ef716f8568
* Added support for the TrainerDay workouts/find API * Added a new (optional) tab to the TrainerDay workouts download dialog * Deferring loading of the classic list of workouts until this tab is activated * Implemented the API for TrainerDays /workouts/find (see https://api.trainerday.com/api-explorer/) * Visualizing the workouts using a colored ErgFilePlot * Modified ErgFilePlot::setData to directly use the data of the given ErgFile instead of falling back to the ErgFile set in the context (required to display multiple plots at once) * Added a simplified version of the existing workouts query sytnax (supported verbs: duration, dominantzone) * Saving the workouts as trainerday-<hash>.erg * Using the segments-section as input for the hash and to identify duplicates * Functionality can be enabled in gcconfig.pri (GC_WANT_TRAINERDAY_API) * Prepared the travis-scripts before_script.sh to inject the API key to Secrets.h * Prepared appveyor.yml to inject the API key to Secrets.h * Patching GC_TRAINERDAY_API_PAGESIZE for appveyor * Configured api key for TrainerDay in appveyor
GoldenCheetah
About
GoldenCheetah is a desktop application for cyclists and triathletes and coaches
- Analyse using summary metrics like BikeStress, TRIMP or RPE
- Extract insight via models like Critical Power and W'bal
- Track and predict performance using models like Banister and PMC
- Optimise aerodynamics using Virtual Elevation
- Train indoors with ANT and BTLE trainers
- Upload and Download with many cloud services including Strava, Withings and Todays Plan
- Import and export data to and from a wide range of bike computers and file formats
- Track body measures, equipment use and setup your own metadata to track
GoldenCheetah provides tools for users to develop their own own metrics, models and charts
- A high-performance and powerful built-in scripting language
- Local Python runtime or embedding a user installed runtime
- Embedded user installed R runtime
GoldenCheetah supports community sharing via the Cloud
- Upload and download user developed metrics
- Upload and download user, Python or R charts
- Import indoor workouts from the ErgDB
- Share anonymised data with researchers via the OpenData initiative
GoldenCheetah is free for everyone to use and modify, released under the GPL v2 open source license with pre-built binaries for Mac, Windows and Linux.
Installing
Golden Cheetah install and build instructions are documented for each platform;
INSTALL-WIN32 For building on Microsoft Windows
INSTALL-LINUX For building on Linux
INSTALL-MAC For building on Apple MacOS
macOS and Linux: 
Windows:
Official release builds, snapshots and development builds are all available from http://www.goldencheetah.org
NOTIO Fork
If you are looking for the NOTIO fork of GoldenCheetah it can be found here: https://github.com/notio-technologies/GCNotio