def test_filter_sql_equivalent(self): """ Test applying a filter on DB. """ data_type = Datatype1() data_type.row1 = "value1" data_type.row2 = "value2" datatypes_factory.DatatypesFactory()._store_datatype(data_type) data_type = Datatype1() data_type.row1 = "value3" data_type.row2 = "value2" datatypes_factory.DatatypesFactory()._store_datatype(data_type) data_type = Datatype1() data_type.row1 = "value1" data_type.row2 = "value3" datatypes_factory.DatatypesFactory()._store_datatype(data_type) test_filter_1 = FilterChain(fields=[FilterChain.datatype + '._row1'], operations=['=='], values=['value1']) test_filter_2 = FilterChain(fields=[FilterChain.datatype + '._row1'], operations=['=='], values=['vaue2']) test_filter_3 = FilterChain(fields=[FilterChain.datatype + '._row1', FilterChain.datatype + '._row2'], operations=['==', 'in'], values=["value1", ['value1', 'value2']]) test_filter_4 = FilterChain(fields=[FilterChain.datatype + '._row1', FilterChain.datatype + '._row2'], operations=['==', 'in'], values=["value1", ['value5', 'value6']]) all_stored_dts = self.count_all_entities(Datatype1) self.assertEqual(3, all_stored_dts) self._evaluate_db_filter(test_filter_1, 2) self._evaluate_db_filter(test_filter_2, 0) self._evaluate_db_filter(test_filter_3, 1) self._evaluate_db_filter(test_filter_4, 0)
def get_input_tree(self): """ Take as Input a Connectivity Object. """ return [{ 'name': 'input_data', 'label': 'Connectivity Matrix', 'type': Connectivity, 'required': True }, { 'name': 'surface_data', 'label': 'Brain Surface', 'type': CorticalSurface, 'description': 'The Brain Surface is used to give you an idea of the connectivity position relative ' 'to the full brain cortical surface. This surface will be displayed as a shadow ' '(only used in 3D Edges viewer).' }, { 'name': 'colors', 'label': 'Node Colors', 'type': ConnectivityMeasure, 'conditions': FilterChain(fields=[FilterChain.datatype + '._nr_dimensions'], operations=["=="], values=[1]), 'description': 'A ConnectivityMesure DataType that establishes a colormap for the nodes ' 'displayed in the 2D Connectivity viewers.' }, { 'name': 'step', 'label': 'Color Threshold', 'type': 'float', 'description': 'All nodes with a value greater than this threshold will be displayed as red discs, ' 'otherwise they will be yellow. (This applies to 2D Connectivity Viewers and the ' 'threshold will depend on the metric used to set the Node Color)' }, { 'name': 'rays', 'label': 'Shapes Dimensions', 'type': ConnectivityMeasure, 'conditions': FilterChain(fields=[FilterChain.datatype + '._nr_dimensions'], operations=["=="], values=[1]), 'description': 'A ConnectivityMeasure datatype used to establish the size of the spheres representing ' 'each node. (It only applies to 3D Nodes viewer).' }]
def test_filter_addition(self): """ test addition in filter chain """ filter1 = FilterChain(fields=[FilterChain.datatype + '.attribute_1'], operations=["=="], values=['test_val']) filter2 = FilterChain(fields=[FilterChain.datatype + '.attribute_2'], operations=['in'], values=[['test_val2', 1]]) test_filter = filter1 + filter2 self.__should_pass( test_filter, TestFiltering.DummyFilterClass(attribute_1='test_val', attribute_2=1)) self.__should_pass( test_filter, TestFiltering.DummyFilterClass(attribute_1='test_val', attribute_2='test_val2')) self.__should_fail( test_filter, TestFiltering.DummyFilterClass(attribute_1='test_val', attribute_2=2)) self.__should_fail( test_filter, TestFiltering.DummyFilterClass(attribute_1='test_val1', attribute_2=1))
