terrence-thesis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Terrence Thesis - Case Study\n",
    "***(Weekly Stress Period) Time Frame 2005-2014--- Changed grid size***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Statements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import flopy\n",
    "import flopy.modflow as mf\n",
    "import flopy.utils as fu\n",
    "import flopy.utils.binaryfile as bf\n",
    "import matplotlib as mpl \n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import platform\n",
    "import shutil\n",
    "import sys\n",
    "import datetime\n",
    "import math\n",
    "from sklearn.metrics import mean_squared_error as mse\n",
    "import flopy.mt3d as mt3\n",
    "from flopy.export import vtk\n",
    "import itertools as it\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup folder where modflow-files are located"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "workspace = os.path.join('mf-data-weekly-grid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "if os.path.exists(workspace):\n",
    "    shutil.rmtree(workspace)\n",
    "\n",
    "if not os.path.exists(workspace):\n",
    "    os.makedirs(workspace)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Print versions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.6.3 (v3.6.3:2c5fed8, Oct  3 2017, 18:11:49) [MSC v.1900 64 bit (AMD64)]\n",
      "numpy version: 1.14.3\n",
      "pandas version: 0.23.4\n",
      "matplotlib version: 3.0.2\n",
      "flopy version: 3.2.10\n"
     ]
    }
   ],
   "source": [
    "print(sys.version)\n",
    "print('numpy version: {}'.format(np.__version__))\n",
    "print('pandas version: {}'.format(pd.__version__))\n",
    "print('matplotlib version: {}'.format(mpl.__version__))\n",
    "print('flopy version: {}'.format(flopy.__version__))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup MF-Package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelname = 'model'\n",
    "exe_name = 'mf2005'\n",
    "\n",
    "if platform.system() == 'Windows':\n",
    "    exe_name += '.exe'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "tm = mf.Modflow(modelname=modelname, exe_name=exe_name, version='mf2005', model_ws=workspace)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup Discretization Package"
   ]
  },
  {
   "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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nrow = 1\n",
    "ncol = 95\n",
    "nlay = 10-1\n",
    "\n",
    "Lx = 950\n",
    "Ly = 1\n",
    "\n",
    "botm = np.ones ( (nlay, nrow, ncol), dtype=np.int32 )\n",
    "top = 33 * np.ones ( (nrow, ncol), dtype=np.int32 )\n",
    "\n",
    "# Range does not include upper bound !!!\n",
    "for i in range ( 30, 33):\n",
    "    top[ :, i ] = 31\n",
    "for i in range ( 47, 48 ):\n",
    "    top[ :, i ] = 28\n",
    "for i in range ( 50, 65):\n",
    "    top[ :, i ] = 32\n",
    "\n",
    "# N1 layer\n",
    "botm[ 0, :, : ] = 28\n",
    "for i in range ( 30, 33):\n",
    "    botm[ 0,:, i ] = 25\n",
    "for i in range ( 50, 65):\n",
    "    botm[ 0,:, i ] = 25\n",
    "## N2 layer\n",
    "for i in range ( 0, 33 ):\n",
    "    botm[ 1, :, i ] = 20\n",
    "for i in range ( 33, 46 ):\n",
    "    botm[ 1, :, i ] = 21.5\n",
    "for i in range ( 46, 48 ):\n",
    "    botm[ 1, :, i ] = 24\n",
    "for i in range ( 48, ncol ):\n",
    "    botm[ 1, :, i ] = 24.5\n",
    "# N3 layer\n",
    "for i in range ( 0, 47 ):\n",
    "    botm[ 2, :, i ] = 17\n",
    "for i in range ( 47, 50 ):\n",
    "    botm[ 2, :, i ] = 16\n",
    "for i in range ( 50, 60 ):\n",
    "    botm[ 2, :, i ] = 15\n",
    "for i in range ( 60, ncol ):\n",
    "    botm[ 2, :, i ] = 20\n",
    "# N4 layer\n",
    "for i in range ( 0, 50 ):\n",
    "    botm[ 3, :, i ] = 15\n",
    "for i in range ( 50, 60 ):\n",
    "    botm[ 3, :, i ] = 14\n",
    "for i in range ( 60, ncol ):\n",
    "    botm[ 3, :, i ] = 16\n",
    "# N5 layer\n",
    "for i in range ( 0, 60 ):\n",
    "    botm[ 4, :, i ] = 13\n",
    "for i in range ( 60, ncol ):\n",
    "    botm[ 4, :, i ] = 15\n",
    "# N6 layer\n",
    "botm[ 5, :, : ] = 10\n",
    "# N7 layer\n",
    "botm[ 6, :, : ] = 9\n",
    "# N8 layer\n",
    "botm[ 7, :, : ] = -12.5\n",
    "# N9 layer\n",
    "botm[ 8, :, : ] = -30\n",
    "\n",
    "delr = Lx / ncol\n",
    "delc = Ly / nrow\n",
    "nper = 505\n",
    "\n",
