ConsensusMap#

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

Bases: object

A container for consensus elements. A ConsensusMap is a container holding 2-dimensional consensus elements (ConsensusFeature) which in turn represent analytes that have been quantified across multiple LC-MS/MS experiments. Each analyte in a ConsensusFeature is linked to its original LC-MS/MS run, the links are maintained by the ConsensusMap class. The map is implemented as a vector of elements of type ConsensusFeature. To be consistent, all maps who are referenced by ConsensusFeature objects (through a unique id) need to be registered in this class. This class supports direct iteration in Python.

__init__(self) → None#
__init__(self, arg: pyopenms._pyopenms_kernel.ConsensusMap, /) → None
__init__(self, arg: int, /) → None

Methods

`consensus_feature_views`()	Returns a list of live views of the consensus features (see consensus_feature_view).
`df_columns`([columns])	Returns a list of column names that to_df() would produce.
`feature_columns`()	Return list of column names that to_feature_arrow() and to_feature_qpx() produce.
`get_df`(args, *kwargs)	Deprecated: Use to_df() instead.
`get_df_columns`(args, *kwargs)	Deprecated: Use df_columns() instead.
`get_intensity_df`()	Generates a pandas DataFrame with feature intensities from each sample in long format.
`get_metadata_df`()	Generates a pandas DataFrame with feature meta data.
`iter_consensus_feature_views`()	Yields live views of the consensus features, one at a time (see consensus_feature_view).
`to_arrow`([columns])	Returns an Apache Arrow Table with consensus feature meta data and intensities.
`to_df`([columns])	Generates a pandas DataFrame with both consensus feature meta data and intensities.
`to_feature_arrow`([reference_file_name, ...])	Export consensus features as Apache Arrow Table following QPX feature schema.
`to_feature_df`(**kwargs)	Export consensus features as pandas DataFrame following QPX feature schema.
`to_feature_parquet`(filename[, compression])	Export consensus features to a Parquet file.
`to_feature_qpx`([qpx_version, creator, ...])	Export consensus features as QPX format (dict with file_metadata and features).

Attributes

`append`	Add a consensus feature to the map (alias for push_back)
`appendColumns`	Add consensus map entries as new columns
`appendRows`	Add consensus map entries as new rows
`clear`	Clears all data and meta data
`clearMetaInfo`	Removes all meta values
`clearRanges`	Clear all ranges
`clearUniqueId`	Clear the unique id.
`consensus_feature_view`	Returns a live view of the consensus feature at index i.
`empty`	Returns True if the map is empty
`ensureUniqueId`	Assigns a valid unique id, but only if the present one is invalid.
`extend`	Overloaded function.
`getColumnHeaders`	Returns the column headers
`getDataProcessing`	Returns a copy of the description of the applied data processing
`getExperimentType`	Non-mutable access to the experiment type
`getIdentifier`	Retrieve document identifier (e.g. an LSID).
`getKeys`	Overloaded function.
`getLoadedFilePath`	Returns the file_name which is the absolute path to the file loaded
`getLoadedFileType`	Returns the file_type (e.g. featureXML, consensusXML, mzData, mzXML, mzML, ...) of the file loaded.
`getMaxIntensity`	Get the maximum intensity value
`getMaxMZ`	Get the maximum m/z value
`getMaxRT`	Get the maximum RT value
`getMetaValue`	Returns the value corresponding to a string, or DataValue::EMPTY if not found
`getMinIntensity`	Get the minimum intensity value
`getMinMZ`	Get the minimum m/z value
`getMinRT`	Get the minimum RT value
`getPrimaryMSRunPath`	Returns the MS run path (stored in ColumnHeaders)
`getProteinIdentifications`	Returns the protein identification runs stored in this map
`getUnassignedPeptideIdentifications`	Returns peptide identifications that are not assigned to any consensus feature
`getUniqueId`	Returns the unique id
`hasInvalidUniqueId`	Returns whether the unique id is invalid.
`hasValidUniqueId`	Returns whether the unique id is valid.
`isMetaEmpty`	Returns if the MetaInfo is empty
`isValid`
`metaRegistry`
`metaValueExists`	Returns whether an entry with the given name exists
`push_back`	Add a consensus feature to the map
`removeMetaValue`	Removes the DataValue corresponding to name if it exists
`reserve`	Reserves space for n consensus features
`setColumnHeaders`	Sets the column headers
`setDataProcessing`	Sets the description of the applied data processing
`setExperimentType`	Mutable access to the experiment type
`setIdentifier`	Sets document identifier (e.g. an LSID).
`setLoadedFilePath`	Sets the file_name according to absolute path of the file loaded, preferably done whilst loading
`setLoadedFileType`	Sets the file_type according to the type of the file loaded from, preferably done whilst loading
`setMetaValue`	Sets the DataValue corresponding to a name
`setPrimaryMSRunPath`	Overloaded function.
`setProteinIdentifications`	Sets the protein identifications
`setUnassignedPeptideIdentifications`	Sets the unassigned PeptideIdentificationList
`setUniqueId`	Overloaded function.
`setUniqueIds`	Sets unique IDs on the map and all its child consensus features
`size`	Returns the number of consensus features
`sortByIntensity`	Sorts the peaks according to ascending intensity.
`sortByMZ`	Sorts the peaks according to m/z position
`sortByMaps`	Sorts with respect to the sets of maps covered by the consensus features (lexicographically)
`sortByPosition`	Lexicographically sorts the peaks by their position (First RT then m/z)
`sortByQuality`	Sorts the peaks according to ascending quality.
`sortByRT`	Sorts the peaks according to RT position
`sortBySize`	Sorts with respect to the size (number of elements)
`sortPeptideIdentificationsByMapIndex`	Sorts PeptideIdentifications of consensus features with respect to their map index.
`updateRanges`	Updates the RT, m/z, and intensity ranges based on contained consensus features

