{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with SBML Ids\n",
    "Usually basico uses the COPASI display names, to work with model elements. That way a consistent naming scheme between the COPASI graphical user interface, and the scripts can be easily maintained. However, for someone inspecting an SBML model, it might be convenient to also look at the SBML ids and identify elements that way. For this reason the data frames returned for compartments, events, parameters, species and reactions now also contain a column `sbml_id`. \n",
    "\n",
    "Lets start as usual with the common imports: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "if '../..' not in sys.path:\n",
    "    sys.path.append('../..')\n",
    "from basico import *\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next lets load a model from the BioModels Database, and look at the elments: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "load_biomodel(64);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have not just the element name availabe, but also their respective `sbml_id`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>sbml_id</th>\n",
       "      <th>initial_concentration</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>High energy phosphates</th>\n",
       "      <td>P</td>\n",
       "      <td>6.310000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glucose 6 Phosphate</th>\n",
       "      <td>G6P</td>\n",
       "      <td>2.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Triose-phosphate</th>\n",
       "      <td>TRIO</td>\n",
       "      <td>0.960000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAD</th>\n",
       "      <td>NAD</td>\n",
       "      <td>1.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Acetaldehyde</th>\n",
       "      <td>ACE</td>\n",
       "      <td>0.170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2-phosphoglycerate</th>\n",
       "      <td>P2G</td>\n",
       "      <td>0.120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1,3-bisphosphoglycerate</th>\n",
       "      <td>BPG</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glucose in Cytosol</th>\n",
       "      <td>GLCi</td>\n",
       "      <td>0.087000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fructose 6 Phosphate</th>\n",
       "      <td>F6P</td>\n",
       "      <td>0.620000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phosphoenolpyruvate</th>\n",
       "      <td>PEP</td>\n",
       "      <td>0.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pyruvate</th>\n",
       "      <td>PYR</td>\n",
       "      <td>1.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fructose-1,6 bisphosphate</th>\n",
       "      <td>F16P</td>\n",
       "      <td>5.510000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3-phosphoglycerate</th>\n",
       "      <td>P3G</td>\n",
       "      <td>0.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NADH</th>\n",
       "      <td>NADH</td>\n",
       "      <td>0.390000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATP concentration</th>\n",
       "      <td>ATP</td>\n",
       "      <td>2.509190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ADP concentration</th>\n",
       "      <td>ADP</td>\n",
       "      <td>1.291619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMP concentration</th>\n",
       "      <td>AMP</td>\n",
       "      <td>0.299190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Extracellular Glucose</th>\n",
       "      <td>GLCo</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glycogen</th>\n",
       "      <td>Glyc</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trehalose</th>\n",
       "      <td>Trh</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CO2</th>\n",
       "      <td>CO2</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Succinate</th>\n",
       "      <td>SUCC</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ethanol</th>\n",
       "      <td>ETOH</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glycerol</th>\n",
       "      <td>GLY</td>\n",
       "      <td>0.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sum of AXP conc</th>\n",
       "      <td>SUM_P</td>\n",
       "      <td>4.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F2,6P</th>\n",
       "      <td>F26BP</td>\n",
       "      <td>0.020000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          sbml_id  initial_concentration\n",
       "name                                                    \n",
       "High energy phosphates          P               6.310000\n",
       "Glucose 6 Phosphate           G6P               2.450000\n",
       "Triose-phosphate             TRIO               0.960000\n",
       "NAD                           NAD               1.200000\n",
       "Acetaldehyde                  ACE               0.170000\n",
       "2-phosphoglycerate            P2G               0.120000\n",
