_tuningcurve.py 文件源码

def _compute_ratemap(self, min_duration=None):

        if min_duration is None:
            min_duration = self._min_duration

        ext = self.trans_func(self._extern, at=self._bst.bin_centers)

        ext_bin_idx = np.digitize(ext, self.bins, True)
        # make sure that all the events fit between extmin and extmax:
        # TODO: this might rather be a warning, but it's a pretty serious warning...
        if ext_bin_idx.max() > self.n_bins:
            raise ValueError("ext values greater than 'ext_max'")
        if ext_bin_idx.min() == 0:
            raise ValueError("ext values less than 'ext_min'")

        ratemap = np.zeros((self.n_units, self.n_bins))

        for tt, bidx in enumerate(ext_bin_idx):
            ratemap[:,bidx-1] += self._bst.data[:,tt]

        # apply minimum observation duration
        for uu in range(self.n_units):
            ratemap[uu][self.occupancy*self._bst.ds < min_duration] = 0

        return ratemap / self._bst.ds