Table of Contents
Produces a single spectrum workspace containing the total summed events in the workspace as a function of a specified log.
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) |
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:
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:
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!
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.
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: 615.00
section 2: 629.00
section 3: 656.00
Totalling 1900 events, matching the 1900 events in the input workspace
Categories: Algorithms | Events