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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 #########
"""Test of the largestn function
.. moduleauthor:: Florent Jaillet
"""
from __future__ import print_function, division
import unittest
import numpy as np
from numpy.testing import assert_array_equal
from copy import deepcopy
from ltfatpy.sigproc.largestn import largestn
from ltfatpy.tests.datasets.read_ref_mat import read_ref_mat
from ltfatpy.tests.datasets.get_dataset_path import get_dataset_path
# NOTE: The reference values used in the tests correspond to results
# obtained with Octave using ltfat 2.1.0
class TestLargestn(unittest.TestCase):
# Called before the tests.
def setUp(self):
print('\nStart TestLargestn')
# Called after the tests.
def tearDown(self):
print('Test done')
def test_known(self):
"""Checking largestn on some known results taken from Octave
"""
filename = get_dataset_path('largestn_ref.mat')
data = read_ref_mat(filename)
for inputs, outputs in data:
xo, Nout = largestn(**inputs)
msg = ('Wrong value in output xo of largestn with inputs ' +
str(inputs))
assert_array_equal(xo, outputs[0], msg)
msg = ('Wrong value in output Nout of largestn with inputs ' +
str(inputs))
self.assertEqual(Nout, outputs[1], msg)
def test_shape(self):
"""Check that input and output shapes match
"""
shapes = ((4,), (4, 3), (4, 3, 2))
thresh_types = ('hard', 'wiener', 'soft')
inputs = {}
inputs['N'] = 2
for shape in shapes:
inputs['xi'] = np.random.random(shape)
for thresh_type in thresh_types:
inputs['thresh_type'] = thresh_type
xo = largestn(**inputs)[0]
msg = ('Wrong shape in output xo of largestn with inputs ' +
str(inputs))
self.assertEqual(xo.shape, shape, msg)
def test_default_param(self):
"""Check that the default value for thresh_type is right
"""
inputs_def = {}
inputs_def['xi'] = np.random.random((4, 3))
inputs_def['N'] = 5
inputs_hard = deepcopy(inputs_def)
inputs_hard['thresh_type'] = 'hard'
xo_def = largestn(**inputs_def)[0]
xo_hard = largestn(**inputs_hard)[0]
msg = ('Wrong default value for thresh_type in largestn when '
'comparing results with inputs ' + str(inputs_def) + ' and ' +
str(inputs_hard))
assert_array_equal(xo_def, xo_hard, msg)
def test_type(self):
"""Check that the type for N is correctly checked
"""
xi = np.ones((4, 3))
# test with N of wrong type (str, when int is expected)
N = 'test'
self.assertRaises(TypeError, largestn, xi, N)
# test with N of wrong type (float, when int is expected)
N = 1.
self.assertRaises(TypeError, largestn, xi, N)
if __name__ == '__main__':
suite = unittest.TestLoader().loadTestsFromTestCase(TestLargestn)
unittest.TextTestRunner(verbosity=2).run(suite)
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