https://github.com/ngs-docs/2016-adv-begin-python

See https://dib-training.readthedocs.org/en/pub/2016-04-04-adv-beg-python.html
https://github.com/ngs-docs/2016-adv-begin-python

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See https://dib-training.readthedocs.org/en/pub/2016-04-04-adv-beg-python.html

• Host: GitHub
• URL: https://github.com/ngs-docs/2016-adv-begin-python
• Owner: ngs-docs
• Created: 2016-04-04T13:14:40.000Z (over 8 years ago)
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  • Readme: README.md

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• awesome-bacteria - 2016-adv-begin-python - Advanced Beginner Python (2016). (Educational resources / Python)

README

        
# Advanced Beginner Python

Materials for [Advanced Beginner

Python](https://dib-training.readthedocs.org/en/pub/2016-04-04-adv-beg-python.html),

offered Apr 4th, 2016, at UC Davis.

These materials are, roughly, meant to be a follow-on from Software

Carpentry's [Programming with

Python](https://swcarpentry.github.io/python-novice-inflammation/).

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](http://ivory.idyll.org/articles/advanced-swc/) but a bit out

of date.

These will be "stub notes" - associated discussion and code will be

* [in the Etherpad](https://etherpad.wikimedia.org/p/2016-adv-begin-python)

* [in the source code repository](https://github.com/ngs-docs/2016-adv-begin-python-source)

## 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](http://software-carpentry.org/conduct/)

* We'll have a coffee break!

* If you don't have a working Python install you can use [this](http://mybinder.org/repo/ctb/2016-mybinder-inflammation)

## Data and problem

* we'll be counting Ns - it's going to be very exciting!

* we'll be using [this FASTQ file of data](https://github.com/ngs-docs/2016-adv-begin-python-source/raw/master/ecoli_ref-100k.fq.gz) from [Chitsaz et al., 2013](http://bix.ucsd.edu/projects/singlecell/nbt_data.html).

* you can download it directly with

```

curl -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](https://coverage.readthedocs.org/) 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.