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Stationary Wavelet Transform¶
Stationary Wavelet Transform (SWT), also known as Undecimated wavelet transform or Algorithme à trous is a translationinvariance modification of the Discrete Wavelet Transform that does not decimate coefficients at every transformation level.
Multilevel swt
¶

pywt.
swt
(data, wavelet, level=None, start_level=0)¶ Performs multilevel Stationary Wavelet Transform.
Parameters: data :
Input signal
wavelet :
Wavelet to use (Wavelet object or name)
level : int, optional
Transform level.
start_level : int, optional
The level at which the decomposition will begin (it allows one to skip a given number of transform steps and compute coefficients starting from start_level) (default: 0)
Returns: coeffs : list
List of approximation and details coefficients pairs in order similar to wavedec function:
[(cAn, cDn), ..., (cA2, cD2), (cA1, cD1)]
where
n
equals input parameter level.If m = start_level is given, then the beginning m steps are skipped:
[(cAm+n, cDm+n), ..., (cAm+1, cDm+1), (cAm, cDm)]
Multilevel swt2
¶

pywt.
swt2
(data, wavelet, level, start_level=0)¶ 2D Stationary Wavelet Transform.
Parameters: data : ndarray
2D array with input data
wavelet : Wavelet object or name string
Wavelet to use
level : int
How many decomposition steps to perform
start_level : int, optional
The level at which the decomposition will start (default: 0)
Returns: coeffs : list
Approximation and details coefficients:
[ (cA_n, (cH_n, cV_n, cD_n) ), (cA_n+1, (cH_n+1, cV_n+1, cD_n+1) ), ..., (cA_n+level, (cH_n+level, cV_n+level, cD_n+level) ) ]
where cA is approximation, cH is horizontal details, cV is vertical details, cD is diagonal details and n is start_level.
Maximum decomposition level  swt_max_level
¶

pywt.
swt_max_level
(input_len)¶ Calculates the maximum level of Stationary Wavelet Transform for data of given length.
Parameters: input_len : int
Input data length.
Returns: max_level : int
Maximum level of Stationary Wavelet Transform for data of given length.