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Smart UseR Frequency analySer, a fast and easy to use frequency analyser.

Project description



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.


First off, create a virtual environment

cd /Path/
python3 -m venv venv/
source venv/bin/activate

Install smurfs through pip

pip install smurfs


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.


If you use this software in your research, consider citing it using Zenodo.


If you use SMURFS to extract LC data from FFIs, you should also cite the awesome people of Eleanor.

Feinstein et al. 2019


Full documentation is available here

Project details

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