Notebooks/deltaG_Kb_calculation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bcfd990c",
   "metadata": {},
   "source": [
    "#### AI/ML directed high-throughput virtual screening of probes to selectively bind with PFOS using SDS as interferent\n",
    "#### Siva Dasetty, Max Topel | Ferguson Lab\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2bbdb11",
   "metadata": {},
   "source": [
    "## imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9104e819",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'calculateFES' from '/scratch/midway2/sivadasetty/pfas/analysis/github/activeLearningPFASLinear/Notebooks/../Codes/calculateFES.py'>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import os.path\n",
    "import sys\n",
    "import importlib\n",
    "import yaml\n",
    "\n",
    "import subprocess\n",
    "\n",
    "from time import time\n",
    "from datetime import datetime\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "\n",
    "import scipy\n",
    "\n",
    "import rdkit\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import AllChem\n",
    "from rdkit import DataStructs\n",
    "from rdkit.Chem.Draw import IPythonConsole\n",
    "from rdkit.Chem.Draw.MolDrawing import MolDrawing, DrawingOptions\n",
    "from rdkit.Chem import Draw\n",
    "from rdkit.Chem.Draw import rdMolDraw2D\n",
    "from rdkit.Chem import Descriptors\n",
    "from rdkit.Chem.Fragments import *\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import StrMethodFormatter\n",
    "from matplotlib import rcParams\n",
    "import matplotlib as mpl\n",
    "from matplotlib  import cm\n",
    "from PIL import Image\n",
    "from matplotlib import colors\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "from matplotlib.offsetbox import (TextArea, DrawingArea, OffsetImage,\n",
    "                                  AnnotationBbox)\n",
    "from matplotlib.cbook import get_sample_data\n",
    "\n",
    "import multiprocessing\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "sys.path.append(\"../Codes/\")\n",
    "\n",
    "\n",
    "import utils_activeLearning_Oliver as ut\n",
    "\n",
    "import utils_functions as utils_functions\n",
    "#import utils_figures as utils_figures\n",
    "\n",
    "importlib.reload(ut)\n",
    "importlib.reload(utils_functions)\n",
    "#importlib.reload(utils_figures)\n",
    "\n",
    "import selfies as sf\n",
    "from oliver_vae.VAE import VAE_encode, VAE_decode\n",
    "\n",
    "import torch\n",
    "from torch import nn\n",
    "\n",
    "import copy\n",
    "\n",
    "import oapackage\n",
    "\n",
    "import importlib\n",
    "import calculateFES\n",
    "importlib.reload(calculateFES)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d393c75a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "bcbcb5be",
   "metadata": {},
   "source": [
    "## plot settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3247578f",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams.update({'font.size': 30})\n",
    "\n",
    "# time stamp\n",
    "now = datetime.now()\n",
