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Ben Klemens introduces a set of open and unlimited tools, and uses them to demonstrate data management, analysis, and simulation techniques essential for dealing with large data sets and computationally intensive procedures. He then demonstrates how to easily apply these tools to the many threads of statistical technique, including classical, Bayesian, maximum likelihood, and Monte Carlo methods.

The book includes nearly one hundred sample programs of all kinds.

The book is devoted mainly to the practitioner of Statistics, but is also useful to mathematicians, computer scientists, researchers and students in the biology, economics and social sciences. I cannot overstate to you the magnitude of the change in my productivity since finding this book. Even after reading the first few chapters, which explain why data analysis is painful and how one can implement a long-term solution, my research moved forward greatly.

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You can interleave code execution with visualizations and prose explanations, turning scientific software into a self-explaining narrative. For example:. Eliot is a different way of explaining how your software works, since it can give you a trace of program execution, with intermediate results of your calculation.

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The downside is that the explanations it offers really only make sense to you as the author of your code. It is possible to do better than either tool on its own. A combination of Eliot and Jupyter could give you a complete trace of the logic of your calculation, loaded into a tool designed for visualization and explanation.

In particular, with some work, it would be possible to take Eliot logs and load them into Jupyter.

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And while at the moment the Eliot is less suitable for storing large arrays or dataframes, this is a solvable problem. If this interests you and you want make it happen, please get in touch.

So take a few minutes to learn more about Eliot , and then go add logging to your own software. Logging for scientific computing: debugging, performance, trust by Itamar Turner-Trauring Last updated 04 Jun , originally created 01 May This is a prose version of a talk I gave at PyCon ; you can also watch a video of the talk.

The nature of scientific computing

The nature of scientific computing For our purposes, scientific computing has three particular characteristics: Logic: It involves complex calculations. Structure: Computation involves processing data and spitting out results, which implies long-running batch processes.

Modeling_with_Data - gsl_stats Modeling with Data gsl_stats...

Three problems with scientific computing Each of these characteristics comes with a corresponding problems to be solved: Logic: Why is your calculation wrong? With complex calculations, this can be hard to determine. Structure: Why is your code slow?

With slow batch processes, slow code is even more painful than usual. Problem 1: Why is your calculation wrong? Which is to say, you need logging of: Which functions called which other functions. Intermediate values as well My preferred way of adding logging to scientific computing is the Eliot logging library , which I started working on in Example: a broken program Consider the following program: def add a , b