Notebook_Tracer_simple_interactive.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "566eb799-dd26-4def-9c8e-fa8917efdd19",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Example 1 - Application of LPMs for Data of a Single Tracer\n",
    "\n",
    "* **General goal**:\n",
    "    * application of LPMs for modelling tracers in an observation well\n",
    "    * getting familiar with different LPM approaches\n",
    "    * fitting LPM parameter values to get optimal simulation results\n",
    "* Starting from three time series data sets:\n",
    "    * precipitation measurements\n",
    "    * tracer concentration in precipitation\n",
    "    * tracer concentration in observation well\n",
    "    \n",
    "- **Steps**:\n",
    "    1. data import and data pre-processing\n",
    "    2. data visualization\n",
    "    3. model setup\n",
    "    4. running the model\n",
    "    \n",
    "**Note: if you are unfamiliar with the `ISOSIMpy` package, do not manipulate any code in this notebook!**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ca45fb4-3515-42e4-b2f5-a4e021ac15af",
   "metadata": {},
   "source": [
    "### Step 1: Data Import"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7462adfa-5c43-4e58-b4ae-0ce3377f7e24",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### 1.1 Setting the Plotting Environment and importing packages"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f553978e-4a92-4cd6-ba00-bd733a3a9a77",
   "metadata": {},
   "source": [
    "&rarr; Setting the plotting environment (`widget` for dynamic plots, `inline` for static plots)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3843d07f-e863-4fef-a695-621ae23761f5",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import io\n",
    "from ipyfilechooser import FileChooser\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from ipywidgets import *\n",
    "from matplotlib.gridspec import GridSpec\n",
    "import matplotlib.ticker\n",
    "from Pre import *\n",
    "from Post import *\n",
    "from Calculate import *\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d483388-0020-4efc-8cb8-b98deff2ac3b",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### 1.2 Selecting Relevant Files"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cda1978b-6443-4d78-b5de-1216ad6c1c8d",
   "metadata": {},
   "source": [
    "&rarr; Select files with relevant data\n",
    "1. Precipitation and source concantration file\n",
    "    - in directory name \"precipitation\"\n",
    "2. Observation well concentration file\n",
    "    - in directory name \"groundwater_samples\"\n",
    "___\n",
    "___\n",
    "\n",
    "The following table shows usable combinations of data-sets (select files according to a **single row** of the table):\n",
    "\n",
    "| **precipitation file** | **groundwater samples file** |\n",
    "|------------------------|------------------------------|\n",
    "| precipitation_3H_1     | GW_samples_3H_1              |\n",
    "| precipitation_3H_2     | GW_samples_3H_2              |\n",
    "| precipitation_CFC11    | GW_samples_CFC11             |\n",
    "| precipitation_CFC12    | GW_samples_CFC12             |\n",
    "| precipitation_CFC113   | GW_samples_CFC113            |\n",
    "| precipitation_SF6      | GW_samples_SF6               |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eafde0f0-3b95-4d00-b874-ffc596e714b5",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4171780f994249d79b7bbf420953c7b1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FileChooser(path='C:\\Users\\MRudolph\\Documents\\GitHub_TUD\\ISOSIMpy\\model_input\\precipitation', filename='', tit…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fc4ab4240b0e41f6b86c942d5a1b9308",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FileChooser(path='C:\\Users\\MRudolph\\Documents\\GitHub_TUD\\ISOSIMpy\\model_input\\groundwater_samples', filename='…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fc_c = FileChooser(\n",
    "    os.path.join(os.getcwd(), \"model_input\", \"precipitation\"),\n",
    "    filename='',\n",
    "    title='<b>Select your time series file of precipitation and tracer concentration:</b>'\n",
    ")\n",
    "\n",
    "display(fc_c)\n",
    "\n",
    "fc_s = FileChooser(\n",
    "    os.path.join(os.getcwd(), \"model_input\", \"groundwater_samples\"),\n",
