%0 Journal Article %1 Chudoba2013414 %A Chudoba, R. %A Sadílek, V. %A Rypl, R. %A Vořechovský, M. %D 2013 %J Computer Physics Communications %K computation integral mathematics python software unread %N 2 %P 414 - 427 %R 10.1016/j.cpc.2012.08.021 %T Using Python for scientific computing: Efficient and flexible evaluation of the statistical characteristics of functions with multivariate random inputs %U http://www.sciencedirect.com/science/article/pii/S0010465512003086 %V 184 %X 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 repository 1 and CPCP library.

@article{Chudoba2013414, 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 repository [1] and CPCP library.}, added-at = {2012-11-23T03:54:31.000+0100}, author = {Chudoba, R. and Sadílek, V. and Rypl, R. and Vořechovský, M.}, biburl = {https://www.bibsonomy.org/bibtex/2d8ad0d86a07be25e7f684d751e019311/drmatusek}, doi = {10.1016/j.cpc.2012.08.021}, interhash = {830821cc881a548c5f5c498cfcf88f6d}, intrahash = {d8ad0d86a07be25e7f684d751e019311}, issn = {0010-4655}, journal = {Computer Physics Communications}, keywords = {computation integral mathematics python software unread}, month = feb, number = 2, pages = {414 - 427}, timestamp = {2012-11-23T03:54:31.000+0100}, title = {Using Python for scientific computing: Efficient and flexible evaluation of the statistical characteristics of functions with multivariate random inputs}, url = {http://www.sciencedirect.com/science/article/pii/S0010465512003086}, volume = 184, year = 2013 }