CaseStudy_klamt2018.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Case study: Optimization for volumetric productivity with yield constraint (Klamt2018)\n",
    "\n",
    "Paper: When do two‐stage processes outperform one‐stage processes? (Klamt *et al.* 2018)\n",
    "\n",
    "Doi: https://doi.org/10.1002/biot.201700539\n",
    "\n",
    "In the following code a lactate producing strain was tested with atp-wasting and without (wildtype). We reproduce the results of the paper here showing that with a **minimum yield of 1.662 mmol/mmol** the volumetric productivity of the one-stage fermentation (OSF) (**Vol_P = 1.23 mmol/L/h**) outperforms the volumetric productivity of the two-stage fermentation (TSF) (**Vol_P = 1.06 mmol/L/h**)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Import packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from IPython.display import display\n",
    "\n",
    "# For storing DataFrames\n",
    "import pickle\n",
    "\n",
    "# Get OptMSP\n",
    "import sys\n",
    "from os import getcwd\n",
    "sys.path.append(getcwd())\n",
    "import MultiStagePackage.OptMSPfunctions as msp\n",
    "\n",
    "# For optimization\n",
    "from pygmo import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read out parameters\n",
    "auxstates = pd.read_csv(\"./MultiStagePackage/auxstates.csv\", sep=\";\")\n",
    "\n",
    "# Index after parameter names\n",
    "auxstates = auxstates.set_index('Name')\n",
    "auxstates = auxstates[5:] # only take TSF parameters\n",
    "\n",
    "# Count parameters for each .csv parameter set\n",
    "numStates = 3\n",
    "numAux = len(auxstates)\n",
    "\n",
    "# Define indices for variables\n",
    "X_TSF, S_TSF, P_TSF = (i for i in range(numStates))\n",
    "mue_TSF, r_S_TSF, r_P_TSF, q_S_TSF, q_P_TSF = (i for i in range(numAux))\n",
    "\n",
    "\n",
    "# inital conditions\n",
    "s = [0.01, 10.0, 0.0]\n",
    "# support variables\n",
    "aux = (auxstates.value).to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### OSF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def growth_phase_wildtype(t,y,aux):\n",
    "    ## Check for values smaller 0\n",
    "    for i in range(len(y)):\n",
    "        if(y[i]<0.0):\n",
    "            y[i] = 0.0\n",
    "    gphase=1\n",
    "    Y_XS=0.022\n",
    "    Y_PX=69.3\n",
    "    Y_ATPS=2\t\n",
    "    Y_PATP=1\t\n",
    "    m_ATP=7.7\n",
    "    v_P=0\t\n",
    "    Y_PS=2\n",
    "    r_Smax=13.3\n",
    "    \n",
    "    if(y[S_TSF]>0.001):\n",
    "        aux[mue_TSF] = Y_XS*(r_Smax-m_ATP/Y_ATPS-v_P/Y_PS)\n",
    "        aux[r_S_TSF] = aux[mue_TSF]/Y_XS+m_ATP/Y_ATPS+v_P/Y_PS\n",
    "        aux[r_P_TSF] = v_P+Y_PATP*m_ATP+Y_PX*aux[mue_TSF]  \n",
    "    else:\n",
    "        aux[mue_TSF] = 0.0\n",
    "        aux[r_S_TSF] = 0.0\n",
    "        aux[r_P_TSF] = 0.0\n",
    "    \n",
    "    aux[q_S_TSF] = aux[r_S_TSF]*y[X_TSF]\n",
    "    aux[q_P_TSF] = aux[r_P_TSF]*y[X_TSF]\n",
    "\n",
    "    dXdt =   aux[mue_TSF]*y[X_TSF]\n",
    "    dSdt = - aux[q_S_TSF]\n",
    "    dPdt =   aux[q_P_TSF]\n",
    "\n",
    "    \n",
    "    dydt = [dXdt, dSdt, dPdt]\n",
    "    return(dydt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### TSF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def growth_phase(t,y,aux):\n",
    "    ## Check for values smaller 0\n",
    "    for i in range(len(y)):\n",
    "        if(y[i]<0.0):\n",
    "            y[i] = 0.0\n",
    "\n",
    "    Y_XS_1=0.098\t\n",
    "    Y_PX_1=0\t\n",
    "    Y_ATPS_1=23.5\t \n",
    "    Y_PATP_1=0\n",
    "    m_ATP_1=7.7\n",
    "    r_Smax_1=10\t\n",
    "    v_P_1=0\n",
    "    Y_PS_1=2\t\n",
    "    if(y[S_TSF]>0.0001):\n",
    "        aux[mue_TSF] = Y_XS_1*(r_Smax_1-m_ATP_1/Y_ATPS_1-v_P_1/Y_PS_1)\n",
    "        aux[r_S_TSF] = aux[mue_TSF]/Y_XS_1+m_ATP_1/Y_ATPS_1+v_P_1/Y_PS_1\n",
    "        aux[r_P_TSF] = v_P_1+Y_PATP_1*m_ATP_1+Y_PX_1*aux[mue_TSF]  \n",
    "    else:\n",
    "        aux[mue_TSF] = 0.0\n",
    "        aux[r_S_TSF] = 0.0\n",
    "        aux[r_P_TSF] = 0.0\n",
    "    \n",
    "    aux[q_S_TSF] = aux[r_S_TSF]*y[X_TSF]\n",
    "    aux[q_P_TSF] = aux[r_P_TSF]*y[X_TSF]\n",
    "\n",
    "    dXdt =   aux[mue_TSF]*y[X_TSF]\n",
    "    dSdt = - aux[q_S_TSF]\n",
    "    dPdt =   aux[q_P_TSF]\n",
    "\n",
    "    dydt = [dXdt, dSdt, dPdt]\n",
    "    return(dydt)\n",
    "\n",
    "\n",
    "def production_phase(t,y,aux):\n",
    "    ## Check for values smaller 0\n",
    "    for i in range(len(y)):\n",
    "        if(y[i]<0.0):\n",
    "            y[i] = 0.0\n",
    "            \n",
