def wrap_values(self, inputs, input_values, skip_missing_values=False):
trans = self.trans
tool = self.tool
incoming = self.incoming
# Wrap tool inputs as necessary
for input in inputs.itervalues():
if input.name not in input_values and skip_missing_values:
continue
value = input_values[input.name]
if isinstance(input, Repeat):
for d in input_values[input.name]:
self.wrap_values(input.inputs,
d,
skip_missing_values=skip_missing_values)
elif isinstance(input, Conditional):
values = input_values[input.name]
current = values["__current_case__"]
self.wrap_values(input.cases[current].inputs,
values,
skip_missing_values=skip_missing_values)
elif isinstance(input, Section):
values = input_values[input.name]
self.wrap_values(input.inputs,
values,
skip_missing_values=skip_missing_values)
elif isinstance(input, DataToolParameter) and input.multiple:
value = input_values[input.name]
dataset_instances = DatasetListWrapper.to_dataset_instances(
value)
input_values[ input.name ] = \
DatasetListWrapper( None,
dataset_instances,
datatypes_registry=trans.app.datatypes_registry,
tool=tool,
name=input.name )
elif isinstance(input, DataToolParameter):
input_values[ input.name ] = \
DatasetFilenameWrapper( input_values[ input.name ],
datatypes_registry=trans.app.datatypes_registry,
tool=tool,
name=input.name )
elif isinstance(input, SelectToolParameter):
input_values[input.name] = SelectToolParameterWrapper(
input,
input_values[input.name],
tool.app,
other_values=incoming)
elif isinstance(input, DataCollectionToolParameter):
input_values[input.name] = DatasetCollectionWrapper(
None,
input_values[input.name],
datatypes_registry=trans.app.datatypes_registry,
tool=tool,
name=input.name,
)
else:
input_values[input.name] = InputValueWrapper(
input, value, incoming)
def wrap_values(self, inputs, input_values, skip_missing_values=False):
trans = self.trans
tool = self.tool
incoming = self.incoming
element_identifier_mapper = ElementIdentifierMapper(self._input_datasets)
# Wrap tool inputs as necessary
for input in inputs.values():
if input.name not in input_values and skip_missing_values:
continue
value = input_values[input.name]
copy_identifiers(destination=value, source=input_values)
if isinstance(input, Repeat):
for d in value:
copy_identifiers(destination=d, source=value)
self.wrap_values(input.inputs, d, skip_missing_values=skip_missing_values)
elif isinstance(input, Conditional):
values = value
current = values["__current_case__"]
self.wrap_values(input.cases[current].inputs, values, skip_missing_values=skip_missing_values)
elif isinstance(input, Section):
values = value
self.wrap_values(input.inputs, values, skip_missing_values=skip_missing_values)
elif isinstance(input, DataToolParameter) and input.multiple:
dataset_instances = DatasetListWrapper.to_dataset_instances(value)
input_values[input.name] = \
DatasetListWrapper(None,
dataset_instances,
datatypes_registry=trans.app.datatypes_registry,
tool=tool,
name=input.name,
formats=input.formats)
elif isinstance(input, DataToolParameter):
wrapper_kwds = dict(
datatypes_registry=trans.app.datatypes_registry,
tool=tool,
name=input.name,
formats=input.formats
)
element_identifier = element_identifier_mapper.identifier(value, input_values)
if element_identifier:
wrapper_kwds["identifier"] = element_identifier
input_values[input.name] = DatasetFilenameWrapper(value, **wrapper_kwds)
elif isinstance(input, SelectToolParameter):
input_values[input.name] = SelectToolParameterWrapper(input, value, other_values=incoming)
elif isinstance(input, DataCollectionToolParameter):
input_values[input.name] = DatasetCollectionWrapper(
None,
value,
datatypes_registry=trans.app.datatypes_registry,
tool=tool,
name=input.name,
)
else:
input_values[input.name] = InputValueWrapper(input, value, incoming)
def wrap_input(input_values, input):
value = input_values[input.name]
if isinstance(input, DataToolParameter) and input.multiple:
dataset_instances = DatasetListWrapper.to_dataset_instances(value)
input_values[input.name] = \
DatasetListWrapper(job_working_directory,
dataset_instances,
compute_environment=self.compute_environment,
datatypes_registry=self.app.datatypes_registry,
tool=self.tool,
name=input.name,
formats=input.formats)
elif isinstance(input, DataToolParameter):
dataset = input_values[input.name]
wrapper_kwds = dict(
datatypes_registry=self.app.datatypes_registry,
tool=self,
name=input.name,
compute_environment=self.compute_environment
)
element_identifier = element_identifier_mapper.identifier(dataset, param_dict)
if element_identifier:
wrapper_kwds["identifier"] = element_identifier
input_values[input.name] = \
DatasetFilenameWrapper(dataset, **wrapper_kwds)
elif isinstance(input, DataCollectionToolParameter):