    "    filename='',\n",
    "    title='<b>Select your file of tracer concentration samples in groundwater:</b>'\n",
    ")\n",
    "\n",
    "display(fc_s)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4a689f1-44e0-41a1-97e8-0241010bf2ca",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### 1.3 Importing and Pre-Processing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbd7ae5c-32e4-4a9e-bce1-1be93b6cc98b",
   "metadata": {},
   "source": [
    "&rarr; Import and pre-process data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "92a30c1a",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "try:\n",
    "    rain = Pre.convertTimeC(pd.read_csv(fc_c.selected, sep=';', header=None))\n",
    "    sample = Pre.convertTimeS(pd.read_csv(fc_s.selected, sep=';', header=None))\n",
    "except:\n",
    "    print('An error occured!')\n",
    "    \n",
    "# check for negative / zero values in the data\n",
    "rain = Pre.checkZero(rain)\n",
    "\n",
    "# add column for precipitation-weighted concentration\n",
    "rain = Pre.weightVolumes(rain)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e92b2d48-3f0f-4d3f-8407-39adfc8c07e4",
   "metadata": {},
   "source": [
    "### Step 2: Data Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "249575bd-faaf-4671-92bb-e124d3c23695",
   "metadata": {},
   "source": [
    "#### 2.1 Tracer Names"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f063bcb3-43d4-42a3-b6b2-59741fd253c9",
   "metadata": {},
   "source": [
    "&rarr; Enter the names of your tracer here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "38f61b27-332b-4df7-af8d-99602de47c62",
   "metadata": {},
   "outputs": [],
   "source": [
    "name_tracer_1 = \"MyTracer1\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7407b1c-68f1-4ce9-856c-eb204e489863",
   "metadata": {},
   "source": [
    "#### 2.2 Plotting Source and Observed Concentration Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eea10e3b-9dd1-4a81-bc59-ffdc630d9071",
   "metadata": {},
   "source": [
    "&rarr; Plotting data in two separate plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "31094b3e",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here you can see your input data and the sample values\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Here you can see your input data and the sample values')\n",
    "sample_vis = sample.drop(sample[sample[3] == 0].index)\n",
    "fig, [ax1, ax2] = plt.subplots(nrows=2, ncols=1, figsize=(8, 5), constrained_layout=True, sharex=True)\n",
    "\n",
    "ax1.plot(rain['Date'], rain[3], label='Obs. %s' % name_tracer_1, color='green')\n",
    "ax1.plot(sample_vis['Date'], sample_vis[1], visible=False)\n",
    "ax1.legend()\n",
    "ax2.scatter(sample_vis['Date'], sample_vis[1], label='Obs. %s' % name_tracer_1, marker='x', zorder=5, c=\"red\")\n",
    "ax2.legend()\n",
    "ax1.set_title(\"Tracer Input\", fontsize=16)\n",
    "ax1.set_ylabel('$c_{tracer}$ [-]', fontsize=14)\n",
    "ax1.set_ylim(0)\n",
    "\n",
    "ax2.set_title(\"Tracer Output\", fontsize=16)\n",
    "ax2.set_ylabel('$c_{tracer}$ [-]', fontsize=14)\n",
    "ax2.set_xlabel('Time', fontsize=14)\n",
    "ax2.set_ylim(0)\n",
    "ax1.grid(zorder=0)\n",
    "ax2.grid(zorder=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3d72de2-3fef-4eb6-8df0-b06fe3042d9f",
   "metadata": {},
   "source": [
    "### Step 3: Model Setup\n",
    "\n",
    "This is all done in the background..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "50bc0889-317d-4842-9a4b-5912bc5dc186",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The tracer concentration in recharge water is assumed to be equal to the concentration in precipitation water.\n"
     ]
    }
   ],
   "source": [
    "# instantiate / initialize the parameterization object\n",
    "Par()\n",
    "\n",
    "# set initial run / model number\n",
    "Par.n = 0\n",
    "\n",
    "# set monthly step (i.e., decay constant is divided by 12 and mean TT is multiplied by 12)\n",
    "Par.step = 0\n",
    "\n",
    "# set option to NOT include the vadose zone (other option not yet implemented)\n",
    "Par.SOILM = 0\n",
    "\n",
    "# handle recharge according to Par.SOILM\n",
    "if Par.SOILM == 0:\n",