    "    Y_ATPS_2=2\n",
    "    Y_PATP_2=1\n",
    "    m_ATP_2=7.7\n",
    "    v_P_2=0\n",
    "    Y_PS_2=2\n",
    "    \n",
    "    if(y[S_TSF]>0.0001):\n",
    "        aux[mue_TSF] = 0.0\n",
    "        aux[r_S_TSF] = m_ATP_2/Y_ATPS_2+v_P_2/Y_PS_2 \n",
    "        aux[r_P_TSF] = v_P_2+Y_PATP_2*m_ATP_2   \n",
    "    else:\n",
    "        aux[mue_TSF] = 0.0\n",
    "        aux[r_S_TSF] = 0.0\n",
    "        aux[r_P_TSF] = 0.0\n",
    "    \n",
    "    aux[q_S_TSF] = aux[r_S_TSF]*y[X_TSF]    # q_S\n",
    "    aux[q_P_TSF] = aux[r_P_TSF]*y[X_TSF]    # q_P\n",
    "\n",
    "    dXdt =   aux[mue_TSF]*y[X_TSF]\n",
    "    dSdt = - aux[q_S_TSF]\n",
    "    dPdt =   aux[q_P_TSF]\n",
    "\n",
    "    dydt = [dXdt, dSdt, dPdt]\n",
    "    return(dydt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "models = [growth_phase, production_phase]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wildtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Volumetric productivity:   1.2291501953507973\n",
      "End product:               16.62054429526739\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define event for finding process end time (when substrate is 0) for vol. prod. calculation\n",
    "def event_sub0(t,y,aux):\n",
    "    if(y[S_TSF] <= 0.001):\n",
    "        return 0    # 0 that event triggers\n",
    "    return 1\n",
    "\n",
    "# Set parameters:\n",
    "aux = (auxstates.value).to_numpy()\n",
    "t = np.linspace(0,20,100)\n",
    "res=[]\n",
    "\n",
    "# Run simulation:\n",
    "wildtype_sim = sp.integrate.solve_ivp(growth_phase_wildtype, t_span=[0, 20], y0=s, t_eval=[20], events=[event_sub0], args=(aux,), dense_output=True, max_step=0.0001)\n",
    "\n",
    "# Calculate vol. prod. and print it\n",
    "res = [wildtype_sim.y_events[0][0][species] for species in range(len(s)) ]\n",
    "res = res + [wildtype_sim.t_events[0][0]] \n",
    "\n",
    "Vol_P=res[P_TSF]/(res[-1])\n",
    "\n",
    "print(\"Volumetric productivity:  \", Vol_P)\n",
    "print(\"End product:              \", res[P_TSF])\n",
    "\n",
    "# Plot\n",
    "plt.plot(t,wildtype_sim.sol(t)[X_TSF],'b-')\n",
    "plt.show()\n",
    "plt.plot(t,wildtype_sim.sol(t)[S_TSF],'b-')\n",
    "plt.show()\n",
    "plt.plot(t,wildtype_sim.sol(t)[P_TSF],'b-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Brute Force Approach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def event_sub0(t,y,aux):\n",
    "    if(y[S_TSF] <= 0.001):\n",
    "        return 0    # 0 that event triggers\n",
    "    return 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jbauer/Documents/1_Projects/TwoStg/MultiStagePackage/OptMSPfunctions.py:535: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  Vol_P=res[indexes[2]]/(res[-2]-t_start)\n"
     ]
    },
    {
     "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>Index</th>\n",
       "      <th>Times</th>\n",
       "      <th>Models</th>\n",
       "      <th>End_T</th>\n",
       "      <th>End_X</th>\n",
       "      <th>End_S</th>\n",
       "      <th>End_P</th>\n",
       "      <th>finished</th>\n",
       "      <th>Vol_P</th>\n",
       "      <th>Y_SubInput</th>\n",
       "      <th>Y_SubUsed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>(0.0, 0.0, 20.0)</td>\n",
       "      <td>[1, 2]</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>(0.0, 0.01, 20.0)</td>\n",
       "      <td>[1, 2]</td>\n",
       "      <td>20.000</td>\n",
       "      <td>0.010</td>\n",
       "      <td>9.222</td>\n",
       "      <td>1.554</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.078</td>\n",
       "      <td>0.155</td>\n",
       "      <td>1.997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>(0.0, 0.02, 20.0)</td>\n",
       "      <td>[1, 2]</td>\n",
       "      <td>20.000</td>\n",
       "      <td>0.010</td>\n",
       "      <td>9.214</td>\n",
       "      <td>1.568</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.078</td>\n",
       "      <td>0.157</td>\n",
       "      <td>1.995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>(0.0, 0.03, 20.0)</td>\n",
       "      <td>[1, 2]</td>\n",
       "      <td>20.000</td>\n",
       "      <td>0.010</td>\n",
       "      <td>9.206</td>\n",
       "      <td>1.582</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.079</td>\n",
       "      <td>0.158</td>\n",
       "      <td>1.992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>(0.0, 0.04, 20.0)</td>\n",
       "      <td>[1, 2]</td>\n",
       "      <td>20.000</td>\n",
       "      <td>0.010</td>\n",
       "      <td>9.198</td>\n",
