{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c39d56ed-ab7a-429c-8b59-0641ce28cb92",
   "metadata": {},
   "source": [
    "# Curve Fitter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4747d1e-1f87-4a1a-8e04-63d1c1146c96",
   "metadata": {},
   "source": [
    "This tutorial demonstrates how to do curve fitting as a pre-processing step to iGFA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9baaa7a2-9ff1-4210-8341-53a22bac209e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "96cb6fdb-cb32-4a69-a1ff-cab0290b4610",
   "metadata": {},
   "outputs": [],
   "source": [
    "sys.path.insert(0, '/home/cabsel/gfa/')\n",
    "from gfapy.curve_fit import curve_fitter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "76a65c97-d8c3-4b60-b0bd-43e4ed801cf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "mainDir = '/home/cabsel/gfa/'\n",
    "inputDir = os.path.join(mainDir, 'inputfiles')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33885095-f04d-4daf-99f4-d8b5132ffa05",
   "metadata": {},
   "source": [
    "Here, we demo how to ingest and smooth the VCD experimental data with a logistic function."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4d7c3a6-143a-48ac-9a54-af634a2c8f80",
   "metadata": {},
   "source": [
    "## Ingest data into Curve Fitter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "29a8efbe-ee28-480a-a461-f7d115585e22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Time (WD)', 'VCD (1E6 VC/mL)', 'fit_VCD (1E6 VC/mL)'], dtype='object')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "expt_data = pd.read_excel(os.path.join(inputDir, 'smoothed_data_lowpH.xlsx'), sheet_name=['vcd', 'titer', 'frac', 'q_prod_matched'])\n",
    "expt_data['VCD'] = (expt_data.pop('vcd').rename(columns={'VCD_1e6cells_mL': 'VCD (1E6 VC/mL)',\n",
    "                                                         'Time_days': 'Time (WD)',\n",
    "                                                         'fit_VCD_1e6cells_mL': 'fit_VCD (1E6 VC/mL)'}).\n",
    "                    set_index('Time (WD)'))\n",
    "display(expt_data['VCD'].reset_index().columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "50bb5778-300c-4765-a679-57f7eed5b88c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Choose a model from below keys or \"Custom:\"\n",
      "['Polynomial', 'Exponential', 'Power', 'Logarithmic', 'Fourier', 'Gaussian', 'Weibull', 'Hill-type', 'Sigmoidal']\n"
     ]
    }
   ],
   "source": [
    "fit_vcd = curve_fitter()\n",
    "fit_vcd.ingest_data(expt_data['VCD'].reset_index(), x_col='Time (WD)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b11facdf-6b6d-4af7-9084-d729fc14d160",
   "metadata": {},
   "source": [
    "Here, the logistic function is of the form: \n",
    "<b>A / (exp(B * x) + C * exp(-D * x)) </b> where A, B, C and D are estimated parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "965b49d8-19f2-47ab-8056-3869c3e07c0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ed9f083b1861412e957dccec8c0219fc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(Dropdown(description='Model:', options=('Polynomial', 'Exponential', 'Power', 'L…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7b50136d639540db85133cfb5af47ed3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vcd_col = 'VCD (1E6 VC/mL)'\n",
    "fit_vcd.fit_jupyter(vcd_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7ba19356-9e35-4def-8ae0-027ba9a7ec9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Time (WD)', ylabel='VCD (1E6 VC/mL)'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.scatterplot(data=fit_vcd.data, x='Time (WD)', y='VCD (1E6 VC/mL)', c='r', label='Original')\n",
    "sns.lineplot(data=fit_vcd.data, x='Time (WD)', y='fit_VCD (1E6 VC/mL)', c='k', ax=ax, label='Fitted')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.1"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "0234d56d126b4997987b21a646f0713b": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "DescriptionStyleModel",
      "state": {
       "description_width": ""
      }
     },
     "0ca6b98edd3e487890d753042ccbe9a2": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "display": "none"
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     },
     "1563002cf4234e3f84bbef2c445ca642": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "DropdownModel",
      "state": {
       "_options_labels": [
        "Polynomial",
        "Exponential",
        "Power",
        "Logarithmic",
        "Fourier",
        "Gaussian",
        "Weibull",
        "Hill-type",
        "Sigmoidal",
        "Custom"
       ],
