1. Getting started with Python for science

This part of the Scipy lecture notes is a self-contained introduction to everything that is needed to use Python for science, from the language itself, to numerical computing or plotting.

• 1.1. Scientific computing with tools and workflow
  • 1.1.1. Why Python?
    • 1.1.1.1. The scientist’s needs
    • 1.1.1.2. Specifications
    • 1.1.1.3. Existing solutions
  • 1.1.2. Scientific Python building blocks
  • 1.1.3. The interactive workflow: IPython and a text editor
    • 1.1.3.1. Command line interaction
    • 1.1.3.2. Elaboration of the algorithm in an editor
    • 1.1.3.3. IPython Tips and Tricks
• 1.2. The Python language
  • 1.2.1. First steps
  • 1.2.2. Basic types
    • 1.2.2.1. Numerical types
    • 1.2.2.2. Containers
      • 1.2.2.2.1. Lists
      • 1.2.2.2.2. Strings
      • 1.2.2.2.3. Dictionaries
      • 1.2.2.2.4. More container types
    • 1.2.2.3. Assignment operator
  • 1.2.3. Control Flow
    • 1.2.3.1. if/elif/else
    • 1.2.3.2. for/range
    • 1.2.3.3. while/break/continue
    • 1.2.3.4. Conditional Expressions
    • 1.2.3.5. Advanced iteration
      • 1.2.3.5.1. Iterate over any sequence
      • 1.2.3.5.2. Keeping track of enumeration number
      • 1.2.3.5.3. Looping over a dictionary
    • 1.2.3.6. List Comprehensions
  • 1.2.4. Defining functions
    • 1.2.4.1. Function definition
    • 1.2.4.2. Return statement
    • 1.2.4.3. Parameters
    • 1.2.4.4. Passing by value
    • 1.2.4.5. Global variables
    • 1.2.4.6. Variable number of parameters
    • 1.2.4.7. Docstrings
    • 1.2.4.8. Functions are objects
    • 1.2.4.9. Methods
    • 1.2.4.10. Exercises
  • 1.2.5. Reusing code: scripts and modules
    • 1.2.5.1. Scripts
    • 1.2.5.2. Importing objects from modules
    • 1.2.5.3. Creating modules
    • 1.2.5.4. ‘__main__’ and module loading
    • 1.2.5.5. Scripts or modules? How to organize your code
      • 1.2.5.5.1. How modules are found and imported
    • 1.2.5.6. Packages
    • 1.2.5.7. Good practices
  • 1.2.6. Input and Output
    • 1.2.6.1. Iterating over a file
      • 1.2.6.1.1. File modes
  • 1.2.7. Standard Library
    • 1.2.7.1. os module: operating system functionality
      • 1.2.7.1.1. Directory and file manipulation
      • 1.2.7.1.2. os.path: path manipulations
      • 1.2.7.1.3. Running an external command
      • 1.2.7.1.4. Walking a directory
      • 1.2.7.1.5. Environment variables:
    • 1.2.7.2. shutil: high-level file operations
    • 1.2.7.3. glob: Pattern matching on files
    • 1.2.7.4. sys module: system-specific information
    • 1.2.7.5. pickle: easy persistence
  • 1.2.8. Exception handling in Python
    • 1.2.8.1. Exceptions
    • 1.2.8.2. Catching exceptions
      • 1.2.8.2.1. try/except
      • 1.2.8.2.2. try/finally
      • 1.2.8.2.3. Easier to ask for forgiveness than for permission
    • 1.2.8.3. Raising exceptions
  • 1.2.9. Object-oriented programming (OOP)
• 1.3. NumPy: creating and manipulating numerical data
  • 1.3.1. The Numpy array object
    • 1.3.1.1. What are Numpy and Numpy arrays?
