\(\renewcommand\AA{\unicode{x212B}}\)

# Running Algorithms - Complex Example¶

We have seen an example of chaining together two algorithms where just the workspace was passed as input. While this is a powerful use of Mantid it is also possible to extract the workspace data out to numpy, perform some operations and then feed this back into a workspace.

The script below shows a more complex example of mixing Mantid’s algorithms with numpy

```
import numpy
white_beam = Load("MAR11060.raw")
# Define constants
norm_spectrum = 2
wb_scale_factor = 100.
rb_params = [10.,1., 80.]
white_beam = NormaliseToMonitor(white_beam,MonitorSpectrum=norm_spectrum)
# crop out monitors
white_beam = CropWorkspace(white_beam, StartWorkspaceIndex=4)
white_beam = ConvertUnits(white_beam, Target='Energy')
white_beam = Rebin(white_beam, rb_params)
""" Extract data to numpy, computes the sum value for each spectra
and puts the results back into a worksapace.
"""
xvalues = white_beam.extractX() # 2D copy of workspace data
yvalues = white_beam.extractY() # 2D copy of workspace data
evalues = white_beam.extractE() # 2D copy of workspace data
# sum across axis that numpy calls 1, i.e sum values across bins for each spectra
sumy = numpy.sum(yvalues, axis=1)
#Compute errors
evalues = evalues ** 2 # Square each value
evalues = numpy.sum(evalues, axis=1) # Sum the squares
evalues = numpy.sqrt(evalues)
xlimits = xvalues[ :, [0,-1] ] # numpy array slice. : takes all rows and [0,-1] selects only column 0 and n-1
# + any other operations that numpy can do
# Put back into workspace.
white_beam = CreateWorkspace(xlimits, sumy, evalues,NSpec=sumy.shape[0])
# Divide sample run
sample = Load("MAR11015")
sample = NormaliseToMonitor(sample,MonitorSpectrum=norm_spectrum)
sample = ConvertUnits(sample, Target='DeltaE',EMode='Direct',EFixed=85)
# crop out monitors
sample = CropWorkspace(sample, StartWorkspaceIndex=4)
sample = Rebin(sample, rb_params)
# Normalize
sample /= white_beam
```