timecorr.autofc

timecorr.autofc(data, timepoint_weights)

Compute auto-correlations within each subject’s data.

This function computes correlations within each subject’s own data, equivalent to standard within-subject dynamic correlations.

Parameters:

datalist of numpy.ndarray or numpy.ndarray

List of timeseries data matrices, each of shape (timepoints, features). If a single array is provided, it will be converted to a list.

timepoint_weightsnumpy.ndarray

T x T matrix of temporal weights for dynamic correlations

Returns:

list of numpy.ndarray

List of correlation matrices (one per subject), each showing within-subject correlations

Examples

>>> import timecorr as tc
>>> import numpy as np
>>> data = [np.random.randn(100, 10) for _ in range(3)]  # 3 subjects
>>> weights = tc.gaussian_weights(100, {'var': 10})
>>> auto_results = tc.autofc(data, weights)
>>> len(auto_results)  # 3