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)	Overload:
appendColumns(self, in_0)	Add consensus map entries as new columns
appendRows(self, in_0)	Add consensus map entries as new rows
clear	Overload:
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.
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 MS run path (stored in ColumnHeaders)
getProteinIdentifications(self)
getUnassignedPeptideIdentifications(self)
getUniqueId(self)	Returns the unique id
get_df()	Generates a pandas DataFrame with both consensus feature meta data and intensities from each sample.
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 (sequence, charge, mz, RT, quality).
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.
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 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	Overload:
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) → List[PeptideIdentification]

getUniqueId(self) → int

Returns the unique id

get_df()

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

Returns: pandas.DataFrame: meta data and intensity DataFrame

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 (sequence, charge, mz, RT, quality).

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

Returns: pandas.DataFrame: DataFrame with metadata for each feature (such as: best identified sequence, charge, centroid RT/mz, fitting quality)

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, 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, 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