Surface NMR processing and inversion GUI
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mrsurvey.py 146KB

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  1. from PyQt5.QtCore import *
  2. import numpy as np
  3. import scipy.signal as signal
  4. import pylab
  5. import sys
  6. import scipy
  7. import copy
  8. import struct
  9. from scipy.io.matlab import mio
  10. from numpy import pi
  11. from math import floor
  12. import matplotlib as mpl
  13. from matplotlib.ticker import FuncFormatter
  14. import matplotlib.font_manager as fm
  15. import matplotlib.pyplot as plt
  16. import matplotlib.ticker
  17. from matplotlib.ticker import MaxNLocator
  18. import multiprocessing
  19. import itertools
  20. import akvo.tressel.adapt as adapt
  21. #import akvo.tressel.cadapt as adapt # cython for more faster
  22. import akvo.tressel.decay as decay
  23. import akvo.tressel.pca as pca
  24. import akvo.tressel.rotate as rotate
  25. import akvo.tressel.cmaps as cmaps
  26. import akvo.tressel.harmonic as harmonic
  27. import cmocean # colormaps for geophysical data
  28. plt.register_cmap(name='viridis', cmap=cmaps.viridis)
  29. plt.register_cmap(name='inferno', cmap=cmaps.inferno)
  30. plt.register_cmap(name='inferno_r', cmap=cmaps.inferno_r)
  31. plt.register_cmap(name='magma', cmap=cmaps.magma)
  32. plt.register_cmap(name='magma_r', cmap=cmaps.magma_r)
  33. def xxloadGMRBinaryFID( rawfname, info ):
  34. """ Reads a single binary GMR file and fills into DATADICT
  35. """
  36. #################################################################################
  37. # figure out key data indices
  38. # Pulse
  39. nps = (int)((info["prePulseDelay"])*info["samp"])
  40. npul = (int)(self.pulseLength[0]*self.samp) #+ 100
  41. # Data
  42. nds = nps+npul+(int)((self.deadTime)*self.samp); # indice pulse 1 data starts
  43. nd1 = (int)(1.*self.samp) # samples in first pulse
  44. invGain = 1./self.RxGain
  45. invCGain = self.CurrentGain
  46. pulse = "Pulse 1"
  47. chan = self.DATADICT[pulse]["chan"]
  48. rchan = self.DATADICT[pulse]["rchan"]
  49. rawFile = open( rawfname, 'rb')
  50. T = N_samp * self.dt
  51. TIMES = np.arange(0, T, self.dt) - .0002 # small offset in GMR DAQ?
  52. for ipm in range(self.nPulseMoments):
  53. buf1 = rawFile.read(4)
  54. buf2 = rawFile.read(4)
  55. N_chan = struct.unpack('>i', buf1 )[0]
  56. N_samp = struct.unpack('>i', buf2 )[0]
  57. DATA = np.zeros([N_samp, N_chan+1])
  58. for ichan in range(N_chan):
  59. DATADUMP = rawFile.read(4*N_samp)
  60. for irec in range(N_samp):
  61. DATA[irec,ichan] = struct.unpack('>f', DATADUMP[irec*4:irec*4+4])[0]
  62. return DATA, TIMES
  63. class SNMRDataProcessor(QObject):
  64. """ Revised class for preprocessing sNMR Data.
  65. Derived types can read GMR files
  66. """
  67. def __init__(self):
  68. QObject.__init__(self)
  69. self.numberOfMoments = 0
  70. self.numberOfPulsesPerMoment = 0
  71. self.pulseType = "NONE"
  72. self.transFreq = 0
  73. self.pulseLength = np.zeros(1)
  74. self.nPulseMoments = 0
  75. self.dt = 0
  76. def mfreqz(self, b,a=1):
  77. """ Plots the frequency response of a filter specified with a and b weights
  78. """
  79. import scipy.signal as signal
  80. pylab.figure(124)
  81. w,h = signal.freqz(b,a)
  82. w /= max(w)
  83. w *= .5/self.dt
  84. h_dB = 20 * pylab.log10 (abs(h))
  85. pylab.subplot(211)
  86. #pylab.plot(w/max(w),h_dB)
  87. pylab.plot(w,h_dB)
  88. pylab.ylim(-150, 5)
  89. pylab.ylabel('Magnitude (dB)')
  90. #pylab.xlabel(r'Normalized Frequency (x$\pi$rad/sample)')
  91. pylab.xlabel(r'Hz')
  92. pylab.title(r'Frequency response')
  93. pylab.subplot(212)
  94. h_Phase = pylab.unwrap(pylab.arctan2(pylab.imag(h), pylab.real(h)))
  95. #pylab.plot(w/max(w),h_Phase)
  96. pylab.plot(w,h_Phase)
  97. pylab.ylabel('Phase (radians)')
  98. pylab.xlabel(r'Hz')
  99. #pylab.xlabel(r'Normalized Frequency (x$\pi$rad/sample)')
  100. pylab.title(r'Phase response')
  101. pylab.subplots_adjust(hspace=0.5)
  102. def mfreqz2(self, b, a, canvas):
  103. "for analysing filt-filt"
  104. import scipy.signal as signal
  105. canvas.reAx2(False,False)
  106. canvas.ax1.tick_params(axis='both', which='major', labelsize=8)
  107. canvas.ax2.tick_params(axis='both', which='major', labelsize=8)
  108. #canvas.ax2.tick_params(axis='both', which='minor', labelsize=6)
  109. #pylab.figure(124)
  110. w,h = signal.freqz(b,a)
  111. w /= max(w)
  112. w *= .5/self.dt
  113. h_dB = 20 * pylab.log10(abs(h*h) + 1e-16)
  114. #ab.subplot(211)
  115. #pylab.plot(w/max(w),h_dB)
  116. canvas.ax1.plot(w,h_dB)
  117. canvas.ax1.set_ylim(-150, 5)
  118. canvas.ax1.set_ylabel('Magnitude [db]', fontsize=8)
  119. #pylab.xlabel(r'Normalized Frequency (x$\pi$rad/sample)')
  120. canvas.ax1.set_xlabel(r'[Hz]', fontsize=8)
  121. canvas.ax1.set_title(r'Frequency response', fontsize=8)
  122. canvas.ax1.grid(True)
  123. tt = np.arange(0, .02, self.dt)
  124. impulse = signal.dimpulse((self.filt_z, self.filt_p, self.filt_k, self.dt), t=tt)
  125. #impulse = signal.dstep((self.filt_z, self.filt_p, self.filt_k, self.dt), t=tt)
  126. #print impulse
  127. #for ii in range(len(impulse[1])):
  128. impulse_dB = 20.*np.log10(np.abs(np.array(impulse[1][0])))
  129. #canvas.ax2.plot(np.array(impulse[0]), impulse_dB)
  130. canvas.ax2.plot(np.array(impulse[0]), impulse[1][0])
  131. #h_Phase = pylab.unwrap(pylab.arctan2(pylab.imag(h), pylab.real(h)))
  132. #canvas.ax2.plot(w,h_Phase)
  133. canvas.ax2.set_ylabel('response [%]', fontsize=8)
  134. canvas.ax2.set_xlabel(r'time [s]', fontsize=8)
  135. canvas.ax2.set_title(r'impulse response', fontsize=8)
  136. #canvas.ax2.grid(True)
  137. canvas.draw()
  138. # search for last
  139. return impulse #[np.where(impulse[1][0] > .01)[-1]]
  140. class GMRDataProcessor(SNMRDataProcessor):
  141. # slots
  142. progressTrigger = pyqtSignal("int")
  143. doneTrigger = pyqtSignal()
  144. enableDSPTrigger = pyqtSignal()
  145. updateProcTrigger = pyqtSignal()
  146. def __init__(self):
  147. SNMRDataProcessor.__init__(self)
  148. self.maxBusV = 0.
  149. self.samp = 50000. # sampling frequency
  150. self.dt = 2e-5 # sampling rate
  151. self.deadTime = .0055 # instrument dead time before measurement
  152. self.prePulseDelay = 0.05 # delay before pulse
  153. self.windead = 0. # FD window filter dead time
  154. self.pulseType = -1
  155. self.transFreq = -1
  156. self.maxBusV = -1
  157. self.pulseLength = -1
  158. self.interpulseDelay = -1 # for T2, Spin Echo
  159. self.repetitionDelay = -1 # delay between first pulse
  160. self.nPulseMoments = -1 # Number of pulse moments per stack
  161. self.TuneCapacitance = -1 # tuning capac in uF
  162. self.nTransVersion = -1 # Transmitter version
  163. self.nDAQVersion = -1 # DAQ software version
  164. self.nInterleaves = -1 # num interleaves
  165. # self.nReceiveChannels = 4 # Num receive channels
  166. self.RotatedAmplitude = False
  167. # self.DATA = np.zeros(1) # Numpy array to hold all data, dimensions resized based on experiment
  168. # self.PULSES = np.zeros(1) # Numpy array to hold all data, dimensions resized based on experiment
  169. def Print(self):
  170. print ("pulse type", self.pulseType)
  171. print ("maxBusV", self.maxBusV)
  172. print ("inner pulse delay", self.interpulseDelay)
  173. print ("tuning capacitance", self.TuneCapacitance)
  174. print ("sampling rate", self.samp)
  175. print ("dt", self.dt)
  176. print ("dead time", self.deadTime)
  177. print ("pre pulse delay", self.prePulseDelay)
  178. print ("number of pulse moments", self.nPulseMoments)
  179. print ("pulse Length", self.pulseLength)
  180. print ("trans freq", self.transFreq)
  181. def readHeaderFile(self, FileName):
  182. HEADER = np.loadtxt(FileName)
  183. pulseTypeDict = {
  184. 1 : lambda: "FID",
  185. 2 : lambda: "T1",
  186. 3 : lambda: "SPINECHO",
  187. 4 : lambda: "4PhaseT1"
  188. }
  189. pulseLengthDict = {
  190. 1 : lambda x: np.ones(1) * x,
  191. 2 : lambda x: np.ones(2) * x,
  192. 3 : lambda x: np.array([x, 2.*x]),
  193. 4 : lambda x: np.ones(2) * x
  194. }
  195. self.pulseType = pulseTypeDict.get((int)(HEADER[0]))()
  196. self.transFreq = HEADER[1]
  197. self.maxBusV = HEADER[2]
  198. self.pulseLength = pulseLengthDict.get((int)(HEADER[0]))(1e-3*HEADER[3])
  199. self.interpulseDelay = 1e-3*HEADER[4] # for T2, Spin Echo
  200. self.repetitionDelay = HEADER[5] # delay between first pulse
  201. self.nPulseMoments = (int)(HEADER[6]) # Number of pulse moments per stack
  202. self.TuneCapacitance = HEADER[7] # tuning capacitance in uF
  203. self.nTransVersion = HEADER[8] # Transmitter version
  204. self.nDAQVersion = HEADER[9] # DAQ software version
  205. self.nInterleaves = HEADER[10] # num interleaves
  206. self.gain()
  207. # default
  208. self.samp = 50000. # sampling frequency
  209. self.dt = 2e-5 # sampling rate
  210. # newer header files contain 64 entries
  211. if self.nDAQVersion >= 2:
  212. #self.deadtime = HEADER[11]
  213. #self.unknown = HEADER[12]
  214. #self.PreAmpGain = HEADER[13]
  215. self.samp = HEADER[14] # sampling frequency
  216. self.dt = 1./self.samp # sampling rate
  217. self.deadTime = .0055 # instrument dead time before measurement
  218. self.prePulseDelay = 0.05 # delay before pulse
  219. #exit()
  220. def gain(self):
  221. #######################################################
  222. # Circuit gain
  223. # From MRSMatlab
  224. w = 2*np.pi*self.transFreq
  225. # 1e6 due to uF of reported capacitance
  226. L_coil = 1e6/(self.TuneCapacitance*(w**2))
  227. R_coil = 1.
  228. Z1_in = .5 + 1j*.5*w
  229. Z2_in = 1./(1j*w*.000001616)
  230. Z_eq_inv = (1./Z1_in) + (1./Z2_in)
  231. Zeq = 1./Z_eq_inv
  232. Zsource = R_coil + 1j*w*L_coil
  233. voltage_in = Zeq / (Zsource + Zeq)
  234. self.circuitGain = np.abs(voltage_in)
  235. self.circuitPhase_deg = (180/np.pi)+np.angle(voltage_in)
  236. circuitImpedance_ohms = np.abs(Zsource + Zeq)
  237. ######################################################
  238. # PreAmp gain
  239. if self.nTransVersion == 4:
  240. self.PreAmpGain = 1000.
  241. elif self.nTransVersion == 1 or self.nTransVersion == 2 or self.nTransVersion == 3 or self.nTransVersion == 6:
  242. self.PreAmpGain = 500.
  243. else:
  244. print ("unsupported transmitter version")
  245. exit(1)
  246. # Total Receiver Gain
  247. self.RxGain = self.circuitGain * self.PreAmpGain
  248. #####################################################
  249. # Current gain
  250. if floor(self.nDAQVersion) == 1:
  251. self.CurrentGain = 150.
  252. elif floor(self.nDAQVersion) == 2:
  253. self.CurrentGain = 180.
  254. def updateProgress(self):
  255. pass
  256. def TDSmartStack(self, outlierTest, MADcutoff, canvas):
  257. #print("Line 300 in mrsurvey")
  258. Stack = {}
  259. # align for stacking and modulate
  260. for pulse in self.DATADICT["PULSES"]:
  261. stack = np.zeros(( len(self.DATADICT[pulse]["chan"]), self.DATADICT["nPulseMoments"],\
  262. len(self.DATADICT["stacks"]), len(self.DATADICT[pulse]["TIMES"]) ))
  263. for ipm in range(self.DATADICT["nPulseMoments"]):
  264. istack = 0
  265. for sstack in self.DATADICT["stacks"]:
  266. if self.pulseType == "FID" or pulse == "Pulse 2":
  267. if floor(self.nDAQVersion) < 2:
  268. mod = 1
  269. else:
  270. mod = (-1.)**(ipm%2) * (-1.)**(sstack%2)
  271. elif self.pulseType == "T1":
  272. #mod = (-1.)**(sstack%2)
  273. #mod = (-1)**(ipm%2) * (-1)**(sstack%2)
  274. #mod = (-1)**(ipm%2) * (-1.**(((sstack-1)/2)%2))
  275. #print("mod", mod, ipm, sstack, (-1.)**(ipm%2), -1.0**(((sstack-1)/2)%2 ))
  276. #mod = (-1.)**((ipm+1)%2) * (-1.**(((sstack)/2)%2))
  277. #mod = (-1.)**((ipm-1)%2) * (-1.)**((sstack-1)%2)
  278. #mod = 1 # (-1.**(((sstack-1)/2)%2))
  279. # These two give great noise estimate
  280. #qcycler = np.array([1,-1,-1,1])
  281. #scycler = np.array([1,-1,1,-1])
  282. qcycler = np.array([ 1, 1])
  283. scycler = np.array([ 1, 1])
  284. mod = qcycler.take([ipm], mode='wrap')*scycler.take([sstack], mode='wrap')
  285. #mod = (-1.)**(ipm%2) * (-1.)**(sstack%2)
  286. elif self.pulseType == "4PhaseT1":
  287. mod = (-1.)**(ipm%2) * (-1.**(((sstack-1)/2)%2))
  288. ichan = 0
  289. for chan in self.DATADICT[pulse]["chan"]:
  290. stack[ichan,ipm,istack,:] += mod*self.DATADICT[pulse][chan][ipm][sstack]
  291. ichan += 1
  292. istack += 1
  293. Stack[pulse] = stack
  294. #########################################
  295. # simple stack and plot of simple stack #
  296. #########################################
  297. canvas.reAxH2(np.shape(stack)[0], False, False)
  298. axes = canvas.fig.axes
  299. SimpleStack = {}
  300. VarStack = {}
  301. for pulse in self.DATADICT["PULSES"]:
  302. SimpleStack[pulse] = {}
  303. VarStack[pulse] = {}
  304. ichan = 0
  305. for chan in self.DATADICT[pulse]["chan"]:
  306. SimpleStack[pulse][chan] = 1e9*np.average( Stack[pulse][ichan], 1 )
  307. VarStack[pulse][chan] = 1e9*np.std( Stack[pulse][ichan], 1 )
  308. ax1 = axes[ 2*ichan ]
  309. #ax1.get_yaxis().get_major_formatter().set_useOffset(False)
  310. y_formatter = matplotlib.ticker.ScalarFormatter(useOffset=False)
  311. ax1.yaxis.set_major_formatter(y_formatter)
  312. ax1.plot( 1e3*self.DATADICT[pulse]["TIMES"], np.average( SimpleStack[pulse][chan], 0 )) #, color='darkblue' )
  313. ax1.set_title("Ch." + str(chan) + ": avg FID", fontsize=8)
  314. ax1.set_xlabel(r"time (ms)", fontsize=8)
  315. if ichan == 0:
  316. ax1.set_ylabel(r"signal [nV]", fontsize=8)
  317. else:
  318. plt.setp(ax1.get_yticklabels(), visible=False)
  319. plt.setp(ax1.get_yaxis().get_offset_text(), visible=False)
  320. # if ichan == 1:
  321. # canvas.ax2.plot( 1e3*self.DATADICT[pulse]["TIMES"], np.average( SimpleStack[pulse][chan], 0 ), color='darkblue' )
  322. # canvas.ax2.set_title("Ch." + str(chan) + ": total average FID", fontsize=8)
  323. # canvas.ax2.set_xlabel(r"time [ms]", fontsize=8)
  324. # if ichan == 2:
  325. # canvas.ax3.plot( 1e3*self.DATADICT[pulse]["TIMES"], np.average( SimpleStack[pulse][chan], 0 ), color='darkblue' )
  326. # canvas.ax3.set_title("Ch." + str(chan) + ": total average FID", fontsize=8)
  327. # canvas.ax3.set_xlabel(r"time [ms]", fontsize=8)
  328. # if ichan == 3:
  329. # canvas.ax4.plot( 1e3*self.DATADICT[pulse]["TIMES"], np.average( SimpleStack[pulse][chan], 0 ), color='darkblue' )
  330. # canvas.ax4.set_title("Ch." + str(chan) + ": total average FID", fontsize=8)
  331. # canvas.ax4.set_xlabel(r"time [ms]", fontsize=8)
  332. ichan += 1
  333. #########################
  334. # Oulier rejectig stack #
  335. #########################
  336. if outlierTest == "MAD":
  337. MADStack = {}
  338. VarStack = {}
  339. #1.4826 is assumption of gaussian noise
  340. madstack = np.zeros(( len(self.DATADICT[pulse]["chan"]),\
  341. self.DATADICT["nPulseMoments"], len(self.DATADICT[pulse]["TIMES"]) ))
  342. varstack = np.zeros(( len(self.DATADICT[pulse]["chan"]),\
  343. self.DATADICT["nPulseMoments"], len(self.DATADICT[pulse]["TIMES"]) ))
  344. for pulse in self.DATADICT["PULSES"]:
  345. MADStack[pulse] = {}
  346. VarStack[pulse] = {}
  347. ichan = 0
  348. for chan in self.DATADICT[pulse]["chan"]:
  349. ax1 = axes[ 2*ichan ]
  350. for ipm in range(self.DATADICT["nPulseMoments"]):
  351. # # brutal loop over time, can this be vectorized?