    "perlen = np.ones ( nper) * 7\n",
    "perlen[0] = 1\n",
    "\n",
    "steady = np.full ( nper, False, dtype=bool )\n",
    "steady[ 0 ] = True\n",
    "dis = mf.ModflowDis(\n",
    "    tm, \n",
    "    nlay=nlay,\n",
    "    nrow=nrow, \n",
    "    ncol=ncol, \n",
    "    delr=delr, \n",
    "    delc=delc, \n",
    "    top=top, \n",
    "    botm=botm, \n",
    "    nper=nper,\n",
    "    perlen = perlen,\n",
    "    steady=steady,\n",
    "    lenuni = 2,\n",
    "    xul = 781658.203,\n",
    "    yul = 5831682.381,\n",
    "    proj4_str = \"EPSG:32632\",\n",
    "    start_datetime = '11/05/2005',\n",
    "    rotation = 3.3\n",
    ")\n",
    "\n",
    "modelxsect= flopy.plot.ModelCrossSection(model=tm, line={'Row':0})\n",
    "linecollection= modelxsect.plot_grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup Basic-Package"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Specify boundary conditions and starting heads.\n",
    "\n",
    "We can set one value for all cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "ibound = np.ones((nlay, nrow, ncol), dtype=np.int32)\n",
    "\n",
    "for i in range ( 1, nlay ):\n",
    "    ibound[ i, :, 0 ] = -1\n",
    "    ibound[ i, :, -1 ] = -1\n",
    "\n",
    "for k in range ( 47, 49 ):\n",
    "    ibound[ 0, :, k ] = 0\n",
    "\n",
    "strt = np.ones ( (nlay, nrow, ncol), dtype=np.int32 ) * 28\n",
    "\n",
    "\n",
    "for i in range ( 30, 33 ):\n",
    "    strt[ 0, :, i ] = 29\n",
    "for j in range ( 50, 65 ):\n",
    "    strt[ 0, :, j ] = 29\n",
    "    \n",
    "bas = mf.ModflowBas(tm, ibound=ibound, strt=strt)\n",
    "\n",
    "#bas.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup Constant Head Boundaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "csv = pd.read_csv('SPA101-weekly.csv', delimiter=' ')\n",
    "chd_values = csv.iloc[ :, 1 ].values\n",
    "\n",
    "#chd for each cell\n",
    "#[lay, row, col, shead, ehead]\n",
    "spchd = {}\n",
    "spchd[0] = [\n",
    "    [1, 0, 0, 28, 28], \n",
    "    [1, 0, ncol-1, 29.5, 29.5 ]\n",
    "]\n",
    "\n",
    "for layer in range (2, nlay):\n",
    "    spchd[0].append ([layer, 0, 0, 28, 28 ])\n",
    "\n",
    "for stress_period in range ( 1, nper ):\n",
    "    spchd[stress_period] = [[1, 0, 0, chd_values[stress_period - 1], chd_values[stress_period]]]\n",
    "    for layer in range (2, nlay):\n",
    "        spchd[stress_period].append ([layer, 0, 0, chd_values[stress_period-1], chd_values[stress_period]])\n",
    "\n",
    "chd = mf.ModflowChd(tm, stress_period_data=spchd)\n",
    "#chd.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup General Head Boundaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "sp = {}\n",
    "sp[0] = []\n",
    "\n",
    "bas12_data = pd.read_csv ( 'Basin1&2-weekly.csv', delimiter=' ' ).iloc[ :, 1 ].values\n",
    "\n",
    "    \n",
    "bas3_data = pd.read_csv ( 'Basin3-weekly.csv', delimiter=' ' ).iloc[ :, 1 ].values\n",
    "\n",
    "for col in range ( 30, 33 ):\n",
    "    sp[ 0 ].append ( [ 0, 0, col,31, 30] )\n",
    "    \n",
    "for col in range ( 50, 65 ):\n",
    "    sp[ 0 ].append ( [ 0, 0, col, 30, 10] )\n",
    "    \n",
    "for stress_period in range ( 1,  nper ):\n",
    "    sp[ stress_period ] = [ ]\n",
    "    for col in range ( 30, 33 ):\n",
    "        if bas3_data[ stress_period - 1 ] == 0:\n",
    "            sp[ stress_period ].append ( [ 0, 0, col, 28 , 3.5] )\n",
    "        else:\n",
    "            sp[ stress_period ].append ( [ 0, 0, col, 30 + 3  * bas3_data[ stress_period - 1 ] , 80 ] ) \n",
    "    for col in range ( 50, 65 ):\n",
    "        if bas12_data[ stress_period - 1 ] == 0:\n",
    "            sp[ stress_period ].append ( [ 0, 0, col, 29 , 1.2])\n",
    "        else:\n",
    "            sp[ stress_period ].append ( [ 0, 0, col, 30 + 4 * bas12_data[ stress_period - 1 ], 40] )\n",
    "ghb = mf.ModflowGhb (tm,\n",
    "                     stress_period_data=sp)\n",
    "#ghb.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup Wells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pumping Well\n",
    "wel_data = pd.read_csv('Pumping_well-weekly.csv', delimiter=' ').iloc[:, -2].values \n",
    "\n",
    "# -1 pumping rate of well 6\n",
    "# -2 average pumping rate of well 5 and well 6\n",
    "\n",
    "spwell = {}\n",
    "spwell[0] = [7, 0, 41, 0]\n",
