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comp_dct.pyx

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  • comp_dct.pyx 4.03 KiB
    # -*- coding: utf-8 -*-
    # ######### COPYRIGHT #########
    # Credits
    # #######
    #
    # Copyright(c) 2015-2018
    # ----------------------
    #
    # * `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) 2005-2018 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 = 1.0.16
    # * 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 dct calculations.
    
    .. moduleauthor:: Denis Arrivault
    """
    
    from __future__ import print_function, division
    import cython
    import numpy as np
    
    from ltfat cimport ltfatInt, dct_kind, dct_cd, dct_d
    from ltfatpy.comp.ltfat cimport DCTI, DCTII, DCTIII, DCTIV
    
    
    # don’t check for out-of-bounds indexing.
    @cython.boundscheck(False)
    # assume no negative indexing.
    @cython.wraparound(False)
    cdef comp_dct_cd(const double complex[:] f, const ltfatInt L,
                     const ltfatInt W, const dct_kind kind, double complex[:] out):
        """ Internal function, do not use it """
        dct_cd(& f[0], L, W, & out[0], kind)
    
    # don’t check for out-of-bounds indexing.
    @cython.boundscheck(False)
    # assume no negative indexing.
    @cython.wraparound(False)
    cdef comp_dct_d(const double[:] f, const ltfatInt L,
                    const ltfatInt W, const dct_kind kind, double[:] out):
        """ Internal function, do not use it """
        dct_d(&f[0], L, W, & out[0], kind)
    
    # don’t check for out-of-bounds indexing.
    @cython.boundscheck(False)
    # assume no negative indexing.
    @cython.wraparound(False)
    cpdef comp_dct(f, type):
        """Function that computes dct
    
        This is a computational subroutine, do not call it directly, use
        :func:`~ltfatpy.fourier.dct.dcti`, :func:`~ltfatpy.fourier.dct.dctii`,
        :func:`~ltfatpy.fourier.dct.dctiii` or func:`~ltfatpy.fourier.dct.dctiv`
        instead.
        """
        cdef ltfatInt L, W
        if (f.dtype.type != np.float64) and (f.dtype.type != np.complex128):
            raise TypeError("f data should be numpy.float64 or complex128")
        if type not in {1, 2, 3, 4}:
            raise TypeError("type should be 1, 2, 3 or 4")
    
        dct_type = {1: DCTI, 2: DCTII, 3: DCTIII, 4: DCTIV}
    
        if f.ndim > 1:
            if f.ndim > 2:
                f = np.squeeze(f, axis=range(2, f.ndim-1))
            L = f.shape[0]
            W = f.shape[1]
            f_combined = f.reshape(L * W, order='F')
        else:
            L = f.shape[0]
            W = 1
            f_combined = f
    
        if f.dtype.type == np.float64:
            res = np.ndarray((L * W), dtype=np.float64)
            comp_dct_d(f_combined, L, W, dct_type[type], res)
        else:
            res = np.ndarray((L * W), dtype=np.complex128)
            comp_dct_cd(f_combined, L, W, dct_type[type], res)
        if W > 1:
            res = np.reshape(res, (L, W), order='F')
        return res