Scientific Python

I have been using python more and more in my scientific work. This has been enabled by several excellent resources. The most important resource is the Enthought Python Distribution (EPD). The EPD makes it trivially easy to have a complete python installation that includes many scientific packages. Best of all, the EPD is available for free under an Academic license. All software included in the EPD is open-source so it is straightforward to install an equivalent set of packages; the only difference is convenience. Enthought also offers many tutorials, webinars, and other support resources for scientific python.

Here, I have put together some of my favorite resources, along with a brief presentation that illustrates the power of SciPy/Numpy through several examples based on my own various projects.

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Basic image analysis in Python

Photo Credits: Harold Vanta

I always like to have a response ready for those times that people inevitably ask “why do you use Python in your research, don’t you know c or Fortran?” I always like that question. “Yes, I know c and Fortran, and I know how painful and slow it can be to code and debug c and Fortran.” Another prime example came to life over this past semester. I was working with a student on a senior capstone project and he was looking at images of the corona discharge around various metal objects. We wanted to analyze the images and map the brightness to the relevant physics (i.e., the electric field surrounding the object).
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