943deb67ea
GENERIC SUPPORT FOR PARSING INTO XDATA
.. Generically parse FIT file messages into XDATA. The current
implementation does this for session, lap and totals messages
but could very easily be extended to any other message type
.. Generic parsing uses metadata rather than hard coding the
message and field types and so on
.. The FIT metadata (FITmetadata.json) has been expanded to
include definitions of message types and all the standard
fields within the message types
.. The existing hard-coded parsing remains to extract data
and apply directly to ridefile samples and metadata. The
generic parser simply adds additional tabs on the data
view as XDATA so users can access it.
CODE REFACTORING, COMMENTS AND BUG FIXES
.. At some point the code needs to be refactored as it is
janky and needs to align with the rest of the codebase
.. Includes a mild refactor renaming some of the classes/structs
and variables to reflect what they actually are, for example:
FitFileReadState -> FitFileParser
FitDefinition -> FitMessage
.. Added lots of code comments and re-organised the code
into clear sections to help navigate what is a very
cumbersome source file, this breaks git blame history
but is worth the loss (you can checkout an earlier commit
to do a full blame)
.. Changed debugging levels to be more helpful
.. Generally I did not change any code, but there were a
couple of serious bugs that needed to be corrected:
Field definitions gets the type wrong in a couple of
places since the type is stored in the low 4 bits:
type = value & 0x1F
The decodeDeveloperFieldDescription function did not
check for NA_VALUEs for scale, offset, native field
.. For less serious bugs I added FIXME comments throughout the code
Fixes #4416
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