4.2

SumEventsByLogValue v1

../_images/SumEventsByLogValue-v1_dlg.png

SumEventsByLogValue dialog.

Summary

Produces a single spectrum workspace containing the total summed events in the workspace as a function of a specified log.

Properties

Name Direction Type Default Description
InputWorkspace Input EventWorkspace Mandatory The input EventWorkspace. Must contain ‘raw’ (unweighted) events
MonitorWorkspace Input EventWorkspace   A workspace containing the monitor counts relating to the input workspace
OutputWorkspace Output Workspace Mandatory The name of the workspace to be created as the output of the algorithm. The output workspace will be a [[TableWorkspace]] in the case that a log holding integer values is given, and a single-spectrum [[Workspace2D]] otherwise.
LogName Input string Mandatory The name of the number series log against which the data should be summed
OutputBinning Input dbl list   Binning parameters for the output workspace (see [[Rebin]] for syntax) (Optional for logs holding integer values, mandatory otherwise)

Description

This algorithm counts up the events in a workspace against the values of a log within the workspace. It will most commonly be used as a sub-algorithm of the RockingCurve algorithm.

The algorithm has two modes:

Table output

This option can be used for integer-typed logs and will produce a table with a row for each integer value between the minimum and maximum contained in the log, and a further column containing the total events for which the log had each value. Further columns will be added for:

  • Monitors, if any - this requires an event workspace with the same name as the input workspace plus a ‘_monitors’ suffix (this is what LoadEventNexus v1 will give).
  • The total time duration, in seconds, during which the log had each value.
  • The integrated proton charge during the period(s) for which the log had each value.
  • The time-weighted average value of any other number-series logs which had more than a single value during the run.

Warning: This mode is intended for logs with a small range (e.g. scan index, period number, status). Be aware that if it is used for a log with a large range, it will create a table row for every integer value between the minimum and maximum log value. This might take a long time!

Single-spectrum option

This option can be used for integer or floating point type logs and requires that the OutputBinning property is specified. It will produce a single spectrum workspace where the X values are derived from the OutputBinning property and the Y values are the total counts in each bin of the log value.

Usage

Example - Single-Spectrum Mode

# a sample workspace with a sample instrument
ws = CreateSampleWorkspace("Event",BankPixelWidth=1)

AddTimeSeriesLog(ws, Name="Log2FilterBy", Time="2010-01-01T00:00:00", Value=1)
AddTimeSeriesLog(ws, Name="Log2FilterBy", Time="2010-01-01T00:10:00", Value=2)
AddTimeSeriesLog(ws, Name="Log2FilterBy", Time="2010-01-01T00:20:00", Value=3)
AddTimeSeriesLog(ws, Name="Log2FilterBy", Time="2010-01-01T00:30:00", Value=1)
AddTimeSeriesLog(ws, Name="Log2FilterBy", Time="2010-01-01T00:40:00", Value=2)
AddTimeSeriesLog(ws, Name="Log2FilterBy", Time="2010-01-01T00:50:00", Value=3)

#split the events by the log value
wsOut = SumEventsByLogValue(ws,LogName="Log2FilterBy",OutputBinning=[1,1,4])

#all of the events should be included
integral = Integration(wsOut)
print("Events were split into %i sections based on the log 'Log2FilterBy'." % wsOut.blocksize())
for i in range(0,wsOut.blocksize()):
  print(" section %i: %.2f" % (i+1,wsOut.readY(0)[i]))
print("Totalling %.0f events, matching the %i events in the input workspace" % (integral.readY(0)[0],ws.getNumberEvents()))

Output:

Events were split into 3 sections based on the log 'Log2FilterBy'.
 section 1: 6...
 section 2: 6...
 section 3: 6...
Totalling 1900 events, matching the 1900 events in the input workspace

Categories: AlgorithmIndex | Events

Source

C++ source: SumEventsByLogValue.cpp (last modified: 2019-07-17)

C++ header: SumEventsByLogValue.h (last modified: 2018-10-05)