Source code for fstg_toolkit.app.figures.data

# 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.
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# abiding by the rules of distribution of free software. You can use,
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import pandas as pd
import plotly.express as px
from plotly.graph_objects import Figure




def areas_per_region_figure(records: list[dict[str, str]]) -> Figure:
    groups = pd.DataFrame.from_records(records, index='Id_Area').groupby('Name_Region')
    df = groups.count()
    df['Areas'] = groups.apply(lambda g: g['Name_Area'].to_list(), include_groups=False)

    return px.bar(df, orientation='h', x='Name_Area', height=600,
                  labels={'Name_Area': 'Number of areas', 'Name_Region': 'Region'},
                  title="Distribution of areas per region", hover_data='Areas')





def subjects_per_factors_figure(records: list[dict[str, str]], factors: list[str]) -> Figure:
    df = pd.DataFrame.from_records(records)[factors + ['Subject']]

    labels = {'Subject': "Number of subjects"}

    if len(df.columns) > 1:
        df = df.groupby(factors).count().reset_index()
    else:
        df = pd.DataFrame(df.count(), columns=["All"]).T
        labels['index'] = ""

    params = dict(zip(('x', 'color'), factors))
    return px.bar(df, y='Subject', **params, height=500,
                  labels=labels,
                  title="Distribution of subjects per factors")