examples/Intro_tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial\n",
    "\n",
    "The `spectral_connectivity` package has two main classes used for computation:\n",
    "+ `Multitaper`\n",
    "+ `Connectivity`. \n",
    "\n",
    "There is also a function called `multitaper_connectivity` which combines the usage of the two classes and outputs a labeled array, which can be convenient for understanding the output and plotting.\n",
    "\n",
    "This tutorial will walk you through the usage of each.\n",
    "\n",
    "## Multitaper\n",
    "\n",
    "`Multitaper` is used to compute the multitaper Fourier transform of a set of signals. It returns Fourier coefficients that can subsequently be used by the `Connectivity` class to compute frequency-domain metrics on the signals such as power and coherence. \n",
    "\n",
    "Let's simulate a set of signals and see how to use the `Multitaper` class. We simulate two signals (`signal`) which oscillate at 200 Hz and are offset in phase by $\\frac{\\pi}{2}$. We also simulate some white noise to add to the signal (`noise`). We want to compute the multitaper Fourier transform of these two signals.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "\n",
    "frequency_of_interest = 200\n",
    "sampling_frequency = 1000\n",
    "time_extent = (0, 60)\n",
    "n_signals = 2\n",
    "\n",
    "\n",
    "n_time_samples = ((time_extent[1] - time_extent[0]) * sampling_frequency) + 1\n",
    "\n",
    "# Signal 1\n",
    "time = np.linspace(time_extent[0], time_extent[1], num=n_time_samples, endpoint=True)\n",
    "signal = np.zeros((n_time_samples, n_signals))\n",
    "signal[:, 0] = np.sin(2 * np.pi * time * frequency_of_interest)\n",
    "\n",
    "# Signal 2\n",
    "phase_offset = np.pi / 2\n",
    "signal[:, 1] = np.sin((2 * np.pi * time * frequency_of_interest) + phase_offset)\n",
    "\n",
    "noise = np.random.normal(0, 4, signal.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot these two signals with and without the noise added:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-10.0, 10.0)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "fig, axes = plt.subplots(2, 1, figsize=(15, 6))\n",
    "axes[0].set_title(\"Signal\", fontweight=\"bold\")\n",
    "axes[0].plot(time, signal[:, 0], label=\"Signal1\")\n",
    "axes[0].plot(time, signal[:, 1], label=\"Signal2\")\n",
    "axes[0].set_xlabel(\"Time\")\n",
    "axes[0].set_ylabel(\"Amplitude\")\n",
    "axes[0].set_xlim((0.95, 1.05))\n",
    "axes[0].set_ylim((-10, 10))\n",
    "axes[0].legend()\n",
    "\n",
    "axes[1].set_title(\"Signal + Noise\", fontweight=\"bold\")\n",
    "axes[1].plot(time, signal + noise)\n",
    "axes[1].set_xlabel(\"Time\")\n",
    "axes[1].set_ylabel(\"Amplitude\")\n",
    "axes[1].set_xlim((0.95, 1.05))\n",
    "axes[1].set_ylim((-10, 10))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's import the `Multitaper` class and see how to use it. From the docstring, we can see there are a number of inputs to the class. We will walk through the most important of these inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0;31mInit signature:\u001b[0m\n",
      "\u001b[0mMultitaper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mtime_series\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0msampling_frequency\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mtime_halfbandwidth_product\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mdetrend_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'constant'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mtime_window_duration\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mtime_window_step\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mn_tapers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mtapers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mstart_time\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mn_fft_samples\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mn_time_samples_per_window\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mn_time_samples_per_step\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mis_low_bias\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mDocstring:\u001b[0m     \n",
      "Transform time-domain signal(s) to the frequency domain by using\n",
      "multiple tapering windows.\n",
      "\n",
      "Attributes\n",
      "----------\n",
      "time_series : array, shape (n_time_samples, n_trials, n_signals) or\n",
      "                           (n_time_samples, n_signals)\n",
      "sampling_frequency : float, optional\n",
      "    Number of samples per time unit the signal(s) are recorded at.\n",
      "time_halfbandwidth_product : float, optional\n",
      "    Specifies the time-frequency tradeoff of the tapers and also the number\n",
      "    of tapers if `n_tapers` is not set.\n",
      "detrend_type : string or None, optional\n",
      "    Subtracting a constant or a linear trend from each time window. If None\n",
      "    then no detrending is done.\n",
      "start_time : float, optional\n",
      "    Start time of time series.\n",
      "time_window_duration : float, optional\n",
      "    Duration of sliding window in which to compute the fft. Defaults to\n",
      "    the entire time if not set.\n",
      "time_window_step : float, optional\n",
      "    Duration of time to skip when moving the window forward. By default,\n",
      "    this equals the duration of the time window.\n",
      "tapers : array, optional, shape (n_time_samples_per_window, n_tapers)\n",
      "    Pass in a pre-computed set of tapers. If `None`, then the tapers are\n",
      "    automically calculated based on the `time_halfbandwidth_product`,\n",
      "    `n_tapers`, and `n_time_samples_per_window`.\n",
      "n_tapers : int, optional\n",
      "    Set the number of tapers. If `None`, the number of tapers is computed\n",
      "    by 2 * `time_halfbandwidth_product` - 1.\n",
      "n_time_samples_per_window : int, optional\n",
      "    Number of samples in each sliding window. If `time_window_duration` is\n",
      "    set, then this is calculated automically.\n",
      "n_time_samples_per_step : int, optional\n",
      "    Number of samples to skip when moving the window forward. If\n",
