MSChromatogram#

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

Bases: MSChromatogram

MSChromatogram with DataFrame export capabilities.

This class extends MSChromatogram with a get_df() method that converts chromatogram data to a pandas DataFrame.

__init__(*args, **kwargs)#

Overload:

__init__(self) → None

Overload:

__init__(self, in_0: MSChromatogram) → None

Methods

`__init__`(args, *kwargs)
`clear`(self, in_0)	Clears all data and meta data
`clearMetaInfo`(self)	Removes all meta values
`clearRanges`(self)	Resets all range dimensions as empty
`findNearest`(self, in_0)	Binary search for the peak nearest to a specific RT :note: Make sure the chromatogram is sorted with respect to RT! Otherwise the result is undefined
`getAcquisitionInfo`(self)	Returns the acquisition info
`getChromatogramType`(self)	Get the chromatogram type
`getComment`(self)	Returns the free-text comment
`getDataProcessing`(self)	Returns the description of the applied processing
`getFloatDataArrays`(self)	Returns a reference to the float meta data arrays
`getInstrumentSettings`(self)	Returns the instrument settings of the current spectrum
`getIntegerDataArrays`(self)	Returns a reference to the integer meta data arrays
`getKeys`(self, keys)	Fills the given vector with a list of all keys for which a value is set
`getMZ`(self)	Returns the mz of the product entry, makes sense especially for MRM scans
`getMaxIntensity`(self)	Returns the maximum intensity
`getMaxRT`(self)	Returns the maximum RT
`getMetaValue`(self, in_0)	Returns the value corresponding to a string, or
`getMinIntensity`(self)	Returns the minimum intensity
`getMinRT`(self)	Returns the minimum RT
`getName`(self)	Returns the name
`getNativeID`(self)	Returns the native identifier for the spectrum, used by the acquisition software.
`getPrecursor`(self)	Returns the precursors
`getProduct`(self)	Returns the product ion
`getSourceFile`(self)	Returns the source file
`getStringDataArrays`(self)	Returns a reference to the string meta data arrays
`get_data_dict`	Returns a dictionary of NumPy arrays with RT, intensities, and metadata.
`get_df`([columns, export_meta_values])	Returns a pandas DataFrame representation of the MSChromatogram.
`get_df_columns`	Returns a list of column names that get_df() would produce for this chromatogram.
`get_peaks`
`isMetaEmpty`(self)	Returns if the MetaInfo is empty
`isSorted`(self)	Checks if all peaks are sorted with respect to ascending RT
`metaRegistry`(self)	Returns a reference to the MetaInfoRegistry
`metaValueExists`(self, in_0)	Returns whether an entry with the given name exists
`push_back`(self, in_0)	Append a peak
`removeMetaValue`(self, in_0)	Removes the DataValue corresponding to name if it exists
`reserve`(self, n)
`resize`(self, n)	Resize the peak array
`setAcquisitionInfo`(self, acquisition_info)	Sets the acquisition info
`setChromatogramType`(self, type)	Sets the chromatogram type
`setComment`(self, comment)	Sets the free-text comment
`setDataProcessing`(self, in_0)	Sets the description of the applied processing
`setFloatDataArrays`(self, fda)	Sets the float meta data arrays
`setInstrumentSettings`(self, instrument_settings)	Sets the instrument settings of the current spectrum
`setIntegerDataArrays`(self, ida)	Sets the integer meta data arrays
`setMetaValue`(self, in_0, in_1)	Sets the DataValue corresponding to a name
`setName`(self, in_0)	Sets the name
`setNativeID`(self, native_id)	Sets the native identifier for the spectrum, used by the acquisition software.
`setPrecursor`(self, precursor)	Sets the precursors
`setProduct`(self, p)	Sets the product ion
`setSourceFile`(self, source_file)	Sets the source file
`setStringDataArrays`(self, sda)	Sets the string meta data arrays
`set_peaks`
`size`(self)
`sortByIntensity`(self, reverse)	Lexicographically sorts the peaks by their intensity
`sortByPosition`(self)	Lexicographically sorts the peaks by their position
`updateRanges`(self)

clear(self, in_0: int) → None#

Clears all data and meta data

Parameters:: clear_meta_data – If true, 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

findNearest(self, in_0: float) → int#

Binary search for the peak nearest to a specific RT :note: Make sure the chromatogram is sorted with respect to RT! Otherwise the result is undefined

Parameters:: rt – The searched for mass-to-charge ratio searched
Returns:: Returns the index of the peak.
Raises:: Exception: Precondition is thrown if the chromatogram is empty (not only in debug mode)

getAcquisitionInfo(self) → AcquisitionInfo#: Returns the acquisition info

getChromatogramType(self) → int#: Get the chromatogram type

getComment(self) → bytes | str | String#: Returns the free-text comment

getDataProcessing(self) → List[DataProcessing]#: Returns the description of the applied processing

getFloatDataArrays(self) → List[FloatDataArray]#: Returns a reference to the float meta data arrays

getInstrumentSettings(self) → InstrumentSettings#: Returns the instrument settings of the current spectrum

getIntegerDataArrays(self) → List[IntegerDataArray]#: Returns a reference to the integer meta data arrays

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

getMZ(self) → float#: Returns the mz of the product entry, makes sense especially for MRM scans

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

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

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

getName(self) → bytes | str | String#: Returns the name

getNativeID(self) → bytes | str | String#: Returns the native identifier for the spectrum, used by the acquisition software.

getPrecursor(self) → Precursor#: Returns the precursors

getProduct(self) → Product#: Returns the product ion

getSourceFile(self) → SourceFile#: Returns the source file

getStringDataArrays(self) → List[StringDataArray]#: Returns a reference to the string meta data arrays

get_data_dict()#

Returns a dictionary of NumPy arrays with RT, intensities, and metadata.