    "now = str(now)[:10]\n",
    "#figures_dir = './Manuscript_Figures_JCED/{}'.format(str(now)).replace(':','-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94cc3825",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "601385ba",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "2708b8c0",
   "metadata": {},
   "source": [
    "#### deltaG and Kb  calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c6cc5266",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nsamples:  3\n",
      "nsamples:  3\n"
     ]
    }
   ],
   "source": [
    "        \n",
    "labelfont=20\n",
    "tickfont=15\n",
    "legendfont=12\n",
    "\n",
    "m=0\n",
    "temperature = 300 # Kelvin\n",
    "kT = 300*0.008314 # kJ/mol\n",
    "\n",
    "simdir = './Data/'\n",
    "\n",
    "\n",
    "for c in [0]: ##range(0,11):\n",
    "    \n",
    "    t = pd.read_csv(simdir + '/folder-pdb-each-cycle/jcycle-'+str(c)+'.txt',sep='\\t',header=None, names=['pdb', 'smiles'])\n",
    "    \n",
    "    #print(t.pdb)\n",
    "    #print(t.shape[1])\n",
    "    \n",
    "    for i in [0]: #range(t.shape[0]):\n",
    "        smiles = t.smiles.values[i]\n",
    "        formula = t.smiles.values[i]\n",
    "        for analyte in ['pfos', 'sds']:\n",
    "            \n",
    "            #print(t.pdb.values[i].split('.')[0])\n",
    "            \n",
    "            if analyte == 'pfos':\n",
    "                simDir='./Data/'\n",
    "                systemDir = './COLVAR_data/pfosd-' + t.pdb.values[i].split('.')[0]\n",
    "                colvarFile = '/pbmd-4cvs-gpu/reweight/colvar_reweight.out'\n",
    "            else:\n",
    "                simDir='./Data/'\n",
    "                systemDir = './COLVAR_data/sds-' + t.pdb.values[i].split('.')[0]\n",
    "                colvarFile = '/pbmd-gpu/reweight/colvar_reweight.out'\n",
    "                \n",
    "            # print if file does not exist\n",
    "            if not os.path.isfile(simDir+systemDir+colvarFile):\n",
    "                print(\"File does not exist\")\n",
    "                print(c, i, simDir+systemDir+colvarFile)\n",
    "                print(\"\\n\")\n",
    "        \n",
    "            b = 100000 # first 100 ns equilibration\n",
    "            e = 1000000 # 300 ns of blocks. 1 micro second data\n",
    "            startBlock = b\n",
    "            blockSize = 100000 * 3\n",
    "            maxBlock = startBlock + blockSize\n",
    "            systemFEList = []\n",
    "            while maxBlock <= e:\n",
    "                colvarFE = calculateFES.get_block_fes_using_cv_weights(temperature,  simDir, systemDir, \n",
    "                                                                       colvarFile, \n",
    "                                                                       [\"time\", \"com\", \"htPFOS\", \"htProbe\", \"angle\", \"bias\"], \n",
    "                                                                       0.1, 3.5, 100, \"com\", \"bias\", 2.5, startBlock, maxBlock)\n",
    "                       \n",
