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 loadGMRBinaryFID( rawfname, istack, 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, nK, f0, f1, plot, canvas):
  463. def harmonicModel(self, nF, \
  464. f0, f0K1, f0KN, f0Ks, f0ns, \
  465. f1, f1K1, f1KN, f1Ks, \
  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. plot = should Akvo plot the results
  478. canvas = mpl plotting axis
  479. """
  480. #print("harmonic modelling...", f0)
  481. #plot = True
  482. if plot:
  483. canvas.reAx2()
  484. #canvas.ax1.tick_params(axis='both', which='major', labelsize=8)
  485. #canvas.ax1.ticklabel_format(style='sci', axis='y') #, scilimits=(0,0))
  486. #canvas.ax2.tick_params(axis='both', which='major', labelsize=8)
  487. #canvas.ax2.ticklabel_format(style='sci', axis='y') #, scilimits=(0,0))
  488. canvas.ax1.set_ylabel(r"signal [nV]", fontsize=8)
  489. canvas.ax2.set_xlabel(r"time [s]", fontsize=8)
  490. canvas.ax2.set_ylabel(r"signal [nV]", fontsize=8)
  491. #canvas.ax1.tick_params(axis='both', which='major', labelsize=8)
  492. #canvas.ax2.tick_params(axis='both', which='major', labelsize=8)
  493. # Data
  494. iFID = 0
  495. # stores previous f0 as starting point in non-linear search
  496. f0p = {}
  497. f1p = {}
  498. for pulse in self.DATADICT["PULSES"]:
  499. for rchan in self.DATADICT[pulse]["rchan"]:
  500. f0p[rchan] = f0
  501. f1p[rchan] = f1+1e-1
  502. for chan in self.DATADICT[pulse]["chan"]:
  503. f0p[chan] = f0
  504. f1p[chan] = f1+1e-1
  505. for pulse in self.DATADICT["PULSES"]:
  506. self.DATADICT[pulse]["TIMES"] = self.DATADICT[pulse]["TIMES"]
  507. Nseg = int( np.floor(len( self.DATADICT[pulse]["TIMES"] ) / f0ns) )
  508. for istack in self.DATADICT["stacks"]:
  509. for ipm in range(self.DATADICT["nPulseMoments"]):
  510. if plot:
  511. #canvas.ax1.clear()
  512. #canvas.ax2.clear()
  513. #for artist in canvas.ax1.lines + canvas.ax1.collections:
  514. # artist.remove()
  515. #canvas.ax1.set_prop_cycle(None)
  516. #for artist in canvas.ax2.lines + canvas.ax2.collections:
  517. # artist.remove()
  518. #canvas.ax2.set_prop_cycle(None)
  519. canvas.softClear()
  520. for ichan in self.DATADICT[pulse]["rchan"]:
  521. canvas.ax1.plot( self.DATADICT[pulse]["TIMES"], 1e9*self.DATADICT[pulse][ichan][ipm][istack], alpha=.5)
  522. #, label = "orig " + pulse + " ipm=" + str(ipm) + " istack=" + str(istack) + " rchan=" + str(ichan))
  523. for ichan in self.DATADICT[pulse]["chan"]:
  524. canvas.ax2.plot( self.DATADICT[pulse]["TIMES"], 1e9*self.DATADICT[pulse][ichan][ipm][istack], alpha=.5)
  525. #, label = "orig " + pulse + " ipm=" + str(ipm) + " istack=" + str(istack) + " chan=" + str(ichan))
  526. canvas.ax1.set_prop_cycle(None)
  527. canvas.ax2.set_prop_cycle(None)
  528. for ichan in self.DATADICT[pulse]["rchan"]:
  529. if nF == 1:
  530. #self.DATADICT[pulse][ichan][ipm][istack] = harmonic.minHarmonic( f0, self.DATADICT[pulse][ichan][ipm][istack], self.samp, nK, self.DATADICT[pulse]["TIMES"] )
  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] = harmonic.minHarmonic( self.DATADICT[pulse][ichan][ipm][istack][iseg*Nseg:(iseg+1)*Nseg], \
  534. self.samp, self.DATADICT[pulse]["TIMES"][iseg*Nseg:(iseg+1)*Nseg], \
  535. f0p[ichan], f0K1, f0KN, f0Ks )
  536. else:
  537. self.DATADICT[pulse][ichan][ipm][istack][iseg*Nseg::], f0p[ichan] = harmonic.minHarmonic( self.DATADICT[pulse][ichan][ipm][istack][iseg*Nseg::], \
  538. self.samp, self.DATADICT[pulse]["TIMES"][iseg*Nseg::], \
  539. f0p[ichan], f0K1, f0KN, f0Ks )
  540. #self.DATADICT[pulse][ichan][ipm][istack] = harmonic.minHarmonic( self.DATADICT[pulse][ichan][ipm][istack], self.samp, self.DATADICT[pulse]["TIMES"], \
  541. # f0, f0K1, f0KN, f0Ks )
  542. elif nF == 2:
  543. #self.DATADICT[pulse][ichan][ipm][istack] = harmonic.minHarmonic2( f0-1e-2, f1+1e-2, self.DATADICT[pulse][ichan][ipm][istack], self.samp, nK, self.DATADICT[pulse]["TIMES"] )
  544. #self.DATADICT[pulse][ichan][ipm][istack] = harmonic.minHarmonic2( self.DATADICT[pulse][ichan][ipm][istack], self.samp, self.DATADICT[pulse]["TIMES"], \
  545. # f0-1e-2, f0K1, f0KN, f0Ks, \
  546. # f1+1e-2, f1K1, f1KN, f1Ks )
  547. for iseg in range(f0ns):
  548. if iseg < f0ns-1:
  549. self.DATADICT[pulse][ichan][ipm][istack][iseg*Nseg:(iseg+1)*Nseg], f0p[ichan], f1p[ichan] = harmonic.minHarmonic2( self.DATADICT[pulse][ichan][ipm][istack][iseg*Nseg:(iseg+1)*Nseg],\
  550. self.samp, self.DATADICT[pulse]["TIMES"][iseg*Nseg:(iseg+1)*Nseg], \
  551. f0p[ichan], f0K1, f0KN, f0Ks, \
  552. f1p[ichan], f1K1, f1KN, f1Ks )
  553. else:
  554. self.DATADICT[pulse][ichan][ipm][istack][iseg*Nseg::], f0p[ichan], f1p[ichan] = harmonic.minHarmonic2( self.DATADICT[pulse][ichan][ipm][istack][iseg*Nseg::],\
  555. self.samp, self.DATADICT[pulse]["TIMES"][iseg*Nseg::], \
  556. f0p[ichan], f0K1, f0KN, f0Ks, \
  557. f1p[ichan], f1K1, f1KN, f1Ks )
  558. # plot
  559. if plot:
  560. canvas.ax1.plot( self.DATADICT[pulse]["TIMES"], 1e9*self.DATADICT[pulse][ichan][ipm][istack], \
  561. label = pulse + " ipm=" + str(ipm) + " istack=" + str(istack) + " rchan=" + str(ichan))
  562. for ichan in self.DATADICT[pulse]["chan"]:
  563. if nF == 1:
  564. #self.DATADICT[pulse][ichan][ipm][istack] = harmonic.minHarmonic( f0, self.DATADICT[pulse][ichan][ipm][istack], self.samp, nK, self.DATADICT[pulse]["TIMES"] )
  565. #self.DATADICT[pulse][ichan][ipm][istack] = harmonic.minHarmonic( self.DATADICT[pulse][ichan][ipm][istack], self.samp, self.DATADICT[pulse]["TIMES"], \
  566. # f0, f0K1, f0KN, f0Ks )
  567. for iseg in range(f0ns):
  568. if iseg < f0ns-1:
  569. self.DATADICT[pulse][ichan][ipm][istack][iseg*Nseg:(iseg+1)*Nseg], f0p[ichan] = 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 )
  572. else:
  573. self.DATADICT[pulse][ichan][ipm][istack][iseg*Nseg::], f0p[ichan] = harmonic.minHarmonic( self.DATADICT[pulse][ichan][ipm][istack][iseg*Nseg::],
  574. self.samp, self.DATADICT[pulse]["TIMES"][iseg*Nseg::], \
  575. f0p[ichan], f0K1, f0KN, f0Ks )
  576. elif nF == 2:
  577. #self.DATADICT[pulse][ichan][ipm][istack] = harmonic.minHarmonic2( f0-1e-2, f1+1e-2, self.DATADICT[pulse][ichan][ipm][istack], self.samp, nK, self.DATADICT[pulse]["TIMES"] )
  578. #self.DATADICT[pulse][ichan][ipm][istack] = harmonic.harmonicEuler( f0, self.DATADICT[pulse][ichan][ipm][istack], self.samp, 20, self.DATADICT[pulse]["TIMES"] )
  579. #self.DATADICT[pulse][ichan][ipm][istack] = harmonic.minHarmonic2( self.DATADICT[pulse][ichan][ipm][istack], self.samp, self.DATADICT[pulse]["TIMES"], \
  580. # f0-1e-2, f0K1, f0KN, f0Ks, \
  581. # f1+1e-2, f1K1, f1KN, f1Ks )
  582. for iseg in range(f0ns):
  583. if iseg < f0ns-1:
  584. self.DATADICT[pulse][ichan][ipm][istack][iseg*Nseg:(iseg+1)*Nseg], f0p[ichan], f1p[ichan] = harmonic.minHarmonic2( self.DATADICT[pulse][ichan][ipm][istack][iseg*Nseg:(iseg+1)*Nseg],\
  585. self.samp, self.DATADICT[pulse]["TIMES"][iseg*Nseg:(iseg+1)*Nseg], \
  586. f0p[ichan], f0K1, f0KN, f0Ks, \
  587. f1p[ichan], f1K1, f1KN, f1Ks )
  588. else:
  589. self.DATADICT[pulse][ichan][ipm][istack][iseg*Nseg::], f0p[ichan], f1p[ichan] = harmonic.minHarmonic2( self.DATADICT[pulse][ichan][ipm][istack][iseg*Nseg::],\
  590. self.samp, self.DATADICT[pulse]["TIMES"][iseg*Nseg::], \
  591. f0p[ichan], f0K1, f0KN, f0Ks, \
  592. f1p[ichan], f1K1, f1KN, f1Ks )
  593. # plot
  594. if plot:
  595. canvas.ax2.plot( self.DATADICT[pulse]["TIMES"], 1e9*self.DATADICT[pulse][ichan][ipm][istack], \
  596. label = pulse + " ipm=" + str(ipm) + " istack=" + str(istack) + " chan=" + str(ichan))
  597. if plot:
  598. canvas.ax1.legend(prop={'size':6}, loc='upper right')
  599. canvas.ax2.legend(prop={'size':6}, loc='upper right')
  600. canvas.draw()
  601. #percent = (int)(1e2*((float)(iFID*self.DATADICT["nPulseMoments"]+(ipm))/(len(self.DATADICT["PULSES"])*self.nPulseMoments)))
  602. percent = (int)(1e2*((ipm+istack*self.nPulseMoments)/(self.nPulseMoments*len(self.DATADICT["stacks"]))))
  603. self.progressTrigger.emit(percent)
  604. iFID += 1
  605. self.doneTrigger.emit()
  606. self.updateProcTrigger.emit()
  607. self.doneTrigger.emit()
  608. def FDSmartStack(self, outlierTest, MADcutoff, canvas):
  609. print("FFT stuff")
  610. self.dataCubeFFT()
  611. Stack = {}
  612. # align phase cycling for stacking and modulate
  613. for pulse in self.DATADICT["PULSES"]:
  614. stack = np.zeros(( len(self.DATADICT[pulse]["chan"]), \
  615. self.DATADICT["nPulseMoments"],\
  616. len(self.DATADICT["stacks"]),\
  617. len(self.DATADICT[pulse][self.DATADICT[pulse]["chan"][0] ]["FFT"]["nu"])//2 + 1),\
  618. dtype=np.complex )
  619. for ipm in range(self.DATADICT["nPulseMoments"]):
  620. istack = 0
  621. for sstack in self.DATADICT["stacks"]:
  622. if self.pulseType == "FID" or pulse == "Pulse 2":
  623. mod = (-1)**(ipm%2) * (-1)**(sstack%2)
  624. elif self.pulseType == "4PhaseT1":
  625. mod = (-1)**(ipm%2) * (-1)**(((sstack-1)/2)%2)
  626. ichan = 0
  627. for chan in self.DATADICT[pulse]["chan"]:
  628. #stack[ichan,ipm,istack,:] += mod*self.DATADICT[pulse][chan][ipm][sstack]
  629. stack[ichan,ipm,istack,:] += mod*self.DATADICT[pulse][chan]["FFT"][sstack][ipm,:]
  630. ichan += 1
  631. istack += 1
  632. Stack[pulse] = stack
  633. #########################################
  634. # simple stack and plot of simple stack #
  635. ########################################https://faculty.apps.utah.edu/#
  636. canvas.reAxH2(np.shape(stack)[0], False, False)
  637. axes = canvas.fig.axes
  638. SimpleStack = {}
  639. VarStack = {}
  640. for pulse in self.DATADICT["PULSES"]:
  641. SimpleStack[pulse] = {}
  642. VarStack[pulse] = {}
  643. ichan = 0
  644. for chan in self.DATADICT[pulse]["chan"]:
  645. SimpleStack[pulse][chan] = 1e9*np.average( Stack[pulse][ichan], 1 )
  646. VarStack[pulse][chan] = 1e9*np.std( Stack[pulse][ichan], 1 )
  647. ax1 = axes[ 2*ichan ]
  648. #ax1.get_yaxis().get_major_formatter().set_useOffset(False)
  649. y_formatter = matplotlib.ticker.ScalarFormatter(useOffset=False)
  650. ax1.yaxis.set_major_formatter(y_formatter)
  651. #ax1.plot( 1e3*self.DATADICT[pulse][chan]["FFT"]["nu"][0:len(SimpleStack[pulse][chan])], np.average(SimpleStack[pulse][chan], 0 )) #, color='darkblue' )
  652. #ax1.pcolor( np.real(SimpleStack[pulse][chan]) ) #, color='darkblue' )
  653. ax1.matshow( np.real(SimpleStack[pulse][chan]), aspect='auto') #, color='darkblue' )
  654. ax1.set_title("Ch." + str(chan) + ": avg FID", fontsize=8)
  655. ax1.set_xlabel(r"time (ms)", fontsize=8)
  656. if ichan == 0:
  657. ax1.set_ylabel(r"signal [nV]", fontsize=8)
  658. else:
  659. plt.setp(ax1.get_yticklabels(), visible=False)
  660. plt.setp(ax1.get_yaxis().get_offset_text(), visible=False)
  661. ichan += 1
  662. #########################
  663. # Oulier rejectig stack #
  664. #########################
  665. if outlierTest == "MAD":
  666. MADStack = {}
  667. VarStack = {}
  668. #1.4826 is assumption of gaussian noise
  669. madstack = np.zeros(( len(self.DATADICT[pulse]["chan"]),\
  670. self.DATADICT["nPulseMoments"],\
  671. len(self.DATADICT[pulse][self.DATADICT[pulse]["chan"][0] ]["FFT"]["nu"])//2 + 1))
  672. varstack = np.zeros(( len(self.DATADICT[pulse]["chan"]),\
  673. self.DATADICT["nPulseMoments"],\
  674. len(self.DATADICT[pulse][self.DATADICT[pulse]["chan"][0] ]["FFT"]["nu"])//2 + 1))
  675. for pulse in self.DATADICT["PULSES"]:
  676. MADStack[pulse] = {}
  677. VarStack[pulse] = {}
  678. ichan = 0
  679. for chan in self.DATADICT[pulse]["chan"]:
  680. ax1 = axes[ 2*ichan ]
  681. for ipm in range(self.DATADICT["nPulseMoments"]):
  682. # # brutal loop over time, can this be vectorized?
