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# Minus v1¶

## Summary¶

The Minus algorithm will subtract the data values and calculate the corresponding error values for two compatible workspaces.

### See Also¶

This algorithm is also known as: Subtract

## Properties¶

Name

Direction

Type

Default

Description

LHSWorkspace

Input

MatrixWorkspace

Mandatory

The name of the input workspace on the left hand side of the operation

RHSWorkspace

Input

MatrixWorkspace

Mandatory

The name of the input workspace on the right hand side of the operation

OutputWorkspace

Output

MatrixWorkspace

Mandatory

The name to call the output workspace

AllowDifferentNumberSpectra

Input

boolean

False

Are workspaces with different number of spectra allowed? For example, the LHSWorkspace might have one spectrum per detector, but the RHSWorkspace could have its spectra averaged per bank. If true, then matching between the LHS and RHS spectra is performed (all detectors in a LHS spectrum have to be in the corresponding RHS) in order to apply the RHS spectrum to the LHS.

ClearRHSWorkspace

Input

boolean

False

For EventWorkspaces only. This will clear out event lists from the RHS workspace as the binary operation is applied. This can prevent excessive memory use, e.g. when subtracting an EventWorkspace from another: memory use will be approximately constant instead of increasing by 50%. At completion, the RHS workspace will be empty.

## Description¶

OutputWorkspace = LHSWorkspace - RHSWorkspace

The algorithm will perform the - operation on the data and associated errors from any two compatible workspaces. Workspaces are compatible if:

• The sizes of the two workspaces are compatible (see below)

• If the two workspaces contain Y bins then the values in these must be identical

• the units of the axes match

• the distribution status/counts units match

For information about how errors are handled and propagated see Error Propagation.

### Compatible Sizes¶

 Workspaces are compatible if they are identically sized in which case the values of each cell within each histogram are subtracted individually. The green arrows shows the order in which the data is subtracted. They are also compatible if they match the size dimension horizontally, in which case the same values are subtracted from each histogram in the workspace. If AllowDifferentNumberSpectra is True, then the LHS and RHS spectra are matched using detector IDs. The corresponding spectra are subtracted from the matching counterpart. If they match in size vertically then the data values will be subtracted from each bin of the histogram.

Finally a workspace containing a single value is compatible to any workspace, in which case the same value will be subtracted uniformly from every bin in each histogram.

The Minus algorithm will subtract the data values and calculate the corresponding error values for two compatible workspaces.

## Usage¶

Example - Minus as an Algorithm

# create histogram workspaces
dataX1 = [0,1,2,3,4,5,6,7,8,9] # or use dataX1=range(0,10)
dataY1 = [0,1,2,3,4,5,6,7,8] # or use dataY1=range(0,9)
dataE1 = [0,1,2,3,4,5,6,7,8] # or use dataE1=range(0,9)
dataX2 = [0,1,2,3,4,5,6,7,8,9] #X-values must be identical
dataY2 = [2,2,2,2,2,2,2,2,2]
dataE2 = [3,3,3,3,3,3,3,3,3]
ws1 = CreateWorkspace(dataX1, dataY1, dataE1)
ws2 = CreateWorkspace(dataX2, dataY2, dataE2)

# perform the algorithm
ws = Minus(ws1, ws2)

print("The X values are: {}".format(ws.readX(0)))
print("The Y values are: {}".format(ws.readY(0)))
print("The updated Error values are: {}".format(ws.readE(0)))


Output:

The X values are: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9.]
The Y values are: [-2. -1.  0.  1.  2.  3.  4.  5.  6.]
The updated Error values are: [ 3.          3.16227766  3.60555128  4.24264069  5.          5.83095189
6.70820393  7.61577311  8.54400375]


Example - Minus as an operator

# create histogram workspaces
dataX1 = [0,1,2,3,4,5,6,7,8,9] # or use dataX1=range(0,10)
dataY1 = [0,1,2,3,4,5,6,7,8] # or use dataY1=range(0,9)
dataE1 = [0,1,2,3,4,5,6,7,8] # or use dataE1=range(0,9)
dataX2 = [0,1,2,3,4,5,6,7,8,9] #X-values must be identical
dataY2 = [2,2,2,2,2,2,2,2,2]
dataE2 = [3,3,3,3,3,3,3,3,3]
ws1 = CreateWorkspace(dataX1, dataY1, dataE1)
ws2 = CreateWorkspace(dataX2, dataY2, dataE2)

# perform the algorithm
ws = ws1 - ws2

print("The X values are: {}".format(ws.readX(0)))
print("The Y values are: {}".format(ws.readY(0)))
print("The updated Error values are: {}".format(ws.readE(0)))


Output:

The X values are: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9.]
The Y values are: [-2. -1.  0.  1.  2.  3.  4.  5.  6.]
The updated Error values are: [ 3.          3.16227766  3.60555128  4.24264069  5.          5.83095189
6.70820393  7.61577311  8.54400375]


Example - Subtract using in-place operator

# create histogram workspaces
dataX1 = [0,1,2,3,4,5,6,7,8,9] # or use dataX1=range(0,10)
dataY1 = [0,1,2,3,4,5,6,7,8] # or use dataY1=range(0,9)
dataE1 = [0,1,2,3,4,5,6,7,8] # or use dataE1=range(0,9)
dataX2 = [0,1,2,3,4,5,6,7,8,9] #X-values must be identical
dataY2 = [2,2,2,2,2,2,2,2,2]
dataE2 = [3,3,3,3,3,3,3,3,3]
ws = CreateWorkspace(dataX1, dataY1, dataE1)
ws1 = CreateWorkspace(dataX2, dataY2, dataE2)

# perform the algorithm
ws -= ws1

print("The X values are: {}".format(ws.readX(0)))
print("The Y values are: {}".format(ws.readY(0)))
print("The updated Error values are: {}".format(ws.readE(0)))


Output:

The X values are: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9.]
The Y values are: [-2. -1.  0.  1.  2.  3.  4.  5.  6.]
The updated Error values are: [ 3.          3.16227766  3.60555128  4.24264069  5.          5.83095189
6.70820393  7.61577311  8.54400375]


Example - Subtract a scalar

# create histogram workspaces
dataX1 = [0,1,2,3,4,5,6,7,8,9] # or use dataX1=range(0,10)
dataY1 = [0,1,2,3,4,5,6,7,8] # or use dataY1=range(0,9)
dataE1 = [0,1,2,3,4,5,6,7,8] # or use dataE1=range(0,9)
ws1 = CreateWorkspace(dataX1, dataY1, dataE1)

# perform the algorithm
ws = ws1 - 2.5

print("The X values are: {}".format(ws.readX(0)))
print("The Y values are: {}".format(ws.readY(0)))
print("The updated Error values are: {}".format(ws.readE(0)))


Output:

The X values are: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9.]
The Y values are: [-2.5 -1.5 -0.5  0.5  1.5  2.5  3.5  4.5  5.5]
The updated Error values are: [ 0.  1.  2.  3.  4.  5.  6.  7.  8.]


Categories: AlgorithmIndex | Arithmetic

## Source¶

C++ header: Minus.h

C++ source: Minus.cpp