The goal of this book is to teach computational scientists how to develop tailored, flexible, and human-efficient working environments built from small programs written in the easy-to-learn, high-level language Python.
The focus is on examples and applications of relevance to computational scientists.
These include binding together existing applications and tools, e.g. for automating simulation, data analysis, and visualization.
The book also covers steering simulations and computational experiments; equipping old programs with graphical user interfaces; making computational Web applications; and creating interactive interfaces with a Maple/Matlab-like syntax to numerical applications in C/C++ or Fortran.
The highly qualified author argues that scripting with Python makes you much more productive, increases the reliability of your scientific work and lets you have more fun - on Unix, Windows and Macintosh.
All the tools and examples in this book are open source codes. The third edition is compatible with the new NumPy implementation and features updated information, correction of errors, and improved associated software tools.
Written for:
Undergraduate students in computer science, computational science and engineering; researchers
The focus is on examples and applications of relevance to computational scientists.
These include binding together existing applications and tools, e.g. for automating simulation, data analysis, and visualization.
The book also covers steering simulations and computational experiments; equipping old programs with graphical user interfaces; making computational Web applications; and creating interactive interfaces with a Maple/Matlab-like syntax to numerical applications in C/C++ or Fortran.
The highly qualified author argues that scripting with Python makes you much more productive, increases the reliability of your scientific work and lets you have more fun - on Unix, Windows and Macintosh.
All the tools and examples in this book are open source codes. The third edition is compatible with the new NumPy implementation and features updated information, correction of errors, and improved associated software tools.
Written for:
Undergraduate students in computer science, computational science and engineering; researchers
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