BicycleParameters

A Python program designed to generate, manipulate, and visualize the parameters of the Whipple-Carvallo bicycle model.

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Documentation
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Render App	Bicycle Dynamics App

Dependencies

Required

• DynamicistToolKit >= 0.5.3
• Matplotlib >= 3.1.2
• NumPy >= 1.17.4
• Python >= 3.7
• SciPy >= 1.3.3
• Uncertainties >= 3.1.2
• yeadon >= 1.3.0

Optional

These are required to run the Dash web application:

• Dash >= 2.0
• dash-bootstrap-components
• Pandas

These are required to build the documentation:

• Sphinx
• Numpydoc

Installation

We recommend installing BicycleParameters with conda or pip.

For conda:

$ conda install -c conda-forge bicycleparameters

For pip:

$ pip install BicycleParameters

The package can also be installed from the source code. The options for getting the source code are:

1. Clone the source code with Git: git clone git://github.com/moorepants/BicycleParameters.git
2. Download the source from Github.
3. Download the source from pypi.

Once you have the source code navigate to the directory and run:

>>> python setup.py install

This will install the software into your system. You can check if it installs with:

$ python -c "import bicycleparameters"

Example Code

>>> import bicycleparameters as bp
>>> import numpy as np
>>> rigid = bp.Bicycle('Rigid')
>>> par = rigid.parameters['Benchmark']
>>> rigid.plot_bicycle_geometry()
>>> speeds = np.linspace(0., 10., num=100)
>>> rigid.plot_eigenvalues_vs_speed(speeds)

Sample Data

Some sample data is included in the repository but a full source with all the raw data files can be downloaded from here:

http://dx.doi.org/10.6084/m9.figshare.1198429

Documentation

Please refer to the online documentation for more information.

Grant Information

This material is partially based upon work supported by the National Science Foundation under Grant No. 0928339. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

This material is partially based upon work supported by the TKI CLICKNL grant "Fiets van de Toekomst"(Grant No. TKI1706).