Source code for gemseo.mlearning.transformers.sensor.jameson
# 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
"""A 1D Jameson sensor."""
from __future__ import annotations
from numpy import abs as np_abs
from numpy import amax
from numpy import ndarray
from gemseo.mlearning.transformers.transformer import Transformer
from gemseo.mlearning.transformers.transformer import TransformerFitOptionType
[docs]
class JamesonSensor(Transformer):
"""A 1D Jameson Sensor."""
def __init__(
self,
name: str = "",
threshold: float = 0.3,
removing_part: float = 0.01,
dimension: int = 1,
) -> None:
"""
Args:
name: A name for this transformer.
threshold: The value to add to the denominator
to avoid zero division.
removing_part: The level of the signal to
remove in order to avoid leading and trailing edge effects.
dimension: The dimension of the mesh.
""" # noqa: D205 D212
super().__init__(name)
self.threshold = threshold
self.removing_part = removing_part
self.dimension = dimension
def _fit(self, data: ndarray, *args: TransformerFitOptionType) -> None:
self.threshold *= amax(data)
@Transformer._use_2d_array
def transform( # noqa: D102
self,
data: ndarray,
) -> 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])
+ 2 * np_abs(data[:, 1:-1])
+ np_abs(data[:, 2:])
+ self.threshold
)
result = abs(data[:, :-2] - 2 * data[:, 1:-1] + data[:, 2:]) / norm
return result[:, min_mesh_size:max_mesh_size]