# Buggy code
def stft(
    x,
    fs=1.0,
    window="hann",
    nperseg=256,
    noverlap=None,
    nfft=None,
    detrend=False,
    return_onesided=True,
    boundary="zeros",
    padded=True,
    axis=-1,
):
    r"""
    Compute the Short Time Fourier Transform (STFT).

    STFTs can be used as a way of quantifying the change of a
    nonstationary signal's frequency and phase content over time.

    Parameters
    ----------
    x : array_like
        Time series of measurement values
    fs : float, optional
        Sampling frequency of the `x` time series. Defaults to 1.0.
    window : str or tuple or array_like, optional
        Desired window to use. If `window` is a string or tuple, it is
        passed to `get_window` to generate the window values, which are
        DFT-even by default. See `get_window` for a list of windows and
        required parameters. If `window` is array_like it will be used
        directly as the window and its length must be nperseg. Defaults
        to a Hann window.
    nperseg : int, optional
        Length of each segment. Defaults to 256.
    noverlap : int, optional
        Number of points to overlap between segments. If `None`,
        ``noverlap = nperseg // 2``. Defaults to `None`. When
        specified, the COLA constraint must be met (see Notes below).
    nfft : int, optional
        Length of the FFT used, if a zero padded FFT is desired. If
        `None`, the FFT length is `nperseg`. Defaults to `None`.
    detrend : str or function or `False`, optional
        Specifies how to detrend each segment. If `detrend` is a
        string, it is passed as the `type` argument to the `detrend`
        function. If it is a function, it takes a segment and returns a
        detrended segment. If `detrend` is `False`, no detrending is
        done. Defaults to `False`.
    return_onesided : bool, optional
        If `True`, return a one-sided spectrum for real data. If
        `False` return a two-sided spectrum. Note that for complex
        data, a two-sided spectrum is always returned. Defaults to
        `True`.
    boundary : str or None, optional
        Specifies whether the input signal is extended at both ends, and
        how to generate the new values, in order to center the first
        windowed segment on the first input point. This has the benefit
        of enabling reconstruction of the first input point when the
        employed window function starts at zero. Valid options are
        ``['even', 'odd', 'constant', 'zeros', None]``. Defaults to
        'zeros', for zero padding extension. I.e. ``[1, 2, 3, 4]`` is
        extended to ``[0, 1, 2, 3, 4, 0]`` for ``nperseg=3``.
    padded : bool, optional
        Specifies whether the input signal is zero-padded at the end to
        make the signal fit exactly into an integer number of window
        segments, so that all of the signal is included in the output.
        Defaults to `True`. Padding occurs after boundary extension, if
        `boundary` is not `None`, and `padded` is `True`, as is the
        default.
    axis : int, optional
        Axis along which the STFT is computed; the default is over the
        last axis (i.e. ``axis=-1``).

    Returns
    -------
    f : ndarray
        Array of sample frequencies.
    t : ndarray
        Array of segment times.
    Zxx : ndarray
        STFT of `x`. By default, the last axis of `Zxx` corresponds
        to the segment times.

    See Also
    --------
    istft: Inverse Short Time Fourier Transform
    check_COLA: Check whether the Constant OverLap Add (COLA) constraint
                is met
    welch: Power spectral density by Welch's method.
    spectrogram: Spectrogram by Welch's method.
    csd: Cross spectral density by Welch's method.
    lombscargle: Lomb-Scargle periodogram for unevenly sampled data

    Notes
    -----
    In order to enable inversion of an STFT via the inverse STFT in
    `istft`, the signal windowing must obey the constraint of "Constant
    OverLap Add" (COLA), and the input signal must have complete
    windowing coverage (i.e. ``(x.shape[axis] - nperseg) %
    (nperseg-noverlap) == 0``). The `padded` argument may be used to
    accomplish this.

    The COLA constraint ensures that every point in the input data is
    equally weighted, thereby avoiding aliasing and allowing full
    reconstruction. Whether a choice of `window`, `nperseg`, and
    `noverlap` satisfy this constraint can be tested with
    `check_COLA`.

