Fig_3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ee8ebc7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import os\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import itertools\n",
    "import warnings\n",
    "from time import time\n",
    "from scipy import stats\n",
    "\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7e09502d",
   "metadata": {},
   "outputs": [],
   "source": [
    "results_pp11 = np.load('data/results_pp11.npy', allow_pickle=True)\n",
    "results_pp11_df = pd.DataFrame.from_records(results_pp11).set_index('cutoff')\n",
    "\n",
    "results_pp11_df['E_avg'] = results_pp11_df[['Ex', 'Ed', 'Ed2']].mean(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a2eaea8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "pp11_df_regimes = pd.DataFrame({'E': [0.732, 0.876, 0.993, 0.867, 0.83], \n",
    "                                'Regime': ['Static', 'Dynamic', 'Dynamic+', 'Average', 'MD']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7a695f0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 350x250 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "\n",
    "g = sns.catplot(data=pp11_df_regimes,\n",
    "                x='Regime', y='E', hue='Regime',\n",
    "                kind=\"bar\", legend=False)\n",
    "\n",
    "plt.axhline(0.88, color='k', linestyle = '--')\n",
    "\n",
    "g.fig.set_size_inches(3.5, 2.5)\n",
    "\n",
    "ax = g.axes\n",
    "\n",
    "ax[0, 0].set_ylim(0, 1.1)\n",
    "ax[0, 0].set_yticks(np.arange(0,1.1, 0.1))\n",
    "        \n",
    "for label in (ax[0, 0].get_xticklabels() + ax[0, 0].get_yticklabels()):\n",
    "    label.set_fontsize(8)\n",
    "\n",
    "ax[0, 0].set_xlabel('', fontsize=10)\n",
    "ax[0, 0].set_ylabel('FRET efficiency', fontsize=8)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "g.savefig('figs/Fig3.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bfa565a5",
   "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": "3.8.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}