''' Testing module for nibetaseries.interfaces.nilearn '''
import nibabel as nib
import numpy as np
import pandas as pd
import os
from ..nilearn import AtlasConnectivity, CensorVolumes
def test_censor_volumes(tmp_path, betaseries_file, brainmask_file):
outlier_file = tmp_path / 'betaseries_outlier.nii.gz'
# make an outlier volume
outlier_idx = 6
beta_img = nib.load(str(betaseries_file))
beta_data = beta_img.get_fdata()
beta_data[..., outlier_idx] += 1000
beta_img.__class__(
beta_data, beta_img.affine, beta_img.header).to_filename(str(outlier_file))
censor_volumes = CensorVolumes(timeseries_file=str(outlier_file),
mask_file=str(brainmask_file))
res = censor_volumes.run()
assert nib.load(res.outputs.censored_file).shape[-1] == beta_img.shape[-1] - 1
assert res.outputs.outliers[outlier_idx]
def test_atlas_connectivity(betaseries_file, atlas_file, atlas_lut):
# read in test files
bs_data = nib.load(str(betaseries_file)).get_data()
atlas_lut_df = pd.read_csv(str(atlas_lut), sep='\t')
# expected output
pcorr = np.corrcoef(bs_data.squeeze())
np.fill_diagonal(pcorr, np.NaN)
regions = atlas_lut_df['regions'].values
pcorr_df = pd.DataFrame(pcorr, index=regions, columns=regions)
expected_zcorr_df = pcorr_df.apply(lambda x: (np.log(1 + x) - np.log(1 - x)) * 0.5)
# run instance of AtlasConnectivity
ac = AtlasConnectivity(timeseries_file=str(betaseries_file),
atlas_file=str(atlas_file),
atlas_lut=str(atlas_lut))
res = ac.run()
output_zcorr_df = pd.read_csv(res.outputs.correlation_matrix,
na_values='n/a',
delimiter='\t',
index_col=0)
os.remove(res.outputs.correlation_matrix)
# test equality of the matrices up to 3 decimals
pd.testing.assert_frame_equal(output_zcorr_df, expected_zcorr_df,
check_less_precise=3)
