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#Operations with integers#
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import numpy as np
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from bin16 import *
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from fft import *
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import math
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import cmath
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import matplotlib.pyplot as plt
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from basic_parameters_Int import *
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from basic_parameters import *
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#This is the number of points used for the FFT
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N = 256
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#These are the limits of our quantization
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liminfB = -1
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limsupB = +1
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liminfE = -10000000
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limsupE = +10000000
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#############################
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#Example of signals taken from a simulation
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a = np.random.rand()
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b = np.sqrt(1 - a*a)
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n = [1,1,1]
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E_para = [0,0,0]
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f = 1500
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fs = 98304
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l = 30000
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[b1,b2,b3,e1,e2] = SignalsWaveMonochromatic(a,b,n,E_para,f,fs,l)
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#############################
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#We add noise
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b1 = b1 + np.random.uniform(1.0*liminfB/100,1.0*limsupB/100,len(b1))
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b2 = b2 + np.random.uniform(1.0*liminfB/100,1.0*limsupB/100,len(b2))
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b3 = b3 + np.random.uniform(1.0*liminfB/100,1.0*limsupB/100,len(b3))
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e1 = e1 + np.random.uniform(1.0*liminfE/100,1.0*limsupE/100,len(e1))
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e2 = e2 + np.random.uniform(1.0*liminfE/100,1.0*limsupE/100,len(e2))
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#This will take our float-number signals and quantize them into integers going from -32768 to 32767
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#The liminf and limsup is what decides the maximum and minimum values for our quantizations
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#quant16 is a function from the bin16 library
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b1Q = np.zeros(len(b1))
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b2Q = np.zeros(len(b2))
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b3Q = np.zeros(len(b3))
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e1Q = np.zeros(len(e1))
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e2Q = np.zeros(len(e2))
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for i in range(len(b1)):
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b1Q[i] = quant16(b1[i],liminfB,limsupB)
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b2Q[i] = quant16(b2[i],liminfB,limsupB)
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b3Q[i] = quant16(b3[i],liminfB,limsupB)
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e1Q[i] = quant16(e1[i],liminfE,limsupE)
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e2Q[i] = quant16(e2[i],liminfE,limsupE)
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#We calculate the Spectral Matrices (averaged in time) for the case of float-numbers operations and also for the integer-numbers operations
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Si,wi = Spectral_MatriceAvgTime_Int(b1Q[::24],b2Q[::24],b3Q[::24],e1Q[::24],e2Q[::24],4096,N)
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Sf,wf = Spectral_MatriceAvgTime(b1[::24],b2[::24],b3[::24],e1[::24],e2[::24],4096,N)
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[PBf,PEf,df,ef,nf] = BasicParameters(Sf,wf)
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[PBi,PEi,di,ei,ni] = BasicParameters_Int(Si,wi)
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mypath = "plotsFloatVsInt"
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if not os.path.isdir(mypath):
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os.makedirs(mypath)
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fm = 4096
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alfa = 1.0 * math.pow(2,30)
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plt.plot(wf/(2*math.pi),PBf,'g')
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plt.scatter(wf/(2*math.pi),1.0 * PBi/alfa)
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plt.title("Power spectrum density of the Magnetic Field (fm = %s Hz)" % fm)
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plt.xlabel("frequency [Hz]")
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plt.ylabel("PB(w)")
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plt.savefig('%s/PB.png' % mypath)
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plt.close()
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alfa = 1.0 * math.pow(2,30)/math.pow(10,14)
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plt.plot(wf/(2*math.pi),PEf,'g')
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plt.scatter(wf/(2*math.pi),1.0 * PEi/alfa)
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plt.title("Power spectrum density of the Electric Field (fm = %s Hz)" % fm)
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plt.xlabel("frequency [Hz]")
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plt.ylabel("PE(w)")
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plt.savefig('%s/PE.png' % mypath)
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plt.close()
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alfa = 16
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plt.plot(wf/(2*math.pi),ef,'g')
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plt.scatter(wf/(2*math.pi),1.0 * ei/alfa)
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plt.title("Ellipticity of the electromagnetic wave (fm = %s Hz)" % fm)
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plt.xlabel("frequency [Hz]")
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plt.ylabel("e(w)")
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plt.savefig('%s/e.png' % mypath)
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plt.close()
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alfa = 8
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plt.plot(wf/(2*math.pi),df, 'g')
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plt.scatter(wf/(2*math.pi),1.0 * di/alfa)
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plt.title("Degree of polarization of the electromagnetic wave (fm = %s Hz)" % fm)
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plt.xlabel("frequency [Hz]")
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plt.ylabel("d(w)")
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plt.savefig('%s/d.png' % mypath)
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plt.close()
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