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Trackster visual analytics: enable datasets that cannot be indexed--and hence cannot be subseted--to be used as tool inputs.

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This commit is contained in:
Jeremy Goecks
2011-03-31 17:22:10 -04:00
parent 3ec9a1c91f
commit 30bb64bbca
1 changed files with 44 additions and 35 deletions
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lib/galaxy/web/controllers/tracks.py
+44 -35
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@@ -670,6 +670,8 @@ class TracksController( BaseController, UsesVisualization, UsesHistoryDatasetAss
if not tool:
return messages.NO_TOOL
tool_params = dict( [ ( p.name, p.value ) for p in original_job.parameters ] )
# TODO: need to handle updates to conditional parameters; conditional
# params are stored in dicts (and dicts within dicts).
tool_params.update( dict( [ ( key, value ) for key, value in kwargs.items() if key in tool.inputs ] ) )
tool_params = tool.params_from_strings( tool_params, self.app )
@@ -685,13 +687,16 @@ class TracksController( BaseController, UsesVisualization, UsesHistoryDatasetAss
messages_list = []
for jida in original_job.input_datasets:
input_dataset = jida.dataset
track_type, data_sources = input_dataset.datatype.get_track_type()
# Convert to datasource that provides 'data' because we need to
# extract the original data.
data_source = data_sources[ 'data' ]
msg = self._convert_dataset( trans, input_dataset, data_source )
if msg is not None:
messages_list.append( msg )
# TODO: put together more robust way to determine if a dataset can be indexed.
if hasattr( input_dataset, 'get_track_type' ):
# Can index dataset.
track_type, data_sources = input_dataset.datatype.get_track_type()
# Convert to datasource that provides 'data' because we need to
# extract the original data.
data_source = data_sources[ 'data' ]
msg = self._convert_dataset( trans, input_dataset, data_source )
if msg is not None:
messages_list.append( msg )
# Return any messages generated during conversions.
return_message = _get_highest_priority_msg( messages_list )
@@ -707,38 +712,42 @@ class TracksController( BaseController, UsesVisualization, UsesHistoryDatasetAss
messages_list = []
for jida in original_job.input_datasets:
input_dataset = jida.dataset
track_type, data_sources = input_dataset.datatype.get_track_type()
data_source = data_sources[ 'data' ]
converted_dataset = input_dataset.get_converted_dataset( trans, data_source )
if hasattr( input_dataset, 'get_track_type' ):
#
# Dataset can be indexed and hence a subset can be extracted.
#
track_type, data_sources = input_dataset.datatype.get_track_type()
data_source = data_sources[ 'data' ]
converted_dataset = input_dataset.get_converted_dataset( trans, data_source )
#
# Create new HDA for input dataset's subset.
#
subset_dataset = trans.app.model.HistoryDatasetAssociation( extension=input_dataset.ext, \
dbkey=input_dataset.dbkey, \
create_dataset=True, \
sa_session=trans.sa_session,
name="Subset [%s:%i-%i] of data %i" % \
( chrom, low, high, input_dataset.hid ),
visible=False )
job_history.add_dataset( subset_dataset )
trans.sa_session.add( subset_dataset )
trans.app.security_agent.set_all_dataset_permissions( subset_dataset.dataset, hda_permissions )
#
# Create new HDA for input dataset's subset.
#
new_dataset = trans.app.model.HistoryDatasetAssociation( extension=input_dataset.ext, \
dbkey=input_dataset.dbkey, \
create_dataset=True, \
sa_session=trans.sa_session,
name="Subset [%s:%i-%i] of data %i" % \
( chrom, low, high, input_dataset.hid ),
visible=False )
job_history.add_dataset( new_dataset )
trans.sa_session.add( new_dataset )
trans.app.security_agent.set_all_dataset_permissions( new_dataset.dataset, hda_permissions )
# Write data subset to new HDA.
data_provider_class = get_data_provider( original_dataset=input_dataset )
data_provider = data_provider_class( original_dataset=input_dataset,
converted_dataset=converted_dataset )
data_provider.write_data_to_file( chrom, low, high, subset_dataset.file_name )
# Write subset of data to new dataset
data_provider_class = get_data_provider( original_dataset=input_dataset )
data_provider = data_provider_class( original_dataset=input_dataset,
converted_dataset=converted_dataset )
data_provider.write_data_to_file( chrom, low, high, new_dataset.file_name )
# TODO: size not working.
subset_dataset.set_size()
subset_dataset.info = "Data subset for trackster"
subset_dataset.set_dataset_state( trans.app.model.Dataset.states.OK )
trans.sa_session.flush()
# TODO: size not working.
new_dataset.set_size()
new_dataset.info = "Data subset for trackster"
new_dataset.set_dataset_state( trans.app.model.Dataset.states.OK )
trans.sa_session.flush()
# Add dataset to tool's parameters.
tool_params[ jida.name ] = subset_dataset
# Add dataset to tool's parameters.
tool_params[ jida.name ] = new_dataset
#
# Start tool and handle outputs.