def test_more_complex_archive(self): archive_filename = os.path.join(os.path.dirname(__file__), 'fixtures', 'BIOMD0000000297.edited.omex') config = get_config() config.REPORT_FORMATS = [report_data_model.ReportFormat.h5, report_data_model.ReportFormat.csv] config.VIZ_FORMATS = [] config.BUNDLE_OUTPUTS = True config.KEEP_INDIVIDUAL_OUTPUTS = True _, log = core.exec_sedml_docs_in_combine_archive(archive_filename, self.dirname, config=config) if log.exception: raise log.exception self.assertEqual(set(['reports.zip', 'reports.h5', 'ex1', 'ex2']).difference(set(os.listdir(self.dirname))), set()) self.assertEqual(set(os.listdir(os.path.join(self.dirname, 'ex1'))), set(['BIOMD0000000297.sedml'])) self.assertEqual(set(os.listdir(os.path.join(self.dirname, 'ex2'))), set(['BIOMD0000000297.sedml'])) self.assertEqual(set(os.listdir(os.path.join(self.dirname, 'ex1', 'BIOMD0000000297.sedml'))), set(['two_species.csv', 'three_species.csv'])) self.assertEqual(set(os.listdir(os.path.join(self.dirname, 'ex2', 'BIOMD0000000297.sedml'))), set(['one_species.csv', 'four_species.csv'])) archive = ArchiveReader().run(os.path.join(self.dirname, 'reports.zip')) self.assertEqual( sorted(file.archive_path for file in archive.files), sorted([ 'ex1/BIOMD0000000297.sedml/two_species.csv', 'ex1/BIOMD0000000297.sedml/three_species.csv', 'ex2/BIOMD0000000297.sedml/one_species.csv', 'ex2/BIOMD0000000297.sedml/four_species.csv', ]), ) report = sedml_data_model.Report( data_sets=[ sedml_data_model.DataSet(id='data_set_time_two_species', label='time'), sedml_data_model.DataSet(id='data_set_Cln4', label='Cln4'), sedml_data_model.DataSet(id='data_set_Swe13', label='Swe13'), ] ) report_results = ReportReader().run(report, self.dirname, 'ex1/BIOMD0000000297.sedml/two_species', format=report_data_model.ReportFormat.h5) self.assertEqual(sorted(report_results.keys()), sorted(['data_set_time_two_species', 'data_set_Cln4', 'data_set_Swe13'])) numpy.testing.assert_allclose(report_results['data_set_time_two_species'], numpy.linspace(0., 1., 10 + 1))
def assert_outputs_created(self, dirname): self.assertTrue(os.path.isfile(os.path.join(dirname, 'reports.h5'))) report = sedml_data_model.Report(data_sets=[ sedml_data_model.DataSet(id='time', label='time'), ]) report_results = ReportReader().run( report, dirname, 'simulation_1.sedml/simulation_1', format=report_data_model.ReportFormat.h5) self.assertEqual(len(report_results[report.data_sets[0].id]), 100 + 1) numpy.testing.assert_almost_equal( report_results[report.data_sets[0].id], numpy.linspace(0., 100., 100 + 1), ) for data_set_result in report_results.values(): self.assertFalse(numpy.any(numpy.isnan(data_set_result)))
def _build_sed_doc(self, algorithm=None): if algorithm is None: algorithm = sedml_data_model.Algorithm( kisao_id='KISAO_0000019', changes=[ sedml_data_model.AlgorithmParameterChange( kisao_id='KISAO_0000211', new_value='2e-8', ), ], ) doc = sedml_data_model.SedDocument() doc.models.append( sedml_data_model.Model( id='model_1', source='model_1.bngl', language=sedml_data_model.ModelLanguage.BNGL.value, changes=[ sedml_data_model.ModelAttributeChange( target='functions.gfunc.expression', new_value='0.5*Atot^2/(10 + Atot^2)'), sedml_data_model.ModelAttributeChange( target='functions.gfunc().expression', new_value='0.5*Atot^2/(10 + Atot^2)'), sedml_data_model.ModelAttributeChange( target='species.A().initialCount', new_value='4'), sedml_data_model.ModelAttributeChange( target='parameters.g1.value', new_value='18.0'), ], )) doc.simulations.append( sedml_data_model.UniformTimeCourseSimulation( id='sim_1_time_course', algorithm=algorithm, initial_time=0., output_start_time=0.1, output_end_time=0.2, number_of_points=20, )) doc.tasks.append( sedml_data_model.Task( id='task_1', model=doc.models[0], simulation=doc.simulations[0], )) doc.data_generators.append( sedml_data_model.DataGenerator( id='data_gen_time', variables=[ sedml_data_model.Variable( id='var_time', symbol=sedml_data_model.Symbol.time, task=doc.tasks[0], ), ], math='var_time', )) doc.data_generators.append( sedml_data_model.DataGenerator( id='data_gen_A', variables=[ sedml_data_model.Variable( id='var_A', target="species.A", task=doc.tasks[0], ), ], math='var_A', )) doc.data_generators.append( sedml_data_model.DataGenerator( id='data_gen_B', variables=[ sedml_data_model.Variable( id='var_B', target='species.B.count', task=doc.tasks[0], ), ], math='var_B', )) doc.data_generators.append( sedml_data_model.DataGenerator( id='data_gen_GeneA_00', variables=[ sedml_data_model.Variable( id='var_GeneA_00', target="molecules.GeneA_00()", task=doc.tasks[0], ), ], math='var_GeneA_00', )) doc.data_generators.append( sedml_data_model.DataGenerator( id='data_gen_GeneA_01', variables=[ sedml_data_model.Variable( id='var_GeneA_01', target="molecules.GeneA_01()", task=doc.tasks[0], ), ], math='var_GeneA_01', )) doc.outputs.append( sedml_data_model.Report( id='report_1', data_sets=[ sedml_data_model.DataSet( id='data_set_time', label='Time', data_generator=doc.data_generators[0]), sedml_data_model.DataSet( id='data_set_A', label='A', data_generator=doc.data_generators[1]), sedml_data_model.DataSet( id='data_set_B', label='B', data_generator=doc.data_generators[2]), sedml_data_model.DataSet( id='data_set_GeneA_00', label='GeneA_00', data_generator=doc.data_generators[3]), sedml_data_model.DataSet( id='data_set_GeneA_01', label='GeneA_01', data_generator=doc.data_generators[4]), ], )) append_all_nested_children_to_doc(doc) return doc
