{
 "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": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot these two signals with and without the noise added:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-10.0, 10.0)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\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))"
   ]
  },
  {
   "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": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31mInit signature:\u001b[39m\n",
      "Multitaper(\n",
      "    time_series: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating]],\n",
      "    sampling_frequency: float = \u001b[32m1000\u001b[39m,\n",
      "    time_halfbandwidth_product: float = \u001b[32m3\u001b[39m,\n",
      "    detrend_type: str | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[33m'constant'\u001b[39m,\n",
      "    time_window_duration: float | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m,\n",
      "    time_window_step: float | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m,\n",
      "    n_tapers: int | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m,\n",
      "    tapers: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating]] | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m,\n",
      "    start_time: float | numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating]] = \u001b[32m0\u001b[39m,\n",
      "    n_fft_samples: int | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m,\n",
      "    n_time_samples_per_window: int | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m,\n",
      "    n_time_samples_per_step: int | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m,\n",
      "    is_low_bias: bool = \u001b[38;5;28;01mTrue\u001b[39;00m,\n",
      ") -> \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "\u001b[31mDocstring:\u001b[39m     \n",
      "Multitaper spectral analysis for robust power spectral density estimation.\n",
      "\n",
      "Transforms time-domain signals to frequency domain using multiple orthogonal\n",
      "tapering windows (Slepian sequences). This approach reduces spectral leakage\n",
      "and provides better spectral estimates than single-taper methods.\n",
      "\n",
      "Parameters\n",
      "----------\n",
      "time_series : NDArray[floating], shape (n_time_samples, n_trials, n_signals)\n",
      "    Input time series data. **Must be 3D array.**\n",
      "    - n_time_samples: number of time points\n",
      "    - n_trials: number of trials (use 1 for single trial)\n",
      "    - n_signals: number of signals/channels\n",
      "    Multiple trials are averaged in the spectral domain.\n",
      "\n",
      "    **Important:** If your data is 1D or 2D, use `prepare_time_series()`\n",
      "    helper function to convert it to the required 3D format.\n",
      "sampling_frequency : float, default=1000\n",
      "    Sampling rate in Hz of the time series data.\n",
      "time_halfbandwidth_product : float, default=3\n",
      "    Time-bandwidth product (often denoted as NW) controlling the trade-off\n",
      "    between frequency resolution and variance reduction.\n",
      "\n",
      "    **Effect on analysis:**\n",
      "    - Determines frequency resolution: Δf = 2·NW / T (where T is window duration)\n",
      "    - Determines number of tapers: n_tapers = floor(2·NW) - 1\n",
      "    - Higher values → More spectral smoothing, better variance reduction\n",
      "    - Lower values → Better frequency resolution, less averaging\n",
      "\n",
      "    **Typical values:**\n",
      "    - NW = 2: Minimal smoothing, 3 tapers, best frequency resolution\n",
      "    - NW = 3: Balanced trade-off (recommended default), 5 tapers\n",
      "    - NW = 4: More smoothing, 7 tapers, strong variance reduction\n",
      "    - NW = 5+: Heavy smoothing, 9+ tapers, very strong variance reduction\n",
      "\n",
      "    **Examples:**\n",
      "    For 1 Hz frequency resolution with 1 second windows: use NW ≤ 0.5\n",
      "    For 5 Hz frequency resolution with 1 second windows: use NW ≤ 2.5\n",
      "    For 10 Hz frequency resolution with 1 second windows: use NW ≤ 5\n",
      "\n",
      "    Use `estimate_frequency_resolution()` to calculate the resolution\n",
      "    for different parameter combinations, or use `suggest_parameters()`\n",
      "    to get recommendations for your specific data and analysis goals.\n",
      "detrend_type : {\"constant\", \"linear\", None}, default=\"constant\"\n",
      "    Type of detrending applied to each time window:\n",
      "    - \"constant\": remove DC component\n",
      "    - \"linear\": remove linear trend\n",
      "    - None: no detrending\n",
      "time_window_duration : float, optional\n",
      "    Duration in seconds of sliding time windows. If None, analyzes entire\n",
      "    time series (no time resolution).\n",
      "time_window_step : float, optional\n",
      "    Step size in seconds between consecutive time windows. If None, uses\n",
      "    non-overlapping windows (step = window duration).\n",
      "n_tapers : int, optional\n",
      "    Number of DPSS tapers to use. If None, computed as\n",
      "    floor(2 * time_halfbandwidth_product) - 1.\n",
      "tapers : NDArray[floating], shape (n_time_samples_per_window, n_tapers), optional\n",
      "    Pre-computed tapering windows. If None, DPSS tapers are computed\n",
      "    automatically.\n",
      "start_time : float or NDArray[floating], default=0\n",
      "    Start time in seconds of the time series data.\n",
      "n_fft_samples : int, optional\n",
      "    Length of FFT. If None, uses next power of 2 >= n_time_samples_per_window.\n",
      "n_time_samples_per_window : int, optional\n",
      "    Number of samples per time window. Computed from time_window_duration\n",
      "    if not provided.\n",
