Fig5ab.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "21b64206-65c6-4bd4-a7c8-28a55ab1d318",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import json\n",
    "from itertools import chain\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "import time\n",
    "\n",
    "import glob\n",
    "\n",
    "params = {\n",
    "   'axes.labelsize': 14,\n",
    "   'font.size': 14,\n",
    "   'legend.fontsize': 14,\n",
    "   'xtick.labelsize': 14,\n",
    "   'ytick.labelsize': 14,\n",
    "   'text.usetex': False,\n",
    "   'figure.figsize': [4.5, 4.5],\n",
    "    'savefig.facecolor':'white'\n",
    "   }\n",
    "\n",
    "mpl.rcParams.update(params)\n",
    "\n",
    "\n",
    "timestr = time.strftime(\"%Y%m%d%H%M\")\n",
    "\n",
    "import random \n",
    "random.seed(12357)\n",
    "np.random.seed(12357)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9a83627-f7b8-4c72-9898-fb2e90acb4c3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "65c2b254-3dd1-45c6-86cb-393414bafcd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0001\n"
     ]
    }
   ],
   "source": [
    "sol_saveat_dt = [1,1e-4][1]\n",
    "\n",
    "print(sol_saveat_dt)\n",
    "\n",
    "inpath = \"output_1e4/\"\n",
    "\n",
    "outpath = \"figures/fig5/\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "68a20e24-5052-4f7f-ad00-82a4ac0f8869",
   "metadata": {},
   "outputs": [],
   "source": [
    "##############################################\n",
    "# set to True to save fig\n",
    "\n",
    "savefigures = True #False #True \n",
    "\n",
    "##############################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2c78cb7f-93d2-4b79-969a-48a4b6123a8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15\n"
     ]
    }
   ],
   "source": [
    "print(len(glob.glob(inpath+\"*_u.csv\"))) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cc2b3428-f0ea-4a32-bb73-69a0f5cb62dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_file_ids = [i.rstrip(\"_u.csv\") for i in glob.glob(inpath+\"*_u.csv\")]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0b2d26c1-7d5b-4e4c-a629-5834725f1259",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "105\n"
     ]
    }
   ],
   "source": [
    "mets2plot2_sel3 = [i[0:-1] for i in [\"Mg_x_n0\",\"NADHmito_n0\", \"QH2mito_n0\", \"CytCredmito_n0\",  \n",
    "            \"ATPmito_n0\", \"ATP_mx_n0\", \"ATP_i_n0\", \"ATP_mi_n0\", \n",
    "            \"MitoMembrPotent_n0\", \"ATP_n0\", \"FUMmito_n0\", \"MALmito_n0\", \"OXAmito_n0\", \"SUCmito_n0\", \"SUCCOAmito_n0\", \"CoAmito_n0\", \n",
    "            \"AKGmito_n0\",  \"ISOCITmito_n0\", \"CITmito_n0\", \"AcCoAmito_n0\", \"AcAc_n0\", \"AcAcCoA_n0\", \"PYRmito_n0\", \"bHB_n0\", \"bHB_ecs0\", \"bHB_b0\",  \"GLU_n0\", \n",
    "            \"NADH_n0\", \"Mg_x_a0\",   \"NADHmito_a0\", \"QH2mito_a0\", \"CytCredmito_a0\",  \n",
    "            \"ATPmito_a0\",   \"ATP_mx_a0\",  \"ATP_i_a0\",   \"ATP_mi_a0\",  \n",
    "            \"MitoMembrPotent_a0\", \"ATP_a0\",  \"FUMmito_a0\", \"MALmito_a0\", \"OXAmito_a0\", \"SUCmito_a0\", \"SUCCOAmito_a0\", \"CoAmito_a0\", \n",
    "            \"AKGmito_a0\", \"ISOCITmito_a0\", \"CITmito_a0\", \"AcCoAmito_a0\", \"PYRmito_a0\", \"GLN_a0\", \"GLUT_a0\", \"Va0\", \"Na_a0\", \"K_a0\", \"K_out0\", \n",
    "              \"VNeu0\", \"Na_n0\",  \"Ca_n0\",   \"Ca_a0\", \"EET_a0\", \"ddHb0\", \"O2cap0\", \n",
    "              \"Glc_b0\", \"Glc_t_t0\", \"Glc_ecsBA0\", \"Glc_a0\", \"Glc_ecsAN0\", \"Glc_n0\", \"G6P_n0\", \"G6P_a0\", \"F6P_n0\", \"F6P_a0\", \"FBP_n0\", \"FBP_a0\", \"f26bp_a0\", \"GLY_a0\", \"G1P_a0\", \"GAP_n0\", \"GAP_a0\", \n",
    "              \"DHAP_n0\", \"DHAP_a0\", \"BPG13_n0\", \"BPG13_a0\", \"NADH_a0\", \"PG3_n0\", \"PG3_a0\", \"PG2_n0\", \"PG2_a0\", \"PEP_n0\", \"PEP_a0\", \"Pyr_n0\", \"Pyr_a0\", \"Lac_b0\", \"Lac_ecs0\", \"Lac_a0\", \"Lac_n0\", \n",