dataset_collection = value
wrapper_kwds = dict(
datatypes_registry=self.app.datatypes_registry,
compute_environment=self.compute_environment,
tool=self,
name=input.name
)
wrapper = DatasetCollectionWrapper(
job_working_directory,
dataset_collection,
**wrapper_kwds
)
input_values[input.name] = wrapper
elif isinstance(input, SelectToolParameter):
if input.multiple:
value = listify(value)
input_values[input.name] = SelectToolParameterWrapper(
input, value, other_values=param_dict, compute_environment=self.compute_environment)
else:
input_values[input.name] = InputValueWrapper(
input, value, param_dict)
def wrap_values(self, inputs, input_values, skip_missing_values=False):
trans = self.trans
tool = self.tool
incoming = self.incoming
element_identifier_mapper = ElementIdentifierMapper(self._input_datasets)
# Wrap tool inputs as necessary
for input in inputs.values():
if input.name not in input_values and skip_missing_values:
continue
value = input_values[input.name]
copy_identifiers(destination=value, source=input_values)
if isinstance(input, Repeat):
for d in value:
copy_identifiers(destination=d, source=value)
self.wrap_values(input.inputs, d, skip_missing_values=skip_missing_values)
elif isinstance(input, Conditional):
values = value
current = values["__current_case__"]
self.wrap_values(input.cases[current].inputs, values, skip_missing_values=skip_missing_values)
elif isinstance(input, Section):
values = value
self.wrap_values(input.inputs, values, skip_missing_values=skip_missing_values)
elif isinstance(input, DataToolParameter) and input.multiple:
dataset_instances = DatasetListWrapper.to_dataset_instances(value)
input_values[input.name] = \
DatasetListWrapper(None,
dataset_instances,
datatypes_registry=trans.app.datatypes_registry,
tool=tool,
name=input.name)
elif isinstance(input, DataToolParameter):
wrapper_kwds = dict(
datatypes_registry=trans.app.datatypes_registry,
tool=tool,
name=input.name
)
element_identifier = element_identifier_mapper.identifier(value, input_values)
if element_identifier:
wrapper_kwds["identifier"] = element_identifier
input_values[input.name] = DatasetFilenameWrapper(value, **wrapper_kwds)
elif isinstance(input, SelectToolParameter):
input_values[input.name] = SelectToolParameterWrapper(input, value, other_values=incoming)
elif isinstance(input, DataCollectionToolParameter):
input_values[input.name] = DatasetCollectionWrapper(
None,
value,
datatypes_registry=trans.app.datatypes_registry,
tool=tool,
name=input.name,
)
else:
input_values[input.name] = InputValueWrapper(input, value, incoming)
def wrap_values( self, inputs, input_values, skip_missing_values=False ):
trans = self.trans
tool = self.tool
incoming = self.incoming
# Wrap tool inputs as necessary
for input in inputs.itervalues():
if input.name not in input_values and skip_missing_values:
continue
value = input_values[ input.name ]
if isinstance( input, Repeat ):
for d in input_values[ input.name ]:
self.wrap_values( input.inputs, d, skip_missing_values=skip_missing_values )
elif isinstance( input, Conditional ):
values = input_values[ input.name ]
current = values[ "__current_case__" ]
self.wrap_values( input.cases[current].inputs, values, skip_missing_values=skip_missing_values )
elif isinstance( input, Section ):
values = input_values[ input.name ]
self.wrap_values( input.inputs, values, skip_missing_values=skip_missing_values )
elif isinstance( input, DataToolParameter ) and input.multiple:
value = input_values[ input.name ]
dataset_instances = DatasetListWrapper.to_dataset_instances( value )
input_values[ input.name ] = \
DatasetListWrapper( None,
dataset_instances,
datatypes_registry=trans.app.datatypes_registry,
tool=tool,
name=input.name )
elif isinstance( input, DataToolParameter ):
input_values[ input.name ] = \
DatasetFilenameWrapper( input_values[ input.name ],
datatypes_registry=trans.app.datatypes_registry,
tool=tool,
name=input.name )
elif isinstance( input, SelectToolParameter ):
input_values[ input.name ] = SelectToolParameterWrapper( input, input_values[ input.name ], tool.app, other_values=incoming )
elif isinstance( input, DataCollectionToolParameter ):
input_values[ input.name ] = DatasetCollectionWrapper(
None,
input_values[ input.name ],
datatypes_registry=trans.app.datatypes_registry,
tool=tool,
name=input.name,
)
else:
input_values[ input.name ] = InputValueWrapper( input, value, incoming )
def wrap_input( input_values, input ):
value = input_values[ input.name ]
if isinstance( input, DataToolParameter ) and input.multiple:
dataset_instances = DatasetListWrapper.to_dataset_instances( value )
input_values[ input.name ] = \
DatasetListWrapper( job_working_directory,
