{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9efa0ee9",
   "metadata": {},
   "source": [
    "# Calibration of steady and transient models\n",
    "\n",
    "This notebook introduces the `timflow.Calibrate` class, which can be used to\n",
    "calibrate steady and/or transient models to head observations.\n",
    "\n",
    "## Contents\n",
    "\n",
    "- [Steady calibration](#steady-calibration)\n",
    "- [Transient calibration](#transient-calibration)\n",
    "- [Combined calibration](#combined-calibration)\n",
    "- [Calibrating on time series with constant offsets](#calibrating-on-time-series-with-constant-offsets)\n",
    "- [Calibrating on time series with time shifts](#calibrating-on-time-series-with-time-shifts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e5d84887",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "import timflow as tf\n",
    "import timflow.steady as tfs\n",
    "import timflow.transient as tft"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aaec704e",
   "metadata": {},
   "source": [
    "## Steady calibration\n",
    "\n",
    "In this section we will test the calibration of a steady model. First, we build a model\n",
    "to represent the \"truth\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c467f670",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of elements, Number of equations: 4 , 2\n",
      "....\n",
      "solution complete\n"
     ]
    }
   ],
   "source": [
    "ml_s = tfs.ModelXsection(naq=1)\n",
    "river_s = tfs.XsectionMaq(\n",
    "    ml_s,\n",
    "    x1=-np.inf,\n",
    "    x2=0.0,\n",
    "    kaq=[10.0],\n",
    "    z=[0.1, 0, -10],\n",
    "    c=[1e-4],\n",
    "    npor=0.3,\n",
    "    topboundary=\"semi\",\n",
    "    hstar=2,\n",
    "    name=\"river\",\n",
    ")\n",
    "\n",
    "polder_s = tfs.XsectionMaq(\n",
    "    ml_s,\n",
    "    x1=0.0,\n",
    "    x2=np.inf,\n",
    "    kaq=[10.0],\n",
    "    z=[1, 0, -10],\n",
    "    c=[500],\n",
    "    npor=0.3,\n",
    "    topboundary=\"semi\",\n",
    "    hstar=0,\n",
    "    name=\"polder\",\n",
    ")\n",
    "ml_s.solve()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a3b6c07c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 3))\n",
    "ax.set_ylim(-10.5, 3.5)\n",
    "ml_s.plots.xsection(\n",
    "    xy=([-100, 0], [100, 0]), names=True, labels=True, params=True, ax=ax\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03ec6d3d",
   "metadata": {},
   "source": [
    "Select 3 head observations that we will use in our calibration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7b26cc78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-50, 1000, 101)\n",
    "h0 = ml_s.headalongline(x=x, y=np.zeros_like(x))\n",
    "\n",
    "x_obs = [10, 100, 500]\n",
    "h_obs = ml_s.headalongline(x=x_obs, y=np.zeros_like(x_obs))[0]\n",
    "\n",
    "plt.figure(figsize=(10, 3))\n",
    "plt.plot(x, h0[0], label='\"Truth\"')\n",
    "plt.plot(x_obs, h_obs, \"kx\", label=\"observations\")\n",
    "plt.legend(loc=(0, 1), frameon=False, ncol=2)\n",
    "plt.ylabel(\"head [m]\")\n",
    "plt.xlabel(\"distance from river [m]\")\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba384cd1",
   "metadata": {},
   "source": [
    "Perform the calibration. We are using the same model we created before, but we are\n",
    "purposely setting the initial parameter estimates to different values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "323a4f32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "                      initial     optimal  pmin  pmax  log_scaled  n_targets  \\\n",
      "name                                                                           \n",
      "kaq_0_0_river_polder      2.0    9.999999  -inf   inf       False          2   \n",
      "c_0_0_polder             10.0  500.000035  -inf   inf       False          1   \n",
      "\n",
      "                      n_models  n_inhoms  \n",
      "name                                      \n",
      "kaq_0_0_river_polder         1         2  \n",
      "c_0_0_polder                 1         1  \n",
      "RMSE: 1.392e-08\n"
     ]
    }
   ],
   "source": [
    "cal = tf.Calibrate(steady_model=ml_s)\n",
    "\n",
    "for i in range(len(x_obs)):\n",
    "    cal.add_steady_head(name=f\"obs{i}\", x=x_obs[i], y=0.0, layer=[0], h=h_obs[i])\n",
    "\n",
    "cal.set_aquifer_parameter(\"kaq\", layers=[0], initial=2.0, inhoms=[\"river\", \"polder\"])\n",
    "cal.set_aquifer_parameter(\"c\", layers=[0], initial=10.0, inhoms=[\"polder\"])\n",
    "\n",
    "cal.fit(printdot=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d724ed73",
   "metadata": {},
   "source": [
    "View the parameters dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d229242c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_f39cc\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_f39cc_level0_col0\" class=\"col_heading level0 col0\" >initial</th>\n",
       "      <th id=\"T_f39cc_level0_col1\" class=\"col_heading level0 col1\" >optimal</th>\n",
       "      <th id=\"T_f39cc_level0_col2\" class=\"col_heading level0 col2\" >pmin</th>\n",
       "      <th id=\"T_f39cc_level0_col3\" class=\"col_heading level0 col3\" >pmax</th>\n",
       "      <th id=\"T_f39cc_level0_col4\" class=\"col_heading level0 col4\" >log_scaled</th>\n",
       "      <th id=\"T_f39cc_level0_col5\" class=\"col_heading level0 col5\" >n_targets</th>\n",
       "      <th id=\"T_f39cc_level0_col6\" class=\"col_heading level0 col6\" >n_models</th>\n",
       "      <th id=\"T_f39cc_level0_col7\" class=\"col_heading level0 col7\" >n_inhoms</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >name</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_f39cc_level0_row0\" class=\"row_heading level0 row0\" >kaq_0_0_river_polder</th>\n",
       "      <td id=\"T_f39cc_row0_col0\" class=\"data row0 col0\" >2.00</td>\n",
       "      <td id=\"T_f39cc_row0_col1\" class=\"data row0 col1\" >10.00</td>\n",
       "      <td id=\"T_f39cc_row0_col2\" class=\"data row0 col2\" >-inf</td>\n",
       "      <td id=\"T_f39cc_row0_col3\" class=\"data row0 col3\" >inf</td>\n",
       "      <td id=\"T_f39cc_row0_col4\" class=\"data row0 col4\" >False</td>\n",
       "      <td id=\"T_f39cc_row0_col5\" class=\"data row0 col5\" >2</td>\n",
       "      <td id=\"T_f39cc_row0_col6\" class=\"data row0 col6\" >1</td>\n",
       "      <td id=\"T_f39cc_row0_col7\" class=\"data row0 col7\" >2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_f39cc_level0_row1\" class=\"row_heading level0 row1\" >c_0_0_polder</th>\n",
       "      <td id=\"T_f39cc_row1_col0\" class=\"data row1 col0\" >10.00</td>\n",
       "      <td id=\"T_f39cc_row1_col1\" class=\"data row1 col1\" >500.00</td>\n",
       "      <td id=\"T_f39cc_row1_col2\" class=\"data row1 col2\" >-inf</td>\n",
       "      <td id=\"T_f39cc_row1_col3\" class=\"data row1 col3\" >inf</td>\n",
       "      <td id=\"T_f39cc_row1_col4\" class=\"data row1 col4\" >False</td>\n",
       "      <td id=\"T_f39cc_row1_col5\" class=\"data row1 col5\" >1</td>\n",
       "      <td id=\"T_f39cc_row1_col6\" class=\"data row1 col6\" >1</td>\n",
       "      <td id=\"T_f39cc_row1_col7\" class=\"data row1 col7\" >1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7b1aa5a678c0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cal.parameters.style.format(formatter=\"{:.2f}\", subset=[\"initial\", \"optimal\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "879e5d1e",
   "metadata": {},
   "source": [
    "Plot the results and compare to the truth."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b9d37f8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hc = ml_s.headalongline(x=x, y=np.zeros_like(x))\n",
    "\n",
    "plt.figure(figsize=(10, 3))\n",
    "plt.plot(x, h0[0], label='\"Truth\"')\n",
    "plt.plot(x, hc[0], ls=\"dashed\", label=\"calibrated\")\n",
    "plt.plot(x_obs, h_obs, \"kx\", label=\"observations\")\n",
    "plt.legend(loc=(0, 1), frameon=False, ncol=3)\n",
    "plt.ylabel(\"head [m]\")\n",
    "plt.xlabel(\"distance from river [m]\")\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f5c3d6a",
   "metadata": {},
   "source": [
    "## Transient calibration\n",
    "\n",
    "In this section, we will test the calibration of a transient model with a sudden rise\n",
    "of 2 m in river water level after t=0.1 days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d9d27971",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "self.neq  2\n",
      "solution complete\n"
     ]
    }
   ],
   "source": [
    "ml_t = tft.ModelXsection(naq=1, tmin=1e-3, tmax=100)\n",
    "\n",
    "river_t = tft.XsectionMaq(\n",
    "    ml_t,\n",
    "    x1=-np.inf,\n",
    "    x2=0.0,\n",
    "    kaq=[10.0],\n",
    "    z=[0.1, 0, -10],\n",
    "    c=[1e-4],\n",
    "    Saq=[1e-3],\n",
    "    topboundary=\"semi\",\n",
    "    tsandhstar=[(0.1, 2)],\n",
    "    name=\"river\",\n",
    ")\n",
    "\n",
    "polder_t = tft.XsectionMaq(\n",
    "    ml_t,\n",
    "    x1=0.0,\n",
    "    x2=np.inf,\n",
    "    kaq=[10.0],\n",
    "    z=[1, 0, -10],\n",
    "    c=[500],\n",
    "    Saq=[1e-3],\n",
    "    topboundary=\"semi\",\n",
    "    name=\"polder\",\n",
    ")\n",
    "ml_t.solve()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fd8c0c30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 3))\n",
    "ax.set_ylim(-10.5, 3.5)\n",
    "ml_t.plots.xsection(\n",
    "    xy=([-100, 0], [100, 0]),\n",
    "    names=True,\n",
    "    labels=True,\n",
    "    params=True,\n",
    "    ax=ax,\n",
    "    sep=\"\\n\",\n",
    "    hstar=2.0,\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49195c34",
   "metadata": {},
   "source": [
    "Plot the head over time along the cross-section. We will extract the heads at the three\n",
    "observation points we used earlier as our observation time series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e64558ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = np.logspace(-1, 1, 11)\n",
    "h0 = ml_t.headalongline(x=x, y=np.zeros_like(x), t=t)\n",
    "\n",
    "plt.figure(figsize=(10, 3))\n",
    "for i in range(len(t)):\n",
