In my last post on IDEs for scientific Python, I couldn’t install, and therefore couldn’t properly review Canopy, a commercial IDE developed by Enthought, who sponsor SciPy. I had a chance to install it on Windows and try it. Canopy’s main screen shows three options: Editor, Package Manager and documentation browser. The package manager offers … More Canopy scientific Python editor for Windows
I’m working on a project using gnumpy, which is a wrapper for CUDAmat, a toolbox for GPU computation in Python. I was having memory management issues, where the script would crash after the memory on the graphics card would fill up. I kept trying to delete the variables via del but this seemed to have … More Memory management in gnumpy
Following my post on IDEs for scientific Python, several commenters mentioned using some combination of their favorite text editor and ipython rather than a dedicated IDE. ipython is an interactive Python interpreter that offers a much enhanced user experience over the regular Python interpreter: with features such as: Integration with matplotlib Autocompletion Syntax highlighting at … More ipython and ipython notebook for Matlab users
TL;DR: use Spyder if you want an IDE with Matlab-like features, or ipython notebook if you want to do literate programming. Python is a general purpose scripting language that can be used for statistical analysis, numeric work, machine learning, and so much more. With packages like SciPy, matplotlib, Scikit, pandas, and TensorFlow, it’s miles ahead … More Evaluating IDEs for scientific Python
I’ve been transferring some code from Matlab to NumPy and my first tests on my Ubuntu 12.10 installation were very disappointing – slow as molasses. Specifically, numpy.dot was taking forever. I figured this might have something to do with BLAS libraries, a subject which is completely beyond mere mortals to understand. Is something wrong with … More NumPy uses the wrong BLAS libraries – solution
