model-explore-mvp.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Exploration Dash Prototype "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/smatthe2/.local/lib/python3.6/site-packages/sklearn/utils/deprecation.py:144: FutureWarning:\n",
      "\n",
      "The sklearn.manifold.t_sne module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.manifold. Anything that cannot be imported from sklearn.manifold is now part of the private API.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "## Imports\n",
    "#\n",
    "\n",
    "import sys\n",
    "sys.path.append('../GillesPy2')\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "from jupyter_dash import JupyterDash\n",
    "from dash import callback_context\n",
    "import dash_core_components as dcc\n",
    "import dash_html_components as html\n",
    "from dash.dependencies import Input, Output, State\n",
    "import pandas as pd\n",
    "import os, ntpath\n",
    "import numpy as np\n",
    "from functools import reduce\n",
    "import numpy as np\n",
    "import gillespy2\n",
    "from gillespy2 import Model, Species, Reaction, Parameter, RateRule, AssignmentRule, FunctionDefinition\n",
    "from gillespy2 import EventAssignment, EventTrigger, Event\n",
    "from sciope.stochmet.stochmet import StochMET\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## GillesPy2 Model\n",
    "#\n",
    "\n",
    "class Dimerization(Model):\n",
    "    def __init__(self, parameter_values=None):\n",
    "        Model.__init__(self, name=\"Dimerization\")\n",
    "        self.volume = 1\n",
    "\n",
    "        # Parameters\n",
    "        self.add_parameter(Parameter(name=\"k_c\", expression=0.005))\n",
    "        self.add_parameter(Parameter(name=\"k_d\", expression=0.08))\n",
    "\n",
    "        # Species\n",
    "        self.add_species(Species(name=\"Monomer\", initial_value=30, mode=\"discrete\"))\n",
    "        self.add_species(Species(name=\"Dimer\", initial_value=0, mode=\"discrete\"))\n",
    "\n",
    "        # Reactions\n",
    "        self.add_reaction(Reaction(name=\"r_creation\", reactants={'Monomer': 2}, products={'Dimer': 1}, rate=self.listOfParameters[\"k_c\"]))\n",
    "        self.add_reaction(Reaction(name=\"r_dissociation\", reactants={'Dimer': 1}, products={'Monomer': 2}, rate=self.listOfParameters[\"k_d\"]))\n",
    "\n",
    "        # Timespan\n",
    "        self.timespan(np.linspace(0, 100, 101))\n",
    "        \n",
    "model = Dimerization()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sciope.utilities.gillespy2 import wrapper\n",
    "settings = {\"number_of_trajectories\":1}\n",
    "simulator = wrapper.get_simulator(gillespy_model=model, run_settings=settings, species_of_interest=['Monomer', 'Dimer'])\n",
    "expression_array = wrapper.get_parameter_expression_array(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dask.distributed import Client\n",
    "#from sciope.designs import latin_hypercube_sampling\n",
    "#from sciope.utilities.summarystats.auto_tsfresh import SummariesTSFRESH\n",
    "import sciope.utilities as util\n",
    "import sciope.designs as designs\n",
    "from sciope.utilities.priors.uniform_prior import UniformPrior\n",
    "\n",
    "sampler = UniformPrior(expression_array*0.8, expression_array*1.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = Client()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sciope.utilities.summarystats.summary_base import SummaryBase\n",
    "\n",
    "summaries = SummaryBase('summaries')\n",
    "summaries.compute = lambda x: x\n",
    "\n",
    "met = StochMET(simulator, sampler, summaries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "met.compute(n_points=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#First lets add some appropiate information about the model and features\n",
    "met.data.configurations['parameter_labels'] = list(model.listOfParameters.keys())\n",
    "met.data.configurations['species_labels'] = list(model.listOfSpecies.keys())\n",
    "met.data.configurations['timepoints'] = model.tspan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = met.data\n",
