Functional Programming Paradigm of Python for Scientific Computation Pipeline Integration

TL;DR

This paper introduces a functional programming paradigm in Python to unify and streamline scientific computation pipelines, enhancing flexibility, performance, and maintainability in interdisciplinary data processing tasks.

Contribution

It proposes a novel FP-based approach tailored for Python that facilitates seamless integration of diverse scientific computation workflows.

Findings

Improved pipeline integration efficiency

Enhanced flexibility in scientific data processing

Reduced maintenance complexity

Abstract

The advent of modern data processing has led to an increasing tendency towards interdisciplinarity, which frequently involves the importation of different technical approaches. Consequently, there is an urgent need for a unified data control system to facilitate the integration of varying libraries. This integration is of profound significance in accelerating prototype verification, optimising algorithm performance and minimising maintenance costs. This paper presents a novel functional programming (FP) paradigm based on the Python architecture and associated suites in programming practice, designed for the integration of pipelines of different data mapping operations. In particular, the solution is intended for the integration of scientific computation flows, which affords a robust yet flexible solution for the aforementioned challenges.