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10 reasons why Python is a complete programming solution for scientific research

SyPy Meetup - April 2016

Paula Sanz-Leon

A normal research day ...

a. Matrix and linear algebra operations (MATLAB).

b. Fast simulations involving ordinary and partial differential equations (C++)

c. Interactive statistical data analysis (R)

d. Interactive and beautiful visualizations (R?, Matlab? Adobe Illustrator ;) ?)

e. Interactive pure maths (Mathematica, Maple, Sage)

What happens when all the code we have written becomes a larger project?

How do we put all the pieces together?

Python is better than glue ...

1 --> All purpose language

You can do from a to e with Python and Python libraries

A normal research day ... in Python

a. Matrix and linear algebra operations (numpy and scipy).

b. Fast simulations involving ordinary and partial differential equations (numpy, cython)

c. Interactive statistical data analysis (pandas, scikit-learn)

d. Interactive and beautiful visualizations (Mayavi, bokeh)

e. Interactive pure maths (Sympy)

2 --> Flexible

  • Provides support for different types of programming paradigms:
    • Procedural linear programming,
    • Functional programming (iterators) makes many programming solutions concise and intuitive.
    • Modular programming (avoids problem with namespaces)
  • Many built in datatypes
    • NumPy: n-dimensional array structure that supports optimized and flexible broadcasting and matrix operations.
    • SciPy: sparse matrices, kd-trees, image objects, time-series.

3 -->Idiomatic

  • Turn notes, doodles and drafts into code and one-liners!
  • Readable.
  • Easy to use --> increased collaboration (arguable)




In [7]:



    


# Define the function
def classify(values, boundary=0):
    """ 
    Classifies values as being: 
    larger (True) or equal/smaller (False) than a boundary.
    """
    return [(True if v > boundary else False) for v in values]



    











In [9]:



    


import numpy as np

my_values = np.random.uniform(0.0, 1.0,1000)

# Call it with a different boundary value
classify(my_values, boundary=0.5)



    


















    
Out[9]:








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 True]

4 --> Interactive (interpreted language) or scripting friendly language

5 --> Plethora of visualization libraries and analysis libraries

Viz
  • Matplotlib
  • bokeh
  • seaborn -
  • vispy
  • pyqtgraph
  • Mayavi
Analysis
  • networkx (graphs)
  • bctpy (graph measures applied to brain networks)
  • scikit-learn
  • pandas (data hndling and statistics)
  • MDP (Modular toolkit Data processing, machine learning)
  • Python API for Tensor Flow (large-scale machine learning)

Scientific Software

  • Nipy (Medical image processing and analysis)
  • BRIAN (neural network simulator)
  • Psychopy (design of experiments for psychophysics)
  • Astropy (astrophysics)
  • MNE (toolkit for the analysis of electroencephalography and magnetoencephalography)

NOTE: Always have a look at pypi (Python index)

6 --> Jupyter

7 --> Vectorization ( operations on n-dimensional arrays)

  • Numpy
  • Scipy
  • Numba (array-oriented and math-heavy Python code can be just-in-time compiled)

8 --> Interoperability

Talks to:

  • Matlab (mlab, mlabwrap, h5py)
  • R (rpy)
  • C (cython)
  • Fortran (fpy)
  • native machine instructions (numba --> CPU, GPU)

9 --> Easy documentation generation

10! --> Free and open-source :) -

  • Canopy by Enthought
  • Anaconda by Continuum analytics.

Summary

  • Open source and completely free, even for commercial use
  • Runs natively on Windows, Mac OS, linux distros (and most of the libraries mentioned here)
  • Quick scripting and large development projects equally well
  • Scalable and well-supported (large-datasets, fast simulations)

What else? ...

Some examples on how to build a brain with Python

Thank you :)!

Content source: pausz/pyladies

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