“`Wotan is an open-source Python package designed to automate the process of removing trends from time-series data, particularly useful in fields such as astronomy and other sciences where time-series analysis is crucial.
What Wotan Does
Wotan is tailored to detrend time-series data, which involves removing systematic trends or variations that are not of interest, allowing researchers to focus on the underlying signals or events, such as transits or flares in astronomical data.Key Features and Functionality
Detrending Methods
Wotan offers a variety of detrending methods to suit different types of data and noise characteristics:- Time-windowed sliding mean: Efficient for data with white (Gaussian) noise.
- Robust time-windowed methods (e.g., `biweight`, `median`, `trimmed`): Superior for data with prominent outliers such as transits or flares.
- Spline-based methods: Useful for data with semi-periodic trends.
- Gaussian Process (GP) methods: Incorporates periodic and non-periodic kernels, with the option to automatically detect the period using a Lomb-Scargle periodogram.
Customization and Flexibility
- Parameter Tuning: Users can adjust parameters such as `window_length`, `cval` (a multiple of the median absolute deviation), and kernel settings to optimize the detrending process for their specific data.
- Segmentation: Wotan can split the data into segments based on gaps or breaks, allowing for more accurate detrending over non-uniformly sampled data.
Efficiency and Robustness
- Efficiency: Methods are optimized for high efficiency, with defaults chosen to achieve good results for Gaussian distributions. For example, the `biweight` method with a `cval` of 5 has shown high efficiency in transit injection retrieval experiments.
- Robustness: Robust methods like `biweight` are designed to handle outliers effectively, making the detrending process more robust against noise and anomalies.
Integration and Dependencies
- Wotan integrates with several popular scientific computing libraries in Python, including `numpy`, `numba`, `scipy`, `statsmodels`, `sklearn`, and `pygam`. It can be installed using pip, and additional dependencies can be installed as needed.
Documentation and Examples
- The package includes extensive documentation, interactive playgrounds, and example tutorials to help users get started and choose the best method for their data.
In summary, Wotan is a powerful and flexible tool for detrending time-series data, offering a range of methods and customization options to handle various types of data and noise, making it a valuable resource for researchers in need of robust and efficient time-series analysis.
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