{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "090b939a",
   "metadata": {},
   "source": [
    "## Simulating with custom results\n",
    "In this example, we'll load  a model, and simulate it, fine tuning the simulation results returned. We start as usual: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b233debd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from basico import *"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5215f121",
   "metadata": {},
   "source": [
    "lets load a model, i'll choose a model from the BioModels Database:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "16fff637",
   "metadata": {},
   "outputs": [],
   "source": [
    "load_biomodel(206);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3125677c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>compartment</th>\n",
       "      <th>type</th>\n",
       "      <th>unit</th>\n",
       "      <th>initial_concentration</th>\n",
       "      <th>initial_particle_number</th>\n",
       "      <th>initial_expression</th>\n",
       "      <th>expression</th>\n",
       "      <th>concentration</th>\n",
       "      <th>particle_number</th>\n",
       "      <th>rate</th>\n",
       "      <th>particle_number_rate</th>\n",
       "      <th>key</th>\n",
       "      <th>sbml_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ATP</th>\n",
       "      <td>compartment</td>\n",
       "      <td>reactions</td>\n",
       "      <td>mmol/l</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1.204428e+21</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Metabolite_1</td>\n",
       "      <td>at</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Triose_Gly3Phos_DHAP</th>\n",
       "      <td>compartment</td>\n",
       "      <td>reactions</td>\n",
       "      <td>mmol/l</td>\n",
       "      <td>0.60</td>\n",
       "      <td>3.613284e+20</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Metabolite_3</td>\n",
       "      <td>s3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Acetaldehyde</th>\n",
       "      <td>compartment</td>\n",
       "      <td>reactions</td>\n",
       "      <td>mmol/l</td>\n",
       "      <td>0.08</td>\n",
       "      <td>4.817713e+19</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Metabolite_7</td>\n",
       "      <td>s6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAD</th>\n",
       "      <td>compartment</td>\n",
       "      <td>reactions</td>\n",
       "      <td>mmol/l</td>\n",
       "      <td>0.60</td>\n",
       "      <td>3.613284e+20</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Metabolite_4</td>\n",
       "      <td>na</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pyruvate</th>\n",
       "      <td>compartment</td>\n",
       "      <td>reactions</td>\n",
       "      <td>mmol/l</td>\n",
       "      <td>8.00</td>\n",
       "      <td>4.817713e+21</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Metabolite_6</td>\n",
       "      <td>s5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glucose</th>\n",
       "      <td>compartment</td>\n",
       "      <td>reactions</td>\n",
       "      <td>mmol/l</td>\n",
       "      <td>1.00</td>\n",
       "      <td>6.022141e+20</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Metabolite_0</td>\n",
       "      <td>s1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>extracellular acetaldehyde</th>\n",
       "      <td>compartment</td>\n",
       "      <td>reactions</td>\n",
       "      <td>mmol/l</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1.204428e+19</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Metabolite_8</td>\n",
       "      <td>s6o</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3PG</th>\n",
       "      <td>compartment</td>\n",
       "      <td>reactions</td>\n",
       "      <td>mmol/l</td>\n",
       "      <td>0.70</td>\n",
       "      <td>4.215499e+20</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Metabolite_5</td>\n",
       "      <td>s4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F16P</th>\n",
       "      <td>compartment</td>\n",
       "      <td>reactions</td>\n",
       "      <td>mmol/l</td>\n",
       "      <td>5.00</td>\n",
       "      <td>3.011070e+21</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Metabolite_2</td>\n",
       "      <td>s2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            compartment       type    unit  \\\n",
       "name                                                         \n",
       "ATP                         compartment  reactions  mmol/l   \n",
       "Triose_Gly3Phos_DHAP        compartment  reactions  mmol/l   \n",
       "Acetaldehyde                compartment  reactions  mmol/l   \n",
       "NAD                         compartment  reactions  mmol/l   \n",
       "Pyruvate                    compartment  reactions  mmol/l   \n",
       "Glucose                     compartment  reactions  mmol/l   \n",
       "extracellular acetaldehyde  compartment  reactions  mmol/l   \n",
       "3PG                         compartment  reactions  mmol/l   \n",
       "F16P                        compartment  reactions  mmol/l   \n",
       "\n",
       "                            initial_concentration  initial_particle_number  \\\n",
       "name                                                                         \n",
       "ATP                                          2.00             1.204428e+21   \n",
       "Triose_Gly3Phos_DHAP                         0.60             3.613284e+20   \n",
       "Acetaldehyde                                 0.08             4.817713e+19   \n",
       "NAD                                          0.60             3.613284e+20   \n",
       "Pyruvate                                     8.00             4.817713e+21   \n",
       "Glucose                                      1.00             6.022141e+20   \n",
       "extracellular acetaldehyde                   0.02             1.204428e+19   \n",
