from PyQt5.QtCore import * import numpy as np import scipy.signal as signal import pylab import sys import scipy from scipy import stats import copy import struct from scipy.io.matlab import mio from numpy import pi from math import floor import matplotlib as mpl from matplotlib.ticker import FuncFormatter import matplotlib.font_manager as fm import matplotlib.pyplot as plt import matplotlib.ticker from matplotlib.ticker import MaxNLocator import multiprocessing import itertools import padasip as pa import akvo.tressel.adapt as adapt #import akvo.tressel.cadapt as adapt # cython for more faster import akvo.tressel.decay as decay import akvo.tressel.pca as pca import akvo.tressel.rotate as rotate import akvo.tressel.cmaps as cmaps import akvo.tressel.harmonic as harmonic import cmocean # colormaps for geophysical data plt.register_cmap(name='viridis', cmap=cmaps.viridis) plt.register_cmap(name='inferno', cmap=cmaps.inferno) plt.register_cmap(name='inferno_r', cmap=cmaps.inferno_r) plt.register_cmap(name='magma', cmap=cmaps.magma) plt.register_cmap(name='magma_r', cmap=cmaps.magma_r) def xxloadGMRBinaryFID( rawfname, info ): """ Reads a single binary GMR file and fills into DATADICT """ ################################################################################# # figure out key data indices # Pulse nps = (int)((info["prePulseDelay"])*info["samp"]) npul = (int)(self.pulseLength[0]*self.samp) #+ 100 # Data nds = nps+npul+(int)((self.deadTime)*self.samp); # indice pulse 1 data starts nd1 = (int)(1.*self.samp) # samples in first pulse invGain = 1./self.RxGain invCGain = self.CurrentGain pulse = "Pulse 1" chan = self.DATADICT[pulse]["chan"] rchan = self.DATADICT[pulse]["rchan"] rawFile = open( rawfname, 'rb') T = N_samp * self.dt TIMES = np.arange(0, T, self.dt) - .0002 # small offset in GMR DAQ? for ipm in range(self.nPulseMoments): buf1 = rawFile.read(4) buf2 = rawFile.read(4) N_chan = struct.unpack('>i', buf1 )[0] N_samp = struct.unpack('>i', buf2 )[0] DATA = np.zeros([N_samp, N_chan+1]) for ichan in range(N_chan): DATADUMP = rawFile.read(4*N_samp) for irec in range(N_samp): DATA[irec,ichan] = struct.unpack('>f', DATADUMP[irec*4:irec*4+4])[0] return DATA, TIMES class SNMRDataProcessor(QObject): """ Revised class for preprocessing sNMR Data. Derived types can read GMR files """ def __init__(self): QObject.__init__(self) self.numberOfMoments = 0 self.numberOfPulsesPerMoment = 0 self.pulseType = "NONE" self.transFreq = 0 self.pulseLength = np.zeros(1) self.nPulseMoments = 0 self.dt = 0 def mfreqz(self, b,a=1): """ Plots the frequency response of a filter specified with a and b weights """ import scipy.signal as signal pylab.figure(124) w,h = signal.freqz(b,a) w /= max(w) w *= .5/self.dt h_dB = 20 * pylab.log10 (abs(h)) pylab.subplot(211) #pylab.plot(w/max(w),h_dB) pylab.plot(w,h_dB) pylab.ylim(-150, 5) pylab.ylabel('Magnitude (dB)') #pylab.xlabel(r'Normalized Frequency (x$\pi$rad/sample)') pylab.xlabel(r'Hz') pylab.title(r'Frequency response') pylab.subplot(212) h_Phase = pylab.unwrap(pylab.arctan2(pylab.imag(h), pylab.real(h))) #pylab.plot(w/max(w),h_Phase) pylab.plot(w,h_Phase) pylab.ylabel('Phase (radians)') pylab.xlabel(r'Hz') #pylab.xlabel(r'Normalized Frequency (x$\pi$rad/sample)') pylab.title(r'Phase response') pylab.subplots_adjust(hspace=0.5) def mfreqz2(self, b, a, canvas): "for analysing filt-filt" import scipy.signal as signal canvas.reAx2(False,False) canvas.ax1.tick_params(axis='both', which='major', labelsize=8) canvas.ax2.tick_params(axis='both', which='major', labelsize=8) #canvas.ax2.tick_params(axis='both', which='minor', labelsize=6) #pylab.figure(124) w,h = signal.freqz(b,a) w /= max(w) w *= .5/self.dt h_dB = 20 * pylab.log10(abs(h*h) + 1e-16) #ab.subplot(211) #pylab.plot(w/max(w),h_dB) canvas.ax1.plot(w,h_dB) canvas.ax1.set_ylim(-150, 5) canvas.ax1.set_ylabel('Magnitude [db]', fontsize=8) #pylab.xlabel(r'Normalized Frequency (x$\pi$rad/sample)') canvas.ax1.set_xlabel(r'[Hz]', fontsize=8) canvas.ax1.set_title(r'Frequency response', fontsize=8) canvas.ax1.grid(True) tt = np.arange(0, .02, self.dt) impulse = signal.dimpulse((self.filt_z, self.filt_p, self.filt_k, self.dt), t=tt) #impulse = signal.dstep((self.filt_z, self.filt_p, self.filt_k, self.dt), t=tt) #print impulse #for ii in range(len(impulse[1])): impulse_dB = 20.*np.log10(np.abs(np.array(impulse[1][0]))) #canvas.ax2.plot(np.array(impulse[0]), impulse_dB) canvas.ax2.plot(np.array(impulse[0]), impulse[1][0]) #h_Phase = pylab.unwrap(pylab.arctan2(pylab.imag(h), pylab.real(h))) #canvas.ax2.plot(w,h_Phase) canvas.ax2.set_ylabel('response [%]', fontsize=8) canvas.ax2.set_xlabel(r'time [s]', fontsize=8) canvas.ax2.set_title(r'impulse response', fontsize=8) #canvas.ax2.grid(True) canvas.draw() # search for last return impulse #[np.where(impulse[1][0] > .01)[-1]] class GMRDataProcessor(SNMRDataProcessor): # slots progressTrigger = pyqtSignal("int") doneTrigger = pyqtSignal() enableDSPTrigger = pyqtSignal() updateProcTrigger = pyqtSignal() def __init__(self): SNMRDataProcessor.__init__(self) self.maxBusV = 0. self.samp = 50000. # sampling frequency self.dt = 2e-5 # sampling rate self.deadTime = .0055 # instrument dead time before measurement self.prePulseDelay = 0.05 # delay before pulse self.windead = 0. # FD window filter dead time self.pulseType = -1 self.transFreq = -1 self.maxBusV = -1 self.pulseLength = -1 self.interpulseDelay = -1 # for T2, Spin Echo self.repetitionDelay = -1 # delay between first pulse self.nPulseMoments = -1 # Number of pulse moments per stack self.TuneCapacitance = -1 # tuning capac in uF self.nTransVersion = -1 # Transmitter version self.nDAQVersion = -1 # DAQ software version self.nInterleaves = -1 # num interleaves # self.nReceiveChannels = 4 # Num receive channels self.RotatedAmplitude = False # self.DATA = np.zeros(1) # Numpy array to hold all data, dimensions resized based on experiment # self.PULSES = np.zeros(1) # Numpy array to hold all data, dimensions resized based on experiment def Print(self): print ("pulse type", self.pulseType) print ("maxBusV", self.maxBusV) print ("inner pulse delay", self.interpulseDelay) print ("tuning capacitance", self.TuneCapacitance) print ("sampling rate", self.samp) print ("dt", self.dt) print ("dead time", self.deadTime) print ("pre pulse delay", self.prePulseDelay) print ("number of pulse moments", self.nPulseMoments) print ("pulse Length", self.pulseLength) print ("trans freq", self.transFreq) def readHeaderFile(self, FileName): HEADER = np.loadtxt(FileName) pulseTypeDict = { 1 : lambda: "FID", 2 : lambda: "T1", 3 : lambda: "SPINECHO", 4 : lambda: "4PhaseT1" } pulseLengthDict = { 1 : lambda x: np.ones(1) * x, 2 : lambda x: np.ones(2) * x, 3 : lambda x: np.array([x, 2.*x]), 4 : lambda x: np.ones(2) * x } self.pulseType = pulseTypeDict.get((int)(HEADER[0]))() self.transFreq = HEADER[1] self.maxBusV = HEADER[2] self.pulseLength = pulseLengthDict.get((int)(HEADER[0]))(1e-3*HEADER[3]) self.interpulseDelay = 1e-3*HEADER[4] # for T2, Spin Echo self.repetitionDelay = HEADER[5] # delay between first pulse self.nPulseMoments = (int)(HEADER[6]) # Number of pulse moments per stack self.TuneCapacitance = HEADER[7] # tuning capacitance in uF self.nTransVersion = HEADER[8] # Transmitter version self.nDAQVersion = HEADER[9] # DAQ software version self.nInterleaves = HEADER[10] # num interleaves self.gain() # default self.samp = 50000. # sampling frequency self.dt = 2e-5 # sampling rate # newer header files contain 64 entries if self.nDAQVersion >= 2: #self.deadtime = HEADER[11] #self.unknown = HEADER[12] #self.PreAmpGain = HEADER[13] self.samp = HEADER[14] # sampling frequency self.dt = 1./self.samp # sampling rate self.deadTime = .0055 # instrument dead time before measurement self.prePulseDelay = 0.05 # delay before pulse #exit() def gain(self): ####################################################### # Circuit gain # From MRSMatlab w = 2*np.pi*self.transFreq # 1e6 due to uF of reported capacitance L_coil = 1e6/(self.TuneCapacitance*(w**2)) R_coil = 1. Z1_in = .5 + 1j*.5*w Z2_in = 1./(1j*w*.000001616) Z_eq_inv = (1./Z1_in) + (1./Z2_in) Zeq = 1./Z_eq_inv Zsource = R_coil + 1j*w*L_coil voltage_in = Zeq / (Zsource + Zeq) self.circuitGain = np.abs(voltage_in) self.circuitPhase_deg = (180/np.pi)+np.angle(voltage_in) circuitImpedance_ohms = np.abs(Zsource + Zeq) ###################################################### # PreAmp gain if self.nTransVersion == 4: self.PreAmpGain = 1000. elif self.nTransVersion == 1 or self.nTransVersion == 2 or self.nTransVersion == 3 or self.nTransVersion == 6: self.PreAmpGain = 500. else: print ("unsupported transmitter version") exit(1) # Total Receiver Gain self.RxGain = self.circuitGain * self.PreAmpGain ##################################################### # Current gain if floor(self.nDAQVersion) == 1: self.CurrentGain = 150. elif floor(self.nDAQVersion) == 2: self.CurrentGain = 180. def updateProgress(self): pass def TDSmartStack(self, outlierTest, MADcutoff, canvas): #print("Line 300 in mrsurvey") Stack = {} # align for stacking and modulate for pulse in self.DATADICT["PULSES"]: stack = np.zeros(( len(self.DATADICT[pulse]["chan"]), self.DATADICT["nPulseMoments"],\ len(self.DATADICT["stacks"]), len(self.DATADICT[pulse]["TIMES"]) )) for ipm in range(self.DATADICT["nPulseMoments"]): istack = 0 for sstack in self.DATADICT["stacks"]: if self.pulseType == "FID" or pulse == "Pulse 2": if floor(self.nDAQVersion) < 2: mod = 1 else: mod = (-1.)**(ipm%2) * (-1.)**(sstack%2) elif self.pulseType == "T1": #mod = (-1.)**(sstack%2) #mod = (-1)**(ipm%2) * (-1)**(sstack%2) #mod = (-1)**(ipm%2) * (-1.**(((sstack-1)/2)%2)) #print("mod", mod, ipm, sstack, (-1.)**(ipm%2), -1.0**(((sstack-1)/2)%2 )) #mod = (-1.)**((ipm+1)%2) * (-1.**(((sstack)/2)%2)) #mod = (-1.)**((ipm-1)%2) * (-1.)**((sstack-1)%2) #mod = 1 # (-1.**(((sstack-1)/2)%2)) # These two give great noise estimate #qcycler = np.array([1,-1,-1,1]) #scycler = np.array([1,-1,1,-1]) qcycler = np.array([ 1, 1]) scycler = np.array([ 1, 1]) mod = qcycler.take([ipm], mode='wrap')*scycler.take([sstack], mode='wrap') #mod = (-1.)**(ipm%2) * (-1.)**(sstack%2) elif self.pulseType == "4PhaseT1": mod = (-1.)**(ipm%2) * (-1.