    "    plt.plot(x, h0[0, i], label=f\"t={t[i]:.1f} d\")\n",
    "plt.legend(loc=(0, 1), frameon=False, ncol=6, fontsize=\"small\")\n",
    "plt.ylabel(\"head [m]\")\n",
    "plt.xlabel(\"distance from river [m]\")\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c2e0f00",
   "metadata": {},
   "source": [
    "Plot our observation data. Note the log-scale of the x-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bc1f0025",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h_obs_series = ml_t.headalongline(x=x_obs, y=np.zeros_like(x_obs), t=t)\n",
    "\n",
    "plt.figure(figsize=(10, 3))\n",
    "for i in range(len(x_obs)):\n",
    "    plt.plot(t, h_obs_series[0, :, i], label=f\"obs{i} (x={x_obs[i]})\", marker=\"o\")\n",
    "plt.legend(loc=(0, 1), frameon=False, ncol=3)\n",
    "plt.ylabel(\"head [m]\")\n",
    "plt.xlabel(\"time [d]\")\n",
    "# plt.xscale(\"log\")\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79efd10f",
   "metadata": {},
   "source": [
    "Create the calibration class, add the observation time series, set the calibration\n",
    "parameters, and calibrate the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "899a2c05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "                      initial     optimal  pmin  pmax  log_scaled  n_targets  \\\n",
      "name                                                                           \n",
      "kaq_0_0_river_polder   2.0000   10.000424  -inf   inf       False          2   \n",
      "c_0_0_polder          10.0000  499.978787  -inf   inf       False          1   \n",
      "Saq_0_0_polder         0.0001    0.001000  -inf   inf       False          1   \n",
      "\n",
      "                      n_models  n_inhoms  \n",
      "name                                      \n",
      "kaq_0_0_river_polder         1         2  \n",
      "c_0_0_polder                 1         1  \n",
      "Saq_0_0_polder               1         1  \n",
      "RMSE: 2.575e-08\n"
     ]
    }
   ],
   "source": [
    "cal = tf.Calibrate(transient_model=ml_t)\n",
    "\n",
    "for i in range(len(x_obs)):\n",
    "    cal.add_head_time_series(\n",
    "        name=f\"obs{i}\",\n",
    "        x=x_obs[i],\n",
    "        y=0.0,\n",
    "        layer=[0],\n",
    "        t=t,\n",
    "        h=h_obs_series[0, :, i],\n",
    "        normalized=True,\n",
    "    )\n",
    "\n",
    "cal.set_aquifer_parameter(\"kaq\", layers=[0], initial=2.0, inhoms=[\"river\", \"polder\"])\n",
    "cal.set_aquifer_parameter(\"c\", layers=[0], initial=10.0, inhoms=[\"polder\"])\n",
    "cal.set_aquifer_parameter(\"Saq\", layers=[0], initial=1e-4, inhoms=[\"polder\"])\n",
    "\n",
    "cal.fit(printdot=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e349649b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_5ceed\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_5ceed_level0_col0\" class=\"col_heading level0 col0\" >initial</th>\n",
       "      <th id=\"T_5ceed_level0_col1\" class=\"col_heading level0 col1\" >optimal</th>\n",
       "      <th id=\"T_5ceed_level0_col2\" class=\"col_heading level0 col2\" >pmin</th>\n",
       "      <th id=\"T_5ceed_level0_col3\" class=\"col_heading level0 col3\" >pmax</th>\n",
       "      <th id=\"T_5ceed_level0_col4\" class=\"col_heading level0 col4\" >log_scaled</th>\n",
       "      <th id=\"T_5ceed_level0_col5\" class=\"col_heading level0 col5\" >n_targets</th>\n",
       "      <th id=\"T_5ceed_level0_col6\" class=\"col_heading level0 col6\" >n_models</th>\n",
       "      <th id=\"T_5ceed_level0_col7\" class=\"col_heading level0 col7\" >n_inhoms</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >name</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_5ceed_level0_row0\" class=\"row_heading level0 row0\" >kaq_0_0_river_polder</th>\n",
       "      <td id=\"T_5ceed_row0_col0\" class=\"data row0 col0\" >2.00e+00</td>\n",
       "      <td id=\"T_5ceed_row0_col1\" class=\"data row0 col1\" >1.00e+01</td>\n",
       "      <td id=\"T_5ceed_row0_col2\" class=\"data row0 col2\" >-inf</td>\n",
       "      <td id=\"T_5ceed_row0_col3\" class=\"data row0 col3\" >inf</td>\n",
       "      <td id=\"T_5ceed_row0_col4\" class=\"data row0 col4\" >False</td>\n",
       "      <td id=\"T_5ceed_row0_col5\" class=\"data row0 col5\" >2</td>\n",
       "      <td id=\"T_5ceed_row0_col6\" class=\"data row0 col6\" >1</td>\n",
       "      <td id=\"T_5ceed_row0_col7\" class=\"data row0 col7\" >2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5ceed_level0_row1\" class=\"row_heading level0 row1\" >c_0_0_polder</th>\n",
       "      <td id=\"T_5ceed_row1_col0\" class=\"data row1 col0\" >1.00e+01</td>\n",
       "      <td id=\"T_5ceed_row1_col1\" class=\"data row1 col1\" >5.00e+02</td>\n",
       "      <td id=\"T_5ceed_row1_col2\" class=\"data row1 col2\" >-inf</td>\n",
       "      <td id=\"T_5ceed_row1_col3\" class=\"data row1 col3\" >inf</td>\n",
       "      <td id=\"T_5ceed_row1_col4\" class=\"data row1 col4\" >False</td>\n",
       "      <td id=\"T_5ceed_row1_col5\" class=\"data row1 col5\" >1</td>\n",
       "      <td id=\"T_5ceed_row1_col6\" class=\"data row1 col6\" >1</td>\n",
       "      <td id=\"T_5ceed_row1_col7\" class=\"data row1 col7\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5ceed_level0_row2\" class=\"row_heading level0 row2\" >Saq_0_0_polder</th>\n",
       "      <td id=\"T_5ceed_row2_col0\" class=\"data row2 col0\" >1.00e-04</td>\n",
       "      <td id=\"T_5ceed_row2_col1\" class=\"data row2 col1\" >1.00e-03</td>\n",
       "      <td id=\"T_5ceed_row2_col2\" class=\"data row2 col2\" >-inf</td>\n",
       "      <td id=\"T_5ceed_row2_col3\" class=\"data row2 col3\" >inf</td>\n",
       "      <td id=\"T_5ceed_row2_col4\" class=\"data row2 col4\" >False</td>\n",
       "      <td id=\"T_5ceed_row2_col5\" class=\"data row2 col5\" >1</td>\n",
       "      <td id=\"T_5ceed_row2_col6\" class=\"data row2 col6\" >1</td>\n",
       "      <td id=\"T_5ceed_row2_col7\" class=\"data row2 col7\" >1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7b1aa454a0d0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cal.parameters.style.format(formatter=\"{:.2e}\", subset=[\"initial\", \"optimal\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bb95ca5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vv/8+zp07h8LCQixdutT0eWruZ0Gr1WL//v1W52a0VY7+nAHX3s8pyTI0GXuY5vWb59Dw5IzP2pEjR3Dbbbfho48+wujRo6/5+s743XXs2DEcOnQIBoMBJSUlmD9/PsaPH4+AALmX5b777sP//d//oaKiAnv27MF3332HmTNnAmj+9+7+/fvRsWNHq2DZVjn6s1ZeXo6JEydiypQpdp8SQORwSo5M4SmWbjrZ5Oh57/x8Slq66aRDXufQoUPS8OHDpdDQUGnAgAHStm3bTNOSk5Olv/3tb6aRiebPny/p9XpJkiRp5syZUlRUlBQcHNxoRKNVq1ZJHTp0kIKDg6XZs2dLtbW1Tb7+G2+8IS1atEiSJHm0o3bt2plG/iktLZUSExOljRs3tug9jRo1SgJgdTt//rwkSfKIO08++aQUFhYmtWvXTnrzzTetlh0wYECrR/HzRD///LPUu3dvKSgoSIqJiZEee+wxSa/XNxqByHLktBdffNFqBKdVq1ZJY8eONT3fs2eP1KdPH8nf318aOXKkdOHCBde9oab88lrTo+dt+bs83QGc8Xlq+LOcnJzc5Ot/9dVX0owZMyRJkqRz585JkZGR0uHDhyVJkiSdTicNHDhQWrFiRYve0+zZsxvVYDmK22uvvSZFRUVJ4eHh0p///Gerka+u9bOwfv166c4772xRLZ7KGZ+za+3nlLT84PImR897L+s9afnB5Q55HUd/1h544AFJpVJJQUFBptstt9zS5Os7+ndXZmamlJqaKgUGBkqxsbHSfffdJxUWFpqmV1dXSzNnzpSCgoKkhIQE6bPPPrNa/lq/d+fPny+98847dtfiyRz9WVu9erUEwOrnIigoyLVviryeIElu8GcxcgtVVVW44YYbsGfPHsXPg8nMzMR7772Hr7/+WtE6iFpLFEXccMMN+Oabbxx6EVxnGD16NJYtW2Y1UhiRp3Cn313XUlpaihtvvBH79+936iHoROQ8DE5ERERERETN4DlOREREREREzVA0OC1ZsgQ33HADQkJCEBMTg6lTp+LkyZPNLvfVV1+he/fu8Pf3R58+fbBx40YXVEtERERERN5K0eC0detWPProo9i1axcyMjKg0+kwYcIEVFVVNbnMjh07MGPGDMyZMwcHDx7E1KlTMXXqVBw9etSFlRMRERERkTdxq3OcioqKEBMTg61bt+Kmm26yOc/06dNRVVWFDRs2mNqGDRuG/v37Ww3bS0RERERE5Cgtv5y2E5WVlQEAIiMjm5xn586dVtdkAICJEydi/fr1dr2GKIrIy8tDSEiI3RdwIyIiIiKitkeSJFRUVKBDhw6mC143xW2CkyiKmD9/Pm688Ub07t27yfkKCgoQGxtr1RYbG4uCggKb89fV1VldpDI3Nxc9e/Z0TNFEREREROTxcnJykJCQcM153CY4Pfroozh69Ci2b9/u0PUuWbIEixcvbtS+cuVKBAYGOvS1iIiIiIjIc1RXV+Phhx9GSEhIs/O6RXB67LHHsGHDBmzbtq3ZpBcXF4fCwkKrtsLCQsTFxdmcf9GiRVaH9pWXlyMxMRFTp05FaGjo9Rd/nXQ6HTIyMjB+/HhoNBqlyyEi8hrc/xIRKcOd9r/l5eV4+OGH7TqFR9HgJEkSHn/8cXz77bfYsmULOnbs2OwyaWlpyMzMxPz5801tGRkZSEtLszm/n58f/Pz8GrVrNBrF/6EsuVs9RETegvtfIiJluMP+tyWvr2hwevTRR7F27Vp89913CAkJMZ2nFBYWhoCAAADArFmzEB8fjyVLlgAAnnzySYwaNQpvvvkmbr31VnzxxRfYt28fPvjgA8XeBxERERERtW2KXsfpvffeQ1lZGUaPHo327dubbuvWrTPNk52djfz8fNPz4cOHY+3atfjggw/Qr18/fP3111i/fv01B5QgIiIiIiK6HoofqtecLVu2NGq7++67cffddzuhIiIiIiIiosYU7XEiIiIiIs/zVsYpLMs8bXPasszTeCvjlIsrch/cNra1he3C4ERERETUhLbwZc8Z1CoBS21sm2WZp7E04xTUquZHKGuruG1sawvbxS2GIyciIiJyR8YvewDwxNhUU7vxy96C8V2VKk1Rxm1huW0st4nltnI3kiRBlABRkiBKEiQJkCyei5J5nob3oiRBAiCKUqNlAPl+Qq9YFFXUYWnGKRSW12L6DYn4Yk8O1u7JxowhiRiZGo0D2SUwn7FSXwNgapPqX0duNzaiUZt5ufoW06zm92V8Gct1otHy5mWMU42n1Eh2rBM2l7duiwjyxc3dY7A04xQOXCxGlFbAyZ9PY8XW827/M2MkSPacaNSGlJeXIywsDGVlZW5zHaeNGzdi8uTJig/HSETkTbj/NXur/q+9tr64LMs8DYMo4SkvDQgAGgWC6w0IkiTBIErQi/KXbr0oQRTlNoMowSBZPG6iTZQk6A3W7cY2UZJgEAG9KDZqM4ii1Wub21C/LtGqTa6v/rGNmk8WVuDM5UoIkL8sp0QFIjEy0PSlWaxfhzFkSECDcCLPY/yibpwm1n87t3wuNbi3XEfjMFP/+g1Dj1d96/UMSoemlmQD9jgRERF5OXfuVTGIEnQGsf4mQW8QoTWI0BskU5vOIEIvitDq5S/5Vu0GqcH85vXoDCJ0ogSdXoReNM5nXtZy+cSIACzNOIW3Mk5BAhAT4oeNR/Lxn0N5TQcb0XZbW/zybnxLF65W48LVakVrcTRBAFSCAAH19/XPVfX3aPBcqH8sCEBheZ1pPfHhAab1yYvJ6wIAAYBQ/xqw0Waez2KZ+gfCNdaJRsvbXmf9/6Z1NFUnbC7fuA31yzS1zh+PFkCSAB8VPKKnyYjBiYiIvAZ7VmSSJKFOL6JOJ6JOb8DU/vG4WikfWpRbWoM7+nXAV/sv4duDuZjStz1SooPw7/2X5HBiGToswomtENNcoGkciOrn09cHGoPodiHDWM7lijpcrqi75rytoRLkIKsSBPioBKhUAtSq+scN2tQqAWrB4nGDeXxstKkFAWp1g+VstdlYzkfdsA349fQVZJ64DLUgwCBJmNgrFhN7xZnDhkWgUNWnAtNzFeqnm0OJHDjq51EJpi/l5nUIpi/fxufGZRoGGqGJe9MyECCoGi8joMFzy9TQAsY/PPiqVdAaREy/IdGjQoKzLMs8jY1HCqAWJOhFAcsyT3vMdmFwIiIir2HZs/LITSmmdqV6VgyihFqdAXV6sdF9nc6A2gb3DafX6UWby5rm0RtQq7O4t1imKev25mDd3hzT8w2H87HhcH6T87uaSgA0alX9TYCPWgVftQo+agEatQo+KgG+PvK95XwNl7Fs91EL8jpUKmh8BGhUjdedefwyfjiSDx+VAL0oYdqAeEwbmACVCvBRqaCu/wLuo1I12dZU2LFsa+2XdCUsyzyNzBOXGx3C2KtDmMd8EXaWpg7vBDyrh8XRjNvhyZs7o1PNSZwL6OZR24XBiYiIvIblCe0GgwGdALz7yxks23wOj47ujHuGJCKnuNoicDQOLE2GEr0BdTqxUdgx3mttrEMvKt+dohIAf40afj4q+PmoUVBeC0A+xKZfYrhFuFDBVy3UhwsVNCpz6LAOIfXzNQwnpkBjHWQaLm+5Ho26PsRYBCElRt5alnkaPxzJb/QlOCU6yCO+7DmDrfO8bA0Y4Y24bWyz3C6P3JSCjRtP4rExnaFWqz1muzA4ERGRx5AkCTU6A6rqDKiq06NKq5cfa/Xy8zq9xTTLeaznC/X3wTu/nAWgBnAOALB8y1ks33JWsffmq1bJ4cUYYjQq+Puore79fFSmkNPw3s9HDX+NfO9ncd9wHeb55cc+Fr0cDQ8turl7jNt/kXE2fgm2zSBKNk/qNz43uMEfBZTCbWOb5XbR6XSmdk/aLgxORERtjDudx2MQJevgYhl2GoYay7BjIxRV1