Fig1c_and_SuppFigS2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bc66269b-0c5a-4d5f-b860-19c7f85c5e6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "202310301520\n"
     ]
    }
   ],
   "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",
    "import glob\n",
    "\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",
    "import random \n",
    "random.seed(12357)\n",
    "np.random.seed(12357)\n",
    "\n",
    "\n",
    "import pprint \n",
    "ppdict = pprint.PrettyPrinter(indent=4, sort_dicts=False)\n",
    "\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "import umap\n",
    "import hdbscan\n",
    "import sklearn.cluster as cluster\n",
    "from sklearn.metrics import adjusted_rand_score, adjusted_mutual_info_score\n",
    "\n",
    "\n",
    "timestr = time.strftime(\"%Y%m%d%H%M\")\n",
    "print(timestr) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ec79ffb8-4748-451f-9e09-da4fb97290ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "sol_saveat_dt = [1,1e-4][0]\n",
    "\n",
    "print(sol_saveat_dt)\n",
    "\n",
    "inpath = \"output/\"\n",
    "outpath = \"figures/fig3/\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "362cf4c6-8571-416c-895f-4c98ab21d22c",
   "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": "076a1ac5-9b51-4347-ae72-e72405a25595",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "190\n"
     ]
    }
   ],
   "source": [
    "! ls -lh output | grep \"_u\\.\" | grep \"sim_\" | wc -l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7652545c-2367-49a2-8aba-cce2f81e7813",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "190\n"
     ]
    }
   ],
   "source": [
    "print(len(glob.glob(inpath+\"*_u.csv\"))) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2e9d4f1e-d2ce-4704-9767-8629bfd31040",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_file_ids = [i.rstrip(\"_u.csv\") for i in glob.glob(inpath+\"*_u.csv\")]\n",
    "\n",
    "\n",
    "input_file_ids = sorted(input_file_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1c4e28f1-18d0-47bd-a39a-fc5ebf8bae0b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "input_dfs = []\n",
    "\n",
    "for fid in input_file_ids:\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/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": 8,
   "id": "3b0f2a57-e5fc-44cd-89e9-d42d93278704",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(58900, 185)\n"
     ]
    }
   ],
   "source": [
    "print(input_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ac3642b9-ef06-4a95-98bb-09dcc98b2aad",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_df = input_df.reindex(sorted(input_df.columns), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7daeb693-b85a-491e-a588-03514f44a022",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_df = input_df.loc[input_df['time_s']>180,:]\n",
    "input_df = input_df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "acbb632f-a389-43a7-8c87-1855ab9197a1",
   "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>0.045969</td>\n",
       "      <td>0.057954</td>\n",
       "      <td>0.045225</td>\n",
       "      <td>0.056787</td>\n",
       "      <td>1.152120</td>\n",
       "      <td>1.131716</td>\n",
       "      <td>0.047784</td>\n",
       "      <td>1.759665</td>\n",
       "      <td>1.856772</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012238</td>\n",
       "      <td>0.991528</td>\n",
       "      <td>0.653282</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.021990</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.023936</td>\n",
       "      <td>0.001238</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.0237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.045</td>\n",
       "      <td>0.045971</td>\n",
       "      <td>0.057954</td>\n",
       "      <td>0.045227</td>\n",
       "      <td>0.056787</td>\n",
       "      <td>1.152133</td>\n",
       "      <td>1.131715</td>\n",
       "      <td>0.047784</td>\n",
       "      <td>1.759688</td>\n",
       "      <td>1.856772</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012238</td>\n",
       "      <td>0.991528</td>\n",
       "      <td>0.653285</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.021989</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.023936</td>\n",
       "      <td>0.001238</td>\n",
       "      <td>182.0</td>\n",
       "      <td>0.0237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.045</td>\n",
       "      <td>0.045973</td>\n",
       "      <td>0.057954</td>\n",
       "      <td>0.045230</td>\n",
       "      <td>0.056787</td>\n",
       "      <td>1.152145</td>\n",
       "      <td>1.131715</td>\n",
       "      <td>0.047784</td>\n",
       "      <td>1.759712</td>\n",
       "      <td>1.856771</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012239</td>\n",
       "      <td>0.991528</td>\n",
