Source code :: main

[Return]
  1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
#!/usr/bin/python2
# -*- coding: utf-8 -*-

import matplotlib
matplotlib.rcParams['backend'] = 'Agg'
matplotlib.rc('font',  size='10')
matplotlib.rc('text', usetex=True)  # FIXME !! nicer but slower !!!


#import pygolem_lite

from scipy.signal import fftconvolve


from numpy import *
#from pygolem_lite.config import *
from pygolem_lite.modules import *
from matplotlib.pyplot import *
import time
#from shutil import copy, move
import os, sys
from pygolem_lite import Shot
#calibration = 662 / 0.891186 #[keV] Cs 137 662 keV  ... X V at 400 V on scintilator

#calibration = 662 / 0.841186 #[keV] Cs 137 662 keV  ... X V at 400 V on scintilator  (podle mě to blo blbě => o 5% zvýšeno)
calibration = 662 / 1.38011 # [keV] Cs 137 662 keV ... novy Vrbuv scintilator
maximum_energy = 1e3 #[keV] maximum to plot in detail
maximum_DAS_voltage = 10 #[V] maximum possible measured voltage
bins = 500 #bins in histogram
calibration_surface = 5.57 #keV/ms
#min_reliable_intensity = 1000
min_reliable_intensity = 0.5 * calibration # above 0.5 V

def prepare_data():
    data = Shot()
    if data.exist('nistandard'):
	tvec, signals = data['nistandard']
	signal_0 = signals[:,5] # 5. channel
    else:
	raise IOError('Missing nistandard')
    start = data['plasma_start']
    end = data['plasma_end']
    plasma = data['plasma']


    signal_0[signal_0 > maximum_DAS_voltage]  = nan

    t = time.time()
    win = 100
    lam = 0.1
    signal, chi2 = GapsFilling(signal_0,win,lam)  # recontruct cutoffs in the signal_0
    print 'filling time', time.time()-t

    save_adv('HXR', tvec, signal)
    save_adv('HXR_orig', tvec, signal_0)
    if plasma:
      saveconst('HXR_mean', mean(signal[(tvec > start) & (tvec < end)]))
    else:
      saveconst('HXR_mean', nan )
    
    
    sigma = len(tvec)/200   # !!! smoothing parameter !!!!
    
    ker = linspace(-4,4, sigma)
    ker = exp( - ker**2)
    ker /= sum(ker)


    
    signal[signal < 0 ] = 0
    signal_smooth = fftconvolve(signal, ker, mode="same")
    signal_smooth*= calibration_surface  #calculate detected HXR power (not the total HXR power) 
    
    save_adv('HXR_smooth', tvec, signal_smooth)



    omit_peaks = range(25) #low intensity noise

    peaks = zeros(len(signal) - 2) #empty array for peaks
    d_data = diff(signal) #first derivative
    d2_data = diff(d_data) #second derivative
    location = (d_data[1:] * d2_data < 0) & (d2_data < 0) #location of data where there are peaks
    peaks[location] = signal[2:][location] #copy only the peaks
    peaks *= calibration #calibrate volatage to get energy

    first_peak = tvec[where(peaks > min_reliable_intensity)[0][0]]
    saveconst('first_peak', first_peak)
    last_peak = tvec[where(peaks > min_reliable_intensity)[0][-1]]
    saveconst('last_peak', last_peak)
    
    #plot histogram from 0.1 to max with 1e3 bins

    peaks = peaks[peaks > 0] # remove negative peaks   
    
    savetxt('peaks', peaks)


    hist, edges = histogram(peaks, bins = bins, range = [0.1, maximum_DAS_voltage * calibration])
    
    #counts, bins, patches = hist(peaks, ) 
    #ylabel("Impulse count")
    #xlabel("Energy [keV]")
    #savefig("HXR_spectrum_full.png")
    #xlim(xmax=maximum_energy) #detail plot
    #savefig("HXR_spectrum_zoom.png")
    #clf()

    hist[omit_peaks]  = 0   #omit the first Be peak

    max_indx = argmax( hist )
    max_peak =  edges[1:][max_indx] / calibration

    saveconst( "max_peak", max_peak  )

    print "done"


def graphs(file_type):

    data = Shot()
    
    if data['plasma']:
        start = data['plasma_start']*1e3
        end = data['plasma_end']*1e3
        last_peak = loadconst('last_peak')*1e3
        end = max(end, last_peak)
    else:
        start= 0; end = 40

