# Paste your code here
import numpy as np
from nilearn.regions.tests.test_signal_extraction import get_data
from nilearn import datasets
from nilearn.maskers import NiftiLabelsMasker
from nibabel import Nifti1Image

# func data
dataset = datasets.fetch_development_fmri(n_subjects=1)
func_filename = dataset.func[0]

# atlas data
atlas = datasets.fetch_atlas_harvard_oxford('cort-maxprob-thr25-2mm')

# create a mask with 3 regions
labels_data = get_data(atlas.maps)
mask_data = (labels_data == 1) + (labels_data == 2) + (labels_data == 5)
mask_img = Nifti1Image(mask_data.astype(np.int8), atlas.maps.affine)

# Instantiate the masker with label image and label values
masker = NiftiLabelsMasker(atlas.maps,
                           labels=atlas.labels,
                           mask_img=mask_img,
                           standardize=True,
                           keep_masked_labels=False
                           )

# fit and transform the masker
_ = masker.fit_transform(func_filename)

# get the region ids and names before and after masking
initial_region_ids = np.unique(labels_data)
initial_region_id_names = {region_id: atlas.labels[i] for i, region_id in enumerate(initial_region_ids)}
print(initial_region_id_names)

region_ids_after_masking = masker.region_ids_
region_names_after_masking = masker.region_names_
print(masker.region_ids_)
print(masker.region_names_)