def create_group(test_user=None, test_project=None, subject="John Doe"):
"""
Create a group of 2 operations, each with at least one resultant DataType.
"""
if test_user is None:
test_user = TestFactory.create_user()
if test_project is None:
test_project = TestFactory.create_project(test_user)
adapter_inst = TestFactory.create_adapter('tvb.tests.framework.adapters.testadapter3', 'TestAdapter3')
adapter_inst.generic_attributes.subject = subject
view_model = adapter_inst.get_view_model()()
args = {RANGE_PARAMETER_1: 'param_5', 'param_5': json.dumps({constants.ATT_MINVALUE: 1,
constants.ATT_MAXVALUE: 2.1,
constants.ATT_STEP: 1})}
algo = adapter_inst.stored_adapter
algo_category = dao.get_category_by_id(algo.fk_category)
# Prepare Operations group. Execute them synchronously
service = OperationService()
operations = service.prepare_operations(test_user.id, test_project, algo, algo_category,
view_model=view_model, **args)[0]
service.launch_operation(operations[0].id, False, adapter_inst)
service.launch_operation(operations[1].id, False, adapter_inst)
resulted_dts = dao.get_datatype_in_group(operation_group_id=operations[0].fk_operation_group)
return resulted_dts, operations[0].fk_operation_group
def create_group(test_user=None, test_project=None, subject="John Doe"):
"""
Create a group of 2 operations, each with at least one resultant DataType.
"""
if test_user is None:
test_user = TestFactory.create_user()
if test_project is None:
test_project = TestFactory.create_project(test_user)
### Retrieve Adapter instance
algo_group = dao.find_group('tvb.tests.framework.adapters.testadapter3', 'TestAdapter3')
algo_category = dao.get_category_by_id(algo_group.fk_category)
algo = dao.get_algorithm_by_group(algo_group.id)
adapter_inst = TestFactory.create_adapter(algo_group=algo_group, test_project=test_project)
adapter_inst.meta_data = {DataTypeMetaData.KEY_SUBJECT: subject}
args = {model.RANGE_PARAMETER_1: 'param_5', 'param_5': [1, 2]}
### Prepare Operations group. Execute them synchronously
service = OperationService()
operations = service.prepare_operations(test_user.id, test_project.id, algo, algo_category, {}, **args)[0]
service.launch_operation(operations[0].id, False, adapter_inst)
service.launch_operation(operations[1].id, False, adapter_inst)
resulted_dts = dao.get_datatype_in_group(operation_group_id=operations[0].fk_operation_group)
return resulted_dts, operations[0].fk_operation_group
def __init__(self):
BaseController.__init__(self)
self.context = SelectedAdapterContext()
self.operation_services = OperationService()
self.simulator_controller = SimulatorController()
analyze_category, groups = self.algorithm_service.get_analyze_groups()
adapters_list = []
for adapter_group in groups:
if len(adapter_group.children) > 1:
ids = [str(child.id) for child in adapter_group.children]
ids = ','.join(ids)
adapter_link = '/flow/show_group_of_algorithms/' + str(
analyze_category.id) + "/" + ids
else:
adapter_link = self.get_url_adapter(
analyze_category.id, adapter_group.children[0].id)
adapters_list.append({
common.KEY_TITLE:
adapter_group.name,
'link':
adapter_link,
'description':
adapter_group.description,
'subsection':
adapter_group.children[0].subsection_name
})
self.analyze_adapters = adapters_list
def update():
"""
Update TVB code to SVN revision version 4455.
This update was done for release 1.0.2.
"""
projects_count = dao.get_all_projects(is_count=True)
for page_start in range(0, projects_count, PAGE_SIZE):
projects_page = dao.get_all_projects(page_start=page_start,
page_size=PAGE_SIZE)
for project in projects_page:
try:
user = dao.get_system_user()
adapter = ObjSurfaceImporter()
OperationService().fire_operation(adapter,
user,
project.id,
visible=False,
surface_type=EEG_CAP,
data_file=DATA_FILE_EEG_CAP)
adapter = ObjSurfaceImporter()
OperationService().fire_operation(adapter,
user,
project.id,
visible=False,
surface_type=FACE,
data_file=DATA_FILE_FACE)
except Exception as excep:
LOGGER.exception(excep)
TvbProfile.current.manager.add_entries_to_config_file(
{stored.KEY_MATLAB_EXECUTABLE: get_matlab_executable()})
def store_exploration_section(self, val_range, step, dt_group_guid):
"""
Launching method for further simulations.
"""
range_list = [float(num) for num in val_range.split(",")]
step_list = [float(num) for num in step.split(",")]
datatype_group_ob = self.project_service.get_datatypegroup_by_gid(dt_group_guid)
operation_grp = datatype_group_ob.parent_operation_group
operation_obj = OperationService.load_operation(datatype_group_ob.fk_from_operation)
parameters = {}
range1name, range1_dict = json.loads(operation_grp.range1)
range2name, range2_dict = json.loads(operation_grp.range2)
parameters[RANGE_PARAMETER_1] = range1name
parameters[RANGE_PARAMETER_2] = range2name
# change the existing simulator parameters to be min max step types
range1_dict = {constants.ATT_MINVALUE: range_list[0],
constants.ATT_MAXVALUE: range_list[1],
constants.ATT_STEP: step_list[0]}
range2_dict = {constants.ATT_MINVALUE: range_list[2],
constants.ATT_MAXVALUE: range_list[3],
constants.ATT_STEP: step_list[1]}
parameters[range1name] = json.dumps(range1_dict) # this is for the x axis parameter
parameters[range2name] = json.dumps(range2_dict) # this is for the y axis parameter
OperationService().group_operation_launch(common.get_logged_user().id, common.get_current_project(),
operation_obj.algorithm.id, operation_obj.algorithm.fk_category,
**parameters)
return [True, 'Stored the exploration material successfully']
def test_get_inputs_for_group(self, datatype_group_factory,
test_adapter_factory):
"""
Tests method get_datatypes_inputs_for_operation_group.
"""
zip_path = os.path.join(os.path.dirname(tvb_data.__file__),
'connectivity', 'connectivity_66.zip')
conn = TestFactory.import_zip_connectivity(self.test_user,
self.test_project, zip_path)
conn.visible = False
dao.store_entity(conn)
group = OperationGroup(self.test_project.id, "group", "range1[1..2]")
group = dao.store_entity(group)
view_model = BaseBCTModel()
view_model.connectivity = conn.gid
adapter = ABCAdapter.build_adapter_from_class(
TransitivityBinaryDirected)
algorithm = adapter.stored_adapter
operation1 = Operation(self.test_user.id,
self.test_project.id,
algorithm.id,
json.dumps({'gid': view_model.gid.hex}),
op_group_id=group.id)
operation2 = Operation(self.test_user.id,
self.test_project.id,
algorithm.id,
json.dumps({'gid': view_model.gid.hex}),
op_group_id=group.id)
dao.store_entities([operation1, operation2])
OperationService()._store_view_model(
operation1, dao.get_project_by_id(self.test_project.id),
view_model)
OperationService()._store_view_model(
operation2, dao.get_project_by_id(self.test_project.id),
view_model)
inputs = self.project_service.get_datatypes_inputs_for_operation_group(
group.id, self.relevant_filter)
assert len(inputs) == 0
inputs = self.project_service.get_datatypes_inputs_for_operation_group(
group.id, self.full_filter)
assert len(inputs) == 1, "Incorrect number of dataTypes."
assert group.id == inputs[0].id, "Retrieved wrong dataType."
conn.visible = True
dao.store_entity(conn)
inputs = self.project_service.get_datatypes_inputs_for_operation_group(
group.id, self.relevant_filter)
assert len(inputs) == 1, "Incorrect number of dataTypes."
inputs = self.project_service.get_datatypes_inputs_for_operation_group(
group.id, self.full_filter)
assert len(inputs) == 1, "Incorrect number of dataTypes."
assert group.id == inputs[0].id, "Retrieved wrong dataType."
def create_group(test_user=None, test_project=None, subject="John Doe"):
"""
Create a group of 2 operations, each with at least one resultant DataType.
"""
if test_user is None:
test_user = TestFactory.create_user()
if test_project is None:
test_project = TestFactory.create_project(test_user)
adapter_inst = TestFactory.create_adapter(
'tvb.tests.framework.adapters.testadapter3', 'TestAdapter3')
adapter_inst.meta_data = {DataTypeMetaData.KEY_SUBJECT: subject}
args = {model.RANGE_PARAMETER_1: 'param_5', 'param_5': [1, 2]}
algo = adapter_inst.stored_adapter
algo_category = dao.get_category_by_id(algo.fk_category)
### Prepare Operations group. Execute them synchronously
service = OperationService()
operations = service.prepare_operations(test_user.id, test_project.id,
algo, algo_category, {},
**args)[0]
service.launch_operation(operations[0].id, False, adapter_inst)
service.launch_operation(operations[1].id, False, adapter_inst)
resulted_dts = dao.get_datatype_in_group(
operation_group_id=operations[0].fk_operation_group)
return resulted_dts, operations[0].fk_operation_group
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.logger = get_logger(self.__class__.__module__)
self.operation_service = OperationService()
self.project_service = ProjectService()
self.user_service = UserService()
self.files_helper = FilesHelper()
def setup_method(self):
"""
Sets up the environment for testing;
creates a `FlowController`
"""
self.init()
self.flow_c = FlowController()
self.burst_c = BurstController()
self.operation_service = OperationService()
def cancel_all_operations(self):
"""
To make sure that no running operations are left which could make some other
test started afterwards to fail, cancel all operations after each test.
"""
LOGGER.info("Stopping all operations.")
op_service = OperationService()
operations = self.get_all_entities(model.Operation)
for operation in operations:
op_service.stop_operation(operation.id)
def setup_method(self):
"""
Reset the database before each test.
"""
self.clean_database()
initialize_storage()
self.test_user = TestFactory.create_user()
self.test_project = TestFactory.create_project(self.test_user)
self.operation_service = OperationService()
self.backup_hdd_size = TvbProfile.current.MAX_DISK_SPACE
def cancel_all_operations(self):
"""
To make sure that no running operations are left which could make some other
test started afterwards to fail, cancel all operations after each test.
"""
LOGGER.info("Stopping all operations.")
op_service = OperationService()
operations = self.get_all_entities(model.Operation)
for operation in operations:
op_service.stop_operation(operation.id)
def test_fire_operation(self):
"""
Test preparation of an adapter and launch mechanism.
"""
adapter = TestFactory.create_adapter("tvb.tests.framework.adapters.dummy_adapter1", "DummyAdapter1")
test_user = TestFactory.create_user(username="******")
test_project = TestFactory.create_project(admin=test_user, name="test_project_fire_sim")
result = OperationService().fire_operation(adapter, test_user, test_project.id,
view_model=adapter.get_view_model()())
assert result.endswith("has finished."), "Operation fail"
def setUp(self):
"""
Sets up the testing environment;
saves config file;
creates a test user, a test project;
creates burst, operation, flow and workflow services
"""
self.test_user = TestFactory.create_user()
self.test_project = TestFactory.create_project(self.test_user)
self.workflow_service = WorkflowService()
self.burst_service = BurstService()
self.operation_service = OperationService()
self.flow_service = FlowService()
def test_noise_2d_happy_flow(self):
"""
Test a simulation with noise.
"""
SIMULATOR_PARAMETERS['integrator'] = u'HeunStochastic'
noise_2d_config = [[1 for _ in xrange(self.CONNECTIVITY_NODES)]
for _ in xrange(2)]
SIMULATOR_PARAMETERS[
'integrator_parameters_option_HeunStochastic_dt'] = u'0.01220703125'
SIMULATOR_PARAMETERS[
'integrator_parameters_option_HeunStochastic_noise'] = u'Additive'
SIMULATOR_PARAMETERS[
'integrator_parameters_option_HeunStochastic_noise_parameters_option_Additive_nsig'] = str(
noise_2d_config)
SIMULATOR_PARAMETERS[
'integrator_parameters_option_HeunStochastic_noise_parameters_option_Additive_ntau'] = u'0.0'
SIMULATOR_PARAMETERS[
'integrator_parameters_option_HeunStochastic_noise_parameters_option_Additive_random_stream'] = u'RandomStream'
SIMULATOR_PARAMETERS[
'integrator_parameters_option_HeunStochastic_noise_parameters_option_Additive_random_stream_parameters_option_RandomStream_init_seed'] = u'42'
filtered_params = self.simulator_adapter.prepare_ui_inputs(
SIMULATOR_PARAMETERS)
self.simulator_adapter.configure(**filtered_params)
if hasattr(self.simulator_adapter, 'algorithm'):
self.assertEqual(
(2, 74, 1),
self.simulator_adapter.algorithm.integrator.noise.nsig.shape)
else:
self.fail("Simulator adapter was not initialized properly")
OperationService().initiate_prelaunch(self.operation,
self.simulator_adapter, {},
**SIMULATOR_PARAMETERS)
sim_result = dao.get_generic_entity(TimeSeriesRegion,
'TimeSeriesRegion', 'type')[0]
self.assertEquals(sim_result.read_data_shape(),
(32, 1, self.CONNECTIVITY_NODES, 1))
SIMULATOR_PARAMETERS[
'integrator_parameters_option_HeunStochastic_noise_parameters_option_Additive_nsig'] = '[1]'
filtered_params = self.simulator_adapter.prepare_ui_inputs(
SIMULATOR_PARAMETERS)
self.simulator_adapter.configure(**filtered_params)
if hasattr(self.simulator_adapter, 'algorithm'):
self.assertEqual(
(1, ),
self.simulator_adapter.algorithm.integrator.noise.nsig.shape)
else:
self.fail("Simulator adapter was not initialized properly")
OperationService().initiate_prelaunch(self.operation,
self.simulator_adapter, {},
**SIMULATOR_PARAMETERS)
def __init__(self, overwrites=None, settings_file=None):
""" Parameters can be overwritten either from a settigns file or from a dictionary. """
if overwrites is not None:
self.overwrites.update(overwrites)
if settings_file is not None:
settings = open(sys.argv[1]).read()
for line in settings.split('\n'):
key, value = line.split('=')
self.overwrites[key.strip()] = value.strip()
if KEY_PROJECT not in self.overwrites:
raise Exception("Settings file should contain the id of the project: %s=1" % KEY_PROJECT)
self.project = dao.get_project_by_id(self.overwrites[KEY_PROJECT])
self.flow_service = FlowService()
self.operation_service = OperationService()
def cancel_all_operations(self):
"""
To make sure that no running operations are left which could make some other
test started afterwards to fail, cancel all operations after each test.
"""
LOGGER.info("Stopping all operations.")
op_service = OperationService()
operations = self.get_all_entities(Operation)
for operation in operations:
try:
op_service.stop_operation(operation.id)
except Exception:
# Ignore potential wrongly written operations by other unit-tests
pass
def launch_synchronously(test_user, test_project, adapter_instance, view_model, algo_category=None):
# Avoid the scheduled execution, as this is asynch, thus launch it immediately
service = OperationService()
algorithm = adapter_instance.stored_adapter
if algo_category is None:
algo_category = dao.get_category_by_id(algorithm.fk_category)
operation = service.prepare_operations(test_user.id, test_project, algorithm, algo_category,
True, view_model=view_model)[0][0]
service.initiate_prelaunch(operation, adapter_instance)
operation = dao.get_operation_by_id(operation.id)
# Check that operation status after execution is success.
assert STATUS_FINISHED == operation.status
# Make sure at least one result exists for each BCT algorithm
return dao.get_generic_entity(DataType, operation.id, 'fk_from_operation')
def reload_burst_operation(self, operation_id, is_group, **_):
"""
Find out from which burst was this operation launched. Set that burst as the selected one and
redirect to the burst page.