append#: Add a consensus feature to the map (alias for push_back)

appendColumns#: Add consensus map entries as new columns

appendRows#: Add consensus map entries as new rows

clear#: Clears all data and meta data

clearMetaInfo#: Removes all meta values

clearRanges#: Clear all ranges

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

consensus_feature_view#: Returns a live view of the consensus feature at index i. The view aliases this object’s storage: edits through it are visible immediately, and it stays valid only until the consensus feature list is resized or reordered. The parent object is kept alive automatically. For an owned copy use cm[i].

consensus_feature_views()#: Returns a list of live views of the consensus features (see consensus_feature_view).

df_columns(columns='default')#: Returns a list of column names that to_df() would produce.

empty#: Returns True if the map is empty

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

extend#

Overloaded function.

extend(self, items: list) -> None

Add multiple consensus features from a list

extend(self, other: pyopenms._pyopenms_kernel.ConsensusMap) -> None

Add features from another ConsensusMap

feature_columns()#

Return list of column names that to_feature_arrow() and to_feature_qpx() produce.

Returns#

list: List of column name strings.

getColumnHeaders#: Returns the column headers

getDataProcessing#: Returns a copy of the description of the applied data processing

getExperimentType#: Non-mutable access to the experiment type

getIdentifier#: Retrieve document identifier (e.g. an LSID)

getKeys#

Overloaded function.

getKeys(self, keys: list) -> None

Fills the given list with all meta value keys

getKeys(self) -> list

Returns all meta value keys as a list

getLoadedFilePath#: Returns the file_name which is the absolute path to the file loaded

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

getMaxIntensity#: Get the maximum intensity value

getMaxMZ#: Get the maximum m/z value

getMaxRT#: Get the maximum RT value

getMetaValue#: Returns the value corresponding to a string, or DataValue::EMPTY if not found

getMinIntensity#: Get the minimum intensity value

getMinMZ#: Get the minimum m/z value

getMinRT#: Get the minimum RT value

getPrimaryMSRunPath#: Returns the MS run path (stored in ColumnHeaders)

getProteinIdentifications#: Returns the protein identification runs stored in this map

getUnassignedPeptideIdentifications#: Returns peptide identifications that are not assigned to any consensus feature

getUniqueId#: Returns the unique id

get_df(*args, **kwargs)#: Deprecated: Use to_df() instead.

get_df_columns(*args, **kwargs)#: Deprecated: Use df_columns() instead.

get_intensity_df()#: Generates a pandas DataFrame with feature intensities from each sample in long format.

get_metadata_df()#: Generates a pandas DataFrame with feature meta data.

