# Test tools API. from base import api from operator import itemgetter from .helpers import DatasetPopulator from .helpers import DatasetCollectionPopulator from .helpers import skip_without_tool class ToolsTestCase( api.ApiTestCase ): def setUp( self ): super( ToolsTestCase, self ).setUp( ) self.dataset_populator = DatasetPopulator( self.galaxy_interactor ) self.dataset_collection_populator = DatasetCollectionPopulator( self.galaxy_interactor ) def test_index( self ): tool_ids = self.__tool_ids() assert "upload1" in tool_ids def test_no_panel_index( self ): index = self._get( "tools", data=dict( in_panel="false" ) ) tools_index = index.json() # No need to flatten out sections, with in_panel=False, only tools are # returned. tool_ids = map( itemgetter( "id" ), tools_index ) assert "upload1" in tool_ids @skip_without_tool( "cat1" ) def test_show_repeat( self ): tool_info = self._show_valid_tool( "cat1" ) parameters = tool_info[ "inputs" ] assert len( parameters ) == 2 assert parameters[ 0 ][ "name" ] == "input1" assert parameters[ 1 ][ "name" ] == "queries" repeat_info = parameters[ 1 ] self._assert_has_keys( repeat_info, "min", "max", "title", "help" ) repeat_params = repeat_info[ "inputs" ] assert len( repeat_params ) == 1 assert repeat_params[ 0 ][ "name" ] == "input2" @skip_without_tool( "random_lines1" ) def test_show_conditional( self ): tool_info = self._show_valid_tool( "random_lines1" ) cond_info = tool_info[ "inputs" ][ 2 ] self._assert_has_keys( cond_info, "cases", "test_param" ) self._assert_has_keys( cond_info[ "test_param" ], 'name', 'type', 'label', 'help' ) cases = cond_info[ "cases" ] assert len( cases ) == 2 case1 = cases[ 0 ] self._assert_has_keys( case1, "value", "inputs" ) assert case1[ "value" ] == "no_seed" assert len( case1[ "inputs" ] ) == 0 case2 = cases[ 1 ] self._assert_has_keys( case2, "value", "inputs" ) case2_inputs = case2[ "inputs" ] assert len( case2_inputs ) == 1 self._assert_has_keys( case2_inputs[ 0 ], 'name', 'type', 'label', 'help' ) assert case2_inputs[ 0 ][ "name" ] == "seed" def _show_valid_tool( self, tool_id ): tool_show_response = self._get( "tools/%s" % tool_id, data=dict( io_details=True ) ) self._assert_status_code_is( tool_show_response, 200 ) tool_info = tool_show_response.json() self._assert_has_keys( tool_info, "inputs", "outputs", "panel_section_id" ) return tool_info def test_upload1_paste( self ): history_id = self.dataset_populator.new_history() payload = self.dataset_populator.upload_payload( history_id, 'Hello World' ) create_response = self._post( "tools", data=payload ) self._assert_has_keys( create_response.json(), 'outputs' ) def test_upload_posix_newline_fixes( self ): windows_content = "1\t2\t3\r4\t5\t6\r" posix_content = windows_content.replace("\r", "\n") result_content = self._upload_and_get_content( windows_content ) self.assertEquals( result_content, posix_content ) def test_upload_disable_posix_fix( self ): windows_content = "1\t2\t3\r4\t5\t6\r" result_content = self._upload_and_get_content( windows_content, to_posix_lines=None ) self.assertEquals( result_content, windows_content ) def test_upload_tab_to_space( self ): table = "1 2 3\n4 5 6\n" result_content = self._upload_and_get_content( table, space_to_tab="Yes" ) self.assertEquals( result_content, "1\t2\t3\n4\t5\t6\n" ) def test_upload_tab_to_space_off_by_default( self ): table = "1 2 3\n4 5 6\n" result_content = self._upload_and_get_content( table ) self.assertEquals( result_content, table ) @skip_without_tool( "cat1" ) def test_run_cat1( self ): # Run simple non-upload tool with an input data parameter. history_id = self.dataset_populator.new_history() new_dataset = self.dataset_populator.new_dataset( history_id, content='Cat1Test' ) inputs = dict( input1=dataset_to_param( new_dataset ), ) outputs = self._cat1_outputs( history_id, inputs=inputs ) self.assertEquals( len( outputs ), 1 ) output1 = outputs[ 0 ] output1_content = self.dataset_populator.get_history_dataset_content( history_id, dataset=output1 ) self.assertEqual( output1_content.strip(), "Cat1Test" ) @skip_without_tool( "cat1" ) def test_run_cat1_with_two_inputs( self ): # Run tool with an multiple data parameter