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)[source]¶ 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.
- mmix_wt ({S, 2C}
-
eimask
(dd, ees=None)[source]¶ 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)[source]¶ 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
- mmix_wt ({S, 2C}