       "1,3-bisphosphoglycerate       BPG               0.000000\n",
       "Glucose in Cytosol           GLCi               0.087000\n",
       "Fructose 6 Phosphate          F6P               0.620000\n",
       "Phosphoenolpyruvate           PEP               0.070000\n",
       "Pyruvate                      PYR               1.850000\n",
       "Fructose-1,6 bisphosphate    F16P               5.510000\n",
       "3-phosphoglycerate            P3G               0.900000\n",
       "NADH                         NADH               0.390000\n",
       "ATP concentration             ATP               2.509190\n",
       "ADP concentration             ADP               1.291619\n",
       "AMP concentration             AMP               0.299190\n",
       "Extracellular Glucose        GLCo              50.000000\n",
       "Glycogen                     Glyc               0.000000\n",
       "Trehalose                     Trh               0.000000\n",
       "CO2                           CO2               1.000000\n",
       "Succinate                    SUCC               0.000000\n",
       "Ethanol                      ETOH              50.000000\n",
       "Glycerol                      GLY               0.150000\n",
       "sum of AXP conc             SUM_P               4.100000\n",
       "F2,6P                       F26BP               0.020000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_species()[['sbml_id', 'initial_concentration']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "similarly we can get the elements by SBML id as well: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>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 concentration</th>\n",
       "      <td>cytosol</td>\n",
       "      <td>assignment</td>\n",
       "      <td>mmol/l</td>\n",
       "      <td>2.50919</td>\n",
       "      <td>1.511070e+21</td>\n",
       "      <td></td>\n",
       "      <td>( [High energy phosphates] - [ADP concentratio...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Metabolite_21</td>\n",
       "      <td>ATP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  compartment        type    unit  initial_concentration  \\\n",
       "name                                                                       \n",
       "ATP concentration     cytosol  assignment  mmol/l                2.50919   \n",
       "\n",
       "                   initial_particle_number initial_expression  \\\n",
       "name                                                            \n",
       "ATP concentration             1.511070e+21                      \n",
       "\n",
       "                                                          expression  \\\n",
       "name                                                                   \n",
       "ATP concentration  ( [High energy phosphates] - [ADP concentratio...   \n",
       "\n",
       "                   concentration  particle_number  rate  particle_number_rate  \\\n",
       "name                                                                            \n",
       "ATP concentration            NaN              NaN   NaN                   NaN   \n",
       "\n",
       "                             key sbml_id  \n",
       "name                                      \n",
       "ATP concentration  Metabolite_21     ATP  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_species(sbml_id='ATP')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Whereas in COPASI each element has a concentration and a particle number, in SBML usually elements deal only with concentrations and amounts. To make it easy to access them, it is convenient to add the expressions for the amount to the model, so that they can be accessed at any point in time. For that a utility function exists. If `use_sbml_ids` is specified, the sbml id of the species will be used in the name (i.e: `amount(sbml_id)`), otherwise it will be named `amount(display name)`. In case `ignore_fixed` is specified, no expressions for fixed species will be created, and similarly assignment expressions can be ignored:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "add_amount_expressions(use_sbml_ids=True, ignore_fixed=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "lets look at the expressions created, we see it is just the concentration multiplied with the compartment size the species is in:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>initial_value</th>\n",
       "      <th>expression</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>amount(GLCi)</th>\n",
       "      <td>0.087000</td>\n",
       "      <td>[Glucose in Cytosol] * Compartments[cytosol].V...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount(G6P)</th>\n",
       "      <td>2.450000</td>\n",
       "      <td>[Glucose 6 Phosphate] * Compartments[cytosol]....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount(F6P)</th>\n",