       "      <td>1.596</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.160</td>\n",
       "      <td>1.990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>479</td>\n",
       "      <td>(0.0, 4.78, 20.0)</td>\n",
       "      <td>[1, 2]</td>\n",
       "      <td>4.867</td>\n",
       "      <td>0.928</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.620</td>\n",
       "      <td>2</td>\n",
       "      <td>0.127</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>480</td>\n",
       "      <td>(0.0, 4.79, 20.0)</td>\n",
       "      <td>[1, 2]</td>\n",
       "      <td>4.850</td>\n",
       "      <td>0.937</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.434</td>\n",
       "      <td>2</td>\n",
       "      <td>0.089</td>\n",
       "      <td>0.043</td>\n",
       "      <td>0.043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>481</td>\n",
       "      <td>(0.0, 4.8, 20.0)</td>\n",
       "      <td>[1, 2]</td>\n",
       "      <td>4.834</td>\n",
       "      <td>0.946</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.246</td>\n",
       "      <td>2</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>482</td>\n",
       "      <td>(0.0, 4.8100000000000005, 20.0)</td>\n",
       "      <td>[1, 2]</td>\n",
       "      <td>4.818</td>\n",
       "      <td>0.955</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.056</td>\n",
       "      <td>2</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>483</td>\n",
       "      <td>(0.0, 4.82, 20.0)</td>\n",
       "      <td>[1, 2]</td>\n",
       "      <td>4.813</td>\n",
       "      <td>0.958</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>483 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Index                            Times  Models   End_T  End_X  End_S  \\\n",
       "0        1                 (0.0, 0.0, 20.0)  [1, 2]   0.000  0.000  0.000   \n",
       "1        2                (0.0, 0.01, 20.0)  [1, 2]  20.000  0.010  9.222   \n",
       "2        3                (0.0, 0.02, 20.0)  [1, 2]  20.000  0.010  9.214   \n",
       "3        4                (0.0, 0.03, 20.0)  [1, 2]  20.000  0.010  9.206   \n",
       "4        5                (0.0, 0.04, 20.0)  [1, 2]  20.000  0.010  9.198   \n",
       "..     ...                              ...     ...     ...    ...    ...   \n",
       "478    479                (0.0, 4.78, 20.0)  [1, 2]   4.867  0.928  0.001   \n",
       "479    480                (0.0, 4.79, 20.0)  [1, 2]   4.850  0.937  0.001   \n",
       "480    481                 (0.0, 4.8, 20.0)  [1, 2]   4.834  0.946  0.000   \n",
       "481    482  (0.0, 4.8100000000000005, 20.0)  [1, 2]   4.818  0.955  0.000   \n",
       "482    483                (0.0, 4.82, 20.0)  [1, 2]   4.813  0.958  0.000   \n",
       "\n",
       "     End_P finished  Vol_P  Y_SubInput  Y_SubUsed  \n",
       "0    0.000        2    NaN       0.000      0.000  \n",
       "1    1.554      inf  0.078       0.155      1.997  \n",
       "2    1.568      inf  0.078       0.157      1.995  \n",
       "3    1.582      inf  0.079       0.158      1.992  \n",
       "4    1.596      inf  0.080       0.160      1.990  \n",
       "..     ...      ...    ...         ...        ...  \n",
       "478  0.620        2  0.127       0.062      0.062  \n",
       "479  0.434        2  0.089       0.043      0.043  \n",
       "480  0.246        2  0.051       0.025      0.025  \n",
       "481  0.056        2  0.012       0.006      0.006  \n",
       "482  0.000        1  0.000       0.000      0.000  \n",
       "\n",
       "[483 rows x 11 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 51.4 s, sys: 63 µs, total: 51.4 s\n",
      "Wall time: 51.4 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Set parameters\n",
    "aux = (auxstates.value).to_numpy()\n",
    "t_start = 0\n",
    "t_end = 20\n",
    "min_duration = 0\n",
    "n_stages = 2\n",
    "den = 100\n",
    "\n",
    "# Run optimization\n",
    "BruteForceNum_2Stage = msp.do_brute_force_num(  combis=[(0,1)],                 # We want only one combination of modules (First growth and then production phase)\n",
    "                                                models=models,                  # The functions of the models \n",
    "                                                n_stages=n_stages,              # The maximum stages the user wants to test out (optimal solution might turn out to not have as many stages)\n",
    "                                                t_start=t_start,                # start time of process\n",
    "                                                t_end=t_end,                    # end time of process\n",