      "n_time_samples_per_step : int, optional\n",
      "    Number of samples to advance between windows. Computed from\n",
      "    time_window_step if not provided.\n",
      "is_low_bias : bool, default=True\n",
      "    If True, exclude tapers with eigenvalues < MIN_EIGENVALUE_THRESHOLD (0.9) to reduce bias.\n",
      "\n",
      "Attributes\n",
      "----------\n",
      "fft : NDArray[complex128]\n",
      "    Complex-valued FFT coefficients with shape\n",
      "    (n_time_windows, n_trials, n_tapers, n_frequencies, n_signals).\n",
      "frequencies : NDArray[float64], shape (n_frequencies,)\n",
      "    Frequency values in Hz corresponding to FFT bins.\n",
      "time : NDArray[float64], shape (n_time_windows,)\n",
      "    Time values in seconds for center of each time window.\n",
      "\n",
      "Examples\n",
      "--------\n",
      "Using the helper function (recommended for 2D data):\n",
      "\n",
      ">>> import numpy as np\n",
      ">>> from spectral_connectivity.transforms import Multitaper, prepare_time_series\n",
      ">>> # EEG recording: 5 seconds at 1000 Hz, 64 channels\n",
      ">>> eeg_data = np.random.randn(5000, 64)  # Shape: (n_time, n_channels)\n",
      ">>> eeg_3d = prepare_time_series(eeg_data, axis='signals')\n",
      ">>> mt = Multitaper(eeg_3d, sampling_frequency=1000, time_halfbandwidth_product=4)\n",
      ">>> print(f\"FFT shape: {mt.fft().shape}\")\n",
      ">>> print(f\"Frequencies: {len(mt.frequencies)} bins, max = {mt.frequencies[-1]:.1f} Hz\")\n",
      "\n",
      "Manual reshaping with np.newaxis (advanced):\n",
      "\n",
      ">>> # Generate test signal: 50Hz + noise\n",
      ">>> fs = 1000  # 1 kHz sampling\n",
      ">>> t = np.arange(0, 1, 1/fs)\n",
      ">>> signal = np.sin(2*np.pi*50*t) + 0.1*np.random.randn(len(t))\n",
      ">>> # Manually reshape to 3D: (n_time, n_trials, n_signals)\n",
      ">>> data = signal[:, np.newaxis, np.newaxis]  # Shape: (1000, 1, 1)\n",
      ">>> mt = Multitaper(data, sampling_frequency=fs, time_halfbandwidth_product=4)\n",
      "\n",
      "Multiple trials (already 3D):\n",
      "\n",
      ">>> # Epoched data: 100 trials, 5 channels, 1 second each at 1000 Hz\n",
      ">>> epoched_data = np.random.randn(1000, 100, 5)  # (n_time, n_trials, n_signals)\n",
      ">>> mt = Multitaper(epoched_data, sampling_frequency=1000)\n",
      ">>> print(f\"Trials: {mt.n_trials}, Signals: {mt.n_signals}\")  # Trials: 100, Signals: 5\n",
      "\n",
      "Notes\n",
      "-----\n",
      "The multitaper method uses discrete prolate spheroidal sequences (DPSS)\n",
      "as tapers, which are optimal for spectral analysis in the sense of minimizing\n",
      "spectral leakage while maximizing energy concentration in the frequency band\n",
      "of interest.\n",
      "\n",
      "References\n",
      "----------\n",
      ".. [1] Thomson, D. J. (1982). Spectrum estimation and harmonic analysis.\n",
      "       Proceedings of the IEEE, 70(9), 1055-1096.\n",
      ".. [2] Percival, D. B., & Walden, A. T. (1993). Spectral Analysis for\n",
      "       Physical Applications. Cambridge University Press.\n",
      "\u001b[31mFile:\u001b[39m           ~/Documents/GitHub/spectral_connectivity/spectral_connectivity/transforms.py\n",
      "\u001b[31mType:\u001b[39m           type\n",
      "\u001b[31mSubclasses:\u001b[39m     "
     ]
    }
   ],
   "source": [
    "from spectral_connectivity import Multitaper\n",
    "from spectral_connectivity.transforms import prepare_time_series\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": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60001, 2)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "signal.shape"
   ]
  },
  {
   "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": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampling_frequency"
   ]
  },
  {
   "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": 6,
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert signal to 3D format (n_time_samples, n_trials, n_signals)\n",
    "signal_with_noise = prepare_time_series(signal + noise, axis=\"signals\")\n",
    "multitaper = Multitaper(\n",
    "    signal_with_noise,\n",
    "    sampling_frequency=sampling_frequency,\n",
    "    time_halfbandwidth_product=5,\n",
    ")\n",
    "multitaper"
   ]
  },
  {
   "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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60.001"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multitaper.time_window_duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60.001"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multitaper.time_window_step"
   ]
  },
  {
   "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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1666638889351844"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multitaper.frequency_resolution"
   ]
  },
  {
   "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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "500.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multitaper.nyquist_frequency"
   ]
  },
  {
   "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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 1, 9, 60025, 2)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fourier_coefficients = multitaper.fft()\n",
    "fourier_coefficients.shape"
   ]
  },
  {
   "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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multitaper.time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.01665973,  0.03331945, ..., -0.04997918,\n",
       "       -0.03331945, -0.01665973], shape=(60025,))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multitaper.frequencies"
   ]
  },
  {
   "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": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31mInit signature:\u001b[39m\n",
