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import numpy as np
import matplotlib.pyplot as plt
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% matplotlib inline
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def ErrorPlot(wavenumber, windowLength):
#add the sine wave and the median filtered wave
data = np.fromfunction( lambda x: np.sin((x-windowLength / 2)/128 * 2 * np.pi * 5), (128 + windowLength / 2, ) )
datafiltered = medianFilter(data, windowLength)
#synchronise both waves and make sure that both arrays have the same length
data = data[ windowLength / 2 : - windowLength ]
datafiltered = datafiltered[ : len(data) ]
error = Errorrate(data,datafiltered,windowLength)
plt.axis([0, y + 1, 0, 1])
plt.xlabel('Window length', fontsize = 20)
plt.ylabel('Error rate', fontsize = 20)
plt.scatter(*error)
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def ErrorRate(data, datafiltered, windowLength):
#calculate the difference between the sine wave and the filtered wave
errorrate = data-datafiltered
#creating a list and save the error rate (y) and window length (x) in it
error = []
errorrate = np.abs(errorrate)
error.append([windowLength ,np.mean(errorrate)])
#zip the error list
error = zip(*error)
return error
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def medianFilter( data, windowLength ):
if (windowLength < len(data)and data.ndim == 1):
#creating an empty liste where the median filtered values will be saved in
tempret = np.zeros(len(data)-windowLength+1)
#check if the window length is even to solve the synchronization problem
if windowLength % 2 == 0:
for c in range(0, len(tempret)):
tempret[c] = np.median( data[ c : c + windowLength + 1 ] )
return tempret
else:
for c in range(0, len(tempret)):
tempret[c] = np.median( data[ c : c + windowLength ] )
return tempret
else:
raise ValueError("windowLength must be smaller than len(data) and data must be a 1D array")
In [41]:
for y in range (0,40,2):
ErrorPlot(5,y)
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