"""
Example of second level design matrix
=====================================

This example shows how a second-level design matrix is specified: assuming that
the data refer to a group of individuals, with one image per subject, the
design matrix typically holds the characteristics of each individual.

This is used in a second-level analysis to assess the impact of these
characteristics on brain signals.

This example requires matplotlib.

"""

from nilearn._utils.helpers import check_matplotlib

check_matplotlib()

import matplotlib.pyplot as plt

# %%
# Create a simple experimental paradigm
# -------------------------------------
# We want to get the group result of a :term:`contrast` for 20 subjects.
n_subjects = 20
subjects_label = [f"sub-{int(i):02}" for i in range(1, n_subjects + 1)]

# %%
# Next, we specify extra information about the subjects to create confounders.
# Without confounders the design matrix would correspond to a one sample test.
import pandas as pd

extra_info_subjects = pd.DataFrame(
    {
        "subject_label": subjects_label,
        "age": range(15, 15 + n_subjects),
        "sex": [0, 1] * (n_subjects // 2),
    }
)

# %%
# Create a second level design matrix
# -----------------------------------
# With that information we can create the second level design matrix.
from nilearn.glm.second_level import make_second_level_design_matrix

design_matrix = make_second_level_design_matrix(
    subjects_label, extra_info_subjects
)

# %%
# Let's plot it.
from nilearn.plotting import plot_design_matrix

fig, ax1 = plt.subplots(1, 1, figsize=(3, 4), constrained_layout=True)

ax = plot_design_matrix(design_matrix, axes=ax1)
ax.set_ylabel("maps")
ax.set_title("Second level design matrix", fontsize=12)
plt.show()