本文整理匯總了Python中configargparse.ArgParser.set_defaults方法的典型用法代碼示例。如果您正苦於以下問題：Python ArgParser.set_defaults方法的具體用法？Python ArgParser.set_defaults怎麽用？Python ArgParser.set_defaults使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類configargparse.ArgParser的用法示例。
在下文中一共展示了ArgParser.set_defaults方法的6個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: _mk_lsq12_parser
# 需要導入模塊: from configargparse import ArgParser [as 別名]
# 或者: from configargparse.ArgParser import set_defaults [as 別名]
def _mk_lsq12_parser():
p = ArgParser(add_help=False)
# group = parser.add_argument_group("LSQ12 registration options",
# "Options for performing a pairwise, affine registration")
p.set_defaults(run_lsq12=True)
p.add_argument("--run-lsq12", dest="run_lsq12",
action="store_true",
help="Actually run the 12 parameter alignment [default = %(default)s]")
p.add_argument("--no-run-lsq12", dest="run_lsq12",
action="store_false",
help="Opposite of --run-lsq12")
p.add_argument("--lsq12-max-pairs", dest="max_pairs",
type=parse_nullable_int, default=25,
help="Maximum number of pairs to register together ('None' implies all pairs). "
"[Default = %(default)s]")
p.add_argument("--lsq12-likefile", dest="like_file",
type=str, default=None,
help="Can optionally specify a 'like'-file for resampling at the end of pairwise "
"alignment. Default is None, which means that the input file will be used. "
"[Default = %(default)s]")
p.add_argument("--lsq12-protocol", dest="protocol",
type=str,
help="Can optionally specify a registration protocol that is different from defaults. "
"Parameters must be specified as in the following example: \n"
"applications_testing/test_data/minctracc_example_linear_protocol.csv \n"
"[Default = %(default)s].")
return p示例2: _mk_segmentation_parser
# 需要導入模塊: from configargparse import ArgParser [as 別名]
# 或者: from configargparse.ArgParser import set_defaults [as 別名]
def _mk_segmentation_parser(parser : ArgParser, default : bool):
group = parser.add_argument_group("Segmentation", "Segmentation options.")
group.add_argument("--run-maget", action='store_true', dest="run_maget",
help="Run MAGeT segmentation. [default = %(default)s]")
group.add_argument("--no-run-maget", dest="run_maget",
action='store_false', help="Don't run MAGeT segmentation")
parser.set_defaults(run_maget=True)
return parser示例3: _mk_thickness_parser
# 需要導入模塊: from configargparse import ArgParser [as 別名]
# 或者: from configargparse.ArgParser import set_defaults [as 別名]
def _mk_thickness_parser(parser : ArgParser):
group = parser.add_argument_group("Thickness", "Thickness calculation options.")
group.add_argument("--run-thickness", action='store_true', dest="run_thickness",
help="Run thickness computation.")
group.add_argument("--no-run-thickness", action='store_false', dest="run_thickness",
help="Don't run thickness computation.")
parser.set_defaults(run_thickness=True)
group.add_argument("--label-mapping", type=str, dest="label_mapping",
help="path to CSV file mapping; see minclaplace/wiki/LaplaceGrid")
group.add_argument("--atlas-fwhm", dest="atlas_fwhm", type=float, # default ?!
help="Blurring kernel (mm) for atlas")
group.add_argument("--thickness-fwhm", dest="thickness_fwhm", type=float, # default??
help="Blurring kernel (mm) for cortical surfaces")
return parser示例4: _mk_stats_parser
# 需要導入模塊: from configargparse import ArgParser [as 別名]
# 或者: from configargparse.ArgParser import set_defaults [as 別名]
def _mk_stats_parser():
p = ArgParser(add_help=False)
# p.add_argument_group("Statistics options",
# "Options for calculating statistics.")
default_fwhms = "0.2"
p.set_defaults(stats_kernels=default_fwhms)
p.set_defaults(calc_stats=True)
p.add_argument("--calc-stats", dest="calc_stats",
action="store_true",
help="Calculate statistics at the end of the registration. [Default = %(default)s]")
p.add_argument("--no-calc-stats", dest="calc_stats",
action="store_false",
help="If specified, statistics are not calculated. Opposite of --calc-stats.")
