Thresholding functions

The thresholding helper module implements the most popular signal thresholding functions.

Thresholding

pywt.threshold(data, value, mode='soft', substitute=0)

Thresholds the input data depending on the mode argument.

In soft thresholding, the data values where their absolute value is less than the value param are replaced with substitute. From the data values with absolute value greater or equal to the thresholding value, a quantity of (signum * value) is subtracted.

In hard thresholding, the data values where their absolute value is less than the value param are replaced with substitute. Data values with absolute value greater or equal to the thresholding value stay untouched.

In greater thresholding, the data is replaced with substitute where data is below the thresholding value. Greater data values pass untouched.

In less thresholding, the data is replaced with substitute where data is above the thresholding value. Less data values pass untouched.

Parameters:

data : array_like

Numeric data.

value : scalar

Thresholding value.

mode : {‘soft’, ‘hard’, ‘greater’, ‘less’}

Decides the type of thresholding to be applied on input data. Default is ‘soft’.

substitute : float, optional

Substitute value (default: 0).

Returns:

output : array

Thresholded array.

Examples

>>> import numpy as np
>>> import pywt
>>> data = np.linspace(1, 4, 7)
>>> data
array([ 1. ,  1.5,  2. ,  2.5,  3. ,  3.5,  4. ])
>>> pywt.threshold(data, 2, 'soft')
array([ 0. ,  0. ,  0. ,  0.5,  1. ,  1.5,  2. ])
>>> pywt.threshold(data, 2, 'hard')
array([ 0. ,  0. ,  2. ,  2.5,  3. ,  3.5,  4. ])
>>> pywt.threshold(data, 2, 'greater')
array([ 0. ,  0. ,  2. ,  2.5,  3. ,  3.5,  4. ])
>>> pywt.threshold(data, 2, 'less')
array([ 1. ,  1.5,  2. ,  0. ,  0. ,  0. ,  0. ])