      "    `time_window_step` is set, then this is calculated automically.\n",
      "is_low_bias : bool, optional\n",
      "    If `True`, excludes tapers with eigenvalues < 0.9\n",
      "\u001b[0;31mInit docstring:\u001b[0m\n",
      "_summary_\n",
      "\n",
      "Parameters\n",
      "----------\n",
      "time_series : array, shape (n_time_samples, n_trials, n_signals) or (n_time_samples, n_signals)\n",
      "sampling_frequency : float, optional\n",
      "    Number of samples per time unit the signal(s) are recorded at.\n",
      "time_halfbandwidth_product : float, optional\n",
      "    Specifies the time-frequency tradeoff of the tapers and also the number\n",
      "    of tapers if `n_tapers` is not set.\n",
      "detrend_type : string or None, optional\n",
      "    Subtracting a constant or a linear trend from each time window. If None\n",
      "    then no detrending is done.\n",
      "start_time : float, optional\n",
      "    Start time of time series.\n",
      "time_window_duration : float, optional\n",
      "    Duration of sliding window in which to compute the fft. Defaults to\n",
      "    the entire time if not set.\n",
      "time_window_step : float, optional\n",
      "    Duration of time to skip when moving the window forward. By default,\n",
      "    this equals the duration of the time window.\n",
      "tapers : array, optional, shape (n_time_samples_per_window, n_tapers)\n",
      "    Pass in a pre-computed set of tapers. If `None`, then the tapers are\n",
      "    automically calculated based on the `time_halfbandwidth_product`,\n",
      "    `n_tapers`, and `n_time_samples_per_window`.\n",
      "n_tapers : int, optional\n",
      "    Set the number of tapers. If `None`, the number of tapers is computed\n",
      "    by 2 * `time_halfbandwidth_product` - 1.\n",
      "n_time_samples_per_window : int, optional\n",
      "    Number of samples in each sliding window. If `time_window_duration` is\n",
      "    set, then this is calculated automically.\n",
      "n_time_samples_per_step : int, optional\n",
      "    Number of samples to skip when moving the window forward. If\n",
      "    `time_window_step` is set, then this is calculated automically.\n",
      "is_low_bias : bool, optional\n",
      "    If `True`, excludes tapers with eigenvalues < 0.9\n",
      "\u001b[0;31mFile:\u001b[0m           ~/Documents/GitHub/spectral_connectivity/spectral_connectivity/transforms.py\n",
      "\u001b[0;31mType:\u001b[0m           type\n",
      "\u001b[0;31mSubclasses:\u001b[0m     \n"
     ]
    }
   ],
   "source": [
    "from spectral_connectivity import Multitaper\n",
    "\n",
    "Multitaper?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### time_series\n",
    "\n",
    "The most important input is `time_series`. This array is what gets transformed into the frequency domain. The order of dimensions of `time_series` are critical for how the data is processed. The array can either have two or three dimensions.\n",
    "\n",
    "If the array has two dimensions, then dimension 1 is `time` and dimensions 2 is `signals`. This is how our simulated signal is arranged. We have 50001 samples in the time dimension and 2 signals:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60001, 2)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "signal.shape\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we have three dimensions, dimension 1 is `time`, dimension 2 is `trials`, and dimensions 3 is `signals`. It is important to know note that dimension 2 now has a different meaning in that it represents trials and not signals now. Dimension 3 is now the signals dimension. We will show an example of this later."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### sampling_frequency\n",
    "\n",
    "The next most important input is the `sampling_frequency`. The `sampling_frequency` is the number of samples per time unit the signal(s) are recorded at. This is set by default to 1000 samples per second. If your signal is sampled at a different rate, this needs to be set. In our simulated signal, we have sampled time at 1000 samples per second:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampling_frequency\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### time_halfbandwidth_product\n",
    "\n",
    "The `time_halfbandwidth_product` controls the frequency resolution of the Fourier transformed signal. It is equal to the duration of the time window multiplied by the half of the frequency resolution desired (the so-called half bandwidth). Remember that the frequency resolution is the number of consecutive frequencies that can be distinguished. So 2 Hz frequency resolution (which has a 1 Hz half bandwidth) means that we can tell the difference between 9 Hz and 11 Hz, but not 10 Hz.\n",
    "\n",
    "Setting this parameter will define the default number of tapers used in the transform (number of tapers = 2 * `time_halfbandwidth_product` - 1.).\n",
    "\n",
    "It is outside the scope of this tutorial to fully explain multitaper analysis, but a good introduction can be found in the book: [Case Studies in Neural Data Analysis](https://mark-kramer.github.io/Case-Studies-Python/04.html?highlight=taper#multitaper).\n",
    "\n",
    "### time_window_duration and time_window_step\n",
    "The `time_window_duration` controls the duration of the segment of time the transformation is computed on. This is specified in seconds and automatically set to the entire length of the signal(s) if it is not specified. For example, if you want to compute a spectrogram, you need to set the `time_window_duration` to something smaller than the length of the signal(s). Note that setting this will affect the frequency resolution as it is part of the calculation of the `time_halfbandwidth_product` (see above).\n",
    "\n",
    "The `time_window_step` control how far the time window is slid. By default, the time window is set to slide the length of the time_window_duration so that each time window is non-overlapping (approximately independent). Setting the step to smaller than the time window duration will make the time windows overlap, so they will be dependent.\n",
    "\n",
    "### Using Multitaper\n",
