44 lines
1.4 KiB
Python
44 lines
1.4 KiB
Python
from __future__ import division
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import numpy as np
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'''
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Reference: https://stackoverflow.com/questions/22354094/pythonic-way-of-detecting-outliers-in-one-dimensional-observation-data
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'''
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def is_outlier(points, thresh=3.5):
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"""
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Returns a boolean array with True if points are outliers and False
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otherwise.
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Parameters:
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-----------
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points : An numobservations by numdimensions array of observations
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thresh : The modified z-score to use as a threshold. Observations with
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a modified z-score (based on the median absolute deviation) greater
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than this value will be classified as outliers.
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Returns:
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--------
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mask : A numobservations-length boolean array.
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References:
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----------
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Boris Iglewicz and David Hoaglin (1993), "Volume 16: How to Detect and
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Handle Outliers", The ASQC Basic References in Quality Control:
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Statistical Techniques, Edward F. Mykytka, Ph.D., Editor.
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"""
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if len(points.shape) == 1:
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points = points[:,None]
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median = np.median(points, axis=0)
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diff = np.sum((points - median)**2, axis=-1)
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diff = np.sqrt(diff)
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med_abs_deviation = np.median(diff)
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modified_z_score = 0.6745 * diff / med_abs_deviation
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return modified_z_score > thresh
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def moving_average(interval, window_size):
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window = np.ones(int(window_size))/float(window_size)
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return np.convolve(interval, window, 'same') |