Metadata-Version: 2.1
Name: varstardetect
Version: 1.1.10
Summary: TESS Variable Star Light Curve Fitter
Home-page: https://github.com/VarStarDetect/varstardetect
Author: Nicolas Carrizosa Arias, Jorge Perez Gonzalez and Andres Cadenas Blanco
Author-email: varstardetect@gmail.com
License: gpl-3.0
Keywords: Star,Astronomy,Star Detection,Detection
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Framework :: Matplotlib
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Astronomy
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE.txt

Varstar Detect
==============

Python package optimized for variable star detection in TESS Secctor 1
data.

 Initialization:
--------------------

To install repository from PyPi: `from varstardectect import Star`

 Looking for stellar variability:
-------------------------------------

Use the `amplitude_test()` function, with the following documentation,
to determine objects which are variable. In which you have to determine 
the directory of the archive file with the sector info, it must be a csv.

    amplitude_test(min, max, amp, directory):
    """                
                       amplitude_test DOCUMENTATION
    ---------------------------------------------------------------------
    Detects variable stars with amplitude higher than threshold.
    ---------------------------------------------------------------------
    INPUTS:     - min: lower star search (TESS) range delimiter
                - max: higher star search (TESS) range delimiter
                - amp: amplitude threshold
                - directory: directory with TESS sector 1 files csv. You can
                  download from
                  https://tess.mit.edu/observations/target-lists/
            -------------------------------------------------------------
    OUTPUTS:    - candidates: 1D numpy array with variable candidate 
                  target IDs
                - chis: 1D numpy array with the chi^2 parameter of each
                  approximation.
                - degree: 1D numpy array with the degree of the optimal
                  degree of the approximation.
                - periods: 1D numpy array with the period of each 
                  approimating function.
                - period_errors: 1D numpy array with the period 
                  uncertainty for each candidate.
                - amplitudes: 1D numpy array with the amplitude of each
                  approximation.
                - amplitude_errors: 1D numpy array with the uncertainty 
                  of the amplitude of each candidate.
    ----------------------------------------------------------------------
    PROCEDURE:
                1. Calculates amplitude for an observed star.
                2. Calculates if amplitude is bigger than threshold.
                3. Returns candidates and their characteristics.
    ----------------------------------------------------------------------
    """

Background:
-----------

The function uses several numerical and statistical methods to filter
and interpret the data obtained form TESS, providing the characteristics
of each star through phenomenological analysis of the lightcurve, given
that it has passed the amplitude test.

DISCLAIMER:
-----------

This is a Beta state of the program. It is unstable therefore itcan have 
bugs. It has not been optimized correctly yet.


