| Name | feets JSON |
| Version |
1.0.1
JSON |
| download |
| home_page | None |
| Summary | feets: feATURE eXTRACTOR FOR tIME sERIES. |
| upload_time | 2025-09-08 21:28:28 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | None |
| license | The MIT License (MIT)
Copyright (c) 2017-2024, Cabral, Juan
Copyright (c) 2025, QuatroPe; Clariá, Felipe
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
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The above copyright notice and this permission notice shall be included in all
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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 |
machine-learning
feature-extraction
timeseries
astronomy
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
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|
<!-- BODY -->
# feets: feATURE eXTRACTOR FOR tIME sERIES
[](https://badge.fury.io/py/feets)
[](http://ascl.net/1806.001)
[](http://feets.readthedocs.io)
[](https://tldrlegal.com/license/mit-license)
[](https://badge.fury.io/py/feets)
## Description
In time-domain astronomy, data gathered from the telescopes is usually represented in the form of light-curves. These are time series that show the brightness variation of an object through a period of time. Based on the variability characteristics of the light-curves, celestial objects can be classified into different groups (quasars, long period variables, eclipsing binaries, etc.) and consequently be studied in depth independentely.
In order to characterize this variability, some of the existing methods use machine learning algorithms that build their decision on the light-curves features. Features, the topic of the following work, are numerical descriptors that aim to characterize and distinguish the different variability classes. They can go from basic statistical measures such as the mean or the standard deviation, to complex time-series characteristics such as the autocorrelation function.
In this package we present a library with a compilation of some of the existing light-curve features. The main goal is to create a collaborative and open tool where every user can characterize or analyze an astronomical photometric database while also contributing to the library by adding new features. However, it is important to highlight that **this library is not restricted to the astronomical field** and could also be applied to any kind of time series.
Our vision is to be capable of analyzing and comparing light-curves from all the available astronomical catalogs in a standard and universal way. This would facilitate and make more efficient tasks as modelling, classification, data cleaning, outlier detection and data analysis in general. Consequently, when studying light-curves, astronomers and data analysts would be on the same wavelength and would not have the necessity to find a way of comparing or matching different features. In order to achieve this goal, the library should be run in every existent survey (MACHO, EROS, OGLE, Catalina, Pan-STARRS, etc) and future surveys (LSST) and the results should be ideally shared in the same open way as this library.
## Installation
### Basic install
Execute:
```sh
$ pip install feets
```
### Development install
Clone this repo, and once inside execute
```sh
$ pip install -r requirements_dev.txt
```
## Documentation and tutorial
For a full documentation and tutorial, visit https://feets.readthedocs.io
## Authors
### Juan BC
jbc.develop@gmail.com
[IATE](https://iate.oac.uncor.edu/) - [UNR](https://unr.edu.ar/)
### Felipe Clariá
felipe.claria@unc.edu.ar
[UNC](https://www.unc.edu.ar/)
## License
feets is under [The MIT License](https://raw.githubusercontent.com/quatrope/feets/master/LICENSE).
A short and simple permissive license with conditions only requiring preservation of copyright and license notices. Licensed works, modifications, and larger works may be distributed under different terms and without source code.
## Citation
If you use feets in a scientific publication, we would appreciate citations to the following paper:
> Cabral, J. B., Sánchez, B., Ramos, F., Gurovich, S., Granitto, P., & Vanderplas, J. (2018).
> From FATS to feets: Further improvements to an astronomical feature extraction tool based on machine learning.
> Astronomy and Computing.
### Bibtex entry
```tex
@article{cabral2018fats,
title={
From FATS to feets: Further improvements to
an astronomical feature extraction tool based on
machine learning},
author={
Cabral, JB and S{\'a}nchez, B and Ramos, F and
Gurovich, S and Granitto, P and Vanderplas, J},
journal={Astronomy and Computing},
year={2018},
publisher={Elsevier}
}
```
### Full publication
http://adsabs.harvard.edu/abs/2018arXiv180902154C
----
Based on "FATS" by Isadora Nun and Pavlos Protopapas (https://github.com/isadoranun/FATS)
Main difference with FATS:
- Removed all the ``sys.exit()`` calls and replaced with Exceptions
- PEP-8
- Python 2 and 3
- Warnings instead of prints
- Only one type of results: numpy array.
- Posibility of register more FeaturesExtractors.
## Code of conduct
`feets` endorses [the Astropy Project code of conduct](http://www.astropy.org/code_of_conduct.html).
