{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import basico\n",
    "import os\n",
    "import json\n",
    "from typing import Dict\n",
    "import pandas as pd\n",
    "from IPython.display import display\n",
    "import vcell_opt\n",
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "data": {
      "text/plain": "                             type unit  initial_value  \\\nname                                                    \n_F_                         fixed        9.648533e+04   \n_F_nmol_                    fixed        9.648533e-05   \n_K_GHK_                     fixed        1.000000e-09   \n_N_pmol_                    fixed        6.022142e+11   \n_PI_                        fixed        3.141593e+00   \n_R_                         fixed        8.314463e+03   \n_T_                         fixed        3.000000e+02   \nK_millivolts_per_volt       fixed        1.000000e+03   \nKf                          fixed        1.000000e+00   \nKMOLE                       fixed        1.660539e-03   \nKr                          fixed        2.000000e+00   \ns0_init_uM                  fixed        5.000000e+00   \ns1_init_uM                  fixed        4.000000e+00   \nSize_c0                     fixed        5.000000e+04   \nJ_r0                   assignment       -3.000000e+00   \ns0                            ode        5.000000e+00   \ns1                            ode        4.000000e+00   \n\n                                    initial_expression  \\\nname                                                     \n_F_                                                      \n_F_nmol_                                                 \n_K_GHK_                                                  \n_N_pmol_                                                 \n_PI_                                                     \n_R_                                                      \n_T_                                                      \nK_millivolts_per_volt                                    \nKf                                                       \nKMOLE                                                    \nKr                                                       \ns0_init_uM                                               \ns1_init_uM                                               \nSize_c0                                                  \nJ_r0                                                     \ns0                     Values[s0_init_uM].InitialValue   \ns1                     Values[s1_init_uM].InitialValue   \n\n                                                              expression  \\\nname                                                                       \n_F_                                                                        \n_F_nmol_                                                                   \n_K_GHK_                                                                    \n_N_pmol_                                                                   \n_PI_                                                                       \n_R_                                                                        \n_T_                                                                        \nK_millivolts_per_volt                                                      \nKf                                                                         \nKMOLE                                                                      \nKr                                                                         \ns0_init_uM                                                                 \ns1_init_uM                                                                 \nSize_c0                                                                    \nJ_r0                   Values[Kf] * Values[s0] + - ( Values[Kr] * Val...   \ns0                                                        - Values[J_r0]   \ns1                                                          Values[J_r0]   \n\n                              value  rate            key  \\\nname                                                       \n_F_                    9.648533e+04   0.0   ModelValue_0   \n_F_nmol_               9.648533e-05   0.0   ModelValue_1   \n_K_GHK_                1.000000e-09   0.0   ModelValue_2   \n_N_pmol_               6.022142e+11   0.0   ModelValue_3   \n_PI_                   3.141593e+00   0.0   ModelValue_4   \n_R_                    8.314463e+03   0.0   ModelValue_5   \n_T_                    3.000000e+02   0.0   ModelValue_6   \nK_millivolts_per_volt  1.000000e+03   0.0   ModelValue_7   \nKf                     1.000000e+00   0.0   ModelValue_8   \nKMOLE                  1.660539e-03   0.0   ModelValue_9   \nKr                     2.000000e+00   0.0  ModelValue_10   \ns0_init_uM             5.000000e+00   0.0  ModelValue_11   \ns1_init_uM             4.000000e+00   0.0  ModelValue_12   \nSize_c0                5.000000e+04   0.0  ModelValue_13   \nJ_r0                  -3.000000e+00   NaN  ModelValue_14   \ns0                     5.000000e+00   3.0  ModelValue_15   \ns1                     4.000000e+00  -3.0  ModelValue_16   \n\n                                     sbml_id  \nname                                          \n_F_                                      _F_  \n_F_nmol_                            _F_nmol_  \n_K_GHK_                              _K_GHK_  \n_N_pmol_                            _N_pmol_  \n_PI_                                    _PI_  \n_R_                                      _R_  \n_T_                                      _T_  \nK_millivolts_per_volt  K_millivolts_per_volt  \nKf                                        Kf  \nKMOLE                                  KMOLE  \nKr                                        Kr  \ns0_init_uM                        s0_init_uM  \ns1_init_uM                        s1_init_uM  \nSize_c0                              Size_c0  \nJ_r0                                    J_r0  \ns0                                        s0  \ns1                                        s1  ",
      "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>type</th>\n      <th>unit</th>\n      <th>initial_value</th>\n      <th>initial_expression</th>\n      <th>expression</th>\n      <th>value</th>\n      <th>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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>_F_</th>\n      <td>fixed</td>\n      <td></td>\n      <td>9.648533e+04</td>\n      <td></td>\n      <td></td>\n      <td>9.648533e+04</td>\n      <td>0.0</td>\n      <td>ModelValue_0</td>\n      <td>_F_</td>\n    </tr>\n    <tr>\n      <th>_F_nmol_</th>\n      <td>fixed</td>\n      <td></td>\n      <td>9.648533e-05</td>\n      <td></td>\n      <td></td>\n      <td>9.648533e-05</td>\n      <td>0.0</td>\n      <td>ModelValue_1</td>\n      <td>_F_nmol_</td>\n    </tr>\n    <tr>\n      <th>_K_GHK_</th>\n      <td>fixed</td>\n      <td></td>\n      <td>1.000000e-09</td>\n      <td></td>\n      <td></td>\n      <td>1.000000e-09</td>\n      <td>0.0</td>\n      <td>ModelValue_2</td>\n      <td>_K_GHK_</td>\n    </tr>\n    <tr>\n      <th>_N_pmol_</th>\n      <td>fixed</td>\n      <td></td>\n      <td>6.022142e+11</td>\n      <td></td>\n      <td></td>\n      <td>6.022142e+11</td>\n      <td>0.0</td>\n      <td>ModelValue_3</td>\n      <td>_N_pmol_</td>\n    </tr>\n    <tr>\n      <th>_PI_</th>\n      <td>fixed</td>\n      <td></td>\n      <td>3.141593e+00</td>\n      <td></td>\n      <td></td>\n      <td>3.141593e+00</td>\n      <td>0.0</td>\n      <td>ModelValue_4</td>\n      <td>_PI_</td>\n    </tr>\n    <tr>\n      <th>_R_</th>\n      <td>fixed</td>\n      <td></td>\n      <td>8.314463e+03</td>\n      <td></td>\n      <td></td>\n      <td>8.314463e+03</td>\n      <td>0.0</td>\n      <td>ModelValue_5</td>\n      <td>_R_</td>\n    </tr>\n    <tr>\n      <th>_T_</th>\n      <td>fixed</td>\n      <td></td>\n      <td>3.000000e+02</td>\n      <td></td>\n      <td></td>\n      <td>3.000000e+02</td>\n      <td>0.0</td>\n      <td>ModelValue_6</td>\n      <td>_T_</td>\n    </tr>\n    <tr>\n      <th>K_millivolts_per_volt</th>\n      <td>fixed</td>\n      <td></td>\n      <td>1.000000e+03</td>\n      <td></td>\n      <td></td>\n      <td>1.000000e+03</td>\n      <td>0.0</td>\n      <td>ModelValue_7</td>\n      <td>K_millivolts_per_volt</td>\n    </tr>\n    <tr>\n      <th>Kf</th>\n      <td>fixed</td>\n      <td></td>\n      <td>1.000000e+00</td>\n      <td></td>\n      <td></td>\n      <td>1.000000e+00</td>\n      <td>0.0</td>\n      <td>ModelValue_8</td>\n      <td>Kf</td>\n    </tr>\n    <tr>\n      <th>KMOLE</th>\n      <td>fixed</td>\n      <td></td>\n      <td>1.660539e-03</td>\n      <td></td>\n      <td></td>\n      <td>1.660539e-03</td>\n      <td>0.0</td>\n      <td>ModelValue_9</td>\n      <td>KMOLE</td>\n    </tr>\n    <tr>\n      <th>Kr</th>\n      <td>fixed</td>\n      <td></td>\n      <td>2.000000e+00</td>\n      <td></td>\n      <td></td>\n      <td>2.000000e+00</td>\n      <td>0.0</td>\n      <td>ModelValue_10</td>\n      <td>Kr</td>\n    </tr>\n    <tr>\n      <th>s0_init_uM</th>\n      <td>fixed</td>\n      <td></td>\n      <td>5.000000e+00</td>\n      <td></td>\n      <td></td>\n      <td>5.000000e+00</td>\n      <td>0.0</td>\n      <td>ModelValue_11</td>\n      <td>s0_init_uM</td>\n    </tr>\n    <tr>\n      <th>s1_init_uM</th>\n      <td>fixed</td>\n      <td></td>\n      <td>4.000000e+00</td>\n      <td></td>\n      <td></td>\n      <td>4.000000e+00</td>\n      <td>0.0</td>\n      <td>ModelValue_12</td>\n      <td>s1_init_uM</td>\n    </tr>\n    <tr>\n      <th>Size_c0</th>\n      <td>fixed</td>\n      <td></td>\n      <td>5.000000e+04</td>\n      <td></td>\n      <td></td>\n      <td>5.000000e+04</td>\n      <td>0.0</td>\n      <td>ModelValue_13</td>\n      <td>Size_c0</td>\n    </tr>\n    <tr>\n      <th>J_r0</th>\n      <td>assignment</td>\n      <td></td>\n      <td>-3.000000e+00</td>\n      <td></td>\n      <td>Values[Kf] * Values[s0] + - ( Values[Kr] * Val...</td>\n      <td>-3.000000e+00</td>\n      <td>NaN</td>\n      <td>ModelValue_14</td>\n      <td>J_r0</td>\n    </tr>\n    <tr>\n      <th>s0</th>\n      <td>ode</td>\n      <td></td>\n      <td>5.000000e+00</td>\n      <td>Values[s0_init_uM].InitialValue</td>\n      <td>- Values[J_r0]</td>\n      <td>5.000000e+00</td>\n      <td>3.0</td>\n      <td>ModelValue_15</td>\n      <td>s0</td>\n    </tr>\n    <tr>\n      <th>s1</th>\n      <td>ode</td>\n      <td></td>\n      <td>4.000000e+00</td>\n      <td>Values[s1_init_uM].InitialValue</td>\n      <td>Values[J_r0]</td>\n      <td>4.000000e+00</td>\n      <td>-3.0</td>\n      <td>ModelValue_16</td>\n      <td>s1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optFile = Path(os.getcwd()) / \"test_data\" / \"optproblem.json\"\n",
    "report_file = Path(os.getcwd()) / \"test_data\" / \"optReport.csv\"\n",
    "with open(optFile, \"rb\") as f_optfile:\n",
    "    vcell_opt_problem: vcell_opt.OptProblem = vcell_opt.OptProblem.from_json_data(json.load(f_optfile))\n",
    "\n",
    "if report_file.exists():\n",
    "    os.remove(report_file)\n",
    "\n",
    "copasi_model = basico.load_model(vcell_opt_problem.math_model_sbml_contents)\n",
    "basico.get_parameters()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "time_course_result = basico.run_time_course(start_time=0, duration=2, use_number=True)\n",
    "\n",
    "exp_data = vcell_opt.get_reference_data(vcell_opt_problem)\n",
    "\n",
    "ax = time_course_result.plot(y='Values[s0]')\n",
    "time_course_result.plot(y='Values[s1]', ax=ax)\n",
    "\n",
    "exp_data.plot(x='Time', y='Values[s1]', style='o', ax=ax);"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "[ParameterDescription(initial_value=0.2, max_value=10.0, min_value=0.1, name='Kf', scale=0.2),\n ParameterDescription(initial_value=0.3, max_value=20.0, min_value=0.2, name='Kr', scale=0.3),\n ParameterDescription(initial_value=2.0, max_value=20.0, min_value=1e-08, name='s0_init_uM', scale=2.0)]"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(vcell_opt_problem.parameter_description_list)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "{'scheduled': False,\n 'update_model': False,\n 'problem': {'Maximize': False,\n  'Randomize Start Values': False,\n  'Calculate Statistics': True,\n  'Create Parameter Sets': False,\n  'Use Time Sens': False},\n 'method': {'name': 'Evolutionary Programming',\n  'Number of Generations': 200.0,\n  'Population