**(((sstack-1)/2)%2)) ichan = 0 for chan in self.DATADICT[pulse]["chan"]: stack[ichan,ipm,istack,:] += mod*self.DATADICT[pulse][chan][ipm][sstack] ichan += 1 istack += 1 Stack[pulse] = stack ######################################### # simple stack and plot of simple stack # ######################################### canvas.reAxH2(np.shape(stack)[0], False, False) axes = canvas.fig.axes SimpleStack = {} VarStack = {} for pulse in self.DATADICT["PULSES"]: SimpleStack[pulse] = {} VarStack[pulse] = {} ichan = 0 for chan in self.DATADICT[pulse]["chan"]: SimpleStack[pulse][chan] = 1e9*np.average( Stack[pulse][ichan], 1 ) VarStack[pulse][chan] = 1e9*np.std( Stack[pulse][ichan], 1 ) ax1 = axes[ 2*ichan ] #ax1.get_yaxis().get_major_formatter().set_useOffset(False) y_formatter = matplotlib.ticker.ScalarFormatter(useOffset=False) ax1.yaxis.set_major_formatter(y_formatter) ax1.plot( 1e3*self.DATADICT[pulse]["TIMES"], np.average( SimpleStack[pulse][chan], 0 )) #, color='darkblue' ) ax1.set_title("Ch." + str(chan) + ": avg FID", fontsize=8) ax1.set_xlabel(r"time (ms)", fontsize=8) if ichan == 0: ax1.set_ylabel(r"signal (nV)", fontsize=8) else: plt.setp(ax1.get_yticklabels(), visible=False) plt.setp(ax1.get_yaxis().get_offset_text(), visible=False) # if ichan == 1: # canvas.ax2.plot( 1e3*self.DATADICT[pulse]["TIMES"], np.average( SimpleStack[pulse][chan], 0 ), color='darkblue' ) # canvas.ax2.set_title("Ch." + str(chan) + ": total average FID", fontsize=8) # canvas.ax2.set_xlabel(r"time [ms]", fontsize=8) # if ichan == 2: # canvas.ax3.plot( 1e3*self.DATADICT[pulse]["TIMES"], np.average( SimpleStack[pulse][chan], 0 ), color='darkblue' ) # canvas.ax3.set_title("Ch." + str(chan) + ": total average FID", fontsize=8) # canvas.ax3.set_xlabel(r"time [ms]", fontsize=8) # if ichan == 3: # canvas.ax4.plot( 1e3*self.DATADICT[pulse]["TIMES"], np.average( SimpleStack[pulse][chan], 0 ), color='darkblue' ) # canvas.ax4.set_title("Ch." + str(chan) + ": total average FID", fontsize=8) # canvas.ax4.set_xlabel(r"time [ms]", fontsize=8) ichan += 1 ######################### # Oulier rejectig stack # ######################### if outlierTest == "MAD": MADStack = {} VarStack = {} #1.4826 is assumption of gaussian noise madstack = np.zeros(( len(self.DATADICT[pulse]["chan"]),\ self.DATADICT["nPulseMoments"], len(self.DATADICT[pulse]["TIMES"]) )) varstack = np.zeros(( len(self.DATADICT[pulse]["chan"]),\ self.DATADICT["nPulseMoments"], len(self.DATADICT[pulse]["TIMES"]) )) for pulse in self.DATADICT["PULSES"]: MADStack[pulse] = {} VarStack[pulse] = {} ichan = 0 for chan in self.DATADICT[pulse]["chan"]: ax1 = axes[ 2*ichan ] for ipm in range(self.DATADICT["nPulseMoments"]): # # brutal loop over time, can this be vectorized? # for it in range(len(self.DATADICT[pulse]["TIMES"])): # x = 1e9 *Stack[pulse][ichan,ipm,:,it] # MAD = 1.4826 * np.median( np.abs(x-np.median(x)) ) # good = 0 # for istack in self.DATADICT["stacks"]: # if (np.abs(x[istack-1]-np.median(x))) / MAD < 2: # good += 1 # madstack[ ichan, ipm, it ] += x[istack-1] # else: # pass # madstack[ichan, ipm, it] /= good # percent = int(1e2* (float)(ipm) / (float)(self.DATADICT["nPulseMoments"]) ) # self.progressTrigger.emit(percent) # Vectorized version of above...much, much faster x = 1e9*copy.deepcopy(Stack[pulse][ichan][ipm,:,:]) # stack and time indices tile_med = np.tile( np.median(x, axis=0), (np.shape(x)[0],1)) MAD = MADcutoff * np.median(np.abs(x - tile_med), axis=0) tile_MAD = np.tile( MAD, (np.shape(x)[0],1)) good = np.abs(x-tile_med)/tile_MAD < 2. # 1.4826 # 2 madstack[ichan][ipm] = copy.deepcopy( np.ma.masked_array(x, good != True).mean(axis=0) ) varstack[ichan][ipm] = copy.deepcopy( np.ma.masked_array(x, good != True).std(axis=0) ) # reporting percent = int(1e2* (float)((ipm)+ichan*self.DATADICT["nPulseMoments"]) / (float)(self.DATADICT["nPulseMoments"] * len(self.DATADICT[pulse]["chan"]))) self.progressTrigger.emit(percent) ax1.plot( 1e3*self.DATADICT[pulse]["TIMES"], np.average( madstack[ichan], 0 ))# , color='darkred') MADStack[pulse][chan] = madstack[ichan] VarStack[pulse][chan] = varstack[ichan] ichan += 1 self.DATADICT["stack"] = MADStack else: self.DATADICT["stack"] = SimpleStack ######################################### # Plot Fourier Transform representation # ######################################### # canvas.fig.subplots_adjust(right=0.8) # cbar_ax = canvas.fig.add_axes([0.85, 0.1, 0.015, 0.355]) # cbar_ax.ticklabel_format(style='sci', scilimits=(0,0), axis='y') im2 = [] im1 = [] for pulse in self.DATADICT["PULSES"]: ichan = 0 axes = canvas.fig.axes vvmin = 1e10 vvmax = 0 for chan in self.DATADICT[pulse]["chan"]: ax1 = axes[2*ichan ] ax2 = axes[2*ichan+1] # TODO fix hard coded number if outlierTest == "MAD": X = np.fft.rfft( MADStack[pulse][chan][0,:] ) nu = np.fft.fftfreq(len( MADStack[pulse][chan][0,:]), d=self.dt) else: X = np.fft.rfft( SimpleStack[pulse][chan][0,:] ) nu = np.fft.fftfreq(len( SimpleStack[pulse][chan][0,:]), d=self.dt) nu = nu[0:len(X)] nu[-1] = np.abs(nu[-1]) df = nu[1] - nu[0] of = 0 istart = int((self.transFreq-50.)/df) iend = int((self.transFreq+50.)/df) of = nu[istart] def freqlabel(xxx, pos): return '%1.0f' %(of + xxx*df) formatter = FuncFormatter(freqlabel) SFFT = np.zeros( (self.DATADICT["nPulseMoments"], len(X)), dtype=np.complex64 ) SFFT[0,:] = X for ipm in range(1, self.DATADICT["nPulseMoments"]): if outlierTest == "MAD": SFFT[ipm,:] = np.fft.rfft( MADStack[pulse][chan][ipm,:] ) else: SFFT[ipm,:] = np.fft.rfft( SimpleStack[pulse][chan][ipm,:] ) # convert to dB and add colorbars #db = 20.*np.log10(np.abs(SFFT[:,istart:iend])) db = (np.abs(SFFT[:,istart:iend])) #db = (np.real(SFFT[:,istart:iend])) #dbr = (np.real(SFFT[:,istart:iend])) #db = (np.imag(SFFT[:,istart:iend])) vvmin = min(vvmin, np.min(db) + 1e-16 ) vvmax = max(vvmax, np.max(db) + 1e-16 ) im2.append(ax2.matshow( db, aspect='auto', cmap=cmocean.cm.ice, vmin=vvmin, vmax=vvmax)) #im1.append(ax1.matshow( dbr, aspect='auto')) #, vmin=vvmin, vmax=vvmax)) #im2.append(ax2.matshow( db, aspect='auto', vmin=vvmin, vmax=vvmax)) #im2 = ax2.matshow( db, aspect='auto', cmap=cmocean.cm.ice, vmin=vvmin, vmax=vvmax) if ichan == 0: #ax2.set_ylabel(r"$q$ (A $\cdot$ s)", fontsize=8) ax2.set_ylabel(r"pulse index", fontsize=8) #ax1.set_ylabel(r"FID (nV)", fontsize=8) else: #ax2.yaxis.set_ticklabels([]) plt.setp(ax2.get_yticklabels(), visible=False) ax2.xaxis.set_major_formatter(formatter) ax2.xaxis.set_ticks_position('bottom') ax2.xaxis.set_major_locator(MaxNLocator(3)) y_formatter = matplotlib.ticker.ScalarFormatter(useOffset=False) ax2.yaxis.set_major_formatter(y_formatter) #if chan == self.DATADICT[pulse]["chan"][-1]: #cb2 = canvas.fig.colorbar(im2, cax=cbar_ax, format='%1.0e') #cb2 = canvas.fig.colorbar(im2[0], ax=ax2, format='%1.0e', orientation='horizontal') #cb2 = canvas.fig.colorbar(im2, ax=ax2, format='%1.0e', orientation='horizontal') #cb2.ax.tick_params(axis='both', which='major', labelsize=8) #cb2.set_label("signal (dB)", fontsize=8) ichan += 1 canvas.fig.subplots_adjust(hspace=.1, wspace=.05, left=.075, right=.95 )#left=None, bottom=None, right=None, top=None, wspace=None, hspace=None) #cb1 = canvas.fig.colorbar(im, ax=axes[0::2], format='%1.0e', orientation='horizontal', shrink=.35, aspect=30) #cb1.ax.tick_params(axis='both', which='major', labelsize=8) #cb1.set_label("$\mathcal{V}_N$ (nV)", fontsize=8) cb2 = canvas.fig.colorbar(im2[-1], ax=axes[1::2], format='%1.0e', orientation='horizontal', shrink=.35, aspect=30) cb2.ax.tick_params(axis='both', which='major', labelsize=8) cb2.set_label("$\mathcal{V}_N$ (nV)", fontsize=8) #canvas.fig.tight_layout() canvas.draw() self.doneTrigger.emit() def harmonicModel(self, nF, \ f0, f0K1, f0KN, f0Ks, f0ns, \ f1, f1K1, f1KN, f1Ks, \ Nsearch, Bounds, procRefs, \ plot, canvas): """ nF = number of base frequencies, must be 1 or 2 f0 = first base frequency f0K1 = first harmonic to model for first base frequency f0KN = last harmonic to model for the first base frequency f0Ks = subharmonic spacing, set to 1 for no subharmonics. f0Ns = number of segments for f0 f1 = second base frequency f1K1 = first harmonic to model for second base frequency f1KN = last harmonic to model for the second base frequency f1Ks = subharmonic spacing for the second base frequency, set to 1 for no subharmonics. Nsearch = the number of harmonics to use when determining base frequency bounds = 1/2 the width of the space where baseline frequency will be searched procRefs = should the reference loops be processed as well plot = should Akvo plot the results canvas = mpl plotting axis """ TDPlot = True if plot: canvas.reAx2(shy=False) canvas.ax1.set_ylabel(r"signal (nV)", fontsize=8) canvas.ax2.set_ylabel(r"signal (nV)", fontsize=8) if TDPlot: canvas.ax2.set_xlabel(r"time (s)", fontsize=8) else: canvas.ax2.set_xlabel(r"frequency (Hz)", fontsize=8) canvas.ax1.set_yscale('log') canvas.ax2.set_yscale('log') # Data iFID = 0 # stores previous f0 as starting point in non-linear search f0p = {} f1p = {} for pulse in self.DATADICT["PULSES"]: for rchan in self.DATADICT[pulse]["rchan"]: f0p[rchan] = f0 f1p[rchan] = f1+1e-1 for chan in self.DATADICT[pulse]["chan"]: f0p[chan] = f0 f1p[chan] = f1+1e-1 for pulse in self.DATADICT["PULSES"]: Nseg = int( np.floor(len( self.DATADICT[pulse]["TIMES"] ) / f0ns) ) for istack in self.DATADICT["stacks"]: for ipm in range(self.DATADICT["nPulseMoments"]): if plot: canvas.softClear() mmaxr = 0 mmaxd = 0 if procRefs: for ichan in self.DATADICT[pulse]["rchan"]: if TDPlot: canvas.ax1.plot( self.DATADICT[pulse]["TIMES"], 1e9*self.DATADICT[pulse][ichan][ipm][istack], alpha=.5) mmaxr = max( mmaxr, np.max(1e9*self.DATADICT[pulse][ichan][ipm][istack])) else: ww = np.fft.fftfreq(len(self.DATADICT[pulse][ichan][ipm][istack]), d=self.dt) X = np.fft.rfft(self.DATADICT[pulse][ichan][ipm][istack]) canvas.ax1.plot(np.abs(ww[0:len(X)]), np.abs(X), alpha=.5) canvas.ax1.set_prop_cycle(None) canvas.ax1.set_ylim(-mmaxr, mmaxr) for ichan in self.DATADICT[pulse]["chan"]: if TDPlot: canvas.ax2.plot( self.DATADICT[pulse]["TIMES"], 1e9*self.DATADICT[pulse][ichan][ipm][istack], alpha=.5) mmaxd = max( mmaxd, np.max(1e9*self.DATADICT[pulse][ichan][ipm][istack])) else: ww = np.fft.fftfreq(len(self.DATADICT[pulse][ichan][ipm][istack]), d=self.dt) X = np.fft.rfft(self.DATADICT[pulse][ichan][ipm][istack]) canvas.ax2.plot(np.abs(ww[0:len(X)]), np.abs(X), alpha=.5) canvas.ax2.set_prop_cycle(None) canvas.ax2.set_ylim(-mmaxd, mmaxd) if procRefs: for ichan in