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FilterEvents v1¶

FilterEvents dialog.

Table of Contents

Summary
- See Also
Properties
Description
Specifying the splitting strategy
Unfiltered Events
Using FilterEvents with fast-changing logs
- Correcting time neutron was at the sample
Difference from FilterByLogValue
Usage
Source

Summary ¶

Filter events from an EventWorkspace to one or multiple EventWorkspaces according to a series of splitters.

Properties ¶

Name	Direction	Type	Default	Description
InputWorkspace	Input	EventWorkspace	Mandatory	An input event workspace
SplitterWorkspace	Input	Workspace	Mandatory	An input SpilltersWorskpace for filtering
OutputWorkspaceBaseName	Input	string	OutputWorkspace	The base name to use for the output workspace. The output workspace names are a combination of this and the index in splitter.
InformationWorkspace	Input	TableWorkspace		Optional output for the information of each splitter workspace index.
OutputTOFCorrectionWorkspace	Output	MatrixWorkspace	TOFCorrectWS	Name of output workspace for TOF correction factor.
FilterByPulseTime	Input	boolean	False	Filter the event by its pulse time only for slow sample environment log. This option can make execution of algorithm faster. But it lowers precision.
GroupWorkspaces	Input	boolean	False	Option to group all the output workspaces. Group name will be OutputWorkspaceBaseName.
OutputWorkspaceIndexedFrom1	Input	boolean	False	If selected, the minimum output workspace is indexed from 1 and continuous.
CorrectionToSample	Input	string	None	Type of correction on neutron events to sample time from detector time. Allowed values: [‘None’, ‘Customized’, ‘Direct’, ‘Elastic’, ‘Indirect’]
DetectorTOFCorrectionWorkspace	Input	TableWorkspace		Name of table workspace containing the log time correction factor for each detector.
IncidentEnergy	Input	number	Optional	Value of incident energy (Ei) in meV in direct mode.
SpectrumWithoutDetector	Input	string	Skip	Approach to deal with spectrum without detectors. Allowed values: [‘Skip’, ‘Skip only if TOF correction’]
SplitSampleLogs	Input	boolean	True	If selected, all sample logs will be splitted by the event splitters. It is not recommended for fast event log splitters.
NumberOutputWS	Output	number		Number of output output workspace splitted.
DBSpectrum	Input	number	Optional	Spectrum (workspace index) for debug purpose.
OutputWorkspaceNames	Output	str list		List of output workspaces names
RelativeTime	Input	boolean	False	Flag to indicate that in the input Matrix splitting workspace,the time indicated by X-vector is relative to either run start time or some indicted time.
FilterStartTime	Input	string		Start time for splitters that can be parsed to DateAndTime.
TimeSeriesPropertyLogs	Input	str list		List of name of sample logs of TimeSeriesProperty format. They will be either excluded from splitting if ExcludedSpecifiedLogs is specified as True. Or They will be the only TimeSeriesProperty sample logs that will be split to child workspaces.
ExcludeSpecifiedLogs	Input	boolean	True	If true, all the TimeSeriesProperty logs listed will be excluded from duplicating. Otherwise, only those specified logs will be split.
DescriptiveOutputNames	Input	boolean	False	If selected, the names of the output workspaces will include information about each slice.

This algorithm filters events from a single Event Workspace to one or multiple EventWorkspaces according to the SplittersWorkspace property. The Event Filtering concept page has a detailed introduction to event filtering.

Specifying the splitting strategy ¶

The SplittersWorkspace describes much of the information for splitting the InputWorkspace into the various output workspaces. It can have one of three types

workspace class	units	rel/abs
MatrixWorkspace	seconds	either
`SplittersWorkspace`	nanoseconds	absolute
TableWorkspace	seconds	either

Whether the values in MatrixWorkspace and TableWorkspace is treated as relative or absolute time is dependent on the value of RelativeTime. In the case of RelativeTime=True, the time is relative to the start of the run (in the ws.run()['run_start']) or, if specified, the FilterStartTime. In the case of RelativeTime=False, the times are relative to the GPS epoch.

Both TableWorkspace and SplittersWorkspace have 3 columns, start, stop, and target which should be a float, float, and string. The event filtering concept page has details on creating the TableWorkspace by hand.

If the SplittersWorkspace is a MatrixWorkspace, it must have a single spectrum with the x-value is the time boundaries and the y-value is the workspace group index.

The optional InformationWorkspace is a TableWorkspace for information of splitters.

Unfiltered Events ¶

Some events are not inside any splitters. They are put to a workspace name ended with _unfiltered. If OutputWorkspaceIndexedFrom1=True, then this workspace will not be created.

