Name: 2016-adv-begin-python
Owner: ngs-docs
Description: See https://dib-training.readthedocs.org/en/pub/2016-04-04-adv-beg-python.html
Created: 2016-04-04 13:14:40.0
Updated: 2016-05-05 19:31:26.0
Pushed: 2016-04-04 16:05:18.0
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README
Advanced Beginner Python
Materials for Advanced Beginner Python,
offered Apr 4th, 2016, at UC Davis.
These materials are, roughly, meant to be a follow-on from Software
Carpentry's Programming with Python.
They are under CC0 Waiver - no copyright is claimed. Use and reuse are
freely allowed.
A long, long time ago (in 2007), I offered a 3 day workshop at
Lawrence Livermore called “Intermediate and Advanced Software
Carpentry in Python.” Those materials are also freely available but a bit out
of date.
These will be “stub notes” - associated discussion and code will be
Welcome to the workshop!
- WHOAMI?
- Materials stay up indefinitely.
- This workshop being live-streamed and recorded.
- Goals: show you the tools, work through them, provide foil for questions.
- We have a Code of conduct
- We'll have a coffee break!
- If you don't have a working Python install you can use this
Data and problem
-L -O https://github.com/ngs-docs/2016-adv-begin-python-source/raw/master/ecoli_ref-100k.fq.gz
Code reuse and modules
- modules provide namespaces for functions, variables, etc.
- they are a simple element of code reuse.
- I organize my code into scripts and modules.
- scripts coordinate execution of code.
- modules provide reusable functions that (usually) multiple scripts use.
if __name__ == '__main__' is only run on execution, not on import.- my convention is to use something like 'main()' as the main function,
executed in the
__main__ block.
- you can build packages by putting multiple modules in a directory,
then placing an
__init__.py file in there.
- argparse is a great way to parse command-line arguments.
#! /usr/bin/env python3.x is a good way to start scripts.
Testing and automated tests
- once you have code that you use in multiple places, you should put more
emphasis on making sure it works.
- 'assert' statements make assertions about the state of variables at a
given point in time.
- Assert statements are invaluable ways to guard your back against weird/
unexpected use of code in future situations. They do little harm.
- functions named 'test_' is generally how Python folk label test code.
- you can use 'nose' or 'py.test' to run all functions named 'test_*'.
- 'test_' functions are unit tests that set up a specific condition and
run your code on it.
- start simple.
- no, really, start simpler than you think is worthwhile.
- if your most basic tests fail, then you really have a problem, don't you?
- code coverage is a good way to target additional tests; see coverage docs for the Python package to use
'virtualenv'
- “virtualenv” is a great tool for creating collections of installed packages
that will never change.
- create a virtualenv with
python -m virtualenv NAME
- activate a virtualenv with
. NAME/bin/activate (in bash).
- deactivate with
deactivate
- pip to install code.
- if you use
#! /usr/bin/env python this will work within virtualenvs.
Fun Python tricks
- run modules in specific versions of Python with
python -m.
- generators: put 'yield' in a Python function to turn it into a generator.
- check out 'enumerate'!
Other principles
- make a cut-down data set so that you can iterate quickly when doing data analysis
- display progress indicators for long analyses.
