Method sum

mne/time_frequency/csd.py:201–273

Calculate the sum CSD in the given frequency range(s). If the exact given frequencies are not available, the nearest frequencies will be chosen. Parameters ---------- fmin : float | list of float | None Lower bound of the frequency range in Hertz

(self, fmin=None, fmax=None)

        )

    def sum(self, fmin=None, fmax=None):
        """Calculate the sum CSD in the given frequency range(s).

        If the exact given frequencies are not available, the nearest
        frequencies will be chosen.

        Parameters
        ----------
        fmin : float | list of float | None
            Lower bound of the frequency range in Hertz. Defaults to the lowest
            frequency available. When a list of frequencies is given, these are
            used as the lower bounds (inclusive) of frequency bins and the sum
            is taken for each bin.
        fmax : float | list of float | None
            Upper bound of the frequency range in Hertz. Defaults to the
            highest frequency available. When a list of frequencies is given,
            these are used as the upper bounds (inclusive) of frequency bins
            and the sum is taken for each bin.

        Returns
        -------
        csd : instance of CrossSpectralDensity
            The CSD matrix, summed across the given frequency range(s).
        """
        if self._is_sum:
            raise RuntimeError(
                "This CSD matrix already represents a mean or sum across frequencies."
            )

        # Deal with the various ways in which fmin and fmax can be specified
        if fmin is None and fmax is None:
            fmin = [self.frequencies[0]]
            fmax = [self.frequencies[-1]]
        else:
            if isinstance(fmin, numbers.Number):
                fmin = [fmin]
            if isinstance(fmax, numbers.Number):
                fmax = [fmax]
            if fmin is None:
                fmin = [self.frequencies[0]] * len(fmax)
            if fmax is None:
                fmax = [self.frequencies[-1]] * len(fmin)

        if any(fmin_ > fmax_ for fmin_, fmax_ in zip(fmin, fmax)):
            raise ValueError(
                "Some lower bounds are higher than the corresponding upper bounds."
            )

        # Find the index of the lower bound of each frequency bin
        fmin_inds = [self._get_frequency_index(f) for f in fmin]
        fmax_inds = [self._get_frequency_index(f) + 1 for f in fmax]

        if len(fmin_inds) != len(fmax_inds):
            raise ValueError("The length of fmin does not match the length of fmax.")

        # Sum across each frequency bin
        n_bins = len(fmin_inds)
        new_data = np.zeros((self._data.shape[0], n_bins), dtype=self._data.dtype)