       "3PG                                          0.70             4.215499e+20   \n",
       "F16P                                         5.00             3.011070e+21   \n",
       "\n",
       "                           initial_expression expression  concentration  \\\n",
       "name                                                                      \n",
       "ATP                                                                 NaN   \n",
       "Triose_Gly3Phos_DHAP                                                NaN   \n",
       "Acetaldehyde                                                        NaN   \n",
       "NAD                                                                 NaN   \n",
       "Pyruvate                                                            NaN   \n",
       "Glucose                                                             NaN   \n",
       "extracellular acetaldehyde                                          NaN   \n",
       "3PG                                                                 NaN   \n",
       "F16P                                                                NaN   \n",
       "\n",
       "                            particle_number  rate  particle_number_rate  \\\n",
       "name                                                                      \n",
       "ATP                                     NaN   0.0                   0.0   \n",
       "Triose_Gly3Phos_DHAP                    NaN   0.0                   0.0   \n",
       "Acetaldehyde                            NaN   0.0                   0.0   \n",
       "NAD                                     NaN   0.0                   0.0   \n",
       "Pyruvate                                NaN   0.0                   0.0   \n",
       "Glucose                                 NaN   0.0                   0.0   \n",
       "extracellular acetaldehyde              NaN   0.0                   0.0   \n",
       "3PG                                     NaN   0.0                   0.0   \n",
       "F16P                                    NaN   0.0                   0.0   \n",
       "\n",
       "                                     key sbml_id  \n",
       "name                                              \n",
       "ATP                         Metabolite_1      at  \n",
       "Triose_Gly3Phos_DHAP        Metabolite_3      s3  \n",
       "Acetaldehyde                Metabolite_7      s6  \n",
       "NAD                         Metabolite_4      na  \n",
       "Pyruvate                    Metabolite_6      s5  \n",
       "Glucose                     Metabolite_0      s1  \n",
       "extracellular acetaldehyde  Metabolite_8     s6o  \n",
       "3PG                         Metabolite_5      s4  \n",
       "F16P                        Metabolite_2      s2  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_species()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "636f8876",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\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>scheme</th>\n",
       "      <th>flux</th>\n",
       "      <th>particle_flux</th>\n",
       "      <th>function</th>\n",
       "      <th>key</th>\n",
       "      <th>sbml_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>v1</th>\n",
       "      <td>Glucose + 2 * ATP = F16P</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Function for v1</td>\n",
       "      <td>Reaction_0</td>\n",
       "      <td>v1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v2</th>\n",
       "      <td>F16P = 2 * Triose_Gly3Phos_DHAP</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Function for v2</td>\n",
       "      <td>Reaction_1</td>\n",
       "      <td>v2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v3</th>\n",
       "      <td>Triose_Gly3Phos_DHAP + NAD = 3PG + ATP</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Function for v3</td>\n",
       "      <td>Reaction_2</td>\n",
       "      <td>v3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v4</th>\n",
       "      <td>3PG = Pyruvate + ATP</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Function for v4</td>\n",
       "      <td>Reaction_3</td>\n",
       "      <td>v4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v5</th>\n",
       "      <td>Pyruvate = Acetaldehyde</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Function for v5</td>\n",
       "      <td>Reaction_4</td>\n",
       "      <td>v5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v7</th>\n",
       "      <td>ATP =</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Function for v7</td>\n",
       "      <td>Reaction_5</td>\n",
       "      <td>v7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v8</th>\n",
       "      <td>Triose_Gly3Phos_DHAP = NAD</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Function for v8</td>\n",
       "      <td>Reaction_6</td>\n",
       "      <td>v8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v9</th>\n",
       "      <td>\"extracellular acetaldehyde\" =</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Function for v9</td>\n",
       "      <td>Reaction_7</td>\n",
       "      <td>v9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v10</th>\n",
       "      <td>Acetaldehyde = 0.1 * \"extracellular acetaldehyde\"</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Function for v10</td>\n",
       "      <td>Reaction_8</td>\n",
       "      <td>v10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v6</th>\n",
       "      <td>Acetaldehyde = NAD</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Function for v6</td>\n",
       "      <td>Reaction_9</td>\n",
       "      <td>v6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v0</th>\n",
       "      <td>= Glucose</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Constant flux (reversible)</td>\n",
       "      <td>Reaction_10</td>\n",
       "      <td>v0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 scheme  flux  particle_flux  \\\n",