      "Connectivity(\n",
      "    fourier_coefficients: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.complexfloating]],\n",
      "    expectation_type: str = \u001b[33m'trials_tapers'\u001b[39m,\n",
      "    frequencies: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating]] | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m,\n",
      "    time: numpy.ndarray[tuple[typing.Any, ...], numpy.dtype[numpy.floating]] | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m,\n",
      "    blocks: int | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m,\n",
      "    dtype: numpy.dtype = <\u001b[38;5;28;01mclass\u001b[39;00m \u001b[33m'numpy.complex128'\u001b[39m>,\n",
      ") -> \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "\u001b[31mDocstring:\u001b[39m     \n",
      "Compute functional and directed connectivity measures from spectral data.\n",
      "\n",
      "This class provides a comprehensive suite of connectivity analysis methods\n",
      "based on cross-spectral matrices derived from Fourier-transformed time series.\n",
      "Methods range from basic coherence to advanced Granger causality measures.\n",
      "\n",
      "Parameters\n",
      "----------\n",
      "fourier_coefficients : NDArray[complexfloating],\n",
      "    shape (n_time_windows, n_trials, n_tapers, n_frequencies, n_signals)\n",
      "    Complex-valued Fourier coefficients from spectral analysis. Must be\n",
      "    two-sided (positive and negative frequencies) for Granger methods.\n",
      "    Usually obtained from multitaper or other spectral estimation methods.\n",
      "    **Validation**: Must be 5-dimensional with at least 2 signals and\n",
      "    contain only finite values (no NaN/Inf).\n",
      "expectation_type : {\"trials_tapers\", \"trials\", \"tapers\", \"time\",\n",
      "    \"time_trials\", \"time_tapers\", \"time_trials_tapers\"},\n",
      "    default=\"trials_tapers\"\n",
      "    Specifies how to average the cross-spectral matrix:\n",
      "    - \"trials_tapers\": average over trials and tapers (most common)\n",
      "    - \"trials\": average over trials only (keep taper dimension)\n",
      "    - \"tapers\": average over tapers only (keep trial dimension)\n",
      "    - \"time\": average over time windows\n",
      "    - combinations: average over multiple dimensions\n",
      "frequencies : NDArray[floating], shape (n_frequencies,), optional\n",
      "    Frequency values in Hz corresponding to FFT bins. If None, uses\n",
      "    normalized frequencies.\n",
      "time : NDArray[floating], shape (n_time_windows,), optional\n",
      "    Time values in seconds for each time window. If None, uses indices.\n",
      "blocks : int, optional\n",
      "    Number of blocks for memory-efficient computation of large connectivity\n",
      "    matrices. When specified, the cross-spectral matrix is computed in\n",
      "    chunks rather than all at once, reducing peak memory usage.\n",
      "\n",
      "    **When to use blocks:**\n",
      "\n",
      "    - Large number of signals (n_signals >= 50, as a rough guideline;\n",
      "      benefit increases with more signals)\n",
      "    - Memory-constrained environments\n",
      "    - High-resolution spectrograms (large n_time_windows × n_frequencies)\n",
      "    - GPU computing with limited VRAM\n",
      "\n",
      "    **When NOT to use blocks:**\n",
      "\n",
      "    - Small datasets (n_signals < 50): The overhead of block management\n",
      "      may exceed the memory benefit, making blocks=None faster\n",
      "    - When speed is critical and memory is abundant\n",
      "\n",
      "    **Memory-Speed Tradeoff:**\n",
      "\n",
      "    - **Without blocks** (default): Fastest for small datasets, but requires\n",
      "      memory for full (n_time_windows × n_frequencies × n_signals × n_signals)\n",
      "      array\n",
      "    - **With blocks**: Reduces peak memory by 70-80% for large arrays\n",
      "      (measured 73% reduction for n_signals=50, blocks=5), with minimal\n",
      "      speed penalty (typically <10%)\n",
      "\n",
      "    **Quick Decision Guide:**\n",
      "\n",
      "    - n_signals < 50: Use default (blocks=None)\n",
      "    - 50 ≤ n_signals < 100: Use blocks=5 if memory is limited\n",
      "    - n_signals ≥ 100: Recommended blocks=5 or blocks=10\n",
      "    - Out of memory errors: Increase blocks value (try doubling)\n",
      "\n",
      "    **Important**: Results are numerically identical whether using blocks\n",
      "    or not (validated to floating-point precision).\n",
      "dtype : np.dtype, default=complex128\n",
      "    Data type for internal computations. Should match input precision.\n",
      "\n",
      "Attributes\n",
      "----------\n",
      "n_observations : int\n",
      "    Effective number of independent observations after averaging,\n",
      "    used for statistical inference.\n",
      "\n",
      "Examples\n",
      "--------\n",
      ">>> import numpy as np\n",
      ">>> from spectral_connectivity import Connectivity\n",
      ">>> # Simulate coherent signals\n",
      ">>> n_times, n_trials, n_tapers, n_freqs, n_signals = 50, 10, 5, 100, 2\n",
      ">>> # Create complex coefficients with some coherence\n",
      ">>> phase_diff = np.pi/4  # 45 degree phase difference\n",
      ">>> coeffs = np.random.randn(n_times, n_trials, n_tapers, n_freqs, n_signals) +     ...          1j * np.random.randn(n_times, n_trials, n_tapers, n_freqs, n_signals)\n",
      ">>> # Add coherence at specific frequencies\n",
      ">>> coeffs[:, :, :, 10, 1] = coeffs[:, :, :, 10, 0] * np.exp(1j * phase_diff)\n",
      ">>>\n",
      ">>> # Compute connectivity\n",
      ">>> conn = Connectivity(coeffs, expectation_type=\"trials_tapers\")\n",
