{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Table of Contents\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading Data\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uses import routines for Matlab binaries\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "from scipy.io import loadmat\n",
    "\n",
    "data = loadmat('ANDA2024_Spectral_DataSets.mat')\n",
    "\n",
    "v4 = data['V4_lfp']\n",
    "flick = data['flicker_signals']\n",
    "v1_lfp = data['V1_lfp']\n",
    "v4_lfp = data['V4_lfp']\n",
    "\n",
    "n_trials, n_time, n_v1cells = v1_lfp.shape  # M trials, N cells\n",
    "\n",
    "# generate time axis and determine sampling frequency, set a few important variables\n",
    "dt = 1e-3\n",
    "t_max = n_time*dt\n",
    "freq_sample = 1/dt\n",
    "t_base = 0.0  # start baseline\n",
    "t_stim = 0.5  # start stimulus\n",
    "t_stop = t_max  # end of trial\n"
   ]
  }
 ],
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