Source code for gemseo.mlearning.transform.sensor.jameson

# -*- coding: utf-8 -*-
# Copyright 2021 IRT Saint Exupéry,
# 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
# 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
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


[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)
[docs] def transform(self, data): """Transform data. :param ndarray data: data to be transformed. :return: transformed data. :rtype: ndarray """ if self.dimension == 1: transformed_data = self._jameson_1_d(data) else: raise NotImplementedError() return transformed_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