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MuonGroupingAsymmetry dialog.
Table of Contents
| Name | Direction | Type | Default | Description |
|---|---|---|---|---|
| InputWorkspace | Input | WorkspaceGroup | Mandatory | Input workspace containing data from detectors which are to be grouped. |
| OutputWorkspace | Output | MatrixWorkspace | Mandatory | Output workspace which will hold the results of the group asymmetry calculation. |
| OutputUnNormWorkspace | Output | MatrixWorkspace | unNormalisedData | The name of the output unnormalized workspace. |
| GroupName | Input | string | The name of the group. Must contain at least one alphanumeric character. | |
| Grouping | Input | int list | 1 | The grouping of detectors, comma separated list of detector IDs or hyphenated ranges of IDs. |
| AsymmetryTimeMin | Input | number | 0 | Start time for the asymmetry estimation (in micro seconds). Defaults to the start time of the InputWorkspace. |
| AsymmetryTimeMax | Input | number | 32 | End time for the asymmetry estimation (in micro seconds). Defaults to the end time of the InputWorkspace. |
| NormalizationIn | Input | number | 0 | If this value is non-zero then this is used for the normalization, instead of being estimated. |
| SummedPeriods | Input | int list | 1 | A list of periods to sum in multiperiod data. |
| SubtractedPeriods | Input | int list | A list of periods to subtract in multiperiod data. |
When interacting with the Muon Analysis interface, operations such as detector grouping, group and pair asymmetry are performed on data. This algorithm performs a “grouping asymmetry” operation, in other words it provides a numerical estimation (without fitting) of the asymmetry. More details can be found at EstimateMuonAsymmetryFromCounts v1.
This algorithm is part of a set of four; with MuonPreProcess v1 being run first; and the output being fed into this one. This allows the replication of the workflow used by the muon analysis interface to produce group data.
A workspace has one or more spectra contained within it; each spectra has a unique detector ID. Assuming the y-values represent counts; a detector grouping operation causes the counts to be summed across the given set of detector IDs which are supplied to the Grouping argument (for example 1,2,3,4,5 and 1-5).
The InputWorkspace must be a WorkspaceGroup, where each workspace within the group represents a single period. Thus, single period data is just a WorkspaceGroup with a single workspace within it.
The group must be given a name via GroupName which can consist of letters, numbers and underscores.
The group name does not affect the data; however the name is used in the muon interface when automatically generating workspace names from group data.
After the grouping is performed, the analysis described in EstimateMuonAsymmetryFromCounts v1 will be run; effectively estimating the muon decay curve and subtracting it from the grouped data and then estimating the asymmetry on the grouped data. The range over which the estimate is performed is controlled by the AsymmetryTimeMin and AsymmetryTimeMax inputs.
Note : The workspaces supplied to the algorithm must have a number of good frames set in their sample logs. The sample log is called “goodfrm” and can be set using;
AddSampleLog(workspace=workspace, LogName="goodfram", LogText ="10")
Both single and multi period data are supported by the algorithm.
The SummedPeriods and SubtractedPeriods inputs are used to control the way that periods are combined. so for example;
would combine periods in the combination \((1+2)-(3+4)\).
Example - Using MuonPreProcess followed by MuonGroupingAsymmetry on Single Period Data
# Create a workspaces with one spectrum
dataX = [0, 1, 2, 3, 4, 5]
dataY = [10, 20, 30, 20, 10]
input_workspace = CreateWorkspace(dataX, dataY, NSpec=1)
input_workspace.getSpectrum(0).setDetectorID(1)
# Workspace must have the number of good frames set
AddSampleLog(Workspace=input_workspace, LogName='goodfrm', LogText="10")
pre_processed_workspace = MuonPreProcess(InputWorkspace=input_workspace)
output = MuonGroupingAsymmetry(InputWorkspace=pre_processed_workspace,
GroupName="fwd",
Grouping=[1])
output_workspace = output[0]
print("X values are : {}".format([round(float(i), 3) for i in output_workspace.readX(0)]))
print("Y values are : {}".format([round(float(i), 3) for i in output_workspace.readY(0)]))
Output:
X values are : [0.0, 1.0, 2.0, 3.0, 4.0, 5.0]
Y values are : [-0.735, -0.163, 0.979, 1.08, 0.639]
Example - Using MuonPreProcess followed by MuonGroupingAsymmetry on Multi Period Data
# Create two workspaces each with two spectra
dataX = [0, 1, 2, 3, 4, 5] * 2
dataY_period1 = [10, 20, 30, 20, 10] + \
[20, 30, 40, 30, 20]
dataY_period2 = [30, 40, 50, 40, 30] + \
[40, 50, 60, 50, 40]
ws1 = CreateWorkspace(dataX, dataY_period1, NSpec=2)
ws2 = CreateWorkspace(dataX, dataY_period2, NSpec=2)
AddSampleLog(Workspace=ws1, LogName='goodfrm', LogText="1")
AddSampleLog(Workspace=ws2, LogName='goodfrm', LogText="1")
for i in range(2):
# set detector IDs to be 1,2,3,4
# these do not have to be the same as the spectrum numbers
# (the spectrum number are 0,1,2,3 in this case)
ws1.getSpectrum(i).setDetectorID(i + 1)
ws2.getSpectrum(i).setDetectorID(i + 1)
# Create multi period data
multi_period_data = GroupWorkspaces(ws1)
multi_period_data.addWorkspace(ws2)
# This time we won't run MuonPreProcess, as we don't want to apply any pre-processing
# and we already have a WorkspaceGroup
output = MuonGroupingAsymmetry(InputWorkspace=multi_period_data,
GroupName="fwd",
Grouping=[1, 2],
SummedPeriods=[1, 2])
output_workspace = output[0]
# We have asked for periods 1+2, with each period summing detectors 1,2
print("X values are : {}".format([round(float(i), 3) for i in output_workspace.readX(0)]))
print("Y values are : {}".format([round(float(i), 3) for i in output_workspace.readY(0)]))
Output:
X values are : [0.0, 1.0, 2.0, 3.0, 4.0, 5.0]
Y values are : [-0.638, -0.201, 0.619, 0.985, 1.235]
Categories: AlgorithmIndex | Muon\DataHandling
C++ header: MuonGroupingAsymmetry.h (last modified: 2020-03-25)
C++ source: MuonGroupingAsymmetry.cpp (last modified: 2021-03-31)