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https://github.com/rougier/scipy-crash-course

Material for a 24 hours course on Scientific Python
https://github.com/rougier/scipy-crash-course

course cython matplotlib numba numpy python scipy

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Material for a 24 hours course on Scientific Python

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README 

        

# Scientific Python Crash Course



![](data/XKCD.png)



This is the material for a 20 hours crash course on Scientific Python

that has been split in 5 beginner and 3 advanced

modules. Contributions and feedbacks are welcome. Released under a

[CC-BY 4.0

International](https://creativecommons.org/licenses/by/4.0/legalcode) license.  



Nicolas P. Rougier





## Material



### [1. Installation](lessons/01-installation.md) 






This lesson aims at providing the student with a clean development environment,

including Python installation and essential packages (using the [Anaconda]

installer), a decent text editor (e.g. [emacs], [vim], [atom], [notepad++]), a

git command line and a shell. We'll also introduce the Python & [IPython]

shells, the [Jupyter] notebook and explains how to run a python

script from the command line or from inside the [IPython] shell.  



**Keywords**: `anaconda`, `shell`, `notebook`, `script`, `git`, `editor`  

**Prerequisites**: None  

**Resources**: None 







### [2. Introduction](lessons/introduction.md) 






We introduce here the Python language. Only the bare minimum necessary for

getting started with Numpy and Scipy is addressed here. To learn more about

the language, consider going through the excellent tutorial

https://docs.python.org/tutorial. Dedicated books are also available, such as

http://www.diveintopython.net/.  



**Keywords**: `python`, `types`, `control flow`, `function`, `package`  

**Prerequisites**: Installation  

**Resources**: [The Python tutorial](https://docs.python.org/3/tutorial/)







### [3. Numerical computing (numpy)](https://scipy-lectures.org/intro/numpy/index.html) 






This lesson gives an overview of [NumPy], the core library for performant

numerical computing, with support for large, multi-dimensional

arrays and matrices, along with a large collection of high-level mathematical

functions to operate on these arrays.



**Keywords**: `numpy`, `array`, `dtype`, `shape`, `broadcast`  

**Prerequisites**: Introduction  

**Resources**: [SciPy Lecture Notes](https://scipy-lectures.org/)







### [4. Data visualization (matplotlib)](https://github.com/rougier/matplotlib-tutorial) 






In this lesson, we are going to explore [Matplotlib] that is the single most

used Python package for 2D-graphics. It provides both a very quick way to

visualize data from Python and to produce publication-quality figures in many

different formats. We'll cover only most common use cases.



**Keywords**: `matplotlib`, `figure`, `plot`, `data`  

**Prerequisites**: Introduction, Numerical computing  

**Resources**: [Scientific Visulization: Python + Matplotlib](https://www.labri.fr/perso/nrougier/scientific-visualization.html)







### [5. Scientific computing (scipy)]() 






We'll explore the [SciPy] library that contains a large number of independent

modules for optimization, linear algebra, integration, interpolation, special

functions, FFT, signal and image processing, ODE solvers and other tasks common

in science and engineering.



**Keywords**: `linear algebra`, `signal processing`, `analysis`, `integration`, `solvers`  

**Prerequisites**: Numerical Computing  

**Resources**: [SciPy Lecture Notes](https://scipy-lectures.org/)







### [6. Version Control (git)](https://swcarpentry.github.io/git-novice/)  






Version control is the lab notebook of the digital world: it’s what

professionals use to keep track of what they’ve done and to collaborate with

other people. Every large software development project relies on it, and most

programmers use it for their small jobs as well. And it isn’t just for

software: books, papers, small data sets, and anything that changes over time

or needs to be shared can and should be stored in a version control system.



**Keywords**: `git`, `github`, `clone`, `commit`, `push`, `fork`  

**Prerequisites**: Installation  

**Resources**: [Software Carpentry](https://software-carpentry.org/)







### [7. Vectorization techniques (numpy)](https://www.labri.fr/perso/nrougier/from-python-to-numpy) 






The goal of this lesson is to explain some vectorization techniques that

can drastically improve computation, with several orders of magnitude in

some cases.



**Keywords**: `Code`, `Problem`, `Spatial`, `Temporal`  

**Prerequisites**: Numerical Computing  

**Resources**: [From Python to Numpy](https://www.labri.fr/perso/nrougier/from-python-to-numpy)







### [8. Interface with C (cython and ctypes)](https://scipy-lectures.org/advanced/interfacing_with_c/interfacing_with_c.html) 






[Cython] is a static compiler for both the Python programming language and the

extended Cython programming language that eases the writing of C extensions.

[Numba] translates Python functions to optimized machine code at runtime (just in

time) using the industry-standard LLVM compiler library.



**Keywords**: `Optimization`, `Cython`, `Numba`, `Compiler`, `JIT`  

**Prerequisites**: Numerical Computing , Vectorization techniques  

**Resources**: [SciPy Lecture Notes](https://scipy-lectures.org/)







### Bibliography

* [SciPy 1.0: fundamental algorithms for scientific computing in Python](https://www.nature.com/articles/s41592-019-0686-2),  

P.Virtanen, R.Gommers & al., Nature Methods, 2020.

* [Array programming with NumPy](https://www.nature.com/articles/s41586-020-2649-2),   

C.R. Harris, K.J. Millman & al., Nature, 2020.

* [How to transform code into scientific contribution](https://www.frontiersin.org/articles/10.3389/fninf.2017.00069/full),  

N.P. Rougier & F.Benureau, Frontiers in Neuroinformatics, 2018.

* [Ten simple rules for better figures](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003833),  

N.P. Rougier, M. Droettboom & P. E. Bourne, Plos Computational Biology, 2014.



### Copyright notice



Copyright © 2021 [Nicolas P. Rougier](http://www.labri.fr/perso/nrougier) —

Released under a [CC-BY 4.0 International](https://creativecommons.org/licenses/by/4.0/legalcode) license.  

Banner image copyright © Randall Monroe ([XKCD #353](https://xkcd.com/353/)) — 

This course has been written in November 2021 using:  

        



[Anaconda]:   https://www.anaconda.com/

[Emacs]:      http://www.emacs.org/

[vim]:        https://www.vim.org/

[Atom]:       https://atom.io/

[Notepad++]:  https://notepad-plus-plus.org/

[IPython]:    http://www.ipython.org/

[Jupyter]:    http://www.jupyter.org/

[NumPy]:      http://www.numpy.org/

[Scipy]:      http://www.scipy.org/

[Matplotlib]: http://www.matplotlib.org/

[Cython]:     https://cython.org/

[Numba]:      https://numba.pydata.org/