feets package¶
Subpackages¶
- feets.datasets package
- feets.extractors package
- Submodules
- feets.extractors.core module
- feets.extractors.ext_amplitude module
- feets.extractors.ext_anderson_darling module
- feets.extractors.ext_autocor_length module
- feets.extractors.ext_beyond1_std module
- feets.extractors.ext_car module
- feets.extractors.ext_color module
- feets.extractors.ext_con module
- feets.extractors.ext_eta_color module
- feets.extractors.ext_eta_e module
- feets.extractors.ext_flux_percentile_ratio module
- feets.extractors.ext_fourier_components module
- feets.extractors.ext_gskew module
- feets.extractors.ext_linear_trend module
- feets.extractors.ext_lomb_scargle module
- feets.extractors.ext_max_slope module
- feets.extractors.ext_mean module
- feets.extractors.ext_mean_variance module
- feets.extractors.ext_median_abs_dev module
- feets.extractors.ext_median_brp module
- feets.extractors.ext_pair_slope_trend module
- feets.extractors.ext_percent_amplitude module
- feets.extractors.ext_percent_difference_flux_percentile module
- feets.extractors.ext_q31 module
- feets.extractors.ext_rcs module
- feets.extractors.ext_skew module
- feets.extractors.ext_slotted_a_length module
- feets.extractors.ext_small_kurtosis module
- feets.extractors.ext_std module
- feets.extractors.ext_stetson module
- feets.extractors.ext_structure_functions module
- Module contents
- feets.libs package
- feets.tests package
Submodules¶
feets.core module¶
core functionalities of feets
-
exception
feets.core.
FeatureNotFound
[source]¶ Bases:
ValueError
-
exception
feets.core.
DataRequiredError
[source]¶ Bases:
ValueError
-
class
feets.core.
FeatureSpace
(data=None, only=None, exclude=None, **kwargs)[source]¶ Bases:
object
Wrapper class, to allow user select the features based on the available time series vectors (magnitude, time, error, second magnitude, etc.) or specify a list of features. The finally selected features for the execution plan are are those that satisfy all the filters.
Parameters: data : array-like, optional, default
None
available time series vectors, which will output all the features that need this data to be calculated.
only : array-like, optional, default
None
List of features, which will output all the features in the list.
exclude : array-like, optional, default
None
List of features, which will not output
kwargs
Extra configuration for the feature extractors. format is
Feature_name={param1: value, param2: value, ...}
Examples
List of features as an input:
>>> fs = feets.FeatureSpace(only=['Std']) >>> features, values = fs.extract(*lc) >>> dict(zip(features, values)) {"Std": .42}
Available data as an input:
>>> fs = feets.FeatureSpace(data=['magnitude','time']) >>> features, values = fs.extract(*lc) >>> dict(zip(features, values)) {...}
List of features and available data as an input:
>>> fs = feets.FeatureSpace( ... only=['Mean','Beyond1Std', 'CAR_sigma','Color'], ... data=['magnitude', 'error']) >>> features, values = fs.extract(*lc) >>> dict(zip(features, values)) {"Beyond1Std": ..., "Mean": ...}
Excluding list as an input
>>> fs = feets.FeatureSpace( ... only=['Mean','Beyond1Std','CAR_sigma','Color'], ... data=['magnitude', 'error'], ... exclude=["Beyond1Std"]) >>> features, values = fs.extract(**lc) >>> dict(zip(features, values)) {"Mean": 23}
Attributes
data
excecution_plan_
exclude
features_
features_as_array_
features_by_data_
features_extractors_
kwargs
only
required_data_
Methods
dict_data_as_array
(d)extract
([time, magnitude, error, …])-
data
¶
-
excecution_plan_
¶
-
exclude
¶
-
extract
(time=None, magnitude=None, error=None, magnitude2=None, aligned_time=None, aligned_magnitude=None, aligned_magnitude2=None, aligned_error=None, aligned_error2=None)[source]¶
-
features_
¶
-
features_as_array_
¶
-
features_by_data_
¶
-
features_extractors_
¶
-
kwargs
¶
-
only
¶
-
required_data_
¶
-
feets.preprocess module¶
Module contents¶
feets: feATURE eXTRACTOR FOR tIME sERIES.
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.