class AutoProtocolParameter(
method_binding_meta(template, ProtocolParameter)):
def __init__(self, nickname=None):
super(AutoProtocolParameter,
self).__init__(data_type,
nickname or template.name,
value=template.value)
class AutoCreatedCascadeModel(
method_binding_meta(template, SimpleCascadeModel)):
def __init__(self, *args):
models = []
for model_def in template.models:
if isinstance(model_def, six.string_types):
models.append(mdt.get_model(model_def)())
else:
models.append(
mdt.get_model(model_def[0])(model_def[1]))
new_args = [deepcopy(template.name), models]
for ind, arg in args:
new_args[ind] = arg
super(AutoCreatedCascadeModel, self).__init__(*new_args)
def _prepare_model(self, iteration_position, model,
output_previous, output_all_previous):
super(AutoCreatedCascadeModel,
self)._prepare_model(iteration_position, model,
output_previous,
output_all_previous)
def parse_value(v):
if isinstance(v, six.string_types):
return output_previous[v]
elif hasattr(v, '__call__'):
return v(output_previous, output_all_previous)
return v
def apply_func(template_element, cb):
items_to_apply = dict(template_element.get(model.name, {}))
items_to_apply.update(
dict(template_element.get(iteration_position, {})))
for key, value in items_to_apply.items():
cb(key, parse_value(value))
apply_func(template.inits,
lambda name, value: model.init(name, value))
apply_func(template.fixes,
lambda name, value: model.fix(name, value))
apply_func(
template.lower_bounds,
lambda name, value: model.set_lower_bound(name, value))
apply_func(
template.upper_bounds,
lambda name, value: model.set_upper_bound(name, value))
self._prepare_model_cb(iteration_position, model,
output_previous, output_all_previous)
class AutoCreatedBatchProfile(method_binding_meta(template, SimpleBatchProfile)):
def _get_subjects(self, data_folder):
dirs = sorted([os.path.basename(f) for f in glob.glob(os.path.join(data_folder, '*'))])
subjects = []
for subject_id in dirs:
subject_base_folder = os.path.join(
data_folder, template.subject_base_folder.format(subject_id=subject_id))
def _prepare_path(template_path):
if template_path is None:
return None
return template_path.format(data_folder=data_folder,
subject_id=subject_id,
subject_base_folder=subject_base_folder)
data_glob = glob.glob(_prepare_path(template.data_fname))
if not list(data_glob):
break
noise_std = self._autoload_noise_std(data_folder, subject_id, file_pattern=template.noise_std_fname)
protocol_loader = BatchFitProtocolLoader(
_prepare_path(template.protocol_auto_dir),
protocol_fname=_prepare_path(template.protocol_fname),
bvec_fname=_prepare_path(template.bvec_fname),
bval_fname=_prepare_path(template.bval_fname),
protocol_columns=template.protocol_columns)
mask_fname = None
if list(glob.glob(_prepare_path(template.mask_fname))):
mask_fname = glob.glob(_prepare_path(template.mask_fname))[0]
data_glob = list(filterfalse(lambda v: v == mask_fname, data_glob))
grad_dev = None
if list(glob.glob(_prepare_path(template.gradient_deviations_fname))):
grad_dev = glob.glob(_prepare_path(template.gradient_deviations_fname))[0]
data_glob = list(filterfalse(lambda v: v == grad_dev, data_glob))
subjects.append(SimpleSubjectInfo(
subject_base_folder, subject_id, data_glob[0],
protocol_loader, mask_fname, noise_std=noise_std,
gradient_deviations=grad_dev))
return subjects
def __str__(self):
return template.name
class AutoFreeParameter(
method_binding_meta(template, FreeParameter)):
def __init__(self, nickname=None):
super(AutoFreeParameter, self).__init__(
data_type,
nickname or template.name,
template.fixed,
template.init_value,
template.lower_bound,
template.upper_bound,
parameter_transform=_resolve_parameter_transform(
template.parameter_transform),
sampling_proposal_std=template.sampling_proposal_std,
sampling_prior=template.sampling_prior,
numdiff_info=numdiff_info)
self.sampling_proposal_modulus = template.sampling_proposal_modulus
class AutoCreatedDMRICompartmentModel(method_binding_meta(template, DMRICompartmentModelFunction)):
def __init__(self, *args, **kwargs):
parameters = []
if len(template.parameters):
parameters = _resolve_parameters(template.parameters)
elif len(template.parameter_list):
# todo remove the parameter_list attribute in future versions
warnings.warn('"parameter_list" is deprecated and will be removed in future versions, '
'please replace with "parameters".')
parameters = _resolve_parameters(template.parameter_list)
dependencies = []
if len(template.dependencies):
dependencies = _resolve_dependencies(template.dependencies)
elif len(template.dependency_list):
# todo remove the dependency_list attribute in future versions
warnings.warn('"dependency_list" is deprecated and will be removed in future versions, '
'please replace with "dependencies".')