Using FilterEvents with fast-changing logs ¶

There are a few parameters to consider when the log filtering is expected to produce a large splitter table. An example of such a case would be a data file for which the events need to be split according to a log with two or more states changing in the kHz range. To reduce the filtering time, one may do the following:

Make sure the SplitterWorkspace input is a MatrixWorkspace. Such a workspace can be produced by using the FastLog = True option when calling GenerateEventsFilter.
Choose the logs to split. Filtering the logs can take a substantial amount of time. To save time, you may want to split only the logs you will need for analysis. To do so, set ExcludeSpecifiedLogs = False and list the logs you need in TimeSeriesPropertyLogs. For example, if we only need to know the accumulated proton charge for each filtered workspace, we would set TimeSeriesPropertyLogs = proton_charge.

Correcting time neutron was at the sample ¶

When filtering fast logs, the time to filter by is the time that the neutron was at the sample. This can be specified using the CorrectionToSample parameter. Either the user specifies the correction parameter for every pixel, or one is calculated. The correction parameters are applied as

\[TOF_{sample} = TOF_{detector} * scale[detectorID] + shift[detectorID]\]

and stored in the OutputTOFCorrectionWorkspace.

CorrectionToSample="None" applies no correction
CorrectionToSample="Elastic" applies \(shift = 0\) with \(scale = L1/(L1+L2)\) for detectors and \(scale = L1/L_{monitor}\) for monitors
CorrectionToSample="Direct" applies \(scale = 0\) and \(shift = L1 / \sqrt{2 E_{fix} / m_n}\). The value supplied in IncidentEnergy will override the value found in the workspace’s value of Ei.
CorrectionToSample="Indirect" applies \(scale = 1\) and \(shift = -1 * L2 / \sqrt{2 E_{fix} / m_n}\) for detectors. For monitors, uses the same corrections as Elastic.
CorrectionToSample="Customized" applies the correction supplied in the DetectorTOFCorrectionWorkspace.

Difference from FilterByLogValue ¶

In FilterByLogValue, EventList.splitByTime() is used. In FilterEvents, it only uses this when FilterByPulse=True. Otherwise, EventList.splitByFullTime() is used. The difference between splitByTime and splitByFullTime is that splitByTime filters events by pulse time, and splitByFullTime considers both pulse time and TOF.

Usage ¶

Example - Filtering event without correction on TOF

ws = Load(Filename='CNCS_7860_event.nxs')
splitws, infows = GenerateEventsFilter(InputWorkspace=ws, UnitOfTime='Nanoseconds', LogName='SampleTemp',
        MinimumLogValue=279.9,  MaximumLogValue=279.98, LogValueInterval=0.01)

FilterEvents(InputWorkspace=ws, SplitterWorkspace=splitws, InformationWorkspace=infows,
        OutputWorkspaceBaseName='tempsplitws',  GroupWorkspaces=True,
        FilterByPulseTime = False, OutputWorkspaceIndexedFrom1 = False,
        CorrectionToSample = "None", SpectrumWithoutDetector = "Skip", SplitSampleLogs = False,
        OutputTOFCorrectionWorkspace='mock')

# Print result
wsgroup = mtd["tempsplitws"]
wsnames = wsgroup.getNames()
for name in sorted(wsnames):
    tmpws = mtd[name]
    print("workspace %s has %d events" % (name, tmpws.getNumberEvents()))

Output:

workspace tempsplitws_0 has 124 events
workspace tempsplitws_1 has 16915 events
workspace tempsplitws_2 has 10009 events
workspace tempsplitws_3 has 6962 events
workspace tempsplitws_4 has 22520 events
workspace tempsplitws_5 has 5133 events
workspace tempsplitws_unfiltered has 50603 events

Example - Filtering event by a user-generated TableWorkspace

import numpy as np
ws = Load(Filename='CNCS_7860_event.nxs')

# create TableWorkspace
split_table_ws = CreateEmptyTableWorkspace()
split_table_ws.addColumn('float', 'start')
split_table_ws.addColumn('float', 'stop')
split_table_ws.addColumn('str', 'target')

split_table_ws.addRow([0., 100., 'a'])
split_table_ws.addRow([200., 300., 'b'])
split_table_ws.addRow([400., 600., 'c'])
split_table_ws.addRow([600., 650., 'b'])

