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. Python scientific computing ecosystem
  • 1.1.1. Why Python?
    • 1.1.1.1. The scientist’s needs
    • 1.1.1.2. Python’s strengths
    • 1.1.1.3. How does Python compare to other solutions?
      • Compiled languages: C, C++, Fortran…
      • Matlab scripting language
      • Julia
      • Other scripting languages: Scilab, Octave, R, IDL, etc.
      • Python
  • 1.1.2. The Scientific Python ecosystem
  • 1.1.3. Before starting: Installing a working environment
  • 1.1.4. The workflow: interactive environments and text editors
    • 1.1.4.1. Interactive work
    • 1.1.4.2. Elaboration of the work in an editor
    • 1.1.4.3. IPython and Jupyter 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
      • Lists
      • Strings
      • Dictionaries
      • 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
      • Iterate over any sequence
      • Keeping track of enumeration number
      • 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
      • 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
      • File modes
  • 1.2.7. Standard Library
    • 1.2.7.1. os module: operating system functionality
      • Directory and file manipulation
      • os.path: path manipulations
      • Running an external command
      • Walking a directory
      • 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
      • try/except
      • try/finally
      • Easier to ask for forgiveness than for permission
    • 1.2.8.3. Raising exceptions
  • 1.2.9. Object-oriented programming (OOP)
• 1.3. Python 2 and Python 3
  • 1.3.1. A very short summary
  • 1.3.2. Breaking changes between Python 2 and Python 3
    • 1.3.2.1. Print function
    • 1.3.2.2. Division
  • 1.3.3. Some new features in Python 3
• 1.4. NumPy: creating and manipulating numerical data
  • 1.4.1. The NumPy array object
    • 1.4.1.1. What are NumPy and NumPy arrays?
      • NumPy arrays
      • NumPy Reference documentation
      • Import conventions
    • 1.4.1.2. Creating arrays
      • Manual construction of arrays
      • Functions for creating arrays
    • 1.4.1.3. Basic data types
    • 1.4.1.4. Basic visualization
    • 1.4.1.5. Indexing and slicing
    • 1.4.1.6. Copies and views
    • 1.4.1.7. Fancy indexing
      • Using boolean masks
      • Indexing with an array of integers
  • 1.4.2. Numerical operations on arrays
    • 1.4.2.1. Elementwise operations
      • Basic operations
      • Other operations
    • 1.4.2.2. Basic reductions
      • Computing sums
      • Other reductions
    • 1.4.2.3. Broadcasting
    • 1.4.2.4. Array shape manipulation
      • Flattening
      • Reshaping
      • Adding a dimension
      • Dimension shuffling
      • Resizing
    • 1.4.2.5. Sorting data
    • 1.4.2.6. Summary
  • 1.4.3. More elaborate arrays
    • 1.4.3.1. More data types
      • Casting
      • Different data type sizes
    • 1.4.3.2. Structured data types
    • 1.4.3.3. maskedarray: dealing with (propagation of) missing data
  • 1.4.4. Advanced operations
    • 1.4.4.1. Polynomials
      • More polynomials (with more bases)
    • 1.4.4.2. Loading data files
      • Text files
      • Images
      • NumPy’s own format
      • Well-known (& more obscure) file formats
  • 1.4.5. Some exercises
    • 1.4.5.1. Array manipulations
    • 1.4.5.2. Picture manipulation: Framing a Face
    • 1.4.5.3. Data statistics
    • 1.4.5.4. Crude integral approximations
    • 1.4.5.5. Mandelbrot set
    • 1.4.5.6. Markov chain
  • 1.4.6. Full code examples
    • 1.4.6.1. Full code examples for the numpy chapter
• 1.5. Matplotlib: plotting
