Analysis of X-ray absorption spectroscopy data often involves the removal of artifacts or glitches from the acquired signal, a process commonly known as deglitching. Glitches result either from specific orientations of monochromator crystals or from scattering by crystallites in the sample itself. Since the precise energy – or wavelength – location and the intensity of glitches in a spectrum cannot always be predicted, deglitching is often performed on a per spectrum basis by the analyst. Some routines have been proposed, but they are prone to arbitrary selection of spectral artifacts and are often inadequate for processing large data sets. Here, a statistically robust algorithm, implemented as a Python program, for the automatic detection and removal of glitches that can be applied to a large number of spectra, is presented. It uses a Savitzky–Golay filter to smooth spectra and the generalized extreme Studentized deviate test to identify outliers. Robust, repeatable, and selective removal of glitches is achieved using this algorithm.

中文翻译：

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X射线吸收光谱数据自动去毛刺算法

X 射线吸收光谱数据的分析通常涉及从采集的信号中去除伪影或毛刺，这一过程通常称为去毛刺. 毛刺是由单色器晶体的特定方向或由样品本身的微晶散射引起的。由于无法始终预测光谱中精确的能量（或波长）位置和毛刺强度，因此分析人员通常在每个光谱的基础上执行去毛刺。已经提出了一些例程，但它们易于任意选择光谱伪影，并且通常不足以处理大型数据集。在这里，提出了一种作为 Python 程序实现的统计稳健算法，用于自动检测和消除可应用于大量光谱的毛刺。它使用 Savitzky-Golay 滤波器来平滑光谱，并使用广义极端学生化偏差测试来识别异常值。稳健、可重复、