"""
is_group = int(is_group)
if not is_group:
operation = OperationService.load_operation(int(operation_id))
else:
op_group = ProjectService.get_operation_group_by_id(operation_id)
first_op = ProjectService.get_operations_in_group(op_group)[0]
operation = OperationService.load_operation(int(first_op.id))
SimulatorController().copy_simulator_configuration(operation.burst.id)
raise cherrypy.HTTPRedirect("/burst/")
def reload_burst_operation(self, operation_id, is_group, **_):
"""
Find out from which burst was this operation launched. Set that burst as the selected one and
redirect to the burst page.
"""
is_group = int(is_group)
if not is_group:
operation = OperationService.load_operation(int(operation_id))
else:
op_group = ProjectService.get_operation_group_by_id(operation_id)
first_op = ProjectService.get_operations_in_group(op_group)[0]
operation = OperationService.load_operation(int(first_op.id))
operation.burst.prepare_after_load()
common.add2session(common.KEY_BURST_CONFIG, operation.burst)
raise cherrypy.HTTPRedirect("/burst/")
def test_bct_all(self):
"""
Iterate all BCT algorithms and execute them.
"""
for adapter_instance in self.bct_adapters:
algorithm = adapter_instance.stored_adapter
operation = TestFactory.create_operation(
algorithm=algorithm,
test_user=self.test_user,
test_project=self.test_project,
operation_status=STATUS_STARTED)
assert STATUS_STARTED == operation.status
# Launch BCT algorithm
submit_data = {algorithm.parameter_name: self.connectivity.gid}
try:
OperationService().initiate_prelaunch(operation,
adapter_instance, {},
**submit_data)
if algorithm.classname in TestBCT.EXPECTED_TO_FAIL_VALIDATION:
raise Exception(
"Algorithm %s was expected to throw input validation "
"exception, but did not!" % (algorithm.classname, ))
operation = dao.get_operation_by_id(operation.id)
# Check that operation status after execution is success.
assert STATUS_FINISHED == operation.status
# Make sure at least one result exists for each BCT algorithm
results = dao.get_generic_entity(DataType, operation.id,
'fk_from_operation')
assert len(results) > 0
except InvalidParameterException as excep:
# Some algorithms are expected to throw validation exception.
if algorithm.classname not in TestBCT.EXPECTED_TO_FAIL_VALIDATION:
raise excep
def do_operation_launch(operation_id):
"""
Event attached to the local queue for executing an operation, when we will have resources available.
"""
LOGGER = get_logger('tvb.core.operation_async_launcher')
try:
LOGGER.debug("Loading operation with id=%s" % operation_id)
curent_operation = dao.get_operation_by_id(operation_id)
stored_adapter = curent_operation.algorithm
LOGGER.debug("Importing Algorithm: " + str(stored_adapter.classname) +
" for Operation:" + str(curent_operation.id))
PARAMS = parse_json_parameters(curent_operation.parameters)
adapter_instance = ABCAdapter.build_adapter(stored_adapter)
## Un-comment bellow for profiling an operation:
## import cherrypy.lib.profiler as profiler
## p = profiler.Profiler("/Users/lia.domide/TVB/profiler/")
## p.run(OperationService().initiate_prelaunch, curent_operation, adapter_instance, {}, **PARAMS)
OperationService().initiate_prelaunch(curent_operation,
adapter_instance, {}, **PARAMS)
LOGGER.debug("Successfully finished operation " + str(operation_id))
except Exception, excep:
LOGGER.error("Could not execute operation " + str(sys.argv[1]))
LOGGER.exception(excep)
parent_burst = dao.get_burst_for_operation_id(operation_id)
if parent_burst is not None:
WorkflowService().mark_burst_finished(parent_burst,
error_message=str(excep))
def _burst_create_connectivity(self):
"""
Create a connectivity that will be used in "non-dummy" burst launches (with the actual simulator).
TODO: This is duplicate code from burstservice_test. Should go into the 'generic' DataType factory
once that is done.
"""
meta = {
DataTypeMetaData.KEY_SUBJECT: "John Doe",
DataTypeMetaData.KEY_STATE: "RAW_DATA"
}
algorithm, algo_group = FlowService(
).get_algorithm_by_module_and_class(SIMULATOR_MODULE, SIMULATOR_CLASS)
self.operation = model.Operation(self.test_user.id,
self.test_project.id,
algo_group.id,
json.dumps(''),
meta=json.dumps(meta),
status=model.STATUS_STARTED)
self.operation = dao.store_entity(self.operation)
storage_path = FilesHelper().get_project_folder(
self.test_project, str(self.operation.id))
connectivity = Connectivity(storage_path=storage_path)
connectivity.weights = numpy.ones((74, 74))
connectivity.centres = numpy.ones((74, 3))
adapter_instance = StoreAdapter([connectivity])
OperationService().initiate_prelaunch(self.operation, adapter_instance,
{})
return connectivity
def fire_operation(self,
adapter_instance,
current_user,
project_id,
visible=True,
**data):
"""
Launch an operation, specified by AdapterInstance, for CurrentUser,
Current Project and a given set of UI Input Data.
"""
operation_name = str(adapter_instance.__class__.__name__)
try:
self.logger.info("Starting operation " + operation_name)
project = dao.get_project_by_id(project_id)
tmp_folder = self.file_helper.get_project_folder(
project, self.file_helper.TEMP_FOLDER)
result = OperationService().initiate_operation(
current_user, project.id, adapter_instance, tmp_folder,
visible, **data)
self.logger.info("Finished operation:" + operation_name)
return result
except TVBException, excep:
self.logger.exception(
"Could not launch operation " + operation_name +
" with the given set of input data, because: " + excep.message)
raise OperationException(excep.message, excep)
def test_get_inputs_for_operation(self):
"""
Tests method get_datatype_and_datatypegroup_inputs_for_operation.
Verifies filters' influence over results is as expected
"""
zip_path = os.path.join(os.path.dirname(tvb_data.__file__),
'connectivity', 'connectivity_66.zip')
conn = TestFactory.import_zip_connectivity(self.test_user,
self.test_project, zip_path)
view_model = BaseBCTModel()
view_model.connectivity = conn.gid
adapter = ABCAdapter.build_adapter_from_class(
TransitivityBinaryDirected)
result = OperationService().fire_operation(adapter,
self.test_user,
self.test_project.id,
view_model=view_model)
conn.visible = False
dao.store_entity(conn)
operation = dao.get_operation_by_id(result[0].id)
inputs = self.project_service.get_datatype_and_datatypegroup_inputs_for_operation(
operation.gid, self.relevant_filter)
assert len(inputs) == 0
inputs = self.project_service.get_datatype_and_datatypegroup_inputs_for_operation(
operation.gid, self.full_filter)
assert len(inputs) == 1, "Incorrect number of inputs."
assert conn.id == inputs[0].id, "Retrieved wrong input dataType."
def cancel_or_remove_operation(self, operation_id, is_group, remove_after_stop=False):
"""
Stop the operation given by operation_id. If is_group is true stop all the
operations from that group.
"""
# Load before we remove, to have its data in memory here
burst_config = BurstService.get_burst_for_operation_id(operation_id, is_group)
if burst_config is not None:
self.burst_service.mark_burst_finished(burst_config, BurstConfiguration.BURST_CANCELED, store_h5_file=False)
while GROUP_BURST_PENDING.get(burst_config.id, False):
pass
GROUP_BURST_PENDING.pop(burst_config.id, False)
result = OperationService.stop_operation(operation_id, is_group, remove_after_stop)
if remove_after_stop:
current_burst = self.context.burst_config
if (current_burst is not None and burst_config is not None and current_burst.id == burst_config.id and
((current_burst.fk_simulation == operation_id and not is_group) or
(current_burst.fk_operation_group == operation_id and is_group))):
self.reset_simulator_configuration()
if burst_config is not None:
burst_config = BurstService.load_burst_configuration(burst_config.id)
if burst_config:
BurstService.remove_burst_configuration(burst_config.id)
return result
def _async_launch_and_prepare(self, burst_config, simulator_index,
simulator_id, user_id):
"""
Prepare operations asynchronously.
"""
try:
operation_ids = self._prepare_operations(burst_config,
simulator_index,
simulator_id, user_id)
self.logger.debug("Starting a total of %s workflows" %
(len(operation_ids, )))
wf_errs = 0
for operation_id in operation_ids:
try:
OperationService().launch_operation(operation_id, True)
except Exception, excep:
self.logger.error(excep)
wf_errs += 1
self.workflow_service.mark_burst_finished(
burst_config, error_message=str(excep))
self.logger.debug("Finished launching workflows. " +
str(len(operation_ids) - wf_errs) +
" were launched successfully, " + str(wf_errs) +
" had error on pre-launch steps")
def test_adapter_huge_memory_requirement(self, test_adapter_factory):
"""
Test that an MemoryException is raised in case adapter cant launch due to lack of memory.
"""
# Prepare adapter
test_adapter_factory(adapter_class=DummyAdapterHugeMemoryRequired)
adapter = TestFactory.create_adapter("tvb.tests.framework.adapters.dummy_adapter3",
"DummyAdapterHugeMemoryRequired")
# Simulate receiving POST data
form = DummyAdapterHugeMemoryRequiredForm()
view_model = form.get_view_model()()
view_model.test = 5
# Prepare operation for launch
operation = Operation(view_model.gid.hex, self.test_user.id, self.test_project.id, adapter.stored_adapter.id,
status=STATUS_STARTED)
operation = dao.store_entity(operation)
# Store ViewModel in H5
parent_folder = StorageInterface().get_project_folder(self.test_project.name, str(operation.id))
h5.store_view_model(view_model, parent_folder)
# Launch operation
with pytest.raises(NoMemoryAvailableException):
OperationService().initiate_prelaunch(operation, adapter)
def test_create_stimulus_surface_with_operation(self):
surface_stimulus_creator = SurfaceStimulusCreator()
view_model = surface_stimulus_creator.get_view_model_class()()
view_model.surface = self.surface.gid
view_model.focal_points_triangles = numpy.array([1, 2, 3])
view_model.spatial = SpatialEquationsEnum.SIGMOID.instance
view_model.spatial.parameters['radius'] = 5.5
view_model.spatial.parameters['offset'] = 0.1
view_model.spatial_amp = 1.0
view_model.spatial_sigma = 1.0
view_model.spatial_offset = 0.0
view_model.temporal = TemporalEquationsEnum.ALPHA.instance
view_model.temporal.parameters['alpha'] = 15.0
view_model.temporal.parameters['beta'] = 40.0
OperationService().fire_operation(surface_stimulus_creator, self.test_user, self.test_project.id,
view_model=view_model)
surface_stimulus_index = TestFactory.get_entity(self.test_project, StimuliSurfaceIndex)
assert surface_stimulus_index.spatial_equation == 'Sigmoid'
assert surface_stimulus_index.temporal_equation == 'Alpha'
assert surface_stimulus_index.fk_surface_gid == self.surface.gid
spatial_eq_params = json.loads(surface_stimulus_index.spatial_parameters)
assert spatial_eq_params['radius'] == 5.5
assert spatial_eq_params['offset'] == 0.1
temporal_eq_params = json.loads(surface_stimulus_index.temporal_parameters)
assert temporal_eq_params['alpha'] == 15.0
assert temporal_eq_params['beta'] == 40.0
def async_launch_and_prepare_simulation(self, burst_config, user, project,
simulator_algo, simulator):
try:
operation = self.operation_service.prepare_operation(
user.id,
project,
simulator_algo,
view_model=simulator,
burst_gid=burst_config.gid,
op_group_id=burst_config.fk_operation_group)
burst_config = self.burst_service.update_simulation_fields(
burst_config, operation.id, simulator.gid)
self.burst_service.store_burst_configuration(burst_config)
wf_errs = 0
try:
OperationService().launch_operation(operation.id, True)
return operation
except Exception as excep:
self.logger.error(excep)
wf_errs += 1
if burst_config:
self.burst_service.mark_burst_finished(
burst_config, error_message=str(excep))
self.logger.debug(
"Finished launching workflow. The operation was launched successfully, "
+ str(wf_errs) + " had error on pre-launch steps")
except Exception as excep:
self.logger.error(excep)
if burst_config:
self.burst_service.mark_burst_finished(
burst_config, error_message=str(excep))
def _compute_operation_details(self, entity_gid, is_group=False):
"""
Returns a dictionary which contains the details for the given operation.
"""
selected_project = common.get_current_project()
op_details = self.project_service.get_operation_details(
entity_gid, is_group)
operation_id = op_details.operation_id
display_reload_btn = True
operation = OperationService.load_operation(operation_id)
if (operation.fk_operation_group is not None) or (operation.burst
is not None):
display_reload_btn = False
else:
op_categ_id = operation.algorithm.fk_category
raw_categories = self.algorithm_service.get_raw_categories()
for category in raw_categories:
if category.id == op_categ_id:
display_reload_btn = False
break
template_specification = {
"entity_gid": entity_gid,
"nodeFields": op_details.get_ui_fields(),
"operationId": operation_id,
"displayReloadBtn": display_reload_btn,
"project": selected_project,
"isRelevant": operation.visible
}
return template_specification
def test_noise_2d_bad_shape(self):
"""
Test a simulation with noise. Pass a wrong shape and expect exception to be raised.
"""
params = copy(SIMULATOR_PARAMETERS)
params['integrator'] = u'HeunStochastic'
noise_4d_config = [[1 for _ in range(self.CONNECTIVITY_NODES)]
for _ in range(4)]
params[
'integrator_parameters_option_HeunStochastic_dt'] = u'0.01220703125'
params[
'integrator_parameters_option_HeunStochastic_noise'] = u'Additive'
params[
'integrator_parameters_option_HeunStochastic_noise_parameters_option_Additive_nsig'] = str(
noise_4d_config)
params[
'integrator_parameters_option_HeunStochastic_noise_parameters_option_Additive_ntau'] = u'0.0'
params[
'integrator_parameters_option_HeunStochastic_noise_parameters_option_Additive_random_stream'] = u'RandomStream'
params[
'integrator_parameters_option_HeunStochastic_noise_parameters_option_Additive_random_stream_parameters_option_RandomStream_init_seed'] = u'42'
filtered_params = self.simulator_adapter.prepare_ui_inputs(params)
self.simulator_adapter.configure(**filtered_params)
if hasattr(self.simulator_adapter, 'algorithm'):
self.assertEqual(
(4, 74),
self.simulator_adapter.algorithm.integrator.noise.nsig.shape)
else:
self.fail("Simulator adapter was not initialized properly")
self.assertRaises(Exception,
OperationService().initiate_prelaunch,
self.operation, self.simulator_adapter, {}, **params)
def setUp(self):
"""
Sets up the environment for testing;
creates a `FlowController`
"""
self.init()
self.flow_c = FlowController()
self.burst_c = BurstController()
self.operation_service = OperationService()
def setup_method(self):
"""
Reset the database before each test.
"""
self.clean_database()
initialize_storage()
self.test_user = TestFactory.create_user()
self.test_project = TestFactory.create_project(self.test_user)
self.operation_service = OperationService()
self.backup_hdd_size = TvbProfile.current.MAX_DISK_SPACE
def stop_operation(self, operation_id, is_group, remove_after_stop=False):
"""
Stop the operation given by operation_id. If is_group is true stop all the
operations from that group.