and grouping (repeat) history_id = self.dataset_populator.new_history() new_dataset1 = self.dataset_populator.new_dataset( history_id, content='Cat1Test' ) new_dataset2 = self.dataset_populator.new_dataset( history_id, content='Cat2Test' ) inputs = { 'input1': dataset_to_param( new_dataset1 ), 'queries_0|input2': dataset_to_param( new_dataset2 ) } outputs = self._cat1_outputs( history_id, inputs=inputs ) self.assertEquals( len( outputs ), 1 ) output1 = outputs[ 0 ] output1_content = self.dataset_populator.get_history_dataset_content( history_id, dataset=output1 ) self.assertEqual( output1_content.strip(), "Cat1Test\nCat2Test" ) @skip_without_tool( "cat1" ) def test_multirun_cat1( self ): history_id = self.dataset_populator.new_history() new_dataset1 = self.dataset_populator.new_dataset( history_id, content='123' ) new_dataset2 = self.dataset_populator.new_dataset( history_id, content='456' ) inputs = { "input1|__multirun__": [ dataset_to_param( new_dataset1 ), dataset_to_param( new_dataset2 ), ], } outputs = self._cat1_outputs( history_id, inputs=inputs ) self.assertEquals( len( outputs ), 2 ) output1 = outputs[ 0 ] output2 = outputs[ 1 ] output1_content = self.dataset_populator.get_history_dataset_content( history_id, dataset=output1 ) output2_content = self.dataset_populator.get_history_dataset_content( history_id, dataset=output2 ) self.assertEquals( output1_content.strip(), "123" ) self.assertEquals( output2_content.strip(), "456" ) @skip_without_tool( "cat1" ) def test_multirun_in_repeat( self ): history_id = self.dataset_populator.new_history() new_dataset1 = self.dataset_populator.new_dataset( history_id, content='123' ) new_dataset2 = self.dataset_populator.new_dataset( history_id, content='456' ) common_dataset = self.dataset_populator.new_dataset( history_id, content='Common' ) inputs = { "input1": dataset_to_param( common_dataset ), 'queries_0|input2|__multirun__': [ dataset_to_param( new_dataset1 ), dataset_to_param( new_dataset2 ), ], } outputs = self._cat1_outputs( history_id, inputs=inputs ) self.assertEquals( len( outputs ), 2 ) output1 = outputs[ 0 ] output2 = outputs[ 1 ] output1_content = self.dataset_populator.get_history_dataset_content( history_id, dataset=output1 ) output2_content = self.dataset_populator.get_history_dataset_content( history_id, dataset=output2 ) self.assertEquals( output1_content.strip(), "Common\n123" ) self.assertEquals( output2_content.strip(), "Common\n456" ) @skip_without_tool( "cat1" ) def test_multirun_on_multiple_inputs( self ): history_id = self.dataset_populator.new_history() new_dataset1 = self.dataset_populator.new_dataset( history_id, content='123' ) new_dataset2 = self.dataset_populator.new_dataset( history_id, content='456' ) new_dataset3 = self.dataset_populator.new_dataset( history_id, content='789' ) new_dataset4 = self.dataset_populator.new_dataset( history_id, content='0ab' ) inputs = { "input1|__multirun__": [ dataset_to_param( new_dataset1 ), dataset_to_param( new_dataset2 ), ], 'queries_0|input2|__multirun__': [ dataset_to_param( new_dataset3 ), dataset_to_param( new_dataset4 ), ], } outputs = self._cat1_outputs( history_id, inputs=inputs ) self.assertEquals( len( outputs ), 2 ) outputs_contents = [ self.dataset_populator.get_history_dataset_content( history_id, dataset=o ).strip() for o in outputs ] assert "123\n789" in outputs_contents assert "456\n0ab" in outputs_contents # TODO: Once cross production (instead of linking inputs) is an option # again redo test with these checks... # self.assertEquals( len( outputs ), 4 ) # assert "123\n0ab" in outputs_contents # assert "456\n789" in outputs_contents @skip_without_tool( "cat1" ) def test_map_over_collection( self ): history_id = self.dataset_populator.new_history() hdca_id = self.