| Name | rxsci JSON |
| Version |
0.26.0
JSON |
| download |
| home_page | |
| Summary | ReactiveX for data science |
| upload_time | 2024-01-24 12:47:49 |
| maintainer | |
| docs_url | None |
| author | Romain Picard |
| requires_python | >=3.8 |
| license | MIT License Copyright (c) 2020, Romain Picard Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
reactivex
streaming
data-science
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
distogram
python-dateutil
Rx
orjson
zstandard
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
|
=======================
|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([
rs.data.roll(window=3, stride=3, pipeline=[
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
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"description": "=======================\n|makinage-logo| RxSci\n=======================\n\n.. |makinage-logo| image:: https://github.com/maki-nage/makinage/raw/master/asset/makinage_logo.png\n\n.. image:: https://img.shields.io/pypi/v/rxsci.svg\n :target: https://pypi.org/project/rxsci/\n :alt: PyPI\n\n.. image:: https://github.com/maki-nage/rxsci/workflows/Python%20package/badge.svg\n :target: https://github.com/maki-nage/rxsci/actions?query=workflow%3A%22Python+package%22\n :alt: Github WorkFlows\n\n.. image:: https://coveralls.io/repos/github/maki-nage/rxsci/badge.svg?branch=master\n :target: https://coveralls.io/github/maki-nage/rxsci?branch=master\n :alt: Code Coverage\n\n.. image:: https://github.com/maki-nage/rxsci/raw/master/asset/apis_read.svg\n :target: https://www.makinage.org/doc/rxsci/latest/index.html\n :alt: Documentation\n\n\nReactiveX operators for data science and machine learning.\n\nRxSci is a set of RxPY operators and observable factories dedicated to data\nscience. Being reactive, RxSci is especially suited to work on streaming data\nand time series.\n\nHowever it can also be used on traditional datasets. Such datasets are processed\nas bounded streams by RxSci. So it is possible to use RxSci for both streaming\ndata and file based datasets. This is especially useful when a\nmachine learning model is trained with a dataset and deployed on streaming data. \n\nGet Started\n============\n\nThis example computes a rolling mean on a window and stride of three elements:\n\n.. code:: Python\n\n import rx\n import rxsci as rs\n\n source = [1, 2, 3, 4, 5, 6, 7]\n\n rx.from_(source).pipe(\n rs.state.with_memory_store([\n rs.data.roll(window=3, stride=3, pipeline=[\n rs.math.mean(reduce=True),\n ]),\n ]),\n ).subscribe(\n on_next=print\n )\n\n.. code:: console\n\n 2.0\n 5.0\n\n\nSee the\n`Maki Nage documentation <https://www.makinage.org/doc/makinage-book/latest/index.html>`_\nfor more information.\n\nInstallation\n=============\n\nRxSci is available on PyPi and can be installed with pip:\n\n.. code:: console\n\n python3 -m pip install rxsci\n",
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