FeatureMap#

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

Bases: FeatureMap

__init__(*args, **kwargs)#

Overload:

__init__(self) → None

Overload:

__init__(self, in_0: FeatureMap) → None

Methods

`__init__`(args, *kwargs)
`clear`
`clearMetaInfo`(self)	Removes all meta values
`clearRanges`(self)	Resets all range dimensions as empty
`clearUniqueId`(self)	Clear the unique id.
`ensureUniqueId`(self)	Assigns a valid unique id, but only if the present one is invalid.
`getDataProcessing`(self)
`getIdentifier`(self)	Retrieve document identifier (e.g. an LSID).
`getKeys`(self, keys)	Fills the given vector with a list of all keys for which a value is set
`getLoadedFilePath`(self)	Returns the file_name which is the absolute path to the file loaded
`getLoadedFileType`(self)	Returns the file_type (e.g. featureXML, consensusXML, mzData, mzXML, mzML, ...) of the file loaded.
`getMaxIntensity`(self)	Returns the maximum intensity
`getMaxMZ`(self)	Returns the maximum m/z
`getMaxRT`(self)	Returns the maximum RT
`getMetaValue`(self, in_0)	Returns the value corresponding to a string, or
`getMinIntensity`(self)	Returns the minimum intensity
`getMinMZ`(self)	Returns the minimum m/z
`getMinRT`(self)	Returns the minimum RT
`getPrimaryMSRunPath`(self, toFill)	Returns the file path to the first MS run
`getProteinIdentifications`(self)	Returns the protein identification runs stored in this map
`getUnassignedPeptideIdentifications`(self)	Returns peptide identifications that are not assigned to any feature
`getUniqueId`(self)	Returns the unique id
`get_assigned_peptide_identifications`()	Generates a list with peptide identifications assigned to a feature.
`get_df`([columns, meta_values, ...])	Generates a pandas DataFrame with information contained in the FeatureMap.
`get_df_columns`([columns, ...])	Returns a list of column names that get_df() would produce.
`hasInvalidUniqueId`(self)	Returns whether the unique id is invalid.
`hasValidUniqueId`(self)	Returns whether the unique id is valid.
`isMetaEmpty`(self)	Returns if the MetaInfo is empty
`isValid`(self, unique_id)	Returns true if the unique_id is valid, false otherwise
`metaRegistry`(self)	Returns a reference to the MetaInfoRegistry
`metaValueExists`(self, in_0)	Returns whether an entry with the given name exists
`push_back`
`removeMetaValue`(self, in_0)	Removes the DataValue corresponding to name if it exists
`setDataProcessing`(self, in_0)	Sets the description of the applied data processing
`setIdentifier`(self, id)	Sets document identifier (e.g. an LSID).
`setLoadedFilePath`(self, file_name)	Sets the file_name according to absolute path of the file loaded, preferably done whilst loading
`setLoadedFileType`(self, file_name)	Sets the file_type according to the type of the file loaded from, preferably done whilst loading
`setMetaValue`(self, in_0, in_1)	Sets the DataValue corresponding to a name
`setPrimaryMSRunPath`
`setProteinIdentifications`(self, in_0)	Sets the protein identifications for this map
`setUnassignedPeptideIdentifications`(self, in_0)	Sets the unassigned peptide identifications
`setUniqueId`(self, rhs)	Assigns a new, valid unique id.
`setUniqueIds`
`size`(self)	Returns the number of features in the map
`sortByIntensity`
`sortByMZ`(self)	Sorts features by mass-to-charge ratio (m/z) in ascending order
`sortByOverallQuality`(self)	Sorts features by overall quality score in ascending order
`sortByPosition`(self)	Sorts features by position using lexicographical comparison
`sortByRT`(self)	Sorts features by retention time (RT) in ascending order
`swap`(self, in_0)
`swapFeaturesOnly`(self, swapfrom)	Swaps the feature content (plus its range information) of this map
`updateRanges`(self)

clear()#

Overload:

clear(self) → None

Clears all feature data and metadata

After calling this, the map will be empty (size() returns 0)