       "      <td>0.620000</td>\n",
       "      <td>[Fructose 6 Phosphate] * Compartments[cytosol]...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount(F16P)</th>\n",
       "      <td>5.510000</td>\n",
       "      <td>[Fructose-1,6 bisphosphate] * Compartments[cyt...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount(TRIO)</th>\n",
       "      <td>0.960000</td>\n",
       "      <td>[Triose-phosphate] * Compartments[cytosol].Volume</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount(BPG)</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[1,3-bisphosphoglycerate] * Compartments[cytos...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount(P3G)</th>\n",
       "      <td>0.900000</td>\n",
       "      <td>[3-phosphoglycerate] * Compartments[cytosol].V...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount(P2G)</th>\n",
       "      <td>0.120000</td>\n",
       "      <td>[2-phosphoglycerate] * Compartments[cytosol].V...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount(PEP)</th>\n",
       "      <td>0.070000</td>\n",
       "      <td>[Phosphoenolpyruvate] * Compartments[cytosol]....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount(PYR)</th>\n",
       "      <td>1.850000</td>\n",
       "      <td>[Pyruvate] * Compartments[cytosol].Volume</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount(ACE)</th>\n",
       "      <td>0.170000</td>\n",
       "      <td>[Acetaldehyde] * Compartments[cytosol].Volume</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount(P)</th>\n",
       "      <td>6.310000</td>\n",
       "      <td>[High energy phosphates] * Compartments[cytoso...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount(NAD)</th>\n",
       "      <td>1.200000</td>\n",
       "      <td>[NAD] * Compartments[cytosol].Volume</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount(NADH)</th>\n",
       "      <td>0.390000</td>\n",
       "      <td>[NADH] * Compartments[cytosol].Volume</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount(ATP)</th>\n",
       "      <td>2.509190</td>\n",
       "      <td>[ATP concentration] * Compartments[cytosol].Vo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount(ADP)</th>\n",
       "      <td>1.291619</td>\n",
       "      <td>[ADP concentration] * Compartments[cytosol].Vo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount(AMP)</th>\n",
       "      <td>0.299190</td>\n",
       "      <td>[AMP concentration] * Compartments[cytosol].Vo...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              initial_value                                         expression\n",
       "name                                                                          \n",
       "amount(GLCi)       0.087000  [Glucose in Cytosol] * Compartments[cytosol].V...\n",
       "amount(G6P)        2.450000  [Glucose 6 Phosphate] * Compartments[cytosol]....\n",
       "amount(F6P)        0.620000  [Fructose 6 Phosphate] * Compartments[cytosol]...\n",
       "amount(F16P)       5.510000  [Fructose-1,6 bisphosphate] * Compartments[cyt...\n",
       "amount(TRIO)       0.960000  [Triose-phosphate] * Compartments[cytosol].Volume\n",
       "amount(BPG)        0.000000  [1,3-bisphosphoglycerate] * Compartments[cytos...\n",
       "amount(P3G)        0.900000  [3-phosphoglycerate] * Compartments[cytosol].V...\n",
       "amount(P2G)        0.120000  [2-phosphoglycerate] * Compartments[cytosol].V...\n",
       "amount(PEP)        0.070000  [Phosphoenolpyruvate] * Compartments[cytosol]....\n",
       "amount(PYR)        1.850000          [Pyruvate] * Compartments[cytosol].Volume\n",
       "amount(ACE)        0.170000      [Acetaldehyde] * Compartments[cytosol].Volume\n",
       "amount(P)          6.310000  [High energy phosphates] * Compartments[cytoso...\n",
       "amount(NAD)        1.200000               [NAD] * Compartments[cytosol].Volume\n",
       "amount(NADH)       0.390000              [NADH] * Compartments[cytosol].Volume\n",
       "amount(ATP)        2.509190  [ATP concentration] * Compartments[cytosol].Vo...\n",
       "amount(ADP)        1.291619  [ADP concentration] * Compartments[cytosol].Vo...\n",
       "amount(AMP)        0.299190  [AMP concentration] * Compartments[cytosol].Vo..."
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_parameters(name='amount(')[['initial_value', 'expression']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "the `run_time_course` function now also takes a parameter to use sbml id's if they are present (it will still use the display names in case an element has no sbml id."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>P</th>\n",
       "      <th>G6P</th>\n",
       "      <th>TRIO</th>\n",
       "      <th>NAD</th>\n",
       "      <th>ACE</th>\n",
       "      <th>P2G</th>\n",
       "      <th>BPG</th>\n",
       "      <th>GLCi</th>\n",
       "      <th>F6P</th>\n",