    "                startBlock = maxBlock\n",
    "                maxBlock = maxBlock + blockSize\n",
    "                \n",
    "                systemFEList.append(colvarFE)\n",
    "                \n",
    "                \n",
    "            avgFE, sdFE, systemFEListUpdated, fe_minList = calculateFES.get_fes_avg_sd(systemFEList, \"com\", 100)\n",
    "          \n",
    "            fe_min = 1000\n",
    "            fe_min = calculateFES.find_min_avg(systemFEList[0].com.values, avgFE, fe_min)\n",
    "            avgFEDF = pd.DataFrame(np.array([systemFEList[0].com.values, avgFE - fe_min, \n",
    "                                             sdFE]).transpose(), columns=[\"com\", \"avg\", \"sd\"])\n",
    "            \n",
    "            minLocation, deltaFE, deltaFESD, freeBoundRegArea = calculateFES.get_delta_fe(avgFEDF, \"com\")\n",
    "            \n",
    "            #blockAvgDeltaFE, blockSDdeltaFE = calculateFES.get_delta_fe_areaMethod(systemFEListUpdated, kT, \"com\", 1)\n",
    "            blockAvgKb, blockSDKb = calculateFES.get_Kb_areaMethod(systemFEListUpdated, kT, \"com\", 1)\n",
    "            \n",
    "            #### WRITE TO FILES\n",
    "            \n",
    "            #### WRITE TO FILE -- delta G\n",
    "            #calculateFES.write_fes_tofile(\"PBMD-4CVs-GPU\", bindingEnergyFileBlock, analyte, smiles, blockAvgDeltaFE, blockSDdeltaFE, formula)\n",
    "  \n",
    "            ## Kb\n",
    "            #KbFileBlock='kb_deltaGRepeated_JCED/wsmiles_Kb-linearProbesBlockArea-b3-pbmd-4cvs.txt'\n",
    "           \n",
    "            # new - per cycle\n",
    "            #KbFileBlock=simdir+'./wsmiles_Kb-linearProbesBlockArea-b3-pbmd-4cvs_cycle_'+str(c)+'.txt'\n",
    "    \n",
    "            #### WRITE TO FILE -- Kb\n",
    "            #calculateFES.write_fes_tofile(\"PBMD-4CVs-GPU\", KbFileBlock, analyte, smiles, blockAvgKb, blockSDKb, formula)\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d10c95b0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "cbca3e54",
   "metadata": {},
   "source": [
    "#### extract delta G from newly computed and compare with previously computed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7798c920",
   "metadata": {},
   "outputs": [],
   "source": [
    "#### read previously computed and newly computed delta G data\n",
    "\n",
    "simdir = '../Data/'\n",
    "\n",
    "\n",
    "new_deltaG_data_cycle_PFOS = []\n",
    "new_deltaG_data_cycle_PFOS_SD = []\n",
    "new_deltaG_data_cycle_SDS = []\n",
    "new_deltaG_data_cycle_SDS_SD = []\n",
    "\n",
    "for analyte in ['PFOS', 'SDS']:\n",
    "    \n",
    "    for c in range(0,11):\n",
    "        \n",
    "        \n",
    "        new_deltaG_data = pd.read_csv(simdir+'./GPR_training_data/wsmiles_deltaG-linearProbesBlockArea-b3-pbmd-4cvs_cycle_'+str(c)+'.txt', delim_whitespace=True, \n",
    "                              names=[\"Method\", \"Analyte\", \"lprobe\", \"lflourines\", \"lhydrogen\", \"hhydrogen\", \n",
    "                                     \"hcarbons\", \"Probe\", \"Avg\", \"SD\", \"smiles\", \"formula\"], comment=\"#\")\n",
