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Creating Workspaces

Most algorithms will want to create a workspace as part of the execution and set this as output. The first step in this process is to declare the output property that will store the workspace after it has been created.

It is currently only possible to create a Workspace2D and TableWorkspace from Python:

• A Workspace2D output should use a WorkspaceProperty or MatrixWorkspaceProperty and

• a TableWorkspace output should use a ITableWorkspaceProperty

The workspace is created using an object called WorkspaceFactory and a method called create. To create a brand new workspace from scratch:

def PyInit(self):
    # Creates a generic output property
    self.declareProperty(WorkspaceProperty(name="OutputWorkspace",
                                           defaultValue="",
                                           direction=Direction.Output))

def PyExec(self):
    # A workspace with 5 rows with 9 bins & 10 bin boundaries
    # (i.e. a histogram)
    output_ws = WorkspaceFactory.create("Workspace2D", NVectors=5,
                                        XLength=10, YLength=9)
    self.setProperty("OutputWorkspace", output_ws)

The above code will create a new 2D workspace filled with zeroes. The setProperty call is important as it is required for Mantid to store the workspace outside of the algorithm.

To set the data in the workspace use the data[X,Y,E] member of the workspace like so:

# ...snipped...
# A workspace with 5 rows with 9 bins & 10 bin boundaries
nrows = 5
nbins = 9
output_ws = WorkspaceFactory.create("Workspace2D", NVectors=nrows,
                                    XLength=nbins+1, YLength=nbins)
for i in range(nrows):
    xdata = output_ws.dataX(i)
    ydata = output_ws.dataY(i)
    edata = output_ws.dataE(i)
    for j in range(nbins):
        xdata[j] = j
        ydata[j] = i*i
        edata[j] = j
    # end for loop
    # final bin boundary
    xdata[nbins] = nbins

self.setProperty("OutputWorkspace", output_ws)

The WorkspaceFactory can also create a workspace using another as a template. In addition to taking the size from the original it will also copy across meta-information such as the instrument and logs. The creation by this method can also create a workspace of a different size, e.g.

# ...snipped...
# A workspace with 5 rows with 9 bins & 10 bin boundaries
nrows = 5
nbins = 9
output_ws = WorkspaceFactory.create("Workspace2D", NVectors=nrows,
                                    XLength=nbins+1, YLength=nbins)

# Copies meta-data and creates a single bin workspace with 5 rows
second_ws = WorkspaceFactory.create(output_ws, NVectors=5,
                                    XLength=2,YLength=1)

# Copies meta-data and creates a workspace with 1 rows that is
# the same length as the original in Y & X
third_ws = WorkspaceFactory.create(output_ws, NVectors=1)

Numpy as Data Source

Numpy arrays can be used to set a 1D array straight from a numpy array. This is more efficient than looping over the arrays and workspaces and setting each element in python:

# ...snipped...
# A workspace with 5 rows with 9 bins & 10 bin boundaries
nrows = 5
nbins = 9
output_ws = WorkspaceFactory.create("Workspace2D", NVectors=nrows,
                                    XLength=nbins+1, YLength=nbins)

xdata = numpy.arange(float(nbins + 1)) # filled with 0->9
ydata = 100*numpy.arange(float(nbins))
edata = numpy.sqrt(ydata) # filled with 0->sqrt(800)

for i in range(nrows):
    output_ws.setX(i, xdata)
    output_ws.setY(i, ydata)
    output_ws.setE(i, edata)

self.setProperty("OutputWorkspace", output_ws)