      ">>> coherence = conn.coherence_magnitude()  # Shape: (n_times, n_freqs, 2, 2)\n",
      ">>> phase_lag = conn.coherence_phase()\n",
      ">>> print(f\"Coherence shape: {coherence.shape}\")\n",
      ">>> print(f\"Peak coherence: {np.max(coherence[:, 10, 0, 1]):.3f}\")\n",
      "\n",
      "Notes\n",
      "-----\n",
      "The class supports both CPU (NumPy) and GPU (CuPy) computation depending\n",
      "on the SPECTRAL_CONNECTIVITY_ENABLE_GPU environment variable. For Granger\n",
      "causality measures, minimum phase decomposition [1]_ is used to estimate\n",
      "transfer functions and noise covariances non-parametrically.\n",
      "\n",
      "References\n",
      "----------\n",
      ".. [1] Dhamala, M., Rangarajan, G., and Ding, M. (2008). Analyzing\n",
      "       information flow in brain networks with nonparametric Granger\n",
      "       causality. NeuroImage 41, 354-362.\n",
      ".. [2] Bastos, A. M., & Schoffelen, J. M. (2016). A tutorial review of\n",
      "       functional connectivity analysis methods and their interpretational\n",
      "       pitfalls. Frontiers in systems neuroscience, 9, 175.\n",
      "\u001b[31mFile:\u001b[39m           ~/Documents/GitHub/spectral_connectivity/spectral_connectivity/connectivity.py\n",
      "\u001b[31mType:\u001b[39m           type\n",
      "\u001b[31mSubclasses:\u001b[39m     "
     ]
    }
   ],
   "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": 15,
   "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",
    ")"
   ]
  },
  {
   "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": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "connectivity = Connectivity.from_multitaper(multitaper)"
   ]
  },
  {
   "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": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 30013, 2)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "power = connectivity.power()\n",
    "power.shape"
   ]
  },
  {
   "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": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30013,)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "connectivity.frequencies.shape"
   ]
  },
  {
   "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": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30013, 2)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "connectivity = Connectivity.from_multitaper(\n",
    "    multitaper, expectation_type=\"time_trials_tapers\", blocks=1\n",
    ")\n",
    "connectivity.power().shape"
   ]
  },
  {
   "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": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Frequency')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(connectivity.frequencies, connectivity.power())\n",
    "plt.ylabel(\"Power\")\n",
    "plt.xlabel(\"Frequency\")"
   ]
  },
  {
   "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": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30013, 2, 2)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coherence = connectivity.coherence_magnitude()\n",
    "coherence.shape"
   ]
  },
  {
   "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": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2a5429070>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(connectivity.frequencies, coherence[:, 0, 1])"
   ]
  },
  {
   "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": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 1001, 2, 2)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multitaper = Multitaper(\n",
    "    signal_with_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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Also note how this changes our frequency resolution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multitaper.frequency_resolution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can plot the coherence over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x2a114d3a0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "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()"
   ]
  },
  {
   "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": 26,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:focus + label {\n",
       "  border: 2px solid var(--xr-font-color0) !important;\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: \"►\";\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: \"▼\";\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: \"(\";\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: \")\";\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: \",\";\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  border-color: var(--xr-background-color-row-odd);\n",
       "  margin-bottom: 0;\n",
       "  padding-top: 2px;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "  border-color: var(--xr-background-color-row-even);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  border-top: 2px dotted var(--xr-background-color);\n",
       "  padding-bottom: 20px !important;\n",
       "  padding-top: 10px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in + label,\n",
       ".xr-var-data-in + label,\n",
       ".xr-index-data-in + label {\n",
       "  padding: 0 1px;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-data > pre,\n",
       ".xr-index-data > pre,\n",
       ".xr-var-data > table > tbody > tr {\n",
       "  background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
       "  color: var(--xr-font-color0);\n",
       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
       "  stroke-width: 0.8px;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;coherence_magnitude&#x27; (time: 30, frequency: 1001, source: 2,\n",
       "                                         target: 2)&gt; Size: 961kB\n",
       "array([[[[       nan, 0.45918036],\n",
       "         [0.45918036,        nan]],\n",
       "\n",