    "\n",
    "Now that we know some of the inputs to `Multitaper`, let's see how to use it. We will give the class a noisy signal (`signal + noise`), the sampling frequency (`sampling_frequency`) which we used to simulate the signals and set the `time_halfbandwidth_product` to 5:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Multitaper(sampling_frequency=1000, time_halfbandwidth_product=5,\n",
       "           time_window_duration=60.001, time_window_step=60.001,\n",
       "           detrend_type='constant', start_time=0, n_tapers=9)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multitaper = Multitaper(\n",
    "    signal + noise, sampling_frequency=sampling_frequency, time_halfbandwidth_product=5\n",
    ")\n",
    "multitaper\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that instantiating the class into the variable (`multitaper`) automatically computed some properties. For example, we can look at `time_window_duration` and `time_window_step`, which both will be the length of the entire signal because we did not specify a shorter duration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60.001"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multitaper.time_window_duration\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60.001"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multitaper.time_window_step\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also look at the frequency resolution (which is determined by the `time_halfbandwidth_product` and the `time_window_duration`). \n",
    "\n",
    "NOTE: The bandwidth is another way to refer to the frequency resolution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1666638889351844"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multitaper.frequency_resolution\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also compute the nyquist frequency, which is the highest resolvable frequency. This is half the sampling frequency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "500.0"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multitaper.nyquist_frequency\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we haven't run the tranformation yet. To do this we can use the method `fft` to get the Fourier coefficients. \n",
    "\n",
    "This will have shape (n_time_windows, n_trials, n_tapers, n_fft_samples, n_signals)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 1, 9, 60750, 2)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fourier_coefficients = multitaper.fft()\n",
    "fourier_coefficients.shape\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can get the time corresponding to each time window (of length `n_time_windows`) and the frequencies (of length `n_fft_samples`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multitaper.time\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.01646091,  0.03292181, ..., -0.04938272,\n",
       "       -0.03292181, -0.01646091])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multitaper.frequencies\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, we have computed the Fourier transform on the entire signal, so we only have a single time corresponding to the beginning of the time window (0.0).\n",
    "\n",
    "Also note that the frequencies given by the Multitaper class are both positive and negative. This is necessary for certain computations.\n",
    "\n",
    "Now that we have computed the `fft` we have all the ingredients we need to compute the connectivity measures."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Connectivity class\n",
    "\n",
    "The `Connectivity` class computes the frequency-domain connectivity measures from the Fourier coeffcients. Let's import the class and look at the docstring:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0;31mInit signature:\u001b[0m\n",
      "\u001b[0mConnectivity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mfourier_coefficients\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mexpectation_type\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'trials_tapers'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mfrequencies\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mtime\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mblocks\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m    \u001b[0mdtype\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m<\u001b[0m\u001b[0;32mclass\u001b[0m \u001b[0;34m'numpy.complex128'\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
      "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mDocstring:\u001b[0m     \n",
      "Computes brain connectivity measures based on the cross spectral\n",
      "matrix.\n",
      "\n",
      "Note that spectral granger methods that require estimation of transfer function\n",
      "and noise covariance use minimum phase decomposition [1] to decompose\n",
      "the cross spectral matrix into square roots, which then can be used to\n",
      "non-parametrically estimate the transfer function and noise covariance.\n",
      "\n",
      "Attributes\n",
      "----------\n",
      "fourier_coefficients : array, shape (n_time_windows, n_trials, n_tapers, n_fft_samples, n_signals)\n",
      "    The compex-valued coefficients from a fourier transform. Note that\n",
      "    this is expected to be the two-sided fourier coefficients\n",
      "    (both the positive and negative lags). This is needed for the\n",
      "    Granger-based methods to work.\n",
      "expectation_type : ('trials_tapers' | 'trials' | 'tapers'), optional\n",
      "    How to average the cross spectral matrix. 'trials_tapers' averages\n",
      "    over the trials and tapers dimensions. 'trials' only averages over\n",
      "    the trials dimensions (leaving tapers) and 'tapers' only averages\n",
      "    over tapers (leaving trials).\n",
      "frequencies : array, shape (n_fft_samples,), optional\n",
      "    Frequency of each sample, by default None\n",
      "time : np.ndarray, shape (n_time_windows,) optional\n",
      "    Time of each window, by default None\n",
      "blocks : int, optional\n",
      "    Number of blocks to split up input arrays to do block computation, by default None\n",
      "dtype : np.dtype, optional\n",
