examples/Notebooks/flopy3_swi2package_ex1.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# FloPy\n",
    "\n",
    "### SWI2 Example 1. Rotating Interface\n",
    "\n",
    "This example problem is the first example problem in the SWI2 documentation (https://pubs.usgs.gov/tm/6a46/) and simulates transient movement of a freshwater\\seawater interface separating two density zones in a two-dimensional vertical plane. The problem domain is 250 m long, 40 m high, and 1 m wide. The aquifer is confined, storage changes are not considered (all MODFLOW stress periods are steady-state), and the top and bottom of the aquifer are horizontal and impermeable.\n",
    "\n",
    "The domain is discretized into 50 columns, 1 row, and 1 layer, with respective cell dimensions of 5 m (`DELR`), 1 m (`DELC`), and 40 m. A constant head of 0 m is specified for column 50. The hydraulic conductivity is 2 m/d and the effective porosity (`SSZ`) is 0.2. A flow of 1 m$^3$/d of seawater is specified in column 1 and causes groundwater to flow from left to right\n",
    "in the model domain.\n",
    "\n",
    "The domain contains one freshwater zone and one seawater zone, separated by an active `ZETA` surface between the zones (`NSRF=1`) that approximates the 50-percent seawater salinity contour. A 400-day period is simulated using a constant time step of 2 days. Fluid density is represented using the stratified option (`ISTRAT=1`) and the elevation of the interface is output every 100 days (every 50 time steps). The densities, $\\rho$, of the freshwater and saltwater are 1,000 and 1,025 kg/m$^3$, respectively. Hence, the dimensionless densities, $\\nu$, of the freshwater and saltwater are 0.0 and 0.025, respectively. The maximum slope of the toe and tip is specified as 0.2 (`TOESLOPE=TIPSLOPE=0.2`), and default tip/toe parameters are used (`ALPHA=BETA=0.1`). Initially, the interface is at a 45$^{\\circ}$ angle from (x,z) = (80,0) to (x,z) = (120,-40). The source/sink terms (`ISOURCE`) are specified to be freshwater everywhere (`ISOURCE=1`) except in cell 1 where saltwater enters the model and `ISOURCE` equals 2. A comparison of results for SWI2 and the exact Dupuit solution of Wilson and Sa Da Costa (1982) are presented below. The constant flow from left to right results in an average velocity of 0.125 m/d. The exact Dupuit solution is a rotating straight interface of which the center moves to the right with this velocity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import `numpy` and `matplotlib`, set all figures to be inline, import `flopy.modflow` and `flopy.utils`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2022-12-15T13:21:17.081640Z"
    }
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.7.15 (default, Oct 18 2022, 09:36:49) \n",
      "[GCC 11.2.0]\n",
      "numpy version: 1.21.6\n",
      "matplotlib version: 3.5.3\n",
      "flopy version: 3.3.6\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "from tempfile import TemporaryDirectory\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# run installed version of flopy or add local path\n",
    "try:\n",
    "    import flopy\n",
    "except:\n",
    "    fpth = os.path.abspath(os.path.join(\"..\", \"..\"))\n",
    "    sys.path.append(fpth)\n",
    "    import flopy\n",
    "\n",
    "print(sys.version)\n",
    "print(\"numpy version: {}\".format(np.__version__))\n",
    "print(\"matplotlib version: {}\".format(mpl.__version__))\n",
    "print(\"flopy version: {}\".format(flopy.__version__))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define model name of your model and the location of MODFLOW executable. All MODFLOW files and output will be stored in the subdirectory defined by the workspace. Create a model named `ml` and specify that this is a MODFLOW-2005 model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2022-12-15T13:21:17.136780Z"
    }
   },
   "outputs": [],
   "source": [
    "modelname = \"swiex1\"\n",
    "\n",
    "# Set name of MODFLOW exe\n",
    "#  assumes executable is in users path statement\n",
    "exe_name = \"mf2005\"\n",
    "\n",
    "temp_dir = TemporaryDirectory()\n",
    "workspace = temp_dir.name\n",
