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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 #########
"""This module contains interface functions for the LTFAT computed
versions of isepdgt calculations.
.. moduleauthor:: Denis Arrivault
"""
from __future__ import print_function, division
import cython
import numpy as np
from ltfatpy.comp.ltfat cimport ltfatInt, dgt_phasetype, idgt_fb_d, idgt_long_d
from ltfatpy.comp.ltfat cimport TIMEINV, FREQINV
# don’t check for out-of-bounds indexing.
@cython.boundscheck(False)
# assume no negative indexing.
@cython.wraparound(False)
cdef comp_idgt_long_d(const double complex[:] coef, const double complex[:] g,
const int L, const int W, const int a, const int M,
const dgt_phasetype ptype, double complex[:] out):
""" Internal function, do not use it """
idgt_long_d(& coef[0], & g[0], L, W, a, M, ptype, &out[0])
# don’t check for out-of-bounds indexing.
@cython.boundscheck(False)
# assume no negative indexing.
@cython.wraparound(False)
cdef comp_idgt_fb_d(const double complex[:] coef, const double complex[:] g,
const int L, const int gl, const int W, const int a,
const int M, const dgt_phasetype ptype,
double complex[:] out):
""" Internal function, do not use it """
idgt_fb_d(& coef[0], & g[0], L, gl, W, a, M, ptype, &out[0])
# don’t check for out-of-bounds indexing.
@cython.boundscheck(False)
# assume no negative indexing.
@cython.wraparound(False)
cpdef comp_isepdgt(coef, g, a, pt):
"""Function that computes separable inverse dgt
This is a computational subroutine, do not call it directly, use
:func:`~ltfatpy.gabor.idgt.idgt` instead.
.. seealso:: :func:`~ltfatpy.gabor.idgt.idgt`
"""
if (coef.dtype.type != np.complex128):
coef = coef.astype(np.complex128)
if (g.dtype.type != np.complex128):
g = g.astype(np.complex128)
if pt != 0 and pt != 1:
raise TypeError("pt should be 0 (FREQINV) or 1 (TIMEINV)")
if coef.ndim <= 1 or coef.ndim > 3:
raise TypeError("Dimensions of dgt coefficients array must be 2 or 3.")
cdef ltfatInt N = coef.shape[1]
cdef ltfatInt L = N * a
cdef ltfatInt M = coef.shape[0]
cdef ltfatInt W = coef.size // (M * N)
if W > 1 and coef.ndim == 2:
raise TypeError("Dimensions of dgt coefficients array do not fit.")
if W == 1 and coef.ndim == 3:
coef = np.squeeze(coef, axis=2)
coef = coef.reshape(coef.size, order='F')
cdef ltfatInt gl = g.shape[0]
if g.ndim > 1:
if g.ndim > 2:
g = np.squeeze(g, axis=range(2, g.ndim-1))
if g.ndim == 2:
gl = gl * g.shape[1]
g = g(gl, order='F')
res = np.ndarray((L * W), dtype=np.complex128)
if gl < L:
comp_idgt_fb_d(coef, g, L, gl, W, a, M, pt, res)
if W > 1:
return np.reshape(res, (L, W), order='F')
return res
comp_idgt_long_d(coef, g, L, W, a, M, pt, res)
if W > 1:
return np.reshape(res, (L, W), order='F')
return res
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