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"description": "\n\n\n<!-- BODY -->\n\n# feets: feATURE eXTRACTOR FOR tIME sERIES\n\n[](https://badge.fury.io/py/feets)\n[](http://ascl.net/1806.001)\n[](http://feets.readthedocs.io)\n[](https://tldrlegal.com/license/mit-license)\n[](https://badge.fury.io/py/feets)\n\n\n## Description\n\n\nIn time-domain astronomy, data gathered from the telescopes is usually represented in the form of light-curves. These are time series that show the brightness variation of an object through a period of time. Based on the variability characteristics of the light-curves, celestial objects can be classified into different groups (quasars, long period variables, eclipsing binaries, etc.) and consequently be studied in depth independentely.\n\nIn order to characterize this variability, some of the existing methods use machine learning algorithms that build their decision on the light-curves features. Features, the topic of the following work, are numerical descriptors that aim to characterize and distinguish the different variability classes. They can go from basic statistical measures such as the mean or the standard deviation, to complex time-series characteristics such as the autocorrelation function.\n\nIn this package we present a library with a compilation of some of the existing light-curve features. The main goal is to create a collaborative and open tool where every user can characterize or analyze an astronomical photometric database while also contributing to the library by adding new features. However, it is important to highlight that **this library is not restricted to the astronomical field** and could also be applied to any kind of time series.\n\nOur vision is to be capable of analyzing and comparing light-curves from all the available astronomical catalogs in a standard and universal way. This would facilitate and make more efficient tasks as modelling, classification, data cleaning, outlier detection and data analysis in general. Consequently, when studying light-curves, astronomers and data analysts would be on the same wavelength and would not have the necessity to find a way of comparing or matching different features. In order to achieve this goal, the library should be run in every existent survey (MACHO, EROS, OGLE, Catalina, Pan-STARRS, etc) and future surveys (LSST) and the results should be ideally shared in the same open way as this library.\n\n## Installation\n\n### Basic install\n\nExecute:\n\n```sh\n$ pip install feets\n```\n\n### Development install\n\nClone this repo, and once inside execute\n\n```sh\n$ pip install -r requirements_dev.txt\n```\n\n## Documentation and tutorial\n\nFor a full documentation and tutorial, visit https://feets.readthedocs.io\n\n## Authors\n\n### Juan BC\n\njbc.develop@gmail.com\n\n[IATE](https://iate.oac.uncor.edu/) - [UNR](https://unr.edu.ar/)\n\n\n### Felipe Clari\u00e1\n\nfelipe.claria@unc.edu.ar\n\n[UNC](https://www.unc.edu.ar/)\n\n\n## License\n\nfeets is under [The MIT License](https://raw.githubusercontent.com/quatrope/feets/master/LICENSE).\n\nA short and simple permissive license with conditions only requiring preservation of copyright and license notices. Licensed works, modifications, and larger works may be distributed under different terms and without source code.\n\n## Citation\n\nIf you use feets in a scientific publication, we would appreciate citations to the following paper:\n\n> Cabral, J. B., S\u00e1nchez, B., Ramos, F., Gurovich, S., Granitto, P., & Vanderplas, J. (2018).\n> From FATS to feets: Further improvements to an astronomical feature extraction tool based on machine learning.\n> Astronomy and Computing.\n\n\n### Bibtex entry\n\n```tex\n @article{cabral2018fats,\n title={\n From FATS to feets: Further improvements to\n an astronomical feature extraction tool based on\n machine learning},\n author={\n Cabral, JB and S{\\'a}nchez, B and Ramos, F and\n Gurovich, S and Granitto, P and Vanderplas, J},\n journal={Astronomy and Computing},\n year={2018},\n publisher={Elsevier}\n }\n```\n\n### Full publication\n\nhttp://adsabs.harvard.edu/abs/2018arXiv180902154C\n\n\n----\n\nBased on \"FATS\" by Isadora Nun and Pavlos Protopapas (https://github.com/isadoranun/FATS)\n\nMain difference with FATS:\n\n- Removed all the ``sys.exit()`` calls and replaced with Exceptions\n- PEP-8\n- Python 2 and 3\n- Warnings instead of prints\n- Only one type of results: numpy array.\n- Posibility of register more FeaturesExtractors.\n\n\n## Code of conduct\n\n`feets` endorses [the Astropy Project code of conduct](http://www.astropy.org/code_of_conduct.html).\n",
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