Publication detail

Using Python for scientific computing: Efficient and flexible evaluation of the statistical characteristics of functions with multivariate random inputs

CHUDOBA, R. SADÍLEK, V. RYPL, R. VOŘECHOVSKÝ, M.

Original Title

Using Python for scientific computing: Efficient and flexible evaluation of the statistical characteristics of functions with multivariate random inputs

Type

journal article - other

Language

English

Original Abstract

This paper examines the feasibility of high-level Python based utilities for numerically intensive applications via an example of a multidimensional integration for the evaluation of the statistical characteristics of a random variable. We discuss the approaches to the implementation of mathematically formulated incremental expressions using high-level scripting code and low-level compiled code. Due to the dynamic typing of the Python language, components of the algorithm can be easily coded in a generic way as algorithmic templates. Using the Enthought Development Suite they can be effectively assembled into a flexible computational framework that can be configured to execute the code for arbitrary combinations of integration schemes and versions of instantiated code. The paper describes the development cycle using a simple running example involving averaging of a random two-parametric function that includes discontinuity. This example is also used to compare the performance of the available algorithmic and executional features. The implemented package including further examples and the results of performance studies have been made available via the free Github repository and CPCP library.

Keywords

C, Enthought traits, Estimation of statistical moments, Loopless programming, Multidimensional integration, NumPy, Python, SciPy

Key words in English

C, Enthought traits, Estimation of statistical moments, Loopless programming, Multidimensional integration, NumPy, Python, SciPy

Authors

CHUDOBA, R.; SADÍLEK, V.; RYPL, R.; VOŘECHOVSKÝ, M.

RIV year

2012

Released

19. 8. 2013

Publisher

Elsevier

Location

USA

ISBN

0010-4655

Periodical

COMPUTER PHYSICS COMMUNICATIONS

Year of study

184

Number

2

State

Kingdom of the Netherlands

Pages from

414

Pages to

427

Pages count

14

BibTex

@article{BUT94313,
  author="Rostislav {Chudoba} and Václav {Sadílek} and Rostislav {Rypl} and Miroslav {Vořechovský}",
  title="Using Python for scientific computing: Efficient and flexible evaluation of the statistical characteristics of functions with multivariate random inputs",
  journal="COMPUTER PHYSICS COMMUNICATIONS",
  year="2013",
  volume="184",
  number="2",
  pages="414--427",
  issn="0010-4655"
}