  352. # for it in range(len(self.DATADICT[pulse]["TIMES"])):
  353. # x = 1e9 *Stack[pulse][ichan,ipm,:,it]
  354. # MAD = 1.4826 * np.median( np.abs(x-np.median(x)) )
  355. # good = 0
  356. # for istack in self.DATADICT["stacks"]:
  357. # if (np.abs(x[istack-1]-np.median(x))) / MAD < 2:
  358. # good += 1
  359. # madstack[ ichan, ipm, it ] += x[istack-1]
  360. # else:
  361. # pass
  362. # madstack[ichan, ipm, it] /= good
  363. # percent = int(1e2* (float)(ipm) / (float)(self.DATADICT["nPulseMoments"]) )
  364. # self.progressTrigger.emit(percent)
  365. # Vectorized version of above...much, much faster
  366. x = 1e9*copy.deepcopy(Stack[pulse][ichan][ipm,:,:]) # stack and time indices
  367. tile_med = np.tile( np.median(x, axis=0), (np.shape(x)[0],1))
  368. MAD = MADcutoff * np.median(np.abs(x - tile_med), axis=0)
  369. tile_MAD = np.tile( MAD, (np.shape(x)[0],1))
  370. good = np.abs(x-tile_med)/tile_MAD < 2. # 1.4826 # 2
  371. madstack[ichan][ipm] = copy.deepcopy( np.ma.masked_array(x, good != True).mean(axis=0) )
  372. varstack[ichan][ipm] = copy.deepcopy( np.ma.masked_array(x, good != True).std(axis=0) )
  373. # reporting
  374. percent = int(1e2* (float)((ipm)+ichan*self.DATADICT["nPulseMoments"]) /
  375. (float)(self.DATADICT["nPulseMoments"] * len(self.DATADICT[pulse]["chan"])))
  376. self.progressTrigger.emit(percent)
  377. ax1.plot( 1e3*self.DATADICT[pulse]["TIMES"], np.average( madstack[ichan], 0 ))# , color='darkred')
  378. MADStack[pulse][chan] = madstack[ichan]
  379. VarStack[pulse][chan] = varstack[ichan]
  380. ichan += 1
  381. self.DATADICT["stack"] = MADStack
  382. else:
  383. self.DATADICT["stack"] = SimpleStack
  384. #########################################
  385. # Plot Fourier Transform representation #
  386. #########################################
  387. # canvas.fig.subplots_adjust(right=0.8)
  388. # cbar_ax = canvas.fig.add_axes([0.85, 0.1, 0.015, 0.355])
  389. # cbar_ax.ticklabel_format(style='sci', scilimits=(0,0), axis='y')
  390. im2 = []
  391. im1 = []
  392. for pulse in self.DATADICT["PULSES"]:
  393. ichan = 0
  394. axes = canvas.fig.axes
  395. vvmin = 1e10
  396. vvmax = 0
  397. for chan in self.DATADICT[pulse]["chan"]:
  398. ax1 = axes[2*ichan ]
  399. ax2 = axes[2*ichan+1] # TODO fix hard coded number
  400. if outlierTest == "MAD":
  401. X = np.fft.rfft( MADStack[pulse][chan][0,:] )
  402. nu = np.fft.fftfreq(len( MADStack[pulse][chan][0,:]), d=self.dt)
  403. else:
  404. X = np.fft.rfft( SimpleStack[pulse][chan][0,:] )
  405. nu = np.fft.fftfreq(len( SimpleStack[pulse][chan][0,:]), d=self.dt)
  406. nu = nu[0:len(X)]
  407. nu[-1] = np.abs(nu[-1])
  408. df = nu[1] - nu[0]
  409. of = 0
  410. istart = int((self.transFreq-50.)/df)
  411. iend = int((self.transFreq+50.)/df)
  412. of = nu[istart]
  413. def freqlabel(xxx, pos):
  414. return '%1.0f' %(of + xxx*df)
  415. formatter = FuncFormatter(freqlabel)
  416. SFFT = np.zeros( (self.DATADICT["nPulseMoments"], len(X)), dtype=np.complex64 )
  417. SFFT[0,:] = X
  418. for ipm in range(1, self.DATADICT["nPulseMoments"]):
  419. if outlierTest == "MAD":
  420. SFFT[ipm,:] = np.fft.rfft( MADStack[pulse][chan][ipm,:] )
  421. else:
  422. SFFT[ipm,:] = np.fft.rfft( SimpleStack[pulse][chan][ipm,:] )
  423. # convert to dB and add colorbars
  424. #db = 20.*np.log10(np.abs(SFFT[:,istart:iend]))
  425. db = (np.abs(SFFT[:,istart:iend]))
  426. #db = (np.real(SFFT[:,istart:iend]))
  427. #dbr = (np.real(SFFT[:,istart:iend]))
  428. #db = (np.imag(SFFT[:,istart:iend]))
  429. vvmin = min(vvmin, np.min(db) + 1e-16 )
  430. vvmax = max(vvmax, np.max(db) + 1e-16 )
  431. im2.append(ax2.matshow( db, aspect='auto', cmap=cmocean.cm.ice, vmin=vvmin, vmax=vvmax))
  432. #im1.append(ax1.matshow( dbr, aspect='auto')) #, vmin=vvmin, vmax=vvmax))
  433. #im2.append(ax2.matshow( db, aspect='auto', vmin=vvmin, vmax=vvmax))
  434. #im2 = ax2.matshow( db, aspect='auto', cmap=cmocean.cm.ice, vmin=vvmin, vmax=vvmax)
  435. if ichan == 0:
  436. ax2.set_ylabel(r"$q$ (A $\cdot$ s)", fontsize=8)
  437. else:
  438. #ax2.yaxis.set_ticklabels([])
  439. plt.setp(ax2.get_yticklabels(), visible=False)
  440. ax2.xaxis.set_major_formatter(formatter)
  441. ax2.xaxis.set_ticks_position('bottom')
  442. ax2.xaxis.set_major_locator(MaxNLocator(3))
  443. y_formatter = matplotlib.ticker.ScalarFormatter(useOffset=False)
  444. ax2.yaxis.set_major_formatter(y_formatter)
  445. #if chan == self.DATADICT[pulse]["chan"][-1]:
  446. #cb2 = canvas.fig.colorbar(im2, cax=cbar_ax, format='%1.0e')
  447. #cb2 = canvas.fig.colorbar(im2[0], ax=ax2, format='%1.0e', orientation='horizontal')
  448. #cb2 = canvas.fig.colorbar(im2, ax=ax2, format='%1.0e', orientation='horizontal')
  449. #cb2.ax.tick_params(axis='both', which='major', labelsize=8)
  450. #cb2.set_label("signal (dB)", fontsize=8)
  451. ichan += 1
  452. canvas.fig.subplots_adjust(hspace=.1, wspace=.05, left=.075, right=.95 )#left=None, bottom=None, right=None, top=None, wspace=None, hspace=None)
  453. #cb1 = canvas.fig.colorbar(im, ax=axes[0::2], format='%1.0e', orientation='horizontal', shrink=.35, aspect=30)
  454. #cb1.ax.tick_params(axis='both', which='major', labelsize=8)
  455. #cb1.set_label("$\mathcal{V}_N$ (nV)", fontsize=8)
  456. cb2 = canvas.fig.colorbar(im2[-1], ax=axes[1::2], format='%1.0e', orientation='horizontal', shrink=.35, aspect=30)
  457. cb2.ax.tick_params(axis='both', which='major', labelsize=8)
  458. cb2.set_label("$\mathcal{V}_N$ (nV)", fontsize=8)
  459. #canvas.fig.tight_layout()
  460. canvas.draw()
  461. self.doneTrigger.emit()
  462. def harmonicModel(self, nF, \
  463. f0, f0K1, f0KN, f0Ks, f0ns, \
  464. f1, f1K1, f1KN, f1Ks, \
  465. Nsearch, Bounds, procRefs, \
  466. plot, canvas):
  467. """ nF = number of base frequencies, must be 1 or 2
  468. f0 = first base frequency
  469. f0K1 = first harmonic to model for first base frequency
  470. f0KN = last harmonic to model for the first base frequency
  471. f0Ks = subharmonic spacing, set to 1 for no subharmonics.
  472. f0Ns = number of segments for f0
  473. f1 = second base frequency
  474. f1K1 = first harmonic to model for second base frequency
  475. f1KN = last harmonic to model for the second base frequency
  476. f1Ks = subharmonic spacing for the second base frequency, set to 1 for no subharmonics.
  477. Nsearch = the number of harmonics to use when determining base frequency
  478. bounds = 1/2 the width of the space where baseline frequency will be searched
  479. procRefs = should the reference loops be processed as well
  480. plot = should Akvo plot the results
  481. canvas = mpl plotting axis
  482. """
  483. if plot:
  484. canvas.reAx2(shy=False)
  485. canvas.ax1.set_ylabel(r"signal [nV]", fontsize=8)
  486. #canvas.ax2.set_xlabel(r"time [s]", fontsize=8)
  487. canvas.ax2.set_ylabel(r"signal [nV]", fontsize=8)
  488. canvas.ax2.set_xlabel(r"frequency [Hz]", fontsize=8)
  489. canvas.ax1.set_yscale('log')
  490. canvas.ax2.set_yscale('log')
  491. # Data
  492. iFID = 0
  493. # stores previous f0 as starting point in non-linear search
  494. f0p = {}
  495. f1p = {}
  496. for pulse in self.DATADICT["PULSES"]:
  497. for rchan in self.DATADICT[pulse]["rchan"]:
  498. f0p[rchan] = f0
  499. f1p[rchan] = f1+1e-1
  500. for chan in self.DATADICT[pulse]["chan"]:
  501. f0p[chan] = f0
  502. f1p[chan] = f1+1e-1
  503. for pulse in self.DATADICT["PULSES"]:
  504. Nseg = int( np.floor(len( self.DATADICT[pulse]["TIMES"] ) / f0ns) )
  505. for istack in self.DATADICT["stacks"]:
  506. for ipm in range(self.DATADICT["nPulseMoments"]):
  507. if plot:
  508. canvas.softClear()
  509. mmaxr = 0
  510. mmaxd = 0
  511. if procRefs:
  512. for ichan in self.DATADICT[pulse]["rchan"]:
  513. #canvas.ax1.plot( self.DATADICT[pulse]["TIMES"], 1e9*self.DATADICT[pulse][ichan][ipm][istack], alpha=.5)
  514. #mmaxr = max( mmaxr, np.max(1e9*self.DATADICT[pulse][ichan][ipm][istack]))
  515. ww = np.fft.fftfreq(len(self.DATADICT[pulse][ichan][ipm][istack]), d=self.dt)
  516. X = np.fft.rfft(self.DATADICT[pulse][ichan][ipm][istack])
  517. canvas.ax1.plot(np.abs(ww[0:len(X)]), np.abs(X), alpha=.5)
  518. canvas.ax1.set_prop_cycle(None)
  519. #canvas.ax1.set_ylim(-mmaxr, mmaxr)
  520. for ichan in self.DATADICT[pulse]["chan"]:
  521. #canvas.ax2.plot( self.DATADICT[pulse]["TIMES"], 1e9*self.DATADICT[pulse][ichan][ipm][istack], alpha=.5)
  522. #mmaxd = max( mmaxd, np.max(1e9*self.DATADICT[pulse][ichan][ipm][istack]))
  523. ww = np.fft.fftfreq(len(self.DATADICT[pulse][ichan][ipm][istack]), d=self.dt)
  524. X = np.fft.rfft(self.DATADICT[pulse][ichan][ipm][istack])
  525. canvas.ax2.plot(np.abs(ww[0:len(X)]), np.abs(X), alpha=.5)
  526. canvas.ax2.set_prop_cycle(None)
  527. #canvas.ax2.set_ylim(-mmaxd, mmaxd)
  528. if procRefs:
  529. for ichan in self.DATADICT[pulse]["rchan"]:
  530. if nF == 1:
  531. for iseg in range(f0ns):
  532. if iseg < f0ns-1:
  533. self.DATADICT[pulse][ichan][ipm][istack][iseg*Nseg:(iseg+1)*Nseg], f0p[ichan] = \
  534. harmonic.minHarmonic( self.DATADICT[pulse][ichan][ipm][istack][iseg*Nseg:(iseg+1)*Nseg], \
  535. self.samp, self.DATADICT[pulse]["TIMES"][iseg*Nseg:(iseg+1)*Nseg], \
  536. f0p[ichan], f0K1, f0KN, f0Ks, Bounds, Nsearch )
  537. else:
  538. self.DATADICT[pulse][ichan][ipm][istack][iseg*Nseg::], f0p[ichan] = \
  539. harmonic.minHarmonic( self.DATADICT[pulse][ichan][ipm][istack][iseg*Nseg::], \
  540. self.samp, self.DATADICT[pulse]["TIMES"][iseg*Nseg::], \
  541. f0p[ichan], f0K1, f0KN, f0Ks, Bounds, Nsearch )
  542. elif nF == 2:
  543. for iseg in range(f0ns):
  544. if iseg < f0ns-1:
  545. self.DATADICT[pulse][ichan][ipm][istack][iseg*Nseg:(iseg+1)*Nseg], f0p[ichan], f1p[ichan] = \
  546. harmonic.minHarmonic2( self.DATADICT[pulse][ichan][ipm][istack][iseg*Nseg:(iseg+1)*Nseg],\
  547. self.samp, self.DATADICT[pulse]["TIMES"][iseg*Nseg:(iseg+1)*Nseg], \
  548. f0p[ichan], f0K1, f0KN, f0Ks, \
  549. f1p[ichan], f1K1, f1KN, f1Ks, Bounds, Nsearch )
  550. else:
  551. self.DATADICT[pulse][ichan][ipm][istack][iseg*Nseg::], f0p[ichan], f1p[ichan] = \
  552. harmonic.minHarmonic2( self.DATADICT[pulse][ichan][ipm][istack][iseg*Nseg::],\
  553. self.samp, self.DATADICT[pulse]["TIMES"][iseg*Nseg::], \
  554. f0p[ichan], f0K1, f0KN, f0Ks, \
  555. f1p[ichan], f1K1, f1KN, f1Ks, Bounds, Nsearch )
  556. # plot
  557. if plot:
  558. #canvas.ax1.plot( self.DATADICT[pulse]["TIMES"], 1e9*self.DATADICT[pulse][ichan][ipm][istack], \
  559. # label = pulse + " ipm=" + str(ipm) + " istack=" + str(istack) + " rchan=" + str(ichan))
  560. ww = np.fft.fftfreq(len(self.DATADICT[pulse][ichan][ipm][istack]), d=self.dt)
  561. X = np.fft.rfft(self.DATADICT[pulse][ichan][ipm][istack])
  562. canvas.ax1.plot(np.abs(ww[0:len(X)]), np.abs(X),\
  563. label = pulse + " ipm=" + str(ipm) + " istack=" + str(istack) + " rchan=" + str(ichan))
  564. for ichan in self.DATADICT[pulse]["chan"]:
  565. if nF == 1:
  566. for iseg in range(f0ns):
  567. if iseg < f0ns-1:
  568. self.DATADICT[pulse][ichan][ipm][istack][iseg*Nseg:(iseg+1)*Nseg], f0p[ichan] = \
  569. harmonic.minHarmonic( self.DATADICT[pulse][ichan][ipm][istack][iseg*Nseg:(iseg+1)*Nseg],
  570. self.samp, self.DATADICT[pulse]["TIMES"][iseg*Nseg:(iseg+1)*Nseg], \
  571. f0p[ichan], f0K1, f0KN, f0Ks, Bounds, Nsearch )
  572. else:
  573. self.DATADICT[pulse][ichan][ipm][istack][iseg*Nseg::], f0p[ichan] = \
  574. harmonic.minHarmonic( self.DATADICT[pulse][ichan][ipm][istack][iseg*Nseg::],
  575. self.samp, self.DATADICT[pulse]["TIMES"][iseg*Nseg::], \
  576. f0p[ichan], f0K1, f0KN, f0Ks, Bounds, Nsearch )
  577. elif nF == 2:
  578. for iseg in range(f0ns):
  579. if iseg < f0ns-1:
  580. self.DATADICT[pulse][ichan][ipm][istack][iseg*Nseg:(iseg+1)*Nseg], f0p[ichan], f1p[ichan] = \
  581. harmonic.minHarmonic2( self.DATADICT[pulse][ichan][ipm][istack][iseg*Nseg:(iseg+1)*Nseg],\
  582. self.samp, self.DATADICT[pulse]["TIMES"][iseg*Nseg:(iseg+1)*Nseg], \
  583. f0p[ichan], f0K1, f0KN, f0Ks, \
  584. f1p[ichan], f1K1, f1KN, f1Ks, Bounds, Nsearch )
  585. else:
  586. self.DATADICT[pulse][ichan][ipm][istack][iseg*Nseg::], f0p[ichan], f1p[ichan] = \
  587. harmonic.minHarmonic2( self.DATADICT[pulse][ichan][ipm][istack][iseg*Nseg::],\
  588. self.samp, self.DATADICT[pulse]["TIMES"][iseg*Nseg::], \
  589. f0p[ichan], f0K1, f0KN, f0Ks, \
  590. f1p[ichan], f1K1, f1KN, f1Ks, Bounds, Nsearch )
  591. # plot
  592. if plot:
  593. #canvas.ax2.plot( self.DATADICT[pulse]["TIMES"], 1e9*self.DATADICT[pulse][ichan][ipm][istack], \
  594. # label = pulse + " ipm=" + str(ipm) + " istack=" + str(istack) + " chan=" + str(ichan))
  595. ww = np.fft.fftfreq(len(self.DATADICT[pulse][ichan][ipm][istack]), d=self.dt)
  596. X = np.fft.rfft(self.DATADICT[pulse][ichan][ipm][istack])
  597. canvas.ax2.plot(np.abs(ww[0:len(X)]), np.abs(X), \
  598. label = pulse + " ipm=" + str(ipm) + " istack=" + str(istack) + " chan=" + str(ichan))
  599. if plot:
  600. if procRefs:
  601. canvas.ax1.legend(prop={'size':6}, loc='upper right')
  602. canvas.ax2.legend(prop={'size':6}, loc='upper right')
  603. canvas.draw()
  604. percent = (int)(1e2*((ipm+istack*self.nPulseMoments)/(self.nPulseMoments*len(self.DATADICT["stacks"]))))
  605. self.progressTrigger.emit(percent)
  606. iFID += 1
  607. self.doneTrigger.emit()
  608. self.updateProcTrigger.emit()
  609. self.doneTrigger.emit()
  610. def FDSmartStack(self, outlierTest, MADcutoff, canvas):
  611. print("FFT stuff")
  612. self.dataCubeFFT()
  613. Stack = {}
  614. # align phase cycling for stacking and modulate
  615. for pulse in self.DATADICT["PULSES"]:
  616. stack = np.zeros(( len(self.DATADICT[pulse]["chan"]), \
  617. self.DATADICT["nPulseMoments"],\
  618. len(self.DATADICT["stacks"]),\
  619. len(self.DATADICT[pulse][self.DATADICT[pulse]["chan"][0] ]["FFT"]["nu"])//2 + 1),\
  620. dtype=np.complex )
  621. for ipm in range(self.DATADICT["nPulseMoments"]):
  622. istack = 0
  623. for sstack in self.DATADICT["stacks"]:
  624. if self.pulseType == "FID" or pulse == "Pulse 2":
  625. mod = (-1)**(ipm%2) * (-1)**(sstack%2)
  626. elif self.pulseType == "4PhaseT1":
  627. mod = (-1)**(ipm%2) * (-1)**(((sstack-1)/2)%2)
  628. ichan = 0
  629. for chan in self.DATADICT[pulse]["chan"]:
  630. #stack[ichan,ipm,istack,:] += mod*self.DATADICT[pulse][chan][ipm][sstack]
  631. stack[ichan,ipm,istack,:] += mod*self.DATADICT[pulse][chan]["FFT"][sstack][ipm,:]
  632. ichan += 1
  633. istack += 1
  634. Stack[pulse] = stack
  635. #########################################
  636. # simple stack and plot of simple stack #
  637. ########################################https://faculty.apps.utah.edu/#
  638. canvas.reAxH2(np.shape(stack)[0], False, False)
  639. axes = canvas.fig.axes
  640. SimpleStack = {}
  641. VarStack = {}
  642. for pulse in self.DATADICT["PULSES"]:
  643. SimpleStack[pulse] = {}
  644. VarStack[pulse] = {}
  645. ichan = 0
  646. for chan in self.DATADICT[pulse]["chan"]:
  647. SimpleStack[pulse][chan] = 1e9*np.average( Stack[pulse][ichan], 1 )
  648. VarStack[pulse][chan] = 1e9*np.std( Stack[pulse][ichan], 1 )
  649. ax1 = axes[ 2*ichan ]
  650. #ax1.get_yaxis().get_major_formatter().set_useOffset(False)
  651. y_formatter = matplotlib.ticker.ScalarFormatter(useOffset=False)
  652. ax1.yaxis.set_major_formatter(y_formatter)
  653. #ax1.plot( 1e3*self.DATADICT[pulse][chan]["FFT"]["nu"][0:len(SimpleStack[pulse][chan])], np.average(SimpleStack[pulse][chan], 0 )) #, color='darkblue' )
  654. #ax1.pcolor( np.real(SimpleStack[pulse][chan]) ) #, color='darkblue' )
  655. ax1.matshow( np.real(SimpleStack[pulse][chan]), aspect='auto') #, color='darkblue' )
  656. ax1.set_title("Ch." + str(chan) + ": avg FID", fontsize=8)
  657. ax1.set_xlabel(r"time (ms)", fontsize=8)
  658. if ichan == 0:
  659. ax1.set_ylabel(r"signal [nV]", fontsize=8)
  660. else:
  661. plt.setp(ax1.get_yticklabels(), visible=False)
  662. plt.setp(ax1.get_yaxis().get_offset_text(), visible=False)
  663. ichan += 1
  664. #########################
  665. # Oulier rejectig stack #
  666. #########################
  667. if outlierTest == "MAD":
  668. MADStack = {}
  669. VarStack = {}
  670. #1.4826 is assumption of gaussian noise
  671. madstack = np.zeros(( len(self.DATADICT[pulse]["chan"]),\
  672. self.DATADICT["nPulseMoments"],\
  673. len(self.DATADICT[pulse][self.DATADICT[pulse]["chan"][0] ]["FFT"]["nu"])//2 + 1))
  674. varstack = np.zeros(( len(self.DATADICT[pulse]["chan"]),\
  675. self.DATADICT["nPulseMoments"],\
  676. len(self.DATADICT[pulse][self.DATADICT[pulse]["chan"][0] ]["FFT"]["nu"])//2 + 1))
  677. for pulse in self.DATADICT["PULSES"]:
  678. MADStack[pulse] = {}
  679. VarStack[pulse] = {}
  680. ichan = 0
  681. for chan in self.DATADICT[pulse]["chan"]:
  682. ax1 = axes[ 2*ichan ]
  683. for ipm in range(self.DATADICT["nPulseMoments"]):
  684. # # brutal loop over time, can this be vectorized?