    "for i in range(1, nper):\n",
    "    spwell[i] = [7, 0, 41, -wel_data[i-1]/67]\n",
    "    \n",
    "wel = mf.ModflowWel(tm, stress_period_data=spwell, ipakcb=53)\n",
    "\n",
    "#wel.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup Soil properties and Flow-Package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "k1 = 1.7  # fine sand 1.728-7.776\n",
    "k2 = 33  # middle sand 7.776-34.56\n",
    "k3 = 80  # stone 34.56-103.68\n",
    "k4 = 0.8  # clay\n",
    "k5 = 40  # middle sand2\n",
    "k6 = 6  # fine sand 1.728-7.776\n",
    "\n",
    "# Set default hk-value value to k2\n",
    "hk = np.ones((nlay, nrow, ncol)) * k2\n",
    "\n",
    "# N1 layer\n",
    "hk[0, :, :] = k1  # fine sand\n",
    "\n",
    "\n",
    "# N2 layer\n",
    "for i in range( 30, 60 ):\n",
    "    hk[1, :, i] = k2  \n",
    "for i in range( 55, 67 ):\n",
    "    hk[1, :, i] = k1  # fine sand\n",
    "\n",
    "#  N3 layer\n",
    "hk[2, :, :] = k5  # middle sand2\n",
    "\n",
    "# N4 layer\n",
    "for i in range( 30, 44 ):\n",
    "    hk[3, :, i] = k3  # sand & stone\n",
    "for i in range( 44, 48 ):\n",
    "    hk[3, :, i] = k4  # clay\n",
    "for i in range( 48, 60 ):\n",
    "    hk[3, :, i] = k3  # sand & stone\n",
    "for i in range( 60, 65 ):\n",
    "    hk[3, :, i] = k4  # clay\n",
    "\n",
    "# N5 layer\n",
    "hk[4, :, :] = k3\n",
    "for i in range(36, 50 ):\n",
    "    hk[4, :, i] = k6  # fine sand\n",
    "for i in range( 50, 60 ):\n",
    "    hk[4, :, i] = k3  # sand & stone\n",
    "for i in range( 60, 65 ):\n",
    "    hk[4, :, i] = k4  # clay\n",
    "\n",
    "# 6 layer\n",
    "hk[5, :, :] = k5  # middle sand2\n",
    "\n",
    "# 7 layer\n",
    "hk[6, :, :] = k5\n",
    "for i in range(36 , 50 ):\n",
    "    hk[6, :, i] = k6  # fine sand\n",
    "\n",
    "# 8 layer\n",
    "hk[7, :, :] = k5  # middle sand2\n",
    "\n",
    "# 9 layer\n",
    "hk[8, :, :] = k5  # middle sand2\n",
    "\n",
    "\n",
    "# vertical HK\n",
    "vk1 = k1*0.3\n",
    "vk2 = k2*0.3\n",
    "vk3 = k3*0.3\n",
    "vk4 = k4*0.3\n",
    "vk5 = k5*0.3\n",
    "vk6 = k6*0.3\n",
    "\n",
    "vka = np.ones((nlay, nrow, ncol), dtype=int) * vk2\n",
    "\n",
    "# N1 layer\n",
    "vka[0, :, :] = vk1  # fine sand\n",
    "\n",
    "\n",
    "# N2 layer\n",
    "for i in range( 30, 55 ):\n",
    "    vka[1, :, i] = vk3  # sand & stone\n",
    "for i in range( 55, 67 ):\n",
    "    vka[1, :, i] = vk4  # clay\n",
    "\n",
    "# N3 layer\n",
    "vka[2, :, :] = vk5  # middle sand2\n",
    "\n",
    "# N4 layer\n",
    "for i in range( 30, 44 ):\n",
    "    vka[3, :, i] = vk3  # sand & stone\n",
    "for i in range( 44, 48 ):\n",
    "    vka[3, :, i] = vk4  # clay\n",
    "for i in range( 48, 60 ):\n",
    "    vka[3, :, i] = vk3  # sand & stone\n",
    "for i in range( 60, 65 ):\n",
    "    vka[3, :, i] = vk4  # clay\n",
    "\n",
    "# N5 layer\n",
    "vka[4, :, :] = vk3\n",
    "for i in range( 36, 50 ):\n",
    "    vka[4, :, i] = vk6  # fine sand\n",
    "for i in range( 50, 60 ):\n",
    "    vka[4, :, i] = vk3  # sand & stone\n",
    "for i in range( 60, 65 ):\n",
    "    vka[4, :, i] = vk4  # clay\n",
    "\n",
    "# N6 layer\n",
    "vka[5, :, :] = vk5  # middle sand2\n",
    "\n",
    "# N7 layer\n",
    "vka[6, :, :] = vk5\n",
    "for i in range( 36, 50):\n",
    "    vka[6, :, i] = vk6  # fine sand\n",
    "\n",
    "# N8 layer\n",
    "vka[7, :, :] = vk5  # middle sand2\n",
    "\n",
    "# N9 layer\n",
    "vka[8, :, :] = vk5  # middle sand2\n",
    "\n",
    "laytyp = [1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
    "\n",
    "# Specific yield \n",
    "sy1 = 0.2 # fine sand \n",
    "sy2 = 0.3 # middle sand\n",
    "sy3 = 0.35 # stone \n",
    "sy4 = 0.05 # clay\n",
    "sy5 = 0.3 # middle sand\n",
    "sy6 = 0.2 # fine sand \n",
    "\n",
    "sy = np.ones((nlay, nrow, ncol)) * sy2\n",
    "\n",
    "# N1 layer\n",
    "sy[0, :, :] = sy1  # fine sand\n",
    "\n",
    "\n",
    "# N2 layer\n",
    "for i in range( 30, 55 ):\n",
    "    sy[1, :, i] = sy3  # sand & stone\n",
    "for i in range( 55, 67 ):\n",
    "    sy[1, :, i] = sy4  # clay\n",
    "\n",
    "# N3 layer\n",
    "sy[2, :, :] = sy5  # middle sand2\n",
    "\n",
    "# N4 layer\n",
    "for i in range( 30, 44 ):\n",
    "    sy[3, :, i] = sy3  # sand & stone\n",
    "for i in range( 44, 48 ):\n",
    "    sy[3, :, i] = sy4  # clay\n",