dependencies = _resolve_dependencies(template.dependency_list)
new_args = [template.name,
template.name,
parameters,
template.cl_code,
dependencies,
template.return_type]
for ind, already_set_arg in enumerate(args):
new_args[ind] = already_set_arg
new_kwargs = {
'model_function_priors': (_resolve_prior(template.extra_prior, template.name,
[p.name for p in parameters],)),
'post_optimization_modifiers': template.post_optimization_modifiers,
'extra_optimization_maps_funcs': builder._get_extra_optimization_map_funcs(
template, parameters),
'extra_sampling_maps_funcs': copy(template.extra_sampling_maps),
'cl_extra': template.cl_extra}
new_kwargs.update(kwargs)
super(AutoCreatedDMRICompartmentModel, self).__init__(*new_args, **new_kwargs)
if hasattr(template, 'init'):
template.init(self)
class AutoCreatedLibraryFunction(method_binding_meta(template, SimpleCLLibrary)):
def __init__(self, *args, **kwargs):
new_args = [template.return_type,
template.name,
_resolve_parameters(template.parameters),
template.cl_code,
]
for ind, already_set_arg in enumerate(args):
new_args[ind] = already_set_arg
new_kwargs = dict(dependencies=_resolve_dependencies(template.dependencies),
cl_extra=template.cl_extra)
new_kwargs.update(kwargs)
super(AutoCreatedLibraryFunction, self).__init__(*new_args, **new_kwargs)
if hasattr(template, 'init'):
template.init(self)
class AutoCreatedWeightModel(method_binding_meta(template, WeightType)):
def __init__(self, *args, **kwargs):
parameters = []
if len(template.parameters):
parameters = _resolve_parameters(template.parameters)
elif len(template.parameter_list):
# todo remove the parameter_list attribute in future versions
warnings.warn('"parameter_list" is deprecated and will be removed in future versions, '
'please replace with "parameters".')
parameters = _resolve_parameters(template.parameter_list)
dependencies = []
if len(template.dependencies):
dependencies = _resolve_dependencies(template.dependencies)
elif len(template.dependency_list):
# todo remove the dependency_list attribute in future versions
warnings.warn('"dependency_list" is deprecated and will be removed in future versions, '
'please replace with "dependencies".')
dependencies = _resolve_dependencies(template.dependency_list)
new_args = [template.name,
template.name,
parameters,
template.cl_code,
]
for ind, already_set_arg in enumerate(args):
new_args[ind] = already_set_arg
new_kwargs = {
'dependencies': dependencies,
'cl_extra': template.cl_extra}
new_kwargs.update(kwargs)
super(AutoCreatedWeightModel, self).__init__(template.return_type, *new_args, **new_kwargs)
if hasattr(template, 'init'):
template.init(self)
class AutoCreatedDMRICompositeModel(
method_binding_meta(template, DMRICompositeModel)):
def __init__(self, model_name=None):
model_name = model_name or deepcopy(template.name)
super(AutoCreatedDMRICompositeModel, self).__init__(
model_name,
CompartmentModelTree(parse(template.model_expression)),
deepcopy(
_resolve_likelihood_function(
template.likelihood_function)),
signal_noise_model=deepcopy(template.signal_noise_model),
enforce_weights_sum_to_one=template.
enforce_weights_sum_to_one,
)
if template.sort_maps:
self._post_optimization_modifiers.append(
_get_map_sorting_modifier(
template.sort_maps,
self._model_functions_info.
get_model_parameter_list()))
self._post_optimization_modifiers.extend(
_get_model_post_optimization_modifiers(
self._model_functions_info.get_model_list()))
self._post_optimization_modifiers.extend(
deepcopy(template.post_optimization_modifiers))
self._extra_optimization_maps_funcs.extend(
_get_model_extra_optimization_maps_funcs(
self._model_functions_info.get_model_list()))
self._extra_optimization_maps_funcs.extend(
deepcopy(template.extra_optimization_maps))
self._extra_sampling_maps_funcs.extend(
_get_model_extra_sampling_maps_funcs(
self._model_functions_info.get_model_list()))
self._extra_sampling_maps_funcs.extend(
deepcopy(template.extra_sampling_maps))
for full_param_name, value in template.inits.items():
self.init(full_param_name, deepcopy(value))
for full_param_name, value in template.fixes.items():
self.fix(full_param_name, deepcopy(value))
for full_param_name, value in template.lower_bounds.items():
self.set_lower_bound(full_param_name, deepcopy(value))
for full_param_name, value in template.upper_bounds.items():
self.set_upper_bound(full_param_name, deepcopy(value))
self.nmr_parameters_for_bic_calculation = self.get_nmr_parameters(
)
self._model_priors.extend(
_resolve_model_prior(
template.extra_prior,
self._model_functions_info.get_model_parameter_list()))
def _get_suitable_volume_indices(self, input_data):
volume_selection = template.volume_selection
if not volume_selection:
return super(AutoCreatedDMRICompositeModel,
self)._get_suitable_volume_indices(input_data)
use_unweighted = volume_selection.get('use_unweighted', True)
use_weighted = volume_selection.get('use_weighted', True)
unweighted_threshold = volume_selection.get(
'unweighted_threshold', 25e6)
protocol = input_data.protocol
if protocol.has_column('g') and protocol.has_column('b'):
if use_weighted:
if 'min_bval' in volume_selection and 'max_bval' in volume_selection:
protocol_indices = protocol.get_indices_bval_in_range(
start=volume_selection['min_bval'],
end=volume_selection['max_bval'])
else:
protocol_indices = protocol.get_weighted_indices(
unweighted_threshold)
else:
protocol_indices = []
if use_unweighted:
protocol_indices = list(protocol_indices) + \
list(protocol.get_unweighted_indices(unweighted_threshold))
else:
return list(range(protocol.length))
return np.unique(protocol_indices)