Skip to content
Snippets Groups Projects
  1. Jan 22, 2024
  2. Jan 21, 2024
  3. Jan 19, 2024
  4. Jan 18, 2024
  5. Jan 17, 2024
  6. Jan 16, 2024
  7. Jan 10, 2024
  8. Jan 08, 2024
    • Gerhard Raven's avatar
      Streamline MonitoringHub and Sink implementations · 8c51ea6d
      Gerhard Raven authored
      - RootHistogramSink: avoid having a 'registry' for each instance,
        instead keep one shared constant global in an anonymous namespace
        so it is hidden from the linker
      - JSONSink: use the fact that `ofstream` closes the corresponding file
        when going out of scope
      - BaseSink: use `std::set` with dedicted comparison operator instead
        of `std::map` which saves a bit of memory
      - MonitoringHub: avoid one level of indirection in the type erasure
        which will type-erased function arguments to be passed in registers
        instead of forcing them to be passed through main memory
      - MonitoringHub: `m_typeIndex` is an invariant once constructed, and
        does not need a type-erased function to be determined 'on the fly'
      8c51ea6d
    • Sebastien Ponce's avatar
      Support GaudiHive in GaudiPython · 4a1e9887
      Sebastien Ponce authored
      Allows to use the hiveWhiteBoard and select the right TES.
      Made it backward compatible in the sense that by default TES number 0 is used seemlessly when number of threads is 1.
      If several threads are used however, an error will be raised if the store is not selected explicitely
      4a1e9887
  9. Jan 03, 2024
  10. Dec 22, 2023
  11. Dec 21, 2023
  12. Dec 20, 2023
    • Sebastien Ponce's avatar
      Implemented Root like histograms · 678df066
      Sebastien Ponce authored
      Their usage is similar to standard histograms, names are prefixed with "Root" and at this stage only non profile, non weighted versions are provided, for dimensions 1,  and 3 only.
      The main difference with standard histograms is that they compute statistical data on the input on top of the strict histogram, in particular they keep sum and sum of squares of all variables so that mean and standard_deviation of the original data can be computed.
      This behavior mimics Root histograms, which allows to convert the new histograms to Root ones and have the expected behavior, while the Gaudi histograms had no statistical data and their conversion to Root was somehow recomputing them (Root did) based on the histogram content. Obviously the values obtained were not correct.
      678df066
  13. Dec 19, 2023
  14. Dec 18, 2023
  15. Dec 06, 2023
  16. Dec 04, 2023
Loading