  685. # for it in range(len(self.DATADICT[pulse]["TIMES"])):
  686. # x = 1e9 *Stack[pulse][ichan,ipm,:,it]
  687. # MAD = 1.4826 * np.median( np.abs(x-np.median(x)) )
  688. # good = 0
  689. # for istack in self.DATADICT["stacks"]:
  690. # if (np.abs(x[istack-1]-np.median(x))) / MAD < 2:
  691. # good += 1
  692. # madstack[ ichan, ipm, it ] += x[istack-1]
  693. # else:
  694. # pass
  695. # madstack[ichan, ipm, it] /= good
  696. # percent = int(1e2* (float)(ipm) / (float)(self.DATADICT["nPulseMoments"]) )
  697. # self.progressTrigger.emit(percent)
  698. # Vectorized version of above...much, much faster
  699. x = 1e9*copy.deepcopy(Stack[pulse][ichan][ipm,:,:]) # stack and time indices
  700. tile_med = np.tile( np.median(x, axis=0), (np.shape(x)[0],1))
  701. MAD = MADcutoff * np.median(np.abs(x - tile_med), axis=0)
  702. tile_MAD = np.tile( MAD, (np.shape(x)[0],1))
  703. good = np.abs(x-tile_med)/tile_MAD < 2. # 1.4826 # 2
  704. madstack[ichan][ipm] = copy.deepcopy( np.ma.masked_array(x, good != True).mean(axis=0) )
  705. varstack[ichan][ipm] = copy.deepcopy( np.ma.masked_array(x, good != True).std(axis=0) )
  706. # reporting
  707. percent = int(1e2* (float)((ipm)+ichan*self.DATADICT["nPulseMoments"]) /
  708. (float)(self.DATADICT["nPulseMoments"] * len(self.DATADICT[pulse]["chan"])))
  709. self.progressTrigger.emit(percent)
  710. ax2 = axes[2*ichan+1] # TODO fix hard coded number
  711. #ax1.plot( 1e3*self.DATADICT[pulse]["TIMES"], np.average( madstack[ichan], 0 ))# , color='darkred')
  712. MADStack[pulse][chan] = madstack[ichan]
  713. VarStack[pulse][chan] = varstack[ichan]
  714. ax2.matshow( np.real(MADStack[pulse][chan]), aspect='auto') #, color='darkblue' )
  715. ichan += 1
  716. self.DATADICT["stack"] = MADStack
  717. else:
  718. self.DATADICT["stack"] = SimpleStack
  719. # #########################################
  720. # # Plot Fourier Transform representation #
  721. # #########################################
  722. #
  723. # # canvas.fig.subplots_adjust(right=0.8)
  724. # # cbar_ax = canvas.fig.add_axes([0.85, 0.1, 0.015, 0.355])
  725. # # cbar_ax.ticklabel_format(style='sci', scilimits=(0,0), axis='y')
  726. # im2 = []
  727. # im1 = []
  728. # for pulse in self.DATADICT["PULSES"]:
  729. # ichan = 0
  730. # axes = canvas.fig.axes
  731. # vvmin = 1e10
  732. # vvmax = 0
  733. # for chan in self.DATADICT[pulse]["chan"]:
  734. # ax1 = axes[2*ichan ]
  735. # ax2 = axes[2*ichan+1] # TODO fix hard coded number
  736. # if outlierTest == "MAD":
  737. # X = np.fft.rfft( MADStack[pulse][chan][0,:] )
  738. # nu = np.fft.fftfreq(len( MADStack[pulse][chan][0,:]), d=self.dt)
  739. # else:
  740. # X = np.fft.rfft( SimpleStack[pulse][chan][0,:] )
  741. # nu = np.fft.fftfreq(len( SimpleStack[pulse][chan][0,:]), d=self.dt)
  742. #
  743. # nu = nu[0:len(X)]
  744. # nu[-1] = np.abs(nu[-1])
  745. # df = nu[1] - nu[0]
  746. # of = 0
  747. #
  748. # istart = int((self.transFreq-50.)/df)
  749. # iend = int((self.transFreq+50.)/df)
  750. # of = nu[istart]
  751. #
  752. # def freqlabel(xxx, pos):
  753. # return '%1.0f' %(of + xxx*df)
  754. # formatter = FuncFormatter(freqlabel)
  755. #
  756. # SFFT = np.zeros( (self.DATADICT["nPulseMoments"], len(X)), dtype=np.complex64 )
  757. # SFFT[0,:] = X
  758. # for ipm in range(1, self.DATADICT["nPulseMoments"]):
  759. # if outlierTest == "MAD":
  760. # SFFT[ipm,:] = np.fft.rfft( MADStack[pulse][chan][ipm,:] )
  761. # else:
  762. # SFFT[ipm,:] = np.fft.rfft( SimpleStack[pulse][chan][ipm,:] )
  763. #
  764. # # convert to dB and add colorbars
  765. # #db = 20.*np.log10(np.abs(SFFT[:,istart:iend]))
  766. # db = (np.abs(SFFT[:,istart:iend]))
  767. # #db = (np.real(SFFT[:,istart:iend]))
  768. # #dbr = (np.real(SFFT[:,istart:iend]))
  769. # #db = (np.imag(SFFT[:,istart:iend]))
  770. #
  771. # vvmin = min(vvmin, np.min (db))
  772. # vvmax = max(vvmax, np.max (db))
  773. # im2.append(ax2.matshow( db, aspect='auto', cmap=cmocean.cm.ice, vmin=vvmin, vmax=vvmax))
  774. # #im1.append(ax1.matshow( dbr, aspect='auto')) #, vmin=vvmin, vmax=vvmax))
  775. # #im2.append(ax2.matshow( db, aspect='auto', vmin=vvmin, vmax=vvmax))
  776. # #im2 = ax2.matshow( db, aspect='auto', cmap=cmocean.cm.ice, vmin=vvmin, vmax=vvmax)
  777. # if ichan == 0:
  778. # ax2.set_ylabel(r"$q$ (A $\cdot$ s)", fontsize=8)
  779. # else:
  780. # #ax2.yaxis.set_ticklabels([])
  781. # plt.setp(ax2.get_yticklabels(), visible=False)
  782. #
  783. # ax2.xaxis.set_major_formatter(formatter)
  784. # ax2.xaxis.set_ticks_position('bottom')
  785. # ax2.xaxis.set_major_locator(MaxNLocator(3))
  786. #
  787. # y_formatter = matplotlib.ticker.ScalarFormatter(useOffset=False)
  788. # ax2.yaxis.set_major_formatter(y_formatter)
  789. #
  790. #
  791. # #if chan == self.DATADICT[pulse]["chan"][-1]:
  792. # #cb2 = canvas.fig.colorbar(im2, cax=cbar_ax, format='%1.0e')
  793. #
  794. # #cb2 = canvas.fig.colorbar(im2[0], ax=ax2, format='%1.0e', orientation='horizontal')
  795. # #cb2 = canvas.fig.colorbar(im2, ax=ax2, format='%1.0e', orientation='horizontal')
  796. # #cb2.ax.tick_params(axis='both', which='major', labelsize=8)
  797. # #cb2.set_label("signal (dB)", fontsize=8)
  798. #
  799. # ichan += 1
  800. #
  801. #
  802. # canvas.fig.subplots_adjust(hspace=.1, wspace=.05, left=.075, right=.95 )#left=None, bottom=None, right=None, top=None, wspace=None, hspace=None)
  803. #
  804. # #cb1 = canvas.fig.colorbar(im, ax=axes[0::2], format='%1.0e', orientation='horizontal', shrink=.35, aspect=30)
  805. # #cb1.ax.tick_params(axis='both', which='major', labelsize=8)
  806. # #cb1.set_label("$\mathcal{V}_N$ (nV)", fontsize=8)
  807. #
  808. # cb2 = canvas.fig.colorbar(im2[-1], ax=axes[1::2], format='%1.0e', orientation='horizontal', shrink=.35, aspect=30)
  809. # cb2.ax.tick_params(axis='both', which='major', labelsize=8)
  810. # cb2.set_label("$\mathcal{V}_N$ (nV)", fontsize=8)
  811. #canvas.fig.tight_layout()
  812. canvas.draw()
  813. self.doneTrigger.emit()
  814. def sumData(self, canvas, fred):
  815. chans = copy.deepcopy(self.DATADICT[self.DATADICT["PULSES"][0]]["chan"]) #= np.array( ( self.DATADICT[pulse]["chan"][0], ) )
  816. nchan = len(chans)
  817. # Sum permutations of two channel combos
  818. for ich in range(nchan-1):
  819. for ch in chans[ich+1:]:
  820. chsum = chans[ich] + "+" + ch
  821. for pulse in self.DATADICT["PULSES"]:
  822. self.DATADICT[pulse][chsum] = {}
  823. for ipm in range(self.DATADICT["nPulseMoments"]):
  824. self.DATADICT[pulse][chsum][ipm] = {}
  825. for istack in self.DATADICT["stacks"]:
  826. self.DATADICT[pulse][chsum][ipm][istack] = self.DATADICT[pulse][chans[ich]][ipm][istack] - self.DATADICT[pulse][ch][ipm][istack]
  827. if chsum == "1+2":
  828. #self.DATADICT[pulse]["rchan"].pop()
  829. #self.DATADICT[pulse]["rchan"].pop()
  830. self.DATADICT[pulse]["chan"].append(chsum)
  831. # Sum all channels
  832. sumall = False
  833. if sumall:
  834. chsum = ""
  835. for ch in chans:
  836. chsum += ch + "+"
  837. chsum = chsum[0:-1] # remove last "+"
  838. for pulse in self.DATADICT["PULSES"]:
  839. self.DATADICT[pulse][chsum] = {}
  840. for ipm in range(self.DATADICT["nPulseMoments"]):
  841. self.DATADICT[pulse][chsum][ipm] = {}
  842. for istack in self.DATADICT["stacks"]:
  843. self.DATADICT[pulse][chsum][ipm][istack] = copy.deepcopy(self.DATADICT[pulse][chans[0]][ipm][istack])
  844. for ch in chans[1:]:
  845. self.DATADICT[pulse][chsum][ipm][istack] += self.DATADICT[pulse][ch][ipm][istack]
  846. self.DATADICT[pulse]["chan"].append(chsum)
  847. # if nchan > 2:
  848. # for ch in chans:
  849. # chsum += ch
  850. # for ch2 in chans[1::]:
  851. # for pulse in self.DATADICT["PULSES"]:
  852. # self.DATADICT[pulse][chsum] = {}
  853. # for istack in self.DATADICT["stacks"]:
  854. # self.DATADICT[pulse][chsum][ipm][istack] = self.DATADICT[pulse][chans[ich]][ipm][istack] + self.DATADICT[pulse][ch][ipm][istack]
  855. self.doneTrigger.emit()
  856. def quadDet(self, clip, method, loss, canvas):
  857. from scipy import signal
  858. self.RotatedAmplitude = True
  859. wL = self.transFreq * 2*np.pi
  860. vL = self.transFreq
  861. #T = 50
  862. dt = self.dt
  863. #DT = 0.01
  864. CA = {} # corrected amplitude
  865. IP = {} # instantaneous phase
  866. NR = {} # Noise residual
  867. RE = {} # Real channel
  868. IM = {} # Imaginary channel
  869. # global maximums for plotting
  870. CAmax = {}
  871. NRmax = {}
  872. REmax = {}
  873. IMmax = {}
  874. E0,phi,df,T2 = 100.,0,0,.2
  875. first = False
  876. self.sigma = {}
  877. for pulse in self.DATADICT["PULSES"]:
  878. CA[pulse] = {}
  879. IP[pulse] = {}
  880. NR[pulse] = {}
  881. RE[pulse] = {}
  882. IM[pulse] = {}
  883. CAmax[pulse] = 0
  884. NRmax[pulse] = 0
  885. REmax[pulse] = 0
  886. IMmax[pulse] = 0
  887. ichan = 0
  888. self.sigma[pulse] = {}
  889. for chan in self.DATADICT[pulse]["chan"]:
  890. CA[pulse][chan] = np.zeros( (self.DATADICT["nPulseMoments"], len(self.DATADICT[pulse]["TIMES"])-clip ) )
  891. IP[pulse][chan] = np.zeros( (self.DATADICT["nPulseMoments"], len(self.DATADICT[pulse]["TIMES"])-clip ) )
  892. NR[pulse][chan] = np.zeros( (self.DATADICT["nPulseMoments"], len(self.DATADICT[pulse]["TIMES"])-clip ) )
  893. RE[pulse][chan] = np.zeros( (self.DATADICT["nPulseMoments"], len(self.DATADICT[pulse]["TIMES"])-clip ) )
  894. IM[pulse][chan] = np.zeros( (self.DATADICT["nPulseMoments"], len(self.DATADICT[pulse]["TIMES"])-clip ) )
  895. for ipm in range(0, self.DATADICT["nPulseMoments"]):
  896. #t = self.DATADICT[pulse]["TIMES"] - self.DATADICT[pulse]["PULSE_TIMES"][-1]
  897. xn = self.DATADICT["stack"][pulse][chan][ipm,:]
  898. ht = signal.hilbert(xn)*np.exp(-1j*wL*self.DATADICT[pulse]["TIMES"])
  899. #############################################################
  900. # Quadrature signal
  901. RE[pulse][chan][ipm,:] = np.real(ht[clip::])
  902. IM[pulse][chan][ipm,:] = np.imag(ht[clip::])
  903. REmax[pulse] = max(REmax[pulse], np.max(np.real(ht[clip::])))
  904. IMmax[pulse] = max(IMmax[pulse], np.max(np.imag(ht[clip::])))
  905. #############################################################
  906. # Instantaneous phase
  907. IP[pulse][chan][ipm,:] = np.angle(ht)[clip::]
  908. #############################################################
  909. # Rotated amplitude
  910. #if ipm != 0:
  911. # [success, E0, df, phi, T2] = decay.quadratureDetect2( ht.real, ht.imag, self.DATADICT[pulse]["TIMES"], (E0,phi,df,T2))
  912. #[success, E0, df, phi, T2] = decay.quadratureDetect( ht.real, ht.imag, self.DATADICT[pulse]["TIMES"] )
  913. #else:
  914. [success, E0, df, phi, T2] = decay.quadratureDetect2( ht.real, ht.imag, self.DATADICT[pulse]["TIMES"], method, loss)
  915. #[success, E0, df, phi, T2] = decay.quadratureDetect2( ht.real, ht.imag, self.DATADICT[pulse]["TIMES"], (E0,phi,df,T2))
  916. #[success, E0, df, phi, T2] = decay.quadratureDetect( ht.real, ht.imag, self.DATADICT[pulse]["TIMES"] )
  917. #print("success", success, "E0", E0, "phi", phi, "df", df, "T2", T2)
  918. D = self.RotateAmplitude( ht.real, ht.imag, phi, df, self.DATADICT[pulse]["TIMES"] )
  919. CA[pulse][chan][ipm,:] = D.imag[clip::] # amplitude data
  920. NR[pulse][chan][ipm,:] = D.real[clip::] # noise data
  921. CAmax[pulse] = max(CAmax[pulse], np.max(D.imag[clip::]) )
  922. NRmax[pulse] = max(NRmax[pulse], np.max(D.real[clip::]) )
  923. self.sigma[pulse][chan] = np.std(NR[pulse][chan])
  924. # reporting
  925. percent = int(1e2* (float)((ipm)+ichan*self.DATADICT["nPulseMoments"]) /
  926. (float)(self.DATADICT["nPulseMoments"] * len(self.DATADICT[pulse]["chan"])))
  927. self.progressTrigger.emit(percent)
  928. ichan += 1
  929. self.DATADICT["CA"] = CA
  930. self.DATADICT["IP"] = IP
  931. self.DATADICT["NR"] = NR
  932. self.DATADICT["RE"] = RE
  933. self.DATADICT["IM"] = IM
  934. self.DATADICT["CAmax"] = CAmax
  935. self.DATADICT["NRmax"] = NRmax
  936. self.DATADICT["REmax"] = REmax
  937. self.DATADICT["IMmax"] = IMmax
  938. self.doneTrigger.emit()
  939. def plotQuadDet(self, clip, phase, canvas):
  940. canvas.reAxH2( len(self.DATADICT[ self.DATADICT["PULSES"][0] ]["chan"] ), False, False)
  941. ###############
  942. # Plot on GUI #
  943. ###############
  944. dcmap = cmocean.cm.curl_r #"seismic_r" #cmocean.cm.balance_r #"RdBu" #YlGn" # "coolwarm_r" # diverging
  945. canvas.reAxH2( len(self.DATADICT[ self.DATADICT["PULSES"][0] ]["chan"] ), False, False)
  946. for pulse in self.DATADICT["PULSES"]:
  947. ichan = 0
  948. axes = canvas.fig.axes
  949. mmaxr = 0.
  950. mmaxi = 0.
  951. if clip > 0:
  952. time_sp = 1e3 * (self.DATADICT[pulse]["TIMES"][clip-1::] - self.DATADICT[pulse]["PULSE_TIMES"][-1] )
  953. else:
  954. time_sp = 1e3 * (self.DATADICT[pulse]["TIMES"] - self.DATADICT[pulse]["PULSE_TIMES"][-1] )
  955. QQ = np.average(self.DATADICT[pulse]["Q"], axis=1 )
  956. for chan in self.DATADICT[pulse]["chan"]:
  957. ax1 = axes[2*ichan ]
  958. ax2 = axes[2*ichan+1] # TODO fix hard coded number
  959. if phase == 0: # Re Im
  960. im1 = ax1.pcolormesh( time_sp, QQ, self.DATADICT["RE"][pulse][chan], cmap=dcmap, rasterized=True,\
  961. vmin=-self.DATADICT["REmax"][pulse] , vmax=self.DATADICT["REmax"][pulse] )
  962. im2 = ax2.pcolormesh( time_sp, QQ, self.DATADICT["IM"][pulse][chan], cmap=dcmap, rasterized=True,\
  963. vmin=-self.DATADICT["IMmax"][pulse], vmax=self.DATADICT["IMmax"][pulse] )
  964. if phase == 1: # Amp phase
  965. im1 = ax1.pcolormesh( time_sp, QQ, self.DATADICT["CA"][pulse][chan], cmap=dcmap, rasterized=True,
  966. vmin=-self.DATADICT["CAmax"][pulse] , vmax=self.DATADICT["CAmax"][pulse] )
  967. #im2 = ax2.pcolormesh( time_sp, QQ, self.DATADICT["IP"][pulse][chan], cmap=cmocean.cm.balance, rasterized=True,\
  968. im2 = ax2.pcolormesh( time_sp, QQ, self.DATADICT["IP"][pulse][chan], cmap=cmocean.cm.delta, rasterized=True,\
  969. vmin=-np.pi, vmax=np.pi)
  970. if phase == 2: # CA NR
  971. im1 = ax1.pcolormesh( time_sp, QQ, self.DATADICT["CA"][pulse][chan], cmap=dcmap, rasterized=True,\
  972. vmin=-self.DATADICT["CAmax"][pulse] , vmax=self.DATADICT["CAmax"][pulse] )
  973. im2 = ax2.pcolormesh( time_sp, QQ, self.DATADICT["NR"][pulse][chan], cmap=dcmap, rasterized=True,\
  974. vmin=-self.DATADICT["NRmax"][pulse] , vmax=self.DATADICT["NRmax"][pulse] )
  975. # cb2 = canvas.fig.colorbar(im2, ax=ax2, format='%1.0e')
  976. # cb2.set_label("Noise residual (nV)", fontsize=8)
  977. # cb2.ax.tick_params(axis='both', which='major', labelsize=8)
  978. # cb1 = canvas.fig.colorbar(im1, ax=ax1, format='%1.0e')
  979. # cb1.set_label("Phased amplitude (nV)", fontsize=8)
  980. # cb1.ax.tick_params(axis='both', which='major', labelsize=8)
  981. # cb2 = canvas.fig.colorbar(im2, ax=ax2, format="%1.0e")
  982. # cb2.set_label("Phase (rad)", fontsize=8)
  983. # cb2.ax.tick_params(axis='both', which='major', labelsize=8)
  984. # cb1 = canvas.fig.colorbar(im1, ax=ax1, format="%1.0e")
  985. # cb1.set_label("FID (nV)", fontsize=8)
  986. # cb1.ax.tick_params(axis='both', which='major', labelsize=8)
  987. # if you save these as pdf or eps, there are artefacts
  988. # for cbar in [cb1,cb2]:
  989. # #cbar.solids.set_rasterized(True)
  990. # cbar.solids.set_edgecolor("face")
  991. # reporting
  992. percent = int(1e2* (float)(ichan)/len(self.DATADICT[pulse]["chan"]))
  993. self.progressTrigger.emit(percent)
  994. if ichan == 0:
  995. ax1.set_ylabel(r"$q$ ( $\mathrm{A}\cdot\mathrm{s}$)", fontsize=8)
  996. ax2.set_ylabel(r"$q$ ( $\mathrm{A}\cdot\mathrm{s}$)", fontsize=8)
  997. else:
  998. #ax2.yaxis.set_ticklabels([])
  999. #ax1.yaxis.set_ticklabels([])
  1000. plt.setp(ax1.get_yticklabels(), visible=False)
  1001. plt.setp(ax2.get_yticklabels(), visible=False)
  1002. ichan += 1
  1003. ax1.set_yscale('log')
  1004. ax2.set_yscale('log')
  1005. plt.setp(ax1.get_xticklabels(), visible=False)
  1006. ax1.set_ylim( np.min(QQ), np.max(QQ) )
  1007. ax2.set_ylim( np.min(QQ), np.max(QQ) )
  1008. ax1.set_xlim( np.min(time_sp), np.max(time_sp) )
  1009. ax2.set_xlim( np.min(time_sp), np.max(time_sp) )
  1010. #ax2.set_xlabel(r"Time since end of pulse (ms)", fontsize=8)
  1011. ax2.set_xlabel(r"Time (ms)", fontsize=8)
  1012. canvas.fig.subplots_adjust(hspace=.15, wspace=.05, left=.075, right=.95, bottom=.1, top=.95 )#left=None, bottom=None, right=None, top=None, wspace=None, hspace=None)
  1013. tick_locator = MaxNLocator(nbins=3)
  1014. cb1 = canvas.fig.colorbar(im1, ax=axes[0::2], format='%1.0e', orientation='horizontal', shrink=.35, aspect=30)
  1015. cb1.ax.tick_params(axis='both', which='major', labelsize=8)
  1016. cb1.set_label("$\mathcal{V}_N$ (nV)", fontsize=8)
  1017. cb1.locator = tick_locator
  1018. cb1.update_ticks()
  1019. tick_locator2 = MaxNLocator(nbins=3)
  1020. cb2 = canvas.fig.colorbar(im2, ax=axes[1::2], format='%1.0e', orientation='horizontal', shrink=.35, aspect=30, pad=.2)
  1021. cb2.ax.tick_params(axis='both', which='major', labelsize=8)
  1022. cb2.set_label("$\mathcal{V}_N$ (nV)", fontsize=8)
  1023. cb2.locator = tick_locator2
  1024. cb2.update_ticks()
  1025. canvas.draw()
  1026. self.doneTrigger.emit()
  1027. def RotateAmplitude(self, X, Y, zeta, df, t):
  1028. V = X + 1j*Y
  1029. return np.abs(V) * np.exp( 1j * ( np.angle(V) - zeta - 2.*np.pi*df*t ) )
  1030. def gateIntegrate( self, gpd, clip, canvas ):
  1031. """ Gate integrate the real, imaginary, phased, and noise residual channels
  1032. """
  1033. self.gated = True
  1034. self.GATED = {}
  1035. for pulse in self.DATADICT["PULSES"]:
  1036. QQ = np.average(self.DATADICT[pulse]["Q"], axis=1 )
  1037. ichan = 0
  1038. for chan in self.DATADICT[pulse]["chan"]:
  1039. self.GATED[chan] = {}
  1040. for ipm in range(0, self.DATADICT["nPulseMoments"]):
  1041. # Time since pulse rather than since record starts...
