Python dao.get_burst_by_id函数代码示例

    def _wait_for_burst(self, burst_config, error_expected=False, timeout=40):
        """
        Method that just waits until a burst configuration is finished or a maximum timeout is reached.

        :param burst_config: the burst configuration that should be waited on
        :param timeout: the maximum number of seconds to wait after the burst
        """
        waited = 0
        while burst_config.status == BurstConfiguration.BURST_RUNNING and waited <= timeout:
            sleep(0.5)
            waited += 0.5
            burst_config = dao.get_burst_by_id(burst_config.id)

        if waited > timeout:
            self.burst_service.stop_burst(burst_config)
            self.fail("Timed out waiting for simulations to finish. We will cancel it")

        if error_expected and burst_config.status != BurstConfiguration.BURST_ERROR:
            self.burst_service.stop_burst(burst_config)
            self.fail("Burst should have failed due to invalid input data.")

        if (not error_expected) and burst_config.status != BurstConfiguration.BURST_FINISHED:
            msg = "Burst status should have been FINISH. Instead got %s %s" % (burst_config.status,
                                                                               burst_config.error_message)
            self.burst_service.stop_burst(burst_config)
            self.fail(msg)

        return burst_config

    def test_launch_group_burst_no_metric(self):
        """
        Test the launch burst method from burst service. Try to launch a burst with test adapter which has
        no metrics associated. This should fail.
        """
        burst_config = self.burst_service.new_burst_configuration(self.test_project.id)

        algo_id = self.flow_service.get_algorithm_by_module_and_class('tvb.tests.framework.adapters.testadapter1',
                                                                      'TestAdapter1').id
        kwargs_replica = {'test1_val1': '[0, 1, 2]', 'test1_val2': '0', model.RANGE_PARAMETER_1: 'test1_val1'}
        test_portlet = dao.get_portlet_by_identifier(self.PORTLET_ID)
        tab_config = {test_portlet.id: [(0, 0), (0, 1), (1, 0)]}
        self._add_portlets_to_burst(burst_config, tab_config)
        burst_config.update_simulator_configuration(kwargs_replica)
        burst_id, _ = self.burst_service.launch_burst(burst_config, 0, algo_id, self.test_user.id)
        burst_config = dao.get_burst_by_id(burst_id)
        # Wait maximum x seconds for burst to finish
        self._wait_for_burst(burst_config, error_expected=True)

        launched_workflows = dao.get_workflows_for_burst(burst_id, is_count=True)
        self.assertEqual(3, launched_workflows, "3 workflows should have been launched due to group parameter.")

        op_groups = self.count_all_entities(model.OperationGroup)
        dt_groups = self.count_all_entities(model.DataTypeGroup)
        self.assertEqual(5, op_groups, "An operation group should have been created for each step.")
        self.assertEqual(5, dt_groups, "An dataType group should have been created for each step.")

 def test_load_tab_configuration(self):
     """
     Create a burst with some predefined portlets in some known positions. Check that the
     load_tab_configuration method does what it is expected, and we get the portlets in the
     corresponding tab positions.
     """
     burst_config = self.burst_service.new_burst_configuration(self.test_project.id)
     SIMULATOR_MODULE = 'tvb.tests.framework.adapters.testadapter1'
     SIMULATOR_CLASS = 'TestAdapter1'
     algo_id = self.flow_service.get_algorithm_by_module_and_class(SIMULATOR_MODULE, SIMULATOR_CLASS).id
     kwargs_replica = {'test1_val1': '0', 'test1_val2': '0'}
     test_portlet = dao.get_portlet_by_identifier(self.PORTLET_ID)
     # Add test_portlet to positions (0,0), (0,1) and (1,0)
     tab_config = {test_portlet.id: [(0, 0), (0, 1), (1, 0)]}
     self._add_portlets_to_burst(burst_config, tab_config)
     burst_config.update_simulator_configuration(kwargs_replica)
     burst_id, _ = self.burst_service.launch_burst(burst_config, 0, algo_id, self.test_user.id)
     burst_config = dao.get_burst_by_id(burst_id)
     burst_config = self._wait_for_burst(burst_config)
     burst_wf = dao.get_workflows_for_burst(burst_config.id)[0]
     wf_step = dao.get_workflow_steps(burst_wf.id)[0]
     burst_config.prepare_after_load()
     for tab in burst_config.tabs:
         for portlet in tab.portlets:
             self.assertTrue(portlet is None, "Before loading the tab configuration all portlets should be none.")
     burst_config = self.burst_service.load_tab_configuration(burst_config, wf_step.fk_operation)
     for tab_idx, tab in enumerate(burst_config.tabs):
         for portlet_idx, portlet in enumerate(tab.portlets):
             if (tab_idx == 0 and portlet_idx in [0, 1]) or (tab_idx == 1 and portlet_idx == 0):
                 self.assertTrue(portlet is not None, "portlet gonfiguration not set")
                 self.assertEqual(test_portlet.id, portlet.portlet_id, "Unexpected portlet entity loaded.")
             else:
                 self.assertTrue(portlet is None, "Before loading the tab configuration all portlets should be none")

