Dld

extra.components.DelayLineDetector

DelayLineDetector(
    data, detector=None, pulses=None, **kwargs
)

Interface for processed delay line detector data.

Raw analog data from quad and hex delay line detectors acquired with GHz digitizers can be reconstructed into hits by the European XFEL offline processing machinery. This component allows convenient access to the resulting sparse data as pulse-labeled pandas series and dataframes.

Note that this component is not able to access data saved by the proprietary SurfaceConcepts TDC integration.

Parameters:

Name	Type	Description	Default
data	DataCollection	Data to access DLD data from.	required
detector	str	Source name of the detector, only needed if the data includes more than one.	None
pulses	PulsePattern	Pulse component to pull pulse information. If omitted, it is constructed from the internal trigger information using any remaining additional keyword argument.	None

control_source property

control_source

Control source.

instrument_source property

instrument_source

Instrument source.

rec_params property

rec_params

Reconstruction parameters.

insert_aligned_columns staticmethod

insert_aligned_columns(df, columns)

Add pulse-indexed data to reduced dataframe.

Parameters:

Name	Type	Description	Default
df	DataFrame	Frame to insert columns into.	required
columns	dict	Mapping of column name to labeled 1D data to insert, may be pandas series, xarray DataArray or KeyData. Must be re-indexable by internal train or pulse index and data is repeated accordingly.	required

Returns:

Type	Description
	None

pulses

pulses(**kwargs)

Get pulse object based on internal triggers.

Any keyword arguments are passed to the underlying DldPulses.

Returns:

Type	Description
DldPulses	Pulse object based on constructed trigger information.

triggers

triggers()

Get triggers as dataframe.

Returns:

Type	Description
DataFrame	Constructed trigger information as dataframe.

edges

edges(channel_index=True, pulse_dim='pulseId')

Get raw edges as series or dataframe.

Parameters:

Name	Type	Description	Default
channel_index	bool	Whether to insert the edge channel as index level and return Series object (default), or as column and return DataFrame object.	True
pulse_dim	{pulseId, pulseIndex, time}	Label for pulse dimension, pulse ID by default.	'pulseId'

Returns:

Type	Description
Series or DataFrame	Raw edge positions as Series (indexed by channel) or DataFrame (with channel as column) object.

signals

signals(
    pulse_dim="pulseId", extra_columns={}, max_method=None
)

Get reconstructed signals as dataframe.

This data is primarily for detector diagnostics purposes and should generally not be used for scientific data analysis, please refer instead to reconstructed x, y, t data obtained from hits().

Parameters:

Name	Type	Description	Default
pulse_dim	{pulseId, pulseIndex, time}	Label for pulse dimension, pulse ID by default.	'pulseId'
extra_columns	dict	Mapping of column name to labeled 1D data to insert, may be pandas series, xarray DataArray or KeyData. Must be re-indexable by internal train or pulse index and data is repeated accordingly.	{}
max_method	int	Maximal reconstruction method to include in the result, by default all hits are included. Generally methods up to and including 10 can be considered safe and > 14 should be treated as risky, please consult processing reports for more details.	None

Returns:

Type	Description
	(pandas.DataFrame) Detector signals.

hits

hits(
    pulse_dim="pulseId", extra_columns={}, max_method=None
)

Get reconstructed hits as dataframe.

By default, this method only includes non-risky hit reconstructions in the returned dataset. Please refer to the max_method argument and the correspondig processing reports for more information

Parameters:

Name	Type	Description	Default
pulse_dim	{pulseId, pulseIndex, time}	Label for pulse dimension, pulse ID by default.	'pulseId'
extra_columns	dict	Mapping of column name to labeled 1D data to insert, may be pandas series, xarray DataArray or KeyData. Must be re-indexable by internal train or pulse index and data is repeated accordingly.	{}
max_method	int	Maximal reconstruction method to include in the result, by default all hits are included. Generally methods up to and including 10 can be considered safe and > 14 should be treated as risky, please consult processing reports for more details.	None

Returns:

Type	Description
	(pandas.DataFrame) Detector hits.