    "              \"NADPH_n0\", \"NADPH_a0\", \"R5P_n0\", \"R5P_a0\",  \"E4P_n0\", \"E4P_a0\", \n",
    "              \"GSH_n0\", \"GSH_a0\", \"cAMP_a0\"\n",
    "]]\n",
    "\n",
    "print(len(mets2plot2_sel3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d3b2d5d0-9751-463b-a41d-21db2642253d",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_file_ids = sorted(input_file_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "583e48ed-7944-440d-a1c6-271a2099de75",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_file_ids_def_glut1_th = [i for i in input_file_ids if ('121_default' in i) or  ('g1_61_def' in i) or ('g1_40' in i) or ('g1_93_blood_glc_ini_dose2' in i)  or ('g1_120_keto_lac_nad_Qtot_na' in i)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6737dda9-72b4-4fca-9dd7-5145fea7eaeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['output_1e4/sim_121_default_extraTag_default__0929_1631',\n",
       " 'output_1e4/sim_g1_120_keto_lac_nad_Qtot_na_extraTag_g1_120_keto_lac_nad_Qtot_na_1.7459_3.2_6.32_0.00512_1010_1304',\n",
       " 'output_1e4/sim_g1_40_glclacbhb_ini_extraTag_g1_40_glclacbhb_ini_1.7459_3.2_6.32_0.00512_1005_1534',\n",
       " 'output_1e4/sim_g1_61_def_extraTag_g1_61_def_1.7459_3.2_6.32_0.00512_0929_1626',\n",
       " 'output_1e4/sim_g1_93_blood_glc_ini_dose2_extraTag_g1_93_blood_glc_ini_dose2_1.7459_3.2_6.32_0.00512_1006_1551']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_file_ids_def_glut1_th"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2339ae5-a75c-4ff6-a8f3-d8994baf90c6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8cdce6e1-52ed-42c5-9d70-a7d9ad719931",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "output_1e4/sim_121_default_extraTag_default__0929_1631\n",
      "output_1e4/sim_g1_120_keto_lac_nad_Qtot_na_extraTag_g1_120_keto_lac_nad_Qtot_na_1.7459_3.2_6.32_0.00512_1010_1304\n",
      "output_1e4/sim_g1_40_glclacbhb_ini_extraTag_g1_40_glclacbhb_ini_1.7459_3.2_6.32_0.00512_1005_1534\n",
      "output_1e4/sim_g1_61_def_extraTag_g1_61_def_1.7459_3.2_6.32_0.00512_0929_1626\n",
      "output_1e4/sim_g1_93_blood_glc_ini_dose2_extraTag_g1_93_blood_glc_ini_dose2_1.7459_3.2_6.32_0.00512_1006_1551\n"
     ]
    }
   ],
   "source": [
    "input_dfs = []\n",
    "\n",
    "for fid in input_file_ids_def_glut1_th:\n",
    "\n",
    "    \n",
    "    print(fid)\n",
    "    \n",
    "    dfn = fid + \"_u.csv\"\n",
    "    tfn = fid + \"_t.csv\"\n",
    "    nfn = fid + \"_unames.csv\"\n",
    "    \n",
    "    df = pd.read_csv(dfn,header=None)\n",
    "    tf = pd.read_csv(tfn,header=None)\n",
    "    nf = pd.read_csv(nfn,header=None)\n",
    "    \n",
    "    df.columns = [i[:-1] for i in nf[0].values.tolist() ]\n",
    "    \n",
    "    df['time_s'] = tf[0].copy()\n",
    "    df['condition'] = fid.replace(\"output_1e4/sim_\",\"\").split(\"_extraTag\",1)[0]\n",
    "    \n",
    "    input_dfs.append(df)\n",
    "    \n",
    "input_df = pd.concat(input_dfs, axis=0, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "75503e83-7165-4793-95c2-baef13b17364",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(15450005, 185)\n"
     ]
    }
   ],
   "source": [
    "print(input_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "11a86fb8-c6f5-4371-8478-b7ab38760d4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_df = input_df.reindex(sorted(input_df.columns), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9956808f-9104-4a52-8fd1-a32795f71083",
   "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>ADP_a</th>\n",
       "      <th>ADP_i_a</th>\n",
       "      <th>ADP_i_n</th>\n",
       "      <th>ADP_mi_a</th>\n",
       "      <th>ADP_mi_n</th>\n",
       "      <th>ADP_mx_a</th>\n",
       "      <th>ADP_mx_n</th>\n",
