python Advanced topics

2. Advanced topics

This part of the Scipy lecture notes is dedicated to advanced usage. It strives to educate the proficient Python coder to be an expert and tackles various specific topics.

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• 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. Array 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: ndimage.measurements
• 2.7. Mathematical optimization: finding minima of functions
  • 2.7.1. Knowing your problem
  • 2.7.2. A review of the different optimizers
  • 2.7.3. Practical guide to optimization with scipy
  • 2.7.4. Special case: non-linear least-squares
  • 2.7.5. Optimization with constraints
• 2.8. Interfacing with C
  • 2.8.1. Introduction
  • 2.8.2. Python-C-Api
  • 2.8.3. Ctypes
  • 2.8.4. SWIG
  • 2.8.5. Cython
  • 2.8.6. Summary
  • 2.8.7. Further Reading and References
  • 2.8.8. Exercises
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http://scipy-lectures.github.io/advanced/index.html