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Compatibility with the Array API standard?

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Bewertung

Schwierigkeit
5/5
Geschätzter Aufwand
Über eine Woche
Anfängerfreundlichkeit
22/100
Issue-Typ
Feature
Klarheit
Größtenteils klar
Aktivitätsstatus
Aktiv
Tech-Stack
numpy, pandas, python, pytorch, tensorflow
Bereich
data

Rechercherichtung

The payload names no specific files, so begin by reading the Array API standard docs and choosing a few representative pvlib functions that rely on NumPy and pandas calls. Record which calls would break with other array libraries, then prototype compatibility in those functions. Done means a written cost-benefit summary with a working prototype and tests run against at least one non-NumPy array library.

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Beschreibung

discussion

The Array API standard makes it possible to write generic code that works with many types of arrays (NumPy, Tensorflow, PyTorch, Dask, JAX, CuPy, MXNet, Xarray, ...). In principle, this would enable a pvlib simulation to be run in parallel, or on a GPU, using only code that looks very similar to the numpy-style code we are familiar with.

The Array API standard is gaining some meaningful traction in the community. For example, SciPy is adding support: see their array types tag, progress tracking tables, and developer documentation. scikit-learn is working on it too.

Adopting the standard could be a fairly straightforward way of enabling new use cases for pvlib. However, I don't have any experience with it myself and can't say anything more concrete than it seems worth looking into. So, following discussion with @echedey-ls, a good first step here is to explore the cost and benefit by prototyping compatibility in a small number of representative pvlib functions (I leave it an open question which functions might be considered representative). That would inform decisions about whether and how to pursue broader adoption across pvlib.

For reference, I copy this description from our GSoC 2025 project ideas list:

Project Title: Compatibility with the Array API

Project Description: An exciting development in the Python ecosystem is the Python array API standard, a uniform interface across the various array libraries (NumPy, Xarray, JAX, etc). Historically, pvlib has been written for compatibility with NumPy arrays and Pandas Series, but making use of the Array API to expand compatibility to other array libraries could be very beneficial. A successful proposal for this project will include some investigation about what it would take to adopt the Array API across pvlib.

Expected Outcomes: A plan/roadmap for adopting the Array API, Array API compatibility in several pvlib modules, and automated tests ensuring compatibility across the various array packages.

Vorherrschende Sprache
Python
Sterne
1.7k
Forks
1.2k
Ø Merge
3 T. 18 Std.
Gemergte PRs (30 T.)
13

Entwicklungsumgebung

Erste Schritte

  1. Lesen Sie das ganze Issue und danach den Beitragsleitfaden des Projekts.
  2. Schreiben Sie ins Issue, dass Sie es übernehmen — das erspart doppelte Arbeit.
  3. Forken Sie das Repository und arbeiten Sie in einem Branch.
  4. Öffnen Sie einen Pull Request, der die Issue-Nummer nennt.

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