Jupyter Notebooks 📓 by Naereen @ GitHub

This repository hosts some Jupyter Notebooks, covering various subjects. Go to nbviewer to read them.

You can also launch an interactive environment to play with the code by yourself, using one of these platforms:

At the beginning, this repository was only here to host some small experiments, for me to learn how to use the wonderful Jupyter tools correctly (baby notebooks 🍼)...

🐚 Bash

The first notebooks I wrote are small tutorials for 🐚 bash commands (or some of my command-line scripts), see for example a tutorial on head and a tail (on GitHub?).

🐍 Python

I am a passionate user of the Python programming language.

Science

This notebook written for the Pi Day 2017 (on GitHub?) demonstrates a dozen of easy algorithms to compute from 10 to 100000 digits of the number pi.
This notebook implements a simple example of the simulated annealing algorithm (on GitHub?) to minimize black-box functions 😎.
This notebook shows a hand-written and clear implementation (on GitHub?) of several Hashing functions, like MD5, SHA1, and all variants of SHA2 (SHA256, SHA224, SHA512, SHA384).
This notebook shows a manual implementation (on GitHub?) of the Lempel-Ziv complexity in pure Python, and then as optimized Python code, with Cython or Numba. I also wrote a version in Julia (in the same notebook), and compare the 4 implementations! I then published my code as a Pypy package, see here on pypi.org.
This notebook shows a hand-written and clear implementation (on GitHub?) of several Pseudo-Random Number Generators, including the famous Mersenne twister algorithm, and then uses it to samples from the most famous discrete and continuous distributions, showcasing use of the Inverse-Transform method and Acceptance-Rejection method (cf. Markov Chain Monte-Carlo methods).
This notebook implements and compares different Runge-Kutta methods (on GitHub?) for integrating Ordinary Differential Equations in Python. And also in Julia (on GitHub?), and also in OCaml (on GitHub?). I wanted to compare the three languages for the same algorithms. Well, obviously, Julia is the fastest and simplest for numerical simulations like this.
🇫🇷 Ce petit notebook en français (sur GitHub ?) implémente les algorithmes de vérifications des numéros de cartes bleues, de sécurité sociale et d'IBAN en Python.
This notebook implements and explore the Exponential Integral function Ei(x) (on GitHub?).
This notebook shows how to solve the equation exp(- a x²)=x both numerically and formally (with the Lambert W function) (on GitHub?).
This notebook implements Kullback-Leibler divergences for some parametric distributions, and KL-UCB indexes, in naive Python and compare with optimized versions using JIT compilation by Numba or C compilation by Cython (on GitHub?).
This short notebook defines and studies the Rényi entropy (on GitHub?).

Teaching

🇫🇷 Ce notebook en français présente les problèmes de bandits multi-bras stochastiques (on GitHub?) (multi-armed bandit, MAB), et les algorithmes dits "de bandits" pour les résoudre (UCB "Upper Confidence Bounds", KL-UCB, Thompson Sampling, Approximated Finite-Horizon Gittins index etc). C'est une bonne introduction aux outils que j'utilise pour ma thèse.
🇫🇷 I corrected some maths & programming problems from the annals of the CentraleSupelec national competitive exam (in France), in this notebook Oraux_CentraleSupelec_PSI__Juin_2017.ipynb (on GitHub?) (for this kind of oral exam). Again in 2018, Oraux_CentraleSupelec_PSI__Juin_2018.ipynb (on GitHub?).

Numerical simulations for dice games (:fr: in French)

I also wrote some notebooks on numerical simulations of dice games 🇫🇷 Voir ce sous-dossier (ou sur GitHub?) / :gb: See this sub-folder (or on GitHub?).

🎨 Art

This notebook shows some "obfuscated" code, producing (on GitHub?) ✨ nice figures... Or maybe they are pieces of code art 🎨 ?
This notebook implements in Python a generator of small "identicon" like the dislayed on GitHub for users without profile pictures (on GitHub?) ✨. See this example:

🇫🇷 Ce notebook montre comment générer des fausses citations latines du Roi Loth, avec une chaîne de Markov, la liste de locutions latines extraite de Wikipédia, et la liste des vraies citations latines du Roi Loth extraite de Wikiquote. Exemples :

I will try to write more artistic notebooks, showcasing nice pieces of code 🎨!

