Metadata-Version: 2.1
Name: sigpro
Version: 0.0.1
Summary: Signal Processing Tools for Machine Mearning
Home-page: https://github.com/signals-dev/SigPro
Author: MIT Data To AI Lab
Author-email: dailabmit@gmail.com
License: MIT license
Description: <p align="left">
        <img width=15% src="https://dai.lids.mit.edu/wp-content/uploads/2018/06/Logo_DAI_highres.png" alt="DAI-Lab" />
        <i>An open source project from Data to AI Lab at MIT.</i>
        </p>
        
        [![Development Status](https://img.shields.io/badge/Development%20Status-2%20--%20Pre--Alpha-yellow)](https://pypi.org/search/?c=Development+Status+%3A%3A+2+-+Pre-Alpha)
        [![PyPi Shield](https://img.shields.io/pypi/v/SigPro.svg)](https://pypi.python.org/pypi/SigPro)
        [![Tests](https://github.com/signals-dev/SigPro/workflows/Run%20Tests/badge.svg)](https://github.com/signals-dev/SigPro/actions?query=workflow%3A%22Run+Tests%22+branch%3Amaster)
        [![Downloads](https://pepy.tech/badge/sigpro)](https://pepy.tech/project/sigpro)
        
        
        # SigPro: Signal Processing Tools for Machine Learning
        
        * License: [MIT](https://github.com/signals-dev/SigPro/blob/master/LICENSE)
        * Development Status: [Pre-Alpha](https://pypi.org/search/?c=Development+Status+%3A%3A+2+-+Pre-Alpha)
        * Homepage: https://github.com/signals-dev/SigPro
        
        ## Overview
        
        SigPro offers an end-to-end solution to efficiently apply multiple *signal processing techniques*
        to convert *raw time series* into *feature time series* that encode the knowledge of domain experts
        in order to solve time series machine learning problems.
        
        # Install
        
        ## Requirements
        
        **SigPro** has been developed and tested on [Python 3.6, 3.7 and 3.8](https://www.python.org/downloads/)
        on GNU/Linux and macOS systems.
        
        Also, although it is not strictly required, the usage of a [virtualenv](
        https://virtualenv.pypa.io/en/latest/) is highly recommended in order to avoid
        interfering with other software installed in the system where **SigPro** is run.
        
        ## Install with pip
        
        The easiest and recommended way to install **SigPro** is using [pip](
        https://pip.pypa.io/en/stable/):
        
        ```bash
        pip install sigpro
        ```
        
        This will pull and install the latest stable release from [PyPi](https://pypi.org/).
        
        If you want to install from source or contribute to the project please read the
        [Contributing Guide](CONTRIBUTING.md).
        
        
        # User Guides
        
        `SigPro` comes with the following user guides:
        
        * [PRIMITIVES.md](PRIMITIVES.md): Information about the primitive families, their expected input
          and output.
        * [USAGE.md](USAGE.md): Instructions about how to usee the three main functionalities of `SigPro`.
        * [DEVELOPMENT.md](DEVELOPMENT.md): Step by step guide about how to write a valid `SigPro`
          primitive and contribute it to either `SigPro` or your own library.
        
        
        # History
        
        ## 0.0.1 - 2021-01-26
        
        First release to PyPI.
        
        This release comes with the first version of the `contributing` module, which makes it easier
        to create new primitives and to test those with the demo data included in this package.
        
        This release also includes the following User Guides:
        
        * [PRIMITIVES.md](https://github.com/signals-dev/SigPro/blob/master/PRIMITIVES.md): Information
          about the primitive families, their expected input and output.
        * [USAGE.md](https://github.com/signals-dev/SigPro/blob/master/USAGE.md): Instructions about how
          to usee the three main functionalities of `SigPro`.
        * [DEVELOPMENT.md](https://github.com/signals-dev/SigPro/blob/master/DEVELOPMENT.md): Step by step
          guide about how to write a valid `SigPro` primitive and contribute it to either `SigPro` or
          your own library.
        
        ### Features
        
        * Demo data: Available demo data to test primitives.
        * First primitives: The following list of primitives were added:
          * `sigpro.aggregations.amplitude.statistical.crest_factor`
          * `sigpro.aggregations.amplitude.statistical.kurtosis`
          * `sigpro.aggregations.amplitude.statistical.mean`
          * `sigpro.aggregations.amplitude.statistical.rms`
          * `sigpro.aggregations.amplitude.statistical.skew`
          * `sigpro.aggregations.amplitude.statistical.std`
          * `sigpro.aggregations.amplitude.statistical.var`
          * `sigpro.transformations.amplitude.identity.identity`
          * `sigpro.transformations.frequency.fft.fft`
          * `sigpro.transformations.frequency.fft.fft_real`
          * `sigpro.transformations.frequency_time.stft.stft`
          * `sigpro.transformations.frequency_time.stft.stft_real`
        * Contributing module.
        * Documentation on how to contribute new primitives and how to run those.
        
Keywords: sigpro signal processing tools machine learning
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6,<3.9
Description-Content-Type: text/markdown
Provides-Extra: test
Provides-Extra: dev
