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Split all workflow extraction testing out of test/api/test_workflows.py.
test_workflows.py was pretty unwieldy and the workflow extraction tests were very different than the other tests in that file (e.g. very different helper functions).
This commit is contained in:
John Chilton
+51
-311
@@ -1,7 +1,5 @@
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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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@@ -46,6 +44,57 @@ class BaseWorkflowsApiTestCase( api.ApiTestCase ):
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self._assert_user_has_workflow_with_name( "%s (imported from API)" % ( workflow[ "name" ] ) )
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return upload_response.json()[ "id" ]
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def _setup_workflow_run( self, workflow, inputs_by='step_id', history_id=None ):
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uploaded_workflow_id = self.workflow_populator.create_workflow( workflow )
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if not history_id:
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history_id = self.dataset_populator.new_history()
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hda1 = self.dataset_populator.new_dataset( history_id, content="1 2 3" )
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hda2 = self.dataset_populator.new_dataset( history_id, content="4 5 6" )
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workflow_request = dict(
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history="hist_id=%s" % history_id,
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workflow_id=uploaded_workflow_id,
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)
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label_map = {
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'WorkflowInput1': self._ds_entry(hda1),
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'WorkflowInput2': self._ds_entry(hda2)
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}
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if inputs_by == 'step_id':
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ds_map = self._build_ds_map( uploaded_workflow_id, label_map )
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workflow_request[ "ds_map" ] = ds_map
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elif inputs_by == "step_index":
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index_map = {
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'0': self._ds_entry(hda1),
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'1': self._ds_entry(hda2)
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}
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workflow_request[ "inputs" ] = dumps( index_map )
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workflow_request[ "inputs_by" ] = 'step_index'
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elif inputs_by == "name":
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workflow_request[ "inputs" ] = dumps( label_map )
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workflow_request[ "inputs_by" ] = 'name'
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return workflow_request, history_id
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def _build_ds_map( self, workflow_id, label_map ):
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workflow_inputs = self._workflow_inputs( workflow_id )
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ds_map = {}
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for key, value in workflow_inputs.iteritems():
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label = value[ "label" ]
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if label in label_map:
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ds_map[ key ] = label_map[ label ]
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return dumps( ds_map )
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def _ds_entry( self, hda ):
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src = 'hda'
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if 'history_content_type' in hda and hda[ 'history_content_type' ] == "dataset_collection":
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src = 'hdca'
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return dict( src=src, id=hda[ "id" ] )
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def _workflow_inputs( self, uploaded_workflow_id ):
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workflow_show_resposne = self._get( "workflows/%s" % uploaded_workflow_id )
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self._assert_status_code_is( workflow_show_resposne, 200 )
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workflow_inputs = workflow_show_resposne.json()[ "inputs" ]
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return workflow_inputs
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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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@@ -358,109 +407,6 @@ class WorkflowsApiTestCase( BaseWorkflowsApiTestCase ):
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step_map[step_index] = step["inputs"][0]["name"]
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return step_map
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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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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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def test_empty_create( self ):
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response = self._post( "workflows" )
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self._assert_status_code_is( response, 400 )
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@@ -475,161 +421,6 @@ class WorkflowsApiTestCase( BaseWorkflowsApiTestCase ):
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self._assert_status_code_is( response, 400 )
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self._assert_error_code_is( response, error_codes.USER_REQUEST_INVALID_PARAMETER )
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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": { "batch": True, "values": [ { "src": "hdca", "id": 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": { "src": "hdca", "id": implicit_hdca1[ "id" ] },
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"f2": { "src": "hdca", "id": 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": { "batch": True, "values": [ { "src": "hdca", "id": 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": { "batch": True, "values": [ { "src": "hdca", "id": 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( "cat1" )
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def test_run_with_pja( self ):
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workflow = self.workflow_populator.load_workflow( name="test_for_pja_run", add_pja=True )
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@@ -776,45 +567,6 @@ class WorkflowsApiTestCase( BaseWorkflowsApiTestCase ):
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assert len( usages ) == 1
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return workflow_id, usages[ 0 ]
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def _setup_workflow_run( self, workflow, inputs_by='step_id', history_id=None ):
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uploaded_workflow_id = self.workflow_populator.create_workflow( workflow )
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if not history_id:
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history_id = self.dataset_populator.new_history()
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hda1 = self.dataset_populator.new_dataset( history_id, content="1 2 3" )
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hda2 = self.dataset_populator.new_dataset( history_id, content="4 5 6" )
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workflow_request = dict(
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history="hist_id=%s" % history_id,
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workflow_id=uploaded_workflow_id,
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)
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label_map = {
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'WorkflowInput1': self._ds_entry(hda1),
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'WorkflowInput2': self._ds_entry(hda2)
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}
|
||||
if inputs_by == 'step_id':
|
||||
ds_map = self._build_ds_map( uploaded_workflow_id, label_map )
|
||||
workflow_request[ "ds_map" ] = ds_map
|
||||
elif inputs_by == "step_index":
|
||||
index_map = {
|
||||
'0': self._ds_entry(hda1),
|
||||
'1': self._ds_entry(hda2)
|
||||
}
|
||||
workflow_request[ "inputs" ] = dumps( index_map )
|
||||
workflow_request[ "inputs_by" ] = 'step_index'
|
||||
elif inputs_by == "name":
|
||||
workflow_request[ "inputs" ] = dumps( label_map )
|
||||
workflow_request[ "inputs_by" ] = 'name'
|
||||
|
||||
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_steps( self, name ):
|
||||
workflow_request, history_id = self._setup_random_x2_workflow( "test_for_replace_step_params" )
|
||||
random_line_steps = self._random_lines_steps( workflow_request )
|
||||
@@ -843,18 +595,6 @@ class WorkflowsApiTestCase( BaseWorkflowsApiTestCase ):
|
||||
)
|
||||
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 ):
|
||||
src = 'hda'
|
||||
if 'history_content_type' in hda and hda[ 'history_content_type' ] == "dataset_collection":
|
||||
src = 'hdca'
|
||||
return dict( src=src, id=hda[ "id" ] )
|
||||
|
||||
def __review_paused_steps( self, uploaded_workflow_id, invocation_id, order_index, action=True ):
|
||||
invocation = self._invocation_details( uploaded_workflow_id, invocation_id )
|
||||
invocation_steps = invocation[ "steps" ]
|
||||
|
||||
Reference in New Issue
Block a user