      • 1.3.1.1.1. Numpy arrays
      • 1.3.1.1.2. Numpy Reference documentation
      • 1.3.1.1.3. Import conventions
    • 1.3.1.2. Creating arrays
      • 1.3.1.2.1. Manual construction of arrays
      • 1.3.1.2.2. Functions for creating arrays
    • 1.3.1.3. Basic data types
    • 1.3.1.4. Basic visualization
    • 1.3.1.5. Indexing and slicing
    • 1.3.1.6. Copies and views
    • 1.3.1.7. Fancy indexing
      • 1.3.1.7.1. Using boolean masks
      • 1.3.1.7.2. Indexing with an array of integers
  • 1.3.2. Numerical operations on arrays
    • 1.3.2.1. Elementwise operations
      • 1.3.2.1.1. Basic operations
      • 1.3.2.1.2. Other operations
    • 1.3.2.2. Basic reductions
      • 1.3.2.2.1. Computing sums
      • 1.3.2.2.2. Other reductions
    • 1.3.2.3. Broadcasting
    • 1.3.2.4. Array shape manipulation
      • 1.3.2.4.1. Flattening
      • 1.3.2.4.2. Reshaping
      • 1.3.2.4.3. Adding a dimension
      • 1.3.2.4.4. Dimension shuffling
      • 1.3.2.4.5. Resizing
    • 1.3.2.5. Sorting data
    • 1.3.2.6. Summary
  • 1.3.3. More elaborate arrays
    • 1.3.3.1. More data types
      • 1.3.3.1.1. Casting
      • 1.3.3.1.2. Different data type sizes
    • 1.3.3.2. Structured data types
    • 1.3.3.3. maskedarray: dealing with (propagation of) missing data
  • 1.3.4. Advanced operations
    • 1.3.4.1. Polynomials
      • 1.3.4.1.1. More polynomials (with more bases)
    • 1.3.4.2. Loading data files
      • 1.3.4.2.1. Text files
      • 1.3.4.2.2. Images
      • 1.3.4.2.3. Numpy’s own format
      • 1.3.4.2.4. Well-known (& more obscure) file formats
  • 1.3.5. Some exercises
    • 1.3.5.1. Array manipulations
    • 1.3.5.2. Picture manipulation: Framing a Face
    • 1.3.5.3. Data statistics
    • 1.3.5.4. Crude integral approximations
    • 1.3.5.5. Mandelbrot set
    • 1.3.5.6. Markov chain
• 1.4. Matplotlib: plotting
  • 1.4.1. Introduction
    • 1.4.1.1. IPython and the matplotlib mode
    • 1.4.1.2. pyplot
  • 1.4.2. Simple plot
    • 1.4.2.1. Plotting with default settings
    • 1.4.2.2. Instantiating defaults
    • 1.4.2.3. Changing colors and line widths
    • 1.4.2.4. Setting limits
    • 1.4.2.5. Setting ticks
    • 1.4.2.6. Setting tick labels
    • 1.4.2.7. Moving spines
    • 1.4.2.8. Adding a legend
    • 1.4.2.9. Annotate some points
    • 1.4.2.10. Devil is in the details
  • 1.4.3. Figures, Subplots, Axes and Ticks
    • 1.4.3.1. Figures
    • 1.4.3.2. Subplots
    • 1.4.3.3. Axes
    • 1.4.3.4. Ticks
      • 1.4.3.4.1. Tick Locators
  • 1.4.4. Other Types of Plots: examples and exercises
    • 1.4.4.1. Regular Plots
    • 1.4.4.2. Scatter Plots
    • 1.4.4.3. Bar Plots
    • 1.4.4.4. Contour Plots
    • 1.4.4.5. Imshow
    • 1.4.4.6. Pie Charts
    • 1.4.4.7. Quiver Plots
    • 1.4.4.8. Grids
    • 1.4.4.9. Multi Plots
    • 1.4.4.10. Polar Axis
    • 1.4.4.11. 3D Plots
    • 1.4.4.12. Text
  • 1.4.5. Beyond this tutorial
    • 1.4.5.1. Tutorials
    • 1.4.5.2. Matplotlib documentation
    • 1.4.5.3. Code documentation
    • 1.4.5.4. Galleries
    • 1.4.5.5. Mailing lists
  • 1.4.6. Quick references
    • 1.4.6.1. Line properties
    • 1.4.6.2. Line styles
    • 1.4.6.3. Markers
    • 1.4.6.4. Colormaps
• 1.5. Scipy : high-level scientific computing
  • 1.5.1. File input/output: scipy.io
  • 1.5.2. Special functions: scipy.special
  • 1.5.3. Linear algebra operations: scipy.linalg
  • 1.5.4. Fast Fourier transforms: scipy.fftpack
  • 1.5.5. Optimization and fit: scipy.optimize