 def test_launch_burst(self):
     """
     Test the launch burst method from burst service.
     """
     first_step_algo = self.flow_service.get_algorithm_by_module_and_class(
         'tvb.tests.framework.adapters.testadapter1', 'TestAdapter1')
     adapter_interface = self.flow_service.prepare_adapter(self.test_project.id, first_step_algo)
     ui_submited_simulator_iface_replica = {}
     kwargs_replica = {}
     for entry in adapter_interface:
         ui_submited_simulator_iface_replica[entry[ABCAdapter.KEY_NAME]] = {model.KEY_PARAMETER_CHECKED: True,
                                                                            model.KEY_SAVED_VALUE: entry[
                                                                                ABCAdapter.KEY_DEFAULT]}
         kwargs_replica[entry[ABCAdapter.KEY_NAME]] = entry[ABCAdapter.KEY_DEFAULT]
     burst_config = self.burst_service.new_burst_configuration(self.test_project.id)
     burst_config.simulator_configuration = ui_submited_simulator_iface_replica
     test_portlet = dao.get_portlet_by_identifier(self.PORTLET_ID)
     tab_config = {test_portlet.id: [(0, 0), (0, 1), (1, 0)]}
     self._add_portlets_to_burst(burst_config, tab_config)
     burst_config.update_simulator_configuration(kwargs_replica)
     burst_id, _ = self.burst_service.launch_burst(burst_config, 0, first_step_algo.id, self.test_user.id)
     burst_config = dao.get_burst_by_id(burst_id)
     self.assertTrue(burst_config.status in (BurstConfiguration.BURST_FINISHED, BurstConfiguration.BURST_RUNNING),
                     "Burst not launched successfully!")
     # Wait maximum x seconds for burst to finish
     self._wait_for_burst(burst_config)

    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

    def _prepare_and_launch_async_burst(self, length=4, is_range=False, nr_ops=0, wait_to_finish=0):
        """
        Launch an asynchronous burst with a simulation having all the default parameters, only the length received as
        a parameters. This is launched with actual simulator and not with a dummy test adapter as replacement.
        :param length: the length of the simulation in milliseconds. This is also used in case we need
            a group burst, in which case we will have `nr_ops` simulations with lengths starting from 
            `length` to `length + nr_ops` milliseconds
        :param is_range: a boolean which switches between a group burst and a non group burst.
            !! even if `is_range` is `True` you still need a non-zero positive `nr_ops` to have an actual group burst
        :param nr_ops: the number of operations in the group burst
        """
        launch_params = self._prepare_simulation_params(length, is_range, nr_ops)

        burst_config = self.burst_service.new_burst_configuration(self.test_project.id)
        burst_config.update_simulator_configuration(launch_params)
        burst_id = self.burst_service.launch_burst(burst_config, 0, self.sim_algorithm.id, self.test_user.id)[0]
        burst_config = dao.get_burst_by_id(burst_id)

