Fig_5_S9.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1b4df2eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import itertools\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9db86e76",
   "metadata": {},
   "source": [
    "## Fig 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "be8fc456",
   "metadata": {},
   "outputs": [],
   "source": [
    "results_0M_20 = np.load('data/results_pairs_ACTR_0M_20.npy', allow_pickle=True)\n",
    "results_0M_20_df = pd.DataFrame.from_records(results_0M_20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "26709f40",
   "metadata": {},
   "outputs": [],
   "source": [
    "results_0M_20_ws = np.load('data/results_pairs_ACTR_0M_ws_20.npy', allow_pickle=True)\n",
    "results_0M_20_ws_df = pd.DataFrame.from_records(results_0M_20_ws)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "70ac0428",
   "metadata": {},
   "outputs": [],
   "source": [
    "# CREATE DF NORMAL VS REWEIGHTED\n",
    "Ex_0M = np.tile([0.61, 0.47, 0.61], 2)\n",
    "\n",
    "Es_0M = np.array(results_0M_20_df['Es'])\n",
    "Es_0M_ws = np.array(results_0M_20_ws_df['Es'])\n",
    "\n",
    "Ed_0M = np.array(results_0M_20_df['Ed'])\n",
    "Ed_0M_ws = np.array(results_0M_20_ws_df['Ed'])\n",
    "\n",
    "Ed2_0M = np.array(results_0M_20_df['Ed2'])\n",
    "Ed2_0M_ws = np.array(results_0M_20_ws_df['Ed2'])\n",
    "\n",
    "E_avg_0M = np.average([Es_0M, Ed_0M, Ed2_0M], axis=0)\n",
    "E_avg_0M_ws = np.average([Es_0M_ws, Ed_0M_ws, Ed2_0M_ws], axis=0)\n",
    "\n",
    "res_0M = np.tile(['3-61', '3-75', '33-75', '3-61', '3-75', '33-75'], 5)\n",
    "allE_0M = np.concatenate([Ex_0M, Es_0M_ws, Es_0M, \n",
    "                          Ed_0M_ws, Ed_0M,\n",
    "                          Ed2_0M_ws, Ed2_0M,\n",
    "                          E_avg_0M_ws, E_avg_0M])\n",
    "\n",
    "type_0M = np.tile(['Reweighted', 'Reweighted', 'Reweighted', 'Normal', 'Normal', 'Normal'], 5)\n",
    "name_0M = np.repeat(['Ex', 'Static', 'Dynamic', 'Dynamic+', 'Average'], 6)\n",
    "ACTR_0M_ws_df = pd.DataFrame({'E': allE_0M, 'name': name_0M, \n",
    "                            'res': res_0M, 'type': type_0M})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f181fa2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 680x270 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# PLOT\n",
    "sns.set_style('whitegrid')\n",
    "\n",
    "g = sns.catplot(data=ACTR_0M_ws_df.loc[(ACTR_0M_ws_df['name'] != 'Ex')], \n",
    "                x='res', y='E', hue='name', col='type',\n",
    "                kind=\"bar\", legend=True)\n",
    "\n",
    "ax = g.axes\n",
    "\n",
    "g.fig.set_size_inches(6.8, 2.7)\n",
    "\n",
    "ax[0, 0].errorbar(['3-61', '3-75', '33-75'], Ex_0M[0:3], linewidth=1, \n",
    "                  marker='.', markersize=10, label='Experiment', linestyle='dashed', color='k')\n",
    "\n",
    "ax[0, 1].errorbar(['3-61', '3-75', '33-75'], Ex_0M[0:3], linewidth=1, \n",
    "                  marker='.', markersize=10, label='Experiment', linestyle='dashed', color='k')\n",
    "\n",
    "for cols in [0, 1]:\n",
    "    \n",
    "    ax[0, cols].set_ylim(0, 1.1)\n",
    "        \n",
    "    for label in (ax[0, cols].get_xticklabels() + ax[0, cols].get_yticklabels()):\n",
    "        label.set_fontsize(8)\n",
    "        \n",
    "ax[0, 0].set_ylabel('FRET efficiency', fontsize=8)\n",
    "ax[0, 0].set_xlabel('Residue pair', fontsize=8)\n",
    "ax[0, 1].set_xlabel('Residue pair', fontsize=8)\n",
    "\n",
    "ax[0, 0].set_title(None)\n",
    "ax[0, 1].set_title(None)\n",
    "\n",
    "ax[0, 0].text(-0.4, 1.04, 'a', fontsize=12)\n",
    "ax[0, 1].text(-0.4, 1.04, 'b', fontsize=12)\n",
    "\n",
    "ax[0, 1].legend(loc='upper center', title='Regime', title_fontsize=8, fontsize=8)\n",
