WorkflowAlgorithmRunner v1¶

Summary¶

Manages dependency resolution, input/output mapping and running of algorithms.

Properties¶

Description¶

This utility algorithm is used to control consecutive executions of another algorithm. After defining a table of desired input and output properties, WorkflowAlgorithmRunner connects the outputs to input properties, declares the order of execution, and runs the algorithm. It is mainly meant for managing the execution of workflow algorithms for data reduction purposes although it can run all types of algorithms.

The runs are specified in a TableWorkspace and given as the SetupTable input property to the algorihtm. The first column has to contain unique string identifiers for each row. The rest of the columns have to be named same as the input/output properties of the managed algorithm.

Each row in the SetupTable corresponds to a single run of the managed algorithm. Its properties are set without modifications from the corresponding columns of the row except for special input/output columns declare in the InputOutputMap. For example, two independent runs of the Scale v1 would be specified as

The Operation column could have been omitted in the above to use the default value of the Operation property of Scale.

InputOutputMap connects the outputs of a run to the inputs of another run. It is a TableWorkspace where the column names correspond to some or all of the managed algorithm’s input properties. The table has only a single row which lists the names of the output properties whose final values will be forwarded to the corresponding input property. To continue the Scale v1 example above, the InputWorkspace and OutputWorkspace properties would be connected by the following single column table:

If the one wanted to give the workspace scaled by ‘2nd run’ as an input for ‘1st run’, the InputOutputMap would look like the following:

This would result in ‘2nd run’ to be executed first to produce the ‘scaled_ws2’ workspace which in turn would be fed to ‘1st run’ as the InputWorkspace property. Behind the scenes, WorkflowAlgorithmRunner will replace the ‘2nd run’ in the InputWorkspace column of ‘1st run’ by scaled_ws2.

In the above, note the quotation marks around the ‘ws2’ and ‘ws1’ property names. These are hard coded input and forced output values, respectively. This tells WorkspaceAlgorithmRunner to omit input/output mapping and to keep these values as they are.

An equivalent python commands to running WorkflowAlgorithmRunner with the above InputOutputMap and SetupTable would be

Scale(InputWorkspace='ws2', OutputWorkspace='scaled_ws2', Factor=100, Operation='Multiply')
Scale(InputWorkspace='scaled_ws2', OutputWorkspace='ws1', Factor=0.42, Operation='Multiply')

Usage¶

Example: Let’s recreate the example in the Description section

# This is our initial workspace. We want to scale it first by 100
# and then by 0.42
CreateSingleValuedWorkspace(OutputWorkspace='ws2', DataValue=1.0)

# Setup the runs for the Scale algorithm
setupTable = WorkspaceFactoryImpl.Instance().createTable()
setupTable.addColumn('str', 'Run name') # First column can have arbitrary name.
# The rest of the columns can be in arbitrary order
setupTable.addColumn('str', 'InputWorkspace')
setupTable.addColumn('double', 'Factor') # Scale expects to get a number here.
setupTable.addColumn('str', 'OutputWorkspace')
row = {
    'Run name': '1st run',
    'InputWorkspace': '2nd run',
    'Factor': 0.42,
    'OutputWorkspace': '"ws1"' # Forced output either by '' or "".
}
setupTable.addRow(row)
row = {
    'Run name': '2nd run',
    'InputWorkspace': "'ws2'",
    'Factor': 100,
    'OutputWorkspace': 'scaled_ws2'
}
setupTable.addRow(row)
AnalysisDataServiceImpl.Instance().addOrReplace('setupTable', setupTable)

# Map OutputWorkspace to InputWorkspace
ioMap = WorkspaceFactoryImpl.Instance().createTable()
ioMap.addColumn('str', 'InputWorkspace')
ioMap.addRow({'InputWorkspace': 'OutputWorkspace'})
AnalysisDataServiceImpl.Instance().addOrReplace('ioMapTable', ioMap)

# Execute the algorithm
WorkflowAlgorithmRunner('Scale', SetupTable=setupTable, InputOutputMap=ioMap)

# Print some results
print('Original input value: {0}'.format(mtd['ws2'].dataY(0)[0]))
print('After scaling by 100: {0}'.format(mtd['scaled_ws2'].dataY(0)[0]))
print('After further scaling by 0.42: {0}'.format(mtd['ws1'].dataY(0)[0]))

Original input value: 1.0
After scaling by 100: 100.0
After further scaling by 0.42: 42.0

Categories: Algorithms | Utility

Source¶

C++ source: WorkflowAlgorithmRunner.cpp

C++ header: WorkflowAlgorithmRunner.h