"""
operation_service = OperationService()
result = False
if int(is_group) == 0:
result = operation_service.stop_operation(operation_id)
if remove_after_stop:
ProjectService().remove_operation(operation_id)
else:
op_group = ProjectService.get_operation_group_by_id(operation_id)
operations_in_group = ProjectService.get_operations_in_group(op_group)
for operation in operations_in_group:
tmp_res = operation_service.stop_operation(operation.id)
if remove_after_stop:
ProjectService().remove_operation(operation.id)
result = result or tmp_res
return result
def transactional_setup_method(self):
"""
Sets up the testing environment;
saves config file;
creates a test user, a test project;
creates burst, operation, flow and workflow services
"""
self.test_user = TestFactory.create_user()
self.test_project = TestFactory.create_project(self.test_user)
self.workflow_service = WorkflowService()
self.burst_service = BurstService()
self.operation_service = OperationService()
self.flow_service = FlowService()
def __init__(self, overwrites=None, settings_file=None):
""" Parameters can be overwritten either from a settigns file or from a dictionary. """
if overwrites is not None:
self.overwrites.update(overwrites)
if settings_file is not None:
settings = open(sys.argv[1]).read()
for line in settings.split('\n'):
key, value = line.split('=')
self.overwrites[key.strip()] = value.strip()
if KEY_PROJECT not in self.overwrites:
raise Exception("Settings file should contain the id of the project: %s=1" % KEY_PROJECT)
self.project = dao.get_project_by_id(self.overwrites[KEY_PROJECT])
self.flow_service = FlowService()
self.operation_service = OperationService()
class ModelValidator(object):
overwrites = {}
def __init__(self, overwrites=None, settings_file=None):
""" Parameters can be overwritten either from a settigns file or from a dictionary. """
if overwrites is not None:
self.overwrites.update(overwrites)
if settings_file is not None:
settings = open(sys.argv[1]).read()
for line in settings.split('\n'):
key, value = line.split('=')
self.overwrites[key.strip()] = value.strip()
if KEY_PROJECT not in self.overwrites:
raise Exception("Settings file should contain the id of the project: %s=1" % KEY_PROJECT)
self.project = dao.get_project_by_id(self.overwrites[KEY_PROJECT])
self.flow_service = FlowService()
self.operation_service = OperationService()
def launch_validation(self):
"""
Prepare the arguments to be submitted and launch actual operations group.
TODO: Now get the results and check if any errors
"""
stored_adapter = self.flow_service.get_algorithm_by_module_and_class(SIMULATOR_MODULE, SIMULATOR_CLASS)
simulator_adapter = ABCAdapter.build_adapter(stored_adapter)
launch_args = {}
flatten_interface = simulator_adapter.flaten_input_interface()
itree_mngr = self.flow_service.input_tree_manager
prepared_flatten_interface = itree_mngr.fill_input_tree_with_options(flatten_interface, self.project.id,
stored_adapter.fk_category)
for entry in prepared_flatten_interface:
value = entry['default']
if isinstance(value, dict):
value = str(value)
if hasattr(value, 'tolist'):
value = value.tolist()
launch_args[entry['name']] = value
launch_args.update(self.overwrites)
nr_of_operations = 1
for key in self.overwrites:
if key.startswith(PARAM_RANGE_PREFIX):
range_values = self.operation_service.get_range_values(launch_args, key)
nr_of_operations *= len(range_values)
do_launch = False
print "Warning! This will launch %s operations. Do you agree? (yes/no)" % nr_of_operations
while 1:
accept = raw_input()
if accept.lower() == 'yes':
do_launch = True
break
if accept.lower() == 'no':
do_launch = False
break
print "Please type either yes or no"
if do_launch:
self.launched_operations = self.flow_service.fire_operation(simulator_adapter, self.project.administrator,
self.project.id, **launch_args)
return self.validate_results(0)
else:
return "Operation canceled by user."
def validate_results(self, last_verified_index):
error_count = 0
while last_verified_index < len(self.launched_operations):
operation_to_check = self.launched_operations[last_verified_index]
operation = dao.get_operation_by_id(operation_to_check.id)
if not operation.has_finished:
sleep(10)
if operation.status == STATUS_ERROR:
sys.stdout.write("E(" + str(operation_to_check.id) + ")")
error_count += 1
last_verified_index += 1
sys.stdout.flush()
if operation.status == STATUS_FINISHED:
last_verified_index += 1
sys.stdout.write('.')
sys.stdout.flush()
if error_count:
return "%s operations in error; %s operations successfully." % (error_count,
len(self.launched_operations) - error_count)
return "All operations finished successfully!"
class OperationServiceTest(BaseTestCase):
"""
Test class for the introspection module. Some tests from here do async launches. For those
cases Transactional tests won't work.
TODO: this is still to be refactored, for being huge, with duplicates and many irrelevant checks
"""
def setUp(self):
"""
Reset the database before each test.
"""
self.clean_database()
initialize_storage()
self.test_user = TestFactory.create_user()
self.test_project = TestFactory.create_project(self.test_user)
self.operation_service = OperationService()
self.backup_hdd_size = TvbProfile.current.MAX_DISK_SPACE
def tearDown(self):
"""
Reset the database when test is done.
"""
TvbProfile.current.MAX_DISK_SPACE = self.backup_hdd_size
self.clean_database()
def _assert_no_dt2(self):
count = dao.count_datatypes(self.test_project.id, Datatype2)
self.assertEqual(0, count)
def _assert_stored_dt2(self, expected_cnt=1):
count = dao.count_datatypes(self.test_project.id, Datatype2)
self.assertEqual(expected_cnt, count)
datatype = dao.try_load_last_entity_of_type(self.test_project.id, Datatype2)
self.assertEqual(datatype.subject, DataTypeMetaData.DEFAULT_SUBJECT, "Wrong data stored.")
return datatype
def test_datatypes_groups(self):
"""
Tests if the dataType group is set correct on the dataTypes resulted from the same operation group.
"""
flow_service = FlowService()
all_operations = dao.get_filtered_operations(self.test_project.id, None)
self.assertEqual(len(all_operations), 0, "There should be no operation")
algogroup = dao.find_group("tvb.tests.framework.adapters.testadapter3", "TestAdapter3")
group, _ = flow_service.prepare_adapter(self.test_project.id, algogroup)
adapter_instance = flow_service.build_adapter_instance(group)
data = {model.RANGE_PARAMETER_1: "param_5", "param_5": [1, 2]}
## Create Group of operations
flow_service.fire_operation(adapter_instance, self.test_user, self.test_project.id, **data)
all_operations = dao.get_filtered_operations(self.test_project.id, None)
self.assertEqual(len(all_operations), 1, "Expected one operation group")
self.assertEqual(all_operations[0][2], 2, "Expected 2 operations in group")
operation_group_id = all_operations[0][3]
self.assertNotEquals(operation_group_id, None, "The operation should be part of a group.")
self.operation_service.stop_operation(all_operations[0][0])
self.operation_service.stop_operation(all_operations[0][1])
## Make sure operations are executed
self.operation_service.launch_operation(all_operations[0][0], False)
self.operation_service.launch_operation(all_operations[0][1], False)
resulted_datatypes = dao.get_datatype_in_group(operation_group_id=operation_group_id)
self.assertTrue(len(resulted_datatypes) >= 2, "Expected at least 2, but: " + str(len(resulted_datatypes)))
dt = dao.get_datatype_by_id(resulted_datatypes[0].id)
datatype_group = dao.get_datatypegroup_by_op_group_id(operation_group_id)
self.assertEqual(dt.fk_datatype_group, datatype_group.id, "DataTypeGroup is incorrect")
def test_initiate_operation(self):
"""
Test the actual operation flow by executing a test adapter.
"""
module = "tvb.tests.framework.adapters.testadapter1"
class_name = "TestAdapter1"
group = dao.find_group(module, class_name)
adapter = FlowService().build_adapter_instance(group)
output = adapter.get_output()
output_type = output[0].__name__
data = {"test1_val1": 5, "test1_val2": 5}
tmp_folder = FilesHelper().get_project_folder(self.test_project, "TEMP")
res = self.operation_service.initiate_operation(
self.test_user, self.test_project.id, adapter, tmp_folder, **data
)
self.assertTrue(res.index("has finished.") > 10, "Operation didn't finish")
group = dao.find_group(module, class_name)
self.assertEqual(group.module, "tvb.tests.framework.adapters.testadapter1", "Wrong data stored.")
self.assertEqual(group.classname, "TestAdapter1", "Wrong data stored.")
dts, count = dao.get_values_of_datatype(self.test_project.id, Datatype1)
self.assertEqual(count, 1)
self.assertEqual(len(dts), 1)
datatype = dao.get_datatype_by_id(dts[0][0])
self.assertEqual(datatype.subject, DataTypeMetaData.DEFAULT_SUBJECT, "Wrong data stored.")
self.assertEqual(datatype.type, output_type, "Wrong data stored.")
def test_delete_dt_free_HDD_space(self):
"""
Launch two operations and give enough available space for user so that both should finish.
"""
module = "tvb.tests.framework.adapters.testadapter3"
class_name = "TestAdapterHDDRequired"
group = dao.find_group(module, class_name)
adapter = FlowService().build_adapter_instance(group)
data = {"test": 100}
TvbProfile.current.MAX_DISK_SPACE = float(adapter.get_required_disk_size(**data))
tmp_folder = FilesHelper().get_project_folder(self.test_project, "TEMP")
self._assert_no_dt2()
self.operation_service.initiate_operation(self.test_user, self.test_project.id, adapter, tmp_folder, **data)
datatype = self._assert_stored_dt2()
# Now free some space and relaunch
ProjectService().remove_datatype(self.test_project.id, datatype.gid)
self._assert_no_dt2()
self.operation_service.initiate_operation(self.test_user, self.test_project.id, adapter, tmp_folder, **data)
self._assert_stored_dt2()
def test_launch_two_ops_HDD_with_space(self):
"""
Launch two operations and give enough available space for user so that both should finish.
"""
module = "tvb.tests.framework.adapters.testadapter3"
class_name = "TestAdapterHDDRequired"
group = dao.find_group(module, class_name)
adapter = FlowService().build_adapter_instance(group)
data = {"test": 100}
TvbProfile.current.MAX_DISK_SPACE = 2 * float(adapter.get_required_disk_size(**data))
tmp_folder = FilesHelper().get_project_folder(self.test_project, "TEMP")
self.operation_service.initiate_operation(self.test_user, self.test_project.id, adapter, tmp_folder, **data)
datatype = self._assert_stored_dt2()
# Now update the maximum disk size to be the size of the previously resulted datatypes (transform from kB to MB)
# plus what is estimated to be required from the next one (transform from B to MB)
TvbProfile.current.MAX_DISK_SPACE = float(datatype.disk_size) + float(adapter.get_required_disk_size(**data))
self.operation_service.initiate_operation(self.test_user, self.test_project.id, adapter, tmp_folder, **data)
self._assert_stored_dt2(2)
def test_launch_two_ops_HDD_full_space(self):
"""
Launch two operations and give available space for user so that the first should finish,
but after the update to the user hdd size the second should not.
"""
module = "tvb.tests.framework.adapters.testadapter3"
class_name = "TestAdapterHDDRequired"
group = dao.find_group(module, class_name)
adapter = FlowService().build_adapter_instance(group)
data = {"test": 100}
TvbProfile.current.MAX_DISK_SPACE = 1 + float(adapter.get_required_disk_size(**data))
tmp_folder = FilesHelper().get_project_folder(self.test_project, "TEMP")
self.operation_service.initiate_operation(self.test_user, self.test_project.id, adapter, tmp_folder, **data)
datatype = self._assert_stored_dt2()
# Now update the maximum disk size to be less than size of the previously resulted datatypes (transform kB to MB)
# plus what is estimated to be required from the next one (transform from B to MB)
TvbProfile.current.MAX_DISK_SPACE = float(datatype.disk_size - 1) + float(
adapter.get_required_disk_size(**data) - 1
)
self.assertRaises(
NoMemoryAvailableException,
self.operation_service.initiate_operation,
self.test_user,
self.test_project.id,
adapter,
tmp_folder,
**data
)
self._assert_stored_dt2()
def test_launch_operation_HDD_with_space(self):
"""
Test the actual operation flow by executing a test adapter.
"""
module = "tvb.tests.framework.adapters.testadapter3"
class_name = "TestAdapterHDDRequired"
group = dao.find_group(module, class_name)
adapter = FlowService().build_adapter_instance(group)
data = {"test": 100}
TvbProfile.current.MAX_DISK_SPACE = float(adapter.get_required_disk_size(**data))
tmp_folder = FilesHelper().get_project_folder(self.test_project, "TEMP")
self.operation_service.initiate_operation(self.test_user, self.test_project.id, adapter, tmp_folder, **data)
self._assert_stored_dt2()
def test_launch_operation_HDD_with_space_started_ops(self):
"""
Test the actual operation flow by executing a test adapter.
"""
space_taken_by_started = 100
module = "tvb.tests.framework.adapters.testadapter3"
class_name = "TestAdapterHDDRequired"
group = dao.find_group(module, class_name)
started_operation = model.Operation(
self.test_user.id,
self.test_project.id,
group.id,
"",
status=model.STATUS_STARTED,
estimated_disk_size=space_taken_by_started,
)
dao.store_entity(started_operation)
adapter = FlowService().build_adapter_instance(group)
data = {"test": 100}
TvbProfile.current.MAX_DISK_SPACE = float(adapter.get_required_disk_size(**data) + space_taken_by_started)
tmp_folder = FilesHelper().get_project_folder(self.test_project, "TEMP")
self.operation_service.initiate_operation(self.test_user, self.test_project.id, adapter, tmp_folder, **data)
self._assert_stored_dt2()
def test_launch_operation_HDD_full_space(self):
"""
Test the actual operation flow by executing a test adapter.
"""
module = "tvb.tests.framework.adapters.testadapter3"
class_name = "TestAdapterHDDRequired"
group = dao.find_group(module, class_name)
adapter = FlowService().build_adapter_instance(group)
data = {"test": 100}
TvbProfile.current.MAX_DISK_SPACE = float(adapter.get_required_disk_size(**data) - 1)
tmp_folder = FilesHelper().get_project_folder(self.test_project, "TEMP")
self.assertRaises(
NoMemoryAvailableException,
self.operation_service.initiate_operation,
self.test_user,
self.test_project.id,
adapter,
tmp_folder,
**data
)
self._assert_no_dt2()
def test_launch_operation_HDD_full_space_started_ops(self):
"""
Test the actual operation flow by executing a test adapter.
"""
space_taken_by_started = 100
module = "tvb.tests.framework.adapters.testadapter3"
class_name = "TestAdapterHDDRequired"
group = dao.find_group(module, class_name)
started_operation = model.Operation(
self.test_user.id,
self.test_project.id,
group.id,
"",
status=model.STATUS_STARTED,
estimated_disk_size=space_taken_by_started,
)
dao.store_entity(started_operation)
adapter = FlowService().build_adapter_instance(group)
data = {"test": 100}
TvbProfile.current.MAX_DISK_SPACE = float(adapter.get_required_disk_size(**data) + space_taken_by_started - 1)
tmp_folder = FilesHelper().get_project_folder(self.test_project, "TEMP")
self.assertRaises(
NoMemoryAvailableException,
self.operation_service.initiate_operation,
self.test_user,
self.test_project.id,
adapter,
tmp_folder,
**data
)
self._assert_no_dt2()
def test_stop_operation(self):
"""
Test that an operation is successfully stopped.