  1042. #if clip > 0:
  1043. # time_sp = 1e3 * (self.DATADICT[pulse]["TIMES"][clip:] - self.DATADICT[pulse]["PULSE_TIMES"][-1] )
  1044. #else:
  1045. time_sp = 1e3 * (self.DATADICT[pulse]["TIMES"] - self.DATADICT[pulse]["PULSE_TIMES"][-1] )
  1046. #GT, GD, GTT, sig_stack, isum = rotate.gateIntegrate( self.DATADICT["CA"][pulse][chan][ipm,:], time_sp, gpd, self.sigma[pulse][chan], 1.5 )
  1047. #GT2, GP, GTT, sig_stack_err, isum = rotate.gateIntegrate( self.DATADICT["NR"][pulse][chan][ipm,:], time_sp, gpd, self.sigma[pulse][chan], 1.5 )
  1048. GT, GCA, GTT, sig_stack, isum = rotate.gateIntegrate( self.DATADICT["CA"][pulse][chan][ipm], time_sp, gpd, self.sigma[pulse][chan], 1.5 )
  1049. GT, GNR, GTT, sig_stack, isum = rotate.gateIntegrate( self.DATADICT["NR"][pulse][chan][ipm], time_sp, gpd, self.sigma[pulse][chan], 1.5 )
  1050. GT, GRE, GTT, sig_stack, isum = rotate.gateIntegrate( self.DATADICT["RE"][pulse][chan][ipm], time_sp, gpd, self.sigma[pulse][chan], 1.5 )
  1051. GT, GIM, GTT, sig_stack, isum = rotate.gateIntegrate( self.DATADICT["IM"][pulse][chan][ipm], time_sp, gpd, self.sigma[pulse][chan], 1.5 )
  1052. GT, GIP, GTT, sig_stack, isum = rotate.gateIntegrate( self.DATADICT["IP"][pulse][chan][ipm], time_sp, gpd, self.sigma[pulse][chan], 1.5 )
  1053. if ipm == 0:
  1054. # self.GATED[chan]["DATA"] = np.zeros( ( self.DATADICT["nPulseMoments"], len(GT)) )
  1055. # self.GATED[chan]["ERR"] = np.zeros( ( self.DATADICT["nPulseMoments"], len(GT)) )
  1056. # self.GATED[chan]["SIGMA"] = np.zeros( ( self.DATADICT["nPulseMoments"], len(GT)) )
  1057. self.GATED[chan]["CA"] = np.zeros( ( self.DATADICT["nPulseMoments"], len(GT)-clip) )
  1058. self.GATED[chan]["NR"] = np.zeros( ( self.DATADICT["nPulseMoments"], len(GT)-clip) )
  1059. self.GATED[chan]["RE"] = np.zeros( ( self.DATADICT["nPulseMoments"], len(GT)-clip) )
  1060. self.GATED[chan]["IM"] = np.zeros( ( self.DATADICT["nPulseMoments"], len(GT)-clip) )
  1061. self.GATED[chan]["IP"] = np.zeros( ( self.DATADICT["nPulseMoments"], len(GT)-clip) )
  1062. self.GATED[chan]["isum"] = isum
  1063. #self.GATED[chan]["DATA"][ipm] = GD.real
  1064. self.GATEDABSCISSA = GT[clip:]
  1065. self.GATEDWINDOW = GTT[clip:]
  1066. #self.GATED[chan]["SIGMA"][ipm] = sig_stack #_err # GP.real
  1067. #self.GATED[chan]["ERR"][ipm] = GP.real
  1068. self.GATED[chan]["CA"][ipm] = GCA.real[clip:]
  1069. self.GATED[chan]["NR"][ipm] = GNR.real[clip:]
  1070. self.GATED[chan]["RE"][ipm] = GRE.real[clip:]
  1071. self.GATED[chan]["IM"][ipm] = GIM.real[clip:]
  1072. self.GATED[chan]["IP"][ipm] = GIP.real[clip:]
  1073. percent = int(1e2* (float)((ipm)+ichan*self.DATADICT["nPulseMoments"]) /
  1074. (float)(self.DATADICT["nPulseMoments"] * len(self.DATADICT[pulse]["chan"])))
  1075. self.progressTrigger.emit(percent)
  1076. self.GATED[chan]["GTT"] = GTT[clip:]
  1077. self.GATED[chan]["GT"] = GT[clip:]
  1078. self.GATED[chan]["QQ"] = QQ
  1079. ichan += 1
  1080. self.doneTrigger.emit()
  1081. def bootstrap_resample(self, X, n=None):
  1082. # from http://nbviewer.jupyter.org/gist/aflaxman/6871948
  1083. """ Bootstrap resample an array_like
  1084. Parameters
  1085. ----------
  1086. X : array_like
  1087. data to resample
  1088. n : int, optional
  1089. length of resampled array, equal to len(X) if n==None
  1090. Results
  1091. -------
  1092. returns X_resamples
  1093. """
  1094. if n == None:
  1095. n = len(X)
  1096. resample_i = np.floor(np.random.rand(n)*len(X)).astype(int)
  1097. return X[resample_i]
  1098. def bootstrap_sigma(self, pulse, chan):
  1099. # bootstrap resample
  1100. nt = len(self.GATED[chan]["GT"])
  1101. nb = 5000
  1102. XS = np.zeros( (nb, nt) )
  1103. for ii in range(nb):
  1104. for it in range(nt):
  1105. if self.GATED[chan]["isum"][it] < 8:
  1106. XS[ii, it] = self.sigma[pulse][chan]
  1107. else:
  1108. if it == 0:
  1109. X = self.bootstrap_resample( np.concatenate( (self.GATED[chan]["NR"][:,it], self.GATED[chan]["NR"][:,it+1], \
  1110. self.GATED[chan]["NR"][:,it+2], self.GATED[chan]["NR"][:,it+3] ) ), n=nt )
  1111. elif it == 1:
  1112. X = self.bootstrap_resample( np.concatenate( (self.GATED[chan]["NR"][:,it-1], self.GATED[chan]["NR"][:,it], \
  1113. self.GATED[chan]["NR"][:,it+1], self.GATED[chan]["NR"][:,it+2] ) ), n=nt )
  1114. elif it == nt-2:
  1115. X = self.bootstrap_resample( np.concatenate( (self.GATED[chan]["NR"][:,it+1], self.GATED[chan]["NR"][:,it], \
  1116. self.GATED[chan]["NR"][:,it-1], self.GATED[chan]["NR"][:,it-2] ) ), n=nt )
  1117. elif it == nt-1:
  1118. X = self.bootstrap_resample( np.concatenate( (self.GATED[chan]["NR"][:,it], self.GATED[chan]["NR"][:,it-1], \
  1119. self.GATED[chan]["NR"][:,it-2], self.GATED[chan]["NR"][:,it-3] ) ), n=nt )
  1120. else:
  1121. X = self.bootstrap_resample( np.concatenate( (self.GATED[chan]["NR"][:,it-2] , self.GATED[chan]["NR"][:,it-1], \
  1122. self.GATED[chan]["NR"][:,it], self.GATED[chan]["NR"][:,it+1], self.GATED[chan]["NR"][:,it+2] )), n=nt )
  1123. XS[ii,it] = np.std(X)
  1124. return XS
  1125. def plotGateIntegrate( self, gpd, clip, phase, canvas ):
  1126. """ Plot the gate integration
  1127. """
  1128. canvas.reAxH2( len(self.DATADICT[ self.DATADICT["PULSES"][0] ]["chan"] ), False, False)
  1129. axes = canvas.fig.axes
  1130. cmap = cmocean.cm.balance_r
  1131. # Calculate maximum for plotting...TODO move into loop above
  1132. vmax1 = 0
  1133. vmax2 = 0
  1134. for pulse in self.DATADICT["PULSES"]:
  1135. for chan in self.DATADICT[pulse]["chan"]:
  1136. if phase == 0:
  1137. vmax1 = max(vmax1, np.max(np.abs(self.GATED[chan]["RE"])))
  1138. vmax2 = max(vmax2, np.max(np.abs(self.GATED[chan]["IM"])))
  1139. elif phase == 1:
  1140. vmax1 = max(vmax1, np.max(np.abs(self.GATED[chan]["CA"])))
  1141. vmax2 = np.pi
  1142. elif phase == 2:
  1143. vmax1 = max(vmax1, np.max(np.abs(self.GATED[chan]["CA"])))
  1144. vmax2 = max(vmax2, np.max(np.abs(self.GATED[chan]["NR"])))
  1145. for pulse in self.DATADICT["PULSES"]:
  1146. ichan = 0
  1147. for chan in self.DATADICT[pulse]["chan"]:
  1148. ax1 = axes[2*ichan ]
  1149. ax2 = axes[2*ichan+1]
  1150. if phase == 0:
  1151. im1 = ax1.pcolormesh(self.GATED[chan]["GTT"], self.GATED[chan]["QQ"], self.GATED[chan]["RE"], cmap=cmap, vmin=-vmax1, vmax=vmax1)
  1152. im2 = ax2.pcolormesh(self.GATED[chan]["GTT"], self.GATED[chan]["QQ"], self.GATED[chan]["IM"], cmap=cmap, vmin=-vmax2, vmax=vmax2)
  1153. elif phase == 1:
  1154. im1 = ax1.pcolormesh(self.GATED[chan]["GTT"], self.GATED[chan]["QQ"], self.GATED[chan]["CA"], cmap=cmap, vmin=-vmax1, vmax=vmax1)
  1155. im2 = ax2.pcolormesh(self.GATED[chan]["GTT"], self.GATED[chan]["QQ"], self.GATED[chan]["IP"], cmap=cmocean.cm.balance, vmin=-vmax2, vmax=vmax2)
  1156. #im2 = ax2.pcolormesh(self.GATED[chan]["GTT"], self.GATED[chan]["QQ"], self.GATED[chan]["IP"], cmap=cmocean.cm.phase, vmin=-vmax2, vmax=vmax2)
  1157. elif phase == 2:
  1158. im1 = ax1.pcolormesh(self.GATED[chan]["GTT"], self.GATED[chan]["QQ"], self.GATED[chan]["CA"], cmap=cmap, vmin=-vmax1, vmax=vmax1)
  1159. XS = self.bootstrap_sigma(pulse, chan)
  1160. #im2 = ax2.pcolormesh(self.GATED[chan]["GTT"], self.GATED[chan]["QQ"], self.GATED[chan]["NR"], cmap=cmap, vmin=-vmax2, vmax=vmax2)
  1161. # bootstrap resample
  1162. # nt = len(self.GATED[chan]["GT"])
  1163. # nb = 5000
  1164. # XS = np.zeros( (nb, nt) )
  1165. # for ii in range(nb):
  1166. # #XS = []
  1167. # for it in range(nt):
  1168. # if self.GATED[chan]["isum"][it] < 8:
  1169. # XS[ii, it] = self.sigma[pulse][chan]
  1170. # else:
  1171. # if it == 0:
  1172. # X = self.bootstrap_resample( np.concatenate( (self.GATED[chan]["NR"][:,it], self.GATED[chan]["NR"][:,it+1], \
  1173. # self.GATED[chan]["NR"][:,it+2], self.GATED[chan]["NR"][:,it+3] ) ), n=nt )
  1174. # if it == 1:
  1175. # X = self.bootstrap_resample( np.concatenate( (self.GATED[chan]["NR"][:,it-1], self.GATED[chan]["NR"][:,it], \
  1176. # self.GATED[chan]["NR"][:,it+1], self.GATED[chan]["NR"][:,it+2] ) ), n=nt )
  1177. # elif it == nt-2:
  1178. # X = self.bootstrap_resample( np.concatenate( (self.GATED[chan]["NR"][:,it+1], self.GATED[chan]["NR"][:,it], \
  1179. # self.GATED[chan]["NR"][:,it-1], self.GATED[chan]["NR"][:,it-2] ) ), n=nt )
  1180. # elif it == nt-1:
  1181. # X = self.bootstrap_resample( np.concatenate( (self.GATED[chan]["NR"][:,it], self.GATED[chan]["NR"][:,it-1], \
  1182. # self.GATED[chan]["NR"][:,it-2], self.GATED[chan]["NR"][:,it-3] ) ), n=nt )
  1183. # else:
  1184. # X = self.bootstrap_resample( np.concatenate( (self.GATED[chan]["NR"][:,it-2] , self.GATED[chan]["NR"][:,it-1], \
  1185. # self.GATED[chan]["NR"][:,it], self.GATED[chan]["NR"][:,it+1], self.GATED[chan]["NR"][:,it+2] )), n=nt )
  1186. # XS[ii,it] = np.std(X)
  1187. #if ii == 0:
  1188. # ax2.plot( self.GATED[chan]["GT"], XS[ii], '-', linewidth=1, markersize=4, alpha=.5, color='lightgrey', label = "bootstrap sim" )
  1189. #else:
  1190. # ax2.plot( self.GATED[chan]["GT"], XS[ii], '-', linewidth=1, markersize=4, alpha=.5, color='lightgrey' )
  1191. ax2.plot( self.GATED[chan]["GT"], np.std(self.GATED[chan]["NR"], axis=0), color='darkgrey', linewidth=2, label="gate std" )
  1192. ax2.plot( np.tile(self.GATED[chan]["GT"], (5000,1) ), XS, '.', color='lightgrey', linewidth=1, alpha=.5 )
  1193. ax2.plot( self.GATED[chan]["GT"], np.average(XS, axis=0), color='black', linewidth=2, label="bootstrap avg." )
  1194. ax2.plot( self.GATED[chan]["GT"], np.median(XS, axis=0), color='black', linewidth=2, label="bootstrap med." )
  1195. ax2.legend()
  1196. im1.set_edgecolor('face')
  1197. if phase != 2:
  1198. im2.set_edgecolor('face')
  1199. plt.setp(ax1.get_xticklabels(), visible=False)
  1200. ax1.set_ylim( np.min(self.GATED[chan]["QQ"]), np.max(self.GATED[chan]["QQ"]) )
  1201. if phase != 2:
  1202. ax2.set_ylim( np.min(self.GATED[chan]["QQ"]), np.max(self.GATED[chan]["QQ"]) )
  1203. ax1.set_xlim( np.min(self.GATED[chan]["GTT"]), np.max(self.GATED[chan]["GTT"]) )
  1204. ax2.set_xlim( np.min(self.GATED[chan]["GTT"]), np.max(self.GATED[chan]["GTT"]) )
  1205. ax1.set_yscale('log')
  1206. ax2.set_yscale('log')
  1207. qlabs = np.append(np.concatenate( ( self.GATED[chan]["QQ"][0:1], self.GATED[chan]["QQ"][9::10] )), self.GATED[chan]["QQ"][-1] )
  1208. ax1.yaxis.set_ticks( qlabs ) # np.append(np.concatenate( (QQ[0:1],QQ[9::10] )), QQ[-1] ) )
  1209. if phase != 2:
  1210. ax2.yaxis.set_ticks( qlabs ) #np.append(np.concatenate( (QQ[0:1],QQ[9::10] )), QQ[-1] ) )
  1211. #formatter = matplotlib.ticker.LogFormatter(10, labelOnlyBase=False)
  1212. formatter = matplotlib.ticker.FuncFormatter(lambda x, pos: str((round(x,1))))
  1213. ax1.yaxis.set_major_formatter(formatter) #matplotlib.ticker.FormatStrFormatter('%d.1'))
  1214. ax2.yaxis.set_major_formatter(formatter) #matplotlib.ticker.FormatStrFormatter('%d.1'))
  1215. ax1.xaxis.set_major_formatter(formatter) #matplotlib.ticker.FormatStrFormatter('%d.1'))
  1216. ax2.xaxis.set_major_formatter(formatter) #matplotlib.ticker.FormatStrFormatter('%d.1'))
  1217. ax1.set_xscale('log')
  1218. ax2.set_xscale('log')
  1219. if ichan == 0:
  1220. ax1.set_ylabel(r"$q$ ( $\mathrm{A}\cdot\mathrm{s}$)", fontsize=8)
  1221. if phase == 2:
  1222. ax2.set_ylabel(r"noise est. (nV)", fontsize=8)
  1223. else:
  1224. ax2.set_ylabel(r"$q$ ( $\mathrm{A}\cdot\mathrm{s}$)", fontsize=8)
  1225. else:
  1226. plt.setp(ax1.get_yticklabels(), visible=False)
  1227. plt.setp(ax2.get_yticklabels(), visible=False)
  1228. ax2.set_xlabel(r"$t-\tau_p$ (ms)", fontsize=8)
  1229. ichan += 1
  1230. #canvas.fig.subplots_adjust(hspace=.1, wspace=.05, left=.075, right=.925 )#left=None, bottom=None, right=None, top=None, wspace=None, hspace=None)
  1231. #canvas.fig.tight_layout()
  1232. #canvas.draw()
  1233. canvas.fig.subplots_adjust(hspace=.15, wspace=.05, left=.075, right=.95, bottom=.1, top=.95 )#left=None, bottom=None, right=None, top=None, wspace=None, hspace=None)
  1234. tick_locator = MaxNLocator(nbins=5)
  1235. cb1 = canvas.fig.colorbar(im1, ax=axes[0::2], format='%1.0e', orientation='horizontal', shrink=.35, aspect=30)
  1236. cb1.ax.tick_params(axis='both', which='major', labelsize=8)
  1237. cb1.set_label("$\mathcal{V}_N$ (nV)", fontsize=8)
  1238. cb1.locator = tick_locator
  1239. cb1.update_ticks()
  1240. if phase != 2:
  1241. cb2 = canvas.fig.colorbar(im2, ax=axes[1::2], format='%1.0e', orientation='horizontal', shrink=.35, aspect=30, pad=.2)
  1242. cb2.ax.tick_params(axis='both', which='major', labelsize=8)
  1243. cb2.set_label("$\mathcal{V}_N$ (nV)", fontsize=8)
  1244. cb2.locator = tick_locator
  1245. cb2.update_ticks()
  1246. canvas.draw()
  1247. self.doneTrigger.emit()
  1248. def FDSmartStack(self, cv, canvas):
  1249. from matplotlib.colors import LogNorm
  1250. from matplotlib.ticker import MaxNLocator
  1251. """
  1252. Currently this stacks 4-phase second pulse data only, we need to generalise
  1253. """
  1254. try:
  1255. canvas.fig.clear()
  1256. except:
  1257. pass
  1258. self.dataCubeFFT( )
  1259. # canvas.ax1 = canvas.fig.add_axes([.1, .1, .8, .8])
  1260. canvas.ax1 = canvas.fig.add_axes([.1, .1, .2, .8])
  1261. canvas.ax2 = canvas.fig.add_axes([.325, .1, .2, .8])
  1262. canvas.ax3 = canvas.fig.add_axes([.55, .1, .2, .8])
  1263. canvas.ax4 = canvas.fig.add_axes([.815, .1, .05, .8]) #cb
  1264. canvas.ax1.tick_params(axis='both', which='major', labelsize=8)
  1265. canvas.ax2.tick_params(axis='both', which='major', labelsize=8)
  1266. canvas.ax3.tick_params(axis='both', which='major', labelsize=8)
  1267. canvas.ax4.tick_params(axis='both', which='major', labelsize=8)
  1268. canvas.ax1.set_ylabel("pulse index", fontsize=8)
  1269. canvas.ax1.set_xlabel(r"$\omega$ bin", fontsize=8)
  1270. canvas.ax2.set_xlabel(r"$\omega$ bin", fontsize=8)
  1271. canvas.ax3.set_xlabel(r"$\omega$ bin", fontsize=8)
  1272. canvas.ax2.yaxis.set_ticklabels([])
  1273. canvas.ax3.yaxis.set_ticklabels([])
  1274. #canvas.ax1.ticklabel_format(style='sci', scilimits=(0,0), axis='y')
  1275. # # Look at pulses
  1276. # for pulse in self.DATADICT["PULSES"]:
  1277. # for istack in self.DATADICT["stacks"]:
  1278. # for ipm in range(0,3):
  1279. # canvas.ax1.plot( self.DATADICT[pulse]["CURRENT"][ipm][istack] , label="istack "+str(istack) + " ipm=" + str(ipm) + pulse )
  1280. # canvas.draw()
  1281. # Create Container for stacks
  1282. # sandbox determine pulse sequence again
  1283. for pulse in self.DATADICT["PULSES"]:
  1284. for ichan in self.DATADICT[pulse]["chan"]:
  1285. #for ipm in range(10,11):
  1286. CONTAINER = {}
  1287. CONTAINER["Cycle 1"] = [] # These are actually subtracted cycles... v+ - v
  1288. CONTAINER["Cycle 2"] = []
  1289. for istack in self.DATADICT["stacks"]:
  1290. #canvas.ax1.clear()
  1291. ipm = 8
  1292. #for ipm in range(self.DATADICT["nPulseMoments"]):
  1293. #canvas.ax1.matshow( np.real(self.DATADICT[pulse][ichan]["FFT"][istack]), aspect='auto' )
  1294. #canvas.draw()
  1295. if not istack%4%4:
  1296. # phase cycle 4, aligned with 1 after sub
  1297. CONTAINER["Cycle 1"].append(-self.DATADICT[pulse][ichan]["FFT"][istack])
  1298. #canvas.ax1.plot( self.DATADICT[pulse]["TIMES"], -self.DATADICT[pulse][ichan][ipm][istack], label="istack "+str(istack)+ " " + pulse )
  1299. elif not istack%4%3:
  1300. # phase cycle 3, aligned with 2 after sub
  1301. CONTAINER["Cycle 2"].append(-self.DATADICT[pulse][ichan]["FFT"][istack])
  1302. #canvas.ax1.plot( self.DATADICT[pulse]["TIMES"], -self.DATADICT[pulse][ichan][ipm][istack], label="istack "+str(istack)+ " " + pulse )
  1303. elif not istack%4%2:
  1304. # phase cycle 2
  1305. CONTAINER["Cycle 2"].append( self.DATADICT[pulse][ichan]["FFT"][istack])
  1306. #canvas.ax1.plot( self.DATADICT[pulse]["TIMES"], self.DATADICT[pulse][ichan][ipm][istack], label="istack "+str(istack)+ " " + pulse )
  1307. else:
  1308. # phase cycle 1
  1309. CONTAINER["Cycle 1"].append( self.DATADICT[pulse][ichan]["FFT"][istack])
  1310. #canvas.ax1.plot( self.DATADICT[pulse]["TIMES"], self.DATADICT[pulse][ichan][ipm][istack], label="istack "+str(istack)+ " " + pulse )
  1311. #canvas.ax1.matshow(np.array(np.average(self.DATADICT[pulse][ichan]["FFT"]), axis=2), aspect='auto' )
  1312. #canvas.ax1.plot( self.DATADICT[pulse]["PULSE_TIMES"], self.DATADICT[pulse]["CURRENT"][ipm][istack] , color='black', label="istack "+str(istack) )
  1313. #canvas.ax1.plot( self.DATADICT[pulse]["CURRENT"][ipm][istack] , label="istack "+str(istack) + " iFID" + str(iFID) )
  1314. #canvas.ax1.plot( self.DATADICT[pulse]["TIMES"], self.DATADICT[pulse][ichan][ipm][istack], label="istack "+str(istack)+ " " + pulse )
  1315. #canvas.ax1.legend(prop={'size':6})
  1316. #canvas.draw()
  1317. # Boostrap
  1318. # stack.
