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problem_2_code.py
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problem_2_code.py
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from __future__ import division
import matplotlib.pyplot as plt
import numpy as np
text_file = open("Data2.txt", "r")
lines = text_file.read().split('\n')
del lines[-1]
lines= list(map(float,lines))
def diff(list):
diffSignal=[]
max=len(lines)-2
const=1/(8*(1/512))
for index in range (2,max) :
diffSignal.append(
const*(
-1*lines[index-2]
-2*lines[index-1]
+2*lines[index+1]
+1*lines[index+2]
)
)
return diffSignal
diffSignal=diff(list)
plt.plot(diffSignal)
plt.show()
def square (diffSignal):
squaredSignal=[]
for element in diffSignal :
squaredSignal.append(element*element)
return squaredSignal
squaredSignal=square(diffSignal)
plt.plot(squaredSignal)
plt.show()
def smooth (squaredSignal):
smoothedSignal=[]
add=0
for index in range (30,len(squaredSignal)-31):
for count in range (0,31) :
add+=(1/31 *(squaredSignal[index-count]))
smoothedSignal.append(add)
add=0
return smoothedSignal
smoothedSignal=smooth(squaredSignal)
plt.plot(smoothedSignal)
plt.show()
def shift(smoothedSignal,shiftAmount):
shiftedSignal=[]
for values in range (0,shiftAmount):
shiftedSignal.append(0)
for index in range (0,len(smoothedSignal)):
shiftedSignal.append(smoothedSignal[index])
return shiftedSignal
def autoCorrelate(smoothedSignal):
add=0
shiftedSignal=[]
autocorr=[]
for i in range (0,2000):
shiftedSignal=shift(smoothedSignal,i)
for j in range (0,len(smoothedSignal)):
add+=smoothedSignal[j]*shiftedSignal[j]
autocorr.append(add)
add=0
return autocorr
autocorr=autoCorrelate(smoothedSignal)
plt.plot(autocorr)
plt.show()
def findPeaks(autocorr):
add=0
for element in autocorr:
if(element>np.mean(autocorr)):
add+=1
return add
print(findPeaks(autocorr))