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223 | #!/usr/bin/python2
# -*- coding: utf-8 -*-
#fASTER VERSION THAN MAIN_2, MORE COMPLICATED
#### Microwaves 2.0
#This algorithm calculate transformation of the sin signal from microwaves density measurement to the phase/amplitude space.
# First step of the calculation is estimate of the base frequency and calculation of the complex exponential
#with the same frequency.In the second step is signal multiplied by this exponential
#and resulting low frequency signal is smoothed over Gaussian window. Finally complex phase and amplitude are calculated.
# Authors: Tomas Odstrcil, Ondrej Grover
from time import time
t = time()
#import matplotlib
#matplotlib.rcParams['backend'] = 'Agg'
#matplotlib.rc('font', size='10')
#matplotlib.rc('text', usetex=True) # FIXME !! nicer but slower !!!
#import matplotlib.pyplot as plt
from numpy import *
from scipy.fftpack import fft, ifft,fftfreq
from scipy.signal import fftconvolve
from pygolem_lite import save_adv,load_adv,saveconst, Shot
from pygolem_lite.modules import multiplot, get_data, paralel_multiplot
import os
import sys
from matplotlib.pylab import *
print 'include time ',time()-t
def Demodulation(y,win,dt):
t = time()
y-= mean(y, axis = 0)
#print mean(abs(y))
n = size(y,0)
N = 2 ** int(ceil(log2(n)))
fourier = fft(y[:,0], N) #calulcate the fourier transfrom for the sine data
#find the carrier frequency
max_frequency_index = argmax(abs(fourier[:N/2]))
#max_frequency_ampl = abs(fourier[max_frequency_index])
f = fftfreq(N,dt)
s = slice(max_frequency_index-100,max_frequency_index+100)
amplitude = abs(fourier[s])
f_carrier = sum(f[s]*amplitude)/sum(amplitude)
#plot(abs(fourier))
#xlim(max_frequency_index-50,max_frequency_index+50)
#savefig('amplitude.png')
#find a unharmonics factor
amplitude = linalg.norm(amplitude)
#norm1 = linalg.norm(y[:,0])*sqrt(N)
norm1 = linalg.norm(fourier[:N/2])
#print
#norm2 = linalg.norm(y[:,0])*sqrt(N)
##print
#print norm1,norm2/sqrt(2), amplitude
k = sqrt(norm1**2-amplitude**2)/norm1
fourier[:] = 0 #cancel out all other frequencies
fourier[max_frequency_index] = 1
cmpl_exp = ifft(fourier)[:n]
gauss = exp(-arange(-3*win,3*win)**2/win**2)
gauss/= sum(gauss)
signal = list()
for i in range(size(y,1)):
#print norm(y[:,i])/norm(fftconvolve(y[:,i]*cmpl_exp,gauss,mode='same' ))
signal.append(fftconvolve(y[:,i]*cmpl_exp,gauss,mode='same' )) #it can be faster by application of the block convolution
#signal.append(y[:,i]*cmpl_exp)
signal = array(signal, copy = False).T
amplitude = abs(signal)
phase = angle(signal)
phase = unwrap(phase, axis = 0)
print 'calc. time', time()-t
return amplitude,phase,f_carrier,k,norm1/(n/2)
def LoadData():
Data = Shot()
Bt_trigger = Data['Tb']
gd = Shot().get_data
tvec, density1 = gd('any', 'density')
tvec, density2 = gd('any', 'density_2')
start = Data['plasma_start']
end = Data['plasma_end']
return tvec, start, end, density1,density2,Bt_trigger
def graphs():
tvec, phase_pila = load_adv('results/phase_saw')
tvec, phase_sinus = load_adv('results/phase_sinus')
tvec, phase = load_adv('results/phase_substracted')
tvec, amplitude = load_adv('results/amplitude_sinus')
tvec, n_e = load_adv('results/electron_density')
#print mean(n_e)
data = [[get_data([tvec,-phase_pila+mean(phase_pila)], 'phase 1', 'phase [rad]', xlim=[0,40], fmt="--"),
get_data([tvec,-phase_sinus+mean(phase_sinus)], 'phase 2', 'phase [rad]', xlim=[0,40], fmt="--" ),
get_data([tvec,phase], 'substracted phase', 'phase [rad]', xlim=[0,40], fmt="k" )],
get_data([tvec,amplitude], 'amplitude', 'amplitude [a.u.]', xlim=[0,40],ylim=[0,None] )]
multiplot(data, '' , 'graphs/demodulation', (10,6) )
data = get_data('electron_density', 'Average electron density', '$<n_e>$ [$10^{19}\,m^{-3}$]', data_rescale=1e-19)
multiplot(data, '' , 'graphs/electron_density', (9,3) )
paralel_multiplot(data, '', 'icon', (4,3), 40)
def main():
for path in ['graphs', 'results' ]:
if not os.path.exists(path):
os.mkdir(path)
if sys.argv[1] == "analysis":
win = 30e-6 #[s]
t = time()
#if the phase difference is negativ, change order of dens1,dens2
tvec,start, end, density2,density1,Bt_trigger = LoadData()
dt = (tvec[-1]-tvec[0])/len(tvec)
density1 = density1[tvec>0]
density2 = density2[tvec>0]
tvec = tvec[tvec>0]
if amax(density1) > amax(density2):
density1,density2 = density2,density1
print 'load time ', time()-t
signals = vstack((density1,density2)).T
amplitude,phase,f_carrier,k,norm_ampl = Demodulation(signals,win/dt,dt)
#signals/= amplitude
#amplitude,phase,f_carrier = Demodulation(signals,win/dt,dt)
downsample = int(win/dt/2)
phase_pila = phase[::downsample,0]
phase_sinus = phase[::downsample,1]
tvec = tvec[::downsample]
phase = phase_pila-phase_sinus
phase -= median(phase[tvec<Bt_trigger])
switched = sign(mean(phase[(tvec > start) & (tvec < end)])) == -1
if switched:
phase *= -1 # rotate the density of cabels were switched
#amplitude = amplitude[::downsample,0]
#else:
amplitude = amplitude[::downsample,0]
amplitude *= norm_ampl/median(amplitude)
save_adv('results/phase_saw', tvec, phase_pila)
save_adv('results/phase_sinus', tvec, phase_sinus)
save_adv('results/phase_substracted', tvec, phase)
save_adv('results/amplitude_sinus', tvec, amplitude)
from scipy.constants import c,m_e,epsilon_0,e
a = 0.01 #[m]
f_0 = 75e9 #[Hz]
lambda_0 = c/f_0
n_e = 4*pi*m_e*epsilon_0*c**2/(e**2*lambda_0)*phase
save_adv('results/electron_density_line', tvec, n_e)
saveconst('results/electron_density_mean', mean(n_e[(tvec > start) & (tvec < end)]))
n_e /= 2*a
save_adv('results/electron_density', tvec, n_e)
saveconst('results/carrier_freq', abs(f_carrier))
saveconst('results/harmonics_distortion', k)
saveconst('results/norm_ampl', norm_ampl)
#norm_ampl
print 'carrier_freq ', f_carrier
print 'harmonics_distortion ',k
print 'norm_ampl ',norm_ampl
if sys.argv[1] == "plots":
graphs()
saveconst('status', 0)
if __name__ == "__main__":
main()
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