    "for i in range( 48, 60 ):\n",
    "    sy[3, :, i] = sy3  # sand & stone\n",
    "for i in range( 60, 65 ):\n",
    "    sy[3, :, i] = sy4  # clay\n",
    "\n",
    "# N5 layer\n",
    "sy[4, :, :] = sy3\n",
    "for i in range( 36, 50 ):\n",
    "    sy[4, :, i] = sy6  # fine sand\n",
    "for i in range( 50, 60 ):\n",
    "    sy[4, :, i] = sy3  # sand & stone\n",
    "for i in range( 60, 65 ):\n",
    "    sy[4, :, i] = sy4  # clay\n",
    "\n",
    "# N6 layer\n",
    "sy[5, :, :] = sy5  # middle sand2\n",
    "\n",
    "# N7 layer\n",
    "sy[6, :, :] = sy5\n",
    "for i in range( 36, 50):\n",
    "    sy[6, :, i] = sy6  # fine sand\n",
    "\n",
    "# N8 layer\n",
    "sy[7, :, :] = sy5  # middle sand2\n",
    "\n",
    "# N9 layer\n",
    "sy[8, :, :] = sy5  # middle sand2\n",
    "ss = 7e-3\n",
    "\n",
    "lpf = mf.ModflowLpf(tm, laytyp=laytyp, hk=hk, layvka=0, vka=vka, sy= sy ,  ss=ss, ipakcb=53)\n",
    "\n",
    "#lpf.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup Solver Package and Output-control with defaults"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "budgetfile = '{}.cbc'.format(modelname)\n",
    "budget_filerecord = [budgetfile]\n",
    "\n",
    "oc = mf.ModflowOc(tm, \n",
    "                   save_every=True,\n",
    "                   save_types=['SAVE HEAD', 'SAVE BUDGET'],\n",
    "                   budget_filerecord=budget_filerecord,\n",
    "                   compact=True\n",
    "                  )\n",
    "\n",
    "\n",
    "pcg = mf.ModflowPcg (tm, rclose=1e-1)\n",
    "\n",
    "lmt = flopy.modflow.ModflowLmt ( tm, output_file_name = modelname + '.ftl', output_file_header='extended',\n",
    "                                 output_file_format='formatted')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Write Model input and run the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'mf-data-weekly-grid\\\\model.bud'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tm.write_input()\n",
    "success = tm.run_model(silent = True)\n",
    "#flopy.export.shapefile_utils.model_attributes_to_shapefile('model.shp', tm, package_names = ['dis'])\n",
    "shutil.copyfile (os.path.join(workspace, modelname + '.cbc'),os.path.join(workspace, modelname + '.bud') )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read and Plot Head Observations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SPA157.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SPA168.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SPA165.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "headobj = bf.HeadFile(os.path.join(workspace, modelname + '.hds'))\n",
    "\n",
    "#observation wells\n",
    "\n",
    "obs = [(1, 0, 65),\n",
    "       (1, 0, 48),\n",
    "       (1, 0, 46)]\n",
    "name = ['SPA157','SPA168','SPA165']\n",
    "\n",
    "for j in range (0,3):\n",
    "    ts = headobj.get_ts ( idx= obs[j] )\n",
    "    np.savetxt ( \"HD.csv\", ts, delimiter=',', header='Days,Head' )\n",
    "    model = pd.read_csv ( 'HD.csv',delimiter=',')\n",
    "    date = []\n",
    "    for d in model['# Days']:\n",
    "        date.append(datetime.datetime(2005,5,11)+datetime.timedelta(d-1)) \n",
    "    model['date'] = date\n",
    "    model['nweek'] = np.int64((model['# Days']-1)/7+1)\n",
    "\n",
    "\n",
    "   \n",
    "    \n",
    "    observation1 = pd.read_csv ( name[j]+'-weekly.csv' , delimiter=' ', header = -1)\n",
    "    date = []\n",
    "    for d in observation1.iloc[:,0]:\n",
    "        date.append(datetime.datetime(2005,5,11)+datetime.timedelta((d-1)*7)) \n",
    "    observation1['date'] = date    \n",
    "    observation1.columns = ['nweek','Head','date']\n",
    "\n",
    "    plt.plot ( model['date'], model.iloc[:,1], label='model' , c = 'black')\n",
    "    plt.plot( observation1['date'], observation1.iloc[:,1], '.', label='Observation1', markersize = 2, c='darkcyan' )\n",
    "    plt.legend ( [ \"model\", \"Observation\" ] )\n",
    "    plt.ylim(25,32)\n",
    "    plt.xlabel ( 'Date [Year]' )\n",
    "    plt.ylabel ( 'Groundwater Level [m]' )\n",
    "    plt.grid(True)\n",
    "    \n",
    "    merge = pd.merge ( model, observation1,left_on='date',right_on='date',how='inner')\n",
    "    merge = merge.iloc[:,[2,1,-1]]\n",
    "    merge.columns = ['date','Head_model','Head_obs']\n",
    "    \n",
    "    h1 = merge['Head_model']\n",