z927O9W694LQUCTYcQUSiynNwwvNsNO43U0XJevjzI9KZZ4aJFt/BJs27X2Id788wJw2zSlLWwXBiciojamtSOkSZI8epjNgNNUD06jICTfV2v1qKzTo1bX9Lk01yvIV40gP5/6mxqBvj4I9vNBoK8awcb2+nkC/XwQbDHPhsN5+HxPDlSQIELAo6M744lxqfBVqzzqHBNHYa9K09rClz0icgwGJyKiNkCSJFRrDSip1uLm7jG4eLUKSzNOISunFP0Tw7HtdBH2XShBj/YhOJpbhntX7jKFnWqtAZX14chZ59yoVQKCLAJNwyBjGXaMjxvOYxmEAjRqqFrZQ7Ms8zQ+35NjdXLyO7+chZ9G7bVfhNmrQkTUPAYnIiI3I4oSymt1KKnWobhKi9JqLUqqdfX38uOSKvlxabXO1Ka1MVLaLycu45cTl03Pj+dX4Hh+RbM1+Pmo6sOLGkHGcGMj7AT5WQcfW2En0Fc+JM0denLawsnJzsBeFSKi5jE4ERE5kVYvmoKPHHS0KK4yPzYHInMYKqvRtfo8Ho1aQESgLyICfREeqMGe88WQII+c9tjNqaYem6bCjrFXx0etcuh2cBdt4eRkIiJSBoMTEXksVw6CYDwUTu4BMvbyWDyusgxH5rYqraHVrxnkq0Z4oC8ig+QQJAciDcLr7yOCfK1CUkSQL4J81VYjpO0+X2waIc1HJeCPozo7ZHt4KvasEBFRazE4EZHHau0gCAZRQnmN7pqHvck9Q+a20modtIbWDXQgCEB4gMYccAJ960OPMQSZH0fWt4cFauDno27V6zXcBhwhjYiI6PoxOBGRx7Ic9etqZR1u6d0en+2+iA2H83FjlyiU1+jw9JeHTOcGlVbrUFx/KJzUyiOyfH1Ucm+PRQiSA4/5sWWvUGSQL0L9Na0eyKA1OEIaERGR4zE4EZHbq9bqkVtSg0slNbhUUl1/Lz/OLa0BAHyy8yI+2XnRtMxvZ67itzNXr7neYD8fhNeHG9Phb/WByFZbRKAvAi0OhXNXHCGNiIjI8RiciEhxVXV65JbaCEX1j69Wae1elwBgbI9Y0zlAlucGGQ+RCw/UIDzAF74+bXMABJ7HQ0RE5HgMTkTkdJV1xh6jaqteIzks1aDYjmAU4u+DhIhAJEQE1N/Mj384nI8VW86aBkHomxDGgEBEREQOxeBERNetsk4vh6HixqHoUkk1Sqp1za4j1CoYWQek+IgAhAVobC63LPM0Vmw5y0EQiIiIyKkYnIioWRW1OqtD6C6V1Mg9SKXy41I7glFYgMZGb5F8Hx8RgFB/28HoWjgIAhEREbkKgxMRobxWZ9VbJPcYmR+X1TQfjMID64NReIPD6SIDEB8egJBWBKPmcBAEIiIichUGJyI354iLvJbV6KxDUYPzjcpr9c3WERGosXmOUXyE84JRczgIAhEREbkKgxORm2vuIq9PjUtFWbUOOVbnFlmPTldhRzCKDPK1OfBCfLh8jlGwH3cXRERE5L34TYjIzVmes5NXWoPU2BBsOJSHgzmliA72xcpfz+Otn083u54oUzBqPPBCfHgAghiMiIiIiJrEb0pEbkqSJJwtqsRvZ67iSG4ZfH1U+GJvjtU8VyrNw3hHB/si3tZw3eHy4XSBvvy4ExEREbUWv0kRuZHC8lr8duYKtp+5gt/OXEFheZ3N+VQC8PIdvREfEYDE+sPpAnzVLq6WiIiIyHswOBEpqLxWh93nik1h6czlSqvpvj4qDEmJxPAuUbhUXIO1e7JNF3ktrtLivmHJClVORERE5F0YnIhcqE5vwMHsUvxW36N06FKZ1ZDZggD0iQ/DjV2iMaJLNAYlR8Bfo8ayzNNYuyebF3klIiIiUgiDE5ETiaKEEwUVph6lPeeLUaMzWM3TMToIN3aJwogu0RjWKQrhgb5W03mRVyIiIiLlMTgROVhOcbUpKO04exXFVVqr6dHBvrixS7TpFh8ecM318SKvRERERMpjcCK6TsVVWuw8e9U0oEN2cbXV9EBfNYZ1isLwzlEYkRqNbrEhEATB7vXzIq9EREREymNwImqhGq0Bey/IAzr8dvYKjuWVQ7Lo9FGrBAxIDJfPU0qNRr+EcPj6qJQrmIiIiIiuG4MTUTP0BhFHcsuw4+xVbD99BfsvlkBrEK3m6RYbUh+UojCkYxSCeTFZIiIiojaF3+6IGpAkCeeuVMnnKZ2+gp3nrqKiVm81T/swf4yo71FK6xyFmBB/haolIiIiIldgcCICcLm8Fr+dvYLtp69ix9kryC+rtZoe6u+DtM7yyHc3dolGx+igFp2nRERERESejcGJvFJF/YVn5ZHvruBUYYMLz6pVGJwSYbqeUu/4MKhVDEpERERE3orBibyCVi/iYHYJfjt7Fb+duYKsnNJGF57t3cF84dnBKfKFZ4mIiIiIAAYnaqNEUcLJQusLz1ZrrS88mxIVaLqWUlqnKEQE+TaxNiIiIiLydgxO1GZcKjFeePYqdpy5gqsNLjwbFeSL4V2iMaJLFIZ3jkZiZKBClRIRERGRp2FwIrfxVsYpqFWCzYu6Lss8DYMoWV0MtqRKi53n5AvP7jhzBReuWl94NkCjxtBOkaYBHbrFhkDF85SIiIiIqBUYnMhtqFUClmacAgCr8LQs8zSWZpzC4zd3wa+ni/DbGfk8paN5ZY0uPNu//sKzN3aOwoCkCF54loiIiIgcgsGJ3IYxLBnD06NjuuD59Uexdk82EiMD8P62c3j3lzNWy3SNDcbwzvKADkM7RSLEX+PyuomIiIio7VM0OG3btg1vvPEG9u/fj/z8fHz77beYOnVqk/Nv2bIFY8aMadSen5+PuLg4J1ZKrvLE2FRIkoSlGadMAQoAcoprAABxof7yyHep8nlKsaG88CwREREROZ+iwamqqgr9+vXDQw89hGnTptm93MmTJxEaGmp6HhMT44zySCGDUyKtnk/oGYsRqfJ5Sp144VkiIiIiUoCiwWnSpEmYNGlSi5eLiYlBeHi44wsit/DCd0cBACoBECWgd3wYZqWlKFsUEREREXk1jzxzvn///mjfvj3Gjx+P3377TelyyIH+b8PvOFtUBQD47/ybsGB8VyzNOIVlmacVroyIiIiIvJlHDQ7Rvn17pKenY/Dgwairq8PKlSsxevRo7N69GwMHDrS5TF1dHerq6kzPy8vLAQA6nQ46nc4ldV+LsQZ3qEVp/9x8Fiu3nwcADOsYgZRIfzxyUwoMBgOWZpyCwWDAY2M6K1wlEbUV3P8SESnDnfa/LalBkCTLAZ2VIwhCs4ND2DJq1CgkJSVhzZo1Nqe/9NJLWLx4caP2tWvXIjCQF0B1Jz9kq7AlX4BWFPBgVwP6R5l/NP97SYAoCZiUKCpYIRERERG1JdXV1Zg5cybKysqsxlCwxaN6nGwZMmQItm/f3uT0RYsWYcGCBabn5eXlSExMxIQJE5rdOK6g0+mQkZGB8ePHQ6Px7qG0dYfysenrI4gN9cPCmSPhozYfSTpZwbqIqG3i/peISBnutP81Ho1mD48PTllZWWjfvn2T0/38/ODn59eoXaPRKP4PZcnd6lHC2j05AID7hiYjwL/xvxkRkTNw/0tEpAx32P+25PUVDU6VlZU4c8Z8QdPz588jKysLkZGRSEpKwqJFi5Cbm4tPP/0UAPD222+jY8eO6NWrF2pra7Fy5Ur88ssv2LRpk1JvgRzkaG4ZDmSXQqMWcM+QJKXLISIiIiKyomhw2rdvn9UFbY2H1M2ePRurV69Gfn4+srOzTdO1Wi2efvpp5ObmIjAwEH379sXPP/9s86K45Fk+3XkBADCpd3u0C2FvExERERG5F0WD0+jRo3GtsSlWr15t9XzhwoVYuHChk6siVyut1uK7rDwAwKy0ZIWrISIiIiJqzCOv40Rty1f7LqFOL6JH+1AMSo5QuhwiIiIiokYYnEhRoijhX7svApB7mwRBULgiIiIiIqLGGJxIUVtPF+Hi1WqE+Pvgjv4dlC6HiIiIiMgmBidS1Jqdcm/THwYnItDX40fHJyIiIqI2isGJFJN9tRqbT14GANw3jINCEBEREZH7YnAixfxr90VIEnBT13boGB2kdDlERERERE1icCJF1OoM+HJfDgBgFnubiIiIiMjNMTiRIr4/lIfSah3iwwMwpnuM0uUQEREREV0TgxO5nCRJpkEh7huWDLWKQ5ATERERkXtjcCKXy8opxZHcMvj6qDD9hkSlyyEiIiIiahaDE7mcsbfptr4dEBnkq3A1RERERETNY3Ail7pSWYcNh/MBALPSOCgEEREREXkGBidyqXV7c6A1iOiXEIZ+ieFKl0NEREREZBcGJ3IZgyhh7e5sAMD9aSnKFkNERERE1AIMTuQymccLkVtag4hADab0ba90OUREREREdmNwIpdZs0seFOIPNyTCX6NWuBoiIiIicpnNS4Ctr9uetvV1ebqbY3AilzhXVIlfT1+BIAD3DeWgEEREREReRaUGNr/aODxtfV1uV7n/H9V9lC6AvIOxt2ls9xgkRgYqXA0RERGRk2xeIoeAUQsbT9v6OiAagDGLXF+X0ozbY/OrUGlr4aPvAtW2N4Bf/w6Mec729nIzDE7kdFV1eny97xIADgpBREREbZyxZwWwDgPGnpUxzylTFwCIImCoA/S1gF5bf1//3GB8bmyrM0/T19UvV9dguq3lm1k/APVvb+JWY00eEpoABidygfVZuaio0yMlKhAju0QrXQ4RERGR81j0rJieG0PTTQuBIXOBioImwkVd88GjqfBiM9g0mG7QKrddbJBUGggeEpoABidyMkmSsGanfJjefcOSoVIJCldERERE1EKSBOiqgZpSoLbU4r6k6baASDksGQMUAGx7Xb65BQHw8Qd8/Czu/Sye+wNq3wbzNHjeaLqf9WO1X+Npez4EfnsbBqihFnVyqPSQ8MTgRE6190IJThRUwF+jwt2DEpUuh4iIiLyZrsY66NSUNAg9TbTVljq4t6Y1oaXhPA2DSQuDjcoHEFz8B+2tr8uh6aZnsaGiJ6aE/A61rcMa3RSDEznVpzsvAACm9o9HWKBG2WKIiIjIMZQcAEFf10yPT2nTgchQd32vLaiBgAggIBzwD7e+D4iwbjv5I5D1L0ClAUQdcON84KZnlAstSrM4x0sc/hSwcSPEkc9ArW7inDA3xOBETnO5vBY/HS0AANyfxiHIiYiI2ozrHQDBoLPvcDdbbfqa66tdUDUOPf7hTQSiBm2+wfYFnq2vy6HJOPCBcbv4Brl9OHAa0WDeHjqdud24PUSDMnW1gF3BadmyZS1e8YMPPoiQkJAWL0dtx+d7cqAXJQxOjkCvDmFKl0NERESOYjkAQm0Z0OtO+dyVw18AXScCEID/Ptd0+NFVXWcBAuAf1kzoCW/cCxQQDviGAConXsrUMjwat5OtASO8zbV6ID1ke9gVnObPn4+EhAS5K80OOTk5mDJlCoOTF9MZRHy2Wx4Ugr1NREREHkpbDZRdAsqygdKc+sc59Y9z5N6Xnf+Ub0an/ivf7OEXBgSE2Q44ttqMIckv1Lnh53pY9qxY8qCeFbLN7kP19u3bh5iYGLvmZWCiTccKcbmiDtHBfpjUu73S5RAREVFDkiT3AJVmW4Qhy5CUA1RfbcEKBSBlhP29QP5h8iF/bU0b6Fkh2+wKTi+++CKCg4PtXulf/vIXREZGtroo8nzGQSFmDEmEr4+b/kWIiIioLRMNQEW+OQSVZjfoMbpk3yFzviFAeCIQllh/n1D/OAk4/h9gxzJ59DaDFuh4E8MBtVl2B6eWWLTISaOokEc4WVCB3eeLoVYJmDk0SelyiIiI2iZdjRx+jD1GZZcsQlIOUJ4LSHYcFhYU0yAYNXjsH2Z7QIStr8uhqeEACADDE7VJHFWPHG7NrgsAgPE9YtE+LEDZYoiIiDyR8TA6y/OJLENSaQ5QfaX59ah8gNB4uXfIVo9RaDyg8W95fRwAgbxQi4PT1atX8cILL2Dz5s24fPkyRFG0ml5cXOyw4sjzVNTq8O2BXADArOEcFIKIiDycs65XJBqAigKLYJRt3WNUdgnQVja/Ht9g696hhj1GwbHOOY+IAyCQF2pxcLr//vtx5swZzJkzB7GxsRC87eJddE3fHMhFldaA1JhgpHWKUrocIiKi69Pa6xXpaq8xGl02UJ4HiPrmXz8oRu4hMgWjJIseo0R5kAUlvotxAATyQi0OTr/++iu2b9+Ofv36OaMe8mCSJJkGhbg/LZmhmoiIPJ+tw8+2/B3Y8howeA4Q2wvYlV7fS2RxWF1VUfPrNh5G16jHKAEISwLC4gEND3knchctDk7du3dHTc11XrGZ2qQdZ6/ibFEVgnzVuHNAvNLlEBERXR9RBErOA9GpQPJwOTxtfg2AJE/f95F8a4om6NqDLoTEtc3huInaqBYHpxUrVuDZZ5/FCy+8gN69e0Oj0VhNDw0NdVhx5FmMvU3TBiYgxF9z7ZmJiIjcibYauPw7UHDEfCs8ZmO4bsn8MKid9UALYYnWh9UFRChzGB0ROUWLg1N4eDjKy8tx8803W7VLkgRBEGAw8GRAb5RXWoOM3wsByIfpERERua2KwvpwdBgoPCo/vnoGkMTG8/r4y4fjiQYgP0s+vE7UAzctBG5u4vwmImqTWhyc7r33Xmg0Gqxdu5aDQ5DJ2t3ZECVgWKdIdI0NUbocIiIiwKCXA1HhUTkkFRwBCo4CVZdtzx/UDojrC8T1Md8iOwPbl1oPvW0cGEKt4UAIRF6kxcHp6NGjOHjwILp16+aMesgD1ekN+GJvNgBgdlqKssUQEZF3qquQD62zPNTu8u+AvrbxvIIKiOpiHZBi+wAhsY3n5fWKiKhei4PT4MGDkZOTw+BEJj8eKcCVSi3iQv0xvqeNXzpERESOIklAea7cc2Q83K7giDyIgy2aICCuNxDbuz4k9QViegC+gfa9Hq9XRET1WhycHn/8cTz55JP485//jD59+jQaHKJv374OK448g3FQiJlDk+CjVilbDBERtR0GHVB0sn6gBovD7WpKbM8f0sG6FymuDxDREVBdx+8mXq+IiOq1ODhNnz4dAPDQQw+Z2gRB4OAQXupobhkOZJdCoxZwz5BEpcshIiJPVVNqHqjBeCs6ARi0jecV1EC77nJPkuWhdkG88DoROU+Lg9P58010hZNXWrPzIgDglt7tERPir3A1RETk9iQJKL1oHqjBGJLKsm3P7xdqEY7qg1K77oCGv3OIyLVaHJySkznUNMnKqnX47lAuAGAWhyAnIqKGdLVyr5HpukhH5bBUV2Z7/rCkBofa9QbCk3ktJCJyC3YFp++//x6TJk1qdD5TUzZu3IgxY8YgICDguooj9/bV/hzU6kR0jwvB4OQIpcshIqLW2rwEUKltn7Oz9fX6ARKuca4PAFRdtb4uUsER4Mop+ZpHDak08gANcX3Nh9vF9pIvGEtE5KbsCk533nknCgoK0K5dO7tWes899yArKwudOnW65nzbtm3DG2+8gf379yM/Px/ffvstpk6des1ltmzZggULFuDYsWNITEzEX//6VzzwwAN21UWOI4oS1uySD9ObPTyF1/MiIvJkKrXtobUth+I2EkV5BDvL6yIVHAEq8myvOyDCPJqd8XC76K6Aj6/z3g8RkRPYFZwkScIDDzwAPz8/u1ZaW2vjmgk2VFVVoV+/fnjooYcwbdq0Zuc/f/48br31VsybNw+fffYZMjMz8fDDD6N9+/aYOHGiXa9JjrH1dBEuXq1GiL8P7ujfQelyiIjoeti6LpExNA18QL4w7IYF9YfbHQN0VbbXE9mp/jwki4vIhnbgoXZE1CbYFZxmz57dopXee++9CA0NbXa+SZMmYdKkSXavNz09HR07dsSbb74JAOjRowe2b9+Ot956i8HJxYyDQtw9KBGBvi0+VY6IiNzNTX8GqorksLRlCSCJAATgwGrgQIN5ffyBmJ4NLiDbC/ALUaBwIiLXsOsb76pVq5xdh1127tyJcePGWbVNnDgR8+fPV6YgL5VTXI3NJy8DAO7noBBERJ6r5CJwfitwfpt8qyyU2yWxfgZJ7m0yBaS+co9SVBdAzT+aEZF38ai9XkFBAWJjY63aYmNjUV5ejpqaGpuDUdTV1aGurs70vLy8HACg0+mg0+mcW7AdjDW4Qy32+mTHeUgSMKJLFBLCfD2qdiIiI0/c/163qisQLv4K1fltEC78CqH0gtVkSeUDQdRDEtQQJAMMwx6DOPalxusRJUD0ou1GRA7lTvvfltTgUcGpNZYsWYLFixc3at+0aRMCAwMVqMi2jIwMpUuwi9YArD2gBiCgu/oyNm7cqHRJRETXxVP2v63hY6hBVOVJRFccQ7uK3xFWm2M1XYQKpUGdUBTcE/66EiQX/4rj7afhVNxUdC1Yjx67/olTF/NwKm6qMm+AiNo0d9j/VldX2z2vRwWnuLg4FBYWWrUVFhYiNDS0yaHPFy1ahAULFpiel5eXIzExERMmTLDrPCxn0+l0yMjIwPjx4+0e7l1J/z6Qi+o9xxAf7o9nZo6EWsUTfonIM3na/tcu+joIufsgXKjvUco7AKHBcOBSTE+IKSMhpdwEKWk4QvxCEPbrP6De9jcYbnoWXUY+gy4AgMkw/NoVPbb9DV1Tu0Ic+YwS74iI2iB32v8aj0azh0cFp7S0tEY9HBkZGUhLS2tyGT8/P5ujAWo0GsX/oSy5Wz22SJKEz/ZcAgDcNywF/n4cSpaIPJ8n7H+bJBrkYcHPbZXPVbq4E9DXWM8Tngx0GgV0lG9CcDuoG65HADDmOahHLbSedvMiQK2GWjRA7anbiIjcljvsf1vy+ooGp8rKSpw5c8b0/Pz588jKykJkZCSSkpKwaNEi5Obm4tNPPwUAzJs3D//85z+xcOFCPPTQQ/jll1/w5Zdf4ocfflDqLXiVrJxSHMktg6+PCtNvSFS6HCIi7yNJwNUzwLkt9YM6/ArUllrPE9QO6HiTHJQ6jQIiUppf77UubmvrorhERF7IruC0bNkyu1f4xBNP2D3vvn37MGbMGNNz4yF1s2fPxurVq5Gfn4/s7GzT9I4dO+KHH37AU089hXfeeQcJCQlYuXIlhyJ3EeMQ5FP6tkdkEHubiIhcojzP3KN0bmvjC836hgApN9YHpdFATA9eN4mIyAnsCk5vvfWW1fOioiJUV1cjPDwcAFBaWorAwEDExMS0KDiNHj0akiQ1OX316tU2lzl48KDdr0GOcbWyDhsO5wMAZqWlKFsMEVFbVl0MXNhuDkpXT1tPV/sCiUPrD78bDXQYwKHBiYhcwK497fnz502P165dixUrVuCjjz5Ct27dAAAnT57E3Llz8cc//tE5VZLi1u3LgdYgom9CGPonhitdDhFR26GtBrJ3moNS/iEAFn9UFFRA+/7m85SShgEa2wMiERGR87T4T1TPP/88vv76a1NoAoBu3brhrbfewl133YV7773XoQWS8gyihM92yYdM3j+MF7wlIrouBh2Qe8AclC7tAQxa63miu5mDUsoIICBckVKJiMisxcEpPz8fer2+UbvBYGg0VDi1Db+cuIzc0hqEB2pwW78OSpdDRORZRBG4fMxi5LsdgLbSep7QBIuR724CQtsrUysRETWpxcFp7Nix+OMf/4iVK1di4MCBAID9+/fjkUcewbhx4xxeICnv050XAADTb0iEv6bRILZERGRJkoCS8+agdP5XoPqK9TwBkUDHkeYBHSI7cUAHIiI31+Lg9PHHH2P27NkYPHiwadxzvV6PiRMnYuXKlQ4vkJR1rqgSv56+AkEA7hvKw/SIiGyqKATObwPObwHObQPKsq2nawKB5OHmIcJj+wAqlSKlEhFR67Q4OLVr1w4bN27EqVOncOLECQBA9+7d0bVrV4cXR8pbs0segvzmbjFIjAxUuBoiIjdRWwZc+M18nlLRcevpKh8g4QZzUIofDPjwMg5ERJ6s1eOXdu3alWGpjavW6vH1/ksAgPvT2NtERF5MVwvk7DYHpbyDgGSwmEEA4vqYhwhPGgb4BStULBEROUOrgtOlS5fw/fffIzs7G1qt9UhAS5cudUhhpLz1B/NQUatHSlQgbkptp3Q5RETXb/MSQKUGRi1sPG3r64BoAMYsku/zsuoPvdsqhyZ9rfX8kZ2tB3QIjHTFOyAiIoW0ODhlZmbi9ttvR6dOnXDixAn07t0bFy5cgCRJpsEiyPNJkmQaFOK+YclQqXjSMhG1ASo1sPlV+fHwp8ztW/4ObHkNSJ0AfD5TvgBtXZn1ssFx5qDUaRQQluC6uomISHEtDk6LFi3CM888g8WLFyMkJAT//ve/ERMTg3vvvRe33HKLM2okBey7WIITBRXw16hw96BEpcshInIMY0/T5lehqipB0lUt1B/+XR4uHABObzLP6x8GpIw0B6Xorhz5jojIi7U4OB0/fhyff/65vLCPD2pqahAcHIyXX34Zd9xxBx555BGHF0mu9+lOeVCIqf3jERaoUbgaIiIHKbskj3AX2gHqPSswwHKaj798bpIxKLXvL/dQERERoRXBKSgoyHReU/v27XH27Fn06tULAHDlypVrLUoe4nJ5LX48kg+Ag0IQURtQngf8/h1w7Fv5XKV6EgABgCSoIMz6DkgYAmj8FSuTiIjcW4uD07Bhw7B9+3b06NEDkydPxtNPP40jR47gm2++wbBhw5xRI7nY53tyoBclDEqOQK8OYUqXQ0TUchUFwO/fy2EpeyfkmAQAgnw9Jd9gCKf/C4PgA7WkB7J3yQM8EBERNaHFwWnp0qWorKwEACxevBiVlZVYt24dUlNTOaJeG6AziFi7Rz5MbxZ7m4jIk1QWAce/A46tlwd3MIUlAInDgF53Aj3vAA6uATa/CsNNz2JDRU9MCfkdauOAEbZG2yMiIkIrglOnTp1Mj4OCgpCenu7QgkhZGb8XorC8DtHBvrild5zS5RARXVvVVeB4fc/ShV8BSTRPS7ihPixNBcLi5batr8uj6o15DuLwp4CNGyGOfAZqtcVoewxPRERkQ6uu41RaWoqvv/4aZ8+exZ///GdERkbiwIEDiI2NRXx8vKNrJBcyDkF+zw1J8PPhSdFE5Iaqi4ETG+SwdG6r9YVoOwyUw1KvqUB4UuNlRQMw5jk5HOl05nZjWBINjZchIiJCK4LT4cOHMW7cOISFheHChQuYO3cuIiMj8c033yA7OxuffvqpM+okFzhVWIFd54qhEoCZQ2184SAiUkpNKXDih/qwtBkQ9eZp7fvVh6U7gYiUa69nzKKmp7GniYiIrqHFwWnBggV44IEH8PrrryMkJMTUPnnyZMycOdOhxZFrrakfgnxCzzh0CA9QuBoi8nq15cDJjXJYOpMJiBY9RLF95F6lXncCUZ0VK5GIiLxHi4PT3r178f777zdqj4+PR0FBgUOKIterqNXhmwOXAHBQCCJSUF0FcPKn+rD0M2CoM0+L6WnuWYpOVa5GIiLySi0OTn5+figvL2/UfurUKbRr184hRZHrfXMgF1VaA7rEBCOtc5TS5RCRN9FWAafqw9LpDEBfa54W3RXoNU0OSzHdlauRiIi8XouD0+23346XX34ZX375JQBAEARkZ2fj//2//4f/+Z//cXiB5HySJGHNLvkwvfuHJUMQBIUrIqI2T1sNnN4kh6VT/wX0NeZpkZ2B3saw1BPgPomIiNxAi4PTm2++ibvuugsxMTGoqanBqFGjUFBQgLS0NLz66qvOqJGcbOfZqzhzuRJBvmpMG8hREYnISXQ18uF3x76VD8fTVZmnRaSYe5bi+jAsERGR22lxcAoLC0NGRga2b9+Ow4cPo7KyEgMHDsS4ceOcUR+5wKf1g0LcOTAeIf4ahashojZFXycP7HDsW3mgB22leVp4kvmcpfb9GZaIiMitteo6TgAwYsQIjBgxwpG1kALySmuQcbwQADArLUXZYoiobdBr5SHDj30rDyFeZ3FebGhC/Wh404D4gQxLRETkMVoVnDIzM5GZmYnLly9DFEWraR9//LFDCiPXWLs7GwZRwrBOkegaG9L8AkREthh08sVoj30LnPgPUFtmnhbSwTx0ePxgQKVSrEwiIqLWanFwWrx4MV5++WUMHjwY7du350ACHqxOb8AXe7MBsLeJiFrBoAcubJPD0vH/ADUl5mnBsUDPqXJYShzKsERERB6vxcEpPT0dq1evxv333++MesiFfjpagCuVWsSG+mF8z1ilyyEiTyAagAvb68PS90D1VfO0oHZAzzvksJSUBqjUytVJRETkYC0OTlqtFsOHD3dGLeRixkEhZg5JhkbNvwYTURNEA5C9Uw5Lv38HVBWZpwVGAT1ul4cPT76RYYmIiNqsFgenhx9+GGvXrsXzzz/vjHrIRY7llWH/xRL4qATMGJKodDlE5G5EEcjZbQ5LlQXmaQERQI/b5AEeUkYC6laPM0REROQx7Pptt2DBAtNjURTxwQcf4Oeff0bfvn2h0VgPX7106VLHVkhOsaa+t+mW3nGICfVXuBoicguSBFzaK4elY+uBijzzNP8woPtt8mF4nUYBal66gIiIvItdwengwYNWz/v37w8AOHr0qFU7B4rwDGXVOqzPygXAQSGIvJ4kAbkHgGPfyD1LZTnmaX6hQPdb68PSGMDHV7k6iYiIFGZXcNq8ebOz6yAX+mp/Dmp1IrrHheCGlAilyyEiR9u8RD7XaNTCxtO2vi6Phtd9Un3P0rdAabZ5um8w0G2yHJa6jAV8/FxXNxERkRvjgeleRhQlrNklH6Y3Ky2FvYREbZFKDWx+VX5sDE+SBPzwNLDvI/kcpW1/N8+vCQK63VIflsYBmgDX10xEROTmGJy8zLbTRbh4tRoh/j6YOqCD0uUQkTMYw9LmV+Xhwv1CgL0fATXFcntNCeATAHSdKIel1AmAb6By9RIREXkABicvYxwU4q5BCQj05T8/UZukqwHCk4CwRGB3urld5QN0mySHpa63AL5BytVIRETkYfjN2YvkFFfjl5OXAQD3D0tWuBoicriCI8D+T4DDXwJ1ZdbTBDXw/y7IvU9ERETUYgxOXuRfuy9CkoCRqdHo1C5Y6XKIyBFqy4Gj/wYOfALkWYyAGp4ERHQEzm8F1L6AQQvses/2gBFERETULAYnL1GrM+DLvfIww+xtIvJwxustHfgEOPoNoKuW21UaoMcUYOAsIGcvsOU1YMxzclja+nrjASOIiIjIbgxOXuI/h/JQUq1DfHgAxvaIVbocImqN6mLg8Dr5cLyi4+b26K7AwNlAv3uAoGg5JFmGJsB6wAjL50RERGQXBicvYRyC/N5hSVCrOAQ5kccQReDCr8CBT4Hj38uH3AHyqHi97pR7l5KGAZaXFhAN1qHJyPhcNLimdiIiojaEwckLZOWU4vClMviqVZg+OFHpcojIHhUFQNZnwIE1QMl5c3tcX2DQbKD3XUBAuO1lxyxqer3saSIiImoVBicv8OnOCwCAKX3bIyrYT9liiKhpogE487N8KN6pnwCpvmfINwToe7d8OF6H/oqWSERE5K0YnNq44iotNhzOBwDcn8ZBIYjcUslF4OC/5FtFnrk9cagclnpN5TWXiIiIFMbg1Mat25sDrV5En/gw9E8MV7ocIjLSa4GTG+WR8c5uBiDJ7QGRQL8Z8rlLMd0VLZGIiIjMVEoXAADLly9HSkoK/P39MXToUOzZs6fJeVevXg1BEKxu/v7+LqzWcxhECf+qHxRiVloyBIGDQhAprugUsOmvwNIewFezgbO/AJCATqOBuz4Gnj4B3PIaQxMREZGbUbzHad26dViwYAHS09MxdOhQvP3225g4cSJOnjyJmJgYm8uEhobi5MmTpucMBLb9cuIycktrEB6owW39OihdDpH30lYDv38nj4yXvcPcHhwHDLhPvkV2VK4+IiIiapbiwWnp0qWYO3cuHnzwQQBAeno6fvjhB3z88cd49tlnbS4jCALi4uJcWaZHMg4KMX1wIvw1amWLIfJG+YflsHT4S6CuTG4TVEDqRPlQvNQJgFrx3TARERHZQdHf2FqtFvv378eiReahc1UqFcaNG4edO3c2uVxlZSWSk5MhiiIGDhyI1157Db169XJFyR7jXFElfj19BYIA3DeMg0IQuUxtOXD0azkw5R00t4cnyWGp/71AKHuAiYiIPI2iwenKlSswGAyIjY21ao+NjcWJEydsLtOtWzd8/PHH6Nu3L8rKyvCPf/wDw4cPx7Fjx5CQkNBo/rq6OtTV1Zmel5eXAwB0Oh10Op0D303rGGtwdC2f7pCv+zIqNRpxIRq3eK9EbZYkQcjdB9XBNRCOr4egq5abVRpI3SZDHDALUspIubcJAPh5dAvO2v8SEdG1udP+tyU1eNwxImlpaUhLSzM9Hz58OHr06IH3338fr7zySqP5lyxZgsWLFzdq37RpEwIDA51aa0tkZGQ4bF11BmDdfjUAAd1Uhdi4caPD1k1EZhp9BRKLf0Py1a0Irc01tVf4tcfF6NHIibgRWk0ocLwKOP6TgpXStThy/0tERPZzh/1vdXW13fMqGpyio6OhVqtRWFho1V5YWGj3OUwajQYDBgzAmTNnbE5ftGgRFixYYHpeXl6OxMRETJgwAaGhoa0v3kF0Oh0yMjIwfvx4aDQah6xz3b5LqDH8jqTIACyYMQIqFQfPIHIYSYRwcbvcu3TyBwgGrdzsEwCp5x0Q+98P/4Qh6CYI6KZwqXRtztj/EhFR89xp/2s8Gs0eigYnX19fDBo0CJmZmZg6dSoAQBRFZGZm4rHHHrNrHQaDAUeOHMHkyZNtTvfz84Ofn1+jdo1Go/g/lCVH1SNJEj7bcwkAMCstBX5+vte9TiICUFEAZH0GHFgDlJw3t8f1BQbNhtDnbgj+Ye5xjQdqEXf7fUBE5C3cYf/bktdX/FC9BQsWYPbs2Rg8eDCGDBmCt99+G1VVVaZR9mbNmoX4+HgsWbIEAPDyyy9j2LBh6NKlC0pLS/HGG2/g4sWLePjhh5V8G25j38USHM8vh79GhbsHJSpdDpFnM+iBMz/LAz2c+gmQDHK7XyjQ5y5g4GygQ39FSyQiIiLXUDw4TZ8+HUVFRXjhhRdQUFCA/v3746effjINGJGdnQ2Vyvw33JKSEsydOxcFBQWIiIjAoEGDsGPHDvTs2VOpt+BWPt0pX/D2jn7xCAvkX1CJWqXkInDwX/KtIs/cnjhMHhmv11TAN0ix8oiIiMj1FA9OAPDYY481eWjeli1brJ6/9dZbeOutt1xQlee5XFGLn47mAwDuT+MQ5EQtotcCJ3+Qe5fObgYgye0BkUC/GXJgiumuaIlERESkHLcITuQYX+zJgc4gYWBSOHrHhyldDpFnKDoFHPgEOPQ5UH3V3N5ptByWuk8BfBqfJ0lERETehcGpjdAbRKzdnQ1AHhSCiK5BWw38/p0cmLItLrYd0l6+QO2A+4DIjsrVR0RERG6HwamNyPi9EAXltYgK8sWkPvYN5U7kdfIPy2Hp8FdAXZncJqiA1InAoNlAl/GAmrtFIiIiaozfENqIT3ZeAADMGJIEPx+1ssUQucrmJYBKDYxa2Hja1tcB0QCkPQoc/RrY/wmQn2WeHp4MDLxf7mEK7eCykomIiMgzMTi1AacKK7DrXDFUAjBzaJLS5RC5jkoNbH5VfmwZnrb8HdjymnyNpR3LAF39VcFVGqDHFHkY8Y6jABWvukRERET2YXBqA9bUD0E+vmcsOoQHKFwNkQsZw5IxPN3wMPDNXPnaSwBQcFi+j+4mD/TQbwYQFOX6OomIiMjjMTh5uIpaHb45cAkAB4UgLzXyaeDqWTk8GQMUAPgEAL2nyYEpcSggCMrVSERERB6PwcnDfXswF1VaAzq3C8LwzvxLOnmRK6eBrLXAoS+sL1ILAbj1H0CfuwF/DstPREREjsHg5MEkScKn9Yfp3T8sGQL/ok5tXW0ZcOxb4OBnwKU95nafAEBfA6h8AFEPVBczNBEREZFDMTh5sJ3nruLM5UoE+qoxbVCC0uUQOYcoAue3yr1Lx/8jByRAHka8y3jANwg49g0w5jn5nKetr9seMIKIiIjoOjA4ebBPd8i9TdMGxiPUX6NwNUQOdvUscOhzIOtzoPySuT26GzDgXqDvdODAp3JIMoYmoPGAEQxPRERE5AAMTh4qv6wGGccLAXBQCGpD6iqAY+uBrM+A7J3mdv8woPdd8jWX4geaB3oQDdahycj4XDS4pGwiIiJq+xicPNTa3dkwiBKGdoxE19gQpcshaj1RBC5ulw/F+/078zWXBBXQ+Wag/0yg262Axr/xsmMWNb1e9jQRERGRAzE4eSCtXsTne3IAsLeJPFjJBfkwvENrgdJsc3tUqhyW+t0DhHZQrDwiIiIiSwxOHujHo/m4UlmH2FA/TOgVq3Q5RParqwSOfy+Pindxu7ndL1S+5lL/e4GEG3jNJSIiInI7DE4eaE39EOQzhiRBo1YpXA1RMyQJuLhDPhTv2LeArqp+ggB0Gi2HpR5TAE2AklUSERERXRODk4c5lleGfRdL4KMSMHNIktLlEDWtNFu+OG3WZ/JheUaRneoPxZsBhHEYfSIiIvIMDE4extjbdEvvOMSE2jhZnkhJ2mr5WktZ/wLObzO3+wYDve6Ue5eShvFQPCIiIvI4DE4epKxah/VZuQA4KAS5EUkCcnbLPUtHvwW0FeZpHW+qPxTvNvlCtUREREQeisHJg3y1Pwe1OhHd40JwQ0qE0uWQtyu7VH8o3lqg+Ky5PTxZDkv97gEikpWrj4iIiMiBGJw8hChK+Ncu+TC9+9OSIfBQJ1KCrgY48QNw8F/AuS0AJLldEwT0mlp/KF4aoOKgJURERNS2MDh5iF/PXMGFq9UI8fPB1P7xSpdD3kSSgEv75POWjn4D1JWbpyWPAAbcC/S4HfALVq5GIiIiIidjcPIQa3ZeAAD8z6AEBPnxn41coDzPfCje1dPm9rAk8wVqIzsqVx8RERGRC/EbuAfIKa5G5onLAOTD9IicRlcLnPxBDktnfwEkUW73CQB63iH3LiWP4KF4RERE5HUYnDzAv3ZfhCQBI1Oj0bkdD4ciB5MkIO8AcPAz4OjXQG2ZeVpSmnzeUs87AP9Q5WokIiIij7YiawVUggrz+s1rNC39UDpEScSf+v9Jgcrsx+Dk5mp1Bny5NwcAcP8w9jaRA1UUAofrD8UrOmFuD00A+s+QL1Ab1Vm5+oiIiKjNUAkqLM9aDgCY03OOqT39UDqWZy3Ho/0fVao0uzE4ubkNh/NRUq1DfHgAxvaIVboc8nT6OuDkj3JYOvMzIBnkdh9/+VpL/e+Vr72kUitbJxEREbUpD/V+CFW6KizPWo7c8lwE64KRczgH7x99H4/2f9RmT5S7YXByc8ZBIWYOTYJaxSHIqRUkCcg/JF+g9shXQE2JeVrCEPm8pV53Av5hytVIREREbs8gGlCpq0R5XTnKtGXN32vLUVZXhgptBWr0Nab1rD+3Xn5wFB4TmgAGJ7d2KKcUhy6VwVetwj03JCpdDnmayiLg8Dq5d+nyMXN7SAd5RLz+M4HoVOXqIyIiIpeTJAk1+hpTqCnXlqO8rtzqeVP3FdoKSMZrOLaCAAGhfqEoq5PPp/YRfDwmNAEMTm7t053yBW9v7dseUcF+CldDHkGvBU5vknuXTm8CRL3crvYDut8q9y51GsND8YiIiJzEVYMg6ERdk4GnuSCkE3XX9doBPgEI8wtDqG9ok/ehfqGN2oM1wfjg8AdYnrUcaqihl/RIP5TuMeGJwclNFVdp8Z/DeQA4BLnX27xEDjqjFjaetvV1QDQAPabIo+Id+RKovmqeHj9IPm+p9zQgIMJ1NRMREXkpy0EQLAOBrUEQJElCpa6yyYBzrQBUra++rjp9VD7XDD5NBiHfMGjUmla9pnEbPNLnEcTnxCM3MdfmtnJXDE5uat3eHGj1IvrEh2FAYrjS5ZCSVGpg86vyY8vwlPES8NtbQHAssPVv5vbgWPlQvH4zgZjuLi2ViIjIWxlD0ISUCcitkANB1uUs9IruhR15O3D0ylEkhSRh26Vt+M/Z/8jBSFsO0XjNxFYK8Q1pUQAy3gf4BEAQXHf+vGVwnNNzDjbmbMTcPnOhUtsOmu6IwckNGUQJ/9olH6Z3f1qyS3+oyQ0Zw9LmV+VD8eIHAj+/BFw5KbdXFgJqX6DbZLl3qfPNgJofbSIiIkfQi3oU1xbjSs0V062oughFNUW4WnMVRTVFpvY6Q53Vsr/l/Ybf8n4zPc+uyAYqGr+Gv9rf5qFtzd0Ha4Kh9pDD70VJNA0EodOZDxU0hqXrDZCuwG9XbmjzicvILa1BeKAGt/froHQ5pJS6CiD/MJCfBVw5DQRGAb++YT1PhwH1h+L9DxAYqUiZREREnqhaVy2HIIvgYwxFpsc1RSipLWnRgAghmhBEB0YjOiAa+wr2QYIEFVR45oZnmjwEzk/d9s9lv9Z5Xe7e02TE4OSGPq3vbfrD4ET4azzjrwh0nWrLgYLDQF6WHJTysoCrZ4CmdtSCCpi3HYjt5boaiYiI3JwoiSipLbEKPrYC0ZWaKy06R0gtqBHlH4WogCi0C2yH6IBo061dgPVzfx9/APKhaXsL9kKj0kAn6lClq8L9Pe931lsnF2BwcjPnr1Rh26kiCAJw31AOCtEm1ZaZe5KMQenqGdvzhsYD7fsDHfrL8xxeJx+WZ9ACJ35gcCIiIkW4auQ4o1p9rVXoMYYi46FyRdXy46u1V2EwXtzdDgE+AVbBx1YoigqIQoRfRIsOibM8n2dev3mm54Dn9K5QYwxObsZ4btPoru2QFBWocDV03WrL5IvPWvYkFZ+1PW9oghyQjEGpfX8guJ08bevrcmga85x8ztPW120PGEFEROQCLRk5rimSJKGsrsz24XKWz6uvoEJn48SgJggQEOEf0ag3qF2gHIKMbe0C2iFQ4/jvWg1DE2DeRgxPno3ByY1Ua/X4al8OAGBWWoqyxVDL1ZQ07kkqPmd73rBEoH2/+oA0QL4PirY9rzEkGUMTYD1ghOVzIiIiF7AVBIyBYV7feZjaZSqOFB1p1DPUMBjpjdcbtIOvyrfJw+QsQ1GEfwQ0qtYNl+0IloMgWPKkQRDINgYnN/JdVh7Ka/VIigzEqK7tlC6HrqWmxLoXKT8LKLlge96wJKBDP4uepAFAUJT9ryUarEOTkfG5aP8hCURERC0lSRIqdBUoqS1BSW0JrtZeRUltCdSCGv3a9cPyrOVYkbUCEiT4q/2Rfjgd6YfT7V5/mF+Y6ZA4qx4i4+P6gRZCNCEeMdJwWxgEgWxjcHITkiTh0531Q5APS4ZK5f47Bq9RXWwdkPKygNKLtucNT7I+1K7DgOsf7W7MoqansaeJiIhaSJREVGgrUFxbjJLaEhTXFpsel9SVWLUb25rrGTKOOldrqAUgX1w1OiAa0f7m4NNwEAVjWPJV+zr9PRM5AoOTmziQXYrj+eXw81Hh7sEJSpfjvaqLgbyD1kGpNNv2vBEp1iGpfT8OCU5ERC4nSiLK6soahaDiumJTL5FlT1FpXWmLBlAwCtIEIcIvApH+kYjwj0CEfwQull/EwcsHoRbUMEgG3NPtHvyp/58Q5hcGlaBywrslUg6Dk5v412753KY7+ndAeCD/8uISVVeB/IMWPUmHgLKmQlLHBgM39AMCIlxWKhERKcPVo8cBgEE0oLSu1NTbYww8DXuBimuKUVInB6HWnDcTogkxBaAI//pAZBGMGt43vNZQ+qF0rD+zvtHIcVEBUTwkjdokBic3UK4F/vt7IQAOCuE0VVfqA5IxKB0CynJszxvZqUFPUl+GJCIiL+WI0eP0oh6ldaXm0FNrHYYaHh5XVlfWoguuGoX6hpp7g/zMYciyh8gYjiL8I67rEDmOHEfeiMHJDey8LEBnkDAgKRy948OULsfzVRY1Piep/JLteSM7W/ckxfUFAsJdUycREbk9W2Fg+cHlSD+cjundpqNfu37YeG6j+RC5uhKr3qHi2mKUa8tb9drhfuGmENQw/FgGpEj/SIT7h7t0JDmOHEfeyC2C0/Lly/HGG2+goKAA/fr1w7vvvoshQ4Y0Of9XX32F559/HhcuXEBqair+/ve/Y/LkyS6s2HH0BhG/FcrHAM9K8/IL3m5eAqjUtgc82Pp6/ehyDQZKqLzceHS78lzb64/q0rgnyZ9BlYhIicPRnE0n6lCrr0WNvsZ8b6htXZuhBlH+UVietdwUoABg3cl1WHdynV31CBAQ7hfeuPfH8t7PPC3cLxw+Krf4mmYTR44jb6T4J3LdunVYsGAB0tPTMXToULz99tuYOHEiTp48iZiYmEbz79ixAzNmzMCSJUswZcoUrF27FlOnTsWBAwfQu3dvBd5Byy1aNRUqqPDqg9/g5xNFKNMKiAzSYHKf9nhu1TSIELHkwfVKl+lyK8qOQnVhG+YB1uFp6+tIP/guxMRh+NOp/1oHpYo8G2sS5JDUsCfJP9Tp78EZ2uIXGkfhtrGN26Vp3Da2OeJwNHtJkgS9qEe1vhq1+lqrkGIMKlbBxdAg1NRPNz2uDzZW0/W10Ev2Xx+oNVSCyhSEGvb+2ApFYb5hUKvUTq2JiJxL8eC0dOlSzJ07Fw8++CAAID09HT/88AM+/vhjPPvss43mf+edd3DLLbfgz3/+MwDglVdeQUZGBv75z38iPd3+awYoSQUVvledBlZNw1ntMwCA6YMS8PKau/G96jRuF1MVrtBBJEnuJZIMgCSaH4sGeZrpsQhIBqjiemN52WHg4LuYV1UEdB4L7Pwn0ksPYXlEOB7N2QUc2dTgRQQgOrVxT5JfiOvfr5O48guNp+G2sY3bpWmW22ZOzzmmdm/cNpIkQYIEURLxUO+HoBf1WJ61HFqDFrN7zcaHhz/EJ79/gj90/QOGtR+GHXk7TKHEGFwsg0qjtmsEotaM6NZaKkGFAJ8A+Kv95Xsf832jNrW/1XTLtszsTHx/9nv4CD7QS3rM6zsPj/R/xGXvg4iUJ0iS1PKzDx1Eq9UiMDAQX3/9NaZOnWpqnz17NkpLS/Hdd981WiYpKQkLFizA/PnzTW0vvvgi1q9fj0OHDjX7muXl5QgLC0NZWRlCQ5XrgXhu1TR8rzqNnpUa+FYlQx2ejf1+WgzW+mJC+wH1wUKU7yGaHxvbITaYx+Jxo+VQfy82mK+pdTdcl7Gt4bobzGc1T+uObd4e6I9tgYG4qboaI6prGzyvA4JjgLCE+ls8EBoP+Pg1v2IPt/3SdmzL3Yab4m/CiIQRjZ57M24b2yy3w43xN2J77nb8mvsrRsaPxIh483ZpzQnogPylu8lp11hna5dzZC0783diZ95ODIsbhtDSUJSFlWF34W4MiRuCQbGDIEoiREmEBAkGyQBJkqzajI+v2SZJEGF+bFpPM21W64FFG0SIYoM2ixotlzfWY1l7U21KUwvqRmEmQF0fapoJM1bhR2O7LUAdAB+Vz3VfNLXhQAi2BkYgIvvpdDps3LgRkydPhkbjunPzbGlJNlA0OOXl5SE+Ph47duxAWlqaqX3hwoXYunUrdu/e3WgZX19ffPLJJ5gxY4apbcWKFVi8eDEKCwsbzV9XV4e6ujrT8/LyciQmJuLKlSuKBicAmLFqME768eRJIiJyT8GaYOswUx9iGoaUptosg4+tNlcOZtBaHx75EO8deQ+P9HkEc/vMbbadiJqn0+mQkZGB8ePHu0Vwio6Otis4KX6onrMtWbIEixcvbtS+adMmBAYGKlCRWR9DH5ySsiAJAgRJwo26QADyX8UkQTA9BgT574L1fzGT0GCaAPNyltOE+uWs1iPUP7Vep2majXVaTbNnnVbrMT821Wdat+2/AAbX5mK3qti0XYaKkaj0j7c5r7c5pjsGCRIECOil6aV0OW6F28Y2e7eL0MTn0R7Xs6ySyx/SHTJtm4G+A6GCCkLD/wTBdnv9NMv/rOYTmmi3Z7qD1ttoPjvW+2vtr9hStwUqqCBCxFD1UIzxG2PeaIb6mx0MMKCy/j9PdqLmBMb6j0V8Tjw25mw0tccjHmP9x+LEqRNW7URkv4yMDKVLQHV1td3zKhqcoqOjoVarG/UUFRYWIi4uzuYycXFxLZp/0aJFWLBggem5scdpwoQJivc47VmzGpIgQCNJ0AkCItXxeOn+LxWtSWmqX/+BDw9vx64IeVhVnajDoPLzmJsyCeLIZ5QuT1EfHvkQR48cNW2Xkd1H8q+c9bhtbON2adqHRz5E1pEsqKGGAQYM6TbE67fNh0c+xJYjW0w9KMYela6pXb1620xG06P2XmsaETXN3Xqc7KVocPL19cWgQYOQmZlpOsdJFEVkZmbiscces7lMWloaMjMzrc5xysjIsDrUz5Kfnx/8/BqfA6PRaBT9h3pu1TR8rz6D2w1dMCTqAey5uhrfq88A/5qOVx/8RrG6FLX1daQfXiEPBNHgOHIcXoF56iaGKvcC6YfS8d6R9xptF5Xa9uhg3oTbxjZul6YZt80jfR5BfE48chNz8d6R97x62zT8eQGAPw38E1RqFX9uiMhplP4+bqzBXoofqrdgwQLMnj0bgwcPxpAhQ/D222+jqqrKNMrerFmzEB8fjyVLlgAAnnzySYwaNQpvvvkmbr31VnzxxRfYt28fPvjgAyXfRosYB4a4XUzFS/evw8aNG+Wepn9NN422543hKb0kyyo0AQ0uPFiSBW/8tc2rszeN28Y2bpemWW6bOT3nYGPORsztM9cUEADv3Da8mCkRUfMUD07Tp09HUVERXnjhBRQUFKB///746aefEBsbCwDIzs6GSqUyzT98+HCsXbsWf/3rX/GXv/wFqampWL9+vcdcwwkARIi4XUzFqw9+A51OZ2p/9cFvgPrrOHkjMWUEHhVu4i/uBviFpmncNrZxuzTNcttY7n+9fdvwYqZERM1TdFQ9JbjLcORG7jQcIxGRN+H+l4hIGe60/21JNlBdcyoRERERERExOBERERERETWHwYmIiIiIiKgZig8O4WrGU7paMma7M+l0OlRXV6O8vFzxYzyJiLwJ979ERMpwp/2vMRPYM+yD1wWniooKAEBiYqLClRARERERkTuoqKhAWFjYNefxulH1RFFEXl4ebr75Zuzbt++613fDDTdg7969rV6+vLwciYmJyMnJcYtR/qhp1/tv7ak87X27S72ursOZr+eMdTtqndezHu5/PYe7fK5dzdPet7vUy/2va9bZVva/kiShoqICHTp0sLoEki1e1+OkUqmQkJAAHx8fh/xDqdVqh6wnNDRU8R8cujZH/Vt7Gk973+5Sr6vrcObrOWPdjlqnI9bD/a/7c5fPtat52vt2l3q5/3XNOtvS/re5niYjrx0c4tFHH3Wr9ZD789Z/a0973+5Sr6vrcObrOWPd3AdTS3jrv7OnvW93qZf7X9es013+vV3J6w7VczfudkFeIiJvwf0vEZEyPHX/67U9Tu7Cz88PL774Ivz8/JQuhYjIq3D/S0SkDE/d/7LHiYiIiIiIqBnscSIiIiIiImoGgxMREREREVEzGJyIiIiIiIiaweBERERERETUDAYnIiIiIiKiZjA4eZA777wTERERuOuuu5QuhYioTduwYQO6deuG1NRUrFy5UulyiIi8irt+5+Vw5B5ky5YtqKiowCeffIKvv/5a6XKIiNokvV6Pnj17YvPmzQgLC8OgQYOwY8cOREVFKV0aEZFXcNfvvOxx8iCjR49GSEiI0mUQEbVpe/bsQa9evRAfH4/g4GBMmjQJmzZtUrosIiKv4a7feRmcHGTbtm247bbb0KFDBwiCgPXr1zeaZ/ny5UhJSYG/vz+GDh2KPXv2uL5QIqI27nr3x3l5eYiPjzc9j4+PR25uritKJyLyeG35OzGDk4NUVVWhX79+WL58uc3p69atw4IFC/Diiy/iwIED6NevHyZOnIjLly+b5unfvz969+7d6JaXl+eqt0FE5PEcsT8mIqLWacv7YB+lC2grJk2ahEmTJjU5fenSpZg7dy4efPBBAEB6ejp++OEHfPzxx3j22WcBAFlZWa4olYioTbve/XGHDh2sephyc3MxZMgQp9dNRNQWOOI7sbtij5MLaLVa7N+/H+PGjTO1qVQqjBs3Djt37lSwMiIi72LP/njIkCE4evQocnNzUVlZiR9//BETJ05UqmQiojbD078Ts8fJBa5cuQKDwYDY2Fir9tjYWJw4ccLu9YwbNw6HDh1CVVUVEhIS8NVXXyEtLc3R5RIRtVn27I99fHzw5ptvYsyYMRBFEQsXLuSIekREDmDvd2J3/c7L4ORBfv75Z6VLICLyCrfffjtuv/12pcsgIvJK7vqdl4fquUB0dDTUajUKCwut2gsLCxEXF6dQVURE3of7YyIi5Xj6PpjByQV8fX0xaNAgZGZmmtpEUURmZqZbdDsSEXkL7o+JiJTj6ftgHqrnIJWVlThz5ozp+fnz55GVlYXIyEgkJSVhwYIFmD17NgYPHowhQ4bg7bffRlVVlWlEESIicgzuj4mIlNOm98ESOcTmzZslAI1us2fPNs3z7rvvSklJSZKvr680ZMgQadeuXcoVTETURnF/TESknLa8DxYkSZIUyGtEREREREQeg+c4ERERERERNYPBiYiIiIiIqBkMTkRERERERM1gcCIiIiIiImoGgxMREREREVEzGJyIiIiIiIiaweBERERERETUDAYnIiIiIiKiZjA4ERGRy23ZsgWCIKC0tNTlry0IAgRBQHh4+DXne+mll9C/f3+X1GR8PWNtb7/9tstel4iI7MPgRERETjV69GjMnz/fqm348OHIz89HWFiYIjWtWrUKp06dUuS1m/LMM88gPz8fCQkJSpdCREQ2+ChdABEReR9fX1/ExcUp9vrh4eGIiYlR7PVtCQ4ORnBwMNRqtdKlEBGRDexxIiIip3nggQewdetWvPPOO6bD0C5cuNDoUL3Vq1cjPDwcGzZsQLdu3RAYGIi77roL1dXV+OSTT5CSkoKIiAg88cQTMBgMpvXX1dXhmWeeQXx8PIKCgjB06FBs2bKlVbX+7W9/Q2xsLEJCQjBnzhzU1tZaTd+7dy/Gjx+P6OhohIWFYdSoUThw4IBp+kMPPYQpU6ZYLaPT6RATE4OPPvoIAPD111+jT58+CAgIQFRUFMaNG4eqqqpW1UtERK7F4ERERE7zzjvvIC0tDXPnzkV+fj7y8/ORmJhoc97q6mosW7YMX3zxBX766Sds2bIFd955JzZu3IiNGzdizZo1eP/99/H111+blnnsscewc+dOfPHFFzh8+DDuvvtu3HLLLTh9+nSL6vzyyy/x0ksv4bXXXsO+ffvQvn17rFixwmqeiooKzJ49G9u3b8euXbuQmpqKyZMno6KiAgDw8MMP46effkJ+fr5pmQ0bNqC6uhrTp09Hfn4+ZsyYgYceegjHjx/Hli1bMG3aNEiS1KJaiYhIGTxUj4iInCYsLAy+vr4IDAxs9tA8nU6H9957D507dwYA3HXXXVizZg0KCwsRHByMnj17YsyYMdi8eTOmT5+O7OxsrFq1CtnZ2ejQoQMA+Tyhn376CatWrcJrr71md51vv/025syZgzlz5gAA/u///g8///yzVa/TzTffbLXMBx98gPDwcGzduhVTpkzB8OHD0a1bN6xZswYLFy4EIJ9LdffddyM4OBinTp2CXq/HtGnTkJycDADo06eP3TUSEZGy2ONERERuITAw0BSaACA2NhYpKSkIDg62art8+TIA4MiRIzAYDOjatavp/KDg4GBs3boVZ8+ebdFrHz9+HEOHDrVqS0tLs3peWFiIuXPnIjU1FWFhYQgNDUVlZSWys7NN8zz88MNYtWqVaf4ff/wRDz30EACgX79+GDt2LPr06YO7774bH374IUpKSlpUJxERKYc9TkRE5BY0Go3Vc0EQbLaJoggAqKyshFqtxv79+xsNqGAZthxl9uzZuHr1Kt555x0kJyfDz88PaWlp0Gq1pnlmzZqFZ599Fjt37sSOHTvQsWNHjBw5EgCgVquRkZGBHTt2YNOmTXj33Xfx3HPPYffu3ejYsaPD6yUiIsdijxMRETmVr6+v1YAOjjJgwAAYDAZcvnwZXbp0sbq1dMS+Hj16YPfu3VZtu3btsnr+22+/4YknnsDkyZPRq1cv+Pn54cqVK1bzREVFYerUqVi1ahVWr16NBx980Gq6IAi48cYbsXjxYhw8eBC+vr749ttvW1QrEREpgz1ORETkVCkpKdi9ezcuXLiA4OBgREZGOmS9Xbt2xb333otZs2bhzTffxIABA1BUVITMzEz07dsXt956q93revLJJ/HAAw9g8ODBuPHGG/HZZ5/h2LFj6NSpk2me1NRUrFmzBoMHD0Z5eTn+/Oc/IyAgoNG6Hn74YUyZMgUGgwGzZ882te/evRuZmZmYMGECYmJisHv3bhQVFaFHjx7XtyGIiMgl2ONERERO9cwzz0CtVqNnz55o166d1TlB12vVqlWYNWsWnn76aXTr1g1Tp07F3r17kZSU1KL1TJ8+Hc8//zwWLlyIQYMG4eLFi3jkkUes5vnoo49QUlKCgQMH4v7778cTTzxh81pQ48aNQ/v27TFx4kTToBUAEBoaim3btmHy5Mno2rUr/vrXv+LNN9/EpEmTWvfmiYjIpQSJ46ASEZEXEQQB3377LaZOneqU9VdWViI+Ph6rVq3CtGnTWrx8SkoK5s+fj/nz5zu+OCIiajX2OBERkdeZMWMGEhISHLpOURRx+fJlvPLKKwgPD8ftt9/eouVfe+01BAcHO7RHjoiIHIc9TkRE5FXOnDkDQB7lzpGj2V24cAEdO3ZEQkICVq9ejbFjx7Zo+eLiYhQXFwMA2rVrh7CwMIfVRkRE14/BiYiIiIiIqBk8VI+IiIiIiKgZDE5ERERERETNYHAiIiIiIiJqBoMTERERERFRMxiciIiIiIiImsHgRERERERE1AwGJyIiIiIiomYwOBERERERETWDwYmIiIiIiKgZ/x8iJZj+JdiQFQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 3))\n",
    "hm = ml_t.headalongline(x=x_obs, y=np.zeros_like(x_obs), t=t)\n",
    "for i in range(len(x_obs)):\n",
    "    plt.plot(\n",
    "        t, h_obs_series[0, :, i], label=f\"obs{i} (x={x_obs[i]})\", marker=\"x\", ls=\"none\"\n",
    "    )\n",
    "    plt.plot(t, hm[0, :, i], label=f\"sim{i}\", color=f\"C{i}\")\n",
    "\n",
    "plt.xscale(\"log\")\n",
    "plt.legend(loc=(0, 1), frameon=False, ncol=6, fontsize=\"small\")\n",
    "plt.xlabel(\"time [days]\")\n",
    "plt.ylabel(\"head [m]\")\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a05b9bf",
   "metadata": {},
   "source": [
    "## Combined calibration\n",
    "\n",
    "Next, we want to attempt a combined calibration. This is done by superposition of a\n",
    "steady model, representing the average situation (i.e. the average river stage), and a\n",
    "transient model that captures the effect of a sudden change in the river water level.\n",
    "The average river stage is 2 m+ref, the sudden rise is 2m at t=0.1 days.\n",
    "\n",
    "We build 2 models, a steady and transient model. Note that we give the `XSection`\n",
    "elements the same names in both models. This allows the calibration class to share\n",
    "parameters between zones (e.g. the polder) across both models.\n",
    "\n",
    "The steady model is added to the transient model. This means the transient model will\n",
    "use the steady model to compute heads and flows: $h = h_\\text{steady} + h_\\text{transient}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "98217e4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of elements, Number of equations: 4 , 2\n",
      "....\n",
      "solution complete\n",
      "self.neq  2\n",
      "solution complete\n"
     ]
    }
   ],
   "source": [
    "ml_s = tfs.ModelXsection(naq=1)\n",
    "river_s = tfs.XsectionMaq(\n",
    "    ml_s,\n",
    "    x1=-np.inf,\n",
    "    x2=0.0,\n",
    "    kaq=[10.0],\n",
    "    z=[1, 0, -10],\n",
    "    c=[1e-4],\n",
    "    npor=0.3,\n",
    "    topboundary=\"semi\",\n",
    "    hstar=2,\n",
    "    name=\"river\",\n",
    ")\n",
    "\n",
    "polder_s = tfs.XsectionMaq(\n",
    "    ml_s,\n",
    "    x1=0.0,\n",
    "    x2=np.inf,\n",
    "    kaq=[10.0],\n",
    "    z=[1, 0, -10],\n",
    "    c=[500],\n",
    "    npor=0.3,\n",
    "    topboundary=\"semi\",\n",
    "    hstar=0,\n",
    "    name=\"polder\",\n",
    ")\n",
    "\n",
    "# add steady model to transient model\n",
    "ml_t = tft.ModelXsection(naq=1, tmin=1e-3, tmax=100, steady=ml_s)\n",
    "\n",
    "river_t = tft.XsectionMaq(\n",
    "    ml_t,\n",
    "    x1=-np.inf,\n",
    "    x2=0.0,\n",
    "    kaq=[10.0],\n",
    "    z=[1, 0, -10],\n",
    "    c=[1e-4],\n",
    "    Saq=[1e-3],\n",
    "    topboundary=\"semi\",\n",
    "    tsandhstar=[(0.1, 2)],\n",
    "    name=\"river\",\n",
    ")\n",
    "\n",
    "polder_t = tft.XsectionMaq(\n",
    "    ml_t,\n",
    "    x1=0.0,\n",
    "    x2=np.inf,\n",
    "    kaq=[10.0],\n",
    "    z=[1, 0, -10],\n",
    "    c=[500],\n",
    "    Saq=[1e-3],\n",
    "    topboundary=\"semi\",\n",
    "    name=\"polder\",\n",
    ")\n",
    "ml_t.solve()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e3764d7",
   "metadata": {},
   "source": [
    "Plot the change in head over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "403f0f6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = np.logspace(-1, 1, 11)\n",
    "h0 = ml_t.headalongline(x=x, y=np.zeros_like(x), t=t)\n",
    "\n",
    "plt.figure(figsize=(10, 3))\n",
    "for i in range(len(t)):\n",
    "    plt.plot(x, h0[0, i], label=f\"t={t[i]:.1f} d\")\n",
    "plt.legend(loc=(0, 1), frameon=False, ncol=6, fontsize=\"small\")\n",
    "plt.ylabel(\"head [m]\")\n",