def test_happy_flow_import(self): """ Test that importing a CFF generates at least one DataType in DB. """ TestConnectivityZip.import_test_connectivity96(self.test_user, self.test_project, subject=TEST_SUBJECT_A) field = FilterChain.datatype + '.subject' filters = FilterChain('', [field], [TEST_SUBJECT_A], ['==']) reference_connectivity = TestFactory.get_entity(self.test_project, Connectivity(), filters) dt_count_before = TestFactory.get_entity_count(self.test_project, Connectivity()) self._import_csv_test_connectivity(reference_connectivity.gid, TEST_SUBJECT_B) dt_count_after = TestFactory.get_entity_count(self.test_project, Connectivity()) assert dt_count_before + 1 == dt_count_after filters = FilterChain('', [field], [TEST_SUBJECT_B], ['like']) imported_connectivity = TestFactory.get_entity(self.test_project, Connectivity(), filters) # check relationship between the imported connectivity and the reference assert (reference_connectivity.centres == imported_connectivity.centres).all() assert (reference_connectivity.orientations == imported_connectivity.orientations).all() assert reference_connectivity.number_of_regions == imported_connectivity.number_of_regions assert (reference_connectivity.region_labels == imported_connectivity.region_labels).all() assert not (reference_connectivity.weights == imported_connectivity.weights).all() assert not (reference_connectivity.tract_lengths == imported_connectivity.tract_lengths).all()
def get_input_tree(self): """ Take as Input a Connectivity Object. """ filters_ui = [UIFilter(linked_elem_name="colors", linked_elem_field=FilterChain.datatype + "._connectivity"), UIFilter(linked_elem_name="rays", linked_elem_field=FilterChain.datatype + "._connectivity")] json_ui_filter = json.dumps([ui_filter.to_dict() for ui_filter in filters_ui]) return [{'name': 'input_data', 'label': 'Connectivity Matrix', 'type': Connectivity, 'required': True, KWARG_FILTERS_UI: json_ui_filter}, {'name': 'surface_data', 'label': 'Brain Surface', 'type': CorticalSurface, 'description': 'The Brain Surface is used to give you an idea of the connectivity position relative ' 'to the full brain cortical surface. This surface will be displayed as a shadow ' '(only used in 3D Edges tab).'}, {'name': 'colors', 'label': 'Node Colors', 'type': ConnectivityMeasure, 'conditions': FilterChain(fields=[FilterChain.datatype + '._nr_dimensions'], operations=["=="], values=[1]), 'description': 'A ConnectivityMeasure DataType that establishes a colormap for the nodes ' 'displayed in the 2D Connectivity tabs.'}, {'name': 'step', 'label': 'Color Threshold', 'type': 'float', 'description': 'All nodes with a value greater or equal (>=) than this threshold will be displayed ' 'as red discs, otherwise (<) they will be yellow. (This applies to 2D Connectivity ' 'tabs and the threshold will depend on the metric used to set the Node Color)'}, {'name': 'rays', 'label': 'Shapes Dimensions', 'type': ConnectivityMeasure, 'conditions': FilterChain(fields=[FilterChain.datatype + '._nr_dimensions'], operations=["=="], values=[1]), 'description': 'A ConnectivityMeasure datatype used to establish the size of the spheres representing ' 'each node. (It only applies to 3D Nodes tab).'}]
def test_get_filtered_by_column(self): """ Test the filter function when retrieving dataTypes with a filter after a column from a class specific table (e.g. DATA_arraywrapper). """ operation_1 = TestFactory.create_operation( test_user=self.test_user, test_project=self.test_project) operation_2 = TestFactory.create_operation( test_user=self.test_user, test_project=self.test_project) one_dim_array = numpy.arange(5) two_dim_array = numpy.array([[1, 2], [2, 3], [1, 4]]) self._store_float_array(one_dim_array, "John Doe 1", operation_1.id) self._store_float_array(one_dim_array, "John Doe 2", operation_1.id) self._store_float_array(two_dim_array, "John Doe 3", operation_2.id) count = self.flow_service.get_available_datatypes( self.test_project.id, "tvb.datatypes.arrays.MappedArray")[1] self.assertEqual(count, 3, "Problems with inserting data") first_filter = FilterChain( fields=[FilterChain.datatype + '._nr_dimensions'], operations=["=="], values=[1]) count = self.flow_service.get_available_datatypes( self.test_project.id, "tvb.datatypes.arrays.MappedArray", first_filter)[1] self.assertEqual(count, 2, "Data was not filtered") second_filter = FilterChain( fields=[FilterChain.datatype + '._nr_dimensions'], operations=["=="], values=[2]) filtered_data = self.flow_service.get_available_datatypes( self.test_project.id, "tvb.datatypes.arrays.MappedArray", second_filter)[0] self.assertEqual(len(filtered_data), 1, "Data