       "      <td>0.653289</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.021989</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.023936</td>\n",
       "      <td>0.001238</td>\n",
       "      <td>183.0</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  0.045969  0.057954  0.045225  0.056787  1.152120  1.131716   \n",
       "1  0.045  0.045971  0.057954  0.045227  0.056787  1.152133  1.131715   \n",
       "2  0.045  0.045973  0.057954  0.045230  0.056787  1.152145  1.131715   \n",
       "\n",
       "      ADP_n  ADPmito_a  ADPmito_n  ...   f26bp_a         h  hIP3Ca_a  \\\n",
       "0  0.047784   1.759665   1.856772  ...  0.012238  0.991528  0.653282   \n",
       "1  0.047784   1.759688   1.856772  ...  0.012238  0.991528  0.653285   \n",
       "2  0.047784   1.759712   1.856771  ...  0.012239  0.991528  0.653289   \n",
       "\n",
       "   mGluRboundRatio_a         n     nBK_a     pgate    sTRP_a  time_s      vV  \n",
       "0                0.0  0.021990  0.000007  0.023936  0.001238   181.0  0.0237  \n",
       "1                0.0  0.021989  0.000007  0.023936  0.001238   182.0  0.0237  \n",
       "2                0.0  0.021989  0.000007  0.023936  0.001238   183.0  0.0237  \n",
       "\n",
       "[3 rows x 185 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "63787583-8828-4500-b2b2-0882aa53abfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "190"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_cnds_l = sorted(input_df['condition'].unique())\n",
    "len(all_cnds_l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "39dc2772-0363-48af-ad88-0aa89500f075",
   "metadata": {},
   "outputs": [],
   "source": [
    "sel_cnds = [\"121_default\",\"g1_61_def\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dbdffc8e-65ee-4c2b-9437-ebbd657ebdfa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37\n"
     ]
    }
   ],
   "source": [
    "sel_mets = [i[0:-1] for i in [     \n",
    "                    \"Glc_t_t0\", \"Glc_ecsBA0\", \"Glc_a0\", \"Glc_ecsAN0\", \"Glc_n0\", \"FBP_n0\", \"FBP_a0\", \"DHAP_n0\", \"DHAP_a0\",\"GAP_n0\", \"GAP_a0\", \"Lac_b0\", \"Lac_ecs0\", \"Lac_a0\", \"Lac_n0\",\n",
    "    \"FUMmito_n0\", \"MALmito_n0\", \"OXAmito_n0\", \"SUCmito_n0\", \"SUCCOAmito_n0\", \"CoAmito_n0\", \n",
    "            \"AKGmito_n0\",  \"ISOCITmito_n0\", \"CITmito_n0\", \"AcCoAmito_n0\",  \"PYRmito_n0\",  \n",
    "    \"FUMmito_a0\", \"MALmito_a0\", \"OXAmito_a0\", \"SUCmito_a0\", \"SUCCOAmito_a0\", \"CoAmito_a0\", \n",
    "            \"AKGmito_a0\", \"ISOCITmito_a0\", \"CITmito_a0\", \"AcCoAmito_a0\", \"PYRmito_a0\"\n",
    "    \n",
    "]]\n",
    "\n",
    "\n",
    "anaplerotics = [i[0:-1] for i in [  \"FUMmito_n0\", \"MALmito_n0\", \"OXAmito_n0\", \"SUCmito_n0\", \"SUCCOAmito_n0\", \"CoAmito_n0\", \n",
    "            \"AKGmito_n0\",  \"ISOCITmito_n0\", \"CITmito_n0\", \"AcCoAmito_n0\",  \"PYRmito_n0\",  \n",
    "    \"FUMmito_a0\", \"MALmito_a0\", \"OXAmito_a0\", \"SUCmito_a0\", \"SUCCOAmito_a0\", \"CoAmito_a0\", \n",
    "            \"AKGmito_a0\", \"ISOCITmito_a0\", \"CITmito_a0\", \"AcCoAmito_a0\", \"PYRmito_a0\"\n",
    "        ]]\n",
    "        \n",
    "jay_selected = [i[0:-1] for i in [\"AKGmito_n0\",\"AKGmito_a0\",  \"OXAmito_n0\",\"OXAmito_a0\", \"SUCCOAmito_n0\",\"SUCCOAmito_a0\", \"FUMmito_n0\",\"FUMmito_a0\"]]\n",
    "\n",
    "print(len(sel_mets))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0cc60905-5be2-4c69-98ea-2686e1cc3774",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a433d92d-30fe-4cd1-9838-16050dcb583e",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_df_mp = input_df.loc[input_df['condition'].isin(sel_cnds),sel_mets + ['condition', 'time_s']].copy()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cf604ea6-8443-4fbb-8761-3734e7f3766a",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_df_mpl = pd.melt(input_df_mp,id_vars=['condition','time_s'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b175ec09-e66f-472c-ad8d-73ae86d5225a",
   "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>condition</th>\n",
       "      <th>time_s</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>121_default</td>\n",
       "      <td>181.0</td>\n",
       "      <td>Glc_t_t</td>\n",
       "      <td>1.399852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>121_default</td>\n",
       "      <td>182.0</td>\n",
       "      <td>Glc_t_t</td>\n",
       "      <td>1.399846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>121_default</td>\n",
       "      <td>183.0</td>\n",
       "      <td>Glc_t_t</td>\n",
       "      <td>1.399840</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     condition  time_s variable     value\n",
       "0  121_default   181.0  Glc_t_t  1.399852\n",
       "1  121_default   182.0  Glc_t_t  1.399846\n",