      "    Data type of the fourier coefficients, by default xp.complex128\n",
      "\n",
      "References\n",
      "----------\n",
      ".. [1] Dhamala, M., Rangarajan, G., and Ding, M. (2008). Analyzing\n",
      "       information flow in brain networks with noxparametric Granger\n",
      "       causality. NeuroImage 41, 354-362.\n",
      "\u001b[0;31mInit docstring:\u001b[0m\n",
      "Parameters\n",
      "----------\n",
      "fourier_coefficients : np.ndarray, shape (n_time_windows, n_trials, n_tapers, n_fft_samples, n_signals)\n",
      "    The compex-valued coefficients from a fourier transform. Note that\n",
      "    this is expected to be the two-sided fourier coefficients\n",
      "    (both the positive and negative lags). This is needed for the\n",
      "    Granger-based methods to work.\n",
      "expectation_type : str, optional\n",
      "    How to average the cross spectral matrix. 'trials_tapers' averages\n",
      "    over the trials and tapers dimensions. 'trials' only averages over\n",
      "    the trials dimensions (leaving tapers) and 'tapers' only averages\n",
      "    over tapers (leaving trials).\n",
      "frequencies : np.ndarray, shape (n_fft_samples,), optional\n",
      "    Frequency of each sample, by default None\n",
      "time : np.ndarray, shape (n_time_windows,) optional\n",
      "    Time of each window, by default None\n",
      "blocks : int, optional\n",
      "    Number of blocks to split up input arrays to do block computation, by default None\n",
      "dtype : np.dtype, optional\n",
      "    Data type of the fourier coefficients, by default xp.complex128\n",
      "\u001b[0;31mFile:\u001b[0m           ~/Documents/GitHub/spectral_connectivity/spectral_connectivity/connectivity.py\n",
      "\u001b[0;31mType:\u001b[0m           type\n",
      "\u001b[0;31mSubclasses:\u001b[0m     \n"
     ]
    }
   ],
   "source": [
    "from spectral_connectivity import Connectivity\n",
    "\n",
    "Connectivity?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the docstring, we can see that the Connectivity class is initialized with the following parameters:\n",
    "+ `fourier_coefficients`\n",
    "+ `expectation_type`\n",
    "+ `frequencies`,\n",
    "+ `time`\n",
    "+ `blocks`\n",
    "+ `dtype`\n",
    "\n",
    "Of these, we have already computed the `fourier_coefficients`, `time`, and `frequencies` from the `Multitaper` class (see last section). We will consider the most important remaining parameters in turn:\n",
    "\n",
    "### expectation_type\n",
    "\n",
    "The `expectation_type` is perhaps the most important of the parameters to consider. It defines the dimensions of the cross-spectrum that are averaged over. You can average over any combination of:\n",
    "+ time\n",
    "+ trials\n",
    "+ tapers\n",
    "\n",
    "By default, `Connectivity` averages over trials and tapers (`trials_tapers`), but depending on your usage, you may want to average over the other dimensions. The dimensions you average over is specified by a string with an underscore (`_`)  between the dimensions.\n",
    "\n",
    "Note that the order of the dimensions must be in the order time, trials, and tapers. So if you wish to average over all three, the correct expectation type is `time_trials_tapers` and not `trials_time_tapers`.\n",
    "\n",
    "### blocks\n",
    "\n",
    "The `blocks` parameter can help when the computation of the connectivity measures is taking up a large amount of RAM. The computation of the cross-spectral matrix can be large depending on the number of time samples, frequencies, trials and tapers. Therefore it is sometimes useful to simplify this by breaking this computation up into smaller arrays (`blocks`). The `blocks` parameter controls the number of blocks the matrix is split into. Setting this parameter appropriately is a matter of experimentation, but users should start with the default of 1 and increase it as needed as memory usage becomes a problem.\n",
    "\n",
    "\n",
    "### Using Connectivity\n",
    "\n",
    "Okay, now that we understand the parameters, we can now instantiate the class in order to compute the connectivity measures. You can instantiate the class as follows:\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "connectivity = Connectivity(\n",
    "    fourier_coefficients=multitaper.fft(),\n",
    "    expectation_type=\"trials_tapers\",\n",
    "    frequencies=multitaper.frequencies,\n",
    "    time=multitaper.time,\n",
    "    blocks=1,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, you can also instantiate the class this way, which is a little more convenient code-wise. Note that in this formulation, you do not call the `.fft` method, the class method `from_multitaper` does this for you:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "connectivity = Connectivity.from_multitaper(multitaper)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are now ready to compute the connectivity measures. These all exists as function methods on the class. So for instance, if you want to compute the power of the two signals, we would use the `power` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 30375, 2)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "power = connectivity.power()\n",
    "power.shape\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice here that only the non-negative frequencies are returned (since for real signals, the power spectrum is symmetric). Also since we didn't average over time (the `expectation_type` is \"trials_tapers\" by default), it is included in the first dimension. We can get the non-negative frequencies using the `frequencies` property. Notice that the shape of the second dimension of power matches:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30375,)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "connectivity.frequencies.shape\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could also average over time here since there is only one time point -- the average will have no effect other than to remove the time dimension from the output. Let's try that here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30375, 2)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "connectivity = Connectivity.from_multitaper(\n",
