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Introduction to Scientific Computing in Python - Agenda
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.. contents::
..
1 Introduction and resources
2 Day 1
3 Day 2
..
Introduction and resources
==========================
While the tutorial will begin with very basic concepts, we will assume that
attendees have given the free online `Python tutorial`_ a very decent read, and
will have installed on their systems all the prerequisite tools.
.. _`Python tutorial`: http://docs.python.org/tut
In addition, the following are good sources of information for the tools we'll
be using (all are linked from the main `SciPy documentation`_ page):
* The `STSci tutorial`_ on interactive data analysis.
* The tentative `NumPy tutorial`_.
* The list of NumPy `functions with examples`_.
* The SciPy community cookbook_.
.. _`SciPy documentation`: http://www.scipy.org/Documentation
.. _`STSci tutorial`: http://www.scipy.org/wikis/topical_software/Tutorial
.. _`NumPy tutorial`: http://www.scipy.org/Tentative_NumPy_Tutorial
.. _`functions with examples`: http://www.scipy.org/Numpy_Example_List_With_Doc
.. _`cookbook`: http://www.scipy.org/Cookbook
Initials indicate who presents what:
* MD: Michael Droetboom
* PG: Perry Greenfield
* FP: Fernando Perez
Day 1
=====
* Python for scientific computing: A high-level overview of the topic of Python
in a scientific context ( simple 30 minute talk).
* Workflow, guided by a simple examples and students typing along. Will show
basics of everyday workflow as we cover the core concepts.
* Basic scalar types: strings and numbers (int, float, complex). Exercise:
Walli's infinte product formula for Pi.
* Basic collections: lists and dicts (mention tuples and sets). Exercise:
word frequency counting.
* Quick review of control flow: if, for, range, while, break, continue.
* Defining functions. Arguments and docstrings.
* Reusing your code: every script is a module, '__main__' (notes on module
loading and reloading)
* Exceptions: a core concept in Python, you really can't use the language
without them.
* Debugging your programs:
* Ye olde print statement.
* %debug in ipython.
* %run -d in ipython.
* winpdb - a free, cross-platform GUI debugger.
* Testing your code: reproducible research from the start. Making a habit
out of having auto-validated code.
* Introduction to NumPy arrays.
* Memory model.
* The dtype concept.
* Creating arrays.
* Basic operations: arithmetic and slicing.
* Indexing modes. Views vs. copies.
* Functions that operate on arrays: the builtins and making your own.
* Saving and reloading arrays on disk.
Exercises: Trapezoid rule integration. Image denoising using FFTs.
* Working with data
* Reading files.
* Simple text parsing.
* CSV files.
* Matplotlib's data loader.
* Python packages and modules, the very basics: __init__.py and $PYTHONPATH.
Day 2
=====
* basic plotting with matplotlib (90 min) [Mike]
* basic line/scatter plotting
* customizing colors, styles
* legend
* backends - what they are and pros/cons of each
* matplotlibrc
* math text
* intermediate numpy (90 min) [Perry]
* advanced indexing
* use of where
* zen, examples of vectorizing
* ieee special number and error handling (5 min)
* masked arrays (10-15 min)
* advanced plotting with matplotlib
* the object-oriented API
* text annotations
* tour of advanced plot types
* polar plots
* histograms
* images
* color maps
* advanced numpy (Below is probably overloaded, will have to cull) (90 min) [Perry]
* memory management (5*10 min)
* memory mapped arrays
* array internals/indexing order issues (10 min)
* general performance issues (5 min)
* record arrays (10 min)
* object arrays (5 min)
* character arrays (5 min)
* interfacing to PIL (5 min)
* standard lib review (fft, random, etc, some of these will be used in examples before this so this seems questionable...) (5 min)
* interfacing to C/C++/Fortran review (10 min)
* no real details, just a survey of different approaches
* scipy review (45 min?) [preferably Travis can do this, did he respond?]