    "pickle.dump(data, open('sample_data.p', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### Start of Dash Setup\n",
    "##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Defaults\n",
    "#\n",
    "\n",
    "default_num_groups = 5\n",
    "default_trajectory_selector = 'slider' # either 'dropdown' or 'slider'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Helpers\n",
    "#\n",
    "\n",
    "def options_factory(iterable, label_accessor=lambda x: x, value_accessor=lambda x: x):\n",
    "    \"\"\"\n",
    "    Factory function for creating a dash dropdown menu's 'options' attribute.\n",
    "\n",
    "    Positional arguments:\n",
    "    iterable -- any iterable data structure.\n",
    "\n",
    "    Keyword arguments (optional):\n",
    "    label_accessor -- function to retrieve the label from a single data point in iterable.\n",
    "    value_accessor -- function to retrieve the value from a single data point in iterable.\n",
    "\n",
    "    Returns a list of dictionaries with keys 'label' and 'value'.\n",
    "    \"\"\"\n",
    "    return [\n",
    "        { 'label': label_accessor(d), 'value': value_accessor(d) }\n",
    "        for d in iterable\n",
    "    ]\n",
    "\n",
    "def get_zeroed_df(df):\n",
    "    zero_df = df - df\n",
    "    zero_df['time'] = df['time'].copy()\n",
    "    return zero_df\n",
    "\n",
    "\n",
    "def default_local_store(dfs, num_groups):\n",
    "    \"\"\"\n",
    "    Initial client-side JSON storage for callbacks to refernece.\n",
    "    \"\"\"\n",
    "    return {\n",
    "        'dfs': [ df.to_json(orient='split') for df in dfs ],\n",
    "        'group_assigns' : [None] * len(dfs),\n",
    "        'group_data' : [get_zeroed_df(dfs[0]).to_json(orient='split')] * default_num_groups\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "## App\n",
    "#\n",
    "\n",
    "external_stylesheets = [\n",
    "    {\n",
    "        'href': 'https://stackpath.bootstrapcdn.com/bootstrap/4.1.3/css/bootstrap.min.css',\n",
    "        'rel': 'stylesheet',\n",
    "        'integrity': 'sha384-MCw98/SFnGE8fJT3GXwEOngsV7Zt27NXFoaoApmYm81iuXoPkFOJwJ8ERdknLPMO',\n",
    "        'crossorigin': 'anonymous'\n",
    "    }\n",
    "]\n",
    "\n",
    "app = JupyterDash(__name__, external_stylesheets=external_stylesheets)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create DataFrames\n",
    "\n",
    "n_samp = len(data.x)\n",
    "species = data.configurations['species_labels']\n",
    "parameters = data.configurations['parameter_labels']\n",
    "timepoints = data.configurations['timepoints']\n",
    "\n",
    "inputs = {}\n",
    "for i, p in enumerate(parameters):\n",
    "    inputs[p] = data.x[:, i]\n",
    "inputs['Sample'] = ['Sample_{}'.format(i) for i in range(n_samp)]\n",
    "outputs = {'time':[]}\n",
    "for i, tp in enumerate(timepoints):\n",
    "    outputs['time'].append(tp)\n",
    "for i in range(n_samp):\n",
    "    for j, s in enumerate(species):\n",
    "        lbl = '{}_{}'.format(s, i)\n",
    "        outputs[lbl] = data.ts[i, 0, j, :]\n",
    "input_df = pd.DataFrame(inputs)\n",
    "output_df = pd.DataFrame(outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Div\n",
    "fig1 = px.scatter(input_df, x=parameters[0], y=parameters[1], hover_name='Sample')\n",
    "df_melt = output_df.melt(id_vars='time', value_vars=['{}_{}'.format(species[0], 0), '{}_{}'.format(species[1], 0)])\n",
    "fig2 = px.line(df_melt, x='time', y ='value', color='variable')\n",
    "\n",
    "app.layout = html.Div(children=[html.Div(\n",
    "    dcc.Graph(id='parameter-space-graph', figure=fig1, clickData=None, hoverData=None, style={'width': '800'}), style={'display': 'inline-block'}),\n",
    "    html.Div(\n",
    "    dcc.Graph(id='time-series-graph', figure=fig2, style={'width': '800'}), style={'display': 'inline-block'})], \n",
    "                      style={'width': '100%', 'display': 'inline-block'})\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Callback for updateable output graph\n",
    "@app.callback(\n",
    "    Output(component_id='time-series-graph', component_property='figure'),\n",