        __timeout = 15
        __waited = 0
        # Wait a maximum of 15 seconds for the burst launch to be performed
        while dao.get_workflows_for_burst(burst_config.id, is_count=True) == 0 and __waited < __timeout:
            sleep(0.5)
            __waited += 0.5

        if wait_to_finish:
            burst_config = self._wait_for_burst(burst_config, timeout=wait_to_finish)
        return burst_config

 def test_remove_started_burst(self):
     """
     Try removing a started burst, which should result in it getting canceled.
     """
     burst_entity = self._prepare_and_launch_async_burst(length=20000)
     self.assertEqual(BurstConfiguration.BURST_RUNNING, burst_entity.status,
                      'A 20000 length simulation should still be started immediately after launch.')
     got_deleted = self.burst_service.cancel_or_remove_burst(burst_entity.id)
     self.assertFalse(got_deleted, "Burst should be cancelled before deleted.")
     burst_entity = dao.get_burst_by_id(burst_entity.id)
     self.assertEqual(BurstConfiguration.BURST_CANCELED, burst_entity.status,
                      'Deleting a running burst should just cancel it first.')
     got_deleted = self.burst_service.cancel_or_remove_burst(burst_entity.id)
     self.assertTrue(got_deleted, "Burst should be deleted if status is cancelled.")
     burst_entity = dao.get_burst_by_id(burst_entity.id)
     self.assertTrue(burst_entity is None, "Removing a canceled burst should delete it from db.")

    def test_launch_burst_invalid_portlet_analyzer_data(self):
        """
        Test that burst is marked as error if invalid data is passed to the first step.
        """
        algo_id = self.flow_service.get_algorithm_by_module_and_class('tvb.tests.framework.adapters.testadapter1',
                                                                      'TestAdapter1').id
        #Adapter tries to do an int(test1_val1) and int(test1_val2) so this should be valid
        burst_config = self.burst_service.new_burst_configuration(self.test_project.id)
        kwargs_replica = {'test1_val1': '1', 'test1_val2': '0'}
        burst_config.update_simulator_configuration(kwargs_replica)

        test_portlet = dao.get_portlet_by_identifier(self.PORTLET_ID)
        portlet_configuration = self.burst_service.new_portlet_configuration(test_portlet.id)
        #Portlet analyzer tries to do int(input) which should fail
        declared_overwrites = {ADAPTER_PREFIX_ROOT + '0test_non_dt_input': 'asa'}
        self.burst_service.update_portlet_configuration(portlet_configuration, declared_overwrites)
        burst_config.tabs[0].portlets[0] = portlet_configuration

        burst_id, _ = self.burst_service.launch_burst(burst_config, 0, algo_id, self.test_user.id)
        burst_config = dao.get_burst_by_id(burst_id)
        #Wait maximum x seconds for burst to finish
        burst_config = self._wait_for_burst(burst_config, error_expected=True)

        burst_wf = dao.get_workflows_for_burst(burst_config.id)[0]
        wf_steps = dao.get_workflow_steps(burst_wf.id)
        self.assertTrue(len(wf_steps) == 2,
                        "Should have exactly 2 wf steps. One for 'simulation' one for portlet analyze operation.")
        simulator_op = dao.get_operation_by_id(wf_steps[0].fk_operation)
        self.assertEqual(model.STATUS_FINISHED, simulator_op.status,
                         "First operation should be simulator which should have 'finished' status.")
        portlet_analyze_op = dao.get_operation_by_id(wf_steps[1].fk_operation)
        self.assertEqual(portlet_analyze_op.status, model.STATUS_ERROR,
                         "Second operation should be portlet analyze step which should have 'error' status.")

 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 test_rename_burst(self):
     """
     Test that renaming of a burst functions properly.
     """
     burst_config = TestFactory.store_burst(self.test_project.id)
     self.burst_service.rename_burst(burst_config.id, "new_burst_name")
     loaded_burst = dao.get_burst_by_id(burst_config.id)
     self.assertEqual(loaded_burst.name, "new_burst_name", "Burst was not renamed properly.")