    .. versionadded:: 0.19.0

    References
    ----------
    .. [1] Oppenheim, Alan V., Ronald W. Schafer, John R. Buck
           "Discrete-Time Signal Processing", Prentice Hall, 1999.
    .. [2] Daniel W. Griffin, Jae S. Limdt "Signal Estimation from
           Modified Short Fourier Transform", IEEE 1984,
           10.1109/TASSP.1984.1164317

    Examples
    --------
    >>> from scipy import signal
    >>> import matplotlib.pyplot as plt

    Generate a test signal, a 2 Vrms sine wave whose frequency is slowly
    modulated around 3kHz, corrupted by white noise of exponentially
    decreasing magnitude sampled at 10 kHz.

    >>> fs = 10e3
    >>> N = 1e5
    >>> amp = 2 * np.sqrt(2)
    >>> noise_power = 0.01 * fs / 2
    >>> time = np.arange(N) / float(fs)
    >>> mod = 500*np.cos(2*np.pi*0.25*time)
    >>> carrier = amp * np.sin(2*np.pi*3e3*time + mod)
    >>> noise = np.random.normal(scale=np.sqrt(noise_power),
    ...                          size=time.shape)
    >>> noise *= np.exp(-time/5)
    >>> x = carrier + noise

    Compute and plot the STFT's magnitude.

    >>> f, t, Zxx = signal.stft(x, fs, nperseg=1000)
    >>> plt.pcolormesh(t, f, np.abs(Zxx), vmin=0, vmax=amp)
    >>> plt.title('STFT Magnitude')
    >>> plt.ylabel('Frequency [Hz]')
    >>> plt.xlabel('Time [sec]')
    >>> plt.show()
    """

    freqs, time, Zxx = _spectral_helper(
        x,
        x,
        fs,
        window,
        nperseg,
        noverlap,
        nfft,
        detrend,
        return_onesided,
        scaling="spectrum",
        axis=axis,
        mode="stft",
        boundary=boundary,
        padded=padded,
    )

    return freqs, time, Zxx


# Traceback (ValueError)
Traceback (most recent call last):
File "eda_cola.py", line 11, in <module>
_, y = signal.istft(Zxx, window=w, nperseg=nperseg, noverlap=noverlap)
File "C:\Users\bruce\Anaconda3\lib\site-packages\scipy\signal\spectral.py", line 1053, in istft
raise ValueError('Window, STFT shape and noverlap do not satisfy the '
ValueError: Window, STFT shape and noverlap do not satisfy the COLA constraint.

# Fixed code
def stft(
    x,
    fs=1.0,
    window="hann",
    nperseg=256,
    noverlap=None,
    nfft=None,
    detrend=False,
    return_onesided=True,
    boundary="zeros",
    padded=True,
    axis=-1,
):
    r"""
    Compute the Short Time Fourier Transform (STFT).

    STFTs can be used as a way of quantifying the change of a
    nonstationary signal's frequency and phase content over time.

    Parameters
    ----------
    x : array_like
        Time series of measurement values
    fs : float, optional
        Sampling frequency of the `x` time series. Defaults to 1.0.
    window : str or tuple or array_like, optional
        Desired window to use. If `window` is a string or tuple, it is
        passed to `get_window` to generate the window values, which are
        DFT-even by default. See `get_window` for a list of windows and
        required parameters. If `window` is array_like it will be used
        directly as the window and its length must be nperseg. Defaults
        to a Hann window.
    nperseg : int, optional
        Length of each segment. Defaults to 256.
    noverlap : int, optional
        Number of points to overlap between segments. If `None`,
        ``noverlap = nperseg // 2``. Defaults to `None`. When
        specified, the COLA constraint must be met (see Notes below).
    nfft : int, optional
        Length of the FFT used, if a zero padded FFT is desired. If
        `None`, the FFT length is `nperseg`. Defaults to `None`.
    detrend : str or function or `False`, optional
        Specifies how to detrend each segment. If `detrend` is a
        string, it is passed as the `type` argument to the `detrend`
        function. If it is a function, it takes a segment and returns a
        detrended segment. If `detrend` is `False`, no detrending is
        done. Defaults to `False`.
    return_onesided : bool, optional
        If `True`, return a one-sided spectrum for real data. If
        `False` return a two-sided spectrum. Note that for complex
        data, a two-sided spectrum is always returned. Defaults to
        `True`.
    boundary : str or None, optional
        Specifies whether the input signal is extended at both ends, and
        how to generate the new values, in order to center the first
        windowed segment on the first input point. This has the benefit
        of enabling reconstruction of the first input point when the
        employed window function starts at zero. Valid options are
        ``['even', 'odd', 'constant', 'zeros', None]``. Defaults to
        'zeros', for zero padding extension. I.e. ``[1, 2, 3, 4]`` is
        extended to ``[0, 1, 2, 3, 4, 0]`` for ``nperseg=3``.
    padded : bool, optional
        Specifies whether the input signal is zero-padded at the end to
        make the signal fit exactly into an integer number of window
        segments, so that all of the signal is included in the output.
        Defaults to `True`. Padding occurs after boundary extension, if
        `boundary` is not `None`, and `padded` is `True`, as is the
        default.
    axis : int, optional
        Axis along which the STFT is computed; the default is over the
        last axis (i.e. ``axis=-1``).

