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_outputs():
output_map = dict()
outputs = DataFinder.output_spec()
for key, metadata in output_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(outputs.traits()[key], metakey), value
def test_DataFinder_outputs():
output_map = dict()
outputs = DataFinder.output_spec()
for key, metadata in output_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(outputs.traits()[key], metakey), value
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)
def rawdataChecker(input_file):
# If the input is a single DCM-file instead of a multi-dim-NifTI, we have to fetch all the other files in the series
if input_file.endswith('.dcm'):
from nipype.interfaces.io import DataFinder
from os import path
from nipype import Node
# Setup a datafinder to find the paths to the specific DICOM files
t1FinderNode = Node(DataFinder(), name='t1Finder')
t1FinderNode.inputs.match_regex = '.*\.dcm'
t1FinderNode.inputs.root_paths = path.split(input_file)[0]
return t1FinderNode.run().outputs.out_paths
else:
return input_file # If other datatype just return the same path
def test_DataFinder_inputs():
input_map = dict(
ignore_exception=dict(
nohash=True,
usedefault=True,
),
ignore_regexes=dict(),
match_regex=dict(usedefault=True, ),
max_depth=dict(),
min_depth=dict(),
root_paths=dict(mandatory=True, ),
unpack_single=dict(usedefault=True, ),
)
inputs = DataFinder.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
def test_DataFinder_inputs():
input_map = dict(ignore_exception=dict(nohash=True,
usedefault=True,
),
ignore_regexes=dict(),
match_regex=dict(usedefault=True,
),
max_depth=dict(),
min_depth=dict(),
root_paths=dict(mandatory=True,
),
unpack_single=dict(usedefault=True,
),
)
inputs = DataFinder.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
mandatory_inputs=True,
name='input_node')
mergedOutputs = pathBuildingNode.outputs.copyable_trait_names()
mergedOutputs.extend(fileNames.keys())
outputNode = Node(IdentityInterface(fields=mergedOutputs),
mandatory_inputs=False,
name='output_node')
# ### Structural Data (T1) preprocessing
# In[ ]:
# Setup a datafinder to find the paths to the specific DICOM files
t1FinderNode = Node(DataFinder(), name='t1Finder')
t1FinderNode.inputs.match_regex = '.*\.dcm'
#df = DataFinder(root_paths = T1RawFolder, match_regex = '.*\.dcm')
#firstFile = df.run().outputs.out_paths[0]
# Set recon-all parameters
reconallNode = Node(freesurfer.preprocess.ReconAll(), name='reconall')
#reconallNode.inputs.T1_files = firstFile
#reconallNode.inputs.subjects_dir = subPath
reconallNode.inputs.subject_id = reconallFolderName
reconallNode.inputs.directive = 'all'
reconallNode.inputs.openmp = cpu_count()
# Convert the T1 mgz image to nifti format for later usage
mriConverter = Node(freesurfer.preprocess.MRIConvert(),
name='convertAparcAseg')
return SC_cap_row_filename, SC_dist_row_filename
import numpy as np
debugPath = '/Users/srothmei/Desktop/charite/toronto/Adalberto/debug/'
roi = 68
subid = 'Adalberto'
tracksPath = debugPath
wmBorder_file = debugPath + 'wmborder.npy'
wmborder = np.load(wmBorder_file)
affine_matrix_file = debugPath + 'affine_matrix.npy'
affine_matrix = np.load(affine_matrix_file)
from nipype import Node
from nipype.interfaces.io import DataFinder
tckFinder = Node(DataFinder(match_regex='.*\.npy', root_paths=tracksPath),
name='tckFinder')
res = tckFinder.run()
track_files = res.outputs.out_paths
#
compute_connectivity_row(roi, subid, affine_matrix, wmborder, tracksPath,
track_files)
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)
