Source code for gemseo.mlearning.transform.sensor.jameson
# -*- coding: utf-8 -*-
# Copyright 2021 IRT Saint Exupéry, https://www.irt-saintexupery.com
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU Lesser General Public
# License version 3 as published by the Free Software Foundation.
#
# 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
# Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
# Contributors:
# INITIAL AUTHORS - initial API and implementation and/or initial
# documentation
# :author: Matthias De Lozzo, Syver Doving Agdestein
# OTHER AUTHORS - MACROSCOPIC CHANGES
"""
Jameson sensor
==============
"""
from __future__ import absolute_import, division, unicode_literals
from future import standard_library
from numpy import abs as np_abs
from numpy import amax
from gemseo.mlearning.transform.transformer import Transformer
standard_library.install_aliases()
[docs]class JamesonSensor(Transformer):
""" Jameson Sensor. """
def __init__(
self, name="JamesonSensor", threshold=0.3, removing_part=0.01, dimension=1
):
"""Constructor.
:param str name: name of the sensor. Default: 'JamesonSensor'.
:param float threshold: value to add to the denominator
to avoid zero division. Default: 0.3.
:param float removing_part: define the level of the signal to
remove in order to avoid leading and trailing edge effects.
ONLY FOR 1D MESH, to redefine for 2D mesh. Default: 0.01.
:param int dimension: mesh dimension. Default: 1.
"""
super(JamesonSensor, self).__init__(name)
self.threshold = threshold
self.removing_part = removing_part
self.dimension = dimension
[docs] def fit(self, data):
"""Fit sensor to data.
:param array data: data to be fitted.
"""
self.threshold = self.threshold * amax(data)
def _jameson_1_d(self, data):
"""Transform data.
:param ndarray data: data to be transformed.
:return: transformed data.
:rtype: ndarray
"""
mesh_size = data.shape[1] - 2
min_mesh_size = int(mesh_size * self.removing_part)
max_mesh_size = int(mesh_size * (1 - self.removing_part))
norm = np_abs(data[:, :-2])
norm += 2 * np_abs(data[:, 1:-1])
norm += np_abs(data[:, 2:])
norm = norm + self.threshold
result = abs(data[:, :-2] - 2 * data[:, 1:-1] + data[:, 2:]) / norm
result = result[:, min_mesh_size:max_mesh_size]
return result