base_dar.py 文件源码

def _remove_far_masked_data(self, mask, list_signals):
        """Remove unnecessary data which is masked
        and far (> self.ordar) from the unmasked data.
        """
        if mask is None:
            return list_signals

        selection = ~mask

        # convolution with a delay kernel,
        # so we keep the close points before the selection
        kernel = np.ones(self.ordar * 2 + 1)
        kernel[-self.ordar:] = 0.
        delayed_selection = fftconvolve(selection, kernel[None, :],
                                        mode='same')
        # remove numerical error from fftconvolve
        delayed_selection[np.abs(delayed_selection) < 1e-13] = 0.

        time_selection = delayed_selection.sum(axis=0) != 0
        epoch_selection = delayed_selection.sum(axis=1) != 0

        if not np.any(time_selection) or not np.any(epoch_selection):
            raise ValueError("The mask seems to hide everything.")

        output_signals = []
        for sig in list_signals:
            if sig is not None:
                sig = sig[..., epoch_selection, :]
                sig = sig[..., :, time_selection]
            output_signals.append(sig)

        return output_signals