       "      <th>ADP_n</th>\n",
       "      <th>ADPmito_a</th>\n",
       "      <th>ADPmito_n</th>\n",
       "      <th>...</th>\n",
       "      <th>f26bp_a</th>\n",
       "      <th>h</th>\n",
       "      <th>hIP3Ca_a</th>\n",
       "      <th>mGluRboundRatio_a</th>\n",
       "      <th>n</th>\n",
       "      <th>nBK_a</th>\n",
       "      <th>pgate</th>\n",
       "      <th>sTRP_a</th>\n",
       "      <th>time_s</th>\n",
       "      <th>vV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.045</td>\n",
       "      <td>0.044347</td>\n",
       "      <td>0.057207</td>\n",
       "      <td>0.043629</td>\n",
       "      <td>0.056055</td>\n",
       "      <td>1.144319</td>\n",
       "      <td>1.129008</td>\n",
       "      <td>0.047784</td>\n",
       "      <td>1.74456</td>\n",
       "      <td>1.851257</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01217</td>\n",
       "      <td>0.981124</td>\n",
       "      <td>0.653157</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.034674</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.035539</td>\n",
       "      <td>0.001247</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.0237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.045</td>\n",
       "      <td>0.044347</td>\n",
       "      <td>0.057207</td>\n",
       "      <td>0.043629</td>\n",
       "      <td>0.056055</td>\n",
       "      <td>1.144319</td>\n",
       "      <td>1.129008</td>\n",
       "      <td>0.047784</td>\n",
       "      <td>1.74456</td>\n",
       "      <td>1.851257</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01217</td>\n",
       "      <td>0.981127</td>\n",
       "      <td>0.653157</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.034671</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.035539</td>\n",
       "      <td>0.001247</td>\n",
       "      <td>1.0001</td>\n",
       "      <td>0.0237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.045</td>\n",
       "      <td>0.044347</td>\n",
       "      <td>0.057207</td>\n",
       "      <td>0.043629</td>\n",
       "      <td>0.056055</td>\n",
       "      <td>1.144319</td>\n",
       "      <td>1.129008</td>\n",
       "      <td>0.047784</td>\n",
       "      <td>1.74456</td>\n",
       "      <td>1.851257</td>\n",
       "      <td>...</td>\n",
       "      <td>0.01217</td>\n",
       "      <td>0.981137</td>\n",
       "      <td>0.653157</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.034662</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.035539</td>\n",
       "      <td>0.001247</td>\n",
       "      <td>1.0002</td>\n",
       "      <td>0.0237</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 185 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ADP_a   ADP_i_a   ADP_i_n  ADP_mi_a  ADP_mi_n  ADP_mx_a  ADP_mx_n  \\\n",
       "0  0.045  0.044347  0.057207  0.043629  0.056055  1.144319  1.129008   \n",
       "1  0.045  0.044347  0.057207  0.043629  0.056055  1.144319  1.129008   \n",
       "2  0.045  0.044347  0.057207  0.043629  0.056055  1.144319  1.129008   \n",
       "\n",
       "      ADP_n  ADPmito_a  ADPmito_n  ...  f26bp_a         h  hIP3Ca_a  \\\n",
       "0  0.047784    1.74456   1.851257  ...  0.01217  0.981124  0.653157   \n",
       "1  0.047784    1.74456   1.851257  ...  0.01217  0.981127  0.653157   \n",
       "2  0.047784    1.74456   1.851257  ...  0.01217  0.981137  0.653157   \n",
       "\n",
       "   mGluRboundRatio_a         n     nBK_a     pgate    sTRP_a  time_s      vV  \n",
       "0                0.0  0.034674  0.000007  0.035539  0.001247  1.0000  0.0237  \n",
       "1                0.0  0.034671  0.000007  0.035539  0.001247  1.0001  0.0237  \n",
       "2                0.0  0.034662  0.000007  0.035539  0.001247  1.0002  0.0237  \n",