 def test_rename_burst(self):
     """
     Create and store a burst, then rename it and check that it
     works as expected.
     """
     burst = self._store_burst(self.test_project.id, 'started', {'test': 'test'}, 'burst1')
     self.burst_c.rename_burst(burst.id, "test_new_burst_name")
     renamed_burst = dao.get_burst_by_id(burst.id)
     self.assertEqual(renamed_burst.name, "test_new_burst_name")

 def test_store_burst_config(self):
     """
     Test that a burst entity is properly stored in db.
     """
     burst_config = TestFactory.store_burst(self.test_project.id)
     self.assertTrue(burst_config.id is not None, 'Burst was not stored properly.')
     stored_entity = dao.get_burst_by_id(burst_config.id)
     self.assertTrue(stored_entity is not None, 'Burst was not stored properly.')
     self._compare_bursts(burst_config, stored_entity)

 def prepare_next_step(self, last_executed_op_id):
     """
     If the operation with id 'last_executed_op_id' resulted after
     the execution of a workflow step then this method will launch
     the operation corresponding to the next step from the workflow.
     """
     try:
         current_step, next_workflow_step = self._get_data(last_executed_op_id)
         if next_workflow_step is not None:
             operation = dao.get_operation_by_id(next_workflow_step.fk_operation)
             dynamic_param_names = next_workflow_step.dynamic_workflow_param_names
             if len(dynamic_param_names) > 0:
                 op_params = json.loads(operation.parameters)
                 for param_name in dynamic_param_names:
                     dynamic_param = op_params[param_name]
                     former_step = dao.get_workflow_step_by_step_index(next_workflow_step.fk_workflow,
                                                                       dynamic_param[wf_cfg.STEP_INDEX_KEY])
                     if type(dynamic_param[wf_cfg.DATATYPE_INDEX_KEY]) is IntType: 
                         datatypes = dao.get_results_for_operation(former_step.fk_operation)
                         op_params[param_name] = datatypes[dynamic_param[wf_cfg.DATATYPE_INDEX_KEY]].gid
                     else:
                         previous_operation = dao.get_operation_by_id(former_step.fk_operation)
                         op_params[param_name] = json.loads(previous_operation.parameters)[
                             dynamic_param[wf_cfg.DATATYPE_INDEX_KEY]]
                 operation.parameters = json.dumps(op_params)
                 operation = dao.store_entity(operation)
             return operation.id
         else:
             if current_step is not None:
                 current_workflow = dao.get_workflow_by_id(current_step.fk_workflow)
                 current_workflow.status = current_workflow.STATUS_FINISHED
                 dao.store_entity(current_workflow)
                 burst_entity = dao.get_burst_by_id(current_workflow.fk_burst)
                 parallel_workflows = dao.get_workflows_for_burst(burst_entity.id)
                 all_finished = True
                 for workflow in parallel_workflows:
                     if workflow.status == workflow.STATUS_STARTED:
                         all_finished = False
                 if all_finished:
                     self.mark_burst_finished(burst_entity, success=True)
                     disk_size = dao.get_burst_disk_size(burst_entity.id)  # Transform from kB to MB
                     if disk_size > 0:
                         user = dao.get_project_by_id(burst_entity.fk_project).administrator
                         user.used_disk_space = user.used_disk_space + disk_size
                         dao.store_entity(user)
             else:
                 operation = dao.get_operation_by_id(last_executed_op_id)
                 disk_size = dao.get_disk_size_for_operation(operation.id)  # Transform from kB to MB
                 if disk_size > 0:
                     user = dao.get_user_by_id(operation.fk_launched_by)
                     user.used_disk_space = user.used_disk_space + disk_size
                     dao.store_entity(user)
         return None
     except Exception, excep:
         self.logger.error(excep)
         self.logger.exception(excep)
         raise WorkflowInterStepsException(excep)

    def export_burst(self, burst_id):
        """
        :param burst_id: ID for existing burst
        :return: JSON of burst representation.
        """
        burst = dao.get_burst_by_id(burst_id)
        if burst is None:
            raise InvalidExportDataException("Could not find burst with ID " + str(burst_id))

        burst_info = self._build_burst_export_dict(burst)
        return json.dumps(burst_info)

 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
     launch_params['simulation_length'] = '10000'
     if is_range:
         launch_params['conduction_speed'] = '[10,15,20]'
         launch_params[model.RANGE_PARAMETER_1] = 'conduction_speed'
     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)