self.DATADICT[pulse]["rchan"]: if nF == 1: for iseg in range(f0ns): if iseg < f0ns-1: 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], \ self.samp, self.DATADICT[pulse]["TIMES"][iseg*Nseg:(iseg+1)*Nseg], \ f0p[ichan], f0K1, f0KN, f0Ks, Bounds, Nsearch ) else: self.DATADICT[pulse][ichan][ipm][istack][iseg*Nseg::], f0p[ichan] = \ harmonic.minHarmonic( self.DATADICT[pulse][ichan][ipm][istack][iseg*Nseg::], \ self.samp, self.DATADICT[pulse]["TIMES"][iseg*Nseg::], \ f0p[ichan], f0K1, f0KN, f0Ks, Bounds, Nsearch ) elif nF == 2: for iseg in range(f0ns): if iseg < f0ns-1: 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],\ self.samp, self.DATADICT[pulse]["TIMES"][iseg*Nseg:(iseg+1)*Nseg], \ f0p[ichan], f0K1, f0KN, f0Ks, \ f1p[ichan], f1K1, f1KN, f1Ks, Bounds, Nsearch ) else: self.DATADICT[pulse][ichan][ipm][istack][iseg*Nseg::], f0p[ichan], f1p[ichan] = \ harmonic.minHarmonic2( self.DATADICT[pulse][ichan][ipm][istack][iseg*Nseg::],\ self.samp, self.DATADICT[pulse]["TIMES"][iseg*Nseg::], \ f0p[ichan], f0K1, f0KN, f0Ks, \ f1p[ichan], f1K1, f1KN, f1Ks, Bounds, Nsearch ) # plot if plot: if TDPlot: canvas.ax1.plot( self.DATADICT[pulse]["TIMES"], 1e9*self.DATADICT[pulse][ichan][ipm][istack], \ label = pulse + " ipm=" + str(ipm) + " istack=" + str(istack) + " rchan=" + str(ichan)) else: ww = np.fft.fftfreq(len(self.DATADICT[pulse][ichan][ipm][istack]), d=self.dt) X = np.fft.rfft(self.DATADICT[pulse][ichan][ipm][istack]) canvas.ax1.plot(np.abs(ww[0:len(X)]), np.abs(X),\ label = pulse + " ipm=" + str(ipm) + " istack=" + str(istack) + " rchan=" + str(ichan)) for ichan in self.DATADICT[pulse]["chan"]: if nF == 1: for iseg in range(f0ns): if iseg < f0ns-1: 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], self.samp, self.DATADICT[pulse]["TIMES"][iseg*Nseg:(iseg+1)*Nseg], \ f0p[ichan], f0K1, f0KN, f0Ks, Bounds, Nsearch ) else: self.DATADICT[pulse][ichan][ipm][istack][iseg*Nseg::], f0p[ichan] = \ harmonic.minHarmonic( self.DATADICT[pulse][ichan][ipm][istack][iseg*Nseg::], self.samp, self.DATADICT[pulse]["TIMES"][iseg*Nseg::], \ f0p[ichan], f0K1, f0KN, f0Ks, Bounds, Nsearch ) elif nF == 2: for iseg in range(f0ns): if iseg < f0ns-1: 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],\ self.samp, self.DATADICT[pulse]["TIMES"][iseg*Nseg:(iseg+1)*Nseg], \ f0p[ichan], f0K1, f0KN, f0Ks, \ f1p[ichan], f1K1, f1KN, f1Ks, Bounds, Nsearch ) else: self.DATADICT[pulse][ichan][ipm][istack][iseg*Nseg::], f0p[ichan], f1p[ichan] = \ harmonic.minHarmonic2( self.DATADICT[pulse][ichan][ipm][istack][iseg*Nseg::],\ self.samp, self.DATADICT[pulse]["TIMES"][iseg*Nseg::], \ f0p[ichan], f0K1, f0KN, f0Ks, \ f1p[ichan], f1K1, f1KN, f1Ks, Bounds, Nsearch ) # plot if plot: if TDPlot: canvas.ax2.plot( self.DATADICT[pulse]["TIMES"], 1e9*self.DATADICT[pulse][ichan][ipm][istack], \ label = pulse + " ipm=" + str(ipm) + " istack=" + str(istack) + " chan=" + str(ichan)) else: ww = np.fft.fftfreq(len(self.DATADICT[pulse][ichan][ipm][istack]), d=self.dt) X = np.fft.rfft(self.DATADICT[pulse][ichan][ipm][istack]) canvas.ax2.plot(np.abs(ww[0:len(X)]), np.abs(X), \ label = pulse + " ipm=" + str(ipm) + " istack=" + str(istack) + " chan=" + str(ichan)) if plot: if procRefs: canvas.ax1.legend(prop={'size':6}, loc='upper right') canvas.ax2.legend(prop={'size':6}, loc='upper right') canvas.draw() percent = (int)(1e2*((ipm+istack*self.nPulseMoments)/(self.nPulseMoments*len(self.DATADICT["stacks"])))) self.progressTrigger.emit(percent) iFID += 1 self.doneTrigger.emit() self.updateProcTrigger.emit() self.doneTrigger.emit() def FDSmartStack(self, outlierTest, MADcutoff, canvas): print("FFT stuff") self.dataCubeFFT() Stack = {} # align phase cycling for stacking and modulate for pulse in self.DATADICT["PULSES"]: stack = np.zeros(( len(self.DATADICT[pulse]["chan"]), \ self.DATADICT["nPulseMoments"],\ len(self.DATADICT["stacks"]),\ len(self.DATADICT[pulse][self.DATADICT[pulse]["chan"][0] ]["FFT"]["nu"])//2 + 1),\ dtype=np.complex ) for ipm in range(self.DATADICT["nPulseMoments"]): istack = 0 for sstack in self.DATADICT["stacks"]: if self.pulseType == "FID" or pulse == "Pulse 2": mod = (-1)**(ipm%2) * (-1)**(sstack%2) elif self.pulseType == "4PhaseT1": mod = (-1)**(ipm%2) * (-1)**(((sstack-1)/2)%2) ichan = 0 for chan in self.DATADICT[pulse]["chan"]: #stack[ichan,ipm,istack,:] += mod*self.DATADICT[pulse][chan][ipm][sstack] stack[ichan,ipm,istack,:] += mod*self.DATADICT[pulse][chan]["FFT"][sstack][ipm,:] ichan += 1 istack += 1 Stack[pulse] = stack ######################################### # simple stack and plot of simple stack # ########################################https://faculty.apps.utah.edu/# canvas.reAxH2(np.shape(stack)[0], False, False) axes = canvas.fig.axes SimpleStack = {} VarStack = {} for pulse in self.DATADICT["PULSES"]: SimpleStack[pulse] = {} VarStack[pulse] = {} ichan = 0 for chan in self.DATADICT[pulse]["chan"]: SimpleStack[pulse][chan] = 1e9*np.average( Stack[pulse][ichan], 1 ) VarStack[pulse][chan] = 1e9*np.std( Stack[pulse][ichan], 1 ) ax1 = axes[ 2*ichan ] #ax1.get_yaxis().get_major_formatter().set_useOffset(False) y_formatter = matplotlib.ticker.ScalarFormatter(useOffset=False) ax1.yaxis.set_major_formatter(y_formatter) #ax1.plot( 1e3*self.DATADICT[pulse][chan]["FFT"]["nu"][0:len(SimpleStack[pulse][chan])], np.average(SimpleStack[pulse][chan], 0 )) #, color='darkblue' ) #ax1.pcolor( np.real(SimpleStack[pulse][chan]) ) #, color='darkblue' ) ax1.matshow( np.real(SimpleStack[pulse][chan]), aspect='auto') #, color='darkblue' ) ax1.set_title("Ch." + str(chan) + ": avg FID", fontsize=8) ax1.set_xlabel(r"time (ms)", fontsize=8) if ichan == 0: ax1.set_ylabel(r"signal [nV]", fontsize=8) else: plt.setp(ax1.get_yticklabels(), visible=False) plt.setp(ax1.get_yaxis().get_offset_text(), visible=False) ichan += 1 ######################### # Oulier rejectig stack # ######################### if outlierTest == "MAD": MADStack = {} VarStack = {} #1.4826 is assumption of gaussian noise madstack = np.zeros(( len(self.DATADICT[pulse]["chan"]),\ self.DATADICT["nPulseMoments"],\ len(self.DATADICT[pulse][self.DATADICT[pulse]["chan"][0] ]["FFT"]["nu"])//2 + 1)) varstack = np.zeros(( len(self.DATADICT[pulse]["chan"]),\ self.DATADICT["nPulseMoments"],\ len(self.DATADICT[pulse][self.DATADICT[pulse]["chan"][0] ]["FFT"]["nu"])//2 + 1)) for pulse in self.DATADICT["PULSES"]: MADStack[pulse] = {} VarStack[pulse] = {} ichan = 0 for chan in self.DATADICT[pulse]["chan"]: ax1 = axes[ 2*ichan ] for ipm in range(self.DATADICT["nPulseMoments"]): # # brutal loop over time, can this be vectorized? # for it in range(len(self.DATADICT[pulse]["TIMES"])): # x = 1e9 *Stack[pulse][ichan,ipm,:,it] # MAD = 1.4826 * np.median( np.abs(x-np.median(x)) ) # good = 0 # for istack in self.DATADICT["stacks"]: # if (np.abs(x[istack-1]-np.median(x))) / MAD < 2: # good += 1 # madstack[ ichan, ipm, it ] += x[istack-1] # else: # pass # madstack[ichan, ipm, it] /= good # percent = int(1e2* (float)(ipm) / (float)(self.DATADICT["nPulseMoments"]) ) # self.progressTrigger.emit(percent) # Vectorized version of above...much, much faster x = 1e9*copy.deepcopy(Stack[pulse][ichan][ipm,:,:]) # stack and time indices tile_med = np.tile( np.median(x, axis=0), (np.shape(x)[0],1)) MAD = MADcutoff * np.median(np.abs(x - tile_med), axis=0) tile_MAD = np.tile( MAD, (np.shape(x)[0],1)) good = np.abs(x-tile_med)/tile_MAD < 2. # 1.4826 # 2 madstack[ichan][ipm] = copy.deepcopy( np.ma.masked_array(x, good != True).mean(axis=0) ) varstack[ichan][ipm] = copy.deepcopy( np.ma.masked_array(x, good != True).std(axis=0) ) # reporting percent = int(1e2* (float)((ipm)+ichan*self.DATADICT["nPulseMoments"]) / (float)(self.DATADICT["nPulseMoments"] * len(self.DATADICT[pulse]["chan"]))) self.progressTrigger.emit(percent) ax2 = axes[2*ichan+1] # TODO fix hard coded number #ax1.plot( 1e3*self.DATADICT[pulse]["TIMES"], np.average( madstack[ichan], 0 ))# , color='darkred') MADStack[pulse][chan] = madstack[ichan] VarStack[pulse][chan] = varstack[ichan] ax2.matshow( np.real(MADStack[pulse][chan]), aspect='auto') #, color='darkblue' ) ichan += 1 self.DATADICT["stack"] = MADStack else: self.DATADICT["stack"] = SimpleStack # ######################################### # # Plot Fourier Transform representation # # ######################################### # # # canvas.fig.subplots_adjust(right=0.8) # # cbar_ax = canvas.fig.add_axes([0.85, 0.1, 0.015, 0.355]) # # cbar_ax.ticklabel_format(style='sci', scilimits=(0,0), axis='y') # im2 = [] # im1 = [] # for pulse in self.DATADICT["PULSES"]: # ichan = 0 # axes = canvas.fig.axes # vvmin = 1e10 # vvmax = 0 # for chan in self.DATADICT[pulse]["chan"]: # ax1 = axes[2*ichan ] # ax2 = axes[2*ichan+1] # TODO fix hard coded number # if outlierTest == "MAD": # X = np.fft.rfft( MADStack[pulse][chan][0,:] ) # nu = np.fft.fftfreq(len( MADStack[pulse][chan][0,:]), d=self.dt) # else: # X = np.fft.rfft( SimpleStack[pulse][chan][0,:] ) # nu = np.fft.fftfreq(len( SimpleStack[pulse][chan][0,:]), d=self.dt) # # nu = nu[0:len(X)] # nu[-1] = np.abs(nu[-1]) # df = nu[1] - nu[0] # of = 0 # # istart = int((self.transFreq-50.)/df) # iend = int((self.transFreq+50.)/df) # of = nu[istart] # # def freqlabel(xxx, pos): # return '%1.0f' %(of + xxx*df) # formatter = FuncFormatter(freqlabel) # # SFFT = np.zeros( (self.DATADICT["nPulseMoments"], len(X)), dtype=np.complex64 ) # SFFT[0,:] = X # for ipm in range(1, self.DATADICT["nPulseMoments"]): # if outlierTest == "MAD": # SFFT[ipm,:] = np.fft.rfft( MADStack[pulse][chan][ipm,:] ) # else: # SFFT[ipm,:] = np.fft.rfft( SimpleStack[pulse][chan][ipm,:] ) # # # convert to dB and add colorbars # #db = 20.