def setup_options(self):
helpString="""
twolevel_model_building
A pydpiper application designed to work with longitudinal data. LSQ12
and nonlinear registration is used to create a consensus average of
every subject. A second level of LSQ12 and nonlinear registrations is
then used to bring all the consensus averages from each subject into
their own consensus average.
Some assumptions:
* at least two timepoints per subject
* future work should be able to extend this to allow single timepoint subjects
* all images must be similar enough to allow registration
The last point is particularly important: the consensus average building process
aligns every image from each subject to every other image from that subject. Early
developmental data or tumour data, where the first image in the series might not be
alignable to the last image in the series, is thus not suited for this approach.
Data is passed to the application through a CSV file. This file has one line per subject,
with each scan per subject listed on the same line and separated by a comma.
"""
# own options go here
nl.addNlinRegOptionGroup(self.parser)
rf.addGenRegOptionGroup(self.parser)
# TODO: better usage description (once I've figured out what the usage will be ...)
self.parser.set_usage("%prog [options] input.csv")
# set help - note that the format is messed up, something that can be fixed if we upgrade
# from optparse to argparse.
self.parser.set_description(helpString)
def setup_options(self):
"""Add option groups from specific modules"""
rf.addGenRegOptionGroup(self.parser)
addNlinRegOptionGroup(self.parser)
addStatsOptions(self.parser)
self.parser.set_usage("%prog [options] input files")
def setup_options(self):
"""Add option groups from specific modules"""
rf.addGenRegOptionGroup(self.parser)
addNlinRegOptionGroup(self.parser)
addStatsOptions(self.parser)
self.parser.set_usage("%prog [options] input files")
def setup_options(self):
"""Add option groups from specific modules"""
addMBMGroup(self.parser)
rf.addGenRegOptionGroup(self.parser)
lsq6.addLSQ6OptionGroup(self.parser)
lsq12.addLSQ12OptionGroup(self.parser)
nlin.addNlinRegOptionGroup(self.parser)
st.addStatsOptions(self.parser)
self.parser.set_usage("%prog [options] input files")
def setup_options(self):
"""Add option groups from specific modules"""
addMBMGroup(self.parser)
rf.addGenRegOptionGroup(self.parser)
lsq6.addLSQ6OptionGroup(self.parser)
lsq12.addLSQ12OptionGroup(self.parser)
nlin.addNlinRegOptionGroup(self.parser)
st.addStatsOptions(self.parser)
self.parser.set_usage("%prog [options] input files")
def setup_options(self):
addGenRegOptionGroup(self.parser)
addMAGeTOptionGroup(self.parser)
addLSQ12OptionGroup(self.parser)
addNlinRegOptionGroup(self.parser)
# make sure that the default non linear registration tool for MAGeT is set to minctracc
# this is what MAGeT was optimized for, and it's a lot faster than mincANTS
self.parser.set_defaults(reg_method="minctracc")
# similarly set the default linear and non linear protocols.
# TODO: ugly hard coded path? Yes...
self.parser.set_defaults(nlin_protocol="/projects/mice/share/arch/linux-3_2_0-36-generic-x86_64-eglibc-2_15/src/pydpiper/applications_testing/test_data/default_nlin_MAGeT_minctracc_prot.csv")
self.parser.set_defaults(lsq12_protocol="/projects/mice/share/arch/linux-3_2_0-36-generic-x86_64-eglibc-2_15/src/pydpiper/applications_testing/test_data/default_linear_MAGeT_prot.csv")
self.parser.set_usage("%prog [options] input files")
def setup_options(self):
group = OptionGroup(self.parser, "Pairwise non-linear options",
"Options for pairwise non-linear registration of lsq6 or lsq12 aligned brains.")
group.add_option("--input-space", dest="input_space",
type="string", default="lsq6",
help="Option to specify space of input-files. Can be lsq6 (default), lsq12 or native.")
self.parser.add_option_group(group)
"""Add option groups from specific modules"""
rf.addGenRegOptionGroup(self.parser)
og.tmpLongitudinalOptionGroup(self.parser)
st.addStatsOptions(self.parser)
self.parser.set_usage("%prog [options] input files")
def setup_options(self):
group = OptionGroup(
self.parser, "Pairwise non-linear options",
"Options for pairwise non-linear registration of lsq6 or lsq12 aligned brains."
)
group.add_option(
"--input-space",
dest="input_space",
type="string",
default="lsq6",
help=
"Option to specify space of input-files. Can be lsq6 (default), lsq12 or native."
)
self.parser.add_option_group(group)
"""Add option groups from specific modules"""
rf.addGenRegOptionGroup(self.parser)
og.tmpLongitudinalOptionGroup(self.parser)
st.addStatsOptions(self.parser)
self.parser.set_usage("%prog [options] input files")
def setup_options(self):
group = OptionGroup(self.parser, "Registration-chain options",
"Options for registering consecutive timepoints of longitudinal data.")
group.add_option("--avg-time-point", dest="avg_time_point",
type="int", default=1,
help="Time point averaged prior to this registration to get common nlin space.")
group.add_option("--input-space", dest="input_space",
type="string", default="lsq6",
help="Option to specify space of input-files. Can be lsq6 (default), lsq12 or native.")
group.add_option("--common-space-name", dest="common_name",
type="string", default="common",
help="Option to specify a name for the common space. This is useful for the \
creation of more readable output file names. Default is common.")
self.parser.add_option_group(group)
"""Add option groups from specific modules"""
rf.addGenRegOptionGroup(self.parser)
og.tmpLongitudinalOptionGroup(self.parser)
st.addStatsOptions(self.parser)
self.parser.set_usage("%prog [options] input.csv")
def setup_options(self):
helpString = """
twolevel_model_building
A pydpiper application designed to work with longitudinal data. LSQ12
and nonlinear registration is used to create a consensus average of
every subject. A second level of LSQ12 and nonlinear registrations is
then used to bring all the consensus averages from each subject into
their own consensus average.
Some assumptions:
* at least two timepoints per subject
* future work should be able to extend this to allow single timepoint subjects
* all images must be similar enough to allow registration
The last point is particularly important: the consensus average building process
aligns every image from each subject to every other image from that subject. Early
developmental data or tumour data, where the first image in the series might not be
alignable to the last image in the series, is thus not suited for this approach.
Data is passed to the application through a CSV file. This file has one line per subject,
with each scan per subject listed on the same line and separated by a comma.
"""
# own options go here
lsq6.addLSQ6OptionGroup(self.parser)
lsq12.addLSQ12OptionGroup(self.parser)
nl.addNlinRegOptionGroup(self.parser)
rf.addGenRegOptionGroup(self.parser)
st.addStatsOptions(self.parser)
# TODO: better usage description (once I've figured out what the usage will be ...)
self.parser.set_usage("%prog [options] input.csv")
# set help - note that the format is messed up, something that can be fixed if we upgrade
# from optparse to argparse.
self.parser.set_description(helpString)
def setup_options(self):
addGenRegOptionGroup(self.parser)
addMAGeTOptionGroup(self.parser)
addLSQ12OptionGroup(self.parser)
addNlinRegOptionGroup(self.parser)
self.parser.set_usage("%prog [options] input files")