Bicycle Parameters 1.1.1 | GitLocker.com Product

Description:

BicycleParameters 1.1.1

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

Download from PyPi

Download from Anaconda

Documentation

CI Status

Render App

Dependencies

Required

DynamicistToolKit >= 0.5.3
Matplotlib >= 3.5.1
NumPy >= 1.21.5
Python >= 3.8
SciPy >= 1.8.0
Uncertainties >= 3.1.5
yeadon >= 1.3.0

Optional
These are required to run the Dash web application:

Dash >= 2.0
dash-bootstrap-components
Pandas >= 1.3.5

These are required to build the documentation:

Sphinx >= 4.3.2
Numpydoc >= 1.2

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:

Clone the source code with Git: git clone git://github.com/moorepants/BicycleParameters.git
Download the source from Github.
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).