{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Estimate time series parameters using pseudo batch transformed data\n",
    "This tutorial will walk through how to estimate a series for the rates and yields from a fed-batch fermentation. This can be useful to detect changes in the phenotype during the fed-batch process. We will work with simulated data, thus the true parameters will be known."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialise session and loading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/s143838/.virtualenvs/pseudobatch-dev/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'stan_version_major': '2', 'stan_version_minor': '29', 'stan_version_patch': '2', 'STAN_THREADS': 'false', 'STAN_MPI': 'false', 'STAN_OPENCL': 'false', 'STAN_NO_RANGE_CHECKS': 'false', 'STAN_CPP_OPTIMS': 'false'}\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "from pseudobatch import pseudobatch_transform_pandas, convert_volumetric_rates_from_pseudo_to_real\n",
    "from pseudobatch.datasets import load_product_inhibited_fedbatch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "fedbatch_df_measurement = load_product_inhibited_fedbatch(sampling_points_only=True)\n",
    "\n",
    "# for visualization purposes we will also load the full dataset\n",
    "fedbatch_df = load_product_inhibited_fedbatch(sampling_points_only=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's quickly inspect the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>sample_volume</th>\n",
       "      <th>c_Biomass</th>\n",
       "      <th>c_Glucose</th>\n",
       "      <th>c_Product</th>\n",
       "      <th>v_Volume</th>\n",
       "      <th>v_Feed_accum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.315046</td>\n",
       "      <td>0.117208</td>\n",
       "      <td>0.676000</td>\n",
       "      <td>1015.906036</td>\n",
       "      <td>15.906036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14.545455</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.026109</td>\n",
       "      <td>0.171224</td>\n",
       "      <td>1.265827</td>\n",
       "      <td>928.960902</td>\n",
       "      <td>28.960902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.090909</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3.083525</td>\n",
       "      <td>0.296168</td>\n",
       "      <td>2.143140</td>\n",
       "      <td>847.314262</td>\n",
       "      <td>47.314262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.636364</td>\n",
       "      <td>100.0</td>\n",
       "      <td>4.568350</td>\n",
       "      <td>0.651151</td>\n",
       "      <td>3.375809</td>\n",
       "      <td>772.816654</td>\n",
       "      <td>72.816654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28.181818</td>\n",
       "      <td>100.0</td>\n",
       "      <td>6.410392</td>\n",
       "      <td>1.735493</td>\n",
       "      <td>4.907704</td>\n",
       "      <td>707.795685</td>\n",
       "      <td>107.795685</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   timestamp  sample_volume  c_Biomass  c_Glucose  c_Product     v_Volume   \n",
       "0  10.000000          100.0   1.315046   0.117208   0.676000  1015.906036  \\\n",
       "1  14.545455          100.0   2.026109   0.171224   1.265827   928.960902   \n",
       "2  19.090909          100.0   3.083525   0.296168   2.143140   847.314262   \n",
       "3  23.636364          100.0   4.568350   0.651151   3.375809   772.816654   \n",
       "4  28.181818          100.0   6.410392   1.735493   4.907704   707.795685   \n",
       "\n",
       "   v_Feed_accum  \n",
       "0     15.906036  \n",
       "1     28.960902  \n",
       "2     47.314262  \n",
       "3     72.816654  \n",
       "4    107.795685  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(fedbatch_df_measurement\n",
