"""
Loading and plotting of a cortical surface atlas
================================================

The Destrieux :term:`parcellation` (:footcite:t:`Destrieux2010`)
in fsaverage5 space as distributed with Freesurfer is used as the chosen atlas.

The :func:`~nilearn.plotting.plot_surf_roi` function is used
to plot the :term:`parcellation` on the pial surface.

See :ref:`plotting` for more details.
"""

# %%
# Data fetcher
# ------------
# Retrieve destrieux parcellation in fsaverage5 space from nilearn
# and create a :obj:`~nilearn.surface.SurfaceImage` instance with it.
from nilearn.datasets import (
    fetch_atlas_surf_destrieux,
    load_fsaverage,
    load_fsaverage_data,
)
from nilearn.surface import SurfaceImage

fsaverage = load_fsaverage("fsaverage5")
destrieux = fetch_atlas_surf_destrieux()
destrieux_atlas = SurfaceImage(
    mesh=fsaverage["pial"],
    data={
        "left": destrieux["map_left"],
        "right": destrieux["map_right"],
    },
)

# Retrieve fsaverage5 surface dataset for the plotting background.
# It contains the surface template as pial and inflated version.
fsaverage_meshes = load_fsaverage()

# The fsaverage meshes contains the FileMesh objects:
print(f"{fsaverage_meshes['pial'].parts['left']=}")
print(f"{fsaverage_meshes['inflated'].parts['left']=}")

# The fsaverage data contains file names pointing to the file locations
# The sulcal depth maps will be is used for shading.
fsaverage_sulcal = load_fsaverage_data(data_type="sulcal")
print(f"{fsaverage_sulcal=}")

# %%
# Visualization
# -------------

# %%
# Destrieux parcellation on pial surface
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
from nilearn.plotting import plot_surf_roi, show

plot_surf_roi(
    roi_map=destrieux_atlas,
    hemi="left",
    view="lateral",
    bg_map=fsaverage_sulcal,
    bg_on_data=True,
    title="Destrieux parcellation on sulcal surface",
    darkness=None,
)

# %%
# Destrieux parcellation on inflated surface with different views
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
for view in ["lateral", "posterior", "ventral"]:
    plot_surf_roi(
        surf_mesh=fsaverage_meshes["inflated"],
        roi_map=destrieux_atlas,
        hemi="left",
        view=view,
        bg_map=fsaverage_sulcal,
        bg_on_data=True,
        title=f"Destrieux parcellation on inflated surface\n{view} view",
        darkness=None,
    )

show()

# %%
# Destrieux parcellation with custom view: explicitly set angle
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
elev, azim = 210.0, 90.0  # appropriate for visualizing, e.g., the OTS
plot_surf_roi(
    surf_mesh=fsaverage_meshes["inflated"],
    roi_map=destrieux_atlas,
    hemi="left",
    view=(elev, azim),
    bg_map=fsaverage_sulcal,
    bg_on_data=True,
    darkness=None,
    title="Arbitrary view of Destrieux parcellation",
)

# %%
# Display connectome from surface parcellation
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
# The following code extracts 3D coordinates of surface parcels
# (also known as labels in the Freesurfer naming convention).
# To do so we get the pial surface of fsaverage subject,
# get the :term:`vertices<vertex>` contained in each parcel
# and compute the mean location to obtain the coordinates.
import numpy as np

from nilearn.plotting import plot_connectome, view_connectome

coordinates = []
for hemi in ["left", "right"]:
    data = destrieux_atlas.data.parts[hemi]
    mesh_coordinates = destrieux_atlas.mesh.parts[hemi].coordinates
    coordinates.extend(
        np.mean(mesh_coordinates[data == k], axis=0)
        for k, label in enumerate(destrieux.labels)
        if "Unknown" not in str(label)
    )
# 3D coordinates of parcels
coordinates = np.array(coordinates)

# We now make a synthetic connectivity matrix that connects labels
# between left and right hemispheres.
n_parcels = len(coordinates)
corr = np.zeros((n_parcels, n_parcels))
n_parcels_hemi = n_parcels // 2
corr[np.arange(n_parcels_hemi), np.arange(n_parcels_hemi) + n_parcels_hemi] = 1
corr = corr + corr.T

plot_connectome(
    adjacency_matrix=corr,
    node_coords=coordinates,
    edge_threshold="90%",
    title="Connectome Destrieux atlas",
)
show()

# %%
# 3D visualization in a web browser
# ---------------------------------
# An alternative to :func:`~nilearn.plotting.plot_surf_roi` is to use
# :func:`~nilearn.plotting.view_surf`
# for more interactive visualizations in a web browser.
# See :ref:`interactive-surface-plotting` for more details.
from nilearn.plotting import view_surf

view = view_surf(
    surf_mesh=fsaverage_meshes["inflated"],
    surf_map=destrieux_atlas,
    cmap="gist_ncar",
    symmetric_cmap=False,
    darkness=None,
)
# In a Jupyter notebook, if ``view`` is the output of a cell,
# it will be displayed below the cell
view

# uncomment this to open the plot in a web browser:
# view.open_in_browser()

# %%
# you can also use :func:`~nilearn.plotting.view_connectome`
# to open an interactive view of the connectome.
view = view_connectome(
    corr,
    coordinates,
    edge_threshold="90%",
)

# uncomment this to open the plot in a web browser:
# view.open_in_browser()
view

# %%
# References
# ----------
#
# .. footbibliography::


# sphinx_gallery_dummy_images=1
