Metadata-Version: 1.2
Name: rxsci
Version: 0.17.1
Summary: ReactiveX for data science
Home-page: https://github.com/maki-nage/rxsci.git
Author: Romain Picard
Author-email: romain.picard@oakbits.com
License: MIT
Project-URL: Documentation, https://rxsci.readthedocs.io
Description: 
        =======================
        |makinage-logo| RxSci
        =======================
        
        .. |makinage-logo| image:: https://github.com/maki-nage/makinage/raw/master/asset/makinage_logo.png
        
        .. image:: https://img.shields.io/pypi/v/rxsci.svg
            :target: https://pypi.org/project/rxsci/
            :alt: PyPI
        
        .. image:: https://github.com/maki-nage/rxsci/workflows/Python%20package/badge.svg
            :target: https://github.com/maki-nage/rxsci/actions?query=workflow%3A%22Python+package%22
            :alt: Github WorkFlows
        
        .. image:: https://coveralls.io/repos/github/maki-nage/rxsci/badge.svg?branch=master
            :target: https://coveralls.io/github/maki-nage/rxsci?branch=master
            :alt: Code Coverage
        
        .. image:: https://github.com/maki-nage/rxsci/raw/master/asset/apis_read.svg
            :target: https://www.makinage.org/doc/rxsci/latest/index.html
            :alt: Documentation
        
        
        ReactiveX operators for data science and machine learning.
        
        RxSci is a set of RxPY operators and observable factories dedicated to data
        science. Being reactive, RxSci is especially suited to work on streaming data
        and time series.
        
        However it can also be used on traditional datasets. Such datasets are processed
        as bounded streams by RxSci. So it is possible to use RxSci for both streaming
        data and file based datasets. This is especially useful when a
        machine learning model is trained with a dataset and deployed on streaming data. 
        
        Get Started
        ============
        
        This example computes a rolling mean on a window and stride of three elements:
        
        .. code:: Python
        
            import rx
            import rxsci as rs
        
            source = [1, 2, 3, 4, 5, 6, 7]
        
            rx.from_(source).pipe(
                rs.state.with_memory_store(rx.pipe(
                    rs.data.roll(window=3, stride=3, pipeline=rx.pipe(
                        rs.math.mean(reduce=True),
                    )),
                )),
            ).subscribe(
                on_next=print
            )
        
        .. code:: console
        
            2.0
            5.0
        
        
        See the
        `Maki Nage documentation <https://www.makinage.org/doc/makinage-book/latest/index.html>`_
        for more information.
        
        Installation
        =============
        
        RxSci is available on PyPi and can be installed with pip:
        
        .. code:: console
        
            python3 -m pip install rxsci
        
Platform: any
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