  1319. #scipy.random.shuffle(x)
  1320. # Stack and calculate the pooled variance (http://en.wikipedia.org/wiki/Pooled_variance)
  1321. """ All this phase cycling wreaks havoc on a normal calculation of std. and variance. Instead, we resort to calculating
  1322. a pooled variance. In this assumption is that the precision of the measurment is constant. This is a poor choice for
  1323. any type of moving sensor.
  1324. """
  1325. # if a window filter has been applied
  1326. #self.WINDOW
  1327. #self.IWindowStart
  1328. #self.iWindowEnd
  1329. #self.FFTtimes
  1330. CONTAINER = .5*(np.array(CONTAINER["Cycle 2"]) - np.array(CONTAINER["Cycle 1"]))
  1331. print ("container shape", np.shape( CONTAINER), self.iWindowStart+1, self.iWindowEnd-1)
  1332. dmin = np.min(np.abs(np.average(np.array(CONTAINER)[:,:,self.iWindowStart+1:self.iWindowEnd-1], axis=0)))
  1333. dmax = np.max(np.abs(np.average(np.array(CONTAINER)[:,:,self.iWindowStart+1:self.iWindowEnd-1], axis=0)))
  1334. mn = canvas.ax1.matshow( 20.*np.log10(np.abs(np.average(np.array(CONTAINER)[:,:, self.iWindowStart+1:self.iWindowEnd-1], axis=0))), aspect='auto', vmin=-120, vmax=-40)
  1335. #mn = canvas.ax1.matshow(20.*np.log10(XA[:,istart:iend+1]), aspect='auto', vmax=-40, vmin=-120) #, norm=LogNorm())
  1336. canvas.ax2.matshow( 20*np.log10(np.std(np.real(np.array(CONTAINER)[:,:,self.iWindowStart+1:self.iWindowEnd-1]), axis=0)), aspect='auto', vmin=-120, vmax=-40)
  1337. canvas.ax3.matshow( 20*np.log10(np.std(np.imag(np.array(CONTAINER)[:,:,self.iWindowStart+1:self.iWindowEnd-1]), axis=0)), aspect='auto', vmin=-120, vmax=-40)
  1338. #canvas.ax1.legend(prop={'size':6})
  1339. cb1 = mpl.colorbar.Colorbar(canvas.ax4, mn)
  1340. cb1.ax.tick_params(labelsize=8)
  1341. cb1.set_label("power [dB]", fontsize=8)
  1342. canvas.ax1.xaxis.set_major_locator(MaxNLocator(4))
  1343. canvas.ax2.xaxis.set_major_locator(MaxNLocator(4))
  1344. canvas.ax3.xaxis.set_major_locator(MaxNLocator(4))
  1345. canvas.draw()
  1346. self.doneTrigger.emit()
  1347. def effectivePulseMoment(self, cv, canvas):
  1348. canvas.reAxH(2)
  1349. nstack = len(self.DATADICT["stacks"])
  1350. canvas.ax1.set_yscale('log')
  1351. for pulse in self.DATADICT["PULSES"]:
  1352. self.DATADICT[pulse]["qeff"] = {}
  1353. self.DATADICT[pulse]["q_nu"] = {}
  1354. for ipm in range(self.DATADICT["nPulseMoments"]):
  1355. self.DATADICT[pulse]["qeff"][ipm] = {}
  1356. self.DATADICT[pulse]["q_nu"][ipm] = {}
  1357. #canvas.ax1.clear()
  1358. #scolours = np.array( ( np.linspace(0.8,0.4,len(self.DATADICT["stacks"])), \
  1359. # np.linspace(0.0,0.6,len(self.DATADICT["stacks"])), \
  1360. # np.linspace(0.6,0.0,len(self.DATADICT["stacks"])) )
  1361. # ).T
  1362. #scolours = plt.cm.Spectral(np.linspace(0,1,len(self.DATADICT["stacks"])))
  1363. #scolours = plt.cm.Blues(np.linspace(0,1,1.5*len(self.DATADICT["stacks"])))
  1364. scolours = cmocean.cm.ice(np.linspace(0,1,1.5*len(self.DATADICT["stacks"])))
  1365. iistack = 0
  1366. for istack in self.DATADICT["stacks"]:
  1367. #self.DATADICT[pulse]["PULSE_TIMES"]
  1368. x = self.DATADICT[pulse]["CURRENT"][ipm][istack]
  1369. X = np.fft.rfft(x)
  1370. v = np.fft.fftfreq(len(x), self.dt)
  1371. v = v[0:len(X)]
  1372. v[-1] = np.abs(v[-1])
  1373. # calculate effective current/moment
  1374. I0 = np.abs(X)/len(X)
  1375. qeff = I0 * (self.DATADICT[pulse]["PULSE_TIMES"][-1]-self.DATADICT[pulse]["PULSE_TIMES"][0])
  1376. canvas.ax1.set_title(r"pulse moment index " +str(ipm), fontsize=10)
  1377. canvas.ax1.set_xlabel(r"$\nu$ [Hz]", fontsize=8)
  1378. canvas.ax1.set_ylabel(r"$q_{eff}$ [A$\cdot$sec]", fontsize=8)
  1379. canvas.ax1.plot(v, qeff, color=scolours[iistack] ) # eff current
  1380. self.DATADICT[pulse]["qeff"][ipm][istack] = qeff
  1381. self.DATADICT[pulse]["q_nu"][ipm][istack] = v
  1382. iistack += 1
  1383. canvas.draw()
  1384. percent = int(1e2* (float)((istack)+ipm*self.DATADICT["nPulseMoments"]) /
  1385. (float)(len(self.DATADICT["PULSES"])*self.DATADICT["nPulseMoments"]*nstack))
  1386. self.progressTrigger.emit(percent)
  1387. canvas.draw()
  1388. self.plotQeffNu(cv, canvas.ax2)
  1389. canvas.draw()
  1390. self.doneTrigger.emit()
  1391. def plotQeffNu(self, cv, ax):
  1392. ####################################
  1393. # TODO label fid1 and fid2, and make a legend, and colour by pulse
  1394. nstack = len(self.DATADICT["stacks"])
  1395. iFID = 0
  1396. for pulse in self.DATADICT["PULSES"]:
  1397. self.DATADICT[pulse]["Q"] = np.zeros( (self.DATADICT["nPulseMoments"], len(self.DATADICT["stacks"])) )
  1398. ilabel = True
  1399. for ipm in range(self.DATADICT["nPulseMoments"]):
  1400. #scolours = np.array([0.,0.,1.])
  1401. scolours = cmocean.cm.ice(np.linspace(0,1,1.5*len(self.DATADICT["stacks"])))
  1402. #scolours = plt.cm.Spectral(np.linspace(0,1,len(self.DATADICT["stacks"])))
  1403. #scolours = plt.cm.Spectral(np.linspace(0,1,len(self.DATADICT["stacks"])))
  1404. istack = 0
  1405. for stack in self.DATADICT["stacks"]:
  1406. # find index
  1407. icv = int (round(cv / self.DATADICT[pulse]["q_nu"][ipm][stack][1]))
  1408. self.DATADICT[pulse]["Q"][ipm,istack] = self.DATADICT[pulse]["qeff"][ipm][stack][icv]
  1409. if ilabel:
  1410. ax.scatter(ipm, self.DATADICT[pulse]["qeff"][ipm][stack][icv], facecolors='none', edgecolors=scolours[istack], label=(str(pulse)))
  1411. ilabel = False
  1412. else:
  1413. ax.scatter(ipm, self.DATADICT[pulse]["qeff"][ipm][stack][icv], facecolors='none', edgecolors=scolours[istack])
  1414. #scolours += np.array((0,1./(nstack+1),-1/(nstack+1.)))
  1415. percent = int(1e2* (float)((istack)+ipm*self.DATADICT["nPulseMoments"]) /
  1416. (float)(len(self.DATADICT["PULSES"])*self.DATADICT["nPulseMoments"]*nstack))
  1417. self.progressTrigger.emit(percent)
  1418. istack += 1
  1419. iFID += 1
  1420. ax.set_xlabel(r"pulse moment index", fontsize=8)
  1421. ax.set_ylabel(r"$q_{eff}$ [A$\cdot$sec]", fontsize=8)
  1422. ax.set_yscale('log')
  1423. ax.set_xlim(0, ax.get_xlim()[1])
  1424. ax.legend(loc='upper right', scatterpoints = 1, prop={'size':6})
  1425. def enableDSP(self):
  1426. self.enableDSPTrigger.emit()
  1427. def adaptiveFilter(self, M, flambda, truncate, mu, PCA, canvas):
  1428. canvas.reAx2(shx=False, shy=False)
  1429. # ax1 is top plot of filter taps
  1430. # ax2 is bottom plot of conditioned signal
  1431. if truncate:
  1432. itrunc =(int) ( round( 1e-3*truncate*self.samp ) )
  1433. print( "adaptive filter size", 1e3*self.dt*M, " [ms]" )
  1434. Filt = adapt.AdaptiveFilter(flambda)
  1435. H = {}
  1436. for pulse in self.DATADICT["PULSES"]:
  1437. H[pulse] = {}
  1438. for ichan in self.DATADICT[pulse]["chan"]:
  1439. H[pulse][ichan] = np.zeros(M)
  1440. iFID = 0
  1441. # original ordering...
  1442. #for pulse in self.DATADICT["PULSES"]:
  1443. # for ipm in range(self.DATADICT["nPulseMoments"]):
  1444. # for istack in self.DATADICT["stacks"]:
  1445. # This order makes more sense, same as data collection, verify
  1446. for istack in self.DATADICT["stacks"]:
  1447. for ipm in range(self.DATADICT["nPulseMoments"]):
  1448. for pulse in self.DATADICT["PULSES"]:
  1449. canvas.softClear()
  1450. mmax = 0
  1451. for ichan in self.DATADICT[pulse]["chan"]:
  1452. canvas.ax2.plot( self.DATADICT[pulse]["TIMES"], 1e9* self.DATADICT[pulse][ichan][ipm][istack], alpha=.5)
  1453. mmax = max(mmax, np.max(1e9*self.DATADICT[pulse][ichan][ipm][istack]))
  1454. canvas.ax2.set_ylim(-mmax, mmax)
  1455. canvas.ax2.set_prop_cycle(None)
  1456. for ichan in self.DATADICT[pulse]["chan"]:
  1457. #H = np.zeros(M)
  1458. RX = []
  1459. for irchan in self.DATADICT[pulse]["rchan"]:
  1460. RX.append(self.DATADICT[pulse][irchan][ipm][istack][::-1])
  1461. if all(H[pulse][ichan]) == 0:
  1462. # call twice to reconcile filter wind-up
  1463. [e,H[pulse][ichan]] = Filt.adapt_filt_Ref( self.DATADICT[pulse][ichan][ipm][istack][::-1],\
  1464. RX,\
  1465. M, mu, PCA, flambda, H[pulse][ichan])
  1466. [e,H[pulse][ichan]] = Filt.adapt_filt_Ref( self.DATADICT[pulse][ichan][ipm][istack][::-1],\
  1467. RX,\
  1468. M, mu, PCA, flambda, H[pulse][ichan])
  1469. else:
  1470. [e,H[pulse][ichan]] = Filt.adapt_filt_Ref( self.DATADICT[pulse][ichan][ipm][istack][::-1],\
  1471. RX,\
  1472. M, mu, PCA, flambda, H[pulse][ichan])
  1473. # replace
  1474. if truncate:
  1475. canvas.ax2.plot( self.DATADICT[pulse]["TIMES"][0:itrunc], 1e9* e[::-1][0:itrunc],\
  1476. label = pulse + " ipm=" + str(ipm) + " istack=" + str(istack) + " ichan=" + str(ichan))
  1477. self.DATADICT[pulse][ichan][ipm][istack] = e[::-1][0:itrunc]
  1478. else:
  1479. canvas.ax2.plot( self.DATADICT[pulse]["TIMES"], 1e9* e[::-1],\
  1480. label = pulse + " ipm=" + str(ipm) + " istack=" + str(istack) + " ichan=" + str(ichan))
  1481. self.DATADICT[pulse][ichan][ipm][istack] = e[::-1]
  1482. canvas.ax1.plot( H[pulse][ichan] ) # , label="taps")
  1483. canvas.ax2.legend(prop={'size':6}, loc='upper right')
  1484. #canvas.ax2.legend(prop={'size':6}, loc='upper right')
  1485. canvas.ax2.set_xlabel(r"time [s]", fontsize=8)
  1486. canvas.ax2.set_ylabel(r"signal [nV]", fontsize=8)
  1487. canvas.ax1.set_xlabel(r"filter index", fontsize=8)
  1488. canvas.ax1.set_ylabel(r"scale factor", fontsize=8)
  1489. canvas.draw()
  1490. # truncate the reference channels too, in case you still need them for something.
  1491. # Otherwise they are no longer aligned with the data
  1492. for rchan in self.DATADICT[pulse]["rchan"]:
  1493. if truncate:
  1494. self.DATADICT[pulse][rchan][ipm][istack] = self.DATADICT[pulse][rchan][ipm][istack][0:itrunc]
  1495. #percent = (int)(1e2*((float)(iFID*self.DATADICT["nPulseMoments"]+(ipm))/( len(self.DATADICT["PULSES"])*self.nPulseMoments)))
  1496. percent = (int)(1e2*((float)(istack*self.DATADICT["nPulseMoments"]+(ipm))/( len(self.DATADICT["PULSES"])*self.nPulseMoments*(len(self.DATADICT["stacks"])+1) )))
  1497. self.progressTrigger.emit(percent)
  1498. # # why is this loop here, istack is not part of rest?
