def main():
parser = ArgumentParser(description=DESCRIP,
epilog=EPILOG,
formatter_class=RawDescriptionHelpFormatter)
parser.add_argument('filename', type=str,
help='4D image filename')
parser.add_argument('--out-path', type=str,
help='path for output image files')
parser.add_argument('--out-fname-label', type=str,
help='mid part of output image filenames')
parser.add_argument('--ncomponents', type=int, default=10,
help='number of PCA components to write')
# parse the command line
args = parser.parse_args()
# process inputs
filename = args.filename
out_path = args.out_path
out_root = args.out_fname_label
ncomps = args.ncomponents
# collect extension for output images
froot, ext, gz = splitext_addext(filename)
pth, fname = os.path.split(froot)
if out_path is None:
out_path = pth
if out_root is None:
out_root = fname
img = nipy.load_image(filename)
res = nads.screen(img, ncomps)
nads.write_screen_res(res, out_path, out_root, ext + gz)
def main():
parser = ArgumentParser(description=DESCRIP,
epilog=EPILOG,
formatter_class=RawDescriptionHelpFormatter)
parser.add_argument('examples_path', type=str,
help='filename of example or directory containing '
'examples to run')
parser.add_argument('--log-path', type=str, default='',
help='path for output logs (default is cwd)')
parser.add_argument('--excludex', type=str, action='append', default=[],
help='regex for files to exclude (add more than one '
'--excludex option for more than one regex filter')
args = parser.parse_args()
# Proc runner
eg_path = abspath(expanduser(args.examples_path))
if args.log_path == '':
log_path = abspath(os.getcwd())
else:
log_path = abspath(expanduser(args.log_path))
excludexes = [re.compile(s) for s in args.excludex]
if isfile(eg_path): # example was a file
proc_logger = PyProcLogger(log_path=log_path,
working_path=dirname(eg_path))
print("Running " + eg_path)
stdout, stderr, code = proc_logger.run_pipes(eg_path)
print('==== Stdout ====')
print(stdout)
print('==== Stderr ====')
print(stderr)
sys.exit(code)
# Multi-run with logging to file
proc_logger = PyProcLogger(log_path=log_path,
working_path=eg_path)
fails = 0
with open(pjoin(log_path, 'summary.txt'), 'wt') as f:
for dirpath, dirnames, filenames in os.walk(eg_path):
for fname in filenames:
full_fname = pjoin(dirpath, fname)
if fname.endswith(".py"):
print(fname, end=': ')
sys.stdout.flush()
for excludex in excludexes:
if excludex.search(fname):
_record('SKIP', fname, f)
break
else: # run test
cmd_name = full_fname.replace(eg_path + psep, '')
cmd_name = cmd_name.replace(psep, '-')
code = proc_logger(cmd_name, full_fname, cwd=dirpath)
if code == 0:
_record('OK', fname, f)
else:
fails += 1
_record('FAIL', fname, f)
sys.exit(fails if fails < 255 else 255)
for k in range(3):
pk = gold_ppm[mask][:, k]
qk = ppm[mask][:, k]
PQ = np.sum(np.sqrt(np.maximum(pk * qk, 0)))
P = np.sum(pk)
Q = np.sum(qk)
dices[k] = 2 * PQ / float(P + Q)
return dices
# Parse command line
description = 'Perform brain tissue classification from skull stripped T1 \
image in CSF, GM and WM. If no mask image is provided, the mask is defined by \
thresholding the input image above zero (strictly).'
parser = ArgumentParser(description=description)
parser.add_argument('img', metavar='img', nargs='+', help='input image')
parser.add_argument('--mask', dest='mask', help='mask image')
parser.add_argument('--niters', dest='niters',
help='number of iterations (default=%d)' % 25)
parser.add_argument('--beta', dest='beta',
help='Markov random field beta parameter (default=%f)' % 0.5)
parser.add_argument('--ngb_size', dest='ngb_size',
help='Markov random field neighborhood system (default=%d)' % 6)
parser.add_argument('--probc', dest='probc', help='csf probability map')
parser.add_argument('--probg', dest='probg',
help='gray matter probability map')
parser.add_argument('--probw', dest='probw',
help='white matter probability map')
args = parser.parse_args()
positive-valued.
See the `nipy.algorithms.group.ParcelAnalysis` class for more general
usage information.