       "\n",
       "[3 rows x 185 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9b3509cb-c4ec-4b2a-a1ba-45799c0bfde6",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_cnds_l = sorted(input_df['condition'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "eaea7fad-61fb-4267-b21b-595dc54e98f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['121_default',\n",
       " 'g1_120_keto_lac_nad_Qtot_na',\n",
       " 'g1_40_glclacbhb_ini',\n",
       " 'g1_61_def',\n",
       " 'g1_93_blood_glc_ini_dose2']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_cnds_l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2905902a-d7a3-407a-9f6b-4725f9e1a765",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4a6172d7-9298-41ee-800e-c83cedb2b786",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6,3))\n",
    "\n",
    "ax.axvspan(200, 230, color='lightgrey', alpha=0.5)\n",
    "\n",
    "ax.plot(input_df.loc[(input_df['time_s']>=180)&(input_df['condition']=='121_default'),'time_s'],input_df.loc[(input_df['time_s']>=180)&(input_df['condition']=='121_default'),'VNeu'],\n",
    "        color='#069e1d',alpha=0.5, label='control')\n",
    "\n",
    "ax.plot(input_df.loc[(input_df['time_s']>=180)&(input_df['condition']=='g1_61_def'),'time_s'],input_df.loc[(input_df['time_s']>=180)&(input_df['condition']=='g1_61_def'),'VNeu'],\n",
    "        color='#fa6f0c',alpha=0.5, label='GLUT1-def.')\n",
    "\n",
    "ax.plot(input_df.loc[(input_df['time_s']>=180)&(input_df['condition']=='g1_93_blood_glc_ini_dose2'),'time_s'],input_df.loc[(input_df['time_s']>=180)&(input_df['condition']=='g1_93_blood_glc_ini_dose2'),'VNeu'],\n",
    "         color='#8a0fd1',alpha=0.8,label='glc')\n",
    "\n",
    "ax.plot(input_df.loc[(input_df['time_s']>=180)&(input_df['condition']=='g1_40_glclacbhb_ini'),'time_s'],input_df.loc[(input_df['time_s']>=180)&(input_df['condition']=='g1_40_glclacbhb_ini'),'VNeu'],\n",
    "        color='#0390fc',alpha=0.8,label='glc-lac-bHB')\n",
    "\n",
    "ax.plot(input_df.loc[(input_df['time_s']>=180)&(input_df['condition']=='g1_120_keto_lac_nad_Qtot_na'),'time_s'],input_df.loc[(input_df['time_s']>=180)&(input_df['condition']=='g1_120_keto_lac_nad_Qtot_na'),'VNeu'],\n",
    "         color='#f70ff7',alpha=0.8,label='lac-bHB-NADH-Q')\n",
    "\n",
    "ax.set_xlabel(\"t, s\")\n",
    "ax.set_ylabel(\"Vm, mV\")\n",
    "\n",
    "#add a legend\n",
    "ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left',frameon=False)\n",
    "\n",
    "ax.spines.top.set_visible(False)\n",
    "ax.spines.right.set_visible(False)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "if savefigures == True:\n",
    "    fig.savefig(outpath + \"Vm_withGlcPrevBest_\" + timestr + \".pdf\",transparent=False)\n",
    "    fig.savefig(outpath + \"Vm_withGlcPrevBest_\" + timestr + \".png\",dpi=300,transparent=False, bbox_inches=\"tight\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55e9b244-31f4-4824-a701-4ede190a49fc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f33c5625-f4e9-49b6-b0c2-c85bc77014eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 180x180 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(2.5,2.5))\n",
    "\n",
    "ax.axvspan(200, 230, color='lightgrey', alpha=0.5)\n",
    "\n",
    "ax.plot(input_df.loc[(input_df['time_s']>=180)&(input_df['condition']=='121_default'),'time_s'],input_df.loc[(input_df['time_s']>=180)&(input_df['condition']=='121_default'),'VNeu'],\n",
    "        color='#069e1d',alpha=1, label='control')\n",
    "\n",
    "ax.set_xlabel(\"t, s\")\n",
    "ax.set_ylabel(\"Vm, mV\")\n",
    "\n",
    "#add a legend\n",
    "ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left',frameon=False)\n",
    "\n",
    "ax.spines.top.set_visible(False)\n",