       "        [[       nan, 0.25961805],\n",
       "         [0.25961805,        nan]],\n",
       "\n",
       "        [[       nan, 0.34489352],\n",
       "         [0.34489352,        nan]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[       nan, 0.15043708],\n",
       "         [0.15043708,        nan]],\n",
       "\n",
       "        [[       nan, 0.2058857 ],\n",
       "         [0.2058857 ,        nan]],\n",
       "\n",
       "        [[       nan, 0.18139946],\n",
       "         [0.18139946,        nan]]],\n",
       "\n",
       "...\n",
       "\n",
       "       [[[       nan, 0.00740096],\n",
       "         [0.00740096,        nan]],\n",
       "\n",
       "        [[       nan, 0.01180062],\n",
       "         [0.01180062,        nan]],\n",
       "\n",
       "        [[       nan, 0.07439174],\n",
       "         [0.07439174,        nan]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[       nan, 0.15354869],\n",
       "         [0.15354869,        nan]],\n",
       "\n",
       "        [[       nan, 0.00108677],\n",
       "         [0.00108677,        nan]],\n",
       "\n",
       "        [[       nan, 0.00625096],\n",
       "         [0.00625096,        nan]]]], shape=(30, 1001, 2, 2))\n",
       "Coordinates:\n",
       "  * time       (time) float64 240B 0.0 2.0 4.0 6.0 8.0 ... 52.0 54.0 56.0 58.0\n",
       "  * frequency  (frequency) float64 8kB 0.0 0.5 1.0 1.5 ... 499.0 499.5 500.0\n",
       "  * source     (source) &lt;U1 8B &#x27;0&#x27; &#x27;1&#x27;\n",
       "  * target     (target) &lt;U1 8B &#x27;0&#x27; &#x27;1&#x27;\n",
       "Attributes: (12/16)\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_sampling_frequency:          1000\n",
       "    mt_start_time:                  0\n",
       "    mt_summarize_parameters:        &lt;bound method Multitaper.summarize_parame...\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' style='display:none'><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>: 1001</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-79511f69-43eb-4462-9e0d-c4a99e881bb9' class='xr-array-in' type='checkbox' checked><label for='section-79511f69-43eb-4462-9e0d-c4a99e881bb9' 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 0.4592 0.4592 nan nan 0.2596 ... nan nan 0.006251 0.006251 nan</span></div><div class='xr-array-data'><pre>array([[[[       nan, 0.45918036],\n",
       "         [0.45918036,        nan]],\n",
       "\n",
       "        [[       nan, 0.25961805],\n",
       "         [0.25961805,        nan]],\n",
       "\n",
       "        [[       nan, 0.34489352],\n",
       "         [0.34489352,        nan]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[       nan, 0.15043708],\n",
       "         [0.15043708,        nan]],\n",
       "\n",
       "        [[       nan, 0.2058857 ],\n",
       "         [0.2058857 ,        nan]],\n",
       "\n",
       "        [[       nan, 0.18139946],\n",
       "         [0.18139946,        nan]]],\n",
       "\n",
       "...\n",
       "\n",
       "       [[[       nan, 0.00740096],\n",
       "         [0.00740096,        nan]],\n",
       "\n",
       "        [[       nan, 0.01180062],\n",
       "         [0.01180062,        nan]],\n",
       "\n",
       "        [[       nan, 0.07439174],\n",
       "         [0.07439174,        nan]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[       nan, 0.15354869],\n",
       "         [0.15354869,        nan]],\n",
       "\n",
       "        [[       nan, 0.00108677],\n",
       "         [0.00108677,        nan]],\n",
       "\n",
       "        [[       nan, 0.00625096],\n",
       "         [0.00625096,        nan]]]], shape=(30, 1001, 2, 2))</pre></div></div></li><li class='xr-section-item'><input id='section-24681a6a-5820-43a9-8fef-bd4b8c939bde' class='xr-section-summary-in' type='checkbox'  checked><label for='section-24681a6a-5820-43a9-8fef-bd4b8c939bde' 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-0c771da3-edf4-46c0-a0b2-1c455fbfc546' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0c771da3-edf4-46c0-a0b2-1c455fbfc546' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07c5dd61-fb58-4430-bdfb-5a60beb0bcb2' class='xr-var-data-in' type='checkbox'><label for='data-07c5dd61-fb58-4430-bdfb-5a60beb0bcb2' 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 ... 499.0 499.5 500.0</div><input id='attrs-2cf52066-1aea-4f67-b3cc-24a11ac46f7a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2cf52066-1aea-4f67-b3cc-24a11ac46f7a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5761783-b204-4620-9a32-09bef7c1340c' class='xr-var-data-in' type='checkbox'><label for='data-b5761783-b204-4620-9a32-09bef7c1340c' 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. , ..., 499. , 499.5, 500. ], shape=(1001,))</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'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;0&#x27; &#x27;1&#x27;</div><input id='attrs-c353ea84-d771-4f14-bf03-f1c5fff11617' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c353ea84-d771-4f14-bf03-f1c5fff11617' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02d7d2cd-7b10-4c1c-8b6a-f4b4cff77e39' class='xr-var-data-in' type='checkbox'><label for='data-02d7d2cd-7b10-4c1c-8b6a-f4b4cff77e39' 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([&#x27;0&#x27;, &#x27;1&#x27;], dtype=&#x27;&lt;U1&#x27;)</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'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;0&#x27; &#x27;1&#x27;</div><input id='attrs-4781edbe-508d-43e6-80ec-2a0ec1aa4e6f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4781edbe-508d-43e6-80ec-2a0ec1aa4e6f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-297d0d30-19ac-4915-95f9-b0431f58b010' class='xr-var-data-in' type='checkbox'><label for='data-297d0d30-19ac-4915-95f9-b0431f58b010' 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([&#x27;0&#x27;, &#x27;1&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3da5fbb2-fa07-4b05-850d-0ba8529ffa56' class='xr-section-summary-in' type='checkbox'  ><label for='section-3da5fbb2-fa07-4b05-850d-0ba8529ffa56' class='xr-section-summary' >Indexes: <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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-6a33677b-a5a4-4121-897e-4408f08a0af3' class='xr-index-data-in' type='checkbox'/><label for='index-6a33677b-a5a4-4121-897e-4408f08a0af3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0.0,  2.0,  4.0,  6.0,  8.0, 10.0, 12.0, 14.0, 16.0, 18.0, 20.0, 22.0,\n",