    "    multitaper, expectation_type=\"time_trials_tapers\", blocks=1\n",
    ")\n",
    "connectivity.power().shape\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the power of both signals. Remember that we simulated them both to be 200 Hz oscillations, offset by $\\frac{\\pi}{2}$ in phase. As expected, the power of both signals is at 200 Hz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Frequency')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(connectivity.frequencies, connectivity.power())\n",
    "plt.ylabel(\"Power\")\n",
    "plt.xlabel(\"Frequency\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's say we want to compute the coherence, a measure of how stable the phase relationships are between the two signals. We use the `coherence_magnitude` method to compute this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30375, 2, 2)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coherence = connectivity.coherence_magnitude()\n",
    "coherence.shape\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The coherence comes in shape (n_frequencies, n_signals, n_signals). Because it is symmetric, we only have to plot the relationship from signal1 to signal2. We can see that the signal is more coherent at 200 Hz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f8a39a70c50>]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(connectivity.frequencies, coherence[:, 0, 1])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding in time\n",
    "\n",
    "Suppose we want to look at how coherence evolves over time. To do so, we have to go back to the `Multitaper` class and specify the `time_window_duration`.  Here we set it to be 2 seconds. Then we can compute the coherence again from the `Connectivity` class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 1000, 2, 2)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multitaper = Multitaper(\n",
    "    signal + noise,\n",
    "    sampling_frequency=sampling_frequency,\n",
    "    time_halfbandwidth_product=5,\n",
    "    time_window_duration=2.0,\n",
    ")\n",
    "connectivity = Connectivity.from_multitaper(\n",
    "    multitaper, expectation_type=\"trials_tapers\"\n",
    ")\n",
    "\n",
    "coherence = connectivity.coherence_magnitude()\n",
    "coherence.shape\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Also note how this changes our frequency resolution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multitaper.frequency_resolution\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can plot the coherence over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f8a0c08d390>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "time_grid, freq_grid = np.meshgrid(\n",
    "    np.append(connectivity.time, time_extent[-1]),\n",
    "    np.append(connectivity.frequencies, multitaper.nyquist_frequency),\n",
    ")\n",
    "\n",
    "mesh = plt.pcolormesh(\n",
    "    time_grid,\n",
    "    freq_grid,\n",
    "    connectivity.coherence_magnitude()[..., 0, 1].squeeze().T,\n",
    "    vmin=0.0,\n",
    "    vmax=1.0,\n",
    "    cmap=\"viridis\",\n",
    ")\n",
    "plt.ylim((0, 300))\n",
    "plt.xlim(time_extent)\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.colorbar()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a number of other connectivity measures besides coherence. See the other notebooks for examples on how to use them.\n",
    "\n",
    "## multitaper_connectivity\n",
    "\n",
    "There is a third option for how to compute the connectivity measures. The output of this option is a labeled array using the xarray package. This can be convenient because one always knows what the dimensions are. In addition, xarray arrays make plotting easy.\n",
    "\n",
    "Let's repeat the example above of computing the coherence over time with this interface and see how this makes things easier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;coherence_magnitude&#x27; (time: 30, frequency: 1000, source: 2, target: 2)&gt;\n",
       "array([[[[       nan, 0.15019062],\n",
       "         [0.15019062,        nan]],\n",
       "\n",
       "        [[       nan, 0.1590459 ],\n",
       "         [0.1590459 ,        nan]],\n",
       "\n",
       "        [[       nan, 0.18504729],\n",
       "         [0.18504729,        nan]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[       nan, 0.13311988],\n",
       "         [0.13311988,        nan]],\n",
       "\n",
       "        [[       nan, 0.03534768],\n",
       "         [0.03534768,        nan]],\n",
       "\n",
       "        [[       nan, 0.00515984],\n",
       "         [0.00515984,        nan]]],\n",
       "\n",
       "...\n",
       "\n",
       "       [[[       nan, 0.00923646],\n",
       "         [0.00923646,        nan]],\n",
       "\n",
       "        [[       nan, 0.0060699 ],\n",
       "         [0.0060699 ,        nan]],\n",
       "\n",
       "        [[       nan, 0.09804826],\n",
       "         [0.09804826,        nan]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[       nan, 0.41137038],\n",
       "         [0.41137038,        nan]],\n",
       "\n",
       "        [[       nan, 0.27513766],\n",
       "         [0.27513766,        nan]],\n",
       "\n",
       "        [[       nan, 0.23863733],\n",
       "         [0.23863733,        nan]]]])\n",
       "Coordinates:\n",
       "  * time       (time) float64 0.0 2.0 4.0 6.0 8.0 ... 50.0 52.0 54.0 56.0 58.0\n",
       "  * frequency  (frequency) float64 0.0 0.5 1.0 1.5 ... 498.0 498.5 499.0 499.5\n",
       "  * source     (source) int64 0 1\n",
       "  * target     (target) int64 0 1\n",
       "Attributes: (12/15)\n",
       "    mt_detrend_type:                constant\n",
       "    mt_frequency_resolution:        5.0\n",
       "    mt_is_low_bias:                 True\n",
       "    mt_n_fft_samples:               2000\n",
       "    mt_n_signals:                   2\n",
       "    mt_n_tapers:                    9\n",
       "    ...                             ...\n",
       "    mt_nyquist_frequency:           500.0\n",
       "    mt_sampling_frequency:          1000\n",
       "    mt_start_time:                  0\n",
       "    mt_time_halfbandwidth_product:  5\n",
