User-defined science module for the Fink broker.
This repository contains science modules used to generate added values to alert collected by the Fink broker. It currently contains:
xmatch: returns the SIMBAD closest counterpart of an alert, based on position.
random_forest_snia: returns the probability of an alert to be a SNe Ia using a Random Forest Classifier (binary classification)
snn: returns the probability of an alert to be a SNe Ia using SuperNNova. Two pre-trained models:
snn_snia_vs_nonia: Ia vs core-collapse SNe
snn_sn_vs_all: SNe vs. anything else (variable stars and other categories in the training)
microlensing: returns the predicted class (among microlensing, variable star, cataclysmic event, and constant event) & probability of an alert to be a microlensing event in each band using LIA.
asteroids: Determine if the alert is an asteroid (experimental).
nalerthist: Number of detections contained in each alert (current+history). Upper limits are not taken into account.
You will find README in each subfolder describing the module.
How to contribute
Learn how to design your science module, and integrate it inside the Fink broker.
If you want to install the package (broker deployment), you can just pip it:
pip install fink_science
Release history Release notifications | RSS feed
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
|Filename, size||File type||Python version||Upload date||Hashes|
|Filename, size fink_science-0.4.0-py3-none-any.whl (8.3 MB)||File type Wheel||Python version py3||Upload date||Hashes View|
|Filename, size fink-science-0.4.0.tar.gz (8.0 MB)||File type Source||Python version None||Upload date||Hashes View|
Hashes for fink_science-0.4.0-py3-none-any.whl