       "2  121_default   183.0  Glc_t_t  1.399840"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_df_mpl.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b6572ed6-f106-428e-a69a-1e931938aaf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_df_mpl['log_conc'] = np.log(input_df_mpl['value'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78bb2b15-7f18-4ef7-8841-e9e995110a3b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d2a0ee26-d5d8-4251-9583-2dcfdfd5c187",
   "metadata": {},
   "outputs": [],
   "source": [
    "lut_cnds = {'121_default':\"#069e1d\", \n",
    "                'g1_61_def':\"#fa6f0c\"\n",
    "               }\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0783d9fb-3118-47c7-ab56-f43057340846",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0fb1edc2-427c-4194-9a6a-53fac59f0911",
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp = pd.DataFrame(input_df_mpl.loc[input_df_mpl['variable'].isin(sel_mets),['variable','value','condition']].groupby(['variable','condition']).apply(np.median))\n",
    "\n",
    "tmp = tmp.sort_values(by=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cebaf6da-7492-4f47-a0d9-d78f4355c94a",
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp = tmp.reset_index(drop=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "dfa15e3c-8880-42a7-8914-9ced8952e7bc",
   "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>variable</th>\n",
       "      <th>condition</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GAP_n</td>\n",
       "      <td>g1_61_def</td>\n",
       "      <td>0.001215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GAP_n</td>\n",
       "      <td>121_default</td>\n",
       "      <td>0.001230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SUCCOAmito_a</td>\n",
       "      <td>g1_61_def</td>\n",
       "      <td>0.001675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SUCCOAmito_a</td>\n",
       "      <td>121_default</td>\n",
       "      <td>0.001684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GAP_a</td>\n",
       "      <td>g1_61_def</td>\n",
       "      <td>0.001815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Glc_t_t</td>\n",
       "      <td>g1_61_def</td>\n",
       "      <td>0.943114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Glc_ecsAN</td>\n",
       "      <td>121_default</td>\n",
       "      <td>1.001148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Glc_a</td>\n",
       "      <td>121_default</td>\n",
       "      <td>1.219554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Glc_ecsBA</td>\n",
       "      <td>121_default</td>\n",
       "      <td>1.298903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Glc_t_t</td>\n",
       "      <td>121_default</td>\n",
       "      <td>1.399190</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>74 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        variable    condition         0\n",
       "0          GAP_n    g1_61_def  0.001215\n",
       "1          GAP_n  121_default  0.001230\n",
       "2   SUCCOAmito_a    g1_61_def  0.001675\n",
       "3   SUCCOAmito_a  121_default  0.001684\n",
       "4          GAP_a    g1_61_def  0.001815\n",
       "..           ...          ...       ...\n",
       "69       Glc_t_t    g1_61_def  0.943114\n",
       "70     Glc_ecsAN  121_default  1.001148\n",
       "71         Glc_a  121_default  1.219554\n",
       "72     Glc_ecsBA  121_default  1.298903\n",
       "73       Glc_t_t  121_default  1.399190\n",
       "\n",
       "[74 rows x 3 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "db545d67-9cb5-449e-a06c-d7d80efe1fbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "range1 = [i for i in tmp.loc[tmp[0]<0.005,'variable'].unique()  if not i in  anaplerotics]\n",
    "len(range1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3f9f84da-93c2-46c9-97fa-3017504a6358",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "range2 = [i for i in tmp.loc[(tmp[0]>=0.005) & (tmp[0]<0.5),'variable'].unique()  if not i in  anaplerotics]\n",
    "len(range2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d9c5417b-4eb8-4b94-9293-6afe71e6814d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "range3 = [i for i in tmp.loc[(tmp[0]>=0.5) & (tmp[0]<1),'variable'].unique() if not i in anaplerotics + ['Glc_n', 'Glc_ecsAN','Glc_a', 'Glc_ecsBA','Glc_t_t']]\n",
    "len(range3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9f903139-c318-413f-9765-8bdf90d896d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "range4 = ['Glc_n', 'Glc_ecsAN','Glc_a', 'Glc_ecsBA','Glc_t_t'] #tmp.loc[(tmp[0]>=1) ,'variable'].unique()\n",
    "len(range4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d95ae660-218d-4e5c-a238-7b79f7abd938",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b6e13c67-510e-414a-bcfa-b9b1c61876cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "range1j = sorted([i for i in tmp.loc[tmp[0]<0.012,'variable'].unique()  if  i in  jay_selected])\n",