*np.log10(np.abs(SFFT[:,istart:iend])) # db = (np.abs(SFFT[:,istart:iend])) # #db = (np.real(SFFT[:,istart:iend])) # #dbr = (np.real(SFFT[:,istart:iend])) # #db = (np.imag(SFFT[:,istart:iend])) # # vvmin = min(vvmin, np.min (db)) # vvmax = max(vvmax, np.max (db)) # im2.append(ax2.matshow( db, aspect='auto', cmap=cmocean.cm.ice, vmin=vvmin, vmax=vvmax)) # #im1.append(ax1.matshow( dbr, aspect='auto')) #, vmin=vvmin, vmax=vvmax)) # #im2.append(ax2.matshow( db, aspect='auto', vmin=vvmin, vmax=vvmax)) # #im2 = ax2.matshow( db, aspect='auto', cmap=cmocean.cm.ice, vmin=vvmin, vmax=vvmax) # if ichan == 0: # ax2.set_ylabel(r"$q$ (A $\cdot$ s)", fontsize=8) # else: # #ax2.yaxis.set_ticklabels([]) # plt.setp(ax2.get_yticklabels(), visible=False) # # ax2.xaxis.set_major_formatter(formatter) # ax2.xaxis.set_ticks_position('bottom') # ax2.xaxis.set_major_locator(MaxNLocator(3)) # # y_formatter = matplotlib.ticker.ScalarFormatter(useOffset=False) # ax2.yaxis.set_major_formatter(y_formatter) # # # #if chan == self.DATADICT[pulse]["chan"][-1]: # #cb2 = canvas.fig.colorbar(im2, cax=cbar_ax, format='%1.0e') # # #cb2 = canvas.fig.colorbar(im2[0], ax=ax2, format='%1.0e', orientation='horizontal') # #cb2 = canvas.fig.colorbar(im2, ax=ax2, format='%1.0e', orientation='horizontal') # #cb2.ax.tick_params(axis='both', which='major', labelsize=8) # #cb2.set_label("signal (dB)", fontsize=8) # # ichan += 1 # # # canvas.fig.subplots_adjust(hspace=.1, wspace=.05, left=.075, right=.95 )#left=None, bottom=None, right=None, top=None, wspace=None, hspace=None) # # #cb1 = canvas.fig.colorbar(im, ax=axes[0::2], format='%1.0e', orientation='horizontal', shrink=.35, aspect=30) # #cb1.ax.tick_params(axis='both', which='major', labelsize=8) # #cb1.set_label("$\mathcal{V}_N$ (nV)", fontsize=8) # # cb2 = canvas.fig.colorbar(im2[-1], ax=axes[1::2], format='%1.0e', orientation='horizontal', shrink=.35, aspect=30) # cb2.ax.tick_params(axis='both', which='major', labelsize=8) # cb2.set_label("$\mathcal{V}_N$ (nV)", fontsize=8) #canvas.fig.tight_layout() canvas.draw() self.doneTrigger.emit() def sumData(self, canvas, fred): chans = copy.deepcopy(self.DATADICT[self.DATADICT["PULSES"][0]]["chan"]) #= np.array( ( self.DATADICT[pulse]["chan"][0], ) ) nchan = len(chans) # Sum permutations of two channel combos for ich in range(nchan-1): for ch in chans[ich+1:]: chsum = chans[ich] + "+" + ch for pulse in self.DATADICT["PULSES"]: self.DATADICT[pulse][chsum] = {} for ipm in range(self.DATADICT["nPulseMoments"]): self.DATADICT[pulse][chsum][ipm] = {} for istack in self.DATADICT["stacks"]: self.DATADICT[pulse][chsum][ipm][istack] = self.DATADICT[pulse][chans[ich]][ipm][istack] - self.DATADICT[pulse][ch][ipm][istack] if chsum == "1+2": #self.DATADICT[pulse]["rchan"].pop() #self.DATADICT[pulse]["rchan"].pop() self.DATADICT[pulse]["chan"].append(chsum) # Sum all channels sumall = False if sumall: chsum = "" for ch in chans: chsum += ch + "+" chsum = chsum[0:-1] # remove last "+" for pulse in self.DATADICT["PULSES"]: self.DATADICT[pulse][chsum] = {} for ipm in range(self.DATADICT["nPulseMoments"]): self.DATADICT[pulse][chsum][ipm] = {} for istack in self.DATADICT["stacks"]: self.DATADICT[pulse][chsum][ipm][istack] = copy.deepcopy(self.DATADICT[pulse][chans[0]][ipm][istack]) for ch in chans[1:]: self.DATADICT[pulse][chsum][ipm][istack] += self.DATADICT[pulse][ch][ipm][istack] self.DATADICT[pulse]["chan"].append(chsum) # if nchan > 2: # for ch in chans: # chsum += ch # for ch2 in chans[1::]: # for pulse in self.DATADICT["PULSES"]: # self.DATADICT[pulse][chsum] = {} # for istack in self.DATADICT["stacks"]: # self.DATADICT[pulse][chsum][ipm][istack] = self.DATADICT[pulse][chans[ich]][ipm][istack] + self.DATADICT[pulse][ch][ipm][istack] self.doneTrigger.emit() def quadDet(self, clip, method, loss, canvas): from scipy import signal self.RotatedAmplitude = True wL = self.transFreq * 2*np.pi vL = self.transFreq #T = 50 dt = self.dt #DT = 0.01 CA = {} # corrected amplitude IP = {} # instantaneous phase NR = {} # Noise residual RE = {} # Real channel IM = {} # Imaginary channel # global maximums for plotting CAmax = {} NRmax = {} REmax = {} IMmax = {} E0,phi,df,T2 = 100.,0,0,.2 first = False self.sigma = {} for pulse in self.DATADICT["PULSES"]: CA[pulse] = {} IP[pulse] = {} NR[pulse] = {} RE[pulse] = {} IM[pulse] = {} CAmax[pulse] = 0 NRmax[pulse] = 0 REmax[pulse] = 0 IMmax[pulse] = 0 ichan = 0 self.sigma[pulse] = {} for chan in self.DATADICT[pulse]["chan"]: CA[pulse][chan] = np.zeros( (self.DATADICT["nPulseMoments"], len(self.DATADICT[pulse]["TIMES"])-clip ) ) IP[pulse][chan] = np.zeros( (self.DATADICT["nPulseMoments"], len(self.DATADICT[pulse]["TIMES"])-clip ) ) NR[pulse][chan] = np.zeros( (self.DATADICT["nPulseMoments"], len(self.DATADICT[pulse]["TIMES"])-clip ) ) RE[pulse][chan] = np.zeros( (self.DATADICT["nPulseMoments"], len(self.DATADICT[pulse]["TIMES"])-clip ) ) IM[pulse][chan] = np.zeros( (self.DATADICT["nPulseMoments"], len(self.DATADICT[pulse]["TIMES"])-clip ) ) #QQ = np.average(self.DATADICT[pulse]["Q"], axis=1 ) #for ipm in np.argsort(QQ): for ipm in range(0, self.DATADICT["nPulseMoments"]): #t = self.DATADICT[pulse]["TIMES"] - self.DATADICT[pulse]["PULSE_TIMES"][-1] xn = self.DATADICT["stack"][pulse][chan][ipm,:] ht = signal.hilbert(xn)*np.exp(-1j*wL*self.DATADICT[pulse]["TIMES"]) ############################################################# # Quadrature signal RE[pulse][chan][ipm,:] = np.real(ht[clip::]) IM[pulse][chan][ipm,:] = np.imag(ht[clip::]) REmax[pulse] = max(REmax[pulse], np.max(np.real(ht[clip::]))) IMmax[pulse] = max(IMmax[pulse], np.max(np.imag(ht[clip::]))) ############################################################# # Instantaneous phase IP[pulse][chan][ipm,:] = np.angle(ht)[clip::] ############################################################# # Rotated amplitude #if ipm != 0: # [success, E0, df, phi, T2] = decay.quadratureDetect2( ht.real, ht.imag, self.DATADICT[pulse]["TIMES"], (E0,phi,df,T2)) #[success, E0, df, phi, T2] = decay.quadratureDetect( ht.real, ht.imag, self.DATADICT[pulse]["TIMES"] ) #else: [success, E0, df, phi, T2] = decay.quadratureDetect2( ht.real, ht.imag, self.DATADICT[pulse]["TIMES"], method, loss) #[success, E0, df, phi, T2] = decay.quadratureDetect2( ht.real, ht.imag, self.DATADICT[pulse]["TIMES"], (E0,phi,df,T2)) #[success, E0, df, phi, T2] = decay.quadratureDetect( ht.real, ht.imag, self.DATADICT[pulse]["TIMES"] ) #print("success", success, "E0", E0, "phi", phi, "df", df, "T2", T2) D = self.RotateAmplitude( ht.real, ht.imag, phi, df, self.DATADICT[pulse]["TIMES"] ) CA[pulse][chan][ipm,:] = D.imag[clip::] # amplitude data NR[pulse][chan][ipm,:] = D.real[clip::] # noise data CAmax[pulse] = max(CAmax[pulse], np.max(D.imag[clip::]) ) NRmax[pulse] = max(NRmax[pulse], np.max(D.real[clip::]) ) self.sigma[pulse][chan] = np.std(NR[pulse][chan]) # reporting percent = int(1e2* (float)((ipm)+ichan*self.DATADICT["nPulseMoments"]) / (float)(self.DATADICT["nPulseMoments"] * len(self.DATADICT[pulse]["chan"]))) self.progressTrigger.emit(percent) ichan += 1 self.DATADICT[pulse]["TIMES"] = self.DATADICT[pulse]["TIMES"][clip::] self.DATADICT["CA"] = CA self.DATADICT["IP"] = IP self.DATADICT["NR"] = NR self.DATADICT["RE"] = RE self.DATADICT["IM"] = IM self.DATADICT["CAmax"] = CAmax self.DATADICT["NRmax"] = NRmax self.DATADICT["REmax"] = REmax self.DATADICT["IMmax"] = IMmax self.doneTrigger.emit() def plotQuadDet(self, clip, phase, canvas): canvas.reAxH2( len(self.DATADICT[ self.DATADICT["PULSES"][0] ]["chan"] ), False, False) ############### # Plot on GUI # ############### dcmap = cmocean.cm.curl_r #"seismic_r" #cmocean.cm.balance_r #"RdBu" #YlGn" # "coolwarm_r" # diverging canvas.reAxH2( len(self.DATADICT[ self.DATADICT["PULSES"][0] ]["chan"] ), False, False) for pulse in self.DATADICT["PULSES"]: ichan = 0 axes = canvas.fig.axes mmaxr = 0. mmaxi = 0. #if clip > 0: # time_sp = 1e3 * (self.DATADICT[pulse]["TIMES"][clip-1::] - self.DATADICT[pulse]["PULSE_TIMES"][-1] ) #else: # time_sp = 1e3 * (self.DATADICT[pulse]["TIMES"] - self.DATADICT[pulse]["PULSE_TIMES"][-1] ) time_sp = 1e3 * (self.DATADICT[pulse]["TIMES"] - self.DATADICT[pulse]["PULSE_TIMES"][-1] ) QQ = np.average(self.DATADICT[pulse]["Q"], axis=1 ) iQ = np.argsort(QQ) for chan in self.DATADICT[pulse]["chan"]: ax1 = axes[2*ichan ] ax2 = axes[2*ichan+1] # TODO fix hard coded number if phase == 0: # Re Im #print("plot dog", np.shape(QQ), np.shape(self.DATADICT["RE"][pulse][chan])) #print("QQ", QQ) im1 = ax1.pcolormesh( time_sp, QQ[iQ], self.DATADICT["RE"][pulse][chan][iQ], cmap=dcmap, \ vmin=-self.DATADICT["REmax"][pulse] , vmax=self.DATADICT["REmax"][pulse] ) im2 = ax2.pcolormesh( time_sp, QQ[iQ], self.DATADICT["IM"][pulse][chan][iQ], cmap=dcmap, \ vmin=-self.DATADICT["IMmax"][pulse], vmax=self.DATADICT["IMmax"][pulse] ) #im1 = ax1.matshow( self.DATADICT["RE"][pulse][chan][iQ], cmap=dcmap, aspect='auto', \ # vmin=-self.DATADICT["REmax"][pulse] , vmax=self.DATADICT["REmax"][pulse] ) #im2 = ax2.matshow( self.DATADICT["IM"][pulse][chan][iQ], cmap=dcmap, aspect='auto', \ # vmin=-self.DATADICT["REmax"][pulse] , vmax=self.DATADICT["REmax"][pulse] ) if phase == 1: # Amp phase im1 = ax1.pcolormesh( time_sp, QQ[iQ], self.DATADICT["CA"][pulse][chan][iQ], cmap=dcmap, \ vmin=-self.DATADICT["CAmax"][pulse] , vmax=self.DATADICT["CAmax"][pulse] ) #im2 = ax2.pcolormesh( time_sp, QQ, self.DATADICT["IP"][pulse][chan], cmap=cmocean.cm.balance, rasterized=True,\ im2 = ax2.pcolormesh( time_sp, QQ[iQ], self.DATADICT["IP"][pulse][chan][iQ], cmap=cmocean.cm.delta, \ vmin=-np.pi, vmax=np.pi) if phase == 2: # CA NR im1 = ax1.pcolormesh( time_sp, QQ[iQ], self.DATADICT["CA"][pulse][chan][iQ], cmap=dcmap, \ vmin=-self.DATADICT["CAmax"][pulse] , vmax=self.DATADICT["CAmax"][pulse] ) im2 = ax2.pcolormesh( time_sp, QQ[iQ], self.DATADICT["NR"][pulse][chan][iQ], cmap=dcmap, \ vmin=-self.DATADICT["NRmax"][pulse] , vmax=self.DATADICT["NRmax"][pulse] ) # cb2 = canvas.fig.colorbar(im2, ax=ax2, format='%1.0e') # cb2.set_label("Noise residual (nV)", fontsize=8) # cb2.ax.tick_params(axis='both', which='major', labelsize=8) # cb1 = canvas.fig.colorbar(im1, ax=ax1, format='%1.0e') # cb1.set_label("Phased amplitude (nV)", fontsize=8) # cb1.ax.tick_params(axis='both', which='major', labelsize=8) # cb2 = canvas.fig.colorbar(im2, ax=ax2, format="%1.0e") # cb2.set_label("Phase (rad)", fontsize=8) # cb2.ax.tick_params(axis='both', which='major', labelsize=8) # cb1 = canvas.fig.colorbar(im1, ax=ax1, format="%1.0e") # cb1.set_label("FID (nV)", fontsize=8) # cb1.ax.tick_params(axis='both', which='major', labelsize=8) # if you save these as pdf or eps, there are artefacts # for cbar in [cb1,cb2]: # #cbar.solids.set_rasterized(True) # cbar.solids.set_edgecolor("face") # reporting percent = int(1e2* (float)(ichan)/len(self.DATADICT[pulse]["chan"])) self.progressTrigger.emit(percent) if ichan == 0: ax1.set_ylabel(r"$q$ ( $\mathrm{A}\cdot\mathrm{s}$)", fontsize=8) ax2.set_ylabel(r"$q$ ( $\mathrm{A}\cdot\mathrm{s}$)", fontsize=8) else: #ax2.yaxis.set_ticklabels([]) #ax1.yaxis.set_ticklabels([]) plt.setp(ax1.get_yticklabels(), visible=False) plt.setp(ax2.get_yticklabels(), visible=False) ichan += 1 ax1.set_yscale('log') ax2.set_yscale('log') plt.setp(ax1.get_xticklabels(), visible=False) ax1.set_ylim( np.min(QQ), np.max(QQ) ) ax2.set_ylim( np.min(QQ), np.max(QQ) ) ax1.set_xlim( np.min(time_sp), np.max(time_sp) ) ax2.set_xlim( np.min(time_sp), np.max(time_sp) ) #ax2.set_xlabel(r"Time since end of pulse (ms)", fontsize=8) ax2.set_xlabel(r"Time (ms)", fontsize=8) 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) tick_locator = MaxNLocator(nbins=3) cb1 = canvas.fig.colorbar(im1, ax=axes[0::2], format='%1.0e', orientation='horizontal', shrink=.35, aspect=30) cb1.ax.tick_params(axis='both', which='major', labelsize=8) cb1.set_label("$\mathcal{V}_N$ (nV)", fontsize=8) cb1.locator = tick_locator cb1.update_ticks() tick_locator2 = MaxNLocator(nbins=3) cb2 = canvas.fig.colorbar(im2, ax=axes[1::2], format='%1.0e', orientation='horizontal', shrink=.35, aspect=30, pad=.2) cb2.ax.tick_params(axis='both', which='major', labelsize=8) if phase == 1: # Amp phase cb2.set_label(r"$\angle \mathcal{V}_N$", fontsize=8) else: cb2.set_label("$\mathcal{V}_N$ (nV)", fontsize=8) cb2.locator = tick_locator2 cb2.update_ticks() canvas.draw() self.doneTrigger.emit() def RotateAmplitude(self, X, Y, zeta, df, t): V = X + 1j*Y return np.abs(V) * np.exp( 1j * ( np.angle(V) - zeta - 2.