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        "description": "![Logo](https://raw.githubusercontent.com/hippke/wotan/master/logo.png)\n\n[![pip](https://img.shields.io/badge/pip-install%20wotan-blue.svg)](https://pypi.org/project/wotan/)\n[![Documentation](https://img.shields.io/badge/documentation-%E2%9C%93-blue.svg)](https://wotan.readthedocs.io/en/latest/index.html)\n[![Image](https://img.shields.io/badge/tutorials-%E2%9C%93-blue.svg)](https://github.com/hippke/wotan/tree/master/tutorials)\n[![Image](https://img.shields.io/badge/arXiv-1906.00966-blue.svg)](https://arxiv.org/abs/1906.00966)\n[![Build Status](https://travis-ci.com/hippke/wotan.svg?branch=master)](https://travis-ci.com/hippke/wotan)\n\nW\u014dtan...\n====================\n\n...offers free and open source algorithms to automagically remove trends from time-series data.\n\n> In Germanic mythology, Odin (/\u02c8o\u02d0\u00f0in\u02d0/ Old High German: W\u014dtan) 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\".\n\nExample usage\n-------------\n```\nfrom wotan import flatten\nflatten_lc, trend_lc = flatten(time, flux, window_length=0.5, method='biweight', return_trend=True)\n```\n\nFor more details, have a look at the [interactive playground](https://colab.research.google.com/github/hippke/wotan/blob/master/tutorials/interactive.ipynb#scrollTo=spnftwRjefhQ), the [documentation](https://wotan.readthedocs.io).\nWe also have examples and tutorials available, such as the [\ud83d\udcd1Example: Basic wotan functionality](https://github.com/hippke/wotan/blob/master/tutorials/01%20Basic%20functionality.ipynb)\n\nAvailable detrending algorithms\n---------------------------------\n\n- Time-windowed sliders with location estimates: [(\ud83d\udcd1Example: Comparison of sliders)](https://github.com/hippke/wotan/blob/master/tutorials/02%20Sliders.ipynb)\n   - ``biweight`` Robust [M-estimator](https://en.wikipedia.org/wiki/M-estimator) using [Tukey's biweight](https://books.google.de/books?id=pGlHAAAAMAAJ) [(\ud83d\udcd1Example)](https://github.com/hippke/wotan/blob/master/examples/biweight.ipynb)\n   - ``huber`` Robust [M-estimator](https://en.wikipedia.org/wiki/M-estimator) from  [Huber (1981)](https://books.google.de/books/about/Robust_Statistics.html?id=hVbhlwEACAAJ&redir_esc=y) (iterative)\n   - ``huber_psi`` Robust [M-estimator](https://en.wikipedia.org/wiki/M-estimator) based on [Huber's \u03c8](https://books.google.de/books/about/Robust_Statistics.html?id=hVbhlwEACAAJ&redir_esc=y) (one-step)\n   - ``hampel`` Robust [M-estimator](https://en.wikipedia.org/wiki/M-estimator) based on [Hampel (1972)](https://www.tandfonline.com/doi/abs/10.1080/01621459.1974.10482962), 3-part descending, known as (a,b,c), 17A, 25A\n   - ``andrewsinewave`` Robust [M-estimator](https://en.wikipedia.org/wiki/M-estimator) using [Andrew's sine wave](http://www.jstor.org/stable/j.ctt13x12sw.3)\n   - ``welsch`` Robust [M-estimator](https://en.wikipedia.org/wiki/M-estimator) from [Welsch](https://doi.org/10.1080/03610917808812083)-[Leclerc](https://doi.org/10.1007/BF00054839)\n   - ``ramsay`` Robust M-estimator from [Ramsay (1977)](https://www.jstor.org/stable/2286228?seq=1#page_scan_tab_contents), known as Ramsay's E<sup>a</sup>\n   - ``tau`` Robust \u03c4 estimator from [Yohai & Zamar (1986)](https://www.tandfonline.com/doi/abs/10.1080/01621459.1988.10478611)\n   - ``hodges`` Rank-based robust R-estimator [Hodges-Lehmann](https://doi.org/10.1214/aoms/1177704172)-[Sen](https://doi.org/10.2307/2527532)\n   - ``median`` The most robust (but least efficient)\n   - ``medfilt`` A cadence-based median filter (*not* time-windowed) for comparison\n   - ``mean`` The least robust (but most efficient for white noise)\n   - ``trim_mean`` Trimmed mean (outliers are removed)\n   - ``winsorize`` Trimmed mean (outliers are [*winsorized*](https://en.wikipedia.org/wiki/Winsorizing) to a specified percentile)\n   - ``hampelfilt`` Trimmed mean (outliers are [replaced](https://link.springer.com/article/10.1186/s13634-016-0383-6) with the median)\n- Splines: [(\ud83d\udcd1Example)](https://github.com/hippke/wotan/blob/master/examples/splines.ipynb)\n   - ``rspline`` Spline with iterative sigma-clipping\n   - ``hspline`` Spline with a robust Huber estimator ([Huber 1981](https://books.google.de/books?id=hVbhlwEACAAJ))\n   - ``pspline`` Penalized spline to automatically select the knot distance [(Eilers 1996)](https://pdfs.semanticscholar.org/5e3d/4cf7824be321af95ac098595957d8a87bf68.pdf), with iterative sigma-clipping\n- Polynomials and sines: [(\ud83d\udcd1Example)](https://github.com/hippke/wotan/blob/master/examples/polynomials_sines_cofiam_cosine_savgol.ipynb)\n   - ``cofiam`` Cosine Filtering with Autocorrelation Minimization ([Kipping et al. 2013](http://adsabs.harvard.edu/abs/2013ApJ...770..101K))\n   - ``cosine`` Sum of sines and cosines, with option for iterative sigma-clipping\n   - ``savgol`` Sliding segments are fit with polynomials ([Savitzky & Golay 1964](https://ui.adsabs.harvard.edu/#abs/1964AnaCh..36.1627S)), cadence-based\n- Regressions: [(\ud83d\udcd1Example)](https://github.com/hippke/wotan/blob/master/examples/regressions_lowess_supersmoother.ipynb)\n   - ``lowess`` Locally weighted scatterplot smoothing ([Cleveland 1979](https://doi.org/10.1080/01621459.1979.10481038))\n   - ``supersmoother`` [Friedman's (1984)](https://www.slac.stanford.edu/pubs/slacpubs/3250/slac-pub-3477.pdf) Super-Smoother, a local linear regression with adaptive bandwidth\n\n   Fitting a model that is a sum of Gaussian bases: [(\ud83d\udcd1Example)](https://github.com/hippke/wotan/blob/master/examples/regressions_ridge_lasso_elasticnet.ipynb)\n   - ``ridge`` Ridge regression (L2 loss, [Tikhonov regularization](https://en.wikipedia.org/wiki/Tikhonov_regularization))\n   - ``lasso`` LASSO regression \n   (L1 loss, [Least Absolute Shrinkage Selector Operator](https://en.wikipedia.org/wiki/Lasso_(statistics)), [Tibshirani (1996)](https://www.jstor.org/stable/2346178?seq=1#page_scan_tab_contents))\n   - ``elasticnet`` [Linear regression model](https://en.wikipedia.org/wiki/Elastic_net_regularization) with 50% L1 and 50% L2 norm regularization\n- ``gp`` Gaussian Processes offering: [(\ud83d\udcd1Example: GP Standard vs. robust)](https://github.com/hippke/wotan/blob/master/tutorials/03%20GPs%20-%20standard%20versus%20robust.ipynb)\n   - ``squared_exp`` Squared-exponential kernel, with option for iterative sigma-clipping\n   - ``matern`` Matern 3/2 kernel, with option for iterative sigma-clipping\n   - ``periodic`` Periodic kernel informed by a user-specified period [(\ud83d\udcd1Example)](https://github.com/hippke/wotan/blob/master/tutorials/04%20GPs%20periodic.ipynb)\n   - ``periodic_auto`` Periodic kernel informed by a Lomb-Scargle periodogram pre-search\n\n\nAvailable features\n-------------------\n\n- ``window_length`` The length of the filter window in units of ``time`` (usually days).\n- ``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.