  1499. # for istack in self.DATADICT["stacks"]:
  1500. # if truncate:
  1501. # self.DATADICT[pulse]["TIMES"] = self.DATADICT[pulse]["TIMES"][0:itrunc]
  1502. # percent = (int)(1e2*((float)(iFID*self.DATADICT["nPulseMoments"]+(ipm))/( len(self.DATADICT["PULSES"])*self.nPulseMoments)))
  1503. # self.progressTrigger.emit(percent)
  1504. # iFID += 1
  1505. if truncate:
  1506. self.DATADICT[pulse]["TIMES"] = self.DATADICT[pulse]["TIMES"][0:itrunc]
  1507. self.doneTrigger.emit()
  1508. self.updateProcTrigger.emit()
  1509. #self.plotFT(canvas)
  1510. def plotFT(self, canvas, istart=0, iend=0):
  1511. try:
  1512. canvas.fig.clear()
  1513. except:
  1514. pass
  1515. canvas.ax1 = canvas.fig.add_axes([.1, .1, .65, .8])
  1516. canvas.ax1c = canvas.fig.add_axes([.8, .1, .05, .8])
  1517. canvas.ax1.tick_params(axis='both', which='major', labelsize=8)
  1518. for pulse in self.DATADICT["PULSES"]:
  1519. for istack in self.DATADICT["stacks"]:
  1520. for ichan in self.DATADICT[pulse]["chan"]:
  1521. # FFT of stack
  1522. XA = np.zeros((self.DATADICT["nPulseMoments"] , len(self.DATADICT[pulse][ichan][0][istack])/2+1))
  1523. nu = np.fft.fftfreq(self.DATADICT[pulse][ichan][0][istack].size, d=self.dt)
  1524. nu[-1] *= -1
  1525. df = nu[1]
  1526. of = 0
  1527. if istart:
  1528. of = nu[istart]
  1529. def freqlabel(x, pos):
  1530. return '%1.0f' %(of + x*df)
  1531. formatter = FuncFormatter(freqlabel)
  1532. canvas.ax1.clear()
  1533. for ipm in range(self.DATADICT["nPulseMoments"]):
  1534. X = np.fft.rfft(self.DATADICT[pulse][ichan][ipm][istack])
  1535. XA[ipm,:] = np.abs(X)
  1536. if istart:
  1537. mn = canvas.ax1.matshow(20.*np.log10(XA[:,istart:iend+1]), aspect='auto', vmax=-40, vmin=-120) #, norm=LogNorm())
  1538. else:
  1539. mn = canvas.ax1.matshow(20.*np.log10(XA), aspect='auto', vmax=-40, vmin=-120) #, norm=LogNorm())
  1540. smin = np.min(20.*np.log10(XA))
  1541. smax = np.max(20.*np.log10(XA))
  1542. canvas.ax1.xaxis.set_major_formatter(formatter)
  1543. cb1 = mpl.colorbar.Colorbar(canvas.ax1c, mn)
  1544. cb1.ax.tick_params(labelsize=8)
  1545. cb1.set_label("signal [dB]", fontsize=8)
  1546. canvas.ax1.set_xlabel(r"$\nu$ [Hz]", fontsize=10)
  1547. canvas.ax1.set_ylabel(r"$q_{index}$", fontsize=10)
  1548. canvas.draw()
  1549. def plotFT(self, canvas, istart=0, iend=0):
  1550. try:
  1551. canvas.fig.clear()
  1552. except:
  1553. pass
  1554. canvas.ax1 = canvas.fig.add_axes([.1, .1, .65, .8])
  1555. canvas.ax1c = canvas.fig.add_axes([.8, .1, .05, .8])
  1556. canvas.ax1.tick_params(axis='both', which='major', labelsize=8)
  1557. for pulse in self.DATADICT["PULSES"]:
  1558. for istack in self.DATADICT["stacks"]:
  1559. for ichan in self.DATADICT[pulse]["chan"]:
  1560. # FFT of stack
  1561. XA = np.zeros((self.DATADICT["nPulseMoments"] , len(self.DATADICT[pulse][ichan][0][istack])//2+1))
  1562. nu = np.fft.fftfreq(self.DATADICT[pulse][ichan][0][istack].size, d=self.dt)
  1563. nu[-1] *= -1
  1564. df = nu[1]
  1565. of = 0
  1566. if istart:
  1567. of = nu[istart]
  1568. def freqlabel(x, pos):
  1569. return '%1.0f' %(of + x*df)
  1570. formatter = FuncFormatter(freqlabel)
  1571. canvas.ax1.clear()
  1572. for ipm in range(self.DATADICT["nPulseMoments"]):
  1573. X = np.fft.rfft(self.DATADICT[pulse][ichan][ipm][istack])
  1574. XA[ipm,:] = np.abs(X)
  1575. if istart:
  1576. mn = canvas.ax1.matshow(20.*np.log10(XA[:,istart:iend+1]), aspect='auto', vmax=-40, vmin=-120, cmap='viridis') #, norm=LogNorm())
  1577. else:
  1578. mn = canvas.ax1.matshow(20.*np.log10(XA), aspect='auto', vmax=-40, vmin=-120, cmap='viridis') #, norm=LogNorm())
  1579. canvas.ax1.xaxis.set_major_formatter(formatter)
  1580. cb1 = mpl.colorbar.Colorbar(canvas.ax1c, mn)
  1581. cb1.ax.tick_params(labelsize=8)
  1582. cb1.set_label("signal [dB]", fontsize=8)
  1583. canvas.ax1.set_xlabel(r"$\nu$ [Hz]", fontsize=10)
  1584. canvas.ax1.set_ylabel(r"$q_{index}$", fontsize=10)
  1585. canvas.draw()
  1586. def dataCubeFFT(self):
  1587. """
  1588. Performs FFT on entire cube of DATA, and REFERENCE channels, but not pulse currents,
  1589. Results are saved to a new field in the data structure
  1590. The GMR varies phase as a function of pulse moment index, so that the first pusle moment is zero phase,
  1591. the second is pi/2 the third is zero. This method corrects for this, so that all pulse moments are in phase.
  1592. Technically we may not want to do this, if there is some system response that this cycles away, and we lose track of
  1593. how many of each cycle we have, could this be problomatic? I think it will come out in the wash as we keep track of the
  1594. rest of the phase cycles. Holy phase cycling batman.
  1595. """
  1596. for pulse in self.DATADICT["PULSES"]:
  1597. for ichan in np.append(self.DATADICT[pulse]["chan"], self.DATADICT[pulse]["rchan"]):
  1598. # FFT of stack
  1599. self.DATADICT[pulse][ichan]["FFT"] = {}
  1600. self.DATADICT[pulse][ichan]["FFT"]["nu"] = np.fft.fftfreq(self.DATADICT[pulse][ichan][0][self.DATADICT["stacks"][0]].size, d=self.dt)
  1601. self.DATADICT[pulse][ichan]["FFT"]["nu"][-1] *= -1
  1602. for istack in self.DATADICT["stacks"]:
  1603. self.DATADICT[pulse][ichan]["FFT"][istack] = np.zeros((self.DATADICT["nPulseMoments"] , len(self.DATADICT[pulse][ichan][0][istack])//2+1), dtype=complex)
  1604. for ipm in range(self.DATADICT["nPulseMoments"]):
  1605. # Mod works for FID pulse sequences, TODO generalize this for 4 phase T1, etc..
  1606. mod = (-1)**(ipm%2) * (-1)**(istack%2)
  1607. self.DATADICT[pulse][ichan]["FFT"][istack][ipm,:] = np.fft.rfft( self.DATADICT[pulse][ichan][ipm][istack] )
  1608. #if ipm%2:
  1609. # odd, phase cycled from previous
  1610. # self.DATADICT[pulse][ichan]["FFT"][istack][ipm,:] = np.fft.rfft(-self.DATADICT[pulse][ichan][ipm][istack])
  1611. #else:
  1612. # even, we define as zero phase, first pulse moment has this
  1613. # self.DATADICT[pulse][ichan]["FFT"][istack][ipm,:] = np.fft.rfft(self.DATADICT[pulse][ichan][ipm][istack])
  1614. def adaptiveFilterFD(self, ftype, band, centre, canvas):
  1615. try:
  1616. canvas.fig.clear()
  1617. except:
  1618. pass
  1619. canvas.ax1 = canvas.fig.add_axes([.1, .5, .7, .4])
  1620. canvas.ax1c = canvas.fig.add_axes([.85, .5, .05, .4])
  1621. canvas.ax1.tick_params(axis='both', which='major', labelsize=8)
  1622. #canvas.ax1.ticklabel_format(style='sci', scilimits=(0,0), axis='y')
  1623. canvas.ax2 = canvas.fig.add_axes([.1, .05, .7, .4])
  1624. canvas.ax2c = canvas.fig.add_axes([.85, .05, .05, .4])
  1625. canvas.ax2.tick_params(axis='both', which='major', labelsize=8)
  1626. #canvas.ax2.ticklabel_format(style='sci', scilimits=(0,0), axis='y')
  1627. self.dataCubeFFT()
  1628. Filt = adapt.AdaptiveFilter(0.)
  1629. for pulse in self.DATADICT["PULSES"]:
  1630. # Compute window function and dimensions
  1631. [WINDOW, nd, wstart, wend, dead] = self.computeWindow(pulse, band, centre, ftype)
  1632. for istack in self.DATADICT["stacks"]:
  1633. for ichan in self.DATADICT[pulse]["chan"]:
  1634. # FFT of stack
  1635. nd = len(self.DATADICT[pulse][ichan][0][istack])
  1636. XX = np.zeros((self.DATADICT["nPulseMoments"] , len(self.DATADICT[pulse][ichan][0][istack])//2+1), dtype=complex)
  1637. nu = np.fft.fftfreq(self.DATADICT[pulse][ichan][0][istack].size, d=self.dt)
  1638. nu[-1] *= -1
  1639. #nu = self.DATADICT[pulse][ichan]["FFT"]["nu"]
  1640. def freqlabel(x, pos):
  1641. return '%1.0f' %((wstart)*nu[1] + x*nu[1])
  1642. formatter = FuncFormatter(freqlabel)
  1643. canvas.ax1.clear()
  1644. for ipm in range(self.DATADICT["nPulseMoments"]):
  1645. X = np.fft.rfft(self.DATADICT[pulse][ichan][ipm][istack])
  1646. XX[ipm,:] = X
  1647. XX = XX*WINDOW
  1648. XX = XX[:,wstart:wend]
  1649. smin = np.min(20.*np.log10(np.abs(XX)))
  1650. smax = np.max(20.*np.log10(np.abs(XX)))
  1651. #if smin != smin:
  1652. smax = -40
  1653. smin = -120
  1654. mn = canvas.ax1.matshow(20.*np.log10(np.abs(XX)), aspect='auto', vmin=smin, vmax=smax) #, norm=LogNorm())
  1655. canvas.ax1.xaxis.set_major_formatter(formatter)
  1656. cb1 = mpl.colorbar.Colorbar(canvas.ax1c, mn)
  1657. RX = []
  1658. for ichan in self.DATADICT[pulse]["rchan"]:
  1659. R = np.zeros((self.DATADICT["nPulseMoments"] , len(self.DATADICT[pulse][ichan][0][istack])//2+1), dtype=complex)
  1660. for ipm in range(self.DATADICT["nPulseMoments"]):
  1661. R[ipm,:] = np.fft.rfft(self.DATADICT[pulse][ichan][ipm][istack])
  1662. RX.append(R[:,wstart:wend])
  1663. XC = Filt.transferFunctionFFT(XX, RX)
  1664. # TODO inverse FFT, but we need to map back to origional matrix size
  1665. #for ichan in self.DATADICT[pulse]["chan"]:
  1666. # for ipm in range(self.DATADICT["nPulseMoments"]):
  1667. # self.DATADICT[pulse][ichan][ipm][istack] = np.fft.irfft(XC[] , nd)
  1668. mc = canvas.ax2.matshow(20.*np.log10(np.abs(XC)), aspect='auto', vmin=smin, vmax=smax) #, norm=LogNorm())
  1669. cb2 = mpl.colorbar.Colorbar(canvas.ax2c, mc)
  1670. cmin = np.min(20.*np.log10(np.abs(XC)))
  1671. cmax = np.max(20.*np.log10(np.abs(XC)))
  1672. canvas.ax2.xaxis.set_major_formatter(formatter)
  1673. #canvas.ax2.colorbar(mn)
  1674. canvas.draw()
  1675. ##############################3
  1676. # TODO inverse FFT to get the damn data back!!!
  1677. # self.progressTrigger.emit(percent)
  1678. # #label = "iFID="+str(iFID) + " ipm=" + str(ipm) + " istack=" + str(istack) + " ichan=" + str(ichan))
  1679. self.doneTrigger.emit()
  1680. def findSpikes(self, x, width, threshold, rollOn):
  1681. import scipy.ndimage as im
  1682. spikes = np.zeros( len(x) )
  1683. med = im.median_filter(x, width,mode='nearest')
  1684. std = np.std(x)
  1685. spikes = (np.abs(x-med) > threshold * std)
  1686. return np.array(np.where(spikes[rollOn::])) + rollOn
  1687. # def despike(self, width, threshold, itype, rollOn, win, canvas):
  1688. # from scipy import interpolate
  1689. # """ This was a stab at a despike filter. Better results were achieved using the SmartStack approach
  1690. # """
  1691. # try:
  1692. # canvas.fig.clear()
  1693. # except:
  1694. # pass
  1695. #
  1696. # canvas.ax1 = canvas.fig.add_axes([.125,.1,.725,.8])
  1697. # canvas.ax1.tick_params(axis='both', which='major', labelsize=8)
  1698. # canvas.ax1.ticklabel_format(style='sci', scilimits=(0,0), axis='y')
  1699. # iFID = 0
  1700. # for pulse in self.DATADICT["PULSES"]:
  1701. # for ipm in range(self.DATADICT["nPulseMoments"]):
  1702. # for istack in self.DATADICT["stacks"]:
  1703. # canvas.ax1.clear()
  1704. # for ichan in np.append(self.DATADICT[pulse]["chan"], self.DATADICT[pulse]["rchan"]):
  1705. # x = self.findSpikes(self.DATADICT[pulse][ichan][ipm][istack], width, threshold, rollOn)
  1706. # canvas.ax1.plot( self.DATADICT[pulse]["TIMES"], self.DATADICT[pulse][ichan][ipm][istack],
  1707. # label = pulse + " ipm=" + str(ipm) + " istack=" + str(istack) + " ichan=" + str(ichan))
  1708. # canvas.ax1.plot( self.DATADICT[pulse]["TIMES"][x], self.DATADICT[pulse][ichan][ipm][istack][x], '.', color='red' , markersize=6 )
  1709. #
  1710. # FIXED = np.zeros(len(x[0]))
  1711. # ii = 0
  1712. # for spike in np.array(x[0]).tolist():
  1713. # f = interpolate.interp1d(np.delete(self.DATADICT[pulse]["TIMES"][spike-win/2:spike+win/2], x[0]-(spike-win/2)), \
  1714. # np.delete(self.DATADICT[pulse][ichan][ipm][istack][spike-win/2:spike+win/2], x[0]-(spike-win/2)), itype)
  1715. # FIXED[ii] = f(self.DATADICT[pulse]["TIMES"][spike])
  1716. # ii += 1
  1717. # canvas.ax1.plot( self.DATADICT[pulse]["TIMES"][x[0]] , FIXED, '.', color='black' , markersize=4 )
  1718. # self.DATADICT[pulse][ichan][ipm][istack][x[0]] = FIXED
  1719. #
  1720. # canvas.ax1.legend(prop={'size':6})
  1721. # canvas.draw()
  1722. # percent = (int)(1e2*((float)(iFID*self.DATADICT["nPulseMoments"]+(ipm))/( len(self.DATADICT["PULSES"])*self.nPulseMoments)))
  1723. # self.progressTrigger.emit(percent)
  1724. # iFID += 1
  1725. # self.doneTrigger.emit()
  1726. def designFilter(self, cf, PB, SB, gpass, gstop, ftype, canvas):
  1727. ''' cf is central frequency
  1728. pb is pass band
  1729. sb is stop band
  1730. '''
  1731. TS = (cf) / (.5/self.dt)
  1732. PB = PB / (.5/self.dt) # 1/2 width pass band Muddy Creek
  1733. SB = SB / (.5/self.dt) # 1/2 width stop band Muddy Creek
  1734. # if butterworth
  1735. #[bord, wn] = signal.buttord([TS-PB,TS+PB], [TS-SB,TS+SB], 1e-1, 5.)
  1736. if ftype=="Butterworth":
  1737. [bord, wn] = signal.buttord([TS-PB,TS+PB], [TS-SB,TS+SB], gpass, gstop)
  1738. [self.filt_b, self.filt_a] = signal.butter(bord, wn, btype='bandpass', output='ba')
  1739. [self.filt_z, self.filt_p, self.filt_k] = signal.butter(bord, wn, btype='band', output='zpk')
  1740. elif ftype == "Chebychev Type II":
  1741. [bord, wn] = signal.cheb2ord([TS-PB,TS+PB], [TS-SB,TS+SB], gpass, gstop)
  1742. [self.filt_b, self.filt_a] = signal.cheby2(bord, gstop, wn, btype='bandpass', output='ba')
  1743. [self.filt_z, self.filt_p, self.filt_k] = signal.cheby2(bord, gstop, wn, btype='band', output='zpk')
  1744. elif ftype == "Elliptic":
  1745. [bord, wn] = signal.ellipord([TS-PB,TS+PB], [TS-SB,TS+SB], gpass, gstop)
  1746. [self.filt_b, self.filt_a] = signal.ellip(bord, gpass, gstop, wn, btype='bandpass', output='ba')
  1747. [self.filt_z, self.filt_p, self.filt_k] = signal.ellip(bord, gpass, gstop, wn, btype='band', output='zpk')
  1748. # if cheby2
  1749. impulse = self.mfreqz2(self.filt_b, self.filt_a, canvas)
  1750. self.fe = -5
  1751. for it in range(len(impulse[0])):
  1752. if abs(impulse[1][0][it][0]) >= .1 * gpass:# gpass:
  1753. self.fe = impulse[0][it]
  1754. canvas.draw()
  1755. return [bord, self.fe]
  1756. def downsample(self, truncate, dec, plot=False, canvas=None):
  1757. """ Downsamples and truncates the raw signal.
  1758. Args
  1759. truncate (float) : the length of the signal to truncate to
  1760. dec (int) : the decimation factor, 1 results in no downsampling
  1761. plot (bool) : perform plots
  1762. canvas : MPL axis for plotting
  1763. """
  1764. if plot:
  1765. canvas.reAx2()
  1766. canvas.ax1.set_ylabel(r"signal [nV]", fontsize=8)
  1767. canvas.ax2.set_xlabel(r"time [s]", fontsize=8)
  1768. canvas.ax2.set_ylabel(r"signal [nV]", fontsize=8)
  1769. self.samp /= dec
  1770. self.dt = 1./self.samp
  1771. iFID = 0
  1772. for pulse in self.DATADICT["PULSES"]:
  1773. RSTIMES = self.DATADICT[pulse]["TIMES"][::dec]
  1774. if truncate:
  1775. itrunc = (int)( 1e-3*truncate*self.samp )
  1776. RSTIMES = RSTIMES[0:itrunc]
  1777. for ipm in range(self.DATADICT["nPulseMoments"]):
  1778. for istack in self.DATADICT["stacks"]:
  1779. if plot:
  1780. canvas.softClear()
  1781. for ichan in np.append(self.DATADICT[pulse]["chan"], self.DATADICT[pulse]["rchan"]):
  1782. # trim off indices that don't divide evenly
  1783. ndi = np.shape(self.DATADICT[pulse][ichan][ipm][istack])[0]%dec
  1784. if ndi:
  1785. #[self.DATADICT[pulse][ichan][ipm][istack], RSTIMES] = signal.resample(self.DATADICT[pulse][ichan][ipm][istack][0:-ndi],\
  1786. # len(self.DATADICT[pulse][ichan][ipm][istack][0:-ndi])//dec,\
  1787. # self.DATADICT[pulse]["TIMES"][0:-ndi], window='hamm')
  1788. self.DATADICT[pulse][ichan][ipm][istack] = signal.decimate(self.DATADICT[pulse][ichan][ipm][istack], dec, n=None, ftype='iir', zero_phase=True)
  1789. else:
  1790. #[self.DATADICT[pulse][ichan][ipm][istack], RSTIMES] = signal.resample(self.DATADICT[pulse][ichan][ipm][istack],\
  1791. # len(self.DATADICT[pulse][ichan][ipm][istack])//dec,\
  1792. # self.DATADICT[pulse]["TIMES"], window='hamm')
  1793. self.DATADICT[pulse][ichan][ipm][istack] = signal.decimate(self.DATADICT[pulse][ichan][ipm][istack], dec, n=None, ftype='iir', zero_phase=True)
  1794. if truncate:
  1795. self.DATADICT[pulse][ichan][ipm][istack] = self.DATADICT[pulse][ichan][ipm][istack][0:itrunc]
  1796. if plot:
  1797. for ichan in self.DATADICT[pulse]["chan"]:
  1798. canvas.ax2.plot( RSTIMES, 1e9*self.DATADICT[pulse][ichan][ipm][istack], \
  1799. label = pulse + " ipm=" + str(ipm) + " istack=" + str(istack) + " ichan=" + str(ichan))
  1800. for ichan in self.DATADICT[pulse]["rchan"]:
  1801. canvas.ax1.plot( RSTIMES, 1e9*self.DATADICT[pulse][ichan][ipm][istack], \
  1802. label = pulse + " ipm=" + str(ipm) + " istack=" + str(istack) + " ichan=" + str(ichan))
  1803. canvas.ax1.legend(prop={'size':6}, loc='upper right')
  1804. canvas.ax2.legend(prop={'size':6}, loc='upper right')
  1805. canvas.draw()
  1806. percent = (int)(1e2*((float)(iFID*self.DATADICT["nPulseMoments"]+(ipm))/( len(self.DATADICT["PULSES"])*self.nPulseMoments)))
  1807. self.progressTrigger.emit(percent)
  1808. iFID += 1
  1809. self.DATADICT[pulse]["TIMES"] = RSTIMES
  1810. #####################################
  1811. # resample pulse data
  1812. for pulse in self.DATADICT["PULSES"]:
  1813. for ipm in range(self.DATADICT["nPulseMoments"]):
  1814. for istack in self.DATADICT["stacks"]:
  1815. ndi = np.shape(self.DATADICT[pulse]["CURRENT"][ipm][istack])[0]%dec
  1816. if ndi:
  1817. [self.DATADICT[pulse]["CURRENT"][ipm][istack], RSPTIMES] = signal.resample(self.DATADICT[pulse]["CURRENT"][ipm][istack][0:-ndi],\
  1818. len(self.DATADICT[pulse]["CURRENT"][ipm][istack][0:-ndi])//dec,\
  1819. self.DATADICT[pulse]["PULSE_TIMES"][0:-ndi], window='hamm')
  1820. else:
  1821. [self.DATADICT[pulse]["CURRENT"][ipm][istack], RSPTIMES] = signal.resample(self.DATADICT[pulse]["CURRENT"][ipm][istack],\
  1822. len(self.DATADICT[pulse]["CURRENT"][ipm][istack])//dec,\
  1823. self.DATADICT[pulse]["PULSE_TIMES"], window='hamm')
  1824. self.DATADICT[pulse]["PULSE_TIMES"] = RSPTIMES
  1825. self.doneTrigger.emit()
  1826. self.updateProcTrigger.emit()
  1827. def computeWindow(self, pulse, band, centre, ftype, canvas=None):
  1828. # Compute window
  1829. nd = len(self.DATADICT[pulse][self.DATADICT[pulse]["chan"][0]][0][self.DATADICT["stacks"][0]]) # num. data
  1830. fft1 = np.fft.rfft(self.DATADICT[pulse][self.DATADICT[pulse]["chan"][0]][0][self.DATADICT["stacks"][0]])
  1831. freqs = np.fft.fftfreq(nd, self.dt)
  1832. df = freqs[1] - freqs[0]
  1833. N = int((round)(band/df))
  1834. if ftype == "Hamming":
  1835. window = np.hamming(N)
  1836. elif ftype == "Hanning":
  1837. window = np.hanning(N)
  1838. elif ftype == "Rectangular":
  1839. window = np.ones(N)
  1840. elif ftype == "Flat top":
  1841. window = signal.flattop(N)
  1842. else:
  1843. print ("in windowFilter, window type undefined")
  1844. WINDOW = np.zeros(len(fft1))
  1845. ifreq = int(round(centre/df))
  1846. istart = ifreq-len(window)//2
  1847. iend = 0
  1848. if N%2:
  1849. WINDOW[ifreq-N//2:ifreq+N//2+1] = window
  1850. iend = ifreq+N//2+1
  1851. else:
  1852. WINDOW[ifreq-N//2:ifreq+N//2] = window
  1853. iend = ifreq+N//2
  1854. self.WINDOW = WINDOW
  1855. self.iWindowStart = istart
  1856. self.iWindowEnd = iend
  1857. self.FFTtimes = nd
  1858. fft1 = np.fft.irfft(WINDOW)
  1859. # calculate dead time
  1860. self.windead = 0.