    #print "end", end, last_peak, data['plasma_end']*1e3
    
    out = [
	[get_data('hxr_signal', 'HXR recalculated', "HXR [a.u.]", xlim=[start, end]),
	get_data('HXR_orig', 'HXR', "HXR [a.u.]", xlim=[start, end])],
	get_data('HXR_smooth', 'HXR smooth', "HXR power [keV/ms]" , xlim=[start, end], ylim=[0,None]),
	get_data('loop_voltage', "Loop voltage" , 'U[V]',  xlim=[start, end], ylim=[0,None])
	]
    print 'multiplot'
    multiplot(out, 'Hard-X ray signal' , 'graph', (9,6),  100,  'vertical', file_type)
    
    # icona
    os.system('convert -resize 150x120\! graph.png icon.png')


    clf()
    print '1. hist'
    try:
	peaks = loadtxt('peaks')
	bins = sqrt(len(peaks))  # empirical rule
    except:
	print "No Xrays were deteced in the signal"
	return
    #my "intelligent" historogram -- it will make a smaller bins if there are higher points 
    #density and larger bins if there are only a few points to keep statistical error on the constant value
    #fig = figure(num=None, figsize=(10, 3), dpi=80, facecolor='w', edgecolor='k')

    peaks.sort()
    x = hstack((peaks[::int(bins)],peaks[-1]))
    y = bins/(diff(x)+1)
    
    #TODO vylapšená verze, otestovat včerně renormalizace 
    #x = (val[bins/2:]+val[:-(bins)/2])/2
    #y = bins/((val[bins/2:]-val[:-(bins)/2]))/2

    
    #y/= sum(diff(x)*y[:-1])
    #y[0] = 0
    
 
    fig = plt.figure(num=None, figsize=(10, 3), dpi=80, facecolor='w', edgecolor='k')

    semilogx(x[:-1], y, drawstyle = 'steps-post')
    axvline(17,  color='r', label=r"Molybden K$\alpha$")
    axvline(20,  color='g', label=r"Molybden K$\beta$")
    axvline(662, color='g', label=r"Cesium 662 KeV")
    legend(loc = 'best')
    ylabel("Impulse count/dE [1/keV]")
    xlabel("Energy [keV]")
    xlim(10,maximum_DAS_voltage*calibration)
    savefig("HXR_spectrum_full.png",   bbox_inches='tight', dpi= 100)
    clf()

    #print '2. hist'
    #fig = figure(num=None, figsize=(10, 3), dpi=80, facecolor='w', edgecolor='k')

    #bins =  sqrt(sum(peaks <  maximum_energy))   # empirical rule
    #hist_range = linspace(0.1, maximum_energy)
    #hist(peaks, hist_range)
    #axvline(17,  color='r', label=r"Molybden K$\alpha$")
    #axvline(20,  color='g', label=r"Molybden K$\beta")
    #axvline(662, color='g', label=r"Cesium 662 KeV")
    #legend(loc = 'best')
    #ylabel("Impulse count")
    #xlabel("Energy [keV]")
    #savefig("HXR_spectrum_zoom.png",   bbox_inches='tight', dpi= 100)
    #clf()

    #print '3. hist'

    
    #scipy.signal.parzen

def postanalysis(file_type):

    data = Shot()
    
    if data['plasma']:
        start = data['plasma_start']*1e3
        end = data['plasma_end']*1e3
    else:
        start= 0; end = 40

    out = [
	[get_data('HXR_signal', 'HXR recalculated', "HXR [a.u.]", xlim=[start, end]),
	get_data('HXR_orig', 'HXR', "HXR [a.u.]", xlim=[start, end])],
	get_data('HXR_smooth', 'HXR smooth', "HXR power [keV/ms]" , xlim=[start, end], ylim=[0,None]),
	get_data('loop_voltage', "Loop voltage" , 'U[V]',  xlim=[start, end], ylim=[0,None])
	]
    print 'multiplot'
    multiplot(out, 'Hard-X ray signal' , 'graph', (9,6),  100,  'vertical', file_type)
    
    
    
def main():

    if sys.argv[1] ==  "acquisition":
	prepare_data()
    if sys.argv[1] ==  "plots":
	graphs('png')
	saveconst('status', 0)
    if sys.argv[1] ==  "postanalysis":
	print "plotting svgz " 
	postanalysis('svgz')
	


if __name__ == "__main__":
    main()

Navigation