    "    .filter(['timestamp', 'sample_volume', 'c_Biomass', 'c_Glucose', 'c_Product', 'v_Volume', 'v_Feed_accum'])\n",
    "    .head()\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataframe is structured exactly similar to the one use in the previous tutorial, though the data is different. We can go straight to the pseudo batch transformation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "glucose_in_feed = fedbatch_df_measurement[\"s_f\"].iloc[0] # The glucose concentration in the feed is constant and store in a row in the dataframe.\n",
    "\n",
    "fedbatch_df_measurement[[\"c_Biomass_pseudo\", \"c_Glucose_pseudo\", \"c_Product_pseudo\", \"c_CO2_pseudo\"]] = pseudobatch_transform_pandas(\n",
    "    fedbatch_df_measurement,\n",
    "    measured_concentration_colnames=[\"c_Biomass\", \"c_Glucose\", \"c_Product\", \"c_CO2\"],\n",
    "    reactor_volume_colname=\"v_Volume\",\n",
    "    accumulated_feed_colname=\"v_Feed_accum\",\n",
    "    sample_volume_colname=\"sample_volume\",\n",
    "    concentration_in_feed=[0, glucose_in_feed, 0, 0], # The biomass, product, CO2 concentrations in the feed are all 0.\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have transformed the data we can estimate parameters."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estimate the growth rate"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to obtain estimates the growth rate as a function of time, i.e. to estimate $\\mu(t)$. First, we will look at the mass balance for biomass. Because we can treat the pseudo batch transformed data as a batch process, we can do the mass balance on pseudo concentration basis instead of mass basis. Assuming that the death rate is 0, the mass balance for the biomass is as follows:\n",
    "$$\n",
    "\\frac{dC^{\\star}_{Biomass}(t)}{dt} = \\mu(t) * C^{\\star}_{Biomass}(t) \n",
    "$$\n",
    "Where $X(t)$ is the biomass in grams, and $\\mu$ is the specific growth rate, The goal is to find $\\mu(t)$, therefore we can isolate $\\mu(t)$ to get \n",
    "$$\n",
    "\\mu(t) = \\frac{dC^{\\star}_{Biomass}(t)}{dt} * C^{\\star}_{Biomass}(t)^{-1}\n",
    "$$ \n",
    "\n",
    "Thus, to calculate the time dependent growth rate, we will need the time derivative of the pseudo biomass concentration ($\\frac{dC^{\\star}_{Biomass}(t)}{dt}$) and the pseudo biomass concentration ($C^{\\star}_{Biomass}(t)$). We already have the pseudo biomass concentration measurements, but we need to estimate $\\frac{dC^{\\star}_{Biomass}(t)}{dt}$. There are many methods that are capable of estimating the derivative of an underlying function (the true growth curve). Typically, these methods assume that the underlying function is continuous. This is not the case for the biomass time series, because samples where drawn from this cultivation. Fortunately, the pseudo batch transformation transform the measurements into a space where underlying growth curve is continuous and therefore the pseudo batch transformation enables the use of many methods to estimate rates as a function of time.\n",
    "\n",
    "Here, we will show how to use finite differences to estimate the growth rate at each measurement time point. First, we will define a function that estimate the time derivative using the finite difference method in the `np.gradient()` function.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def finite_difference_derivative(df: pd.DataFrame, x_colname: str, y_colname: str, log_transform_y: bool = False)->np.ndarray:\n",
    "    x = df[x_colname].to_numpy()\n",
    "    if log_transform_y:\n",
    "        y = df[y_colname].transform(np.log).to_numpy()\n",
    "    else:\n",
    "        y = df[y_colname].to_numpy()\n",
