ET_manuscript_Extended_Figure_3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load files\n",
    "pre_df = pd.read_csv('L5-ET-pre-motif-percentage.csv')\n",
    "post_df = pd.read_csv('L5-ET-post-motif-percentage.csv')\n",
    "\n",
    "# data on X-axis\n",
    "pre_mean = pre_df.iloc[:,1:].mean()\n",
    "pre_sem = pre_df.iloc[:,1:].sem()\n",
    "post_mean = post_df.iloc[:,1:].mean()\n",
    "post_sem = post_df.iloc[:,1:].sem()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot\n",
    "fig = plt.figure(figsize=(10,6))\n",
    "\n",
    "#plt.figure(figsize=(10,6))\n",
    "\n",
    "N = 20\n",
    "ind = np.arange(N)\n",
    "\n",
    "width = 0.35       \n",
    "plt.ylim([0,60])\n",
    "\n",
    "plt.grid(axis = 'y',linestyle = '--', linewidth = 0.5, zorder=1)\n",
    "\n",
    "plt.bar(ind, pre_mean, width, label='Synapses from presynaptic Motif groups onto postsynaptic ET cells', color='lightsteelblue', zorder=2)\n",
    "plt.bar(ind + width, post_mean, width, label='Synapses from presynaptic ET cells onto postsynaptic Motif groups',color='darkblue', zorder=2)\n",
    "\n",
    "yerr1=pre_sem\n",
    "yerr2=post_sem\n",
    "\n",
    "plt.vlines( ind, pre_mean, pre_mean+yerr1, color='black')\n",
    "plt.vlines( ind+ width, post_mean, post_mean+yerr2, color='black')\n",
    "\n",
    "plt.xlabel('Motif group #', fontsize=15)\n",
    "plt.ylabel('Percentage of synapse', fontsize=15)\n",
    "#plt.title('Data from 39 ETs',fontsize=18)\n",
    "\n",
    "plt.xticks(ind + width / 2, (np.arange(1,21)), fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "plt.legend(loc='best',framealpha=0,fontsize=14,bbox_to_anchor=(0.65, 0.65))\n",
    "\n",
    "\n",
    "for pos in ['right', 'top']:\n",
    "    plt.gca().spines[pos].set_visible(False)\n",
    "\n",
    "fig.savefig('Extended_Fig2.eps', bbox_inches='tight')\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}