    "    [Input(component_id='parameter-space-graph', component_property='clickData'),\n",
    "    Input(component_id='parameter-space-graph', component_property='selectedData')]\n",
    ")\n",
    "def update_time_series(clk_data, selectedData):\n",
    "    df_melt = output_df.melt(id_vars='time', value_vars=['{}_{}'.format(species[0], 0), '{}_{}'.format(species[1], 0)])\n",
    "    fig = px.line(df_melt, x='time', y ='value', color='variable')\n",
    "    if clk_data:\n",
    "        loc_1 = input_df.loc[input_df[parameters[0]] == clk_data['points'][0]['x']]\n",
    "        loc_2 = input_df.loc[input_df[parameters[1]] == clk_data['points'][0]['y']]\n",
    "        indx = input_df[parameters[0]][input_df[parameters[0]] == clk_data['points'][0]['x']].index.tolist()[0]\n",
    "        df_melt = output_df.melt(id_vars='time', value_vars=['{}_{}'.format(species[0], indx), '{}_{}'.format(species[1], indx)])\n",
    "        if loc_1.equals(loc_2):\n",
    "            fig = px.line(df_melt, x='time', y='value', color='variable')\n",
    "    if selectedData:\n",
    "        df_melt = output_df.melt(id_vars='time', value_vars=['{}_{}'.format(species[0], 0), '{}_{}'.format(species[1], 0)])\n",
    "        fig = px.line(df_melt, x='time', y ='value', color='variable')\n",
    "        indices = []\n",
    "        for point in selectedData['points']:\n",
    "            loc_1 = input_df.loc[input_df[parameters[0]] == point['x']]\n",
    "            loc_2 = input_df.loc[input_df[parameters[0]] == point['y']]\n",
    "            indices.append(input_df[parameters[0]][input_df[parameters[0]] == point['x']].index.tolist()[0])\n",
    "        values = []\n",
    "        for i in indices:\n",
    "            values.append('{}_{}'.format(species[0], i))\n",
    "            values.append('{}_{}'.format(species[1], i))\n",
    "        df_melt = output_df.melt(id_vars='time', value_vars=values)\n",
    "        fig = px.line(df_melt, x='time', y='value', color='variable')\n",
    "    return fig\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dash app running on http://127.0.0.1:8050/\n"
     ]
    }
   ],
   "source": [
    "## Run\n",
    "#\n",
    "\n",
    "# app.run_server(mode='inline', debug=True)\n",
    "app.run_server(debug=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>k_c</th>\n",
       "      <th>k_d</th>\n",
       "      <th>Sample</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.005064</td>\n",
       "      <td>0.101790</td>\n",
       "      <td>Sample_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.006229</td>\n",
       "      <td>0.078347</td>\n",
       "      <td>Sample_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.006215</td>\n",
       "      <td>0.108408</td>\n",
       "      <td>Sample_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.005749</td>\n",
       "      <td>0.082067</td>\n",
       "      <td>Sample_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.007006</td>\n",
       "      <td>0.118582</td>\n",
       "      <td>Sample_4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.004181</td>\n",
       "      <td>0.070791</td>\n",
       "      <td>Sample_5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.006246</td>\n",
       "      <td>0.111056</td>\n",
       "      <td>Sample_6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.006401</td>\n",
       "      <td>0.083480</td>\n",
       "      <td>Sample_7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.006091</td>\n",
       "      <td>0.114940</td>\n",
       "      <td>Sample_8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.007426</td>\n",
       "      <td>0.089185</td>\n",
       "      <td>Sample_9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.006928</td>\n",
       "      <td>0.087389</td>\n",
       "      <td>Sample_10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.006480</td>\n",
       "      <td>0.115141</td>\n",
       "      <td>Sample_11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.007425</td>\n",
       "      <td>0.108545</td>\n",
