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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 Root Mean Square calculation
Ported from ltfat_2.1.0/sigproc/rms.m
.. moduleauthor:: Denis Arrivault
"""
from __future__ import print_function, division
import numpy as np
from numpy import linalg as LA
from ltfatpy.comp.assert_sigreshape_pre import assert_sigreshape_pre
from ltfatpy.comp.assert_sigreshape_post import assert_sigreshape_post
def rms(f, ac=False, dim=None):
r"""RMS value of signal
- Usage:
| ``y = rms(f)``
- Input parameters:
:param numpy.ndarray f: Input signal
:param bool ac: ``True`` if calculation should only consider the AC
component of the signal (i.e. the mean is removed). ``False`` by
default.
:param int dim: Dimension along which norm is applied (first non-singleton
dimension as default)
- Output parameters:
:returns: RMS value
:rtype: float
``rms(f)`` computes the RMS (Root Mean Square) value of a finite sampled
signal sampled at a uniform sampling rate. This is a vector norm
equal to the :math:`l^2` averaged by the length of the signal.
If the input is a matrix or ND-array, the RMS is computed along the
first (non-singleton) dimension, and a vector of values is returned.
The RMS value of a signal ``f`` of length ``N`` is computed by
.. N
rms(f) = 1/sqrt(N) ( sum |f(n)|^2 )^(1/2)
n=1
.. math::
rms(f) = \\frac{1}{\\sqrt N} \\left( \\sum_{n=1}^N |f(n)|^2
\\right)^{\\frac{1}{2}}
"""
# It is better to use 'norm' instead of explicitly summing the squares, as
# norm (hopefully) attempts to avoid numerical overflow.
(f, L, _unused, W, dim, permutedsize, order) = \
assert_sigreshape_pre(f, dim=dim)
permutedshape = (1,) + permutedsize[1:]
y = np.zeros(permutedshape)
if W == 1:
if ac:
y[0] = LA.norm(f[:, 0] - np.mean(f[:, 0])) / np.sqrt(L)
else:
y[0] = LA.norm(f[:, 0]) / np.sqrt(L)
else:
if ac:
for ii in range(W):
y[0, ii] = LA.norm(f[:, ii] - np.mean(f[:, ii])) / np.sqrt(L)
else:
for ii in range(W):
y[0, ii] = LA.norm(f[:, ii]) / np.sqrt(L)
y = assert_sigreshape_post(y, dim, permutedshape, order)
return y
|
