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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 Sampled, periodized hyperbolic secant calculation
Ported from ltfat_2.1.0/fourier/psech.m
.. moduleauthor:: Denis Arrivault
"""
import numpy as np
def psech(L, tfr=None, s=None, **kwargs):
r"""Sampled, periodized hyperbolic secant
- Usage:
| ``(g, tfr) = psech(L)``
| ``(g, tfr) = psech(L, tfr)``
| ``(g, tfr) = psech(L, s=...)``
- Input parameters:
:param int L: length of vector.
:param float tfr: ratio between time and frequency support.
:param int s: number of samples (equivalent to :math:`tfr=s^2/L`)
- Output parameters:
:returns: ``(g, tfr)``
:rtype: tuple
:var numpy.ndarray g: periodized hyperbolic cosine
:var float tfr: calculated ratio between time and frequency support
``psech(L,tfr)`` computes samples of a periodized hyperbolic secant.
The function returns a regular sampling of the periodization
of the function :math:`sech(\\pi\\cdot x)`
The returned function has norm equal to 1.
The parameter **tfr** determines the ratio between the effective support
of **g** and the effective support of the DFT of **g**. If **tfr** > 1
then **g** has a wider support than the DFT of **g**.
``psech(L)`` does the same setting than **tfr** = 1.
``psech(L,s)`` returns a hyperbolic secant with an effective support of
**s** samples. This means that approx. 96% of the energy or 74% or the
area under the graph is contained within **s** samples. This is
equivalent to ``psech(L,s^2/L)``.
``(g,tfr) = psech( ... )`` returns the time-to-frequency support ratio.
This is useful if you did not specify it (i.e. used the **s** input
format).
The function is whole-point even. This implies that
``fft(psech(L,tfr))`` is real for any **L** and **tfr**.
If this function is used to generate a window for a Gabor frame, then
the window giving the smallest frame bound ratio is generated by
``psech(L,a*M/L)``.
- Examples:
This example creates a ``psech`` function, and demonstrates that it
is its own Discrete Fourier Transform:
>>> import numpy as np
>>> import numpy.linalg as nla
>>> g = psech(128)[0] # DFT invariance: Should be close to zero.
>>> diff = nla.norm(g-np.fft.fft(g)/np.sqrt(128))
>>> np.abs(diff) < 10e-10
True
.. seealso:: :func:`~ltfatpy.fourier.pgauss.pgauss`, :func:`pbspline`,
:func:`pherm`
- References:
:cite:`jast02-1`
"""
if not isinstance(L, int):
raise TypeError('L must be an integer')
if s is not None:
if not isinstance(s, int):
raise TypeError('s must be an integer')
tfr = float(s**2 / L)
elif tfr is None:
tfr = 1
safe = 12
g = np.zeros(L)
sqrtl = np.sqrt(L)
w = tfr
# Outside the interval [-safe,safe] then sech(pi*x) is numerically zero.
nk = np.ceil(safe / np.sqrt(L / np.sqrt(w)))
lr = np.arange(L)
for k in np.arange(-nk, nk+1):
g = g + (1 / np.cosh(np.pi * (lr / sqrtl - k * sqrtl) / np.sqrt(w)))
# Normalize it.
g = g * np.sqrt(np.pi / (2 * np.sqrt(L*w)))
return(g, tfr)
if __name__ == '__main__': # pragma: no cover
import doctest
doctest.testmod()
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