# filter events
FilterEvents(InputWorkspace=ws, SplitterWorkspace=split_table_ws,
        OutputWorkspaceBaseName='tempsplitws3',  GroupWorkspaces=True,
        FilterByPulseTime = False, OutputWorkspaceIndexedFrom1 = False,
        CorrectionToSample = "None", SpectrumWithoutDetector = "Skip", SplitSampleLogs = False,
        OutputTOFCorrectionWorkspace='mock',
        RelativeTime=True)

# Print result
wsgroup = mtd["tempsplitws3"]
wsnames = wsgroup.getNames()
for name in sorted(wsnames):
    tmpws = mtd[name]
    print("workspace %s has %d events" % (name, tmpws.getNumberEvents()))
    split_log = tmpws.run().getProperty('splitter')
    entry_0 = np.datetime_as_string(split_log.times[0].astype(np.dtype('M8[s]')), timezone='UTC')
    entry_1 = np.datetime_as_string(split_log.times[1].astype(np.dtype('M8[s]')), timezone='UTC')
    print('event splitter log: entry 0 and entry 1 are {0} and {1}.'.format(entry_0, entry_1))

Output:

workspace tempsplitws3_a has 77580 events
event splitter log: entry 0 and entry 1 are 2010-03-25T16:08:37Z and 2010-03-25T16:10:17Z.
workspace tempsplitws3_b has 0 events
event splitter log: entry 0 and entry 1 are 2010-03-25T16:08:37Z and 2010-03-25T16:11:57Z.
workspace tempsplitws3_c has 0 events
event splitter log: entry 0 and entry 1 are 2010-03-25T16:08:37Z and 2010-03-25T16:15:17Z.
workspace tempsplitws3_unfiltered has 34686 events
event splitter log: entry 0 and entry 1 are 2010-03-25T16:08:37Z and 2010-03-25T16:10:17Z.

Example - Filtering event by pulse time

ws = Load(Filename='CNCS_7860_event.nxs')
splitws, infows = GenerateEventsFilter(InputWorkspace=ws, UnitOfTime='Nanoseconds', LogName='SampleTemp',
        MinimumLogValue=279.9,  MaximumLogValue=279.98, LogValueInterval=0.01)

FilterEvents(InputWorkspace=ws,
    SplitterWorkspace=splitws,
    InformationWorkspace=infows,
    OutputWorkspaceBaseName='tempsplitws',
    GroupWorkspaces=True,
    FilterByPulseTime = True,
    OutputWorkspaceIndexedFrom1 = True,
    CorrectionToSample = "None",
    SpectrumWithoutDetector = "Skip",
    SplitSampleLogs = False,
    OutputTOFCorrectionWorkspace='mock')

# Print result
wsgroup = mtd["tempsplitws"]
wsnames = wsgroup.getNames()
for name in sorted(wsnames):
    tmpws = mtd[name]
    print("workspace %s has %d events" % (name, tmpws.getNumberEvents()))

Output:

workspace tempsplitws_1 has 123 events
workspace tempsplitws_2 has 16951 events
workspace tempsplitws_3 has 9972 events
workspace tempsplitws_4 has 7019 events
workspace tempsplitws_5 has 22529 events
workspace tempsplitws_6 has 5067 events

Example - Filtering event with correction on TOF

ws = Load(Filename='CNCS_7860_event.nxs')
splitws, infows = GenerateEventsFilter(InputWorkspace=ws, UnitOfTime='Nanoseconds', LogName='SampleTemp',
        MinimumLogValue=279.9,  MaximumLogValue=279.98, LogValueInterval=0.01)

FilterEvents(InputWorkspace=ws, SplitterWorkspace=splitws, InformationWorkspace=infows,
    OutputWorkspaceBaseName='tempsplitws',
    GroupWorkspaces=True,
    FilterByPulseTime = False,
    OutputWorkspaceIndexedFrom1 = False,
    CorrectionToSample = "Direct",
    IncidentEnergy=3,
    SpectrumWithoutDetector = "Skip",
    SplitSampleLogs = False,
    OutputTOFCorrectionWorkspace='mock')

# Print result
wsgroup = mtd["tempsplitws"]
wsnames = wsgroup.getNames()
for name in sorted(wsnames):
    tmpws = mtd[name]
    print("workspace %s has %d events" % (name, tmpws.getNumberEvents()))

Output:

workspace tempsplitws_0 has 123 events
workspace tempsplitws_1 has 16951 events
workspace tempsplitws_2 has 9972 events
workspace tempsplitws_3 has 7019 events
workspace tempsplitws_4 has 22514 events
workspace tempsplitws_5 has 5082 events
workspace tempsplitws_unfiltered has 50605 events

Categories: AlgorithmIndex | Events\EventFiltering