mirror of
https://github.com/galaxyproject/galaxy.git
synced 2026-10-24 12:13:33 +08:00
John Chilton
6c0432980b
The old 'ds_map' parameter remains in place for backward compatibility and with the same behavior. A new parameter 'inputs' can now be specified instead however, and its keys corresponding to the steps 'order_index' instead of the raw unencoded database ids 'ds_map' uses. This variant has the nice property that this map can be constructed without prior knowledge of how Galaxy will assign ids during import - hence it is easier to use and more portable. Additionally, 'inputs' is more flexiable and can revert to the old behavior by specifying a new parameter 'inputs_by' as 'step_id'. 'inputs_by' can also be 'name' - this is even more human friendly because it will assign the ids based on data input names (this is what I intend to use mostly, but I have not made it the default because not all workflows will have data inputs with distinct names). Finally, the new name 'inputs' will be more appropriate once data collection inputs can be explicitly mapped via the API.
494 lines
25 KiB
Python
494 lines
25 KiB
Python
from base import api
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from json import dumps
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from json import loads
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import operator
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import time
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from .helpers import WorkflowPopulator
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from .helpers import DatasetPopulator
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from .helpers import DatasetCollectionPopulator
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from .helpers import skip_without_tool
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from base.interactor import delete_request # requests like delete
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# Workflow API TODO:
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# - Allow history_id as param to workflow run action. (hist_id)
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# - Allow post to workflows/<workflow_id>/run in addition to posting to
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# /workflows with id in payload.
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# - Much more testing obviously, always more testing.
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class WorkflowsApiTestCase( api.ApiTestCase ):
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def setUp( self ):
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super( WorkflowsApiTestCase, self ).setUp()
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self.workflow_populator = WorkflowPopulator( self.galaxy_interactor )
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self.dataset_populator = DatasetPopulator( self.galaxy_interactor )
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self.dataset_collection_populator = DatasetCollectionPopulator( self.galaxy_interactor )
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def test_delete( self ):
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workflow_id = self.workflow_populator.simple_workflow( "test_delete" )
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workflow_name = "test_delete (imported from API)"
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self._assert_user_has_workflow_with_name( workflow_name )
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workflow_url = self._api_url( "workflows/%s" % workflow_id, use_key=True )
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delete_response = delete_request( workflow_url )
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self._assert_status_code_is( delete_response, 200 )
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# Make sure workflow is no longer in index by default.
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assert workflow_name not in self.__workflow_names()
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def test_other_cannot_delete( self ):
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workflow_id = self.workflow_populator.simple_workflow( "test_other_delete" )
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with self._different_user():
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workflow_url = self._api_url( "workflows/%s" % workflow_id, use_key=True )
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delete_response = delete_request( workflow_url )
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self._assert_status_code_is( delete_response, 403 )
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def test_index( self ):
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index_response = self._get( "workflows" )
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self._assert_status_code_is( index_response, 200 )
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assert isinstance( index_response.json(), list )
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def test_import( self ):
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data = dict(
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workflow=dumps( self.workflow_populator.load_workflow( name="test_import" ) ),
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)
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upload_response = self._post( "workflows/upload", data=data )
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self._assert_status_code_is( upload_response, 200 )
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self._assert_user_has_workflow_with_name( "test_import (imported from API)" )
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def test_export( self ):
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uploaded_workflow_id = self.workflow_populator.simple_workflow( "test_for_export" )
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download_response = self._get( "workflows/%s/download" % uploaded_workflow_id )
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self._assert_status_code_is( download_response, 200 )
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downloaded_workflow = download_response.json()
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assert downloaded_workflow[ "name" ] == "test_for_export (imported from API)"
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assert len( downloaded_workflow[ "steps" ] ) == 3
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first_input = downloaded_workflow[ "steps" ][ "0" ][ "inputs" ][ 0 ]
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assert first_input[ "name" ] == "WorkflowInput1"
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@skip_without_tool( "cat1" )
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def test_run_workflow_by_index( self ):
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self.__run_cat_workflow( inputs_by='step_index' )
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@skip_without_tool( "cat1" )
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def test_run_workflow_by_name( self ):
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self.__run_cat_workflow( inputs_by='name' )
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@skip_without_tool( "cat1" )
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def test_run_workflow( self ):
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self.__run_cat_workflow( inputs_by='step_id' )
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def __run_cat_workflow( self, inputs_by ):
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workflow = self.workflow_populator.load_workflow( name="test_for_run" )
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workflow_request, history_id = self._setup_workflow_run( workflow, inputs_by=inputs_by )
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# TODO: This should really be a post to workflows/<workflow_id>/run or
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# something like that.
