{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this short tutorial, we will compute the static connectivity of the EEG singals."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- - -"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy\n",
    "from scipy import io"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "eeg = np.load(\"data/eeg_eyes_opened.npy\")\n",
    "\n",
    "num_trials, num_channels, num_samples = np.shape(eeg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "eeg_ts = np.squeeze(eeg[0, :, :])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Static connectivity\n",
    "\n",
    "As a first example, we are going to compute the static connectivity of the EEG signals using the IPLV estimator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "\n",
    "from dyconnmap.fc import iplv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the frequency band we are interested to examine, in Hz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "band = [1.0, 4.0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the sampling frequency, in Hz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sampling_frequency = 160.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will invoke the estimator using the full by-name arguments. The last arguement, `pairs` is `None` by default, which means all \"full connectivity\", otherwise you check the documentation about the structure of the value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts, avg = iplv(eeg_ts, fb=band, fs=sampling_frequency, pairs=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f\"\"\"Time series array shape: {np.shape(ts)}\n",
    "Average time series array shape: {np.shape(avg)}\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make the connectivity matrix symmetric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_symm = avg + avg.T\n",
    "np.fill_diagonal(avg_symm, 1.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot\n",
    "\n",
    "Plot the matrix using the standard Matplotlib functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "mtx_min = 0.0 # we know it's 0.0 because of the estimator's properties\n",
    "mtx_max = np.max(avg)\n",
    "\n",
    "plt.figure(figsize=(6, 6))\n",
    "cax = plt.imshow(avg_symm, vmin=mtx_min, vmax=mtx_max, cmap=plt.cm.Spectral)\n",
    "cb = plt.colorbar(fraction=0.046, pad=0.04)\n",
    "cb.ax.set_ylabel('Imaginary PLV', fontdict={'fontsize': 20})\n",
    "plt.title('Connectivity Matrix', fontdict={'fontsize': 20})\n",
    "plt.xlabel('ROI', fontdict={'fontsize': 20})\n",
    "plt.ylabel('ROI', fontdict={'fontsize': 20})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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