    "                                                min_duration=min_duration,      # minimal duration of each sub-process/module\n",
    "                                                s=s,                            # initial values of species\n",
    "                                                events=[event_sub0],            # event function for denoting real end time of fermentation (here when no substrate is present anymore; in other use cases different)\n",
    "                                                density=den,                    # density or step time (how frequent the switching times are sampled -> see for more information OptMSP_SupportFunctions.ipynb notebook)\n",
    "                                                arguments=(aux,),               # auxiliary variables needed for model (not constant)\n",
    "                                                indexes=[X_TSF, S_TSF, P_TSF])  # indexes to denote at which position in the model are biomass, substrate and product (default is [0,1,2]; -> see for more information ModelingTutorial.ipynb notebook)\n",
    "\n",
    "display(BruteForceNum_2Stage)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Index</th>\n",
       "      <th>Times</th>\n",
       "      <th>Models</th>\n",
       "      <th>End_T</th>\n",
       "      <th>End_X</th>\n",
       "      <th>End_S</th>\n",
       "      <th>End_P</th>\n",
       "      <th>finished</th>\n",
       "      <th>Vol_P</th>\n",
       "      <th>Y_SubInput</th>\n",
       "      <th>Y_SubUsed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>300</td>\n",
       "      <td>(0.0, 2.99, 20.0)</td>\n",
       "      <td>[1, 2]</td>\n",
       "      <td>15.674</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.001</td>\n",
       "      <td>16.619</td>\n",
       "      <td>2</td>\n",
       "      <td>1.06</td>\n",
       "      <td>1.662</td>\n",
       "      <td>1.662</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Index              Times  Models   End_T  End_X  End_S   End_P finished  \\\n",
       "299    300  (0.0, 2.99, 20.0)  [1, 2]  15.674   0.17  0.001  16.619        2   \n",
       "\n",
       "     Vol_P  Y_SubInput  Y_SubUsed  \n",
       "299   1.06       1.662      1.662  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Filter for the minimum yield requirement:\n",
    "filtered_df = BruteForceNum_2Stage[ BruteForceNum_2Stage['Y_SubUsed'] >= 1.662]  \n",
    "filtered_df = filtered_df.sort_values([\"Vol_P\"], ascending=[False]).dropna(axis=0)[:1]\n",
    "display(filtered_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from '/home/jbauer/miniconda3/envs/twostage/lib/python3.8/site-packages/matplotlib/pyplot.py'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x1500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aux = (auxstates.value).to_numpy() # Reset the auxiliary variable to default starting values\n",
    "msp.do_custom_plot( df=filtered_df, \n",
    "                    models_num=models, \n",
    "                    s=s, \n",
    "                    title=\"Result two-stage - brute force\", \n",
    "                    events=[event_sub0], \n",
    "                    arguments=(aux,), \n",
    "                    indexes=[X_TSF, S_TSF, P_TSF])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimization Approach"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Optimization approach with product as constraint\n",
    "\n",
    "because the input substrate is 10 mmol we can also optimize by end product (16.62 mmol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Fevals:          ppar:            bw:            dx:            df:      Violated:    Viol. Norm:        ideal1:\n",
      "      1       0.350711       0.987289        16.3071       0.357947              1        9.46007      -0.357947\n",
      "     51       0.386267       0.520795        6.40814        1.44869              1        4.96827       -1.52569\n",
      "    101       0.421822       0.274719       0.763624        0.35737              1       0.559525       -1.16832\n",
      "    151       0.457378       0.144914        0.81892       0.552375              1       0.559525       -1.16832\n",
      "    201       0.492933      0.0764422       0.332376       0.218464              1       0.559525       -1.16832\n",
      "    251       0.528489      0.0403233       0.351782       0.245348              0              0      -0.922975\n",
      "    301       0.564044      0.0212705      0.0710353      0.0471373              0              0      -0.922975\n",
