def deriveMetric(input, first, second, oper):
# derive the metric
derivor = DeriveMetricOperation(input, first, second, oper)
derived = derivor.processData().get(0)
newName = derived.getMetrics().toArray()[0]
# merge new metric with the trial
merger = MergeTrialsOperation(input)
merger.addInput(derived)
merged = merger.processData().get(0)
#print "new metric: " + newName
return merged, newName
def scaleMetric(input, metric, value, oper):
# derive the metric
scaler = ScaleMetricOperation(input, metric, value, oper)
scaled = scaler.processData().get(0)
newName = scaled.getMetrics().toArray()[0]
# merge new metric with the trial
merger = MergeTrialsOperation(input)
merger.addInput(scaled)
merged = merger.processData().get(0)
#print "new metric: " + newName
return merged, newName
def deriveMetric(input, first, second, oper):
# derive the metric
derivor = DeriveMetricOperation(input, first, second, oper)
# check to see if this metric already has been derived
merged = None
newName = None
if (derivor.exists()):
print "Exists: ", newName
merged = input
newName = derivor.getNewName()
else:
derived = derivor.processData().get(0)
newName = derivor.getNewName()
# merge new metric with the trial
merger = MergeTrialsOperation(input)
merger.addInput(derived)
merged = merger.processData().get(0)
# save the newly derived metric
# saver = SaveResultOperation(derived)
# saver.setForceOverwrite(False)
# saver.processData()
print "new metric: ", newName
return merged, newName