  1861. for ift in np.arange(100,0,-1):
  1862. #print( ift, fft1[ift] )
  1863. if (abs(fft1[ift])/abs(fft1[0])) > 1e-2:
  1864. #print ("DEAD TIME", 1e3*self.DATADICT[pulse]["TIMES"][ift] - 1e3*self.DATADICT[pulse]["TIMES"][0] )
  1865. dead = 1e3*self.DATADICT[pulse]["TIMES"][ift] - 1e3*self.DATADICT[pulse]["TIMES"][0]
  1866. self.windead = self.DATADICT[pulse]["TIMES"][ift] - self.DATADICT[pulse]["TIMES"][0]
  1867. break
  1868. if canvas != None:
  1869. canvas.fig.clear()
  1870. canvas.ax1 = canvas.fig.add_axes([.1, .6, .75, .35])
  1871. canvas.ax2 = canvas.fig.add_axes([.1, .1, .75, .35])
  1872. canvas.ax1.plot(WINDOW)
  1873. canvas.ax2.plot( 1e3* self.DATADICT[pulse]["TIMES"][0:100] - 1e3*self.DATADICT[pulse]["TIMES"][0], fft1[0:100] )
  1874. canvas.ax2.set_xlabel("time (ms)")
  1875. canvas.ax2.set_title("IFFT")
  1876. canvas.draw()
  1877. return [WINDOW, nd, istart, iend, dead, ift]
  1878. def windowFilter(self, ftype, band, centre, trunc, canvas):
  1879. ###############################
  1880. # Window Filter (Ormsby filter http://www.xsgeo.com/course/filt.htm)
  1881. # apply window
  1882. iFID = 0
  1883. for pulse in self.DATADICT["PULSES"]:
  1884. [WINDOW, nd, istart, iend, dead, idead] = self.computeWindow(pulse, band, centre, ftype)
  1885. for istack in self.DATADICT["stacks"]:
  1886. for ipm in range(self.DATADICT["nPulseMoments"]):
  1887. for ichan in np.append(self.DATADICT[pulse]["chan"], self.DATADICT[pulse]["rchan"]):
  1888. fft = np.fft.rfft( self.DATADICT[pulse][ichan][ipm][istack] )
  1889. fft *= WINDOW
  1890. if trunc:
  1891. self.DATADICT[pulse][ichan][ipm][istack] = np.fft.irfft(fft, nd)[idead:-idead]
  1892. else:
  1893. self.DATADICT[pulse][ichan][ipm][istack] = np.fft.irfft(fft, nd)
  1894. percent = (int)(1e2*((float)(iFID*self.DATADICT["nPulseMoments"]+(ipm))/(len(self.DATADICT["PULSES"])*self.nPulseMoments)))
  1895. self.progressTrigger.emit(percent)
  1896. iFID += 1
  1897. if trunc:
  1898. self.DATADICT[pulse]["TIMES"] = self.DATADICT[pulse]["TIMES"][idead:-idead]
  1899. [WINDOWxx, ndxx, istart, iend, deadxx, ideadxx] = self.computeWindow(pulse, band, centre, ftype)
  1900. self.plotFT(canvas, istart, iend)
  1901. self.doneTrigger.emit()
  1902. def bandpassFilter(self, canvas, blank, plot=True):
  1903. if plot:
  1904. canvas.reAx2()
  1905. canvas.ax1.set_ylabel(r"signal [nV]", fontsize=8)
  1906. canvas.ax2.set_xlabel(r"time [s]", fontsize=8)
  1907. canvas.ax2.set_ylabel(r"signal [nV]", fontsize=8)
  1908. ife = (int)( max(self.fe, self.windead) * self.samp )
  1909. # Data
  1910. iFID = 0
  1911. for pulse in self.DATADICT["PULSES"]:
  1912. self.DATADICT[pulse]["TIMES"] = self.DATADICT[pulse]["TIMES"][ife:-ife]
  1913. for ipm in range(self.DATADICT["nPulseMoments"]):
  1914. for istack in self.DATADICT["stacks"]:
  1915. if plot:
  1916. canvas.softClear()
  1917. mmax = 0
  1918. for ichan in self.DATADICT[pulse]["rchan"]:
  1919. canvas.ax1.plot( self.DATADICT[pulse]["TIMES"], 1e9*self.DATADICT[pulse][ichan][ipm][istack][ife:-ife], alpha=.5)
  1920. mmax = max( mmax, np.max(1e9*self.DATADICT[pulse][ichan][ipm][istack][ife:-ife]))
  1921. for ichan in self.DATADICT[pulse]["chan"]:
  1922. canvas.ax2.plot( self.DATADICT[pulse]["TIMES"], 1e9*self.DATADICT[pulse][ichan][ipm][istack][ife:-ife], alpha=.5)
  1923. mmax = max( mmax, np.max(1e9*self.DATADICT[pulse][ichan][ipm][istack][ife:-ife]))
  1924. canvas.ax2.set_prop_cycle(None)
  1925. canvas.ax1.set_prop_cycle(None)
  1926. canvas.ax1.set_ylim(-mmax, mmax)
  1927. for ichan in self.DATADICT[pulse]["rchan"]:
  1928. # reflect signal back on itself to reduce gibbs effects on early times
  1929. #nr = len( self.DATADICT[pulse][ichan][ipm][istack] ) - 1 + ife
  1930. #refl = np.append( -1*self.DATADICT[pulse][ichan][ipm][istack][::-1][0:-1], self.DATADICT[pulse][ichan][ipm][istack] )
  1931. #reflfilt = signal.filtfilt( self.filt_b, self.filt_a, refl )
  1932. #self.DATADICT[pulse][ichan][ipm][istack] = reflfilt[nr:-ife]
  1933. # don't reflect
  1934. self.DATADICT[pulse][ichan][ipm][istack] = \
  1935. signal.filtfilt(self.filt_b, self.filt_a, self.DATADICT[pulse][ichan][ipm][istack])[ife:-ife]
  1936. # plot
  1937. if plot:
  1938. canvas.ax1.plot( self.DATADICT[pulse]["TIMES"], 1e9*self.DATADICT[pulse][ichan][ipm][istack], \
  1939. label = pulse + " ipm=" + str(ipm) + " istack=" + str(istack) + " rchan=" + str(ichan))
  1940. for ichan in self.DATADICT[pulse]["chan"]:
  1941. # reflect signal back on itself to reduce gibbs effects on early times
  1942. #nr = len( self.DATADICT[pulse][ichan][ipm][istack] ) - 1 + ife
  1943. #refl = np.append( -1*self.DATADICT[pulse][ichan][ipm][istack][::-1][0:-1], self.DATADICT[pulse][ichan][ipm][istack] )
  1944. #reflfilt = signal.filtfilt( self.filt_b, self.filt_a, refl )
  1945. #self.DATADICT[pulse][ichan][ipm][istack] = reflfilt[nr:-ife]
  1946. # don't reflect
  1947. self.DATADICT[pulse][ichan][ipm][istack] = \
  1948. scipy.signal.filtfilt(self.filt_b, self.filt_a, self.DATADICT[pulse][ichan][ipm][istack])[ife:-ife]
  1949. # plot
  1950. if plot:
  1951. canvas.ax2.plot( self.DATADICT[pulse]["TIMES"], 1e9*self.DATADICT[pulse][ichan][ipm][istack], \
  1952. label = "data " + pulse + " ipm=" + str(ipm) + " istack=" + str(istack) + " chan=" + str(ichan))
  1953. if plot:
  1954. canvas.ax1.legend(prop={'size':6}, loc='upper right')
  1955. canvas.ax2.legend(prop={'size':6}, loc='upper right')
  1956. canvas.draw()
  1957. percent = (int)(1e2*((float)(iFID*self.DATADICT["nPulseMoments"]+(ipm))/(len(self.DATADICT["PULSES"])*self.nPulseMoments)))
  1958. self.progressTrigger.emit(percent)
  1959. iFID += 1
  1960. self.doneTrigger.emit()
  1961. self.updateProcTrigger.emit()
  1962. def loadGMRBinaryFID( self, rawfname, istack ):
  1963. """ Reads a single binary GMR file and fills into DATADICT
  1964. """
  1965. #################################################################################
  1966. # figure out key data indices
  1967. # Pulse
  1968. nps = (int)((self.prePulseDelay)*self.samp)
  1969. npul = (int)(self.pulseLength[0]*self.samp) #+ 100
  1970. # Data
  1971. nds = nps+npul+(int)((self.deadTime)*self.samp); # indice pulse 1 data starts
  1972. nd1 = (int)(1.*self.samp) # samples in first pulse
  1973. invGain = 1./self.RxGain
  1974. invCGain = self.CurrentGain
  1975. pulse = "Pulse 1"
  1976. chan = self.DATADICT[pulse]["chan"]
  1977. rchan = self.DATADICT[pulse]["rchan"]
  1978. rawFile = open( rawfname, 'rb')
  1979. for ipm in range(self.nPulseMoments):
  1980. buf1 = rawFile.read(4)
  1981. buf2 = rawFile.read(4)
  1982. N_chan = struct.unpack('>i', buf1 )[0]
  1983. N_samp = struct.unpack('>i', buf2 )[0]
  1984. T = N_samp * self.dt
  1985. TIMES = np.arange(0, T, self.dt) - .0002 # small offset in GMR DAQ?
  1986. DATA = np.zeros([N_samp, N_chan+1])
  1987. for ichan in range(N_chan):
  1988. DATADUMP = rawFile.read(4*N_samp)
  1989. for irec in range(N_samp):
  1990. DATA[irec,ichan] = struct.unpack('>f', DATADUMP[irec*4:irec*4+4])[0]
  1991. # Save into Data Cube
  1992. for ichan in chan:
  1993. self.DATADICT["Pulse 1"][ichan][ipm][istack] = DATA[:,eval(ichan)+3][nds:nds+nd1] * invGain
  1994. self.DATADICT["Pulse 1"]["TIMES"] = TIMES[nds:nds+nd1]
  1995. self.DATADICT["Pulse 1"]["CURRENT"][ipm][istack] = DATA[:,1][nps:nps+npul] * invCGain
  1996. self.DATADICT["Pulse 1"]["PULSE_TIMES"] = TIMES[nps:nps+npul]
  1997. # reference channels?
  1998. for ichan in rchan:
  1999. self.DATADICT["Pulse 1"][ichan][ipm][istack] = DATA[:,eval(ichan)+3][nds:nds+nd1] * invGain
  2000. self.DATADICT["Pulse 1"]["TIMES"] = TIMES[nds:nds+nd1]
  2001. def loadGMRASCIIFID( self, rawfname, istack ):
  2002. """Based on the geoMRI instrument manufactured by VistaClara. Imports
  2003. a suite of raw .lvm files with the following format (on one line)
  2004. time(s) DC_Bus/100(V) Current+/75(A) Curr-/75(A) Voltage+/200(V) \
  2005. Ch1(V) Ch2(V) Ch3(V) Ch4(V)
  2006. Sampling rate is assumed at 50 kHz
  2007. """
  2008. import pandas as pd
  2009. #################################################################################
  2010. # figure out key data indices
  2011. # Pulse
  2012. nps = (int)((self.prePulseDelay)*self.samp)
  2013. npul = (int)(self.pulseLength[0]*self.samp) #+ 100
  2014. # Data
  2015. nds = nps+npul+(int)((self.deadTime)*self.samp); # indice pulse 1 data starts
  2016. nd1 = (int)(1.*self.samp) - nds # samples in first pulse
  2017. ndr = (int)(1.*self.samp) # samples in record
  2018. invGain = 1./self.RxGain
  2019. invCGain = self.CurrentGain
  2020. pulse = "Pulse 1"
  2021. chan = self.DATADICT[pulse]["chan"]
  2022. rchan = self.DATADICT[pulse]["rchan"]
  2023. T = 1.5 #N_samp * self.dt
  2024. TIMES = np.arange(0, T, self.dt) - .0002 # small offset in GMR DAQ?
  2025. self.DATADICT["Pulse 1"]["TIMES"] = TIMES[nds:nds+nd1]
  2026. self.DATADICT["Pulse 1"]["PULSE_TIMES"] = TIMES[nps:nps+npul]
  2027. # pandas is much faster than numpy for io
  2028. #DATA = np.loadtxt(rawfname)
  2029. DATA = pd.read_csv(rawfname, header=None, sep="\t").values
  2030. for ipm in range(self.nPulseMoments):
  2031. for ichan in np.append(chan,rchan):
  2032. self.DATADICT["Pulse 1"][ichan][ipm][istack] = DATA[:, eval(ichan)+4][nds:(nds+nd1)] * invGain
  2033. self.DATADICT["Pulse 1"]["CURRENT"][ipm][istack] = DATA[:,2][nps:nps+npul] * invCGain
  2034. nds += ndr
  2035. nps += ndr
  2036. def loadGMRASCIIT1( self, rawfname, istack ):
  2037. """Based on the geoMRI instrument manufactured by VistaClara. Imports
  2038. a suite of raw .lvm files with the following format (on one line)
  2039. time(s) DC_Bus/100(V) Current+/75(A) Curr-/75(A) Voltage+/200(V) \
  2040. Ch1(V) Ch2(V) Ch3(V) Ch4(V)
  2041. Sampling rate is assumed at 50 kHz
  2042. """
  2043. import pandas as pd
  2044. #################################################################################
  2045. # figure out key data indices
  2046. # Pulse
  2047. nps = (int)((self.prePulseDelay)*self.samp)
  2048. npul = (int)(self.pulseLength[0]*self.samp) #+ 100
  2049. # phase cycling
  2050. # Older T1 GMR data had a curious phase cycling
  2051. npc = 2 #(int)( self.samp / self.transFreq / 6 )
  2052. #print("npc", npc)
  2053. # Data
  2054. nds = nps+npul+(int)((self.deadTime)*self.samp); # indice pulse 1 data starts
  2055. nd1 = (int)( (self.interpulseDelay) * self.samp) - nds # samples in first pulse
  2056. ndr = (int)( (self.interpulseDelay) * self.samp) # samples in record
  2057. invGain = 1./self.RxGain
  2058. invCGain = self.CurrentGain
  2059. pulse = "Pulse 1"
  2060. chan = self.DATADICT[pulse]["chan"]
  2061. rchan = self.DATADICT[pulse]["rchan"]
