Source code for gemseo.mlearning.transform.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.
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# 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.
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# 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.transform.transformer import Transformer
from gemseo.mlearning.transform.transformer import TransformerFitOptionType


[docs]class JamesonSensor(Transformer): """A 1D Jameson Sensor.""" def __init__( self, name: str = "JamesonSensor", 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. """ super().__init__(name) self.threshold = threshold self.removing_part = removing_part self.dimension = dimension def _fit( self, data: ndarray, *args: TransformerFitOptionType, ) -> None: self.threshold = self.threshold * amax(data)
[docs] def transform( 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]) 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