       "      <th>PEP</th>\n",
       "      <th>...</th>\n",
       "      <th>Values[amount(P2G)]</th>\n",
       "      <th>Values[amount(PEP)]</th>\n",
       "      <th>Values[amount(PYR)]</th>\n",
       "      <th>Values[amount(ACE)]</th>\n",
       "      <th>Values[amount(P)]</th>\n",
       "      <th>Values[amount(NAD)]</th>\n",
       "      <th>Values[amount(NADH)]</th>\n",
       "      <th>Values[amount(ATP)]</th>\n",
       "      <th>Values[amount(ADP)]</th>\n",
       "      <th>Values[amount(AMP)]</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",
       "      <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>0.00</th>\n",
       "      <td>6.310000</td>\n",
       "      <td>2.450000</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.087000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>1.850000</td>\n",
       "      <td>0.170000</td>\n",
       "      <td>6.310000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>2.509190</td>\n",
       "      <td>1.291619</td>\n",
       "      <td>0.299190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.01</th>\n",
       "      <td>6.530115</td>\n",
       "      <td>2.491309</td>\n",
       "      <td>2.325367</td>\n",
       "      <td>1.055557</td>\n",
       "      <td>0.011908</td>\n",
       "      <td>0.051758</td>\n",
       "      <td>0.000412</td>\n",
       "      <td>0.097408</td>\n",
       "      <td>0.351027</td>\n",
       "      <td>0.075957</td>\n",
       "      <td>...</td>\n",
       "      <td>0.051758</td>\n",
       "      <td>0.075957</td>\n",
       "      <td>3.176736</td>\n",
       "      <td>0.011908</td>\n",
       "      <td>6.530115</td>\n",
       "      <td>1.055557</td>\n",
       "      <td>0.534443</td>\n",
       "      <td>2.669381</td>\n",
       "      <td>1.191352</td>\n",
       "      <td>0.239267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.02</th>\n",
       "      <td>6.481760</td>\n",
       "      <td>2.428287</td>\n",
       "      <td>2.355642</td>\n",
       "      <td>1.010053</td>\n",
       "      <td>0.012240</td>\n",
       "      <td>0.039849</td>\n",
       "      <td>0.000327</td>\n",
       "      <td>0.097234</td>\n",
       "      <td>0.341946</td>\n",
       "      <td>0.062268</td>\n",
       "      <td>...</td>\n",
       "      <td>0.039849</td>\n",
       "      <td>0.062268</td>\n",
       "      <td>3.864468</td>\n",
       "      <td>0.012240</td>\n",
       "      <td>6.481760</td>\n",
       "      <td>1.010053</td>\n",
       "      <td>0.579947</td>\n",
       "      <td>2.633713</td>\n",
       "      <td>1.214334</td>\n",
       "      <td>0.251953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.03</th>\n",
       "      <td>6.419771</td>\n",
       "      <td>2.357805</td>\n",
       "      <td>2.326751</td>\n",
       "      <td>1.033128</td>\n",
       "      <td>0.013500</td>\n",
       "      <td>0.040591</td>\n",
       "      <td>0.000326</td>\n",
       "      <td>0.099157</td>\n",
       "      <td>0.328112</td>\n",
       "      <td>0.062661</td>\n",
       "      <td>...</td>\n",
       "      <td>0.040591</td>\n",
       "      <td>0.062661</td>\n",
       "      <td>4.355273</td>\n",
       "      <td>0.013500</td>\n",
       "      <td>6.419771</td>\n",
       "      <td>1.033128</td>\n",
       "      <td>0.556872</td>\n",
       "      <td>2.588384</td>\n",
       "      <td>1.243002</td>\n",
       "      <td>0.268613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.04</th>\n",
       "      <td>6.465266</td>\n",
       "      <td>2.295451</td>\n",
       "      <td>2.288924</td>\n",
       "      <td>1.076765</td>\n",
       "      <td>0.015151</td>\n",
       "      <td>0.043923</td>\n",
       "      <td>0.000361</td>\n",
       "      <td>0.098315</td>\n",
       "      <td>0.323283</td>\n",
       "      <td>0.068018</td>\n",
       "      <td>...</td>\n",
       "      <td>0.043923</td>\n",
       "      <td>0.068018</td>\n",
       "      <td>4.792030</td>\n",
       "      <td>0.015151</td>\n",
       "      <td>6.465266</td>\n",
       "      <td>1.076765</td>\n",
       "      <td>0.513235</td>\n",
       "      <td>2.621609</td>\n",
       "      <td>1.222048</td>\n",
       "      <td>0.256343</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",
       "      <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>0.96</th>\n",
       "      <td>6.470716</td>\n",
       "      <td>1.124734</td>\n",
       "      <td>0.799562</td>\n",
       "      <td>1.550632</td>\n",
       "      <td>0.195576</td>\n",
       "      <td>0.049470</td>\n",
       "      <td>0.000391</td>\n",
       "      <td>0.094281</td>\n",
       "      <td>0.127878</td>\n",
       "      <td>0.084961</td>\n",
       "      <td>...</td>\n",
       "      <td>0.049470</td>\n",
       "      <td>0.084961</td>\n",
       "      <td>9.734328</td>\n",
       "      <td>0.195576</td>\n",