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run_workflow_response = self._post( "workflows", data=workflow_request )
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self._assert_status_code_is( run_workflow_response, 200 )
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self.dataset_populator.wait_for_history( history_id, assert_ok=True )
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@skip_without_tool( "cat1" )
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def test_extract_from_history( self ):
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history_id = self.dataset_populator.new_history()
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# Run the simple test workflow and extract it back out from history
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cat1_job_id = self.__setup_and_run_cat1_workflow( history_id=history_id )
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contents_response = self._get( "histories/%s/contents" % history_id )
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input_hids = map( lambda c: c[ "hid" ], contents_response.json()[ 0:2 ] )
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downloaded_workflow = self._extract_and_download_workflow(
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from_history_id=history_id,
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dataset_ids=dumps( input_hids ),
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job_ids=dumps( [ cat1_job_id ] ),
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workflow_name="test import from history",
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)
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self.assertEquals( downloaded_workflow[ "name" ], "test import from history" )
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self.__assert_looks_like_cat1_example_workflow( downloaded_workflow )
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def test_extract_with_copied_inputs( self ):
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old_history_id = self.dataset_populator.new_history()
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# Run the simple test workflow and extract it back out from history
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self.__setup_and_run_cat1_workflow( history_id=old_history_id )
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history_id = self.dataset_populator.new_history()
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# Bug cannot mess up hids or these don't extract correctly. See Trello card here:
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# https://trello.com/c/mKzLbM2P
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# # create dummy dataset to complicate hid mapping
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# self.dataset_populator.new_dataset( history_id, content="dummydataset" )
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# offset = 1
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offset = 0
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old_contents = self._get( "histories/%s/contents" % old_history_id ).json()
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for old_dataset in old_contents:
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self.__copy_content_to_history( history_id, old_dataset )
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new_contents = self._get( "histories/%s/contents" % history_id ).json()
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input_hids = map( lambda c: c[ "hid" ], new_contents[ (offset + 0):(offset + 2) ] )
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cat1_job_id = self.__job_id( history_id, new_contents[ (offset + 2) ][ "id" ] )
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downloaded_workflow = self._extract_and_download_workflow(
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from_history_id=history_id,
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dataset_ids=dumps( input_hids ),
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job_ids=dumps( [ cat1_job_id ] ),
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workflow_name="test import from history",
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)
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self.__assert_looks_like_cat1_example_workflow( downloaded_workflow )
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def __assert_looks_like_cat1_example_workflow( self, downloaded_workflow ):
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assert len( downloaded_workflow[ "steps" ] ) == 3
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input_steps = self._get_steps_of_type( downloaded_workflow, "data_input", expected_len=2 )
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tool_step = self._get_steps_of_type( downloaded_workflow, "tool", expected_len=1 )[ 0 ]
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input1 = tool_step[ "input_connections" ][ "input1" ]
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input2 = tool_step[ "input_connections" ][ "queries_0|input2" ]
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print downloaded_workflow
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self.assertEquals( input_steps[ 0 ][ "id" ], input1[ "id" ] )
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self.assertEquals( input_steps[ 1 ][ "id" ], input2[ "id" ] )
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def __setup_and_run_cat1_workflow( self, history_id ):
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workflow = self.workflow_populator.load_workflow( name="test_for_extract" )
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workflow_request, history_id = self._setup_workflow_run( workflow, history_id=history_id )
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run_workflow_response = self._post( "workflows", data=workflow_request )
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self._assert_status_code_is( run_workflow_response, 200 )
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self.dataset_populator.wait_for_history( history_id, assert_ok=True, timeout=10 )
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return self.__cat_job_id( history_id )
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def __cat_job_id( self, history_id ):
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data = dict( history_id=history_id, tool_id="cat1" )
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jobs_response = self._get( "jobs", data=data )
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self._assert_status_code_is( jobs_response, 200 )
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cat1_job_id = jobs_response.json()[ 0 ][ "id" ]
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return cat1_job_id
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def __job_id( self, history_id, dataset_id ):
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url = "histories/%s/contents/%s/provenance" % ( history_id, dataset_id )
