"""
Visualizing global patterns with a carpet plot
==============================================

A common quality control step for functional MRI data is to visualize the data
over time in a carpet plot (also known as a Power plot or a grayplot).

The :func:`~nilearn.plotting.plot_carpet()` function generates a carpet plot
from a 4D functional image.
"""

# %%
# Fetching data from ADHD dataset
# -------------------------------
from nilearn.datasets import fetch_adhd
from nilearn.plotting import plot_carpet

adhd_dataset = fetch_adhd(n_subjects=1)

# plot_carpet can infer TR from the image header,
# but preprocessing can often overwrite that particular header field,
# so we will be explicit.
t_r = 2.0

# Print basic information on the dataset
print(
    f"First subject functional nifti image (4D) is at: {adhd_dataset.func[0]}"
)

# %%
# Deriving a mask
# ---------------
from nilearn import masking

# Build an EPI-based mask because we have no anatomical data
mask_img = masking.compute_epi_mask(adhd_dataset.func[0])

# %%
# Visualizing global patterns over time
# -------------------------------------
import matplotlib.pyplot as plt

display = plot_carpet(
    adhd_dataset.func[0],
    mask_img,
    t_r=t_r,
    standardize="zscore_sample",
    title="global patterns over time",
)

display.show()

# %%
# Deriving a label-based mask
# ---------------------------
# Create a gray matter/white matter/cerebrospinal fluid mask from
# ICBM152 tissue probability maps.
import numpy as np

from nilearn import image
from nilearn.datasets import fetch_icbm152_2009

atlas = fetch_icbm152_2009()
atlas_img = image.concat_imgs((atlas["gm"], atlas["wm"], atlas["csf"]))
map_labels = {"Gray Matter": 1, "White Matter": 2, "Cerebrospinal Fluid": 3}

atlas_data = atlas_img.get_fdata()
discrete_version = np.argmax(atlas_data, axis=3) + 1
discrete_version[np.max(atlas_data, axis=3) == 0] = 0
discrete_atlas_img = image.new_img_like(
    atlas_img, discrete_version.astype(np.float32)
)


# %%
# Visualizing global patterns, separated by tissue type
# -----------------------------------------------------
fig, ax = plt.subplots(figsize=(10, 10))

display = plot_carpet(
    adhd_dataset.func[0],
    discrete_atlas_img,
    t_r=t_r,
    mask_labels=map_labels,
    axes=ax,
    standardize="zscore_sample",
    title="global patterns over time separated by tissue type",
)

plt.show()

# sphinx_gallery_dummy_images=1
