- Generalize handling of common Weights and user-defined Weights
- Added first integration of LAW tasks to stear an analysis running PocketCoffea workflows
- Many improvements in the plotting scripts
- Added Dataset discovery cli to dynamically query for dataset and build the dataset definition file
- Cleaning up of the default skimming function (**Breaking changes!** see below)
- Added CDCI tests of utils and full configuration tests
- New parameters exploration CLI
- Added more executors
- Tested the Swan AF and INFN AF
Breaking changes
Default skim
- Some skimming cuts were included by default in the base workflow and may be unnoticed by the users
[PR193](https://github.com/PocketCoffea/PocketCoffea/pull/193). For maximum transparency we have removed those cuts
from the base workflow and made the cutting functions available to be used in the configuration. The functions were:
- nPV_good > 0 selection
- goldenJson
- event flags for data and MC.
Users **must now include** the following cut functions in their `skim` configuration to keep the same cutflow in their
analysis:
python
from pocket_coffea.lib.cut_functions import get_nPVgood, goldenJson, eventFlags
cfg = Configurator(
skim = [get_nPVgood(1), eventFlags, goldenJson]
)
This change is enough to preserve the same cuts applied before 0.9.5.
Jet selection function
The signature of the jet cleaning function used often in the object preselection step of processor has been changes to
add explicitely the year argument.
python
- def jet_selection(events, jet_type, params, leptons_collection=""):
+ def jet_selection(events, jet_type, params, *year*, leptons_collection=""):
This signature change can be unnoticed by users using the `leptons_collection` argument. Please cross-check your
function usage.