p.add_argument("--stats-kernels", dest="stats_kernels",
type=str,
help="comma separated list of blurring kernels for analysis. [Default = %(default)s].")
return p示例5: _mk_lsq12_parser
# 需要導入模塊: from configargparse import ArgParser [as 別名]
# 或者: from configargparse.ArgParser import set_defaults [as 別名]
def _mk_lsq12_parser():
p = ArgParser(add_help=False)
# group = parser.add_argument_group("LSQ12 registration options",
# "Options for performing a pairwise, affine registration")
p.set_defaults(run_lsq12=True)
p.set_defaults(generate_tournament_style_lsq12_avg=False)
p.add_argument("--run-lsq12", dest="run_lsq12",
action="store_true",
help="Actually run the 12 parameter alignment [default = %(default)s]")
p.add_argument("--no-run-lsq12", dest="run_lsq12",
action="store_false",
help="Opposite of --run-lsq12")
p.add_argument("--lsq12-max-pairs", dest="max_pairs",
type=parse_nullable_int, default=25,
help="Maximum number of pairs to register together ('None' implies all pairs). "
"[Default = %(default)s]")
p.add_argument("--lsq12-likefile", dest="like_file",
type=str, default=None,
help="Can optionally specify a 'like'-file for resampling at the end of pairwise "
"alignment. Default is None, which means that the input file will be used. "
"[Default = %(default)s]")
p.add_argument("--lsq12-protocol", dest="protocol",
type=str,
help="Can optionally specify a registration protocol that is different from defaults. "
"Parameters must be specified as in the following example: \n"
"applications_testing/test_data/minctracc_example_linear_protocol.csv \n"
"[Default = %(default)s].")
#p.add_argument("--generate-tournament-style-lsq12-avg", dest="generate_tournament_style_lsq12_avg",
# action="store_true",
# help="Instead of creating the average of the lsq12 resampled files "
# "by simply averaging them directly, create an iterative average "
# "as follows. Perform a non linear registration between pairs "
# "of files. Resample each file halfway along that transformation "
# "in order for them to end up in the middle. Average those two files. "
# "Then continue on to the next level as in a tournament. [default = %(default)s]")
#p.add_argument("--no-generate-tournament-style-lsq12-avg", dest="generate_tournament_style_lsq12_avg",
# action="store_false",
# help="Opposite of --generate-tournament-style-lsq12-avg")
return p示例6: _mk_lsq6_parser
# 需要導入模塊: from configargparse import ArgParser [as 別名]
# 或者: from configargparse.ArgParser import set_defaults [as 別名]
def _mk_lsq6_parser(with_nuc : bool = True,
with_inormalize : bool = True):
p = ArgParser(add_help=False)
p.set_defaults(lsq6_method="lsq6_large_rotations")
p.set_defaults(nuc = True if with_nuc else False)
p.set_defaults(inormalize = True if with_inormalize else False)
p.set_defaults(copy_header_info=False)
# TODO: should this actually be part of the LSQ6 component? What would it return in this case?
p.set_defaults(run_lsq6=True)
p.add_argument("--run-lsq6", dest="run_lsq6",
action="store_true",
help="Actually run the 6 parameter alignment [default = %(default)s]")
p.add_argument("--no-run-lsq6", dest="run_lsq6",
action="store_false",
help="Opposite of --run-lsq6")
# TODO should be part of some mutually exclusive group ...
p.add_argument("--init-model", dest="init_model",
type=str, default=None,
help="File in standard space in the initial model. The initial model "
"can also have a file in native space and potentially a transformation "
"file. See our wiki (https://wiki.mouseimaging.ca/) for detailed "
"information on initial models. [Default = %(default)s]")
p.add_argument("--lsq6-target", dest="lsq6_target",
type=str, default=None,
help="File to be used as the target for the initial (often 6-parameter) alignment. "
"[Default = %(default)s]")
p.add_argument("--bootstrap", dest="bootstrap",
action="store_true", default=False,
help="Use the first input file to the pipeline as the target for the "
"initial (often 6-parameter) alignment. [Default = %(default)s]")
# TODO: add information about the pride of models to the code in such a way that it
# is reflected on GitHub
p.add_argument("--pride-of-models", dest="pride_of_models",
type=str, default=None,
help="(selected longitudinal pipelines only!) Specify a csv file that contains the mapping of "
"all your initial models at different time points. The idea is that you might "
"want to use different initial models for the time points in your data. "
"The csv file should have one column called \"model_file\", and one column "
"called \"time_point\". The time points can be given in either integer values "
"or float values. Each model file should point to the file in standard space "
"for that particular model. [Default = %(default)s]")
