"""
Regions Extraction of Default Mode Networks using Smith Atlas
=============================================================

This simple example shows how to extract regions from Smith atlas
resting state networks.

In particular, we show how Default Mode Network regions are extracted
using :class:`~nilearn.regions.RegionExtractor` from regions module
"""

# %%
# Fetching the smith :term:`ICA` 10 RSN by importing datasets utilities
from nilearn import datasets

atlas_networks = datasets.fetch_atlas_smith_2009(resting=True, dimension=10)[
    "maps"
]

# %%
# Import region extractor to extract atlas networks
from nilearn.regions import RegionExtractor

# min_region_size in voxel volume mm^3
extraction = RegionExtractor(
    atlas_networks,
    min_region_size=800,
    threshold=98,
    thresholding_strategy="percentile",
    verbose=1,
)

# Just call fit() to execute region extraction procedure
extraction.fit()
regions_img = extraction.regions_img_

# %%
# Visualization
# Show region extraction results by importing image & plotting utilities
from nilearn import plotting
from nilearn.image import index_img
from nilearn.plotting import find_xyz_cut_coords

# Showing region extraction results using 4D maps visualization tool
plotting.plot_prob_atlas(
    regions_img,
    display_mode="z",
    cut_coords=1,
    view_type="contours",
    title="Regions extracted.",
)

# To reduce the complexity, we choose to display all the regions
# extracted from network 3
import numpy as np

DMN_network = index_img(atlas_networks, 3)
plotting.plot_stat_map(
    DMN_network,
    display_mode="z",
    cut_coords=1,
    title="Network 3",
    colorbar=False,
)

regions_indices_network3 = np.where(np.array(extraction.index_) == 3)
for index in regions_indices_network3[0]:
    cur_img = index_img(extraction.regions_img_, index)
    coords = find_xyz_cut_coords(cur_img)
    plotting.plot_stat_map(
        cur_img,
        display_mode="z",
        cut_coords=coords[2:3],
        title="Blob of network3",
        colorbar=False,
        cmap="inferno",
        vmax=15,
    )

plotting.show()
