It is possible to replicate plots you’ve made previously in MantidPlot entirely using python script in Workbench. For all these exercises do not make any changes to the default imports given by the Workbench script editor. You can execute the script after each step to make sure that there are no errors raised.
Load the File “GEM38370_Focussed_Legacy.nxs” using the algorithm: GEM38370_Focussed_Legacy = Load("GEM38370_Focussed_Legacy")
Before plotting from workspaces you will need to set your subplot up with the Mantid projection so it can interpret them. To do this include:
fig,ax = plt.subplots(subplot_kw = {"projection": "mantid"})
This will let you plot spectra from workspaces by including a spectrum number specNum or workspace index wkspIndex in the plot command.
ax.plot(RawData, specNum = 2) or ax.plot(RawData, wkspIndex = 3)
Plot the other spectra 3-7.
Now that the subplot is set up to plot spectra you can plot other curves in the same window using the same command with a different specNum or wkspIndex, by modifying the script you have written already you can use a for loop to plot all spectra in sequence.
for i in range(6):
ax.plot(GEM38370_Focussed_Legacy, wkspIndex = i)
change the d-spacing axis range to 0-10 angstroms and set the x-axis to a logarithmic scale
Since ax is a matplotlib subplot we can us the built-in matplotlib attributes to change the the axis range ax.set_xlim([0.0,10.0]) and scale ax.set_xscale('symlog'). You can then use the fig.show() function to show your plot
By following these steps you should end up with code that looks something like this:
# The following line helps with future compatibility with Python 3
# print must now be used as a function, e.g print('Hello','World')
from __future__ import (absolute_import, division, print_function, unicode_literals)
# import mantid algorithms, numpy and matplotlib
from mantid.simpleapi import *
import matplotlib.pyplot as plt
import numpy as np
GEM38370_Focussed_Legacy = Load("GEM38370_Focussed_Legacy")
fig,ax = plt.subplots(subplot_kw = {"projection": "mantid"})
for i in range(6):
ax.plot(GEM38370_Focussed_Legacy, wkspIndex = i)
ax.set_xlim([0.0,10.0])
ax.set_xscale('symlog')
fig.show()
That will produce a figure looking like this:
Load the EventWorkspace HYS_11388_event.nxs
using the SumSpectra() algorithm sum across each spectra in the workspace, assigning the result to a new workspace Sum
using the Rebin() algorithm rebin your new Sum workspace with bin width of 100 microseconds and events being preserved and assign it to another new workspace binned.
Hint: your Params should be set to 100 and PreserveEvents should equal True
plot the spectrum of binned in the same was the previous part.
Filter out events recored before 4000 microseconds using the FilterByTime() algorithm into a workspace called FilteredByTime, and plot FilteredByTime to the same figure.
Using the Matplotlib function add a legend to your plot ax.legend() and then show your figure.
By following these steps you should end up with code that looks something like this:
# The following line helps with future compatibility with Python 3
# print must now be used as a function, e.g print('Hello','World')
from __future__ import (absolute_import, division, print_function, unicode_literals)
# import mantid algorithms, numpy and matplotlib
from mantid.simpleapi import *
import matplotlib.pyplot as plt
import numpy as np
HYS_11388_event = Load("HYS_11388_event")
Sum = SumSpectra(HYS_11388_event)
binned = Rebin(Sum,Params = 100,PreserveEvents=True)
fig,ax = plt.subplots(subplot_kw = {"projection": "mantid"})
ax.plot(binned, wkspIndex = 0)
FilteredByTime = FilterByTime(binned, StartTime = 4000)
ax.plot(FilteredByTime, wkspIndex = 0)
ax.legend()
fig.show()
That will produce a figure looking like this:
