v0.4.0
Summary
This release brings many improvements of which the most relevant are summarised
here depending on their scope within the pipeline workflow.
Performance-wise, protopipe
caught up with the EventDisplay
and CTAMARS
historical pipelines starting from about 500 GeV onwards.
Below this threshold, even if compatible with requirements, the sensitivity diverges.
-
All pipeline
- upgrade to the API of
ctapipe 0.9.1
- documentation also on
readthedocs
and link toZenodo
- Continuous Integration is now performed on
GitHub
- New benchmarks have been added
- Reference analysis and benchmarks results have been updated
- upgrade to the API of
-
Data training
- calibration benchmarks need only
ctapipe-stage1-process
write_dl1
has becomedata_training
- DL1 parameters and (optionally) images are merged in a single file
- DL1 parameters names as in
ctapipe
and they are in degrees (TelescopeFrame
) - scale correction with the effective focal length
- fixed bugs and wrong behaviors
- calibration benchmarks need only
-
Modeling and DL2 production
- fixed bugs and wrong behaviors
- Added missing features to get closer to
CTAMARS
-
DL3
Contributors
- Michele Peresano (@HealthyPear)
- Gaia Verna (@gaia-verna)
- Alice Donini (@adonini)
What is changed since v0.3.0
Pull-requests that contain changes belonging to multiple classes are repeated.
🚀 General features
- Performance using Pyirf (#83) @gaia-verna
- Towards using Pyirf (#79) @gaia-verna
- Upgrade of DL2 production (#77) @HealthyPear
- Upgrade calibration benchmarks (#59) @HealthyPear
- Upgrade of data training (#58) @HealthyPear
🐛 Bug Fixes
- Fix zenodo configuration file and add LICENSE file (#106) @HealthyPear
- Fix calibration benchmarking settings (#100) @HealthyPear
- Fix plot of simulated signal and noise of 2nd pass image extraction (#99) @HealthyPear
- Upgrade of DL2 production (#77) @HealthyPear
- Upgrade of data training (#58) @HealthyPear
🧰 Maintenance
- Fix zenodo configuration file and add LICENSE file (#106) @HealthyPear
- Update documentation + general maintenance (#62) @HealthyPear
- Use mamba to create virtual enviroment for the CI (#101) @HealthyPear
- Upgrade all other notebooks and their docs version (#76) @HealthyPear
- Upgrade calibration benchmarks (#59) @HealthyPear
- Upgrade of data training (#58) @HealthyPear
- Enable CI from GitHub actions (#84) @HealthyPear