__build_pair( history_id, [ "123", "456" ] ) inputs = { # Such inputs can be simple hdca ids (for GUI) or # {src: "hdca", id: } for API. This tests the # first, next test method tests other. "input1|__collection_multirun__": hdca_id, } create = self._run_cat1( history_id, inputs=inputs, assert_ok=True ) outputs = create[ 'outputs' ] jobs = create[ 'jobs' ] implicit_collections = create[ 'implicit_collections' ] self.assertEquals( len( jobs ), 2 ) self.assertEquals( len( outputs ), 2 ) self.assertEquals( len( implicit_collections ), 1 ) output1 = outputs[ 0 ] output2 = outputs[ 1 ] output1_content = self.dataset_populator.get_history_dataset_content( history_id, dataset=output1 ) output2_content = self.dataset_populator.get_history_dataset_content( history_id, dataset=output2 ) self.assertEquals( output1_content.strip(), "123" ) self.assertEquals( output2_content.strip(), "456" ) @skip_without_tool( "cat1" ) def test_map_over_nested_collections( self ): history_id = self.dataset_populator.new_history() hdca_id = self.__build_nested_list( history_id ) inputs = { "input1|__collection_multirun__": dict( src="hdca", id=hdca_id ), } create = self._run_cat1( history_id, inputs=inputs, assert_ok=True ) outputs = create[ 'outputs' ] jobs = create[ 'jobs' ] implicit_collections = create[ 'implicit_collections' ] self.assertEquals( len( jobs ), 4 ) self.assertEquals( len( outputs ), 4 ) self.assertEquals( len( implicit_collections ), 1 ) implicit_collection = implicit_collections[ 0 ] self._assert_has_keys( implicit_collection, "collection_type", "elements" ) assert implicit_collection[ "collection_type" ] == "list:paired" assert len( implicit_collection[ "elements" ] ) == 2 first_element, second_element = implicit_collection[ "elements" ] assert first_element[ "element_identifier" ] == "test0" assert second_element[ "element_identifier" ] == "test1" first_object = first_element[ "object" ] assert first_object[ "collection_type" ] == "paired" assert len( first_object[ "elements" ] ) == 2 first_object_forward_element = first_object[ "elements" ][ 0 ] self.assertEquals( outputs[ 0 ][ "id" ], first_object_forward_element[ "object" ][ "id" ] ) @skip_without_tool( "cat1" ) def test_map_over_two_collections( self ): history_id = self.dataset_populator.new_history() hdca1_id = self.__build_pair( history_id, [ "123", "456" ] ) hdca2_id = self.__build_pair( history_id, [ "789", "0ab" ] ) inputs = { "input1|__collection_multirun__": hdca1_id, "queries_0|input2|__collection_multirun__": hdca2_id, } outputs = self._cat1_outputs( history_id, inputs=inputs ) self.assertEquals( len( outputs ), 2 ) output1 = outputs[ 0 ] output2 = outputs[ 1 ] output1_content = self.dataset_populator.get_history_dataset_content( history_id, dataset=output1 ) output2_content = self.dataset_populator.get_history_dataset_content( history_id, dataset=output2 ) self.assertEquals( output1_content.strip(), "123\n789" ) self.assertEquals( output2_content.strip(), "456\n0ab" ) @skip_without_tool( "cat1" ) def test_cannot_map_over_incompatible_collections( self ): history_id = self.dataset_populator.new_history() hdca1_id = self.__build_pair( history_id, [ "123", "456" ] ) hdca2_id = self.dataset_collection_populator.create_list_in_history( history_id ).json()[ "id" ] inputs = { "input1|__collection_multirun__": hdca1_id, "queries_0|input2|__collection_multirun__": hdca2_id, } run_response = self._run_cat1( history_id, inputs ) # TODO: Fix this error checking once switch over to new API decorator # on server. assert run_response.status_code >= 400 @skip_without_tool( "multi_data_param" ) def test_reduce_collections( self ): history_id = self.dataset_populator.new_history() hdca1_id = self.__build_pair( history_id, [ "123", "456" ] ) hdca2_id = self.dataset_collection_populator.create_list_in_history( history_id ).json()[ "id" ] inputs = { "f1": "__collection_reduce__|%s" % hdca1_id, "f2": "__collection_reduce__|%s" % hdca2_id, } create = self._run( "multi_data_param", history_id, inputs, assert_ok=True ) outputs = create[ 'outputs' ] jobs = create[ 'jobs' ] assert len( jobs ) == 1 assert len( outputs ) == 2 output1 = outputs[ 0 ] output2 = outputs[ 1 ] output1_content = self.dataset_populator.get_history_dataset_content( history_id, dataset=output1 ) output2_content = self.dataset_populator.get_history_dataset_content( history_id, dataset=output2 ) assert output1_content.strip() == "123\n456" assert len( output2_content.strip().split("\n") ) == 3, output2_content @skip_without_tool( "collection_paired_test" ) def test_subcollection_mapping( self ): history_id = self.dataset_populator.new_history() hdca_list_id = self.