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prov_response = self._get( url, data=dict( follow=False ) )
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self._assert_status_code_is( prov_response, 200 )
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return prov_response.json()[ "job_id" ]
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@skip_without_tool( "collection_paired_test" )
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def test_extract_workflows_with_dataset_collections( self ):
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history_id = self.dataset_populator.new_history()
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hdca = self.dataset_collection_populator.create_pair_in_history( history_id ).json()
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hdca_id = hdca[ "id" ]
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inputs = {
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"f1": dict( src="hdca", id=hdca_id )
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}
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run_output = self.dataset_populator.run_tool(
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tool_id="collection_paired_test",
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inputs=inputs,
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history_id=history_id,
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)
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job_id = run_output[ "jobs" ][ 0 ][ "id" ]
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self.dataset_populator.wait_for_history( history_id, assert_ok=True )
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downloaded_workflow = self._extract_and_download_workflow(
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from_history_id=history_id,
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dataset_collection_ids=dumps( [ hdca[ "hid" ] ] ),
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job_ids=dumps( [ job_id ] ),
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workflow_name="test import from history",
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)
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collection_steps = self._get_steps_of_type( downloaded_workflow, "data_collection_input", expected_len=1 )
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collection_step = collection_steps[ 0 ]
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collection_step_state = loads( collection_step[ "tool_state" ] )
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self.assertEquals( collection_step_state[ "collection_type" ], u"paired" )
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@skip_without_tool( "random_lines1" )
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def test_extract_mapping_workflow_from_history( self ):
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history_id = self.dataset_populator.new_history()
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hdca, job_id1, job_id2 = self.__run_random_lines_mapped_over_pair( history_id )
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downloaded_workflow = self._extract_and_download_workflow(
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from_history_id=history_id,
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dataset_collection_ids=dumps( [ hdca[ "hid" ] ] ),
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job_ids=dumps( [ job_id1, job_id2 ] ),
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workflow_name="test import from mapping history",
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)
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self.__assert_looks_like_randomlines_mapping_workflow( downloaded_workflow )
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def test_extract_copied_mapping_from_history( self ):
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old_history_id = self.dataset_populator.new_history()
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hdca, job_id1, job_id2 = self.__run_random_lines_mapped_over_pair( old_history_id )
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history_id = self.dataset_populator.new_history()
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old_contents = self._get( "histories/%s/contents" % old_history_id ).json()
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for old_content in old_contents:
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self.__copy_content_to_history( history_id, old_content )
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# API test is somewhat contrived since there is no good way
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# to retrieve job_id1, job_id2 like this for copied dataset
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# collections I don't think.
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downloaded_workflow = self._extract_and_download_workflow(
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from_history_id=history_id,
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dataset_collection_ids=dumps( [ hdca[ "hid" ] ] ),
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job_ids=dumps( [ job_id1, job_id2 ] ),
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workflow_name="test import from history",
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)
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self.__assert_looks_like_randomlines_mapping_workflow( downloaded_workflow )
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@skip_without_tool( "random_lines1" )
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@skip_without_tool( "multi_data_param" )
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def test_extract_reduction_from_history( self ):
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history_id = self.dataset_populator.new_history()
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hdca = self.dataset_collection_populator.create_pair_in_history( history_id, contents=["1 2 3\n4 5 6", "7 8 9\n10 11 10"] ).json()
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hdca_id = hdca[ "id" ]
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inputs1 = {
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"input|__collection_multirun__": hdca_id,
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"num_lines": 2
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}
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implicit_hdca1, job_id1 = self._run_tool_get_collection_and_job_id( history_id, "random_lines1", inputs1 )
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inputs2 = {
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"f1": "__collection_reduce__|%s" % ( implicit_hdca1[ "id" ] ),
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"f2": "__collection_reduce__|%s" % ( implicit_hdca1[ "id" ] )
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}
