Of course a key function of creating this chart is calculating TSS per workout, which requires an FTP/CP score. Here's an example of a modified PMC which is also incorporating HRV data and readiness scores from my oura ring, as well as non ride/run workouts which leverage HRss or weightlifting "WSS" (which I am calculating on my own), which can all be toggled with the top 3 teal buttons to show activity type specific CTL (fitness), but leverage ATL (fatigue) across all workouts (credits to Elevate app for some of my design choices): I'm building out my own charts which pull in metrics from a lot of other data sources which golden cheetah does not leverage, however I still want to use the golden cheetah metrics, hence why I am trying to replicate them. So ultimately what I am trying to do is build a small personal app that can run the CP model every day on a rolling period (perhaps rolling 90 days) and show me what my CP is every day based on the best efforts from that passed rolling time period.
Use goldencheetah to view heartrate code#
I'm a little new to the statistics portions of all of this, but have enough python experience to code some stuff out once I understand the math. Thanks for the response Happy to help out with the project once you start it up again.
PS 2: If you have suggestions for this library please let me know. PS: I have plans to extend and improve this library (I haven't worked on it for a while.) but am considering to remove to WorkoutDataFrame interface because I think it's confusing for some people and is an extra interface for me to maintain. You will get better results when you only feed it the data from some maximal efforts or your "mean max power" over a longer period of time.Ĭan you explain what you are trying to achieve? In this case you seem to be feeding the model data from a single workout which will not yield the best results.
In an ideal world this p_max model parameter would indeed be identical to your maximal power but this greatly depends on the data you are feeding the model. Here's an visual representation of both the 2-parameter and 3-parameter critical power models: The reason that the p_max that the model is returning is different from the actual power max is because this is the fitted model parameter which represents the interception of the critical power model with the vertical axis (t=0). the "3 parameter non linear model" you are using in your second message). These efforts are then fitted to a critical power model (e.g. The CP (and W' for that matter) are generally computed from a small number of maximal efforts. Your first error (first message) is indeed caused by the missing CP for the athlete. Great to hear that you're using this library. TypeError: unsupported operand type(s) for -: 'NoneType' and 'int' Static_tau = tau_w_prime_balance(power, cp)įile "C:\Users\Ethan\PycharmProjects\fitness\venv\lib\site-packages\sweat\pdm\w_prime_balance.py", line 14, in tau_w_prime_balance Tau = get_tau_method(power, cp, tau_dynamic, tau_value)įile "C:\Users\Ethan\PycharmProjects\fitness\venv\lib\site-packages\sweat\pdm\w_prime_balance.py", line 25, in get_tau_method Return method(power, cp, w_prime, *args, **kwargs)įile "C:\Users\Ethan\PycharmProjects\fitness\venv\lib\site-packages\sweat\pdm\w_prime_balance.py", line 46, in w_prime_balance_waterworth _prime, algorithm, *args, **kwargs)įile "C:\Users\Ethan\PycharmProjects\fitness\venv\lib\site-packages\sweat\pdm\w_prime_balance.py", line 112, in w_prime_balance Pydev_imports.execfile(filename, global_vars, local_vars) # execute the scriptįile "C:\Program Files\JetBrains\P圜harm Community Edition 2019.2.1\plugins\python-ce\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfileĮxec(compile(contents+"\n", file, 'exec'), glob, loc)įile "C:/Users/Ethan/PycharmProjects/fitness/index.py", line 254, in įile "C:\Users\Ethan\PycharmProjects\fitness\pages\pandasTesting.py", line 37, in testįile "C:\Users\Ethan\PycharmProjects\fitness\venv\lib\site-packages\sweat\io\models\dataframes.py", line 35, in compute_w_prime_balance File "C:\Program Files\JetBrains\P圜harm Community Edition 2019.2.1\plugins\python-ce\helpers\pydev\_pydev_bundle\pydev_umd.py", line 197, in runfile
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