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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 to find Gabor parameters to generate image
Ported from ltfat_2.1.0/gabor/gabimagepars.m
.. moduleauthor:: Florent Jaillet
"""
from __future__ import print_function, division
import numpy as np
from ltfatpy.tools.lcm import lcm
def gabimagepars(Ls, x, y):
"""Find Gabor parameters to generate image
- Usage:
| ``(a, M, L, N, Ngood) = gabimagepars(Ls, x, y)``
- Input parameters:
:param int Ls: Signal length
:param int x: Approximate number of pixels in the time direction
:param int y: Number of pixels in the frequency direction
- Output parameters:
:returns: ``(a, M, L, N, Ngood)``
:rtype: tuple
:var int a: Length of time shift
:var int M: Number of frequency channels
:var int L: Length of transform to do
:var int N: Total number of time steps
:var int Ngood: Number of time steps (columns in the coefficients
matrix) that contain relevant information. The columns from
``Ngood-1`` until ``N-1`` only contain information from a
zero-extension of the signal.
``(a, M, L, N, Ngood) = gabimagepars(Ls, x, y)`` will compute a reasonable
set of parameters **a**, **M** and **L** to produce a nice Gabor 'image' of
a signal of length **Ls**.
If you use this function to calculate a grid size for analysis of a
real-valued signal (using :func:`~ltfatpy.gabor.dgtreal.dgtreal`), please
input twice of the desired size **y**. This is because
:func:`~ltfatpy.gabor.dgtreal.dgtreal` only returns half as many
coefficients in the frequency direction as :func:`~ltfatpy.gabor.dgt.dgt`.
For this function to work properly, the specified numbers for **x** and
**y** must not be large prime numbers.
- Example:
We wish to compute a Gabor image of a real valued signal ``f``
of length 7500. The image should have an approximate resolution of
600 x 800 pixels:
>>> from matplotlib.pyplot import show
>>> from ltfatpy import linus, gabimagepars, dgtreal, plotdgtreal
>>> f, fs = linus()
>>> f = f[4000:4000+7500]
>>> a, M, L, N, Ngood = gabimagepars(7500, 800, 2*600)
>>> c = dgtreal(f, 'gauss', a, M)[0]
>>> _ = plotdgtreal(c, a, M, fs=fs, dynrange=90)
>>> show()
The size of ``c`` is ``(M/2)+1 x N`` equal to 601 x 700 pixels.
.. image:: images/gabimagepars.png
:width: 600px
:alt: Gabor image of f
:align: center
.. seealso:: :func:`~ltfatpy.gabor.dgt.dgt`,
:func:`~ltfatpy.gabor.dgtreal.dgtreal`,
:func:`~ltfatpy.gabor.sgram.sgram`
"""
# Note: There is an inaccuracy in the help of the function gabimagepars in
# ltfat 2.1.0 for Octave.
# This inaccuracy concerns the size of the resulting coefficients in the
# example. See the description and confirmation here:
# http://sourceforge.net/p/ltfat/bugs/120/
# This inaccuracy is corrected in the docstring of this Python port.
if min(x, y) > Ls:
# Small values case, just do an STFT
M = Ls
N = Ls
a = 1
Ngood = N
L = Ls
else:
# Set M and N to be what the user specified
M = y
N = x
# Determine the minimum transform size.
K = lcm(M, N)
# This L is good, but is it not the same as DGT will choose.
Llong = np.ceil(Ls/K)*K
# Fix a from the long L
a = int(Llong/N)
# Now we have fixed a and M, so we can use the standard method of
# choosing L
Lsmallest = lcm(a, M)
L = int(np.ceil(Ls/Lsmallest)*Lsmallest)
# We did not get N as desired.
N = int(L/a)
# Number of columns to display
Ngood = int(np.ceil(Ls/a))
if M <= a:
raise ValueError(
'Cannot generate a frame, the signal is too long '
'as compared to the size of the image. Increase x and y.')
return (a, M, L, N, Ngood)
if __name__ == '__main__': # pragma: no cover
import doctest
doctest.testmod()
|