    "len(range1j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b52cb89f-b5bf-4a83-b030-1631460f7972",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "range2j = sorted([i for i in tmp.loc[tmp[0]>=0.012,'variable'].unique()  if  i in  jay_selected])\n",
    "len(range2j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0a6d6837-8938-4431-8f4a-ed1446bb5d95",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>SUCCOAmito_n</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>SUCCOAmito_n</td>\n",
       "      <td>121_default</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>OXAmito_a</td>\n",
       "      <td>g1_61_def</td>\n",
       "      <td>0.004460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>OXAmito_a</td>\n",
       "      <td>121_default</td>\n",
       "      <td>0.004532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>OXAmito_n</td>\n",
       "      <td>g1_61_def</td>\n",
       "      <td>0.011016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>OXAmito_n</td>\n",
       "      <td>121_default</td>\n",
       "      <td>0.011122</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        variable    condition         0\n",
       "2   SUCCOAmito_a    g1_61_def  0.001675\n",
       "3   SUCCOAmito_a  121_default  0.001684\n",
       "9   SUCCOAmito_n    g1_61_def  0.002565\n",
       "11  SUCCOAmito_n  121_default  0.002669\n",
       "18     OXAmito_a    g1_61_def  0.004460\n",
       "19     OXAmito_a  121_default  0.004532\n",
       "22     OXAmito_n    g1_61_def  0.011016\n",
       "23     OXAmito_n  121_default  0.011122"
      ]
     },
     "execution_count": 29,
     "metadata": {},
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   "source": [
    "tmp.loc[tmp['variable'].isin(range1j)]"
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   "cell_type": "code",
   "execution_count": 30,
   "id": "47c4a738-7f77-4e94-84aa-bb0df57b9204",
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   "outputs": [
    {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>AKGmito_a</td>\n",
       "      <td>121_default</td>\n",
       "      <td>0.014752</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>AKGmito_a</td>\n",
       "      <td>g1_61_def</td>\n",
       "      <td>0.014951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>FUMmito_a</td>\n",
       "      <td>g1_61_def</td>\n",
       "      <td>0.049835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>FUMmito_a</td>\n",
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       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>AKGmito_n</td>\n",
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       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>FUMmito_n</td>\n",
       "      <td>g1_61_def</td>\n",
       "      <td>0.069111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>FUMmito_n</td>\n",
       "      <td>121_default</td>\n",
       "      <td>0.069525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>AKGmito_n</td>\n",
       "      <td>121_default</td>\n",
       "      <td>0.071883</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "     variable    condition         0\n",
       "26  AKGmito_a  121_default  0.014752\n",
       "27  AKGmito_a    g1_61_def  0.014951\n",
       "38  FUMmito_a    g1_61_def  0.049835\n",
       "39  FUMmito_a  121_default  0.050373\n",
       "40  AKGmito_n    g1_61_def  0.067736\n",
       "41  FUMmito_n    g1_61_def  0.069111\n",
       "42  FUMmito_n  121_default  0.069525\n",
       "43  AKGmito_n  121_default  0.071883"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp.loc[tmp['variable'].isin(range2j)]"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7e9feb0-ec29-474b-8e6d-e02d71e60e00",
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   "outputs": [],
   "source": []
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   "cell_type": "code",
   "execution_count": 31,
   "id": "9bda0ee2-291a-49fd-9c07-c5ae5127389d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "range1a = sorted([i for i in tmp.loc[tmp[0]<0.05,'variable'].unique() if (i in anaplerotics) and (i not in jay_selected) ])\n",
    "len(range1a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fe9fd39c-d19d-4b54-8ba5-34d016790bea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "range2a = sorted([i for i in tmp.loc[(tmp[0]>=0.05),'variable'].unique() if (i in anaplerotics) and (i not in jay_selected)  and (i not in range1a)])\n",
    "len(range2a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d083c0b5-61c1-494c-9ea6-d7cef5d9af3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CoAmito_a</td>\n",
       "      <td>g1_61_def</td>\n",
       "      <td>0.002495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CoAmito_a</td>\n",
       "      <td>121_default</td>\n",
