',\n",
       " 'DATA            1135  ref/hk_7.ref\\n',\n",
       " 'DATA            1136  ref/vka7.ref\\n',\n",
       " 'DATA            1137  ref/ss_layer_7.ref\\n',\n",
       " 'DATA            1138  ref/hk_8.ref\\n',\n",
       " 'DATA            1139  ref/vka8.ref\\n',\n",
       " 'DATA            1140  ref/ss_layer_8.ref\\n',\n",
       " 'DATA            1141  ref/hk_9.ref\\n',\n",
       " 'DATA            1142  ref/vka9.ref\\n',\n",
       " 'DATA            1143  ref/ss_layer_9.ref\\n',\n",
       " 'DATA            1144  ref/hk_10.ref\\n',\n",
       " 'DATA            1145  ref/vka10.ref\\n',\n",
       " 'DATA            1146  ref/ss_layer_10.ref\\n',\n",
       " 'DATA            1147  ref/rech_0.ref\\n',\n",
       " 'DATA            1148  ref/important_recharge.ref\\n']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open(os.path.join(ml.model_ws, ml.name + \".nam\"), \"r\").readlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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