  683. # for it in range(len(self.DATADICT[pulse]["TIMES"])):
  684. # x = 1e9 *Stack[pulse][ichan,ipm,:,it]
  685. # MAD = 1.4826 * np.median( np.abs(x-np.median(x)) )
  686. # good = 0
  687. # for istack in self.DATADICT["stacks"]:
  688. # if (np.abs(x[istack-1]-np.median(x))) / MAD < 2:
  689. # good += 1
  690. # madstack[ ichan, ipm, it ] += x[istack-1]
  691. # else:
  692. # pass
  693. # madstack[ichan, ipm, it] /= good
  694. # percent = int(1e2* (float)(ipm) / (float)(self.DATADICT["nPulseMoments"]) )
  695. # self.progressTrigger.emit(percent)
  696. # Vectorized version of above...much, much faster
  697. x = 1e9*copy.deepcopy(Stack[pulse][ichan][ipm,:,:]) # stack and time indices
  698. tile_med = np.tile( np.median(x, axis=0), (np.shape(x)[0],1))
  699. MAD = MADcutoff * np.median(np.abs(x - tile_med), axis=0)
  700. tile_MAD = np.tile( MAD, (np.shape(x)[0],1))
  701. good = np.abs(x-tile_med)/tile_MAD < 2. # 1.4826 # 2
  702. madstack[ichan][ipm] = copy.deepcopy( np.ma.masked_array(x, good != True).mean(axis=0) )
  703. varstack[ichan][ipm] = copy.deepcopy( np.ma.masked_array(x, good != True).std(axis=0) )
  704. # reporting
  705. percent = int(1e2* (float)((ipm)+ichan*self.DATADICT["nPulseMoments"]) /
  706. (float)(self.DATADICT["nPulseMoments"] * len(self.DATADICT[pulse]["chan"])))
  707. self.progressTrigger.emit(percent)
  708. ax2 = axes[2*ichan+1] # TODO fix hard coded number
  709. #ax1.plot( 1e3*self.DATADICT[pulse]["TIMES"], np.average( madstack[ichan], 0 ))# , color='darkred')
  710. MADStack[pulse][chan] = madstack[ichan]
  711. VarStack[pulse][chan] = varstack[ichan]
  712. ax2.matshow( np.real(MADStack[pulse][chan]), aspect='auto') #, color='darkblue' )
  713. ichan += 1
  714. self.DATADICT["stack"] = MADStack
  715. else:
  716. self.DATADICT["stack"] = SimpleStack
  717. # #########################################
  718. # # Plot Fourier Transform representation #
  719. # #########################################
  720. #
  721. # # canvas.fig.subplots_adjust(right=0.8)
  722. # # cbar_ax = canvas.fig.add_axes([0.85, 0.1, 0.015, 0.355])
  723. # # cbar_ax.ticklabel_format(style='sci', scilimits=(0,0), axis='y')
  724. # im2 = []
  725. # im1 = []
  726. # for pulse in self.DATADICT["PULSES"]:
  727. # ichan = 0
  728. # axes = canvas.fig.axes
  729. # vvmin = 1e10
  730. # vvmax = 0
  731. # for chan in self.DATADICT[pulse]["chan"]:
  732. # ax1 = axes[2*ichan ]
  733. # ax2 = axes[2*ichan+1] # TODO fix hard coded number
  734. # if outlierTest == "MAD":
  735. # X = np.fft.rfft( MADStack[pulse][chan][0,:] )
  736. # nu = np.fft.fftfreq(len( MADStack[pulse][chan][0,:]), d=self.dt)
  737. # else:
  738. # X = np.fft.rfft( SimpleStack[pulse][chan][0,:] )
  739. # nu = np.fft.fftfreq(len( SimpleStack[pulse][chan][0,:]), d=self.dt)
  740. #
  741. # nu = nu[0:len(X)]
  742. # nu[-1] = np.abs(nu[-1])
  743. # df = nu[1] - nu[0]
  744. # of = 0
  745. #
  746. # istart = int((self.transFreq-50.)/df)
  747. # iend = int((self.transFreq+50.)/df)
  748. # of = nu[istart]
  749. #
  750. # def freqlabel(xxx, pos):
  751. # return '%1.0f' %(of + xxx*df)
  752. # formatter = FuncFormatter(freqlabel)
  753. #
  754. # SFFT = np.zeros( (self.DATADICT["nPulseMoments"], len(X)), dtype=np.complex64 )
  755. # SFFT[0,:] = X
  756. # for ipm in range(1, self.DATADICT["nPulseMoments"]):
  757. # if outlierTest == "MAD":
  758. # SFFT[ipm,:] = np.fft.rfft( MADStack[pulse][chan][ipm,:] )
  759. # else:
  760. # SFFT[ipm,:] = np.fft.rfft( SimpleStack[pulse][chan][ipm,:] )
  761. #
  762. # # convert to dB and add colorbars
  763. # #db = 20.*np.log10(np.abs(SFFT[:,istart:iend]))
  764. # db = (np.abs(SFFT[:,istart:iend]))
  765. # #db = (np.real(SFFT[:,istart:iend]))
  766. # #dbr = (np.real(SFFT[:,istart:iend]))
  767. # #db = (np.imag(SFFT[:,istart:iend]))
  768. #
  769. # vvmin = min(vvmin, np.min (db))
  770. # vvmax = max(vvmax, np.max (db))
  771. # im2.append(ax2.matshow( db, aspect='auto', cmap=cmocean.cm.ice, vmin=vvmin, vmax=vvmax))
  772. # #im1.append(ax1.matshow( dbr, aspect='auto')) #, vmin=vvmin, vmax=vvmax))
  773. # #im2.append(ax2.matshow( db, aspect='auto', vmin=vvmin, vmax=vvmax))
  774. # #im2 = ax2.matshow( db, aspect='auto', cmap=cmocean.cm.ice, vmin=vvmin, vmax=vvmax)
  775. # if ichan == 0:
  776. # ax2.set_ylabel(r"$q$ (A $\cdot$ s)", fontsize=8)
  777. # else:
  778. # #ax2.yaxis.set_ticklabels([])
  779. # plt.setp(ax2.get_yticklabels(), visible=False)
  780. #
  781. # ax2.xaxis.set_major_formatter(formatter)
  782. # ax2.xaxis.set_ticks_position('bottom')
  783. # ax2.xaxis.set_major_locator(MaxNLocator(3))
  784. #
  785. # y_formatter = matplotlib.ticker.ScalarFormatter(useOffset=False)
  786. # ax2.yaxis.set_major_formatter(y_formatter)
  787. #
  788. #
  789. # #if chan == self.DATADICT[pulse]["chan"][-1]:
  790. # #cb2 = canvas.fig.colorbar(im2, cax=cbar_ax, format='%1.0e')
  791. #
  792. # #cb2 = canvas.fig.colorbar(im2[0], ax=ax2, format='%1.0e', orientation='horizontal')
  793. # #cb2 = canvas.fig.colorbar(im2, ax=ax2, format='%1.0e', orientation='horizontal')
  794. # #cb2.ax.tick_params(axis='both', which='major', labelsize=8)
  795. # #cb2.set_label("signal (dB)", fontsize=8)
  796. #
  797. # ichan += 1
  798. #
  799. #
  800. # canvas.fig.subplots_adjust(hspace=.1, wspace=.05, left=.075, right=.95 )#left=None, bottom=None, right=None, top=None, wspace=None, hspace=None)
  801. #
  802. # #cb1 = canvas.fig.colorbar(im, ax=axes[0::2], format='%1.0e', orientation='horizontal', shrink=.35, aspect=30)
  803. # #cb1.ax.tick_params(axis='both', which='major', labelsize=8)
  804. # #cb1.set_label("$\mathcal{V}_N$ (nV)", fontsize=8)
  805. #
  806. # cb2 = canvas.fig.colorbar(im2[-1], ax=axes[1::2], format='%1.0e', orientation='horizontal', shrink=.35, aspect=30)
  807. # cb2.ax.tick_params(axis='both', which='major', labelsize=8)
  808. # cb2.set_label("$\mathcal{V}_N$ (nV)", fontsize=8)
  809. #canvas.fig.tight_layout()
  810. canvas.draw()
  811. self.doneTrigger.emit()
  812. def sumData(self, canvas, fred):
  813. chans = copy.deepcopy(self.DATADICT[self.DATADICT["PULSES"][0]]["chan"]) #= np.array( ( self.DATADICT[pulse]["chan"][0], ) )
  814. nchan = len(chans)
  815. # Sum permutations of two channel combos
  816. for ich in range(nchan-1):
  817. for ch in chans[ich+1:]:
  818. chsum = chans[ich] + "+" + ch
  819. for pulse in self.DATADICT["PULSES"]:
  820. self.DATADICT[pulse][chsum] = {}
  821. for ipm in range(self.DATADICT["nPulseMoments"]):
  822. self.DATADICT[pulse][chsum][ipm] = {}
  823. for istack in self.DATADICT["stacks"]:
  824. self.DATADICT[pulse][chsum][ipm][istack] = self.DATADICT[pulse][chans[ich]][ipm][istack] - self.DATADICT[pulse][ch][ipm][istack]
  825. if chsum == "1+2":
  826. #self.DATADICT[pulse]["rchan"].pop()
  827. #self.DATADICT[pulse]["rchan"].pop()
  828. self.DATADICT[pulse]["chan"].append(chsum)
  829. # Sum all channels
  830. sumall = False
  831. if sumall:
  832. chsum = ""
  833. for ch in chans:
  834. chsum += ch + "+"
  835. chsum = chsum[0:-1] # remove last "+"
  836. for pulse in self.DATADICT["PULSES"]:
  837. self.DATADICT[pulse][chsum] = {}
  838. for ipm in range(self.DATADICT["nPulseMoments"]):
  839. self.DATADICT[pulse][chsum][ipm] = {}
  840. for istack in self.DATADICT["stacks"]:
  841. self.DATADICT[pulse][chsum][ipm][istack] = copy.deepcopy(self.DATADICT[pulse][chans[0]][ipm][istack])
  842. for ch in chans[1:]:
  843. self.DATADICT[pulse][chsum][ipm][istack] += self.DATADICT[pulse][ch][ipm][istack]
  844. self.DATADICT[pulse]["chan"].append(chsum)
  845. # if nchan > 2:
  846. # for ch in chans:
  847. # chsum += ch
  848. # for ch2 in chans[1::]:
  849. # for pulse in self.DATADICT["PULSES"]:
  850. # self.DATADICT[pulse][chsum] = {}
  851. # for istack in self.DATADICT["stacks"]:
  852. # self.DATADICT[pulse][chsum][ipm][istack] = self.DATADICT[pulse][chans[ich]][ipm][istack] + self.DATADICT[pulse][ch][ipm][istack]
  853. self.doneTrigger.emit()
  854. def quadDet(self, clip, method, loss, canvas):
  855. from scipy import signal
  856. self.RotatedAmplitude = True
  857. wL = self.transFreq * 2*np.pi
  858. vL = self.transFreq
  859. #T = 50
  860. dt = self.dt
  861. #DT = 0.01
  862. CA = {} # corrected amplitude
  863. IP = {} # instantaneous phase
  864. NR = {} # Noise residual
  865. RE = {} # Real channel
  866. IM = {} # Imaginary channel
  867. # global maximums for plotting
  868. CAmax = {}
  869. NRmax = {}
  870. REmax = {}
  871. IMmax = {}
  872. E0,phi,df,T2 = 100.,0,0,.2
  873. first = False
  874. self.sigma = {}
  875. for pulse in self.DATADICT["PULSES"]:
  876. CA[pulse] = {}
  877. IP[pulse] = {}
  878. NR[pulse] = {}
  879. RE[pulse] = {}
  880. IM[pulse] = {}
  881. CAmax[pulse] = 0
  882. NRmax[pulse] = 0
  883. REmax[pulse] = 0
  884. IMmax[pulse] = 0
  885. ichan = 0
  886. self.sigma[pulse] = {}
  887. for chan in self.DATADICT[pulse]["chan"]:
  888. CA[pulse][chan] = np.zeros( (self.DATADICT["nPulseMoments"], len(self.DATADICT[pulse]["TIMES"])-clip ) )
  889. IP[pulse][chan] = np.zeros( (self.DATADICT["nPulseMoments"], len(self.DATADICT[pulse]["TIMES"])-clip ) )
  890. NR[pulse][chan] = np.zeros( (self.DATADICT["nPulseMoments"], len(self.DATADICT[pulse]["TIMES"])-clip ) )
  891. RE[pulse][chan] = np.zeros( (self.DATADICT["nPulseMoments"], len(self.DATADICT[pulse]["TIMES"])-clip ) )
  892. IM[pulse][chan] = np.zeros( (self.DATADICT["nPulseMoments"], len(self.DATADICT[pulse]["TIMES"])-clip ) )
  893. for ipm in range(0, self.DATADICT["nPulseMoments"]):
  894. #t = self.DATADICT[pulse]["TIMES"] - self.DATADICT[pulse]["PULSE_TIMES"][-1]
  895. xn = self.DATADICT["stack"][pulse][chan][ipm,:]
  896. ht = signal.hilbert(xn)*np.exp(-1j*wL*self.DATADICT[pulse]["TIMES"])
  897. #############################################################
  898. # Quadrature signal
  899. RE[pulse][chan][ipm,:] = np.real(ht[clip::])
  900. IM[pulse][chan][ipm,:] = np.imag(ht[clip::])
  901. REmax[pulse] = max(REmax[pulse], np.max(np.real(ht[clip::])))
  902. IMmax[pulse] = max(IMmax[pulse], np.max(np.imag(ht[clip::])))
  903. #############################################################
  904. # Instantaneous phase
  905. IP[pulse][chan][ipm,:] = np.angle(ht)[clip::]
  906. #############################################################
  907. # Rotated amplitude
  908. #if ipm != 0:
  909. # [success, E0, df, phi, T2] = decay.quadratureDetect2( ht.real, ht.imag, self.DATADICT[pulse]["TIMES"], (E0,phi,df,T2))
  910. #[success, E0, df, phi, T2] = decay.quadratureDetect( ht.real, ht.imag, self.DATADICT[pulse]["TIMES"] )
  911. #else:
  912. [success, E0, df, phi, T2] = decay.quadratureDetect2( ht.real, ht.imag, self.DATADICT[pulse]["TIMES"], method, loss)
  913. #[success, E0, df, phi, T2] = decay.quadratureDetect2( ht.real, ht.imag, self.DATADICT[pulse]["TIMES"], (E0,phi,df,T2))
  914. #[success, E0, df, phi, T2] = decay.quadratureDetect( ht.real, ht.imag, self.DATADICT[pulse]["TIMES"] )
  915. #print("success", success, "E0", E0, "phi", phi, "df", df, "T2", T2)
  916. D = self.RotateAmplitude( ht.real, ht.imag, phi, df, self.DATADICT[pulse]["TIMES"] )
  917. CA[pulse][chan][ipm,:] = D.imag[clip::] # amplitude data
  918. NR[pulse][chan][ipm,:] = D.real[clip::] # noise data
  919. CAmax[pulse] = max(CAmax[pulse], np.max(D.imag[clip::]) )
  920. NRmax[pulse] = max(NRmax[pulse], np.max(D.real[clip::]) )
  921. self.sigma[pulse][chan] = np.std(NR[pulse][chan])
  922. # reporting
  923. percent = int(1e2* (float)((ipm)+ichan*self.DATADICT["nPulseMoments"]) /
  924. (float)(self.DATADICT["nPulseMoments"] * len(self.DATADICT[pulse]["chan"])))
  925. self.progressTrigger.emit(percent)
  926. ichan += 1
  927. self.DATADICT["CA"] = CA
  928. self.DATADICT["IP"] = IP
  929. self.DATADICT["NR"] = NR
  930. self.DATADICT["RE"] = RE
  931. self.DATADICT["IM"] = IM
  932. self.DATADICT["CAmax"] = CAmax
  933. self.DATADICT["NRmax"] = NRmax
  934. self.DATADICT["REmax"] = REmax
  935. self.DATADICT["IMmax"] = IMmax
  936. self.doneTrigger.emit()
  937. def plotQuadDet(self, clip, phase, canvas):
  938. canvas.reAxH2( len(self.DATADICT[ self.DATADICT["PULSES"][0] ]["chan"] ), False, False)
  939. ###############
  940. # Plot on GUI #
  941. ###############
  942. dcmap = cmocean.cm.curl_r #"seismic_r" #cmocean.cm.balance_r #"RdBu" #YlGn" # "coolwarm_r" # diverging
  943. canvas.reAxH2( len(self.DATADICT[ self.DATADICT["PULSES"][0] ]["chan"] ), False, False)
  944. for pulse in self.DATADICT["PULSES"]:
  945. ichan = 0
  946. axes = canvas.fig.axes
  947. mmaxr = 0.
  948. mmaxi = 0.
