Wōtan...

...offers free and open source algorithms to automagically remove trends from time-series data.

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', return_trend=True)

For more details, have a look at the interactive playground, the documentation. We also have examples and tutorials available, such as the 📑Example: Basic wotan functionality

Available detrending algorithms

Time-windowed sliders with location estimates: (📑Example: Comparison of sliders)
- biweight Robust M-estimator using Tukey's biweight (📑Example)
- huber Robust M-estimator from Huber (1981) (iterative)
- huber_psi Robust M-estimator based on Huber's ψ (one-step)
- hampel Robust M-estimator based on Hampel (1972), 3-part descending, known as (a,b,c), 17A, 25A
- andrewsinewave Robust M-estimator using Andrew's sine wave
- welsch Robust M-estimator from Welsch-Leclerc
- ramsay Robust M-estimator from Ramsay (1977), known as Ramsay's E^a
- tau Robust τ estimator from Yohai & Zamar (1986)
- hodges Rank-based robust R-estimator Hodges-Lehmann-Sen
- median The most robust (but least efficient)
- medfilt A cadence-based median filter (not time-windowed) for comparison
- mean The least robust (but most efficient for white noise)
- trim_mean Trimmed mean (outliers are removed)
- winsorize Trimmed mean (outliers are winsorized to a specified percentile)
- hampelfilt Trimmed mean (outliers are replaced with the median)
Splines: (📑Example)
- rspline Spline with iterative sigma-clipping
- hspline Spline with a robust Huber estimator (Huber 1981)
- pspline Penalized spline to automatically select the knot distance (Eilers 1996), with iterative sigma-clipping
Polynomials and sines: (📑Example)
- cofiam Cosine Filtering with Autocorrelation Minimization (Kipping et al. 2013)
- cosine Sum of sines and cosines, with option for iterative sigma-clipping
- savgol Sliding segments are fit with polynomials (Savitzky & Golay 1964), cadence-based
Regressions: (📑Example)
- lowess Locally weighted scatterplot smoothing (Cleveland 1979)
- supersmoother Friedman's (1984) Super-Smoother, a local linear regression with adaptive bandwidth
Fitting a model that is a sum of Gaussian bases: (📑Example)
- ridge Ridge regression (L2 loss, Tikhonov regularization)
- lasso LASSO regression (L1 loss, Least Absolute Shrinkage Selector Operator, Tibshirani (1996))
- elasticnet Linear regression model with 50% L1 and 50% L2 norm regularization
gp Gaussian Processes offering: (📑Example: GP Standard vs. robust)
- squared_exp Squared-exponential kernel, with option for iterative sigma-clipping
- matern Matern 3/2 kernel, with option for iterative sigma-clipping
- periodic Periodic kernel informed by a user-specified period (📑Example)
- 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.
transit_mask Mask known transits during detrending (📑Example)

What method to choose?

It depends on your data and what you like to achieve (relevant xkcd). If possible, try it out! Use wotan with a selection of methods, iterate over their parameter space, and choose what gives the best results for your research.

If that is too much effort, you should first examine your data.

Is it mostly white (Gaussian) noise? Use a time-windowed sliding mean. This is the most efficient method for white noise.
With prominent outliers (such as transits or flares), use a robust time-windowed method such as the biweight. This is usually superior to the median or trimmed methods.
Are there (semi-) periodic trends? In addition to a time-windowed biweight, try a spline-based method. Experimenting with periodic GPs is worthwhile.

Installation

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:

huber, ramsay, and hampel depend on statsmodels
hspline and gp depend on sklearn
pspline depends on pygam
supersmoother depends on supersmoother

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

Originality

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 (iterative sigma-clipping) penalized splines for automatic knot distance determination and outlier resistance
Robustified (iterative sigma-clipping) Gaussian processes
GP with a periodic kernel informed by a Lomb-Scargle periodogram pre-search
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

Attribution

Please cite Hippke et al. (2019, AJ, 158, 143) if you find this code useful in your research. The BibTeX entry for the paper is:

@ARTICLE{2019AJ....158..143H,
       author = {{Hippke}, Michael and {David}, Trevor J. and {Mulders}, Gijs D. and
         {Heller}, Ren{\'e}},
        title = "{W{\={o}}tan: Comprehensive Time-series Detrending in Python}",
      journal = {\aj},
     keywords = {eclipses, methods: data analysis, methods: statistical, planetary systems, planets and satellites: detection, Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics},
         year = "2019",
        month = "Oct",
       volume = {158},
       number = {4},
          eid = {143},
        pages = {143},
          doi = {10.3847/1538-3881/ab3984},
archivePrefix = {arXiv},
       eprint = {1906.00966},
 primaryClass = {astro-ph.EP},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2019AJ....158..143H},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

ksetdekov / wotan