       "name                                                                           \n",
       "v1                             Glucose + 2 * ATP = F16P   0.0            0.0   \n",
       "v2                      F16P = 2 * Triose_Gly3Phos_DHAP   0.0            0.0   \n",
       "v3               Triose_Gly3Phos_DHAP + NAD = 3PG + ATP   0.0            0.0   \n",
       "v4                                 3PG = Pyruvate + ATP   0.0            0.0   \n",
       "v5                              Pyruvate = Acetaldehyde   0.0            0.0   \n",
       "v7                                               ATP =    0.0            0.0   \n",
       "v8                           Triose_Gly3Phos_DHAP = NAD   0.0            0.0   \n",
       "v9                      \"extracellular acetaldehyde\" =    0.0            0.0   \n",
       "v10   Acetaldehyde = 0.1 * \"extracellular acetaldehyde\"   0.0            0.0   \n",
       "v6                                   Acetaldehyde = NAD   0.0            0.0   \n",
       "v0                                            = Glucose   0.0            0.0   \n",
       "\n",
       "                        function          key sbml_id  \n",
       "name                                                   \n",
       "v1               Function for v1   Reaction_0      v1  \n",
       "v2               Function for v2   Reaction_1      v2  \n",
       "v3               Function for v3   Reaction_2      v3  \n",
       "v4               Function for v4   Reaction_3      v4  \n",
       "v5               Function for v5   Reaction_4      v5  \n",
       "v7               Function for v7   Reaction_5      v7  \n",
       "v8               Function for v8   Reaction_6      v8  \n",
       "v9               Function for v9   Reaction_7      v9  \n",
       "v10             Function for v10   Reaction_8     v10  \n",
       "v6               Function for v6   Reaction_9      v6  \n",
       "v0    Constant flux (reversible)  Reaction_10      v0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_reactions()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27f81765",
   "metadata": {},
   "source": [
    "### Simulations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f348c061",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Time'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_time_course(duration=1).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2496455",
   "metadata": {},
   "source": [
    "### Custom Selection list\n",
    "Sometimes you just want to select certain elements, for which you want to select the output. This can be done using `run_time_course_with_output`, where the first element is an array of display names, for which you'd like to collect the output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e3aedf9d",
   "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>Time</th>\n",
       "      <th>[ATP]</th>\n",
       "      <th>ATP.Rate</th>\n",
       "      <th>(v1).Flux</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>23.471501</td>\n",
       "      <td>64.705882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.01</td>\n",
       "      <td>2.103916</td>\n",
       "      <td>23.471501</td>\n",
       "      <td>52.477019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02</td>\n",
       "      <td>2.127439</td>\n",
       "      <td>23.471501</td>\n",
       "      <td>50.238184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03</td>\n",
       "      <td>2.147108</td>\n",
       "      <td>23.471501</td>\n",
       "      <td>49.133261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.04</td>\n",
       "      <td>2.172374</td>\n",
       "      <td>23.471501</td>\n",
       "      <td>48.222173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0.96</td>\n",
       "      <td>0.700117</td>\n",
       "      <td>23.471501</td>\n",
       "      <td>58.593102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>0.97</td>\n",
       "      <td>1.226780</td>\n",
       "      <td>23.471501</td>\n",
       "      <td>40.493568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0.98</td>\n",
       "      <td>1.833802</td>\n",
       "      <td>23.471501</td>\n",
       "      <td>29.080133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0.99</td>\n",
       "      <td>2.349971</td>\n",
       "      <td>23.471501</td>\n",
       "      <td>24.298749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>1.00</td>\n",
       "      <td>2.708829</td>\n",
       "      <td>23.471501</td>\n",
       "      <td>23.269926</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Time     [ATP]   ATP.Rate  (v1).Flux\n",
       "0    0.00  2.000000  23.471501  64.705882\n",
       "1    0.01  2.103916  23.471501  52.477019\n",
       "2    0.02  2.127439  23.471501  50.238184\n",
       "3    0.03  2.147108  23.471501  49.133261\n",
       "4    0.04  2.172374  23.471501  48.222173\n",
       "..    ...       ...        ...        ...\n",
       "96   0.96  0.700117  23.471501  58.593102\n",
       "97   0.97  1.226780  23.471501  40.493568\n",
       "98   0.98  1.833802  23.471501  29.080133\n",
       "99   0.99  2.349971  23.471501  24.298749\n",
       "100  1.00  2.708829  23.471501  23.269926\n",
       "\n",
       "[101 rows x 4 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run_time_course_with_output(['Time', '[ATP]', 'ATP.Rate', '(v1).Flux'], duration=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff4c3a53",
   "metadata": {},
   "source": [
    "as you see just, as with `run_time_course` a pandas dataframe is returned, the only difference is, that the index column is not automatically set to the time column (as you might not want to collect time!). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d20b0375",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Time'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "run_time_course_with_output(['Time', '[ATP]', 'ATP.Rate', '(v1).Flux'], duration=1).set_index('Time').plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e93b4af",
