Metadata-Version: 1.1
Name: rouleur
Version: 0.0.3
Summary: Cycling performance modelling with Python
Home-page: https://github.com/jmackie4/rouleur
Author: Jordan Mackie
Author-email: jmackie@protonmail.com
License: MIT
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.](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)
        
Keywords: cycling power modelling
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.5