       "      <td>0.002532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>CoAmito_n</td>\n",
       "      <td>g1_61_def</td>\n",
       "      <td>0.002562</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CoAmito_n</td>\n",
       "      <td>121_default</td>\n",
       "      <td>0.002613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>AcCoAmito_a</td>\n",
       "      <td>121_default</td>\n",
       "      <td>0.004306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>AcCoAmito_a</td>\n",
       "      <td>g1_61_def</td>\n",
       "      <td>0.004354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>PYRmito_a</td>\n",
       "      <td>121_default</td>\n",
       "      <td>0.013180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>PYRmito_a</td>\n",
       "      <td>g1_61_def</td>\n",
       "      <td>0.013314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>ISOCITmito_n</td>\n",
       "      <td>121_default</td>\n",
       "      <td>0.033115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>ISOCITmito_n</td>\n",
       "      <td>g1_61_def</td>\n",
       "      <td>0.033120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>ISOCITmito_a</td>\n",
       "      <td>121_default</td>\n",
       "      <td>0.036082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>ISOCITmito_a</td>\n",
       "      <td>g1_61_def</td>\n",
       "      <td>0.036085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>AcCoAmito_n</td>\n",
       "      <td>121_default</td>\n",
       "      <td>0.040713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>AcCoAmito_n</td>\n",
       "      <td>g1_61_def</td>\n",
       "      <td>0.040849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>PYRmito_n</td>\n",
       "      <td>121_default</td>\n",
       "      <td>0.047497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>PYRmito_n</td>\n",
       "      <td>g1_61_def</td>\n",
       "      <td>0.048444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        variable    condition         0\n",
       "6      CoAmito_a    g1_61_def  0.002495\n",
       "7      CoAmito_a  121_default  0.002532\n",
       "8      CoAmito_n    g1_61_def  0.002562\n",
       "10     CoAmito_n  121_default  0.002613\n",
       "16   AcCoAmito_a  121_default  0.004306\n",
       "17   AcCoAmito_a    g1_61_def  0.004354\n",
       "24     PYRmito_a  121_default  0.013180\n",
       "25     PYRmito_a    g1_61_def  0.013314\n",
       "30  ISOCITmito_n  121_default  0.033115\n",
       "31  ISOCITmito_n    g1_61_def  0.033120\n",
       "32  ISOCITmito_a  121_default  0.036082\n",
       "33  ISOCITmito_a    g1_61_def  0.036085\n",
       "34   AcCoAmito_n  121_default  0.040713\n",
       "35   AcCoAmito_n    g1_61_def  0.040849\n",
       "36     PYRmito_n  121_default  0.047497\n",
       "37     PYRmito_n    g1_61_def  0.048444"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp.loc[tmp['variable'].isin(range1a)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "90902e36-171e-4c01-b4a2-41b95030b815",
   "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>variable</th>\n",
       "      <th>condition</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>MALmito_a</td>\n",
       "      <td>g1_61_def</td>\n",
       "      <td>0.257336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>MALmito_a</td>\n",
       "      <td>121_default</td>\n",
       "      <td>0.260312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>CITmito_n</td>\n",
       "      <td>g1_61_def</td>\n",
       "      <td>0.341266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>CITmito_n</td>\n",
       "      <td>121_default</td>\n",
       "      <td>0.341310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>CITmito_a</td>\n",
       "      <td>121_default</td>\n",
       "      <td>0.359859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>CITmito_a</td>\n",
       "      <td>g1_61_def</td>\n",
       "      <td>0.359894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>SUCmito_n</td>\n",
       "      <td>121_default</td>\n",
       "      <td>0.370445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>SUCmito_n</td>\n",
       "      <td>g1_61_def</td>\n",
       "      <td>0.374446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>MALmito_n</td>\n",
       "      <td>g1_61_def</td>\n",
       "      <td>0.381224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>MALmito_n</td>\n",
       "      <td>121_default</td>\n",
       "      <td>0.383448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>SUCmito_a</td>\n",
       "      <td>121_default</td>\n",
       "      <td>0.580259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>SUCmito_a</td>\n",
       "      <td>g1_61_def</td>\n",
       "      <td>0.583513</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     variable    condition         0\n",
       "44  MALmito_a    g1_61_def  0.257336\n",
