rouleur 0.0.1

Cycling performance modelling with Python

# rouleur: Cycling performance modelling

Makes the physical modelling of cycling trivially easy.

For example, let’s try and estimate the power required for Wiggo’s current hour record:

`pycon >>> from rouleur import CyclingParams, calculate_air_density >>> >>> record = 54.526 # km/h >>> record *= 1000 / 60**2 # m/s >>> rho = calculate_air_density(30, 777, 0.6) # about right >>> pars = CyclingParams( >>> rider_velocity=record, >>> air_density=rho, >>> CdA=0.19, Crr=0.0025, >>> chain_efficiency_factor=0.98, >>> road_gradient=0, >>> mass_total=82) >>> >>> pars.solve_for.power_output() 440.9565671224358 `

That’s all there is to it.

The API consists almost exclusively of the CyclingParams class, which holds all the parameters required for modelling a cyclist. The class constructor combines a number of sensible defaults with any (keyword) arguments passed. Details of recognised keyword arguments—i.e. model parameters—can be found in the class docstring (help(CyclingParams)).

Instances then have a number of solver methods accessible via parameters.solve_for.* synatx.

# References

This package is an implementation of a number of published algorithms. Important references are:

1. [Martin JC, Milliken DL, Cobb JE, McFadden KL, Coggan AR. Validation of a Mathematical Model for Road Cycling Power. Journal of Applied Biomechanics 14: 276–291, 1998.](http://journals.humankinetics.com/doi/10.1123/jab.14.3.276)

2. [Martin JC, Gardner AS, Barras M, Martin DT. Modeling sprint cycling using field-derived parameters and forward integration. Med Sci Sports Exerc 38: 592–597, 2006.](https://www.ncbi.nlm.nih.gov/pubmed/16540850)

3. [Atkinson G, Peacock O, Passfield L. Variable versus constant power strategies during cycling time-trials: Prediction of time savings using an up-to-date mathematical model. Journal of Sports Sciences 25: 1001–1009, 2007.](https://www.ncbi.nlm.nih.gov/pubmed/17497402)

4. [Wells MS, Marwood S. Effects of power variation on cycle performance during simulated hilly time-trials. European Journal of Sport Science 16: 912–918, 2016.](https://www.ncbi.nlm.nih.gov/pubmed/26949050)