Metadata-Version: 2.1
Name: smurfs
Version: 1.1.0
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
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
Requires-Dist: astropy
Requires-Dist: matplotlib
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: pytest
Requires-Dist: scipy
Requires-Dist: uncertainties
Requires-Dist: astroquery
Requires-Dist: lightkurve
Requires-Dist: eleanor-mamu
Requires-Dist: lmfit
Requires-Dist: jupyter
Requires-Dist: pyfcomb
Requires-Dist: Sphinx
Requires-Dist: recommonmark
Requires-Dist: sphinx-rtd-theme
Requires-Dist: tess-point (>=0.3.8)

# SMURFS
![SMURFS Image](https://i.imgur.com/wWe1q0y.png)

The **SMURFS** (**SM**art **U**se**R** **F**requency analy**S**er) provides automatic extraction of frequencies from
a Timeseries data. It provides various interfaces, from a standalone command line tool, to jupyter and python 
integrations. It also automatically computes possible frequency combinations, directly downloads of TESS/Kepler/K2 
data and 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
```
Executing this command will make smurfs search for light curves of the star. It starts by using the 
[lightkurve.search.search_lightcurvefile](https://docs.lightkurve.org/api/lightkurve.search.search_lightcurvefile.html#lightkurve.search.search_lightcurvefile)
method, which queries MAST for processed light curves of the object. If this doesn't return any light curves, SMURFS 
will then check if the star has been observed by the TESS mission. It queries Simbad for the coordinates of the object 
and then checks if that point was observed by TESS. If so, we use [TessCut](https://mast.stsci.edu/tesscut/) and 
the [Eleanor](https://adina.feinste.in/eleanor/) pipeline to extract the light curve. 

In the case of Gamma Doradus, we have existing TESS SC light curves. Smurfs will give the following output:
![Gamma Doradus output](images/gamma_doradus_output.png)

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.

## Documentation

Full documenation is available [here](https://smurfs.readthedocs.io/en/master/)

## Features

SMURFS provides various nice to have features, setting it apart
from common frequency analysers. These include

* Python only. No more Fortran, IDL or other more obfuscating languages 
* Fast runs due to the usage of optimized libraries, including numpy, scipy and astropy,
dedicated to scientific work
* Generates a full result set that can be used for further analysis, including 
spectra of the first and last frequency, spectrograms, machine readable results and so on.