*np.pi*df*t ) ) def gateIntegrate( self, gpd, clip, canvas ): """ Gate integrate the real, imaginary, phased, and noise residual channels """ self.gated = True self.GATED = {} for pulse in self.DATADICT["PULSES"]: QQ = np.average(self.DATADICT[pulse]["Q"], axis=1 ) iQ = np.argsort(QQ) ichan = 0 for chan in self.DATADICT[pulse]["chan"]: self.GATED[chan] = {} for ipm in range(0, self.DATADICT["nPulseMoments"]): #for ipm in iQ: # Time since pulse end rather than since record starts... #if clip > 0: # time_sp = 1e3 * (self.DATADICT[pulse]["TIMES"][clip:] - self.DATADICT[pulse]["PULSE_TIMES"][-1] ) #else: time_sp = 1e3 * (self.DATADICT[pulse]["TIMES"] - self.DATADICT[pulse]["PULSE_TIMES"][-1] ) #GT, GD, GTT, sig_stack, isum = rotate.gateIntegrate( self.DATADICT["CA"][pulse][chan][ipm,:], time_sp, gpd, self.sigma[pulse][chan], 1.5 ) #GT2, GP, GTT, sig_stack_err, isum = rotate.gateIntegrate( self.DATADICT["NR"][pulse][chan][ipm,:], time_sp, gpd, self.sigma[pulse][chan], 1.5 ) # err GT, GCA, GTT, sig_stack, isum = rotate.gateIntegrate( self.DATADICT["CA"][pulse][chan][ipm], time_sp, gpd, self.sigma[pulse][chan], 2 ) GT, GNR, GTT, sig_stack, isum = rotate.gateIntegrate( self.DATADICT["NR"][pulse][chan][ipm], time_sp, gpd, self.sigma[pulse][chan], 2 ) GT, GRE, GTT, sig_stack, isum = rotate.gateIntegrate( self.DATADICT["RE"][pulse][chan][ipm], time_sp, gpd, self.sigma[pulse][chan], 2 ) GT, GIM, GTT, sig_stack, isum = rotate.gateIntegrate( self.DATADICT["IM"][pulse][chan][ipm], time_sp, gpd, self.sigma[pulse][chan], 2 ) GT, GIP, GTT, sig_stack, isum = rotate.gateIntegrate( self.DATADICT["IP"][pulse][chan][ipm], time_sp, gpd, self.sigma[pulse][chan], 2 ) #if ipm == iQ[0]: if ipm == 0: # self.GATED[chan]["DATA"] = np.zeros( ( self.DATADICT["nPulseMoments"], len(GT)) ) # self.GATED[chan]["ERR"] = np.zeros( ( self.DATADICT["nPulseMoments"], len(GT)) ) # self.GATED[chan]["SIGMA"] = np.zeros( ( self.DATADICT["nPulseMoments"], len(GT)) ) self.GATED[chan]["CA"] = np.zeros( ( self.DATADICT["nPulseMoments"], len(GT)-clip) ) self.GATED[chan]["NR"] = np.zeros( ( self.DATADICT["nPulseMoments"], len(GT)-clip) ) self.GATED[chan]["BN"] = np.zeros( ( self.DATADICT["nPulseMoments"], len(GT)-clip) ) self.GATED[chan]["RE"] = np.zeros( ( self.DATADICT["nPulseMoments"], len(GT)-clip) ) self.GATED[chan]["IM"] = np.zeros( ( self.DATADICT["nPulseMoments"], len(GT)-clip) ) self.GATED[chan]["IP"] = np.zeros( ( self.DATADICT["nPulseMoments"], len(GT)-clip) ) self.GATED[chan]["isum"] = isum # Bootstrap noise #self.GATED[chan]["isum"] print("bootstrappin") Means = rotate.bootstrapWindows( self.DATADICT["NR"][pulse][chan][ipm], 20000, isum[isum!=1], adapt=True) # MAD, only for windows > 1 c = stats.norm.ppf(3./4.) sig_stack[isum!=1] = np.ma.median(np.ma.abs(Means), axis=1) / c self.GATED[chan]["BN"][ipm] = sig_stack[clip:] print("end bootstrappin") #self.GATED[chan]["DATA"][ipm] = GD.real self.GATEDABSCISSA = GT[clip:] self.GATEDWINDOW = GTT[clip:] #self.GATED[chan]["SIGMA"][ipm] = sig_stack #_err # GP.real #self.GATED[chan]["ERR"][ipm] = GP.real #self.GATED[chan]["CA"][iQ[ipm]] = GCA.real[clip:] #self.GATED[chan]["NR"][iQ[ipm]] = GNR.real[clip:] #self.GATED[chan]["RE"][iQ[ipm]] = GRE.real[clip:] #self.GATED[chan]["IM"][iQ[ipm]] = GIM.real[clip:] #self.GATED[chan]["IP"][iQ[ipm]] = GIP.real[clip:] self.GATED[chan]["CA"][ipm] = GCA.real[clip:] self.GATED[chan]["NR"][ipm] = GNR.real[clip:] self.GATED[chan]["RE"][ipm] = GRE.real[clip:] self.GATED[chan]["IM"][ipm] = GIM.real[clip:] self.GATED[chan]["IP"][ipm] = GIP.real[clip:] percent = int(1e2* (float)((ipm)+ichan*self.DATADICT["nPulseMoments"]) / (float)(self.DATADICT["nPulseMoments"] * len(self.DATADICT[pulse]["chan"]))) self.progressTrigger.emit(percent) self.GATED[chan]["CA"] = self.GATED[chan]["CA"][iQ,:] self.GATED[chan]["NR"] = self.GATED[chan]["NR"][iQ,:] self.GATED[chan]["RE"] = self.GATED[chan]["RE"][iQ,:] self.GATED[chan]["IM"] = self.GATED[chan]["IM"][iQ,:] self.GATED[chan]["IP"] = self.GATED[chan]["IP"][iQ,:] self.GATED[chan]["GTT"] = GTT[clip:] self.GATED[chan]["GT"] = GT[clip:] self.GATED[chan]["QQ"] = QQ[iQ] ichan += 1 self.doneTrigger.emit() def bootstrap_resample(self, X, n=None): # from http://nbviewer.jupyter.org/gist/aflaxman/6871948 """ Bootstrap resample an array_like Parameters ---------- X : array_like data to resample n : int, optional length of resampled array, equal to len(X) if n==None Results ------- returns X_resamples """ if n == None: n = len(X) resample_i = np.floor(np.random.rand(n)*len(X)).astype(int) return X[resample_i] def bootstrap_sigma(self, pulse, chan): # bootstrap resample nt = len(self.GATED[chan]["GT"]) nb = 5000 XS = np.zeros( (nb, nt) ) for ii in range(nb): for it in range(nt): if self.GATED[chan]["isum"][it] < 8: XS[ii, it] = self.sigma[pulse][chan] else: if it == 0: X = self.bootstrap_resample( np.concatenate( (self.GATED[chan]["NR"][:,it], \ self.GATED[chan]["NR"][:,it+1], \ self.GATED[chan]["NR"][:,it+2], \ self.GATED[chan]["NR"][:,it+3] ) ), n=nt ) elif it == 1: X = self.bootstrap_resample( np.concatenate( (self.GATED[chan]["NR"][:,it-1], self.GATED[chan]["NR"][:,it], \ self.GATED[chan]["NR"][:,it+1], self.GATED[chan]["NR"][:,it+2] ) ), n=nt ) elif it == nt-2: X = self.bootstrap_resample( np.concatenate( (self.GATED[chan]["NR"][:,it+1], self.GATED[chan]["NR"][:,it], \ self.GATED[chan]["NR"][:,it-1], self.GATED[chan]["NR"][:,it-2] ) ), n=nt ) elif it == nt-1: X = self.bootstrap_resample( np.concatenate( (self.GATED[chan]["NR"][:,it], self.GATED[chan]["NR"][:,it-1], \ self.GATED[chan]["NR"][:,it-2], self.GATED[chan]["NR"][:,it-3] ) ), n=nt ) else: X = self.bootstrap_resample( np.concatenate( (self.GATED[chan]["NR"][:,it-2] , self.GATED[chan]["NR"][:,it-1], \ self.GATED[chan]["NR"][:,it], self.GATED[chan]["NR"][:,it+1], self.GATED[chan]["NR"][:,it+2] )), n=nt ) XS[ii,it] = np.std(X) return XS def plotGateIntegrate( self, gpd, clip, phase, canvas ): """ Plot the gate integration """ canvas.reAxH2( len(self.DATADICT[ self.DATADICT["PULSES"][0] ]["chan"] ), False, False) axes = canvas.fig.axes #cmap = cmocean.cm.balance_r dcmap = cmocean.cm.curl_r #"seismic_r" #cmocean.cm.balance_r #"RdBu" #YlGn" # "coolwarm_r" # diverging # Calculate maximum for plotting...TODO move into loop above vmax1 = 0 vmax2 = 0 for pulse in self.DATADICT["PULSES"]: for chan in self.DATADICT[pulse]["chan"]: if phase == 0: vmax1 = max(vmax1, np.max(np.abs(self.GATED[chan]["RE"]))) vmax2 = max(vmax2, np.max(np.abs(self.GATED[chan]["IM"]))) elif phase == 1: vmax1 = max(vmax1, np.max(np.abs(self.GATED[chan]["CA"]))) vmax2 = np.pi elif phase == 2: vmax1 = max(vmax1, np.max(np.abs(self.GATED[chan]["CA"]))) vmax2 = max(vmax2, np.max(np.abs(self.GATED[chan]["NR"]))) for pulse in self.DATADICT["PULSES"]: ichan = 0 for chan in self.DATADICT[pulse]["chan"]: ax1 = axes[2*ichan ] ax2 = axes[2*ichan+1] if phase == 0: im1 = ax1.pcolormesh(self.GATED[chan]["GTT"], self.GATED[chan]["QQ"], self.GATED[chan]["RE"], cmap=dcmap, vmin=-vmax1, vmax=vmax1) im2 = ax2.pcolormesh(self.GATED[chan]["GTT"], self.GATED[chan]["QQ"], self.GATED[chan]["IM"], cmap=dcmap, vmin=-vmax2, vmax=vmax2) #im1 = ax1.matshow(self.GATED[chan]["RE"], cmap=dcmap, vmin=-vmax1, vmax=vmax1, aspect='auto') #im2 = ax2.matshow(self.GATED[chan]["IM"], cmap=dcmap, vmin=-vmax2, vmax=vmax2, aspect='auto') elif phase == 1: im1 = ax1.pcolormesh(self.GATED[chan]["GTT"], self.GATED[chan]["QQ"], self.GATED[chan]["CA"], cmap=dcmap, vmin=-vmax1, vmax=vmax1) im2 = ax2.pcolormesh(self.GATED[chan]["GTT"], self.GATED[chan]["QQ"], self.GATED[chan]["IP"], cmap=cmocean.cm.delta, vmin=-vmax2, vmax=vmax2) #im2 = ax2.pcolormesh(self.GATED[chan]["GTT"], self.GATED[chan]["QQ"], self.GATED[chan]["IP"], cmap=cmocean.cm.phase, vmin=-vmax2, vmax=vmax2) elif phase == 2: im1 = ax1.pcolormesh(self.GATED[chan]["GTT"], self.GATED[chan]["QQ"], self.GATED[chan]["CA"], cmap=dcmap, vmin=-vmax1, vmax=vmax1) #XS = self.bootstrap_sigma(pulse, chan) #im2 = ax2.pcolormesh(self.GATED[chan]["GTT"], self.GATED[chan]["QQ"], self.GATED[chan]["NR"], cmap=cmap, vmin=-vmax2, vmax=vmax2) # bootstrap resample # nt = len(self.GATED[chan]["GT"]) # nb = 5000 # XS = np.zeros( (nb, nt) ) # for ii in range(nb): # #XS = [] # for it in range(nt): # if self.GATED[chan]["isum"][it] < 8: # XS[ii, it] = self.sigma[pulse][chan] # else: # if it == 0: # X = self.bootstrap_resample( np.concatenate( (self.GATED[chan]["NR"][:,it], self.GATED[chan]["NR"][:,it+1], \ # self.GATED[chan]["NR"][:,it+2], self.GATED[chan]["NR"][:,it+3] ) ), n=nt ) # if it == 1: # X = self.bootstrap_resample( np.concatenate( (self.GATED[chan]["NR"][:,it-1], self.GATED[chan]["NR"][:,it], \ # self.GATED[chan]["NR"][:,it+1], self.GATED[chan]["NR"][:,it+2] ) ), n=nt ) # elif it == nt-2: # X = self.bootstrap_resample( np.concatenate( (self.GATED[chan]["NR"][:,it+1], self.GATED[chan]["NR"][:,it], \ # self.GATED[chan]["NR"][:,it-1], self.GATED[chan]["NR"][:,it-2] ) ), n=nt ) # elif it == nt-1: # X = self.bootstrap_resample( np.concatenate( (self.GATED[chan]["NR"][:,it], self.GATED[chan]["NR"][:,it-1], \ # self.GATED[chan]["NR"][:,it-2], self.GATED[chan]["NR"][:,it-3] ) ), n=nt ) # else: # X = self.bootstrap_resample( np.concatenate( (self.GATED[chan]["NR"][:,it-2] , self.GATED[chan]["NR"][:,it-1], \ # self.GATED[chan]["NR"][:,it], self.GATED[chan]["NR"][:,it+1], self.GATED[chan]["NR"][:,it+2] )), n=nt ) # XS[ii,it] = np.std(X) #if ii == 0: # ax2.plot( self.GATED[chan]["GT"], XS[ii], '-', linewidth=1, markersize=4, alpha=.5, color='lightgrey', label = "bootstrap sim" ) #else: # ax2.plot( self.GATED[chan]["GT"], XS[ii], '-', linewidth=1, markersize=4, alpha=.5, color='lightgrey' ) ax2.plot( self.GATED[chan]["GT"], np.std(self.GATED[chan]["NR"], axis=0), color='darkgrey', linewidth=2, label="gate std" ) ax2.plot( self.GATED[chan]["GT"], np.average(self.GATED[chan]["BN"], axis=0), color='black', linewidth=2, label="boot average" ) #ax2.plot( np.tile(self.GATED[chan]["GT"], (5000,1) ), XS, '.', color='lightgrey', linewidth=1, alpha=.5 ) #ax2.plot( self.GATED[chan]["GT"], np.average(XS, axis=0), color='black', linewidth=2, label="bootstrap avg." ) #ax2.plot( self.GATED[chan]["GT"], np.median(XS, axis=0), color='black', linewidth=2, label="bootstrap med." ) ax2.legend() im1.set_edgecolor('face') if phase != 2: im2.set_edgecolor('face') plt.setp(ax1.get_xticklabels(), visible=False) ax1.set_ylim( np.min(self.GATED[chan]["QQ"]), np.max(self.GATED[chan]["QQ"]) ) if phase != 2: ax2.set_ylim( np.min(self.GATED[chan]["QQ"]), np.max(self.GATED[chan]["QQ"]) ) ax1.set_xlim( np.min(self.GATED[chan]["GTT"]), np.max(self.GATED[chan]["GTT"]) ) ax2.set_xlim( np.min(self.GATED[chan]["GTT"]), np.max(self.GATED[chan]["GTT"]) ) ax1.set_yscale('log') ax2.set_yscale('log') qlabs = np.append(np.concatenate( ( self.GATED[chan]["QQ"][0:1], self.GATED[chan]["QQ"][9::10] )), self.GATED[chan]["QQ"][-1] ) ax1.yaxis.set_ticks( qlabs ) # np.append(np.concatenate( (QQ[0:1],QQ[9::10] )), QQ[-1] ) ) if phase != 2: ax2.yaxis.set_ticks( qlabs ) #np.append(np.concatenate( (QQ[0:1],QQ[9::10] )), QQ[-1] ) ) #formatter = matplotlib.ticker.LogFormatter(10, labelOnlyBase=False) formatter = matplotlib.ticker.FuncFormatter(lambda x, pos: str((round(x,1)))) ax1.set_xscale('log') ax2.set_xscale('log') ax1.yaxis.set_major_formatter(formatter) #matplotlib.ticker.FormatStrFormatter('%d.1')) ax2.yaxis.set_major_formatter(formatter) #matplotlib.ticker.FormatStrFormatter('%d.1')) ax1.xaxis.set_major_formatter(formatter) #matplotlib.ticker.FormatStrFormatter('%d.1')) ax2.xaxis.set_major_formatter(formatter) #matplotlib.ticker.FormatStrFormatter('%d.1')) if ichan == 0: ax1.set_ylabel(r"$q$ ( $\mathrm{A}\cdot\mathrm{s}$)", fontsize=8) if phase == 2: ax2.set_ylabel(r"noise est. (nV)", fontsize=8) else: ax2.set_ylabel(r"$q$ ( $\mathrm{A}\cdot\mathrm{s}$)", fontsize=8) else: plt.setp(ax1.get_yticklabels(), visible=False) plt.setp(ax2.get_yticklabels(), visible=False) ax2.set_xlabel(r"$t-\tau_p$ (ms)", fontsize=8) ichan += 1 #canvas.fig.subplots_adjust(hspace=.1, wspace=.05, left=.075, right=.925 )#left=None, bottom=None, right=None, top=None, wspace=None, hspace=None) #canvas.fig.tight_layout() #canvas.draw() 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) tick_locator = MaxNLocator(nbins=5) cb1 = canvas.fig.colorbar(im1, ax=axes[0::2], format='%1.0e', orientation='horizontal', shrink=.35, aspect=30) cb1.ax.tick_params(axis='both', which='major', labelsize=8) cb1.set_label("$\mathcal{V}_N$ (nV)", fontsize=8) #cb1.locator = tick_locator #cb1.update_ticks() if phase != 2: cb2 = canvas.fig.colorbar(im2, ax=axes[1::2], format='%1.0e', orientation='horizontal', shrink=.35, aspect=30, pad=.2) cb2.ax.tick_params(axis='both', which='major', labelsize=8) cb2.set_label("$\mathcal{V}_N$ (nV)", fontsize=8) cb2.locator = tick_locator cb2.update_ticks() canvas.draw() self.doneTrigger.emit() def FDSmartStack(self, cv, canvas): from matplotlib.colors import LogNorm from matplotlib.ticker import MaxNLocator """ Currently this stacks 4-phase second pulse data only, we need to generalise """ try: canvas.fig.clear() except: pass self.dataCubeFFT( ) # canvas.ax1 = canvas.fig.add_axes([.1, .1, .8, .8]) canvas.ax1 = canvas.fig.add_axes([.1, .1, .2, .8]) canvas.ax2 = canvas.fig.add_axes([.325, .1, .2, .8]) canvas.ax3 = canvas.fig.add_axes([.55, .1, .2, .8]) canvas.ax4 = canvas.fig.add_axes([.815, .1, .05, .8]) #cb canvas.ax1.tick_params(axis='both', which='major', labelsize=8) canvas.ax2.tick_params(axis='both', which='major', labelsize=8) canvas.ax3.tick_params(axis='both', which='major', labelsize=8) canvas.ax4.tick_params(axis='both', which='major', labelsize=8) canvas.ax1.set_ylabel("pulse index", fontsize=8) canvas.ax1.set_xlabel(r"$\omega$ bin", fontsize=8) canvas.ax2.set_xlabel(r"$\omega$ bin", fontsize=8) canvas.ax3.set_xlabel(r"$\omega$ bin", fontsize=8) canvas.ax2.yaxis.set_ticklabels([]) canvas.ax3.yaxis.set_ticklabels([]) #canvas.ax1.ticklabel_format(style='sci', scilimits=(0,0), axis='y') # # Look at pulses # for pulse in self.DATADICT["PULSES"]: # for istack in self.DATADICT["stacks"]: # for ipm in range(0,3): # canvas.ax1.plot( self.DATADICT[pulse]["CURRENT"][ipm][istack] , label="istack "+str(istack) + " ipm=" + str(ipm) + pulse ) # canvas.draw() # Create Container for stacks # sandbox determine pulse sequence again for pulse in self.DATADICT["PULSES"]: for ichan in self.DATADICT[pulse]["chan"]: #for ipm in range(10,11): CONTAINER = {} CONTAINER["Cycle 1"] = [] # These are actually subtracted cycles... v+ - v CONTAINER["Cycle 2"] = [] for istack in self.DATADICT["stacks"]: #canvas.ax1.clear() ipm = 8 #for ipm in range(self.DATADICT["nPulseMoments"]): #canvas.ax1.matshow( np.real(self.DATADICT[pulse][ichan]["FFT"][istack]), aspect='auto' ) #canvas.draw() if not istack%4%4: # phase cycle 4, aligned with 1 after sub CONTAINER["Cycle 1"].append(-self.DATADICT[pulse][ichan]["FFT"][istack]) #canvas.ax1.plot( self.DATADICT[pulse]["TIMES"], -self.DATADICT[pulse][ichan][ipm][istack], label="istack "+str(istack)+ " " + pulse ) elif not istack%4%3: # phase cycle 3, aligned with 2 after sub CONTAINER["Cycle 2"].append(-self.DATADICT[pulse][ichan]["FFT"][istack]) #canvas.ax1.plot( self.DATADICT[pulse]["TIMES"], -self.DATADICT[pulse][ichan][ipm][istack], label="istack "+str(istack)+ " " + pulse ) elif not istack%4%2: # phase cycle 2 CONTAINER["Cycle 2"].append( self.DATADICT[pulse][ichan]["FFT"][istack]) #canvas.ax1.plot( self.DATADICT[pulse]["TIMES"], self.DATADICT[pulse][ichan][ipm][istack], label="istack "+str(istack)+ " " + pulse ) else: # phase cycle 1 CONTAINER["Cycle 1"].append( self.DATADICT[pulse][ichan]["FFT"][istack]) #canvas.ax1.plot( self.DATADICT[pulse]["TIMES"], self.DATADICT[pulse][ichan][ipm][istack], label="istack "+str(istack)+ " " + pulse ) #canvas.ax1.matshow(np.array(np.average(self.DATADICT[pulse][ichan]["FFT"]), axis=2), aspect='auto' ) #canvas.ax1.plot( self.DATADICT[pulse]["PULSE_TIMES"], self.DATADICT[pulse]["CURRENT"][ipm][istack] , color='black', label="istack "+str(istack) ) #canvas.ax1.plot( self.DATADICT[pulse]["CURRENT"][ipm][istack] , label="istack "+str(istack) + " iFID" + str(iFID) ) #canvas.ax1.plot( self.DATADICT[pulse]["TIMES"], self.DATADICT[pulse][ichan][ipm][istack], label="istack "+str(istack)+ " " + pulse ) #canvas.ax1.legend(prop={'size':6}) #canvas.draw() # Boostrap # stack. #scipy.random.shuffle(x) # Stack and calculate the pooled variance (http://en.wikipedia.org/wiki/Pooled_variance) """ All this phase cycling wreaks havoc on a normal calculation of std. and variance. Instead, we resort to calculating a pooled variance. In this assumption is that the precision of the measurment is constant. This is a poor choice for any type of moving sensor. """ # if a window filter has been applied #self.WINDOW #self.IWindowStart #self.iWindowEnd #self.FFTtimes CONTAINER = .5*(np.array(CONTAINER["Cycle 2"]) - np.array(CONTAINER["Cycle 1"])) print ("container shape", np.shape( CONTAINER), self.iWindowStart+1, self.iWindowEnd-1) dmin = np.min(np.abs(np.average(np.array(CONTAINER)[:,:,self.iWindowStart+1:self.iWindowEnd-1], axis=0))) dmax = np.max(np.abs(np.average(np.array(CONTAINER)[:,:,self.iWindowStart+1:self.iWindowEnd-1], axis=0))) 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) #mn = canvas.ax1.matshow(20.*np.log10(XA[:,istart:iend+1]), aspect='auto', vmax=-40, vmin=-120) #, norm=LogNorm()) 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) 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) #canvas.ax1.legend(prop={'size':6}) cb1 = mpl.colorbar.Colorbar(canvas.ax4, mn) cb1.ax.tick_params(labelsize=8) cb1.set_label("power [dB]", fontsize=8) canvas.ax1.xaxis.set_major_locator(MaxNLocator(4)) canvas.ax2.xaxis.set_major_locator(MaxNLocator(4)) canvas.ax3.xaxis.set_major_locator(MaxNLocator(4)) canvas.draw() self.doneTrigger.emit() def effectivePulseMoment(self, cv, canvas): canvas.reAxH(2) nstack = len(self.DATADICT["stacks"]) #canvas.ax1.set_yscale('log') for pulse in self.DATADICT["PULSES"]: self.DATADICT[pulse]["qeff"] = {} self.DATADICT[pulse]["q_nu"] = {} for ipm in range(self.DATADICT["nPulseMoments"]): self.DATADICT[pulse]["qeff"][ipm] = {} self.DATADICT[pulse]["q_nu"][ipm] = {} #canvas.ax1.clear() #scolours = np.array( ( np.linspace(0.8,0.4,len(self.DATADICT["stacks"])), \ # np.linspace(0.0,0.6,len(self.DATADICT["stacks"])), \ # np.linspace(0.6,0.0,len(self.DATADICT["stacks"])) ) # ).T #scolours = plt.cm.Spectral(np.linspace(0,1,len(self.DATADICT["stacks"]))) #scolours = plt.cm.Blues(np.linspace(0,1,1.5*len(self.DATADICT["stacks"]))) scolours = cmocean.cm.ice(np.linspace(0,1,1.5*len(self.DATADICT["stacks"]))) iistack = 0 for istack in self.DATADICT["stacks"]: #self.DATADICT[pulse]["PULSE_TIMES"] x = self.DATADICT[pulse]["CURRENT"][ipm][istack] X = np.fft.rfft(x) v = np.fft.fftfreq(len(x), self.dt) v = v[0:len(X)] v[-1] = np.abs(v[-1]) # calculate effective current/moment I0 = np.abs(X)/len(X) qeff = I0 * (self.DATADICT[pulse]["PULSE_TIMES"][-1]-self.DATADICT[pulse]["PULSE_TIMES"][0]) # frequency plot #canvas.ax1.set_title(r"pulse moment index " +str(ipm), fontsize=10) #canvas.ax1.set_xlabel(r"$\nu$ [Hz]", fontsize=8) #canvas.ax1.set_ylabel(r"$q_{eff}$ [A$\cdot$sec]", fontsize=8) #canvas.ax1.plot(v, qeff, color=scolours[iistack] ) # eff current # time plot