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reduction_run_output = self.dataset_populator.run_tool(
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tool_id="multi_data_param",
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inputs=inputs2,
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history_id=history_id,
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)
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job_id2 = reduction_run_output[ "jobs" ][ 0 ][ "id" ]
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self.dataset_populator.wait_for_history( history_id, assert_ok=True, timeout=20 )
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downloaded_workflow = self._extract_and_download_workflow(
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from_history_id=history_id,
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dataset_collection_ids=dumps( [ hdca[ "hid" ] ] ),
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job_ids=dumps( [ job_id1, job_id2 ] ),
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workflow_name="test import reduction",
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)
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assert len( downloaded_workflow[ "steps" ] ) == 3
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collect_step_idx = self._assert_first_step_is_paired_input( downloaded_workflow )
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tool_steps = self._get_steps_of_type( downloaded_workflow, "tool", expected_len=2 )
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random_lines_map_step = tool_steps[ 0 ]
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reduction_step = tool_steps[ 1 ]
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random_lines_input = random_lines_map_step[ "input_connections" ][ "input" ]
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assert random_lines_input[ "id" ] == collect_step_idx
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reduction_step_input = reduction_step[ "input_connections" ][ "f1" ]
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assert reduction_step_input[ "id"] == random_lines_map_step[ "id" ]
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def __copy_content_to_history( self, history_id, content ):
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if content[ "history_content_type" ] == "dataset":
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payload = dict(
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source="hda",
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content=content["id"]
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)
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response = self._post( "histories/%s/contents/datasets" % history_id, payload )
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else:
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payload = dict(
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source="hdca",
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content=content["id"]
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)
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response = self._post( "histories/%s/contents/dataset_collections" % history_id, payload )
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self._assert_status_code_is( response, 200 )
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return response.json()
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def __run_random_lines_mapped_over_pair( self, history_id ):
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hdca = self.dataset_collection_populator.create_pair_in_history( history_id, contents=["1 2 3\n4 5 6", "7 8 9\n10 11 10"] ).json()
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hdca_id = hdca[ "id" ]
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inputs1 = {
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"input|__collection_multirun__": hdca_id,
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"num_lines": 2
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}
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implicit_hdca1, job_id1 = self._run_tool_get_collection_and_job_id( history_id, "random_lines1", inputs1 )
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inputs2 = {
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"input|__collection_multirun__": implicit_hdca1[ "id" ],
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"num_lines": 1
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}
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_, job_id2 = self._run_tool_get_collection_and_job_id( history_id, "random_lines1", inputs2 )
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return hdca, job_id1, job_id2
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def __assert_looks_like_randomlines_mapping_workflow( self, downloaded_workflow ):
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# Assert workflow is input connected to a tool step with one output
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# connected to another tool step.
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assert len( downloaded_workflow[ "steps" ] ) == 3
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collect_step_idx = self._assert_first_step_is_paired_input( downloaded_workflow )
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tool_steps = self._get_steps_of_type( downloaded_workflow, "tool", expected_len=2 )
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tool_step_idxs = []
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tool_input_step_idxs = []
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for tool_step in tool_steps:
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self._assert_has_key( tool_step[ "input_connections" ], "input" )
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input_step_idx = tool_step[ "input_connections" ][ "input" ][ "id" ]
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tool_step_idxs.append( tool_step[ "id" ] )
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tool_input_step_idxs.append( input_step_idx )
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assert collect_step_idx not in tool_step_idxs
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assert tool_input_step_idxs[ 0 ] == collect_step_idx
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assert tool_input_step_idxs[ 1 ] == tool_step_idxs[ 0 ]
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def _run_tool_get_collection_and_job_id( self, history_id, tool_id, inputs ):
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run_output1 = self.dataset_populator.run_tool(
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tool_id=tool_id,
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inputs=inputs,
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history_id=history_id,
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)
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implicit_hdca = run_output1[ "implicit_collections" ][ 0 ]