    "    h2 = merge['Head_obs']\n",
    "    rmse = math.sqrt ( mse ( h1, h2 ) )\n",
    "    nrmse = rmse/(np.max(h2)-np.min(h2))\n",
    "    rmse = '{:01.2f}'.format(rmse)\n",
    "    nrmse = '{:01.2f}'.format(nrmse)\n",
    "    \n",
    "    plt.title( name[j] + '(NRMSE = ' + str ( nrmse )+')', loc='right')\n",
    "    title=name[j]+\".pdf\"\n",
    "    print(title)\n",
    "    #plt.savefig(title, dpi=300, bbox_inches='tight')\n",
    "    plt.show ()\n",
    "    \n",
    "    \n",
    "    u = []\n",
    "    u = h1-h2\n",
    "    a = h1 - h1\n",
    "\n",
    "    \n",
    "    x = [25,32]  \n",
    "    y = [25,32]\n",
    "    plt.scatter(h1,h2,s = 5,c='black')\n",
    "    plt.plot(x,y,c='red')\n",
    "    plt.ylim(25,32)\n",
    "    plt.xlim(25,32)\n",
    "    plt.xlabel('Simulation results [m]')\n",
    "    plt.ylabel('Observations [m]')\n",
    "\n",
    "    plt.title(name[j] + '(RMSE = ' + str ( rmse )+')', loc='right')\n",
    "    title=name[j]+\"2.pdf\"\n",
    "    #plt.savefig(title, dpi=300, bbox_inches='tight')\n",
    "    plt.show()\n",
    "    \n",
    "    os.remove('HD.csv')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MT3DMS "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Specified value of ftlfree conflicts with FTL file format\n",
      "Switching ftlfree from False to True\n",
      "adding Package:  ADV\n",
      "adding Package:  BTN\n",
      "adding Package:  DSP\n",
      "adding Package:  GCG\n",
      "adding Package:  RCT\n",
      "adding Package:  SSM\n",
      "\n",
      "Writing packages:\n",
      "   Package:  ADV\n",
      "   Package:  BTN\n",
      "   Package:  DSP\n",
      "   Package:  GCG\n",
      "   Package:  RCT\n",
      "   Package:  SSM\n",
      " \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(False, [])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if os.path.isfile('Basin-T-weekly.csv'):\n",
    "    heat = pd.read_csv('Basin-T-weekly.csv',delimiter=' ',header = -1)\n",
    "else:\n",
    "    os.chdir(\"..\")\n",
    "    heat = pd.read_csv('Basin-T-weekly.csv',delimiter=' ',header = -1)\n",
    "\n",
    "heat_basin = heat.iloc[:,-1].values\n",
    "\n",
    "mt = flopy.mt3d.Mt3dms(modelname = 'heat',version='mt3dms',  exe_name='mt3dms5b', \n",
    "                       model_ws = workspace, \n",
    "                       modflowmodel = tm, ftlfilename = modelname + \".ftl\", verbose = True)\n",
    "\n",
    "adv = mt3.Mt3dAdv(mt, mixelm = -1, percel = 0.75)\n",
    "icbund = np.ones((nlay, nrow, ncol), dtype=np.int32)\n",
    "icbund[:,:,0] = -1\n",
    "icbund[:,:,-1] = -1\n",
    "sconc_1 = np.ones((nlay, nrow, ncol)) * 10\n",
    "\n",
    "for i in range ( 30, 55):\n",
    "    sconc_1[ :, :, i ] = 18\n",
    "for i in range ( 38, 45):\n",
    "    sconc_1[ :, :, i ] = 13.5\n",
    "for i in range ( 30, 33):\n",
    "    sconc_1[ 0, :, i] = 12\n",
    "for i in range ( 50, 65):\n",
    "    sconc_1[ 0, :, i] = 12\n",
    "    \n",
    "obs = [ ( 7, 0, 41)]\n",
    "\n",
    "btn = mt3.Mt3dBtn(mt,icbund= icbund, ncomp=1,mcomp=0, sconc = sconc_1, prsity = sy, species_names=['heat'], obs=obs,\n",
    "                nprobs=7)\n",
    "\n",
    "dsp = mt3.Mt3dDsp(mt, al = 17, trpt= 0.1,trpv = 0.03, dmcoef= 0.17)\n",
    "\n",
    "gcg = mt3.Mt3dGcg(mt, mxiter=1,iter1=50,isolve = 2,cclose = 1e-2,ncrs =1)             \n",
    "rct = mt3.Mt3dRct(mt,isothm=1,ireact=0, prsity2= sy, igetsc=0, rhob = 1.87e3, sp1= 0.28e-3)\n",
    "\n",
    "\n",
    "ssm_data = {}\n",
    "ssm_data[0] = []\n",
    "\n",
    "itype = flopy.mt3d.Mt3dSsm.itype_dict()\n",
    "\n",
    "for i in range( 30, 33):\n",
    "    ssm_data[0] = ssm_data[0]+ [[0, 0, i, 14 , itype[ 'GHB' ]]]\n",
    "for i in range ( 50, 65):\n",
    "    ssm_data[0].append( [ 0, 0, i, 14, itype[ 'GHB' ] ] )\n",
    "    \n",
    "for j in range (1,280):\n",
    "    ssm_data[ j ] = [ ]\n",
    "    for i in range ( 30, 33):\n",
    "         ssm_data[ j ].append ( [ 0, 0, i, heat_basin[ j - 1 ], itype[ 'GHB' ] ] )\n",
    "    for i in range ( 50, 65):\n",
    "         ssm_data[ j ].append ( [ 0, 0, i, heat_basin[ j - 1 ], itype[ 'GHB' ] ] )\n",
    "for j in range(280,nper):\n",
    "    ssm_data[ j ] = [ ]\n",
    "    for i in range ( 30, 33):\n",
    "        ssm_data[ j ].append ( [ 0, 0, i, 14, itype[ 'GHB' ] ] )\n",
    "    for i in range ( 50, 65):\n",