    "plt.xlabel(\"distance from river [m]\")\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5c62ad3",
   "metadata": {},
   "source": [
    "Now we get our head time series from the model to use as our observation time series in\n",
    "the calibration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0b4d5499",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h_obs_series = ml_t.headalongline(x=x_obs, y=np.zeros_like(x_obs), t=t)\n",
    "\n",
    "plt.figure(figsize=(10, 3))\n",
    "for i in range(len(x_obs)):\n",
    "    plt.plot(t, h_obs_series[0, :, i], label=f\"obs{i} (x={x_obs[i]})\", marker=\"x\", ls=\"-\")\n",
    "plt.legend(loc=(0, 1), frameon=False, ncol=3)\n",
    "plt.ylabel(\"head [m]\")\n",
    "plt.xlabel(\"time [d]\")\n",
    "plt.grid()\n",
    "plt.xscale(\"log\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fe33bc6",
   "metadata": {},
   "source": [
    "Now we will calibrate both models simultaneously by passing both models to the\n",
    "calibrate class. We need to add the steady model separately so that the calibration\n",
    "class can link the parameters between the models.\n",
    "\n",
    "We add the head observations as before. \n",
    "\n",
    "Note that when defining the calibration parameters, we can now set the names of the\n",
    "`XSections` we want to target, as well as the `model`. The `model` can be `\"steady\"`,\n",
    "`\"transient\"` or `\"both\"`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e5ba87ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "                  initial     optimal          pmin     pmax  log_scaled  \\\n",
      "name                                                                       \n",
      "kaq_0_0_polder     2.0000    9.999954  2.000000e+00    100.0       False   \n",
      "c_0_0_polder    1000.0000  500.002305  1.000000e+00  10000.0       False   \n",
      "Saq_0_0_polder     0.0001    0.001000  1.000000e-10      1.0        True   \n",
      "\n",
      "                n_targets  n_models  n_inhoms  \n",
      "name                                           \n",
      "kaq_0_0_polder          2         2         1  \n",
      "c_0_0_polder            2         2         1  \n",
      "Saq_0_0_polder          1         1         1  \n",
      "RMSE: 2.472e-08\n"
     ]
    }
   ],
   "source": [
    "cal = tf.Calibrate(transient_model=ml_t, steady_model=ml_s)\n",
    "\n",
    "for i in range(len(x_obs)):\n",
    "    cal.add_head_time_series(\n",
    "        name=f\"obs{i}\",\n",
    "        x=x_obs[i],\n",
    "        y=0.0,\n",
    "        layer=[0],\n",
    "        t=t,\n",
    "        h=h_obs_series[0, :, i],\n",
    "        normalized=True,\n",
    "    )\n",
    "\n",
    "cal.set_aquifer_parameter(\n",
    "    \"kaq\",\n",
    "    layers=[0],\n",
    "    initial=2.0,\n",
    "    pmin=2.0,\n",
    "    pmax=100.0,\n",
    "    inhoms=[\"polder\"],\n",
    "    model=\"both\",\n",
    ")\n",
    "cal.set_aquifer_parameter(\n",
    "    \"c\",\n",
    "    layers=[0],\n",
    "    initial=1000.0,\n",
    "    pmin=1.0,\n",
    "    pmax=10_000,\n",
    "    inhoms=[\"polder\"],\n",
    "    model=\"both\",\n",
    ")\n",
    "cal.set_aquifer_parameter(\n",
    "    \"Saq\",\n",
    "    layers=[0],\n",
    "    initial=1e-4,\n",
    "    inhoms=[\"polder\"],\n",
    "    pmin=1e-10,\n",
    "    pmax=1.0,\n",
    "    model=\"transient\",\n",
    "    log_scale=True,\n",
    ")\n",
    "\n",
    "# cal.lmfit()\n",
    "cal.fit(printdot=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "018aee81",
   "metadata": {},
   "source": [
    "Let's view the parameters dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "629cb4f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_458e3\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_458e3_level0_col0\" class=\"col_heading level0 col0\" >initial</th>\n",
       "      <th id=\"T_458e3_level0_col1\" class=\"col_heading level0 col1\" >optimal</th>\n",
       "      <th id=\"T_458e3_level0_col2\" class=\"col_heading level0 col2\" >pmin</th>\n",
       "      <th id=\"T_458e3_level0_col3\" class=\"col_heading level0 col3\" >pmax</th>\n",
       "      <th id=\"T_458e3_level0_col4\" class=\"col_heading level0 col4\" >log_scaled</th>\n",
       "      <th id=\"T_458e3_level0_col5\" class=\"col_heading level0 col5\" >n_targets</th>\n",
       "      <th id=\"T_458e3_level0_col6\" class=\"col_heading level0 col6\" >n_models</th>\n",
       "      <th id=\"T_458e3_level0_col7\" class=\"col_heading level0 col7\" >n_inhoms</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >name</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_458e3_level0_row0\" class=\"row_heading level0 row0\" >kaq_0_0_polder</th>\n",
       "      <td id=\"T_458e3_row0_col0\" class=\"data row0 col0\" >2.00e+00</td>\n",
       "      <td id=\"T_458e3_row0_col1\" class=\"data row0 col1\" >1.00e+01</td>\n",
       "      <td id=\"T_458e3_row0_col2\" class=\"data row0 col2\" >2.00e+00</td>\n",
       "      <td id=\"T_458e3_row0_col3\" class=\"data row0 col3\" >1.00e+02</td>\n",
       "      <td id=\"T_458e3_row0_col4\" class=\"data row0 col4\" >False</td>\n",
       "      <td id=\"T_458e3_row0_col5\" class=\"data row0 col5\" >2</td>\n",
       "      <td id=\"T_458e3_row0_col6\" class=\"data row0 col6\" >2</td>\n",
       "      <td id=\"T_458e3_row0_col7\" class=\"data row0 col7\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_458e3_level0_row1\" class=\"row_heading level0 row1\" >c_0_0_polder</th>\n",
       "      <td id=\"T_458e3_row1_col0\" class=\"data row1 col0\" >1.00e+03</td>\n",
       "      <td id=\"T_458e3_row1_col1\" class=\"data row1 col1\" >5.00e+02</td>\n",
       "      <td id=\"T_458e3_row1_col2\" class=\"data row1 col2\" >1.00e+00</td>\n",
       "      <td id=\"T_458e3_row1_col3\" class=\"data row1 col3\" >1.00e+04</td>\n",
       "      <td id=\"T_458e3_row1_col4\" class=\"data row1 col4\" >False</td>\n",
       "      <td id=\"T_458e3_row1_col5\" class=\"data row1 col5\" >2</td>\n",
       "      <td id=\"T_458e3_row1_col6\" class=\"data row1 col6\" >2</td>\n",
       "      <td id=\"T_458e3_row1_col7\" class=\"data row1 col7\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_458e3_level0_row2\" class=\"row_heading level0 row2\" >Saq_0_0_polder</th>\n",
       "      <td id=\"T_458e3_row2_col0\" class=\"data row2 col0\" >1.00e-04</td>\n",
       "      <td id=\"T_458e3_row2_col1\" class=\"data row2 col1\" >1.00e-03</td>\n",
       "      <td id=\"T_458e3_row2_col2\" class=\"data row2 col2\" >1.00e-10</td>\n",
       "      <td id=\"T_458e3_row2_col3\" class=\"data row2 col3\" >1.00e+00</td>\n",
       "      <td id=\"T_458e3_row2_col4\" class=\"data row2 col4\" >True</td>\n",
       "      <td id=\"T_458e3_row2_col5\" class=\"data row2 col5\" >1</td>\n",
       "      <td id=\"T_458e3_row2_col6\" class=\"data row2 col6\" >1</td>\n",
       "      <td id=\"T_458e3_row2_col7\" class=\"data row2 col7\" >1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7b1aa5a04410>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cal.parameters.style.format(\n",
    "    formatter=\"{:.2e}\", subset=[\"initial\", \"optimal\", \"pmin\", \"pmax\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "934f75a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 3))\n",
    "hm = ml_t.headalongline(x=x_obs, y=np.zeros_like(x_obs), t=t)\n",
    "for i in range(len(x_obs)):\n",
    "    plt.plot(\n",
    "        t, h_obs_series[0, :, i], label=f\"obs{i} (x={x_obs[i]})\", marker=\"x\", ls=\"none\"\n",
    "    )\n",
    "    plt.plot(t, hm[0, :, i], label=f\"sim{i}\", color=f\"C{i}\")\n",
    "\n",
    "plt.xscale(\"log\")\n",
    "plt.legend(loc=(0, 1), frameon=False, ncol=6)\n",
    "plt.xlabel(\"time [days]\")\n",
    "plt.ylabel(\"head [m]\")\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ddb5b4b",
   "metadata": {},
   "source": [
    "## Calibrating on time series with constant offsets\n",
    "\n",
    "The Calibrate class supports adding unknown constants for each head time series. This\n",
    "is useful when the data has been measured relative to some reference level, but we are\n",
    "only interested in the variation in heads. This could be used for calibrating to head\n",
    "changes, while ignoring the absolute levels. Or, it could be used to calibrate on head\n",
    "observations that lie along a river, where the reference level might change slightly\n",
    "as we move downstream along that river.\n",
    "\n",
    "In this example, we're simply adding an increasing constant value (1, 2 or 3 m) to\n",
    "each head observation from the previous example.\n",
    "\n",
    "Note that when adding the head time series, we now specify the constant and provide it\n",
    "with a tuple containing three values: `constant = (initial, pmin, pmax)`. This\n",
    "represents the initial guess of the reference level, and the upper and lower bounds.\n",
    "The upper and lower bounds are optional and may be ommitted by passing a single float."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "805185af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "                  initial     optimal          pmin     pmax  log_scaled  \\\n",
      "name                                                                       \n",
      "obs0_constant      0.1000    1.000000 -1.000000e+01     10.0       False   \n",
      "obs1_constant      0.1000    2.000000 -1.000000e+01     10.0       False   \n",
      "obs2_constant      0.1000    3.000000 -1.000000e+01     10.0       False   \n",
      "kaq_0_0_polder     2.0000    9.999735  2.000000e+00    100.0       False   \n",
      "c_0_0_polder    1000.0000  500.013246  1.000000e+00  10000.0       False   \n",
      "Saq_0_0_polder     0.0001    0.001000  1.000000e-10      1.0        True   \n",
      "\n",
      "                n_targets  n_models  n_inhoms  \n",
      "name                                           \n",
      "obs0_constant           1         0         0  \n",
      "obs1_constant           1         0         0  \n",
      "obs2_constant           1         0         0  \n",
      "kaq_0_0_polder          2         2         1  \n",
      "c_0_0_polder            2         2         1  \n",
