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)	Overload:
clear	Overload:
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.
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.
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)
getUnassignedPeptideIdentifications(self)
getUniqueId(self)	Returns the unique id
get_assigned_peptide_identifications()	Generates a list with peptide identifications assigned to a feature.
get_df([meta_values, ...])	Generates a pandas DataFrame with information contained in the FeatureMap.
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	Overload:
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.
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	Overload:
setProteinIdentifications(self, in_0)	Sets the protein identifications
setUnassignedPeptideIdentifications(self, in_0)	Sets the unassigned peptide identifications
setUniqueId(self, rhs)	Assigns a new, valid unique id.
setUniqueIds
size(self)
sortByIntensity	Overload:
sortByMZ(self)	Sorts features by m/z position
sortByOverallQuality(self)	Sorts features by ascending overall quality.
sortByPosition(self)	Sorts features by position.
sortByRT(self)	Sorts features by RT position
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 data and meta data

Overload:

clear(self, clear_meta_data: bool) → None

Clears all data and meta data. If ‘true’ is passed as an argument, all meta data is cleared in addition to the data

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]

getUnassignedPeptideIdentifications(self) → List[PeptideIdentification]

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: [PeptideIdentification]: list of PeptideIdentification objects

get_df(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: 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

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

Overload:

push_back(self, spec: MRMFeature) → None

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

setUnassignedPeptideIdentifications(self, in_0: List[PeptideIdentification]) → None

Sets the unassigned peptide identifications

setUniqueId(self, rhs: int) → None

Assigns a new, valid unique id. Always returns 1

setUniqueIds()

size(self) → int

sortByIntensity()

Overload:

sortByIntensity(self) → None

Sorts the peaks according to ascending intensity

Overload:

sortByIntensity(self, reverse: bool) → None

Sorts the peaks according to ascending intensity. Order is reversed if argument is true ( reverse = true )

sortByMZ(self) → None

Sorts features by m/z position

sortByOverallQuality(self) → None

Sorts features by ascending overall quality. Order is reversed if argument is true ( reverse = true )

sortByPosition(self) → None

Sorts features by position. Lexicographical comparison (first RT then m/z) is done

sortByRT(self) → None

Sorts features by RT position

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