dataset_instances,
dataset_paths=input_dataset_paths,
datatypes_registry=self.app.datatypes_registry,
tool=self.tool,
name=input.name )
elif isinstance( input, DataToolParameter ):
# FIXME: We're populating param_dict with conversions when
# wrapping values, this should happen as a separate
# step before wrapping (or call this wrapping step
# something more generic) (but iterating this same
# list twice would be wasteful)
# Add explicit conversions by name to current parent
for conversion_name, conversion_extensions, conversion_datatypes in input.conversions:
# If we are at building cmdline step, then converters
# have already executed
conv_ext, converted_dataset = input_values[ input.name ].find_conversion_destination( conversion_datatypes )
# When dealing with optional inputs, we'll provide a
# valid extension to be used for None converted dataset
if not conv_ext:
conv_ext = conversion_extensions[0]
# input_values[ input.name ] is None when optional
# dataset, 'conversion' of optional dataset should
# create wrapper around NoneDataset for converter output
if input_values[ input.name ] and not converted_dataset:
# Input that converter is based from has a value,
# but converted dataset does not exist
raise Exception( 'A path for explicit datatype conversion has not been found: %s --/--> %s'
% ( input_values[ input.name ].extension, conversion_extensions ) )
else:
# Trick wrapper into using target conv ext (when
# None) without actually being a tool parameter
input_values[ conversion_name ] = \
DatasetFilenameWrapper( converted_dataset,
datatypes_registry=self.app.datatypes_registry,
tool=Bunch( conversion_name=Bunch( extensions=conv_ext ) ),
name=conversion_name )
# Wrap actual input dataset
dataset = input_values[ input.name ]
wrapper_kwds = dict(
datatypes_registry=self.app.datatypes_registry,
tool=self,
name=input.name
)
if dataset:
# A None dataset does not have a filename
real_path = dataset.file_name
if real_path in input_dataset_paths:
wrapper_kwds[ "dataset_path" ] = input_dataset_paths[ real_path ]
identifier_key = identifier_key_dict.get(dataset, None)
if identifier_key:
element_identifier = param_dict.get(identifier_key, None)
if element_identifier:
wrapper_kwds[ "identifier" ] = element_identifier
input_values[ input.name ] = \
DatasetFilenameWrapper( dataset, **wrapper_kwds )
elif isinstance( input, DataCollectionToolParameter ):
dataset_collection = value
wrapper_kwds = dict(
datatypes_registry=self.app.datatypes_registry,
dataset_paths=input_dataset_paths,
tool=self,
name=input.name
)
wrapper = DatasetCollectionWrapper(
job_working_directory,
dataset_collection,
**wrapper_kwds
)
input_values[ input.name ] = wrapper
elif isinstance( input, SelectToolParameter ):
input_values[ input.name ] = SelectToolParameterWrapper(
input, value, self.app, other_values=param_dict, path_rewriter=self.unstructured_path_rewriter )
else:
input_values[ input.name ] = InputValueWrapper(
input, value, param_dict )
def wrap_input(input_values, input):
value = input_values[input.name]
if isinstance(input, DataToolParameter) and input.multiple:
dataset_instances = DatasetListWrapper.to_dataset_instances(value)
input_values[input.name] = \
DatasetListWrapper(job_working_directory,
dataset_instances,
compute_environment=self.compute_environment,
datatypes_registry=self.app.datatypes_registry,
tool=self.tool,
name=input.name,
formats=input.formats)
elif isinstance(input, DataToolParameter):
# FIXME: We're populating param_dict with conversions when
# wrapping values, this should happen as a separate
# step before wrapping (or call this wrapping step
# something more generic) (but iterating this same
# list twice would be wasteful)
# Add explicit conversions by name to current parent
for conversion_name, conversion_extensions, conversion_datatypes in input.conversions:
# If we are at building cmdline step, then converters
# have already executed
direct_match, conv_ext, converted_dataset = input_values[input.name].find_conversion_destination(conversion_datatypes)
# When dealing with optional inputs, we'll provide a
# valid extension to be used for None converted dataset
if not direct_match and not conv_ext:
conv_ext = conversion_extensions[0]
# input_values[ input.name ] is None when optional
# dataset, 'conversion' of optional dataset should
# create wrapper around NoneDataset for converter output
if input_values[input.name] and not converted_dataset:
# Input that converter is based from has a value,
# but converted dataset does not exist
raise Exception('A path for explicit datatype conversion has not been found: %s --/--> %s'
% (input_values[input.name].extension, conversion_extensions))
else:
