There is no denying that the labor of scientists in the 21st century is so much easier than in previous generations. This is, among other reasons, because we have reinvented discovery into Networked Science; members of any scientific community with similar goals gather in large interdisciplinary teams and cooperate together to achieve complex mission-oriented goals. This new paradigm on the approach to research is also reflected in the computational resources employed by researchers. These are not restricted any more to a single piece of commercial software, created and maintained by a lone company, but libraries of code that sit on top of programming languages. The same professionals, who require fast and robust computational tools for their everyday work, get together and create these libraries in an open-source philosophy, in such a way that the resources are thoroughly tested, and improvements occur at faster pace than any commercial product could ever offer.
This book presents the most robust programming environment till date – a system based on two libraries of the computer language Python: NumPy and SciPy. In the following sections we wish to guide you on the usage of this system, through examples of state-of-the-art research in different branches of science and engineering.