# -*- 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 #########
""" Module of signal normalization
Ported from ltfat_2.1.0/sigproc/normalize.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
from ltfatpy.sigproc.rms import rms
from ltfatpy.gabor.s0norm import s0norm
[docs]def normalize(f, norm='2', dim=None):
"""Normalize input signal by specified norm
- Usage:
| ``(f, fnorm) = normalize(f)``
| ``(f, fnorm) = normalize(f, 'area')``
| ``(f, fnorm) = normalize(f, dim=2)``
| ...
:param numpy.ndarray f: Input signal
:param str norm: Name of the norm to apply
:param int dim: Dimension along which norm is applied (first non-singleton
dimension as default)
- Output parameters:
:return: ``(f, fnorm)``
:rtype: tuple
:var numpy.ndarray f: normalized signal
:var numpy.ndarray fnorm: norm of the signal
``normalize(f,...)`` will normalize the signal **f** by the specified norm.
The norm is specified as a string and may be one of:
============ ==========================================================
``'1'`` Normalize the :math:`l^1` norm to be *1*.
``'area'`` Normalize the area of the signal to be *1*. This is
exactly the same as ``'1'``.
``'2'`` Normalize the :math:`l^2` norm to be *1*. This is the
default
``'energy'`` Normalize the energy of the signal to be *1*. This is
exactly the same as ``'2'``.
``'inf'`` Normalize the :math:`l^{\inf}` norm to be *1*.
``'peak'`` Normalize the peak value of the signal to be *1*.
This is exactly the same as ``'inf'``.
``'rms'`` Normalize the Root Mean Square (RMS) norm of the signal to
be *1*.
``'s0'`` Normalize the S0-norm to be *1*.
``'wav'`` Normalize to the :math:`l^{\inf}` norm to be *0.99* to
avoid possible clipping introduced by the quantization
procedure when saving as a wav file. This only works with
floating point data types.
``'null'`` Do NOT normalize, output is identical to input.
============ ==========================================================
.. seealso::
:func:`~ltfatpy.sigproc.rms.rms`,
:func:`~ltfatpy.gabor.s0norm.s0norm`
"""
if not isinstance(norm, str):
raise TypeError('norm should be string.')
norm = norm.lower()
f = f.copy()
(f, _unused, _unused, W, dim, permutedshape, order) = \
assert_sigreshape_pre(f, dim=dim)
if np.issubdtype(f.dtype, np.integer) and norm == 'wav':
raise TypeError('Integer data types are unsupported for wav norm.')
fnorm = np.zeros((W, ))
for ii in range(W):
if norm == '1' or norm == 'area':
fnorm[ii] = LA.norm(f[:, ii], 1)
f[:, ii] = f[:, ii] / fnorm[ii]
elif norm == '2' or norm == 'energy':
fnorm[ii] = LA.norm(f[:, ii], 2)
f[:, ii] = f[:, ii] / fnorm[ii]
elif norm == 'inf' or norm == 'peak':
fnorm[ii] = LA.norm(f[:, ii], np.inf)
f[:, ii] = f[:, ii] / fnorm[ii]
elif norm == 'rms':
fnorm[ii] = rms(f[:, ii])
f[:, ii] = f[:, ii] / fnorm[ii]
elif norm == 's0':
fnorm[ii] = s0norm(f[:, ii])
f[:, ii] = f[:, ii] / fnorm[ii]
elif norm == 'wav':
if np.issubdtype(f.dtype, np.floating):
fnorm[ii] = LA.norm(f[:, ii], np.inf)
f[:, ii] = 0.99 * f[:, ii] / fnorm[ii]
else:
raise TypeError("TO DO: Normalizing integer data types not"
"supported yet.")
f = assert_sigreshape_post(f, dim, permutedshape, order)
return (f, fnorm)