Metadata-Version: 2.1
Name: pyrotor
Version: 1.0.2
Summary: Trajectory optimization package based on data
Home-page: https://github.com/bguedj/pyrotor
Author-email: arthur.talpaert@isen.yncrea.fr
License: MIT
Platform: UNKNOWN
Description-Content-Type: text/markdown
Requires-Dist: cvxopt (==1.2.4)
Requires-Dist: numpy (==1.19.1)
Requires-Dist: pandas (==0.25.3)
Requires-Dist: scipy (==1.5.2)
Requires-Dist: mock (==3.0.5)
Requires-Dist: pytest (==5.3.1)
Requires-Dist: pickleshare (==0.7.5)
Requires-Dist: scikit-learn (==0.23.2)
Requires-Dist: sphinx (==2.3.1)
Requires-Dist: recommonmark (==0.6.0)
Requires-Dist: sphinx-rtd-theme (==0.5.0)


[![](https://travis-ci.com/bguedj/pyrotor.svg?token=mBozP3BYCpx6zxMpJQAQ&branch=master)](https://travis-ci.com/github/bguedj/pyrotor)

# PyRotor - Python Route Trajectory Optimiser
PyRotor is a Python library for trajectory optimisation problems. Initially developed for the aeronautic setting, it is intended to be generic and to be used in a wide range of applications.

PyRotor leverages available trajectory data to focus the search space and to estimate some properties which are then incorporated in the optimisation problem. This constraints in a natural and simple way the optimisation problem whose solution inherits realistic patterns from the data. In particular it does not require any knowledge on the dynamics of the system.

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### Documentation and examples

An online documentation is available [here](https://pyrotor.readthedocs.io/en/latest/) but is still under construction. Thereotical details on the methodology will be soon available in a theoretical paper.

The [examples](https://github.com/bguedj/pyrotor/tree/master/examples) folder provides currently two examples showing the use of PyRotor.

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### Installation
Run the following command:
```Bash
$ pip install pyrotor
```

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### Reference
- Coming soon


