2009/05/11 by Helmut G. Katzgraber, Katzgraber, Helmut G.
Computer Science · Decision Sciences · #Computational Physics (physics.comp-ph) #Computational Physics and Python Applications #Data Visualization and Analytics #FOS: Physical sciences #Scientific Computing and Data Management
paper · pdf · doi:10.48550/arxiv.0905.1628
openalex publication_date 2009/05/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Writing complex computer programs to study scientific problems requires careful planning and an in-depth knowledge of programming languages and tools. In this chapter the importance of using the right tool for the right problem is emphasized. Common tools to organize computer programs, as well as to debug and improve them are discussed, followed by simple data reduction strategies and visualization tools. Furthermore, some useful scientific libraries such as boost, GSL, LEDA and numerical recipes are outlined.