was not filtered") self.assertEqual(filtered_data[0][3], "John Doe 3") third_filter = FilterChain( fields=[FilterChain.datatype + '._length_1d'], operations=["=="], values=[3]) filtered_data = self.flow_service.get_available_datatypes( self.test_project.id, "tvb.datatypes.arrays.MappedArray", third_filter)[0] self.assertEqual(len(filtered_data), 1, "Data was not filtered correct") self.assertEqual(filtered_data[0][3], "John Doe 3") try: if os.path.exists('One_dim.txt'): os.remove('One_dim.txt') if os.path.exists('Two_dim.txt'): os.remove('Two_dim.txt') if os.path.exists('One_dim-1.txt'): os.remove('One_dim-1.txt') except Exception: pass
def _build_custom_filter(filter_data): """ Param filter_data should be at this point a dictionary of the form: {'type' : 'fitler_type', 'value' : 'fitler_value'} If 'filter_type' is not handled just return None. """ filter_data = json.loads(filter_data) if filter_data['type'] == 'from_burst': return FilterChain('Burst', [FilterChain.datatype + '.fk_parent_burst'], [filter_data['value']], operations=["=="]) if filter_data['type'] == 'from_datatype': return FilterChain('Datatypes', [FilterChain.operation + '.parameters'], [filter_data['value']], operations=["like"]) return None
def get_input_tree(self): return [{'name': 'data_0', 'label': 'Connectivity Measures 1', 'type': ConnectivityMeasure, 'required': True, 'conditions': FilterChain(fields=[FilterChain.datatype + '._nr_dimensions'], operations=["=="], values=[1]), 'description': 'Punctual values for each node in the connectivity matrix. ' 'This will give the colors of the resulting topographic image.'}, {'name': 'data_1', 'label': 'Connectivity Measures 2', 'type': ConnectivityMeasure, 'conditions': FilterChain(fields=[FilterChain.datatype + '._nr_dimensions'], operations=["=="], values=[1]), 'description': 'Comparative values'}, {'name': 'data_2', 'label': 'Connectivity Measures 3', 'type': ConnectivityMeasure, 'conditions': FilterChain(fields=[FilterChain.datatype + '._nr_dimensions'], operations=["=="], values=[1]), 'description': 'Comparative values'}]
def _import(self, import_file_path, surface_gid, connectivity_gid): """ This method is used for importing region mappings :param import_file_path: absolute path of the file to be imported """ # Retrieve Adapter instance test_subject = "test" importer = TestFactory.create_adapter( 'tvb.adapters.uploaders.region_mapping_importer', 'RegionMapping_Importer') args = { 'mapping_file': import_file_path, 'surface': surface_gid, 'connectivity': connectivity_gid, DataTypeMetaData.KEY_SUBJECT: test_subject } # Launch import Operation FlowService().fire_operation(importer, self.test_user, self.test_project.id, **args) # During setup we import a CFF which creates an additional RegionMapping # So, here we have to find our mapping (just imported) data_filter = FilterChain(fields=[FilterChain.datatype + ".subject"], operations=["=="], values=[test_subject]) region_mapping = self._get_entity(RegionMapping, data_filter) return region_mapping
def get_input_tree(self): return [{ 'name': 'measure', 'label': 'Measure', 'type': MappedArray, 'required': True, 'description': 'A measure to view on anatomy', 'conditions': FilterChain(fields=[FilterChain.datatype + '._nr_dimensions'], operations=[">="], values=[2]) }, { 'name': 'region_mapping_volume', 'label': 'Region mapping', 'type': RegionVolumeMapping, 'required': False, }, { 'name': 'data_slice', 'label': 'slice indices in numpy syntax', 'type': 'str', 'required': False }, _MappedArrayVolumeBase.get_background_input_tree()]
def get_input_tree(self): """ Compute interface based on introspected algorithms found. """ algorithm = BaseTimeseriesMetricAlgorithm() algorithm.trait.bound = self.INTERFACE_ATTRIBUTES_ONLY tree = algorithm.interface[self.INTERFACE_ATTRIBUTES] tree[0]['conditions'] = FilterChain( fields=[FilterChain.datatype + '._nr_dimensions'], operations=["=="], values=[4]) algo_names = self.available_algorithms.keys() options = [] for name in algo_names: options.append({ ABCAdapter.KEY_NAME: name, ABCAdapter.KEY_VALUE: name }) tree.append({ 'name': 'algorithms', 'label': 'Selected metrics to be applied', 'type': ABCAdapter.TYPE_MULTIPLE, 'required': False, 'options': options, 'description': 'The selected metric algorithms will be applied on the input TimeSeries' }) return tree