This method extracts chromatogram data including peaks, precursor/product info, and optional meta values into a dictionary format suitable for conversion to a pandas DataFrame.

Args:

columns (list or None): List of column names to include. If None, includes: all default columns. Use get_df_columns(‘all’) to see all available columns.
export_meta_values (bool): Whether to include meta values in the output.: Only applies when columns=None. Defaults to True.

Returns:

dict: Dictionary with requested columns as keys and numpy arrays as values.

Default columns include:

‘rt’: numpy array of retention time values (float64)
‘intensity’: numpy array of intensity values (float32)
‘precursor_mz’: precursor m/z (float64)
‘precursor_charge’: precursor charge (uint16)
‘product_mz’: product m/z (float64)
‘native_id’: chromatogram native identifier
Additional meta value columns (if export_meta_values=True)

Non-default columns (must be explicitly requested): - ‘chromatogram_type’: type of chromatogram - ‘comment’: chromatogram comment

Example:

>>> # Get all columns (default)
>>> data = chrom.get_data_dict()

>>> # Get only specific columns for performance
>>> data = chrom.get_data_dict(columns=['rt', 'intensity'])

>>> # Get all available columns including non-defaults
>>> all_cols = chrom.get_df_columns('all')
>>> data = chrom.get_data_dict(columns=all_cols)

get_df(columns: None | List[str] = None, export_meta_values: bool = True) → DataFrame#

Returns a pandas DataFrame representation of the MSChromatogram.

This method converts the chromatogram data (peaks, metadata, precursor/product info) into a pandas DataFrame format.

Args:

columns (list or None): List of column names to include. If None,: includes all default columns. Use get_df_columns() to discover available columns.
export_meta_values (bool): Whether to include meta values. Only applies: when columns=None. Defaults to True.

Returns:

pd.DataFrame: DataFrame with requested columns. Default columns include:

rt: retention time (in seconds)
intensity: signal intensity at each time point
precursor_mz: precursor m/z
precursor_charge: precursor charge
product_mz: product m/z
native_id: chromatogram native identifier
Additional meta value columns (if export_meta_values=True)

Non-default columns (must be explicitly requested):

chromatogram_type: type of chromatogram
comment: chromatogram comment

Example:

>>> # Get all default columns
>>> df = chrom.get_df()

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

>>> # Get only specific columns (faster)
>>> df = chrom.get_df(columns=['rt', 'intensity'])

>>> # Get all columns including non-defaults
>>> cols = chrom.get_df_columns('all')
>>> df = chrom.get_df(columns=cols)

get_df_columns()#

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

Useful for discovering available columns before export, especially when selecting specific columns for performance optimization.

Args:

columns (str): ‘default’ for standard columns, ‘all’ for all available: columns including non-default ones (chromatogram_type, comment).
export_meta_values (bool): Whether to include meta value column names.: Defaults to True.

Returns:

list: List of column name strings.

Example:

>>> # See default columns
>>> cols = chrom.get_df_columns()
['rt', 'intensity', 'precursor_mz', ...]

>>> # See ALL available columns
>>> cols = chrom.get_df_columns('all')
['rt', 'intensity', ..., 'chromatogram_type', 'comment']

>>> # Export everything
>>> df = chrom.get_df(columns=chrom.get_df_columns('all'))

get_peaks()#

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

isSorted(self) → bool#: Checks if all peaks are sorted with respect to ascending RT

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, in_0: ChromatogramPeak) → None#: Append a peak

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

reserve(self, n: int) → None#

resize(self, n: int) → None#: Resize the peak array

setAcquisitionInfo(self, acquisition_info: AcquisitionInfo) → None#: Sets the acquisition info

setChromatogramType(self, type: int) → None#: Sets the chromatogram type

setComment(self, comment: bytes | str | String) → None#: Sets the free-text comment

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

setFloatDataArrays(self, fda: List[FloatDataArray]) → None#: Sets the float meta data arrays

setInstrumentSettings(self, instrument_settings: InstrumentSettings) → None#: Sets the instrument settings of the current spectrum

setIntegerDataArrays(self, ida: List[IntegerDataArray]) → None#: Sets the integer meta data arrays

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

setName(self, in_0: bytes | str | String) → None#: Sets the name

setNativeID(self, native_id: bytes | str | String) → None#: Sets the native identifier for the spectrum, used by the acquisition software.

setPrecursor(self, precursor: Precursor) → None#: Sets the precursors

setProduct(self, p: Product) → None#: Sets the product ion

setSourceFile(self, source_file: SourceFile) → None#: Sets the source file

setStringDataArrays(self, sda: List[StringDataArray]) → None#: Sets the string meta data arrays

set_peaks()#

size(self) → int#

sortByIntensity(self, reverse: bool) → None#

Lexicographically sorts the peaks by their intensity

Sorts the peaks according to ascending intensity. Meta data arrays will be sorted accordingly

sortByPosition(self) → None#

Lexicographically sorts the peaks by their position

The chromatogram is sorted with respect to position. Meta data arrays will be sorted accordingly

updateRanges(self) → None#