def test_init_combine_archive_log(self): archive = combine_data_model.CombineArchive(contents=[ combine_data_model.CombineArchiveContent( location='./exp_2.sedml', format=combine_data_model.CombineArchiveContentFormat.SED_ML, master=False), combine_data_model.CombineArchiveContent( location='./exp_1.sedml', format=combine_data_model.CombineArchiveContentFormat.SED_ML, master=False), ]) exp_1 = sedml_data_model.SedDocument() model_1 = sedml_data_model.Model( id='model_1', language=sedml_data_model.ModelLanguage.SBML.value, source='./model.xml') exp_1.models.append(model_1) sim_1 = sedml_data_model.SteadyStateSimulation( id='sim_1', algorithm=sedml_data_model.Algorithm(kisao_id='KISAO_0000019')) exp_1.simulations.append(sim_1) task_1 = sedml_data_model.Task(id='task_1', model=model_1, simulation=sim_1) task_2 = sedml_data_model.Task(id='task_2', model=model_1, simulation=sim_1) exp_1.tasks.append(task_1) exp_1.tasks.append(task_2) data_gen_1 = sedml_data_model.DataGenerator( id='data_gen_1', math='param_1', parameters=[sedml_data_model.Parameter(id='param_1', value=1.)]) data_gen_2 = sedml_data_model.DataGenerator( id='data_gen_2', math='param_2', parameters=[sedml_data_model.Parameter(id='param_2', value=2.)]) exp_1.data_generators.append(data_gen_1) exp_1.data_generators.append(data_gen_2) exp_1.outputs.append( sedml_data_model.Report( id='report_1', data_sets=[ sedml_data_model.DataSet(id='data_set_1', label='data_set_1', data_generator=data_gen_1), sedml_data_model.DataSet(id='data_set_2', label='data_set_2', data_generator=data_gen_2), ])) exp_1.outputs.append( sedml_data_model.Plot2D( id='plot_2', curves=[ sedml_data_model.Curve( id='curve_1', x_data_generator=data_gen_1, y_data_generator=data_gen_1, x_scale=sedml_data_model.AxisScale.log, y_scale=sedml_data_model.AxisScale.log), sedml_data_model.Curve( id='curve_2', x_data_generator=data_gen_2, y_data_generator=data_gen_2, x_scale=sedml_data_model.AxisScale.log, y_scale=sedml_data_model.AxisScale.log), ])) SedmlSimulationWriter().run(exp_1, os.path.join(self.dirname, 'exp_1.sedml'), validate_models_with_languages=False) exp_2 = sedml_data_model.SedDocument() model_2 = sedml_data_model.Model( id='model_2', language=sedml_data_model.ModelLanguage.SBML.value, source='./model.xml') exp_2.models.append(model_2) sim_2 = sedml_data_model.SteadyStateSimulation( id='sim_2', algorithm=sedml_data_model.Algorithm(kisao_id='KISAO_0000019')) exp_2.simulations.append(sim_2) task_3 = sedml_data_model.Task(id='task_3', model=model_2, simulation=sim_2) exp_2.tasks.append(task_3) data_gen_3 = sedml_data_model.DataGenerator( id='data_gen_3', math='param_3', parameters=[sedml_data_model.Parameter(id='param_3', value=1.)]) data_gen_4 = sedml_data_model.DataGenerator( id='data_gen_4', math='param_4', parameters=[sedml_data_model.Parameter(id='param_4', value=2.)]) exp_2.data_generators.append(data_gen_3) exp_2.data_generators.append(data_gen_4) exp_2.outputs.append(sedml_data_model.Report(id='report_3')) exp_2.outputs.append(sedml_data_model.Plot2D(id='plot_4')) exp_2.outputs.append( sedml_data_model.Plot3D( id='plot_5', surfaces=[ sedml_data_model.Surface( id='surface_1', x_data_generator=data_gen_3, y_data_generator=data_gen_3, z_data_generator=data_gen_4, x_scale=sedml_data_model.AxisScale.log, y_scale=sedml_data_model.AxisScale.log, z_scale=sedml_data_model.AxisScale.log), ])) SedmlSimulationWriter().run(exp_2, os.path.join(self.dirname, 'exp_2.sedml'), validate_semantics=False, validate_models_with_languages=False) status = utils.init_combine_archive_log( archive, self.dirname, logged_features=( sedml_data_model.SedDocument, sedml_data_model.Task, sedml_data_model.Report, sedml_data_model.Plot2D, sedml_data_model.Plot3D, sedml_data_model.DataSet, sedml_data_model.Curve, sedml_data_model.Surface, ), supported_features=( sedml_data_model.SedDocument, sedml_data_model.Task, sedml_data_model.Report, sedml_data_model.DataSet, ), ) expected = { 'status': 'QUEUED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'sedDocuments': [ { 'location': 'exp_2.sedml', 'status': 'QUEUED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'tasks': [ { 'id': 'task_3', 'status': 'QUEUED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'algorithm': None, 'simulatorDetails': None, }, ], 'outputs': [ { 'id': 'report_3', 'status': 'QUEUED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'dataSets': [] }, { 'id': 'plot_4', 'status': 'SKIPPED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'curves': [] }, { 'id': 'plot_5', 'status': 'SKIPPED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'surfaces': [ { 'id': 'surface_1', 'status': 'SKIPPED' }, ] }, ], }, { 'location': 'exp_1.sedml', 'status': 'QUEUED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'tasks': [ { 'id': 'task_1', 'status': 'QUEUED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'algorithm': None, 'simulatorDetails': None }, { 'id': 'task_2', 'status': 'QUEUED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'algorithm': None, 'simulatorDetails': None }, ], 'outputs': [ { 'id': 'report_1', 'status': 'QUEUED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'dataSets': [ { 'id': 'data_set_1', 'status': 'QUEUED' }, { 'id': 'data_set_2', 'status': 'QUEUED' }, ] }, { 'id': 'plot_2', 'status': 'SKIPPED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'curves': [ { 'id': 'curve_1', 'status': 'SKIPPED' }, { 'id': 'curve_2', 'status': 'SKIPPED' }, ] }, ], }, ], } self.assertEqual(status.to_json()['sedDocuments'][1]['outputs'][1], expected['sedDocuments'][1]['outputs'][1]) self.assertEqual(status.sed_documents['exp_1.sedml'].parent, status) self.assertEqual( status.sed_documents['exp_1.sedml'].tasks['task_1'].parent, status.sed_documents['exp_1.sedml']) self.assertEqual( status.sed_documents['exp_1.sedml'].outputs['report_1'].parent, status.sed_documents['exp_1.sedml']) status = utils.init_combine_archive_log(archive, self.dirname) for doc in status.sed_documents.values(): doc.status = data_model.Status.QUEUED for task in doc.tasks.values(): task.status = data_model.Status.QUEUED for output in doc.outputs.values(): output.status = data_model.Status.QUEUED if isinstance(output, data_model.ReportLog): els = output.data_sets elif isinstance(output, data_model.Plot2DLog): els = output.curves elif isinstance(output, data_model.Plot3DLog): els = output.surfaces else: raise ValueError(output.