Main program

The purpose of the main program is to load the data and slice definitions, generate or load the intermediate multidimensional event workspaces, and create the required histograms. Usually one would also plot any one or two dimensional slices using matplotlib.

A simple script would look something like this:

from imp import reload
import matplotlib.pyplot as plt
import sys
sys.path.append('/opt/Mantid/bin')
sys.path.append('/opt/Mantid/lib')
sys.path.append('/SNS/groups/dgs/DGS_SC_scripts')

from mantid import plots
from reduce_data_to_MDE import *
from slice_utils import *
import define_data_example
import define_slices_example

#############################################
# MAIN PROGRAM
#############################################
reload(define_data_example)
reload(define_slices_example)

datasets=define_data_example.define_data_set()
reduce_data_to_MDE(datasets)

slice_descriptions=define_slices_example.define_data_slices(extra='_dataset0')

make_slice(datasets[0],slice_descriptions[0])

fig,ax=plt.subplots(subplot_kw={'projection':'mantid'})
c=plot_slice(slice_descriptions[0], ax=ax, cbar_label='Intensity')
set_axes_parameters(ax,**slice_descriptions[0]['Axes_parameters'])
fig.show()

Import section

This is lines 1-12 in the above example. Since the program uses Mantid as the main processing library, one might add it to the sys.path (lines 3-5). You might ommit these if you run this script from within a Mantid environment (mantidpython or MantidWorkbench). Similarly, the location of the utlility functions called in this program needs to be available (line 6). You can skip this information if you already added this files to your python path, or if they are in the same folder as the current script.

We need to import the following libraries:

reduce_data_to_MDE - utility library to load/ process the raw data to MDEventWorkspace
slice_utils - library to generate slices (and optionally plot them)
Your data description, define_data_example in this case
Your slice description, define_slices_example in this case
Optionally the mantid.plots library, in case you want to use matplotlib to plot your data

One might run the program multiple times, just changing data or slice descriptions. Since those functions are in libraries already loaded in python, just running import again will not get the latest updates. For that one must force reload the libraries (lines 17 and 18).

Getting the data

To get the data, the first step is to get the corresponding definitions. This is achieved using a call to the user defined data description, in line 20. The reduce_data_to_MDE function (line 21) takes the list of data descriptions and makes sure that, for each description, the workspace with the given MDEName is present in memory. Optionally, if the BackgroundMdeName is not None, it will also get those workspaces.

Note: Since the generation of the intermediate MDEvent data structure is slow, the behavior of the reduce_data_to_MDE function is as follows

If there is a workspace in memory with the name MDEName no further processing will happen
If the MDEName workspace is not in memory, it will try it to load it from the file with the same name, extension .nxs, in the MdeFolder
If the above steps fail, the function will try to generate the workspace from the parameters given in the data description.

A similar behavior is present for the background data. In the case one would like to re-reduce the original data, then one would need to delete the workspace from memory and delete it from the disk.

Generating slices

Similar to getting the data, generating slices is a two step process. In the first step, a call (line 23) to the userdefined slice description library returns a list of dictionaries, one for each slice. If more than one dataset is to be cut, it is recommended to use the extra parameter to generate a new set of slice descriptions for each dataset, so that the names of the histogrammed workspaces are different.

The second step is to generate the histogram (1D, 2D, 3D, …). This is done using the make_slice function (line 25). The main parameters are a single dataset description and a single slice description. Most of the calculation is done using the MDNorm algorithm. Additional parameters allow for overriding the measured efficiency workspace (usually Vanadium), and for saving the data in ASCII or HDF5 file (processed MD histogram workspace in Mantid).

The main result is the generation of an MDHistogram workspace with the name given in the slice description.

Plotting slices

Optionally, one can plot the histograms as part of this script. One can use the Mantid/matplotlib libraries. For convenience, a plot_slice function can be used for line plots (wrapping a call to matplotlib errorbar function), or for 2D plots (using matplotlib pcolormesh and colorbar functions). The example in the script above will generate a 2D plot on the screen.

Notes about polarization

For polarized mode, one would like to apply a flipping ratio correction to the data. This needs to account for both the spin flip and non spin flip data. For convenience, a make_slices_FR_corrected function is available. It uses two datasets, it applies the flipping ratio corrections, and it will use the make_slice function to produce histograms for both spin states.