def _make_haxby_subject_data(match, response):
sub_files = [
'bold.nii.gz', 'labels.txt', 'mask4_vt.nii.gz',
'mask8b_face_vt.nii.gz', 'mask8b_house_vt.nii.gz',
'mask8_face_vt.nii.gz', 'mask8_house_vt.nii.gz', 'anat.nii.gz'
]
return list_to_archive(Path(match.group(1), f) for f in sub_files)
def _adhd_example_subject(match, request):
contents = [
Path("data", match.group(1), match.expand(r"\1_regressors.csv")),
Path("data", match.group(1),
match.expand(r"\1_rest_tshift_RPI_voreg_mni.nii.gz"))
]
return list_to_archive(contents)
def test_fetch_surf_fsaverage(mesh, tmp_path, request_mocker):
# Define attribute list that nilearn meshs should contain
# (each attribute should eventually map to a _.gii.gz file
# named after the attribute)
mesh_attributes = {
"{}_{}".format(part, side)
for part in
["area", "curv", "infl", "pial", "sphere", "sulc", "thick", "white"]
for side in ["left", "right"]
}
# Mock fsaverage3, 4, 6, 7 download (with actual url)
fs_urls = [
"https://osf.io/asvjk/download",
"https://osf.io/x2j49/download",
"https://osf.io/um5ag/download",
"https://osf.io/q7a5k/download",
]
for fs_url in fs_urls:
request_mocker.url_mapping[fs_url] = list_to_archive(
["{}.gii.gz".format(name) for name in mesh_attributes])
dataset = struct.fetch_surf_fsaverage(mesh, data_dir=str(tmp_path))
assert mesh_attributes.issubset(set(dataset.keys()))
assert dataset.description != ''
def test_fetch_surf_fsaverage5_sphere(tmp_path, request_mocker):
request_mocker.url_mapping["*b79fy*"] = list_to_archive(
["sphere_right.gii", "sphere_left.gii"])
for mesh in ['fsaverage5_sphere']:
dataset = struct.fetch_surf_fsaverage(mesh, data_dir=str(tmp_path))
keys = {'sphere_left', 'sphere_right'}
assert keys.issubset(set(dataset.keys()))
assert dataset.description != ''
def test_fetch_surf_fsaverage(mesh, tmp_path, request_mocker):
keys = {
'pial_left', 'pial_right', 'infl_left', 'infl_right', 'sulc_left',
'sulc_right', 'white_left', 'white_right'
}
request_mocker.url_mapping["*fsaverage.tar.gz"] = list_to_archive([
Path("fsaverage") / "{}.gii".format(k.replace("infl", "inflated"))
for k in keys
])
dataset = struct.fetch_surf_fsaverage(mesh, data_dir=str(tmp_path))
assert keys.issubset(set(dataset.keys()))
assert dataset.description != ''
def test_dict_to_archive(tmp_path):
subdir = tmp_path / "tmp"
subdir.mkdir()
(subdir / "labels.csv").touch()
(subdir / "img.nii.gz").touch()
archive_spec = {
"empty_data":
subdir,
"empty_data_path.txt":
str(subdir),
Path("data", "labels.csv"):
"a,b,c",
Path("data", "img.nii.gz"):
generate_fake_fmri()[0],
Path("a", "b", "c"): (100).to_bytes(length=1,
byteorder="big",
signed=False),
}
targz = _testing.dict_to_archive(archive_spec)
extract_dir = tmp_path / "extract"
extract_dir.mkdir()
archive_path = tmp_path / "archive"
with archive_path.open("wb") as f:
f.write(targz)
with tarfile.open(str(archive_path)) as tarf:
tarf.extractall(str(extract_dir))
img = image.load_img(str(extract_dir / "data" / "img.nii.gz"))
assert img.shape == (10, 11, 12, 17)
with (extract_dir / "a" / "b" / "c").open("rb") as f:
assert int.from_bytes(f.read(), byteorder="big", signed=False) == 100
with open(str(extract_dir / "empty_data" / "labels.csv")) as f:
assert f.read() == ""
zip_archive = _testing.dict_to_archive(
{
"readme.txt": "hello",
"archive": targz
}, "zip")
with archive_path.open("wb") as f:
f.write(zip_archive)
with zipfile.ZipFile(str(archive_path)) as zipf:
with zipf.open("archive", "r") as f:
assert f.read() == targz
from_list = _testing.list_to_archive(archive_spec.keys())
with archive_path.open("wb") as f:
f.write(from_list)
with tarfile.open(str(archive_path)) as tarf:
assert sorted(map(Path, tarf.getnames())) == sorted(
list(map(Path, archive_spec.keys())) +
[Path("."), Path("a"),
Path("a", "b"),
Path("data")])