Overload:

clear(self, clear_meta_data: bool) → None

Clears feature data and optionally metadata

Parameters:: clear_meta_data – If True, also clears all metadata; if False, keeps metadata

clearMetaInfo(self) → None#: Removes all meta values

clearRanges(self) → None#: Resets all range dimensions as empty

clearUniqueId(self) → int#: Clear the unique id. The new unique id will be invalid. Returns 1 if the unique id was changed, 0 otherwise

ensureUniqueId(self) → int#: Assigns a valid unique id, but only if the present one is invalid. Returns 1 if the unique id was changed, 0 otherwise

getDataProcessing(self) → List[DataProcessing]#

getIdentifier(self) → bytes | str | String#: Retrieve document identifier (e.g. an LSID)

getKeys(self, keys: List[bytes]) → None#: Fills the given vector with a list of all keys for which a value is set

getLoadedFilePath(self) → bytes | str | String#: Returns the file_name which is the absolute path to the file loaded

getLoadedFileType(self) → int#: Returns the file_type (e.g. featureXML, consensusXML, mzData, mzXML, mzML, …) of the file loaded

getMaxIntensity(self) → float#: Returns the maximum intensity

getMaxMZ(self) → float#: Returns the maximum m/z

getMaxRT(self) → float#: Returns the maximum RT

getMetaValue(self, in_0: bytes | str | String) → int | float | bytes | str | List[int] | List[float] | List[bytes]#: Returns the value corresponding to a string, or

getMinIntensity(self) → float#: Returns the minimum intensity

getMinMZ(self) → float#: Returns the minimum m/z

getMinRT(self) → float#: Returns the minimum RT

getPrimaryMSRunPath(self, toFill: List[bytes]) → None#: Returns the file path to the first MS run

getProteinIdentifications(self) → List[ProteinIdentification]#

Returns the protein identification runs stored in this map

Returns:: Protein identification data from database searches

Protein identifications contain metadata about search parameters and protein hits

getUnassignedPeptideIdentifications(self) → PeptideIdentificationList#

Returns peptide identifications that are not assigned to any feature

Returns:: Unassigned peptide identification results

These are peptide IDs that could not be matched to features, possibly due to feature detection issues or filtering

getUniqueId(self) → int#: Returns the unique id

get_assigned_peptide_identifications()#

Generates a list with peptide identifications assigned to a feature.

Adds ‘ID_native_id’ (feature spectrum id), ‘ID_filename’ (primary MS run path of corresponding ProteinIdentification) and ‘feature_id’ (unique ID of corresponding Feature) as meta values to the peptide hits. A DataFrame from the assigned peptides generated with peptide_identifications_to_df(assigned_peptides) can be merged with the FeatureMap DataFrame with: merged_df = _pd.merge(feature_df, assigned_peptide_df, on=[‘feature_id’, ‘ID_native_id’, ‘ID_filename’])

Returns: _PeptideIdentificationList: list of PeptideIdentification objects

get_df(columns: None | List[str] = None, meta_values: None | List[str] | str = None, export_peptide_identifications: bool = True)#

Generates a pandas DataFrame with information contained in the FeatureMap.

Optionally the feature meta values and information for the assigned PeptideHit can be exported.

Parameters: columns (list or None): List of column names to include. If None,

includes all columns. Use get_df_columns() to discover available columns.

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

export_peptide_identifications (bool): export sequence and score for best PeptideHit assigned to a feature. Additionally the ID_filename (file name of the corresponding ProteinIdentification) and the ID_native_id (spectrum ID of the corresponding Feature) are exported. They are also annotated as meta values when collecting all assigned PeptideIdentifications from a FeatureMap with FeatureMap.get_assigned_peptide_identifications(). A DataFrame from the assigned peptides generated with peptide_identifications_to_df(assigned_peptides) can be merged with the FeatureMap DataFrame with: merged_df = pd.merge(feature_df, assigned_peptide_df, on=[‘feature_id’, ‘ID_native_id’, ‘ID_filename’])

Returns: pandas.DataFrame: feature information stored in a DataFrame

Example:

>>> # Get all columns
>>> df = fmap.get_df()

>>> # Discover available columns
>>> print(fmap.get_df_columns())

>>> # Get only specific columns
>>> df = fmap.get_df(columns=['feature_id', 'mz', 'rt', 'intensity'])

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

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

Useful for discovering available columns before export.

