flo fae2b935e1 Support Multisport fit files
GoldenCheetah lacks support for splitting multisport fit files.
These files combine multiple sports recorded into a single
activity. Those sports are marked by session entries. These
session entries are parsed but ignored.

Fit files are parsed on-the-fly without caching data. This is
great in terms of memory useage but bad in terms of splitting
the activity into sessions because of the fit specification.
The specification allows session entries to appear either
grouped at the beginning of the file or at the end of the session
spread throughout the file.

We do cache the most relevant data entries along with the
session field entries. This hopefully adds as little overhead
as possible while parsing and in memory useage, but allows
us to determine if there are multiple session entries in one
file. If so, we can split the single file into multiple ones,
each representing a single sport (activity). Eg.: If a triathlon
is recorded using the multisport method it is split up into
the following activities:

 - Swim
 - Transition
 - Bike
 - Transition
 - Run

This corrects the metric calculation. Prior to this change
the parsed activity is tagged as a run activity and the whole
data - swim/bike HR, bike cadence, ... - was taken into account
for the run metrics calculation. Now only the relevant part of
the file is taken into account.

Laps as well as XData records are also split up to the files
created out of a single multisport file and are aligned in time.

It turned out that it is best to treat transitions as run.

Fixes: #3211
2020-02-19 17:45:53 -03:00
2018-06-02 11:01:43 +01:00
2020-01-13 15:34:09 +00:00
2015-09-18 08:49:39 +01:00
2019-03-12 18:20:32 +00:00
2020-02-19 17:45:53 -03:00
2020-02-10 11:57:42 +00:00
2019-02-10 16:18:51 +01:00
2020-02-18 12:25:51 -03:00
2018-05-30 12:40:12 -03:00
2018-06-02 11:01:43 +01:00
2019-03-12 19:16:22 +00:00
2019-11-29 14:26:17 -03:00
2020-01-15 20:58:30 -03:00
2017-11-07 21:38:24 -05:00

GoldenCheetah

About

GoldenCheetah is an open-source data analysis tool primarily written in C++ with Qt for cyclists and triathletes with support for training as well.

GoldenCheetah can connect with indoor trainers and cycling equipment such as cycling computers and power meters to import data.

In addition, GoldenCheetah can connect to cloud services.

It can then manipulate and view the data, as well as analyze it.

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 OS X

OSX: Build Status

Windows: Build status

Coverity Status

Alternatively, official builds are available from http://www.goldencheetah.org

whilst the latest developer builds are available from https://github.com/GoldenCheetah/GoldenCheetah/releases

Languages
Standard ML 68.3%
C++ 28.1%
C 2.7%
Yacc 0.2%
QMake 0.2%
Other 0.1%