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) → Union[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: Union[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