       "       24.0, 26.0, 28.0, 30.0, 32.0, 34.0, 36.0, 38.0, 40.0, 42.0, 44.0, 46.0,\n",
       "       48.0, 50.0, 52.0, 54.0, 56.0, 58.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>frequency</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-b738f0a5-0490-43f4-8e08-d87170c7419b' class='xr-index-data-in' type='checkbox'/><label for='index-b738f0a5-0490-43f4-8e08-d87170c7419b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0.0,   0.5,   1.0,   1.5,   2.0,   2.5,   3.0,   3.5,   4.0,   4.5,\n",
       "       ...\n",
       "       495.5, 496.0, 496.5, 497.0, 497.5, 498.0, 498.5, 499.0, 499.5, 500.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;frequency&#x27;, length=1001))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>source</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-751efd0f-36f5-4f67-943d-99cd2fe702ad' class='xr-index-data-in' type='checkbox'/><label for='index-751efd0f-36f5-4f67-943d-99cd2fe702ad' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;0&#x27;, &#x27;1&#x27;], dtype=&#x27;object&#x27;, name=&#x27;source&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>target</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-526412c4-9992-42b0-a6a5-8691db5b4479' class='xr-index-data-in' type='checkbox'/><label for='index-526412c4-9992-42b0-a6a5-8691db5b4479' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;0&#x27;, &#x27;1&#x27;], dtype=&#x27;object&#x27;, name=&#x27;target&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a86fbc48-d258-4b72-996e-b559ff36286f' class='xr-section-summary-in' type='checkbox'  ><label for='section-a86fbc48-d258-4b72-996e-b559ff36286f' class='xr-section-summary' >Attributes: <span>(16)</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_summarize_parameters :</span></dt><dd>&lt;bound method Multitaper.summarize_parameters of Multitaper(sampling_frequency=1000, time_halfbandwidth_product=5,\n",
       "           time_window_duration=2.0, time_window_step=2.0,\n",
       "           detrend_type=&#x27;constant&#x27;, start_time=0, n_tapers=9)&gt;</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>"
      ],
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       "<xarray.DataArray 'coherence_magnitude' (time: 30, frequency: 1001, source: 2,\n",
       "                                         target: 2)> Size: 961kB\n",
       "array([[[[       nan, 0.45918036],\n",
       "         [0.45918036,        nan]],\n",
       "\n",
       "        [[       nan, 0.25961805],\n",
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       "\n",
       "        [[       nan, 0.34489352],\n",
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       "\n",
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       "\n",
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       "         [0.18139946,        nan]]],\n",
       "\n",
       "...\n",
       "\n",
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       "\n",
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       "         [0.07439174,        nan]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[       nan, 0.15354869],\n",
       "         [0.15354869,        nan]],\n",
       "\n",
       "        [[       nan, 0.00108677],\n",
       "         [0.00108677,        nan]],\n",
       "\n",
       "        [[       nan, 0.00625096],\n",
       "         [0.00625096,        nan]]]], shape=(30, 1001, 2, 2))\n",
       "Coordinates:\n",
       "  * time       (time) float64 240B 0.0 2.0 4.0 6.0 8.0 ... 52.0 54.0 56.0 58.0\n",
       "  * frequency  (frequency) float64 8kB 0.0 0.5 1.0 1.5 ... 499.0 499.5 500.0\n",
       "  * source     (source) <U1 8B '0' '1'\n",
       "  * target     (target) <U1 8B '0' '1'\n",
       "Attributes: (12/16)\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_sampling_frequency:          1000\n",
       "    mt_start_time:                  0\n",
       "    mt_summarize_parameters:        <bound method Multitaper.summarize_parame...\n",
       "    mt_time_halfbandwidth_product:  5\n",
       "    mt_time_window_duration:        2.0\n",
       "    mt_time_window_step:            2.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from spectral_connectivity import multitaper_connectivity\n",
    "\n",
    "coherence = multitaper_connectivity(\n",
    "    signal_with_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"
   ]
  },
  {
   "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": 27,
   "metadata": {},
   "outputs": [
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       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  border-top: 2px dotted var(--xr-background-color);\n",
       "  padding-bottom: 20px !important;\n",
       "  padding-top: 10px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in + label,\n",
       ".xr-var-data-in + label,\n",
       ".xr-index-data-in + label {\n",
       "  padding: 0 1px;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-data > pre,\n",
       ".xr-index-data > pre,\n",
       ".xr-var-data > table > tbody > tr {\n",
       "  background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
       "  color: var(--xr-font-color0);\n",
       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