       "45  MALmito_a  121_default  0.260312\n",
       "46  CITmito_n    g1_61_def  0.341266\n",
       "47  CITmito_n  121_default  0.341310\n",
       "48  CITmito_a  121_default  0.359859\n",
       "49  CITmito_a    g1_61_def  0.359894\n",
       "50  SUCmito_n  121_default  0.370445\n",
       "51  SUCmito_n    g1_61_def  0.374446\n",
       "52  MALmito_n    g1_61_def  0.381224\n",
       "53  MALmito_n  121_default  0.383448\n",
       "55  SUCmito_a  121_default  0.580259\n",
       "56  SUCmito_a    g1_61_def  0.583513"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp.loc[tmp['variable'].isin(range2a)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae170143-f42d-43d9-9c79-6136bfae844d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ba54a8f-8fc7-4301-b2c5-4ace15338be2",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "55057c1e-1c67-4e7f-ba9f-ce722fcfc535",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(4,2))\n",
    "ax = plt.subplot(111)\n",
    "\n",
    "\n",
    "sns.barplot(ax=ax,\n",
    "            data=input_df_mpl.loc[input_df_mpl['variable'].isin(range1)], \n",
    "            y='value', x='variable', \n",
    "            hue=\"condition\",palette=lut_cnds,orient='v',ci=95)\n",
    "\n",
    "ax.spines.top.set_visible(False)\n",
    "ax.spines.right.set_visible(False)\n",
    "\n",
    "ax.set_xlabel(\"\")\n",
    "ax.set_ylabel(\"Conc., mM\")\n",
    "ax.tick_params(axis='x', rotation=45)\n",
    "\n",
    "ax.legend( bbox_to_anchor=(1.05, 1), loc='upper left',frameon=False)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "if savefigures == True:\n",
    "    \n",
    "    spec_id = \"bars_mets1_\"\n",
    "    \n",
    "    fig.savefig(outpath +  spec_id +  timestr + \".pdf\",transparent=False, bbox_inches=\"tight\")\n",
    "    fig.savefig(outpath +  spec_id +  timestr + \".png\",dpi=300,transparent=False, bbox_inches=\"tight\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "494ef806-95a8-43f2-896e-ec367878e19a",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(input_df_mpl.loc[input_df_mpl['variable'].isin(range1),['variable','condition','value']].groupby(['variable','condition'])['value'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d7d896e-4d95-462c-a6c4-afbb8ae10c9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(input_df_mpl.loc[input_df_mpl['variable'].isin(range1),['variable','condition','value']].groupby(['variable','condition'])['value'].std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "81a16e70-a000-429b-a219-12a12ffbee0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 144x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(2,2))\n",
    "ax = plt.subplot(111)\n",
    "\n",
    "\n",
    "sns.barplot(ax=ax,\n",
    "            data=input_df_mpl.loc[input_df_mpl['variable'].isin(range2)], \n",
    "            y='value', x='variable', \n",
    "            hue=\"condition\",palette=lut_cnds,orient='v',ci=95)\n",
    "\n",
    "ax.spines.top.set_visible(False)\n",
    "ax.spines.right.set_visible(False)\n",
    "\n",
    "ax.set_xlabel(\"\")\n",
    "ax.set_ylabel(\"Conc., mM\")\n",
    "ax.tick_params(axis='x', rotation=45)\n",
    "\n",
    "ax.legend( bbox_to_anchor=(1.05, 1), loc='upper left',frameon=False)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "if savefigures == True:\n",
    "    \n",
    "    spec_id = \"bars_mets2_\"\n",
    "    \n",
    "    fig.savefig(outpath +  spec_id +  timestr + \".pdf\",transparent=False, bbox_inches=\"tight\")\n",
    "    fig.savefig(outpath +  spec_id +  timestr + \".png\",dpi=300,transparent=False, bbox_inches=\"tight\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6784daf2-bd8f-499f-a2fe-49751b8417e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(input_df_mpl.loc[input_df_mpl['variable'].isin(range2),['variable','condition','value']].groupby(['variable','condition'])['value'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c55e947-1352-414a-900c-b0ec49458e70",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(input_df_mpl.loc[input_df_mpl['variable'].isin(range2),['variable','condition','value']].groupby(['variable','condition'])['value'].std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a032cbee-427d-4898-8ea9-83fa75909265",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(4,2))\n",
    "ax = plt.subplot(111)\n",
    "\n",
    "\n",
    "sns.barplot(ax=ax,\n",
    "            data=input_df_mpl.loc[input_df_mpl['variable'].isin(range3)], \n",
    "            y='value', x='variable', \n",
    "            hue=\"condition\",palette=lut_cnds,orient='v',ci=95)\n",
    "\n",
    "ax.spines.top.set_visible(False)\n",
    "ax.spines.right.set_visible(False)\n",
    "\n",
    "ax.set_xlabel(\"\")\n",
    "ax.set_ylabel(\"Conc., mM\")\n",
    "ax.tick_params(axis='x', rotation=45)\n",
    "\n",