  949. if clip > 0:
  950. time_sp = 1e3 * (self.DATADICT[pulse]["TIMES"][clip-1::] - self.DATADICT[pulse]["PULSE_TIMES"][-1] )
  951. else:
  952. time_sp = 1e3 * (self.DATADICT[pulse]["TIMES"] - self.DATADICT[pulse]["PULSE_TIMES"][-1] )
  953. QQ = np.average(self.DATADICT[pulse]["Q"], axis=1 )
  954. for chan in self.DATADICT[pulse]["chan"]:
  955. ax1 = axes[2*ichan ]
  956. ax2 = axes[2*ichan+1] # TODO fix hard coded number
  957. if phase == 0: # Re Im
  958. im1 = ax1.pcolormesh( time_sp, QQ, self.DATADICT["RE"][pulse][chan], cmap=dcmap, rasterized=True,\
  959. vmin=-self.DATADICT["REmax"][pulse] , vmax=self.DATADICT["REmax"][pulse] )
  960. im2 = ax2.pcolormesh( time_sp, QQ, self.DATADICT["IM"][pulse][chan], cmap=dcmap, rasterized=True,\
  961. vmin=-self.DATADICT["IMmax"][pulse], vmax=self.DATADICT["IMmax"][pulse] )
  962. if phase == 1: # Amp phase
  963. im1 = ax1.pcolormesh( time_sp, QQ, self.DATADICT["CA"][pulse][chan], cmap=dcmap, rasterized=True,
  964. vmin=-self.DATADICT["CAmax"][pulse] , vmax=self.DATADICT["CAmax"][pulse] )
  965. im2 = ax2.pcolormesh( time_sp, QQ, self.DATADICT["IP"][pulse][chan], cmap=cmocean.cm.phase, rasterized=True,\
  966. vmin=-np.pi, vmax=np.pi)
  967. if phase == 2: # CA NR
  968. im1 = ax1.pcolormesh( time_sp, QQ, self.DATADICT["CA"][pulse][chan], cmap=dcmap, rasterized=True,\
  969. vmin=-self.DATADICT["CAmax"][pulse] , vmax=self.DATADICT["CAmax"][pulse] )
  970. im2 = ax2.pcolormesh( time_sp, QQ, self.DATADICT["NR"][pulse][chan], cmap=dcmap, rasterized=True,\
  971. vmin=-self.DATADICT["NRmax"][pulse] , vmax=self.DATADICT["NRmax"][pulse] )
  972. # cb2 = canvas.fig.colorbar(im2, ax=ax2, format='%1.0e')
  973. # cb2.set_label("Noise residual (nV)", fontsize=8)
  974. # cb2.ax.tick_params(axis='both', which='major', labelsize=8)
  975. # cb1 = canvas.fig.colorbar(im1, ax=ax1, format='%1.0e')
  976. # cb1.set_label("Phased amplitude (nV)", fontsize=8)
  977. # cb1.ax.tick_params(axis='both', which='major', labelsize=8)
  978. # cb2 = canvas.fig.colorbar(im2, ax=ax2, format="%1.0e")
  979. # cb2.set_label("Phase (rad)", fontsize=8)
  980. # cb2.ax.tick_params(axis='both', which='major', labelsize=8)
  981. # cb1 = canvas.fig.colorbar(im1, ax=ax1, format="%1.0e")
  982. # cb1.set_label("FID (nV)", fontsize=8)
  983. # cb1.ax.tick_params(axis='both', which='major', labelsize=8)
  984. # if you save these as pdf or eps, there are artefacts
  985. # for cbar in [cb1,cb2]:
  986. # #cbar.solids.set_rasterized(True)
  987. # cbar.solids.set_edgecolor("face")
  988. # reporting
  989. percent = int(1e2* (float)(ichan)/len(self.DATADICT[pulse]["chan"]))
  990. self.progressTrigger.emit(percent)
  991. if ichan == 0:
  992. ax1.set_ylabel(r"$q$ ( $\mathrm{A}\cdot\mathrm{s}$)", fontsize=8)
  993. ax2.set_ylabel(r"$q$ ( $\mathrm{A}\cdot\mathrm{s}$)", fontsize=8)
  994. else:
  995. #ax2.yaxis.set_ticklabels([])
  996. #ax1.yaxis.set_ticklabels([])
  997. plt.setp(ax1.get_yticklabels(), visible=False)
  998. plt.setp(ax2.get_yticklabels(), visible=False)
  999. ichan += 1
  1000. ax1.set_yscale('log')
  1001. ax2.set_yscale('log')
  1002. plt.setp(ax1.get_xticklabels(), visible=False)
  1003. ax1.set_ylim( np.min(QQ), np.max(QQ) )
  1004. ax2.set_ylim( np.min(QQ), np.max(QQ) )
  1005. ax1.set_xlim( np.min(time_sp), np.max(time_sp) )
  1006. ax2.set_xlim( np.min(time_sp), np.max(time_sp) )
  1007. #ax2.set_xlabel(r"Time since end of pulse (ms)", fontsize=8)
  1008. ax2.set_xlabel(r"Time (ms)", fontsize=8)
  1009. 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)
  1010. tick_locator = MaxNLocator(nbins=3)
  1011. cb1 = canvas.fig.colorbar(im1, ax=axes[0::2], format='%1.0e', orientation='horizontal', shrink=.35, aspect=30)
  1012. cb1.ax.tick_params(axis='both', which='major', labelsize=8)
  1013. cb1.set_label("$\mathcal{V}_N$ (nV)", fontsize=8)
  1014. cb1.locator = tick_locator
  1015. cb1.update_ticks()
  1016. tick_locator2 = MaxNLocator(nbins=3)
  1017. cb2 = canvas.fig.colorbar(im2, ax=axes[1::2], format='%1.0e', orientation='horizontal', shrink=.35, aspect=30, pad=.2)
  1018. cb2.ax.tick_params(axis='both', which='major', labelsize=8)
  1019. cb2.set_label("$\mathcal{V}_N$ (nV)", fontsize=8)
  1020. cb2.locator = tick_locator2
  1021. cb2.update_ticks()
  1022. canvas.draw()
  1023. self.doneTrigger.emit()
  1024. def RotateAmplitude(self, X, Y, zeta, df, t):
  1025. V = X + 1j*Y
  1026. return np.abs(V) * np.exp( 1j * ( np.angle(V) - zeta - 2.*np.pi*df*t ) )
  1027. def gateIntegrate( self, gpd, clip, canvas ):
  1028. """ Gate integrate the real, imaginary, phased, and noise residual channels
  1029. """
  1030. self.gated = True
  1031. self.GATED = {}
  1032. for pulse in self.DATADICT["PULSES"]:
  1033. QQ = np.average(self.DATADICT[pulse]["Q"], axis=1 )
  1034. ichan = 0
  1035. for chan in self.DATADICT[pulse]["chan"]:
  1036. self.GATED[chan] = {}
  1037. for ipm in range(0, self.DATADICT["nPulseMoments"]):
  1038. # Time since pulse rather than since record starts...
  1039. #if clip > 0:
  1040. # time_sp = 1e3 * (self.DATADICT[pulse]["TIMES"][clip:] - self.DATADICT[pulse]["PULSE_TIMES"][-1] )
  1041. #else:
  1042. time_sp = 1e3 * (self.DATADICT[pulse]["TIMES"] - self.DATADICT[pulse]["PULSE_TIMES"][-1] )
  1043. #GT, GD, GTT, sig_stack, isum = rotate.gateIntegrate( self.DATADICT["CA"][pulse][chan][ipm,:], time_sp, gpd, self.sigma[pulse][chan], 1.5 )
  1044. #GT2, GP, GTT, sig_stack_err, isum = rotate.gateIntegrate( self.DATADICT["NR"][pulse][chan][ipm,:], time_sp, gpd, self.sigma[pulse][chan], 1.5 )
  1045. GT, GCA, GTT, sig_stack, isum = rotate.gateIntegrate( self.DATADICT["CA"][pulse][chan][ipm], time_sp, gpd, self.sigma[pulse][chan], 1.5 )
  1046. GT, GNR, GTT, sig_stack, isum = rotate.gateIntegrate( self.DATADICT["NR"][pulse][chan][ipm], time_sp, gpd, self.sigma[pulse][chan], 1.5 )
  1047. GT, GRE, GTT, sig_stack, isum = rotate.gateIntegrate( self.DATADICT["RE"][pulse][chan][ipm], time_sp, gpd, self.sigma[pulse][chan], 1.5 )
  1048. GT, GIM, GTT, sig_stack, isum = rotate.gateIntegrate( self.DATADICT["IM"][pulse][chan][ipm], time_sp, gpd, self.sigma[pulse][chan], 1.5 )
  1049. GT, GIP, GTT, sig_stack, isum = rotate.gateIntegrate( self.DATADICT["IP"][pulse][chan][ipm], time_sp, gpd, self.sigma[pulse][chan], 1.5 )
  1050. if ipm == 0:
  1051. # self.GATED[chan]["DATA"] = np.zeros( ( self.DATADICT["nPulseMoments"], len(GT)) )
  1052. # self.GATED[chan]["ERR"] = np.zeros( ( self.DATADICT["nPulseMoments"], len(GT)) )
  1053. # self.GATED[chan]["SIGMA"] = np.zeros( ( self.DATADICT["nPulseMoments"], len(GT)) )
  1054. self.GATED[chan]["CA"] = np.zeros( ( self.DATADICT["nPulseMoments"], len(GT)-clip) )
  1055. self.GATED[chan]["NR"] = np.zeros( ( self.DATADICT["nPulseMoments"], len(GT)-clip) )
  1056. self.GATED[chan]["RE"] = np.zeros( ( self.DATADICT["nPulseMoments"], len(GT)-clip) )
  1057. self.GATED[chan]["IM"] = np.zeros( ( self.DATADICT["nPulseMoments"], len(GT)-clip) )
  1058. self.GATED[chan]["IP"] = np.zeros( ( self.DATADICT["nPulseMoments"], len(GT)-clip) )
  1059. self.GATED[chan]["isum"] = isum
  1060. #self.GATED[chan]["DATA"][ipm] = GD.real
  1061. self.GATEDABSCISSA = GT[clip:]
  1062. self.GATEDWINDOW = GTT[clip:]
  1063. #self.GATED[chan]["SIGMA"][ipm] = sig_stack #_err # GP.real
  1064. #self.GATED[chan]["ERR"][ipm] = GP.real
  1065. self.GATED[chan]["CA"][ipm] = GCA.real[clip:]
  1066. self.GATED[chan]["NR"][ipm] = GNR.real[clip:]
  1067. self.GATED[chan]["RE"][ipm] = GRE.real[clip:]
  1068. self.GATED[chan]["IM"][ipm] = GIM.real[clip:]
  1069. self.GATED[chan]["IP"][ipm] = GIP.real[clip:]
  1070. percent = int(1e2* (float)((ipm)+ichan*self.DATADICT["nPulseMoments"]) /
  1071. (float)(self.DATADICT["nPulseMoments"] * len(self.DATADICT[pulse]["chan"])))
  1072. self.progressTrigger.emit(percent)
  1073. self.GATED[chan]["GTT"] = GTT[clip:]
  1074. self.GATED[chan]["GT"] = GT[clip:]
  1075. self.GATED[chan]["QQ"] = QQ
  1076. ichan += 1
  1077. self.doneTrigger.emit()
  1078. def bootstrap_resample(self, X, n=None):
  1079. # from http://nbviewer.jupyter.org/gist/aflaxman/6871948
  1080. """ Bootstrap resample an array_like
  1081. Parameters
  1082. ----------
  1083. X : array_like
  1084. data to resample
  1085. n : int, optional
  1086. length of resampled array, equal to len(X) if n==None
  1087. Results
  1088. -------
  1089. returns X_resamples
  1090. """
  1091. if n == None:
  1092. n = len(X)
  1093. resample_i = np.floor(np.random.rand(n)*len(X)).astype(int)
  1094. return X[resample_i]
  1095. def bootstrap_sigma(self, pulse, chan):
  1096. # bootstrap resample
  1097. nt = len(self.GATED[chan]["GT"])
  1098. nb = 5000
  1099. XS = np.zeros( (nb, nt) )
  1100. for ii in range(nb):
  1101. for it in range(nt):
  1102. if self.GATED[chan]["isum"][it] < 8:
  1103. XS[ii, it] = self.sigma[pulse][chan]
  1104. else:
  1105. if it == 0:
  1106. X = self.bootstrap_resample( np.concatenate( (self.GATED[chan]["NR"][:,it], self.GATED[chan]["NR"][:,it+1], \
  1107. self.GATED[chan]["NR"][:,it+2], self.GATED[chan]["NR"][:,it+3] ) ), n=nt )
  1108. elif it == 1:
  1109. X = self.bootstrap_resample( np.concatenate( (self.GATED[chan]["NR"][:,it-1], self.GATED[chan]["NR"][:,it], \
  1110. self.GATED[chan]["NR"][:,it+1], self.GATED[chan]["NR"][:,it+2] ) ), n=nt )
  1111. elif it == nt-2:
  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-1:
  1115. X = self.bootstrap_resample( np.concatenate( (self.GATED[chan]["NR"][:,it], self.GATED[chan]["NR"][:,it-1], \
  1116. self.GATED[chan]["NR"][:,it-2], self.GATED[chan]["NR"][:,it-3] ) ), n=nt )
  1117. else:
  1118. X = self.bootstrap_resample( np.concatenate( (self.GATED[chan]["NR"][:,it-2] , self.GATED[chan]["NR"][:,it-1], \
  1119. self.GATED[chan]["NR"][:,it], self.GATED[chan]["NR"][:,it+1], self.GATED[chan]["NR"][:,it+2] )), n=nt )
  1120. XS[ii,it] = np.std(X)
  1121. return XS
  1122. def plotGateIntegrate( self, gpd, clip, phase, canvas ):
  1123. """ Plot the gate integration
  1124. """
  1125. canvas.reAxH2( len(self.DATADICT[ self.DATADICT["PULSES"][0] ]["chan"] ), False, False)
  1126. axes = canvas.fig.axes
  1127. cmap = cmocean.cm.balance_r
  1128. # Calculate maximum for plotting...TODO move into loop above
  1129. vmax1 = 0
  1130. vmax2 = 0
  1131. for pulse in self.DATADICT["PULSES"]:
  1132. for chan in self.DATADICT[pulse]["chan"]:
  1133. if phase == 0:
  1134. vmax1 = max(vmax1, np.max(np.abs(self.GATED[chan]["RE"])))
  1135. vmax2 = max(vmax2, np.max(np.abs(self.GATED[chan]["IM"])))
  1136. elif phase == 1:
  1137. vmax1 = max(vmax1, np.max(np.abs(self.GATED[chan]["CA"])))
  1138. vmax2 = np.pi
  1139. elif phase == 2:
  1140. vmax1 = max(vmax1, np.max(np.abs(self.GATED[chan]["CA"])))
  1141. vmax2 = max(vmax2, np.max(np.abs(self.GATED[chan]["NR"])))
  1142. for pulse in self.DATADICT["PULSES"]:
  1143. ichan = 0
  1144. for chan in self.DATADICT[pulse]["chan"]:
  1145. ax1 = axes[2*ichan ]
  1146. ax2 = axes[2*ichan+1]
  1147. if phase == 0:
  1148. im1 = ax1.pcolormesh(self.GATED[chan]["GTT"], self.GATED[chan]["QQ"], self.GATED[chan]["RE"], cmap=cmap, vmin=-vmax1, vmax=vmax1)
  1149. im2 = ax2.pcolormesh(self.GATED[chan]["GTT"], self.GATED[chan]["QQ"], self.GATED[chan]["IM"], cmap=cmap, vmin=-vmax2, vmax=vmax2)
  1150. elif phase == 1:
  1151. im1 = ax1.pcolormesh(self.GATED[chan]["GTT"], self.GATED[chan]["QQ"], self.GATED[chan]["CA"], cmap=cmap, vmin=-vmax1, vmax=vmax1)
  1152. im2 = ax2.pcolormesh(self.GATED[chan]["GTT"], self.GATED[chan]["QQ"], self.GATED[chan]["IP"], cmap=cmocean.cm.phase, vmin=-vmax2, vmax=vmax2)
  1153. elif phase == 2:
  1154. im1 = ax1.pcolormesh(self.GATED[chan]["GTT"], self.GATED[chan]["QQ"], self.GATED[chan]["CA"], cmap=cmap, vmin=-vmax1, vmax=vmax1)
  1155. XS = self.bootstrap_sigma(pulse, chan)
  1156. #im2 = ax2.pcolormesh(self.GATED[chan]["GTT"], self.GATED[chan]["QQ"], self.GATED[chan]["NR"], cmap=cmap, vmin=-vmax2, vmax=vmax2)
  1157. # bootstrap resample
  1158. # nt = len(self.GATED[chan]["GT"])
  1159. # nb = 5000
  1160. # XS = np.zeros( (nb, nt) )
  1161. # for ii in range(nb):
  1162. # #XS = []
  1163. # for it in range(nt):
  1164. # if self.GATED[chan]["isum"][it] < 8:
  1165. # XS[ii, it] = self.sigma[pulse][chan]
  1166. # else:
  1167. # if it == 0:
  1168. # X = self.bootstrap_resample( np.concatenate( (self.GATED[chan]["NR"][:,it], self.GATED[chan]["NR"][:,it+1], \
  1169. # self.GATED[chan]["NR"][:,it+2], self.GATED[chan]["NR"][:,it+3] ) ), n=nt )
  1170. # if it == 1:
  1171. # X = self.bootstrap_resample( np.concatenate( (self.GATED[chan]["NR"][:,it-1], self.GATED[chan]["NR"][:,it], \
