1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226
|
# -*- 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 #########
"""Read .mat files generated with the Octave version of ltfat for validation
.. moduleauthor:: Florent Jaillet
"""
from __future__ import print_function, division
import numpy as np
import scipy.io
def _squeeze_array(val):
"""Remove single-dimensional entries from the shape of Numpy arrays
- Input parameter:
:param val: A Python object of any type
- Output parameter:
:returns: If **val** is an instance of numpy.ndarray, the squeezed version
of **val** is returned, otherwise, **val** is returned
:rtype: Same type as **val**
If **val** is an instance of numpy.ndarray, this function returns the
input array **val**, but with all of the dimensions of length 1 removed.
The output is always **val** itself or a view into **val**.
"""
res = val
if isinstance(val, np.ndarray):
res = np.squeeze(res)
return res
def _adapt_type(val):
"""Adapt the type of the input
- Input parameter:
:param val: A Python object of any type
- Output parameter:
:returns: If the type and content of **val** can be adapted to better match
the expected types and values used in the ltfatpy functions, the
adapted version of **val** is returned, otherwise, **val** is returned
The following conversions are done when **val** is an instance of
numpy.ndarray:
- Arrays with dtype numpy.unicode are converted to standard Python
strings, and if their content is ``u'_bool_True_'`` or
``u'_bool_False_'`` they are converted to the corresponding boolean value
(output of type bool)
- If the array contains a single value, it is extracted from the array and
converted to the appropriate Python object. If this value is a float
having an integer value, it is converted to int.
"""
res = val
if isinstance(val, np.ndarray):
if np.issubdtype(val.dtype, np.str_):
# convert the unicode numpy arrays to standard unicode strings
res = val.tolist()
# convert boolean values
if val == "_bool_True_":
res = True
elif val == "_bool_False_":
res = False
elif val.size == 1:
# convert the single value in the array to the corresponding
# standard Python object
res = np.squeeze(val).tolist()
# force type int for integer values
if isinstance(res, float):
if res.is_integer():
res = int(res)
return res
def read_ref_mat(filename, squeeze_arrays=True, adapt_types=True):
"""Read reference data saved in a .mat file
- Input parameter:
:param str filename: File name of the .mat file containing reference data
saved in the expected format using Octave or MATLAB
:param bool squeeze_arrays: Flag specifying if the arrays must be squeezed
when reading the data (if ``True``, the dimensions of length 1 are
removed in the shapes of the arrays)
:param bool adapt_types: Flag specifying if the data types must be adapted
when reading the data (if ``True`` the types are adapted, see below for
details)
- Output parameters:
:returns: A list of tuples of the form ``(inputs, outputs)``, each tuple
giving the outputs expected for a given set of inputs
:rtype: list
:var dict inputs: The inputs as expected by the tested Python function
(note that it implies that there can be some differences with the
inputs used to generate the data in Octave or MATLAB)
:var list outputs: The expected outputs when running the tested Python
function with inputs given in **inputs**
In Octave or MATLAB, the data must be stored in the .mat file as a cell
array named ``data``.
Each item of this cell array must be a cell array containing three cell
arrays, the first containing the keys of **inputs**, the second the values
of **inputs**, and the third the values of **outputs**.
Here is an example to illustrate the expected use:
In Octave or MATLAB, define the following variable::
data = {{{'fun', 'dim', 'var'}, {'abs', 1, 1.3}, {[3., 2.2], 1.5}}, ...
{{'fun', 'do_it'}, {'dgt', '_bool_True_'}, {[1.4, 1.2]}}};
and save it using the save command::
save('test_filename.mat', 'data', '-V6');
In Python, using :func:`read_ref_mat` with::
read_ref_mat('test_filename.mat')
you get the following output::
[({u'dim': 1, u'fun': u'abs', u'var': 1.3}, [array([3. , 2.2]), 1.5]),
({u'do_it': True, u'fun': u'dgt'}, [array([1.4, 1.2])])]
If ``adapt_types=True``, the following conversions are done for data that
are an instance of numpy.ndarray:
- Arrays with dtype numpy.unicode are converted to standard Python
strings, and if their content is ``u'_bool_True_'`` or
``u'_bool_False_'`` they are converted to the corresponding boolean value
(output of type bool)
- If the array contains a single value, it is extracted from the array and
converted to the appropriate Python object. If this value is a float
having an integer value, it is converted to int.
"""
data = scipy.io.loadmat(filename, chars_as_strings=True)["data"]
res = list()
for item in data[0, :]:
if item[0, 0].size > 0:
# the tolist in the following is used to convert the unicode array
# to standard unicode string
inputs = dict(
(
(key[0].tolist(), val)
for key, val in zip(item[0, 0][0, :], item[0, 1][0, :])
)
)
else:
inputs = dict()
outputs = item[0, 2][0, :].tolist()
if squeeze_arrays:
inputs = {key: _squeeze_array(val) for key, val in inputs.items()}
outputs = [_squeeze_array(val) for val in outputs]
if adapt_types:
inputs = {key: _adapt_type(val) for key, val in inputs.items()}
outputs = [_adapt_type(val) for val in outputs]
res.append((inputs, outputs))
return res
|