      "    351         0.5996      0.0112202       0.165472       0.113778              0              0       -1.03469\n",
      "    401       0.635156     0.00591864      0.0392434      0.0275503              0              0       -1.05521\n",
      "    451       0.670711     0.00312208      0.0291998      0.0205192              0              0       -1.05521\n",
      "    501       0.706267      0.0016469      0.0240064       0.016869              0              0       -1.05521\n",
      "    551       0.741822    0.000868738      0.0160559       0.011299              0              0       -1.05983\n",
      "    601       0.777378    0.000458259     0.00656443     0.00461883              0              0       -1.05983\n",
      "    651       0.812933    0.000241732     0.00192467     0.00136798              0              0       -1.05987\n",
      "    701       0.848489    0.000127513    0.000944701    0.000668911              0              0          -1.06\n",
      "    751       0.884044    6.72632e-05    0.000664261    0.000464263              0              0       -1.06029\n",
      "    801         0.9196    3.54813e-05    0.000682124    0.000479978              0              0       -1.06048\n",
      "    851       0.955156    1.87164e-05    0.000623521    0.000447371              0              0       -1.06048\n"
     ]
    }
   ],
   "source": [
    "def event_sub0(t,y,aux):\n",
    "    if(y[S_TSF] <= 0.001):\n",
    "        return 0    # 0 that event triggers\n",
    "    return 1\n",
    "\n",
    "## 1.) Define objective (here volumetric productivity)\n",
    "def volprod(simulation, indexes, initial_values, tstart, tend):\n",
    "    # return simulation[indexes[2]]/(initial_values[indexes[1]]-simulation[indexes[1]])\n",
    "    return simulation[P_TSF]/(simulation[-2]-tstart)\n",
    "\n",
    "## Optional: Define constraint (here end product)\n",
    "def constraint_product(simulation, indexes, initial_values, tstart, tend):\n",
    "    return simulation[P_TSF]\n",
    "\n",
    "## 2.) Define the algorithm that will be used (in our case IHS) with the number of generations (fitness function evaluations) and a seed for reproducibility\n",
    "# Note: per default there are always 10 evaluations so the total number of fitness function evaluations is 10 + the number of you pass to the gen attribute\n",
    "algo_ihs = algorithm(ihs(gen=900, seed=12345))  \n",
    "algo_ihs.set_verbosity(50)  # output best performing each x-th generation\n",
    "\n",
    "## 3.) Define optimization problem\n",
    "# Defining the problem through creating a msp.Optimizer object\n",
    "problem_sim_volP_1 = problem(\n",
    "    decorator_problem(\n",
    "        msp.Optimizer(\n",
    "            s=s,                                # initial values of species\n",
    "            models=models,                      # The functions of the models \n",
    "            indexes=[X_TSF, S_TSF, P_TSF],      # indexes to denote at which position in the model are biomass, substrate and product (default is [0,1,2]; -> see for more information ModelingTutorial.ipynb notebook)\n",
    "            tstart=0,                           # start time of process\n",
    "            tend=20,                            # end time of process\n",
    "            max_stage=2,                        # The maximum stages the user wants to test out (optimal solution might turn out to not have as many stages)\n",
    "            min_duration=1,                     # minimal duration of each sub-process/module\n",
    "            arguments=(aux,),                   # auxiliary variables needed for model (not constant)\n",
    "            events=[event_sub0],                # event function for denoting real end time of fermentation (here when no substrate is present anymore; in other use cases different)\n",
    "            objective=volprod,                # Objective function (input as list!!)\n",
    "            optmod=\"max\",                       # Maximization or minimization? (\"max\" or \"min\")\n",
    "            extracon=[constraint_product],      # Constraints function(s) (input as list!!)\n",
    "            extracon_vals=[16.62],              # The values for the corresponding constraints provided in extracon (input as list!!)\n",
    "            extracon_optmod=[\"greater_than\"]    # The denotion if extracon_vals is greater or lower than the constraint value (here it means therefore: end product > 16.62) use \"greater_than\" or \"lower_than\"\n",
    "        ),\n",
    "        fitness_decorator=msp.f_log_decor,\n",