Size': 20.0,\n  'Random Number Generator': 1.0,\n  'Seed': 3.0},\n 'report': {'filename': '',\n  'report_definition': 'Parameter Estimation',\n  'append': True,\n  'confirm_overwrite': True}}"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "[{'name': 'Values[Kf]', 'lower': 0.1, 'upper': 10.0},\n {'name': 'Values[Kr]', 'lower': 0.2, 'upper': 20.0},\n {'name': 'Values[s0_init_uM]', 'lower': 1e-08, 'upper': 20.0}]"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "                    lower upper  start affected  \\\nname                                              \nValues[Kf]            0.1  10.0    1.0       []   \nValues[Kr]            0.2  20.0    2.0       []   \nValues[s0_init_uM]  1e-08  20.0    5.0       []   \n\n                                                                   cn  \nname                                                                   \nValues[Kf]          CN=Root,Model=NoName,Vector=Values[Kf],Referen...  \nValues[Kr]          CN=Root,Model=NoName,Vector=Values[Kr],Referen...  \nValues[s0_init_uM]  CN=Root,Model=NoName,Vector=Values[s0_init_uM]...  ",
      "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>lower</th>\n      <th>upper</th>\n      <th>start</th>\n      <th>affected</th>\n      <th>cn</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    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Values[Kf]</th>\n      <td>0.1</td>\n      <td>10.0</td>\n      <td>1.0</td>\n      <td>[]</td>\n      <td>CN=Root,Model=NoName,Vector=Values[Kf],Referen...</td>\n    </tr>\n    <tr>\n      <th>Values[Kr]</th>\n      <td>0.2</td>\n      <td>20.0</td>\n      <td>2.0</td>\n      <td>[]</td>\n      <td>CN=Root,Model=NoName,Vector=Values[Kr],Referen...</td>\n    </tr>\n    <tr>\n      <th>Values[s0_init_uM]</th>\n      <td>1e-08</td>\n      <td>20.0</td>\n      <td>5.0</td>\n      <td>[]</td>\n      <td>CN=Root,Model=NoName,Vector=Values[s0_init_uM]...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "basico.add_experiment('exp1', data=exp_data)\n",
    "task_settings = basico.get_task_settings('Parameter Estimation')\n",
    "task_settings['method'] = vcell_opt.get_copasi_opt_method_settings(vcell_opt_problem)\n",
    "display(task_settings)\n",
    "basico.set_task_settings('Parameter Estimation', task_settings)\n",
    "\n",
    "# add report definition and set report file\n",
    "basico.add_report('parest report', task=basico.T.PARAMETER_ESTIMATION,\n",
    "                  body=['CN=Root,Vector=TaskList[Parameter Estimation],Problem=Parameter Estimation,Reference=Function Evaluations',\n",
    "                        '\\\\\\t',\n",
    "                        'CN=Root,Vector=TaskList[Parameter Estimation],Problem=Parameter Estimation,Reference=Best Value',\n",
    "                        '\\\\\\t',\n",
    "                        'CN=Root,Vector=TaskList[Parameter Estimation],Problem=Parameter Estimation,Reference=Best Parameters'\n",
    "                        ],\n",
    "                  )\n",
    "basico.assign_report(\"parest report\", task=basico.T.PARAMETER_ESTIMATION, filename=str(report_file), append=True)\n",
    "\n",
    "fit_items = vcell_opt.get_fit_parameters(vcell_opt_problem)\n",
    "display(fit_items)\n",
    "basico.set_fit_parameters(fit_items)\n",
    "display(basico.get_fit_parameters())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:matplotlib.axes._axes:*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n"
     ]
    },
    {
     "data": {
      "text/plain": "[(<Figure size 640x480 with 1 Axes>,\n  <AxesSubplot: title={'center': 'exp1'}, xlabel='Time', ylabel='Values[s1]'>)]"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results: pd.DataFrame = basico.run_parameter_estimation(update_model=True)\n",
    "basico.plot_per_experiment()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot: xlabel='iteration'>"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# read last line in report and see that the parameter values match\n",
    "iterations = []\n",
    "objective_functions = []\n",
    "with open(report_file, \"r\") as f_reportfile:\n",
    "    for line in f_reportfile:\n",
    "        tokens = line.split(\"\\t\")\n",
