from __future__ import division
from pylab import *
from scipy import *
from numpy import sin, linspace, pi, correlate
from pylab import plot, show, title, xlabel, ylabel, subplot
from scipy import fft, arange, signal, misc
from scipy import array

def intAC(eFieldSVEA):
    '''
    Compuite the optical autocorrelation trace
    '''
    result = signal.correlate(eFieldSVEA, eFieldSVEA, mode='full')

    return result[result.size/2:]

def autocorr(x):
    result = correlate(x, x, mode='full')
    return result[result.size/2:]

def plotSpectrum(y,Fs):
 """
 Plots a Single-Sided Amplitude Spectrum of y(t)
 """
 n = len(y) # length of the signal
 k = arange(n)
 T = n/Fs
 frq = k/T # two sides frequency range
 frq = frq[range(n/2)] # one side frequency range

 Y = rfft(y)/n # fft computing and normalization
 Y = Y[range(n/2)]

 plot(frq,abs(Y),'r') # plotting the spectrum
 xlabel('f (Hz)')
 ylabel('|Y(f)|')

infile = open('wcdma2.csv', 'r')

wcdmaMeasurements = []
timestamps = []

Fs = 32496000

for value in infile:
    value = value.split('\n')[0]
    wcdmaMeasurements.append(value)
    #print str(int(value)*1000)

for i in range(len(wcdmaMeasurements)):
    timestamps.append(float((i/Fs)))
    #print str(i*1/fs)

print type(array(wcdmaMeasurements))

test = signal.correlate(array(wcdmaMeasurements),array(wcdmaMeasurements), mode='full')

#subplot(311)
#plot(timestamps, wcdmaMeasurements)
#xlabel('t (s)')
#ylabel('y(t)')
#title("Signal in Time and Frequency Domain")
#subplot(312)
#plot(intAC(array(wcdmaMeasurements)))
#xlabel('t (s)')
#ylabel('Corr Coeff')
#subplot(313)
#plotSpectrum(wcdmaMeasurements,Fs)
#show()