def responders(l2_dir,
roi="DSURQEc_ctx",
data_root="~/ni_data/ofM.dr",
roi_root="~/ni_data/templates/roi"):
data_regex = "(?P<subject>.+)/tstat1.nii.gz"
data_path = "{data_root}/l2/{l2_dir}/".format(data_root=data_root,
l2_dir=l2_dir)
data_path = path.expanduser(data_path)
roi_path = "{roi_root}/{roi}.nii.gz".format(roi_root=roi_root, roi=roi)
roi_path = path.expanduser(roi_path)
data_find = DataFinder()
data_find.inputs.root_paths = data_path
data_find.inputs.match_regex = path.join(data_path, data_regex)
found_data = data_find.run().outputs
masker = NiftiMasker(mask_img=roi_path)
voxeldf = pd.DataFrame({})
for subject, data_file in zip(found_data.subject, found_data.out_paths):
subject_data = {}
print(subject, data_file)
img = nib.load(data_file)
img = masker.fit_transform(img)
img = img.flatten()
subject_data["subject"] = subject
for i in img:
voxel_data = deepcopy(subject_data)
voxel_data["t"] = i
df_ = pd.DataFrame(voxel_data, index=[None])
voxeldf = pd.concat([voxeldf, df_])
voxeldf.to_csv('{}/ctx_responders.csv'.format(data_path))
def test_datafinder_unpack():
outdir = mkdtemp()
single_res = os.path.join(outdir, "findme.txt")
open(single_res, 'a').close()
open(os.path.join(outdir, "dontfindme"), 'a').close()
from nipype.interfaces.io import DataFinder
df = DataFinder()
df.inputs.root_paths = outdir
df.inputs.match_regex = '.+/(?P<basename>.+)\.txt'
df.inputs.unpack_single = True
result = df.run()
print result.outputs.out_paths
yield assert_equal, result.outputs.out_paths, single_res
def test_datafinder_unpack():
outdir = mkdtemp()
single_res = os.path.join(outdir, "findme.txt")
open(single_res, 'a').close()
open(os.path.join(outdir, "dontfindme"), 'a').close()
from nipype.interfaces.io import DataFinder
df = DataFinder()
df.inputs.root_paths = outdir
df.inputs.match_regex = '.+/(?P<basename>.+)\.txt'
df.inputs.unpack_single = True
result = df.run()
print result.outputs.out_paths
yield assert_equal, result.outputs.out_paths, single_res
def test_datafinder_depth():
outdir = mkdtemp()
os.makedirs(os.path.join(outdir, '0', '1', '2', '3'))
from nipype.interfaces.io import DataFinder
df = DataFinder()
df.inputs.root_paths = os.path.join(outdir, '0')
for min_depth in range(4):
for max_depth in range(min_depth, 4):
df.inputs.min_depth = min_depth
df.inputs.max_depth = max_depth
result = df.run()
expected = [str(x) for x in range(min_depth, max_depth + 1)]
for path, exp_fname in zip(result.outputs.out_paths, expected):
_, fname = os.path.split(path)
yield assert_equal, fname, exp_fname
shutil.rmtree(outdir)
def test_datafinder_depth():
outdir = mkdtemp()
os.makedirs(os.path.join(outdir, '0', '1', '2', '3'))
from nipype.interfaces.io import DataFinder
df = DataFinder()
df.inputs.root_paths = os.path.join(outdir, '0')
for min_depth in range(4):
for max_depth in range(min_depth, 4):
df.inputs.min_depth = min_depth
df.inputs.max_depth = max_depth
result = df.run()
expected = [str(x) for x in range(min_depth, max_depth + 1)]
for path, exp_fname in zip(result.outputs.out_paths, expected):
_, fname = os.path.split(path)
yield assert_equal, fname, exp_fname
shutil.rmtree(outdir)
def responders(