    "    print('The tracer concentration in recharge water is assumed to be equal to the concentration in precipitation water.')\n",
    "    Cin = rain['weighted_c']\n",
    "elif Par.SOILM == 1:\n",
    "    pass\n",
    "elif Par.SOILM == 2:\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "379fb041-5505-4f1a-a664-9c8ef531673d",
   "metadata": {},
   "source": [
    "### Step 4: Setting Model Parameters, Running the Model, and Plotting the Results\n",
    "\n",
    "You will see two `     Run Interact     `-buttons below.\n",
    "\n",
    "You have to **press the <u>upper button</u> to run the model** with the current parameter values.\n",
    "\n",
    "You have to **press the <u>lower button</u> to plot the results** with the current plotting settings.\n",
    "\n",
    "If you want to update anything (model parameters / plotting settings), you have to press the corresponding button for the changes to take effect!\n",
    "___\n",
    "___"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "099843e6-0b43-4d28-a6f0-d5695d3c948c",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model type and parameter values:\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5278a94f4bb4422d81f2e14bbba8937a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(RadioButtons(description='Modeltype:', layout=Layout(width='max-content'), options=('Pis…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bd212f8aeb2943f9bdbbec89d2e4957f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(BoundedIntText(value=1950, description='First Year to Show', max=2022, min=1950, style=D…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_results(min_year, max_year, min_conc, max_conc, log_scale)>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Model type and parameter values:')\n",
    "def setParameters(model, mean_tt, half_time, pd_, eta):\n",
    "    if model == 'Piston Flow Model (MTT, HL)':\n",
    "        Par.MODNUM = 1\n",
    "    elif model == 'Exponential Model (MTT, HL)':\n",
    "        Par.MODNUM = 2\n",
    "    elif model == 'Dispersion Model (MTT, HL, PD)':\n",
    "        Par.MODNUM = 3\n",
    "    elif model == 'Linear Model (MTT, HL)':\n",
    "        Par.MODNUM = 4\n",
    "    elif model == 'Exponential - Piston Flow Model (MTT, HL, PD, E)':\n",
    "        Par.MODNUM = 5\n",
    "    Par.TT = mean_tt\n",
    "    Par.Thalf_1 = half_time\n",
    "    Par.PD = pd_\n",
    "    Par.eta = eta\n",
    "    \n",
    "    \"\"\" run model \"\"\"\n",
    "    \n",
    "    # define global variables for results and rsq\n",
    "    global result\n",
    "    global rsq\n",
    "\n",
    "    # increase model (run) number by 1\n",
    "    Par.n += 1\n",
    "    \n",
    "    # obtain the result\n",
    "    result = Tracer(Par, Cin, rain)\n",
    "    \n",
    "    \"\"\" calculate fit metrics \"\"\"\n",
    "    \n",
    "    ## use only available data\n",
    "    \n",
    "    # get the number of available observations\n",
    "    orig_length = len(sample_vis[1])\n",
    "    \n",
    "    # make pandas Series (a time series) from model results\n",
    "    results_series = pd.Series(data=result.result, index=rain['Date'])\n",
    "    \n",
    "    # get model results at dates which are available from the observations\n",
    "    results_filtered = results_series.filter(items=sample_vis['Date'].dt.round(\"d\"))\n",
    "    \n",
    "    # set the index of the observations Series to be the Date column\n",
    "    sample_ = sample_vis.set_index(['Date'])\n",
    "    \n",
    "    # get observations at dates which are available from the (already reduced / truncated) model results\n",
    "    sample_ = sample_.loc[results_filtered.index]\n",
    "    \n",
    "    # compute the correlation matrix for results and observations and select the cross-correlation\n",
    "    corr = np.corrcoef(x=sample_.iloc[:, 1], y=results_filtered[:])[0, 1]\n",
    "    # calculate the R^2\n",
    "    rsq = corr ** 2.\n",
    "    \n",
    "    # calculate the MSE\n",
    "    mse = np.sum((sample_.iloc[:, 1] - results_filtered.values) ** 2.) / len(results_filtered[:])\n",
    "    \n",
    "    # print the results\n",
    "    print(\"The R^2 is {:1.3f} and the MSE is {:1.3f}, based on {} out of {} possible values\".format(rsq, mse, len(results_filtered[:]), orig_length))\n",
    "    \n",
