Does this skill support Awkward Array 1.x?

No, this skill is specifically designed for Awkward Array 2.0 to ensure developers use the most modern and performant APIs available.

How does it handle complex slicing and filtering?

The skill provides guidance on boolean masking, argmin/argmax slicing, and axis-specific operations to ensure accurate data extraction from jagged structures.

Does it provide help with NumPy integration?

Yes, it contains specific references for NumPy interoperability, helping you mix standard array operations with Awkward's jagged structures effectively.

Can it help with High Energy Physics (HEP) data?

Yes, it includes specific patterns for HEP-style event data models, including combinatorics and record construction frequently used in particle physics.

Awkward Array 2.0 Development

Name: Awkward Array 2.0 Development
Author: gordonwatts

bygordonwatts

데이터 과학 및 ML

Streamlines the development of Python workflows using Awkward Array for jagged, nested, and record-based data structures.

소개

This skill provides specialized guidance for navigating the complexities of Awkward Array 2.0, particularly in high-energy physics (HEP) and scientific computing environments. It assists developers in building efficient event data models using ak.zip, performing advanced combinatorics like ak.cartesian, and managing complex jagged array manipulations with best practices for axes and NumPy interoperability. By leveraging a modular reference system, it ensures code is performant, follows the latest API standards, and avoids common pitfalls in multidimensional data analysis.

주요 기능

Performance-oriented filtering, masking, and aggregation strategies
Expert guidance on axis rules, flattening, and NumPy interoperability
0 GitHub stars
Complex record construction and event data modeling (EDM)
Awkward 2.0 API compliance and syntax enforcement
Advanced combinatorics including Cartesian products and combinations

사용 사례

Building and managing event data models for High Energy Physics (HEP) analysis
Optimizing data pipelines involving nested records and multi-dimensional boolean masking
Debugging complex jagged array operations and axis-related errors in Python

소개

주요 기능

Performance-oriented filtering, masking, and aggregation strategies
Expert guidance on axis rules, flattening, and NumPy interoperability
0 GitHub stars
Complex record construction and event data modeling (EDM)
Awkward 2.0 API compliance and syntax enforcement
Advanced combinatorics including Cartesian products and combinations

사용 사례

Building and managing event data models for High Energy Physics (HEP) analysis
Optimizing data pipelines involving nested records and multi-dimensional boolean masking
Debugging complex jagged array operations and axis-related errors in Python