Exemple #1
0
 def gen_composite(self, condname, overwrite=False):
     """Create linear composite for condition.
     """
     #TODO overwrite
     data = (run.load(standardized=True, threshold=True) for run in self.iter_runs(condname))
     shape = self.iter_runs(condname).next().load().shape
     composite = sum_tc(data, shape=shape, standardize_out=False)
     cond = self.get_cond(condname)
     dset = cond.require_dataset('composite', shape=composite.shape, dtype=composite.dtype)
     dset[...] = composite
Exemple #2
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def isc_within_diff(A, B, standardized=False):
    """Contrast within-group subject-total correlation for A and B.

    This function operates on the timecourse data, so is slower
    than isc_corrmat_within_diff. Inputs may be multi-dimensional.
    The last dimension is used for correlations (e.g. time should be last).

    Arguments:
        A (list): List of timecourse data for each member of group A.
        B (list): Timecourses of same length as A.

    Returns:
        ndarray with isc for A minus isc for B.

    """

    isc = lambda L, ttl: nanmean([corcomposite(dat, ttl, standardized=standardized) for dat in L],
                                 axis=0)
    A_composite = sum_tc(A)
    B_composite = sum_tc(B)
    A_mean_isc = isc(A, A_composite)
    B_mean_isc = isc(B, B_composite)
    return A_mean_isc - B_mean_isc