       "      <td>Sample_12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.006234</td>\n",
       "      <td>0.084777</td>\n",
       "      <td>Sample_13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.005474</td>\n",
       "      <td>0.076960</td>\n",
       "      <td>Sample_14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.004416</td>\n",
       "      <td>0.093449</td>\n",
       "      <td>Sample_15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.004571</td>\n",
       "      <td>0.098056</td>\n",
       "      <td>Sample_16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.006207</td>\n",
       "      <td>0.108499</td>\n",
       "      <td>Sample_17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.006574</td>\n",
       "      <td>0.072043</td>\n",
       "      <td>Sample_18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.004855</td>\n",
       "      <td>0.117566</td>\n",
       "      <td>Sample_19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.005680</td>\n",
       "      <td>0.079744</td>\n",
       "      <td>Sample_20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.007001</td>\n",
       "      <td>0.079156</td>\n",
       "      <td>Sample_21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.005957</td>\n",
       "      <td>0.077940</td>\n",
       "      <td>Sample_22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.005947</td>\n",
       "      <td>0.073759</td>\n",
       "      <td>Sample_23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.007027</td>\n",
       "      <td>0.088003</td>\n",
       "      <td>Sample_24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.005152</td>\n",
       "      <td>0.077104</td>\n",
       "      <td>Sample_25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.006303</td>\n",
       "      <td>0.117687</td>\n",
       "      <td>Sample_26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.007387</td>\n",
       "      <td>0.094000</td>\n",
       "      <td>Sample_27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.007087</td>\n",
       "      <td>0.109303</td>\n",
       "      <td>Sample_28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.005578</td>\n",
       "      <td>0.066900</td>\n",
       "      <td>Sample_29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.006566</td>\n",
       "      <td>0.114827</td>\n",
       "      <td>Sample_30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.004488</td>\n",
       "      <td>0.115247</td>\n",
       "      <td>Sample_31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.006048</td>\n",
       "      <td>0.091782</td>\n",
       "      <td>Sample_32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.005917</td>\n",
       "      <td>0.118435</td>\n",
       "      <td>Sample_33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.006709</td>\n",
       "      <td>0.113676</td>\n",
       "      <td>Sample_34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.005807</td>\n",
       "      <td>0.092688</td>\n",
       "      <td>Sample_35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>0.004966</td>\n",
       "      <td>0.102082</td>\n",
       "      <td>Sample_36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>0.007034</td>\n",
       "      <td>0.097872</td>\n",
       "      <td>Sample_37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>0.006221</td>\n",
       "      <td>0.103497</td>\n",
       "      <td>Sample_38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.006851</td>\n",
       "      <td>0.111952</td>\n",
       "      <td>Sample_39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>0.004655</td>\n",
       "      <td>0.106218</td>\n",
       "      <td>Sample_40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0.004221</td>\n",
       "      <td>0.107022</td>\n",
       "      <td>Sample_41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0.004938</td>\n",
       "      <td>0.102355</td>\n",
       "      <td>Sample_42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>0.007420</td>\n",
       "      <td>0.100008</td>\n",