  1172. # self.GATED[chan]["NR"][:,it+1], self.GATED[chan]["NR"][:,it+2] ) ), n=nt )
  1173. # elif it == nt-2:
  1174. # X = self.bootstrap_resample( np.concatenate( (self.GATED[chan]["NR"][:,it+1], self.GATED[chan]["NR"][:,it], \
  1175. # self.GATED[chan]["NR"][:,it-1], self.GATED[chan]["NR"][:,it-2] ) ), n=nt )
  1176. # elif it == nt-1:
  1177. # X = self.bootstrap_resample( np.concatenate( (self.GATED[chan]["NR"][:,it], self.GATED[chan]["NR"][:,it-1], \
  1178. # self.GATED[chan]["NR"][:,it-2], self.GATED[chan]["NR"][:,it-3] ) ), n=nt )
  1179. # else:
  1180. # X = self.bootstrap_resample( np.concatenate( (self.GATED[chan]["NR"][:,it-2] , self.GATED[chan]["NR"][:,it-1], \
  1181. # self.GATED[chan]["NR"][:,it], self.GATED[chan]["NR"][:,it+1], self.GATED[chan]["NR"][:,it+2] )), n=nt )
  1182. # XS[ii,it] = np.std(X)
  1183. #if ii == 0:
  1184. # ax2.plot( self.GATED[chan]["GT"], XS[ii], '-', linewidth=1, markersize=4, alpha=.5, color='lightgrey', label = "bootstrap sim" )
  1185. #else:
  1186. # ax2.plot( self.GATED[chan]["GT"], XS[ii], '-', linewidth=1, markersize=4, alpha=.5, color='lightgrey' )
  1187. ax2.plot( self.GATED[chan]["GT"], np.std(self.GATED[chan]["NR"], axis=0), color='darkgrey', linewidth=2, label="gate std" )
  1188. ax2.plot( np.tile(self.GATED[chan]["GT"], (5000,1) ), XS, '.', color='lightgrey', linewidth=1, alpha=.5 )
  1189. ax2.plot( self.GATED[chan]["GT"], np.average(XS, axis=0), color='black', linewidth=2, label="bootstrap avg." )
  1190. ax2.plot( self.GATED[chan]["GT"], np.median(XS, axis=0), color='black', linewidth=2, label="bootstrap med." )
  1191. ax2.legend()
  1192. im1.set_edgecolor('face')
  1193. if phase != 2:
  1194. im2.set_edgecolor('face')
  1195. plt.setp(ax1.get_xticklabels(), visible=False)
  1196. ax1.set_ylim( np.min(self.GATED[chan]["QQ"]), np.max(self.GATED[chan]["QQ"]) )
  1197. if phase != 2:
  1198. ax2.set_ylim( np.min(self.GATED[chan]["QQ"]), np.max(self.GATED[chan]["QQ"]) )
  1199. ax1.set_xlim( np.min(self.GATED[chan]["GTT"]), np.max(self.GATED[chan]["GTT"]) )
  1200. ax2.set_xlim( np.min(self.GATED[chan]["GTT"]), np.max(self.GATED[chan]["GTT"]) )
  1201. ax1.set_yscale('log')
  1202. ax2.set_yscale('log')
  1203. qlabs = np.append(np.concatenate( ( self.GATED[chan]["QQ"][0:1], self.GATED[chan]["QQ"][9::10] )), self.GATED[chan]["QQ"][-1] )
  1204. ax1.yaxis.set_ticks( qlabs ) # np.append(np.concatenate( (QQ[0:1],QQ[9::10] )), QQ[-1] ) )
  1205. if phase != 2:
  1206. ax2.yaxis.set_ticks( qlabs ) #np.append(np.concatenate( (QQ[0:1],QQ[9::10] )), QQ[-1] ) )
  1207. #formatter = matplotlib.ticker.LogFormatter(10, labelOnlyBase=False)
  1208. formatter = matplotlib.ticker.FuncFormatter(lambda x, pos: str((round(x,1))))
  1209. ax1.yaxis.set_major_formatter(formatter) #matplotlib.ticker.FormatStrFormatter('%d.1'))
  1210. ax2.yaxis.set_major_formatter(formatter) #matplotlib.ticker.FormatStrFormatter('%d.1'))
  1211. ax1.xaxis.set_major_formatter(formatter) #matplotlib.ticker.FormatStrFormatter('%d.1'))
  1212. ax2.xaxis.set_major_formatter(formatter) #matplotlib.ticker.FormatStrFormatter('%d.1'))
  1213. ax1.set_xscale('log')
  1214. ax2.set_xscale('log')
  1215. if ichan == 0:
  1216. ax1.set_ylabel(r"$q$ ( $\mathrm{A}\cdot\mathrm{s}$)", fontsize=8)
  1217. if phase == 2:
  1218. ax2.set_ylabel(r"noise est. (nV)", fontsize=8)
  1219. else:
  1220. ax2.set_ylabel(r"$q$ ( $\mathrm{A}\cdot\mathrm{s}$)", fontsize=8)
  1221. else:
  1222. plt.setp(ax1.get_yticklabels(), visible=False)
  1223. plt.setp(ax2.get_yticklabels(), visible=False)
  1224. ax2.set_xlabel(r"$t-\tau_p$ (ms)", fontsize=8)
  1225. ichan += 1
  1226. #canvas.fig.subplots_adjust(hspace=.1, wspace=.05, left=.075, right=.925 )#left=None, bottom=None, right=None, top=None, wspace=None, hspace=None)
  1227. #canvas.fig.tight_layout()
  1228. #canvas.draw()
  1229. 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)
  1230. tick_locator = MaxNLocator(nbins=5)
  1231. cb1 = canvas.fig.colorbar(im1, ax=axes[0::2], format='%1.0e', orientation='horizontal', shrink=.35, aspect=30)
  1232. cb1.ax.tick_params(axis='both', which='major', labelsize=8)
  1233. cb1.set_label("$\mathcal{V}_N$ (nV)", fontsize=8)
  1234. cb1.locator = tick_locator
  1235. cb1.update_ticks()
  1236. if phase != 2:
  1237. cb2 = canvas.fig.colorbar(im2, ax=axes[1::2], format='%1.0e', orientation='horizontal', shrink=.35, aspect=30, pad=.2)
  1238. cb2.ax.tick_params(axis='both', which='major', labelsize=8)
  1239. cb2.set_label("$\mathcal{V}_N$ (nV)", fontsize=8)
  1240. cb2.locator = tick_locator
  1241. cb2.update_ticks()
  1242. canvas.draw()
  1243. self.doneTrigger.emit()
  1244. def FDSmartStack(self, cv, canvas):
  1245. from matplotlib.colors import LogNorm
  1246. from matplotlib.ticker import MaxNLocator
  1247. """
  1248. Currently this stacks 4-phase second pulse data only, we need to generalise
  1249. """
  1250. try:
  1251. canvas.fig.clear()
  1252. except:
  1253. pass
  1254. self.dataCubeFFT( )
  1255. # canvas.ax1 = canvas.fig.add_axes([.1, .1, .8, .8])
  1256. canvas.ax1 = canvas.fig.add_axes([.1, .1, .2, .8])
  1257. canvas.ax2 = canvas.fig.add_axes([.325, .1, .2, .8])
  1258. canvas.ax3 = canvas.fig.add_axes([.55, .1, .2, .8])
  1259. canvas.ax4 = canvas.fig.add_axes([.815, .1, .05, .8]) #cb
  1260. canvas.ax1.tick_params(axis='both', which='major', labelsize=8)
  1261. canvas.ax2.tick_params(axis='both', which='major', labelsize=8)
  1262. canvas.ax3.tick_params(axis='both', which='major', labelsize=8)
  1263. canvas.ax4.tick_params(axis='both', which='major', labelsize=8)
  1264. canvas.ax1.set_ylabel("pulse index", fontsize=8)
  1265. canvas.ax1.set_xlabel(r"$\omega$ bin", fontsize=8)
  1266. canvas.ax2.set_xlabel(r"$\omega$ bin", fontsize=8)
  1267. canvas.ax3.set_xlabel(r"$\omega$ bin", fontsize=8)
  1268. canvas.ax2.yaxis.set_ticklabels([])
  1269. canvas.ax3.yaxis.set_ticklabels([])
  1270. #canvas.ax1.ticklabel_format(style='sci', scilimits=(0,0), axis='y')
  1271. # # Look at pulses
  1272. # for pulse in self.DATADICT["PULSES"]:
  1273. # for istack in self.DATADICT["stacks"]:
  1274. # for ipm in range(0,3):
  1275. # canvas.ax1.plot( self.DATADICT[pulse]["CURRENT"][ipm][istack] , label="istack "+str(istack) + " ipm=" + str(ipm) + pulse )
  1276. # canvas.draw()
  1277. # Create Container for stacks
  1278. # sandbox determine pulse sequence again
  1279. for pulse in self.DATADICT["PULSES"]:
  1280. for ichan in self.DATADICT[pulse]["chan"]:
  1281. #for ipm in range(10,11):
  1282. CONTAINER = {}
  1283. CONTAINER["Cycle 1"] = [] # These are actually subtracted cycles... v+ - v
  1284. CONTAINER["Cycle 2"] = []
  1285. for istack in self.DATADICT["stacks"]:
  1286. #canvas.ax1.clear()
  1287. ipm = 8
  1288. #for ipm in range(self.DATADICT["nPulseMoments"]):
  1289. #canvas.ax1.matshow( np.real(self.DATADICT[pulse][ichan]["FFT"][istack]), aspect='auto' )
  1290. #canvas.draw()
  1291. if not istack%4%4:
  1292. # phase cycle 4, aligned with 1 after sub
  1293. CONTAINER["Cycle 1"].append(-self.DATADICT[pulse][ichan]["FFT"][istack])
  1294. #canvas.ax1.plot( self.DATADICT[pulse]["TIMES"], -self.DATADICT[pulse][ichan][ipm][istack], label="istack "+str(istack)+ " " + pulse )
  1295. elif not istack%4%3:
  1296. # phase cycle 3, aligned with 2 after sub
  1297. CONTAINER["Cycle 2"].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%2:
  1300. # phase cycle 2
  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. else:
  1304. # phase cycle 1
  1305. CONTAINER["Cycle 1"].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. #canvas.ax1.matshow(np.array(np.average(self.DATADICT[pulse][ichan]["FFT"]), axis=2), aspect='auto' )
  1308. #canvas.ax1.plot( self.DATADICT[pulse]["PULSE_TIMES"], self.DATADICT[pulse]["CURRENT"][ipm][istack] , color='black', label="istack "+str(istack) )
  1309. #canvas.ax1.plot( self.DATADICT[pulse]["CURRENT"][ipm][istack] , label="istack "+str(istack) + " iFID" + str(iFID) )
  1310. #canvas.ax1.plot( self.DATADICT[pulse]["TIMES"], self.DATADICT[pulse][ichan][ipm][istack], label="istack "+str(istack)+ " " + pulse )
  1311. #canvas.ax1.legend(prop={'size':6})
  1312. #canvas.draw()
  1313. # Boostrap
  1314. # stack.
  1315. #scipy.random.shuffle(x)
  1316. # Stack and calculate the pooled variance (http://en.wikipedia.org/wiki/Pooled_variance)
  1317. """ All this phase cycling wreaks havoc on a normal calculation of std. and variance. Instead, we resort to calculating
  1318. a pooled variance. In this assumption is that the precision of the measurment is constant. This is a poor choice for
  1319. any type of moving sensor.
  1320. """
  1321. # if a window filter has been applied
  1322. #self.WINDOW
  1323. #self.IWindowStart
  1324. #self.iWindowEnd
  1325. #self.FFTtimes
  1326. CONTAINER = .5*(np.array(CONTAINER["Cycle 2"]) - np.array(CONTAINER["Cycle 1"]))
  1327. print ("container shape", np.shape( CONTAINER), self.iWindowStart+1, self.iWindowEnd-1)
  1328. dmin = np.min(np.abs(np.average(np.array(CONTAINER)[:,:,self.iWindowStart+1:self.iWindowEnd-1], axis=0)))
  1329. dmax = np.max(np.abs(np.average(np.array(CONTAINER)[:,:,self.iWindowStart+1:self.iWindowEnd-1], axis=0)))
  1330. 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)
  1331. #mn = canvas.ax1.matshow(20.*np.log10(XA[:,istart:iend+1]), aspect='auto', vmax=-40, vmin=-120) #, norm=LogNorm())
  1332. 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)
  1333. 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)
  1334. #canvas.ax1.legend(prop={'size':6})
  1335. cb1 = mpl.colorbar.Colorbar(canvas.ax4, mn)
  1336. cb1.ax.tick_params(labelsize=8)
  1337. cb1.set_label("power [dB]", fontsize=8)
  1338. canvas.ax1.xaxis.set_major_locator(MaxNLocator(4))
  1339. canvas.ax2.xaxis.set_major_locator(MaxNLocator(4))
  1340. canvas.ax3.xaxis.set_major_locator(MaxNLocator(4))
  1341. canvas.draw()
  1342. self.doneTrigger.emit()
  1343. def effectivePulseMoment(self, cv, canvas):
  1344. canvas.reAxH(2)
  1345. nstack = len(self.DATADICT["stacks"])
  1346. canvas.ax1.set_yscale('log')
  1347. for pulse in self.DATADICT["PULSES"]:
  1348. self.DATADICT[pulse]["qeff"] = {}
  1349. self.DATADICT[pulse]["q_nu"] = {}
  1350. for ipm in range(self.DATADICT["nPulseMoments"]):
  1351. self.DATADICT[pulse]["qeff"][ipm] = {}
  1352. self.DATADICT[pulse]["q_nu"][ipm] = {}
  1353. #canvas.ax1.clear()
  1354. #scolours = np.array( ( np.linspace(0.8,0.4,len(self.DATADICT["stacks"])), \
  1355. # np.linspace(0.0,0.6,len(self.DATADICT["stacks"])), \
  1356. # np.linspace(0.6,0.0,len(self.DATADICT["stacks"])) )
  1357. # ).T
  1358. #scolours = plt.cm.Spectral(np.linspace(0,1,len(self.DATADICT["stacks"])))
  1359. #scolours = plt.cm.Blues(np.linspace(0,1,1.5*len(self.DATADICT["stacks"])))
  1360. scolours = cmocean.cm.ice(np.linspace(0,1,1.5*len(self.DATADICT["stacks"])))
  1361. iistack = 0
  1362. for istack in self.DATADICT["stacks"]:
  1363. #self.DATADICT[pulse]["PULSE_TIMES"]
  1364. x = self.DATADICT[pulse]["CURRENT"][ipm][istack]
  1365. X = np.fft.rfft(x)
  1366. v = np.fft.fftfreq(len(x), self.dt)
  1367. v = v[0:len(X)]
  1368. v[-1] = np.abs(v[-1])
  1369. # calculate effective current/moment
  1370. I0 = np.abs(X)/len(X)
  1371. qeff = I0 * (self.DATADICT[pulse]["PULSE_TIMES"][-1]-self.DATADICT[pulse]["PULSE_TIMES"][0])
  1372. canvas.ax1.set_title(r"pulse moment index " +str(ipm), fontsize=10)
  1373. canvas.ax1.set_xlabel(r"$\nu$ [Hz]", fontsize=8)
  1374. canvas.ax1.set_ylabel(r"$q_{eff}$ [A$\cdot$sec]", fontsize=8)
  1375. canvas.ax1.plot(v, qeff, color=scolours[iistack] ) # eff current
  1376. self.DATADICT[pulse]["qeff"][ipm][istack] = qeff
  1377. self.DATADICT[pulse]["q_nu"][ipm][istack] = v
  1378. iistack += 1
  1379. canvas.draw()
  1380. percent = int(1e2* (float)((istack)+ipm*self.DATADICT["nPulseMoments"]) /
  1381. (float)(len(self.DATADICT["PULSES"])*self.DATADICT["nPulseMoments"]*nstack))
  1382. self.progressTrigger.emit(percent)
  1383. canvas.draw()
  1384. self.plotQeffNu(cv, canvas.ax2)
  1385. canvas.draw()
  1386. self.doneTrigger.emit()
  1387. def plotQeffNu(self, cv, ax):
  1388. ####################################
  1389. # TODO label fid1 and fid2, and make a legend, and colour by pulse
  1390. nstack = len(self.DATADICT["stacks"])
  1391. iFID = 0
  1392. for pulse in self.DATADICT["PULSES"]:
  1393. self.DATADICT[pulse]["Q"] = np.zeros( (self.DATADICT["nPulseMoments"], len(self.DATADICT["stacks"])) )
  1394. ilabel = True
  1395. for ipm in range(self.DATADICT["nPulseMoments"]):
  1396. #scolours = np.array([0.,0.,1.])