"""
module = "tvb.tests.framework.adapters.testadapter2"
class_name = "TestAdapter2"
group = dao.find_group(module, class_name)
adapter = FlowService().build_adapter_instance(group)
data = {"test": 5}
algo_group = adapter.algorithm_group
algo_category = dao.get_category_by_id(algo_group.fk_category)
algo = dao.get_algorithm_by_group(algo_group.id)
operations, _ = self.operation_service.prepare_operations(
self.test_user.id, self.test_project.id, algo, algo_category, {}, **data
)
self.operation_service._send_to_cluster(operations, adapter)
self.operation_service.stop_operation(operations[0].id)
operation = dao.get_operation_by_id(operations[0].id)
self.assertEqual(operation.status, model.STATUS_CANCELED, "Operation should have been canceled!")
def test_stop_operation_finished(self):
"""
Test that an operation that is already finished is not changed by the stop operation.
"""
module = "tvb.tests.framework.adapters.testadapter1"
class_name = "TestAdapter1"
group = dao.find_group(module, class_name)
adapter = FlowService().build_adapter_instance(group)
data = {"test1_val1": 5, "test1_val2": 5}
algo_group = adapter.algorithm_group
algo_category = dao.get_category_by_id(algo_group.fk_category)
algo = dao.get_algorithm_by_group(algo_group.id)
operations, _ = self.operation_service.prepare_operations(
self.test_user.id, self.test_project.id, algo, algo_category, {}, **data
)
self.operation_service._send_to_cluster(operations, adapter)
operation = dao.get_operation_by_id(operations[0].id)
operation.status = model.STATUS_FINISHED
dao.store_entity(operation)
self.operation_service.stop_operation(operations[0].id)
operation = dao.get_operation_by_id(operations[0].id)
self.assertEqual(operation.status, model.STATUS_FINISHED, "Operation shouldn't have been canceled!")
def test_array_from_string(self):
"""
Simple test for parse array on 1d, 2d and 3d array.
"""
row = {
"description": "test.",
"default": "None",
"required": True,
"label": "test: ",
"attributes": None,
"quantifier": "manual",
"elementType": "float",
"type": "array",
"options": None,
"name": "test",
}
input_data_string = "[ [1 2 3] [4 5 6]]"
output = string2array(input_data_string, " ", row["elementType"])
self.assertEqual(output.shape, (2, 3), "Dimensions not properly parsed")
for i in output[0]:
self.assertTrue(i in [1, 2, 3])
for i in output[1]:
self.assertTrue(i in [4, 5, 6])
input_data_string = "[1, 2, 3, 4, 5, 6]"
output = string2array(input_data_string, ",", row["elementType"])
self.assertEqual(output.shape, (6,), "Dimensions not properly parsed")
for i in output:
self.assertTrue(i in [1, 2, 3, 4, 5, 6])
input_data_string = "[ [ [1,1], [2, 2] ], [ [3 ,3], [4,4] ] ]"
output = string2array(input_data_string, ",", row["elementType"])
self.assertEqual(output.shape, (2, 2, 2), "Wrong dimensions.")
for i in output[0][0]:
self.assertTrue(i == 1)
for i in output[0][1]:
self.assertTrue(i == 2)
for i in output[1][0]:
self.assertTrue(i == 3)
for i in output[1][1]:
self.assertTrue(i == 4)
row = {
"description": "test.",
"default": "None",
"required": True,
"label": "test: ",
"attributes": None,
"quantifier": "manual",
"elementType": "str",
"type": "array",
"options": None,
"name": "test",
}
input_data_string = "[1, 2, 3, 4, 5, 6]"
output = string2array(input_data_string, ",", row["elementType"])
for i in output:
self.assertTrue(i in [1, 2, 3, 4, 5, 6])
def test_wrong_array_from_string(self):
"""Test that parsing an array from string is throwing the expected
exception when wrong input string"""
row = {
"description": "test.",
"default": "None",
"required": True,
"label": "test: ",
"attributes": None,
"quantifier": "manual",
"elementType": "float",
"type": "array",
"options": None,
"name": "test",
}
input_data_string = "[ [1,2 3] [4,5,6]]"
self.assertRaises(ValueError, string2array, input_data_string, ",", row["elementType"])
input_data_string = "[ [1,2,wrong], [4, 5, 6]]"
self.assertRaises(ValueError, string2array, input_data_string, ",", row["elementType"])
row = {
"description": "test.",
"default": "None",
"required": True,
"label": "test: ",
"attributes": None,
"quantifier": "manual",
"elementType": "str",
"type": "array",
"options": None,
"name": "test",
}
output = string2array(input_data_string, ",", row["elementType"])
self.assertEqual(output.shape, (2, 3))
self.assertEqual(output[0][2], "wrong", "String data not converted properly")
input_data_string = "[ [1,2 3] [4,5,6]]"
output = string2array(input_data_string, ",", row["elementType"])
self.assertEqual(output[0][1], "2 3")
def test_reduce_dimension_component(self):
"""
This method tests if the data passed to the launch method of
the NDimensionArrayAdapter adapter is correct. The passed data should be a list
of arrays with one dimension.
"""
inserted_count = FlowService().get_available_datatypes(
self.test_project.id, "tvb.datatypes.arrays.MappedArray"
)[1]
self.assertEqual(inserted_count, 0, "Expected to find no data.")
# create an operation
algorithm_id = (
FlowService()
.get_algorithm_by_module_and_class(
"tvb.tests.framework.adapters.ndimensionarrayadapter", "NDimensionArrayAdapter"
)[0]
.id
)
operation = model.Operation(
self.test_user.id,
self.test_project.id,
algorithm_id,
"test params",
meta=json.dumps({DataTypeMetaData.KEY_STATE: "RAW_DATA"}),
status=model.STATUS_FINISHED,
)
operation = dao.store_entity(operation)
# save the array wrapper in DB
adapter_instance = NDimensionArrayAdapter()
PARAMS = {}
self.operation_service.initiate_prelaunch(operation, adapter_instance, {}, **PARAMS)
inserted_data = FlowService().get_available_datatypes(self.test_project.id, "tvb.datatypes.arrays.MappedArray")[
0
]
self.assertEqual(len(inserted_data), 1, "Problems when inserting data")
gid = inserted_data[0][2]
entity = dao.get_datatype_by_gid(gid)
# from the 3D array do not select any array
PARAMS = {
"python_method": "reduce_dimension",
"input_data": gid,
"input_data_dimensions_0": "requiredDim_1",
"input_data_dimensions_1": "",
"input_data_dimensions_2": "",
}
try:
self.operation_service.initiate_prelaunch(operation, adapter_instance, {}, **PARAMS)
self.fail("Test should not pass. The resulted array should be a 1D array.")
except Exception:
# OK, do nothing; we were expecting to produce a 1D array
pass
# from the 3D array select only a 1D array
first_dim = [gid + "_1_0", "requiredDim_1"]
PARAMS = {
"python_method": "reduce_dimension",
"input_data": gid,
"input_data_dimensions_0": first_dim,
"input_data_dimensions_1": gid + "_2_1",
}
self.operation_service.initiate_prelaunch(operation, adapter_instance, {}, **PARAMS)
expected_result = entity.array_data[:, 0, 1]
actual_result = adapter_instance.launch_param
self.assertEqual(len(actual_result), len(expected_result), "Not the same size for results!")
self.assertTrue(numpy.equal(actual_result, expected_result).all())
# from the 3D array select a 2D array
first_dim = [gid + "_1_0", gid + "_1_1", "requiredDim_2"]
PARAMS = {
"python_method": "reduce_dimension",
"input_data": gid,
"input_data_dimensions_0": first_dim,
"input_data_dimensions_1": gid + "_2_1",
}
self.operation_service.initiate_prelaunch(operation, adapter_instance, {}, **PARAMS)
expected_result = entity.array_data[slice(0, None), [0, 1], 1]
actual_result = adapter_instance.launch_param
self.assertEqual(len(actual_result), len(expected_result), "Not the same size for results!")
self.assertTrue(numpy.equal(actual_result, expected_result).all())
# from 3D array select 1D array by applying SUM function on the first
# dimension and average function on the second dimension
PARAMS = {
"python_method": "reduce_dimension",
"input_data": gid,
"input_data_dimensions_0": ["requiredDim_1", "func_sum"],
"input_data_dimensions_1": "func_average",
"input_data_dimensions_2": "",
}
self.operation_service.initiate_prelaunch(operation, adapter_instance, {}, **PARAMS)
aux = numpy.sum(entity.array_data, axis=0)
expected_result = numpy.average(aux, axis=0)
actual_result = adapter_instance.launch_param
self.assertEqual(len(actual_result), len(expected_result), "Not the same size of results!")
self.assertTrue(numpy.equal(actual_result, expected_result).all())
# from 3D array select a 2D array and apply op. on the second dimension
PARAMS = {
"python_method": "reduce_dimension",
"input_data": gid,
"input_data_dimensions_0": ["requiredDim_2", "func_sum", "expected_shape_x,512", "operations_x,>"],
"input_data_dimensions_1": "",
"input_data_dimensions_2": "",
}
try:
self.operation_service.initiate_prelaunch(operation, adapter_instance, {}, **PARAMS)
self.fail("Test should not pass! The second dimension of the array should be >512.")
except Exception:
# OK, do nothing;
pass
class BurstService(object):
"""
Service layer for Burst related entities.
"""
def __init__(self):
self.operation_service = OperationService()
self.workflow_service = WorkflowService()
self.logger = get_logger(self.__class__.__module__)
self.cache_portlet_configurators = {}
def build_portlet_interface(self, portlet_configuration, project_id):
"""
From a portlet_id and a project_id, first build the portlet
entity then get it's configurable interface.
:param portlet_configuration: a portlet configuration entity. It holds at the
least the portlet_id, and in case any default parameters were saved
they can be rebuilt from the analyzers // visualizer parameters
:param project_id: the id of the current project
:returns: the portlet interface will be of the following form::
[{'interface': adapter_interface,
'prefix': prefix_for_parameter_names,
'subalg': {algorithm_field_name: default_algorithm_value},
'algo_group': algorithm_group,
'alg_ui_name': displayname},
......]
A list of dictionaries for each adapter that makes up the portlet.
"""
portlet_configurer = self._get_portlet_configurer(portlet_configuration.portlet_id)
portlet_interface = portlet_configurer.get_configurable_interface()
for adapter_conf in portlet_interface:
interface = adapter_conf.interface
itree_mngr = InputTreeManager()
interface = itree_mngr.fill_input_tree_with_options(interface, project_id,
adapter_conf.stored_adapter.fk_category)
adapter_conf.interface = itree_mngr.prepare_param_names(interface)
portlet_configurer.update_default_values(portlet_interface, portlet_configuration)
portlet_configurer.prefix_adapters_parameters(portlet_interface)
return portlet_interface
def _get_portlet_configurer(self, portlet_id):
if portlet_id not in self.cache_portlet_configurators:
portlet_entity = dao.get_portlet_by_id(portlet_id)
if portlet_entity is None:
raise InvalidPortletConfiguration("No portlet entity located in database with id=%s. " % portlet_id)
self.cache_portlet_configurators[portlet_id] = PortletConfigurer(portlet_entity)
self.logger.debug("Recently parsed portlet XML:" + str([portlet_entity]))
return self.cache_portlet_configurators[portlet_id]
def update_portlet_configuration(self, portlet_configuration, submited_parameters):
"""
:param portlet_configuration: the portlet configuration that needs to be updated
:param submited_parameters: a list of parameters as submitted from the UI. This
is a dictionary in the form :
{'dynamic' : {name:value pairs}, 'static' : {name:value pairs}}
All names are prefixed with adapter specific generated prefix.
"""
portlet_configurer = self._get_portlet_configurer(portlet_configuration.portlet_id)
return portlet_configurer.update_portlet_configuration(portlet_configuration, submited_parameters)
def new_burst_configuration(self, project_id):
"""
Return a new burst configuration entity with all the default values.
"""
burst_configuration = model.BurstConfiguration(project_id)
burst_configuration.selected_tab = 0
# Now set the default portlets for the specified burst configuration.
# The default portlets are specified in the __init__.py script from tvb root.
for tab_idx, value in DEFAULT_PORTLETS.items():
for sel_idx, portlet_identifier in value.items():
portlet = BurstService.get_portlet_by_identifier(portlet_identifier)
if portlet is not None:
portlet_configuration = self.new_portlet_configuration(portlet.id, tab_idx, sel_idx,
portlet.algorithm_identifier)
burst_configuration.set_portlet(tab_idx, sel_idx, portlet_configuration)
return burst_configuration
@staticmethod
def _store_burst_config(burst_config):
"""
Store a burst configuration entity.
"""
burst_config.prepare_before_save()
saved_entity = dao.store_entity(burst_config)
return saved_entity.id
@staticmethod
def get_available_bursts(project_id):
"""
Return all the burst for the current project.
"""
bursts = dao.get_bursts_for_project(project_id, page_size=MAX_BURSTS_DISPLAYED) or []
for burst in bursts:
burst.prepare_after_load()
return bursts
@staticmethod
def populate_burst_disk_usage(bursts):
"""
Adds a disk_usage field to each burst object.
The disk usage is computed as the sum of the datatypes generated by a burst
"""
sizes = dao.compute_bursts_disk_size([b.id for b in bursts])
for b in bursts:
b.disk_size = format_bytes_human(sizes[b.id])
@staticmethod
def rename_burst(burst_id, new_name):
"""
Rename the burst given by burst_id, setting it's new name to
burst_name.
"""
burst = dao.get_burst_by_id(burst_id)
burst.name = new_name
dao.store_entity(burst)
def load_burst(self, burst_id):
"""
:param burst_id: the id of the burst that should be loaded
Having this input the method should:
- load the entity from the DB
- get all the workflow steps for the saved burst id
- go trough the visualization workflow steps to create the tab
configuration of the burst using the tab_index and index_in_tab
fields saved on each workflow_step
"""
burst = dao.get_burst_by_id(burst_id)
burst.prepare_after_load()
burst.reset_tabs()
burst_workflows = dao.get_workflows_for_burst(burst.id)
group_gid = None
if len(burst_workflows) == 1:
# A simple burst with no range parameters
burst = self.__populate_tabs_from_workflow(burst, burst_workflows[0])
elif len(burst_workflows) > 1:
# A burst workflow with a range of values, created multiple workflows and need
# to launch parameter space exploration with the resulted group
self.__populate_tabs_from_workflow(burst, burst_workflows[0])
executed_steps = dao.get_workflow_steps(burst_workflows[0].id)
operation = dao.get_operation_by_id(executed_steps[0].fk_operation)
if operation.operation_group:
workflow_group = dao.get_datatypegroup_by_op_group_id(operation.operation_group.id)
group_gid = workflow_group.gid
return burst, group_gid
@staticmethod
def __populate_tabs_from_workflow(burst_entity, workflow):
"""
Given a burst and a workflow populate the tabs of the burst with the PortletConfigurations
generated from the steps of the workflow.
"""
visualizers = dao.get_visualization_steps(workflow.id)
for entry in visualizers:
## For each visualize step, also load all of the analyze steps.
portlet_cfg = PortletConfiguration(entry.fk_portlet)
portlet_cfg.set_visualizer(entry)
analyzers = dao.get_workflow_steps_for_position(entry.fk_workflow, entry.tab_index, entry.index_in_tab)
portlet_cfg.set_analyzers(analyzers)
burst_entity.tabs[entry.tab_index].portlets[entry.index_in_tab] = portlet_cfg
return burst_entity
def load_tab_configuration(self, burst_entity, op_id):
"""
Given a burst entity and an operation id, find the workflow to which the op_id
belongs and the load the burst_entity's tab configuration with those workflow steps.
