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Detrending algorithms

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...offers free and open source algorithms to automagically remove stellar trends from light curves for exoplanet transit detection.

In Germanic mythology, Odin (/ˈoːðinː/ Old High German: Wōtan) is a widely revered god. He gave one of his eyes to Mimir in return for wisdom. Thus, in order to achieve a goal, one sometimes has to turn a blind eye. In Richard Wagner's "Der Ring des Nibelungen", Wotan is the King of the Gods (god of light, air, and wind) and a bass-baritone. According to Wagner, he is the "pinnacle of intelligence".

Example usage

from wotan import flatten
flatten_lc, trend_lc = flatten(time, flux, window_length=0.5, method='biweight')

For more details, have a look at the interactive playground, the documentation and tutorials.

Available detrending algorithms

  • Time-windowed sliders with robust location estimates:
  • Splines:
  • Polynomials and others:
  • gp Gaussian Processes offering:
    • squared_exp Squared-exponential kernel
    • matern Matern 3/2 kernel
    • periodic Periodic kernel informed by a user-specified period
    • periodic_auto Periodic kernel informed by a Lomb-Scargle periodogram pre-search

Available features

  • window_length The length of the filter window in units of time (usually days).
  • break_tolerance If there are large gaps in time, especially with corresponding flux level offsets, the detrending is much improved when splitting the data into several sub-lightcurves and applying the filter to each individually. Comes with an empirical default and is fully adjustable.
  • edge_cutoff Trends near edges are less robust. Depending on the data, it may be beneficial to remove edges.
  • cval Tuning parameter for the robust estimators (see documentation)
  • return_trend If True, the method will return a tuple of two elements (flattened_flux, trend_flux) where trend_flux is the removed trend. Otherwise, it will only return flattened_flux.


To install the released version, type

$ pip install wotan

which automatically installs numpy, numba and scipy if not present. Depending on the algorithm, additional dependencies exist:

  • lowess and huber depend on statsmodels
  • hspline and gp depend on `sklearn
  • pspline depends on pygam
  • supersmoother depends on supersmoother
  • untrendy depends on untrendy

To install all additional dependencies, type $ pip install statsmodels sklearn supersmoother untrendy pygam.


Please cite Hippke et al. (2019, XXX) if you find this code useful in your research. The BibTeX entry for the paper is:

      adsnote = {Provided by the SAO/NASA Astrophysics Data System}


As all scientific work, wōtan is standing on the shoulders of giants. Particularly, many detrending methods are wrapped from existing packages. Original contributions include:

  • A time-windowed detrending master module with edge treatments and segmentation options
  • Robust location estimates using Newton-Raphson iteration to a precision threshold for Tukey's biweight, Andrew's sine wave, and the Welsch-Leclerc. This is probably a "first", which reduces jitter in the location estimate by ~10 ppm
  • Robustified penalized splines for automatic knot distance determination and outlier resistance
  • Bringing together many methods in one place in a common interface, with sensible defaults
  • Providing documentation, tutorials, and a paper which compares and benchmarks the methods

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