XIMParquetFile

class pyopenms.XIMParquetFile(*args, **kwargs)

Bases: object

OpenMS class XIMParquetFile

__init__(self, arg: pyopenms._pyopenms_format.XIMParquetFile, /) → None

__init__(self, arg: str, /) → None

__init__(self, arg: list[str], /) → None

Methods

df_columns()	Return the parquet schema column names.
get_analyte_df([nest_transitions, columns])	Return unique analyte metadata as a pandas DataFrame.
get_analyte_dict([nest_transitions, columns])	Return unique analyte metadata as a dict.
get_data_dict([explode, precursor_id, ...])	Return mobilogram data as a dict.
get_run_df()	Return unique run metadata as a pandas DataFrame.
get_run_dict()	Return unique run metadata as a dict.
query_mobilograms()	Return a chainable query builder for mobilograms.
to_arrow([explode])	Returns an Apache Arrow Table representation of the mobilograms.
to_df([explode])	Return mobilogram data as a pandas DataFrame.

Attributes

getAnalytes	Return unique analyte metadata as a dict
getColumns	Return parquet schema column names as a list
getFilename	Reader for multiple OpenSWATH mobilogram Parquet files (.xim).
getFilenames
getMobilograms	Return mobilogram data as a dict
getRuns	Return unique run metadata as a dict

df_columns()

Return the parquet schema column names.

Returns

list

List of column names.

getAnalytes

Return unique analyte metadata as a dict

getColumns

Return parquet schema column names as a list

getFilename

Reader for multiple OpenSWATH mobilogram Parquet files (.xim).

getFilenames

getMobilograms

Return mobilogram data as a dict

getRuns

Return unique run metadata as a dict

get_analyte_df(nest_transitions=True, columns=None)

Return unique analyte metadata as a pandas DataFrame.

Parameters

nest_transitionsbool

Aggregate transition fields per precursor.

columnslist, optional

List of column names to return.

Returns

pandas.DataFrame

DataFrame with analyte metadata.

Raises

ImportError

If pandas is not installed.

get_analyte_dict(nest_transitions=True, columns=None)

Return unique analyte metadata as a dict.

If nest_transitions=False, each row represents a unique precursor-transition pair with scalar transition fields. If nest_transitions=True, each row represents a unique precursor and transition fields are lists.

Parameters

nest_transitionsbool

Aggregate transition fields per precursor.

columnslist, optional

List of column names to return. If None, uses all columns.

Returns

dict

Dict of lists keyed by column name.

get_data_dict(explode=False, precursor_id=-1, transition_id=-1, modified_sequence='', precursor_charge=-1, product_charge=-1, ms_level=-1, run_id=-1, mobilogram_type='', feature_id=-1, feature_rt=-1.0, filter_expr='', **kwargs)

Return mobilogram data as a dict.

If explode=True, returns long format with mobility/intensity rows. Otherwise, mobility and intensity are stored as lists.

Parameters

explodebool

If True, return long format with one row per mobility/intensity.

precursor_idint

Optional precursor id (-1 to ignore).

transition_idint

Optional transition id (-1 to ignore).

modified_sequencestr

Optional modified sequence filter (empty to ignore).

precursor_chargeint

Optional precursor charge filter (-1 to ignore).

product_chargeint

Optional product charge filter (-1 to ignore).

ms_levelint

Optional MS level filter (-1 to ignore).

run_idint

Optional run id filter (-1 to ignore).

mobilogram_typestr

Optional mobilogram type filter (empty to ignore).

feature_idint

Optional feature id filter (-1 to ignore).

feature_rtfloat

Optional feature RT filter (<0 to ignore).

filter_exprstr

Optional filter expression string.

Returns

dict

Dict of lists keyed by column name.

get_run_df()

Return unique run metadata as a pandas DataFrame.

Returns

pandas.DataFrame

DataFrame with run_id and source_file.

Raises

ImportError

If pandas is not installed.

get_run_dict()

Return unique run metadata as a dict.

Returns

dict

Dict with run_id and source_file lists.

query_mobilograms()

Return a chainable query builder for mobilograms.

Example::

df = xim.query_mobilograms().filter_precursor_id(123).to_df()

Returns

_MobilogramQuery

Query builder with filter methods and to_df()/to_dict().

to_arrow(explode=False)

Returns an Apache Arrow Table representation of the mobilograms.

If explode=True, returns long format with mobility/intensity rows.

Parameters

explodebool

If True, return long format with one row per mobility/intensity.

Returns

pyarrow.Table

Arrow Table with mobilogram data.

Raises

ImportError

If pyarrow is not installed.

to_df(explode=False)

Return mobilogram data as a pandas DataFrame.

If explode=True, returns long format with mobility/intensity rows.

Parameters

explodebool

If True, return long format with one row per mobility/intensity.

Returns

pandas.DataFrame

DataFrame with mobilogram data.

Raises

ImportError

If pandas is not installed.