canvas.ax1.plot(1e2*(self.DATADICT[pulse]["PULSE_TIMES"]-self.DATADICT[pulse]["PULSE_TIMES"][0]), x, color=scolours[iistack]) self.DATADICT[pulse]["qeff"][ipm][istack] = qeff self.DATADICT[pulse]["q_nu"][ipm][istack] = v iistack += 1 canvas.ax1.set_xlabel("time (ms)", fontsize=8) canvas.ax1.set_ylabel("current (A)", fontsize=8) canvas.draw() percent = int(1e2* (float)((istack)+ipm*self.DATADICT["nPulseMoments"]) / (float)(len(self.DATADICT["PULSES"])*self.DATADICT["nPulseMoments"]*nstack)) self.progressTrigger.emit(percent) canvas.draw() self.plotQeffNu(cv, canvas.ax2) canvas.draw() self.doneTrigger.emit() def plotQeffNu(self, cv, ax): #################################### # TODO label fid1 and fid2, and make a legend, and colour by pulse nstack = len(self.DATADICT["stacks"]) iFID = 0 for pulse in self.DATADICT["PULSES"]: self.DATADICT[pulse]["Q"] = np.zeros( (self.DATADICT["nPulseMoments"], len(self.DATADICT["stacks"])) ) ilabel = True for ipm in range(self.DATADICT["nPulseMoments"]): #scolours = np.array([0.,0.,1.]) scolours = cmocean.cm.ice(np.linspace(0,1,1.5*len(self.DATADICT["stacks"]))) #scolours = plt.cm.Spectral(np.linspace(0,1,len(self.DATADICT["stacks"]))) #scolours = plt.cm.Spectral(np.linspace(0,1,len(self.DATADICT["stacks"]))) istack = 0 for stack in self.DATADICT["stacks"]: # find index icv = int(round(cv / self.DATADICT[pulse]["q_nu"][ipm][stack][1])) self.DATADICT[pulse]["Q"][ipm,istack] = self.DATADICT[pulse]["qeff"][ipm][stack][icv] if ilabel: ax.scatter(ipm, self.DATADICT[pulse]["qeff"][ipm][stack][icv], facecolors='none', edgecolors=scolours[istack], label=(str(pulse))) ilabel = False else: ax.scatter(ipm, self.DATADICT[pulse]["qeff"][ipm][stack][icv], facecolors='none', edgecolors=scolours[istack]) #scolours += np.array((0,1./(nstack+1),-1/(nstack+1.))) percent = int(1e2* (float)((istack)+ipm*self.DATADICT["nPulseMoments"]) / (float)(len(self.DATADICT["PULSES"])*self.DATADICT["nPulseMoments"]*nstack)) self.progressTrigger.emit(percent) istack += 1 iFID += 1 ax.set_xlabel(r"pulse moment index", fontsize=8) ax.set_ylabel(r"$q_{eff}$ [A$\cdot$sec]", fontsize=8) ax.set_yscale('log') ax.set_xlim(0, ax.get_xlim()[1]) ax.legend(loc='upper right', scatterpoints = 1, prop={'size':6}) def enableDSP(self): self.enableDSPTrigger.emit() def adaptiveFilter(self, M, flambda, truncate, mu, PCA, canvas): canvas.reAx2(shx=False, shy=False) # ax1 is top plot of filter taps # ax2 is bottom plot of conditioned signal if truncate: itrunc =(int) ( round( 1e-3*truncate*self.samp ) ) print( "adaptive filter size", 1e3*self.dt*M, " [ms]" ) Filt = adapt.AdaptiveFilter(flambda) H = {} for pulse in self.DATADICT["PULSES"]: H[pulse] = {} for ichan in self.DATADICT[pulse]["chan"]: H[pulse][ichan] = np.zeros(M*len( self.DATADICT[pulse]["rchan"] )) iFID = 0 # original ordering... #for pulse in self.DATADICT["PULSES"]: # for ipm in range(self.DATADICT["nPulseMoments"]): # for istack in self.DATADICT["stacks"]: # This order makes more sense, same as data collection, verify for istack in self.DATADICT["stacks"]: for ipm in range(self.DATADICT["nPulseMoments"]): for pulse in self.DATADICT["PULSES"]: canvas.softClear() mmax = 0 for ichan in self.DATADICT[pulse]["chan"]: canvas.ax2.plot( self.DATADICT[pulse]["TIMES"], 1e9* self.DATADICT[pulse][ichan][ipm][istack], alpha=.5) mmax = max(mmax, np.max(1e9*self.DATADICT[pulse][ichan][ipm][istack])) canvas.ax2.set_ylim(-mmax, mmax) canvas.ax2.set_prop_cycle(None) for ichan in self.DATADICT[pulse]["chan"]: #H = np.zeros(M) RX = [] for irchan in self.DATADICT[pulse]["rchan"]: RX.append(self.DATADICT[pulse][irchan][ipm][istack][::-1]) # Reset each time? #H[pulse][ichan] *= 0 #if all(H[pulse][ichan]) == 0: if False: #################################################################################### # Padasip adaptive filter implimentations, do not allow for variable filter length #################################################################################### # identification # #f = pa.filters.FilterRLS(n=len(self.DATADICT[pulse]["rchan"]), mu=0.99, w="zeros") # #f = pa.filters.FilterGNGD(n=len(self.DATADICT[pulse]["rchan"]), mu=0.1) # # Nope #f = pa.filters.FilterLMS(n=len(self.DATADICT[pulse]["rchan"]), mu=0.1) # # NOPE #f = pa.filters.AdaptiveFilter(model="NLMS", n=len(self.DATADICT[pulse]["rchan"]), mu=0.1, w="random") # NOPE #f = pa.filters.AdaptiveFilter(model="GNGD", n=len(self.DATADICT[pulse]["rchan"]), mu=0.1) # horrendous #f = pa.filters.FilterNLMF(n=len(self.DATADICT[pulse]["rchan"]), mu=0.005, w="random") # BAD #f = pa.filters.FilterSSLMS(n=len(self.DATADICT[pulse]["rchan"]), mu=0.01, w="zeros") # pretty good f = pa.filters.FilterNSSLMS(n=len(self.DATADICT[pulse]["rchan"]), mu=0.1, w="zeros") # pretty good y, e, H[pulse][ichan] = f.run(self.DATADICT[pulse][ichan][ipm][istack][::-1], np.array(RX).T) # #################################################################################### e = self.DATADICT[pulse][ichan][ipm][istack][::-1] - y elif True: # check for change in filter coefficients and rerun if things are changing too rapidly, # this is especially true for the first run hm1 = np.copy(H[pulse][ichan]) [e, H[pulse][ichan]] = Filt.adapt_filt_Ref( self.DATADICT[pulse][ichan][ipm][istack][::-1],\ RX,\ M, mu, PCA, flambda, H[pulse][ichan]) iloop = 0 #while False: while (np.linalg.norm( H[pulse][ichan] - hm1) > .05): hm1 = np.copy(H[pulse][ichan]) [e, H[pulse][ichan]] = Filt.adapt_filt_Ref( self.DATADICT[pulse][ichan][ipm][istack][::-1],\ RX,\ M, mu, PCA, flambda, H[pulse][ichan]) iloop += 1 print("Recalled ", iloop, "times with norm=", np.linalg.norm(hm1-H[pulse][ichan])) else: [e,H[pulse][ichan]] = Filt.adapt_filt_Ref( self.DATADICT[pulse][ichan][ipm][istack][::-1],\ RX,\ M, mu, PCA, flambda, H[pulse][ichan]) # replace if truncate: canvas.ax2.plot( self.DATADICT[pulse]["TIMES"][0:itrunc], 1e9* e[::-1][0:itrunc],\ label = pulse + " ipm=" + str(ipm) + " istack=" + str(istack) + " ichan=" + str(ichan)) self.DATADICT[pulse][ichan][ipm][istack] = e[::-1][0:itrunc] else: canvas.ax2.plot( self.DATADICT[pulse]["TIMES"], 1e9* e[::-1],\ label = pulse + " ipm=" + str(ipm) + " istack=" + str(istack) + " ichan=" + str(ichan)) self.DATADICT[pulse][ichan][ipm][istack] = e[::-1] canvas.ax1.plot( H[pulse][ichan].reshape(-1, len(RX)) ) # , label="taps") canvas.ax2.legend(prop={'size':6}, loc='upper right') #canvas.ax2.legend(prop={'size':6}, loc='upper right') mh = np.max(np.abs( H[pulse][ichan] )) canvas.ax1.set_ylim( -mh, mh ) canvas.ax2.set_xlabel(r"time [s]", fontsize=8) canvas.ax2.set_ylabel(r"signal [nV]", fontsize=8) canvas.ax1.set_xlabel(r"filter index", fontsize=8) canvas.ax1.set_ylabel(r"scale factor", fontsize=8) canvas.draw() # truncate the reference channels too, in case you still need them for something. # Otherwise they are no longer aligned with the data for rchan in self.DATADICT[pulse]["rchan"]: if truncate: self.DATADICT[pulse][rchan][ipm][istack] = self.DATADICT[pulse][rchan][ipm][istack][0:itrunc] #percent = (int)(1e2*((float)(iFID*self.DATADICT["nPulseMoments"]+(ipm))/( len(self.DATADICT["PULSES"])*self.nPulseMoments))) percent = (int)(1e2*((float)(istack*self.DATADICT["nPulseMoments"]+(ipm))/( len(self.DATADICT["PULSES"])*self.nPulseMoments*(len(self.DATADICT["stacks"])+1) ))) self.progressTrigger.emit(percent) # # why is this loop here, istack is not part of rest? # for istack in self.DATADICT["stacks"]: # if truncate: # self.DATADICT[pulse]["TIMES"] = self.DATADICT[pulse]["TIMES"][0:itrunc] # percent = (int)(1e2*((float)(iFID*self.DATADICT["nPulseMoments"]+(ipm))/( len(self.DATADICT["PULSES"])*self.nPulseMoments))) # self.progressTrigger.emit(percent) # iFID += 1 if truncate: self.DATADICT[pulse]["TIMES"] = self.DATADICT[pulse]["TIMES"][0:itrunc] self.doneTrigger.emit() self.updateProcTrigger.emit() #self.plotFT(canvas) def plotFT(self, canvas, istart=0, iend=0): try: canvas.fig.clear() except: pass canvas.ax1 = canvas.fig.add_axes([.1, .1, .65, .8]) canvas.ax1c = canvas.fig.add_axes([.8, .1, .05, .8]) canvas.ax1.tick_params(axis='both', which='major', labelsize=8) for pulse in self.DATADICT["PULSES"]: for istack in self.DATADICT["stacks"]: for ichan in self.DATADICT[pulse]["chan"]: # FFT of stack XA = np.zeros((self.DATADICT["nPulseMoments"] , len(self.DATADICT[pulse][ichan][0][istack])/2+1)) nu = np.fft.fftfreq(self.DATADICT[pulse][ichan][0][istack].size, d=self.dt) nu[-1] *= -1 df = nu[1] of = 0 if istart: of = nu[istart] def freqlabel(x, pos): return '%1.0f' %(of + x*df) formatter = FuncFormatter(freqlabel) canvas.ax1.clear() for ipm in range(self.DATADICT["nPulseMoments"]): X = np.fft.rfft(self.DATADICT[pulse][ichan][ipm][istack]) XA[ipm,:] = np.abs(X) if istart: mn = canvas.ax1.matshow(20.*np.log10(XA[:,istart:iend+1]), aspect='auto', vmax=-40, vmin=-120) #, norm=LogNorm()) else: mn = canvas.ax1.matshow(20.*np.log10(XA), aspect='auto', vmax=-40, vmin=-120) #, norm=LogNorm()) smin = np.min(20.*np.log10(XA)) smax = np.max(20.*np.log10(XA)) canvas.ax1.xaxis.set_major_formatter(formatter) cb1 = mpl.colorbar.Colorbar(canvas.ax1c, mn) cb1.ax.tick_params(labelsize=8) cb1.set_label("signal [dB]", fontsize=8) canvas.ax1.set_xlabel(r"$\nu$ [Hz]", fontsize=10) canvas.ax1.set_ylabel(r"$q_{index}$", fontsize=10) canvas.draw() def plotFT(self, canvas, istart=0, iend=0): try: canvas.fig.clear() except: pass canvas.ax1 = canvas.fig.add_axes([.1, .1, .65, .8]) canvas.ax1c = canvas.fig.add_axes([.8, .1, .05, .8]) canvas.ax1.tick_params(axis='both', which='major', labelsize=8) for pulse in self.DATADICT["PULSES"]: for istack in self.DATADICT["stacks"]: for ichan in self.DATADICT[pulse]["chan"]: # FFT of stack XA = np.zeros((self.DATADICT["nPulseMoments"] , len(self.DATADICT[pulse][ichan][0][istack])//2+1)) nu = np.fft.fftfreq(self.DATADICT[pulse][ichan][0][istack].size, d=self.dt) nu[-1] *= -1 df = nu[1] of = 0 if istart: of = nu[istart] def freqlabel(x, pos): return '%1.0f' %(of + x*df) formatter = FuncFormatter(freqlabel) canvas.ax1.clear() for ipm in range(self.DATADICT["nPulseMoments"]): X = np.fft.rfft(self.DATADICT[pulse][ichan][ipm][istack]) XA[ipm,:] = np.abs(X) if istart: mn = canvas.ax1.matshow(20.*np.log10(XA[:,istart:iend+1]), aspect='auto', vmax=-40, vmin=-120, cmap='viridis') #, norm=LogNorm()) else: mn = canvas.ax1.matshow(20.