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import numpy as np
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages


    

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%matplotlib inline


    

In [6]:

    

def medianFilter( data, windowLength ): 
    if (windowLength < len(data)and data.ndim == 1):
        tempret = np.zeros(len(data)-windowLength+1)  # creating an array where the filtered values will be saved in
        if windowLength % 2 ==0:                      # check if the window length is odd or even because with even window length we get an unsynchrone
            for c in range(0, len(tempret)):
                tempret[c] = np.median( data[ c : c + windowLength +1 ] )  # write the values of the median filtered wave in tempret, calculate the median of all values in the window
            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")


    

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def medianSinPlot( waveNumber, windowLength ):
    data = np.fromfunction( lambda x: np.sin((x-windowLength / 2)/128 * 2 * np.pi * waveNumber), (128 + windowLength / 2, ) ) #creating an array with a sine wave
    noise = np.random.normal(0,0.2,(128 + windowLength / 2))       # creating the noise as an array, filled with random numbers, with the same length as the data array
    signal = data + noise                                          # generate the noised signal
    datafiltered = medianFilter(signal, windowLength)              #calculate the filtered wave with the medianFiltered function
    signal = signal[ windowLength / 2 : - windowLength ]           # slice the data array to synchronize both waves
    datafiltered = datafiltered[ : len(signal) ]                   # cut the filtered wave to the same length as the data wave
    plt.plot( signal )
    plt.plot( datafiltered )
    plt.plot( signal-datafiltered )


    

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pp = PdfPages( 'median sin with white noise.pdf')
for z in range (2,8):  
    fig = plt.figure(z, figsize=(30,20))       #creating different figures in one plot, z is the window length
    for x in range(1, 5):
        for y in range(1, 6):
            plt.subplot(5, 5, x + (y-1)*4)     #creating different subplots in one figure, with x and y the wave number is calculated
            wavenum = (x-1) + (y-1)*4
            medianSinPlot( wavenum, z )
            plt.suptitle('Median filtered, noised sine waves with window length ' + str(z), fontsize = 60)
            plt.xlabel(("Wave number = "+str((x-1) + (y-1)*4)), fontsize=18)
    pp.savefig(fig)
pp.close()


    

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