{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quick start"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial will show how to use the pseodo batch transformation function in python and explain the data structure of the input data."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading fedbatch data\n",
    "We will start with setting up the python session and loading the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from pseudobatch import pseudobatch_transform_pandas, pseudobatch_transform\n",
    "from pseudobatch.datasets import load_standard_fedbatch"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The pseudobatch package includes few datasets of simulated data that are used to showcase the package. We will load the simplest one which is a exponential fed-batch fermentation operated in substrate limited mode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "fedbatch_df = load_standard_fedbatch(sampling_points_only=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets start with getting an overview of the data that we have imported by looking at a part of the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>sample_volume</th>\n",
       "      <th>timestamp</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>170.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.337852</td>\n",
       "      <td>0.075016</td>\n",
       "      <td>0.694735</td>\n",
       "      <td>1015.906036</td>\n",
       "      <td>15.906036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>170.0</td>\n",
       "      <td>17.142857</td>\n",
       "      <td>2.664023</td>\n",
       "      <td>0.075103</td>\n",
       "      <td>1.794378</td>\n",
       "      <td>867.753513</td>\n",
       "      <td>37.753513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>170.0</td>\n",
       "      <td>24.285714</td>\n",
       "      <td>5.175767</td>\n",
       "      <td>0.075053</td>\n",
       "      <td>3.877080</td>\n",
       "      <td>733.638872</td>\n",
       "      <td>73.638872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>170.0</td>\n",
       "      <td>31.428571</td>\n",
       "      <td>9.612284</td>\n",
       "      <td>0.075015</td>\n",
       "      <td>7.555778</td>\n",
       "      <td>619.956743</td>\n",
       "      <td>129.956743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>170.0</td>\n",
       "      <td>38.571429</td>\n",
       "      <td>16.561967</td>\n",
       "      <td>0.075011</td>\n",
       "      <td>13.318358</td>\n",
       "      <td>533.452797</td>\n",
       "      <td>213.452797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>170.0</td>\n",
       "      <td>45.714286</td>\n",
       "      <td>25.635276</td>\n",
       "      <td>0.075009</td>\n",
       "      <td>20.841818</td>\n",
       "      <td>479.658734</td>\n",
       "      <td>329.658734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>170.0</td>\n",
       "      <td>52.857143</td>\n",
       "      <td>35.029969</td>\n",
       "      <td>0.075033</td>\n",
       "      <td>28.631766</td>\n",
       "      <td>462.904906</td>\n",
       "      <td>482.904906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>170.0</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>42.688520</td>\n",
       "      <td>0.075012</td>\n",
       "      <td>34.982129</td>\n",
       "      <td>490.982268</td>\n",
       "      <td>680.982268</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sample_volume  timestamp  c_Biomass  c_Glucose  c_Product     v_Volume   \n",
       "0          170.0  10.000000   1.337852   0.075016   0.694735  1015.906036  \\\n",
       "1          170.0  17.142857   2.664023   0.075103   1.794378   867.753513   \n",
       "2          170.0  24.285714   5.175767   0.075053   3.877080   733.638872   \n",
       "3          170.0  31.428571   9.612284   0.075015   7.555778   619.956743   \n",
       "4          170.0  38.571429  16.561967   0.075011  13.318358   533.452797   \n",
       "5          170.0  45.714286  25.635276   0.075009  20.841818   479.658734   \n",
       "6          170.0  52.857143  35.029969   0.075033  28.631766   462.904906   \n",
       "7          170.0  60.000000  42.688520   0.075012  34.982129   490.982268   \n",
       "\n",
       "   v_Feed_accum  \n",
       "0     15.906036  \n",
       "1     37.753513  \n",
       "2     73.638872  \n",
       "3    129.956743  \n",
       "4    213.452797  \n",
       "5    329.658734  \n",
       "6    482.904906  \n",
       "7    680.982268  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(fedbatch_df\n",
    "    .filter(['sample_volume', 'timestamp', 'c_Biomass', 'c_Glucose', 'c_Product', 'v_Volume', 'v_Feed_accum'])\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows some of the columns in the dataframe. \n",
    "\n",
    "- `sample_volume` columns contain the sample volume at the given time point. In this dataset, we have online measurements thus more measurements than samples and therefore at most timepoint the sample volume is 0.\n",
    "- `timestamp` describe the timepoint\n",
    "- `c_Biomass`, `c_Glucose`, and `c_Product` is the online concentration measurements \n",
    "- `v_Volume` is the volume of the bioreactor. **IMPORTANT:** at points where a sample is taken this value represents the volume just **before** the sample was drawn.\n",
    "- `v_Feed_accum` is the accumulated feed added until that timepoint.\n",
    "\n",
    "This is also the data structure which the `pseudobatch_transform()` expects."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transform a single measurement time series\n",
    "We will now transform the biomass time series data using the `pseudobatch_transform()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "pseudo_biomass = pseudobatch_transform(\n",
    "    measured_concentration=fedbatch_df['c_Biomass'].to_numpy(),\n",
    "    reactor_volume=fedbatch_df['v_Volume'].to_numpy(),\n",
    "    accumulated_feed=fedbatch_df['v_Feed_accum'].to_numpy(),\n",
    "    concentration_in_feed=0, # There is no biomass in the feeding medium \n",