       "  stroke-width: 0.8px;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;coherence_magnitude&#x27; (time: 30, frequency: 401, source: 2,\n",
       "                                         target: 2)&gt; Size: 385kB\n",
       "array([[[[       nan, 0.09630359],\n",
       "         [0.09630359,        nan]],\n",
       "\n",
       "        [[       nan, 0.06003863],\n",
       "         [0.06003863,        nan]],\n",
       "\n",
       "        [[       nan, 0.08824564],\n",
       "         [0.08824564,        nan]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[       nan, 0.06787789],\n",
       "         [0.06787789,        nan]],\n",
       "\n",
       "        [[       nan, 0.04864566],\n",
       "         [0.04864566,        nan]],\n",
       "\n",
       "        [[       nan, 0.03176439],\n",
       "         [0.03176439,        nan]]],\n",
       "\n",
       "...\n",
       "\n",
       "       [[[       nan, 0.04377205],\n",
       "         [0.04377205,        nan]],\n",
       "\n",
       "        [[       nan, 0.03295512],\n",
       "         [0.03295512,        nan]],\n",
       "\n",
       "        [[       nan, 0.04093662],\n",
       "         [0.04093662,        nan]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[       nan, 0.02097841],\n",
       "         [0.02097841,        nan]],\n",
       "\n",
       "        [[       nan, 0.0131043 ],\n",
       "         [0.0131043 ,        nan]],\n",
       "\n",
       "        [[       nan, 0.07531392],\n",
       "         [0.07531392,        nan]]]], shape=(30, 401, 2, 2))\n",
       "Coordinates:\n",
       "  * time       (time) float64 240B 0.0 2.0 4.0 6.0 8.0 ... 52.0 54.0 56.0 58.0\n",
       "  * frequency  (frequency) float64 3kB 100.0 100.5 101.0 ... 299.0 299.5 300.0\n",
       "  * source     (source) &lt;U1 8B &#x27;0&#x27; &#x27;1&#x27;\n",
       "  * target     (target) &lt;U1 8B &#x27;0&#x27; &#x27;1&#x27;\n",
       "Attributes: (12/16)\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_sampling_frequency:          1000\n",
       "    mt_start_time:                  0\n",
       "    mt_summarize_parameters:        &lt;bound method Multitaper.summarize_parame...\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' style='display:none'><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-055a0504-0354-419b-8b7b-5db1a089c722' class='xr-array-in' type='checkbox' checked><label for='section-055a0504-0354-419b-8b7b-5db1a089c722' 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 0.0963 0.0963 nan nan 0.06004 ... nan nan 0.07531 0.07531 nan</span></div><div class='xr-array-data'><pre>array([[[[       nan, 0.09630359],\n",
       "         [0.09630359,        nan]],\n",
       "\n",
       "        [[       nan, 0.06003863],\n",
       "         [0.06003863,        nan]],\n",
       "\n",
       "        [[       nan, 0.08824564],\n",
       "         [0.08824564,        nan]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[       nan, 0.06787789],\n",
       "         [0.06787789,        nan]],\n",
       "\n",
       "        [[       nan, 0.04864566],\n",
       "         [0.04864566,        nan]],\n",
       "\n",
       "        [[       nan, 0.03176439],\n",
       "         [0.03176439,        nan]]],\n",
       "\n",
       "...\n",
       "\n",
       "       [[[       nan, 0.04377205],\n",
       "         [0.04377205,        nan]],\n",
       "\n",
       "        [[       nan, 0.03295512],\n",
       "         [0.03295512,        nan]],\n",
       "\n",
       "        [[       nan, 0.04093662],\n",
       "         [0.04093662,        nan]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[       nan, 0.02097841],\n",
       "         [0.02097841,        nan]],\n",
       "\n",
       "        [[       nan, 0.0131043 ],\n",
       "         [0.0131043 ,        nan]],\n",
       "\n",
       "        [[       nan, 0.07531392],\n",
       "         [0.07531392,        nan]]]], shape=(30, 401, 2, 2))</pre></div></div></li><li class='xr-section-item'><input id='section-78f92f55-38e8-444d-bc4b-02a7f8671562' class='xr-section-summary-in' type='checkbox'  checked><label for='section-78f92f55-38e8-444d-bc4b-02a7f8671562' 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-544819c3-bed9-40a5-983b-2b3fc55ac2c7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-544819c3-bed9-40a5-983b-2b3fc55ac2c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43e98521-5ecd-4ac6-b3a8-d925792a3d8d' class='xr-var-data-in' type='checkbox'><label for='data-43e98521-5ecd-4ac6-b3a8-d925792a3d8d' 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-18bfee61-40af-41b1-bddc-f52e306f3f9c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-18bfee61-40af-41b1-bddc-f52e306f3f9c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea249706-53df-43b0-8d72-97682dca42cc' class='xr-var-data-in' type='checkbox'><label for='data-ea249706-53df-43b0-8d72-97682dca42cc' 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. ], shape=(401,))</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'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;0&#x27; &#x27;1&#x27;</div><input id='attrs-27e3f297-9f43-4945-9dba-fe2a675b2860' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-27e3f297-9f43-4945-9dba-fe2a675b2860' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43d0601e-c6c4-46a1-81c6-fc91e04a9004' class='xr-var-data-in' type='checkbox'><label for='data-43d0601e-c6c4-46a1-81c6-fc91e04a9004' 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([&#x27;0&#x27;, &#x27;1&#x27;], dtype=&#x27;&lt;U1&#x27;)</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'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;0&#x27; &#x27;1&#x27;</div><input id='attrs-3c6cd476-f75d-49f1-aaf8-651a8b94f92d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3c6cd476-f75d-49f1-aaf8-651a8b94f92d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2c0b85cc-a4b0-421b-9cdf-54e179f9bf6e' class='xr-var-data-in' type='checkbox'><label for='data-2c0b85cc-a4b0-421b-9cdf-54e179f9bf6e' 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([&#x27;0&#x27;, &#x27;1&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-95fc0600-17f1-483c-911f-08dc9e580fc6' class='xr-section-summary-in' type='checkbox'  ><label for='section-95fc0600-17f1-483c-911f-08dc9e580fc6' class='xr-section-summary' >Indexes: <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-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-fabf0954-4c56-4eb0-98ca-698d858216db' class='xr-index-data-in' type='checkbox'/><label for='index-fabf0954-4c56-4eb0-98ca-698d858216db' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0.0,  2.0,  4.0,  6.0,  8.0, 10.0, 12.0, 14.0, 16.0, 18.0, 20.0, 22.0,\n",