\n- ``edge_cutoff`` Trends near edges are less robust. Depending on the data, it may be beneficial to remove edges.\n- ``cval`` Tuning parameter for the robust estimators (see [documentation](https://wotan.readthedocs.io/en/latest/index.html))\n- ``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``.\n- ``transit_mask`` Mask known transits during detrending [(\ud83d\udcd1Example)](https://github.com/hippke/wotan/blob/master/examples/transit%20mask.ipynb)\n\n\nWhat method to choose?\n-----------------------\nIt depends on your data and what you like to achieve ([relevant xkcd](https://xkcd.com/2048/)). 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.\n\nIf that is too much effort, you should first examine your data.\n- Is it mostly white (Gaussian) noise? Use a time-windowed sliding mean. This is the most efficient method for white noise.\n- 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.\n- Are there (semi-) periodic trends? In addition to a time-windowed biweight, try a spline-based method. Experimenting with periodic GPs is worthwhile.\n\n\nInstallation\n------------\nTo install the released version, type\n\n    $ pip install wotan\n\nwhich automatically installs `numpy`, `numba` and ``scipy`` if not present. Depending on the algorithm, additional dependencies exist:\n\n- `huber`, `ramsay`, and `hampel` depend on `statsmodels`\n- `hspline` and `gp` depend on `sklearn`\n- `pspline` depends on `pygam`\n- `supersmoother` depends on `supersmoother`\n\nTo install all additional dependencies, type ``$ pip install statsmodels sklearn supersmoother pygam``.\n\n\nOriginality\n----------------\nAs all scientific work, w\u014dtan is [*standing on the shoulders of giants*](https://en.wikiquote.org/wiki/Isaac_Newton). Particularly, many detrending methods are wrapped from existing packages. Original contributions include:\n- A time-windowed detrending master module with edge treatments and segmentation options\n- 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\n- Robustified (iterative sigma-clipping) penalized splines for automatic knot distance determination and outlier resistance\n- Robustified (iterative sigma-clipping) Gaussian processes\n- GP with a periodic kernel informed by a Lomb-Scargle periodogram pre-search\n- Bringing together many methods in one place in a common interface, with sensible defaults\n- Providing documentation, tutorials, and a [paper](https://arxiv.org/abs/1906.00966) which compares and benchmarks the methods\n\n\nAttribution\n----------------\nPlease cite [Hippke et al. (2019, AJ in press)](https://arxiv.org/abs/1906.00966) if you find this code useful in your research. The BibTeX entry for the paper is:\n\n```\n@ARTICLE{2019arXiv190600966H,\n       author = {{Hippke}, Michael and {David}, Trevor J. and {Mulders}, Gijs D. and\n         {Heller}, Ren{\\'e}},\n        title = \"{Wotan: Comprehensive time-series de-trending in Python}\",\n      journal = {arXiv e-prints},\n     keywords = {Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics},\n         year = \"2019\",\n        month = \"Jun\",\n          eid = {arXiv:1906.00966},\n        pages = {arXiv:1906.00966},\narchivePrefix = {arXiv},\n       eprint = {1906.00966},\n primaryClass = {astro-ph.EP},\n       adsurl = {https://ui.adsabs.harvard.edu/abs/2019arXiv190600966H},\n      adsnote = {Provided by the SAO/NASA Astrophysics Data System}\n}\n```\n\n\n",
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