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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 N largest coefficients extraction
Ported from ltfat_2.1.0/sigproc/largestn.m
.. moduleauthor:: Florent Jaillet
"""
import numpy as np
from ltfatpy.sigproc.thresh import thresh
def largestn(xi, N, thresh_type='hard'):
"""Keep N largest coefficients
- Usage:
| ``(xo, Nout) = largestn(xi, N)``
| ``(xo, Nout) = largestn(xi, N, thresh_type)``
- Input parameters:
:param numpy.ndarray xi: Input array
:param int N: Number of kept coefficients
:param str thresh_type: Optional flag specifying the type of thresholding
(see possible values below)
- Output parameters:
:returns: ``(xo, Nout)``
:rtype: tuple
:var numpy.ndarray xo: Array of the same shape as **xi** keeping
the **N** largest coefficients
:var int Nout: Number of coefficients kept
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.
============ ======================================================
If the coefficients represents a signal expanded in an orthonormal
basis then this will be the best N-term approximation.
.. note::
If soft- or Wiener thresholding is selected, only ``N-1``
coefficients will actually be returned. This is caused by the Nth
coefficient being set to zero.
.. seealso::
:func:`~ltfatpy.sigproc.largestr.largestr`
- References:
:cite:`ma98`
"""
if not isinstance(N, int):
raise TypeError('N must be an int.')
# Sort the absolute values of the coefficients.
sxi = np.sort(abs(xi.flatten()))
# Find the coefficient sitting at position N through the array,
# and use this as a threshing value.
if N <= 0:
# Choose a thresh value higher than max
lamb = sxi[-1] + 1.
else:
lamb = sxi[-N]
xo, Nout = thresh(xi, lamb, thresh_type)
return (xo, Nout)
|
