cBioPortal/clinical-timeline

Name: clinical-timeline

Owner: cBioPortal

Description: Use d3-timeline to visualize clinical data

Created: 2015-06-04 00:21:58.0

Updated: 2017-10-21 02:35:51.0

Pushed: 2017-12-15 02:21:40.0

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Size: 3995

Language: JavaScript

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README

clinical-timeline

Build Status Code Climate Coverage Status Join the chat at https://gitter.im/cBioPortal/clinical-timeline API Doc

clinical-timeline builds upon the popular d3-timeline library, which displays a collection of timepoints in JSON format as a timeline. It focusses on visualization of clinical data on a timeline and extends the JSON format and the API with some new features and various plugins.

With the correct configuration one can instantiate a timeline as follows:

timeline-example

The index.html showcases clinical-timeline's features in an exhuastive way.

Building

Building the javascript files in clinical-timeline is handled by a Makefile. Makefiles are primarily used for building software on unix, but they are also great task runners. Executing the command make build in the root directory of clinical-timeline generates the distributable version of the timeline clinical-timeline.min.js in the dist/ directory post concatination and minification. Also the command make clean can be used to clean the dist/ directory. JSDoc syntax should be strictly followed for documentation as it is automatically build by doclets.

Examples

Note : Kindly browse over HTTP instead of HTTPS for the rawgit versions.

Documentation

Documentation for the library can be found here

Contributing

clinical-timeline is being actively developed and any kind of contribution to the repository is highly encouraged. For detailed instructions on contributing, one can use the Contributing Guide.

Checks before sending a PR:

clinical-timeline uses code-climate to maintain code quality and coveralls for code-coverage. Please ensure that the repo GPA and code-coverage doesn't falls if not increase post the new commit. Adding new unit tests is most welcomed.

License

LGPL


This work is supported by the National Institutes of Health's National Center for Advancing Translational Sciences, Grant Number U24TR002306. This work is solely the responsibility of the creators and does not necessarily represent the official views of the National Institutes of Health.