       "    mt_time_window_duration:        2.0\n",
       "    mt_time_window_step:            2.0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'coherence_magnitude'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 30</li><li><span class='xr-has-index'>frequency</span>: 1000</li><li><span class='xr-has-index'>source</span>: 2</li><li><span class='xr-has-index'>target</span>: 2</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-9ae284f5-5824-4d7d-b585-d99bc6a316cd' class='xr-array-in' type='checkbox' checked><label for='section-9ae284f5-5824-4d7d-b585-d99bc6a316cd' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan 0.1502 0.1502 nan nan 0.159 ... 0.2751 nan nan 0.2386 0.2386 nan</span></div><div class='xr-array-data'><pre>array([[[[       nan, 0.15019062],\n",
       "         [0.15019062,        nan]],\n",
       "\n",
       "        [[       nan, 0.1590459 ],\n",
       "         [0.1590459 ,        nan]],\n",
       "\n",
       "        [[       nan, 0.18504729],\n",
       "         [0.18504729,        nan]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[       nan, 0.13311988],\n",
       "         [0.13311988,        nan]],\n",
       "\n",
       "        [[       nan, 0.03534768],\n",
       "         [0.03534768,        nan]],\n",
       "\n",
       "        [[       nan, 0.00515984],\n",
       "         [0.00515984,        nan]]],\n",
       "\n",
       "...\n",
       "\n",
       "       [[[       nan, 0.00923646],\n",
       "         [0.00923646,        nan]],\n",
       "\n",
       "        [[       nan, 0.0060699 ],\n",
       "         [0.0060699 ,        nan]],\n",
       "\n",
       "        [[       nan, 0.09804826],\n",
       "         [0.09804826,        nan]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[       nan, 0.41137038],\n",
       "         [0.41137038,        nan]],\n",
       "\n",
       "        [[       nan, 0.27513766],\n",
       "         [0.27513766,        nan]],\n",
       "\n",
       "        [[       nan, 0.23863733],\n",
       "         [0.23863733,        nan]]]])</pre></div></div></li><li class='xr-section-item'><input id='section-5d4bc836-62f6-418d-a7d7-c07532d83e4d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5d4bc836-62f6-418d-a7d7-c07532d83e4d' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 2.0 4.0 6.0 ... 54.0 56.0 58.0</div><input id='attrs-285cf70a-51b6-495e-b9c5-c0b9c3c47b55' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-285cf70a-51b6-495e-b9c5-c0b9c3c47b55' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7364aa2-ed80-47f3-81ae-3ba77e066539' class='xr-var-data-in' type='checkbox'><label for='data-a7364aa2-ed80-47f3-81ae-3ba77e066539' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0.,  2.,  4.,  6.,  8., 10., 12., 14., 16., 18., 20., 22., 24., 26.,\n",
       "       28., 30., 32., 34., 36., 38., 40., 42., 44., 46., 48., 50., 52., 54.,\n",
       "       56., 58.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>frequency</span></div><div class='xr-var-dims'>(frequency)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.5 1.0 ... 498.5 499.0 499.5</div><input id='attrs-3be09694-5073-4fba-ba68-fb2b9230de72' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3be09694-5073-4fba-ba68-fb2b9230de72' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9f0a69c-4dd1-49f9-9be5-09c197dab883' class='xr-var-data-in' type='checkbox'><label for='data-d9f0a69c-4dd1-49f9-9be5-09c197dab883' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0. ,   0.5,   1. , ..., 498.5, 499. , 499.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>source</span></div><div class='xr-var-dims'>(source)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1</div><input id='attrs-eef662cc-2ebd-4102-9758-a7b176c48b0d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eef662cc-2ebd-4102-9758-a7b176c48b0d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd40416c-0460-430c-9253-c95e913e5e90' class='xr-var-data-in' type='checkbox'><label for='data-bd40416c-0460-430c-9253-c95e913e5e90' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>target</span></div><div class='xr-var-dims'>(target)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1</div><input id='attrs-ff43790c-547a-4963-b0a4-72be5aa49981' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ff43790c-547a-4963-b0a4-72be5aa49981' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b466f339-86dd-4dbe-9736-d316aaa1d55c' class='xr-var-data-in' type='checkbox'><label for='data-b466f339-86dd-4dbe-9736-d316aaa1d55c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e9257604-d0b0-4ad4-ad26-7ca026d1d379' class='xr-section-summary-in' type='checkbox'  ><label for='section-e9257604-d0b0-4ad4-ad26-7ca026d1d379' class='xr-section-summary' >Attributes: <span>(15)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>mt_detrend_type :</span></dt><dd>constant</dd><dt><span>mt_frequency_resolution :</span></dt><dd>5.0</dd><dt><span>mt_is_low_bias :</span></dt><dd>True</dd><dt><span>mt_n_fft_samples :</span></dt><dd>2000</dd><dt><span>mt_n_signals :</span></dt><dd>2</dd><dt><span>mt_n_tapers :</span></dt><dd>9</dd><dt><span>mt_n_time_samples_per_step :</span></dt><dd>2000</dd><dt><span>mt_n_time_samples_per_window :</span></dt><dd>2000</dd><dt><span>mt_n_trials :</span></dt><dd>1</dd><dt><span>mt_nyquist_frequency :</span></dt><dd>500.0</dd><dt><span>mt_sampling_frequency :</span></dt><dd>1000</dd><dt><span>mt_start_time :</span></dt><dd>0</dd><dt><span>mt_time_halfbandwidth_product :</span></dt><dd>5</dd><dt><span>mt_time_window_duration :</span></dt><dd>2.0</dd><dt><span>mt_time_window_step :</span></dt><dd>2.0</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'coherence_magnitude' (time: 30, frequency: 1000, source: 2, target: 2)>\n",
       "array([[[[       nan, 0.15019062],\n",
       "         [0.15019062,        nan]],\n",
       "\n",
       "        [[       nan, 0.1590459 ],\n",
       "         [0.1590459 ,        nan]],\n",
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      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from spectral_connectivity import multitaper_connectivity\n",
    "\n",
    "coherence = multitaper_connectivity(\n",
    "    signal + noise,\n",
