Metadata-Version: 2.1
Name: smurfs
Version: 1.1.4
Summary: Smart UseR Frequency analySer, a fast and easy to use frequency analyser.
Home-page: https://github.com/muma7490/SMURFS
Author: Marco Müllner
Author-email: muellnermarco@gmail.com
License: UNKNOWN
Description: # SMURFS
        ![SMURFS Image](https://i.imgur.com/Uh2UhpZ.png)
        
        **SMURFS** provides automatic extraction of frequencies from
        timeseries. It provides various interfaces, from a standalone command line tool, to jupyter and python 
        integrations and computes possible frequency combinations, directly downloads and reduces (if necessary) data 
        of TESS/Kepler/K2 observations and much much more.
        ## Getting started
        
        To install smurfs, you need python > 3.5, pip as well as cmake. If you don't have these, install them through the
        packet manager of your choice (f.e. _brew_(Mac) or _apt_ (Debian)). For pip check 
        [here](https://pip.pypa.io/en/stable/installing/).
        
        ## Installation
        
        First off, create a virtual environment
        
        ```bash
        cd /Path/
        python3 -m venv venv/
        source venv/bin/activate
        ```
        
        Install smurfs through pip
        
        ```bash
        pip install smurfs
        ```
        
        ## Quickstart
        
        Using SMURFS as a standalone command line tool is very simple. Simply call ```smurfs``` with a **target**, signal to noise
        ratio cutoff and the window size. The target can be either:
        
        - A path to a file, containing 2 columns with time and flux
        - Any name of a star, that is resolvable by Simbad and has been observed by the **Kepler**,**K2** or **TESS** missions.
        
        As an example, we can take a look at the star Gamma Doradus:
        ```
        smurfs "Gamma Doradus" 4 2
        ```
        
        SMURFS creates a result folder after running the code. In this case it has the following structure
        ```
        - Gamma_Doradus
            - data
                - _combinations.csv
                - _result.csv
                - LC_residual.txt
                - LC.txt
                - PS_residual.txt
                - PS.txt         
            - plots
                - LC_residual.pdf
                - LC.pdf
                - PS_residual.pdf
                - PS_result.pdf
                - PS.pdf
        ```
        The ```LC*.txt``` files contain the light curves, original and residual. The ```PS*.txt``` files contain the 
        original as well as the residual amplitude spectrum. ```_combinations.csv``` shows all combination frequencies for the 
        result and ```_result.csv``` gives the result for a given run.
        
        ## Citing
        
        If you use this software in your research, consider citing  it using Zenodo.
        
        [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3635801.svg)](https://doi.org/10.5281/zenodo.3635801)
        
        If you use SMURFS to extract LC data from FFIs, you should also cite the awesome people of Eleanor.
        
        [Feinstein et al. 2019](https://ui.adsabs.harvard.edu/abs/2019PASP..131i4502F/abstract)
        
        
         
        ## Documentation
        
        Full documentation is available [here](https://smurfs.readthedocs.io/en/master/)
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