  1397. scolours = cmocean.cm.ice(np.linspace(0,1,1.5*len(self.DATADICT["stacks"])))
  1398. #scolours = plt.cm.Spectral(np.linspace(0,1,len(self.DATADICT["stacks"])))
  1399. #scolours = plt.cm.Spectral(np.linspace(0,1,len(self.DATADICT["stacks"])))
  1400. istack = 0
  1401. for stack in self.DATADICT["stacks"]:
  1402. # find index
  1403. icv = int (round(cv / self.DATADICT[pulse]["q_nu"][ipm][stack][1]))
  1404. self.DATADICT[pulse]["Q"][ipm,istack] = self.DATADICT[pulse]["qeff"][ipm][stack][icv]
  1405. if ilabel:
  1406. ax.scatter(ipm, self.DATADICT[pulse]["qeff"][ipm][stack][icv], facecolors='none', edgecolors=scolours[istack], label=(str(pulse)))
  1407. ilabel = False
  1408. else:
  1409. ax.scatter(ipm, self.DATADICT[pulse]["qeff"][ipm][stack][icv], facecolors='none', edgecolors=scolours[istack])
  1410. #scolours += np.array((0,1./(nstack+1),-1/(nstack+1.)))
  1411. percent = int(1e2* (float)((istack)+ipm*self.DATADICT["nPulseMoments"]) /
  1412. (float)(len(self.DATADICT["PULSES"])*self.DATADICT["nPulseMoments"]*nstack))
  1413. self.progressTrigger.emit(percent)
  1414. istack += 1
  1415. iFID += 1
  1416. ax.set_xlabel(r"pulse moment index", fontsize=8)
  1417. ax.set_ylabel(r"$q_{eff}$ [A$\cdot$sec]", fontsize=8)
  1418. ax.set_yscale('log')
  1419. ax.set_xlim(0, ax.get_xlim()[1])
  1420. ax.legend(loc='upper right', scatterpoints = 1, prop={'size':6})
  1421. def enableDSP(self):
  1422. self.enableDSPTrigger.emit()
  1423. def adaptiveFilter(self, M, flambda, truncate, mu, PCA, canvas):
  1424. canvas.reAx2(shx=False, shy=False)
  1425. canvas.ax2.tick_params(axis='both', which='major', labelsize=8)
  1426. canvas.ax2.ticklabel_format(style='sci', scilimits=(0,0), axis='y')
  1427. canvas.ax2.tick_params(axis='both', which='major', labelsize=8)
  1428. canvas.ax2.ticklabel_format(style='sci', scilimits=(0,0), axis='y')
  1429. if truncate:
  1430. itrunc =(int) ( round( 1e-3*truncate*self.samp ) )
  1431. print( "adaptive filter size", 1e3*self.dt*M, " [ms]" )
  1432. Filt = adapt.AdaptiveFilter(flambda)
  1433. H = {}
  1434. for pulse in self.DATADICT["PULSES"]:
  1435. H[pulse] = {}
  1436. for ichan in self.DATADICT[pulse]["chan"]:
  1437. H[pulse][ichan] = np.zeros(M)
  1438. iFID = 0
  1439. # original ordering...
  1440. #for pulse in self.DATADICT["PULSES"]:
  1441. # for ipm in range(self.DATADICT["nPulseMoments"]):
  1442. # for istack in self.DATADICT["stacks"]:
  1443. # This order makes more sense, same as data collection, verify
  1444. for istack in self.DATADICT["stacks"]:
  1445. for ipm in range(self.DATADICT["nPulseMoments"]):
  1446. for pulse in self.DATADICT["PULSES"]:
  1447. canvas.ax1.clear()
  1448. canvas.ax2.clear()
  1449. for ichan in self.DATADICT[pulse]["chan"]:
  1450. #H = np.zeros(M)
  1451. canvas.ax1.plot( self.DATADICT[pulse]["TIMES"], 1e9* self.DATADICT[pulse][ichan][ipm][istack],\
  1452. label = "noisy")
  1453. RX = []
  1454. for irchan in self.DATADICT[pulse]["rchan"]:
  1455. RX.append(self.DATADICT[pulse][irchan][ipm][istack][::-1])
  1456. if all(H[pulse][ichan]) == 0:
  1457. # call twice to reconcile filter wind-up
  1458. [e,H[pulse][ichan]] = Filt.adapt_filt_Ref( self.DATADICT[pulse][ichan][ipm][istack][::-1],\
  1459. RX,\
  1460. M, mu, PCA, flambda, H[pulse][ichan])
  1461. [e,H[pulse][ichan]] = Filt.adapt_filt_Ref( self.DATADICT[pulse][ichan][ipm][istack][::-1],\
  1462. RX,\
  1463. M, mu, PCA, flambda, H[pulse][ichan])
  1464. else:
  1465. [e,H[pulse][ichan]] = Filt.adapt_filt_Ref( self.DATADICT[pulse][ichan][ipm][istack][::-1],\
  1466. RX,\
  1467. M, mu, PCA, flambda, H[pulse][ichan])
  1468. # replace
  1469. if truncate:
  1470. canvas.ax1.plot( self.DATADICT[pulse]["TIMES"][0:itrunc], 1e9* e[::-1][0:itrunc],\
  1471. label = pulse + " ipm=" + str(ipm) + " istack=" + str(istack) + " ichan=" + str(ichan))
  1472. self.DATADICT[pulse][ichan][ipm][istack] = e[::-1][0:itrunc]
  1473. else:
  1474. canvas.ax1.plot( self.DATADICT[pulse]["TIMES"], 1e9* e[::-1],\
  1475. label = pulse + " ipm=" + str(ipm) + " istack=" + str(istack) + " ichan=" + str(ichan))
  1476. self.DATADICT[pulse][ichan][ipm][istack] = e[::-1]
  1477. canvas.ax2.plot( H[pulse][ichan] , label="taps")
  1478. canvas.ax1.legend(prop={'size':6})
  1479. canvas.ax2.legend(prop={'size':6})
  1480. canvas.ax1.set_xlabel(r"time [s]", fontsize=8)
  1481. canvas.ax1.set_ylabel(r"signal [nV]", fontsize=8)
  1482. canvas.ax2.set_xlabel(r"filter index", fontsize=8)
  1483. canvas.ax2.set_ylabel(r"scale factor", fontsize=8)
  1484. canvas.draw()
  1485. # truncate the reference channels too, in case you still need them for something.
  1486. # Otherwise they are no longer aligned with the data
  1487. for rchan in self.DATADICT[pulse]["rchan"]:
  1488. if truncate:
  1489. self.DATADICT[pulse][rchan][ipm][istack] = self.DATADICT[pulse][rchan][ipm][istack][0:itrunc]
  1490. #percent = (int)(1e2*((float)(iFID*self.DATADICT["nPulseMoments"]+(ipm))/( len(self.DATADICT["PULSES"])*self.nPulseMoments)))
  1491. percent = (int)(1e2*((float)(istack*self.DATADICT["nPulseMoments"]+(ipm))/( len(self.DATADICT["PULSES"])*self.nPulseMoments*(len(self.DATADICT["stacks"])+1) )))
  1492. self.progressTrigger.emit(percent)
  1493. # # why is this loop here, istack is not part of rest?
  1494. # for istack in self.DATADICT["stacks"]:
  1495. # if truncate:
  1496. # self.DATADICT[pulse]["TIMES"] = self.DATADICT[pulse]["TIMES"][0:itrunc]
  1497. # percent = (int)(1e2*((float)(iFID*self.DATADICT["nPulseMoments"]+(ipm))/( len(self.DATADICT["PULSES"])*self.nPulseMoments)))
  1498. # self.progressTrigger.emit(percent)
  1499. # iFID += 1
  1500. if truncate:
  1501. self.DATADICT[pulse]["TIMES"] = self.DATADICT[pulse]["TIMES"][0:itrunc]
  1502. self.doneTrigger.emit()
  1503. self.updateProcTrigger.emit()
  1504. #self.plotFT(canvas)
  1505. def plotFT(self, canvas, istart=0, iend=0):
  1506. try:
  1507. canvas.fig.clear()
  1508. except:
  1509. pass
  1510. canvas.ax1 = canvas.fig.add_axes([.1, .1, .65, .8])
  1511. canvas.ax1c = canvas.fig.add_axes([.8, .1, .05, .8])
  1512. canvas.ax1.tick_params(axis='both', which='major', labelsize=8)
  1513. for pulse in self.DATADICT["PULSES"]:
  1514. for istack in self.DATADICT["stacks"]:
  1515. for ichan in self.DATADICT[pulse]["chan"]:
  1516. # FFT of stack
  1517. XA = np.zeros((self.DATADICT["nPulseMoments"] , len(self.DATADICT[pulse][ichan][0][istack])/2+1))
  1518. nu = np.fft.fftfreq(self.DATADICT[pulse][ichan][0][istack].size, d=self.dt)
  1519. nu[-1] *= -1
  1520. df = nu[1]
  1521. of = 0
  1522. if istart:
  1523. of = nu[istart]
  1524. def freqlabel(x, pos):
  1525. return '%1.0f' %(of + x*df)
  1526. formatter = FuncFormatter(freqlabel)
  1527. canvas.ax1.clear()
  1528. for ipm in range(self.DATADICT["nPulseMoments"]):
  1529. X = np.fft.rfft(self.DATADICT[pulse][ichan][ipm][istack])
  1530. XA[ipm,:] = np.abs(X)
  1531. if istart:
  1532. mn = canvas.ax1.matshow(20.*np.log10(XA[:,istart:iend+1]), aspect='auto', vmax=-40, vmin=-120) #, norm=LogNorm())
  1533. else:
  1534. mn = canvas.ax1.matshow(20.*np.log10(XA), aspect='auto', vmax=-40, vmin=-120) #, norm=LogNorm())
  1535. smin = np.min(20.*np.log10(XA))
  1536. smax = np.max(20.*np.log10(XA))
  1537. canvas.ax1.xaxis.set_major_formatter(formatter)
  1538. cb1 = mpl.colorbar.Colorbar(canvas.ax1c, mn)
  1539. cb1.ax.tick_params(labelsize=8)
  1540. cb1.set_label("signal [dB]", fontsize=8)
  1541. canvas.ax1.set_xlabel(r"$\nu$ [Hz]", fontsize=10)
  1542. canvas.ax1.set_ylabel(r"$q_{index}$", fontsize=10)
  1543. canvas.draw()
  1544. def plotFT(self, canvas, istart=0, iend=0):
  1545. try:
  1546. canvas.fig.clear()
  1547. except:
  1548. pass
  1549. canvas.ax1 = canvas.fig.add_axes([.1, .1, .65, .8])
  1550. canvas.ax1c = canvas.fig.add_axes([.8, .1, .05, .8])
  1551. canvas.ax1.tick_params(axis='both', which='major', labelsize=8)
  1552. for pulse in self.DATADICT["PULSES"]:
  1553. for istack in self.DATADICT["stacks"]:
  1554. for ichan in self.DATADICT[pulse]["chan"]:
  1555. # FFT of stack
  1556. XA = np.zeros((self.DATADICT["nPulseMoments"] , len(self.DATADICT[pulse][ichan][0][istack])//2+1))
  1557. nu = np.fft.fftfreq(self.DATADICT[pulse][ichan][0][istack].size, d=self.dt)
  1558. nu[-1] *= -1
  1559. df = nu[1]
  1560. of = 0
  1561. if istart:
  1562. of = nu[istart]
  1563. def freqlabel(x, pos):
  1564. return '%1.0f' %(of + x*df)
  1565. formatter = FuncFormatter(freqlabel)
  1566. canvas.ax1.clear()
  1567. for ipm in range(self.DATADICT["nPulseMoments"]):
  1568. X = np.fft.rfft(self.DATADICT[pulse][ichan][ipm][istack])
  1569. XA[ipm,:] = np.abs(X)
  1570. if istart:
  1571. mn = canvas.ax1.matshow(20.*np.log10(XA[:,istart:iend+1]), aspect='auto', vmax=-40, vmin=-120, cmap='viridis') #, norm=LogNorm())
  1572. else:
  1573. mn = canvas.ax1.matshow(20.*np.log10(XA), aspect='auto', vmax=-40, vmin=-120, cmap='viridis') #, norm=LogNorm())
  1574. canvas.ax1.xaxis.set_major_formatter(formatter)
  1575. cb1 = mpl.colorbar.Colorbar(canvas.ax1c, mn)
  1576. cb1.ax.tick_params(labelsize=8)
  1577. cb1.set_label("signal [dB]", fontsize=8)
  1578. canvas.ax1.set_xlabel(r"$\nu$ [Hz]", fontsize=10)
  1579. canvas.ax1.set_ylabel(r"$q_{index}$", fontsize=10)
  1580. canvas.draw()
  1581. def dataCubeFFT(self):
  1582. """
  1583. Performs FFT on entire cube of DATA, and REFERENCE channels, but not pulse currents,
  1584. Results are saved to a new field in the data structure
  1585. The GMR varies phase as a function of pulse moment index, so that the first pusle moment is zero phase,
  1586. the second is pi/2 the third is zero. This method corrects for this, so that all pulse moments are in phase.
  1587. Technically we may not want to do this, if there is some system response that this cycles away, and we lose track of
  1588. 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
  1589. rest of the phase cycles. Holy phase cycling batman.
  1590. """
  1591. for pulse in self.DATADICT["PULSES"]:
  1592. for ichan in np.append(self.DATADICT[pulse]["chan"], self.DATADICT[pulse]["rchan"]):
  1593. # FFT of stack
  1594. self.DATADICT[pulse][ichan]["FFT"] = {}
  1595. self.DATADICT[pulse][ichan]["FFT"]["nu"] = np.fft.fftfreq(self.DATADICT[pulse][ichan][0][self.DATADICT["stacks"][0]].size, d=self.dt)
  1596. self.DATADICT[pulse][ichan]["FFT"]["nu"][-1] *= -1
  1597. for istack in self.DATADICT["stacks"]:
  1598. self.DATADICT[pulse][ichan]["FFT"][istack] = np.zeros((self.DATADICT["nPulseMoments"] , len(self.DATADICT[pulse][ichan][0][istack])//2+1), dtype=complex)
  1599. for ipm in range(self.DATADICT["nPulseMoments"]):
  1600. # Mod works for FID pulse sequences, TODO generalize this for 4 phase T1, etc..
  1601. mod = (-1)**(ipm%2) * (-1)**(istack%2)
  1602. self.DATADICT[pulse][ichan]["FFT"][istack][ipm,:] = np.fft.rfft( self.DATADICT[pulse][ichan][ipm][istack] )
  1603. #if ipm%2:
  1604. # odd, phase cycled from previous
  1605. # self.DATADICT[pulse][ichan]["FFT"][istack][ipm,:] = np.fft.rfft(-self.DATADICT[pulse][ichan][ipm][istack])
  1606. #else:
  1607. # even, we define as zero phase, first pulse moment has this
  1608. # self.DATADICT[pulse][ichan]["FFT"][istack][ipm,:] = np.fft.rfft(self.DATADICT[pulse][ichan][ipm][istack])
  1609. def adaptiveFilterFD(self, ftype, band, centre, canvas):
  1610. try:
  1611. canvas.fig.clear()
  1612. except:
  1613. pass
  1614. canvas.ax1 = canvas.fig.add_axes([.1, .5, .7, .4])
  1615. canvas.ax1c = canvas.fig.add_axes([.85, .5, .05, .4])
  1616. canvas.ax1.tick_params(axis='both', which='major', labelsize=8)
  1617. #canvas.ax1.ticklabel_format(style='sci', scilimits=(0,0), axis='y')
  1618. canvas.ax2 = canvas.fig.add_axes([.1, .05, .7, .4])
  1619. canvas.ax2c = canvas.fig.add_axes([.85, .05, .05, .4])
  1620. canvas.ax2.tick_params(axis='both', which='major', labelsize=8)
  1621. #canvas.ax2.ticklabel_format(style='sci', scilimits=(0,0), axis='y')
  1622. self.dataCubeFFT()
  1623. Filt = adapt.AdaptiveFilter(0.)
  1624. for pulse in self.DATADICT["PULSES"]:
  1625. # Compute window function and dimensions
  1626. [WINDOW, nd, wstart, wend, dead] = self.computeWindow(pulse, band, centre, ftype)
  1627. for istack in self.DATADICT["stacks"]:
  1628. for ichan in self.DATADICT[pulse]["chan"]:
  1629. # FFT of stack
  1630. nd = len(self.DATADICT[pulse][ichan][0][istack])
  1631. XX = np.zeros((self.DATADICT["nPulseMoments"] , len(self.DATADICT[pulse][ichan][0][istack])//2+1), dtype=complex)
  1632. nu = np.fft.fftfreq(self.DATADICT[pulse][ichan][0][istack].size, d=self.dt)
  1633. nu[-1] *= -1
  1634. #nu = self.DATADICT[pulse][ichan]["FFT"]["nu"]
  1635. def freqlabel(x, pos):
  1636. return '%1.0f' %((wstart)*nu[1] + x*nu[1])
  1637. formatter = FuncFormatter(freqlabel)
  1638. canvas.ax1.clear()
  1639. for ipm in range(self.DATADICT["nPulseMoments"]):
  1640. X = np.fft.rfft(self.DATADICT[pulse][ichan][ipm][istack])
  1641. XX[ipm,:] = X
  1642. XX = XX*WINDOW
  1643. XX = XX[:,wstart:wend]
  1644. smin = np.min(20.*np.log10(np.abs(XX)))
  1645. smax = np.max(20.*np.log10(np.abs(XX)))
  1646. #if smin != smin:
  1647. smax = -40
  1648. smin = -120
  1649. mn = canvas.ax1.matshow(20.*np.log10(np.abs(XX)), aspect='auto', vmin=smin, vmax=smax) #, norm=LogNorm())
  1650. canvas.ax1.xaxis.set_major_formatter(formatter)
  1651. cb1 = mpl.colorbar.Colorbar(canvas.ax1c, mn)
  1652. RX = []
  1653. for ichan in self.DATADICT[pulse]["rchan"]:
  1654. R = np.zeros((self.DATADICT["nPulseMoments"] , len(self.DATADICT[pulse][ichan][0][istack])//2+1), dtype=complex)
  1655. for ipm in range(self.DATADICT["nPulseMoments"]):
  1656. R[ipm,:] = np.fft.rfft(self.DATADICT[pulse][ichan][ipm][istack])
  1657. RX.append(R[:,wstart:wend])
  1658. XC = Filt.transferFunctionFFT(XX, RX)
  1659. # TODO inverse FFT, but we need to map back to origional matrix size
  1660. #for ichan in self.DATADICT[pulse]["chan"]:
  1661. # for ipm in range(self.DATADICT["nPulseMoments"]):
  1662. # self.DATADICT[pulse][ichan][ipm][istack] = np.fft.irfft(XC[] , nd)
  1663. mc = canvas.ax2.matshow(20.*np.log10(np.abs(XC)), aspect='auto', vmin=smin, vmax=smax) #, norm=LogNorm())
  1664. cb2 = mpl.colorbar.Colorbar(canvas.ax2c, mc)
  1665. cmin = np.min(20.*np.log10(np.abs(XC)))
  1666. cmax = np.max(20.*np.log10(np.abs(XC)))
  1667. canvas.ax2.xaxis.set_major_formatter(formatter)
  1668. #canvas.ax2.colorbar(mn)
  1669. canvas.draw()
  1670. ##############################3
  1671. # TODO inverse FFT to get the damn data back!!!
