# Copyright 2018-2023
# Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich GmbH

# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at

#     http://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""
Adding a custom parcellation map
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Sometimes you might want to use a custom parcellation map to perform feature
queries in siibra. For this example, we retrieve a freely available AICHA -
Atlas of Intrinsic Connectivity of Homotopic Areas (Tzourio-Mazoyer N, Landeau B,
Papathanassiou D, Crivello F, Etard O, Delcroix N, Mazoyer B, Joliot M (2002)
Automated anatomical labeling of activations in SPM using a macroscopic
anatomical parcellation of the MNI MRI single-subject brain. Neuroimage
15:273-289.). This atlas provided in the MNI ICBM 152 space.
"""


# %%
import siibra
from nilearn import plotting

# %%
# Load the custom parcellation map from the online resource. For the retrieval,
# siibra's zipfile connector is very helpful. We can use the resulting NIfTI
# to create a custom parcellation map inside siibra.

# connect to the online zip file
base_url = "https://data-proxy.ebrains.eu/api/v1/buckets/p4791e-ext-d000069_AICHA_pub/v2.0"

# the NIfTI file is easily retrieved:
nii = siibra.retrieval.requests.HttpRequest(f"{base_url}/AICHA1mm.nii").get()
# and create a volume on space MNI152 (note that this assumes our
# external knowledge that the map is in MNI152 space)
volume = siibra.volumes.from_nifti(nii, 'mni152', "AICHA")

# The text file with label mappings has a custom format. We provide a tsv
# decoder to extract the list of region/label pairs since the txt file is tab
# separated.
volume_info = siibra.retrieval.requests.HttpRequest(f"{base_url}/AICHA1mm_vol3.txt", func=siibra.retrieval.requests.DECODERS['.tsv']).get()
volume_info

# %%
# Now we can add this map to current configuration:
regionnames = volume_info['nom_l'].tolist()
labels = [int(label) for label in volume_info['color']]
aicha_map = siibra.volumes.parcellationmap.from_volume(
    name="AICHA - Atlas of Intrinsic Connectivity of Homotopic Areas",
    volume=volume,
    regionnames=regionnames,
    regionlabels=labels
)

# %%
# Let us plot the full map to test
plotting.plot_roi(aicha_map.fetch())

# %%
# Now let's fetch a specific region
plotting.plot_roi(aicha_map.fetch("G_Fusiform-1-L"))

# %%
# We can already use this map to find spatial features, such as BigBrain
# intensity profiles, gene expressions, volume of interests, sections...
region = aicha_map.parcellation.get_region('S_Rolando-1-L')
intensity_profiles = siibra.features.get(
    region,
    siibra.features.cellular.BigBrainIntensityProfile
)[0]
print(f"{len(intensity_profiles)} {intensity_profiles.name} found.")


# %%
# On the other hand, some features are only anchored to a semantic region
# object and the link to the custom region is not directly known to siibra.
# However, siibra circumvents this by comparing volumes of these regions to
# assign a link between them.
volume = region.get_regional_mask('mni152')
receptor_profiles = siibra.features.get(
    volume,
    siibra.features.molecular.ReceptorDensityFingerprint
)
print(f"{len(receptor_profiles)} {receptor_profiles[0].name} found intersecting with this region.")

# The relation of volume to the anatomical anchor is provided in
# `anchor.last_match_description`
for d in receptor_profiles:
    print(d.anchor.last_match_description)


# %%
# To get a more detailed assignment scores, one can make use of map assignment
# discussed in Anatomical Assignment, see
# :ref:`sphx_glr_examples_05_anatomical_assignment_002_activation_maps.py`.