timecorr.gaussian_weights

timecorr.gaussian_weights(T, params={'var': 100})

Generate Gaussian weighting function for dynamic correlations.

This function creates a time-varying weighting matrix where each timepoint receives weights according to a Gaussian distribution centered at that timepoint. Useful for computing smooth dynamic correlations.

Parameters:

Tint

Number of timepoints in the timeseries

paramsdict, optional

Dictionary containing Gaussian parameters. Default: {‘var’: 100} - ‘var’ : float, variance of the Gaussian kernel

Returns:

numpy.ndarray

T x T matrix of Gaussian weights, where weights[i,j] represents the weight given to timepoint j when computing correlations at timepoint i

Examples

>>> import timecorr as tc
>>> weights = tc.gaussian_weights(50, {'var': 10})
>>> print(weights.shape)  # (50, 50)