ET_Manuscript_Extended_Figure_6.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "93ce0719",
   "metadata": {},
   "source": [
    "# EXTENDED FIGURE 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a653c610",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import requests\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import cloudvolume\n",
    "from caveclient import CAVEclient\n",
    "import nglui\n",
    "from statistics import mean\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "979d4493",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load dataframe\n",
    "synapse_table = pd.read_feather(\"ET_extended_synapse_table.feather\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "57a1c316",
   "metadata": {},
   "outputs": [],
   "source": [
    "syn_per_con = []\n",
    "\n",
    "pre_soma_IDs = synapse_table.pre_pt_root_id.unique()\n",
    "\n",
    "for ii,pre_soma_ID in enumerate(pre_soma_IDs): \n",
    "    \n",
    "    #print(pre_soma_ID)\n",
    "    \n",
    "    post_soma_IDs =  synapse_table[(synapse_table['pre_pt_root_id'] == pre_soma_ID)].post_pt_root_id.unique()\n",
    "    \n",
    "    for ii,post_soma_ID in enumerate(post_soma_IDs):\n",
    "        \n",
    "        values = {\n",
    "            \n",
    "            \n",
    "#          print(len(synapse_table[(synapse_table['pre_pt_root_id'] == pre_soma_ID) &\n",
    "#                                     (synapse_table['post_pt_root_id'] == post_soma_ID)]))         \n",
    "                    'syn_per_con': len(synapse_table[(synapse_table['pre_pt_root_id'] == pre_soma_ID) &\n",
    "                                        (synapse_table['post_pt_root_id'] == post_soma_ID)]),\n",
    "                    \n",
    "                    'subclass': synapse_table[(synapse_table['pre_pt_root_id'] == pre_soma_ID) &\n",
    "                                        (synapse_table['post_pt_root_id'] == post_soma_ID)].consensus_class.iloc[0],\n",
    "                    \n",
    "                    'pre_pt_root_id': pre_soma_ID, \n",
    "                    \n",
    "                    'post_pt_root_id': post_soma_ID, \n",
    "                    \n",
    "        }\n",
    "        syn_per_con.append(values)\n",
    "    \n",
    "syn_per_con_df = pd.DataFrame(syn_per_con)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "74c1aa8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "f, ax = plt.subplots(figsize=(8,10))\n",
    "\n",
    "ax = sns.histplot(data=syn_per_con_df, x=\"syn_per_con\", hue=\"subclass\", multiple=\"dodge\", shrink=.8)\n",
    "\n",
    "\n",
    "ax.set_yscale('log') \n",
    "plt.savefig('Extended_6.eps')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6889c2d7",
   "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": 5
}