"""
originating_workflow = dao.get_workflow_for_operation_id(op_id)
burst_entity = self.__populate_tabs_from_workflow(burst_entity, originating_workflow)
return burst_entity
def new_portlet_configuration(self, portlet_id, tab_nr=-1, index_in_tab=-1, portlet_name='Default'):
"""
Return a new portlet configuration entity with default parameters.
:param portlet_id: the id of the portlet for which a configuration will be stored
:param tab_nr: the index of the currently selected tab
:param index_in_tab: the index from the currently selected tab
"""
portlet_configurer = self._get_portlet_configurer(portlet_id)
configuration = portlet_configurer.create_new_portlet_configuration(portlet_name)
for wf_step in configuration.analyzers:
wf_step.tab_index = tab_nr
wf_step.index_in_tab = index_in_tab
configuration.visualizer.tab_index = tab_nr
configuration.visualizer.index_in_tab = index_in_tab
return configuration
@staticmethod
def get_available_portlets():
"""
:returns: a list of all the available portlet entites
"""
return dao.get_available_portlets()
@staticmethod
def get_portlet_by_id(portlet_id):
"""
:returns: the portlet entity with the id =@portlet_id
"""
return dao.get_portlet_by_id(portlet_id)
@staticmethod
def get_portlet_by_identifier(portlet_identifier):
"""
:returns: the portlet entity with the algorithm identifier =@portlet_identifier
"""
return dao.get_portlet_by_identifier(portlet_identifier)
def launch_burst(self, burst_configuration, simulator_index, simulator_id, user_id, launch_mode=LAUNCH_NEW):
"""
Given a burst configuration and all the necessary data do the actual launch.
:param burst_configuration: BurstConfiguration
:param simulator_index: the position within the workflows step list that the simulator will take. This is needed
so that the rest of the portlet workflow steps know what steps do their dynamic parameters come from.
:param simulator_id: the id of the simulator adapter as stored in the DB. It's needed to load the simulator algo
group and category that are then passed to the launcher's prepare_operation method.
:param user_id: the id of the user that launched this burst
:param launch_mode: new/branch/continue
"""
## 1. Prepare BurstConfiguration entity
if launch_mode == LAUNCH_NEW:
## Fully new entity for new simulation
burst_config = burst_configuration.clone()
if burst_config.name is None:
new_id = dao.get_max_burst_id() + 1
burst_config.name = 'simulation_' + str(new_id)
else:
## Branch or Continue simulation
burst_config = burst_configuration
simulation_state = dao.get_generic_entity(SIMULATION_DATATYPE_MODULE + "." + SIMULATION_DATATYPE_CLASS,
burst_config.id, "fk_parent_burst")
if simulation_state is None or len(simulation_state) < 1:
exc = BurstServiceException("Simulation State not found for %s, "
"thus we are unable to branch from it!" % burst_config.name)
self.logger.error(exc)
raise exc
simulation_state = simulation_state[0]
burst_config.update_simulation_parameter("simulation_state", simulation_state.gid)
burst_config = burst_configuration.clone()
count = dao.count_bursts_with_name(burst_config.name, burst_config.fk_project)
burst_config.name = burst_config.name + "_" + launch_mode + str(count)
## 2. Create Operations and do the actual launch
if launch_mode in [LAUNCH_NEW, LAUNCH_BRANCH]:
## New Burst entry in the history
burst_id = self._store_burst_config(burst_config)
thread = threading.Thread(target=self._async_launch_and_prepare,
kwargs={'burst_config': burst_config,
'simulator_index': simulator_index,
'simulator_id': simulator_id,
'user_id': user_id})
thread.start()
return burst_id, burst_config.name
else:
## Continue simulation
## TODO
return burst_config.id, burst_config.name
@transactional
def _prepare_operations(self, burst_config, simulator_index, simulator_id, user_id):
"""
Prepare all required operations for burst launch.
"""
project_id = burst_config.fk_project
burst_id = burst_config.id
workflow_step_list = []
starting_index = simulator_index + 1
sim_algo = FlowService().get_algorithm_by_identifier(simulator_id)
metadata = {DataTypeMetaData.KEY_BURST: burst_id}
launch_data = burst_config.get_all_simulator_values()[0]
operations, group = self.operation_service.prepare_operations(user_id, project_id, sim_algo,
sim_algo.algorithm_category, metadata,
**launch_data)
group_launched = group is not None
if group_launched:
starting_index += 1
for tab in burst_config.tabs:
for portlet_cfg in tab.portlets:
### For each portlet configuration stored, update the step index ###
### and also change the dynamic parameters step indexes to point ###
### to the simulator outputs. ##
if portlet_cfg is not None:
analyzers = portlet_cfg.analyzers
visualizer = portlet_cfg.visualizer
for entry in analyzers:
entry.step_index = starting_index
self.workflow_service.set_dynamic_step_references(entry, simulator_index)
workflow_step_list.append(entry)
starting_index += 1
### Change the dynamic parameters to point to the last adapter from this portlet execution.
visualizer.step_visible = False
if len(workflow_step_list) > 0 and isinstance(workflow_step_list[-1], model.WorkflowStep):
self.workflow_service.set_dynamic_step_references(visualizer, workflow_step_list[-1].step_index)
else:
self.workflow_service.set_dynamic_step_references(visualizer, simulator_index)
### Only for a single operation have the step of visualization, otherwise is useless.
if not group_launched:
workflow_step_list.append(visualizer)
if group_launched:
### For a group of operations, make sure the metric for PSE view
### is also computed, immediately after the simulation.
metric_algo = FlowService().get_algorithm_by_module_and_class(MEASURE_METRICS_MODULE, MEASURE_METRICS_CLASS)
metric_interface = FlowService().prepare_adapter(project_id, metric_algo)
dynamics = {}
for entry in metric_interface:
# We have a select that should be the dataType and a select multiple with the
# required metric algorithms to be evaluated. Only dynamic parameter should be
# the select type.
if entry[KEY_TYPE] == TYPE_SELECT:
dynamics[entry[KEY_NAME]] = {WorkflowStepConfiguration.DATATYPE_INDEX_KEY: 0,
WorkflowStepConfiguration.STEP_INDEX_KEY: simulator_index}
metric_step = model.WorkflowStep(algorithm_id=metric_algo.id, step_index=simulator_index + 1,
static_param={}, dynamic_param=dynamics)
metric_step.step_visible = False
workflow_step_list.insert(0, metric_step)
workflows = self.workflow_service.create_and_store_workflow(project_id, burst_id, simulator_index,
simulator_id, operations)
self.operation_service.prepare_operations_for_workflowsteps(workflow_step_list, workflows, user_id,
burst_id, project_id, group, operations)
operation_ids = [operation.id for operation in operations]
return operation_ids
def _async_launch_and_prepare(self, burst_config, simulator_index, simulator_id, user_id):
"""
Prepare operations asynchronously.
"""
try:
operation_ids = self._prepare_operations(burst_config, simulator_index, simulator_id, user_id)
self.logger.debug("Starting a total of %s workflows" % (len(operation_ids, )))
wf_errs = 0
for operation_id in operation_ids:
try:
OperationService().launch_operation(operation_id, True)
except Exception as excep:
self.logger.error(excep)
wf_errs += 1
self.workflow_service.mark_burst_finished(burst_config, error_message=str(excep))
self.logger.debug("Finished launching workflows. " + str(len(operation_ids) - wf_errs) +
" were launched successfully, " + str(wf_errs) + " had error on pre-launch steps")
except Exception as excep:
self.logger.error(excep)
self.workflow_service.mark_burst_finished(burst_config, error_message=str(excep))
@staticmethod
def launch_visualization(visualization, frame_width=None, frame_height=None, is_preview=True):
"""
:param visualization: a visualization workflow step
"""
dynamic_params = visualization.dynamic_param
static_params = visualization.static_param
parameters_dict = static_params
current_project_id = 0
# Current operation id needed for export mechanism. So far just use ##
# the operation of the workflow_step from which the inputs are taken ####
for param in dynamic_params:
step_index = dynamic_params[param][WorkflowStepConfiguration.STEP_INDEX_KEY]
datatype_index = dynamic_params[param][WorkflowStepConfiguration.DATATYPE_INDEX_KEY]
referred_workflow_step = dao.get_workflow_step_by_step_index(visualization.fk_workflow, step_index)
referred_operation_id = referred_workflow_step.fk_operation
referred_operation = dao.get_operation_by_id(referred_operation_id)
current_project_id = referred_operation.fk_launched_in
if type(datatype_index) is IntType:
# Entry is the output of a previous step ##
datatypes = dao.get_results_for_operation(referred_operation_id)
parameters_dict[param] = datatypes[datatype_index].gid
else:
# Entry is the input of a previous step ###
parameters_dict[param] = json.loads(referred_operation.parameters)[datatype_index]
algorithm = dao.get_algorithm_by_id(visualization.fk_algorithm)
adapter_instance = ABCAdapter.build_adapter(algorithm)
adapter_instance.current_project_id = current_project_id
prepared_inputs = adapter_instance.prepare_ui_inputs(parameters_dict)
if frame_width is not None:
prepared_inputs[ABCDisplayer.PARAM_FIGURE_SIZE] = (frame_width, frame_height)
if is_preview:
result = adapter_instance.generate_preview(**prepared_inputs)
else:
result = adapter_instance.launch(**prepared_inputs)
return result, parameters_dict
def update_history_status(self, id_list):
"""
For each burst_id received in the id_list read new status from DB and return a list [id, new_status] pair.
"""
result = []
for b_id in id_list:
burst = dao.get_burst_by_id(b_id)
burst.prepare_after_load()
if burst is not None:
if burst.status == burst.BURST_RUNNING:
running_time = datetime.now() - burst.start_time
else:
running_time = burst.finish_time - burst.start_time
running_time = format_timedelta(running_time, most_significant2=False)
if burst.status == burst.BURST_ERROR:
msg = 'Check Operations page for error Message'
else:
msg = ''
result.append([burst.id, burst.status, burst.is_group, msg, running_time])
else:
self.logger.debug("Could not find burst with id=" + str(b_id) + ". Might have been deleted by user!!")
return result
def stop_burst(self, burst_entity):
"""
Stop all the entities for the current burst and set the burst status to canceled.
"""
burst_wfs = dao.get_workflows_for_burst(burst_entity.id)
any_stopped = False
for workflow in burst_wfs:
wf_steps = dao.get_workflow_steps(workflow.id)
for step in wf_steps:
if step.fk_operation is not None:
self.logger.debug("We will stop operation: %d" % step.fk_operation)
any_stopped = self.operation_service.stop_operation(step.fk_operation) or any_stopped
if any_stopped and burst_entity.status != burst_entity.BURST_CANCELED:
self.workflow_service.mark_burst_finished(burst_entity, model.BurstConfiguration.BURST_CANCELED)
return True
return False
@transactional
def cancel_or_remove_burst(self, burst_id):
"""
Cancel (if burst is still running) or Remove the burst given by burst_id.
:returns True when Remove operation was done and False when Cancel
"""
burst_entity = dao.get_burst_by_id(burst_id)
if burst_entity.status == burst_entity.BURST_RUNNING:
self.stop_burst(burst_entity)
return False
service = ProjectService()
## Remove each DataType in current burst.
## We can not leave all on cascade, because it won't work on SQLite for mapped dataTypes.
datatypes = dao.get_all_datatypes_in_burst(burst_id)
## Get operations linked to current burst before removing the burst or else
## the burst won't be there to identify operations any more.
remaining_ops = dao.get_operations_in_burst(burst_id)
# Remove burst first to delete work-flow steps which still hold foreign keys to operations.
correct = dao.remove_entity(burst_entity.__class__, burst_id)
if not correct:
raise RemoveDataTypeException("Could not remove Burst entity!")
for datatype in datatypes:
service.remove_datatype(burst_entity.fk_project, datatype.gid, False)
## Remove all Operations remained.
correct = True
remaining_op_groups = set()
project = dao.get_project_by_id(burst_entity.fk_project)
for oper in remaining_ops:
is_remaining = dao.get_generic_entity(oper.__class__, oper.id)
if len(is_remaining) == 0:
### Operation removed cascaded.
continue
if oper.fk_operation_group is not None and oper.fk_operation_group not in remaining_op_groups:
is_remaining = dao.get_generic_entity(model.OperationGroup, oper.fk_operation_group)
if len(is_remaining) > 0:
remaining_op_groups.add(oper.fk_operation_group)
correct = correct and dao.remove_entity(model.OperationGroup, oper.fk_operation_group)
correct = correct and dao.remove_entity(oper.__class__, oper.id)
service.structure_helper.remove_operation_data(project.name, oper.id)
if not correct:
raise RemoveDataTypeException("Could not remove Burst because a linked operation could not be dropped!!")
return True
@staticmethod
def get_portlet_status(portlet_cfg):
"""
Get the status of a portlet configuration.
"""
if portlet_cfg.analyzers:
for analyze_step in portlet_cfg.analyzers:
operation = dao.try_get_operation_by_id(analyze_step.fk_operation)
if operation is None:
return model.STATUS_ERROR, "Operation has been removed"
if operation.status != model.STATUS_FINISHED:
return operation.status, operation.additional_info or ''
else:
## Simulator is first step so now decide if we are waiting for input or output ##
visualizer = portlet_cfg.visualizer
wait_on_outputs = False
for entry in visualizer.dynamic_param:
if type(visualizer.dynamic_param[entry][WorkflowStepConfiguration.DATATYPE_INDEX_KEY]) == IntType:
wait_on_outputs = True
break
if wait_on_outputs:
simulator_step = dao.get_workflow_step_by_step_index(visualizer.fk_workflow, 0)
operation = dao.try_get_operation_by_id(simulator_step.fk_operation)
if operation is None:
error_msg = ("At least one simulation result was not found, it might have been removed. <br\>"
"You can copy and relaunch current simulation, if you are interested in having "
"your results re-computed.")
return model.STATUS_ERROR, error_msg
else:
return operation.status, operation.additional_info or ''
return model.STATUS_FINISHED, ''
def __init__(self):
self.operation_service = OperationService()
self.workflow_service = WorkflowService()
self.logger = get_logger(self.__class__.__module__)
self.cache_portlet_configurators = {}
class TestOperationService(BaseTestCase):
"""
Test class for the introspection module. Some tests from here do async launches. For those
cases Transactional tests won't work.
TODO: this is still to be refactored, for being huge, with duplicates and many irrelevant checks
"""
def setup_method(self):
"""
Reset the database before each test.
"""
self.clean_database()
initialize_storage()
self.test_user = TestFactory.create_user()
self.test_project = TestFactory.create_project(self.test_user)
self.operation_service = OperationService()
self.backup_hdd_size = TvbProfile.current.MAX_DISK_SPACE
def teardown_method(self):
"""
Reset the database when test is done.
"""
TvbProfile.current.MAX_DISK_SPACE = self.backup_hdd_size
self.clean_database()
def _assert_no_dt2(self):
count = dao.count_datatypes(self.test_project.id, Datatype2)
assert 0 == count
def _assert_stored_dt2(self, expected_cnt=1):
count = dao.count_datatypes(self.test_project.id, Datatype2)
assert expected_cnt == count
datatype = dao.try_load_last_entity_of_type(self.test_project.id, Datatype2)
assert datatype.subject == DataTypeMetaData.DEFAULT_SUBJECT, "Wrong data stored."
return datatype
def test_datatypes_groups(self):
"""
Tests if the dataType group is set correct on the dataTypes resulted from the same operation group.