    "    sampling_frequency=sampling_frequency,\n",
    "    time_halfbandwidth_product=5,\n",
    "    time_window_duration=2.0,\n",
    "    method=\"coherence_magnitude\",\n",
    ")\n",
    "\n",
    "coherence\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the labeled output conveniently gives us the time and frequencies and signal labels for each dimension. Manipulating xarray by their label name is convenient. Lets' say we only want frequencies between 100 and 300 Hz. We can grab this data in a convenient way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;coherence_magnitude&#x27; (time: 30, frequency: 401, source: 2, target: 2)&gt;\n",
       "array([[[[       nan, 0.20811035],\n",
       "         [0.20811035,        nan]],\n",
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       "\n",
       "        [[       nan, 0.27178454],\n",
       "         [0.27178454,        nan]]],\n",
       "\n",
       "...\n",
       "\n",
       "       [[[       nan, 0.1233722 ],\n",
       "         [0.1233722 ,        nan]],\n",
       "\n",
       "        [[       nan, 0.01402156],\n",
       "         [0.01402156,        nan]],\n",
       "\n",
       "        [[       nan, 0.01869211],\n",
       "         [0.01869211,        nan]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[       nan, 0.10371416],\n",
       "         [0.10371416,        nan]],\n",
       "\n",
       "        [[       nan, 0.13492168],\n",
       "         [0.13492168,        nan]],\n",
       "\n",
       "        [[       nan, 0.02686476],\n",
       "         [0.02686476,        nan]]]])\n",
       "Coordinates:\n",
       "  * time       (time) float64 0.0 2.0 4.0 6.0 8.0 ... 50.0 52.0 54.0 56.0 58.0\n",
       "  * frequency  (frequency) float64 100.0 100.5 101.0 101.5 ... 299.0 299.5 300.0\n",
       "  * source     (source) int64 0 1\n",
       "  * target     (target) int64 0 1\n",
       "Attributes: (12/15)\n",
       "    mt_detrend_type:                constant\n",
       "    mt_frequency_resolution:        5.0\n",
       "    mt_is_low_bias:                 True\n",
       "    mt_n_fft_samples:               2000\n",
       "    mt_n_signals:                   2\n",
       "    mt_n_tapers:                    9\n",
       "    ...                             ...\n",
       "    mt_nyquist_frequency:           500.0\n",
       "    mt_sampling_frequency:          1000\n",
       "    mt_start_time:                  0\n",
       "    mt_time_halfbandwidth_product:  5\n",
       "    mt_time_window_duration:        2.0\n",
       "    mt_time_window_step:            2.0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'coherence_magnitude'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 30</li><li><span class='xr-has-index'>frequency</span>: 401</li><li><span class='xr-has-index'>source</span>: 2</li><li><span class='xr-has-index'>target</span>: 2</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-2c4aff86-4448-47f3-86ba-1c866db5bb23' class='xr-array-in' type='checkbox' checked><label for='section-2c4aff86-4448-47f3-86ba-1c866db5bb23' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>nan 0.2081 0.2081 nan nan 0.235 ... 0.1349 nan nan 0.02686 0.02686 nan</span></div><div class='xr-array-data'><pre>array([[[[       nan, 0.20811035],\n",
       "         [0.20811035,        nan]],\n",
       "\n",
       "        [[       nan, 0.23500603],\n",
       "         [0.23500603,        nan]],\n",
       "\n",
       "        [[       nan, 0.11101969],\n",
       "         [0.11101969,        nan]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[       nan, 0.13770835],\n",
       "         [0.13770835,        nan]],\n",
       "\n",
       "        [[       nan, 0.17166775],\n",
       "         [0.17166775,        nan]],\n",
       "\n",
       "        [[       nan, 0.27178454],\n",
       "         [0.27178454,        nan]]],\n",
       "\n",
       "...\n",
       "\n",
       "       [[[       nan, 0.1233722 ],\n",
       "         [0.1233722 ,        nan]],\n",
       "\n",
       "        [[       nan, 0.01402156],\n",
       "         [0.01402156,        nan]],\n",
       "\n",
       "        [[       nan, 0.01869211],\n",
       "         [0.01869211,        nan]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[       nan, 0.10371416],\n",
       "         [0.10371416,        nan]],\n",
       "\n",
       "        [[       nan, 0.13492168],\n",
       "         [0.13492168,        nan]],\n",
       "\n",
       "        [[       nan, 0.02686476],\n",
       "         [0.02686476,        nan]]]])</pre></div></div></li><li class='xr-section-item'><input id='section-8e4d01ea-7170-4372-9d23-a2f9b8a10c72' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8e4d01ea-7170-4372-9d23-a2f9b8a10c72' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 2.0 4.0 6.0 ... 54.0 56.0 58.0</div><input id='attrs-c13514ef-f1e0-4f52-a8f0-ddc38135866c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c13514ef-f1e0-4f52-a8f0-ddc38135866c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37313f69-7509-465a-a37a-caad2bd12ead' class='xr-var-data-in' type='checkbox'><label for='data-37313f69-7509-465a-a37a-caad2bd12ead' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0.,  2.,  4.,  6.,  8., 10., 12., 14., 16., 18., 20., 22., 24., 26.,\n",
       "       28., 30., 32., 34., 36., 38., 40., 42., 44., 46., 48., 50., 52., 54.,\n",