l2_dir,
roi="dsurqec_200micron_roi-dr",
data_root="~/ni_data/ofM.dr",
roi_root='/usr/share/mouse-brain-atlases',
save_inplace=True,
save_as='',
):
data_regex = "(?P<subject>.+)/.*?_tstat\.nii\.gz"
data_path = "{data_root}/l2/{l2_dir}/".format(data_root=data_root,
l2_dir=l2_dir)
data_path = path.expanduser(data_path)
roi_path = "{roi_root}/{roi}.nii".format(roi_root=roi_root, roi=roi)
roi_path = path.expanduser(roi_path)
data_find = DataFinder()
data_find.inputs.root_paths = data_path
data_find.inputs.match_regex = path.join(data_path, data_regex)
found_data = data_find.run().outputs
print(found_data)
masker = NiftiMasker(mask_img=roi_path)
voxeldf = pd.DataFrame({})
for subject, data_file in zip(found_data.subject, found_data.out_paths):
subject_data = {}
print(subject, data_file)
img = nib.load(data_file)
img = masker.fit_transform(img)
img = img.flatten()
subject_data["subject"] = subject
for i in img:
voxel_data = deepcopy(subject_data)
voxel_data["t"] = i
df_ = pd.DataFrame(voxel_data, index=[None])
voxeldf = pd.concat([voxeldf, df_])
if save_inplace:
voxeldf.to_csv('{}/ctx_responders.csv'.format(data_path))
else:
voxeldf.to_csv(path.abspath(path.expanduser(save_as)))
def test_datafinder_copydir():
outdir = mkdtemp()
open(os.path.join(outdir, "findme.txt"), 'a').close()
open(os.path.join(outdir, "dontfindme"), 'a').close()
open(os.path.join(outdir, "dontfindmealsotxt"), 'a').close()
open(os.path.join(outdir, "findmetoo.txt"), 'a').close()
open(os.path.join(outdir, "ignoreme.txt"), 'a').close()
open(os.path.join(outdir, "alsoignore.txt"), 'a').close()
from nipype.interfaces.io import DataFinder
df = DataFinder()
df.inputs.root_paths = outdir
df.inputs.match_regex = '.+/(?P<basename>.+)\.txt'
df.inputs.ignore_regexes = ['ignore']
result = df.run()
expected = ["findme.txt", "findmetoo.txt"]
for path, expected_fname in zip(result.outputs.out_paths, expected):
_, fname = os.path.split(path)
yield assert_equal, fname, expected_fname
yield assert_equal, result.outputs.basename, ["findme", "findmetoo"]
shutil.rmtree(outdir)
def test_datafinder_copydir():
outdir = mkdtemp()
open(os.path.join(outdir, "findme.txt"), 'a').close()
open(os.path.join(outdir, "dontfindme"), 'a').close()
open(os.path.join(outdir, "dontfindmealsotxt"), 'a').close()
open(os.path.join(outdir, "findmetoo.txt"), 'a').close()
open(os.path.join(outdir, "ignoreme.txt"), 'a').close()
open(os.path.join(outdir, "alsoignore.txt"), 'a').close()
from nipype.interfaces.io import DataFinder
df = DataFinder()
df.inputs.root_paths = outdir
df.inputs.match_regex = '.+/(?P<basename>.+)\.txt'
df.inputs.ignore_regexes = ['ignore']
result = df.run()
expected = ["findme.txt", "findmetoo.txt"]
for path, expected_fname in zip(result.outputs.out_paths, expected):
_, fname = os.path.split(path)
yield assert_equal, fname, expected_fname
yield assert_equal, result.outputs.basename, ["findme", "findmetoo"]
shutil.rmtree(outdir)
from nipype.interfaces.io import DataFinder df = DataFinder() df.inputs.root_paths = '/tmp/exp_01' df.inputs.match_regex = '.+/(?P<series_dir>.+)/(?P<basename>.+)\.nii.gz' result = df.run() print(result.outputs.out_paths)