    "ax.spines.right.set_visible(False)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "if savefigures == True:\n",
    "    fig.savefig(outpath + \"Vm_fig5b_1_\" + timestr + \".pdf\",transparent=False)\n",
    "    fig.savefig(outpath + \"Vm_fig5b_1_\" + timestr + \".png\",dpi=300,transparent=False, bbox_inches=\"tight\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5b266012-faa9-431f-974b-2325ee163c8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 180x180 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(2.5,2.5))\n",
    "\n",
    "ax.axvspan(200, 230, color='lightgrey', alpha=0.5)\n",
    "\n",
    "ax.plot(input_df.loc[(input_df['time_s']>=180)&(input_df['condition']=='g1_61_def'),'time_s'],input_df.loc[(input_df['time_s']>=180)&(input_df['condition']=='g1_61_def'),'VNeu'],\n",
    "        color='#fa6f0c',alpha=1, label='GLUT1-def.')\n",
    "\n",
    "ax.set_xlabel(\"t, s\")\n",
    "ax.set_ylabel(\"Vm, mV\")\n",
    "\n",
    "#add a legend\n",
    "ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left',frameon=False)\n",
    "\n",
    "ax.spines.top.set_visible(False)\n",
    "ax.spines.right.set_visible(False)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "if savefigures == True:\n",
    "    fig.savefig(outpath + \"Vm_fig5b_2_\" + timestr + \".pdf\",transparent=False)\n",
    "    fig.savefig(outpath + \"Vm_fig5b_2_\" + timestr + \".png\",dpi=300,transparent=False, bbox_inches=\"tight\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "dad2ea70-2260-4193-bcb2-3ffe7ef9d37c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 180x180 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(2.5,2.5))\n",
    "\n",
    "ax.axvspan(200, 230, color='lightgrey', alpha=0.5)\n",
    "\n",
    "ax.plot(input_df.loc[(input_df['time_s']>=180)&(input_df['condition']=='g1_93_blood_glc_ini_dose2'),'time_s'],input_df.loc[(input_df['time_s']>=180)&(input_df['condition']=='g1_93_blood_glc_ini_dose2'),'VNeu'],\n",
    "         color='#8a0fd1',alpha=1,label='glc')\n",
    "\n",
    "ax.set_xlabel(\"t, s\")\n",
    "ax.set_ylabel(\"Vm, mV\")\n",
    "\n",
    "#add a legend\n",
    "ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left',frameon=False)\n",
    "\n",
    "ax.spines.top.set_visible(False)\n",
    "ax.spines.right.set_visible(False)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "if savefigures == True:\n",
    "    fig.savefig(outpath + \"Vm_fig5b_3_\" + timestr + \".pdf\",transparent=False)\n",
    "    fig.savefig(outpath + \"Vm_fig5b_3_\" + timestr + \".png\",dpi=300,transparent=False, bbox_inches=\"tight\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c9a0eae6-ad2d-4382-9649-5ac29dbd58ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 180x180 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(2.5,2.5))\n",
    "\n",
    "ax.axvspan(200, 230, color='lightgrey', alpha=0.5)\n",
    "\n",
    "ax.plot(input_df.loc[(input_df['time_s']>=180)&(input_df['condition']=='g1_40_glclacbhb_ini'),'time_s'],input_df.loc[(input_df['time_s']>=180)&(input_df['condition']=='g1_40_glclacbhb_ini'),'VNeu'],\n",
    "        color='#0390fc',alpha=1,label='glc-lac-bHB')\n",
    "\n",
    "ax.set_xlabel(\"t, s\")\n",
    "ax.set_ylabel(\"Vm, mV\")\n",
    "\n",
    "#add a legend\n",
    "ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left',frameon=False)\n",
    "\n",
    "ax.spines.top.set_visible(False)\n",
    "ax.spines.right.set_visible(False)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "if savefigures == True:\n",
    "    fig.savefig(outpath + \"Vm_fig5b_4_\" + timestr + \".pdf\",transparent=False)\n",
    "    fig.savefig(outpath + \"Vm_fig5b_4_\" + timestr + \".png\",dpi=300,transparent=False, bbox_inches=\"tight\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1ede8bf5-d049-49e5-92e5-aadd3ee26a2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 180x180 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(2.5,2.5))\n",