"""
flow_service = FlowService()
all_operations = dao.get_filtered_operations(self.test_project.id, None)
assert len(all_operations) == 0, "There should be no operation"
adapter_instance = TestFactory.create_adapter('tvb.tests.framework.adapters.testadapter3', 'TestAdapter3')
data = {model.RANGE_PARAMETER_1: 'param_5', 'param_5': [1, 2]}
## Create Group of operations
flow_service.fire_operation(adapter_instance, self.test_user, self.test_project.id, **data)
all_operations = dao.get_filtered_operations(self.test_project.id, None)
assert len(all_operations) == 1, "Expected one operation group"
assert all_operations[0][2] == 2, "Expected 2 operations in group"
operation_group_id = all_operations[0][3]
assert operation_group_id != None, "The operation should be part of a group."
self.operation_service.stop_operation(all_operations[0][0])
self.operation_service.stop_operation(all_operations[0][1])
## Make sure operations are executed
self.operation_service.launch_operation(all_operations[0][0], False)
self.operation_service.launch_operation(all_operations[0][1], False)
resulted_datatypes = dao.get_datatype_in_group(operation_group_id=operation_group_id)
assert len(resulted_datatypes) >= 2, "Expected at least 2, but: " + str(len(resulted_datatypes))
dt = dao.get_datatype_by_id(resulted_datatypes[0].id)
datatype_group = dao.get_datatypegroup_by_op_group_id(operation_group_id)
assert dt.fk_datatype_group == datatype_group.id, "DataTypeGroup is incorrect"
def test_initiate_operation(self):
"""
Test the actual operation flow by executing a test adapter.
"""
module = "tvb.tests.framework.adapters.testadapter1"
class_name = "TestAdapter1"
adapter = TestFactory.create_adapter(module, class_name)
output = adapter.get_output()
output_type = output[0].__name__
data = {"test1_val1": 5, "test1_val2": 5}
tmp_folder = FilesHelper().get_project_folder(self.test_project, "TEMP")
res = self.operation_service.initiate_operation(self.test_user, self.test_project.id, adapter,
tmp_folder, **data)
assert res.index("has finished.") > 10, "Operation didn't finish"
group = dao.get_algorithm_by_module(module, class_name)
assert group.module == 'tvb.tests.framework.adapters.testadapter1', "Wrong data stored."
assert group.classname == 'TestAdapter1', "Wrong data stored."
dts, count = dao.get_values_of_datatype(self.test_project.id, Datatype1)
assert count == 1
assert len(dts) == 1
datatype = dao.get_datatype_by_id(dts[0][0])
assert datatype.subject == DataTypeMetaData.DEFAULT_SUBJECT, "Wrong data stored."
assert datatype.type == output_type, "Wrong data stored."
def test_delete_dt_free_HDD_space(self):
"""
Launch two operations and give enough available space for user so that both should finish.
"""
adapter = TestFactory.create_adapter("tvb.tests.framework.adapters.testadapter3", "TestAdapterHDDRequired")
data = {"test": 100}
TvbProfile.current.MAX_DISK_SPACE = float(adapter.get_required_disk_size(**data))
tmp_folder = FilesHelper().get_project_folder(self.test_project, "TEMP")
self._assert_no_dt2()
self.operation_service.initiate_operation(self.test_user, self.test_project.id, adapter, tmp_folder, **data)
datatype = self._assert_stored_dt2()
# Now free some space and relaunch
ProjectService().remove_datatype(self.test_project.id, datatype.gid)
self._assert_no_dt2()
self.operation_service.initiate_operation(self.test_user, self.test_project.id, adapter, tmp_folder, **data)
self._assert_stored_dt2()
def test_launch_two_ops_HDD_with_space(self):
"""
Launch two operations and give enough available space for user so that both should finish.
"""
adapter = TestFactory.create_adapter("tvb.tests.framework.adapters.testadapter3", "TestAdapterHDDRequired")
data = {"test": 100}
TvbProfile.current.MAX_DISK_SPACE = 2 * float(adapter.get_required_disk_size(**data))
tmp_folder = FilesHelper().get_project_folder(self.test_project, "TEMP")
self.operation_service.initiate_operation(self.test_user, self.test_project.id, adapter, tmp_folder, **data)
datatype = self._assert_stored_dt2()
#Now update the maximum disk size to be the size of the previously resulted datatypes (transform from kB to MB)
#plus what is estimated to be required from the next one (transform from B to MB)
TvbProfile.current.MAX_DISK_SPACE = float(datatype.disk_size) + float(adapter.get_required_disk_size(**data))
self.operation_service.initiate_operation(self.test_user, self.test_project.id, adapter, tmp_folder, **data)
self._assert_stored_dt2(2)
def test_launch_two_ops_HDD_full_space(self):
"""
Launch two operations and give available space for user so that the first should finish,
but after the update to the user hdd size the second should not.
"""
adapter = TestFactory.create_adapter("tvb.tests.framework.adapters.testadapter3", "TestAdapterHDDRequired")
data = {"test": 100}
TvbProfile.current.MAX_DISK_SPACE = (1 + float(adapter.get_required_disk_size(**data)))
tmp_folder = FilesHelper().get_project_folder(self.test_project, "TEMP")
self.operation_service.initiate_operation(self.test_user, self.test_project.id, adapter, tmp_folder, **data)
datatype = self._assert_stored_dt2()
#Now update the maximum disk size to be less than size of the previously resulted datatypes (transform kB to MB)
#plus what is estimated to be required from the next one (transform from B to MB)
TvbProfile.current.MAX_DISK_SPACE = float(datatype.disk_size - 1) + \
float(adapter.get_required_disk_size(**data) - 1)
with pytest.raises(NoMemoryAvailableException):
self.operation_service.initiate_operation(self.test_user,self.test_project.id, adapter, tmp_folder, **data)
self._assert_stored_dt2()
def test_launch_operation_HDD_with_space(self):
"""
Test the actual operation flow by executing a test adapter.
"""
adapter = TestFactory.create_adapter("tvb.tests.framework.adapters.testadapter3", "TestAdapterHDDRequired")
data = {"test": 100}
TvbProfile.current.MAX_DISK_SPACE = float(adapter.get_required_disk_size(**data))
tmp_folder = FilesHelper().get_project_folder(self.test_project, "TEMP")
self.operation_service.initiate_operation(self.test_user, self.test_project.id, adapter, tmp_folder, **data)
self._assert_stored_dt2()
def test_launch_operation_HDD_with_space_started_ops(self):
"""
Test the actual operation flow by executing a test adapter.
"""
space_taken_by_started = 100
adapter = TestFactory.create_adapter("tvb.tests.framework.adapters.testadapter3", "TestAdapterHDDRequired")
started_operation = model.Operation(self.test_user.id, self.test_project.id, adapter.stored_adapter.id, "",
status=model.STATUS_STARTED, estimated_disk_size=space_taken_by_started)
dao.store_entity(started_operation)
data = {"test": 100}
TvbProfile.current.MAX_DISK_SPACE = float(adapter.get_required_disk_size(**data) + space_taken_by_started)
tmp_folder = FilesHelper().get_project_folder(self.test_project, "TEMP")
self.operation_service.initiate_operation(self.test_user, self.test_project.id, adapter, tmp_folder, **data)
self._assert_stored_dt2()
def test_launch_operation_HDD_full_space(self):
"""
Test the actual operation flow by executing a test adapter.
"""
adapter = TestFactory.create_adapter("tvb.tests.framework.adapters.testadapter3", "TestAdapterHDDRequired")
data = {"test": 100}
TvbProfile.current.MAX_DISK_SPACE = float(adapter.get_required_disk_size(**data) - 1)
tmp_folder = FilesHelper().get_project_folder(self.test_project, "TEMP")
with pytest.raises(NoMemoryAvailableException):
self.operation_service.initiate_operation(self.test_user, self.test_project.id, adapter, tmp_folder, **data)
self._assert_no_dt2()
def test_launch_operation_HDD_full_space_started_ops(self):
"""
Test the actual operation flow by executing a test adapter.
"""
space_taken_by_started = 100
adapter = TestFactory.create_adapter("tvb.tests.framework.adapters.testadapter3", "TestAdapterHDDRequired")
started_operation = model.Operation(self.test_user.id, self.test_project.id, adapter.stored_adapter.id, "",
status=model.STATUS_STARTED, estimated_disk_size=space_taken_by_started)
dao.store_entity(started_operation)
data = {"test": 100}
TvbProfile.current.MAX_DISK_SPACE = float(adapter.get_required_disk_size(**data) + space_taken_by_started - 1)
tmp_folder = FilesHelper().get_project_folder(self.test_project, "TEMP")
with pytest.raises(NoMemoryAvailableException):
self.operation_service.initiate_operation(self.test_user,self.test_project.id, adapter, tmp_folder, **data)
self._assert_no_dt2()
def test_stop_operation(self):
"""
Test that an operation is successfully stopped.
"""
adapter = TestFactory.create_adapter("tvb.tests.framework.adapters.testadapter2", "TestAdapter2")
data = {"test": 5}
algo = adapter.stored_adapter
algo_category = dao.get_category_by_id(algo.fk_category)
operations, _ = self.operation_service.prepare_operations(self.test_user.id, self.test_project.id, algo,
algo_category, {}, **data)
self.operation_service._send_to_cluster(operations, adapter)
self.operation_service.stop_operation(operations[0].id)
operation = dao.get_operation_by_id(operations[0].id)
assert operation.status, model.STATUS_CANCELED == "Operation should have been canceled!"
def test_stop_operation_finished(self):
"""
Test that an operation that is already finished is not changed by the stop operation.
"""
adapter = TestFactory.create_adapter("tvb.tests.framework.adapters.testadapter1", "TestAdapter1")
data = {"test1_val1": 5, 'test1_val2': 5}
algo = adapter.stored_adapter
algo_category = dao.get_category_by_id(algo.fk_category)
operations, _ = self.operation_service.prepare_operations(self.test_user.id, self.test_project.id, algo,
algo_category, {}, **data)
self.operation_service._send_to_cluster(operations, adapter)
operation = dao.get_operation_by_id(operations[0].id)
operation.status = model.STATUS_FINISHED
dao.store_entity(operation)
self.operation_service.stop_operation(operations[0].id)
operation = dao.get_operation_by_id(operations[0].id)
assert operation.status, model.STATUS_FINISHED == "Operation shouldn't have been canceled!"
def test_array_from_string(self):
"""
Simple test for parse array on 1d, 2d and 3d array.
"""
row = {'description': 'test.',
'default': 'None',
'required': True,
'label': 'test: ',
'attributes': None,
'elementType': 'float',
'type': 'array',
'options': None,
'name': 'test'}
input_data_string = '[ [1 2 3] [4 5 6]]'
output = string2array(input_data_string, ' ', row['elementType'])
assert output.shape, (2, 3) == "Dimensions not properly parsed"
for i in output[0]:
assert i in [1, 2, 3]
for i in output[1]:
assert i in [4, 5, 6]
input_data_string = '[1, 2, 3, 4, 5, 6]'
output = string2array(input_data_string, ',', row['elementType'])
assert output.shape == (6,), "Dimensions not properly parsed"
for i in output:
assert i in [1, 2, 3, 4, 5, 6]
input_data_string = '[ [ [1,1], [2, 2] ], [ [3 ,3], [4,4] ] ]'
output = string2array(input_data_string, ',', row['elementType'])
assert output.shape == (2, 2, 2), "Wrong dimensions."
for i in output[0][0]:
assert i == 1
for i in output[0][1]:
assert i == 2
for i in output[1][0]:
assert i == 3
for i in output[1][1]:
assert i == 4
row = {'description': 'test.',
'default': 'None',
'required': True,
'label': 'test: ',
'attributes': None,
'elementType': 'str',
'type': 'array',
'options': None,
'name': 'test'}
input_data_string = '[1, 2, 3, 4, 5, 6]'
output = string2array(input_data_string, ',', row['elementType'])
for i in output:
assert i in [1, 2, 3, 4, 5, 6]
def test_wrong_array_from_string(self):
"""Test that parsing an array from string is throwing the expected
exception when wrong input string"""
row = {'description': 'test.',
'default': 'None',
'required': True,
'label': 'test: ',
'attributes': None,
'elementType': 'float',
'type': 'array',
'options': None,
'name': 'test'}
input_data_string = '[ [1,2 3] [4,5,6]]'
with pytest.raises(ValueError):
string2array(input_data_string, ',', row['elementType'])
input_data_string = '[ [1,2,wrong], [4, 5, 6]]'
with pytest.raises(ValueError):
string2array(input_data_string, ',', row['elementType'])
row = {'description': 'test.',
'default': 'None',
'required': True,
'label': 'test: ',
'attributes': None,
'elementType': 'str',
'type': 'array',
'options': None,
'name': 'test'}
output = string2array(input_data_string, ',', row['elementType'])
assert output.shape == (2, 3)
assert output[0][2] == 'wrong', 'String data not converted properly'
input_data_string = '[ [1,2 3] [4,5,6]]'
output = string2array(input_data_string, ',', row['elementType'])
assert output[0][1] == '2 3'
def test_reduce_dimension_component(self):
"""
This method tests if the data passed to the launch method of
the NDimensionArrayAdapter adapter is correct. The passed data should be a list
of arrays with one dimension.
"""
inserted_count = FlowService().get_available_datatypes(self.test_project.id,
"tvb.datatypes.arrays.MappedArray")[1]
assert inserted_count == 0, "Expected to find no data."
#create an operation
algorithm_id = FlowService().get_algorithm_by_module_and_class('tvb.tests.framework.adapters.ndimensionarrayadapter',
'NDimensionArrayAdapter').id
operation = model.Operation(self.test_user.id, self.test_project.id, algorithm_id, 'test params',
meta=json.dumps({DataTypeMetaData.KEY_STATE: "RAW_DATA"}),
status=model.STATUS_FINISHED)
operation = dao.store_entity(operation)
#save the array wrapper in DB
adapter_instance = NDimensionArrayAdapter()
PARAMS = {}
self.operation_service.initiate_prelaunch(operation, adapter_instance, {}, **PARAMS)
inserted_data = FlowService().get_available_datatypes(self.test_project.id,
"tvb.datatypes.arrays.MappedArray")[0]
assert len(inserted_data) == 1, "Problems when inserting data"
gid = inserted_data[0][2]
entity = dao.get_datatype_by_gid(gid)
#from the 3D array do not select any array
PARAMS = {"python_method": "reduce_dimension", "input_data": gid,
"input_data_dimensions_0": "requiredDim_1",
"input_data_dimensions_1": "",
"input_data_dimensions_2": ""}
try:
self.operation_service.initiate_prelaunch(operation, adapter_instance, {}, **PARAMS)
raise AssertionError("Test should not pass. The resulted array should be a 1D array.")
except Exception:
# OK, do nothing; we were expecting to produce a 1D array
pass
#from the 3D array select only a 1D array
first_dim = [gid + '_1_0', 'requiredDim_1']
PARAMS = {"python_method": "reduce_dimension", "input_data": gid,
"input_data_dimensions_0": first_dim,
"input_data_dimensions_1": gid + "_2_1"}
self.operation_service.initiate_prelaunch(operation, adapter_instance, {}, **PARAMS)
expected_result = entity.array_data[:, 0, 1]
actual_result = adapter_instance.launch_param
assert len(actual_result) == len(expected_result), "Not the same size for results!"
assert numpy.equal(actual_result, expected_result).all()
#from the 3D array select a 2D array
first_dim = [gid + '_1_0', gid + '_1_1', 'requiredDim_2']
PARAMS = {"python_method": "reduce_dimension", "input_data": gid,
"input_data_dimensions_0": first_dim,
"input_data_dimensions_1": gid + "_2_1"}
self.operation_service.initiate_prelaunch(operation, adapter_instance, {}, **PARAMS)
expected_result = entity.array_data[slice(0, None), [0, 1], 1]
actual_result = adapter_instance.launch_param
assert len(actual_result) == len(expected_result), "Not the same size for results!"
assert numpy.equal(actual_result, expected_result).all()
#from 3D array select 1D array by applying SUM function on the first
#dimension and average function on the second dimension
PARAMS = {"python_method": "reduce_dimension", "input_data": gid,
"input_data_dimensions_0": ["requiredDim_1", "func_sum"],
"input_data_dimensions_1": "func_average",
"input_data_dimensions_2": ""}
self.operation_service.initiate_prelaunch(operation, adapter_instance, {}, **PARAMS)
aux = numpy.sum(entity.array_data, axis=0)
expected_result = numpy.average(aux, axis=0)
actual_result = adapter_instance.launch_param
assert len(actual_result) == len(expected_result), "Not the same size of results!"
assert numpy.equal(actual_result, expected_result).all()
#from 3D array select a 2D array and apply op. on the second dimension
PARAMS = {"python_method": "reduce_dimension", "input_data": gid,
"input_data_dimensions_0": ["requiredDim_2", "func_sum",
"expected_shape_x,512", "operations_x,>"],
"input_data_dimensions_1": "",
"input_data_dimensions_2": ""}
try:
self.operation_service.initiate_prelaunch(operation, adapter_instance, {}, **PARAMS)
raise AssertionError("Test should not pass! The second dimension of the array should be >512.")
except Exception:
# OK, do nothing;
pass
class TestWorkflow(TransactionalTestCase):
"""
Test that workflow conversion methods are valid.