Experiments with Python

This small notebook (on GitHub?) is a fun experiment, where I tried to use James Powell (@dutc) rwatch module to write a Python context manager to add a Gaussian white noise to every numbers inside the context... Something like: with noise(): x = 10 will produce x = 10.325 for instance... It fails, but I almost got it, and it works (without breaking the interpreter) for complex numbers. That's already intersting!
This notebook (on GitHub?) is a small experiment, written quickly, about floating-point error propagation when using a non-naive polynomial multiplication with evaluation-and-interpolation. Sadly, this approach fails!
A tiny presentation on how to do time/memory profiling (on GitHub?) from inside the Jupyter notebook interface, with various approaches.

Experiments with Jupyter

This notebook shows how to register a custom HTML writer for builtins or user-defined types in IPython and Jupyter (on GitHub?), for the sake of the example I wrote a nice LaTeX/MathJax-powered print function that nicely displays polynomials from the numpy.polynomial.Polynomial module or class.
A small benchmark between Python, Pypy and Julia for the Romberg numerical integration algorithm (on GitHub?). Julia is the fastest, but Pypy is very fast too 🐍 !
Demo of the RISE Jupyter extension to easily write a dynamic slideshow in a Jupyter notebook, for Python (on GitHub?) and for OCaml (on GitHub?) 📢.

With Julia

This notebook shows a implementation of a naive algorithm to compute eigen values and eigen vectors for full rank matrices (on GitHub?, on Julia.

🐫 OCaml

Some notebooks are written in French 🇫🇷, mainly documents written for the preparation to the highly competitive French national exam to become a professor (aka the "agrégation"), as since 2016 I am a teaching assistant at ENS de Rennes in the Computer Science department, for the "Fundamental Computer Science" minor option (D) for the agrégation exam. 🇫🇷 Voir ce sous-dossier (ou sur GitHub) / :gb: See this sub-folder (or on GitHub).

And more will come... soon! 🚅

1. How to read these documents?

1.a. View the notebooks statically 📝

Either directly in GitHub: see the list of notebooks;
Or on nbviewer.jupiter.org: list of notebooks.

1.b. Play with the notebooks dynamically 💥

Anyone can use the mybinder.org website (by clicking on the icon above) to run the notebook in her/his web-browser. You can then play with it as long as you like, for instance by modifying the values or experimenting with the code.

Note: Only the Python kernel is supported on the MyBinder interface!

2. Requirements to run the notebooks locally?

All the requirements can be installed with pip and by running a few python -m ... commands.

Note: if you use Python 3 instead of Python 2, you might have to replace pip and python by pip3 and python3 in the next commands (if both pip and pip3 are installed).

2.a. Jupyter Notebook and IPython

sudo pip install jupyter ipython

It will also install all the dependencies, afterward you should have a jupyter-notebook command (or a jupyter command, to be ran as jupyter notebook) available in your PATH:

$ whereis jupyter-notebook
jupyter-notebook: /usr/local/bin/jupyter-notebook
$ jupyter-notebook --version  # version >= 4 is recommended
4.2.1

2.b. Other kernels (optional):

They are only needed to run the notebooks written for Bash or Octave (or OCaml):

2.b.1. GNU Bash kernel

You have to have bash installed (already there on all Linux distribution, installable with brew or macports on Mac OS, available on Windows through cygwin):

$ bash --version | head -n1
GNU bash, version 4.3.42(1)-release (x86_64-pc-linux-gnu)

And then install the kernel bash_kernel with these two commands:

sudo pip install bash_kernel
python -m bash_kernel.install

2.b.2. GNU Octave kernel

You have to have octave installed (installable with the package manager on major Linux distributions (apt-get, pacman, yum etc), installable with brew or macports on Mac OS, available on Windows through cygwin or natively):

$ octave --version | head -n1
GNU Octave, version 4.0.0

Your version of Octave should be installed with gnuplot support (it is usually the case):

$ gnuplot --version | head -n1
gnuplot 5.0 patchlevel 1

And then install the kernel bash_kernel with these two commands:

sudo pip install octave_kernel
python -m octave_kernel.install

2.b.3. OCaml kernel: `OCaml-Jupyter`

It is quite easy to install, with opam:

opam install jupyter

I started by using this OCaml kernel called IOCaml. The instructions were not so simple, cf. the tutorial by @andrewray on iocaml's wiki. It was prone to some bugs, and I had to manually implement a script to be able to convert the notebooks to PDF with jupyter-nbconvert --to pdf. Note that I also had to write a custom Exporter for jupyter-nbconvert in order to convert the notebooks to OCaml scripts (.ml).

💁 More information?

More information about notebooks (on the documentation of IPython) or on the FAQ on Jupyter's website.

More information about mybinder.org: on this example repository.