Distributed arrays for parallel applications

I came across recently a very promising module: DistArray. The idea behind DistArray is to

provide general multidimensional NumPy-like distributed arrays to Python. It intends to bring the strengths of NumPy to data-parallel high-performance computing. 

Neat!

Some examples for easily creating distributed arrays are given in this IPython notebook.

Unfortunately I could not test DistArray so far because I am getting weird errors in my system, probably related to installation issues with my MPI installation.

Frequentism and Bayesianism

Jake VanderPlas has been writing a series of posts discussing frequentism and bayesianism. They are well-written, clear and insightful and use IPython for the statistical analysis. Here, I compiled his posts on the topic for convenience.

Frequentism and Bayesianism: A Practical Introduction
where he synthesizes the philosophical and pragmatic aspects of the frequentist and Bayesian approaches as they relate to the analysis of scientific data.

Frequentism and Bayesianism II: When Results Differ
where he discusses the difference between frequentist and Bayesian in the treatment of nuisance parameters.

Frequentism and Bayesianism III: Confidence, Credibility, and why Frequentism and Science do not Mix
where he discusses the subtle difference between frequentist confidence intervals and Bayesian credible intervals.

Frequentism and Bayesianism IV: How to be a Bayesian in Python
where he describes how to do Bayesian statistics in python with emcee, PyMC and PyStan.

Linear regression with errors in X and Y with Python: BCES

I finally had the chance to upload my BCES linear regression python code to Github!

If you need to do linear regression with measurement errors in X and Y, including intrinsic scatter, please check it out. Even better, if you have suggestions to improve or speed up the code, please contribute by all means!

LaTeX tricks

Here is a collection of useful latex tricks I have been using in my papers.

How to add "sticky note" annotations to your manuscript

In the preamble of the document, add
\usepackage{todonotes}


For sticky notes:
\todo{your sticky note comment}

For notes placed in the text:
\todo[inline]{your sticky note comment}

For a missing figure box, such that you want to remind yourself to add a plot there later:

\missingfigure{figure description}

Read this for more options and the documentation of the todonotes package.

How to highlight or cross-out text

In the preamble of the document, add
\usepackage{color, soul}
\setstcolor{red}


For highlighted text:
\hl{your highlighted text}

For crossing-out text:

Check this out for more options.

For commenting out larger chunks of text

In the preamble of the document, add
\usepackage{verbatim}


Then put the text you want to comment out inside the environment:
\begin{comment}
everything your want to not show up in the document
\end{comment}

Can be way more powerful and faster than appending the % character to the beginning of each line.

Video tutorial: Bayesian data analysis with PyMC 3

I highly recommend the tutorial by Thomas Wiecki on using PyMC 3 to perform Bayesian data analysis. Some of the cool things he demonstrates in this ~50 min video:

• summary of Bayesian analysis and Bayesian theorem
• application for parameter estimation for a few introductory examples: coin-flipping experiment, simple linear regression
• new features of PyMC 3 with respect to v2
• how to construct a model in PyMC 3 and a few notes on samplers
• more advanced example: application to time series of correlated stocks, then linear regression of correlated stocks with a time-dependent slope (!!)

Interactive plots in ipython notebooks

A couple of ways of doing interactive plots and animations in an ipython notebook:

• Using plotly
• Using a little hack called ipywidgets

I am sure there are many other ways, and if you know of nice ways please post in the comments. 

Links via reddit.

Citing IPython in papers

If you feel like citing IPython in your papers, you can actually cite the following paper by Pérez & Granger (2007).

You can find the appropriate bibtex citation here.