def get_models_list():
"""Get a list of all available composite models
Returns:
list of str: A list of available model names.
"""
from mdt.lib.components import get_component_list
return list(sorted(get_component_list('composite_models')))
def get_models_meta_info():
"""Get the meta information tags for all the models returned by get_models_list()
Returns:
dict of dict: The first dictionary indexes the model names to the meta tags, the second holds the meta
information.
"""
from mdt.lib.components import get_meta_info, get_component_list
return {model: get_meta_info('composite_models', model) for model in get_component_list('composite_models')}
def get_models_meta_info():
"""Get the meta information tags for all the models returned by get_models_list()
Returns:
dict of dict: The first dictionary indexes the model names to the meta tags, the second holds the meta
information.
"""
from mdt.lib.components import list_cascade_models, list_composite_models, get_meta_info, get_component_list
meta_info = {}
for model_type in ('composite_models', 'cascade_models'):
model_list = get_component_list(model_type)
for model in model_list:
meta_info.update({model: get_meta_info(model_type, model)})
return meta_info
def get_best_batch_profile(data_folder):
"""Get the batch profile that best matches the given directory.
Args:
data_folder (str): the directory for which to get the best batch profile.
Returns:
BatchProfile: the best matching batch profile.
"""
profiles = [
get_batch_profile(name)()
for name in get_component_list('batch_profiles')
]
best_crawler = None
best_subjects_count = 0
for profile in profiles:
if profile.is_suitable(data_folder):
tmp_count = len(profile.get_subjects(data_folder))
if tmp_count > best_subjects_count:
best_crawler = profile
best_subjects_count = tmp_count
return best_crawler
def _get_arg_parser(self, doc_parser=False):
description = textwrap.dedent(__doc__)
examples = textwrap.dedent('''
mdt-batch-fit . NODDI
mdt-batch-fit /data/mgh 'BallStick_r1' --batch-profile 'HCP_MGH'
mdt-batch-fit . CHARMED_r1 --subjects-id 1003 1004 --subjects-index 0 1 2
mdt-batch-fit . BallStick_r1 Tensor --dry-run
''')
epilog = self._format_examples(doc_parser, examples)
batch_profiles = get_component_list('batch_profiles')
parser = argparse.ArgumentParser(
description=description,
epilog=epilog,
formatter_class=argparse.RawTextHelpFormatter)
parser.add_argument('data_folder',
help='the directory with the subject to fit'
).completer = FilesCompleter()
parser.add_argument('models_to_fit',
type=str,
nargs='*',
help="The models to fit")
parser.add_argument('-o', '--output_folder',
help='the directory for the output, defaults to an output dir next to the input dir.')\
.completer = FilesCompleter()
parser.add_argument(
'-b',
'--batch_profile',
default=None,
choices=batch_profiles,
help=
'The batch profile (by name) to use during fitting. If not given a'
'batch profile is auto-detected.')
parser.add_argument(
'--cl-device-ind',
type=int,
nargs='*',
choices=self.available_devices,
help=
"The index of the device we would like to use. This follows the indices "
"in mdt-list-devices and defaults to the first GPU.")
parser.add_argument(
'--recalculate',
dest='recalculate',
action='store_true',
help="Recalculate the model(s) if the output exists.")
parser.add_argument(
'--no-recalculate',
dest='recalculate',
action='store_false',
help=
"Do not recalculate the model(s) if the output exists. (default)")
parser.set_defaults(recalculate=False)
parser.add_argument(
'--use-gradient-deviations',
dest='use_gradient_deviations',
action='store_true',
help=
"Uses the gradient deviations. If not set, the default in the profile is used."
)
parser.add_argument(
'--no-gradient-deviations',
dest='use_gradient_deviations',
action='store_false',
help=
"Disable the use of gradient deviations. If not set, the default "
"in the profile is used.")
parser.set_defaults(use_gradient_deviations=None)
parser.add_argument('--double',
dest='double_precision',
action='store_true',
help="Calculate in double precision.")
parser.add_argument('--float',
dest='double_precision',
action='store_false',
help="Calculate in single precision. (default)")
parser.set_defaults(double_precision=False)
parser.add_argument(
'--subjects-index',
type=int,
nargs='*',
help=
"The indices of the subjects we would like to fit. This reduces the set of "
"subjects.")
parser.add_argument(
'--subjects-id',
type=str,
nargs='*',
help=
"The id of the subjects we would like to fit. This reduces the set of "
"subjects.")
parser.add_argument(
'--dry-run',
dest='dry_run',
action='store_true',
help="Shows what it will do without the dry run argument.")
parser.set_defaults(dry_run=False)
parser.add_argument(
'--tmp-results-dir',
dest='tmp_results_dir',
default='True',
type=str,
help=
'The directory for the temporary results. The default ("True") uses the config file '
'setting. Set to the literal "None" to disable.'
).completer = FilesCompleter()
return parser