tedana.decomposition._utils

Utility functions for tedana decomposition

Functions

dwtmat(mmix)	Wavelet transform data using order 2 Daubechies wavelet.
eimask(dd[, ees])	Returns mask for data between [0.001, 5] * 98th percentile of dd
idwtmat(mmix_wt, n_coefs_approx)	Invert wavelet transform data with order 2 Daubechies wavelet.

dwtmat(mmix)

Wavelet transform data using order 2 Daubechies wavelet.

Parameters:mmix ({S, T} numpy.ndarray) – Data to wavelet transform. Returns:

• mmix_wt ({S, 2C} numpy.ndarray) – Wavelet-transformed data. Approximation and detail coefficients are horizontally concatenated for each row in mmix.
• n_coefs_approx (int) – The number of approximation coefficients from the wavelet transformation. Used to split the wavelet-transformed data into approximation and detail coefficients.

eimask(dd, ees=None)

Returns mask for data between [0.001, 5] * 98th percentile of dd

Parameters:

• dd ((S x E x T) array_like) – Input data, where S is samples, E is echos, and T is time
• ees ((N,) list) – Indices of echos to assess from dd in calculating output

Returns:

imask – Boolean array denoting

Return type:

(S x N) numpy.ndarray

idwtmat(mmix_wt, n_coefs_approx)

Invert wavelet transform data with order 2 Daubechies wavelet.

Parameters:

• mmix_wt ({S, 2C} numpy.ndarray) – Wavelet-transformed data. Approximation and detail coefficients are horizontally concatenated for each row in mmix.
• n_coefs_approx (int) – The number of approximation coefficients from the wavelet transformation. Used to split the wavelet-transformed data into approximation and detail coefficients.

Returns:

mmix_iwt – Inverse wavelet-transformed data.

Return type:

{S, T} numpy.ndarray