ScPyT

Scientific Computing in Python, a set of tutorials and useful examples. All come from my own experience.

What is this repository?

Scientific Computing scans a wide spectrum including basic numeric programming, linear algebra, all kinds of distributions, and realizing Maximum Likelihood Estimation (MLE), Expectation-Maximization (EM) algorithm, Monte-Carlo Markov Chain (MCMC) sampling. This tutorial aims to help you to master the basic skills to implement relevant algorithms in python.

In the second part, titled as Practical tricks, I will share some useful code snippets which relate to some confusing points when using numpy, I got into these traps before, so I hope it can serve as a reminder to me and other readers.

1. Understanding Numpy and ndarray
2. Linear algebra in python
3. Ordinal Differential Equation
4. Bayesian Probabilistic model
5. Frequentist statistical model (MLE, optimization methods, EM, factor analysis, etc)
6. stay tuned...

Practical tricks

In this Section, I want to share some caveats that numpy user may benefit from:

1. Tuple Index in Numpy
2. Missing value in Python
3. Pairwise distance when having missing value
4. argsort, argwhere, argmin, argmax
5. random package and np.random()
6. sorted array, index array, rank array, inverse index array
7. np where function
8. Parallelization in python scientific computing
9. LaTex notes