Algorithms

Many signal processing algorithms follow a similar pattern in OpenMS.

algorithm = NameOfTheAlgorithmClass()
exp = MSExperiment()

# populate exp, for example load from file
# ...

# run the algorithm on data
algorithm.filterExperiment(exp)

In many cases, the processing algorithms have a set of parameters that can be adjusted. These are accessible through Algorithm.getParameters() and yield a Param object (see Parameter handling) which can be manipulated. After changing parameters, one can use Algorithm.setParameters() to propagate the new parameters to the algorithm:

algorithm = NameOfTheAlgorithmClass()
param = algorithm.getParameters()
param.setValue("algo_parameter", "new_value")
algorithm.setParameters(param)

exp = MSExperiment()

# populate exp, for example load from file
# ...

algorithm.filterExperiment(exp)

Since they work on a single MSExperiment object, little input is needed to execute a filter directly on the data. Examples of filters that follow this pattern are GaussFilter, SavitzkyGolayFilter as well as the spectral filters NLargest, Normalizer, SpectraMerger, ThresholdMower, WindowMower.

Using the same example file as before, we can execute a GaussFilter on our test data as follows:

import pyopenms as oms
from urllib.request import urlretrieve

gh = "https://raw.githubusercontent.com/OpenMS/OpenMS/develop/doc/pyopenms"
urlretrieve(gh + "/src/data/tiny.mzML", "test.mzML")

gf = oms.GaussFilter()
exp = oms.MSExperiment()
oms.MzMLFile().load("test.mzML", exp)
gf.filterExperiment(exp)
# oms.MzMLFile().store("test.filtered.mzML", exp)