Python Scientific lecture notes

Python para programación científica

Descripción

- Rich collection of already existing bricks corresponding to classical numerical methods or basic actions: we don’t want to re-program the plotting of a curve, a Fourier transform or a fitting algorithm. Don’t reinvent the wheel! - Easy to learn: computer science is neither our job nor our education. We want to be able to draw a curve, smooth a signal, do a Fourier transform in a few minutes. - Easy communication with collaborators, students, customers, to make the code live within a lab or a company: the code should be as readable as a book. Thus, the language should contain as few syntax symbols or unneeded routines as possible that would divert the reader from the mathematical or scientific understanding of the code. - Efficient code that executes quickly... but needless to say that a very fast code becomes useless if we spend too much time writing it. So, we need both a quick development time and a quick execution time. - A single environment/language for everything, if possible, to avoid learning a new software for each new problem.

Detalles

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