      "Saq_0_0_polder          1         1         1  \n",
      "RMSE: 1.873e-08\n"
     ]
    }
   ],
   "source": [
    "cal = tf.Calibrate(transient_model=ml_t, steady_model=ml_s)\n",
    "\n",
    "for i in range(len(x_obs)):\n",
    "    cal.add_head_time_series(\n",
    "        name=f\"obs{i}\",\n",
    "        x=x_obs[i],\n",
    "        y=0.0,\n",
    "        layer=[0],\n",
    "        t=t,\n",
    "        h=h_obs_series[0, :, i] + (i + 1.0),\n",
    "        constant=(0.1, -10, 10),\n",
    "    )\n",
    "\n",
    "cal.set_aquifer_parameter(\n",
    "    \"kaq\",\n",
    "    layers=[0],\n",
    "    initial=2.0,\n",
    "    pmin=2.0,\n",
    "    pmax=100.0,\n",
    "    inhoms=[\"polder\"],\n",
    "    model=\"both\",\n",
    ")\n",
    "cal.set_aquifer_parameter(\n",
    "    \"c\",\n",
    "    layers=[0],\n",
    "    initial=1000.0,\n",
    "    pmin=1.0,\n",
    "    pmax=10_000,\n",
    "    inhoms=[\"polder\"],\n",
    "    model=\"both\",\n",
    ")\n",
    "cal.set_aquifer_parameter(\n",
    "    \"Saq\",\n",
    "    layers=[0],\n",
    "    initial=1e-4,\n",
    "    inhoms=[\"polder\"],\n",
    "    pmin=1e-10,\n",
    "    pmax=1.0,\n",
    "    model=\"transient\",\n",
    "    log_scale=True,\n",
    ")\n",
    "\n",
    "# cal.lmfit()\n",
    "cal.fit(printdot=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91885d05",
   "metadata": {},
   "source": [
    "The parameters dataframe shows that we were able to get the correct values of the\n",
    "constants."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5852d1ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_259c3\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_259c3_level0_col0\" class=\"col_heading level0 col0\" >initial</th>\n",
       "      <th id=\"T_259c3_level0_col1\" class=\"col_heading level0 col1\" >optimal</th>\n",
       "      <th id=\"T_259c3_level0_col2\" class=\"col_heading level0 col2\" >pmin</th>\n",
       "      <th id=\"T_259c3_level0_col3\" class=\"col_heading level0 col3\" >pmax</th>\n",
       "      <th id=\"T_259c3_level0_col4\" class=\"col_heading level0 col4\" >log_scaled</th>\n",
       "      <th id=\"T_259c3_level0_col5\" class=\"col_heading level0 col5\" >n_targets</th>\n",
       "      <th id=\"T_259c3_level0_col6\" class=\"col_heading level0 col6\" >n_models</th>\n",
       "      <th id=\"T_259c3_level0_col7\" class=\"col_heading level0 col7\" >n_inhoms</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >name</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_259c3_level0_row0\" class=\"row_heading level0 row0\" >obs0_constant</th>\n",
       "      <td id=\"T_259c3_row0_col0\" class=\"data row0 col0\" >0.10</td>\n",
       "      <td id=\"T_259c3_row0_col1\" class=\"data row0 col1\" >1.00</td>\n",
       "      <td id=\"T_259c3_row0_col2\" class=\"data row0 col2\" >-10.00</td>\n",
       "      <td id=\"T_259c3_row0_col3\" class=\"data row0 col3\" >10.00</td>\n",
       "      <td id=\"T_259c3_row0_col4\" class=\"data row0 col4\" >False</td>\n",
       "      <td id=\"T_259c3_row0_col5\" class=\"data row0 col5\" >1</td>\n",
       "      <td id=\"T_259c3_row0_col6\" class=\"data row0 col6\" >0</td>\n",
       "      <td id=\"T_259c3_row0_col7\" class=\"data row0 col7\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_259c3_level0_row1\" class=\"row_heading level0 row1\" >obs1_constant</th>\n",
       "      <td id=\"T_259c3_row1_col0\" class=\"data row1 col0\" >0.10</td>\n",
       "      <td id=\"T_259c3_row1_col1\" class=\"data row1 col1\" >2.00</td>\n",
       "      <td id=\"T_259c3_row1_col2\" class=\"data row1 col2\" >-10.00</td>\n",
       "      <td id=\"T_259c3_row1_col3\" class=\"data row1 col3\" >10.00</td>\n",
       "      <td id=\"T_259c3_row1_col4\" class=\"data row1 col4\" >False</td>\n",
       "      <td id=\"T_259c3_row1_col5\" class=\"data row1 col5\" >1</td>\n",
       "      <td id=\"T_259c3_row1_col6\" class=\"data row1 col6\" >0</td>\n",
       "      <td id=\"T_259c3_row1_col7\" class=\"data row1 col7\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_259c3_level0_row2\" class=\"row_heading level0 row2\" >obs2_constant</th>\n",
       "      <td id=\"T_259c3_row2_col0\" class=\"data row2 col0\" >0.10</td>\n",
       "      <td id=\"T_259c3_row2_col1\" class=\"data row2 col1\" >3.00</td>\n",
       "      <td id=\"T_259c3_row2_col2\" class=\"data row2 col2\" >-10.00</td>\n",
       "      <td id=\"T_259c3_row2_col3\" class=\"data row2 col3\" >10.00</td>\n",
       "      <td id=\"T_259c3_row2_col4\" class=\"data row2 col4\" >False</td>\n",
       "      <td id=\"T_259c3_row2_col5\" class=\"data row2 col5\" >1</td>\n",
       "      <td id=\"T_259c3_row2_col6\" class=\"data row2 col6\" >0</td>\n",
       "      <td id=\"T_259c3_row2_col7\" class=\"data row2 col7\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_259c3_level0_row3\" class=\"row_heading level0 row3\" >kaq_0_0_polder</th>\n",
       "      <td id=\"T_259c3_row3_col0\" class=\"data row3 col0\" >2.00</td>\n",
       "      <td id=\"T_259c3_row3_col1\" class=\"data row3 col1\" >10.00</td>\n",
       "      <td id=\"T_259c3_row3_col2\" class=\"data row3 col2\" >2.00</td>\n",
       "      <td id=\"T_259c3_row3_col3\" class=\"data row3 col3\" >100.00</td>\n",
       "      <td id=\"T_259c3_row3_col4\" class=\"data row3 col4\" >False</td>\n",
       "      <td id=\"T_259c3_row3_col5\" class=\"data row3 col5\" >2</td>\n",
       "      <td id=\"T_259c3_row3_col6\" class=\"data row3 col6\" >2</td>\n",
       "      <td id=\"T_259c3_row3_col7\" class=\"data row3 col7\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_259c3_level0_row4\" class=\"row_heading level0 row4\" >c_0_0_polder</th>\n",
       "      <td id=\"T_259c3_row4_col0\" class=\"data row4 col0\" >1000.00</td>\n",
       "      <td id=\"T_259c3_row4_col1\" class=\"data row4 col1\" >500.01</td>\n",
       "      <td id=\"T_259c3_row4_col2\" class=\"data row4 col2\" >1.00</td>\n",
       "      <td id=\"T_259c3_row4_col3\" class=\"data row4 col3\" >10000.00</td>\n",
       "      <td id=\"T_259c3_row4_col4\" class=\"data row4 col4\" >False</td>\n",
       "      <td id=\"T_259c3_row4_col5\" class=\"data row4 col5\" >2</td>\n",
       "      <td id=\"T_259c3_row4_col6\" class=\"data row4 col6\" >2</td>\n",
       "      <td id=\"T_259c3_row4_col7\" class=\"data row4 col7\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_259c3_level0_row5\" class=\"row_heading level0 row5\" >Saq_0_0_polder</th>\n",
       "      <td id=\"T_259c3_row5_col0\" class=\"data row5 col0\" >0.00</td>\n",
       "      <td id=\"T_259c3_row5_col1\" class=\"data row5 col1\" >0.00</td>\n",
       "      <td id=\"T_259c3_row5_col2\" class=\"data row5 col2\" >0.00</td>\n",
       "      <td id=\"T_259c3_row5_col3\" class=\"data row5 col3\" >1.00</td>\n",
       "      <td id=\"T_259c3_row5_col4\" class=\"data row5 col4\" >True</td>\n",
       "      <td id=\"T_259c3_row5_col5\" class=\"data row5 col5\" >1</td>\n",
       "      <td id=\"T_259c3_row5_col6\" class=\"data row5 col6\" >1</td>\n",
       "      <td id=\"T_259c3_row5_col7\" class=\"data row5 col7\" >1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7b1aa5ba20d0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cal.parameters.style.format(\n",
    "    formatter=\"{:.2f}\", subset=[\"initial\", \"optimal\", \"pmin\", \"pmax\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bcfb5e0",
   "metadata": {},
   "source": [
    "Plot the calibration results. We now use the head time series objects contained within\n",
    "the Calibration class to plot the observations. When we apply the constants, we see the\n",
    "model fits perfectly with the data. If we set `apply_constant=False`, we would see that\n",
    "the observations are shifted vertically relative to the model simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "40ea3054",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 3))\n",
    "hm = ml_t.headalongline(x=x_obs, y=np.zeros_like(x_obs), t=t)\n",
    "for i in range(len(x_obs)):\n",
    "    cal.observations_dict[f\"obs{i}\"].plot(\n",
    "        ax=plt.gca(), marker=\"x\", ls=\"none\", apply_constant=True\n",
    "    )\n",
    "    plt.plot(t, hm[0, :, i], label=f\"sim{i}\", color=f\"C{i}\")\n",
    "\n",
    "plt.xscale(\"log\")\n",
    "plt.legend(loc=(0, 1), frameon=False, ncol=6)\n",
    "plt.xlabel(\"time [days]\")\n",
    "plt.ylabel(\"head [m]\")\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c096114f",
   "metadata": {},
   "source": [
    "## Calibrating on time series with time shifts\n",
    "\n",
    "Another option the new Calibration class provides is to set time shift parameters in the calibration. This allows the optimization to shift the head time series in time. This can be useful when there are phase shifts in the data that are not represented in our model, e.g. when the observations lie along a river, where a flood wave might arrive slightly earlier in upstream observation wells relative to the downstream observation wells. \n",
    "\n",
    "In this example, we're simply adding an increasing time shift (0.1, 0.2 and 0.3 days) to\n",
    "each head observation series from the previous example.\n",
    "\n",
    "Note that when adding the head time series, we now specify the time shift and provide it\n",
    "with a tuple containing three values: `time_shift = (initial, pmin, pmax)`. This\n",
    "represents the initial guess of the time shift, and the upper and lower bounds.\n",
    "The upper and lower bounds are optional and may be ommitted by passing a single float.\n",
    "\n",
    "We recreate the transient cross-section model here to reduce the order of the `tmin`\n",
    "parameter, because shifting the observation time series can cause observations to lie\n",
    "close to the changes in boundary conditions, which will introduce NaNs into the\n",
    "simulation. These are filtered out, but the optimization might still be affected, so it\n",
    "is recommended to also reduce the `tmin` to avoid this as much as possible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1e6a8fbe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of elements, Number of equations: 4 , 2\n",