       "       24.0, 26.0, 28.0, 30.0, 32.0, 34.0, 36.0, 38.0, 40.0, 42.0, 44.0, 46.0,\n",
       "       48.0, 50.0, 52.0, 54.0, 56.0, 58.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>frequency</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-2e0209e8-a26b-4566-adad-3579448c94ac' class='xr-index-data-in' type='checkbox'/><label for='index-2e0209e8-a26b-4566-adad-3579448c94ac' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([100.0, 100.5, 101.0, 101.5, 102.0, 102.5, 103.0, 103.5, 104.0, 104.5,\n",
       "       ...\n",
       "       295.5, 296.0, 296.5, 297.0, 297.5, 298.0, 298.5, 299.0, 299.5, 300.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;frequency&#x27;, length=401))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>source</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-213c1f2a-79bd-4523-a2ea-c1586d1818c1' class='xr-index-data-in' type='checkbox'/><label for='index-213c1f2a-79bd-4523-a2ea-c1586d1818c1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;0&#x27;, &#x27;1&#x27;], dtype=&#x27;object&#x27;, name=&#x27;source&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>target</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-1ccbc5a0-09f2-47c0-90ef-c87c60c89c5f' class='xr-index-data-in' type='checkbox'/><label for='index-1ccbc5a0-09f2-47c0-90ef-c87c60c89c5f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;0&#x27;, &#x27;1&#x27;], dtype=&#x27;object&#x27;, name=&#x27;target&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7b585ce4-3a76-4bb4-bf19-1b992912b582' class='xr-section-summary-in' type='checkbox'  ><label for='section-7b585ce4-3a76-4bb4-bf19-1b992912b582' class='xr-section-summary' >Attributes: <span>(16)</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_summarize_parameters :</span></dt><dd>&lt;bound method Multitaper.summarize_parameters of Multitaper(sampling_frequency=1000, time_halfbandwidth_product=5,\n",
       "           time_window_duration=2.0, time_window_step=2.0,\n",
       "           detrend_type=&#x27;constant&#x27;, start_time=0, n_tapers=9)&gt;</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,\n",
       "                                         target: 2)> Size: 385kB\n",
       "array([[[[       nan, 0.09630359],\n",
       "         [0.09630359,        nan]],\n",
       "\n",
       "        [[       nan, 0.06003863],\n",
       "         [0.06003863,        nan]],\n",
       "\n",
       "        [[       nan, 0.08824564],\n",
       "         [0.08824564,        nan]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[       nan, 0.06787789],\n",
       "         [0.06787789,        nan]],\n",
       "\n",
       "        [[       nan, 0.04864566],\n",
       "         [0.04864566,        nan]],\n",
       "\n",
       "        [[       nan, 0.03176439],\n",
       "         [0.03176439,        nan]]],\n",
       "\n",
       "...\n",
       "\n",
       "       [[[       nan, 0.04377205],\n",
       "         [0.04377205,        nan]],\n",
       "\n",
       "        [[       nan, 0.03295512],\n",
       "         [0.03295512,        nan]],\n",
       "\n",
       "        [[       nan, 0.04093662],\n",
       "         [0.04093662,        nan]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[       nan, 0.02097841],\n",
       "         [0.02097841,        nan]],\n",
       "\n",
       "        [[       nan, 0.0131043 ],\n",
       "         [0.0131043 ,        nan]],\n",
       "\n",
       "        [[       nan, 0.07531392],\n",
       "         [0.07531392,        nan]]]], shape=(30, 401, 2, 2))\n",
       "Coordinates:\n",
       "  * time       (time) float64 240B 0.0 2.0 4.0 6.0 8.0 ... 52.0 54.0 56.0 58.0\n",
       "  * frequency  (frequency) float64 3kB 100.0 100.5 101.0 ... 299.0 299.5 300.0\n",
       "  * source     (source) <U1 8B '0' '1'\n",
       "  * target     (target) <U1 8B '0' '1'\n",
       "Attributes: (12/16)\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_sampling_frequency:          1000\n",
       "    mt_start_time:                  0\n",
       "    mt_summarize_parameters:        <bound method Multitaper.summarize_parame...\n",
       "    mt_time_halfbandwidth_product:  5\n",
       "    mt_time_window_duration:        2.0\n",
       "    mt_time_window_step:            2.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coherence.sel(frequency=slice(100, 300))"
   ]
  },
  {
   "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": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.plot.facetgrid.FacetGrid at 0x2a54dd730>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x600 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coherence.sel(frequency=slice(100, 300)).plot(\n",
    "    x=\"time\", y=\"frequency\", row=\"source\", col=\"target\"\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": {
  "jupytext": {
   "formats": "ipynb,py:percent"
  },
  "kernelspec": {
   "display_name": "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": "3.12.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}