    "        \n",
    "\n",
    "        if analyte == 'PFOS':\n",
    "            new_deltaG_data_cycle_PFOS.append(new_deltaG_data[(new_deltaG_data[\"Analyte\"] == analyte)].Avg.values)\n",
    "            new_deltaG_data_cycle_PFOS_SD.append(new_deltaG_data[(new_deltaG_data[\"Analyte\"] == analyte)].SD.values)\n",
    "        else:\n",
    "            new_deltaG_data_cycle_SDS.append(new_deltaG_data[(new_deltaG_data[\"Analyte\"] == analyte)].Avg.values)\n",
    "            new_deltaG_data_cycle_SDS_SD.append(new_deltaG_data[(new_deltaG_data[\"Analyte\"] == analyte)].SD.values)\n",
    "                    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c985169b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "565548c8",
   "metadata": {},
   "source": [
    "#### deltaG_data_cycle_PFOS (Figure in main text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "aa6de0b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cycle: 0: 1: 0:\n",
      "cycle: 1: 1: 1:\n",
      "cycle: 2: 1: 2:\n",
      "cycle: 3: 1: 3:\n",
      "cycle: 4: 1: 4:\n",
      "cycle: 5: 1: 5:\n",
      "cycle: 6: 2: 0:\n",
      "cycle: 7: 2: 1:\n",
      "cycle: 8: 2: 2:\n",
      "cycle: 9: 2: 3:\n",
      "cycle: 10: 2: 4:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 700x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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mu0MCACAl7W1r067a2iNLuoVCMtLSNMXv18zly5kRBABJFM8653vb2tRUXS2Z5oB7weH/bqqq0sTCQnI4gLjEk5uAYzFjHDGLZT0H1n4AAABus2LFCnV1dWn9+vVx/XxBQYEmT5486KO2tjbB0QIA4E3MOgTgJLF2rvBKp4tY1jnfVVsrIz094jZGerp2rV6dqPAApKhYchNwLArjiFks6zmw9gMAAHCTHTt26L777tOdd96pU089Na736Orq0r59+wZ9BAKBBEcMAID3DDvr0DTVVFXFerUAki7W9bJTdX3t7mDwSHePCF1DpSM5vGPrVtcPGAAAuBeFccQllvUc7Fr7wSsjMwEAQGwmTJggwzCifmzfvl2SdOjQIV1zzTU6++yzdcMNN8T9+3Nzc5WXlzfoIzs7O0H/SgAAvItZhwCcINbOFanc6eJwIND3bx6OGQrpMAOGAQA2YY1xxC2W9RxGsvZDrD/DGmQAAKS28vJyHThwIOrtc3JyJEm33Xab3nrrLf36179WWlr840d3795NARwAgDj1zTocpsBy9KxD2mgCSLRY18tO9fW1R2Vny0hLi6o4bqSlaRTXSwAAm1AYx4hlZGZGfREay7bxFLjb6+vVVF0tIz19wMjMjoYGza+rS9oMdQAA4Az33ntvXD/X3t6uv/71ryooKBj09VtuuUW33HKLvv3tb2vNmjUjiBAAAAwlnlmHIymMxzOAH4D3hTtXRGoNHu5ckVdSEvP2XpORmakpfv+R2fKRPgOfT/l+P/kWAGAbCuNwpHgK3Kk+MhMAAIzMRRddpPz8/AHP79mzRy0tLZo5c6a+8IUv6Oyzz7YhOgAAUoNVsw7pNgdgKLF2rgj+7//S6UJScU2NOhoaIm5j9vaqeNkyawICAGAQFMaRVPGMvI63wB3VyMy0NM+OzAQAACPzne98Z9DnN23apJaWFpWVlWnFihUJ+V3MTgOQql544QWVlJQoFArprrvuSlhehXdYMeuQbnMAIom1c8XBt96ytNOFU02ePVvz6+rUVFU14B6t4fPJ7O3V/Lo67ssCAGwV/+KJQAR729rUUFamtVlZqs/J0dqsLDWUlWnfzp3D/my4wB1JuPVQWN9IzggXzdKRUYl7nnhC3cFgdP8QAACABBrJORIAuF0wGFRFRYUyPVgMQGIV19TI7O2NuE28sw6HHYxvmmqqquLYDKSwcOeKaBhpacr67Gdj2t7L62sXVVaqvLVV+X5/32dipKUp3+9XeWsrg44AALajMJ7iuoNBHdq/P6GF4vb6ej02Z86R0d3HjLzeXFqq9nXrIsYTVYH7qNZDUmwjOWWaer2pKbptAQAAEmQk50gA4AW33Xaburq6mCWOYYVnHcowZPj6Nzs0fD7JMOKedRjPYHwAqSXcueLY/HMsw+dT/sKFyvzMZ2La3ouzxY+WV1Ii/5YtuvHgQV3/9tu68eBB+bdsYaY4AMARKIynqGTNVhrpyOtYWxUdDgQkxTaSU5Je2bAh6m0BAEBqq6iokGmaIyrkMDsNQKrbuXOn7rnnHt19992aPHmy3eHABZIx6zDewfgAUk+snSuS2enCrTIyMzVm0iTPDwQAALgLhfEUlMzZSiMdeR1rq6Jw66GMzEyddvHFUcf556ee4gIXAABYhtlpAFLZRx99pIqKCp177rlaunSp3eHARRI96zDewfgAUk+snSuS2ekCAAAkDoXxFJPM2UqJGHkda6uio0ccFn7zm1HHygUuAACwCrPTAKS6FStWqKurS+vXr4/r5wsKCjR58uRBH7W1tQmOFk6UqFmH8Q7GB5CaYu1cwfraAAA4X+TqIzwnPFsp0o3Z8GylWEcwxjPyerCL2uKaGnU0NET++UFaD518/vmSYUimOezv5wIXAABYJVHnSADgRjt27NB9992nNWvW6NRTT43rPbq6uoZ8LcCAZ8QgPBi/s7Ex8n0Rn0/5fj/HYwDKKylRXkmJuoNBHQ4ENCo7O2JuiHV7AABgLWaMp5Bkz1ZK1MjreFsPZWRmKn/BAmm4NqWDzDYHAABIFmanAXC7CRMmyDCMqB/bt2+XJB06dEjXXHONzj77bN1www1x//7c3Fzl5eUN+sgmZyJGrAMMIB6xdq5gfW0AAJyJGeMpJNmzlRI58rqoslITCwu1a/VqdWzdKjMU6ms9VLxs2ZCz2eOdbQ4AAJAszE4D4Hbl5eU6cOBA1Nvn5ORIkm677Ta99dZb+vWvf620KAcIDWb37t0UwJEw4cH4TVVVAzrqGT6fzN5e1gEGAAAAPIrCeAoJz1aKpjge72ylRBam42k9xAUuAABwIgbvAXCze++9N66fa29v11//+lcVFBQM+vott9yiW265Rd/+9re1Zs2aEUQIxCbewfgAAAAA3I3CeAqxYrZSMgrTGZmZMcXCBS4AAHAaBu8BSEUXXXSR8vPzBzy/Z88etbS0aObMmfrCF76gs88+24bokOpYBxgAAABIPRTGU4wVs5WcUJjmAhcAADiNE86RAMBK3/nOdwZ9ftOmTWppaVFZWZlWrFhhcVRAf7EOxgcAAADgXhTGU4xVs5WcUpjmAhcAADiJU86RAAAAAAAAgFSTZncAsF5RZaXKW1uV7/fLSDvyJxCerVTe2qqiysqE/a6MzEyNmTSJG74AAABH4RwJAAAATtUdDOrQ/v3qDgbtDgUAACChmDGeopitBAAAAACwQ0VFhSoqKuwOAwBwjL1tbdpVW6vOJ5/sW/Znit+vmcuXs+wPAADwBGaMpzhmKwHe8sILLyg9PV2GYeiHP/yh3eHEhZHpAAAAAABYq72+Xo/NmaPOxkaZoZAkyQyF1NnYqM2lpWpft87mCAEAAEbOssL4oUOH9Mgjj+iyyy7T1KlTlZmZqeOOO05z587V5s2brQoDADwrGAyqoqJCmS4d6LK3rU0NZWVam5Wl+pwcrc3KUkNZmfbt3Gl3aAAAAAAAeNbetjY1VVdLpimzp6ffa2ZPj2Saaqqq4vocgKe9/PLLWrRokfLy8jR69Gh99rOf1Ze//GU999xzdocGIIEsK4y3trbqyiuvVHNzs2bMmKGbbrpJl1xyif7whz9o0aJF+ta3vmVVKADgSbfddpu6urq0YsUKu0OJGSPTAQAAAACwx67aWhnp6RG3MdLTtWv1aosiAgBrPfzww/riF7+of/u3f9P8+fO1fPlyXXzxxdq/f7+ef/55u8MDkECWrTGem5urn/3sZ7r00kuVkZHR9/ydd96ps846S/fdd5++8Y1vaObMmVaFBACesXPnTt1zzz1at25dvxzrBsOOTJfUVFWliYWFrGkGAAAAAEACdQeDfWuKR2L29Khj61Z1B4MsyQjAU/793/9d3/zmNzVr1iz96le/0vjx4/u93nPM/UoA7mbZjPHp06dr0aJFAwo2kyZN0nXXXSdJ2rFjh1XhAIBnfPTRR6qoqNC5556rpUuX2h1OzBiZDgAAAACAPQ4HAsMWxcPMUEiHA4EkRwQA1vre976n3t5ePfzwwwOK4pLk81k2vxSABRyxR4eL5SQYAIjdihUr1NXVpW3btsX18wUFBUpLG3ycVE1NjWpqakYSXkSMTAcAAAAAwD6jsrNlpKVFVRw30tI0KjvbgqgAwBoffPCBtm3bphkzZig/P187duzQ73//e/l8Pp111lk655xz7A4RQILZXokOj8QxDEPz588fdns7CzgA4DQ7duzQfffdpzVr1ujUU0+N6z26urqGfC2Q5JHg8YxMpzAOAAAAAEBiZGRmaorfr87GxgHLmx3N8PmU7/dzTQ7AU1566SWFQiGdeOKJ+upXv6rGxsZ+r59//vn613/9V40bN86mCAEkmu2F8ZUrV+qVV17RNddcozPPPHPY7e0s4ABAMkyYMEHvvfde1Ns/99xzOvfcc3Xo0CFdc801Ovvss3XDDTfE/ftzc3OHHHCUneSR4IxMBwAAAADAXsU1NepoaIi4jdnbq+Jly6wJCAAs8pe//EWS9Mtf/lITJkxQQ0OD5s2bp7feekv/9//+Xz311FO69tpr9fOf/zzi+zChE3CPmAvj8RZwBvOTn/xEd911l2bMmKF77rknqvezs4ADAMlQXl6uAwcORL19Tk6OJOm2227TW2+9pV//+tdD5sVo7N6927b8ych0AAAAAADsNXn2bM2vq1NTVZWM9PR+1+eGzyezt1fz6+qUV1JiY5QAMLR461ahTybr9Pb2at26dfL7/ZKO1Joef/xxTZ06Vf/6r/+qu+++WyeeeOKQ78eETsA9Yi6Mx1vAOdbGjRtVWVmpwsJCPfPMM8rKyorq/ews4ABAMtx7771x/Vx7e7v++te/qqCgYNDXb7nlFt1yyy369re/rTVr1owgwuRiZDoAAAAAAPYqqqzUxMJC7Vq9Wh1bt8oMhWSkpSnf71fxsmUUxQE4Wrx1q3CL9PT0dF100UX9thk9erQuuOACrV+/Xv/+7/8esTDOhE7APWIujMdbwDnahg0btHTpUp1xxhl69tlndfzxx4/4PQEg1Vx00UXKz88f8PyePXvU0tKimTNn6gtf+ILOPvtsG6KLHiPTAQAAAACwX15JifJKStQdDOpwIKBR2dl0bgPgCvHWrT73uc9Jkj796U8rIyNjwOvHHXecJCkYDEZ8HyZ0Au5h+RrjGzZs0JIlS/T5z39ezc3NmjhxotUhAIAnfOc73xn0+U2bNqmlpUVlZWVasWKFxVHFh5HpAAAAAAA4Q0ZmJgVxAClhypQpOumkk/TGG29o7969mjx5cr/X//M//1OSdMopp9gQHYBksLQw/uCDD2rp0qUqKChQc3OzTjjhBCt/PQDAwRiZDgAAAAAAEB/upwCxMwxDlZWVuvXWW3Xrrbdq06ZNfS3Rd+zYoaefflqnnHKKZs6caXOkABLFssJ4c3Ozli5dKtM0NWfOHNXX1w/YpqioSAsWLLAqJCBqnFgC1mFkuv3IeQCA4XCsAIDEIq8C1vPKfre3rU27amvV+eSTfR34pvj9mrl8OR3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\n",
