/usr/lib/python2.7/dist-packages/pywt/_thresholding.py is in python-pywt 0.5.1-1.1ubuntu4.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 | # Copyright (c) 2006-2012 Filip Wasilewski <http://en.ig.ma/>
# Copyright (c) 2012-2016 The PyWavelets Developers
# <https://github.com/PyWavelets/pywt>
# See COPYING for license details.
"""
The thresholding helper module implements the most popular signal thresholding
functions.
"""
from __future__ import division, print_function, absolute_import
__all__ = ['threshold']
import numpy as np
def soft(data, value, substitute=0):
data = np.asarray(data)
magnitude = np.absolute(data)
sign = np.sign(data)
thresholded = (magnitude - value).clip(0) * sign
cond = np.less(magnitude, value)
return np.where(cond, substitute, thresholded)
def hard(data, value, substitute=0):
data = np.asarray(data)
cond = np.less(np.absolute(data), value)
return np.where(cond, substitute, data)
def greater(data, value, substitute=0):
data = np.asarray(data)
return np.where(np.less(data, value), substitute, data)
def less(data, value, substitute=0):
data = np.asarray(data)
return np.where(np.greater(data, value), substitute, data)
thresholding_options = {'soft': soft,
'hard': hard,
'greater': greater,
'less': less}
def 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. ])
"""
try:
return thresholding_options[mode](data, value, substitute)
except KeyError:
# Make sure error is always identical by sorting keys
keys = ("'{0}'".format(key) for key in
sorted(thresholding_options.keys()))
raise ValueError("The mode parameter only takes values from: {0}."
.format(', '.join(keys)))
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