       "      <td>6.470716</td>\n",
       "      <td>1.550632</td>\n",
       "      <td>0.039368</td>\n",
       "      <td>2.625605</td>\n",
       "      <td>1.219506</td>\n",
       "      <td>0.254889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.97</th>\n",
       "      <td>6.463801</td>\n",
       "      <td>1.120309</td>\n",
       "      <td>0.798489</td>\n",
       "      <td>1.550554</td>\n",
       "      <td>0.195101</td>\n",
       "      <td>0.049284</td>\n",
       "      <td>0.000388</td>\n",
       "      <td>0.094464</td>\n",
       "      <td>0.127152</td>\n",
       "      <td>0.084495</td>\n",
       "      <td>...</td>\n",
       "      <td>0.049284</td>\n",
       "      <td>0.084495</td>\n",
       "      <td>9.698833</td>\n",
       "      <td>0.195101</td>\n",
       "      <td>6.463801</td>\n",
       "      <td>1.550554</td>\n",
       "      <td>0.039446</td>\n",
       "      <td>2.620535</td>\n",
       "      <td>1.222730</td>\n",
       "      <td>0.256734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.98</th>\n",
       "      <td>6.457177</td>\n",
       "      <td>1.116112</td>\n",
       "      <td>0.797470</td>\n",
       "      <td>1.550473</td>\n",
       "      <td>0.194615</td>\n",
       "      <td>0.049106</td>\n",
       "      <td>0.000385</td>\n",
       "      <td>0.094640</td>\n",
       "      <td>0.126464</td>\n",
       "      <td>0.084049</td>\n",
       "      <td>...</td>\n",
       "      <td>0.049106</td>\n",
       "      <td>0.084049</td>\n",
       "      <td>9.663914</td>\n",
       "      <td>0.194615</td>\n",
       "      <td>6.457177</td>\n",
       "      <td>1.550473</td>\n",
       "      <td>0.039527</td>\n",
       "      <td>2.615684</td>\n",
       "      <td>1.225809</td>\n",
       "      <td>0.258507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.99</th>\n",
       "      <td>6.450834</td>\n",
       "      <td>1.112132</td>\n",
       "      <td>0.796503</td>\n",
       "      <td>1.550390</td>\n",
       "      <td>0.194118</td>\n",
       "      <td>0.048936</td>\n",
       "      <td>0.000383</td>\n",
       "      <td>0.094809</td>\n",
       "      <td>0.125811</td>\n",
       "      <td>0.083622</td>\n",
       "      <td>...</td>\n",
       "      <td>0.048936</td>\n",
       "      <td>0.083622</td>\n",
       "      <td>9.629595</td>\n",
       "      <td>0.194118</td>\n",
       "      <td>6.450834</td>\n",
       "      <td>1.550390</td>\n",
       "      <td>0.039610</td>\n",
       "      <td>2.611044</td>\n",
       "      <td>1.228747</td>\n",
       "      <td>0.260209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.00</th>\n",
       "      <td>6.444763</td>\n",
       "      <td>1.108357</td>\n",
       "      <td>0.795584</td>\n",
       "      <td>1.550304</td>\n",
       "      <td>0.193613</td>\n",
       "      <td>0.048772</td>\n",
       "      <td>0.000380</td>\n",
       "      <td>0.094971</td>\n",
       "      <td>0.125191</td>\n",
       "      <td>0.083214</td>\n",
       "      <td>...</td>\n",
       "      <td>0.048772</td>\n",
       "      <td>0.083214</td>\n",
       "      <td>9.595898</td>\n",
       "      <td>0.193613</td>\n",
       "      <td>6.444763</td>\n",
       "      <td>1.550304</td>\n",
       "      <td>0.039696</td>\n",
       "      <td>2.606606</td>\n",
       "      <td>1.231551</td>\n",
       "      <td>0.261843</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             P       G6P      TRIO       NAD       ACE       P2G       BPG  \\\n",
       "Time                                                                         \n",
       "0.00  6.310000  2.450000  0.960000  1.200000  0.170000  0.120000  0.000000   \n",
       "0.01  6.530115  2.491309  2.325367  1.055557  0.011908  0.051758  0.000412   \n",
       "0.02  6.481760  2.428287  2.355642  1.010053  0.012240  0.039849  0.000327   \n",
       "0.03  6.419771  2.357805  2.326751  1.033128  0.013500  0.040591  0.000326   \n",
       "0.04  6.465266  2.295451  2.288924  1.076765  0.015151  0.043923  0.000361   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "0.96  6.470716  1.124734  0.799562  1.550632  0.195576  0.049470  0.000391   \n",
       "0.97  6.463801  1.120309  0.798489  1.550554  0.195101  0.049284  0.000388   \n",
       "0.98  6.457177  1.116112  0.797470  1.550473  0.194615  0.049106  0.000385   \n",
       "0.99  6.450834  1.112132  0.796503  1.550390  0.194118  0.048936  0.000383   \n",
       "1.00  6.444763  1.108357  0.795584  1.550304  0.193613  0.048772  0.000380   \n",
       "\n",
       "          GLCi       F6P       PEP  ...  Values[amount(P2G)]  \\\n",
       "Time                                ...                        \n",
       "0.00  0.087000  0.620000  0.070000  ...             0.120000   \n",
       "0.01  0.097408  0.351027  0.075957  ...             0.051758   \n",
