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1. Getting started with Python for science
¶
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
1.2.2.3. 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
1.2.3.6. 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
1.2.5.6. Packages
1.2.5.7. Good practices
1.2.6. Input and Output
1.2.6.1. Iterating over a file
1.2.7. Standard Library
1.2.7.1.
os
module: operating system functionality
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
1.2.8.3. 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?
1.3.1.2. Reference documentation
1.3.1.3. Import conventions
1.3.1.4. Creating arrays
1.3.1.5. Functions for creating arrays
1.3.1.6. Basic data types
1.3.1.7. Basic visualization
1.3.1.8. Indexing and slicing
1.3.1.9. Copies and views
1.3.1.10. Fancy indexing
1.3.2. Numerical operations on arrays
1.3.2.1. Elementwise operations
1.3.2.2. Basic reductions
1.3.2.3. Broadcasting
1.3.2.4. Array shape manipulation
1.3.2.5. Sorting data
1.3.2.6. Summary
1.3.3. More elaborate arrays
1.3.3.1. More data types
1.3.3.2. Structured data types
1.3.3.3.
maskedarray
: dealing with (propagation of) missing data
1.3.4. Advanced operations
1.3.4.1. Polynomials
1.3.4.2. Loading data files
1.3.5. Some exercises
1.4. Matplotlib: plotting
1.4.1. Introduction
1.4.1.1. IPython and the pylab mode
1.4.1.2. pylab
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
1.4.4. 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
1.5.11.14. Non linear least squares curve fitting: application to point extraction in topographical lidar data
1.5.11.15. Image processing application: counting bubbles and unmolten grains
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
1.5.11.14. Non linear least squares curve fitting: application to point extraction in topographical lidar data
1.5.11.15. Image processing application: counting bubbles and unmolten grains
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
1.5.11.14. Non linear least squares curve fitting: application to point extraction in topographical lidar data
1.5.11.15. Image processing application: counting bubbles and unmolten grains
1.5.11.16. Example of solution for the image processing exercise: unmolten grains in glass
1.6. Getting help and finding documentation
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