Skip to main content
This is a pre-production deployment of Warehouse. Changes made here affect the production instance of PyPI (
Help us improve Python packaging - Donate today!

Cycling performance modelling with Python

Project Description

# 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.*.

# 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.](
  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.](
  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.](
  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.](

Release History

This version
History Node


History Node


History Node


Download Files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, Size & Hash SHA256 Hash Help File Type Python Version Upload Date
(14.6 kB) Copy SHA256 Hash SHA256
Source None Feb 21, 2017

Supported By

Elastic Elastic Search Pingdom Pingdom Monitoring Dyn Dyn DNS Sentry Sentry Error Logging CloudAMQP CloudAMQP RabbitMQ Heroku Heroku PaaS Kabu Creative Kabu Creative UX & Design Fastly Fastly CDN DigiCert DigiCert EV Certificate Google Google Cloud Servers