    "\n",
    "h,l = ax[0, 1].get_legend_handles_labels()\n",
    "legend = ax[0,0].legend(handles=h, labels=l, title='Regime', title_fontsize=8, fontsize=8, loc=(.35,.55))\n",
    "ax[0, 1].get_legend().remove()\n",
    "\n",
    "legend.get_frame().set_alpha(None)\n",
    "\n",
    "g._legend.remove()\n",
    "\n",
    "g.savefig('figs/Fig5.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc11b88c",
   "metadata": {},
   "source": [
    "## Fig S9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b1219195",
   "metadata": {},
   "outputs": [],
   "source": [
    "results_2_5M_20 = np.load('data/results_pairs_ACTR_2_5M_20.npy', allow_pickle=True)\n",
    "results_2_5M_20_df = pd.DataFrame.from_records(results_2_5M_20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "81a64fdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "results_2_5M_20_ws = np.load('data/results_pairs_ACTR_2_5M_ws_20.npy', allow_pickle=True)\n",
    "results_2_5M_20_ws_df = pd.DataFrame.from_records(results_2_5M_20_ws)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "85bf9d2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "results_5M_20 = np.load('data/results_pairs_ACTR_5M_20.npy', allow_pickle=True)\n",
    "results_5M_20_df = pd.DataFrame.from_records(results_5M_20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "20340c37",
   "metadata": {},
   "outputs": [],
   "source": [
    "results_5M_20_ws = np.load('data/results_pairs_ACTR_5M_ws_20.npy', allow_pickle=True)\n",
    "results_5M_20_ws_df = pd.DataFrame.from_records(results_5M_20_ws)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9972299e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# CREATE DF NORMAL VS REWEIGHTED 2.5 M + 5 M\n",
    "Ex_2_5M = np.tile([0.49, 0.38, 0.51], 2)\n",
    "\n",
    "Es_2_5M = np.array(results_2_5M_20_df['Es'])\n",
    "Es_2_5M_ws = np.array(results_2_5M_20_ws_df['Es'])\n",
    "\n",
    "Ed_2_5M = np.array(results_2_5M_20_df['Ed'])\n",
    "Ed_2_5M_ws = np.array(results_2_5M_20_ws_df['Ed'])\n",
    "\n",
    "Ed2_2_5M = np.array(results_2_5M_20_df['Ed2'])\n",
    "Ed2_2_5M_ws = np.array(results_2_5M_20_ws_df['Ed2'])\n",
    "\n",
    "E_avg_2_5M = np.average([Es_2_5M, Ed_2_5M, Ed2_2_5M], axis=0)\n",
    "E_avg_2_5M_ws = np.average([Es_2_5M_ws, Ed_2_5M_ws, Ed2_2_5M_ws], axis=0)\n",
    "\n",
    "res_2_5M = np.tile(['3-61', '3-75', '33-75', '3-61', '3-75', '33-75'], 5)\n",
    "allE_2_5M = np.concatenate([Ex_2_5M, Es_2_5M_ws, Es_2_5M,\n",
    "                            Ed_2_5M_ws, Ed_2_5M,\n",
    "                            Ed2_2_5M_ws, Ed2_2_5M, \n",
    "                            E_avg_2_5M_ws, E_avg_2_5M])\n",
    "\n",
    "type_2_5M = np.tile(['Reweighted', 'Reweighted', 'Reweighted', 'Normal', 'Normal', 'Normal'], 5)\n",
    "name_2_5M = np.repeat(['Ex', 'Static', 'Dynamic', 'Dynamic+', 'Average'], 6)\n",
    "concentration_2_5M = np.repeat('2.5 M', 30)\n",
    "ACTR_2_5M_ws_df = pd.DataFrame({'E': allE_2_5M, 'name': name_2_5M, \n",
    "                                'res': res_2_5M, 'type': type_2_5M, \n",
    "                                'concentration': concentration_2_5M})\n",
    "\n",
    "\n",
    "Ex_5M = np.tile([0.42, 0.34, 0.46], 2)\n",
    "\n",
    "Es_5M = np.array(results_5M_20_df['Es'])\n",
    "Es_5M_ws = np.array(results_5M_20_ws_df['Es'])\n",
    "\n",
    "Ed_5M = np.array(results_5M_20_df['Ed'])\n",
    "Ed_5M_ws = np.array(results_5M_20_ws_df['Ed'])\n",
    "\n",
    "Ed2_5M = np.array(results_5M_20_df['Ed2'])\n",
    "Ed2_5M_ws = np.array(results_5M_20_ws_df['Ed2'])\n",
    "\n",
    "E_avg_5M = np.average([Es_5M, Ed_5M, Ed2_5M], axis=0)\n",
    "E_avg_5M_ws = np.average([Es_5M_ws, Ed_5M_ws, Ed2_5M_ws], axis=0)\n",
    "\n",