# TODO: do we need to implement this option? This was for Kieran Short, but the procedure
# he will be using in the future most likely will not involve this option.
# group.add_argument("--lsq6-alternate-data-prefix", dest="lsq6_alternate_prefix",
# type=str, default=None,
# help="Specify a prefix for an augmented data set to use for the 6 parameter "
# "alignment. Assumptions: there is a matching alternate file for each regular input "
# "file, e.g. input files are: input_1.mnc input_2.mnc ... input_n.mnc. If the "
# "string provided for this flag is \"aug_\", then the following files should exist: "
# "aug_input_1.mnc aug_input_2.mnc ... aug_input_n.mnc. These files are assumed to be "
# "in the same orientation/location as the regular input files. They will be used for "
# "for the 6 parameter alignment. The transformations will then be used to transform "
# "the regular input files, with which the pipeline will continue.")
p.add_argument("--lsq6-simple", dest="lsq6_method",
action="store_const", const="lsq6_simple",
help="Run a 6 parameter alignment assuming that the input files are roughly "
"aligned: same space, similar orientation. Keep in mind that if you use an "
"initial model with both a standard and a native space, the assumption is "
"that the input files are already roughly aligned to the native space. "
"Three iterations are run: 1st is 17 times stepsize blur, 2nd is 9 times "
"stepsize gradient, 3rd is 4 times stepsize blur. [Default = %(default)s]")
p.add_argument("--lsq6-centre-estimation", dest="lsq6_method",
action="store_const", const="lsq6_centre_estimation",
help="Run a 6 parameter alignment assuming that the input files have a "
"similar orientation, but are scanned in different coils/spaces. [Default = %(default)s]")
p.add_argument("--lsq6-large-rotations", dest="lsq6_method",
action="store_const", const="lsq6_large_rotations",
help="Run a 6 parameter alignment assuming that the input files have a random "
"orientation and are scanned in different coils/spaces. A brute force search over "
"the x,y,z rotation space is performed to find the best 6 parameter alignment. "
"[Default = %(default)s]")
p.add_argument("--lsq6-large-rotations-tmp-dir", dest="rotation_tmp_dir",
type=str, default="/dev/shm/",
help="Specify the directory that rotational_minctracc.py uses for temporary files. "
"By default we use /dev/shm/, because this program involves a lot of I/O, and "
"this is probably one of the fastest way to provide this. [Default = %(default)s]")
p.add_argument("--lsq6-large-rotations-parameters", dest="rotation_params",
type=str, default="5,4,10,8",
help="Settings for the large rotation alignment. factor=factor based on smallest file "
"resolution: 1) blur factor, 2) resample step size factor, 3) registration step size "
"factor, 4) w_translations factor ***** if you are working with mouse brain data "
" the defaults do not have to be based on the file resolution; a default set of "
" settings works for all mouse brain. In order to use those setting, specify: "
"\"mousebrain\" as the argument for this option. ***** [default = %(default)s]")
p.add_argument("--lsq6-rotational-range", dest="rotation_range",
type=int, default=50,
help="Settings for the rotational range in degrees when running the large rotation "
"alignment. [Default = %(default)s]")
p.add_argument("--lsq6-rotational-interval", dest="rotation_interval",
type=int, default=10,
help="Settings for the rotational interval in degrees when running the large rotation "
"alignment. [Default = %(default)s]")
p.add_argument("--nuc", dest="nuc",
action="store_true",
help="Perform non-uniformity correction. [Default = %(default)s]")
p.add_argument("--no-nuc", dest="nuc",
action="store_false",
help="If specified, do not perform non-uniformity correction. Opposite of --nuc.")
p.add_argument("--inormalize", dest="inormalize",
action="store_true",
#.........這裏部分代碼省略.........