    "ax.legend( bbox_to_anchor=(1.05, 1), loc='upper left',frameon=False)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "if savefigures == True:\n",
    "    \n",
    "    spec_id = \"bars_mets3_\"\n",
    "    \n",
    "    fig.savefig(outpath +  spec_id +  timestr + \".pdf\",transparent=False, bbox_inches=\"tight\")\n",
    "    fig.savefig(outpath +  spec_id +  timestr + \".png\",dpi=300,transparent=False, bbox_inches=\"tight\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8cc14cb3-cb1f-48de-9a02-5e4d02ff2335",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(input_df_mpl.loc[input_df_mpl['variable'].isin(range3),['variable','condition','value']].groupby(['variable','condition'])['value'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d62b6831-2abf-412f-8ce3-3fea176b1785",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(input_df_mpl.loc[input_df_mpl['variable'].isin(range3),['variable','condition','value']].groupby(['variable','condition'])['value'].std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8ac15e78-26ce-4d25-a5fe-ed24a8784a13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(5,2))\n",
    "ax = plt.subplot(111)\n",
    "\n",
    "\n",
    "sns.barplot(ax=ax,\n",
    "            data=input_df_mpl.loc[input_df_mpl['variable'].isin(range4)], \n",
    "            y='value', x='variable', \n",
    "            hue=\"condition\",palette=lut_cnds,orient='v',ci=95)\n",
    "\n",
    "ax.spines.top.set_visible(False)\n",
    "ax.spines.right.set_visible(False)\n",
    "\n",
    "ax.set_xlabel(\"\")\n",
    "ax.set_ylabel(\"Conc., mM\")\n",
    "ax.tick_params(axis='x', rotation=45)\n",
    "\n",
    "ax.legend( bbox_to_anchor=(1.05, 1), loc='upper left',frameon=False)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "if savefigures == True:\n",
    "    \n",
    "    spec_id = \"bars_mets4_\"\n",
    "    \n",
    "    fig.savefig(outpath +  spec_id +  timestr + \".pdf\",transparent=False, bbox_inches=\"tight\")\n",
    "    fig.savefig(outpath +  spec_id +  timestr + \".png\",dpi=300,transparent=False, bbox_inches=\"tight\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e31e4cd-3b83-4079-9496-1415685dcce5",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(input_df_mpl.loc[input_df_mpl['variable'].isin(range4),['variable','condition','value']].groupby(['variable','condition'])['value'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "debbef2d-9110-4ead-a639-c32307a971a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(input_df_mpl.loc[input_df_mpl['variable'].isin(range4),['variable','condition','value']].groupby(['variable','condition'])['value'].std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4cbaeb47-a06b-4a40-bd1b-9d3a918d644d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(4,2))\n",
    "ax = plt.subplot(111)\n",
    "\n",
    "\n",
    "sns.barplot(ax=ax,\n",
    "            data=input_df_mpl.loc[input_df_mpl['variable'].isin(range1j)].sort_values('variable'), \n",
    "            y='value', x='variable', \n",
    "            hue=\"condition\",palette=lut_cnds,orient='v',ci=95)\n",
    "\n",
    "ax.spines.top.set_visible(False)\n",
    "ax.spines.right.set_visible(False)\n",
    "\n",
    "ax.set_xlabel(\"\")\n",
    "ax.set_ylabel(\"Conc., mM\")\n",
    "ax.tick_params(axis='x', rotation=45)\n",
    "\n",
    "ax.legend( bbox_to_anchor=(1.05, 1), loc='upper left',frameon=False)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "if savefigures == True:\n",
    "    \n",
    "    spec_id = \"bars_mets1j_\"\n",
    "    \n",
    "    fig.savefig(outpath +  spec_id +  timestr + \".pdf\",transparent=False, bbox_inches=\"tight\")\n",
    "    fig.savefig(outpath +  spec_id +  timestr + \".png\",dpi=300,transparent=False, bbox_inches=\"tight\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4afb3bed-7347-4580-bd6d-1f145b6a3f47",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(input_df_mpl.loc[input_df_mpl['variable'].isin(range1j),['variable','condition','value']].groupby(['variable','condition'])['value'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eef71817-9c0a-4072-be50-406b1952d2a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(input_df_mpl.loc[input_df_mpl['variable'].isin(range1j),['variable','condition','value']].groupby(['variable','condition'])['value'].std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "da0bd988-5b26-4933-b858-8d1d0e8f8422",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(4,2))\n",
    "ax = plt.subplot(111)\n",
    "\n",
    "\n",
    "sns.barplot(ax=ax,\n",
    "            data=input_df_mpl.loc[input_df_mpl['variable'].isin(range2j)].sort_values('variable'), \n",
    "            y='value', x='variable', \n",
    "            hue=\"condition\",palette=lut_cnds,orient='v',ci=95)\n",
    "\n",
    "ax.spines.top.set_visible(False)\n",
    "ax.spines.right.set_visible(False)\n",
    "\n",
    "ax.set_xlabel(\"\")\n",
    "ax.set_ylabel(\"Conc., mM\")\n",