       "description": "Model:",
       "index": 8,
       "layout": "IPY_MODEL_c06c9645b00a401fa9923ffdcb38cf17",
       "style": "IPY_MODEL_4455fc4d5ca6408a8977123e8820caa4"
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     },
     "1a648b4ea84749b8816e7466b97cfbc7": {
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      "model_module_version": "2.0.0",
      "model_name": "LayoutModel",
      "state": {
       "align_items": "flex-start"
      }
     },
     "34bd5564e10e4df98982f43f81f3c458": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "TextStyleModel",
      "state": {
       "description_width": "",
       "font_size": null,
       "text_color": null
      }
     },
     "352b21fe82984d54a8fc452635a68d8a": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "2.0.0",
      "model_name": "DropdownModel",
      "state": {
       "_options_labels": [
        "Logistic",
        "4PL",
        "Alternate Logistic",
        "Gompertz"
       ],
       "description": "Submodel:",
       "index": 2,
       "layout": "IPY_MODEL_79ba74c790ec4b328c58e52f9816f9bf",
       "style": "IPY_MODEL_f1fd269fb38e4c69a9cbc5643632532f"
      }
     },
     "3730079da2bb4f33b1e93170b45c3c9e": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_b647012082fd436cad84a67cb57d778f",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "0.019431404560929*(-0.0505094526190915*exp(0.0505094526190915*x) + 11.1222374486757*exp(-0.455014025087188*x))/(0.040910295899263*exp(0.0505094526190915*x) + exp(-0.455014025087188*x))**2\n"
        },
        {
         "data": {
          "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Parameter</th>\n      <th>Value</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>A</td>\n      <td>11.61018</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>B</td>\n      <td>0.050509</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>C</td>\n      <td>24.443724</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>D</td>\n      <td>0.455014</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>R-squared</td>\n      <td>0.998939</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>RMSE</td>\n      <td>0.063782</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Equation</td>\n      <td>A/(C*exp(-D*x) + exp(B*x))</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
          "text/plain": "   Parameter                       Value\n0          A                    11.61018\n1          B                    0.050509\n2          C                   24.443724\n3          D                    0.455014\n4  R-squared                    0.998939\n5       RMSE                    0.063782\n6   Equation  A/(C*exp(-D*x) + exp(B*x))"
         },
         "metadata": {},
         "output_type": "display_data"
        },
        {
         "data": {
          "image/png": 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",
          "text/plain": "<Figure size 640x480 with 1 Axes>"
         },
         "metadata": {},
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        {
         "name": "stdout",
         "output_type": "stream",
         "text": "0.019431404560929*(-0.0505094526190915*exp(0.0505094526190915*x) + 11.1222374486757*exp(-0.455014025087188*x))/(0.040910295899263*exp(0.0505094526190915*x) + exp(-0.455014025087188*x))**2\n"
        },
        {
         "data": {
          "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Parameter</th>\n      <th>Value</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>A</td>\n      <td>11.61018</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>B</td>\n      <td>0.050509</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>C</td>\n      <td>24.443724</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>D</td>\n      <td>0.455014</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>R-squared</td>\n      <td>0.998939</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>RMSE</td>\n      <td>0.063782</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Equation</td>\n      <td>A/(C*exp(-D*x) + exp(B*x))</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
          "text/plain": "   Parameter                       Value\n0          A                    11.61018\n1          B                    0.050509\n2          C                   24.443724\n3          D                    0.455014\n4  R-squared                    0.998939\n5       RMSE                    0.063782\n6   Equation  A/(C*exp(-D*x) + exp(B*x))"
         },
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        },
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