"""
def transactional_setup_method(self):
"""
Sets up the testing environment;
saves config file;
creates a test user, a test project;
creates burst, operation, flow and workflow services
"""
self.test_user = TestFactory.create_user()
self.test_project = TestFactory.create_project(self.test_user)
self.workflow_service = WorkflowService()
self.burst_service = BurstService()
self.operation_service = OperationService()
self.flow_service = FlowService()
def transactional_teardown_method(self):
"""
Remove project folders and clean up database.
"""
FilesHelper().remove_project_structure(self.test_project.name)
self.delete_project_folders()
def __create_complex_workflow(self, workflow_step_list):
"""
Creates a burst with a complex workflow with a given list of workflow steps.
:param workflow_step_list: a list of workflow steps that will be used in the
creation of a new workflow for a new burst
"""
burst_config = TestFactory.store_burst(self.test_project.id)
stored_dt = datatypes_factory.DatatypesFactory()._store_datatype(Datatype1())
first_step_algorithm = self.flow_service.get_algorithm_by_module_and_class("tvb.tests.framework.adapters.testadapter1",
"TestAdapterDatatypeInput")
metadata = {DataTypeMetaData.KEY_BURST: burst_config.id}
kwargs = {"test_dt_input": stored_dt.gid, 'test_non_dt_input': '0'}
operations, group = self.operation_service.prepare_operations(self.test_user.id, self.test_project.id,
first_step_algorithm,
first_step_algorithm.algorithm_category,
metadata, **kwargs)
workflows = self.workflow_service.create_and_store_workflow(project_id=self.test_project.id,
burst_id=burst_config.id,
simulator_index=0,
simulator_id=first_step_algorithm.id,
operations=operations)
self.operation_service.prepare_operations_for_workflowsteps(workflow_step_list, workflows, self.test_user.id,
burst_config.id, self.test_project.id, group,
operations)
#fire the first op
if len(operations) > 0:
self.operation_service.launch_operation(operations[0].id, False)
return burst_config.id
def test_workflow_generation(self):
"""
A simple test just for the fact that a workflow is created an ran,
no dynamic parameters are passed. In this case we create a two steps
workflow: step1 - tvb.tests.framework.adapters.testadapter2.TestAdapter2
step2 - tvb.tests.framework.adapters.testadapter1.TestAdapter1
The first adapter doesn't return anything and the second returns one
tvb.datatypes.datatype1.Datatype1 instance. We check that the steps
are actually ran by checking that two operations are created and that
one dataType is stored.
"""
workflow_step_list = [TestFactory.create_workflow_step("tvb.tests.framework.adapters.testadapter2",
"TestAdapter2", step_index=1,
static_kwargs={"test2": 2}),
TestFactory.create_workflow_step("tvb.tests.framework.adapters.testadapter1",
"TestAdapter1", step_index=2,
static_kwargs={"test1_val1": 1, "test1_val2": 1})]
self.__create_complex_workflow(workflow_step_list)
stored_datatypes = dao.get_datatypes_in_project(self.test_project.id)
assert len(stored_datatypes) == 2, "DataType from second step was not stored."
assert stored_datatypes[0].type == 'Datatype1', "Wrong type was stored."
assert stored_datatypes[1].type == 'Datatype1', "Wrong type was stored."
finished, started, error, _, _ = dao.get_operation_numbers(self.test_project.id)
assert finished == 3, "Didnt start operations for both adapters in workflow."
assert started == 0, "Some operations from workflow didnt finish."
assert error == 0, "Some operations finished with error status."
def test_workflow_dynamic_params(self):
"""
A simple test just for the fact that dynamic parameters are passed properly
between two workflow steps:
step1 - tvb.tests.framework.adapters.testadapter1.TestAdapter1
step2 - tvb.tests.framework.adapters.testadapter3.TestAdapter3
The first adapter returns a tvb.datatypes.datatype1.Datatype1 instance.
The second adapter has this passed as a dynamic workflow parameter.
We check that the steps are actually ran by checking that two operations
are created and that two dataTypes are stored.
"""
workflow_step_list = [TestFactory.create_workflow_step("tvb.tests.framework.adapters.testadapter1",
"TestAdapter1", step_index=1,
static_kwargs={"test1_val1": 1, "test1_val2": 1}),
TestFactory.create_workflow_step("tvb.tests.framework.adapters.testadapter3",
"TestAdapter3", step_index=2,
dynamic_kwargs={
"test": {wf_cfg.DATATYPE_INDEX_KEY: 0,
wf_cfg.STEP_INDEX_KEY: 1}})]
self.__create_complex_workflow(workflow_step_list)
stored_datatypes = dao.get_datatypes_in_project(self.test_project.id)
assert len(stored_datatypes) == 3, "DataType from all step were not stored."
for result_row in stored_datatypes:
assert result_row.type in ['Datatype1', 'Datatype2'], "Wrong type was stored."
finished, started, error, _, _ = dao.get_operation_numbers(self.test_project.id)
assert finished == 3, "Didn't start operations for both adapters in workflow."
assert started == 0, "Some operations from workflow didn't finish."
assert error == 0, "Some operations finished with error status."
def test_configuration2workflow(self):
"""
Test that building a WorkflowStep from a WorkflowStepConfiguration. Make sure all the data is
correctly passed. Also check that any base_wf_step is incremented to dynamic parameters step index.
"""
workflow_step = TestFactory.create_workflow_step("tvb.tests.framework.adapters.testadapter1", "TestAdapter1",
static_kwargs={"static_param": "test"},
dynamic_kwargs={"dynamic_param": {wf_cfg.STEP_INDEX_KEY: 0,
wf_cfg.DATATYPE_INDEX_KEY: 0}},
step_index=1, base_step=5)
assert workflow_step.step_index == 1, "Wrong step index in created workflow step."
assert workflow_step.static_param == {'static_param': 'test'}, 'Different static parameters on step.'
assert workflow_step.dynamic_param == {'dynamic_param': {wf_cfg.STEP_INDEX_KEY: 5,
wf_cfg.DATATYPE_INDEX_KEY: 0}},\
"Dynamic parameters not saved properly, or base workflow index not added to step index."
def test_create_workflow(self):
"""
Test that a workflow with all the associated workflow steps is actually created.
"""
workflow_step_list = [TestFactory.create_workflow_step("tvb.tests.framework.adapters.testadapter2",
"TestAdapter2", step_index=1,
static_kwargs={"test2": 2}),
TestFactory.create_workflow_step("tvb.tests.framework.adapters.testadapter1",
"TestAdapter1", step_index=2,
static_kwargs={"test1_val1": 1, "test1_val2": 1})]
burst_id = self.__create_complex_workflow(workflow_step_list)
workflow_entities = dao.get_workflows_for_burst(burst_id)
assert len(workflow_entities) == 1, "For some reason workflow was not stored in database."
workflow_steps = dao.get_workflow_steps(workflow_entities[0].id)
assert len(workflow_steps) == len(workflow_step_list) + 1, "Wrong number of workflow steps created."
class FlowContollerTest(BaseControllersTest):
""" Unit tests for FlowController """
def setUp(self):
"""
Sets up the environment for testing;
creates a `FlowController`
"""
self.init()
self.flow_c = FlowController()
self.burst_c = BurstController()
self.operation_service = OperationService()
def tearDown(self):
""" Cleans up the testing environment """
self.cleanup()
self.clean_database()
def test_context_selected(self):
"""
Remove the project from CherryPy session and check that you are redirected to projects page.
"""
del cherrypy.session[common.KEY_PROJECT]
self._expect_redirect('/project/viewall', self.flow_c.step)
def test_invalid_step(self):
"""
Pass an invalid step and make sure we are redirected to tvb start page.
"""
self._expect_redirect('/tvb', self.flow_c.step)
def test_valid_step(self):
"""
For all algorithm categories check that a submenu is generated and the result
page has it's title given by category name.
"""
categories = dao.get_algorithm_categories()
for categ in categories:
result_dict = self.flow_c.step(categ.id)
self.assertTrue(common.KEY_SUBMENU_LIST in result_dict,
"Expect to have a submenu with available algorithms for category.")
self.assertEqual(result_dict["section_name"], categ.displayname.lower())
def test_step_connectivity(self):
"""
Check that the correct section name and connectivity sub-menu are returned for the connectivity step.
"""
result_dict = self.flow_c.step_connectivity()
self.assertEqual(result_dict['section_name'], 'connectivity')
self.assertEqual(result_dict['submenu_list'], self.flow_c.connectivity_submenu)
def test_default(self):
"""
Test default method from step controllers. Check that the submit link is ok, that a mainContent
is present in result dict and that the isAdapter flag is set to true.
"""
cherrypy.request.method = "GET"
categories = dao.get_algorithm_categories()
for categ in categories:
algo_groups = dao.get_groups_by_categories([categ.id])
for algo in algo_groups:
result_dict = self.flow_c.default(categ.id, algo.id)
self.assertEqual(result_dict[common.KEY_SUBMIT_LINK], '/flow/%i/%i' % (categ.id, algo.id))
self.assertTrue('mainContent' in result_dict)
self.assertTrue(result_dict['isAdapter'])
def test_default_cancel(self):
"""
On cancel we should get a redirect to the back page link.
"""
cherrypy.request.method = "POST"
categories = dao.get_algorithm_categories()
algo_groups = dao.get_groups_by_categories([categories[0].id])
self._expect_redirect('/project/viewoperations/%i' % self.test_project.id, self.flow_c.default,
categories[0].id, algo_groups[0].id, cancel=True, back_page='operations')
def test_default_invalid_key(self):
"""
Pass invalid keys for adapter and step and check you get redirect to tvb entry
page with error set.
"""
self._expect_redirect('/tvb?error=True', self.flow_c.default, 'invalid', 'invalid')
def test_read_datatype_attribute(self):
"""
Read an attribute from a datatype.
"""
dt = DatatypesFactory().create_datatype_with_storage("test_subject", "RAW_STATE",
'this is the stored data'.split())
returned_data = self.flow_c.read_datatype_attribute(dt.gid, "string_data")
self.assertEqual(returned_data, '["this", "is", "the", "stored", "data"]')
def test_read_datatype_attribute_method_call(self):
"""
Call method on given datatype.
"""
dt = DatatypesFactory().create_datatype_with_storage("test_subject", "RAW_STATE",
'this is the stored data'.split())
args = {'length': 101}
returned_data = self.flow_c.read_datatype_attribute(dt.gid, 'return_test_data', **args)
self.assertTrue(returned_data == str(range(101)))
def test_get_simple_adapter_interface(self):
adapter = dao.find_group('tvb.tests.framework.adapters.testadapter1', 'TestAdapter1')
result = self.flow_c.get_simple_adapter_interface(adapter.id)
expected_interface = TestAdapter1().get_input_tree()
self.assertEqual(result['inputList'], expected_interface)
def _long_burst_launch(self, is_range=False):
self.burst_c.index()
connectivity = DatatypesFactory().create_connectivity()[1]
launch_params = copy.deepcopy(SIMULATOR_PARAMETERS)
launch_params['connectivity'] = dao.get_datatype_by_id(connectivity.id).gid
if not is_range:
launch_params['simulation_length'] = '10000'
else:
launch_params['simulation_length'] = '[10000,10001,10002]'
launch_params[model.RANGE_PARAMETER_1] = 'simulation_length'
launch_params = {"simulator_parameters": json.dumps(launch_params)}
burst_id = json.loads(self.burst_c.launch_burst("new", "test_burst", **launch_params))['id']
return dao.get_burst_by_id(burst_id)
def _wait_for_burst_ops(self, burst_config):
""" sleeps until some operation of the burst is created"""
waited = 1
timeout = 50
operations = dao.get_operations_in_burst(burst_config.id)
while not len(operations) and waited <= timeout:
sleep(1)
waited += 1
operations = dao.get_operations_in_burst(burst_config.id)
operations = dao.get_operations_in_burst(burst_config.id)
return operations
def test_stop_burst_operation(self):
burst_config = self._long_burst_launch()
operation = self._wait_for_burst_ops(burst_config)[0]
self.assertFalse(operation.has_finished)
self.flow_c.stop_burst_operation(operation.id, 0, False)
operation = dao.get_operation_by_id(operation.id)
self.assertEqual(operation.status, model.STATUS_CANCELED)
def test_stop_burst_operation_group(self):
burst_config = self._long_burst_launch(True)
operations = self._wait_for_burst_ops(burst_config)
operations_group_id = 0
for operation in operations:
self.assertFalse(operation.has_finished)
operations_group_id = operation.fk_operation_group
self.flow_c.stop_burst_operation(operations_group_id, 1, False)
for operation in operations:
operation = dao.get_operation_by_id(operation.id)
self.assertEqual(operation.status, model.STATUS_CANCELED)
def test_remove_burst_operation(self):
burst_config = self._long_burst_launch()
operation = self._wait_for_burst_ops(burst_config)[0]
self.assertFalse(operation.has_finished)
self.flow_c.stop_burst_operation(operation.id, 0, True)
operation = dao.try_get_operation_by_id(operation.id)
self.assertTrue(operation is None)
def test_remove_burst_operation_group(self):
burst_config = self._long_burst_launch(True)
operations = self._wait_for_burst_ops(burst_config)
operations_group_id = 0
for operation in operations:
self.assertFalse(operation.has_finished)
operations_group_id = operation.fk_operation_group
self.flow_c.stop_burst_operation(operations_group_id, 1, True)
for operation in operations:
operation = dao.try_get_operation_by_id(operation.id)
self.assertTrue(operation is None)
def _launch_test_algo_on_cluster(self, **data):
module = "tvb.tests.framework.adapters.testadapter1"
class_name = "TestAdapter1"
group = dao.find_group(module, class_name)
adapter = FlowService().build_adapter_instance(group)
algo_group = adapter.algorithm_group
algo_category = dao.get_category_by_id(algo_group.fk_category)
algo = dao.get_algorithm_by_group(algo_group.id)
operations, _ = self.operation_service.prepare_operations(self.test_user.id, self.test_project.id, algo,
algo_category, {}, ABCAdapter.LAUNCH_METHOD, **data)
self.operation_service._send_to_cluster(operations, adapter)
return operations
def test_stop_operations(self):
data = {"test1_val1": 5, 'test1_val2': 5}
operations = self._launch_test_algo_on_cluster(**data)
operation = dao.get_operation_by_id(operations[0].id)
self.assertFalse(operation.has_finished)
self.flow_c.stop_operation(operation.id, 0, False)
operation = dao.get_operation_by_id(operation.id)
self.assertEqual(operation.status, model.STATUS_CANCELED)
def test_stop_operations_group(self):
data = {model.RANGE_PARAMETER_1: "test1_val1", "test1_val1": '5,6,7', 'test1_val2': 5}
operations = self._launch_test_algo_on_cluster(**data)
operation_group_id = 0
for operation in operations:
operation = dao.get_operation_by_id(operation.id)
self.assertFalse(operation.has_finished)
operation_group_id = operation.fk_operation_group
self.flow_c.stop_operation(operation_group_id, 1, False)
for operation in operations:
operation = dao.get_operation_by_id(operation.id)
self.assertEqual(operation.status, model.STATUS_CANCELED)
def __init__(self):
self.operation_service = OperationService()
self.workflow_service = WorkflowService()
self.logger = get_logger(self.__class__.__module__)
class BurstService:
"""
Service layer for Burst related entities.
