Source code for gemseo.post.dataset.color_evolution
# 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 - API and implementation and/or documentation
# :author: Matthias De Lozzo
# OTHER AUTHORS - MACROSCOPIC CHANGES
"""Evolution of the variables by means of a color scale."""
from __future__ import annotations
from typing import TYPE_CHECKING
from gemseo.post.dataset.dataset_plot import DatasetPlot
if TYPE_CHECKING:
from collections.abc import Iterable
from numpy.typing import NDArray
from gemseo.datasets.dataset import Dataset
[docs]
class ColorEvolution(DatasetPlot):
"""Evolution of the variables by means of a color scale.
Based on the matplotlib function :meth:`imshow`.
Tip:
Use :attr:`.colormap` to set a matplotlib colormap, e.g. ``"seismic"``.
"""
def __init__(
self,
dataset: Dataset,
variables: Iterable[str] | None = None,
use_log: bool = False,
opacity: float = 0.6,
**options: bool | float | str | None,
) -> None:
"""
Args:
variables: The variables of interest
If ``None``, use all the variables.
use_log: Whether to use a symmetric logarithmic scale.
opacity: The level of opacity (0 = transparent; 1 = opaque).
**options: The options for the matplotlib function :meth:`imshow`.
""" # noqa: D205, D212, D415
options_ = {
"interpolation": "nearest",
"aspect": "auto",
}
options_.update(options)
super().__init__(
dataset,
variables=variables,
use_log=use_log,
opacity=opacity,
options=options_,
)
def _create_specific_data_from_dataset(self) -> tuple[NDArray[float], list[str]]:
"""
Returns:
The data to be plotted,
the names of the variables.
""" # noqa: D205, D212, D415
variable_names = (
self._specific_settings.variables or self.dataset.variable_names
)
return (
self.dataset.get_view(variable_names=variable_names).to_numpy().T,
variable_names,
)