    "        ssm_data[ j ].append ( [ 0, 0, i, 14, itype[ 'GHB' ] ] )\n",
    "\n",
    "ssm = flopy.mt3d.Mt3dSsm(mt, stress_period_data=ssm_data)\n",
    "\n",
    "mt.write_input()\n",
    "mt.run_model(silent = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if os.path.isfile('N5_T-weekly2.csv'):\n",
    "    n = pd.read_csv('N5_T-weekly2.csv',delimiter=',',header=0, index_col = 0)\n",
    "    w = pd.read_csv('N6_T-weekly.csv',delimiter=',',header=0, index_col = 0)\n",
    "else:\n",
    "    os.chdir(\"..\")\n",
    "    n = pd.read_csv('N5_T-weekly2.csv',delimiter=',',header=0, index_col = 0)\n",
    "    w = pd.read_csv('N6_T-weekly.csv',delimiter=',',header=0 ,index_col = 0)\n",
    "if os.path.isfile('MT3D001.OBS'):\n",
    "    t = pd.read_csv('MT3D001.OBS',delimiter=' ', nrows= 1150,skiprows=[0,1],skipinitialspace=True,skip_blank_lines=True,usecols=[1,2])\n",
    "\n",
    "else:\n",
    "    os.chdir(workspace)\n",
    "    t = pd.read_csv('MT3D001.OBS',delimiter=' ', nrows= 1150,skiprows=[0,1],skipinitialspace=True,skip_blank_lines=True,usecols=[1,2])\n",
    "\n",
    "t1 = t.iloc[:,0].values\n",
    "y1 = []\n",
    "for i in t1:\n",
    "    y1.append([datetime.date(2005,5,11)+datetime.timedelta(i-1)])\n",
    "t2 = t.iloc[:,1].values\n",
    "\n",
    "\n",
    "\n",
    "n1 = n['days']\n",
    "y2 = []\n",
    "for i in n1:\n",
    "    y2.append([datetime.date(2005,5,11)+datetime.timedelta(np.float64(i-1))])\n",
    "n2 = n.iloc[:,0].values\n",
    "\n",
    "w1 = w['days']\n",
    "y3 = []\n",
    "for i in w1:\n",
    "    y3.append([datetime.date(2005,5,11)+datetime.timedelta(np.float64(i-1))])\n",
    "w2 = w.iloc[:,0].values\n",
    "\n",
    "plt.plot(y2,n2,c='darkcyan')\n",
    "plt.plot(y1,t2,c='black')\n",
    "plt.legend(['model','Average observation 5&6','ObservationN6'])\n",
    "plt.xlabel ( 'Date [Year]' )\n",
    "plt.ylabel ( 'Temperature [°C]' )\n",
    "plt.title('Temperature Simulation')\n",
    "plt.show()\n",
    "plt.close()\n",
    "\n",
    "x = pd.merge ( t, n,left_on= t.iloc[:,0].values,right_on='days',how='inner')\n",
    "\n",
    "v1 = x.iloc[:,1].values\n",
    "v2 = x.iloc[:,2].values\n",
    "x1 = [7,18.5]\n",
    "y1 = [7,18.5]\n",
    "plt.scatter(v1,v2,s = 5,c='slategray')\n",
    "plt.plot(x1,y1,c='darkcyan')\n",
    "plt.ylim(7,18.5)\n",
    "plt.xlim(7,18.5)\n",
    "plt.xlabel ( 'Model Results' )\n",
    "plt.ylabel ( 'Observation Temperature (Well NO.5)' )\n",
    "rmse =  math.sqrt ( mse ( v1, v2 ) )\n",
    "nrmse = rmse/(np.max(v2)-np.min(v1))\n",
    "rmse = '{:01.2f}'.format(rmse)\n",
    "nrmse= '{:01.2f}'.format(nrmse)\n",
    "plt.title('Well NO.5 (RMSE = ' + str (rmse)+', NRMSE= '+ str(nrmse)+')')\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MODPATH 6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This is an Example for time series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, [])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mp = flopy.modpath.Modpath(modelname= modelname,\n",
    "                               exe_name='C:/modflow/Packages/mp6',\n",
    "                               modflowmodel= tm,\n",
    "                               dis_file= tm.name  +'.dis',\n",
    "                               head_file= tm.name +'.hds',\n",
    "                               budget_file= tm.name + '.cbc',\n",
    "                               )\n",
    "options = [3,   #SimulationType           # 1 endpoint; 2 pathline; 3 timeseries\n",
    "           1,   #TrackingDirection            # 1 forward; 2 backward\n",
    "           1,   #WeakSinkOption            # 1 pass; 2 stop\n",
    "           1,   #WeakSourceOption         # 1 pass; 2 stop\n",
    "           1,   #ReferemceTimeOption       # 1 time value; 2 stress period, time step, relative offset\n",
    "           1,   #StopOption                  # 1 stop with simulation 2; extend if steady state 3; specify time\n",
    "           1,   #ParticleGenerationOption    # 1 automatic; 2 external file\n",
    "           2,   #TimePointOption             # 1 none; 2 number at fixed intervals; 3 array\n",
    "           1,   #BudgetOutputOption          # 1 none; 2 summary; 3 list of cells; 4 trace mode\n",
    "           1,   #ZoneArrayOption          # 1 none; 2 read zone array(s) \n",
    "           1,   #RetardationOption           # 1 none; 2 read array(s) \n",
