<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>
[](https://pypi.org/search/?c=Development+Status+%3A%3A+2+-+Pre-Alpha)
[](https://pypi.python.org/pypi/SigPro)
[](https://github.com/sintel-dev/SigPro/actions?query=workflow%3A%22Run+Tests%22+branch%3Amaster)
[](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 3.8, 3.9, 3.10, and 3.11](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
## 0.2.1 - 2024-04-24
### Features
* Frequency primitive - [Issue #60](https://github.com/sintel-dev/SigPro/pull/60)
## 0.2.0 - 2024-02-02
### Features
* Demo Notebooks for Pipeline usage - [Issue #55](https://github.com/sintel-dev/SigPro/issues/55) by @andyx13
* Added `contributing_primitive` and `basic_primitives` module to assist with new primitive creation/usage - [Issue #54](https://github.com/sintel-dev/SigPro/issues/54) by @andyx13
* Incorrect classification for stft.json and stft_real.json - [Issue #53](https://github.com/sintel-dev/SigPro/issues/53) by @andyx13
* Support for more complex pipeline architectures - [Issue #52](https://github.com/sintel-dev/SigPro/issues/52) by @andyx13
* Update primitive interfaces - [Issue #51](https://github.com/sintel-dev/SigPro/issues/51) by @andyx13
* Syntax for pipeline creation - [Issue #41](https://github.com/sintel-dev/SigPro/issues/41) by @andyx13
* Load demo dataset at random index - [Issue #35](https://github.com/sintel-dev/SigPro/issues/35) by @andyx13
## 0.1.2 - 2023-12-11
### Features
* Python version update - [Issue #44](https://github.com/sintel-dev/SigPro/issues/44) by @andyx13
* Add demo notebook and per-primitive documentation - [Issue #47](https://github.com/sintel-dev/SigPro/issues/47) by @andyx13
## 0.1.1 - 2023-04-06
### Features
* Accepting single value data frame format - [Issue #36](https://github.com/sintel-dev/SigPro/issues/36) by @frances-h @sarahmish
* Update demos - [Issue #26](https://github.com/sintel-dev/SigPro/pull/26) by @frances-h
## 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.
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"description": "<p align=\"left\">\n<img width=15% src=\"https://dai.lids.mit.edu/wp-content/uploads/2018/06/Logo_DAI_highres.png\" alt=\"DAI-Lab\" />\n<i>An open source project from Data to AI Lab at MIT.</i>\n</p>\n\n[](https://pypi.org/search/?c=Development+Status+%3A%3A+2+-+Pre-Alpha)\n[](https://pypi.python.org/pypi/SigPro)\n[](https://github.com/sintel-dev/SigPro/actions?query=workflow%3A%22Run+Tests%22+branch%3Amaster)\n[](https://pepy.tech/project/sigpro)\n\n\n# SigPro: Signal Processing Tools for Machine Learning\n\n* License: [MIT](https://github.com/sintel-dev/SigPro/blob/master/LICENSE)\n* Development Status: [Pre-Alpha](https://pypi.org/search/?c=Development+Status+%3A%3A+2+-+Pre-Alpha)\n* Homepage: https://github.com/sintel-dev/SigPro\n\n## Overview\n\nSigPro offers an end-to-end solution to efficiently apply multiple *signal processing techniques*\nto convert *raw time series* into *feature time series* that encode the knowledge of domain experts\nin order to solve time series machine learning problems.\n\n# Install\n\n## Requirements\n\n**SigPro** has been developed and tested on [Python 3.8, 3.9, 3.10, and 3.11](https://www.python.org/downloads/)\non GNU/Linux and macOS systems.\n\nAlso, although it is not strictly required, the usage of a [virtualenv](\nhttps://virtualenv.pypa.io/en/latest/) is highly recommended in order to avoid\ninterfering with other software installed in the system where **SigPro** is run.\n\n## Install with pip\n\nThe easiest and recommended way to install **SigPro** is using [pip](\nhttps://pip.pypa.io/en/stable/):\n\n```bash\npip install sigpro\n```\n\nThis will pull and install the latest stable release from [PyPi](https://pypi.org/).\n\nIf you want to install from source or contribute to the project please read the\n[Contributing Guide](CONTRIBUTING.md).\n\n\n# User Guides\n\n`SigPro` comes with the following user guides:\n\n* [PRIMITIVES.md](PRIMITIVES.md): Information about the primitive families, their expected input\n and output.