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job_id = run_output1[ "jobs" ][ 0 ][ "id" ]
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self.dataset_populator.wait_for_history( history_id, assert_ok=True, timeout=20 )
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return implicit_hdca, job_id
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def _assert_first_step_is_paired_input( self, downloaded_workflow ):
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collection_steps = self._get_steps_of_type( downloaded_workflow, "data_collection_input", expected_len=1 )
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collection_step = collection_steps[ 0 ]
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collection_step_state = loads( collection_step[ "tool_state" ] )
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self.assertEquals( collection_step_state[ "collection_type" ], u"paired" )
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collect_step_idx = collection_step[ "id" ]
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return collect_step_idx
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def _extract_and_download_workflow( self, **extract_payload ):
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create_workflow_response = self._post( "workflows", data=extract_payload )
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self._assert_status_code_is( create_workflow_response, 200 )
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new_workflow_id = create_workflow_response.json()[ "id" ]
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download_response = self._get( "workflows/%s/download" % new_workflow_id )
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self._assert_status_code_is( download_response, 200 )
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downloaded_workflow = download_response.json()
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return downloaded_workflow
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def _get_steps_of_type( self, downloaded_workflow, type, expected_len=None ):
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steps = [ s for s in downloaded_workflow[ "steps" ].values() if s[ "type" ] == type ]
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if expected_len is not None:
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n = len( steps )
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assert n == expected_len, "Expected %d steps of type %s, found %d" % ( expected_len, type, n )
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return sorted( steps, key=operator.itemgetter("id") )
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@skip_without_tool( "random_lines1" )
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def test_run_replace_params_by_tool( self ):
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workflow_request, history_id = self._setup_random_x2_workflow( "test_for_replace_tool_params" )
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workflow_request[ "parameters" ] = dumps( dict( random_lines1=dict( num_lines=5 ) ) )
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run_workflow_response = self._post( "workflows", data=workflow_request )
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self._assert_status_code_is( run_workflow_response, 200 )
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self.dataset_populator.wait_for_history( history_id, assert_ok=True )
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# Would be 8 and 6 without modification
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self.__assert_lines_hid_line_count_is( history_id, 2, 5 )
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self.__assert_lines_hid_line_count_is( history_id, 3, 5 )
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@skip_without_tool( "random_lines1" )
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def test_run_replace_params_by_steps( self ):
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workflow_request, history_id = self._setup_random_x2_workflow( "test_for_replace_step_params" )
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workflow_summary_response = self._get( "workflows/%s" % workflow_request[ "workflow_id" ] )
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self._assert_status_code_is( workflow_summary_response, 200 )
|
|
steps = workflow_summary_response.json()[ "steps" ]
|
|
last_step_id = str( max( map( int, steps.keys() ) ) )
|
|
params = dumps( { last_step_id: dict( num_lines=5 ) } )
|
|
workflow_request[ "parameters" ] = params
|
|
run_workflow_response = self._post( "workflows", data=workflow_request )
|
|
self._assert_status_code_is( run_workflow_response, 200 )
|
|
self.dataset_populator.wait_for_history( history_id, assert_ok=True )
|
|
# Would be 8 and 6 without modification
|
|
self.__assert_lines_hid_line_count_is( history_id, 2, 8 )
|
|
self.__assert_lines_hid_line_count_is( history_id, 3, 5 )
|
|
|
|
def test_pja_import_export( self ):
|
|
workflow = self.workflow_populator.load_workflow( name="test_for_pja_import", add_pja=True )
|
|
uploaded_workflow_id = self.workflow_populator.create_workflow( workflow )
|
|
download_response = self._get( "workflows/%s/download" % uploaded_workflow_id )
|
|
downloaded_workflow = download_response.json()
|
|
self._assert_has_keys( downloaded_workflow[ "steps" ], "0", "1", "2" )
|
|
pjas = downloaded_workflow[ "steps" ][ "2" ][ "post_job_actions" ].values()
|
|
assert len( pjas ) == 1, len( pjas )
|
|
pja = pjas[ 0 ]
|
|
self._assert_has_keys( pja, "action_type", "output_name", "action_arguments" )
|
|
|
|
@skip_without_tool( "cat1" )
|
|
def test_invocation_usage( self ):
|
|
workflow = self.workflow_populator.load_workflow( name="test_usage" )
|
|
workflow_request, history_id = self._setup_workflow_run( workflow )
|
|
workflow_id = workflow_request[ "workflow_id" ]
|
|
response = self._get( "workflows/%s/usage" % workflow_id )
|
|
self._assert_status_code_is( response, 200 )
|
|
assert len( response.json() ) == 0
|
|
run_workflow_response = self._post( "workflows", data=workflow_request )
|
|
self._assert_status_code_is( run_workflow_response, 200 )
|
|
|
|
response = self._get( "workflows/%s/usage" % workflow_id )
|
|
self._assert_status_code_is( response, 200 )
|
|
usages = response.json()
|
|
assert len( usages ) == 1
|
|
|
|
usage_details_response = self._get( "workflows/%s/usage/%s" % ( workflow_id, usages[ 0 ][ "id" ] ) )
|
|
self._assert_status_code_is( usage_details_response, 200 )
|
|
usage_details = usage_details_response.json()
|
|
# Assert some high-level things about the structure of data returned.
|
|
self._assert_has_keys( usage_details, "inputs", "steps" )
|
|
for step in usage_details[ "steps" ].values():
|
|
self._assert_has_keys( step, "workflow_step_id", "order_index" )
|
|
|
|
@skip_without_tool( "cat1" )
|
|
def test_post_job_action( self ):
|
|
""" Tests both import and execution of post job actions.
