Metadata-Version: 2.1
Name: robot-analysis-interface
Version: 1.0rc5
Summary: Robot Analysis Infrastructure
Home-page: https://gitlab.tuebingen.mpg.de/SoW/amd-robot-plotting-framework
Author: Jean-Claude Passy, Maximilien Naveau
Author-email: jean-claude.passy@tuebingen.mpg.de, maximilien.naveau@tuebingen.mpg.de
License: BSD-3-Clause
Project-URL: Documentation, https://machines-in-motion.github.io/code_documentation/amd-robot-plotting-framework/docs/sphinx/html/index.html
Description: [![Build Status](
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                https://atlas.is.localnet/bamboo/browse/MGC-RAI/latest)
        
        Robot Analysis Interface
        ========================
        
        The **R**obot **A**nalysis **I**nterface (`RAI`) is a package to analyze
        synchronously sensors data and video captures of the scene from robotic experiments.
        
        It is written in `Python` and uses `Qt` and `OpenCV` bindings under the hood.
        It is supported on `Linux` operating systems.
        
        Dependencies
        ------------
        
        `RAI` has the following dependencies:
        
        * [OpenCV](https://opencv.org/)
        * [PyQt](https://riverbankcomputing.com/software/pyqt/intro)
        * [pyqtgraph](http://www.pyqtgraph.org/)
        * [numpy](https://numpy.org/)
        
        The `setup.py` will take care of installing these packages.
        
        However, you can also install them manually by running:
        
        ```
        $ pip install -r requirements
        ```
        which will also install the dependencies for building the [documentation](#Documentation).
        
        
        Installation
        ------------
        
        `RAI` releases can be instaled from [PyPI](https://pypi.org/) by doing
        ```
        $ pip install robot_analysis_interface
        ```
        
        If you prefer to install from source, clone this repository,
        go to the root directory and type in
        ```
        $ pip install .
        ```
        
        or directly on the compressed file:
        
        ```
        $ pip install robot_analysis_interface-xx.tar.gz
        ```
        
        ---
        **NOTE**
        
        It is recommended to install everything in a dedicated virtual environment.
        
        ---
        
        Getting started
        ---------------
        
        ### Quick way to open a data file/folder:
        
        To open a data file or folder, you can use the script provided in this repository. Several type of data can be parsed:
        
        - The [SL](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.142.4178&rep=rep1&type=pdf) `d-file` format:
        ```bash
        $ rai --data=d00021 --session=my_session.json
        ```
        - A file dumped from the `DataCollector` class available from the RAI API:
        ```bash
        $ rai --data=data_collector_file.npy --session=my_session.json
        $ rai --data=data_collector_file.npz --session=my_session.json
        ```
        - A data folder dumped by the [dynamic-graph](https://github.com/stack-of-tasks/dynamic-graph) framework.
        ```bash
        $ rai --data=2019-08-12_02-03-19/ --session=my_session.json
        ```
        - The default behavior looks for the newest data file/folder that are dumped
          by the dynamic-graph or SL is the current folder.
        ```bash
        $ rai
        ```
        
        Note from these commands:
        - the (optional) data file or folder defined with the `--data` argument,
        - the (optional) JSON file one can use to define to restore a RAI session with the `--session` argument,
        - without the `--data` argument the launcher looks into the current folder for
          a folder with the highest date name (e.g. `2019-08-12_02-03-19`), if none
          are found, it looks for the d-file with the highest index.
        - without  `--session` argument the launcher open a clean session.
        
        ### Using the RAI python interface
        
        To use the RAI, import the package in your *Python* session:
        
        ```
        >>> import RAI
        ```
        
        RAI comes with demos, which can be run using the following commands:
        ```
        >>> RAI.demos.demo()
        ```
        
        Documentation
        -------------
        
        The full documentation can be found [here](https://machines-in-motion.github.io/code_documentation/robot-analysis-interface).
        
        It can also be built from source the following way:
        ```
        $ pip install sphinx sphinx_bootstrap_theme
        $ cd doc
        $ make html
        ```
        and open the file `build/html/index.html` in your web browser.
        
        Unit test
        ---------
        
        The RAI has been tested using the Python unit testing framework.
        The tests can be run using the following command in the root directory:
        
            python -m unittest discover
        
        It is also possible to run the tests using nose:
        
            pip install nose
            nosetests -v tests
        
        If you wish to run headless tests, use [xvfb](https://www.x.org/archive/X11R7.6/doc/man/man1/Xvfb.1.xhtml).
        
        
        Authors
        -------
        
        Jean-Claude Passy <jean-claude.passy@tuebingen.mpg.de>
        
        Maximilien Naveau <maximilien.naveau@tuebingen.mpg.de>
        
        License
        -------
        
        BSD 3-Clause (see LICENSE.md)
        
        Copyright
        ---------
        
        © 2017, Max Planck Society / Software Workshop - Max Planck Institute for Intelligent Systems
        
Platform: Linux
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Requires-Python: >=3.6
Description-Content-Type: text/markdown