       "      <td>Sample_43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>0.005182</td>\n",
       "      <td>0.102353</td>\n",
       "      <td>Sample_44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.004089</td>\n",
       "      <td>0.110349</td>\n",
       "      <td>Sample_45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.006234</td>\n",
       "      <td>0.102384</td>\n",
       "      <td>Sample_46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>0.007424</td>\n",
       "      <td>0.074687</td>\n",
       "      <td>Sample_47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>0.005555</td>\n",
       "      <td>0.078399</td>\n",
       "      <td>Sample_48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.005380</td>\n",
       "      <td>0.111632</td>\n",
       "      <td>Sample_49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         k_c       k_d     Sample\n",
       "0   0.005064  0.101790   Sample_0\n",
       "1   0.006229  0.078347   Sample_1\n",
       "2   0.006215  0.108408   Sample_2\n",
       "3   0.005749  0.082067   Sample_3\n",
       "4   0.007006  0.118582   Sample_4\n",
       "5   0.004181  0.070791   Sample_5\n",
       "6   0.006246  0.111056   Sample_6\n",
       "7   0.006401  0.083480   Sample_7\n",
       "8   0.006091  0.114940   Sample_8\n",
       "9   0.007426  0.089185   Sample_9\n",
       "10  0.006928  0.087389  Sample_10\n",
       "11  0.006480  0.115141  Sample_11\n",
       "12  0.007425  0.108545  Sample_12\n",
       "13  0.006234  0.084777  Sample_13\n",
       "14  0.005474  0.076960  Sample_14\n",
       "15  0.004416  0.093449  Sample_15\n",
       "16  0.004571  0.098056  Sample_16\n",
       "17  0.006207  0.108499  Sample_17\n",
       "18  0.006574  0.072043  Sample_18\n",
       "19  0.004855  0.117566  Sample_19\n",
       "20  0.005680  0.079744  Sample_20\n",
       "21  0.007001  0.079156  Sample_21\n",
       "22  0.005957  0.077940  Sample_22\n",
       "23  0.005947  0.073759  Sample_23\n",
       "24  0.007027  0.088003  Sample_24\n",
       "25  0.005152  0.077104  Sample_25\n",
       "26  0.006303  0.117687  Sample_26\n",
       "27  0.007387  0.094000  Sample_27\n",
       "28  0.007087  0.109303  Sample_28\n",
       "29  0.005578  0.066900  Sample_29\n",
       "30  0.006566  0.114827  Sample_30\n",
       "31  0.004488  0.115247  Sample_31\n",
       "32  0.006048  0.091782  Sample_32\n",
       "33  0.005917  0.118435  Sample_33\n",
       "34  0.006709  0.113676  Sample_34\n",
       "35  0.005807  0.092688  Sample_35\n",
       "36  0.004966  0.102082  Sample_36\n",
       "37  0.007034  0.097872  Sample_37\n",
       "38  0.006221  0.103497  Sample_38\n",
       "39  0.006851  0.111952  Sample_39\n",
       "40  0.004655  0.106218  Sample_40\n",
       "41  0.004221  0.107022  Sample_41\n",
       "42  0.004938  0.102355  Sample_42\n",
       "43  0.007420  0.100008  Sample_43\n",
       "44  0.005182  0.102353  Sample_44\n",
       "45  0.004089  0.110349  Sample_45\n",
       "46  0.006234  0.102384  Sample_46\n",
       "47  0.007424  0.074687  Sample_47\n",
       "48  0.005555  0.078399  Sample_48\n",
       "49  0.005380  0.111632  Sample_49"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>time</th>\n",
       "      <th>Monomer_0</th>\n",
       "      <th>Dimer_0</th>\n",
       "      <th>Monomer_1</th>\n",
       "      <th>Dimer_1</th>\n",
       "      <th>Monomer_2</th>\n",
       "      <th>Dimer_2</th>\n",
       "      <th>Monomer_3</th>\n",
       "      <th>Dimer_3</th>\n",
       "      <th>Monomer_4</th>\n",
       "      <th>...</th>\n",
       "      <th>Monomer_45</th>\n",
       "      <th>Dimer_45</th>\n",
       "      <th>Monomer_46</th>\n",
       "      <th>Dimer_46</th>\n",
       "      <th>Monomer_47</th>\n",
       "      <th>Dimer_47</th>\n",
       "      <th>Monomer_48</th>\n",
       "      <th>Dimer_48</th>\n",
       "      <th>Monomer_49</th>\n",
       "      <th>Dimer_49</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>...</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>...</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>...</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</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>96</th>\n",