|
|
"""
|
|
workflow = self.workflow_populator.load_workflow( name="test_for_pja_run", add_pja=True )
|
|
workflow_request, history_id = self._setup_workflow_run( workflow )
|
|
run_workflow_response = self._post( "workflows", data=workflow_request )
|
|
self._assert_status_code_is( run_workflow_response, 200 )
|
|
self.dataset_populator.wait_for_history( history_id, assert_ok=True )
|
|
time.sleep(.1) # Give another little bit of time for rename (needed?)
|
|
contents = self._get( "histories/%s/contents" % history_id ).json()
|
|
# loading workflow with add_pja=True causes workflow output to be
|
|
# renamed to 'the_new_name'.
|
|
assert "the_new_name" in map( lambda hda: hda[ "name" ], contents )
|
|
|
|
def _setup_workflow_run( self, workflow, history_id=None ):
|
|
uploaded_workflow_id = self.workflow_populator.create_workflow( workflow )
|
|
if not history_id:
|
|
history_id = self.dataset_populator.new_history()
|
|
hda1 = self.dataset_populator.new_dataset( history_id, content="1 2 3" )
|
|
hda2 = self.dataset_populator.new_dataset( history_id, content="4 5 6" )
|
|
label_map = {
|
|
'WorkflowInput1': self._ds_entry(hda1),
|
|
'WorkflowInput2': self._ds_entry(hda2)
|
|
}
|
|
workflow_request = dict(
|
|
history="hist_id=%s" % history_id,
|
|
workflow_id=uploaded_workflow_id,
|
|
ds_map=self._build_ds_map( uploaded_workflow_id, label_map ),
|
|
)
|
|
return workflow_request, history_id
|
|
|
|
def _build_ds_map( self, workflow_id, label_map ):
|
|
workflow_inputs = self._workflow_inputs( workflow_id )
|
|
ds_map = {}
|
|
for key, value in workflow_inputs.iteritems():
|
|
label = value[ "label" ]
|
|
if label in label_map:
|
|
ds_map[ key ] = label_map[ label ]
|
|
return dumps( ds_map )
|
|
|
|
def _setup_random_x2_workflow( self, name ):
|
|
workflow = self.workflow_populator.load_random_x2_workflow( name )
|
|
uploaded_workflow_id = self.workflow_populator.create_workflow( workflow )
|
|
workflow_inputs = self._workflow_inputs( uploaded_workflow_id )
|
|
key = workflow_inputs.keys()[ 0 ]
|
|
history_id = self.dataset_populator.new_history()
|
|
ten_lines = "\n".join( map( str, range( 10 ) ) )
|
|
hda1 = self.dataset_populator.new_dataset( history_id, content=ten_lines )
|
|
workflow_request = dict(
|
|
history="hist_id=%s" % history_id,
|
|
workflow_id=uploaded_workflow_id,
|
|
ds_map=dumps( {
|
|
key: self._ds_entry(hda1),
|
|
} ),
|
|
)
|
|
return workflow_request, history_id
|
|
|
|
def _workflow_inputs( self, uploaded_workflow_id ):
|
|
workflow_show_resposne = self._get( "workflows/%s" % uploaded_workflow_id )
|
|
self._assert_status_code_is( workflow_show_resposne, 200 )
|
|
workflow_inputs = workflow_show_resposne.json()[ "inputs" ]
|
|
return workflow_inputs
|
|
|
|
def _ds_entry( self, hda ):
|
|
return dict( src="hda", id=hda[ "id" ] )
|
|
|
|
def _assert_user_has_workflow_with_name( self, name ):
|
|
names = self.__workflow_names()
|
|
assert name in names, "No workflows with name %s in users workflows <%s>" % ( name, names )
|
|
|
|
def __assert_lines_hid_line_count_is( self, history, hid, lines ):
|
|
contents_url = "histories/%s/contents" % history
|
|
history_contents_response = self._get( contents_url )
|
|
self._assert_status_code_is( history_contents_response, 200 )
|
|
hda_summary = filter( lambda hc: hc[ "hid" ] == hid, history_contents_response.json() )[ 0 ]
|
|
hda_info_response = self._get( "%s/%s" % ( contents_url, hda_summary[ "id" ] ) )
|
|
self._assert_status_code_is( hda_info_response, 200 )
|
|
self.assertEquals( hda_info_response.json()[ "metadata_data_lines" ], lines )
|
|
|
|
def __workflow_names( self ):
|
|
index_response = self._get( "workflows" )
|
|
self._assert_status_code_is( index_response, 200 )
|
|
names = map( lambda w: w[ "name" ], index_response.json() )
|
|
return names
|