Comulative sample type flags

[sofiagiappichini]

New flags are introduced for the cases where multiple subtypes of samples are used, but a flag for the overall type is also needed. For now, is_data, is_dyjets, and is_wjets are supported. Individual subtype flags are maintained alongside them.

[Copilot]

Pull request overview

This PR introduces cumulative sample type flags to complement existing individual subtype flags. The change allows for broader categorization of samples (e.g., is_data, is_dyjets, is_wjets) while maintaining granular subtype flags.

Changes:

• Added cumulative flags for data, dyjets, and wjets sample types based on substring matching
• Updated subproject commit reference for jsonpog-integration

Reviewed changes

Copilot reviewed 2 out of 2 changed files in this pull request and generated no comments.

File	Description
data/jsonpog-integration	Updated subproject commit reference
code_generation/configuration.py	Added cumulative sample type flags (is_data, is_dyjets, is_wjets) with substring-based detection

Comments suppressed due to low confidence (2)

code_generation/configuration.py:1

• Line 173 sets is_dyjets instead of is_wjets in the else block for the wjets check. This should be sample_parameters['is_wjets'] = False.

from __future__ import annotations  # needed for type annotations in > python 3.7

code_generation/configuration.py:1

• The if-else blocks can be simplified to directly assign the boolean result of the substring check, reducing code duplication. For example: sample_parameters['is_data'] = 'data' in self.sample.

from __future__ import annotations  # needed for type annotations in > python 3.7

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[moritzmolch]

I'm fine with the changes, my only comment is just a stylistic proposal.

[moritzmolch]

You can save some lines of code by shortening the if-else statements to:

sample_parameters["is_data"] = "data" in self.sample

The same can be done for the "is_dyjets" and "is_wjets" flags.

[nshadskiy]

I suggest to make it more general by looking at how the sample name starts with. This should also work if later other similar cases would be introduced to an analysis. The splitting is then set to always be an underscore.

for sampletype in self.available_sample_types:
    if self.sample == sampletype or self.sample.startswith(sampletype + "_"):
        sample_parameters["is_{}".format(sampletype)] = True
    else:
        sample_parameters["is_{}".format(sampletype)] = False