       "      <td>96.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>97.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>98.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>99.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>100.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101 rows × 101 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      time  Monomer_0  Dimer_0  Monomer_1  Dimer_1  Monomer_2  Dimer_2  \\\n",
       "0      0.0       30.0      0.0       30.0      0.0       30.0      0.0   \n",
       "1      1.0       20.0      5.0       20.0      5.0       20.0      5.0   \n",
       "2      2.0       20.0      5.0       16.0      7.0       16.0      7.0   \n",
       "3      3.0       22.0      4.0       10.0     10.0       14.0      8.0   \n",
       "4      4.0       22.0      4.0       12.0      9.0       12.0      9.0   \n",
       "..     ...        ...      ...        ...      ...        ...      ...   \n",
       "96    96.0       16.0      7.0       10.0     10.0       12.0      9.0   \n",
       "97    97.0       16.0      7.0       10.0     10.0       16.0      7.0   \n",
       "98    98.0       16.0      7.0       12.0      9.0       16.0      7.0   \n",
       "99    99.0       14.0      8.0       10.0     10.0       10.0     10.0   \n",
       "100  100.0       18.0      6.0       10.0     10.0       12.0      9.0   \n",
       "\n",
       "     Monomer_3  Dimer_3  Monomer_4  ...  Monomer_45  Dimer_45  Monomer_46  \\\n",
       "0         30.0      0.0       30.0  ...        30.0       0.0        30.0   \n",
       "1         20.0      5.0       20.0  ...        26.0       2.0        24.0   \n",
       "2         20.0      5.0       20.0  ...        22.0       4.0        18.0   \n",
       "3         18.0      6.0       22.0  ...        18.0       6.0        16.0   \n",
       "4         18.0      6.0       16.0  ...        16.0       7.0        14.0   \n",
       "..         ...      ...        ...  ...         ...       ...         ...   \n",
       "96        18.0      6.0        8.0  ...        14.0       8.0        10.0   \n",
       "97        18.0      6.0        8.0  ...        16.0       7.0        10.0   \n",
       "98        16.0      7.0        8.0  ...        14.0       8.0        10.0   \n",
       "99        16.0      7.0        8.0  ...        16.0       7.0        10.0   \n",
       "100       16.0      7.0        8.0  ...        14.0       8.0         8.0   \n",
       "\n",
       "     Dimer_46  Monomer_47  Dimer_47  Monomer_48  Dimer_48  Monomer_49  \\\n",
       "0         0.0        30.0       0.0        30.0       0.0        30.0   \n",
       "1         3.0        24.0       3.0        22.0       4.0        22.0   \n",
       "2         6.0        16.0       7.0        18.0       6.0        18.0   \n",
       "3         7.0        12.0       9.0        12.0       9.0        14.0   \n",
       "4         8.0         8.0      11.0        10.0      10.0        14.0   \n",
       "..        ...         ...       ...         ...       ...         ...   \n",
       "96       10.0        12.0       9.0         8.0      11.0        10.0   \n",
       "97       10.0        12.0       9.0         6.0      12.0        12.0   \n",
       "98       10.0        12.0       9.0         6.0      12.0        14.0   \n",
       "99       10.0        12.0       9.0         8.0      11.0        12.0   \n",
       "100      11.0        10.0      10.0         6.0      12.0        12.0   \n",
       "\n",
       "     Dimer_49  \n",
       "0         0.0  \n",
       "1         4.0  \n",
       "2         6.0  \n",
       "3         8.0  \n",
       "4         8.0  \n",
       "..        ...  \n",
       "96       10.0  \n",
       "97        9.0  \n",
       "98        8.0  \n",
       "99        9.0  \n",
       "100       9.0  \n",
       "\n",
       "[101 rows x 101 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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