def _mask_butterfly(mask, nii, newname, restart):
"""Apply mask to nii, save as newname."""
print("Masking {0} with {1}.".format(os.path.basename(nii), mask))
# Create a name for the temp roi data
# then use the mask, saving with the tmpname
name = newname + "_" + os.path.basename(nii)
name = os.path.join(path, "roinii", name)
# Ensure we are compressing the
# data by adding .nii.gz instead of,
# say, .nii
fullname = os.path.basename(name)
filename, fileextension = os.path.splitext(fullname)
name = os.path.join(
os.path.dirname(name), "{0}.nii.gz".format(filename))
# If the file exists and we are
# restarting continue on.
if restart and os.path.isfile(name):
print("{0} exists, continuing on.".format(name))
else:
# Mask! Save as name
masknii(mask, nii, save=name)
return name
def _mask_butterfly(mask, nii, newname, restart):
"""Apply mask to nii, save as newname."""
print("Masking {0} with {1}.".format(os.path.basename(nii), mask))
# Create a name for the temp roi data
# then use the mask, saving with the tmpname
name = newname + "_" + os.path.basename(nii)
name = os.path.join(path, "roinii", name)
# Ensure we are compressing the
# data by adding .nii.gz instead of,
# say, .nii
fullname = os.path.basename(name)
filename, fileextension = os.path.splitext(fullname)
name = os.path.join(os.path.dirname(name), "{0}.nii.gz".format(filename))
# If the file exists and we are
# restarting continue on.
if restart and os.path.isfile(name):
print("{0} exists, continuing on.".format(name))
else:
# Mask! Save as name
masknii(mask, nii, save=name)
return name
def create(args):
"""Create a roi dataset using all Ploran 2007 (i.e. butterfly)
datasets.
Parameters
----------
mask - name of a roi found in the Harvard Oxford atlas (see roi
package for details)
newname - what (if anything) to rename the roi data as,
often the default name (i.e. mask) are rather long
subdatatable - a dictionary whose keys are subject numbers
and whose values are absoluate paths to that Ss
whole brain (functional) data.
basepath - the top-level directory where all the Ss BOLD
(and other) data lives.
"""
# Process args, a dunmb way to make
# this function compatible with pool.map()
mask, newname, subdatatable, basepath = args
print("Mask is {0}.".format(mask))
maskednames = []
for s in sorted(subdatatable.keys()):
print("Running subject {0}.".format(s))
# Look up the location of that Ss data,
# and mask it, finally save the masked
# file to disk
datadir = subdatatable[s]
saveas = os.path.join(basepath,
'roinii', "{0}_{1}.nii.gz".format(newname, s))
masknii(mask, datadir, save=saveas)
def create(args):
"""Create a roi dataset using all Ploran 2007 (i.e. butterfly)
datasets.
Parameters
----------
mask - name of a roi found in the Harvard Oxford atlas (see roi
package for details)
newname - what (if anything) to rename the roi data as,
often the default name (i.e. mask) are rather long
subdatatable - a dictionary whose keys are subject numbers
and whose values are absoluate paths to that Ss
whole brain (functional) data.
basepath - the top-level directory where all the Ss BOLD
(and other) data lives.
"""
# Process args, a dunmb way to make
# this function compatible with pool.map()
mask, newname, subdatatable, basepath = args
print("Mask is {0}.".format(mask))
maskednames = []
for s in sorted(subdatatable.keys()):
print("Running subject {0}.".format(s))
# Look up the location of that Ss data,
# and mask it, finally save the masked
# file to disk
datadir = subdatatable[s]
saveas = os.path.join(basepath, 'roinii',
"{0}_{1}.nii.gz".format(newname, s))
masknii(mask, datadir, save=saveas)