def test_complex_filter(self): """ Test a filter with at least 2 conditions """ test_filter = FilterChain(fields=[ FilterChain.datatype + '.attribute_1', FilterChain.datatype + '.attribute_2' ], operations=["==", 'in'], values=['test_val', ['test_val2', 1]]) self.__should_pass( test_filter, TestFiltering.DummyFilterClass(attribute_1='test_val', attribute_2=1)) self.__should_pass( test_filter, TestFiltering.DummyFilterClass(attribute_1='test_val', attribute_2=1)) self.__should_fail( test_filter, TestFiltering.DummyFilterClass(attribute_1='test_val', attribute_2=2)) self.__should_fail( test_filter, TestFiltering.DummyFilterClass(attribute_1='test_val1', attribute_2=1))
def get_input_tree(self): return [ { 'name': 'region_mapping_volume', 'label': 'Region mapping', 'type': RegionVolumeMapping, 'required': True, }, { 'name': 'connectivity_measure', 'label': 'Connectivity measure', 'type': ConnectivityMeasure, 'required': False, 'description': 'A connectivity measure', 'conditions': FilterChain(fields=[FilterChain.datatype + '._nr_dimensions'], operations=["=="], values=[1]) }, ]
def _import(self, import_file_path, surface_gid, connectivity_gid): """ This method is used for importing region mappings :param import_file_path: absolute path of the file to be imported """ ### Retrieve Adapter instance group = dao.find_group( 'tvb.adapters.uploaders.region_mapping_importer', 'RegionMapping_Importer') importer = ABCAdapter.build_adapter(group) args = { 'mapping_file': import_file_path, 'surface': surface_gid, 'connectivity': connectivity_gid, DataTypeMetaData.KEY_SUBJECT: "test" } now = datetime.datetime.now() ### Launch import Operation FlowService().fire_operation(importer, self.test_user, self.test_project.id, **args) # During setup we import a CFF which creates an additional RegionMapping # So, here we have to find our mapping (just imported) data_filter = FilterChain( fields=[FilterChain.datatype + ".create_date"], operations=[">"], values=[now]) region_mapping = self._get_entity(RegionMapping(), data_filter) return region_mapping
def get_input_tree(self): return [{ 'name': 'time_series', 'label': 'Time Series (Region or Surface)', 'type': TimeSeries, 'required': True, 'conditions': FilterChain(fields=[ FilterChain.datatype + '.type', FilterChain.datatype + '._has_surface_mapping' ], operations=["in", "=="], values=[['TimeSeriesRegion', 'TimeSeriesSurface'], True]) }, { 'name': 'shell_surface', 'label': 'Shell Surface', 'type': Surface, 'required': False, 'description': "Surface to be displayed semi-transparently, for visual purposes only." }]
def getfiltereddatatypes(self, name, parent_div, tree_session_key, filters): """ Given the name from the input tree, the dataType required and a number of filters, return the available dataType that satisfy the conditions imposed. """ previous_tree = self.context.get_session_tree_for_key(tree_session_key) if previous_tree is None: common.set_error_message("Adapter Interface not in session for filtering!") raise cherrypy.HTTPRedirect("/tvb?error=True") current_node = self._get_node(previous_tree, name) if current_node is None: raise Exception("Could not find node :" + name) datatype = current_node[ABCAdapter.KEY_DATATYPE] filters = json.loads(filters) availablefilter = json.loads(FilterChain.get_filters_for_type(datatype)) for i, filter_ in enumerate(filters[FILTER_FIELDS]): # Check for filter input of type 'date' as these need to be converted if filter_ in availablefilter and availablefilter[filter_][FILTER_TYPE] == 'date': try: temp_date = string2date(filters[FILTER_VALUES][i], False) filters[FILTER_VALUES][i] = temp_date except ValueError: raise # In order for the filter object not to "stack up" on multiple calls to # this method, create a deepCopy to work with if ABCAdapter.KEY_CONDITION in current_node: new_filter = copy.deepcopy(current_node[ABCAdapter.KEY_CONDITION]) else: new_filter = FilterChain() new_filter.fields.extend(filters[FILTER_FIELDS]) new_filter.operations.extend(filters[FILTER_OPERATIONS]) new_filter.values.extend(filters[FILTER_VALUES]) # Get dataTypes that match the filters from DB then populate with values values, total_count = InputTreeManager().populate_option_values_for_dtype( common.get_current_project().id, datatype, new_filter, self.context.get_current_step()) # Create a dictionary that matches what the template expects parameters = {ABCAdapter.KEY_NAME: name, ABCAdapter.KEY_FILTERABLE: availablefilter, ABCAdapter.KEY_TYPE: ABCAdapter.TYPE_SELECT, ABCAdapter.KEY_OPTIONS: values, ABCAdapter.KEY_DATATYPE: datatype} if total_count > MAXIMUM_DATA_TYPES_DISPLAYED: parameters[KEY_WARNING] = WARNING_OVERFLOW if ABCAdapter.KEY_REQUIRED in current_node: parameters[ABCAdapter.KEY_REQUIRED] = current_node[ABCAdapter.KEY_REQUIRED] if len(values) > 0 and string2bool(str(parameters[ABCAdapter.KEY_REQUIRED])): parameters[ABCAdapter.KEY_DEFAULT] = str(values[-1][ABCAdapter.KEY_VALUE]) previous_selected = self.context.get_current_default(name) if previous_selected in [str(vv['value']) for vv in values]: parameters[ABCAdapter.KEY_DEFAULT] = previous_selected template_specification = {"inputRow": parameters, "disabled": False, "parentDivId": parent_div, common.KEY_SESSION_TREE: tree_session_key} return self.fill_default_attributes(template_specification)
def test_invalid_filter(self): """ Error test-case when evaluating filter in Python. """ test_filter = FilterChain(fields = [FilterChain.datatype + '.attribute_1'], operations = ["in"], values = [None]) self.assertRaises(InvalidFilterEntity, test_filter.get_python_filter_equivalent, FilteringTest.DummyFilterClass(attribute_1 = ['test_val', 'test2']))
def test_bad_reference(self): TestFactory.import_cff(test_user=self.test_user, test_project=self.test_project) field = FilterChain.datatype + '.subject' filters = FilterChain('', [field], [TEST_SUBJECT_A], ['!=']) bad_reference_connectivity = TestFactory.get_entity(self.test_project, Connectivity(), filters) with pytest.raises(OperationException): self._import_csv_test_connectivity(bad_reference_connectivity.gid, TEST_SUBJECT_A)
def test_invalid_input(self): """ Error test-case. """ test_filter = FilterChain(fields = [FilterChain.datatype + '.other_attribute_1'], operations = ["in"], values = ['test']) self.assertRaises(InvalidFilterChainInput, test_filter.get_python_filter_equivalent, FilteringTest.DummyFilterClass(attribute_1 = ['test_val', 'test2']))
def test_filter_sql_equivalent(self): """ Test applying a filter on DB. """ data_type = Datatype1() data_type.row1 = "value1" data_type.row2 = "value2" datatypes_factory.DatatypesFactory()._store_datatype(data_type) data_type = Datatype1() data_type.row1 = "value3" data_type.row2 = "value2" datatypes_factory.DatatypesFactory()._store_datatype(data_type) data_type = Datatype1() data_type.row1 = "value1" data_type.row2 = "value3" datatypes_factory.DatatypesFactory()._store_datatype(data_type) test_filter_1 = FilterChain(fields=[FilterChain.datatype + '._row1'], operations=['=='], values=['value1']) test_filter_2 = FilterChain(fields=[FilterChain.datatype + '._row1'], operations=['=='], values=['vaue2']) test_filter_3 = FilterChain(fields=[ FilterChain.datatype + '._row1', FilterChain.datatype + '._row2' ], operations=['==', 'in'], values=["value1", ['value1', 'value2']]) test_filter_4 = FilterChain(fields=[ FilterChain.datatype + '._row1', FilterChain.datatype + '._row2' ], operations=['==', 'in'], values=["value1", ['value5', 'value6']]) all_stored_dts = self.get_all_entities(Datatype1) self.assertTrue( len(all_stored_dts) == 3, "Expected 3 DTs to be stored for " "test_filte_sql_equivalent. Got %s instead." % (len(all_stored_dts, ))) self._evaluate_db_filter(test_filter_1, 2) self._evaluate_db_filter(test_filter_2, 0) self._evaluate_db_filter(test_filter_3, 1) self._evaluate_db_filter(test_filter_4, 0)
