Abstract

Computational science (scientific computing or scientific computation) is concerned with constructing mathematical models as well as quantitative analysis techniques and using computers to analyze as well as solve scientific problems. In practical use, it is basically the application of computer simulation as well as other forms of computation from numerical analysis and theoretical computer science to problems in different scientific disciplines. The scientific computing approach is to gain understanding, basically through the analysis of mathematical models implemented on computers. Python is frequently used for highperformance scientific applications and widely used in academia as well as scientific projects because it is easy to write and performs well. Due to its high performance nature, scientific computing in Python often utilizes external libraries like NumPy, SciPy and Matplotlib etc.

Highlights

  • Science has basically been divided into theoretical and experimental disciplines, but during the last several decades computing has emerged as an important part of science

  • It is relatively rare that source code used in simulations for published papers are provided to readers, in contrast to the open nature of theoretical and experimental work

  • NumPy allows python to serve as a high-level language for manipulating numerical data, much such as IDL, MATLAB, or Yorick

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Summary

Introduction

Science has basically been divided into theoretical and experimental disciplines, but during the last several decades computing has emerged as an important part of science. Scientific computing is closely based to theory, but it has many characteristics in common with experimental work. It is often viewed as a new third branch of science. It is relatively rare that source code used in simulations for published papers are provided to readers, in contrast to the open nature of theoretical and experimental work. It is not uncommon that source code for simulation software is withheld as well as considered a competitive advantage. This problem has recently started to attract increasing attention, as well as a number of editorials in high-profile journals have called for increased openness in

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