scientimate.replaceoutlier

xReplaced, outlier_Indx = scientimate.replaceoutlier(x, WindowSize=15, zscore_threshold=2, interpMethod='linear', dispout='no')

Description

Remove outliers in the time series using moving z-score window

Inputs

x

Input data

WindowSize=15

Window size (number of adjacent elements) that is used for moving window, should be equal or larger than 3

zscore_threshold=2

z-score threshold to define outliers

data in range of x < (xmean-zscore_threshold*std) or x > (xmean+zscore_threshold*std) considered outliers

interpMethod=’linear’

Interpolation method for replacing spike points:

Matlab/Octave: ‘linear’, ‘nearest’, ‘next’, ‘previous’, ‘pchip’, ‘cubic’, ‘spline’

Python/Scipy : ‘linear’, ‘nearest’, ‘zero’, ‘slinear’, ‘quadratic’, ‘cubic’

dispout=’no’

Define to display outputs or not (‘yes’: display, ‘no’: not display)

Outputs

xReplaced

Replaced data

outlier_Indx

Logical index of replaced points

Examples

import scientimate as sm
import numpy as np
import scipy as sp
from scipy import signal

fs=128
t=np.linspace(0,9.5,10*fs)
x=np.sin(2*np.pi*0.3*t)+0.1*np.sin(2*np.pi*4*t)
spikeloc=np.arange(10,len(t),100)
x[np.int64(spikeloc+np.round(2*np.random.randn(len(spikeloc))))]=np.sign(np.random.randn(len(spikeloc)))
x[220:225]=1.5
x=x+5
xReplaced,outlier_Indx=sm.replaceoutlier(x,37,2,'linear','yes')

fs=2
t=np.linspace(0,1023.5,1024*fs)
x=sp.signal.detrend(0.5*np.cos(2*np.pi*0.2*t)+(-0.1+(0.1-(-0.1)))*np.random.rand(1024*fs))
spikeloc=np.arange(10,len(t),100)
x[np.int64(spikeloc+np.round(2*np.random.randn(len(spikeloc))))]=np.sign(np.random.randn(len(spikeloc)))
xReplaced,outlier_Indx=sm.replaceoutlier(x,21,2,'linear','yes')

References