  • 1.5.6. Statistics and random numbers: scipy.stats
    • 1.5.6.1. Histogram and probability density function
    • 1.5.6.2. Percentiles
    • 1.5.6.3. Statistical tests
  • 1.5.7. Interpolation: scipy.interpolate
  • 1.5.8. Numerical integration: scipy.integrate
  • 1.5.9. Signal processing: scipy.signal
  • 1.5.10. Image processing: scipy.ndimage
    • 1.5.10.1. Geometrical transformations on images
    • 1.5.10.2. Image filtering
    • 1.5.10.3. Mathematical morphology
    • 1.5.10.4. Measurements on images
  • 1.5.11. Summary exercises on scientific computing
    • 1.5.11.13. Maximum wind speed prediction at the Sprogø station
      • 1.5.11.13.1. Statistical approach
      • 1.5.11.13.2. Computing the cumulative probabilities
      • 1.5.11.13.3. Prediction with UnivariateSpline
      • 1.5.11.13.4. Exercise with the Gumbell distribution
    • 1.5.11.14. Non linear least squares curve fitting: application to point extraction in topographical lidar data
      • 1.5.11.14.1. Introduction
      • 1.5.11.14.2. Loading and visualization
      • 1.5.11.14.3. Fitting a waveform with a simple Gaussian model
        • 1.5.11.14.3.1. Model
        • 1.5.11.14.3.2. Initial solution
        • 1.5.11.14.3.3. Fit
      • 1.5.11.14.4. Going further
    • 1.5.11.15. Image processing application: counting bubbles and unmolten grains
      • 1.5.11.15.1. Statement of the problem
      • 1.5.11.15.2. Proposed solution
    • 1.5.11.16. Example of solution for the image processing exercise: unmolten grains in glass
    • 1.5.11.13. Maximum wind speed prediction at the Sprogø station
      • 1.5.11.13.1. Statistical approach
      • 1.5.11.13.2. Computing the cumulative probabilities
      • 1.5.11.13.3. Prediction with UnivariateSpline
      • 1.5.11.13.4. Exercise with the Gumbell distribution
    • 1.5.11.14. Non linear least squares curve fitting: application to point extraction in topographical lidar data
      • 1.5.11.14.1. Introduction
      • 1.5.11.14.2. Loading and visualization
      • 1.5.11.14.3. Fitting a waveform with a simple Gaussian model
        • 1.5.11.14.3.1. Model
        • 1.5.11.14.3.2. Initial solution
        • 1.5.11.14.3.3. Fit
      • 1.5.11.14.4. Going further
    • 1.5.11.15. Image processing application: counting bubbles and unmolten grains
      • 1.5.11.15.1. Statement of the problem
      • 1.5.11.15.2. Proposed solution
    • 1.5.11.16. Example of solution for the image processing exercise: unmolten grains in glass
    • 1.5.11.13. Maximum wind speed prediction at the Sprogø station
      • 1.5.11.13.1. Statistical approach
      • 1.5.11.13.2. Computing the cumulative probabilities
      • 1.5.11.13.3. Prediction with UnivariateSpline
      • 1.5.11.13.4. Exercise with the Gumbell distribution
    • 1.5.11.14. Non linear least squares curve fitting: application to point extraction in topographical lidar data
      • 1.5.11.14.1. Introduction
      • 1.5.11.14.2. Loading and visualization
      • 1.5.11.14.3. Fitting a waveform with a simple Gaussian model
        • 1.5.11.14.3.1. Model
        • 1.5.11.14.3.2. Initial solution
        • 1.5.11.14.3.3. Fit
      • 1.5.11.14.4. Going further
    • 1.5.11.15. Image processing application: counting bubbles and unmolten grains
      • 1.5.11.15.1. Statement of the problem
      • 1.5.11.15.2. Proposed solution
    • 1.5.11.16. Example of solution for the image processing exercise: unmolten grains in glass
• 1.6. Getting help and finding documentation