Euroscipy 2010 tutorials

Contents:

• Scientific computing: why Python?
  • The scientist’s needs
  • Specifications
  • Existing solutions
• Building blocks of scientific computing with Python
• A (very short) introduction to Python
  • First steps
  • Basic types
  • Assignment operator
  • Control Flow
  • Defining functions
  • Reusing code: scripts and modules
  • Input and Output
  • Standard Library
  • Exceptions handling in Python
  • Object-oriented programming (OOP)
• NumPy: creating and manipulating numerical data
  • Creating NumPy data arrays
  • Graphical data representation : matplotlib and Mayavi
  • Indexing
  • Slicing
  • Manipulating the shape of arrays
  • Exercises : some simple array creations
  • Real data: read/write arrays from/to files
  • Simple mathematical and statistical operations on arrays
  • Fancy indexing
  • Broadcasting
  • Synthesis exercises: framing Lena
• Getting help and finding documentation
• Matplotlib
  • Introduction
  • IPython
  • pylab
  • Simple Plots
  • Properties
  • Text
  • Ticks
  • Figures, Subplots, and Axes
  • Other Types of Plots
  • The Class Library
• Scipy : high-level scientific computing
  • Scipy builds upon Numpy
  • File input/output: scipy.io
  • Signal processing: scipy.signal
  • Special functions: scipy.special
  • Statistics and random numbers: scipy.stats
  • Linear algebra operations: scipy.linalg
  • Numerical integration: scipy.integrate
  • Fast Fourier transforms: scipy.fftpack
  • Interpolation: scipy.interpolate
  • Optimization and fit: scipy.optimize
  • Image processing: scipy.ndimage
  • Summary exercices on scientific computing
• Sparse Matrices in SciPy
  • Introduction
  • Storage Schemes
  • Linear System Solvers
  • Other Interesting Packages
• Sympy : Symbolic Mathematics in Python
  • Objectives
  • What is SymPy?
  • First Steps with SymPy
  • Algebraic manipulations
  • Calculus
  • Equation solving
  • Linear Algebra
• 3D plotting with Mayavi
  • A simple example
  • 3D plotting functions
  • Figures and decorations
  • Interaction
• Advanced Numpy
  • Abstract
  • Advanced Numpy
  • Life of ndarray
  • Universal functions
  • Interoperability features
  • Siblings: chararray, maskedarray, matrix
  • Summary
  • Hit list of the future for Numpy core
  • Contributing to Numpy/Scipy
  • Python 2 and 3, single code base