{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "%matplotlib qt\n",
    "import matplotlib.pyplot as pl\n",
    "import sys\n",
    "import scipy as sp\n",
    "from files4brainNetViewer import BNVfiles\n",
    "import copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pathRel = './data/relevanceOfPulses_G0p3L0p7.npz'\n",
    "with np.load(pathRel) as data:\n",
    "    sCorTe  = data['sCorTe']\n",
    "    fitTe   = data['fitTe']\n",
    "    pulseTe = data['pulseTe']\n",
    "    fitRef  = data['fitRef']\n",
    "    sCorRef = data['sCorRef']\n",
    "    pliRef  = data['pliRef']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "relevance = np.zeros(fitTe.shape)\n",
    "match = np.zeros(pliRef.shape[1:])\n",
    "for i in range(68):\n",
    "    relevance[:,i,:] = fitRef - fitTe[:,i,:]\n",
    "for i in range(4):\n",
    "    match[:,i] = np.mean( np.sign(pliRef[:,:,i]) * kao.eFCs[i,:] ,axis=0)\n",
    "MDrele  = np.median(relevance,axis=0)\n",
    "MErele  = np.mean(relevance,axis=0)\n",
    "SDrele  = np.std(relevance,axis=0)\n",
    "MDpu    = np.median(pulseTe, axis=0)\n",
    "MEpu    = np.mean(pulseTe, axis=0)\n",
    "SDpu    = np.std(pulseTe, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do file to visualize nodes in matlab 3D brain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bnv = BNVfiles()\n",
    "for i in range(4):\n",
    "    name = 'NODESstage_' + str(i+1)\n",
    "    bnv.doNodes( colors=MEpu[:,i], sizes=MErele[:,i], name=name)\n",
    "    name = 'EDGESstage_' + str(i+1)\n",
    "    bnv.doEdges(edges=kao.eFCs[i,:], weights=match[:,i], name=name)"
   ]
  }
 ],
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