scientimate.replacemissing1d#

xReplaced, NaN_Indx = scientimate.replacemissing1d(x, what2replace='both', interpMethod='linear', dispout='no')

Description#

Replace missing data points in 1d data such as time series

Inputs#

x

Input data

what2replace=’both’
What needs to be replaced
‘NaN’: replacing NaN data points
‘Inf’: replacing Inf data points
‘both’: replacing NaN and Inf data points
Number: replacing data points equal to Number
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

NaN_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.nan
x=x+5
x[220:225]=np.nan
xReplaced,NaN_Indx=sm.replacemissing1d(x,'NaN','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=x+1
x[np.int64(spikeloc+np.round(2*np.random.randn(len(spikeloc))))]=0
xReplaced,NaN_Indx=sm.replacemissing1d(x,0,'linear','yes')

References#