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# -*- coding: utf-8 -*-
# ######### COPYRIGHT #########
# Credits
# #######
#
# Copyright(c) 2015-2025
# ----------------------
#
# * `LabEx Archimède <http://labex-archimede.univ-amu.fr/>`_
# * `Laboratoire d'Informatique Fondamentale <http://www.lif.univ-mrs.fr/>`_
# (now `Laboratoire d'Informatique et Systèmes <http://www.lis-lab.fr/>`_)
# * `Institut de Mathématiques de Marseille <http://www.i2m.univ-amu.fr/>`_
# * `Université d'Aix-Marseille <http://www.univ-amu.fr/>`_
#
# This software is a port from LTFAT 2.1.0 :
# Copyright (C) 2005-2025 Peter L. Soendergaard <peter@sonderport.dk>.
#
# Contributors
# ------------
#
# * Denis Arrivault <contact.dev_AT_lis-lab.fr>
# * Florent Jaillet <contact.dev_AT_lis-lab.fr>
#
# Description
# -----------
#
# ltfatpy is a partial Python port of the
# `Large Time/Frequency Analysis Toolbox <http://ltfat.sourceforge.net/>`_,
# a MATLAB®/Octave toolbox for working with time-frequency analysis and
# synthesis.
#
# Version
# -------
#
# * ltfatpy version = 1.1.2
# * LTFAT version = 2.1.0
#
# Licence
# -------
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
# ######### COPYRIGHT #########
""" Module of coefficient thresholding
Ported from ltfat_2.1.0/sigproc/thresh.m
.. moduleauthor:: Florent Jaillet
"""
from __future__ import print_function, division
import numpy as np
def thresh(xi, lamb, thresh_type='hard'):
"""Coefficient thresholding
- Usage:
| ``(xo, N) = thresh(xi, lamb)``
| ``(xo, N) = thresh(xi, lamb, thresh_type)``
- Input parameters:
:param numpy.ndarray xi: Input array
:param lamb: Threshold
:type lamb: float or numpy.ndarray
:param str thresh_type: Optional flag specifying the type of thresholding
(see possible values below)
- Output parameters:
:returns: ``(xo, N)``
:rtype: tuple
:var numpy.ndarray xo: Array of the same shape as **xi**
containing data from **xi** after thresholding
:var int N: Number of coefficients kept
``thresh(xi, lamb)`` will perform hard thresholding on **xi**, i.e. all
elements with absolute value less than scalar **lamb** will be set to zero.
``thresh(xi, lamb, 'soft')`` will perform soft thresholding on **xi**, i.e.
**lamb** will be substracted from the absolute value of every element of
**xi**.
The lamb parameter can also be a vector with number of elements
equal to ``xi.size`` or it can be a numpy array of the same shape
as **xi**. **lamb** is then applied element-wise and in a column major
order if **lamb** is a vector.
The parameter **thresh_type** can take the following values:
============ ======================================================
``'hard'`` Perform hard thresholding. This is the default.
``'wiener'`` Perform empirical Wiener shrinkage. This is in between
soft and hard thresholding.
``'soft'`` Perform soft thresholding.
============ ======================================================
The function ``wthresh`` in the Matlab Wavelet toolbox implements some of
the same functionality.
- Example:
The following code produces a plot to demonstrate the difference
between hard and soft thresholding for a simple linear input:
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from ltfatpy.sigproc.thresh import thresh
>>> t = np.linspace(-4, 4, 100)
>>> _ = plt.plot(t, thresh(t, 1., 'soft')[0], 'r',
... t, thresh(t, 1., 'hard')[0], '.b',
... t, thresh(t, 1., 'wiener')[0], '--g')
>>> _ = plt.legend(('Soft thresh.', 'Hard thresh.', 'Wiener thresh.'),
... loc='upper left')
>>> plt.show()
.. image:: images/thresh.png
:width: 700px
:alt: thresh image
:align: center
.. seealso::
:func:`~ltfatpy.sigproc.largestr.largestr`,
:func:`~ltfatpy.sigproc.largestn.largestn`
- References:
:cite:`lim1979enhancement,ghael1997improved`
"""
# Note: This function doesn't support the handling of sparse matrices
# available in the Octave version. Only full numpy arrays are supported in
# input and output.
error_msg = ('lamb must be a float or a numpy vector with '
'lamb.size == xi.size or whatever shape xi has such that '
'lamb.shape == xi.shape')
if not (isinstance(lamb, float) or isinstance(lamb, np.ndarray)):
raise TypeError(error_msg)
if isinstance(lamb, np.ndarray): # lamb is not scalar
if lamb.size != xi.size:
# lamb does not have the same number of elements
raise ValueError(error_msg)
# Reshape lamb if it is a vector
if lamb.shape != xi.shape:
lamb = lamb.reshape(xi.shape, order='F')
# Dense case (this Python port doesn't handle the sparse matrix case)
xo = np.zeros(xi.shape, dtype=xi.dtype)
# Create a mask with a value of 1 for non-zero elements. For full
# matrices, this is faster than the significance map.
if thresh_type == 'hard':
mask = abs(xi) >= lamb
N = np.count_nonzero(mask)
xo = xi * mask
elif thresh_type == 'soft':
# In the following lines, the +0 is significant: It turns
# -0 into +0, oh! the joy of numerics.
# Note: It is not sure that the "+0." needed in Octave is also needed
# in Python, but it is kept here for safety.
xa = abs(xi)-lamb
mask = xa >= 0.
xo = (mask*xa + 0.) * np.sign(xi)
N = np.count_nonzero(mask) - np.count_nonzero(xa == 0.)
elif thresh_type == 'wiener':
with np.errstate(divide='ignore'):
# NOTE: divide by 0 warnings are ignored because they are handled
# below
xa = lamb / abs(xi)
xa[np.isinf(xa)] = 0
xa = 1. - xa**2
mask = xa > 0
xo = xi * xa * mask
N = np.count_nonzero(mask)
return (xo, N)
if __name__ == '__main__': # pragma: no cover
import doctest
doctest.testmod()
|
