3dAnalysis-L4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This notebook produces data used in Supplementary Table 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SPDX-License-Identifier: GPL-3.0-or-later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib widget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import os\n",
    "from scipy.stats import linregress\n",
    "import matplotlib\n",
    "font = {'size'   : 12}\n",
    "\n",
    "matplotlib.rc('font', **font)\n",
    "import conntility\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Samples cells from exemplary regions and gets scaling factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import bluepysnap as bp\n",
    "s = bp.Simulation('/gpfs/bbp.cscs.ch/project/proj83/scratch/home/tharayil/BlueRecording/examples/whiskerFlick/data/simulation/simulation_config.json')\n",
    "circuit = s.circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "nodes = circuit.nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loads a generator that produces the cell types for all cells in the subvolume\n",
    "\n",
    "nFl = nodes.get('hex_O1',properties=['mtype'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loads node ids for Layer 4 PC cells from each of the above regions\n",
    "\n",
    "Fl = next(nFl)[1]\n",
    "Fl = np.array(list(Fl[Fl['mtype'].isin(['L4_TPC','L4_UPC'])].index))[:,1].astype(int)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29117"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Fl) # Number of L4 PCs, used in Supplementary table 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import h5py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loads the weights file\n",
    "\n",
    "coeffsFile = \"/gpfs/bbp.cscs.ch/project/proj83/scratch/home/tharayil/BlueRecording/examples/whiskerFlick/electrodeFile/coeffs_eeg.h5\"\n",
    "\n",
    "coeffs = h5py.File(coeffsFile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Node ids from weights file\n",
    "allNodeIds = coeffs['S1nonbarrel_neurons']['node_ids']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Finds indices in the weights file of nodes in the selected regions\n",
    "\n",
    "flIndex = np.where(np.isin(allNodeIds[:],Fl))[0]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(1776)\n",
    "\n",
    "# Samples nodes from the selected regions\n",
    "\n",
    "flIndex = np.sort(np.random.choice(flIndex,1000,replace=False))\n",
    "\n",
    "\n",
    "FLshort = allNodeIds[:][flIndex]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Finds offsets for the compartment weights for the selected nodes\n",
    "\n",
    "flOffsets = [coeffs['S1nonbarrel_neurons']['offsets'][:][flIndex],coeffs['S1nonbarrel_neurons']['offsets'][:][flIndex+1]]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Gets compartment weights for the selected nodes (shifted such that the minimum weight for each neuron is 0)\n",
    "\n",
    "scaleFacFl = []\n",
    "ranges = []\n",
    "\n",
    "\n",
    "for i in range(len(flOffsets[0])):\n",
    "    \n",
    "    sfl = coeffs['electrodes']['S1nonbarrel_neurons']['scaling_factors'][flOffsets[0][i]:flOffsets[1][i]][:,0]\n",
    "    sfl -= np.min(sfl)\n",
    "\n",
    "    scaleFacFl.append( sfl )\n",
    "    \n",
    "    ranges.append(np.percentile(sfl,90) - np.percentile(sfl,10))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = np.mean(ranges) # Range of weights, used in supplementary table 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5233842072239206e-09"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r"
   ]
  }
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