def _remove_entity(self, data_class, before_number):
"""
Try to remove entity. Fail otherwise.
"""
dts, count = get_filtered_datatypes(self.test_project.id, data_class)
assert count == before_number
for dt in dts:
data_gid = dt[2]
self.project_service.remove_datatype(self.test_project.id, data_gid)
res = dao.get_datatype_by_gid(data_gid)
assert res is None, "The entity was not deleted"
_, count = get_filtered_datatypes(self.test_project.id, data_class)
assert 0 == count
def test_zip_import(self, prepare_importer_data):
"""
This method tests import of TVB data in zip format (which imply multiple data types
in the same zip file - exported from a group)
"""
self._import(self.zip_file_path)
_, count = get_filtered_datatypes(self.test_project.id, self.datatype.module + "." + self.datatype.type)
assert 9, count == "9 datatypes should have been imported from group."
def test_h5_import(self, prepare_importer_data):
"""
This method tests import of TVB data in h5 format. Single data type / import
"""
self._import(self.h5_file_path)
data_types, count = get_filtered_datatypes(self.test_project.id,
self.datatype.module + "." + self.datatype.type)
assert 1, len(data_types) == "Project should contain only one data type."
assert 1, count == "Project should contain only one data type."
data_type_entity = load_entity_by_gid(data_types[0][2])
assert data_type_entity is not None, "Datatype should not be none"
assert self.datatype.gid, data_type_entity.gid == "Imported datatype should have the same gid"
def populate_option_values_for_dtype(self, project_id, type_name, filter_condition=None,
category_key=None, complex_dt_attributes=None):
'''
Converts all datatypes that match the project_id, type_name and filter_condition
to a {name: , value:} dict used to populate options in the input tree ui
'''
data_type_cls = get_class_by_name(type_name)
#todo: send category instead of category_key to avoid redundant queries
#NOTE these functions are coupled via data_list, _populate_values makes no sense without _get_available_datatypes
data_list, total_count = get_filtered_datatypes(project_id, data_type_cls,
filter_condition)
values = self._populate_values(data_list, data_type_cls,
category_key, complex_dt_attributes)
return values, total_count
def build(test_user, test_project):
view_model = BaseBCTModel()
view_model.connectivity = get_filtered_datatypes(test_project.id, ConnectivityIndex, page_size=1)[0][0][2]
adapter = ABCAdapter.build_adapter_from_class(TransitivityBinaryDirected)
op = OperationService().fire_operation(adapter, test_user, test_project.id, view_model=view_model)
# wait for the operation to finish
tries = 5
while not op.has_finished and tries > 0:
sleep(5)
tries = tries - 1
op = dao.get_operation_by_id(op.id)
value_wrapper = try_get_last_datatype(test_project.id, ValueWrapperIndex)
count = dao.count_datatypes(test_project.id, ValueWrapperIndex)
assert 1 == count
return value_wrapper
def populate_option_values_for_dtype(self, project_id, type_name, filter_condition=None,
category_key=None):
'''
Converts all datatypes that match the project_id, type_name and filter_condition
to a {name: , value:} dict used to populate options in the input tree ui
'''
# todo: normalize all itree[KEY_TYPE] to be a python type, not a str, not a None etc
if isinstance(type_name, basestring):
data_type_cls = get_class_by_name(type_name)
else:
data_type_cls = type_name
#todo: send category instead of category_key to avoid redundant queries
#NOTE these functions are coupled via data_list, _populate_values makes no sense without _get_available_datatypes
data_list, total_count = get_filtered_datatypes(project_id, data_type_cls,
filter_condition)
values = self._populate_values(data_list, data_type_cls, category_key)
return values, total_count
def build(project):
count = dao.count_datatypes(project.id, DataTypeMatrix)
assert count == 0
op = operation_factory(test_project=project)
conn = connectivity_index_factory(op=op)
count = _create_measure(conn, op, project)
assert count == 1
count = _create_measure(conn, op, project)
assert count == 2
count = _create_measure(conn, op, project)
assert count == 3
return get_filtered_datatypes(project.id, DataTypeMatrix)[0]
def populate_option_values_for_dtype(self,
project_id,
type_name,
filter_condition=None,
category_key=None,
complex_dt_attributes=None):
'''
Converts all datatypes that match the project_id, type_name and filter_condition
to a {name: , value:} dict used to populate options in the input tree ui
'''
data_type_cls = get_class_by_name(type_name)
#todo: send category instead of category_key to avoid redundant queries
#NOTE these functions are coupled via data_list, _populate_values makes no sense without _get_available_datatypes
data_list, total_count = get_filtered_datatypes(
project_id, data_type_cls, filter_condition)
values = self._populate_values(data_list, data_type_cls, category_key,
complex_dt_attributes)
return values, total_count
def build(project):
count = dao.count_datatypes(project.id, DataTypeMatrix)
assert count == 0
op = operation_factory(test_project=project)
conn = connectivity_index_factory(op=op)
storage_path = FilesHelper().get_project_folder(
op.project, str(op.id))
count = _create_measure(conn, op, storage_path, project.id)
assert count == 1
count = _create_measure(conn, op, storage_path, project.id)
assert count == 2
count = _create_measure(conn, op, storage_path, project.id)
assert count == 3
return get_filtered_datatypes(project.id, DataTypeMatrix)[0]
def get_available_datatypes(project_id, data_type_cls, filters=None):
"""
Return all dataTypes that match a given name and some filters.
:param data_type_cls: either a fully qualified class name or a class object
"""
return get_filtered_datatypes(project_id, data_type_cls, filters)
def get_available_datatypes(project_id, data_type_cls, filters=None):
"""
Return all dataTypes that match a given name and some filters.
:param data_type_cls: either a fully qualified class name or a class object
"""
return get_filtered_datatypes(project_id, data_type_cls, filters)