      "....\n",
      "solution complete\n",
      "self.neq  2\n",
      "solution complete\n"
     ]
    }
   ],
   "source": [
    "# add steady model to transient model\n",
    "ml_t = tft.ModelXsection(naq=1, tmin=1e-6, tmax=100, steady=ml_s)\n",
    "\n",
    "river_t = tft.XsectionMaq(\n",
    "    ml_t,\n",
    "    x1=-np.inf,\n",
    "    x2=0.0,\n",
    "    kaq=[10.0],\n",
    "    z=[1, 0, -10],\n",
    "    c=[1e-4],\n",
    "    Saq=[1e-3],\n",
    "    topboundary=\"semi\",\n",
    "    tsandhstar=[(0.1, 2)],\n",
    "    name=\"river\",\n",
    ")\n",
    "\n",
    "polder_t = tft.XsectionMaq(\n",
    "    ml_t,\n",
    "    x1=0.0,\n",
    "    x2=np.inf,\n",
    "    kaq=[10.0],\n",
    "    z=[1, 0, -10],\n",
    "    c=[500],\n",
    "    Saq=[1e-3],\n",
    "    topboundary=\"semi\",\n",
    "    name=\"polder\",\n",
    ")\n",
    "ml_t.solve()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bcdff213",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning, some of the times ( 6.021147109236402e-07 ) are smaller than tmin = 9.9999999e-07 after a change in boundary condition. Nans are substituted.\n",
      "Warning, some of the times ( 8.672320787050936e-07 ) are smaller than tmin = 9.9999999e-07 after a change in boundary condition. Nans are substituted.\n",
      "Warning, some of the times ( 9.512140915768352e-07 ) are smaller than tmin = 9.9999999e-07 after a change in boundary condition. Nans are substituted.\n",
      "Warning, some of the times ( 9.146629446066257e-07 ) are smaller than tmin = 9.9999999e-07 after a change in boundary condition. Nans are substituted.\n",
      "Warning, some of the times ( 9.253381204887834e-07 ) are smaller than tmin = 9.9999999e-07 after a change in boundary condition. Nans are substituted.\n",
      "Warning, some of the times ( 9.441022927048071e-07 ) are smaller than tmin = 9.9999999e-07 after a change in boundary condition. Nans are substituted.\n",
      "Warning, some of the times ( 9.851228263690892e-07 ) are smaller than tmin = 9.9999999e-07 after a change in boundary condition. Nans are substituted.\n",
      "\n",
      "                     initial     optimal          pmin     pmax  log_scaled  \\\n",
      "name                                                                          \n",
      "obs0_time_shift     0.041667    0.100002  0.000000e+00      1.0       False   \n",
      "obs1_time_shift     0.041667    0.199999  0.000000e+00      1.0       False   \n",
      "obs2_time_shift     0.041667    0.299953  0.000000e+00      1.0       False   \n",
      "kaq_0_0_polder      2.000000    9.984054  2.000000e+00    100.0       False   \n",
      "c_0_0_polder     1000.000000  500.797334  1.000000e+00  10000.0       False   \n",
      "Saq_0_0_polder      0.000100    0.000998  1.000000e-10      1.0        True   \n",
      "\n",
      "                 n_targets  n_models  n_inhoms  \n",
      "name                                            \n",
      "obs0_time_shift          1         0         0  \n",
      "obs1_time_shift          1         0         0  \n",
      "obs2_time_shift          1         0         0  \n",
      "kaq_0_0_polder           2         2         1  \n",
      "c_0_0_polder             2         2         1  \n",
      "Saq_0_0_polder           1         1         1  \n",
      "RMSE: 1.539e-06\n"
     ]
    }
   ],
   "source": [
    "cal = tf.Calibrate(transient_model=ml_t, steady_model=ml_s)\n",
    "\n",
    "for i in range(len(x_obs)):\n",
    "    cal.add_head_time_series(\n",
    "        name=f\"obs{i}\",\n",
    "        x=x_obs[i],\n",
    "        y=0.0,\n",
    "        layer=[0],\n",
    "        t=t + 0.1 * (i + 1),\n",
    "        h=h_obs_series[0, :, i],\n",
    "        time_shift=(1 / 24.0, 0.0, 1.0),  # initial guess is we're 1 hour off\n",
    "    )\n",
    "\n",
    "cal.set_aquifer_parameter(\n",
    "    \"kaq\",\n",
    "    layers=[0],\n",
    "    initial=2.0,\n",
    "    pmin=2.0,\n",
    "    pmax=100.0,\n",
    "    inhoms=[\"polder\"],\n",
    "    model=\"both\",\n",
    ")\n",
    "cal.set_aquifer_parameter(\n",
    "    \"c\",\n",
    "    layers=[0],\n",
    "    initial=1000.0,\n",
    "    pmin=1.0,\n",
    "    pmax=10_000,\n",
    "    inhoms=[\"polder\"],\n",
    "    model=\"both\",\n",
    ")\n",
    "cal.set_aquifer_parameter(\n",
    "    \"Saq\",\n",
    "    layers=[0],\n",
    "    initial=1e-4,\n",
    "    inhoms=[\"polder\"],\n",
    "    pmin=1e-10,\n",
    "    pmax=1.0,\n",
    "    model=\"transient\",\n",
    "    log_scale=True,\n",
    ")\n",
    "\n",
    "# cal.lmfit()\n",
    "cal.fit(printdot=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7bc239b",
   "metadata": {},
   "source": [
    "We got a few warnings about the computation time lying to close to a change in boundary\n",
    "condition, but the results looks good nonetheless. The parameters dataframe shows the\n",
    "time shifts are estimated correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3d2b392b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th id=\"T_e3f61_level0_col1\" class=\"col_heading level0 col1\" >optimal</th>\n",
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       "      <th class=\"index_name level0\" >name</th>\n",
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       "    <tr>\n",
       "      <th id=\"T_e3f61_level0_row0\" class=\"row_heading level0 row0\" >obs0_time_shift</th>\n",
       "      <td id=\"T_e3f61_row0_col0\" class=\"data row0 col0\" >0.04</td>\n",
       "      <td id=\"T_e3f61_row0_col1\" class=\"data row0 col1\" >0.10</td>\n",
       "      <td id=\"T_e3f61_row0_col2\" class=\"data row0 col2\" >0.00</td>\n",
       "      <td id=\"T_e3f61_row0_col3\" class=\"data row0 col3\" >1.00</td>\n",
       "      <td id=\"T_e3f61_row0_col4\" class=\"data row0 col4\" >False</td>\n",
       "      <td id=\"T_e3f61_row0_col5\" class=\"data row0 col5\" >1</td>\n",
       "      <td id=\"T_e3f61_row0_col6\" class=\"data row0 col6\" >0</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_e3f61_level0_row1\" class=\"row_heading level0 row1\" >obs1_time_shift</th>\n",
       "      <td id=\"T_e3f61_row1_col0\" class=\"data row1 col0\" >0.04</td>\n",
       "      <td id=\"T_e3f61_row1_col1\" class=\"data row1 col1\" >0.20</td>\n",
       "      <td id=\"T_e3f61_row1_col2\" class=\"data row1 col2\" >0.00</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_e3f61_level0_row2\" class=\"row_heading level0 row2\" >obs2_time_shift</th>\n",
       "      <td id=\"T_e3f61_row2_col0\" class=\"data row2 col0\" >0.04</td>\n",
       "      <td id=\"T_e3f61_row2_col1\" class=\"data row2 col1\" >0.30</td>\n",
       "      <td id=\"T_e3f61_row2_col2\" class=\"data row2 col2\" >0.00</td>\n",
       "      <td id=\"T_e3f61_row2_col3\" class=\"data row2 col3\" >1.00</td>\n",
       "      <td id=\"T_e3f61_row2_col4\" class=\"data row2 col4\" >False</td>\n",
       "      <td id=\"T_e3f61_row2_col5\" class=\"data row2 col5\" >1</td>\n",
       "      <td id=\"T_e3f61_row2_col6\" class=\"data row2 col6\" >0</td>\n",
       "      <td id=\"T_e3f61_row2_col7\" class=\"data row2 col7\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e3f61_level0_row3\" class=\"row_heading level0 row3\" >kaq_0_0_polder</th>\n",
       "      <td id=\"T_e3f61_row3_col0\" class=\"data row3 col0\" >2.00</td>\n",
       "      <td id=\"T_e3f61_row3_col1\" class=\"data row3 col1\" >9.98</td>\n",
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       "      <td id=\"T_e3f61_row3_col7\" class=\"data row3 col7\" >1</td>\n",
       "    </tr>\n",
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       "      <td id=\"T_e3f61_row4_col2\" class=\"data row4 col2\" >1.00</td>\n",
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       "      <td id=\"T_e3f61_row4_col4\" class=\"data row4 col4\" >False</td>\n",
       "      <td id=\"T_e3f61_row4_col5\" class=\"data row4 col5\" >2</td>\n",
       "      <td id=\"T_e3f61_row4_col6\" class=\"data row4 col6\" >2</td>\n",
       "      <td id=\"T_e3f61_row4_col7\" class=\"data row4 col7\" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e3f61_level0_row5\" class=\"row_heading level0 row5\" >Saq_0_0_polder</th>\n",
       "      <td id=\"T_e3f61_row5_col0\" class=\"data row5 col0\" >0.00</td>\n",
       "      <td id=\"T_e3f61_row5_col1\" class=\"data row5 col1\" >0.00</td>\n",
       "      <td id=\"T_e3f61_row5_col2\" class=\"data row5 col2\" >0.00</td>\n",
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       "      <td id=\"T_e3f61_row5_col6\" class=\"data row5 col6\" >1</td>\n",
       "      <td id=\"T_e3f61_row5_col7\" class=\"data row5 col7\" >1</td>\n",
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       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7b1aa410da90>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cal.parameters.style.format(\n",
    "    formatter=\"{:.2f}\", subset=[\"initial\", \"optimal\", \"pmin\", \"pmax\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34a3b64c",
   "metadata": {},
   "source": [
    "Plot the calibration results. We now use the head time series objects contained within\n",
    "the Calibration class to plot the observations. When we apply the time shifts, we see the\n",
    "model fits perfectly with the data. If we set `apply_time_shift=False`, we would see that\n",
    "the observations are shifted in time relative to the model simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6e237ba0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 3))\n",
    "hm = ml_t.headalongline(x=x_obs, y=np.zeros_like(x_obs), t=t)\n",
    "for i in range(len(x_obs)):\n",
    "    cal.observations_dict[f\"obs{i}\"].plot(\n",
    "        ax=plt.gca(), marker=\"x\", ls=\"none\", apply_time_shift=True\n",
    "    )\n",
    "    plt.plot(t, hm[0, :, i], label=f\"sim{i}\", color=f\"C{i}\")\n",
    "\n",
    "plt.xscale(\"log\")\n",
    "plt.legend(loc=(0, 1), frameon=False, ncol=6)\n",
    "plt.xlabel(\"time [days]\")\n",
    "plt.ylabel(\"head [m]\")\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cda26f2f",
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