__class__) for id in els.keys(): els[id] = data_model.Status.QUEUED status.finalize() self.assertEqual( status.to_json(), { 'status': 'SKIPPED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'sedDocuments': [ { 'location': 'exp_2.sedml', 'status': 'SKIPPED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'tasks': [ { 'id': 'task_3', 'status': 'SKIPPED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'algorithm': None, 'simulatorDetails': None }, ], 'outputs': [ { 'id': 'report_3', 'status': 'SKIPPED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'dataSets': [] }, { 'id': 'plot_4', 'status': 'SKIPPED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'curves': [] }, { 'id': 'plot_5', 'status': 'SKIPPED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'surfaces': [ { 'id': 'surface_1', 'status': 'SKIPPED' }, ] }, ], }, { 'location': 'exp_1.sedml', 'status': 'SKIPPED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'tasks': [ { 'id': 'task_1', 'status': 'SKIPPED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'algorithm': None, 'simulatorDetails': None }, { 'id': 'task_2', 'status': 'SKIPPED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'algorithm': None, 'simulatorDetails': None }, ], 'outputs': [ { 'id': 'report_1', 'status': 'SKIPPED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'dataSets': [ { 'id': 'data_set_1', 'status': 'SKIPPED' }, { 'id': 'data_set_2', 'status': 'SKIPPED' }, ] }, { 'id': 'plot_2', 'status': 'SKIPPED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'curves': [ { 'id': 'curve_1', 'status': 'SKIPPED' }, { 'id': 'curve_2', 'status': 'SKIPPED' }, ] }, ], }, ], }) status = utils.init_combine_archive_log(archive, self.dirname) status.status = data_model.Status.RUNNING for doc in status.sed_documents.values(): doc.status = data_model.Status.RUNNING for task in doc.tasks.values(): task.status = data_model.Status.RUNNING for output in doc.outputs.values(): output.status = data_model.Status.RUNNING if isinstance(output, data_model.ReportLog): els = output.data_sets elif isinstance(output, data_model.Plot2DLog): els = output.curves elif isinstance(output, data_model.Plot3DLog): els = output.surfaces else: raise ValueError(output.__class__) for id in els.keys(): els[id] = data_model.Status.RUNNING status.finalize() self.assertEqual( status.to_json(), { 'status': 'FAILED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'sedDocuments': [ { 'location': 'exp_2.sedml', 'status': 'FAILED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'tasks': [ { 'id': 'task_3', 'status': 'FAILED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'algorithm': None, 'simulatorDetails': None }, ], 'outputs': [ { 'id': 'report_3', 'status': 'FAILED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'dataSets': [] }, { 'id': 'plot_4', 'status': 'FAILED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'curves': [] }, { 'id': 'plot_5', 'status': 'FAILED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'surfaces': [ { 'id': 'surface_1', 'status': 'FAILED' }, ] }, ], }, { 'location': 'exp_1.sedml', 'status': 'FAILED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'tasks': [ { 'id': 'task_1', 'status': 'FAILED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'algorithm': None, 'simulatorDetails': None }, { 'id': 'task_2', 'status': 'FAILED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'algorithm': None, 'simulatorDetails': None }, ], 'outputs': [ { 'id': 'report_1', 'status': 'FAILED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'dataSets': [ { 'id': 'data_set_1', 'status': 'FAILED' }, { 'id': 'data_set_2', 'status': 'FAILED' }, ] }, { 'id': 'plot_2', 'status': 'FAILED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'curves': [ { 'id': 'curve_1', 'status': 'FAILED' }, { 'id': 'curve_2', 'status': 'FAILED' }, ] }, ], }, ], }) # test logging subsets of possible features -- no data sets, curves, surfaces status = utils.init_combine_archive_log( archive, self.dirname, logged_features=( sedml_data_model.SedDocument, sedml_data_model.Task, sedml_data_model.Report, sedml_data_model.Plot2D, sedml_data_model.Plot3D, ), supported_features=( sedml_data_model.SedDocument, sedml_data_model.Task, sedml_data_model.Report, sedml_data_model.DataSet, ), ) self.assertEqual( status.to_json(), { 'status': 'QUEUED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'sedDocuments': [ { 'location': 'exp_2.sedml', 'status': 'QUEUED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'tasks': [ { 'id': 'task_3', 'status': 'QUEUED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'algorithm': None, 'simulatorDetails': None, }, ], 'outputs': [ { 'id': 'report_3', 'status': 'QUEUED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'dataSets': None }, { 'id': 'plot_4', 'status': 'SKIPPED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'curves': None }, { 'id': 'plot_5', 'status': 'SKIPPED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'surfaces': None }, ], }, { 'location': 'exp_1.sedml', 'status': 'QUEUED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'tasks': [ { 'id': 'task_1', 'status': 'QUEUED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'algorithm': None, 'simulatorDetails': None }, { 'id': 'task_2', 'status': 