MRMTransitionGroupCP#

class pyopenms.MRMTransitionGroupCP(*args, **kwargs)#

Bases: MRMTransitionGroupCP

__init__(*args, **kwargs)#

Overload:

__init__(self) → None

Overload:

__init__(self, in_0: MRMTransitionGroupCP) → None

Methods

`__init__`(args, *kwargs)
`addChromatogram`(self, chromatogram, key)
`addFeature`(self, feature)
`addPrecursorChromatogram`(self, chromatogram, key)
`addTransition`(self, transition, key)
`chromatogramIdsMatch`(self)
`getBestFeature`(self)
`getChromatogram`(self, key)
`getChromatograms`(self)
`getFeatures`(self)
`getFeaturesMuteable`(self)
`getLibraryIntensity`(self, result)
`getPrecursorChromatogram`(self, key)
`getPrecursorChromatograms`(self)
`getTransition`(self, key)
`getTransitionGroupID`(self)
`getTransitions`(self)
`getTransitionsMuteable`(self)
`get_chromatogram_df`([columns, ...])	Returns a DataFrame representation of the Chromatograms stored in MRMTransitionGroupCP.
`get_chromatogram_df_columns`([columns, ...])	Returns a list of column names that get_chromatogram_df() would produce.
`get_feature_df`([columns, meta_values])	Returns a DataFrame representation of the Features stored in MRMTransitionGroupCP.
`get_feature_df_columns`([columns])	Returns a list of column names that get_feature_df() would produce.
`hasChromatogram`(self, key)
`hasPrecursorChromatogram`(self, key)
`hasTransition`(self, key)
`isInternallyConsistent`(self)
`setTransitionGroupID`(self, tr_gr_id)
`size`(self)
`subset`(self, tr_ids)

addChromatogram(self, chromatogram: MSChromatogram, key: bytes | str | String) → None#

addFeature(self, feature: MRMFeature) → None#

addPrecursorChromatogram(self, chromatogram: MSChromatogram, key: bytes | str | String) → None#

addTransition(self, transition: ReactionMonitoringTransition, key: bytes | str | String) → None#

chromatogramIdsMatch(self) → bool#

getBestFeature(self) → MRMFeature#

getChromatogram(self, key: bytes | str | String) → MSChromatogram#

getChromatograms(self) → List[MSChromatogram]#

getFeatures(self) → List[MRMFeature]#

getFeaturesMuteable(self) → List[MRMFeature]#

getLibraryIntensity(self, result: List[float]) → None#

getPrecursorChromatogram(self, key: bytes | str | String) → MSChromatogram#

getPrecursorChromatograms(self) → List[MSChromatogram]#

getTransition(self, key: bytes | str | String) → ReactionMonitoringTransition#

getTransitionGroupID(self) → bytes | str | String#

getTransitions(self) → List[ReactionMonitoringTransition]#

getTransitionsMuteable(self) → List[ReactionMonitoringTransition]#

get_chromatogram_df(columns: None | List[str] = None, export_meta_values: bool = True) → DataFrame#

Returns a DataFrame representation of the Chromatograms stored in MRMTransitionGroupCP.

Args:

columns (list or None): List of column names to include. If None,: includes all default columns.
export_meta_values (bool): Whether to export meta values. Only applies: when columns=None.

Returns:

pd.DataFrame: DataFrame representation of the chromatograms.

Example:

>>> # Get all default columns
>>> df = mrm.get_chromatogram_df()

>>> # Discover available columns
>>> print(mrm.get_chromatogram_df_columns())

>>> # Get only specific columns
>>> df = mrm.get_chromatogram_df(columns=['rt', 'intensity'])

get_chromatogram_df_columns(columns: str = 'default', export_meta_values: bool = True) → List[str]#

Returns a list of column names that get_chromatogram_df() would produce.

Args:: columns (str): ‘default’ for standard columns, ‘all’ for all available columns. export_meta_values (bool): Whether to include meta value columns.
Returns:: list: List of column name strings.

get_feature_df(columns: None | List[str] = None, meta_values: None | List[str] | str = None) → DataFrame#

Returns a DataFrame representation of the Features stored in MRMTransitionGroupCP.

Args:

columns (list or None): List of column names to include. If None,: includes all columns. Use get_feature_df_columns() to discover available columns.

meta_values: meta values to include (None, [custom list of meta value names] or ‘all’)

Returns:

pd.DataFrame: DataFrame representation of the Features.

Example:

>>> # Get all columns
>>> df = mrm.get_feature_df()

>>> # Discover available columns
>>> print(mrm.get_feature_df_columns())

>>> # Get only specific columns
>>> df = mrm.get_feature_df(columns=['feature_id', 'RT', 'intensity'])

get_feature_df_columns(columns: str = 'default') → List[str]#

Returns a list of column names that get_feature_df() would produce.

Args:: columns (str): ‘default’ for core columns, ‘all’ to include all meta values.
Returns:: list: List of column name strings.

hasChromatogram(self, key: bytes | str | String) → bool#

hasPrecursorChromatogram(self, key: bytes | str | String) → bool#

hasTransition(self, key: bytes | str | String) → bool#

isInternallyConsistent(self) → bool#

setTransitionGroupID(self, tr_gr_id: bytes | str | String) → None#

size(self) → int#

subset(self, tr_ids: List[bytes | str]) → MRMTransitionGroupCP#