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# SumSpectra v1¶

## Summary¶

The SumSpectra algorithm adds the data values in each time bin across a range of spectra; the output workspace has a single spectrum. If the input is an EventWorkspace, the output is also an EventWorkspace; otherwise it will be a Workspace2D.

SumNeighbours

## Properties¶

Name

Direction

Type

Default

Description

InputWorkspace

Input

MatrixWorkspace

Mandatory

The workspace containing the spectra to be summed.

OutputWorkspace

Output

MatrixWorkspace

Mandatory

The name of the workspace to be created as the output of the algorithm. A workspace of this name will be created and stored in the Analysis Data Service.

StartWorkspaceIndex

Input

number

0

The first Workspace index to be included in the summing

EndWorkspaceIndex

Input

number

Optional

The last Workspace index to be included in the summing

ListOfWorkspaceIndices

Input

int list

A list of workspace indices as a string with ranges, for example: 5-10,15,20-23. Optional: if not specified, then the Start/EndWorkspaceIndex fields are used alone. If specified, the range and the list are combined (without duplicating indices). For example, a range of 10 to 20 and a list ‘12,15,26,28’ gives ‘10-20,26,28’.

IncludeMonitors

Input

boolean

True

Whether to include monitor spectra in the summation.

WeightedSum

Input

boolean

False

Instead of the usual spectra sum, calculate the weighted sum. This has the form: $$nSpectra \times\Sigma(Signal_i/Error_i^2)/\Sigma(1/Error_i^2)$$ This property is ignored for event workspace. The sums are defined for $$Error_i != 0$$ only, so the values with zero error are dropped from the summation. To estimate the number of dropped values see the description.

RemoveSpecialValues

Input

boolean

False

If enabled floating point special values such as NaN or Inf are removed before the spectra are summed.

MultiplyBySpectra

Input

boolean

True

For unnormalized data one should multiply the weighted sum by the number of spectra contributing to the bin.

UseFractionalArea

Input

boolean

True

Normalize the output workspace to the fractional area for RebinnedOutput workspaces.

## Description¶

Takes a workspace as input and sums all of the spectra within it maintaining the existing bin structure and units. Any masked spectra are ignored. The result is stored as a new workspace containing a single spectra.

If we define a the $$i^{th}$$ spectrum with bins $$j$$. The unweighted sum is just (WeightedSum=False)

$Signal[j] = \displaystyle\Sigma_{i \in spectra} Signal_i[j]$

The weighted sum (WeightedSum=True and MultiplyBySpectra=True, ignored for event workspaces), the sum is defined (skipping $$Signal_i[j]$$ when $$Error_i[j] == 0$$),

$Signal[j] = NSpectra \times \displaystyle\Sigma_{i \in spectra} \left(\frac{Signal_i[j]}{Error_i^2[j]}\right) / \Sigma_{i \in spectra}\left(\frac{1}{Error_i^2[j]}\right)$

$$NSpectra$$ is the number of spectra contributing to that bin. If the weights contributing to the sum are equal, these result in the same value. This should be used for unnormalized (e.g. not divided by vanadium spectrum) data. If the data has been normalized (e.g. divided by vanadium spectrum for total scattering) then multiplying by the number of spectra contributing to the bin is incorrect, use WeightedSum=True and MultiplyBySpectra=False to sum as

$Signal[j] = \displaystyle\Sigma_{i \in spectra} \left(\frac{Signal_i[j]}{Error_i^2[j]}\right) / \Sigma_{i \in spectra}\left(\frac{1}{Error_i^2[j]}\right)$

The algorithm adds to the OutputWorkspace three additional properties (Log values). The properties (Log) names are:

• NumAllSpectra is the number of spectra contributed to the sum

• NumMaskSpectra is the spectra dropped from the summations because they are masked. Monitors are not included in this total if IncludeMonitors=False.

• NumZeroSpectra is the number of zero bins in histogram workspace or empty spectra for event workspace. These spectra are dropped from the summation of histogram workspace when WeightedSum=True.

Assuming pWS is the output workspace handle, from Python these properties can be accessed using:

nSpectra       = pWS.getRun().getLogData("NumAllSpectra").value
nZeroSpectra   = pWS.getRun().getLogData("NumZeroSpectra").value


## Usage¶

Example - a simple example of running SumSpectra.

ws = CreateSampleWorkspace("Histogram", Random=True)
print("Workspace has %d spectra" % ws.getNumberHistograms())

ws = SumSpectra(ws)
print("Workspace has %d spectra" % ws.getNumberHistograms())


Output:

Workspace has 200 spectra
Workspace has 1 spectra


Example - running SumSpectra with a list of indices.

ws = CreateSampleWorkspace("Histogram", Random=True)
print("Workspace has %d spectra" % ws.getNumberHistograms())

ws = SumSpectra(ws, ListOfWorkspaceIndices='0-3, 10-13')
print("Workspace has %d spectra" % ws.getNumberHistograms())


Output:

Workspace has 200 spectra
Workspace has 1 spectra


Example - a running SumSpectra with a start and end index.

ws = CreateSampleWorkspace("Histogram", Random=True)
print("Workspace has %d spectra" % ws.getNumberHistograms())

ws = SumSpectra(ws, StartWorkspaceIndex=0, EndWorkspaceIndex=9)
print("Workspace has %d spectra" % ws.getNumberHistograms())


Output:

Workspace has 200 spectra
Workspace has 1 spectra


Example - a running SumSpectra in weighted sum mode.

ws = CreateSampleWorkspace("Histogram", Random=True)
print("Workspace has %d spectra" % ws.getNumberHistograms())

ws = SumSpectra(ws, WeightedSum=True)
print("Workspace has %d spectra" % ws.getNumberHistograms())


Output:

Workspace has 200 spectra
Workspace has 1 spectra


Categories: AlgorithmIndex | Transforms\Grouping