{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dealing with multiple feeds\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In some cases the fed-batch process uses multiple feeding mediums. This is typically the case in cultivation of mammalians cells. This tutorial will show how to use the pseudo batch transformation to handle multiple feeding mediums. We will, again, use simulated data to showcase the workflow. This fed-batch process seeks to mimic a cultivation of mammalians cells. These simulated cells require two substrates to grow; glucose and glutamine. The bioreactor is fed with two different feed mediums, one containing concentrated glucose and one complex medium that contain glucose, and glutamine. Furthermore, the feeding is not done through a \"continuous\" exponential feed, but instead as pulse feed, i.e. at certain time points a given volume of each feed is added to the bioreactor.\n",
    "\n",
    "In the simulation 12 samples are drawn over the course of the fermentation and 0.1 hours after each sample feed is added."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we will setup the environment and load the 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 numpy as np\n",
    "\n",
    "from pseudobatch import pseudobatch_transform_pandas\n",
    "from pseudobatch.datasets import load_cho_cell_like_fedbatch\n",
    "from pseudobatch.datasets._dataloaders import _prepare_simulated_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "fedbatch_df_measurement = load_cho_cell_like_fedbatch(sampling_points_only=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inspect the dataset"
   ]
  },
  {
   "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_accum1</th>\n",
       "      <th>v_Feed_accum2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1.644817</td>\n",
       "      <td>47.882089</td>\n",
       "      <td>0.940479</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>3.007637</td>\n",
       "      <td>50.337209</td>\n",
       "      <td>2.106177</td>\n",
       "      <td>935.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>5.466735</td>\n",
       "      <td>50.535418</td>\n",
       "      <td>4.170358</td>\n",
       "      <td>870.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>9.758328</td>\n",
       "      <td>47.107608</td>\n",
       "      <td>7.737765</td>\n",
       "      <td>805.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>16.782229</td>\n",
       "      <td>38.381155</td>\n",
       "      <td>13.547532</td>\n",
       "      <td>740.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   timestamp  sample_volume  c_Biomass  c_Glucose  c_Product  v_Volume   \n",
       "0       10.0          170.0   1.644817  47.882089   0.940479    1000.0  \\\n",
       "1       16.0          170.0   3.007637  50.337209   2.106177     935.0   \n",
       "2       22.0          170.0   5.466735  50.535418   4.170358     870.0   \n",
       "3       28.0          170.0   9.758328  47.107608   7.737765     805.0   \n",
       "4       34.0          170.0  16.782229  38.381155  13.547532     740.0   \n",
       "\n",
       "   v_Feed_accum1  v_Feed_accum2  \n",
       "0            0.0            0.0  \n",
       "1          100.0            5.0  \n",
       "2          200.0           10.0  \n",
       "3          300.0           15.0  \n",
       "4          400.0           20.0  "
      ]
     },
     "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_accum1', 'v_Feed_accum2'])\n",
    "    .head()\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataframe is structured very similar to the one used in the previous tutorial. This time the fed-batch process utilized two feed streams and therefore there are two accumulated feed columns. It is important to not the even though the feed is added in pulses the pseudo batch still takes accumulated amounts.\n",
    "\n",
    "In this simulation feed 1 contained only glucose, while feed 2 contain both glucose and Glutamine. We will store the concentrations in some variables here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "glucose_in_feed1 = fedbatch_df_measurement.c_Glucose_feed1.iloc[0] \n",
    "glucose_in_feed2 = 0  \n",
    "Glutamine_in_feed1 = fedbatch_df_measurement.c_Glutamine_feed1.iloc[0]\n",
    "Glutamine_in_feed2 = fedbatch_df_measurement.c_Glutamine_feed2.iloc[0]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Applying the pseudo batch transformation\n",
    "We can now apply the pseudo batch transformation to the data. Notice that to include both feeds we simply supply a list of the columns names in the `accumulated_feed_colnames` argument. The `concentrations_in_feed` is specified as follows: The outer list iterates over the measured concentrations (in this case Biomass, Glucose, Product, CO2, Glutamine), while the inner list iterates over the feeding mediums. **Important:** the order HAS to be the same for `concetration_in_feed` and both `measured_concentration_colnames` and `accumulated_feed_colname`!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "fedbatch_df_measurement[[\"c_Biomass_pseudo\", \"c_Glucose_pseudo\", \"c_Product_pseudo\", \"c_CO2_pseudo\", \"c_Glutamine_pseudo\"]] = pseudobatch_transform_pandas(\n",
    "    df=fedbatch_df_measurement,\n",
    "    measured_concentration_colnames=['c_Biomass', 'c_Glucose', 'c_Product', 'c_CO2', 'c_Glutamine'],\n",
    "    reactor_volume_colname='v_Volume',\n",
    "    accumulated_feed_colname=['v_Feed_accum1', 'v_Feed_accum2'],\n",
    "    concentration_in_feed=[[0, 0], [glucose_in_feed1, glucose_in_feed2], [0, 0], [0, 0], [Glutamine_in_feed1, Glutamine_in_feed2]],\n",
    "    sample_volume_colname='sample_volume'\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can take a quick look at what the pseudo batch transformed data looks like by plotting it against time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cols = ['c_Biomass_pseudo', 'c_Glucose_pseudo','v_Feed_accum1','c_Product_pseudo',  'c_Glutamine_pseudo', 'v_Feed_accum2']\n",
    "fig, axes = plt.subplots(nrows=2, ncols=len(plot_cols)//2, figsize=(10, 8))\n",
    "for ax, col in zip(axes.flatten(), plot_cols):\n",
    "    ax.scatter(fedbatch_df_measurement.timestamp, fedbatch_df_measurement[col])\n",
    "    ax.set_title(col)\n",
    "\n",
    "    if col.startswith('v_'):\n",
    "        ax.set_ylabel('Volume')\n",
    "\n",
    "fig.supxlabel('Feeding time [h]')\n",
    "fig.supylabel('Pseudo batch concentration')\n",
    "fig.tight_layout()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estimate substrate yields\n",
    "We can now estimate the yield coefficient for the two substrates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated Yxglucose:  1.8500000000000019\n",
      "True Yxglucose:  1.85\n",
      "Estimated Yxglutamine:  0.02000000000000001\n",
      "True Yxglutamine:  0.02\n"
     ]
    }
   ],
   "source": [
    "Yxglucose, _ = np.polyfit(fedbatch_df_measurement.c_Biomass_pseudo.to_numpy(), fedbatch_df_measurement.c_Glucose_pseudo.to_numpy(), 1)\n",
    "Yxglutamine, _ = np.polyfit(fedbatch_df_measurement.c_Biomass_pseudo.to_numpy(), fedbatch_df_measurement.c_Glutamine_pseudo.to_numpy(), 1)\n",
    "\n",
    "print(\"Estimated Yxglucose: \", np.abs(Yxglucose))\n",
    "print(\"True Yxglucose: \", fedbatch_df_measurement.Yxs.iloc[0])\n",
    "print(\"Estimated Yxglutamine: \", np.abs(Yxglutamine))\n",
    "print(\"True Yxglutamine: \", fedbatch_df_measurement.Yxs2.iloc[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you see the yields are correctly estimated also when multiple feed mediums are used. Please refer to the other tutorials for guides on estimation of rates or other parameters."
   ]
  }
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