Source code for

# 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
"""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 import DatasetPlot

    from import Iterable
    from 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`.""" # TODO: API: remove this attribute and use the option instead. opacity_level: float """The opacity level for the faces, between 0 and 1.""" 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 self.opacity_level = opacity_level 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, )