Review of the book Learning Scipy for Numerical and Scientific Computing

Hi all,

I've finished reading the book "Learning Scipy for Numerical and Scientific Computing".  This book comes to the scientific python series that PacktPub are bringing to the Python Developers! Congratulations!  As the title informs: it includes Scipy, Numpy and Matplotlib.  I only missed some further information about IPython, but it wasn't the goal of the book, so it goes well even leaved out.

It covers several important topics that are not as commonly covered, specially with several snippets illustrating special functions presented at Scipy library. For the developers it will be another great reference book to complement the native docs that comes with the library.  I enjoyed the author focused more on numerical analysis functions, it is one of the most used functions at the library.  

The bool also brings chapters on more specific applications: signal processing, data mining and computational geometry.  There is an extra chapter about the integration with another languages, but I found it not dense enough to explain those integrations. I really missed more start-off examples showing how to install the f2py or how to use Scipy with C/C++. 

Overall the Learning Scipy for Numerical and Scientific Computing book is a good book on Scipy covering  lots of mathematics with examples in Python. The book has a good size and it helps the scientists and scientific developers (by the way the non-developers will face some difficulties due to the heavy math that comes with the examples) to have a good overview on the library before exploring the reference material.

Thanks Kenny for the invitation to review this book, and congratulations to Francisco for bringing one more technical book for scientific python computing to the series!

Regards,

Marcel Caraciolo

Slides for Scientific Computing Meeting: Benchy and GeoMapper Visualization

Hi all,

Yesterday it happened the XXVI local meeting of the Python Users Group at Pernambuco (PUG-PE).  In the occasion I had the opportunity to present two talks about scientific computing with python.

The first one was the lightweight framework for benchmark analysis on Python Scripts called Benchy, which I developed for about one week to help me on checking performance of several algorithms that I developed in Python.  I covered the framework at my last post which can be found here.

Here are the slides for the presentation:

Benchy: Lightweight framework for Performance Benchmarks from Marcel Caraciolo

The second talk was about a new type of visualization that I developed for social network analysis in order to check the degree of connections between the users at the socialnetwork using their geolocation data to present in a map.

The result was beautiful plots using this new type of visualization.  Amazing!

The slides are available here:

GeoMapper, Python Script for Visualizing Data on Social Networks with Geo-location data from Marcel Caraciolo

I hope you enjoy the slides, any further information feel free to comment!

Regards,

Marcel Caraciolo