# 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
# OTHER AUTHORS - MACROSCOPIC CHANGES
"""Draw the boxplots of some variables from a :class:`.Dataset`.
A boxplot represents the median and the first and third quartiles of numerical data. The
variability outside the inter quartile domain can be represented with lines, called
*whiskers*. The numerical data that are significantly different are called *outliers*
and can be plotted as individual points beyond the whiskers.
"""
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 collections.abc import Sequence
from typing import Any
from gemseo.datasets.dataset import Dataset
[docs]
class Boxplot(DatasetPlot):
"""Draw the boxplots of some variables from a :class:`.Dataset`."""
def __init__(
self,
dataset: Dataset,
*datasets: Dataset,
variables: Iterable[str] = (),
center: bool = False,
scale: bool = False,
use_vertical_bars: bool = True,
add_confidence_interval: bool = False,
add_outliers: bool = True,
opacity_level: float = 0.25,
**boxplot_options: Any,
) -> None:
"""
Args:
*datasets: Datasets containing other series of data to plot.
variables: The names of the variables to plot.
If empty, use all the variables.
center: Whether to center the variables so that they have a zero mean.
scale: Whether to scale the variables so that they have a unit variance.
use_vertical_bars: Whether to use vertical bars.
add_confidence_interval: Whether to add the confidence interval (CI)
around the median; a CI is also called *notch*.
add_outliers: Whether to add the outliers.
opacity_level: The opacity level for the faces, between 0 and 1.
**boxplot_options: The options of the wrapped boxplot function.
""" # noqa: D205, D212, D415
self.__n_datasets = 1 + len(datasets)
self.__names = dataset.get_columns(variables)
self.__origin = 0
super().__init__(
dataset,
datasets=datasets,
variables=variables,
center=center,
scale=scale,
use_vertical_bars=use_vertical_bars,
add_confidence_interval=add_confidence_interval,
add_outliers=add_outliers,
boxplot_options=boxplot_options,
opacity_level=opacity_level,
)
def _create_specific_data_from_dataset(
self,
) -> tuple[Sequence[str], list[float], int, float, list[str], float]:
"""
Returns:
The names of the variables,
the positions of the variables on the x-axis,
the number of datasets,
the x-offset,
the names of the variables,
the level of opacity.
""" # noqa: D205, D212, D415
self._n_items = self.__n_datasets
return (
self._specific_settings.variables or self.dataset.variable_names,
[
(self.__n_datasets - 1) / self.__n_datasets + i * self.__n_datasets
for i, _ in enumerate(self.__names)
],
self.__n_datasets,
self.__origin,
self.__names,
self._specific_settings.opacity_level,
)