'QUEUED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'algorithm': None, 'simulatorDetails': None }, ], 'outputs': [ { 'id': 'report_1', 'status': 'QUEUED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'dataSets': None }, { 'id': 'plot_2', 'status': 'SKIPPED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'curves': None }, ], }, ], }) # test logging subsets of possible features -- no plots status = utils.init_combine_archive_log( archive, self.dirname, logged_features=( sedml_data_model.SedDocument, sedml_data_model.Task, sedml_data_model.Report, ), supported_features=( sedml_data_model.SedDocument, sedml_data_model.Task, sedml_data_model.Report, sedml_data_model.DataSet, ), ) self.assertEqual( status.to_json(), { 'status': 'QUEUED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'sedDocuments': [ { 'location': 'exp_2.sedml', 'status': 'QUEUED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'tasks': [ { 'id': 'task_3', 'status': 'QUEUED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'algorithm': None, 'simulatorDetails': None, }, ], 'outputs': [ { 'id': 'report_3', 'status': 'QUEUED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'dataSets': None }, ], }, { 'location': 'exp_1.sedml', 'status': 'QUEUED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'tasks': [ { 'id': 'task_1', 'status': 'QUEUED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'algorithm': None, 'simulatorDetails': None }, { 'id': 'task_2', 'status': 'QUEUED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'algorithm': None, 'simulatorDetails': None }, ], 'outputs': [ { 'id': 'report_1', 'status': 'QUEUED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'dataSets': None }, ], }, ], }) # test logging subsets of possible features -- no outputs status = utils.init_combine_archive_log( archive, self.dirname, logged_features=( sedml_data_model.SedDocument, sedml_data_model.Task, ), supported_features=( sedml_data_model.SedDocument, sedml_data_model.Task, sedml_data_model.Report, sedml_data_model.DataSet, ), ) self.assertEqual( status.to_json(), { 'status': 'QUEUED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'sedDocuments': [ { 'location': 'exp_2.sedml', 'status': 'QUEUED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'tasks': [ { 'id': 'task_3', 'status': 'QUEUED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'algorithm': None, 'simulatorDetails': None, }, ], 'outputs': None, }, { 'location': 'exp_1.sedml', 'status': 'QUEUED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'tasks': [ { 'id': 'task_1', 'status': 'QUEUED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'algorithm': None, 'simulatorDetails': None }, { 'id': 'task_2', 'status': 'QUEUED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'algorithm': None, 'simulatorDetails': None }, ], 'outputs': None, }, ], }) # test logging subsets of possible features -- no tasks or outputs status = utils.init_combine_archive_log( archive, self.dirname, logged_features=(sedml_data_model.SedDocument, ), supported_features=( sedml_data_model.SedDocument, sedml_data_model.Task, sedml_data_model.Report, sedml_data_model.DataSet, ), ) self.assertEqual( status.to_json(), { 'status': 'QUEUED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'sedDocuments': [ { 'location': 'exp_2.sedml', 'status': 'QUEUED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'tasks': None, 'outputs': None, }, { 'location': 'exp_1.sedml', 'status': 'QUEUED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'tasks': None, 'outputs': None, }, ], }) # test logging subsets of possible features -- no SED documents status = utils.init_combine_archive_log( archive, self.dirname, logged_features=(), supported_features=( sedml_data_model.SedDocument, sedml_data_model.Task, sedml_data_model.Report, sedml_data_model.DataSet, ), ) self.assertEqual( status.to_json(), { 'status': 'QUEUED', 'exception': None, 'skipReason': None, 'output': None, 'duration': None, 'sedDocuments': None, })
def test(self): model = data_model.Model( id='model1', name='Model1', source='model.sbml', language='urn:sedml:language:sbml', changes=[ data_model.ModelAttributeChange(target='/sbml:sbml/sbml:model[id=\'a\']/@id', new_value='234'), data_model.ModelAttributeChange(target='/sbml:sbml/sbml:model[id=\'b\']/@id', new_value='432'), data_model.AddElementModelChange(target='/sbml:sbml/sbml:model[id=\'b\']/@id', new_elements='432'), data_model.ReplaceElementModelChange(target='/sbml:sbml/sbml:model[id=\'b\']/@id', new_elements='432'), data_model.RemoveElementModelChange(target='/sbml:sbml/sbml:model[id=\'b\']/@id'), data_model.ComputeModelChange( target='/sbml:sbml/sbml:model[id=\'b\']/@id', parameters=[ data_model.Parameter(id='p_1', value=1.5), data_model.Parameter(id='p_2', value=2.5), ], variables=[ data_model.Variable( id='v_1', model=None, target="/sbml:sbml/sbml:model/sbml:listOfParameters/sbml:parameter[@id='param_1']", ), data_model.Variable( id='v_1', model=None, target="/sbml:sbml/sbml:model/sbml:listOfParameters/sbml:parameter[@id='param_1']", ), ], math='p_1 * v_1 + p_2 * v_2', ), ], ) for var in model.changes[-1].variables: var.model = model ss_simulation = data_model.SteadyStateSimulation( id='simulation1', name='Simulation1', algorithm=data_model.Algorithm( kisao_id='KISAO_0000029', changes=[ data_model.AlgorithmParameterChange(kisao_id='KISAO_0000001', new_value='1.234'), ]), ) one_step_simulation = data_model.OneStepSimulation( id='simulation1', name='Simulation1', algorithm=data_model.Algorithm( kisao_id='KISAO_0000029', changes=[ data_model.AlgorithmParameterChange(kisao_id='KISAO_0000001', new_value='1.234'), ]), step=10.) time_course_simulation = data_model.UniformTimeCourseSimulation( id='simulation1', name='Simulation1', algorithm=data_model.Algorithm( kisao_id='KISAO_0000029', changes=[ data_model.AlgorithmParameterChange(kisao_id='KISAO_0000001', new_value='1.234'), data_model.AlgorithmParameterChange(kisao_id='KISAO_0000002', new_value='4.321'), ]), initial_time=10., output_start_time=20., output_end_time=30, number_of_steps=10) task = data_model.Task(id='task1', name='Task1', model=model, simulation=time_course_simulation) for var in model.changes[-1].variables: var.task = task report = data_model.Report( id='report1', name='Report1', data_sets=[ data_model.DataSet( id='dataset1', name='Dataset1', label='Dataset-1', data_generator=data_model.DataGenerator( id='dataGen1', name='DataGen1', variables=[ data_model.Variable( id='DataGenVar1', name='DataGenVar1', target='/sbml:sbml/sbml:model/@id', task=task, model=model) ], parameters=[ data_model.Parameter( id='DataGenParam1', name='DataGenParam1', value=2.) ], math='DataGenVar1 - DataGenParam1', ) ), ], ) plot2d = data_model.Plot2D( name='Plot2D', curves=[ data_model.Curve( id='curve1', name='Curve1', x_scale=data_model.AxisScale.linear, y_scale=data_model.AxisScale.log, x_data_generator=data_model.DataGenerator( id='xDataGen1', name='XDataGen1', variables=[ data_model.Variable( id='xDataGenVar1', name='XDataGenVar1', target='/sbml:sbml/sbml:model/@id', task=task, model=model) ], parameters=[ data_model.Parameter( id='xDataGenParam1', name='XDataGenParam1', value=2.) ], math='xDataGenVar1 * xDataGenParam1', ), y_data_generator=None, ), data_model.Curve( id='curve2', name='Curve2', x_scale=data_model.AxisScale.linear, y_scale=data_model.AxisScale.log, x_data_generator=None, y_data_generator=data_model.DataGenerator( id='yDataGen1', name='yDataGen1', variables=[ data_model.Variable( id='yDataGenVar1', name='YDataGenVar1', target='/sbml:sbml/sbml:model/@id', task=task, model=model) ], parameters=[ data_model.Parameter( id='yDataGenParam1', name='YDataGenParam1', value=2.) ], math='yDataGenParam1 + YDataGenParam1', ), ), ] ) plot3d = data_model.Plot3D( name='Plot3D', surfaces=[ data_model.Surface( id='curve1', name='Curve1', x_scale=data_model.AxisScale.linear, y_scale=data_model.AxisScale.log, z_scale=data_model.AxisScale.linear, x_data_generator=data_model.DataGenerator( id='xDataGen1', name='XDataGen1', variables=[ data_model.Variable( id='xDataGenVar1', name='XDataGenVar1', target='/sbml:sbml/sbml:model/@id', task=task, model=model) ], parameters=[ data_model.Parameter( id='xDataGenParam1', name='XDataGenParam1', value=2.) ], math='xDataGenVar1 * xDataGenParam1', ), y_data_generator=None, z_data_generator=None, ), data_model.Surface( id='curve2', name='Curve2', x_scale=data_model.AxisScale.linear, y_scale=data_model.AxisScale.log, z_scale=data_model.AxisScale.log, x_data_generator=None, y_data_generator=None, z_data_generator=data_model.DataGenerator( id='zDataGen1', name='zDataGen1', variables=[ data_model.Variable( id='zDataGenVar1', name='ZDataGenVar1', target='/sbml:sbml/sbml:model/@id', task=task, model=model) ], parameters=[ data_model.Parameter( id='zDataGenParam1', name='ZDataGenParam1', value=2.) ], math='zDataGenParam1 / ZDataGenParam1', ), ), ] ) document = data_model.SedDocument( level=1, version=3, models=[model], simulations=[ss_simulation, one_step_simulation, time_course_simulation], tasks=[task], data_generators=[plot2d.curves[0].x_data_generator], outputs=[report, plot2d, plot3d], metadata=Metadata(), ) self.assertEqual( ss_simulation.to_tuple(), ( ss_simulation.id, ss_simulation.name, ( ss_simulation.algorithm.kisao_id, ( ( ss_simulation.algorithm.changes[0].kisao_id, ss_simulation.algorithm.changes[0].new_value, ), ), ), ) ) self.assertEqual( one_step_simulation.to_tuple(), ( one_step_simulation.id, one_step_simulation.name, ( one_step_simulation.algorithm.kisao_id, ( ( one_step_simulation.algorithm.changes[0].kisao_id, one_step_simulation.algorithm.changes[0].new_value, ), ), ), one_step_simulation.step ) ) self.assertEqual(report.to_tuple()[0], report.id) self.assertEqual(plot2d.to_tuple()[2][0][4], plot2d.curves[0].x_data_generator.id) self.assertEqual(document.to_tuple(), ( document.level, document.version, (model.to_tuple(),), tuple(none_sorted((ss_simulation.to_tuple(), one_step_simulation.to_tuple(), time_course_simulation.to_tuple()))), (task.to_tuple(),), (plot2d.curves[0].x_data_generator.to_tuple(),), tuple(none_sorted((report.to_tuple(), plot2d.to_tuple(), plot3d.to_tuple()))), document.metadata.to_tuple(), )) change = model.changes[0] change2 = copy.deepcopy(change) self.assertTrue(change.is_equal(change2)) change2.target = None self.assertFalse(change.is_equal(change2)) change = model.changes[-1] change2 = copy.deepcopy(change) self.assertTrue(change.is_equal(change2)) change2 = copy.deepcopy(change) change2.target = None self.assertFalse(change.is_equal(change2)) change2 = copy.deepcopy(change) change2.parameters[0].value = -1 self.assertFalse(change.is_equal(change2)) change2 = copy.deepcopy(change) change2.variables[0].id = 'variable_1' self.assertFalse(change.is_equal(change2)) change2 = copy.deepcopy(change) change2.variables[0].model.id = 'different_model' self.assertFalse(change.is_equal(change2)) change2 = copy.deepcopy(change) change2.math = 'x * y' self.assertFalse(change.is_equal(change2)) model2 = copy.deepcopy(model) self.assertTrue(model.is_equal(model2)) model2.id = None self.assertFalse(model.is_equal(model2)) alg_change = time_course_simulation.algorithm.changes[0] alg_change_2 = copy.deepcopy(alg_change) self.assertTrue(alg_change.is_equal(alg_change_2)) alg_change_2.new_value = None self.assertFalse(alg_change.is_equal(alg_change_2)) alg = time_course_simulation.algorithm alg2 = copy.deepcopy(alg) self.assertTrue(alg.is_equal(alg2)) alg2.kisao_id = None self.assertFalse(alg.is_equal(alg2)) one_step_simulation_2 = copy.deepcopy(one_step_simulation) self.assertTrue(one_step_simulation.is_equal(one_step_simulation_2)) one_step_simulation_2.step = -1. self.assertFalse(one_step_simulation.is_equal(one_step_simulation_2)) time_course_simulation_2 = copy.deepcopy(time_course_simulation) self.assertTrue(time_course_simulation.is_equal(time_course_simulation_2)) time_course_simulation_2.initial_time = -1. self.assertFalse(time_course_simulation.is_equal(time_course_simulation_2)) task2 = copy.deepcopy(task) self.assertTrue(task.is_equal(task2)) task2.id = None self.assertFalse(task.is_equal(task2)) var = copy.deepcopy(report.data_sets[0].data_generator.variables[0]) var2 = copy.deepcopy(var) self.assertTrue(var.is_equal(var2)) var2.target = None self.assertFalse(var.is_equal(var2)) data_generator = copy.deepcopy(report.data_sets[0].data_generator) data_generator_2 = copy.deepcopy(data_generator) self.assertTrue(data_generator.is_equal(data_generator_2)) data_generator_2.variables = [] self.assertFalse(data_generator.is_equal(data_generator_2)) data_set = copy.deepcopy(report.data_sets[0]) data_set_2 = copy.deepcopy(data_set) self.assertTrue(data_set.is_equal(data_set_2)) data_set_2.label = None self.assertFalse(data_set.is_equal(data_set_2)) report2 = copy.deepcopy(report) self.assertTrue(report.is_equal(report2)) report2.data_sets = [] self.assertFalse(report.is_equal(report2)) curve = copy.deepcopy(plot2d.curves[0]) curve2 = copy.deepcopy(curve) self.assertTrue(curve.is_equal(curve2)) curve2.x_scale = None self.assertFalse(curve.is_equal(curve2)) surface = copy.deepcopy(plot3d.surfaces[1]) surface2 = copy.deepcopy(surface) self.assertTrue(surface.is_equal(surface2)) surface2.z_data_generator = None self.assertFalse(surface.is_equal(surface2)) plot2d_2 = copy.deepcopy(plot2d) self.assertTrue(plot2d.is_equal(plot2d_2)) plot2d_2.curves = [] self.assertFalse(plot2d.is_equal(plot2d_2)) plot3d_2 = copy.deepcopy(plot3d) self.assertTrue(plot3d.is_equal(plot3d_2)) plot3d_2.name = None self.assertFalse(plot3d.is_equal(plot3d_2)) document_2 = copy.deepcopy(document) self.assertTrue(document.is_equal(document_2)) document_2.models = [] self.assertFalse(document.is_equal(document_2))
def _build_sed_doc(self, algorithm=None): if algorithm is None: algorithm = sedml_data_model.Algorithm( kisao_id='KISAO_0000088', changes=[ sedml_data_model.AlgorithmParameterChange( kisao_id='KISAO_0000211', new_value='1e-10', ), ], ) doc = sedml_data_model.SedDocument() doc.models.append(sedml_data_model.Model( id='model_1', source='model_1.xml', language=sedml_data_model.ModelLanguage.SBML.value, changes=[], )) doc.simulations.append(sedml_data_model.UniformTimeCourseSimulation( id='sim_1_time_course', algorithm=algorithm, initial_time=0., output_start_time=0.1, output_end_time=0.2, number_of_points=20, )) doc.tasks.append(sedml_data_model.Task( id='task_1', model=doc.models[0], simulation=doc.simulations[0], )) doc.data_generators.append(sedml_data_model.DataGenerator( id='data_gen_time', variables=[ sedml_data_model.Variable( id='var_time', symbol=sedml_data_model.Symbol.time, task=doc.tasks[0], ), ], math='var_time', )) doc.data_generators.append(sedml_data_model.DataGenerator( id='data_gen_BE', variables=[ sedml_data_model.Variable( id='var_BE', target="/sbml:sbml/sbml:model/sbml:listOfSpecies/sbml:species[@id='BE']", target_namespaces=self.NAMESPACES, task=doc.tasks[0], ), ], math='var_BE', )) doc.data_generators.append(sedml_data_model.DataGenerator( id='data_gen_Cdh1', variables=[ sedml_data_model.Variable( id='var_Cdh1', target='/sbml:sbml/sbml:model/sbml:listOfSpecies/sbml:species[@id="Cdh1"]', target_namespaces=self.NAMESPACES, task=doc.tasks[0], ), ], math='var_Cdh1', )) doc.data_generators.append(sedml_data_model.DataGenerator( id='data_gen_Cdc20', variables=[ sedml_data_model.Variable( id='var_Cdc20', target="/sbml:sbml/sbml:model/sbml:listOfSpecies/sbml:species[@id='Cdc20']", target_namespaces=self.NAMESPACES, task=doc.tasks[0], ), ], math='var_Cdc20', )) doc.outputs.append(sedml_data_model.Report( id='report_1', data_sets=[ sedml_data_model.DataSet(id='data_set_time', label='Time', data_generator=doc.data_generators[0]), sedml_data_model.DataSet(id='data_set_BE', label='BE', data_generator=doc.data_generators[1]), sedml_data_model.DataSet(id='data_set_Cdh1', label='Cdh1', data_generator=doc.data_generators[2]), sedml_data_model.DataSet(id='data_set_Cdc20', label='Cdc20', data_generator=doc.data_generators[3]), ], )) append_all_nested_children_to_doc(doc) return doc
def test_write_read(self): shutil.copy( os.path.join(os.path.dirname(__file__), '..', 'fixtures', 'BIOMD0000000075.xml'), os.path.join(self.tmp_dir, 'model.sbml')) model1 = data_model.Model( id='model1', name='Model1', source='model.sbml', language='urn:sedml:language:sbml', changes=[ data_model.ModelAttributeChange( target='/sbml:sbml/sbml:model[id=\'a\']/@id', new_value='234'), data_model.ModelAttributeChange( target='/sbml:sbml/sbml:model[id=\'b\']/@id', new_value='432'), data_model.AddElementModelChange( target= '/sbml:sbml/sbml:model[id=\'b\']/sbml:listOfParameters', new_elements= '<sbml:parameter xmlns:sbml="http://www.sbml.org/sbml/level2/version3" id="new_parameter" value="1.0"/>' ), data_model.AddElementModelChange( target= '/sbml:sbml/sbml:model[id=\'b\']/sbml:listOfParameters', new_elements= '<parameter id="new_parameter_1" value="1.0"/><parameter id="new_parameter_2" value="1.0"/>' ), data_model.ReplaceElementModelChange( target= '/sbml:sbml/sbml:model[id=\'b\']/sbml:listOfParameters/sbml:parameter[@id=\'p1\']', new_elements='<parameter