FIAMSDataProcessor

class pyopenms.FIAMSDataProcessor

Bases: object

Cython implementation of _FIAMSDataProcessor

Original C++ documentation is available here

– Inherits from [‘DefaultParamHandler’]

ADD PYTHON DOCUMENTATION HERE

__init__()

Overload:

__init__(self) → None

Data processing for FIA-MS data

Overload:

__init__(self, in_0: FIAMSDataProcessor) → None

Methods

__init__	Overload:
convertToFeatureMap(self, input_)	Convert a spectrum to a feature map with the corresponding polarity
extractPeaks(self, input_)	Pick peaks from the summed spectrum
getDefaults(self)	Returns the default parameters
getName(self)	Returns the name
getParameters(self)	Returns the parameters
getSubsections(self)
run(self, experiment, n_seconds, output, ...)	Run the full analysis for the experiment for the given time interval
setName(self, in_0)	Sets the name
setParameters(self, param)	Sets the parameters
trackNoise(self, input_)	Estimate noise for each peak

convertToFeatureMap(self, input_: MSSpectrum) → FeatureMap

Convert a spectrum to a feature map with the corresponding polarity

Applies SavitzkyGolayFilter and PeakPickerHiRes

Parameters:

input – Input a picked spectrum

Returns:

A feature map with the peaks converted to features and polarity from the parameters

extractPeaks(self, input_: MSSpectrum) → MSSpectrum

Pick peaks from the summed spectrum

Parameters:

input – Input vector of spectra

Returns:

A spectrum with picked peaks

getDefaults(self) → Param

Returns the default parameters

getName(self) → bytes | str | String

Returns the name

getParameters(self) → Param

Returns the parameters

getSubsections(self) → List[bytes]

run(self, experiment: MSExperiment, n_seconds: float, output: MzTab, load_cached_spectrum: bool) → bool

Run the full analysis for the experiment for the given time interval

The workflow steps are: - the time axis of the experiment is cut to the interval from 0 to n_seconds - the spectra are summed into one along the time axis with the bin size determined by mz and instrument resolution - data is smoothed by applying the Savitzky-Golay filter - peaks are picked - the accurate mass search for all the picked peaks is performed

The intermediate summed spectra and picked peaks can be saved to the filesystem. Also, the results of the accurate mass search and the signal-to-noise information of the resulting spectrum is saved.

Parameters:

• experiment – Input MSExperiment

• n_seconds – Input number of seconds

• load_cached_spectrum – Load the cached picked spectrum if exists

• output – Output of the accurate mass search results

Returns:

A boolean indicating if the picked spectrum was loaded from the cached file

setName(self, in_0: bytes | str | String) → None

Sets the name

setParameters(self, param: Param) → None

Sets the parameters

trackNoise(self, input_: MSSpectrum) → MSSpectrum

Estimate noise for each peak

Uses SignalToNoiseEstimatorMedianRapid

Parameters:

input – Input a picked spectrum

Returns:

A spectrum object storing logSN information