Add ability to bin non-10M data with missing modules

Open
#105 0 comments 0 reactions 0 assignees View on GitHub

Nobody has claimed this yet.

Assessment

Difficulty
4/5
Estimated time
3-5 days
Newbie friendliness
35/100
Issue type
Feature
Clarity
Mostly clear
Activity status
Stale
Tech stack
python
Domain
data-engineering

Research direction

No files, tests, or entry points are named. Start by locating the dataset-binning validation and the diagnostic-tool entry points, then trace where a full Tristan 10M detector and 100 expected files are required. Done means incomplete datasets with missing module files can be binned and remain usable by the supported diagnostic tools.

Written by the indexing model from the issue text.

Description

At the moment, it is not possible to bin any datasets that are not from a full Tristan 10M detector. In a recent beamtime there has been an issue with one module which had to be switched off, leading to 10 missing files from the expected 100. While this is not ideal, and hopefully won't repeat itself, we should still be able to process and run diagnostic tools on incomplete datasets which may still have some useful data for the users.

(Diagnostic side in progress...)

Dominant language
Python
Stars
2
Forks
1
No merged PRs in 30d

Contributor guide

No contributing guide indexed for this repository

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

More from DiamondLightSource/python-tristan

All issues in DiamondLightSource/python-tristan

Similar issues

More Python issues

Get new issues in your inbox

A short digest of beginner-friendly GitHub issues.