    "res_5M = np.tile(['3-61', '3-75', '33-75', '3-61', '3-75', '33-75'], 5)\n",
    "allE_5M = np.concatenate([Ex_5M, Es_5M_ws, Es_5M,\n",
    "                          Ed_5M_ws, Ed_5M,\n",
    "                          Ed2_5M_ws, Ed2_5M, \n",
    "                          E_avg_5M_ws, E_avg_5M])\n",
    "\n",
    "type_5M = np.tile(['Reweighted', 'Reweighted', 'Reweighted', 'Normal', 'Normal', 'Normal'], 5)\n",
    "name_5M = np.repeat(['Ex', 'Static', 'Dynamic', 'Dynamic+', 'Average'], 6)\n",
    "concentration_5M = np.repeat('5 M', 30)\n",
    "ACTR_5M_ws_df = pd.DataFrame({'E': allE_5M, 'name': name_5M, \n",
    "                              'res': res_5M, 'type': type_5M, \n",
    "                              'concentration': concentration_5M})\n",
    "\n",
    "ACTR_2_5_5_ws_df = pd.concat([ACTR_2_5M_ws_df, ACTR_5M_ws_df], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "1287dc57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x430 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# PLOT 2.5 M + 5 M\n",
    "sns.set_style('whitegrid')\n",
    "\n",
    "g = sns.catplot(data=ACTR_2_5_5_ws_df.loc[(ACTR_2_5_5_ws_df['name'] != 'Ex')], \n",
    "                x='res', y='E', hue='name', col='type', row='concentration',\n",
    "                kind=\"bar\", legend=True, sharex=False)\n",
    "\n",
    "ax = g.axes\n",
    "\n",
    "g.fig.set_size_inches(5, 4.3)\n",
    "\n",
    "ax[0, 0].errorbar(['3-61', '3-75', '33-75'], Ex_2_5M[0:3], linewidth=1, \n",
    "                  marker='.', markersize=10, label='Experiment', linestyle='dashed', color='k')\n",
    "\n",
    "ax[0, 1].errorbar(['3-61', '3-75', '33-75'], Ex_2_5M[0:3], linewidth=1, \n",
    "                  marker='.', markersize=10, label='Experiment', linestyle='dashed', color='k')\n",
    "\n",
    "ax[1, 0].errorbar(['3-61', '3-75', '33-75'], Ex_5M[0:3], linewidth=1, \n",
    "                  marker='.', markersize=10, label='Experiment', linestyle='dashed', color='k')\n",
    "\n",
    "ax[1, 1].errorbar(['3-61', '3-75', '33-75'], Ex_5M[0:3], linewidth=1, \n",
    "                  marker='.', markersize=10, label='Experiment', linestyle='dashed', color='k')\n",
    "\n",
    "for row in [0, 1]:\n",
    "    for cols in [0, 1]:\n",
    "        \n",
    "        ax[row, cols].set_ylim(0, 1.1)\n",
    "        \n",
    "        for label in (ax[row, cols].get_xticklabels() + ax[row, cols].get_yticklabels()):\n",
    "            label.set_fontsize(8)\n",
    "        \n",
    "ax[0, 0].set_ylabel('FRET efficiency', fontsize=8)\n",
    "ax[1, 0].set_ylabel('FRET efficiency', fontsize=8)\n",
    "\n",
    "ax[1, 0].set_xlabel('Residue pair', fontsize=8)\n",
    "ax[1, 1].set_xlabel('Residue pair', fontsize=8)\n",
    "\n",
    "ax[0, 0].set_title(None)\n",
    "ax[0, 1].set_title(None)\n",
    "\n",
    "ax[1, 0].set_title('', fontsize=8)\n",
    "ax[1, 1].set_title('', fontsize=8)\n",
    "\n",
    "legend = ax[1, 1].legend(loc=(.2,.42), title='Regime', title_fontsize=8, fontsize=8)\n",
    "\n",
    "# Letters\n",
    "ax[0, 0].text(-0.4, 1.02, 'a', fontsize=12)\n",
    "ax[1, 0].text(-0.4, 1.02, 'c', fontsize=12)\n",
    "\n",
    "ax[0, 1].text(-0.4, 1.02, 'b', fontsize=12)\n",
    "ax[1, 1].text(-0.4, 1.02, 'd', fontsize=12)\n",
    "\n",
    "h,l = ax[1, 1].get_legend_handles_labels()\n",
    "legend = ax[1,0].legend(loc=(.2,.42), handles=h, labels=l, title='Regime', title_fontsize=8, fontsize=8)\n",
    "ax[1, 1].get_legend().remove()\n",
    "\n",
    "g._legend.remove()\n",
    "\n",
    "legend.get_frame().set_alpha(None)\n",
    "\n",
    "# plt.tight_layout()\n",
    "\n",
    "g.savefig('figs/FigS9.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ca1cc98",
   "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
}