kernel-varying convolution presubmission inquiry

[pibion]

Submitting Author: Name (@github_handle)
Package Name:
One-Line Description of Package:
Repository Link (if existing):
EiC: @coatless

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Code of Conduct & Commitment to Maintain Package

• I agree to abide by pyOpenSci's Code of Conduct during the review process and in maintaining my package after should it be accepted.
• I have read and will commit to package maintenance after the review as per the pyOpenSci Policies Guidelines.

Description

My team and I (especially @Arqu1100) have been working on energy-dependent convolutions for a nuclear physics application. Such a convolution is useful for incorporating beam resolution (which varies by energy) into predicted data. An identical varying-kernel convolution is used in astronomy to match spectra resolution across energy ranges.

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checks that satisfy community requirements. If your package fits into an
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• Astropy: My package adheres to Astropy community standards
• Pangeo: My package adheres to the Pangeo standards listed in the pyOpenSci peer review guidebook

Scope

• Please indicate which category or categories this package falls under:

  • Data retrieval
  • Data extraction
  • Data processing/munging
  • Data deposition
  • Data validation and testing
  • Data visualization
  • Workflow automation
  • Citation management and bibliometrics
  • Scientific software wrappers
  • Database interoperability

Domain Specific

• Geospatial
• Education

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• Explain how and why the package falls under these categories (briefly, 1-2 sentences). For community partnerships, check also their specific guidelines as documented in the links above. Please note any areas you are unsure of:

This is a scientific software wrapper for a convolution that is useful to the nuclear physics community.

• Who is the target audience and what are the scientific applications of this package?

The target audience is the nuclear beam physics community. The specific application of this package is to improve R-matrix fits by more accurately representing what the experimental data would look like.

• Are there other Python packages that accomplish similar things? If so, how does yours differ?

There is another python package that does a similar thing, ppfx. It is available on PyPi at https://pypi.org/project/ppxf/, specifically the varsmooth function. This is incorporated into SDSS-Mangadap (https://sdss-mangadap.readthedocs.io/en/latest/resolution.html). The issue is that these libraries are very difficult to find when coming from the nuclear physics community.

• Any other questions or issues we should be aware of:

My main question is whether this package is in scope for PyOpenSci.

P.S. Have feedback/comments about our review process? Leave a comment here

[coatless]

@pibion please fill out the upper YAML information in the ticket:

Submitting Author: Name (@github_handle)
Package Name:
One-Line Description of Package:
Repository Link (if existing):
EiC: TBD