    "    return np.gradient(y, x)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can estimate $\\mu(t)$ as describe in the equation above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "mu_hat = finite_difference_derivative(fedbatch_df_measurement, \"timestamp\", \"c_Biomass_pseudo\", log_transform_y=False) / fedbatch_df_measurement.c_Biomass_pseudo"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot $\\hat\\mu(t)$ as a function of time to inspect is the growth rate was constant over course of the cultivation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x12dc9b430>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "growth_rate_fig, growth_rate_ax = plt.subplots(figsize=(6, 5))\n",
    "growth_rate_ax.scatter(fedbatch_df_measurement.timestamp, mu_hat, label=\"Estimated growth rate (pseudo batch)\", color = \"C1\")\n",
    "growth_rate_ax.set_title(\"Growth rate\")\n",
    "growth_rate_ax.set_xlabel(\"Feeding time [h]\")\n",
    "growth_rate_ax.set_ylabel(\"Growth rate [1/h]\")\n",
    "growth_rate_ax.legend()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this specific growth rate time series we can see that the rate decreases during the fed-batch process. This hints that something may build up during the process and inhibiting the growth.\n",
    "Because this data is simulated we know the true growth rate and we can compare this to the estimated growth rates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "growth_rate_ax.plot(fedbatch_df.timestamp, fedbatch_df['mu_true'], label=\"Simulated growth rate\", color = 'grey')[0]\n",
    "growth_rate_ax.legend()\n",
    "growth_rate_fig"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the estimate is in general close to true growth rate. but close to the boundaries the estimate gets worse. This is because the `np.gradient()` function use a rolling window af 5 data points (centered) to estimate the derivative, but at the boundaries there are not enough data points to fill the window. Therefore the quality of the estimate decreases. Remember that this is simulated data, and therefore we have the accurate measurements. That is not the case in the real world, here the measurements will be noisy, which will be reflected in the accuracy of the growth rate estimates."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estimate specific consumption or production rates\n",
    "Similarly to what we did for growth rate we can setup mass balances for the individual species again on pseudo concentration basis.\n",
    "$$\n",
    "\\frac{dC^{\\star}_{species}(t)}{dt} = r_{species} * C^{\\star}_{Biomass}(t)\n",
    "$$\n",
    "Where $C^{\\star}_{species}(t)$ pseudo concentration of $species$, $r_{species}$ is the specific rate of the $species$, and $C^{\\star}_{Biomass}(t)$ pseudo biomass concentration. The goal is to find $r_{species}(t)$, therefore we can isolate $r_{species}(t)$ to get \n",
    "$$\n",
    "r_{species} = \\frac{dC^{\\star}_{species}(t)}{dt} * C^{\\star}_{Biomass}(t)^{-1}\n",
    "$$ \n",
    "\n",
    "Similarly to the growth rate, we can estimate $\\frac{dC^{\\star}_{species}(t)}{dt}$ as the first order derivative of the time evolution of the species mass. Again using finite difference method we can estimate the rate as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "r_glucose_uptake = -1*finite_difference_derivative(fedbatch_df_measurement, \"timestamp\", \"c_Glucose_pseudo\", log_transform_y=False) / fedbatch_df_measurement.c_Biomass_pseudo\n",
    "r_product_production = finite_difference_derivative(fedbatch_df_measurement, \"timestamp\", \"c_Product_pseudo\", log_transform_y=False) / fedbatch_df_measurement.c_Biomass_pseudo\n",
    "r_co2_production = finite_difference_derivative(fedbatch_df_measurement, \"timestamp\", \"c_CO2_pseudo\", log_transform_y=False) / fedbatch_df_measurement.c_Biomass_pseudo"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the estimated rate along with the true rates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the results\n",
    "fig, axes = plt.subplots(nrows = 1, ncols = 3, figsize=(14, 4))\n",
