ConsensusMap#

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

Bases: ConsensusMap

__init__(*args, **kwargs)#

Overload:

__init__(self) → None

Overload:

__init__(self, in_0: ConsensusMap) → None

Methods

`__init__`(args, *kwargs)
`appendColumns`(self, in_0)	Add consensus map entries as new columns
`appendRows`(self, in_0)	Add consensus map entries as new rows
`clear`
`clearMetaInfo`(self)	Removes all meta values
`clearRanges`(self)	Resets all range dimensions as empty
`clearUniqueId`(self)	Clear the unique id.
`empty`(self)
`ensureUniqueId`(self)	Assigns a valid unique id, but only if the present one is invalid.
`getColumnHeaders`
`getDataProcessing`(self)	Returns a const reference to the description of the applied data processing
`getExperimentType`(self)	Non-mutable access to the experiment type
`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 MS run path (stored in ColumnHeaders)
`getProteinIdentifications`(self)
`getUnassignedPeptideIdentifications`(self)
`getUniqueId`(self)	Returns the unique id
`get_df`([columns])	Generates a pandas DataFrame with both consensus feature meta data and intensities from each sample.
`get_df_columns`([columns])	Returns a list of column names that get_df() would produce.
`get_intensity_df`()	Generates a pandas DataFrame with feature intensities from each sample in long format (over files).
`get_metadata_df`()	Generates a pandas DataFrame with feature meta data.
`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`(self, spec)
`removeMetaValue`(self, in_0)	Removes the DataValue corresponding to name if it exists
`reserve`(self, s)
`setColumnHeaders`
`setDataProcessing`(self, in_0)	Sets the description of the applied data processing
`setExperimentType`(self, experiment_type)	Mutable access to the experiment type
`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
`setUnassignedPeptideIdentifications`(self, ...)	Sets the unassigned PeptideIdentificationList
`setUniqueId`(self, rhs)	Assigns a new, valid unique id.
`setUniqueIds`
`size`(self)
`sortByIntensity`
`sortByMZ`(self)	Sorts the peaks according to m/z position
`sortByMaps`(self)	Sorts with respect to the sets of maps covered by the consensus features (lexicographically)
`sortByPosition`(self)	Lexicographically sorts the peaks by their position (First RT then m/z)
`sortByQuality`
`sortByRT`(self)	Sorts the peaks according to RT position
`sortBySize`(self)	Sorts with respect to the size (number of elements)
`sortPeptideIdentificationsByMapIndex`(self)	Sorts PeptideIdentifications of consensus features with respect to their map index.
`updateRanges`(self)

appendColumns(self, in_0: ConsensusMap) → ConsensusMap#: Add consensus map entries as new columns

appendRows(self, in_0: ConsensusMap) → ConsensusMap#: Add consensus map entries as new rows

clear()#

Overload:

clear(self, clear_meta_data: bool) → None

Clears all data and meta data

Overload:

clear(self) → None

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

empty(self) → bool#

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

getColumnHeaders()#

getDataProcessing(self) → List[DataProcessing]#: Returns a const reference to the description of the applied data processing

getExperimentType(self) → bytes | str | String#: Non-mutable access to the experiment type

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 MS run path (stored in ColumnHeaders)

getProteinIdentifications(self) → List[ProteinIdentification]#

getUnassignedPeptideIdentifications(self) → PeptideIdentificationList#

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

get_df(columns: None | List[str] = None)#

Generates a pandas DataFrame with both consensus feature meta data and intensities from each sample.

Args:

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

Returns:

pandas.DataFrame: meta data and intensity DataFrame

Example:

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

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

>>> # Get only specific columns
>>> df = cmap.get_df(columns=['sequence', 'mz', 'intensity'])

get_df_columns(columns: str = 'default') → 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’ for all available columns.

Returns:

list: List of column name strings.

Example:

>>> cmap.get_df_columns()
['sequence', 'charge', 'rt', 'mz', 'quality', 'intensity_file1', ...]

get_intensity_df()#

Generates a pandas DataFrame with feature intensities from each sample in long format (over files).

For labelled analyses channel intensities will be in one row, therefore resulting in a semi-long/block format. Resulting DataFrame can be joined with result from get_metadata_df by their index ‘id’.

Returns: pandas.DataFrame: intensity DataFrame

get_metadata_df()#

Generates a pandas DataFrame with feature meta data.

Columns: sequence, charge, rt, mz, quality (indexed by ‘id’).

Resulting DataFrame can be joined with result from get_intensity_df by their index ‘id’.

Returns:

pandas.DataFrame: DataFrame with metadata for each feature (sequence, charge,: rt, mz, quality). All column names are lowercase snake_case.

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(self, spec: ConsensusFeature) → None#

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

reserve(self, s: int) → None#

setColumnHeaders()#

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

setExperimentType(self, experiment_type: bytes | str | String) → None#: Mutable access to the experiment type

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 paths to the primary MS run (stored in ColumnHeaders)

Overload:

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

setProteinIdentifications(self, in_0: List[ProteinIdentification]) → None#: Sets the protein identifications

setUnassignedPeptideIdentifications(self, unassigned_peptide_identifications: PeptideIdentificationList) → None#: Sets the unassigned PeptideIdentificationList

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

setUniqueIds()#

size(self) → int#

sortByIntensity()#

Overload:

sortByIntensity(self, reverse: bool) → None

Sorts the peaks according to ascending intensity.

Overload:

sortByIntensity(self) → None

sortByMZ(self) → None#: Sorts the peaks according to m/z position

sortByMaps(self) → None#: Sorts with respect to the sets of maps covered by the consensus features (lexicographically)

sortByPosition(self) → None#: Lexicographically sorts the peaks by their position (First RT then m/z)

sortByQuality()#

Overload:

sortByQuality(self, reverse: bool) → None

Sorts the peaks according to ascending quality.

Overload:

sortByQuality(self) → None

sortByRT(self) → None#: Sorts the peaks according to RT position

sortBySize(self) → None#: Sorts with respect to the size (number of elements)

sortPeptideIdentificationsByMapIndex(self) → None#: Sorts PeptideIdentifications of consensus features with respect to their map index.

updateRanges(self) → None#