Python Scientific Lecture Notes

Download

• PDF, 2 pages per side
• PDF, 1 page per side
• HTML and example files
• Source code (github)

This document

Teaching material on the scientific Python ecosystem, a quick introduction to central tools and techniques. The different chapters each correspond to a 1 to 2 hours course with increasing level of expertise, from beginner to expert.

• Authors
• License
• What’s new

---

• 1. Getting started with Python for science
  • 1.1. Scientific computing with tools and workflow
    • 1.1.1. Why Python?
    • 1.1.2. Scientific Python building blocks
    • 1.1.3. The interactive workflow: IPython and a text editor
  • 1.2. The Python language
    • 1.2.1. First steps
    • 1.2.2. Basic types
    • 1.2.3. Assignment operator
    • 1.2.4. Control Flow
    • 1.2.5. Defining functions
    • 1.2.6. Reusing code: scripts and modules
    • 1.2.7. Input and Output
    • 1.2.8. Standard Library
    • 1.2.9. Exception handling in Python
    • 1.2.10. Object-oriented programming (OOP)
  • 1.3. NumPy: creating and manipulating numerical data
    • 1.3.1. The numpy array object
    • 1.3.2. Numerical operations on arrays
    • 1.3.3. Moving on
    • 1.3.4. Under the hood
  • 1.4. Getting help and finding documentation
  • 1.5. Matplotlib
    • 1.5.1. Introduction
    • 1.5.2. IPython and the pylab mode
    • 1.5.3. Simple Plots
    • 1.5.4. Properties
    • 1.5.5. Text
    • 1.5.6. Ticks
    • 1.5.7. Figures, Subplots, and Axes
    • 1.5.8. Other Types of Plots
    • 1.5.9. The Class Library
  • 1.6. Scipy : high-level scientific computing
    • 1.6.1. Scipy builds upon Numpy
    • 1.6.2. File input/output: scipy.io
    • 1.6.3. Signal processing: scipy.signal
    • 1.6.4. Special functions: scipy.special
    • 1.6.5. Statistics and random numbers: scipy.stats
    • 1.6.6. Linear algebra operations: scipy.linalg
    • 1.6.7. Numerical integration: scipy.integrate
    • 1.6.8. Fast Fourier transforms: scipy.fftpack
    • 1.6.9. Interpolation: scipy.interpolate
    • 1.6.10. Optimization and fit: scipy.optimize
    • 1.6.11. Image processing: scipy.ndimage
    • 1.6.12. Summary exercises on scientific computing
• 2. Advanced topics
  • 2.1. Advanced Python Constructs
    • 2.1.1. Iterators, generator expressions and generators
    • 2.1.2. Decorators
    • 2.1.3. Context managers
  • 2.2. Advanced Numpy
    • 2.2.1. Life of ndarray
    • 2.2.2. Universal functions
    • 2.2.3. Interoperability features
    • 2.2.4. Siblings: chararray, maskedarray, matrix
    • 2.2.5. Summary
    • 2.2.6. Contributing to Numpy/Scipy
  • 2.3. Debugging code
    • 2.3.1. Avoiding bugs
    • 2.3.2. Debugging workflow
    • 2.3.3. Using the Python debugger
    • 2.3.4. Debugging segmentation faults using gdb
  • 2.4. Optimizing code
    • 2.4.1. Optimization workflow
    • 2.4.2. Profiling Python code
    • 2.4.3. Making code go faster
    • 2.4.4. Writing faster numerical code
  • 2.5. Sparse Matrices in SciPy
    • 2.5.1. Introduction
    • 2.5.2. Storage Schemes
    • 2.5.3. Linear System Solvers
    • 2.5.4. Other Interesting Packages
  • 2.6. Image manipulation and processing using Numpy and Scipy
    • 2.6.1. Opening and writing to image files
    • 2.6.2. Displaying images
    • 2.6.3. Basic manipulations
    • 2.6.4. Image filtering
    • 2.6.5. Feature extraction
    • 2.6.6. Measuring objects properties
  • 2.7. Traits
    • 2.7.1. Introduction
    • 2.7.2. Example
    • 2.7.3. What are Traits
    • 2.7.4. References
  • 2.8. 3D plotting with Mayavi
    • 2.8.1. A simple example
    • 2.8.2. 3D plotting functions
    • 2.8.3. Figures and decorations
    • 2.8.4. Interaction
  • 2.9. Sympy : Symbolic Mathematics in Python
    • 2.9.1. First Steps with SymPy
    • 2.9.2. Algebraic manipulations
    • 2.9.3. Calculus
    • 2.9.4. Equation solving
    • 2.9.5. Linear Algebra
  • 2.10. scikit-learn: machine learning in Python
    • 2.10.1. Loading an example dataset
    • 2.10.2. Classification
    • 2.10.3. Clustering: grouping observations together
    • 2.10.4. Dimension Reduction with Principal Component Analysis
    • 2.10.5. Putting it all together: face recognition
    • 2.10.6. Linear model: from regression to sparsity
    • 2.10.7. Model selection: choosing estimators and their parameters

---

Version: 2012.0-1-g042a4f5 (output of git describe for project repository)

PythonScientific.pdf PythonScientific-simple.pdf