Python in Academia by Marco Bardoscia

1. Python in Academia
Marco Bardoscia
International Centre forTheoretical Physics

2. Background
> Theoretical (statistical) physicist
> Economic systems (financial markets, economies)
> Social systems (social networks)
> Computational problems (SAT extensions)
> Biological systems (metabolic networks)

3. Tools in Science
“Using regular adhesive tape they managed to obtain
a flake of carbon with a thickness of just one atom.”
nobelprize.org

4. ComputationalTools
> MATLAB
> A single FORTRAN file (5000 lines of code)
> C++ for data analysis
> R

5. ComputationalTools
> MATLAB
> A single FORTRAN file (5000 lines of code)
> C++ for data analysis
> R
… mostly true for small groups.

6. > General-purpose
> Plenty of packages
> Nicely extendable
> Interactive
But Python is:

7. > General-purpose
> Plenty of packages
> Nicely extendable
> Interactive
But Python is:
… but many people in academia care about different things!

8. What people care
about:
> Is it fast (enough)?
> Is it at least always faster than MATLAB?
> What about my legacy code?
(wrapping in Cython is hardly a choice)
> Can I get nice publication-ready plots?
(using defaults?)

9. What about
reproducibility?
If what I have done is interesting, someone else will do
it from scratch. If they find the same results (modulo
some tiny details) using an entirely different code,
then we didn’t do anything terribly wrong…

10. > Up-to-date comparison with Matlab for the most
relevant functions
> Nice(r) defaults for Matplotlib plots
> Simpler integration with compiled code
What can be done?