# -*- coding: utf-8 -*-
# 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
r"""Draw a scatter matrix from a :class:`.Dataset`.
The :class:`.ScatterMatrix` class implements the scatter plot matrix,
which is a way to visualize :math:`n` samples of a
multi-dimensional vector
.. math::
x=(x_1,x_2,\ldots,x_d)\in\mathbb{R}^d
in several 2D subplots where the (i,j) subplot represents the cloud
of points
.. math::
\left(x_i^{(k)},x_j^{(k)}\right)_{1\leq k \leq n}
while the (i,i) subplot represents the empirical distribution of the samples
.. math::
x_i^{(1)},\ldots,x_i^{(n)}
by means of an histogram or a kernel density estimator.
A variable name can be passed to the :meth:`.DatasetPlot.execute` method
by means of the :code:`classifier` keyword in order to color the curves
according to the value of the variable name. This is useful when the data is
labeled.
"""
from __future__ import division, unicode_literals
from typing import List, Mapping, Optional
import matplotlib.pyplot as plt
from matplotlib.figure import Figure
from pandas import DataFrame
from gemseo.post.dataset.dataset_plot import DatasetPlot
try:
from pandas.plotting import scatter_matrix
except ImportError:
from pandas import scatter_matrix
[docs]class ScatterMatrix(DatasetPlot):
"""Scatter plot matrix."""
def _plot(
self,
properties, # type: Mapping
classifier=None, # type: Optional[str]
kde=False, # type: bool
size=25, # type: int
marker="o", # type: str
): # type: (...) -> List[Figure]
"""
Args:
classifier: The name of the variable to build the cluster.
kde: The type of the distribution representation.
If True, plot kernel-density estimator on the diagonal.
Otherwise, use histograms.
size: The size of the points.
marker: The marker for the points.
"""
figsize_x = properties.get(self.FIGSIZE_X) or 10
figsize_y = properties.get(self.FIGSIZE_Y) or 10
if classifier is not None and classifier not in self.dataset.variables:
raise ValueError(
"Classifier must be one of these names: "
+ ", ".join(self.dataset.variables)
)
if kde:
diagonal = "kde"
else:
diagonal = "hist"
dataframe = self.dataset.export_to_dataframe()
if classifier is None:
self._scatter_matrix(
dataframe, diagonal, size, marker, figsize_x, figsize_y
)
else:
self._scatter_matrix_for_group(
classifier, dataframe, diagonal, size, marker, figsize_x, figsize_y
)
return [plt.gcf()]
def _scatter_matrix_for_group(
self,
classifier, # type: str
dataframe, # type: DataFrame
diagonal, # type: str
size, # type: int
marker, # type: str
figsize_x, # type: int
figsize_y, # type: int
): # type: (...) -> None
"""Scatter matrix plot for group.
Args:
classifier: The name of the variable to group the data.
dataframe: The data to plot.
diagonal: The type of distribution representation, either "kde" or "hist".
size: The size of the points.
marker: The marker for the points.
figsize_x: The size of the figure in horizontal direction (inches).
figsize_y: The size of the figure in vertical direction (inches).
"""
palette = dict(enumerate("bgrcmyk"))
groups = self.dataset.get_data_by_names([classifier], False)[:, 0:1]
colors = [palette[group[0] % len(palette)] for group in groups]
_, varname = self._get_label(classifier)
dataframe = dataframe.drop(varname, 1)
dataframe.columns = self._get_variables_names(dataframe)
scatter_matrix(
dataframe,
diagonal=diagonal,
color=colors,
s=size,
marker=marker,
figsize=(figsize_x, figsize_y),
)
def _scatter_matrix(
self,
dataframe, # type: DataFrame
diagonal, # type: str
size, # type: int
marker, # type: str
figsize_x, # type: int
figsize_y, # type: int
): # type: (...) -> None
"""Scatter matrix plot for group.
Args:
dataframe: The data to plot.
diagonal: The type of distribution representation, either "kde" or "hist".
size: The size of the points.
marker: The marker for the points.
figsize_x: The size of the figure in horizontal direction (inches).
figsize_y: The size of the figure in vertical direction (inches).
Returns:
The figure.
"""
dataframe.columns = self._get_variables_names(dataframe)
scatter_matrix(
dataframe,
diagonal=diagonal,
figsize=(figsize_x, figsize_y),
s=size,
marker=marker,
)