    "    )\n",
    ")\n",
    "# Define a relative tolerance\n",
    "problem_sim_volP_1.c_tol = 1e-3\n",
    "\n",
    "## 4.) Starting the optimization\n",
    "aux = (auxstates.value).to_numpy() # Reset the auxiliary variable to default starting values\n",
    "res_opt_1 = algo_ihs.evolve(population(problem_sim_volP_1, size=10, seed=12345)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Constraints found. Dataframe is filtered first\n",
      "        Score      Models                  Times               Constraints\n",
      "226 -0.875837  (0.0, 1.0)  (2.7200877052157826,)     [-0.8104571387072497]\n",
      "241 -0.869775  (0.0, 1.0)   (2.711408127884879,)     [-0.7755063505885715]\n",
      "246 -0.885756  (0.0, 1.0)  (2.7351933000526856,)     [-0.7704131172203823]\n",
      "249 -0.869775  (0.0, 1.0)   (2.711408127884879,)     [-0.7755063505885715]\n",
      "254 -0.922975  (0.0, 1.0)   (2.791122957417314,)     [-0.6167225962338243]\n",
      "..        ...         ...                    ...                       ...\n",
      "854 -1.060031  (0.0, 1.0)  (2.9895876067433087,)  [-0.0005059979389834268]\n",
      "866 -1.060045  (0.0, 1.0)   (2.989602488956357,)  [-0.0007284520667809602]\n",
      "872 -1.060256  (0.0, 1.0)   (2.989887791620673,)   [-0.000622588517217082]\n",
      "882 -1.060045  (0.0, 1.0)   (2.989602488956357,)  [-0.0007284520667809602]\n",
      "886 -1.060112  (0.0, 1.0)  (2.9896935070112955,)  [-0.0006820125574762415]\n",
      "\n",
      "[152 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "log_dataframe_1 = pd.DataFrame(res_opt_1.problem.extract(decorator_problem).dv_log)[1:] \n",
    "\n",
    "best_res_opt_1 = msp.do_opt_to_df(  df=log_dataframe_1,     # log dataframe from optimization\n",
    "                                    n_best=1)               # Number of best performing processes that are returned\n",
    "\n",
    "aux = (auxstates.value).to_numpy()\n",
    "df_1=msp.do_convert(opt_res=best_res_opt_1,             # Output from do_opt_to_df()\n",
    "                    models_num=models,                  # models (in numeric form)\n",
    "                    t_start=0, \n",
    "                    t_end=20, \n",
    "                    s=s, \n",
    "                    events=[event_sub0], \n",
    "                    arguments=(aux,), \n",
    "                    indexes=[X_TSF, S_TSF, P_TSF])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Best result from optimization:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Index</th>\n",
       "      <th>Times</th>\n",
       "      <th>Models</th>\n",
       "      <th>End_T</th>\n",
       "      <th>End_X</th>\n",
       "      <th>End_S</th>\n",
       "      <th>End_P</th>\n",
       "      <th>finished</th>\n",
       "      <th>Vol_P</th>\n",
       "      <th>Y_SubInput</th>\n",
       "      <th>Y_SubUsed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>[0.0, 2.99, 20.0]</td>\n",
       "      <td>[1, 2]</td>\n",
       "      <td>15.676</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.621</td>\n",
       "      <td>2</td>\n",
       "      <td>1.06</td>\n",
       "      <td>1.662</td>\n",
       "      <td>1.662</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Index              Times  Models   End_T  End_X  End_S   End_P finished  \\\n",
       "0      1  [0.0, 2.99, 20.0]  [1, 2]  15.676   0.17    0.0  16.621        2   \n",
       "\n",
       "   Vol_P  Y_SubInput  Y_SubUsed  \n",
       "0   1.06       1.662      1.662  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from '/home/jbauer/miniconda3/envs/twostage/lib/python3.8/site-packages/matplotlib/pyplot.py'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x1500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "msp.do_custom_plot(df_1, models, s, \"Result two-stage - optimization - product constraint\", [event_sub0], (aux,))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Optimization approach with yield as constraint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Fevals:          ppar:            bw:            dx:            df:      Violated:    Viol. Norm:        ideal1:\n",
      "      1       0.350711       0.987289        16.3071       0.357947              0              0      -0.357947\n",
      "     51       0.386267       0.520795        14.8155       0.280947              0              0      -0.357947\n",
      "    101       0.421822       0.274719        14.8155       0.280947              0              0      -0.357947\n",
      "    151       0.457378       0.144914       0.672586       0.294313              0              0       -0.65226\n",