    "           1    #AdvectiveObservationsOption # 1 none; 2 saved for all time pts 3; saved for final time pt\n",
    "          ] \n",
    "mpb = flopy.modpath.ModpathBas(mp, hdry=tm.lpf.hdry, laytyp=tm.lpf.laytyp, ibound=1, prsity = sy)\n",
    "\n",
    "particles = 4 # particle numbers\n",
    "\n",
    "ifaces = []\n",
    "face_ct = []\n",
    "\n",
    "group = []\n",
    "group_placement = []\n",
    "group_name = []\n",
    "\n",
    "icnt = 0\n",
    "for col in it.chain(range(30,33),range(50,65)):\n",
    "    group_name.append('GHB{}'.format(icnt))\n",
    "    group.append([0, 0, col, 0, 0, col])\n",
    "    ifaces.append([[1, particles, particles], # side  \n",
    "                   [2, particles, particles], # side \n",
    "                   [3, particles, particles], # side \n",
    "                   [4, particles, particles], # side \n",
    "                   [6, particles, particles]])# bottom\n",
    "    face_ct.append(5)\n",
    "    group_placement.append([1, 1, 1, 0, 1, 1])\n",
    "    icnt += 1\n",
    "    \n",
    "sim = flopy.modpath.ModpathSim(mp, \n",
    "                 option_flags= options,\n",
    "                 ref_time= 214, # the beginning time when you release the particles  \n",
    "                 group_name=group_name, \n",
    "                 group_placement=group_placement,\n",
    "                 group_region = group,  face_ct= face_ct,\n",
    "                 ifaces=ifaces,\n",
    "                 time_ct = 50  # the number of time steps needs to be read\n",
    "                )\n",
    "mp.write_input()\n",
    "mp.run_model(silent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def modpath (t):\n",
    "    mp = flopy.modpath.Modpath(modelname= modelname,\n",
    "                               exe_name='C:/modflow/Packages/mp6',\n",
    "                               modflowmodel= tm,\n",
    "                               dis_file= tm.name  +'.dis',\n",
    "                               head_file= tm.name +'.hds',\n",
    "                               budget_file= tm.name + '.cbc',\n",
    "                                   )\n",
    "    options = [2,   #SimulationType           # 1 endpoint; 2 pathline; 3 timeseries\n",
    "               1,   #TrackingDirection            # 1 forward; 2 backward\n",
    "               1,   #WeakSinkOption            # 1 pass; 2 stop\n",
    "               1,   #WeakSourceOption         # 1 pass; 2 stop\n",
    "               1,   #ReferemceTimeOption       # 1 time value; 2 stress period, time step, relative offset\n",
    "               1,   #StopOption                  # 1 stop with simulation 2; extend if steady state 3; specify time\n",
    "               1,   #ParticleGenerationOption    # 1 automatic; 2 external file\n",
    "               2,   #TimePointOption             # 1 none; 2 number at fixed intervals; 3 array\n",
    "               1,   #BudgetOutputOption          # 1 none; 2 summary; 3 list of cells; 4 trace mode\n",
    "               1,   #ZoneArrayOption          # 1 none; 2 read zone array(s) \n",
    "               1,   #RetardationOption           # 1 none; 2 read array(s) \n",
    "               1    #AdvectiveObservationsOption # 1 none; 2 saved for all time pts 3; saved for final time pt\n",
    "              ] \n",
    "    mpb = flopy.modpath.ModpathBas(mp, hdry=tm.lpf.hdry, laytyp=tm.lpf.laytyp, ibound=1, prsity = sy)\n",
    "    \n",
    "    particles = 4 # particle numbers\n",
    "    \n",
    "    ifaces = []\n",
    "    face_ct = []\n",
    "    \n",
    "    group = []\n",
    "    group_placement = []\n",
    "    group_name = []\n",
    "    \n",
    "    icnt = 0\n",
    "    for col in it.chain(range(30,33),range(50,65)):\n",
    "        group_name.append('GHB{}'.format(icnt))\n",
    "        group.append([0, 0, col, 0, 0, col])\n",
    "        ifaces.append([[1, particles, particles], # side  \n",
    "                       [2, particles, particles], # side \n",
    "                       [3, particles, particles], # side \n",
    "                       [4, particles, particles], # side \n",
    "                       [6, particles, particles]])# bottom\n",
    "        face_ct.append(5)\n",
    "        group_placement.append([1, 1, 1, 0, 1, 1])\n",
    "        icnt += 1\n",
    "        \n",
    "    sim = flopy.modpath.ModpathSim(mp, \n",
