Metadata-Version: 2.1
Name: sigpro
Version: 0.1.1.dev0
Summary: Signal Processing Tools for Machine Mearning
Home-page: https://github.com/sintel-dev/SigPro
Author: MIT Data To AI Lab
Author-email: dailabmit@gmail.com
License: MIT license
Keywords: sigpro signal processing tools machine learning
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
License-File: LICENSE
License-File: AUTHORS.rst

<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>

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[![Downloads](https://pepy.tech/badge/sigpro)](https://pepy.tech/project/sigpro)


# SigPro: Signal Processing Tools for Machine Learning

* License: [MIT](https://github.com/sintel-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/sintel-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

## 0.1.0 - 2021-11-14

### Features
* Rework SigPro to be class based

## 0.0.3 - 2021-09-27

### Features
* Add `process_signals` function to take a collection of primitives and create features for the given data. 

## 0.0.2 - 2021-02-05

### Bug Fixes

* `MANIFEST.in`: copy the json files of the primitives with the package installation.

## 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/sintel-dev/SigPro/blob/master/PRIMITIVES.md): Information
  about the primitive families, their expected input and output.
* [USAGE.md](https://github.com/sintel-dev/SigPro/blob/master/USAGE.md): Instructions about how
  to usee the three main functionalities of `SigPro`.
* [DEVELOPMENT.md](https://github.com/sintel-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.