    "    ## use interpolation (interpolating results to observation dates)\n",
    "    \n",
    "    # make pandas Series (a time series) from model results\n",
    "    results_series = pd.Series(data=result.result, index=rain['Date'])\n",
    "    # force an index with daily frequency and interpolate\n",
    "    # NOTE: this is necessary as ISOSIMpy only returns results at the first of each month\n",
    "    #     but observations are available at other times / days so we need to have those observation\n",
    "    #     dates in the index as well\n",
    "    results_series = results_series.reindex(pd.date_range(start=results_series.index[0], end=results_series.index[-1], freq=\"D\")).interpolate()\n",
    "    # truncate the resulting series to start at the first observation date and to end at the\n",
    "    #     last obervation date (at latest)\n",
    "    results_series = results_series.truncate(before=sample_vis['Date'].iloc[0], after=sample_vis['Date'].iloc[-1])\n",
    "    # drop remaining NaN values\n",
    "    results_series = results_series.reindex(sample_vis['Date']).dropna()\n",
    "    \n",
    "    # set the index of the observation data to be the date column and truncate to the date-range\n",
    "    #     of the results\n",
    "    sample_ = sample_vis.set_index(['Date'])\n",
    "    sample_ = sample_.truncate(before=results_series.index[0], after=results_series.index[-1])\n",
    "    \n",
    "    # compute the correlation matrix for results and observations and select the cross-correlation\n",
    "    corr = np.corrcoef(x=sample_.iloc[:, 1].values, y=results_series.iloc[:].values)[0, 1]\n",
    "    # calculate the R^2\n",
    "    rsq = corr ** 2.\n",
    "    \n",
    "    # calculate the MSE\n",
    "    mse = np.sum((sample_.iloc[:, 1].values - results_series.iloc[:].values) ** 2.) / len(results_series[:])\n",
    "    \n",
    "    # print the results\n",
    "    print(\"The R^2 is {:1.3f} and the MSE is {:1.3f}, based on all values (linear interpolation)\".format(rsq, mse))\n",
    "    \n",
    "    print(\"\\n\\nThe following line does not mean anything...\")\n",
    "    \n",
    "    \"\"\" write log \"\"\"\n",
    "    \n",
    "    with open(r'model_output/log.txt', 'a') as f:\n",
    "        f.write(str(datetime.now()))\n",
    "        f.write(';' + str(Par.n))\n",
    "        f.write(';' + str(Par.MODNUM))\n",
    "        f.write(';' + str(Par.TT))\n",
    "        f.write(';' + str(Par.Thalf_1))\n",
    "        f.write(';' + str(Par.PD))\n",
    "        f.write(';' + str(Par.eta))\n",
    "        f.write(';' + str(\"{:1.2f}\".format(rsq)))\n",
    "        f.write(';' + \"    # date / time; run index; model type index; mean TT; half life; dispersion; exp. flow fraction\")\n",
    "        f.write('\\n')\n",
    "        \n",
    "    \"\"\" write results \"\"\"\n",
    "    \n",
    "    results_series.to_csv(r'model_output/results_{}.csv'.format(Par.n), sep=';')\n",
    "    \n",
    "    return result, rsq\n",
    "    \n",
    "def plot_results(min_year, max_year, min_conc, max_conc, log_scale):\n",
    "    print('Here you can see your input data and the simulation results')\n",
    "    sample_vis = sample.drop(sample[sample[3] == 0].index)\n",
    "    fig3, ax3 = plt.subplots(nrows=1, ncols=1, figsize=(8, 3), constrained_layout=True)\n",
    "    ax3.plot(rain['Date'], rain[3], label='Input %s' % name_tracer_1, color='green')\n",
    "    ax3.axvline(rain.iloc[np.argmax(rain[3]), 4], ls=\"--\", c=\"black\")\n",
    "    ax3.legend()\n",
    "    ax3.grid(zorder=0)\n",
    "    ax3.tick_params(axis='x', which='minor', bottom=False)\n",
    "    \n",
    "    if log_scale == True:\n",
    "        scale_ = \"log\"\n",
    "    else:\n",
    "        scale_ = \"linear\"\n",
    "    \n",
    "    ax3.set(title='Observed Tracer Source Concentration (Max. at {})'.format(rain.iloc[np.argmax(rain[3]), 4]),\n",
    "            ylabel='$c$ [-]',\n",
    "            xlabel='t',\n",
    "            ylim=(min_conc, max_conc),\n",
    "            xlim=(pd.to_datetime(\"{}-01-01\".format(int(min_year))), pd.to_datetime(\"{}-01-01\".format(int(max_year)))),\n",
    "            yscale=scale_)\n",
    "    \n",
    "    fig4, ax4 = plt.subplots(nrows=1, ncols=1, figsize=(8, 3), constrained_layout=True)\n",
