/mobility-index

Primary LanguageJupyter NotebookMIT LicenseMIT

Project Organization, Virtualization, and Continuous Integration

(Tips and Tools for Reproducible Projects with Python)

So far you have learnt:

  • how to analyze complex geospatial datasets
  • how to write readable code
  • how to write testable code
  • how to write version-controlled code

Plan for today is to discuss:

  • organizing project repositories
  • using virtual environments
  • creating modules
  • building packages
  • setting up automatic testing

Directory Structure

  • How should we structure our data science project repositories?

Example:

.
+-- data
|   +-- raw
|   +-- processed
|
+-- src
|   +-- PythonModules
|   +-- tests
|   
+-- notebooks
|   +-- exploratory
|   +-- expositionary
|
+-- references
|   +-- papers
|   +-- tutorials
|
+-- results 
+-- README.md
+-- LICENSE.txt

No one solution: adjust as the project evolves.

Comprehensive Project Templates:

Exercise 1:

Reorganize the repo

Distributions & Package Managers

Conda vs pip

What is Conda?

  • Anaconda is a Python distribution slightly different from the default Python distribution, and comes with its own package manager (conda).

  • Conda packages come in the form of .whl files (wheel files). They are precompiled packages: i.e. they are compiled for each specific operating system. They are fast to install. (Installing Numpy from scratch takes forever compiling C code) Miniconda is even faster to install as it is bare bones: better for deploying: have only what you need.

What is pip?

Package manager for Python. Install packages from PyPi. There are packages in pip which are not in conda.

pip install vs conda install

pip freeze

conda list

There are also additional conda packages on conda-forge. You can install them by

conda install -c conda-forge package_name

and you can build your own.

Virtual Environments

What is a Python virtual environment?

A folder with all Python executables and libraries and a link to them. Virtual environments take space!

Pure Python: virtualenv, pipenv

Anaconda Python: conda environment

Create conda envs by:

conda create --name newEnv python=2 extra_packages

View environments:

conda env list

On Unix:

source activate newEnv
do stuff
conda install more_packages
source deactivate

On Windows:

activate newEnv
do stuff
conda install more_package
deactivate

Saving environments:

conda env export -f exported_env.yml

Load an environment from .yml file:

conda env create -f exported_env.yml

You can do the same thing with pip:

pip freeze > requirements.txt
pip install -r requirements.txt

We can see that the list of packages is pretty long (because of the dependencies), and quite specific.

Sometimes you just want to list the ones which you need (and not specify the version). You can write create the following requirements.txt file:

geopandas
shapely

You can also use conda to install from a requirements file:

conda install --file requirements.txt

Jupyter Notebooks and virtual environments:

  • Make sure to install Jupyter within virtual environment
  • Run jupyter notebook within the activated environment

Setting up an environment switch within the Jupyter notebook:

https://ipython.readthedocs.io/en/stable/install/kernel_install.html

More Virtualization

Exercise 2:

  • create a virtual environment mob_idx with geopandas and shapely
  • start a notebook within it and test the imports
  • deactivate the enviornment
  • export the package list

Cross-platform Directory Paths

  • Make paths independent of platform and all relative to directory structure
	import os
	
	# current path
	current_path = os.getcwd()
	
	# join paths for Windows and Unix
	code_path = os.path.join(current_path, "src")
	
	# make sure paths/files exist before reading
	os.path_exists() 
	os.path.isfile()

Modules & Packages

  • move functions from notebooks to a .py file
  • import the .py file as a module
import module_name
module_name.function()

import module_name as mod 
mod.function()

from module_name import myFunction
function()

  • order of importing modules

    • first checks system and Python modules
    • checks local directory or modules in added system paths

  • reloading modules (Python 3)

from imp import reload
reload(module_name)

  • install module as a package

  • run the setup.py file

python setup.py install package_name

and you will be able to import the package from anywhere!

  • subpackages

    • put __init__.py in every folder
    .
+-- package   
|   +-- __init__.py
|   +-- subpackage
|   |   +-- __init__.py
|   |   +-- module.py


    from subpackage import module

  • relative imports

from ..subpackage import  module

Exercise 3

  • Create the functions described in the notebooks
  • Move them to the mobility_index folder
  • Test importing them from the notebook

Testing

  • Locally

     conda install nose

    For each function in library.py write a test function:

     +-- src
 |   +-- library.py
 |   +-- tests
 |       +-- test_library.py

    Run the tests:

     nosetests

In practice, we most probably we will forget to run nosetests after every change we make in the code, luckily, we can do it automatically using continuous integration.

  • Remotely:

    Exercise: let's set up Travis-CI for the Mobility Index project.

    • log in to Travis-CI with your github account
    • search for the repository you want to activate with travis and switch it on
    • write a .travis.yml specifying the build requirements and tests

    Travis-CI Tutorial

    Test Coverage - Coveralls

    Exercise (extra): explore how you can set up automatic coverage check

Editors

  • PyCharm - integration with GitHub
  • Atom - coloring in Github (extra packages)
  • JupyterLab (web based -> can run on server)
  • Spyder Matlab-like IDE

Documentation

Extra Resources

Hitchhikers Guide for packaging

Survey

Survey