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FilterByLogValue-v1.rst

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Description

Filters out events using the entries in the Sample Logs.

Sample logs consist of a series of pairs. The first step in filtering is to generate a list of start-stop time intervals that will be kept, using those logs.

  • Each log value is compared to the min/max value filters to determine whether it is "good" or not.
    • For a single log value that satisfies the criteria at time T, all events between T+-Tolerance (LogBoundary=Centre), or T and T+Tolerance (LogBoundary=Left) are kept.
    • If there are several consecutive log values matching the filter, events between T1-Tolerance and T2+Tolerance, where T2 is the last "good" value (LogBoundary=Centre), or T1 and T2, where T2 is the first "bad" value (LogBoundary=Left) are kept.
  • The filter is then applied to all events in all spectra. Any events with pulse times outside of any "good" time ranges are removed.

There is no interpolation of log values between the discrete sample log times at this time. However, the log value is assumed to be constant at times before its first point and after its last. For example, if the first temperature measurement was at time=10 seconds and a temperature within the acceptable range, then all events between 0 and 10 seconds will be included also. If a log has a single point in time, then that log value is assumed to be constant for all time and if it falls within the range, then all events will be kept.

PulseFilter (e.g. for Veto Pulses)

If you select PulseFilter, then events will be filtered OUT in notches around each time in the selected sample log, and the MinValue/MaxValue parameters are ignored. For example:

  • If you have 3 entries at times:
    • 10, 20, 30 seconds.
    • A TimeTolerance of 1 second.
  • Then the events at the following times will be EXCLUDED from the output:
    • 9-11; 19-21; 29-30 seconds.

The typical use for this is to filter out "veto" pulses from a SNS event nexus file. Some of these files have a sample log called "veto_pulse_time" that only contains times of the pulses to be rejected. For example, this call will filter out veto pulses:

VetoPulseTime

ws=CreateSampleWorkspace("Event") AddTimeSeriesLog(ws, Name="veto_pulse_time", Time="2010-01-01T00:00:00", Value=1) AddTimeSeriesLog(ws, Name="veto_pulse_time", Time="2010-01-01T00:10:00", Value=0) AddTimeSeriesLog(ws, Name="veto_pulse_time", Time="2010-01-01T00:20:00", Value=1) AddTimeSeriesLog(ws, Name="veto_pulse_time", Time="2010-01-01T00:30:00", Value=0) AddTimeSeriesLog(ws, Name="veto_pulse_time", Time="2010-01-01T00:40:00", Value=1) AddTimeSeriesLog(ws, Name="veto_pulse_time", Time="2010-01-01T00:50:00", Value=0)

VetoPulseTime

ws = FilterByLogValue(ws, LogName="veto_pulse_time", PulseFilter="1")

Comparing with other event filtering algorithms

Wiki page EventFiltering has a detailed introduction on event filtering in MantidPlot.

Usage

Example - Filtering by a simple time series Log

FilterByLogValue

ws = CreateSampleWorkspace("Event",BankPixelWidth=1)

AddTimeSeriesLog(ws, Name="proton_charge", Time="2010-01-01T00:00:00", Value=100) AddTimeSeriesLog(ws, Name="proton_charge", Time="2010-01-01T00:10:00", Value=100) AddTimeSeriesLog(ws, Name="proton_charge", Time="2010-01-01T00:20:00", Value=100) AddTimeSeriesLog(ws, Name="proton_charge", Time="2010-01-01T00:30:00", Value=100) AddTimeSeriesLog(ws, Name="proton_charge", Time="2010-01-01T00:40:00", Value=15) AddTimeSeriesLog(ws, Name="proton_charge", Time="2010-01-01T00:50:00", Value=100)

print "The unfiltered workspace %s has %i events and a peak value of %.2f" % (ws, ws.getNumberEvents(),ws.readY(0)[50])

wsOut = FilterByLogValue(ws,"proton_charge",MinimumValue=75, MaximumValue=150)

print "The filtered workspace %s has %i events and a peak value of %.2f" % (wsOut, wsOut.getNumberEvents(),wsOut.readY(0)[50])

Output:

FilterByLogValue

The unfiltered workspace ws has 8000 events and a peak value of 1030.00 The filtered workspace wsOut has 4058 events and a peak value of 502.00