def search_and_export_ts(project_id, export_folder=os.path.join("~", "TVB")): #### This is the simplest filter you could write: filter and entity by Subject filter_connectivity = FilterChain( fields=[FilterChain.datatype + '.subject'], operations=["=="], values=[DataTypeMetaData.DEFAULT_SUBJECT]) connectivities = _retrieve_entities_by_filters(Connectivity, project_id, filter_connectivity) #### A more complex filter: by linked entity (connectivity), BOLD monitor, sampling, operation param: filter_timeseries = FilterChain( fields=[ FilterChain.datatype + '._connectivity', FilterChain.datatype + '._title', FilterChain.datatype + '._sample_period', FilterChain.datatype + '._sample_rate', FilterChain.operation + '.parameters' ], operations=["==", "like", ">=", "<=", "like"], values=[ connectivities[0].gid, "Bold", "500", "0.002", '"conduction_speed": "3.0"' ]) #### If you want to filter another type of TS, change the kind class bellow, #### instead of TimeSeriesRegion use TimeSeriesEEG, or TimeSeriesSurface, etc. timeseries = _retrieve_entities_by_filters(TimeSeriesRegion, project_id, filter_timeseries) for ts in timeseries: print("=============================") print(ts.summary_info) print(" Original file: " + str(ts.get_storage_file_path())) destination_file = os.path.expanduser( os.path.join(export_folder, ts.get_storage_file_name())) FilesHelper.copy_file(ts.get_storage_file_path(), destination_file) if os.path.exists(destination_file): print(" TS file copied at: " + destination_file) else: print( " Some error happened when trying to copy at destination folder!!" )
def get_input_tree(self): """ Take as Input a Connectivity Object. """ return [{'name': 'datatype_group', 'label': 'Datatype Group', 'type': DataTypeGroup, 'required': True, 'conditions': FilterChain(fields=[FilterChain.datatype + ".no_of_ranges"], operations=["=="], values=[2])}]
def get_input_tree(self): return [{'name': 'time_series', 'label': 'Time Series (Region or Surface)', 'type': TimeSeries, 'required': True, 'conditions': FilterChain(fields=[FilterChain.datatype + '.type', FilterChain.datatype + '._nr_dimensions'], operations=["in", "=="], values=[['TimeSeriesRegion', 'TimeSeriesSurface'], 4]), 'description': 'Depending on the simulation length and your browser capabilities, you might experience' ' after multiple runs, browser crashes. In such cases, it is recommended to empty the' ' browser cache and try again. Sorry for the inconvenience.'}]
def build_datatype_filters(selected=RELEVANT_VIEW, single_filter=None): """ Return all visibility filters for data structure page, or only one filter. """ filters = {StaticFiltersFactory.FULL_VIEW: FilterChain(StaticFiltersFactory.FULL_VIEW), StaticFiltersFactory.RELEVANT_VIEW: FilterChain(StaticFiltersFactory.RELEVANT_VIEW, [FilterChain.datatype + '.visible'], [True], operations=["=="])} if selected is None or len(selected) == 0: selected = StaticFiltersFactory.RELEVANT_VIEW if selected in filters: filters[selected].selected = True if single_filter is not None: if single_filter in filters: return filters[single_filter] else: ### We have some custom filter to build return StaticFiltersFactory._build_custom_filter(single_filter) return filters.values()
def test_filter_add_condition(self): """ Test that adding a condition to a filter is working. """ test_filter = FilterChain(fields = [FilterChain.datatype + '.attribute_1'], operations = ["=="], values = ['test_val']) filter_input = FilteringTest.DummyFilterClass(attribute_1 = 'test_val', attribute_2 = 1) self.__should_pass(test_filter, filter_input) test_filter.add_condition(FilterChain.datatype + '.attribute_2', '==', 2) self.__should_fail(test_filter, filter_input)
def get_input_tree(self): return [ dict(name="connectivity", label=self._ui_connectivity_label, type=Connectivity, required=True, conditions=FilterChain( fields=[FilterChain.datatype + '._undirected'], operations=["=="], values=['1'])) ]