"""
def __init__(self):
self.operation_service = OperationService()
self.workflow_service = WorkflowService()
self.logger = get_logger(self.__class__.__module__)
def build_portlet_interface(self, portlet_configuration, project_id):
"""
From a portlet_id and a project_id, first build the portlet
entity then get it's configurable interface.
:param portlet_configuration: a portlet configuration entity. It holds at the
least the portlet_id, and in case any default parameters were saved
they can be rebuilt from the analyzers // visualizer parameters
:param project_id: the id of the current project
:returns: the portlet interface will be of the following form::
[{'interface': adapter_interface,
'prefix': prefix_for_parameter_names,
'subalg': {algorithm_field_name: default_algorithm_value},
'algo_group': algorithm_group,
'alg_ui_name': displayname},
......]
A list of dictionaries for each adapter that makes up the portlet.
"""
portlet_entity = dao.get_portlet_by_id(portlet_configuration.portlet_id)
if portlet_entity is None:
raise InvalidPortletConfiguration(
"No portlet entity located in database with id=%s. "
"Portlet configuration %s is not valid." % (portlet_configuration.portlet_id, portlet_configuration)
)
portlet_configurer = PortletConfigurer(portlet_entity)
portlet_interface = portlet_configurer.get_configurable_interface()
self.logger.debug("Created interface for portlet " + str([portlet_entity]))
for adapter_conf in portlet_interface:
interface = adapter_conf.interface
interface = FlowService().prepare_parameters(interface, project_id, adapter_conf.group.fk_category)
interface = ABCAdapter.prepare_param_names(interface, adapter_conf.prefix)
adapter_conf.interface = interface
portlet_configurer.update_default_values(portlet_interface, portlet_configuration)
portlet_configurer.prefix_adapters_parameters(portlet_interface)
return portlet_interface
@staticmethod
def update_portlet_configuration(portlet_configuration, submited_parameters):
"""
:param portlet_configuration: the portlet configuration that needs to be updated
:param submited_parameters: a list of parameters as submitted from the UI. This
is a dictionary in the form :
{'dynamic' : {name:value pairs}, 'static' : {name:value pairs}}
All names are prefixed with adapter specific generated prefix.
"""
portlet_entity = dao.get_portlet_by_id(portlet_configuration.portlet_id)
portlet_configurer = PortletConfigurer(portlet_entity)
return portlet_configurer.update_portlet_configuration(portlet_configuration, submited_parameters)
@staticmethod
def new_burst_configuration(project_id):
"""
Return a new burst configuration entity with all the default values.
"""
burst_configuration = model.BurstConfiguration(project_id)
burst_configuration.selected_tab = 0
BurstService.set_default_portlets(burst_configuration)
return burst_configuration
@staticmethod
def set_default_portlets(burst_configuration):
"""
Sets the default portlets for the specified burst configuration.
The default portlets are specified in the __init__.py script from tvb root.
"""
for tab_idx, value in DEFAULT_PORTLETS.items():
for sel_idx, portlet_identifier in value.items():
portlet = BurstService.get_portlet_by_identifier(portlet_identifier)
if portlet is not None:
portlet_configuration = BurstService.new_portlet_configuration(
portlet.id, tab_idx, sel_idx, portlet.algorithm_identifier
)
burst_configuration.set_portlet(tab_idx, sel_idx, portlet_configuration)
@staticmethod
def _store_burst_config(burst_config):
"""
Store a burst configuration entity.
"""
burst_config.prepare_before_save()
saved_entity = dao.store_entity(burst_config)
return saved_entity.id
@staticmethod
def get_available_bursts(project_id):
"""
Return all the burst for the current project.
"""
bursts = dao.get_bursts_for_project(project_id, page_size=MAX_BURSTS_DISPLAYED) or []
for burst in bursts:
burst.prepare_after_load()
return bursts
@staticmethod
def populate_burst_disk_usage(bursts):
"""
Adds a disk_usage field to each burst object.
The disk usage is computed as the sum of the datatypes generated by a burst
"""
sizes = dao.compute_bursts_disk_size([b.id for b in bursts])
for b in bursts:
b.disk_size = format_bytes_human(sizes[b.id])
@staticmethod
def rename_burst(burst_id, new_name):
"""
Rename the burst given by burst_id, setting it's new name to
burst_name.
"""
burst = dao.get_burst_by_id(burst_id)
burst.name = new_name
dao.store_entity(burst)
def load_burst(self, burst_id):
"""
:param burst_id: the id of the burst that should be loaded
Having this input the method should:
- load the entity from the DB
- get all the workflow steps for the saved burst id
- go trough the visualization workflow steps to create the tab
configuration of the burst using the tab_index and index_in_tab
fields saved on each workflow_step
"""
burst = dao.get_burst_by_id(burst_id)
burst.prepare_after_load()
burst.reset_tabs()
burst_workflows = dao.get_workflows_for_burst(burst.id)
group_gid = None
if len(burst_workflows) == 1:
# A simple burst with no range parameters
burst = self.__populate_tabs_from_workflow(burst, burst_workflows[0])
elif len(burst_workflows) > 1:
# A burst workflow with a range of values, created multiple workflows and need
# to launch parameter space exploration with the resulted group
self.__populate_tabs_from_workflow(burst, burst_workflows[0])
executed_steps = dao.get_workflow_steps(burst_workflows[0].id)
operation = dao.get_operation_by_id(executed_steps[0].fk_operation)
if operation.operation_group:
workflow_group = dao.get_datatypegroup_by_op_group_id(operation.operation_group.id)
group_gid = workflow_group.gid
return burst, group_gid
@staticmethod
def __populate_tabs_from_workflow(burst_entity, workflow):
"""
Given a burst and a workflow populate the tabs of the burst with the PortletConfigurations
generated from the steps of the workflow.
"""
visualizers = dao.get_visualization_steps(workflow.id)
for entry in visualizers:
## For each visualize step, also load all of the analyze steps.
portlet_cfg = PortletConfiguration(entry.fk_portlet)
portlet_cfg.set_visualizer(entry)
analyzers = dao.get_workflow_steps_for_position(entry.fk_workflow, entry.tab_index, entry.index_in_tab)
portlet_cfg.set_analyzers(analyzers)
burst_entity.tabs[entry.tab_index].portlets[entry.index_in_tab] = portlet_cfg
return burst_entity
def load_tab_configuration(self, burst_entity, op_id):
"""
Given a burst entity and an operation id, find the workflow to which the op_id
belongs and the load the burst_entity's tab configuration with those workflow steps.
"""
originating_workflow = dao.get_workflow_for_operation_id(op_id)
burst_entity = self.__populate_tabs_from_workflow(burst_entity, originating_workflow)
return burst_entity
@staticmethod
def new_portlet_configuration(portlet_id, tab_nr=-1, index_in_tab=-1, portlet_name="Default"):
"""
Return a new portlet configuration entitiy with default parameters.
:param portlet_id: the id of the portlet for which a configuration will
be stored
:param tab_nr: the index of the currently selected tab
:param index_in_tab: the index from the currently selected tab
"""
portlet_entity = dao.get_portlet_by_id(portlet_id)
if portlet_entity is None:
raise InvalidPortletConfiguration("No portlet entity located in database with id=%s." % portlet_id)
portlet_configurer = PortletConfigurer(portlet_entity)
configuration = portlet_configurer.create_new_portlet_configuration(portlet_name)
for wf_step in configuration.analyzers:
wf_step.tab_index = tab_nr
wf_step.index_in_tab = index_in_tab
configuration.visualizer.tab_index = tab_nr
configuration.visualizer.index_in_tab = index_in_tab
return configuration
@staticmethod
def get_available_portlets():
"""
:returns: a list of all the available portlet entites
"""
return dao.get_available_portlets()
@staticmethod
def get_portlet_by_id(portlet_id):
"""
:returns: the portlet entity with the id =@portlet_id
"""
return dao.get_portlet_by_id(portlet_id)
@staticmethod
def get_portlet_by_identifier(portlet_identifier):
"""
:returns: the portlet entity with the algorithm identifier =@portlet_identifier
"""
return dao.get_portlet_by_identifier(portlet_identifier)
def launch_burst(self, burst_configuration, simulator_index, simulator_id, user_id, launch_mode=LAUNCH_NEW):
"""
Given a burst configuration and all the necessary data do the actual launch.
:param burst_configuration: BurstConfiguration
:param simulator_index: the position within the workflows step list that the simulator will take. This is needed
so that the rest of the portlet workflow steps know what steps do their dynamic parameters come from.
:param simulator_id: the id of the simulator adapter as stored in the DB. It's needed to load the simulator algo
group and category that are then passed to the launcher's prepare_operation method.
:param user_id: the id of the user that launched this burst
:param launch_mode: new/branch/continue
"""
## 1. Prepare BurstConfiguration entity
if launch_mode == LAUNCH_NEW:
## Fully new entity for new simulation
burst_config = burst_configuration.clone()
if burst_config.name is None:
new_id = dao.get_max_burst_id() + 1
burst_config.name = "simulation_" + str(new_id)
else:
## Branch or Continue simulation
burst_config = burst_configuration
simulation_state = dao.get_generic_entity(
SIMULATION_DATATYPE_MODULE + "." + SIMULATION_DATATYPE_CLASS, burst_config.id, "fk_parent_burst"
)
if simulation_state is None or len(simulation_state) < 1:
exc = BurstServiceException(
"Simulation State not found for %s, " "thus we are unable to branch from it!" % burst_config.name
)
self.logger.error(exc)
raise exc
simulation_state = simulation_state[0]
burst_config.update_simulation_parameter("simulation_state", simulation_state.gid)
burst_config = burst_configuration.clone()
count = dao.count_bursts_with_name(burst_config.name, burst_config.fk_project)
burst_config.name = burst_config.name + "_" + launch_mode + str(count)
## 2. Create Operations and do the actual launch
if launch_mode in [LAUNCH_NEW, LAUNCH_BRANCH]:
## New Burst entry in the history
burst_id = self._store_burst_config(burst_config)
thread = threading.Thread(
target=self._async_launch_and_prepare,
kwargs={
"burst_config": burst_config,
"simulator_index": simulator_index,
"simulator_id": simulator_id,
"user_id": user_id,
},
)
thread.start()
return burst_id, burst_config.name
else:
## Continue simulation
## TODO
return burst_config.id, burst_config.name
@transactional
def _prepare_operations(self, burst_config, simulator_index, simulator_id, user_id):
"""
Prepare all required operations for burst launch.
"""
project_id = burst_config.fk_project
burst_id = burst_config.id
workflow_step_list = []
starting_index = simulator_index + 1
sim_algo = FlowService().get_algorithm_by_identifier(simulator_id)
metadata = {DataTypeMetaData.KEY_BURST: burst_id}
launch_data = burst_config.get_all_simulator_values()[0]
operations, group = self.operation_service.prepare_operations(
user_id, project_id, sim_algo, sim_algo.algo_group.group_category, metadata, **launch_data
)
group_launched = group is not None
if group_launched:
starting_index += 1
for tab in burst_config.tabs:
for portlet_cfg in tab.portlets:
### For each portlet configuration stored, update the step index ###
### and also change the dynamic parameters step indexes to point ###
### to the simulator outputs. ##
if portlet_cfg is not None:
analyzers = portlet_cfg.analyzers
visualizer = portlet_cfg.visualizer
for entry in analyzers:
entry.step_index = starting_index
self.workflow_service.set_dynamic_step_references(entry, simulator_index)
workflow_step_list.append(entry)
starting_index += 1
### Change the dynamic parameters to point to the last adapter from this portlet execution.
visualizer.step_visible = False
if len(workflow_step_list) > 0 and isinstance(workflow_step_list[-1], model.WorkflowStep):
self.workflow_service.set_dynamic_step_references(visualizer, workflow_step_list[-1].step_index)
else:
self.workflow_service.set_dynamic_step_references(visualizer, simulator_index)
### Only for a single operation have the step of visualization, otherwise is useless.
if not group_launched:
workflow_step_list.append(visualizer)
if group_launched:
### For a group of operations, make sure the metric for PSE view
### is also computed, immediately after the simulation.
metric_algo, metric_group = FlowService().get_algorithm_by_module_and_class(
MEASURE_METRICS_MODULE, MEASURE_METRICS_CLASS
)
_, metric_interface = FlowService().prepare_adapter(project_id, metric_group)
dynamics = {}
for entry in metric_interface:
# We have a select that should be the dataType and a select multiple with the
# required metric algorithms to be evaluated. Only dynamic parameter should be
# the select type.
if entry[ABCAdapter.KEY_TYPE] == "select":
dynamics[entry[ABCAdapter.KEY_NAME]] = {
WorkflowStepConfiguration.DATATYPE_INDEX_KEY: 0,
WorkflowStepConfiguration.STEP_INDEX_KEY: simulator_index,
}
metric_step = model.WorkflowStep(
algorithm_id=metric_algo.id, step_index=simulator_index + 1, static_param={}, dynamic_param=dynamics
)
metric_step.step_visible = False
workflow_step_list.insert(0, metric_step)
workflows = self.workflow_service.create_and_store_workflow(
project_id, burst_id, simulator_index, simulator_id, operations
)
self.operation_service.prepare_operations_for_workflowsteps(
workflow_step_list, workflows, user_id, burst_id, project_id, group, operations
)
operation_ids = [operation.id for operation in operations]
return operation_ids
def _async_launch_and_prepare(self, burst_config, simulator_index, simulator_id, user_id):
"""
Prepare operations asynchronously.
"""
try:
operation_ids = self._prepare_operations(burst_config, simulator_index, simulator_id, user_id)
self.logger.debug("Starting a total of %s workflows" % (len(operation_ids)))
wf_errs = 0
for operation_id in operation_ids:
try:
OperationService().launch_operation(operation_id, True)
except Exception, excep:
self.logger.error(excep)
wf_errs += 1
self.workflow_service.mark_burst_finished(burst_config, error_message=str(excep))
self.logger.debug(
"Finished launching workflows. "
+ str(len(operation_ids) - wf_errs)
+ " were launched successfully, "
+ str(wf_errs)
+ " had error on pre-launch steps"
)
except Exception, excep:
self.logger.error(excep)
self.workflow_service.mark_burst_finished(burst_config, error_message=str(excep))