id="p1" value="1.0"/>'), data_model.ReplaceElementModelChange( target= '/sbml:sbml/sbml:model[id=\'b\']/sbml:listOfParameters/sbml:parameter[@id=\'p1\']', new_elements= '<parameter id="p1" value="1.0"/><parameter id="p1" value="1.0"/>' ), data_model.RemoveElementModelChange( target= '/sbml:sbml/sbml:model[id=\'b\']/sbml:listOfParameters/sbml:parameter[@id=\'p1\']', ), data_model.ComputeModelChange( target= '/sbml:sbml/sbml:model[id=\'b\']/sbml:listOfParameters/sbml:parameter[@id=\'p1\']', parameters=[ data_model.Parameter(id='a', value=1.5), data_model.Parameter(id='b', value=2.25), ], variables=[ data_model.Variable( id='x', target='variable_target_x', ), data_model.Variable( id='y', target='variable_target_y', ), ], math='a * x + b * y', ), ], ) for var in model1.changes[-1].variables: var.model = model1 model2 = data_model.Model( id='model2', name='Model2', source='model.sbml', language='urn:sedml:language:sbml', changes=[ data_model.ModelAttributeChange( target='/sbml:sbml/sbml:model[id=\'a\']/@id', new_value='234', ), data_model.ModelAttributeChange( target='/sbml:sbml/sbml:model[id=\'b\']/@id', new_value='432'), ], ) ss_simulation = data_model.SteadyStateSimulation( id='simulation1', name='Simulation1', algorithm=data_model.Algorithm( kisao_id='KISAO_0000019', changes=[ data_model.AlgorithmParameterChange( kisao_id='KISAO_0000211', new_value='1.234'), ]), ) one_step_simulation = data_model.OneStepSimulation( id='simulation2', name='Simulation2', algorithm=data_model.Algorithm( kisao_id='KISAO_0000019', changes=[ data_model.AlgorithmParameterChange( kisao_id='KISAO_0000211', new_value='1.234'), ]), step=10.) time_course_simulation = data_model.UniformTimeCourseSimulation( id='simulation3', name='Simulation3', algorithm=data_model.Algorithm( kisao_id='KISAO_0000019', changes=[ data_model.AlgorithmParameterChange( kisao_id='KISAO_0000209', new_value='1.234'), data_model.AlgorithmParameterChange( kisao_id='KISAO_0000211', new_value='4.321'), ]), initial_time=10., output_start_time=20., output_end_time=30, number_of_steps=10) task1 = data_model.Task(id='task1', name='Task1', model=model1, simulation=time_course_simulation) task2 = data_model.Task(id='task2', name='Task2', model=model2, simulation=time_course_simulation) task3 = data_model.RepeatedTask( id='task3', name='Task3', reset_model_for_each_iteration=True, changes=[], sub_tasks=[ data_model.SubTask(task=task1, order=1), data_model.SubTask(task=task2, order=2), ], ranges=[ data_model.UniformRange( id='range1', start=10., end=20., number_of_steps=4, type=data_model.UniformRangeType.linear), data_model.UniformRange(id='range2', start=10., end=20., number_of_steps=4, type=data_model.UniformRangeType.log), data_model.VectorRange(id='range3', values=[3., 5., 7., 11., 13.]), ], ) task3.ranges.append( data_model.FunctionalRange( id='range4', range=task3.ranges[0], parameters=[ data_model.Parameter( id='x2', value=2.0, ), ], variables=[ data_model.Variable( id='y2', model=model1, target= "/sbml:sbml/sbml:model/sbml:listOfParameters/sbml:Parameter[@id='param_1']", ), ], math='{} * {} + {}'.format(task3.ranges[0].id, 'x2', 'y2'), ), ) task3.range = task3.ranges[1] task3.changes.append( data_model.SetValueComputeModelChange( model=model1, target= "/sbml:sbml/sbml:model/sbml:listOfParameters/sbml:parameter[@id='p1']", range=task3.ranges[0], parameters=[], variables=[], math='range1', ), ) task3.changes.append( data_model.SetValueComputeModelChange( model=model1, target= "/sbml:sbml/sbml:model/sbml:listOfParameters/sbml:parameter[@id='p1']", range=task3.ranges[0], parameters=[], variables=[ data_model.Variable( id='range1_var1', model=model1, target= "/sbml:sbml/sbml:model/sbml:listOfParameters/sbml:parameter[@id='p1']", ) ], math='range1 * range1_var1', ), ) task3.changes.append( data_model.SetValueComputeModelChange( model=model1, target= "/sbml:sbml/sbml:model/sbml:listOfParameters/sbml:parameter[@id='p1']", range=task3.ranges[0], parameters=[ data_model.Parameter(id='range1_p1', value=2.5), ], variables=[], math='range1 * range1_p1', ), ) report = data_model.Report( id='report1', name='Report1', data_sets=[ data_model.DataSet( id='dataset1', name='Dataset1', label='Dataset-1', data_generator=data_model.DataGenerator( id='dataGen1', name='DataGen1', variables=[ data_model.Variable( id='DataGenVar1', name='DataGenVar1', target='/sbml:sbml/sbml:model/@id', task=task1, ) ], parameters=[ data_model.Parameter(id='DataGenParam1', name='DataGenParam1', value=2.) ], math='DataGenVar1 - DataGenParam1', )), ], ) plot2d = data_model.Plot2D( id='plot2d', name='Plot2D', curves=[ data_model.Curve( id='curve1', name='Curve1', x_scale=data_model.AxisScale.linear, y_scale=data_model.AxisScale.log, x_data_generator=data_model.DataGenerator( id='xDataGen2', name='XDataGen2', variables=[ data_model.Variable(id='xDataGenVar1', name='XDataGenVar1', symbol='urn:sedml:symbol:time', task=task2) ], parameters=[ data_model.Parameter(id='xDataGenParam1', name='XDataGenParam1', value=2.) ], math='xDataGenVar1 * xDataGenParam1', ), y_data_generator=data_model.DataGenerator( id='yDataGen3', name='yDataGen3', variables=[ data_model.Variable( id='xDataGenVar2', name='XDataGenVar1', target='/sbml:sbml/sbml:model/@id', task=task2, ) ], parameters=[ data_model.Parameter(id='xDataGenParam2', name='XDataGenParam1', value=2.) ], math='xDataGenVar2 * xDataGenParam2', ), ), data_model.Curve( id='curve2', name='Curve2', x_scale=data_model.AxisScale.linear, y_scale=data_model.AxisScale.log, x_data_generator=data_model.DataGenerator( id='yDataGen4', name='yDataGen4', variables=[ data_model.Variable( id='yDataGenVar1', target='/sbml:sbml/sbml:model/@id', task=task1, ) ], parameters=[ data_model.Parameter(id='YDataGenParam1', value=2.) ], math='yDataGenVar1 + YDataGenParam1', ), y_data_generator=data_model.DataGenerator( id='yDataGen5', name='yDataGen5', variables=[ data_model.Variable( id='yDataGenVar2', target='/sbml:sbml/sbml:model/@id', task=task1, ) ], parameters=[ data_model.Parameter(id='YDataGenParam2', value=2.), ], math='yDataGenVar2 + YDataGenParam2', ), ), ]) plot3d = data_model.Plot3D( id='plot3d', name='Plot3D', surfaces=[ data_model.Surface( id='surface1', name='Surface1', x_scale=data_model.AxisScale.linear, y_scale=data_model.AxisScale.log, z_scale=data_model.AxisScale.linear, x_data_generator=data_model.DataGenerator( id='xDataGen6', name='XDataGen6', variables=[ data_model.Variable( id='xDataGenVar3', name='XDataGenVar2', target='/sbml:sbml/sbml:model/@id', task=task2, ) ], parameters=[ data_model.Parameter(id='xDataGenParam3', name='XDataGenParam1', value=2.) ], math='xDataGenVar3 * xDataGenParam3', ), y_data_generator=data_model.DataGenerator( id='xDataGen8', name='XDataGen8', variables=[ data_model.Variable( id='xDataGenVar4', name='XDataGenVar2', target='/sbml:sbml/sbml:model/@id', task=task2, ) ], parameters=[ data_model.Parameter(id='xDataGenParam4', name='XDataGenParam1', value=2.) ], math='xDataGenVar4 * xDataGenParam4', ), z_data_generator=data_model.DataGenerator( id='xDataGen9', name='XDataGen9', variables=[ data_model.Variable( id='xDataGenVar5', name='XDataGenVar2', target='/sbml:sbml/sbml:model/@id', task=task2, ) ], parameters=[ data_model.Parameter(id='xDataGenParam5', name='XDataGenParam1', value=2.) ], math='xDataGenVar5 * xDataGenParam5', ), ), data_model.Surface( id='surface2', name='Surface2', x_scale=data_model.AxisScale.linear, y_scale=data_model.AxisScale.log, z_scale=data_model.AxisScale.log, x_data_generator=data_model.DataGenerator( id='xDataGen10', name='XDataGen10', variables=[ data_model.Variable( id='xDataGenVar6', name='XDataGenVar2', target='/sbml:sbml/sbml:model/@id', task=task2, ) ], parameters=[ data_model.Parameter(id='xDataGenParam6', name='XDataGenParam1', value=2.) ], math='xDataGenVar6 * xDataGenParam6', ), y_data_generator=data_model.DataGenerator( id='xDataGen11', name='XDataGen11', variables=[ data_model.Variable( id='xDataGenVar7', name='XDataGenVar2', target='/sbml:sbml/sbml:model/@id', task=task2, ) ], parameters=[ data_model.Parameter(id='xDataGenParam7', name='XDataGenParam1', value=2.) ], math='xDataGenVar7 * xDataGenParam7', ), z_data_generator=data_model.DataGenerator( id='xDataGen12', name='XDataGen12', variables=[ data_model.Variable( id='xDataGenVar8', name='XDataGenVar2', target='/sbml:sbml/sbml:model/@id', task=task2, ) ], parameters=[ data_model.Parameter(id='xDataGenParam8', name='XDataGenParam1', value=2.) ], math='xDataGenVar8 * xDataGenParam8', ), ), ]) now = datetime.datetime(2020, 1, 2, 1, 2, 3, tzinfo=dateutil.tz.tzutc()) document = data_model.SedDocument( level=1, version=3, models=[model1], simulations=[time_course_simulation], tasks=[task1], data_generators=([d.data_generator for d in report.data_sets]), outputs=[report], metadata=Metadata( description="description", tags=['tag-1', 'tag-2'], authors=[ Person(given_name='first', other_name='middle', family_name='last'), ], references=ExternalReferences( identifiers=None, citations=[ Citation(title='creative title', authors='Author-1 & Author-2', journal='major journal', volume='10', issue='20', pages='30-40', year=2020, identifiers=[ Identifier( namespace="doi", id='10.0/1.0', url="https://doi.org/10.0/1.0"), ]) ]), license=OntologyTerm( namespace='SPDX', id='MIT', url='https://spdx.org/licenses/MIT.html', ), created=now, updated=now, ), ) self._set_target_namespaces(document) filename = os.path.join(self.tmp_dir, 'test.xml') io.SedmlSimulationWriter().run(document, filename) document2 = io.SedmlSimulationReader().run(filename) self.assertTrue(document.is_equal(document2)) document = data_model.SedDocument( level=1, version=3, models=[model1, model2], simulations=[ ss_simulation, one_step_simulation, time_course_simulation ], tasks=[task1, task2, task3], data_generators=([d.data_generator for d in report.data_sets] + [c.x_data_generator for c in plot2d.curves] + [c.y_data_generator for c in plot2d.curves] + [s.x_data_generator for s in plot3d.surfaces] + [s.y_data_generator for s in plot3d.surfaces] + [s.z_data_generator for s in plot3d.surfaces]), outputs=[report, plot2d, plot3d], metadata=Metadata( description="description", license=OntologyTerm( namespace='SPDX', id='MIT', url='https://spdx.org/licenses/MIT.html', ), ), ) self._set_target_namespaces(document) filename = os.path.join(self.tmp_dir, 'test.xml') io.SedmlSimulationWriter().run(document, filename) document2 = io.SedmlSimulationReader().run(filename) self.assertTrue(document.is_equal(document2)) document2.metadata.license.namespace = None document2.metadata.license.url = None io.SedmlSimulationWriter().run(document2, filename) document3 = io.SedmlSimulationReader().run(filename) self.assertTrue(document2.is_equal(document3)) document3.metadata = None io.SedmlSimulationWriter().run(document3, filename) document4 = io.SedmlSimulationReader().run(filename) self.assertTrue(document4.is_equal(document3)) document.models[0].changes[ 2].new_elements = '<parameter id="new_parameter" value="1.0/>' document.models[0].changes[ 4].new_elements = '<parameter id="new_parameter" value="1.0"/>' with self.assertRaisesRegex(ValueError, 'invalid XML'): io.SedmlSimulationWriter().run(document, filename) document.models[0].changes[ 2].new_elements = '<parameter id="new_parameter" value="1.0"/>' document.models[0].changes[ 4].new_elements = '<parameter id="new_parameter" value="1.0/>' with self.assertRaisesRegex(ValueError, 'invalid XML'): io.SedmlSimulationWriter().run(document, filename)