    "        iterations.append(float(tokens[0]))\n",
    "        objective_functions.append(float(tokens[1]))\n",
    "\n",
    "df = pd.DataFrame({\"iteration\": iterations, \"objectFunc\": objective_functions})\n",
    "df.plot(x=\"iteration\", y=\"objectFunc\", logy=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "                    lower upper       sol affected\nname                                              \nValues[Kf]            0.1  10.0  0.812494       []\nValues[Kr]            0.2  20.0  0.687506       []\nValues[s0_init_uM]  1e-08  20.0  0.000031       []",
      "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>lower</th>\n      <th>upper</th>\n      <th>sol</th>\n      <th>affected</th>\n    </tr>\n    <tr>\n      <th>name</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Values[Kf]</th>\n      <td>0.1</td>\n      <td>10.0</td>\n      <td>0.812494</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>Values[Kr]</th>\n      <td>0.2</td>\n      <td>20.0</td>\n      <td>0.687506</td>\n      <td>[]</td>\n    </tr>\n    <tr>\n      <th>Values[s0_init_uM]</th>\n      <td>1e-08</td>\n      <td>20.0</td>\n      <td>0.000031</td>\n      <td>[]</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "0.8124941553266871"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['lower', 'upper', 'sol', 'affected'], dtype='object')\n",
      "Index(['Values[Kf]', 'Values[Kr]', 'Values[s0_init_uM]'], dtype='object', name='name')\n"
     ]
    }
   ],
   "source": [
    "fit_solution = basico.task_parameterestimation.get_parameters_solution()\n",
    "display(fit_solution)\n",
    "#display(fit_solution.loc[fit_solution['name'] == 'values[Kf]']['sol'])\n",
    "display(fit_solution.loc['Values[Kf]']['sol'])\n",
    "print(fit_solution.columns)\n",
    "print(fit_solution.index)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "{'Kf': 0.8124941553266871,\n 'Kr': 0.6875063620515809,\n 's0_init_uM': 3.108315653769391e-05}"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  datapoints: 25 of 25\n"
     ]
    },
    {
     "data": {
      "text/plain": "{'obj': 4.9787144868957555e-14,\n 'rms': 4.4626066315084305e-08,\n 'sd': 4.757154653254475e-08,\n 'f_evals': 8,\n 'failed_evals_exception': 0,\n 'failed_evals_nan': 0,\n 'cpu_time': 0.000851,\n 'data_points': 25,\n 'valid_data_points': 25,\n 'evals_per_sec': 0.000106375,\n 'parameters': [{'name': 'Values[Kf].InitialValue',\n   'lower': '0.1',\n   'start': 0.8124941553266871,\n   'value': 0.8124941553266871,\n   'upper': '10.0',\n   'std_dev': 9.099386340882232e-07,\n   'coeff_of_variation': 0.0001119932528895984,\n   'gradient': 0.005045944421179314},\n  {'name': 'Values[Kr].InitialValue',\n   'lower': '0.2',\n   'start': 0.6875063620515809,\n   'value': 0.6875063620515809,\n   'upper': '20.0',\n   'std_dev': 9.107832607394269e-07,\n   'coeff_of_variation': 0.00013247633927656577,\n   'gradient': 0.021536089913057506},\n  {'name': 'Values[s0_init_uM].InitialValue',\n   'lower': '1e-08',\n   'start': 3.108315653769391e-05,\n   'value': 3.108315653769391e-05,\n   'upper': '20.0',\n   'std_dev': 4.483413363362403e-06,\n   'coeff_of_variation': 14.423932002933674,\n   'gradient': 8.076673316815898e-08}],\n 'experiments': [{'name': 'exp1',\n   'valid_points': 25,\n   'total_points': 25,\n   'obj': 4.978428364309541e-14,\n   'rms': 4.4624783985178195e-08,\n   'error_mean': 6.777800365398434e-10,\n   'error_mean_sd': 4.4619636483793596e-08,\n   'dependent_data': [{'name': 'Values[s1]',\n     'data_points': 25,\n     'obj': 4.978428364309541e-14,\n     'rms': 4.4624783985178195e-08,\n     'error_mean': 6.777800365398434e-10}]}],\n 'variables': [{'name': 'Values[s1]',\n   'data_points': 25,\n   'obj': 4.978428364309541e-14,\n   'rms': 4.4624783985178195e-08,\n   'error_mean': 6.777800365398434e-10,\n   'error_mean_sd': 4.4619636483793596e-08}]}"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display(vcell_opt.result_set_from_fit(fit_solution))\n",
    "basico.task_parameterestimation.get_fit_statistic(include_parameters=True, include_experiments=True, include_fitted=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}