    Returns
    -------
    f : ndarray
        Array of sample frequencies.
    t : ndarray
        Array of segment times.
    Zxx : ndarray
        STFT of `x`. By default, the last axis of `Zxx` corresponds
        to the segment times.

    See Also
    --------
    istft: Inverse Short Time Fourier Transform
    check_COLA: Check whether the Constant OverLap Add (COLA) constraint
                is met
    check_NOLA: Check whether the Nonzero Overlap Add (NOLA) constraint is met
    welch: Power spectral density by Welch's method.
    spectrogram: Spectrogram by Welch's method.
    csd: Cross spectral density by Welch's method.
    lombscargle: Lomb-Scargle periodogram for unevenly sampled data

    Notes
    -----
    In order to enable inversion of an STFT via the inverse STFT in
    `istft`, the signal windowing must obey the constraint of "Nonzero
    OverLap Add" (NOLA), and the input signal must have complete
    windowing coverage (i.e. ``(x.shape[axis] - nperseg) %
    (nperseg-noverlap) == 0``). The `padded` argument may be used to
    accomplish this.

    Given a time-domain signal :math:`x[n]`, a window :math:`w[n]`, and a hop
    size :math:`H` = `nperseg - noverlap`, the windowed frame at time index
    :math:`t` is given by

    .. math:: x_{t}[n]=x[n]w[n-tH]

    The overlap-add (OLA) reconstruction equation is given by

    .. math:: x[n]=\frac{\sum_{t}x_{t}[n]w[n-tH]}{\sum_{t}w^{2}[n-tH]}

    The NOLA constraint ensures that every normalization term that appears
    in the denomimator of the OLA reconstruction equation is nonzero. Whether a
    choice of `window`, `nperseg`, and `noverlap` satisfy this constraint can
    be tested with `check_NOLA`.

    .. versionadded:: 0.19.0

    References
    ----------
    .. [1] Oppenheim, Alan V., Ronald W. Schafer, John R. Buck
           "Discrete-Time Signal Processing", Prentice Hall, 1999.
    .. [2] Daniel W. Griffin, Jae S. Lim "Signal Estimation from
           Modified Short Fourier Transform", IEEE 1984,
           10.1109/TASSP.1984.1164317

    Examples
    --------
    >>> from scipy import signal
    >>> import matplotlib.pyplot as plt

    Generate a test signal, a 2 Vrms sine wave whose frequency is slowly
    modulated around 3kHz, corrupted by white noise of exponentially
    decreasing magnitude sampled at 10 kHz.

    >>> fs = 10e3
    >>> N = 1e5
    >>> amp = 2 * np.sqrt(2)
    >>> noise_power = 0.01 * fs / 2
    >>> time = np.arange(N) / float(fs)
    >>> mod = 500*np.cos(2*np.pi*0.25*time)
    >>> carrier = amp * np.sin(2*np.pi*3e3*time + mod)
    >>> noise = np.random.normal(scale=np.sqrt(noise_power),
    ...                          size=time.shape)
    >>> noise *= np.exp(-time/5)
    >>> x = carrier + noise

    Compute and plot the STFT's magnitude.

    >>> f, t, Zxx = signal.stft(x, fs, nperseg=1000)
    >>> plt.pcolormesh(t, f, np.abs(Zxx), vmin=0, vmax=amp)
    >>> plt.title('STFT Magnitude')
    >>> plt.ylabel('Frequency [Hz]')
    >>> plt.xlabel('Time [sec]')
    >>> plt.show()
    """

    freqs, time, Zxx = _spectral_helper(
        x,
        x,
        fs,
        window,
        nperseg,
        noverlap,
        nfft,
        detrend,
        return_onesided,
        scaling="spectrum",
        axis=axis,
        mode="stft",
        boundary=boundary,
        padded=padded,
    )

    return freqs, time, Zxx