https://github.com/quatrope/feets
feATURE eXTRACTOR FOR tIME sERIES
https://github.com/quatrope/feets
astronomy astropy feature-extraction machine-learning python2 python3 scipy timeseries
Last synced: 22 days ago
JSON representation
feATURE eXTRACTOR FOR tIME sERIES
- Host: GitHub
- URL: https://github.com/quatrope/feets
- Owner: quatrope
- License: mit
- Created: 2017-03-29T03:58:58.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2025-04-08T23:38:26.000Z (23 days ago)
- Last Synced: 2025-04-10T00:18:08.991Z (22 days ago)
- Topics: astronomy, astropy, feature-extraction, machine-learning, python2, python3, scipy, timeseries
- Language: Python
- Homepage:
- Size: 30.2 MB
- Stars: 53
- Watchers: 6
- Forks: 13
- Open Issues: 51
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
feets: feATURE eXTRACTOR FOR tIME sERIES
========================================
[](https://badge.fury.io/py/feets)
[](http://ascl.net/1806.001)
[](https://travis-ci.org/quatrope/feets)
[](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 (for a
visual representation see video below). 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.
Basic Install
-------------
Execute
``` {.sourceCode .bash}
$ pip install feets
```
Development Install
-------------------
1. Clone this repo and then inside the local
2. Execute
``` {.sourceCode .bash}
$ pip install -e .
```
Tutorial
--------
Authors
-------
Juan BC
[IATE](http://iate.oac.uncor.edu/) - [UNR](http://unr.edu.ar/)
------------------------------------------------------------------------
Based on "FATS" by Isadora Nun and Pavlos Protopapas
()
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.