    "\n",
    "ml = flopy.modflow.Modflow(\n",
    "    modelname, version=\"mf2005\", exe_name=exe_name, model_ws=workspace\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the number of layers, rows and columns, and the cell size along the rows (`delr`) and along the columns (`delc`). Then create a discretization file. Specify the top and bottom of the aquifer. The heads are computed quasi-steady state (hence a steady MODFLOW run) while the interface will move. There is one stress period with a length of 400 days and 200 steps (so one step is 2 days). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:21:17.142405Z",
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     "shell.execute_reply": "2022-12-15T13:21:17.148635Z"
    }
   },
   "outputs": [],
   "source": [
    "nlay = 1\n",
    "nrow = 1\n",
    "ncol = 50\n",
    "delr = 5.0\n",
    "delc = 1.0\n",
    "nper, perlen, nstp = 1, 400.0, 200\n",
    "discret = flopy.modflow.ModflowDis(\n",
    "    ml,\n",
    "    nlay=nlay,\n",
    "    nrow=nrow,\n",
    "    ncol=ncol,\n",
    "    delr=delr,\n",
    "    delc=delc,\n",
    "    top=0,\n",
    "    botm=-40.0,\n",
    "    steady=True,\n",
    "    nper=nper,\n",
    "    perlen=perlen,\n",
    "    nstp=nstp,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All cells are active (`ibound=1`), while the last cell is fixed head (`ibound=-1`). The starting values of the head are not important, as the heads are computed every time with a steady run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:21:17.153616Z",
     "iopub.status.busy": "2022-12-15T13:21:17.153069Z",
     "iopub.status.idle": "2022-12-15T13:21:17.158482Z",
     "shell.execute_reply": "2022-12-15T13:21:17.157590Z"
    }
   },
   "outputs": [],
   "source": [
    "ibound = np.ones((nrow, ncol))\n",
    "ibound[0, -1] = -1\n",
    "bas = flopy.modflow.ModflowBas(ml, ibound=ibound, strt=0.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the hydraulic conductivity. The aquifer is confined (`laytype=0`) and the intercell hydraulic conductivity is the harmonic meand (`layavg=0`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:21:17.162959Z",
     "iopub.status.busy": "2022-12-15T13:21:17.162484Z",
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     "shell.execute_reply": "2022-12-15T13:21:17.167140Z"
    }
   },
   "outputs": [],
   "source": [
    "lpf = flopy.modflow.ModflowLpf(ml, hk=2.0, laytyp=0, layavg=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inflow on the right side of the model is 1 m$^3$/d (layer 0, row 0, column 0, discharge 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:21:17.172187Z",
     "iopub.status.busy": "2022-12-15T13:21:17.171613Z",
     "iopub.status.idle": "2022-12-15T13:21:17.177062Z",
     "shell.execute_reply": "2022-12-15T13:21:17.175912Z"
    }
   },
   "outputs": [],
   "source": [
    "wel = flopy.modflow.ModflowWel(ml, stress_period_data={0: [[0, 0, 0, 1]]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the output control to save heads and interface every 50 steps, and define the pcg solver with default arguments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:21:17.181435Z",
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     "shell.execute_reply": "2022-12-15T13:21:17.185475Z"
    }
   },
   "outputs": [],
   "source": [
    "spd = {}\n",
    "for istp in range(49, nstp + 1, 50):\n",
    "    spd[(0, istp)] = [\"save head\", \"print budget\"]\n",
    "    spd[(0, istp + 1)] = []\n",
    "\n",
    "oc = flopy.modflow.ModflowOc(ml, stress_period_data=spd)\n",
    "pcg = flopy.modflow.ModflowPcg(ml)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The intial interface is straight. The isource is one everywhere (inflow and outflow is fresh (zone 1)) except for the first cell (index=0) which has saltwater inflow (zone 2)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:21:17.190668Z",
     "iopub.status.busy": "2022-12-15T13:21:17.190190Z",
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     "shell.execute_reply": "2022-12-15T13:21:17.197928Z"
    }
   },
   "outputs": [],
   "source": [
    "z = np.zeros((nrow, ncol), float)\n",