      "    201       0.492933      0.0764422       0.332376       0.223937              0              0      -0.876197\n",
      "    251       0.528489      0.0403233       0.113488      0.0835181              0              0      -0.876197\n",
      "    301       0.564044      0.0212705      0.0766441      0.0509133              0              0       -0.92711\n",
      "    351         0.5996      0.0112202       0.165472       0.114066              0              0       -1.04118\n",
      "    401       0.635156     0.00591864      0.0648179      0.0453343              0              0       -1.05063\n",
      "    451       0.670711     0.00312208      0.0269863      0.0189513              0              0       -1.05575\n",
      "    501       0.706267      0.0016469      0.0205947      0.0144885              0              0       -1.06126\n",
      "    551       0.741822    0.000868738     0.00904431     0.00636439              0              0       -1.06212\n",
      "    601       0.777378    0.000458259     0.00129965    0.000923191              0              0       -1.06218\n",
      "    651       0.812933    0.000241732    0.000554592     0.00039861              0              0       -1.06218\n",
      "    701       0.848489    0.000127513    0.000261106    0.000201155              0              0       -1.06232\n",
      "    751       0.884044    6.72632e-05    0.000261106    0.000201155              0              0       -1.06232\n",
      "    801         0.9196    3.54813e-05    0.000429534    0.000321116              0              0       -1.06244\n",
      "    851       0.955156    1.87164e-05    0.000251478    0.000188594              0              0       -1.06244\n"
     ]
    }
   ],
   "source": [
    "def event_sub0(t,y,aux):\n",
    "    if(y[S_TSF] <= 0.001):\n",
    "        return 0    # 0 that event triggers\n",
    "    return 1\n",
    "\n",
    "## 1.) Define objective (here volumetric productivity)\n",
    "def volprod(simulation, indexes, initial_values, tstart, tend ):\n",
    "    # return simulation[indexes[2]]/(initial_values[indexes[1]]-simulation[indexes[1]])\n",
    "    return simulation[P_TSF]/(simulation[-2]-tstart)\n",
    "\n",
    "## Optional: Define constraint (here yield)\n",
    "def constraint_product_yield(simulation, indexes, initial_values, tstart, tend):\n",
    "    return simulation[P_TSF]/(initial_values[S_TSF]-simulation[S_TSF])\n",
    "\n",
    "## 2.) Define the algorithm that will be used (in our case IHS) with the number of generations (fitness function evaluations) and a seed for reproducibility\n",
    "# Note: per default there are always 10 evaluations so the total number of fitness function evaluations is 10 + the number of you pass to the gen attribute\n",
    "algo_ihs = algorithm(ihs(gen=900, seed=12345))  # total numer of evaluations = 10 + 1000\n",
    "algo_ihs.set_verbosity(50)  # output best performing each 50th generation\n",
    "\n",
    "## 3.) Define optimization problem\n",
    "# Defining the problem through creating a msp.Optimizer object\n",
    "problem_sim_volP_2 = problem(\n",
    "    decorator_problem(\n",
    "        msp.Optimizer(\n",
    "            s=s,                                # initial values of species\n",
    "            models=models,                      # The functions of the models \n",
    "            indexes=[X_TSF, S_TSF, P_TSF],      # indexes to denote at which position in the model are biomass, substrate and product (default is [0,1,2]; -> see for more information ModelingTutorial.ipynb notebook)\n",
    "            tstart=0,                           # start time of process\n",
    "            tend=20,                            # end time of process\n",
    "            max_stage=2,                        # The maximum stages the user wants to test out (optimal solution might turn out to not have as many stages)\n",
    "            min_duration=1,                     # minimal duration of each sub-process/module\n",
    "            arguments=(aux,),                   # auxiliary variables needed for model (not constant)\n",
    "            events=[event_sub0],                # event function for denoting real end time of fermentation (here when no substrate is present anymore; in other use cases different)\n",
    "            objective=volprod,                  # Objective function\n",
    "            optmod=\"max\",                       # Maximization or minimization? (\"max\" or \"min\")\n",
    "            extracon=[constraint_product_yield],# Constraints function(s) (input as list!!)\n",