   "metadata": {},
   "source": [
    "The other difference, hen using this method is, that the selection is not automatically saved to COPASI, so when you prepare a model and run it later in the COPASI UI, the output selection is not persisted. You would have to create plot elements or reports for that."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd90eb04",
   "metadata": {},
   "source": [
    "### Custom Time Points\n",
    "Some times you might only want to return values at certain times, this is possible as well (with either `run_time_course` and `run_time_course_with_output`, adding the 'values' argument:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "83540ccc",
   "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>ATP</th>\n",
       "      <th>Triose_Gly3Phos_DHAP</th>\n",
       "      <th>Acetaldehyde</th>\n",
       "      <th>NAD</th>\n",
       "      <th>Pyruvate</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>extracellular acetaldehyde</th>\n",
       "      <th>3PG</th>\n",
       "      <th>F16P</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>2.708848</td>\n",
       "      <td>0.593576</td>\n",
       "      <td>0.074087</td>\n",
       "      <td>0.579888</td>\n",
       "      <td>8.415963</td>\n",
       "      <td>0.856600</td>\n",
       "      <td>0.023069</td>\n",
       "      <td>0.672236</td>\n",
       "      <td>5.160410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>3.001586</td>\n",
       "      <td>0.574146</td>\n",
       "      <td>0.078183</td>\n",
       "      <td>0.620547</td>\n",
       "      <td>8.217482</td>\n",
       "      <td>1.290106</td>\n",
       "      <td>0.024351</td>\n",
       "      <td>0.848794</td>\n",
       "      <td>4.759722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.0</th>\n",
       "      <td>2.923411</td>\n",
       "      <td>0.508574</td>\n",
       "      <td>0.080717</td>\n",
       "      <td>0.645962</td>\n",
       "      <td>8.019722</td>\n",
       "      <td>1.851914</td>\n",
       "      <td>0.025662</td>\n",
       "      <td>0.903727</td>\n",
       "      <td>4.376452</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ATP  Triose_Gly3Phos_DHAP  Acetaldehyde       NAD  Pyruvate  \\\n",
       "Time                                                                     \n",
       "0.0   2.000000              0.600000      0.080000  0.600000  8.000000   \n",
       "1.0   2.708848              0.593576      0.074087  0.579888  8.415963   \n",
       "2.0   3.001586              0.574146      0.078183  0.620547  8.217482   \n",
       "4.0   2.923411              0.508574      0.080717  0.645962  8.019722   \n",
       "\n",
       "       Glucose  extracellular acetaldehyde       3PG      F16P  \n",
       "Time                                                            \n",
       "0.0   1.000000                    0.020000  0.700000  5.000000  \n",
       "1.0   0.856600                    0.023069  0.672236  5.160410  \n",
       "2.0   1.290106                    0.024351  0.848794  4.759722  \n",
       "4.0   1.851914                    0.025662  0.903727  4.376452  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run_time_course(values=[0, 1, 2, 4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "129f0a0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>[ATP]</th>\n",
       "      <th>ATP.Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "    <tr>\n",
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       "      <td>1.0</td>\n",
       "      <td>2.708848</td>\n",
       "      <td>23.471501</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.001586</td>\n",
       "      <td>23.471501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2.923411</td>\n",
       "      <td>23.471501</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "   Time     [ATP]   ATP.Rate\n",
       "0   0.0  2.000000  23.471501\n",
       "1   1.0  2.708848  23.471501\n",
       "2   2.0  3.001586  23.471501\n",
       "3   4.0  2.923411  23.471501"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run_time_course_with_output(['Time', '[ATP]', 'ATP.Rate'], values=[0, 1, 2, 4])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0830e51e",
   "metadata": {},
   "source": [
    "### For expert users\n",
    "For some elements, we do not have an easy mapping between object and display names, to help around that issue `run_time_course_with_output` allows the use of CN's as well. Though of course they are quite error prone and not as nice to read!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cf27ffa3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>Glucose.ParticleNumber</th>\n",
       "      <th>Compartments[compartment].Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>6.022141e+20</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.158565e+20</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>7.769203e+20</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.115249e+21</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time  Glucose.ParticleNumber  Compartments[compartment].Volume\n",
       "0   0.0            6.022141e+20                               1.0\n",
       "1   1.0            5.158565e+20                               1.0\n",
       "2   2.0            7.769203e+20                               1.0\n",
       "3   4.0            1.115249e+21                               1.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run_time_course_with_output([\n",
    "    'CN=Root,Model=Wolf2000_Glycolytic_Oscillations,Reference=Time', \n",
    "    'CN=Root,Model=Wolf2000_Glycolytic_Oscillations,Vector=Compartments[compartment],Vector=Metabolites[Glucose],Reference=ParticleNumber', \n",
    "    'CN=Root,Model=Wolf2000_Glycolytic_Oscillations,Vector=Compartments[compartment],Reference=Volume'], \n",
    "    values=[0, 1, 2, 4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90f7a3c3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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