    "axes[0].plot(fedbatch_df[\"timestamp\"], fedbatch_df[\"mu_true\"]*fedbatch_df['Yxs'], label=\"Simulated glucose uptake rate\", color=\"grey\")\n",
    "axes[0].scatter(fedbatch_df_measurement[\"timestamp\"], r_glucose_uptake, label=\"Estimated glucose uptake rate (pseudo batch)\", color = \"C1\")\n",
    "axes[0].set_title(\"Glucose uptake rate\")\n",
    "axes[1].plot(fedbatch_df[\"timestamp\"], fedbatch_df[\"mu_true\"]*fedbatch_df['Yxp'], label=\"Simulated product formation rate\", color=\"grey\")\n",
    "axes[1].scatter(fedbatch_df_measurement[\"timestamp\"], r_product_production, label=\"Estimated product formation rate (pseudo batch)\", color = \"C1\")\n",
    "axes[1].set_title(\"Product formation rate\")\n",
    "axes[2].plot(fedbatch_df[\"timestamp\"], fedbatch_df[\"mu_true\"]*fedbatch_df['Yxco2'], label=\"Simulated CO2 formation rate\", color=\"grey\")\n",
    "axes[2].scatter(fedbatch_df_measurement[\"timestamp\"], r_co2_production, label=\"Estimated CO2 formation rate (pseudo batch)\", color = \"C1\")\n",
    "axes[2].set_title(\"CO2 formation rate\")\n",
    "\n",
    "# setup legend\n",
    "true_patch = mpatches.Patch(color='grey', label=r'True $r$')\n",
    "pseudo_patch = mpatches.Patch(color='C1', label=r'Estimated $r$')\n",
    "\n",
    "fig.legend(\n",
    "    handles=[true_patch, pseudo_patch],\n",
    "    loc=\"lower center\", ncol=2, bbox_to_anchor=(0.5, -0.12)\n",
    ")\n",
    "\n",
    "fig.supxlabel(\"Feeding time [h]\")\n",
    "axes[0].set_ylabel(\"Specific rate [g/gDW/h]\")\n",
    "fig.tight_layout()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because this is simulated data the plot these plot are very similar only scaled differently, but it shows how estimate the rates as function of time based on pseudo batch transformed data."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estimate volumetric rates \n",
    "\n",
    "Sometimes one is interested in the volumetric rates. These can also be calculated from the pseudo batch transformed data, but it requires a bit extra care. \n",
    "\n",
    "\n",
    "Recall that in a batch process there is no volume change and therefore all changes in concentrations are due to cellular behavior (if we ignore phenomena as evaporation and gas stripping). Thus the volumetric rates can be calculated from the pseudo batch transformed data by estimating the differential quotient and the concentration time series data. \n",
    "\n",
    "$$\n",
    "q^{\\star}_{species} = \\frac{dC_{species}^{\\star}}{dt}\n",
    "$$\n",
    "\n",
    "Though this volumetric rate describes the rate of change in the pseudo concentration, which is not the same as the rate of change of the real concentration. We can convert it to real concentration using the function `convert_volumetric_rates_from_pseudo_to_real()`. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following we will use the finite difference method to estimate $\\frac{dC_{species}^{\\star}}{dt}$ and them convert it into real concentrations to obtain as estimate of the volumetric rates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Estimate finite difference derivatives\n",
    "fedbatch_df_measurement['q_star_biomass_production'] = finite_difference_derivative(fedbatch_df_measurement, \"timestamp\", \"c_Biomass_pseudo\", log_transform_y=False)\n",
    "fedbatch_df_measurement['q_star_glucose_uptake'] = finite_difference_derivative(fedbatch_df_measurement, \"timestamp\", \"c_Glucose_pseudo\", log_transform_y=False) \n",
    "fedbatch_df_measurement['q_star_product_production'] = finite_difference_derivative(fedbatch_df_measurement, \"timestamp\", \"c_Product_pseudo\", log_transform_y=False)\n",
    "fedbatch_df_measurement['q_star_co2_production'] = finite_difference_derivative(fedbatch_df_measurement, \"timestamp\", \"c_CO2_pseudo\", log_transform_y=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert to real volumetric rates\n",
