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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 dgt function
.. moduleauthor:: Denis Arrivault
"""
from __future__ import print_function, division
import unittest
import numpy as np
from ltfatpy.gabor.dgt import dgt
from ltfatpy.gabor.dgtreal import dgtreal
from ltfatpy.tests.datasets.read_dgt_signal_ex_mat import DgtSignals
from ltfatpy.fourier.pgauss import pgauss
from ltfatpy.tests.datasets.get_dataset_path import get_dataset_path
class TestDgt(unittest.TestCase):
# Called before the tests.
def setUp(self):
self.filename = get_dataset_path('dgt_signal_ex.mat')
# Called after the tests.
def tearDown(self):
print('Test done')
def test_default(self):
""" Comparing results with Matlab generated ones
"""
dgs = DgtSignals(self.filename)
(TYPE, PHASETYPE, L, W, a, M, gl, SIGNAL, WINDOW, DGT, DUAL_WINDOW,
IDGT) = dgs.read_next_signal()
while TYPE != '':
mess = "\n\nTYPE = " + TYPE + "\nPHASETYPE = " + PHASETYPE
mess += "\nL = " + str(L) + "\nW = " + str(W) + "\na = " + str(a)
mess += "\nM = " + str(M) + "\ngl = " + str(gl) + "\nSIGNAL =\n"
mess += str(SIGNAL) + "\nWINDOW =\n" + str(WINDOW)
mess += "\nDGT\n" + str(DGT) + "\nDUAL_WINDOW\n"
mess += str(DUAL_WINDOW) + "\nIDGT\n" + str(IDGT)
if (TYPE == "REAL"):
pydgt = dgtreal(SIGNAL, WINDOW, a, M, L, PHASETYPE)[0]
else:
pydgt = dgt(SIGNAL, WINDOW, a, M, L, PHASETYPE)[0]
mess += "\npydgt = \n" + str(pydgt)
self.assertTrue(np.linalg.norm(pydgt-DGT) <= 1e-10, mess)
(TYPE, PHASETYPE, L, W, a, M, gl, SIGNAL, WINDOW, DGT, DUAL_WINDOW,
IDGT) = dgs.read_next_signal()
def test_exceptions_cplx(self):
f = np.random.random_sample((128))
a = 10
M = 40
self.assertRaises(ValueError, dgt, f, "Gauss", a, M, 100)
L = 160
(_, Ls, g) = dgt(f, "Gauss", a, M)
self.assertEqual(Ls, 128)
gs = pgauss(160, a*M/L)[0]
np.testing.assert_array_almost_equal(g, gs, a)
self.assertRaises(ValueError, dgt, f, g, a, M, L, "timeinverse")
g = np.random.random_sample((200))
self.assertRaises(ValueError, dgt, f, g, a, M)
f = np.random.random_sample((1, 1, 128))
c = dgt(f, "Gauss", a, M)[0]
self.assertEqual(c.shape, (40, 16))
def test_exceptions_real(self):
f = np.random.random_sample((128))
a = 10
M = 40
self.assertRaises(ValueError, dgtreal, f, "Gauss", 10, 40, 100)
L = 160
(_, Ls, g) = dgtreal(f, "Gauss", 10, 40)
self.assertEqual(Ls, 128)
gs = pgauss(160, a*M/L)[0]
np.testing.assert_array_almost_equal(g, gs, 10)
self.assertRaises(ValueError, dgtreal, f, g, a, M, L, "timeinverse")
g = np.array(np.random.random_sample((40)) + 1j *
np.random.random_sample((40)))
self.assertRaises(ValueError, dgtreal, f, g, a, M)
if __name__ == '__main__':
suite = unittest.TestLoader().loadTestsFromTestCase(TestDgt)
unittest.TextTestRunner(verbosity=2).run(suite)
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