  1672. # self.progressTrigger.emit(percent)
  1673. # #label = "iFID="+str(iFID) + " ipm=" + str(ipm) + " istack=" + str(istack) + " ichan=" + str(ichan))
  1674. self.doneTrigger.emit()
  1675. def findSpikes(self, x, width, threshold, rollOn):
  1676. import scipy.ndimage as im
  1677. spikes = np.zeros( len(x) )
  1678. med = im.median_filter(x, width,mode='nearest')
  1679. std = np.std(x)
  1680. spikes = (np.abs(x-med) > threshold * std)
  1681. return np.array(np.where(spikes[rollOn::])) + rollOn
  1682. # def despike(self, width, threshold, itype, rollOn, win, canvas):
  1683. # from scipy import interpolate
  1684. # """ This was a stab at a despike filter. Better results were achieved using the SmartStack approach
  1685. # """
  1686. # try:
  1687. # canvas.fig.clear()
  1688. # except:
  1689. # pass
  1690. #
  1691. # canvas.ax1 = canvas.fig.add_axes([.125,.1,.725,.8])
  1692. # canvas.ax1.tick_params(axis='both', which='major', labelsize=8)
  1693. # canvas.ax1.ticklabel_format(style='sci', scilimits=(0,0), axis='y')
  1694. # iFID = 0
  1695. # for pulse in self.DATADICT["PULSES"]:
  1696. # for ipm in range(self.DATADICT["nPulseMoments"]):
  1697. # for istack in self.DATADICT["stacks"]:
  1698. # canvas.ax1.clear()
  1699. # for ichan in np.append(self.DATADICT[pulse]["chan"], self.DATADICT[pulse]["rchan"]):
  1700. # x = self.findSpikes(self.DATADICT[pulse][ichan][ipm][istack], width, threshold, rollOn)
  1701. # canvas.ax1.plot( self.DATADICT[pulse]["TIMES"], self.DATADICT[pulse][ichan][ipm][istack],
  1702. # label = pulse + " ipm=" + str(ipm) + " istack=" + str(istack) + " ichan=" + str(ichan))
  1703. # canvas.ax1.plot( self.DATADICT[pulse]["TIMES"][x], self.DATADICT[pulse][ichan][ipm][istack][x], '.', color='red' , markersize=6 )
  1704. #
  1705. # FIXED = np.zeros(len(x[0]))
  1706. # ii = 0
  1707. # for spike in np.array(x[0]).tolist():
  1708. # f = interpolate.interp1d(np.delete(self.DATADICT[pulse]["TIMES"][spike-win/2:spike+win/2], x[0]-(spike-win/2)), \
  1709. # np.delete(self.DATADICT[pulse][ichan][ipm][istack][spike-win/2:spike+win/2], x[0]-(spike-win/2)), itype)
  1710. # FIXED[ii] = f(self.DATADICT[pulse]["TIMES"][spike])
  1711. # ii += 1
  1712. # canvas.ax1.plot( self.DATADICT[pulse]["TIMES"][x[0]] , FIXED, '.', color='black' , markersize=4 )
  1713. # self.DATADICT[pulse][ichan][ipm][istack][x[0]] = FIXED
  1714. #
  1715. # canvas.ax1.legend(prop={'size':6})
  1716. # canvas.draw()
  1717. # percent = (int)(1e2*((float)(iFID*self.DATADICT["nPulseMoments"]+(ipm))/( len(self.DATADICT["PULSES"])*self.nPulseMoments)))
  1718. # self.progressTrigger.emit(percent)
  1719. # iFID += 1
  1720. # self.doneTrigger.emit()
  1721. def designFilter(self, cf, PB, SB, gpass, gstop, ftype, canvas):
  1722. ''' cf is central frequency
  1723. pb is pass band
  1724. sb is stop band
  1725. '''
  1726. TS = (cf) / (.5/self.dt)
  1727. PB = PB / (.5/self.dt) # 1/2 width pass band Muddy Creek
  1728. SB = SB / (.5/self.dt) # 1/2 width stop band Muddy Creek
  1729. # if butterworth
  1730. #[bord, wn] = signal.buttord([TS-PB,TS+PB], [TS-SB,TS+SB], 1e-1, 5.)
  1731. if ftype=="Butterworth":
  1732. [bord, wn] = signal.buttord([TS-PB,TS+PB], [TS-SB,TS+SB], gpass, gstop)
  1733. [self.filt_b, self.filt_a] = signal.butter(bord, wn, btype='bandpass', output='ba')
  1734. [self.filt_z, self.filt_p, self.filt_k] = signal.butter(bord, wn, btype='band', output='zpk')
  1735. elif ftype == "Chebychev Type II":
  1736. [bord, wn] = signal.cheb2ord([TS-PB,TS+PB], [TS-SB,TS+SB], gpass, gstop)
  1737. [self.filt_b, self.filt_a] = signal.cheby2(bord, gstop, wn, btype='bandpass', output='ba')
  1738. [self.filt_z, self.filt_p, self.filt_k] = signal.cheby2(bord, gstop, wn, btype='band', output='zpk')
  1739. elif ftype == "Elliptic":
  1740. [bord, wn] = signal.ellipord([TS-PB,TS+PB], [TS-SB,TS+SB], gpass, gstop)
  1741. [self.filt_b, self.filt_a] = signal.ellip(bord, gpass, gstop, wn, btype='bandpass', output='ba')
  1742. [self.filt_z, self.filt_p, self.filt_k] = signal.ellip(bord, gpass, gstop, wn, btype='band', output='zpk')
  1743. # if cheby2
  1744. impulse = self.mfreqz2(self.filt_b, self.filt_a, canvas)
  1745. self.fe = -5
  1746. for it in range(len(impulse[0])):
  1747. if abs(impulse[1][0][it][0]) >= .1 * gpass:# gpass:
  1748. self.fe = impulse[0][it]
  1749. canvas.draw()
  1750. return [bord, self.fe]
  1751. def downsample(self, truncate, dec, plot=False, canvas=None):
  1752. """ Downsamples and truncates the raw signal.
  1753. Args
  1754. truncate (float) : the length of the signal to truncate to
  1755. dec (int) : the decimation factor, 1 results in no downsampling
  1756. plot (bool) : perform plots
  1757. canvas : MPL axis for plotting
  1758. """
  1759. if plot:
  1760. canvas.reAx2()
  1761. canvas.ax1.set_ylabel(r"signal [nV]", fontsize=8)
  1762. canvas.ax2.set_xlabel(r"time [s]", fontsize=8)
  1763. canvas.ax2.set_ylabel(r"signal [nV]", fontsize=8)
  1764. self.samp /= dec
  1765. self.dt = 1./self.samp
  1766. if truncate:
  1767. itrunc = (int)( 1e-3*truncate*self.samp )
  1768. iFID = 0
  1769. for pulse in self.DATADICT["PULSES"]:
  1770. for ipm in range(self.DATADICT["nPulseMoments"]):
  1771. for istack in self.DATADICT["stacks"]:
  1772. if plot:
  1773. canvas.softClear()
  1774. for ichan in np.append(self.DATADICT[pulse]["chan"], self.DATADICT[pulse]["rchan"]):
  1775. # trim off indices that don't divide evenly
  1776. ndi = np.shape(self.DATADICT[pulse][ichan][ipm][istack])[0]%dec
  1777. if ndi:
  1778. [self.DATADICT[pulse][ichan][ipm][istack], RSTIMES] = signal.resample(self.DATADICT[pulse][ichan][ipm][istack][0:-ndi],\
  1779. len(self.DATADICT[pulse][ichan][ipm][istack][0:-ndi])//dec,\
  1780. self.DATADICT[pulse]["TIMES"][0:-ndi], window='hamm')
  1781. else:
  1782. [self.DATADICT[pulse][ichan][ipm][istack], RSTIMES] = signal.resample(self.DATADICT[pulse][ichan][ipm][istack],\
  1783. len(self.DATADICT[pulse][ichan][ipm][istack])//dec,\
  1784. self.DATADICT[pulse]["TIMES"], window='hamm')
  1785. if truncate:
  1786. self.DATADICT[pulse][ichan][ipm][istack] = self.DATADICT[pulse][ichan][ipm][istack][0:itrunc]
  1787. RSTIMES = RSTIMES[0:itrunc]
  1788. if plot:
  1789. for ichan in self.DATADICT[pulse]["chan"]:
  1790. canvas.ax2.plot( RSTIMES, 1e9*self.DATADICT[pulse][ichan][ipm][istack], \
  1791. label = pulse + " ipm=" + str(ipm) + " istack=" + str(istack) + " ichan=" + str(ichan))
  1792. for ichan in self.DATADICT[pulse]["rchan"]:
  1793. canvas.ax1.plot( RSTIMES, 1e9*self.DATADICT[pulse][ichan][ipm][istack], \
  1794. label = pulse + " ipm=" + str(ipm) + " istack=" + str(istack) + " ichan=" + str(ichan))
  1795. canvas.ax1.legend(prop={'size':6}, loc='upper right')
  1796. canvas.ax2.legend(prop={'size':6}, loc='upper right')
  1797. canvas.draw()
  1798. percent = (int)(1e2*((float)(iFID*self.DATADICT["nPulseMoments"]+(ipm))/( len(self.DATADICT["PULSES"])*self.nPulseMoments)))
  1799. self.progressTrigger.emit(percent)
  1800. iFID += 1
  1801. self.DATADICT[pulse]["TIMES"] = RSTIMES
  1802. #####################################
  1803. # resample pulse data
  1804. for pulse in self.DATADICT["PULSES"]:
  1805. for ipm in range(self.DATADICT["nPulseMoments"]):
  1806. for istack in self.DATADICT["stacks"]:
  1807. ndi = np.shape(self.DATADICT[pulse]["CURRENT"][ipm][istack])[0]%dec
  1808. if ndi:
  1809. [self.DATADICT[pulse]["CURRENT"][ipm][istack], RSPTIMES] = signal.resample(self.DATADICT[pulse]["CURRENT"][ipm][istack][0:-ndi],\
  1810. len(self.DATADICT[pulse]["CURRENT"][ipm][istack][0:-ndi])//dec,\
  1811. self.DATADICT[pulse]["PULSE_TIMES"][0:-ndi], window='hamm')
  1812. else:
  1813. [self.DATADICT[pulse]["CURRENT"][ipm][istack], RSPTIMES] = signal.resample(self.DATADICT[pulse]["CURRENT"][ipm][istack],\
  1814. len(self.DATADICT[pulse]["CURRENT"][ipm][istack])//dec,\
  1815. self.DATADICT[pulse]["PULSE_TIMES"], window='hamm')
  1816. self.DATADICT[pulse]["PULSE_TIMES"] = RSPTIMES
  1817. self.doneTrigger.emit()
  1818. self.updateProcTrigger.emit()
  1819. def computeWindow(self, pulse, band, centre, ftype, canvas=None):
  1820. # Compute window
  1821. nd = len(self.DATADICT[pulse][self.DATADICT[pulse]["chan"][0]][0][self.DATADICT["stacks"][0]])
  1822. fft1 = np.fft.rfft(self.DATADICT[pulse][self.DATADICT[pulse]["chan"][0]][0][self.DATADICT["stacks"][0]])
  1823. freqs = np.fft.fftfreq(nd, self.dt)
  1824. df = freqs[1] - freqs[0]
  1825. N = int((round)(band/df))
  1826. if ftype == "Hamming":
  1827. window = np.hamming(N)
  1828. elif ftype == "Hanning":
  1829. window = np.hanning(N)
  1830. elif ftype == "Rectangular":
  1831. window = np.ones(N)
  1832. elif ftype == "Flat top":
  1833. window = signal.flattop(N)
  1834. else:
  1835. print ("in windowFilter, window type undefined")
  1836. WINDOW = np.zeros(len(fft1))
  1837. ifreq = int(round(centre/df))
  1838. istart = ifreq-len(window)//2
  1839. iend = 0
  1840. if N%2:
  1841. WINDOW[ifreq-N//2:ifreq+N//2+1] = window
  1842. iend = ifreq+N//2+1
  1843. else:
  1844. WINDOW[ifreq-N//2:ifreq+N//2] = window
  1845. iend = ifreq+N//2
  1846. self.WINDOW = WINDOW
  1847. self.iWindowStart = istart
  1848. self.iWindowEnd = iend
  1849. self.FFTtimes = nd
  1850. fft1 = np.fft.irfft(WINDOW)
  1851. # calculate dead time
  1852. self.windead = 0.
