InternalCalibration

class pyopenms.InternalCalibration

Bases: object

Cython implementation of _InternalCalibration

Documentation is available at http://www.openms.de/current_doxygen/html/classOpenMS_1_1InternalCalibration.html: – Inherits from [‘ProgressLogger’]

__init__()

Cython signature: void InternalCalibration() A mass recalibration method using linear/quadratic interpolation (robust/weighted) of given reference masses
Cython signature: void InternalCalibration(InternalCalibration &)

Methods

`__init__`	Cython signature: void InternalCalibration()
`applyTransformation`	Cython signature: void applyTransformation(MSExperiment & exp, IntList & target_mslvl, MZTrafoModel & trafo)
`calibrate`	Cython signature: bool calibrate(MSExperiment, libcpp_vector[int], MZTrafoModel_MODELTYPE, double rt_chunk, bool use_RANSAC, double post_ppm_median, double post_ppm_MAD, String file_models, String file_models_plot, String file_residuals, String file_residuals_plot, String rscript_executable)
`endProgress`	Cython signature: void endProgress() Ends the progress display
`fillCalibrants`	Cython signature: size_t fillCalibrants(MSExperiment, libcpp_vector[InternalCalibration_LockMass], double tol_ppm, bool lock_require_mono, bool lock_require_iso, CalibrationData & failed_lock_masses, bool verbose)
`getCalibrationPoints`	Cython signature: CalibrationData getCalibrationPoints()
`getLogType`	Cython signature: LogType getLogType() Returns the type of progress log being used
`nextProgress`	Cython signature: void nextProgress() Increment progress by 1 (according to range begin-end)
`setLogType`	Cython signature: void setLogType(LogType) Sets the progress log that should be used.
`setProgress`	Cython signature: void setProgress(ptrdiff_t value) Sets the current progress
`startProgress`	Cython signature: void startProgress(ptrdiff_t begin, ptrdiff_t end, String label)

applyTransformation(): Cython signature: void applyTransformation(MSExperiment & exp, IntList & target_mslvl, MZTrafoModel & trafo)

calibrate()

Cython signature: bool calibrate(MSExperiment, libcpp_vector[int], MZTrafoModel_MODELTYPE, double rt_chunk, bool use_RANSAC, double post_ppm_median, double post_ppm_MAD, String file_models, String file_models_plot, String file_residuals, String file_residuals_plot, String rscript_executable)

For each spectrum, a calibration model will be computed and applied. Make sure to call fillCalibrants() before, so a model can be created. —– The MSExperiment will be sorted by RT and m/z if unsorted. —– :param exp: MSExperiment holding the Raw data to calibrate :param target_mslvl: MS-levels where calibration should be applied to :param model_type: Linear or quadratic model; select based on your instrument :param rt_chunk: RT-window size (one-sided) of calibration points to collect around each spectrum. Set to negative values, to build one global model instead. :param use_RANSAC: Remove outliers before fitting a model?! :param post_ppm_median: The median ppm error of the calibrants must be at least this good after calibration; otherwise this method returns false(fail) :param post_ppm_MAD: The median absolute deviation of the calibrants must be at least this good after calibration; otherwise this method returns false(fail) :param file_models: Output CSV filename, where model parameters are written to (pass empty string to skip) :param file_models_plot: Output PNG image model parameters (pass empty string to skip) :param file_residuals: Output CSV filename, where ppm errors of calibrants before and after model fitting parameters are written to (pass empty string to skip) :param file_residuals_plot: Output PNG image of the ppm errors of calibrants (pass empty string to skip) :param rscript_executable: Full path to the Rscript executable :returns: true upon successful calibration

endProgress(): Cython signature: void endProgress() Ends the progress display

fillCalibrants()

Cython signature: size_t fillCalibrants(MSExperiment, libcpp_vector[InternalCalibration_LockMass], double tol_ppm, bool lock_require_mono, bool lock_require_iso, CalibrationData & failed_lock_masses, bool verbose)

Filters can be used to exclude spurious peaks, i.e. require the calibrant peak to be monoisotopic or to have a +1 isotope (should not be used for very low abundant calibrants) If a calibrant is not found, it is added to a ‘failed_lock_masses’ database which is returned and not stored internally. The intensity of the peaks describe the reason for failed detection: 0.0 - peak not found with the given ppm tolerance; 1.0 - peak is not monoisotopic (can only occur if ‘lock_require_mono’ is true) 2.0 - peak has no +1 isotope (can only occur if ‘lock_require_iso’ is true) —– :param exp: Peak map containing the lock masses :param ref_masses: List of lock masses :param tol_ppm: Search window for lock masses in ‘exp’ :param lock_require_mono: Require that a lock mass is the monoisotopic peak (i.e. not an isotope peak) – lock mass is rejected otherwise :param lock_require_iso: Require that a lock mass has isotope peaks to its right – lock mass is rejected otherwise :param failed_lock_masses: Set of calibration masses which were not found, i.e. their expected m/z and RT positions :param verbose: Print information on ‘lock_require_XXX’ matches during search :returns: Number of calibration masses found

Cython signature: size_t fillCalibrants(FeatureMap, double)

Extracts only the first hit from the first peptide identification of each feature Hits are sorted beforehand Ambiguities should be resolved before, e.g. using IDFilter RT and m/z are taken from the features, not from the identifications (for an exception see below)! —– Unassigned peptide identifications are also taken into account! RT and m/z are naturally taken from the IDs, since to feature is assigned If you do not want these IDs, remove them from the feature map before calling this function —– A filtering step is done in the m/z dimension using ‘tol_ppm’ Since precursor masses could be annotated wrongly (e.g. isotope peak instead of mono), larger outliers are removed before accepting an ID as calibrant —– :param fm: FeatureMap with peptide identifications :param tol_ppm: Only accept ID’s whose theoretical mass deviates at most this much from annotated :returns: Number of calibration masses found

Cython signature: size_t fillCalibrants(libcpp_vector[PeptideIdentification], double)

Extracts only the first hit from each peptide identification Hits are sorted beforehand Ambiguities should be resolved before, e.g. using IDFilter —– Unassigned peptide identifications are also taken into account! RT and m/z are naturally taken from the IDs, since to feature is assigned If you do not want these IDs, remove them from the feature map before calling this function —– A filtering step is done in the m/z dimension using ‘tol_ppm’ Since precursor masses could be annotated wrongly (e.g. isotope peak instead of mono), larger outliers are removed before accepting an ID as calibrant —– :param pep_ids: Peptide ids (e.g. from an idXML file) :param tol_ppm: Only accept ID’s whose theoretical mass deviates at most this much from annotated :returns: Number of calibration masses found

getCalibrationPoints()

Cython signature: CalibrationData getCalibrationPoints()

Returns: Container of calibration points

getLogType(): Cython signature: LogType getLogType() Returns the type of progress log being used

nextProgress(): Cython signature: void nextProgress() Increment progress by 1 (according to range begin-end)

setLogType(): Cython signature: void setLogType(LogType) Sets the progress log that should be used. The default type is NONE!

setProgress(): Cython signature: void setProgress(ptrdiff_t value) Sets the current progress

startProgress(): Cython signature: void startProgress(ptrdiff_t begin, ptrdiff_t end, String label)