# Trick wrapper into using target conv ext (when
# None) without actually being a tool parameter
input_values[conversion_name] = \
DatasetFilenameWrapper(converted_dataset,
datatypes_registry=self.app.datatypes_registry,
tool=Bunch(conversion_name=Bunch(extensions=conv_ext)),
name=conversion_name)
# Wrap actual input dataset
dataset = input_values[input.name]
wrapper_kwds = dict(
datatypes_registry=self.app.datatypes_registry,
tool=self,
name=input.name,
compute_environment=self.compute_environment
)
element_identifier = element_identifier_mapper.identifier(dataset, param_dict)
if element_identifier:
wrapper_kwds["identifier"] = element_identifier
input_values[input.name] = \
DatasetFilenameWrapper(dataset, **wrapper_kwds)
elif isinstance(input, DataCollectionToolParameter):
dataset_collection = value
wrapper_kwds = dict(
datatypes_registry=self.app.datatypes_registry,
compute_environment=self.compute_environment,
tool=self,
name=input.name
)
wrapper = DatasetCollectionWrapper(
job_working_directory,
dataset_collection,
**wrapper_kwds
)
input_values[input.name] = wrapper
elif isinstance(input, SelectToolParameter):
if input.multiple:
value = listify(value)
input_values[input.name] = SelectToolParameterWrapper(
input, value, other_values=param_dict, compute_environment=self.compute_environment)
else:
input_values[input.name] = InputValueWrapper(
input, value, param_dict)
def wrap_input(input_values, input):
if isinstance(input, DataToolParameter) and input.multiple:
dataset_instances = input_values[input.name]
if isinstance(dataset_instances,
model.HistoryDatasetCollectionAssociation):
dataset_instances = dataset_instances.collection.dataset_instances[:]
input_values[ input.name ] = \
DatasetListWrapper( dataset_instances,
dataset_paths=input_dataset_paths,
datatypes_registry=self.app.datatypes_registry,
tool=self.tool,
name=input.name )
elif isinstance(input, DataToolParameter):
## FIXME: We're populating param_dict with conversions when
## wrapping values, this should happen as a separate
## step before wrapping (or call this wrapping step
## something more generic) (but iterating this same
## list twice would be wasteful)
# Add explicit conversions by name to current parent
for conversion_name, conversion_extensions, conversion_datatypes in input.conversions:
# If we are at building cmdline step, then converters
# have already executed
conv_ext, converted_dataset = input_values[
input.name].find_conversion_destination(
conversion_datatypes)
# When dealing with optional inputs, we'll provide a
# valid extension to be used for None converted dataset
if not conv_ext:
conv_ext = conversion_extensions[0]
# input_values[ input.name ] is None when optional
# dataset, 'conversion' of optional dataset should
# create wrapper around NoneDataset for converter output
if input_values[input.name] and not converted_dataset:
# Input that converter is based from has a value,
# but converted dataset does not exist
raise Exception(
'A path for explicit datatype conversion has not been found: %s --/--> %s'
% (input_values[input.name].extension,
conversion_extensions))
else:
# Trick wrapper into using target conv ext (when
# None) without actually being a tool parameter
input_values[ conversion_name ] = \
DatasetFilenameWrapper( converted_dataset,
datatypes_registry=self.app.datatypes_registry,
tool=Bunch( conversion_name=Bunch( extensions=conv_ext ) ),
name=conversion_name )
# Wrap actual input dataset
dataset = input_values[input.name]
wrapper_kwds = dict(
datatypes_registry=self.app.datatypes_registry,
tool=self,
name=input.name)
if dataset:
#A None dataset does not have a filename
real_path = dataset.file_name
if real_path in input_dataset_paths:
wrapper_kwds["dataset_path"] = input_dataset_paths[
real_path]
input_values[ input.name ] = \
DatasetFilenameWrapper( dataset, **wrapper_kwds )
elif isinstance(input, DataCollectionToolParameter):
dataset_collection = input_values[input.name]
wrapper_kwds = dict(
datatypes_registry=self.app.datatypes_registry,
dataset_paths=input_dataset_paths,
tool=self,
name=input.name)
wrapper = DatasetCollectionWrapper(dataset_collection,
**wrapper_kwds)
input_values[input.name] = wrapper
elif isinstance(input, SelectToolParameter):
input_values[input.name] = SelectToolParameterWrapper(
input,
input_values[input.name],
self.app,
other_values=param_dict,
path_rewriter=self.unstructured_path_rewriter)
elif isinstance(input, LibraryDatasetToolParameter):
# TODO: Handle input rewrites in here? How to test LibraryDatasetToolParameters?
input_values[input.name] = LibraryDatasetValueWrapper(
input, input_values[input.name], param_dict)
else:
input_values[input.name] = InputValueWrapper(
input, input_values[input.name], param_dict)