       "       56., 58.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>frequency</span></div><div class='xr-var-dims'>(frequency)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>100.0 100.5 101.0 ... 299.5 300.0</div><input id='attrs-0339196c-f7a5-4a04-816d-eba137661e66' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0339196c-f7a5-4a04-816d-eba137661e66' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-019e5b8b-9934-439c-92bf-b90a84ad878d' class='xr-var-data-in' type='checkbox'><label for='data-019e5b8b-9934-439c-92bf-b90a84ad878d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([100. , 100.5, 101. , ..., 299. , 299.5, 300. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>source</span></div><div class='xr-var-dims'>(source)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1</div><input id='attrs-2e85e91c-27fa-451e-ab7f-be104cdb07f7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2e85e91c-27fa-451e-ab7f-be104cdb07f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32b99868-89ab-40b6-bdfb-e8109e221c6c' class='xr-var-data-in' type='checkbox'><label for='data-32b99868-89ab-40b6-bdfb-e8109e221c6c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>target</span></div><div class='xr-var-dims'>(target)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1</div><input id='attrs-db5eedb1-ebfd-4e32-ba0a-d06a347ba157' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-db5eedb1-ebfd-4e32-ba0a-d06a347ba157' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34dce43c-10cd-4cfa-9913-aef2f6d94eaf' class='xr-var-data-in' type='checkbox'><label for='data-34dce43c-10cd-4cfa-9913-aef2f6d94eaf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-14b6c39a-cd74-4433-a164-f6d6173a97a5' class='xr-section-summary-in' type='checkbox'  ><label for='section-14b6c39a-cd74-4433-a164-f6d6173a97a5' class='xr-section-summary' >Attributes: <span>(15)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>mt_detrend_type :</span></dt><dd>constant</dd><dt><span>mt_frequency_resolution :</span></dt><dd>5.0</dd><dt><span>mt_is_low_bias :</span></dt><dd>True</dd><dt><span>mt_n_fft_samples :</span></dt><dd>2000</dd><dt><span>mt_n_signals :</span></dt><dd>2</dd><dt><span>mt_n_tapers :</span></dt><dd>9</dd><dt><span>mt_n_time_samples_per_step :</span></dt><dd>2000</dd><dt><span>mt_n_time_samples_per_window :</span></dt><dd>2000</dd><dt><span>mt_n_trials :</span></dt><dd>1</dd><dt><span>mt_nyquist_frequency :</span></dt><dd>500.0</dd><dt><span>mt_sampling_frequency :</span></dt><dd>1000</dd><dt><span>mt_start_time :</span></dt><dd>0</dd><dt><span>mt_time_halfbandwidth_product :</span></dt><dd>5</dd><dt><span>mt_time_window_duration :</span></dt><dd>2.0</dd><dt><span>mt_time_window_step :</span></dt><dd>2.0</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'coherence_magnitude' (time: 30, frequency: 401, source: 2, target: 2)>\n",
       "array([[[[       nan, 0.20811035],\n",
       "         [0.20811035,        nan]],\n",
       "\n",
       "        [[       nan, 0.23500603],\n",
       "         [0.23500603,        nan]],\n",
       "\n",
       "        [[       nan, 0.11101969],\n",
       "         [0.11101969,        nan]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[       nan, 0.13770835],\n",
       "         [0.13770835,        nan]],\n",
       "\n",
       "        [[       nan, 0.17166775],\n",
       "         [0.17166775,        nan]],\n",
       "\n",
       "        [[       nan, 0.27178454],\n",
       "         [0.27178454,        nan]]],\n",
       "\n",
       "...\n",
       "\n",
       "       [[[       nan, 0.1233722 ],\n",
       "         [0.1233722 ,        nan]],\n",
       "\n",
       "        [[       nan, 0.01402156],\n",
       "         [0.01402156,        nan]],\n",
       "\n",
       "        [[       nan, 0.01869211],\n",
       "         [0.01869211,        nan]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[       nan, 0.10371416],\n",
       "         [0.10371416,        nan]],\n",
       "\n",
       "        [[       nan, 0.13492168],\n",
       "         [0.13492168,        nan]],\n",
       "\n",
       "        [[       nan, 0.02686476],\n",
       "         [0.02686476,        nan]]]])\n",
       "Coordinates:\n",
       "  * time       (time) float64 0.0 2.0 4.0 6.0 8.0 ... 50.0 52.0 54.0 56.0 58.0\n",
       "  * frequency  (frequency) float64 100.0 100.5 101.0 101.5 ... 299.0 299.5 300.0\n",
       "  * source     (source) int64 0 1\n",
       "  * target     (target) int64 0 1\n",
       "Attributes: (12/15)\n",
       "    mt_detrend_type:                constant\n",
       "    mt_frequency_resolution:        5.0\n",
       "    mt_is_low_bias:                 True\n",
       "    mt_n_fft_samples:               2000\n",
       "    mt_n_signals:                   2\n",
       "    mt_n_tapers:                    9\n",
       "    ...                             ...\n",
       "    mt_nyquist_frequency:           500.0\n",
       "    mt_sampling_frequency:          1000\n",
       "    mt_start_time:                  0\n",
       "    mt_time_halfbandwidth_product:  5\n",
       "    mt_time_window_duration:        2.0\n",
       "    mt_time_window_step:            2.0"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coherence.sel(frequency=slice(100, 300))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also plot this in an easy way that puts the labels on the plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.plot.facetgrid.FacetGrid at 0x7f8a08c30d50>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x432 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "coherence.sel(frequency=slice(100, 300)).plot(\n",
    "    x=\"time\", y=\"frequency\", row=\"source\", col=\"target\"\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fully explaining using xarray arrays is beyond the scope of this tutorial, but see the [xarray documentation](https://docs.xarray.dev/en/stable/) to explore all the possibilities."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using GPUs\n",
    "\n",
    "The `spectral_connectivity` package can do computations on the GPU to speed up the code. To do this, you must have the package `cupy` installed AND have the environmental variable `SPECTRAL_CONNECTIVITY_ENABLE_GPU` set. \n",
    "\n",
    "WARNING: the environmental variable must be set before the first import of the `spectral_connectivity` package or the GPU enabled functions will not work.\n",
    "\n",
    "To check if you are using the GPU on first import, use the logging.basicConfig to get messages that will tell you which version is being used.\n",
    "\n",
    "It is easiest to use `conda` to install `cupy` because it handles the installation of the correct version of `cudatoolkit`. Note that `cupy` will not work on Macs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.10 ('spectral_connectivity')",
   "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": "3.7.12"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "92a01d5ca5327f6bae6704d5c268274dc652170a5ef2c529397f4bd5c8a1c159"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}