Performance trends chart for Golden Cheetah 3.5 written in Python

[Florian Lungu]

Hope everyone and their families are all doing well. Just posting to share a cycling focused performance trends chart for Golden Cheetah 3.5 written in Python.

The chart shows monthly maximum performance metrics year-over-year. Any metric that looks low, I'll go out and put in an effort to do better or train to improve it. I find it much more useful during the training year than year-to-date best efforts or even all-time best efforts. The chart has a slider at the bottom to switch the view from CTL / 20min power / 10min power / 5min power / 1min power / 10sec power / 1sec power.

The link below has the Python code as well as the Performance Track.gchart that can be directly uploaded into Golden Cheetah. Enjoy!!!

https://github.com/florianlungu/gc_performance_tracker_py

Any issues or feedback is welcome.

Florian

My notes from installing Golden Cheetah 3.5 & Python on Windows 10

Install Python 3.6.4
https://www.python.org/ftp/python/3.6.4/python-3.6.4-amd64.exe

Make sure path is set in Environment Variables during install
C:\Users\Home\AppData\Local\

Programs\Python\Python36\

Install Anaconda 3.7
https://repo.anaconda.com/archive/Anaconda3-2019.10-Windows-x86_64.exe

Open DOS and enter the following commands.
C:\WINDOWS\system32>pip install sip==4.19.8
C:\WINDOWS\system32>pip install numpy
C:\WINDOWS\system32>pip install pandas
C:\WINDOWS\system32>pip install scipy
C:\WINDOWS\system32>pip install scikit-learn
C:\WINDOWS\system32>pip install Lmfit
C:\WINDOWS\system32>pip install colour
C:\WINDOWS\system32>pip install plotly

Install Golden Cheetah 3.5
In Golden Cheetah click on Tools > Options > Select the checkbox for enable Python
And set the Python Home to: C:/Users/Home/AppData/Local/Programs/Python/Python36

Restart Golden Cheetah

[Mark Liversedge]

In version 3.6 development you can plot into a Qt chart instead of Plotly. Its quite powerful and easy to do.

We've added it to the R and Python charts as well as a new UserChart.

Looks like this from python:

Here is a user chart fitting the new odpm model using OLS:

And the docs:

https://github.com/GoldenCheetah/GoldenCheetah/wiki/UG_Special-Topics_Working-with-User-Charts

Mark

[Mark Liversedge]

Also, if you're interested in the odpm and how it stacks up to the 3 parameter CP model:

This is a user chart fitting to filtered MMP (get rid of those submax efforts).

Mark