# Copyright 2025 ICube (University of Strasbourg - CNRS)
# author: Julien PONTABRY (ICube)
#
# This software is a computer program whose purpose is to provide a toolkit
# to model, process and analyze the longitudinal reorganization of brain
# connectivity data, as functional MRI for instance.
#
# This software is governed by the CeCILL-B license under French law and
# abiding by the rules of distribution of free software. You can use,
# modify and/or redistribute the software under the terms of the CeCILL-B
# license as circulated by CEA, CNRS and INRIA at the following URL
# "http://www.cecill.info".
#
# As a counterpart to the access to the source code and rights to copy,
# modify and redistribute granted by the license, users are provided only
# with a limited warranty and the software's author, the holder of the
# economic rights, and the successive licensors have only limited
# liability.
#
# In this respect, the user's attention is drawn to the risks associated
# with loading, using, modifying and/or developing or reproducing the
# software by the user in light of its specific status of free software,
# that may mean that it is complicated to manipulate, and that also
# therefore means that it is reserved for developers and experienced
# professionals having in-depth computer knowledge. Users are therefore
# encouraged to load and test the software's suitability as regards their
# requirements in conditions enabling the security of their systems and/or
# data to be ensured and, more generally, to use and operate it in the
# same conditions as regards security.
#
# The fact that you are presently reading this means that you have had
# knowledge of the CeCILL-B license and that you accept its terms.
import dash_bootstrap_components as dbc
from dash import Input, Output, State, callback, dash_table, dcc, html
from dash.exceptions import PreventUpdate
from .common import (
plotly_config,
create_factors_options_controls,
build_factors_options,
)
from ..figures.data import areas_per_region_figure, subjects_per_factors_figure
from ...io import GraphsDataset
desc_columns = [{'name': "Area id", 'id': 'Id_Area'},
{'name': "Area name", 'id': 'Name_Area'},
{'name': "Region name", 'id': 'Name_Region'}]
corr_columns = [{'name': "Subject", 'id': 'Subject'}]
layout = [
dbc.Row(html.H2("Description of regions/areas")),
dbc.Row([
dbc.Col([
dcc.Loading(
children=dash_table.DataTable(
columns=desc_columns, page_size=15, id='desc-table',
sort_action='native', filter_action='native', style_as_list_view=True,
style_header={'fontWeight': 'bold', 'textAlign': 'center'}),
type='circle', overlay_style={'visibility': 'visible', 'filter': 'blur(2px)'})
]),
dbc.Col([
dcc.Loading(
dcc.Graph(figure={}, id='desc-count-plot',
config=dict(**plotly_config,
modeBarButtonsToRemove=['select2d', 'lasso2d'])),
type='circle', overlay_style={'visibility': 'visible', 'filter': 'blur(2px)'})
])
]),
dbc.Row(html.H2("Subjects")),
dbc.Row([
dbc.Col([
dcc.Loading(
children=dash_table.DataTable(
columns=corr_columns, page_size=15, id='subjects-table',
sort_action='native', filter_action='native', style_as_list_view=True,
style_header={'fontWeight': 'bold', 'textAlign': 'center'}),
type='circle', overlay_style={'visibility': 'visible', 'filter': 'blur(2px)'})
]),
dbc.Col([
dcc.Loading([
dbc.Row(dcc.Graph(
figure={}, id='subjects-dist-plot',
config=dict(**plotly_config, modeBarButtonsToRemove=['select2d', 'lasso2d']))),
dbc.Row(create_factors_options_controls('data-subjects-dist'))
],
type='circle', overlay_style={'visibility': 'visible', 'filter': 'blur(2px)'}
)
])
]),
]
[docs]
@callback(
Output('desc-table', 'data'),
Output('desc-count-plot', 'figure'),
Output('subjects-table', 'columns'),
Output('subjects-table', 'data'),
Output('data-subjects-dist-factors', 'options'),
Output('data-subjects-dist-factors', 'value'),
Input('store-dataset', 'data')
)
def dataset_changed(store_dataset):
if store_dataset is None:
return PreventUpdate
# update the columns of subjects table
n_factors = len(store_dataset['factors'])
columns = [{'name': f"Factor {i}", 'id': f'Factor{i}'}
for i in range(1, n_factors+1)]
columns.append({'name': "Subject", 'id': 'Subject'})
# compute plot for areas distribution
areas_count_fig = areas_per_region_figure(store_dataset['areas_desc'])
# update the factors selection for factors distribution plot
factors, default_factors = build_factors_options(GraphsDataset.deserialize(store_dataset))
return store_dataset['areas_desc'], areas_count_fig, columns, store_dataset['subjects'], \
factors, default_factors
[docs]
@callback(
Output('subjects-dist-plot', 'figure'),
Input('data-subjects-dist-factors', 'value'),
State('store-dataset', 'data'),
prevent_initial_call=True
)
def factors_selection_changed(factors, store_dataset):
if store_dataset is None:
raise PreventUpdate
return subjects_per_factors_figure(store_dataset['subjects'], factors)