       "0.02  0.097234  0.341946  0.062268  ...             0.039849   \n",
       "0.03  0.099157  0.328112  0.062661  ...             0.040591   \n",
       "0.04  0.098315  0.323283  0.068018  ...             0.043923   \n",
       "...        ...       ...       ...  ...                  ...   \n",
       "0.96  0.094281  0.127878  0.084961  ...             0.049470   \n",
       "0.97  0.094464  0.127152  0.084495  ...             0.049284   \n",
       "0.98  0.094640  0.126464  0.084049  ...             0.049106   \n",
       "0.99  0.094809  0.125811  0.083622  ...             0.048936   \n",
       "1.00  0.094971  0.125191  0.083214  ...             0.048772   \n",
       "\n",
       "      Values[amount(PEP)]  Values[amount(PYR)]  Values[amount(ACE)]  \\\n",
       "Time                                                                  \n",
       "0.00             0.070000             1.850000             0.170000   \n",
       "0.01             0.075957             3.176736             0.011908   \n",
       "0.02             0.062268             3.864468             0.012240   \n",
       "0.03             0.062661             4.355273             0.013500   \n",
       "0.04             0.068018             4.792030             0.015151   \n",
       "...                   ...                  ...                  ...   \n",
       "0.96             0.084961             9.734328             0.195576   \n",
       "0.97             0.084495             9.698833             0.195101   \n",
       "0.98             0.084049             9.663914             0.194615   \n",
       "0.99             0.083622             9.629595             0.194118   \n",
       "1.00             0.083214             9.595898             0.193613   \n",
       "\n",
       "      Values[amount(P)]  Values[amount(NAD)]  Values[amount(NADH)]  \\\n",
       "Time                                                                 \n",
       "0.00           6.310000             1.200000              0.390000   \n",
       "0.01           6.530115             1.055557              0.534443   \n",
       "0.02           6.481760             1.010053              0.579947   \n",
       "0.03           6.419771             1.033128              0.556872   \n",
       "0.04           6.465266             1.076765              0.513235   \n",
       "...                 ...                  ...                   ...   \n",
       "0.96           6.470716             1.550632              0.039368   \n",
       "0.97           6.463801             1.550554              0.039446   \n",
       "0.98           6.457177             1.550473              0.039527   \n",
       "0.99           6.450834             1.550390              0.039610   \n",
       "1.00           6.444763             1.550304              0.039696   \n",
       "\n",
       "      Values[amount(ATP)]  Values[amount(ADP)]  Values[amount(AMP)]  \n",
       "Time                                                                 \n",
       "0.00             2.509190             1.291619             0.299190  \n",
       "0.01             2.669381             1.191352             0.239267  \n",
       "0.02             2.633713             1.214334             0.251953  \n",
       "0.03             2.588384             1.243002             0.268613  \n",
       "0.04             2.621609             1.222048             0.256343  \n",
       "...                   ...                  ...                  ...  \n",
       "0.96             2.625605             1.219506             0.254889  \n",
       "0.97             2.620535             1.222730             0.256734  \n",
       "0.98             2.615684             1.225809             0.258507  \n",
       "0.99             2.611044             1.228747             0.260209  \n",
       "1.00             2.606606             1.231551             0.261843  \n",
       "\n",
       "[101 rows x 34 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "run_time_course(use_sbml_id=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = run_time_course()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "so lets plot just the amounts we got:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "amount_columns = list(df.columns)\n",
    "amount_columns = [name for name in amount_columns if 'amount(' in name]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "df[amount_columns].plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "of course the added global parameters can be easily removed: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "remove_amount_expressions()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and now we can plot the concentrations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "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(use_sbml_id=True).plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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