    "\n",
    "ax.axvspan(200, 230, color='lightgrey', alpha=0.5)\n",
    "\n",
    "ax.plot(input_df.loc[(input_df['time_s']>=180)&(input_df['condition']=='g1_120_keto_lac_nad_Qtot_na'),'time_s'],input_df.loc[(input_df['time_s']>=180)&(input_df['condition']=='g1_120_keto_lac_nad_Qtot_na'),'VNeu'],\n",
    "         color='#f70ff7',alpha=1,label='lac-bHB-NADH-Q')\n",
    "\n",
    "ax.set_xlabel(\"t, s\")\n",
    "ax.set_ylabel(\"Vm, mV\")\n",
    "\n",
    "#add a legend\n",
    "ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left',frameon=False)\n",
    "\n",
    "ax.spines.top.set_visible(False)\n",
    "ax.spines.right.set_visible(False)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "if savefigures == True:\n",
    "    fig.savefig(outpath + \"Vm_fig5b_5_\" + timestr + \".pdf\",transparent=False)\n",
    "    fig.savefig(outpath + \"Vm_fig5b_5_\" + timestr + \".png\",dpi=300,transparent=False, bbox_inches=\"tight\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a638ceff-e84f-4888-b343-3358d66586f2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1218c685-aba4-49d7-b046-cf4b81a75a41",
   "metadata": {},
   "source": [
    "### ATP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4df6780d-f9e9-42e4-940f-411e684a2d55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6,3))\n",
    "\n",
    "ax.axvspan(200, 230, color='lightgrey', alpha=0.5)\n",
    "\n",
    "ax.plot(input_df.loc[(input_df['time_s']>=180)&(input_df['condition']=='g1_61_def'),'time_s'],input_df.loc[(input_df['time_s']>=180)&(input_df['condition']=='g1_61_def'),'ATP_n'],color='#fa6f0c',alpha=0.8, label='GLUT1-def.')\n",
    "\n",
    "ax.plot(input_df.loc[(input_df['time_s']>=180)&(input_df['condition']=='121_default'),'time_s'],input_df.loc[(input_df['time_s']>=180)&(input_df['condition']=='121_default'),'ATP_n'],color='#069e1d',alpha=0.8, label='control')\n",
    "\n",
    "ax.plot(input_df.loc[(input_df['time_s']>=180)&(input_df['condition']=='g1_40_glclacbhb_ini'),'time_s'],input_df.loc[(input_df['time_s']>=180)&(input_df['condition']=='g1_40_glclacbhb_ini'),'ATP_n'],color='#0390fc',alpha=0.8,label='Glc-Lac-bHB therapy')\n",
    "\n",
    "ax.plot(input_df.loc[(input_df['time_s']>=180)&(input_df['condition']=='g1_93_blood_glc_ini_dose2'),'time_s'],input_df.loc[(input_df['time_s']>=180)&(input_df['condition']=='g1_93_blood_glc_ini_dose2'),'ATP_n'],\n",
    "         color='#8a0fd1',alpha=0.8,label='Glc therapy')\n",
    "\n",
    "ax.plot(input_df.loc[(input_df['time_s']>=180)&(input_df['condition']=='g1_120_keto_lac_nad_Qtot_na'),'time_s'],input_df.loc[(input_df['time_s']>=180)&(input_df['condition']=='g1_120_keto_lac_nad_Qtot_na'),'ATP_n'],\n",
    "         color='#f70ff7',alpha=0.8,label='Lac-bHB-NADH-Q therapy')\n",
    "\n",
    "\n",
    "ax.set_xlabel(\"t, s\")\n",
    "ax.set_ylabel(\"ATP n, mM\")\n",
    "\n",
    "#add a legend\n",
    "ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left',frameon=False)\n",
    "\n",
    "ax.spines.top.set_visible(False)\n",
    "ax.spines.right.set_visible(False)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "if savefigures == True:\n",
    "    fig.savefig(outpath + \"ATP_withGlcPrevBest_\" + timestr + \".pdf\",transparent=False)\n",
    "    fig.savefig(outpath + \"ATP_withGlcPrevBest_\" + timestr + \".png\",dpi=300,transparent=False, bbox_inches=\"tight\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e969abd7-e753-469f-b830-125bdcdfff16",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28f13a2b-a7c7-4942-bd75-9ac2a7c154a9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0656028e-29a1-49a7-8a8f-e8d59950eb21",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}