  1853. for ift in np.arange(100,0,-1):
  1854. #print( ift, fft1[ift] )
  1855. if fft1[ift] > .001:
  1856. #print ("DEAD TIME", 1e3*self.DATADICT[pulse]["TIMES"][ift] - 1e3*self.DATADICT[pulse]["TIMES"][0] )
  1857. dead = 1e3*self.DATADICT[pulse]["TIMES"][ift] - 1e3*self.DATADICT[pulse]["TIMES"][0]
  1858. self.windead = self.DATADICT[pulse]["TIMES"][ift] - self.DATADICT[pulse]["TIMES"][0]
  1859. break
  1860. if canvas != None:
  1861. canvas.fig.clear()
  1862. canvas.ax1 = canvas.fig.add_axes([.1, .6, .75, .35])
  1863. canvas.ax2 = canvas.fig.add_axes([.1, .1, .75, .35])
  1864. canvas.ax1.plot(WINDOW)
  1865. canvas.ax2.plot( 1e3* self.DATADICT[pulse]["TIMES"][0:100] - 1e3*self.DATADICT[pulse]["TIMES"][0], fft1[0:100] )
  1866. canvas.ax2.set_xlabel("time (ms)")
  1867. canvas.ax2.set_title("IFFT")
  1868. canvas.draw()
  1869. return [WINDOW, nd, istart, iend, dead]
  1870. def windowFilter(self, ftype, band, centre, canvas):
  1871. ###############################
  1872. # Window Filter (Ormsby filter http://www.xsgeo.com/course/filt.htm)
  1873. # apply window
  1874. iFID = 0
  1875. for pulse in self.DATADICT["PULSES"]:
  1876. [WINDOW, nd, istart, iend,dead] = self.computeWindow(pulse, band, centre, ftype)
  1877. for istack in self.DATADICT["stacks"]:
  1878. for ipm in range(self.DATADICT["nPulseMoments"]):
  1879. for ichan in np.append(self.DATADICT[pulse]["chan"], self.DATADICT[pulse]["rchan"]):
  1880. fft = np.fft.rfft( self.DATADICT[pulse][ichan][ipm][istack] )
  1881. fft *= WINDOW
  1882. self.DATADICT[pulse][ichan][ipm][istack] = np.fft.irfft(fft , nd)
  1883. percent = (int)(1e2*((float)(iFID*self.DATADICT["nPulseMoments"]+(ipm))/(len(self.DATADICT["PULSES"])*self.nPulseMoments)))
  1884. self.progressTrigger.emit(percent)
  1885. iFID += 1
  1886. self.plotFT(canvas, istart, iend)
  1887. self.doneTrigger.emit()
  1888. def bandpassFilter(self, canvas, blank, plot=True):
  1889. if plot:
  1890. canvas.reAx2()
  1891. canvas.ax1.tick_params(axis='both', which='major', labelsize=8)
  1892. canvas.ax1.ticklabel_format(style='sci', scilimits=(0,0), axis='y')
  1893. canvas.ax2.tick_params(axis='both', which='major', labelsize=8)
  1894. canvas.ax2.ticklabel_format(style='sci', scilimits=(0,0), axis='y')
  1895. ife = (int)( max(self.fe, self.windead) * self.samp )
  1896. # Data
  1897. iFID = 0
  1898. for pulse in self.DATADICT["PULSES"]:
  1899. self.DATADICT[pulse]["TIMES"] = self.DATADICT[pulse]["TIMES"][ife:-ife]
  1900. for ipm in range(self.DATADICT["nPulseMoments"]):
  1901. for istack in self.DATADICT["stacks"]:
  1902. canvas.ax1.clear()
  1903. canvas.ax2.clear()
  1904. #for ichan in np.append(self.DATADICT[pulse]["chan"], self.DATADICT[pulse]["rchan"]):
  1905. for ichan in self.DATADICT[pulse]["rchan"]:
  1906. # reflect signal back on itself to reduce gibbs effects on early times
  1907. #nr = len( self.DATADICT[pulse][ichan][ipm][istack] ) - 1 + ife
  1908. #refl = np.append( -1*self.DATADICT[pulse][ichan][ipm][istack][::-1][0:-1], self.DATADICT[pulse][ichan][ipm][istack] )
  1909. #reflfilt = signal.filtfilt( self.filt_b, self.filt_a, refl )
  1910. #self.DATADICT[pulse][ichan][ipm][istack] = reflfilt[nr:-ife]
  1911. # don't reflect
  1912. self.DATADICT[pulse][ichan][ipm][istack] = \
  1913. signal.filtfilt(self.filt_b, self.filt_a, self.DATADICT[pulse][ichan][ipm][istack])[ife:-ife]
  1914. # plot
  1915. if plot:
  1916. canvas.ax1.plot( self.DATADICT[pulse]["TIMES"], 1e9*self.DATADICT[pulse][ichan][ipm][istack], \
  1917. label = pulse + " ipm=" + str(ipm) + " istack=" + str(istack) + " rchan=" + str(ichan))
  1918. for ichan in self.DATADICT[pulse]["chan"]:
  1919. # reflect signal back on itself to reduce gibbs effects on early times
  1920. #nr = len( self.DATADICT[pulse][ichan][ipm][istack] ) - 1 + ife
  1921. #refl = np.append( -1*self.DATADICT[pulse][ichan][ipm][istack][::-1][0:-1], self.DATADICT[pulse][ichan][ipm][istack] )
  1922. #reflfilt = signal.filtfilt( self.filt_b, self.filt_a, refl )
  1923. #self.DATADICT[pulse][ichan][ipm][istack] = reflfilt[nr:-ife]
  1924. # don't reflect
  1925. self.DATADICT[pulse][ichan][ipm][istack] = \
  1926. scipy.signal.filtfilt(self.filt_b, self.filt_a, self.DATADICT[pulse][ichan][ipm][istack])[ife:-ife]
  1927. # plot
  1928. if plot:
  1929. canvas.ax2.plot( self.DATADICT[pulse]["TIMES"], 1e9*self.DATADICT[pulse][ichan][ipm][istack], \
  1930. label = "data " + pulse + " ipm=" + str(ipm) + " istack=" + str(istack) + " chan=" + str(ichan))
  1931. if plot:
  1932. canvas.ax1.set_xlabel(r"time [s]", fontsize=8)
  1933. canvas.ax1.set_ylabel(r"signal [nV]", fontsize=8)
  1934. canvas.ax2.set_xlabel(r"time [s]", fontsize=8)
  1935. canvas.ax2.set_ylabel(r"signal [nV]", fontsize=8)
  1936. canvas.ax1.legend(prop={'size':6})
  1937. canvas.ax2.legend(prop={'size':6})
  1938. canvas.draw()
  1939. percent = (int)(1e2*((float)(iFID*self.DATADICT["nPulseMoments"]+(ipm))/(len(self.DATADICT["PULSES"])*self.nPulseMoments)))
  1940. self.progressTrigger.emit(percent)
  1941. iFID += 1
  1942. self.doneTrigger.emit()
  1943. self.updateProcTrigger.emit()
  1944. def loadGMRBinaryFID( self, rawfname, istack ):
  1945. """ Reads a single binary GMR file and fills into DATADICT
  1946. """
  1947. #################################################################################
  1948. # figure out key data indices
  1949. # Pulse
  1950. nps = (int)((self.prePulseDelay)*self.samp)
  1951. npul = (int)(self.pulseLength[0]*self.samp) #+ 100
  1952. # Data
  1953. nds = nps+npul+(int)((self.deadTime)*self.samp); # indice pulse 1 data starts
  1954. nd1 = (int)(1.*self.samp) # samples in first pulse
  1955. invGain = 1./self.RxGain
  1956. invCGain = self.CurrentGain
  1957. pulse = "Pulse 1"
  1958. chan = self.DATADICT[pulse]["chan"]
  1959. rchan = self.DATADICT[pulse]["rchan"]
  1960. rawFile = open( rawfname, 'rb')
  1961. for ipm in range(self.nPulseMoments):
  1962. buf1 = rawFile.read(4)
  1963. buf2 = rawFile.read(4)
  1964. N_chan = struct.unpack('>i', buf1 )[0]
  1965. N_samp = struct.unpack('>i', buf2 )[0]
  1966. T = N_samp * self.dt
  1967. TIMES = np.arange(0, T, self.dt) - .0002 # small offset in GMR DAQ?
  1968. DATA = np.zeros([N_samp, N_chan+1])
  1969. for ichan in range(N_chan):
  1970. DATADUMP = rawFile.read(4*N_samp)
  1971. for irec in range(N_samp):
  1972. DATA[irec,ichan] = struct.unpack('>f', DATADUMP[irec*4:irec*4+4])[0]
  1973. # Save into Data Cube
  1974. for ichan in chan:
  1975. self.DATADICT["Pulse 1"][ichan][ipm][istack] = DATA[:,eval(ichan)+3][nds:nds+nd1] * invGain
  1976. self.DATADICT["Pulse 1"]["TIMES"] = TIMES[nds:nds+nd1]
  1977. self.DATADICT["Pulse 1"]["CURRENT"][ipm][istack] = DATA[:,1][nps:nps+npul] * invCGain
  1978. self.DATADICT["Pulse 1"]["PULSE_TIMES"] = TIMES[nps:nps+npul]
  1979. # reference channels?
  1980. for ichan in rchan:
  1981. self.DATADICT["Pulse 1"][ichan][ipm][istack] = DATA[:,eval(ichan)+3][nds:nds+nd1] * invGain
  1982. self.DATADICT["Pulse 1"]["TIMES"] = TIMES[nds:nds+nd1]
  1983. def loadGMRASCIIFID( self, rawfname, istack ):
  1984. """Based on the geoMRI instrument manufactured by VistaClara. Imports
  1985. a suite of raw .lvm files with the following format (on one line)
  1986. time(s) DC_Bus/100(V) Current+/75(A) Curr-/75(A) Voltage+/200(V) \
  1987. Ch1(V) Ch2(V) Ch3(V) Ch4(V)
  1988. Sampling rate is assumed at 50 kHz
  1989. """
  1990. import pandas as pd
  1991. #################################################################################
  1992. # figure out key data indices
  1993. # Pulse
  1994. nps = (int)((self.prePulseDelay)*self.samp)
  1995. npul = (int)(self.pulseLength[0]*self.samp) #+ 100
  1996. # Data
  1997. nds = nps+npul+(int)((self.deadTime)*self.samp); # indice pulse 1 data starts
  1998. nd1 = (int)(1.*self.samp) - nds # samples in first pulse
  1999. ndr = (int)(1.*self.samp) # samples in record
  2000. invGain = 1./self.RxGain
  2001. invCGain = self.CurrentGain
  2002. pulse = "Pulse 1"
  2003. chan = self.DATADICT[pulse]["chan"]
  2004. rchan = self.DATADICT[pulse]["rchan"]
  2005. T = 1.5 #N_samp * self.dt
  2006. TIMES = np.arange(0, T, self.dt) - .0002 # small offset in GMR DAQ?
  2007. self.DATADICT["Pulse 1"]["TIMES"] = TIMES[nds:nds+nd1]
  2008. self.DATADICT["Pulse 1"]["PULSE_TIMES"] = TIMES[nps:nps+npul]
  2009. # pandas is much faster than numpy for io
  2010. #DATA = np.loadtxt(rawfname)
  2011. DATA = pd.read_csv(rawfname, header=None, sep="\t").values
  2012. for ipm in range(self.nPulseMoments):
  2013. for ichan in np.append(chan,rchan):
  2014. self.DATADICT["Pulse 1"][ichan][ipm][istack] = DATA[:, eval(ichan)+4][nds:(nds+nd1)] * invGain
  2015. self.DATADICT["Pulse 1"]["CURRENT"][ipm][istack] = DATA[:,2][nps:nps+npul] * invCGain
  2016. nds += ndr
  2017. nps += ndr
  2018. def loadGMRASCIIT1( self, rawfname, istack ):
  2019. """Based on the geoMRI instrument manufactured by VistaClara. Imports
  2020. a suite of raw .lvm files with the following format (on one line)
  2021. time(s) DC_Bus/100(V) Current+/75(A) Curr-/75(A) Voltage+/200(V) \
  2022. Ch1(V) Ch2(V) Ch3(V) Ch4(V)
  2023. Sampling rate is assumed at 50 kHz
  2024. """
  2025. import pandas as pd
  2026. #################################################################################
  2027. # figure out key data indices
  2028. # Pulse
  2029. nps = (int)((self.prePulseDelay)*self.samp)
  2030. npul = (int)(self.pulseLength[0]*self.samp) #+ 100
  2031. # phase cycling
  2032. # Older T1 GMR data had a curious phase cycling
  2033. npc = 2 #(int)( self.samp / self.transFreq / 6 )
  2034. #print("npc", npc)
  2035. # Data
  2036. nds = nps+npul+(int)((self.deadTime)*self.samp); # indice pulse 1 data starts
  2037. nd1 = (int)( (self.interpulseDelay) * self.samp) - nds # samples in first pulse
  2038. ndr = (int)( (self.interpulseDelay) * self.samp) # samples in record
  2039. invGain = 1./self.RxGain
  2040. invCGain = self.CurrentGain
  2041. pulse = "Pulse 1"
  2042. chan = self.DATADICT[pulse]["chan"]
  2043. rchan = self.DATADICT[pulse]["rchan"]
  2044. T = 1.5 #N_samp * self.dt
  2045. TIMES = np.arange(0, T, self.dt) - .0002 # small offset in GMR DAQ?
  2046. self.DATADICT["Pulse 1"]["TIMES"] = TIMES[nds:nds+nd1]
  2047. self.DATADICT["Pulse 1"]["PULSE_TIMES"] = TIMES[nps:nps+npul]
  2048. # pandas is much faster than numpy for io
  2049. #DATA = np.loadtxt(rawfname)
  2050. DATA = pd.read_csv(rawfname, header=None, sep="\t").values
  2051. for ipm in range(self.nPulseMoments):
  2052. for ichan in np.append(chan,rchan):
  2053. if ipm%2:
  2054. self.DATADICT["Pulse 1"][ichan][ipm][istack] = DATA[:, eval(ichan)+4][(nds+npc):(nds+nd1+npc)] * invGain
  2055. #self.DATADICT["Pulse 1"][ichan][ipm][istack] = DATA[:, eval(ichan)+4][nds:(nds+nd1)] * invGain
  2056. self.DATADICT["Pulse 1"]["CURRENT"][ipm][istack] = DATA[:,2][nps+npc:nps+npul+npc] * invCGain
  2057. else:
  2058. self.DATADICT["Pulse 1"][ichan][ipm][istack] = DATA[:, eval(ichan)+4][nds:(nds+nd1)] * invGain
  2059. self.DATADICT["Pulse 1"]["CURRENT"][ipm][istack] = DATA[:,2][nps:nps+npul] * invCGain
  2060. nds += ndr
  2061. nps += ndr
  2062. def loadFIDData(self, base, procStacks, chanin, rchanin, FIDProc, canvas, deadTime, plot):
  2063. '''
  2064. Loads a GMR FID dataset, reads binary and ASCII format files
  2065. '''
  2066. canvas.reAx3(True,False)
  2067. chan = []
  2068. for ch in chanin:
  2069. chan.append(str(ch))
  2070. rchan = []
  2071. for ch in rchanin:
  2072. rchan.append(str(ch))
  2073. # not in any headers but this has changed, NOT the place to do this. MOVE
  2074. #self.prePulseDelay = 0.01 # delay before pulse
  2075. self.deadTime = deadTime # instrument dead time before measurement
  2076. self.samp = 50000. # in case this is a reproc, these might have
  2077. self.dt = 1./self.samp # changed
  2078. #################################################################################
  2079. # Data structures
  2080. PULSES = [FIDProc]
  2081. PULSES = ["Pulse 1"]
  2082. self.DATADICT = {}
  2083. self.DATADICT["nPulseMoments"] = self.nPulseMoments
  2084. self.DATADICT["stacks"] = procStacks
  2085. self.DATADICT["PULSES"] = PULSES
  2086. for pulse in PULSES:
  2087. self.DATADICT[pulse] = {}
  2088. self.DATADICT[pulse]["chan"] = chan # TODO these should not be a subet of pulse! for GMR all
  2089. self.DATADICT[pulse]["rchan"] = rchan # data are consistent
  2090. self.DATADICT[pulse]["CURRENT"] = {}
  2091. for ichan in np.append(chan,rchan):
  2092. self.DATADICT[pulse][ichan] = {}
  2093. for ipm in range(self.nPulseMoments):
  2094. self.DATADICT[pulse][ichan][ipm] = {}
  2095. self.DATADICT[pulse]["CURRENT"][ipm] = {}
  2096. for istack in procStacks:
  2097. self.DATADICT[pulse][ichan][ipm][istack] = np.zeros(3)
  2098. self.DATADICT[pulse]["CURRENT"][ipm][istack] = np.zeros(3)
  2099. ##############################################
  2100. # Read in binary (.lvm) data
  2101. iistack = 0
  2102. fnames = []
  2103. for istack in procStacks:
  2104. if self.nDAQVersion <= 1.0:
  2105. try:
  2106. self.loadGMRASCIIFID( base + "_" + str(istack), istack )
  2107. except:
  2108. self.loadGMRASCIIFID( base + "_" + str(istack) + ".lvm", istack )
  2109. elif self.nDAQVersion < 2.3:
  2110. #rawfname = base + "_" + str(istack)
  2111. self.loadGMRASCIIFID( base + "_" + str(istack), istack )
  2112. else:
  2113. self.loadGMRBinaryFID( base + "_" + str(istack) + ".lvm", istack )
  2114. #fnames.append( base + "_" + str(istack) + ".lvm" )
  2115. percent = (int) (1e2*((float)((iistack*self.nPulseMoments+ipm+1)) / (len(procStacks)*self.nPulseMoments)))
  2116. self.progressTrigger.emit(percent)
  2117. iistack += 1
  2118. # multiprocessing load data
  2119. #info = {}
  2120. #info["prePulseDelay"] = self.prePulseDelay
  2121. #with multiprocessing.Pool() as pool:
  2122. # results = pool.starmap( loadGMRBinaryFID, zip(itertools.repeat(self), fnames, info ) ) # zip(np.tile(vc, (ns, 1)), np.tile(vgc, (ns,1)), itertools.repeat(sys.argv[1]), itertools.repeat(sys.argv[2]), EPS_CMR))
  2123. # Plotting
  2124. if plot:
  2125. iistack = 0
  2126. for istack in procStacks:
  2127. for ipm in range(self.nPulseMoments):
  2128. canvas.ax1.clear()
  2129. canvas.ax2.clear()
  2130. canvas.ax3.clear()
  2131. #canvas.fig.patch.set_facecolor('blue')
  2132. for ichan in chan:
  2133. canvas.ax1.plot(self.DATADICT["Pulse 1"]["PULSE_TIMES"], self.DATADICT["Pulse 1"]["CURRENT"][ipm][istack] , color='black')
  2134. 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')
  2135. for ichan in rchan:
  2136. 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')
  2137. canvas.ax3.legend(prop={'size':6})
  2138. canvas.ax2.legend(prop={'size':6})
  2139. canvas.ax1.set_title("stack "+str(istack)+" pulse index " + str(ipm), fontsize=8)
  2140. canvas.ax1.set_xlabel("time [s]", fontsize=8)
  2141. canvas.ax1.set_ylabel("Current [A]", fontsize=8)
  2142. canvas.ax1.ticklabel_format(style='sci', scilimits=(0,0), axis='y')
  2143. canvas.ax2.set_ylabel("RAW signal [V]", fontsize=8)
  2144. canvas.ax2.tick_params(axis='both', which='major', labelsize=8)
  2145. canvas.ax2.tick_params(axis='both', which='minor', labelsize=6)
  2146. canvas.ax2.set_xlabel("time [s]", fontsize=8)
  2147. canvas.ax2.ticklabel_format(style='sci', scilimits=(0,0), axis='y')
  2148. canvas.ax3.ticklabel_format(style='sci', scilimits=(0,0), axis='y')
  2149. canvas.draw()
  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. #with multiprocessing.Pool() as pool:
  2210. # results = pool.starmap( loadGMRBinaryFID, zip(itertools.repeat(self), fnames, info ) ) # zip(np.tile(vc, (ns, 1)), np.tile(vgc, (ns,1)), itertools.repeat(sys.argv[1]), itertools.repeat(sys.argv[2]), EPS_CMR))
  2211. # Plotting
  2212. if plot:
  2213. iistack = 0
  2214. for istack in procStacks:
  2215. #for ipm in range(0,7,1):
  2216. for ipm in range(self.nPulseMoments):
  2217. canvas.ax1.clear()
  2218. canvas.ax2.clear()
  2219. canvas.ax3.clear()
  2220. #canvas.fig.patch.set_facecolor('blue')
  2221. for ichan in chan:
  2222. canvas.ax1.plot(self.DATADICT["Pulse 1"]["PULSE_TIMES"], self.DATADICT["Pulse 1"]["CURRENT"][ipm][istack] , color='black')
  2223. 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')
  2224. for ichan in rchan:
  2225. 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')
  2226. canvas.ax3.legend(prop={'size':6})
  2227. canvas.ax2.legend(prop={'size':6})
  2228. canvas.ax1.set_title("stack "+str(istack)+" pulse index " + str(ipm), fontsize=8)
  2229. canvas.ax1.set_xlabel("time [s]", fontsize=8)
  2230. canvas.ax1.set_ylabel("Current [A]", fontsize=8)
  2231. canvas.ax1.ticklabel_format(style='sci', scilimits=(0,0), axis='y')
  2232. canvas.ax2.set_ylabel("RAW signal [V]", fontsize=8)
  2233. canvas.ax2.tick_params(axis='both', which='major', labelsize=8)
  2234. canvas.ax2.tick_params(axis='both', which='minor', labelsize=6)
  2235. canvas.ax2.set_xlabel("time [s]", fontsize=8)
  2236. canvas.ax2.ticklabel_format(style='sci', scilimits=(0,0), axis='y')
  2237. canvas.ax3.ticklabel_format(style='sci', scilimits=(0,0), axis='y')
  2238. canvas.draw()
  2239. #canvas.draw()
  2240. percent = (int) (1e2*((float)((iistack*self.nPulseMoments+ipm+1)) / (len(procStacks)*self.nPulseMoments)))
  2241. self.progressTrigger.emit(percent)
  2242. iistack += 1
  2243. self.enableDSP()
  2244. self.doneTrigger.emit()
  2245. def load4PhaseT1Data(self, base, procStacks, chan, rchan, FIDProc, canvas, deadTime, plot):
  2246. """
  2247. Designed to load GMR 4-phase data which use the following convention for phase cycles
  2248. P1 P2
  2249. Stack 1 -> 0 0 <-- <--
  2250. Stack 2 -> 0 pi/2 | <-- <--
  2251. Stack 3 -> pi/2 0 <-- | <--
  2252. Stack 4 -> pi/2 pi/2 <-- <--
  2253. The cycle is determined by stack indice. Walbrecker proposes for pulse2 data (Stack2 - Stack1) / 2
  2254. equivalently (Stack 4 - Stack3) will yield the same voltage response wrt. the second pulse.
