2.1.6.1.5. sf_tools.signal.noise module

NOISE ROUTINES

This module contains methods for adding and removing noise from data.

Author:Samuel Farrens <samuel.farrens@gmail.com>
Version:1.2
Date:04/01/2017
sf_tools.signal.noise.add_noise(data, sigma=1.0, noise_type='gauss')[source]

Add noise to data

This method adds Gaussian or Poisson noise to the input data

Parameters:
  • data (np.ndarray, list or tuple) – Input data array
  • sigma (float or list, optional) – Standard deviation of the noise to be added (‘gauss’ only)
  • noise_type (str {'gauss', 'poisson'}) – Type of noise to be added (default is ‘gauss’)
Returns:
Return type:

np.ndarray input data with added noise
Raises:
  • ValueError – If noise_type is not ‘gauss’ or ‘poisson’
  • ValueError – If number of sigma values does not match the first dimension of the input data

Examples

>>> import numpy as np
>>> from sf_tools.signal.noise import add_noise
>>> x = np.arange(9).reshape(3, 3).astype(float)
>>> x
array([[ 0.,  1.,  2.],
       [ 3.,  4.,  5.],
       [ 6.,  7.,  8.]])
>>> np.random.seed(1)
>>> add_noise(x, noise_type='poisson')
array([[  0.,   2.,   2.],
       [  4.,   5.,  10.],
       [ 11.,  15.,  18.]])

>>> import numpy as np
>>> from sf_tools.signal.noise import add_noise
>>> x = np.zeros(5)
>>> x
array([ 0.,  0.,  0.,  0.,  0.])
>>> np.random.seed(1)
>>> add_noise(x, sigma=2.0)
array([ 3.24869073, -1.22351283, -1.0563435 , -2.14593724,  1.73081526])
sf_tools.signal.noise.thresh(data, threshold, threshold_type='hard')[source]

Threshold data

This method perfoms hard or soft thresholding on the input data

Parameters:
  • data (np.ndarray, list or tuple) – Input data array
  • threshold (float) – Threshold level
  • threshold_type (str {'hard', 'soft'}) – Type of noise to be added (default is ‘hard’)
Returns:
Return type:

np.ndarray thresholded data
Raises:

ValueError – If threshold_type is not ‘hard’ or ‘soft’

Notes

Implements one of the following two equations:

  • Hard Threshold
    \[\begin{split}\mathrm{HT}_\lambda(x) = \begin{cases} x & \text{if } |x|\geq\lambda \\ 0 & \text{otherwise} \end{cases}\end{split}\]
  • Soft Threshold
    \[\begin{split}\mathrm{ST}_\lambda(x) = \begin{cases} x-\lambda\text{sign}(x) & \text{if } |x|\geq\lambda \\ 0 & \text{otherwise} \end{cases}\end{split}\]

Examples

>>> import numpy as np
>>> from sf_tools.signal.noise import thresh
>>> np.random.seed(1)
>>> x = np.random.randint(-9, 9, 10)
>>> x
array([-4,  2,  3, -1,  0,  2, -4,  6, -9,  7])
>>> thresh(x, 4)
array([-4,  0,  0,  0,  0,  0, -4,  6, -9,  7])

>>> import numpy as np
>>> from sf_tools.signal.noise import thresh
>>> np.random.seed(1)
>>> x = np.random.ranf((3, 3))
>>> x
array([[  4.17022005e-01,   7.20324493e-01,   1.14374817e-04],
       [  3.02332573e-01,   1.46755891e-01,   9.23385948e-02],
       [  1.86260211e-01,   3.45560727e-01,   3.96767474e-01]])
>>> thresh(x, 0.2, threshold_type='soft')
array([[ 0.217022  ,  0.52032449, -0.        ],
       [ 0.10233257, -0.        , -0.        ],
       [-0.        ,  0.14556073,  0.19676747]])