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Functional Programming Paradigm of Python for Scientific Computation Pipeline Integration

Chen Zhang, Lecheng Jia, Wei Zhang, Ning Wen

TL;DR

A novel functional programming (FP) paradigm based on the Python architecture and associated suites in programming practice is presented, designed for the integration of pipelines of different data mapping operations, intended for the integration of scientific computation flows.

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.

Functional Programming Paradigm of Python for Scientific Computation Pipeline Integration

TL;DR

A novel functional programming (FP) paradigm based on the Python architecture and associated suites in programming practice is presented, designed for the integration of pipelines of different data mapping operations, intended for the integration of scientific computation flows.

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.
Paper Structure (12 sections, 5 figures)

This paper contains 12 sections, 5 figures.

Table of Contents

  1. Introduction
  2. Background and Architecture
  3. Function suite
  4. Analysis and discussion
  5. Conclusion
  6. Acknowledgements

Figures (5)

  • Figure 1: The essence of data manipulation: transforming data to represent it from one form to another.
  • Figure 2: The technical divergence between data research and engineering
  • Figure 3: Illustration of the FP Python infrastructure used for the runtime checker (navy), function wrapper (gray), and tester (lime); the light salmon background is the framework's feature set.
  • Figure 4: Beeswarm and box plots of benchmark tests on invocation time consumption, for an identical simple functions. NoDeco: without decorator; FlowConf: decorated by inflow/outflow control; ArgConf: decorated by argument configuration; and FlowConf+ArgConf: with both decorators.
  • Figure 5: Demonstration of experimental pipe applied to natural image processing. (a) original 8-bit grayscale image; (b) preprocessed image after cropping, denoising, and resampling; and final images filtered by (c) Prewitt; (d) Canny; and (e) Laplacian of Gaussian.