Args:

columns (str): ‘default’ for standard columns, ‘all’ to include all meta values. export_peptide_identifications (bool): Whether to include peptide ID columns.

Returns:

list: List of column name strings.

Example:

>>> fmap.get_df_columns()
['feature_id', 'peptide_sequence', 'charge', 'rt', 'mz', ...]

hasInvalidUniqueId(self) → int#: Returns whether the unique id is invalid. Returns 1 if the unique id is invalid, 0 otherwise

hasValidUniqueId(self) → int#: Returns whether the unique id is valid. Returns 1 if the unique id is valid, 0 otherwise

isMetaEmpty(self) → bool#: Returns if the MetaInfo is empty

isValid(self, unique_id: int) → bool#: Returns true if the unique_id is valid, false otherwise

metaRegistry(self) → MetaInfoRegistry#: Returns a reference to the MetaInfoRegistry

metaValueExists(self, in_0: bytes | str | String) → bool#: Returns whether an entry with the given name exists

push_back()#

Overload:

push_back(self, spec: Feature) → None

Adds a Feature to the map

Parameters:: spec – The feature to add to the map

Overload:

push_back(self, spec: MRMFeature) → None

Adds an MRMFeature to the map

Parameters:: spec – The MRM feature to add to the map

removeMetaValue(self, in_0: bytes | str | String) → None#: Removes the DataValue corresponding to name if it exists

setDataProcessing(self, in_0: List[DataProcessing]) → None#: Sets the description of the applied data processing

setIdentifier(self, id: bytes | str | String) → None#: Sets document identifier (e.g. an LSID)

setLoadedFilePath(self, file_name: bytes | str | String) → None#: Sets the file_name according to absolute path of the file loaded, preferably done whilst loading

setLoadedFileType(self, file_name: bytes | str | String) → None#: Sets the file_type according to the type of the file loaded from, preferably done whilst loading

setMetaValue(self, in_0: bytes | str | String, in_1: int | float | bytes | str | List[int] | List[float] | List[bytes]) → None#: Sets the DataValue corresponding to a name

setPrimaryMSRunPath()#

Overload:

setPrimaryMSRunPath(self, s: List[bytes]) → None

Sets the file path to the primary MS run (usually the mzML file obtained after data conversion from raw files)

Overload:

setPrimaryMSRunPath(self, s: List[bytes], e: MSExperiment) → None

Sets the file path to the primary MS run using the mzML annotated in the MSExperiment argument e

setProteinIdentifications(self, in_0: List[ProteinIdentification]) → None#

Sets the protein identifications for this map

Parameters:: protein_ids – Protein identification results to associate with this map

setUnassignedPeptideIdentifications(self, in_0: PeptideIdentificationList) → None#

Sets the unassigned peptide identifications

Parameters:: peptide_ids – Peptide IDs not assigned to features

setUniqueId(self, rhs: int) → None#: Assigns a new, valid unique id. Always returns 1

setUniqueIds()#

size(self) → int#

Returns the number of features in the map

Returns:: Number of features stored in this container

sortByIntensity()#

Overload:

sortByIntensity(self) → None

Sorts features by ascending intensity

After sorting, features can be accessed in order from lowest to highest intensity

Overload:

sortByIntensity(self, reverse: bool) → None

Sorts features by intensity with optional reverse order

Parameters:: reverse – If True, sorts in descending order (highest to lowest intensity)

sortByMZ(self) → None#

Sorts features by mass-to-charge ratio (m/z) in ascending order

Useful for mass-based grouping or analysis

sortByOverallQuality(self) → None#

Sorts features by overall quality score in ascending order

Higher quality scores indicate better feature detection confidence

sortByPosition(self) → None#

Sorts features by position using lexicographical comparison

Compares RT first, then m/z for features with the same RT

sortByRT(self) → None#

Sorts features by retention time (RT) in ascending order

This is useful for time-based analysis or visualization

swap(self, in_0: FeatureMap) → None#

swapFeaturesOnly(self, swapfrom: FeatureMap) → None#: Swaps the feature content (plus its range information) of this map

updateRanges(self) → None#