    "z[0, 16:24] = np.arange(-2.5, -40, -5)\n",
    "z[0, 24:] = -40\n",
    "z = [z]  # zeta needs to be\n",
    "isource = np.ones((nrow, ncol), int)\n",
    "isource[0, 0] = 2\n",
    "#\n",
    "swi = flopy.modflow.ModflowSwi2(\n",
    "    ml,\n",
    "    nsrf=1,\n",
    "    istrat=1,\n",
    "    toeslope=0.2,\n",
    "    tipslope=0.2,\n",
    "    nu=[0, 0.025],\n",
    "    zeta=z,\n",
    "    ssz=0.2,\n",
    "    isource=isource,\n",
    "    nsolver=1,\n",
    "    iswizt=55,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write the MODFLOW input files and run the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:21:17.203466Z",
     "iopub.status.busy": "2022-12-15T13:21:17.202821Z",
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     "shell.execute_reply": "2022-12-15T13:21:17.243657Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, [])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml.write_input()\n",
    "ml.run_model(silent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the head and zeta data from the file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:21:17.248671Z",
     "iopub.status.busy": "2022-12-15T13:21:17.248349Z",
     "iopub.status.idle": "2022-12-15T13:21:17.260690Z",
     "shell.execute_reply": "2022-12-15T13:21:17.259695Z"
    }
   },
   "outputs": [],
   "source": [
    "# read model heads\n",
    "hfile = flopy.utils.HeadFile(os.path.join(ml.model_ws, modelname + \".hds\"))\n",
    "head = hfile.get_alldata()\n",
    "# read model zeta\n",
    "zfile = flopy.utils.CellBudgetFile(\n",
    "    os.path.join(ml.model_ws, modelname + \".zta\")\n",
    ")\n",
    "kstpkper = zfile.get_kstpkper()\n",
    "zeta = []\n",
    "for kk in kstpkper:\n",
    "    zeta.append(zfile.get_data(kstpkper=kk, text=\"ZETASRF  1\")[0])\n",
    "zeta = np.array(zeta)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a graph and add the solution of Wilson and Sa da Costa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:21:17.265251Z",
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     "shell.execute_reply": "2022-12-15T13:21:17.508278Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 6))\n",
    "# define x-values of xcells and plot interface\n",
    "x = np.arange(0, ncol * delr, delr) + delr / 2.0\n",
    "label = [\n",
    "    \"SWI2\",\n",
    "    \"_\",\n",
    "    \"_\",\n",
    "    \"_\",\n",
    "]  # labels with an underscore are not added to legend\n",
    "for i in range(4):\n",
    "    zt = np.ma.masked_outside(zeta[i, 0, 0, :], -39.99999, -0.00001)\n",
    "    plt.plot(x, zt, \"r-\", lw=1, zorder=10, label=label[i])\n",
    "# Data for the Wilson - Sa da Costa solution\n",
    "k = 2.0\n",
    "n = 0.2\n",
    "nu = 0.025\n",
    "H = 40.0\n",
    "tzero = H * n / (k * nu) / 4.0\n",
    "Ltoe = np.zeros(4)\n",
    "v = 0.125\n",
    "t = np.arange(100, 500, 100)\n",
    "label = [\n",
    "    \"Wilson and Sa Da Costa (1982)\",\n",
    "    \"_\",\n",
    "    \"_\",\n",
    "    \"_\",\n",
    "]  # labels with an underscore are not added to legend\n",
    "for i in range(4):\n",
    "    Ltoe[i] = H * np.sqrt(k * nu * (t[i] + tzero) / n / H)\n",
    "    plt.plot(\n",
    "        [100 - Ltoe[i] + v * t[i], 100 + Ltoe[i] + v * t[i]],\n",
    "        [0, -40],\n",
    "        \"0.75\",\n",
    "        lw=8,\n",
    "        zorder=0,\n",
    "        label=label[i],\n",
    "    )\n",
    "# Scale figure and add legend\n",
    "plt.axis(\"scaled\")\n",
    "plt.xlim(0, 250)\n",
    "plt.ylim(-40, 0)\n",
    "plt.legend(loc=\"best\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use ModelCrossSection plotting class and plot_surface() method to plot zeta surfaces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:21:17.513688Z",
     "iopub.status.busy": "2022-12-15T13:21:17.513408Z",
     "iopub.status.idle": "2022-12-15T13:21:18.173889Z",
     "shell.execute_reply": "2022-12-15T13:21:18.172686Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16, 3))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "modelxsect = flopy.plot.PlotCrossSection(model=ml, line={\"Row\": 0})\n",
    "label = [\"SWI2\", \"_\", \"_\", \"_\"]\n",
