global_racetrajectory_optimization | repository contains multiple approaches

by TUMFTM Python Version: Current License: LGPL-3.0

X-Ray Key Features Code Snippets Community Discussions Vulnerabilities Install Support

kandi X-RAY | global_racetrajectory_optimization Summary

global_racetrajectory_optimization is a Python library. global_racetrajectory_optimization has no bugs, it has no vulnerabilities, it has build file available, it has a Weak Copyleft License and it has low support. You can download it from GitHub.

This repository contains multiple approaches for generating global racetrajectories.

Support

Quality

Security

License

Reuse

Support

global_racetrajectory_optimization has a low active ecosystem.

It has 302 star(s) with 119 fork(s). There are 21 watchers for this library.

It had no major release in the last 6 months.

There are 3 open issues and 4 have been closed. On average issues are closed in 0 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of global_racetrajectory_optimization is current.

Quality

global_racetrajectory_optimization has 0 bugs and 0 code smells.

Security

global_racetrajectory_optimization has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

global_racetrajectory_optimization code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

global_racetrajectory_optimization is licensed under the LGPL-3.0 License. This license is Weak Copyleft.

Weak Copyleft licenses have some restrictions, but you can use them in commercial projects.

Reuse

global_racetrajectory_optimization releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

global_racetrajectory_optimization saves you 1236 person hours of effort in developing the same functionality from scratch.

It has 2782 lines of code, 63 functions and 32 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed global_racetrajectory_optimization and discovered the below as its top functions. This is intended to give you an instant insight into global_racetrajectory_optimization implemented functionality, and help decide if they suit your requirements.

Compute smoothed time series
Calculate battery loss
Calculate the internal residuals
Calculate the DOC from sf
Calculate the approximate friction coefficient map
Establishes the grid centers
Extracts the friction coefficients from the reference line
Gets the restriction of the given positions
Plots the friction map
Plots the frictionmaps from a file
Plots friction map from a dictionary
Plots Voronoi from a file
Function to plot the Voronoi grid of the given tree
Plot the refline of a reftrack
Calculate the cross product of refline vectors
Check if reference track is closed
Returns the restriction of the given positions
Calculate the boundaries of the track

Get all kandi verified functions for this library.

global_racetrajectory_optimization Key Features

No Key Features are available at this moment for global_racetrajectory_optimization.

global_racetrajectory_optimization Examples and Code Snippets

No Code Snippets are available at this moment for global_racetrajectory_optimization.

Community Discussions

No Community Discussions are available at this moment for global_racetrajectory_optimization.Refer to stack overflow page for discussions.

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install global_racetrajectory_optimization

You can download it from GitHub.
You can use global_racetrajectory_optimization like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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