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Releases: cp3-llbb/SAMADhi

Version 2.0

27 Aug 15:16
bb1a204
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This major releases includes few big changes:

  • MadWeight tables were removed. We never used them in CP3 and we moved to MoMEMta, which doesn't require a database of weight anymore.
  • A new table of analyses was setup, to group results
  • A new version of the web frontend was deployed
  • More analysis and cleanup scripts

Official release for the 2017 data analysis

11 Jun 07:31
e165365
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This release was used for HH and ZA 2017 run 2 analysis

v1.1

08 Aug 19:44
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This minor release includes changes that were proposed during a dedicated tutorial session in CP3.

  • For samples and results, the path is resolved/expanded before we store it in the database. It makes more sense than relative paths.
  • For results, the path may also be a file, not only a directory
  • When entering a sample with no explicit #processed events, the code tries to guess it from the parent sample and/or dataset.
  • Modification of the MadWeight bookkeeping.
    Event weights are now tracked in association with a LHCO file.
    These files must be stored as (derived) samples and be directly or indirectly associated with a dataset.

Other small improvements are also included, mostly in the behavior of the CLI scripts.

Note that while considered as a minor release, the changes included make the database incompatible with the previous one. Conversion is nevertheless trivial and most of the code is unchanged.

First public release

05 Jul 22:29
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With version 1.0, SAMADhi reaches a turning point in its development, with a first version available to users. Functionality matches the initial goals for the llbb CP3 group:

  • tracking of the CMS datasets used by the group
  • tracking of the samples produced for analysis
  • tracking of the analysis results (produced from one or more sample)
  • central bookkeeping of Madweight configurations and event weights
    The system keeps track of the relations between these items.

SAMADhi comes with a set of command line clients to fill the database and search its content. Examples (code snippets) for using the database in python analysis code are provided.

Enjoy!