Flake fixes

from scipy import signal, stats

from scipy.signal import signaltools

from scipy.signal.windows import dpss

from scipy.signal.spectral import _triage_segments

logging.basicConfig(level=logging.DEBUG)

    nenbw = len(win) * (s2 / s1**2)

    enbw = fs * (s2 / s1**2)

    return nenbw, enbw

def _set_detrend(detrend, axis):

    """Set a detrending function to be applied to STFT windows prior to FFT.

            # Set a rectangular boxcar of 1s as the window if None is requested

            # - keeps following code cleaner.

            self.window_type = 'boxcar'

        self.window, self.nperseg = signal.spectral._triage_segments(self.window_type, self.nperseg, input_length=self.input_len)

        self.window, self.nperseg = _triage_segments(self.window_type, self.nperseg, input_length=self.input_len)

        self.nfft = _set_nfft(self.nfft, self.nperseg)

        self.noverlap = _set_noverlap(self.noverlap, self.nperseg)

        self.nstep = self.nperseg - self.noverlap

        p = np.nanmean(p, axis=0).real

    elif config.average == 'median':

        p = np.nanmedian(p, axis=0).real

    elif config.average == None:

    elif config.average is None:

        pass

    else:

        msg = "'average' value of '{0}' not recognised - please use 'mean' or 'median'"

                msg = "Regressor '{0}' shape ({1}) not matched to input data length ({2})"

                raise ValueError(msg.format(key, len(cov[key]), input_len))

        ret = cov  # pass back out

    elif cov == None:

    elif cov is None:

        ret = {}  # No regressors defined

    else:

        # Check array_like inputs

    else:

        # Sometimes this is stored in a sub model...

        betas = fit_method.estimator_.coef_.T

    extras = (fit_method)

    # Compute COPES and VARCOPES

    copes = contrasts.dot(betas)