"""
from os.path import join
from glob import glob
from nipy import load_image
from nipy.algorithms.group import parcel_analysis
from nipy.externals.argparse import ArgumentParser
# Parse command line
description = 'Run a parcel-based second-level analysis from a set of\
first-level effect images.'
parser = ArgumentParser(description=description)
parser.add_argument('con_path', metavar='con_path',
help='directory where 1st-level images are to be found')
parser.add_argument('msk_file', metavar='msk_file',
help='group mask file')
parser.add_argument('parcel_file', metavar='parcel_file',
help='parcellation image file')
args = parser.parse_args()
# Load first-level images
con_files = glob(join(args.con_path, '*.nii'))
con_imgs = [load_image(f) for f in con_files]
# Load group mask
msk_img = load_image(args.msk_file)
for k in range(3):
pk = gold_ppm[mask][:, k]
qk = ppm[mask][:, k]
PQ = np.sum(np.sqrt(np.maximum(pk * qk, 0)))
P = np.sum(pk)
Q = np.sum(qk)
dices[k] = 2 * PQ / float(P + Q)
return dices
# Parse command line
description = 'Perform brain tissue classification from skull stripped T1 \
image in CSF, GM and WM. If no mask image is provided, the mask is defined by \
thresholding the input image above zero (strictly).'
parser = ArgumentParser(description=description)
parser.add_argument('img', metavar='img', nargs='+', help='input image')
parser.add_argument('--mask', dest='mask', help='mask image')
parser.add_argument('--niters',
dest='niters',
help='number of iterations (default=%d)' % 25)
parser.add_argument('--beta',
dest='beta',
help='Markov random field beta parameter (default=%f)' %
0.5)
parser.add_argument(
'--ngb_size',
dest='ngb_size',
help='Markov random field neighborhood system (default=%d)' % 6)
parser.add_argument('--probc', dest='probc', help='csf probability map')
parser.add_argument('--probg',
positive-valued.
See the `nipy.algorithms.group.ParcelAnalysis` class for more general
usage information.
"""
from os.path import join
from glob import glob
from nipy import load_image
from nipy.algorithms.group import parcel_analysis
from nipy.externals.argparse import ArgumentParser
# Parse command line
description = "Run a parcel-based second-level analysis from a set of\
first-level effect images."
parser = ArgumentParser(description=description)
parser.add_argument("con_path", metavar="con_path", help="directory where 1st-level images are to be found")
parser.add_argument("msk_file", metavar="msk_file", help="group mask file")
parser.add_argument("parcel_file", metavar="parcel_file", help="parcellation image file")
args = parser.parse_args()
# Load first-level images
con_files = glob(join(args.con_path, "*.nii"))
con_imgs = [load_image(f) for f in con_files]
# Load group mask
msk_img = load_image(args.msk_file)
# Load parcellation
parcel_img = load_image(args.parcel_file)
positive-valued.
See the `nipy.algorithms.group.ParcelAnalysis` class for more general
usage information.
"""
from os.path import join
from glob import glob
from nipy import load_image
from nipy.algorithms.group import parcel_analysis
from nipy.externals.argparse import ArgumentParser
# Parse command line
description = 'Run a parcel-based second-level analysis from a set of\
first-level effect images.'
parser = ArgumentParser(description=description)
parser.add_argument('con_path', metavar='con_path',
help='directory where 1st-level images are to be found')
parser.add_argument('msk_file', metavar='msk_file',
help='group mask file')
parser.add_argument('parcel_file', metavar='parcel_file',
help='parcellation image file')
args = parser.parse_args()
# Load first-level images
con_files = glob(join(args.con_path, '*.nii'))
con_imgs = [load_image(f) for f in con_files]
# Load group mask
msk_img = load_image(args.msk_file)
for k in range(3):
pk = gpm[k].get_data()[mask]
qk = ppm.get_data()[mask][:, k]
PQ = np.sum(np.sqrt(np.maximum(pk * qk, 0)))
P = np.sum(pk)
Q = np.sum(qk)
dices[k] = 2 * PQ / float(P + Q)
return dices
# Parse command line
description = 'Perform brain tissue classification from skull stripped T1 \
image in CSF, GM and WM. If no mask image is provided, the mask is defined by \
thresholding the input image above zero (strictly).'
parser = ArgumentParser(description=description)
parser.add_argument('img', metavar='img', nargs='+', help='input image')
parser.add_argument('--mask', dest='mask', help='mask image')
parser.add_argument('--k_csf', dest='k_csf',
help='number of CSF classes (default=%d)' % K_CSF)
parser.add_argument('--k_gm', dest='k_gm',
help='number of GM classes (default=%d)' % K_GM)
parser.add_argument('--k_wm', dest='k_wm',
help='number of WM classes (default=%d)' % K_WM)
parser.add_argument('--niters', dest='niters',
help='number of iterations (default=%d)' % NITERS)
parser.add_argument('--beta', dest='beta',
help='Markov random field beta parameter (default=%f)' % BETA)
parser.add_argument('--scheme', dest='scheme',
help='message passing scheme (mf, icm or bp, default=%s)' % SCHEME)
parser.add_argument('--noise', dest='noise',