hasInvalidUniqueId#: Returns whether the unique id is invalid. Returns 1 if the unique id is invalid, 0 otherwise

hasValidUniqueId#: Returns whether the unique id is valid. Returns 1 if the unique id is valid, 0 otherwise

isMetaEmpty#: Returns if the MetaInfo is empty

isValid = <nanobind.nb_func object>#

iter_consensus_feature_views()#: Yields live views of the consensus features, one at a time (see consensus_feature_view).

metaRegistry = <nanobind.nb_func object>#

metaValueExists#: Returns whether an entry with the given name exists

push_back#: Add a consensus feature to the map

removeMetaValue#: Removes the DataValue corresponding to name if it exists

reserve#: Reserves space for n consensus features

setColumnHeaders#: Sets the column headers

setDataProcessing#: Sets the description of the applied data processing

setExperimentType#: Mutable access to the experiment type

setIdentifier#: Sets document identifier (e.g. an LSID)

setLoadedFilePath#: Sets the file_name according to absolute path of the file loaded, preferably done whilst loading

setLoadedFileType#: Sets the file_type according to the type of the file loaded from, preferably done whilst loading

setMetaValue#: Sets the DataValue corresponding to a name

setPrimaryMSRunPath#

Overloaded function.

setPrimaryMSRunPath(self, s: list[str]) -> None

Sets the file paths to the primary MS run (stored in ColumnHeaders)

setPrimaryMSRunPath(self, s: list[str], e: pyopenms._pyopenms_experiment.MSExperiment) -> None

setProteinIdentifications#: Sets the protein identifications

setUnassignedPeptideIdentifications#: Sets the unassigned PeptideIdentificationList

setUniqueId#

Overloaded function.

setUniqueId(self) -> int

Assigns a new, valid unique id. Always returns 1

setUniqueId(self, rhs: int) -> None

Assigns the given unique id

setUniqueId(self, rhs: str) -> None

Assigns a unique id from a string (extracts UInt64 after last underscore)

setUniqueIds#: Sets unique IDs on the map and all its child consensus features

size#: Returns the number of consensus features

sortByIntensity#: Sorts the peaks according to ascending intensity.

sortByMZ#: Sorts the peaks according to m/z position

sortByMaps#: Sorts with respect to the sets of maps covered by the consensus features (lexicographically)

sortByPosition#: Lexicographically sorts the peaks by their position (First RT then m/z)

sortByQuality#: Sorts the peaks according to ascending quality.

sortByRT#: Sorts the peaks according to RT position

sortBySize#: Sorts with respect to the size (number of elements)

sortPeptideIdentificationsByMapIndex#: Sorts PeptideIdentifications of consensus features with respect to their map index.

to_arrow(columns=None)#: Returns an Apache Arrow Table with consensus feature meta data and intensities.

to_df(columns=None)#: Generates a pandas DataFrame with both consensus feature meta data and intensities.

to_feature_arrow(reference_file_name=None, columns=None, include_modifications=True, scan_format='native_id')#

Export consensus features as Apache Arrow Table following QPX feature schema.

Parameters#

reference_file_namestr, optional: Reference file name to use for unmatched features.
columnslist, optional: Columns to include. If None, includes all.
include_modificationsbool: Include modification information (default: True).
scan_formatstr: Format for scan numbers (default: ‘native_id’).

Returns#

pyarrow.Table: Arrow table with feature data.

to_feature_df(**kwargs)#

Export consensus features as pandas DataFrame following QPX feature schema.

Parameters#

**kwargs: Passed to to_feature_arrow().

Returns#

pandas.DataFrame: DataFrame with feature data.

to_feature_parquet(filename, compression='snappy', **kwargs)#

Export consensus features to a Parquet file.

Parameters#

filenamestr: Output file path.
compressionstr: Compression codec (default: ‘snappy’).
**kwargs: Passed to to_feature_arrow().

to_feature_qpx(qpx_version='1.1', creator='pyopenms', software_provider='OpenMS', **kwargs)#

Export consensus features as QPX format (dict with file_metadata and features).

Parameters#

qpx_versionstr: QPX schema version.
creatorstr: Creator identifier.
software_providerstr: Software provider name.
**kwargs: Passed to to_feature_arrow().

Returns#

dict: Dictionary with ‘file_metadata’ and ‘features’ keys.

updateRanges#: Updates the RT, m/z, and intensity ranges based on contained consensus features