    "                     option_flags= options,\n",
    "                     ref_time= t, # the beginning time when you release the particles  \n",
    "                     group_name=group_name, \n",
    "                     group_placement=group_placement,\n",
    "                     group_region = group,  face_ct= face_ct,\n",
    "                     ifaces=ifaces,\n",
    "                    )\n",
    "    mp.write_input()\n",
    "    mp.run_model(silent=True)\n",
    "    pthobj = flopy.utils.PathlineFile(os.path.join(modelname +'.mppth'))\n",
    "    pdobj = flopy.utils.EndpointFile(os.path.join(modelname +'.mpend'))\n",
    "    shutil.copyfile ( modelname + '.mppth', str(t) + modelname + '.path' )\n",
    "    shutil.copyfile ( modelname + '.mpend', str(t) + modelname + '.end' )\n",
    "    \n",
    "    \n",
    "    options[0] = 3\n",
    "    \n",
    "    sim = flopy.modpath.ModpathSim(mp, \n",
    "                     option_flags= options,\n",
    "                     ref_time= t, # the beginning time when you release the particles  \n",
    "                     group_name=group_name, \n",
    "                     group_placement=group_placement,\n",
    "                     group_region = group,  face_ct= face_ct,\n",
    "                     ifaces=ifaces,\n",
    "                     time_ct = 100  # the number of time steps needs to be read\n",
    "                    )\n",
    "    mp.write_input()\n",
    "    mp.run_model(silent=True)        \n",
    "    shutil.copyfile ( modelname + '.mp.tim_ser', str(t) + modelname + '.timeseries' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: unit 32 of package MPSIM already in use\n",
      "****Warning -- two packages of the same type:  <class 'flopy.modpath.mpsim.ModpathSim'> <class 'flopy.modpath.mpsim.ModpathSim'>\n",
      "replacing existing Package...\n",
      "WARNING: unit 32 of package MPSIM already in use\n",
      "****Warning -- two packages of the same type:  <class 'flopy.modpath.mpsim.ModpathSim'> <class 'flopy.modpath.mpsim.ModpathSim'>\n",
      "replacing existing Package...\n",
      "WARNING: unit 32 of package MPSIM already in use\n",
      "****Warning -- two packages of the same type:  <class 'flopy.modpath.mpsim.ModpathSim'> <class 'flopy.modpath.mpsim.ModpathSim'>\n",
      "replacing existing Package...\n",
      "WARNING: unit 32 of package MPSIM already in use\n",
      "****Warning -- two packages of the same type:  <class 'flopy.modpath.mpsim.ModpathSim'> <class 'flopy.modpath.mpsim.ModpathSim'>\n",
      "replacing existing Package...\n",
      "WARNING: unit 32 of package MPSIM already in use\n",
      "****Warning -- two packages of the same type:  <class 'flopy.modpath.mpsim.ModpathSim'> <class 'flopy.modpath.mpsim.ModpathSim'>\n",
      "replacing existing Package...\n",
      "WARNING: unit 32 of package MPSIM already in use\n",
      "****Warning -- two packages of the same type:  <class 'flopy.modpath.mpsim.ModpathSim'> <class 'flopy.modpath.mpsim.ModpathSim'>\n",
      "replacing existing Package...\n",
      "WARNING: unit 32 of package MPSIM already in use\n",
      "****Warning -- two packages of the same type:  <class 'flopy.modpath.mpsim.ModpathSim'> <class 'flopy.modpath.mpsim.ModpathSim'>\n",
      "replacing existing Package...\n",
      "WARNING: unit 32 of package MPSIM already in use\n",
      "****Warning -- two packages of the same type:  <class 'flopy.modpath.mpsim.ModpathSim'> <class 'flopy.modpath.mpsim.ModpathSim'>\n",
      "replacing existing Package...\n",
      "WARNING: unit 32 of package MPSIM already in use\n",
      "****Warning -- two packages of the same type:  <class 'flopy.modpath.mpsim.ModpathSim'> <class 'flopy.modpath.mpsim.ModpathSim'>\n",
      "replacing existing Package...\n",
      "WARNING: unit 32 of package MPSIM already in use\n",
      "****Warning -- two packages of the same type:  <class 'flopy.modpath.mpsim.ModpathSim'> <class 'flopy.modpath.mpsim.ModpathSim'>\n",
      "replacing existing Package...\n"
     ]
    }
   ],
   "source": [
    "time = [\n",
    "    79,\n",
    "    257,\n",
    "    438,\n",
    "    622,\n",
    "    790,\n",
    "    969,\n",
    "    1152,\n",
    "    1345,\n",
    "    1538,\n",
    "    1739\n",
    "]\n",
    "for i in time:\n",
    "    modpath(round(i))"
   ]
  }
 ],
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