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Best practices

Best practices#

Columnflow convenience tools#

  • Columnflow defines EMPTY_FLOAT, a float variable containing the value -99999.0. This variable is typically used to replace null values in awkward arrays.

  • In many cases, one wants to access an object that does not exist for every event (e.g. accessing the transverse momentum of the 3rd jet events.Jet[:, 2].pt, even though some events may only contain two jets). In that case, the Route class and its apply() function can be used to access this object by replacing missing values with the given value, e.g. jet3_pt = Route("Jet.pt[:, 2]").apply(events, null_value=EMPTY_FLOAT).

  • Columnflow allows the use of some fields out of the events array, like the Jet field, as a Lorentz vector to apply operations on. For this purpose, you might use the attach_coffea_behavior() function. This function can be applied on the events array using

events = self[attach_coffea_behavior](events, **kwargs)

If the name of the field does not correspond to a standard coffea field name, e.g. “BtaggedJets”, which should provide the same behaviour as a normal jet, the behaviour can still be set, using

collections = {x: {"type_name": "Jet"} for x in ["BtaggedJets"]}
events = self[attach_coffea_behavior](events, collections=collections, **kwargs)

  • shortcuts / winning strategies / walktrough guides e.g. pilot parameter TODO

  • config utils: TODO

  • categorization

    • mutually exclusive leaf categories TODO

General advices#

  • When storage space is a limiting factor, it is good practice to produce and store (if possible) columns only after the reduction, using the ProduceColumns task.

Using python scripts removed from the standard workflow#

  • Use a particular cf_sandbox for a python script not implemented in the columnflow workflow: Write cf_sandbox venv_columnar_dev bash on the command line.

  • Call tasks objects in a python script removed from the standard workflow: An imported task can be run through the law_run() command, its output can be accessed through the “output” function of the task. An example is given in the following code snippet.

from columnflow.tasks.selection import SelectEvents

# run some task
task = SelectEvents(version="v1", dataset="tt_sl_powheg", walltime="1h")
task.law_run()

# do something with the output
output = task.output()["collection"]
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