      "text/plain": [
       "<Figure size 2500x1500 with 11 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "mean_distance_pareto = []\n",
    "mean_distance_pareto_only = []\n",
    "\n",
    "labelfont=20\n",
    "tickfont=18\n",
    "legendfont=8\n",
    "\n",
    "import matplotlib.gridspec as gridspec\n",
    "\n",
    "fig = plt.figure(figsize=([7,4]))\n",
    "\n",
    "#gs = gridspec.GridSpec(2,6)\n",
    "#gs.update(wspace=1.5, hspace=0.3)\n",
    "\n",
    "fig = plt.figure(figsize=[25,15])\n",
    "fig.tight_layout(pad=100.0)\n",
    "spec = gridspec.GridSpec(ncols=6, nrows=4)\n",
    "fig.subplots_adjust(bottom=0.05, hspace=.4, wspace=.5)\n",
    "\n",
    "ax = ['ax1', 'ax2', 'ax3', 'ax4', 'ax5', 'ax6', 'ax7', 'ax8', 'ax9', 'ax10', 'ax11', 'ax12', 'ax13', 'ax14']\n",
    "\n",
    "\n",
    "kT = 1 #300*0.008314\n",
    "preFactor = 2*kT\n",
    "\n",
    "##############################################################################################################################################            \n",
    "col=0\n",
    "row=1\n",
    "cycles=11\n",
    "for c in range(cycles):\n",
    "    \n",
    "    tickfont=14\n",
    "    \n",
    "    if c!=0:\n",
    "        if c % 6 == 0:\n",
    "            col = 0\n",
    "            row += 1\n",
    "        else:\n",
    "            col += 1\n",
    "    \n",
    "    print(\"cycle: %d: %d: %d:\"% (c, row, col))\n",
    "    \n",
    "    #ax[c] = fig.add_subplot(2, 6, c+3)\n",
    "    pc = c+2\n",
    "    ax[pc] = fig.add_subplot(spec[row, col])\n",
    "    ax[pc].tick_params(axis=\"x\", labelsize=tickfont, direction='in', length=6, width=2, colors='black', rotation=0)\n",
    "    ax[pc].tick_params(axis=\"y\", labelsize=tickfont, direction='in', length=6, width=2, colors='black')\n",
    "    ax[pc].tick_params(which='minor', direction='in', length=3, width=2)\n",
    "    #spec.update(wspace=0.45, hspace=0.7)\n",
    "    \n",
    "    new_selectivity = np.array(new_deltaG_data_cycle_PFOS[c]).flatten() - np.array(new_deltaG_data_cycle_SDS[c]).flatten()\n",
    "    new_selectivityError = np.sqrt(np.array(new_deltaG_data_cycle_PFOS_SD[c]).flatten()**2 + np.array(new_deltaG_data_cycle_SDS_SD[c]).flatten()**2)\n",
    "    \n",
    "    ax[pc].scatter(-np.array(new_deltaG_data_cycle_PFOS[c]).flatten()/kT, \n",
    "                  -new_selectivity/kT, \n",
    "                  color=\"darkred\", s=50, alpha=1, marker='o', label='init')\n",
    "    \n",
    "\n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "097bb420",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ecd4910",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b01e8e3",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}