Colorgorical
Colorgorical is a tool to make categorical color palettes for information visualizations. Users are able to customize palette design by (1) specifying the number of colors, (2) selecting the importance of discriminability and aesthetic preference, (3) limiting the CIE LCh hues and lightnesses of palette colors, and (4) providing a palette to build off of.
The tool itself runs as a web server. The server is implemented using Tornado and the backend palette construction is implemented using a mixture of NumPy and C. The front-end is minimalistic and only relies on Bootstrap and D3.
For more information about Colorgorical, consult either the inlined docstrings
or the paper located in src/public/static/pdf.
Running Colorgorical
After cloning the repo, you first have to compile the C code so that it is
usable by Colorgorical. To do so, run the ``setup.sh'' script. Alternatively,
you can navigate to /src/model and run `python setup.py build_ext --inplace`.
Once you have compiled the C code, navigate back to the project's root. The
webserver can be called using python run.py --server. If you want to change
the port just use --port ####.
Dependencies: Colorgorical was designed to run with Python 2.7 and was
implemented using NumPy v.1.10, Tornado 4.3, and setuptools 20.7; however,
Colorgorical should be compatible with most versions of these libraries.
The C code is ANSI C valid and was verified to be compilable with the Apple
Developer Tools C compiler (Apple LLVM version 7.0.2, clang-700.1.81) and with
gcc v.4.9.2. All client-side dependencies are pre-included and are listed within
bower.json.
About Colorgorical's development
Colorgorical was developed as a research tool to test the relation between color preference and discriminability when creating categorical color palettes, given that there is an implicit trade off between the two. For this reason we did not reduce the number of sliders in the interface, given the original aim of development.
Contributing
Contributions are more than welcome; however, we ask that contributors adhere to 80-column line breaks, use spaces, are consistent with current indentation, and follow the other style conventions that already exist in the code base. We follow Google's style guidelines for Python, and a loose-fitting take on JPL's C style guidelines. We request that C code remains ANSI C valid.