  2062. T = 1.5 #N_samp * self.dt
  2063. TIMES = np.arange(0, T, self.dt) - .0002 # small offset in GMR DAQ?
  2064. self.DATADICT["Pulse 1"]["TIMES"] = TIMES[nds:nds+nd1]
  2065. self.DATADICT["Pulse 1"]["PULSE_TIMES"] = TIMES[nps:nps+npul]
  2066. # pandas is much faster than numpy for io
  2067. #DATA = np.loadtxt(rawfname)
  2068. DATA = pd.read_csv(rawfname, header=None, sep="\t").values
  2069. for ipm in range(self.nPulseMoments):
  2070. for ichan in np.append(chan,rchan):
  2071. if ipm%2:
  2072. self.DATADICT["Pulse 1"][ichan][ipm][istack] = DATA[:, eval(ichan)+4][(nds+npc):(nds+nd1+npc)] * invGain
  2073. #self.DATADICT["Pulse 1"][ichan][ipm][istack] = DATA[:, eval(ichan)+4][nds:(nds+nd1)] * invGain
  2074. self.DATADICT["Pulse 1"]["CURRENT"][ipm][istack] = DATA[:,2][nps+npc:nps+npul+npc] * invCGain
  2075. else:
  2076. self.DATADICT["Pulse 1"][ichan][ipm][istack] = DATA[:, eval(ichan)+4][nds:(nds+nd1)] * invGain
  2077. self.DATADICT["Pulse 1"]["CURRENT"][ipm][istack] = DATA[:,2][nps:nps+npul] * invCGain
  2078. nds += ndr
  2079. nps += ndr
  2080. def loadFIDData(self, base, procStacks, chanin, rchanin, FIDProc, canvas, deadTime, plot):
  2081. '''
  2082. Loads a GMR FID dataset, reads binary and ASCII format files
  2083. '''
  2084. canvas.reAx3(True,False)
  2085. chan = []
  2086. for ch in chanin:
  2087. chan.append(str(ch))
  2088. rchan = []
  2089. for ch in rchanin:
  2090. rchan.append(str(ch))
  2091. self.deadTime = deadTime # instrument dead time before measurement
  2092. self.samp = 50000. # in case this is a reproc, these might have
  2093. self.dt = 1./self.samp # changed
  2094. #################################################################################
  2095. # Data structures
  2096. PULSES = [FIDProc]
  2097. PULSES = ["Pulse 1"]
  2098. self.DATADICT = {}
  2099. self.DATADICT["nPulseMoments"] = self.nPulseMoments
  2100. self.DATADICT["stacks"] = procStacks
  2101. self.DATADICT["PULSES"] = PULSES
  2102. for pulse in PULSES:
  2103. self.DATADICT[pulse] = {}
  2104. self.DATADICT[pulse]["chan"] = chan # TODO these should not be a subet of pulse! for GMR all
  2105. self.DATADICT[pulse]["rchan"] = rchan # data are consistent
  2106. self.DATADICT[pulse]["CURRENT"] = {}
  2107. for ichan in np.append(chan,rchan):
  2108. self.DATADICT[pulse][ichan] = {}
  2109. for ipm in range(self.nPulseMoments):
  2110. self.DATADICT[pulse][ichan][ipm] = {}
  2111. self.DATADICT[pulse]["CURRENT"][ipm] = {}
  2112. for istack in procStacks:
  2113. self.DATADICT[pulse][ichan][ipm][istack] = np.zeros(3)
  2114. self.DATADICT[pulse]["CURRENT"][ipm][istack] = np.zeros(3)
  2115. ##############################################
  2116. # Read in binary (.lvm) data
  2117. iistack = 0
  2118. for istack in procStacks:
  2119. if self.nDAQVersion <= 1.0:
  2120. try:
  2121. self.loadGMRASCIIFID( base + "_" + str(istack), istack )
  2122. except:
  2123. self.loadGMRASCIIFID( base + "_" + str(istack) + ".lvm", istack )
  2124. elif self.nDAQVersion < 2.3:
  2125. self.loadGMRASCIIFID( base + "_" + str(istack), istack )
  2126. else:
  2127. self.loadGMRBinaryFID( base + "_" + str(istack) + ".lvm", istack )
  2128. if plot:
  2129. for ipm in range(self.nPulseMoments):
  2130. canvas.softClear()
  2131. for ichan in chan:
  2132. canvas.ax1.plot(self.DATADICT["Pulse 1"]["PULSE_TIMES"], self.DATADICT["Pulse 1"]["CURRENT"][ipm][istack] , color='black')
  2133. canvas.ax3.plot(self.DATADICT["Pulse 1"]["TIMES"], self.DATADICT["Pulse 1"][ichan][ipm][istack], label="Pulse 1 FID data ch. "+str(ichan)) #, color='blue')
  2134. for ichan in rchan:
  2135. canvas.ax2.plot(self.DATADICT["Pulse 1"]["TIMES"], self.DATADICT["Pulse 1"][ichan][ipm][istack], label="Pulse 1 FID ref ch. "+str(ichan)) #, color='blue')
  2136. canvas.ax3.legend(prop={'size':6}, loc='upper right')
  2137. canvas.ax2.legend(prop={'size':6}, loc='upper right')
  2138. canvas.ax1.set_title("stack "+str(istack)+" pulse index " + str(ipm), fontsize=8)
  2139. canvas.ax1.set_ylabel("Current [A]", fontsize=8)
  2140. canvas.ax1.ticklabel_format(style='sci', scilimits=(0,0), axis='y')
  2141. canvas.ax2.set_ylabel("RAW signal [V]", fontsize=8)
  2142. canvas.ax3.set_ylabel("RAW signal [V]", fontsize=8)
  2143. canvas.ax2.tick_params(axis='both', which='major', labelsize=8)
  2144. canvas.ax2.tick_params(axis='both', which='minor', labelsize=6)
  2145. canvas.ax2.set_xlabel("time [s]", fontsize=8)
  2146. canvas.draw()
  2147. percent = (int) (1e2*((float)((iistack*self.nPulseMoments+ipm+1)) / (len(procStacks)*self.nPulseMoments)))
  2148. self.progressTrigger.emit(percent)
  2149. iistack += 1
  2150. # percent = (int) (1e2*((float)((iistack*self.nPulseMoments+ipm+1)) / (len(procStacks)*self.nPulseMoments)))
  2151. # self.progressTrigger.emit(percent)
  2152. # iistack += 1
  2153. self.enableDSP()
  2154. self.doneTrigger.emit()
  2155. def loadT1Data(self, base, procStacks, chanin, rchanin, FIDProc, canvas, deadTime, plot):
  2156. '''
  2157. Loads a GMR T1 dataset, reads binary and ASCII format files
  2158. '''
  2159. canvas.reAx3(True,False)
  2160. chan = []
  2161. for ch in chanin:
  2162. chan.append(str(ch))
  2163. rchan = []
  2164. for ch in rchanin:
  2165. rchan.append(str(ch))
  2166. # not in any headers but this has changed, NOT the place to do this. MOVE
  2167. #self.prePulseDelay = 0.01 # delay before pulse
  2168. self.deadTime = deadTime # instrument dead time before measurement
  2169. self.samp = 50000. # in case this is a reproc, these might have
  2170. self.dt = 1./self.samp # changed
  2171. #################################################################################
  2172. # Data structures
  2173. PULSES = [FIDProc]
  2174. self.DATADICT = {}
  2175. self.DATADICT["nPulseMoments"] = self.nPulseMoments
  2176. self.DATADICT["stacks"] = procStacks
  2177. self.DATADICT["PULSES"] = PULSES
  2178. for pulse in PULSES:
  2179. self.DATADICT[pulse] = {}
  2180. self.DATADICT[pulse]["chan"] = chan # TODO these should not be a subet of pulse! for GMR all
  2181. self.DATADICT[pulse]["rchan"] = rchan # data are consistent
  2182. self.DATADICT[pulse]["CURRENT"] = {}
  2183. for ichan in np.append(chan,rchan):
  2184. self.DATADICT[pulse][ichan] = {}
  2185. for ipm in range(self.nPulseMoments):
  2186. self.DATADICT[pulse][ichan][ipm] = {}
  2187. self.DATADICT[pulse]["CURRENT"][ipm] = {}
  2188. for istack in procStacks:
  2189. self.DATADICT[pulse][ichan][ipm][istack] = np.zeros(3)
  2190. self.DATADICT[pulse]["CURRENT"][ipm][istack] = np.zeros(3)
  2191. ##############################################
  2192. # Read in binary (.lvm) data
  2193. iistack = 0
  2194. fnames = []
  2195. for istack in procStacks:
  2196. if self.nDAQVersion < 2.3:
  2197. #rawfname = base + "_" + str(istack)
  2198. #self.loadGMRASCIIFID( base + "_" + str(istack), istack )
  2199. self.loadGMRASCIIT1( base + "_" + str(istack), istack )
  2200. else:
  2201. self.loadGMRBinaryFID( base + "_" + str(istack) + ".lvm", istack )
  2202. #fnames.append( base + "_" + str(istack) + ".lvm" )
  2203. percent = (int) (1e2*((float)((iistack*self.nPulseMoments+ipm+1)) / (len(procStacks)*self.nPulseMoments)))
  2204. self.progressTrigger.emit(percent)
  2205. iistack += 1
  2206. # multiprocessing load data
  2207. #info = {}
  2208. #info["prePulseDelay"] = self.prePulseDelay
  2209. #info["samp"] = self.samp
  2210. #with multiprocessing.Pool() as pool:
  2211. # results = pool.starmap( xxloadGMRBinaryFID, ( fnames, zip(itertools.repeat(info)) ) )
  2212. # Plotting
  2213. if plot:
  2214. iistack = 0
  2215. for istack in procStacks:
  2216. #for ipm in range(0,7,1):
  2217. for ipm in range(self.nPulseMoments):
  2218. canvas.ax1.clear()
  2219. canvas.ax2.clear()
  2220. canvas.ax3.clear()
  2221. #canvas.fig.patch.set_facecolor('blue')
  2222. for ichan in chan:
  2223. canvas.ax1.plot(self.DATADICT["Pulse 1"]["PULSE_TIMES"], self.DATADICT["Pulse 1"]["CURRENT"][ipm][istack] , color='black')
  2224. canvas.ax3.plot(self.DATADICT["Pulse 1"]["TIMES"], self.DATADICT["Pulse 1"][ichan][ipm][istack], label="Pulse 1 FID data ch. "+str(ichan)) #, color='blue')
  2225. for ichan in rchan:
  2226. canvas.ax2.plot(self.DATADICT["Pulse 1"]["TIMES"], self.DATADICT["Pulse 1"][ichan][ipm][istack], label="Pulse 1 FID ref ch. "+str(ichan)) #, color='blue')
  2227. canvas.ax3.legend(prop={'size':6}, loc='upper right')
  2228. canvas.ax2.legend(prop={'size':6}, loc='upper right')
  2229. canvas.ax1.set_title("stack "+str(istack)+" pulse index " + str(ipm), fontsize=8)
  2230. canvas.ax1.set_xlabel("time [s]", fontsize=8)
  2231. canvas.ax1.set_ylabel("Current [A]", fontsize=8)
  2232. canvas.ax1.ticklabel_format(style='sci', scilimits=(0,0), axis='y')
  2233. canvas.ax2.set_ylabel("RAW signal [V]", fontsize=8)
  2234. canvas.ax2.tick_params(axis='both', which='major', labelsize=8)
  2235. canvas.ax2.tick_params(axis='both', which='minor', labelsize=6)
  2236. canvas.ax2.set_xlabel("time [s]", fontsize=8)
  2237. canvas.ax2.ticklabel_format(style='sci', scilimits=(0,0), axis='y')
  2238. canvas.ax3.ticklabel_format(style='sci', scilimits=(0,0), axis='y')
  2239. canvas.draw()
  2240. #canvas.draw()
  2241. percent = (int) (1e2*((float)((iistack*self.nPulseMoments+ipm+1)) / (len(procStacks)*self.nPulseMoments)))
  2242. self.progressTrigger.emit(percent)
  2243. iistack += 1
  2244. self.enableDSP()
  2245. self.doneTrigger.emit()
  2246. def load4PhaseT1Data(self, base, procStacks, chan, rchan, FIDProc, canvas, deadTime, plot):
  2247. """
  2248. Designed to load GMR 4-phase data which use the following convention for phase cycles
  2249. P1 P2
  2250. Stack 1 -> 0 0 <-- <--
  2251. Stack 2 -> 0 pi/2 | <-- <--
  2252. Stack 3 -> pi/2 0 <-- | <--
  2253. Stack 4 -> pi/2 pi/2 <-- <--
  2254. The cycle is determined by stack indice. Walbrecker proposes for pulse2 data (Stack2 - Stack1) / 2
  2255. equivalently (Stack 4 - Stack3) will yield the same voltage response wrt. the second pulse.
  2256. Alternatively Stack 4 can be converted to be aligned with Stack 1 by negating, and Stack 3 Can be aligned with Stack 2 by negating
  2257. Then there are just the two phase cycles that can be stacked like normal.
  2258. Unfortunately, we need to stack each cycle first, then perform corrections for phase cycling. The reason for this is that otherwise,
  2259. the entire point is lost, as the signal that is desired to be cancelled out may not be balanced evenly across the stacks. That is to say,
  2260. if there is an uneven number of a certain phase cycle.
  2261. We could, I suppose impose this condition, but I think I would rather not?
  2262. + more samples for std. deviation calculation
  2263. + single spikes will have less residual effect
  2264. - can no longer do normality tests etc. and remove data that are suspect.
  2265. - requires a dumb stack, and may also require removal of entire stacks of data
  2266. Additonally, the GMR varies phase as a function of pulse moment index, so that the first pusle moment is zero phase, the second is pi/2 the third is zero ...
  2267. This however, is altered by the above convention. It gets a little complicated...
  2268. """
  2269. import struct
  2270. canvas.reAx3()
  2271. # not in any headers but this has changed, NOT the place to do this. MOVE
  2272. self.prePulseDelay = 0.01 # delay before pulse
  2273. self.deadTime = deadTime # instrument dead time before measurement
  2274. self.samp = 50000. # in case this is a reproc, these might have
  2275. self.dt = 1./self.samp # changed
  2276. invGain = 1./self.RxGain
  2277. invCGain = self.CurrentGain
  2278. #################################################################################
  2279. # figure out key data indices
  2280. # Pulse
  2281. nps = (int)((self.prePulseDelay)*self.samp)
  2282. nps2 = (int)((self.prePulseDelay+self.interpulseDelay)*self.samp)
  2283. npul = (int)(self.pulseLength[0]*self.samp) #+ 100
  2284. np2 = (int)(self.pulseLength[1]*self.samp) #+ 100
  2285. # Data
  2286. nds = nps+npul+(int)((self.deadTime)*self.samp); # indice pulse 1 data starts
  2287. nd1 = (int)((self.interpulseDelay)*self.samp) # samples in first pulse
  2288. nd2s = nps+npul+nd1+(int)((self.deadTime)*self.samp); # indice pulse 2 data starts
  2289. nd2 = (int)((1.)*self.samp) # samples in first pulse
  2290. nd1 -= (int)((.028)*self.samp) + nps # some time to get ready for next pulse
  2291. #################################################################################
  2292. # Data structures
  2293. PULSES = [FIDProc]
  2294. if FIDProc == "Both":
  2295. PULSES = ["Pulse 1","Pulse 2"]
  2296. self.DATADICT = {}
  2297. self.DATADICT["nPulseMoments"] = self.nPulseMoments
  2298. self.DATADICT["stacks"] = procStacks
  2299. self.DATADICT["PULSES"] = PULSES
  2300. for pulse in PULSES:
  2301. self.DATADICT[pulse] = {}
  2302. self.DATADICT[pulse]["chan"] = chan
  2303. self.DATADICT[pulse]["rchan"] = rchan
  2304. self.DATADICT[pulse]["CURRENT"] = {}
  2305. for ichan in np.append(chan,rchan):
  2306. self.DATADICT[pulse][ichan] = {}
  2307. for ipm in range(self.nPulseMoments):
  2308. self.DATADICT[pulse][ichan][ipm] = {}
  2309. self.DATADICT[pulse]["CURRENT"][ipm] = {}
  2310. for istack in procStacks:
  2311. self.DATADICT[pulse][ichan][ipm][istack] = np.zeros(3)
  2312. self.DATADICT[pulse]["CURRENT"][ipm][istack] = np.zeros(3)
  2313. ##############################################
  2314. # Read in binary data
  2315. iistack = 0
  2316. for istack in procStacks:
  2317. rawFile = open(base + "_" + str(istack) + ".lvm", 'rb')
  2318. for ipm in range(self.nPulseMoments):
  2319. N_chan = struct.unpack('>i', rawFile.read(4))[0]
  2320. N_samp = struct.unpack('>i', rawFile.read(4))[0]
  2321. T = N_samp * self.dt
  2322. TIMES = np.arange(0, T, self.dt) - .0002 # small offset in GMR DAQ?
  2323. DATA = np.zeros([N_samp, N_chan+1])
  2324. for ichan in range(N_chan):
  2325. DATADUMP = rawFile.read(4*N_samp)
  2326. for irec in range(N_samp):
  2327. DATA[irec,ichan] = struct.unpack('>f', DATADUMP[irec*4:irec*4+4])[0]
  2328. if plot:
  2329. #canvas.ax1.clear()
  2330. #canvas.ax2.clear()
  2331. canvas.softClear()
  2332. li = np.shape( DATA[:,4][nd2s:nd2s+nd2] )[0]
  2333. ######################################
  2334. # save into DATA cube
  2335. # TODO, changing iFID to 'Pulse 1' or 'Pulse 2'
  2336. for ichan in chan:
  2337. if FIDProc == "Pulse 1":
  2338. self.DATADICT["Pulse 1"][ichan][ipm][istack] = DATA[:,ichan+3][nds:nds+nd1] * invGain
  2339. self.DATADICT["Pulse 1"]["TIMES"] = TIMES[nds:nds+nd1]
  2340. self.DATADICT["Pulse 1"]["CURRENT"][ipm][istack] = DATA[:,1][nps:nps+npul] * invCGain
  2341. self.DATADICT["Pulse 1"]["PULSE_TIMES"] = TIMES[nps:nps+npul]
  2342. if plot:
  2343. canvas.ax3.plot(self.DATADICT["Pulse 1"]["TIMES"], self.DATADICT["Pulse 1"][ichan][ipm][istack], label="Pulse 1 FID data ch. "+str(ichan)) #, color='blue')
  2344. canvas.ax1.plot(self.DATADICT["Pulse 1"]["PULSE_TIMES"], self.DATADICT["Pulse 1"]["CURRENT"][ipm][istack] , color='black')
  2345. elif FIDProc == "Pulse 2":
  2346. print("TODO fix y scale")
  2347. self.DATADICT["Pulse 2"][ichan][ipm][istack] = DATA[:,ichan+3][nd2s:nd2s+nd2] *invGain
  2348. self.DATADICT["Pulse 2"]["TIMES"] = TIMES[nd2s:nd2s+nd2]
  2349. self.DATADICT["Pulse 2"]["CURRENT"][ipm][istack] = DATA[:,1][nps2:nps2+np2] * invCGain
  2350. self.DATADICT["Pulse 2"]["PULSE_TIMES"] = TIMES[nps2:nps2+np2]
  2351. if plot:
  2352. canvas.ax3.plot(self.DATADICT["Pulse 2"]["TIMES"], self.DATADICT["Pulse 2"][ichan][ipm][istack], label="Pulse 2 FID data ch. "+str(ichan)) #, color='blue')
  2353. canvas.ax1.plot( self.DATADICT["Pulse 2"]["PULSE_TIMES"], self.DATADICT["Pulse 2"]["CURRENT"][ipm][istack], color='black' )
  2354. else:
  2355. self.DATADICT["Pulse 1"][ichan][ipm][istack] = DATA[:,ichan+3][nds:nds+nd1] * invGain
  2356. self.DATADICT["Pulse 2"][ichan][ipm][istack] = DATA[:,ichan+3][nd2s:nd2s+nd2] * invGain
  2357. self.DATADICT["Pulse 1"]["TIMES"] = TIMES[nds:nds+nd1]
  2358. self.DATADICT["Pulse 2"]["TIMES"] = TIMES[nd2s:nd2s+nd2]
  2359. self.DATADICT["Pulse 1"]["CURRENT"][ipm][istack] = DATA[:,1][nps:nps+npul] * invCGain
  2360. self.DATADICT["Pulse 1"]["PULSE_TIMES"] = TIMES[nps:nps+npul]
  2361. self.DATADICT["Pulse 2"]["CURRENT"][ipm][istack] = DATA[:,1][nps2:nps2+np2] * invCGain
  2362. self.DATADICT["Pulse 2"]["PULSE_TIMES"] = TIMES[nps2:nps2+np2]
  2363. if plot:
  2364. canvas.ax3.plot(self.DATADICT["Pulse 1"]["TIMES"], self.DATADICT["Pulse 1"][ichan][ipm][istack], label="Pulse 1 FID data ch. "+str(ichan)) #, color='blue')
  2365. canvas.ax3.plot(self.DATADICT["Pulse 2"]["TIMES"], self.DATADICT["Pulse 2"][ichan][ipm][istack], label="Pulse 2 FID data ch. "+str(ichan)) #, color='blue')
  2366. canvas.ax1.plot( self.DATADICT["Pulse 1"]["PULSE_TIMES"], self.DATADICT["Pulse 1"]["CURRENT"][ipm][istack] , color='black' )
  2367. canvas.ax1.plot( self.DATADICT["Pulse 2"]["PULSE_TIMES"], self.DATADICT["Pulse 2"]["CURRENT"][ipm][istack] , color='black')
  2368. for ichan in rchan:
  2369. if FIDProc == "Pulse 1":
  2370. self.DATADICT["Pulse 1"][ichan][ipm][istack] = DATA[:,ichan+3][nds:nds+nd1] * invGain
  2371. self.DATADICT["Pulse 1"]["TIMES"] = TIMES[nds:nds+nd1]
  2372. if plot:
  2373. canvas.ax2.plot(self.DATADICT["Pulse 1"]["TIMES"], self.DATADICT["Pulse 1"][ichan][ipm][istack], label="Pulse 1 FID ref ch. "+str(ichan)) #, color='blue')
  2374. elif FIDProc == "Pulse 2":
  2375. self.DATADICT["Pulse 2"][ichan][ipm][istack] = DATA[:,ichan+3][nd2s:nd2s+nd2] * invGain
  2376. self.DATADICT["Pulse 2"]["TIMES"] = TIMES[nd2s:nd2s+nd2]
  2377. if plot:
  2378. canvas.ax2.plot(self.DATADICT["Pulse 2"]["TIMES"], self.DATADICT["Pulse 2"][ichan][ipm][istack], label="Pulse 2 FID ref ch. "+str(ichan)) #, color='blue')
  2379. else:
  2380. self.DATADICT["Pulse 1"][ichan][ipm][istack] = DATA[:,ichan+3][nds:nds+nd1] * invGain
  2381. self.DATADICT["Pulse 2"][ichan][ipm][istack] = DATA[:,ichan+3][nd2s:nd2s+nd2] * invGain
  2382. self.DATADICT["Pulse 1"]["TIMES"] = TIMES[nds:nds+nd1]
  2383. self.DATADICT["Pulse 2"]["TIMES"] = TIMES[nd2s:nd2s+nd2]
  2384. if plot:
  2385. canvas.ax2.plot(self.DATADICT["Pulse 1"]["TIMES"], self.DATADICT["Pulse 1"][ichan][ipm][istack], label="Pulse 1 FID ref ch. "+str(ichan)) #, color='blue')
  2386. canvas.ax2.plot(self.DATADICT["Pulse 2"]["TIMES"], self.DATADICT["Pulse 2"][ichan][ipm][istack], label="Pulse 2 FID ref ch. "+str(ichan)) #, color='blue')
  2387. if plot:
  2388. canvas.ax3.legend(prop={'size':6}, loc='upper right')
  2389. canvas.ax2.legend(prop={'size':6}, loc='upper right')
  2390. canvas.ax1.set_title("stack "+str(istack)+" pulse index " + str(ipm), fontsize=8)
  2391. canvas.ax1.set_xlabel("time [s]", fontsize=8)
  2392. canvas.ax3.set_ylabel("RAW signal [V]", fontsize=8)
  2393. canvas.ax2.set_ylabel("RAW signal [V]", fontsize=8)
  2394. canvas.ax1.set_ylabel("Current [A]", fontsize=8)
  2395. #canvas.ax2.tick_params(axis='both', which='major', labelsize=8)
  2396. #canvas.ax2.tick_params(axis='both', which='minor', labelsize=6)
  2397. #canvas.ax2.ticklabel_format(style='sci', scilimits=(0,0), axis='y')
  2398. #canvas.ax1.ticklabel_format(style='sci', scilimits=(0,0), axis='y')
  2399. canvas.draw()
  2400. # update GUI of where we are
  2401. percent = (int) (1e2*((float)((iistack*self.nPulseMoments+ipm+1)) / (len(procStacks)*self.nPulseMoments)))
  2402. self.progressTrigger.emit(percent)
  2403. iistack += 1
  2404. self.enableDSP()
  2405. self.doneTrigger.emit()
  2406. if __name__ == "__main__":
  2407. if len(sys.argv) < 4:
  2408. print( "mrsurvey path/to/header <stack1> <stackN> ")
  2409. exit()
  2410. GMR = GMRDataProcessor()
  2411. GMR.readHeaderFile(sys.argv[1])
  2412. GMR.Print()
  2413. if GMR.pulseType == "FID":
  2414. GMR.loadFIDData(sys.argv[1], sys.argv[2], sys.argv[3], 5)
  2415. if GMR.pulseType == "4PhaseT1":
  2416. GMR.load4PhaseT1Data(sys.argv[1], sys.argv[2], sys.argv[3], 5)
  2417. pylab.show()