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Utilities

Find raw data using OnCat

For many experiments users already used sequence names or comments to group together runs for autoreduction. If that’s the case, this information is stored in the https://oncat.ornl.gov database. There are some functions available to access the database and retrieve the information. It is recommended to write a separate script to log in to ONCat, retrieve run information, and store it in a json file. An example script can be found below:

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import pyoncat
from oncat_util import oncat_login, write_json_from_oncat

# login to oncat if required
try:
    ocl.Facility.list()
except (NameError, pyoncat.LoginRequiredError, pyoncat.InvalidRefreshTokenError):
    ocl = oncat_login()

# generate file and print the keys
keys = write_json_from_oncat(filename="/SNS/some_path/deleteme.json",
                             login=ocl,
                             ipts_number="27023",
                             instrument="ARCS",
                             group_by_angle=True)
print(keys)

Save the script in your folder, say as retrieve_data_IPTS-27023.py. Open a terminal, navigate to your folder, start mantidpython, then type run -i retrieve_data_IPTS-27023.py. The script will save a file, /SNS/some_path/deleteme.json, and will print the keys (the names of the datasets you used in autoreduction).

The beginning of the output file will look like

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{"Sample_800meV_flux_5K": [[223212], [223213], [223214], [223215], [223216], [223217], [223218], [223219], [223220], [223221], [223222], [223223], [223224], [223225], [223226], [223227], [223228], [223229], [223230], [223231], [223232], [223233], [223234], [223235], [223236], [223237], [223238], [223239], [223240], [223241], [223242], [223243], [223244], [223245], [223246], [223247]], "Sample_30meV_new_5K": [[229833, 230557]]

and the keys are

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dict_keys(['Sample_800meV_flux_5K', 'Sample_30meV_new_5K', 'None', ' ', 'Sample_30meV_new_18K', 'Sample_300meV_flux_5K', 'Sample_60meV_res_5K', 'Sample_120meV_flux_5K', 'Sample_30meV_new_300K', 'Sample_30meV_res_5K', 'Sample_30meV_new_100K', 'Sample_500meV_flux_5K'])

To use this information, change the value of Runs to

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'Runs':{'file':'/SNS/some_path/deleteme.json', 'ky':"Sample_800meV_flux_5K"}

It might happen that the information in the ONCat database is not accurate, or some files must be added or excluded from one dataset. The write_json_from_oncat function can add or exclude runs from a dataset, and it can group entries by angle. Please see the documentation in the function. Also, there are several examples available in the oncat_example.py file.

Reduce raw data to MDEvent workspaces

The reduce_data_to_MDE function takes a lists of dataset description and will try to get the associated workspace in memory. If already there, the function does not try to load or generate them again. For each dataset in the list, if the workspaces are not in memory, it will try to load them from the disk. Finally, if this step fails, it will call the generate_mde(dataset) function, and optionally the generate_BG_mde(data_set,compress_events_tof) to produce muti-dimensional event workspaces. In the case of background, all files are summed together, then transformed to an angle-independent momentum trasfer frame (laboratory frame). In future releases, these two functions will be combined into asingle one, with an additional flag to specify if the result is real data or background.

Generating histograms

Correction functions

Plotting functions