  • 1.5.1. Introduction
    • 1.5.1.1. IPython, Jupyter, and matplotlib modes
    • 1.5.1.2. pyplot
  • 1.5.2. Simple plot
    • 1.5.2.1. Plotting with default settings
    • 1.5.2.2. Instantiating defaults
    • 1.5.2.3. Changing colors and line widths
    • 1.5.2.4. Setting limits
    • 1.5.2.5. Setting ticks
    • 1.5.2.6. Setting tick labels
    • 1.5.2.7. Moving spines
    • 1.5.2.8. Adding a legend
    • 1.5.2.9. Annotate some points
    • 1.5.2.10. Devil is in the details
  • 1.5.3. Figures, Subplots, Axes and Ticks
    • 1.5.3.1. Figures
    • 1.5.3.2. Subplots
    • 1.5.3.3. Axes
    • 1.5.3.4. Ticks
      • Tick Locators
  • 1.5.4. Other Types of Plots: examples and exercises
    • 1.5.4.1. Regular Plots
    • 1.5.4.2. Scatter Plots
    • 1.5.4.3. Bar Plots
    • 1.5.4.4. Contour Plots
    • 1.5.4.5. Imshow
    • 1.5.4.6. Pie Charts
    • 1.5.4.7. Quiver Plots
    • 1.5.4.8. Grids
    • 1.5.4.9. Multi Plots
    • 1.5.4.10. Polar Axis
    • 1.5.4.11. 3D Plots
    • 1.5.4.12. Text
  • 1.5.5. Beyond this tutorial
    • 1.5.5.1. Tutorials
    • 1.5.5.2. Matplotlib documentation
    • 1.5.5.3. Code documentation
    • 1.5.5.4. Galleries
    • 1.5.5.5. Mailing lists
  • 1.5.6. Quick references
    • 1.5.6.1. Line properties
    • 1.5.6.2. Line styles
    • 1.5.6.3. Markers
    • 1.5.6.4. Colormaps
  • 1.5.7. Full code examples
    • 1.5.7.1. Code samples for Matplotlib
    • 1.5.7.2. Code for the chapter’s exercises
    • 1.5.7.3. Example demoing choices for an option
    • 1.5.7.4. Code generating the summary figures with a title
• 1.6. Scipy : high-level scientific computing
  • 1.6.1. File input/output: scipy.io
  • 1.6.2. Special functions: scipy.special
  • 1.6.3. Linear algebra operations: scipy.linalg
  • 1.6.4. Interpolation: scipy.interpolate
  • 1.6.5. Optimization and fit: scipy.optimize
    • 1.6.5.1. Curve fitting
    • 1.6.5.2. Finding the minimum of a scalar function
    • 1.6.5.3. Finding the roots of a scalar function
  • 1.6.6. Statistics and random numbers: scipy.stats
    • 1.6.6.1. Distributions: histogram and probability density function
    • 1.6.6.2. Mean, median and percentiles
    • 1.6.6.3. Statistical tests
  • 1.6.7. Numerical integration: scipy.integrate
    • 1.6.7.1. Function integrals
    • 1.6.7.2. Integrating differential equations
  • 1.6.8. Fast Fourier transforms: scipy.fftpack
  • 1.6.9. Signal processing: scipy.signal
  • 1.6.10. Image manipulation: scipy.ndimage
    • 1.6.10.1. Geometrical transformations on images
    • 1.6.10.2. Image filtering
    • 1.6.10.3. Mathematical morphology
    • 1.6.10.4. Connected components and measurements on images
  • 1.6.11. Summary exercises on scientific computing
    • 1.6.11.1. Maximum wind speed prediction at the Sprogø station
      • Statistical approach
      • Computing the cumulative probabilities
      • Prediction with UnivariateSpline
      • Exercise with the Gumbell distribution
    • 1.6.11.2. Non linear least squares curve fitting: application to point extraction in topographical lidar data
      • Introduction
      • Loading and visualization
      • Fitting a waveform with a simple Gaussian model
        • Model
        • Initial solution
        • Fit
      • Going further
    • 1.6.11.3. Image processing application: counting bubbles and unmolten grains
      • Statement of the problem
    • 1.6.11.4. Example of solution for the image processing exercise: unmolten grains in glass
  • 1.6.12. Full code examples for the scipy chapter
    • 1.6.12.18. Solutions of the exercises for scipy
• 1.7. Getting help and finding documentation