    "            extracon_vals=[1.662],              # The values for the corresponding constraints provided in extracon (input as list!!)\n",
    "            extracon_optmod=[\"greater_than\"]    # The denotion if extracon_vals is greater or lower than the constraint value (here it means therefore: yield > 1.662) use \"greater_than\" or \"lower_than\"\n",
    "        ),\n",
    "        fitness_decorator=msp.f_log_decor,\n",
    "    )\n",
    ")\n",
    "# Define a relative tolerance that \n",
    "problem_sim_volP_2.c_tol = 1e-3\n",
    "\n",
    "## 4.) Starting the optimization\n",
    "aux = (auxstates.value).to_numpy() # Reset the auxiliary variable to default starting values\n",
    "res_opt_2 = algo_ihs.evolve(population(problem_sim_volP_2, size=10, seed=12345))  # start with 10 candidate solutions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Constraints found. Dataframe is filtered first\n",
      "        Score      Models                  Times             Constraints\n",
      "0   -0.357947  (0.0, 1.0)  (1.7156709370460683,)   [-0.1230881770759662]\n",
      "1   -0.077000  (1.0, 1.0)  (10.577402498158406,)   [-0.3380000000018941]\n",
      "2   -0.077000  (1.0, 1.0)   (9.314673543758984,)   [-0.3380000000018941]\n",
      "6   -0.077000  (1.0, 1.0)  (15.531196744036084,)  [-0.33800000000189456]\n",
      "7   -0.077000  (1.0, 1.0)  (17.342648154258875,)   [-0.3380000000018937]\n",
      "..        ...         ...                    ...                     ...\n",
      "893 -0.077000  (1.0, 1.0)  (2.9926855709986144,)  [-0.33800000000189456]\n",
      "897 -0.077000  (1.0, 1.0)  (2.9929443820182278,)   [-0.3380000000018941]\n",
      "904 -0.077000  (1.0, 1.0)    (2.99276368554601,)   [-0.3380000000018941]\n",
      "906 -0.077000  (1.0, 1.0)  (2.9928644172226684,)   [-0.3380000000018897]\n",
      "908 -0.077000  (1.0, 1.0)  (2.9929450574613554,)  [-0.33800000000189456]\n",
      "\n",
      "[493 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "log_dataframe_2 = pd.DataFrame(res_opt_2.problem.extract(decorator_problem).dv_log)[1:] \n",
    "\n",
    "best_res_opt_2 = msp.do_opt_to_df(  df=log_dataframe_2,     # log dataframe from optimization\n",
    "                                    n_best=1)               # Number of best performing processes that are returned\n",
    "\n",
    "aux = (auxstates.value).to_numpy()\n",
    "df_2=msp.do_convert(opt_res=best_res_opt_2,             # Output from do_opt_to_df()\n",
    "                    models_num=models,                  # models (in numeric form)\n",
    "                    t_start=0, \n",
    "                    t_end=20, \n",
    "                    s=s, \n",
    "                    events=[event_sub0], \n",
    "                    arguments=(aux,), \n",
    "                    indexes=[X_TSF, S_TSF, P_TSF])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Best result from optimization:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Index</th>\n",
       "      <th>Times</th>\n",
       "      <th>Models</th>\n",
       "      <th>End_T</th>\n",
       "      <th>End_X</th>\n",
       "      <th>End_S</th>\n",
       "      <th>End_P</th>\n",
       "      <th>finished</th>\n",
       "      <th>Vol_P</th>\n",
       "      <th>Y_SubInput</th>\n",
       "      <th>Y_SubUsed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>[0.0, 2.99, 20.0]</td>\n",
       "      <td>[1, 2]</td>\n",
       "      <td>15.677</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.621</td>\n",
       "      <td>2</td>\n",
       "      <td>1.06</td>\n",
       "      <td>1.662</td>\n",
       "      <td>1.662</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Index              Times  Models   End_T  End_X  End_S   End_P finished  \\\n",
       "0      1  [0.0, 2.99, 20.0]  [1, 2]  15.677   0.17    0.0  16.621        2   \n",
       "\n",
       "   Vol_P  Y_SubInput  Y_SubUsed  \n",
       "0   1.06       1.662      1.662  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Potting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from '/home/jbauer/miniconda3/envs/twostage/lib/python3.8/site-packages/matplotlib/pyplot.py'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x1500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "msp.do_custom_plot(df_2, models, s, \"Result two-stage - optimization - yield constraint\", [event_sub0], (aux,))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "twostage",
   "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": "3.8.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}