    "for k in range(zeta.shape[0]):\n",
    "    modelxsect.plot_surface(\n",
    "        zeta[k, :, :, :],\n",
    "        masked_values=[0, -40.0],\n",
    "        color=\"red\",\n",
    "        lw=1,\n",
    "        label=label[k],\n",
    "    )\n",
    "linecollection = modelxsect.plot_grid()\n",
    "ax.set_title(\"ModelCrossSection.plot_surface()\")\n",
    "# Data for the Wilson - Sa da Costa solution\n",
    "k = 2.0\n",
    "n = 0.2\n",
    "nu = 0.025\n",
    "H = 40.0\n",
    "tzero = H * n / (k * nu) / 4.0\n",
    "Ltoe = np.zeros(4)\n",
    "v = 0.125\n",
    "t = np.arange(100, 500, 100)\n",
    "label = [\n",
    "    \"Wilson and Sa Da Costa (1982)\",\n",
    "    \"_\",\n",
    "    \"_\",\n",
    "    \"_\",\n",
    "]  # labels with an underscore are not added to legend\n",
    "for i in range(4):\n",
    "    Ltoe[i] = H * np.sqrt(k * nu * (t[i] + tzero) / n / H)\n",
    "    ax.plot(\n",
    "        [100 - Ltoe[i] + v * t[i], 100 + Ltoe[i] + v * t[i]],\n",
    "        [0, -40],\n",
    "        \"blue\",\n",
    "        lw=1,\n",
    "        zorder=0,\n",
    "        label=label[i],\n",
    "    )\n",
    "# Scale figure and add legend\n",
    "ax.axis(\"scaled\")\n",
    "ax.set_xlim(0, 250)\n",
    "ax.set_ylim(-40, 0)\n",
    "ax.legend(loc=\"best\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use ModelCrossSection plotting class and plot_fill_between() method to fill between zeta surfaces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:21:18.178610Z",
     "iopub.status.busy": "2022-12-15T13:21:18.177918Z",
     "iopub.status.idle": "2022-12-15T13:21:18.430081Z",
     "shell.execute_reply": "2022-12-15T13:21:18.429053Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16, 3))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "modelxsect = flopy.plot.PlotCrossSection(model=ml, line={\"Row\": 0})\n",
    "modelxsect.plot_fill_between(zeta[3, :, :, :])\n",
    "linecollection = modelxsect.plot_grid()\n",
    "ax.set_title(\"ModelCrossSection.plot_fill_between()\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert zeta surfaces to relative seawater concentrations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:21:18.434570Z",
     "iopub.status.busy": "2022-12-15T13:21:18.434162Z",
     "iopub.status.idle": "2022-12-15T13:21:18.705174Z",
     "shell.execute_reply": "2022-12-15T13:21:18.704179Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.00000001 1.00000001 1.00000001 1.00000001 1.00000001 1.00000001\n",
      " 1.00000001 1.00000001 1.00000001 1.00000001 1.00000001 1.00000001\n",
      " 1.00000001 1.00000001 1.00000001 1.00000001 1.00000001 0.97957368\n",
      " 0.93777831 0.89900599 0.8606122  0.82240776 0.78431243 0.74628681\n",
      " 0.70830902 0.6703652  0.63244558 0.59454289 0.55665098 0.5187645\n",
      " 0.4808785  0.44298783 0.40508729 0.36717053 0.32923027 0.29125705\n",
      " 0.25323816 0.21515508 0.17697897 0.13865538 0.10004826 0.06099987\n",
      " 0.02483263 0.         0.         0.         0.         0.\n",
      " 0.         0.        ]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, Y = np.meshgrid(x, [0, -40])\n",
    "zc = flopy.plot.SwiConcentration(model=ml)\n",
    "conc = zc.calc_conc(zeta={0: zeta[3, :, :, :]}) / 0.025\n",
    "print(conc[0, 0, :])\n",
    "v = np.vstack((conc[0, 0, :], conc[0, 0, :]))\n",
    "plt.imshow(v, extent=[0, 250, -40, 0], cmap=\"Reds\")\n",
    "cb = plt.colorbar(orientation=\"horizontal\")\n",
    "cb.set_label(\"percent seawater\")\n",
    "plt.contour(X, Y, v, [0.25, 0.5, 0.75], linewidths=[2, 1.5, 1], colors=\"black\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-15T13:21:18.709050Z",
     "iopub.status.busy": "2022-12-15T13:21:18.708672Z",
     "iopub.status.idle": "2022-12-15T13:21:18.713957Z",
     "shell.execute_reply": "2022-12-15T13:21:18.713078Z"
    }
   },
   "outputs": [],
   "source": [
    "try:\n",
    "    # ignore PermissionError on Windows\n",
    "    temp_dir.cleanup()\n",
    "except:\n",
    "    pass"
   ]
  }
 ],
 "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": "3.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}