\n* [USAGE.md](USAGE.md): Instructions about how to usee the three main functionalities of `SigPro`.\n* [DEVELOPMENT.md](DEVELOPMENT.md): Step by step guide about how to write a valid `SigPro`\n primitive and contribute it to either `SigPro` or your own library.\n\n\n# History\n\n## 0.2.1 - 2024-04-24\n\n### Features\n\n* Frequency primitive - [Issue #60](https://github.com/sintel-dev/SigPro/pull/60)\n\n\n## 0.2.0 - 2024-02-02\n\n### Features\n* Demo Notebooks for Pipeline usage - [Issue #55](https://github.com/sintel-dev/SigPro/issues/55) by @andyx13\n* Added `contributing_primitive` and `basic_primitives` module to assist with new primitive creation/usage - [Issue #54](https://github.com/sintel-dev/SigPro/issues/54) by @andyx13\n* Incorrect classification for stft.json and stft_real.json - [Issue #53](https://github.com/sintel-dev/SigPro/issues/53) by @andyx13\n* Support for more complex pipeline architectures - [Issue #52](https://github.com/sintel-dev/SigPro/issues/52) by @andyx13\n* Update primitive interfaces - [Issue #51](https://github.com/sintel-dev/SigPro/issues/51) by @andyx13\n* Syntax for pipeline creation - [Issue #41](https://github.com/sintel-dev/SigPro/issues/41) by @andyx13\n* Load demo dataset at random index - [Issue #35](https://github.com/sintel-dev/SigPro/issues/35) by @andyx13\n\n\n## 0.1.2 - 2023-12-11\n\n### Features\n* Python version update - [Issue #44](https://github.com/sintel-dev/SigPro/issues/44) by @andyx13\n* Add demo notebook and per-primitive documentation - [Issue #47](https://github.com/sintel-dev/SigPro/issues/47) by @andyx13\n\n\n## 0.1.1 - 2023-04-06\n\n### Features\n* Accepting single value data frame format - [Issue #36](https://github.com/sintel-dev/SigPro/issues/36) by @frances-h @sarahmish\n* Update demos - [Issue #26](https://github.com/sintel-dev/SigPro/pull/26) by @frances-h\n\n\n## 0.1.0 - 2021-11-14\n\n### Features\n* Rework SigPro to be class based\n\n\n## 0.0.3 - 2021-09-27\n\n### Features\n* Add `process_signals` function to take a collection of primitives and create features for the given data. \n\n\n## 0.0.2 - 2021-02-05\n\n### Bug Fixes\n\n* `MANIFEST.in`: copy the json files of the primitives with the package installation.\n\n\n## 0.0.1 - 2021-01-26\n\nFirst release to PyPI.\n\nThis release comes with the first version of the `contributing` module, which makes it easier\nto create new primitives and to test those with the demo data included in this package.\n\nThis release also includes the following User Guides:\n\n* [PRIMITIVES.md](https://github.com/sintel-dev/SigPro/blob/master/PRIMITIVES.md): Information\n about the primitive families, their expected input and output.\n* [USAGE.md](https://github.com/sintel-dev/SigPro/blob/master/USAGE.md): Instructions about how\n to usee the three main functionalities of `SigPro`.\n* [DEVELOPMENT.md](https://github.com/sintel-dev/SigPro/blob/master/DEVELOPMENT.md): Step by step\n guide about how to write a valid `SigPro` primitive and contribute it to either `SigPro` or\n your own library.\n\n### Features\n\n* Demo data: Available demo data to test primitives.\n* First primitives: The following list of primitives were added:\n * `sigpro.aggregations.amplitude.statistical.crest_factor`\n * `sigpro.aggregations.amplitude.statistical.kurtosis`\n * `sigpro.aggregations.amplitude.statistical.mean`\n * `sigpro.aggregations.amplitude.statistical.rms`\n * `sigpro.aggregations.amplitude.statistical.skew`\n * `sigpro.aggregations.amplitude.statistical.std`\n * `sigpro.aggregations.amplitude.statistical.var`\n * `sigpro.transformations.amplitude.identity.identity`\n * `sigpro.transformations.frequency.fft.fft`\n * `sigpro.transformations.frequency.fft.fft_real`\n * `sigpro.transformations.frequency_time.stft.stft`\n * `sigpro.transformations.frequency_time.stft.stft_real`\n* Contributing module.\n* Documentation on how to contribute new primitives and how to run those.\n",
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