1. Python for Scientific Computing
Wim R.M. Cardoen
Center for High Performance Computing
Fall 2010

2. Overview Python NumPy SciPy Matplotlib Python using C/C++ and Fortran
IPython
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3. Why Python? High-level interpreted language
Very clear syntax =>easy-to-read
Open source
Platform independent
Large number of libraries
Binding to all standard GUI kits (TkInter, Qt, ..)
Powerful automated testing tools
Easily integrated with C/C++, and Fortran
Actively used and extended by scientists
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4. Python Language Clear syntax -> easy to understand
Indentation for blocks
Use of namespace, exception handling
Automatic doc.: doc strings & pydoc
Dynamic typing
Supports both procedural & OO approach
Easy code testing
Garbage collecting
Functional programming, iterators,…
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7. Numpy:Why? The “trad.” Python list:
can contain data of whatever type =>
memory el. may have different sizes
the list can grow & shrink dynamically
passing through list by loops/iterators
=> not very efficient for numerical calculations.
We need something more efficient:
python + fast math=> NumPy
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8. Multi-dimensional array object i.e. ndarray
1. homogeneous (all have the same type)
2. indexing/slicing
3. vectorization
4. broadcasting
Derived objects such as masked arrays, matrices
1. matrix: derived from ndarray: always 2D
attributes: T(transpose), H (hermit. conj.), I (invert)
2. masked arrays: missing or invalid entries
Universal func.: operates on ndarrays in el-by-el fashion
NumPy: basic toolkit for other packages e.g. SciPy,...
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10. NumPy and Scipy require: Fast Linear Algebra routines:
What’s under the hood?
NumPy and Scipy require:
Fast Linear Algebra routines:
BLAS, LAPACK / ATLAS / GOTO-BLAS
FFT Routines: routines for computing the DFT
UMFPACK: routines for solving unsymmetric sparse linear systems
Alternative: MKL (Intel)
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11. SciPy collection of mathematical algorithms
contains several subpackages, e.g.:
scipy.linalg
scipy.fftpack
scipy.optimize
scipy.integrate
scipy.interpolate
scipy.special …
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21. MatplotLib produces publication quality 2D plots
supports Latex commands
>= 0.99: inclusion of mplot3D (3D pics)
Note:
Mayavi: 3D Scientific Data Visualization
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28. Source: http://en.wikipedia.org/wiki/Delaunay_triangulation
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29. Interfacing C/C++ and Python
Approaches for C/C++:
SWIG: Simplied Wrapper & Interface Generator
Weave: inclusion C/C++ code in Python
PyInline: inline other languages in Python
Pyrex: python with C-data types
Approaches for Fortan: f2py
f2py -c --fcompiler=gfortran -m essai essai.f90
import essai
essai.trial(13) # subroutine trial(x) defined in “essai.f90”
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34. Ipython Enhanced interactive Python shell TAB-completion
explore objects using ?
%run command
debug python script
output/input cache
history
Suppress output
call system commands …
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35. Additional packages MPI: MPI4PY SymPy: Symbolic Math
PIL: Python Imaging Library
Mayavi Interactive 3D Visualization
PyMol : 3D Molecular Viewer
NLopt: Non-linear optimization
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36. Links: http://www.python.org/ http://numpy.scipy.org
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