def get_input_tree(self): """ Return a list of lists describing the interface to the analyzer. This is used by the GUI to generate the menus and fields necessary for defining a simulation. """ algorithm = NodeCovariance() algorithm.trait.bound = self.INTERFACE_ATTRIBUTES_ONLY tree = algorithm.interface[self.INTERFACE_ATTRIBUTES] tree[0]['conditions'] = FilterChain(fields = [FilterChain.datatype + '._nr_dimensions'], operations = ["=="], values = [4]) return tree
def get_input_tree(self): # todo: filter connectivity measures: same length as regions and 1-dimensional filters_ui = [ UIFilter(linked_elem_name="region_map", linked_elem_field=FilterChain.datatype + "._surface"), # UIFilter(linked_elem_name="connectivity_measure", # linked_elem_field=FilterChain.datatype + "._surface") ] json_ui_filter = json.dumps( [ui_filter.to_dict() for ui_filter in filters_ui]) return [{ 'name': 'surface', 'label': 'Brain surface', 'type': Surface, 'required': True, 'description': '', KWARG_FILTERS_UI: json_ui_filter }, { 'name': 'region_map', 'label': 'Region mapping', 'type': RegionMapping, 'required': False, 'description': 'A region map' }, { 'name': 'connectivity_measure', 'label': 'Connectivity measure', 'type': ConnectivityMeasure, 'required': False, 'description': 'A connectivity measure', 'conditions': FilterChain(fields=[FilterChain.datatype + '._nr_dimensions'], operations=["=="], values=[1]) }, { 'name': 'shell_surface', 'label': 'Shell Surface', 'type': Surface, 'required': False, 'description': "Face surface to be displayed semi-transparently, for orientation only." }]
def test_get_filtered_datatypes(self): """ Test the filter function when retrieving dataTypes. """ #Create some test operations start_dates = [datetime.now(), datetime.strptime("08-06-2010", "%m-%d-%Y"), datetime.strptime("07-21-2010", "%m-%d-%Y"), datetime.strptime("05-06-2010", "%m-%d-%Y"), datetime.strptime("07-21-2011", "%m-%d-%Y")] end_dates = [datetime.now(), datetime.strptime("08-12-2010", "%m-%d-%Y"), datetime.strptime("08-12-2010", "%m-%d-%Y"), datetime.strptime("08-12-2011", "%m-%d-%Y"), datetime.strptime("08-12-2011", "%m-%d-%Y")] for i in range(5): operation = model.Operation(self.test_user.id, self.test_project.id, self.algo_inst.id, 'test params', status="FINISHED", start_date=start_dates[i], completion_date=end_dates[i]) operation = dao.store_entity(operation) storage_path = FilesHelper().get_project_folder(self.test_project, str(operation.id)) if i < 4: datatype_inst = Datatype1() datatype_inst.type = "Datatype1" datatype_inst.subject = "John Doe" + str(i) datatype_inst.state = "RAW" datatype_inst.set_operation_id(operation.id) dao.store_entity(datatype_inst) else: for _ in range(2): datatype_inst = Datatype2() datatype_inst.storage_path = storage_path datatype_inst.type = "Datatype2" datatype_inst.subject = "John Doe" + str(i) datatype_inst.state = "RAW" datatype_inst.string_data = ["data"] datatype_inst.set_operation_id(operation.id) dao.store_entity(datatype_inst) returned_data = self.flow_service.get_available_datatypes(self.test_project.id, "tvb_test.datatypes.datatype1.Datatype1") for row in returned_data: if row[1] != 'Datatype1': self.fail("Some invalid data was returned!") self.assertEqual(4, len(returned_data), "Invalid length of result") filter_op = FilterChain(fields=[FilterChain.datatype + ".state", FilterChain.operation + ".start_date"], values=["RAW", datetime.strptime("08-01-2010", "%m-%d-%Y")], operations=["==", ">"]) returned_data = self.flow_service.get_available_datatypes(self.test_project.id, "tvb_test.datatypes.datatype1.Datatype1", filter_op) returned_subjects = [one_data[3] for one_data in returned_data] if "John Doe0" not in returned_subjects or "John Doe1" not in returned_subjects or len(returned_subjects) != 2: self.fail("DataTypes were not filtered properly!")
def _get_filter(cls, nodes_list): """Get default filter""" fields = None values = None operations = None for node in nodes_list: if node.nodeName == ELEM_COND_FIELDS: fields = eval(node.getAttribute(ATT_FILTER_VALUES)) if node.nodeName == ELEM_COND_OPS: operations = eval(node.getAttribute(ATT_FILTER_VALUES)) if node.nodeName == ELEM_COND_VALUES: values = eval(node.getAttribute(ATT_FILTER_VALUES)) return FilterChain(fields=fields, values=values, operations=operations)