Python package to read McStas data and export as python objects in different formats or as other files.

The current version of the code is supposed to be a starting point to figure out how such a package should be structured, especially with regards to the API for the user. The demo notebook shows how the package can be used.

The core package only depends on

• h5py
• numpy

The different export formats, such as scipp, should not be core dependencies but only used when exporting to that format.

To run the demo notebook one will need

• Recent McStas through conda (>3.5.20)
• mcstasscript
• matplotlib
• scipp
• scippneutron

pip install mcstastox 

Note that still an early version of the package is on pip, but the API is not stable yet.

python -m pip install mcstastox

To for example export to scipp, one needs to specify which component is the source and which is the sample.

Then all monitors with pixel id's are loaded, this is purely supported by Monitor_nD with option strings such as:

square x bins=512 y bins=256, neutron pixel min=0 t, list all neutrons square x bins=1024 y bins=512, neutron pixel min=131073 t, list all neutrons 

The next pixel min should be large enough to accommodate all pixels of the previous monitors, so here above 512*256.

This data can then be loaded to a simple scipp object with:

import mcstastox with mcstastox.Read(file_path) as loaded_data: scipp_data = loaded_data.export_scipp_simple(source_name="source", sample_name="sample_position") 

This data can also be loaded to a scipp data group with:

import mcstastox with mcstastox.Read(file_path) as loaded_data: scipp_data_group = loaded_data.export_scipp(source_name="source", sample_name="sample_position") 

This takes less space and events are already grouped by pixel ids