#%% import matplotlib.pyplot as plt import matplotlib as mpl import numpy as np import scipy.stats as st import re with open('data_captured.dat', 'rb') as f: # hex = "" # for byte in f.read(): # hex += "{0:02x}".format(byte) # # pattern = re.compile(r'((00)(..){3}){4}') # result = pattern.search(hex) # # f.seek(result.span()[0]//2) dw = f.read(4) data = [] while dw: data.append(int.from_bytes(dw[1:4], byteorder='big', signed=True)) dw = f.read(4) #myarray = np.asarray(dw) #mu, sigma = 0, 0.1 #count, bins, _ = plt.hist(myarray, normed=True) #plt.plot(bins, 1./(np.sqrt(2*np.pi)*sigma)*np.exp(-(bins-mu)**2/(2*sigma**2)), lw=2, c='r') #s_fit = np.linspace(min(data), max(data)) #plt.plot(s_fit, st.norm(mu, sigma).pdf(s_fit), lw=2, c='r') plt.plot(data) plt.show() #pattern = ''' # ^ #begin of the string # ('\x00') #first should be 00 or aa # #('\x'..){3} #3 any other bytes # $ # ''' #s = np.loadtxt('data_captured.dat') # plot first 20 points of the resulting data (why not all 1000?) #plt.plot(s[:20]) #plt.show() #from scipy.signal import lfilter #sout = lfilter(lpf, 1, s) #plt.plot(20*np.log10(abs(np.fft.fft(s)))) #plt.plot(20*np.log10(abs(np.fft.fft(sout)))) #plt.show()