    "fedbatch_df_measurement['q_biomass'] = convert_volumetric_rates_from_pseudo_to_real(fedbatch_df_measurement.q_star_biomass_production, reactor_volume=fedbatch_df_measurement.v_Volume, sample_volume=fedbatch_df_measurement.sample_volume)\n",
    "fedbatch_df_measurement['q_glucose'] = convert_volumetric_rates_from_pseudo_to_real(fedbatch_df_measurement.q_star_glucose_uptake, reactor_volume=fedbatch_df_measurement.v_Volume, sample_volume=fedbatch_df_measurement.sample_volume)\n",
    "fedbatch_df_measurement['q_product'] = convert_volumetric_rates_from_pseudo_to_real(fedbatch_df_measurement.q_star_product_production, reactor_volume=fedbatch_df_measurement.v_Volume, sample_volume=fedbatch_df_measurement.sample_volume)\n",
    "fedbatch_df_measurement['q_co2'] = convert_volumetric_rates_from_pseudo_to_real(fedbatch_df_measurement.q_star_co2_production, reactor_volume=fedbatch_df_measurement.v_Volume, sample_volume=fedbatch_df_measurement.sample_volume)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For comparison we will now calculate the true volumetric rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# true volumetric rates\n",
    "true_volumetric_rates = fedbatch_df.filter(['timestamp']).copy()\n",
    "\n",
    "true_volumetric_rates['q_biomass'] = fedbatch_df.mu_true * fedbatch_df.c_Biomass\n",
    "true_volumetric_rates['q_glucose'] = fedbatch_df.mu_true * fedbatch_df.c_Biomass * -1*fedbatch_df.Yxs\n",
    "true_volumetric_rates['q_product'] = fedbatch_df.mu_true * fedbatch_df.c_Biomass * fedbatch_df.Yxp\n",
    "true_volumetric_rates['q_co2'] = fedbatch_df.mu_true * fedbatch_df.c_Biomass * fedbatch_df.Yxco2"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can plot true and estimate volumetric rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the results\n",
    "\n",
    "fig, axes = plt.subplots(nrows = 1, ncols = 4, figsize=(14, 4))\n",
    "axes[0].plot(true_volumetric_rates[\"timestamp\"], true_volumetric_rates[\"q_biomass\"], label=\"Simulated glucose uptake rate\", color=\"grey\")\n",
    "axes[0].scatter(fedbatch_df_measurement[\"timestamp\"], fedbatch_df_measurement['q_biomass'], color = \"C1\")\n",
    "axes[0].set_title(\"Volumetric biomass production rate\")\n",
    "\n",
    "axes[1].plot(true_volumetric_rates[\"timestamp\"], true_volumetric_rates[\"q_glucose\"], color=\"grey\")\n",
    "axes[1].scatter(fedbatch_df_measurement[\"timestamp\"], fedbatch_df_measurement['q_glucose'], color = \"C1\")\n",
    "axes[1].set_title(\"Volumetric glucose uptake rate\")\n",
    "\n",
    "axes[2].plot(true_volumetric_rates[\"timestamp\"], true_volumetric_rates[\"q_product\"], color=\"grey\")\n",
    "axes[2].scatter(fedbatch_df_measurement[\"timestamp\"], fedbatch_df_measurement['q_product'], color = \"C1\")\n",
    "axes[2].set_title(\"Volumetric product production rate\")\n",
    "\n",
    "axes[3].plot(true_volumetric_rates[\"timestamp\"], true_volumetric_rates[\"q_co2\"], color=\"grey\")\n",
    "axes[3].scatter(fedbatch_df_measurement[\"timestamp\"], fedbatch_df_measurement['q_co2'], color = \"C1\")\n",
    "axes[3].set_title(\"Volumetric product production rate\")\n",
    "\n",
    "# setup legend\n",
    "true_patch = mpatches.Patch(color='grey', label=r'True $q$')\n",
    "pseudo_patch = mpatches.Patch(color='C1', label=r'Estimated $q$')\n",
    "\n",
    "fig.legend(\n",
    "    handles=[true_patch, pseudo_patch],\n",
    "    loc=\"lower center\", ncol=2, bbox_to_anchor=(0.5, -0.12)\n",
    ")\n",
    "fig.supxlabel(\"Feeding time [h]\")\n",
    "axes[0].set_ylabel(\"Volumetric rate [g/L/h]\")\n",
    "fig.tight_layout()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the estimate the volumetric rates are very good, but again similar to the specific rates it is difficult to get good estimates close the boundaries of the time series."
   ]
  }
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