To help educators and learners understand the progression of skills involved in transitioning from beginner to expert for any given programming problem set.
scr/cli.py: Entry point for the various sub-components. Exposes a number of sub-commands. To see them, type:
. ./venv/bin/activate
cd src
python cli.py --help
To see the usage of any subcommand, (e.g the order task) type:
python cli.py order --help
Documentation specific to how each subcommand works can be seen in the appropriate submodules.
2. src/visalize/app.py: Minimal Flask-app that serves the static files, as well as generated graph, snippets, etc.
See the order module for details on the partial ordering.
We use GNU Make for... well, Makefile stuff. The code runs on Python 2.7, and the Makefile assumes virtualenv is installed. The Makefile uses UNIX-like paths: this may cause compatibility issues with certain Windows environments.
Clone the repository, and run make
git clone git@github.com:Sharadh/prog-square.git
make run
This will create the venv, install required packages (hat-tip to pip-tools for dependency compiling and management), and compute the partial ordering graph for the default dataset.
To start a web-server that lets you visualize the partial ordering generated:
make serve
This starts a web-server at localhost:5000 by default. Navigate to http://localhost:5000/home.
There is also a convenience endpoint that bundles the two above steps
make visualize
To run on a different dataset, just modify the SOURCE environment variable inline with the make command
SOURCE=data/hello make
The default dataset is the practice-python dataset of ~20 problems and their solutions.
The repository also exposes make targets to retrieve the most recent StackOverflow posts tagged python. To run the ordering analysis on these tasks, first pull the code snippets (into the data/so_temp directory), and then visualize this source.
make pull_so_recent
SOURCE=data/so_temp make visualize
There's no easy way to say this... tests are not up yet. Stay tuned (or better still, contribute some unit-tests!)
Currently, the only tests that are run are from the make lint endpoint: this runs the pep8 and flake8 linters against the *.py files in the src directory.
This project is licensed under the MIT license - see the LICENSE.md file for details