  2255. 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
  2256. Then there are just the two phase cycles that can be stacked like normal.
  2257. Unfortunately, we need to stack each cycle first, then perform corrections for phase cycling. The reason for this is that otherwise,
  2258. 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,
  2259. if there is an uneven number of a certain phase cycle.
  2260. We could, I suppose impose this condition, but I think I would rather not?
  2261. + more samples for std. deviation calculation
  2262. + single spikes will have less residual effect
  2263. - can no longer do normality tests etc. and remove data that are suspect.
  2264. - requires a dumb stack, and may also require removal of entire stacks of data
  2265. 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 ...
  2266. This however, is altered by the above convention. It gets a little complicated...
  2267. """
  2268. import struct
  2269. canvas.reAx2()
  2270. # not in any headers but this has changed, NOT the place to do this. MOVE
  2271. self.prePulseDelay = 0.01 # delay before pulse
  2272. self.deadTime = deadTime # instrument dead time before measurement
  2273. self.samp = 50000. # in case this is a reproc, these might have
  2274. self.dt = 1./self.samp # changed
  2275. invGain = 1./self.RxGain
  2276. invCGain = self.CurrentGain
  2277. #################################################################################
  2278. # figure out key data indices
  2279. # Pulse
  2280. nps = (int)((self.prePulseDelay)*self.samp)
  2281. nps2 = (int)((self.prePulseDelay+self.interpulseDelay)*self.samp)
  2282. npul = (int)(self.pulseLength[0]*self.samp) #+ 100
  2283. np2 = (int)(self.pulseLength[1]*self.samp) #+ 100
  2284. # Data
  2285. nds = nps+npul+(int)((self.deadTime)*self.samp); # indice pulse 1 data starts
  2286. nd1 = (int)((self.interpulseDelay)*self.samp) # samples in first pulse
  2287. nd2s = nps+npul+nd1+(int)((self.deadTime)*self.samp); # indice pulse 2 data starts
  2288. nd2 = (int)((1.)*self.samp) # samples in first pulse
  2289. nd1 -= (int)((.028)*self.samp) + nps # some time to get ready for next pulse
  2290. #################################################################################
  2291. # Data structures
  2292. PULSES = [FIDProc]
  2293. if FIDProc == "Both":
  2294. PULSES = ["Pulse 1","Pulse 2"]
  2295. self.DATADICT = {}
  2296. self.DATADICT["nPulseMoments"] = self.nPulseMoments
  2297. self.DATADICT["stacks"] = procStacks
  2298. self.DATADICT["PULSES"] = PULSES
  2299. for pulse in PULSES:
  2300. self.DATADICT[pulse] = {}
  2301. self.DATADICT[pulse]["chan"] = chan
  2302. self.DATADICT[pulse]["rchan"] = rchan
  2303. self.DATADICT[pulse]["CURRENT"] = {}
  2304. for ichan in np.append(chan,rchan):
  2305. self.DATADICT[pulse][ichan] = {}
  2306. for ipm in range(self.nPulseMoments):
  2307. self.DATADICT[pulse][ichan][ipm] = {}
  2308. self.DATADICT[pulse]["CURRENT"][ipm] = {}
  2309. for istack in procStacks:
  2310. self.DATADICT[pulse][ichan][ipm][istack] = np.zeros(3)
  2311. self.DATADICT[pulse]["CURRENT"][ipm][istack] = np.zeros(3)
  2312. ##############################################
  2313. # Read in binary data
  2314. iistack = 0
  2315. for istack in procStacks:
  2316. rawFile = open(base + "_" + str(istack) + ".lvm", 'rb')
  2317. for ipm in range(self.nPulseMoments):
  2318. N_chan = struct.unpack('>i', rawFile.read(4))[0]
  2319. N_samp = struct.unpack('>i', rawFile.read(4))[0]
  2320. T = N_samp * self.dt
  2321. TIMES = np.arange(0, T, self.dt) - .0002 # small offset in GMR DAQ?
  2322. DATA = np.zeros([N_samp, N_chan+1])
  2323. for ichan in range(N_chan):
  2324. DATADUMP = rawFile.read(4*N_samp)
  2325. for irec in range(N_samp):
  2326. DATA[irec,ichan] = struct.unpack('>f', DATADUMP[irec*4:irec*4+4])[0]
  2327. if plot:
  2328. canvas.ax1.clear()
  2329. canvas.ax2.clear()
  2330. li = np.shape( DATA[:,4][nd2s:nd2s+nd2] )[0]
  2331. ######################################
  2332. # save into DATA cube
  2333. # TODO, changing iFID to 'Pulse 1' or 'Pulse 2'
  2334. for ichan in chan:
  2335. if FIDProc == "Pulse 1":
  2336. self.DATADICT["Pulse 1"][ichan][ipm][istack] = DATA[:,ichan+3][nds:nds+nd1] * invGain
  2337. self.DATADICT["Pulse 1"]["TIMES"] = TIMES[nds:nds+nd1]
  2338. self.DATADICT["Pulse 1"]["CURRENT"][ipm][istack] = DATA[:,1][nps:nps+npul] * invCGain
  2339. self.DATADICT["Pulse 1"]["PULSE_TIMES"] = TIMES[nps:nps+npul]
  2340. if plot:
  2341. canvas.ax1.plot(self.DATADICT["Pulse 1"]["TIMES"], self.DATADICT["Pulse 1"][ichan][ipm][istack], label="Pulse 1 FID data ch. "+str(ichan)) #, color='blue')
  2342. canvas.ax2.plot(self.DATADICT["Pulse 1"]["PULSE_TIMES"], self.DATADICT["Pulse 1"]["CURRENT"][ipm][istack] , color='black')
  2343. elif FIDProc == "Pulse 2":
  2344. self.DATADICT["Pulse 2"][ichan][ipm][istack] = DATA[:,ichan+3][nd2s:nd2s+nd2] *invGain
  2345. self.DATADICT["Pulse 2"]["TIMES"] = TIMES[nd2s:nd2s+nd2]
  2346. self.DATADICT["Pulse 2"]["CURRENT"][ipm][istack] = DATA[:,1][nps2:nps2+np2] * invCGain
  2347. self.DATADICT["Pulse 2"]["PULSE_TIMES"] = TIMES[nps2:nps2+np2]
  2348. if plot:
  2349. canvas.ax1.plot(self.DATADICT["Pulse 2"]["TIMES"], self.DATADICT["Pulse 2"][ichan][ipm][istack], label="Pulse 2 FID data ch. "+str(ichan)) #, color='blue')
  2350. canvas.ax2.plot( self.DATADICT["Pulse 2"]["PULSE_TIMES"], self.DATADICT["Pulse 2"]["CURRENT"][ipm][istack], color='black' )
  2351. else:
  2352. self.DATADICT["Pulse 1"][ichan][ipm][istack] = DATA[:,ichan+3][nds:nds+nd1] * invGain
  2353. self.DATADICT["Pulse 2"][ichan][ipm][istack] = DATA[:,ichan+3][nd2s:nd2s+nd2] * invGain
  2354. self.DATADICT["Pulse 1"]["TIMES"] = TIMES[nds:nds+nd1]
  2355. self.DATADICT["Pulse 2"]["TIMES"] = TIMES[nd2s:nd2s+nd2]
  2356. self.DATADICT["Pulse 1"]["CURRENT"][ipm][istack] = DATA[:,1][nps:nps+npul] * invCGain
  2357. self.DATADICT["Pulse 1"]["PULSE_TIMES"] = TIMES[nps:nps+npul]
  2358. self.DATADICT["Pulse 2"]["CURRENT"][ipm][istack] = DATA[:,1][nps2:nps2+np2] * invCGain
  2359. self.DATADICT["Pulse 2"]["PULSE_TIMES"] = TIMES[nps2:nps2+np2]
  2360. if plot:
  2361. canvas.ax1.plot(self.DATADICT["Pulse 1"]["TIMES"], self.DATADICT["Pulse 1"][ichan][ipm][istack], label="Pulse 1 FID data ch. "+str(ichan)) #, color='blue')
  2362. canvas.ax1.plot(self.DATADICT["Pulse 2"]["TIMES"], self.DATADICT["Pulse 2"][ichan][ipm][istack], label="Pulse 2 FID data ch. "+str(ichan)) #, color='blue')
  2363. canvas.ax2.plot( self.DATADICT["Pulse 1"]["PULSE_TIMES"], self.DATADICT["Pulse 1"]["CURRENT"][ipm][istack] , color='black' )
  2364. canvas.ax2.plot( self.DATADICT["Pulse 2"]["PULSE_TIMES"], self.DATADICT["Pulse 2"]["CURRENT"][ipm][istack] , color='black')
  2365. for ichan in rchan:
  2366. if FIDProc == "Pulse 1":
  2367. self.DATADICT["Pulse 1"][ichan][ipm][istack] = DATA[:,ichan+3][nds:nds+nd1] * invGain
  2368. self.DATADICT["Pulse 1"]["TIMES"] = TIMES[nds:nds+nd1]
  2369. if plot:
  2370. canvas.ax1.plot(self.DATADICT["Pulse 1"]["TIMES"], self.DATADICT["Pulse 1"][ichan][ipm][istack], label="Pulse 1 FID ref ch. "+str(ichan)) #, color='blue')
  2371. elif FIDProc == "Pulse 2":
  2372. self.DATADICT["Pulse 2"][ichan][ipm][istack] = DATA[:,ichan+3][nd2s:nd2s+nd2] * invGain
  2373. self.DATADICT["Pulse 2"]["TIMES"] = TIMES[nd2s:nd2s+nd2]
  2374. if plot:
  2375. canvas.ax1.plot(self.DATADICT["Pulse 2"]["TIMES"], self.DATADICT["Pulse 2"][ichan][ipm][istack], label="Pulse 2 FID ref ch. "+str(ichan)) #, color='blue')
  2376. else:
  2377. self.DATADICT["Pulse 1"][ichan][ipm][istack] = DATA[:,ichan+3][nds:nds+nd1] * invGain
  2378. self.DATADICT["Pulse 2"][ichan][ipm][istack] = DATA[:,ichan+3][nd2s:nd2s+nd2] * invGain
  2379. self.DATADICT["Pulse 1"]["TIMES"] = TIMES[nds:nds+nd1]
  2380. self.DATADICT["Pulse 2"]["TIMES"] = TIMES[nd2s:nd2s+nd2]
  2381. if plot:
  2382. canvas.ax1.plot(self.DATADICT["Pulse 1"]["TIMES"], self.DATADICT["Pulse 1"][ichan][ipm][istack], label="Pulse 1 FID ref ch. "+str(ichan)) #, color='blue')
  2383. canvas.ax1.plot(self.DATADICT["Pulse 2"]["TIMES"], self.DATADICT["Pulse 2"][ichan][ipm][istack], label="Pulse 2 FID ref ch. "+str(ichan)) #, color='blue')
  2384. if plot:
  2385. canvas.ax1.legend(prop={'size':6})
  2386. canvas.ax1.set_title("stack "+str(istack)+" pulse index " + str(ipm), fontsize=8)
  2387. canvas.ax1.set_xlabel("time [s]", fontsize=8)
  2388. canvas.ax1.set_ylabel("RAW signal [V]", fontsize=8)
  2389. canvas.ax2.set_ylabel("Current [A]", fontsize=8)
  2390. canvas.ax2.tick_params(axis='both', which='major', labelsize=8)
  2391. canvas.ax2.tick_params(axis='both', which='minor', labelsize=6)
  2392. canvas.ax2.ticklabel_format(style='sci', scilimits=(0,0), axis='y')
  2393. canvas.ax1.ticklabel_format(style='sci', scilimits=(0,0), axis='y')
  2394. canvas.draw()
  2395. # update GUI of where we are
  2396. percent = (int) (1e2*((float)((iistack*self.nPulseMoments+ipm+1)) / (len(procStacks)*self.nPulseMoments)))
  2397. self.progressTrigger.emit(percent)
  2398. iistack += 1
  2399. self.enableDSP()
  2400. self.doneTrigger.emit()
  2401. if __name__ == "__main__":
  2402. if len(sys.argv) < 4:
  2403. print( "mrsurvey path/to/header <stack1> <stackN> ")
  2404. exit()
  2405. GMR = GMRDataProcessor()
  2406. GMR.readHeaderFile(sys.argv[1])
  2407. GMR.Print()
  2408. if GMR.pulseType == "FID":
  2409. GMR.loadFIDData(sys.argv[1], sys.argv[2], sys.argv[3], 5)
  2410. if GMR.pulseType == "4PhaseT1":
  2411. GMR.load4PhaseT1Data(sys.argv[1], sys.argv[2], sys.argv[3], 5)
  2412. pylab.show()