    "    ax4.scatter(sample_vis['Date'], sample_vis[1], label='Output %s' % name_tracer_1, marker='x', zorder=5, c=\"red\")\n",
    "    ax4.plot(rain['Date'], result.result, label='Run Nr. {}, $R^2$={:1.2f}'.format(Par.n, rsq))\n",
    "    ax4.axvline(rain.iloc[np.argmax(result.result), 4], ls=\"--\", c=\"black\")\n",
    "    ax4.legend()\n",
    "    ax4.grid(zorder=0)\n",
    "    ax4.set(title='Observed and Simulated Tracer Output Conc. (Max. at {})'.format(rain.iloc[np.argmax(result.result), 4]),\n",
    "            ylabel='$c$ [-]',\n",
    "            xlabel='t',\n",
    "            ylim=(min_conc, max_conc),\n",
    "            xlim=(pd.to_datetime(\"{}-01-01\".format(int(min_year))), pd.to_datetime(\"{}-12-31\".format(int(max_year)))),\n",
    "            yscale=scale_)\n",
    "    \n",
    "    # save figure\n",
    "    # model mtt hl pd eta\n",
    "    plt.savefig(\"model_run_{}_{}_{}_{}_{}.png\".format(Par.MODNUM, Par.TT, Par.Thalf_1, Par.PD, Par.eta), dpi=400)\n",
    "    \n",
    "    return\n",
    "    \n",
    "interact_manual(setParameters,\n",
    "    model = widgets.RadioButtons(options=['Piston Flow Model (MTT, HL)',\n",
    "                                       'Exponential Model (MTT, HL)',\n",
    "                                       'Dispersion Model (MTT, HL, PD)',\n",
    "                                       'Linear Model (MTT, HL)',\n",
    "                                       'Exponential - Piston Flow Model (MTT, HL, PD, E)'],\n",
    "                              layout={'width': 'max-content'},\n",
    "                              description='Modeltype:',\n",
    "        style={'description_width': 'initial'}),\n",
    "    mean_tt = widgets.BoundedIntText(value=10, min=1, max=100, step=1,\n",
    "                                     description='Mean Travel Time (MTT) [a]:',\n",
    "                                    style={'description_width': 'initial'}),\n",
    "    half_time = widgets.BoundedFloatText(value=10, min=1, max=100, step=0.001,\n",
    "                                         description='Half Life (HL) [a]:',\n",
    "                                        style={'description_width': 'initial'}),\n",
    "    pd_ = widgets.BoundedFloatText(value=0.1, min=0.01, max=1000, step=0.01,\n",
    "                             description='Dispersion Parameter (PD) [-]:',\n",
    "                                 style={'description_width': 'initial'}),\n",
    "    eta = widgets.BoundedFloatText(value=0.5, min=0.01, max=1, step=0.01,\n",
    "                             description='Exp. Flow Fraction (E) [-]:',\n",
    "                                  style={'description_width': 'initial'}))\n",
    "\n",
    "print(\"\\n\")\n",
    "\n",
    "interact_manual(\n",
    "    plot_results,\n",
    "    min_year=widgets.BoundedIntText(\n",
    "        value=1950,\n",
    "        min=1950,\n",
    "        max=2022,\n",
    "        step=1,\n",
    "        description=\"First Year to Show\",\n",
    "        style={'description_width': 'initial'}\n",
    "    ),\n",
    "    max_year=widgets.BoundedIntText(\n",
    "        value=2023,\n",
    "        min=1951,\n",
    "        max=2023,\n",
    "        step=1,\n",
    "        description=\"Last Year to Show\",\n",
    "        style={'description_width': 'initial'}\n",
    "    ),\n",
    "    min_conc=widgets.BoundedFloatText(\n",
    "        value=1e-1,\n",
    "        min=1e-4,\n",
    "        max=1e4,\n",
    "        step=1e-4,\n",
    "        description=\"Minimum Concentration to Show\",\n",
    "        style={'description_width': 'initial'}\n",
    "    ),\n",
    "    max_conc=widgets.BoundedFloatText(\n",
    "        value=1e4,\n",
    "        min=1e-4,\n",
    "        max=1e4,\n",
    "        step=1e-4,\n",
    "        description=\"Maximum Concentration to Show\",\n",
    "        style={'description_width': 'initial'}\n",
    "    ),\n",
    "    log_scale=widgets.Dropdown(\n",
    "        options=[('Logarithmic', True), ('Linear', False)],\n",
    "        value=True,\n",
    "        description='y-Axis Scale:',\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bebbe5a3-adf9-46a7-a9f2-fe68d58ee556",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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