data/examples/advanced_examples/plot_dataset_bubbles.py

"""
===================
Dataset bubble plot
===================

This tutorial shows how to use the :func:`moabb.analysis.plotting.dataset_bubble_plot`
function to visualize, at a glance, the number of subjects and sessions in each dataset
and the number of trials per session.

"""

# Authors: Pierre Guetschel
#
# License: BSD (3-clause)

import matplotlib.pyplot as plt

from moabb.analysis.plotting import dataset_bubble_plot
from moabb.datasets import (
    BNCI2014_001,
    Cho2017,
    Hinss2021,
    Lee2019_ERP,
    Sosulski2019,
    Thielen2021,
    Wang2016,
)
from moabb.datasets.utils import plot_datasets_cluster, plot_datasets_grid


###############################################################################
# Visualizing one dataset
# -----------------------
#
# The :func:`moabb.analysis.plotting.dataset_bubble_plot` is fairly simple to use.
# It takes a :class:`moabb.datasets.base.BaseDataset` as input and plots
# its characteristics.
#
# Each bubble represents one subject. The size of the bubble is
# proportional to the number of trials per subject on a log scale,
# the color represents the paradigm, and the alpha is proportional to
# the number of sessions.
#
# You can adjust plotting parameters, such as the scale of the bubbles, but
# we will leave the default values for this example.
# More details on the parameters can be found in the doc (:func:`moabb.analysis.plotting.dataset_bubble_plot`).


dataset = Lee2019_ERP()
dataset_bubble_plot(dataset)
plt.show()

###############################################################################
# Alternatively, ou can also plot hexagons instead of circles,
# using the ``shape`` parameter.


dataset = Lee2019_ERP()
dataset_bubble_plot(dataset, shape="hexagon", gap=0.1)
plt.show()


##############################################################################
# In this example, we can see that the :class:`moabb.datasets.Lee2019_ERP` dataset
# has many subjects (54), 2 sessions, and a fairly large number of trials per session.
#
# Visualizing multiple datasets simultaneously
# --------------------------------------------
#
# Multiple datasets can be visualized at once by using the ``ax`` and ``center`` parameters.
# The ``ax`` parameter allows you to re-plot on the same axis, while the ``center`` parameter
# allows you to specify the center of each dataset.
# The following example shows how to plot multiple datasets on the same axis.

ax = plt.gca()
dataset_bubble_plot(Lee2019_ERP(), ax=ax, center=(10, 10), legend=False)
dataset_bubble_plot(BNCI2014_001(), ax=ax, center=(-2, 33), legend=False)
dataset_bubble_plot(Wang2016(), ax=ax, center=(37, -1), legend=True)
dataset_bubble_plot(Thielen2021(), ax=ax, center=(38, 16), legend=False)
dataset_bubble_plot(Hinss2021(), ax=ax, center=(30, 22), legend=False)
dataset_bubble_plot(Cho2017(), ax=ax, center=(33, 35), legend=False)
dataset_bubble_plot(Sosulski2019(), ax=ax, center=(13, 42), legend=False)
plt.show()

###############################################################################
# Another parameter available is ``size_mode``. It allows you to choose how the size
# of the bubbles is calculated. You can choose to use the number of trials per subject
# (``size_mode="count"``) or the duration of experiment data per subject
# (``size_mode="duration"``). The experiment data duration is calculated
# as the number of trials multiplied by the duration of each trial.
#
# Here is the same plot as above, but using ``size_mode="duration"``:

ax = plt.gca()
kwargs = {"size_mode": "duration", "scale": 0.4, "ax": ax}
dataset_bubble_plot(Lee2019_ERP(), center=(10, 10), legend=False, **kwargs)
dataset_bubble_plot(BNCI2014_001(), center=(-2, 33), legend=False, **kwargs)
dataset_bubble_plot(Wang2016(), center=(35, -1), legend=True, **kwargs)
dataset_bubble_plot(Thielen2021(), center=(39, 16), legend=False, **kwargs)
dataset_bubble_plot(Hinss2021(), center=(27, 22), legend=False, **kwargs)
dataset_bubble_plot(Cho2017(), center=(33, 35), legend=False, **kwargs)
dataset_bubble_plot(Sosulski2019(), center=(13, 42), legend=False, **kwargs)
plt.show()


###############################################################################
# We can observe, for example, that the ``Thielen2021`` contains few trials
# per subject but very long trials (31,5 seconds) while ``Lee2019_ERP`` contains
# many but short trials (1 second).
#
# Visualizing a custom dataset
# ----------------------------
#
# You can also visualize your own dataset by manually specifying the following parameters:
#
# - ``dataset_name``: name of the dataset
# - ``n_subjects``: number of subjects
# - ``n_sessions``: number of sessions
# - ``n_trials``: number of trials per session
# - ``paradigm``: paradigm name
# - ``trial_len``: duration of one trial, in seconds
#
# Here is an example of a custom dataset with 100 subjects, and 10000 trials per session:

dataset_bubble_plot(
    dataset_name="My custom dataset",
    n_subjects=100,
    n_sessions=1,
    n_trials=10000,
    paradigm="imagery",
    trial_len=5.0,
)
plt.show()

###############################################################################
# Visualizing all MOABB datasets
# ------------------------------
#
# Finally, you can visualize all datasets available in MOABB at once
# by using the :func:`moabb.datasets.utils.plot_datasets_grid` function.
# The datasets are sorted in alphabetical order and displayed on a grid.
#
# When using this function, we recommend saving the figure as a PDF or SVG
# file, as the figure is quite large and may be long to render.

fig = plot_datasets_grid(n_col=5)
fig.tight_layout()
plt.show()

###############################################################################
# Alternatively, you can also use the :func:`moabb.datasets.utils.plot_datasets_cluster`
# function to visualize the datasets in more compact format.

fig = plot_datasets_cluster()
fig.tight_layout()
plt.show()