    "    sample_volume=fedbatch_df['sample_volume'].to_numpy()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transform multiple time series"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above show how to transform measurements from a single species (here biomass). However often fermentation datasets often also holds measurements substrate and products, and by-products.\n",
    "\n",
    "We expect most users to work with their data in Pandas Dataframes therefore provide a convenience wrapper function that can be applied directly to a Pandas DataFrame. Under the hood this just loops over a list of columns and calls the `pseudobatch_transform()` on each of them. Because we provide a list of species to transform, we also need to provide a list of concentrations in the feed medium. The two lists has to follow the same order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "conc_in_feed = [0, 100, 0] # [Biomass, Glucose, Product] in g/L\n",
    "\n",
    "fedbatch_df[[\"c_Biomass_pseudo\", \"c_Glucose_pseudo\", \"c_Product_pseudo\"]] = pseudobatch_transform_pandas(\n",
    "    fedbatch_df,\n",
    "    measured_concentration_colnames=[\"c_Biomass\", \"c_Glucose\", \"c_Product\"],\n",
    "    reactor_volume_colname=\"v_Volume\",\n",
    "    accumulated_feed_colname=\"v_Feed_accum\",\n",
    "    sample_volume_colname=\"sample_volume\",\n",
    "    concentration_in_feed=conc_in_feed,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note 1** In the above, we show how to transform data from a single culture/replicate. In [this tutorial](httpsss/biosustain.github.io/pseudobatch/Tutorials/6%20-%20Real%20data%20example%3A%20Yeast%20Biolector%20Fermentation.html) we show how transform multiple cultures in one go using the Pandas groupby functionality.\n",
    "\n",
    "**Note 2** Volatile compounds (for example CO2 and O2) requires special treatment, find more about that in [this tutorial](https://biosustain.github.io/pseudobatch/Tutorials/8%20-%20Special%20case%20gaseous%20species.html)."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have the pseudo-batch transformed data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "\n",
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       "    }\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>c_Biomass_pseudo</th>\n",
       "      <th>c_Glucose_pseudo</th>\n",
       "      <th>c_Product_pseudo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.337852</td>\n",
       "      <td>0.075016</td>\n",
       "      <td>0.694735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17.142857</td>\n",
       "      <td>2.732828</td>\n",
       "      <td>-2.505689</td>\n",
       "      <td>1.840722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24.285714</td>\n",
       "      <td>5.582507</td>\n",
       "      <td>-7.777595</td>\n",
       "      <td>4.181762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31.428571</td>\n",
       "      <td>11.403591</td>\n",
       "      <td>-18.546600</td>\n",
       "      <td>8.963843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>38.571429</td>\n",
       "      <td>23.294434</td>\n",
       "      <td>-40.544661</td>\n",
       "      <td>18.732292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   timestamp  c_Biomass_pseudo  c_Glucose_pseudo  c_Product_pseudo\n",
       "0  10.000000          1.337852          0.075016          0.694735\n",
       "1  17.142857          2.732828         -2.505689          1.840722\n",
       "2  24.285714          5.582507         -7.777595          4.181762\n",
       "3  31.428571         11.403591        -18.546600          8.963843\n",
       "4  38.571429         23.294434        -40.544661         18.732292"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fedbatch_df.filter(['timestamp', \"c_Biomass_pseudo\", \"c_Glucose_pseudo\", \"c_Product_pseudo\"]).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing the pseudobatch concentrations"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can quickly visualize the data here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Time [h]')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fedbatch_df.plot(\n",
    "    x=\"timestamp\", \n",
    "    y=[\"c_Biomass_pseudo\", \"c_Glucose_pseudo\", \"c_Product_pseudo\"], \n",
    "    marker=\"o\",\n",
    "    figsize=(10, 5)\n",
    ")\n",
    "plt.ylabel(\"Pseudobatch concentration\")\n",
    "plt.xlabel(\"Time [h]\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In pseudo batch transformed data consumed species typically becomes negative. This simply shows that the culture have consumed more glucose than was initially present because of the feed. To make a prettier plot we can simply subtract the final pseudo batch concentration from all measurements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Time [h]')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fedbatch_df[\"c_Glucose_pseudo_positive\"] = fedbatch_df[\"c_Glucose_pseudo\"] - fedbatch_df[\"c_Glucose_pseudo\"].iloc[-1]\n",
    "fedbatch_df.plot(\n",
    "    x=\"timestamp\", \n",
    "    y=[\"c_Biomass_pseudo\", \"c_Glucose_pseudo_positive\", \"c_Product_pseudo\"], \n",
    "    marker=\"o\",\n",
    "    figsize=(10, 5)\n",
    ")\n",
    "plt.ylabel(\"Pseudobatch concentration\")\n",
    "plt.xlabel(\"Time [h]\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the data also looks more like what we usually expect from a batch culture. Remember that the y-axis has the units pseudo concentration, thus it is difficult to interpret the actual values. It is safest to only interpret time evolution trends of the pseudo batch concentration data."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This quick start only shows how to use the pseudo batch transformation. In the tutorials you will find examples of how to integrate the pseudo batch transformation into regular cell cultivation workflows."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.5 ('.venv_fedbatch-data-correction': venv)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "0337f5dfa8bf2ee335f62d4679bbb5183dd2c214a8c6ed07ec0592e911fc9b16"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}