__build_nested_list( history_id ) inputs = { "f1|__collection_multirun__": "%s|paired" % hdca_list_id } # Following wait not really needed - just getting so many database # locked errors with sqlite. self.dataset_populator.wait_for_history( history_id, assert_ok=True ) outputs = self._run_and_get_outputs( "collection_paired_test", history_id, inputs ) assert len( outputs ), 2 output1 = outputs[ 0 ] output2 = outputs[ 1 ] output1_content = self.dataset_populator.get_history_dataset_content( history_id, dataset=output1 ) output2_content = self.dataset_populator.get_history_dataset_content( history_id, dataset=output2 ) assert output1_content.strip() == "123\n456", output1_content assert output2_content.strip() == "789\n0ab", output2_content @skip_without_tool( "collection_mixed_param" ) def test_combined_mapping_and_subcollection_mapping( self ): history_id = self.dataset_populator.new_history() nested_list_id = self.__build_nested_list( history_id ) create_response = self.dataset_collection_populator.create_list_in_history( history_id, contents=["xxx", "yyy"] ) list_id = create_response.json()[ "id" ] inputs = { "f1|__collection_multirun__": "%s|paired" % nested_list_id, "f2|__collection_multirun__": list_id, } self.dataset_populator.wait_for_history( history_id, assert_ok=True ) outputs = self._run_and_get_outputs( "collection_mixed_param", history_id, inputs ) assert len( outputs ), 2 output1 = outputs[ 0 ] output2 = outputs[ 1 ] output1_content = self.dataset_populator.get_history_dataset_content( history_id, dataset=output1 ) output2_content = self.dataset_populator.get_history_dataset_content( history_id, dataset=output2 ) assert output1_content.strip() == "123\n456\nxxx", output1_content assert output2_content.strip() == "789\n0ab\nyyy", output2_content def _cat1_outputs( self, history_id, inputs ): return self._run_outputs( self._run_cat1( history_id, inputs ) ) def _run_and_get_outputs( self, tool_id, history_id, inputs ): return self._run_outputs( self._run( tool_id, history_id, inputs ) ) def _run_outputs( self, create_response ): self._assert_status_code_is( create_response, 200 ) return create_response.json()[ 'outputs' ] def _run_cat1( self, history_id, inputs, assert_ok=False ): return self._run( 'cat1', history_id, inputs, assert_ok=assert_ok ) def _run( self, tool_id, history_id, inputs, assert_ok=False ): payload = self.dataset_populator.run_tool_payload( tool_id=tool_id, inputs=inputs, history_id=history_id, ) create_response = self._post( "tools", data=payload ) if assert_ok: self._assert_status_code_is( create_response, 200 ) create = create_response.json() self._assert_has_keys( create, 'outputs' ) return create else: return create_response def _upload_and_get_content( self, content, **upload_kwds ): history_id = self.dataset_populator.new_history() new_dataset = self.dataset_populator.new_dataset( history_id, content=content, **upload_kwds ) self.dataset_populator.wait_for_history( history_id, assert_ok=True ) return self.dataset_populator.get_history_dataset_content( history_id, dataset=new_dataset ) def __tool_ids( self ): index = self._get( "tools" ) tools_index = index.json() # In panels by default, so flatten out sections... tools = [] for tool_or_section in tools_index: if "elems" in tool_or_section: tools.extend( tool_or_section[ "elems" ] ) else: tools.append( tool_or_section ) tool_ids = map( itemgetter( "id" ), tools ) return tool_ids def __build_nested_list( self, history_id ): hdca1_id = self.__build_pair( history_id, [ "123", "456" ] ) hdca2_id = self.__build_pair( history_id, [ "789", "0ab" ] ) response = self.dataset_collection_populator.create_list_from_pairs( history_id, [ hdca1_id, hdca2_id ] ) self._assert_status_code_is( response, 200 ) hdca_list_id = response.json()[ "id" ] return hdca_list_id def __build_pair( self, history_id, contents ): create_response = self.dataset_collection_populator.create_pair_in_history( history_id, contents=contents ) hdca_id = create_response.json()[ "id" ] return hdca_id def dataset_to_param( dataset ): return dict( src='hda', id=dataset[ 'id' ] )