    "ax.tick_params(axis='x', rotation=45)\n",
    "\n",
    "ax.legend( bbox_to_anchor=(1.05, 1), loc='upper left',frameon=False)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "if savefigures == True:\n",
    "    \n",
    "    spec_id = \"bars_mets2j_\"\n",
    "    \n",
    "    fig.savefig(outpath +  spec_id +  timestr + \".pdf\",transparent=False, bbox_inches=\"tight\")\n",
    "    fig.savefig(outpath +  spec_id +  timestr + \".png\",dpi=300,transparent=False, bbox_inches=\"tight\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af3837f5-fb92-4bf9-bc34-e11369ace18a",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(input_df_mpl.loc[input_df_mpl['variable'].isin(range2j),['variable','condition','value']].groupby(['variable','condition'])['value'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26c374c4-bee8-470d-80bb-4d330dcc2892",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(input_df_mpl.loc[input_df_mpl['variable'].isin(range2j),['variable','condition','value']].groupby(['variable','condition'])['value'].std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "325481a6-44aa-4d65-ae00-de49409326ed",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "dbed00d7-4364-4db4-acae-9d28827f0c1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,2))\n",
    "ax = plt.subplot(111)\n",
    "\n",
    "\n",
    "sns.barplot(ax=ax,\n",
    "            data=input_df_mpl.loc[input_df_mpl['variable'].isin(range1a)].sort_values('variable'), \n",
    "            y='value', x='variable', \n",
    "            hue=\"condition\",palette=lut_cnds,orient='v',ci=95)\n",
    "\n",
    "ax.spines.top.set_visible(False)\n",
    "ax.spines.right.set_visible(False)\n",
    "\n",
    "ax.set_xlabel(\"\")\n",
    "ax.set_ylabel(\"Conc., mM\")\n",
    "ax.tick_params(axis='x', rotation=45)\n",
    "\n",
    "ax.legend( bbox_to_anchor=(1.05, 1), loc='upper left',frameon=False)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "if savefigures == True:\n",
    "    \n",
    "    spec_id = \"bars_mets1a_\"\n",
    "    \n",
    "    fig.savefig(outpath +  spec_id +  timestr + \".pdf\",transparent=False, bbox_inches=\"tight\")\n",
    "    fig.savefig(outpath +  spec_id +  timestr + \".png\",dpi=300,transparent=False, bbox_inches=\"tight\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de137aba-2d8d-4960-8121-d5c1a9769de9",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(input_df_mpl.loc[input_df_mpl['variable'].isin(range1a),['variable','condition','value']].groupby(['variable','condition'])['value'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c8fd867-3789-4ee4-adda-1592ab1b1bb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(input_df_mpl.loc[input_df_mpl['variable'].isin(range1a),['variable','condition','value']].groupby(['variable','condition'])['value'].std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "da6ad23d-f93c-42a7-a63e-36945578f58b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6,2))\n",
    "ax = plt.subplot(111)\n",
    "\n",
    "\n",
    "sns.barplot(ax=ax,\n",
    "            data=input_df_mpl.loc[input_df_mpl['variable'].isin(range2a)].sort_values('variable'), \n",
    "            y='value', x='variable', \n",
    "            hue=\"condition\",palette=lut_cnds,orient='v',ci=95)\n",
    "\n",
    "ax.spines.top.set_visible(False)\n",
    "ax.spines.right.set_visible(False)\n",
    "\n",
    "ax.set_xlabel(\"\")\n",
    "ax.set_ylabel(\"Conc., mM\")\n",
    "ax.tick_params(axis='x', rotation=45)\n",
    "\n",
    "ax.legend( bbox_to_anchor=(1.05, 1), loc='upper left',frameon=False)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "if savefigures == True:\n",
    "    \n",
    "    spec_id = \"bars_mets2a_\"\n",
    "    \n",
    "    fig.savefig(outpath +  spec_id +  timestr + \".pdf\",transparent=False, bbox_inches=\"tight\")\n",
    "    fig.savefig(outpath +  spec_id +  timestr + \".png\",dpi=300,transparent=False, bbox_inches=\"tight\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7efc18ab-8218-4f93-bbea-c6ff367a0e81",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(input_df_mpl.loc[input_df_mpl['variable'].isin(range2a),['variable','condition','value']].groupby(['variable','condition'])['value'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b87eb7eb-8720-47af-bc9a-ee1922b8afc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.DataFrame(input_df_mpl.loc[input_df_mpl['variable'].isin(range2a),['variable','condition','value']].groupby(['variable','condition'])['value'].std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4859158-1e6d-4050-b813-b67060ade5e4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "372b3c20-72af-4ff3-9fe0-712062e22c21",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d44b5a3e-fd02-461b-bd05-88aaf47dfbee",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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