*np.log10(XA), aspect='auto', vmax=-40, vmin=-120, cmap='viridis') #, norm=LogNorm()) canvas.ax1.xaxis.set_major_formatter(formatter) cb1 = mpl.colorbar.Colorbar(canvas.ax1c, mn) cb1.ax.tick_params(labelsize=8) cb1.set_label("signal [dB]", fontsize=8) canvas.ax1.set_xlabel(r"$\nu$ [Hz]", fontsize=10) canvas.ax1.set_ylabel(r"$q_{index}$", fontsize=10) canvas.draw() def dataCubeFFT(self): """ Performs FFT on entire cube of DATA, and REFERENCE channels, but not pulse currents, Results are saved to a new field in the data structure 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. This method corrects for this, so that all pulse moments are in phase. Technically we may not want to do this, if there is some system response that this cycles away, and we lose track of 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 rest of the phase cycles. Holy phase cycling batman. """ for pulse in self.DATADICT["PULSES"]: for ichan in np.append(self.DATADICT[pulse]["chan"], self.DATADICT[pulse]["rchan"]): # FFT of stack self.DATADICT[pulse][ichan]["FFT"] = {} self.DATADICT[pulse][ichan]["FFT"]["nu"] = np.fft.fftfreq(self.DATADICT[pulse][ichan][0][self.DATADICT["stacks"][0]].size, d=self.dt) self.DATADICT[pulse][ichan]["FFT"]["nu"][-1] *= -1 for istack in self.DATADICT["stacks"]: self.DATADICT[pulse][ichan]["FFT"][istack] = np.zeros((self.DATADICT["nPulseMoments"] , len(self.DATADICT[pulse][ichan][0][istack])//2+1), dtype=complex) for ipm in range(self.DATADICT["nPulseMoments"]): # Mod works for FID pulse sequences, TODO generalize this for 4 phase T1, etc.. mod = (-1)**(ipm%2) * (-1)**(istack%2) self.DATADICT[pulse][ichan]["FFT"][istack][ipm,:] = np.fft.rfft( self.DATADICT[pulse][ichan][ipm][istack] ) #if ipm%2: # odd, phase cycled from previous # self.DATADICT[pulse][ichan]["FFT"][istack][ipm,:] = np.fft.rfft(-self.DATADICT[pulse][ichan][ipm][istack]) #else: # even, we define as zero phase, first pulse moment has this # self.DATADICT[pulse][ichan]["FFT"][istack][ipm,:] = np.fft.rfft(self.DATADICT[pulse][ichan][ipm][istack]) def adaptiveFilterFD(self, ftype, band, centre, canvas): try: canvas.fig.clear() except: pass canvas.ax1 = canvas.fig.add_axes([.1, .5, .7, .4]) canvas.ax1c = canvas.fig.add_axes([.85, .5, .05, .4]) canvas.ax1.tick_params(axis='both', which='major', labelsize=8) #canvas.ax1.ticklabel_format(style='sci', scilimits=(0,0), axis='y') canvas.ax2 = canvas.fig.add_axes([.1, .05, .7, .4]) canvas.ax2c = canvas.fig.add_axes([.85, .05, .05, .4]) canvas.ax2.tick_params(axis='both', which='major', labelsize=8) #canvas.ax2.ticklabel_format(style='sci', scilimits=(0,0), axis='y') self.dataCubeFFT() Filt = adapt.AdaptiveFilter(0.) for pulse in self.DATADICT["PULSES"]: # Compute window function and dimensions [WINDOW, nd, wstart, wend, dead, idead] = self.computeWindow(pulse, band, centre, ftype) for istack in self.DATADICT["stacks"]: for ichan in self.DATADICT[pulse]["chan"]: # FFT of stack nd = len(self.DATADICT[pulse][ichan][0][istack]) XX = np.zeros((self.DATADICT["nPulseMoments"] , len(self.DATADICT[pulse][ichan][0][istack])//2+1), dtype=complex) nu = np.fft.fftfreq(self.DATADICT[pulse][ichan][0][istack].size, d=self.dt) nu[-1] *= -1 #nu = self.DATADICT[pulse][ichan]["FFT"]["nu"] def freqlabel(x, pos): return '%1.0f' %((wstart)*nu[1] + x*nu[1]) formatter = FuncFormatter(freqlabel) canvas.ax1.clear() for ipm in range(self.DATADICT["nPulseMoments"]): X = np.fft.rfft(self.DATADICT[pulse][ichan][ipm][istack]) XX[ipm,:] = X XX = XX*WINDOW XX = XX[:,wstart:wend] smin = np.min(20.*np.log10(np.abs(XX))) smax = np.max(20.*np.log10(np.abs(XX))) #if smin != smin: smax = -40 smin = -120 mn = canvas.ax1.matshow(20.*np.log10(np.abs(XX)), aspect='auto', vmin=smin, vmax=smax) #, norm=LogNorm()) canvas.ax1.xaxis.set_major_formatter(formatter) cb1 = mpl.colorbar.Colorbar(canvas.ax1c, mn) RX = [] for ichan in self.DATADICT[pulse]["rchan"]: R = np.zeros((self.DATADICT["nPulseMoments"] , len(self.DATADICT[pulse][ichan][0][istack])//2+1), dtype=complex) for ipm in range(self.DATADICT["nPulseMoments"]): R[ipm,:] = np.fft.rfft(self.DATADICT[pulse][ichan][ipm][istack]) RX.append(R[:,wstart:wend]) XC = Filt.transferFunctionFFT(XX, RX) # TODO inverse FFT, but we need to map back to origional matrix size #for ichan in self.DATADICT[pulse]["chan"]: # for ipm in range(self.DATADICT["nPulseMoments"]): # self.DATADICT[pulse][ichan][ipm][istack] = np.fft.irfft(XC[] , nd) mc = canvas.ax2.matshow(20.*np.log10(np.abs(XC)), aspect='auto', vmin=smin, vmax=smax) #, norm=LogNorm()) cb2 = mpl.colorbar.Colorbar(canvas.ax2c, mc) cmin = np.min(20.*np.log10(np.abs(XC))) cmax = np.max(20.*np.log10(np.abs(XC))) canvas.ax2.xaxis.set_major_formatter(formatter) #canvas.ax2.colorbar(mn) canvas.draw() ##############################3 # TODO inverse FFT to get the damn data back!!! # self.progressTrigger.emit(percent) # #label = "iFID="+str(iFID) + " ipm=" + str(ipm) + " istack=" + str(istack) + " ichan=" + str(ichan)) self.doneTrigger.emit() def findSpikes(self, x, width, threshold, rollOn): import scipy.ndimage as im spikes = np.zeros( len(x) ) med = im.median_filter(x, width,mode='nearest') std = np.std(x) spikes = (np.abs(x-med) > threshold * std) return np.array(np.where(spikes[rollOn::])) + rollOn # def despike(self, width, threshold, itype, rollOn, win, canvas): # from scipy import interpolate # """ This was a stab at a despike filter. Better results were achieved using the SmartStack approach # """ # try: # canvas.fig.clear() # except: # pass # # canvas.ax1 = canvas.fig.add_axes([.125,.1,.725,.8]) # canvas.ax1.tick_params(axis='both', which='major', labelsize=8) # canvas.ax1.ticklabel_format(style='sci', scilimits=(0,0), axis='y') # iFID = 0 # for pulse in self.DATADICT["PULSES"]: # for ipm in range(self.DATADICT["nPulseMoments"]): # for istack in self.DATADICT["stacks"]: # canvas.ax1.clear() # for ichan in np.append(self.DATADICT[pulse]["chan"], self.DATADICT[pulse]["rchan"]): # x = self.findSpikes(self.DATADICT[pulse][ichan][ipm][istack], width, threshold, rollOn) # canvas.ax1.plot( self.DATADICT[pulse]["TIMES"], self.DATADICT[pulse][ichan][ipm][istack], # label = pulse + " ipm=" + str(ipm) + " istack=" + str(istack) + " ichan=" + str(ichan)) # canvas.ax1.plot( self.DATADICT[pulse]["TIMES"][x], self.DATADICT[pulse][ichan][ipm][istack][x], '.', color='red' , markersize=6 ) # # FIXED = np.zeros(len(x[0])) # ii = 0 # for spike in np.array(x[0]).tolist(): # f = interpolate.interp1d(np.delete(self.DATADICT[pulse]["TIMES"][spike-win/2:spike+win/2], x[0]-(spike-win/2)), \ # np.delete(self.DATADICT[pulse][ichan][ipm][istack][spike-win/2:spike+win/2], x[0]-(spike-win/2)), itype) # FIXED[ii] = f(self.DATADICT[pulse]["TIMES"][spike]) # ii += 1 # canvas.ax1.plot( self.DATADICT[pulse]["TIMES"][x[0]] , FIXED, '.', color='black' , markersize=4 ) # self.DATADICT[pulse][ichan][ipm][istack][x[0]] = FIXED # # canvas.ax1.legend(prop={'size':6}) # canvas.draw() # percent = (int)(1e2*((float)(iFID*self.DATADICT["nPulseMoments"]+(ipm))/( len(self.DATADICT["PULSES"])*self.nPulseMoments))) # self.progressTrigger.emit(percent) # iFID += 1 # self.doneTrigger.emit() def designFilter(self, cf, PB, SB, gpass, gstop, ftype, canvas): ''' cf is central frequency pb is pass band sb is stop band ''' TS = (cf) / (.5/self.dt) PB = PB / (.5/self.dt) # 1/2 width pass band Muddy Creek SB = SB / (.5/self.dt) # 1/2 width stop band Muddy Creek # if butterworth #[bord, wn] = signal.buttord([TS-PB,TS+PB], [TS-SB,TS+SB], 1e-1, 5.) if ftype=="Butterworth": [bord, wn] = signal.buttord([TS-PB,TS+PB], [TS-SB,TS+SB], gpass, gstop) [self.filt_b, self.filt_a] = signal.butter(bord, wn, btype='bandpass', output='ba') [self.filt_z, self.filt_p, self.filt_k] = signal.butter(bord, wn, btype='band', output='zpk') elif ftype == "Chebychev Type II": [bord, wn] = signal.cheb2ord([TS-PB,TS+PB], [TS-SB,TS+SB], gpass, gstop) [self.filt_b, self.filt_a] = signal.cheby2(bord, gstop, wn, btype='bandpass', output='ba') [self.filt_z, self.filt_p, self.filt_k] = signal.cheby2(bord, gstop, wn, btype='band', output='zpk') elif ftype == "Elliptic": [bord, wn] = signal.ellipord([TS-PB,TS+PB], [TS-SB,TS+SB], gpass, gstop) [self.filt_b, self.filt_a] = signal.ellip(bord, gpass, gstop, wn, btype='bandpass', output='ba') [self.filt_z, self.filt_p, self.filt_k] = signal.ellip(bord, gpass, gstop, wn, btype='band', output='zpk') # if cheby2 impulse = self.mfreqz2(self.filt_b, self.filt_a, canvas) self.fe = -5 for it in range(len(impulse[0])): if abs(impulse[1][0][it][0]) >= .1 * gpass:# gpass: self.fe = impulse[0][it] canvas.draw() return [bord, self.fe] def downsample(self, truncate, dec, plot=False, canvas=None): """ Downsamples and truncates the raw signal. Args truncate (float) : the length of the signal to truncate to dec (int) : the decimation factor, 1 results in no downsampling plot (bool) : perform plots canvas : MPL axis for plotting """ if plot: canvas.reAx2() canvas.ax1.set_ylabel(r"signal [nV]", fontsize=8) canvas.ax2.set_xlabel(r"time [s]", fontsize=8) canvas.ax2.set_ylabel(r"signal [nV]", fontsize=8) self.samp /= dec self.dt = 1./self.samp iFID = 0 for pulse in self.DATADICT["PULSES"]: RSTIMES = self.DATADICT[pulse]["TIMES"][::dec] if truncate: itrunc = (int)( 1e-3*truncate*self.samp ) RSTIMES = RSTIMES[0:itrunc] for ipm in range(self.DATADICT["nPulseMoments"]): for istack in self.DATADICT["stacks"]: if plot: canvas.softClear() for ichan in np.append(self.DATADICT[pulse]["chan"], self.DATADICT[pulse]["rchan"]): # trim off indices that don't divide evenly ndi = np.shape(self.DATADICT[pulse][ichan][ipm][istack])[0]%dec if ndi: #[self.DATADICT[pulse][ichan][ipm][istack], RSTIMES] = signal.resample(self.DATADICT[pulse][ichan][ipm][istack][0:-ndi],\ # len(self.DATADICT[pulse][ichan][ipm][istack][0:-ndi])//dec,\ # self.DATADICT[pulse]["TIMES"][0:-ndi], window='hamm') self.DATADICT[pulse][ichan][ipm][istack] = signal.decimate(self.DATADICT[pulse][ichan][ipm][istack], dec, n=None, ftype='iir', zero_phase=True) else: #[self.DATADICT[pulse][ichan][ipm][istack], RSTIMES] = signal.resample(self.DATADICT[pulse][ichan][ipm][istack],\ # len(self.DATADICT[pulse][ichan][ipm][istack])//dec,\ # self.DATADICT[pulse]["TIMES"], window='hamm') self.DATADICT[pulse][ichan][ipm][istack] = signal.decimate(self.DATADICT[pulse][ichan][ipm][istack], dec, n=None, ftype='iir', zero_phase=True) if truncate: self.DATADICT[pulse][ichan][ipm][istack] = self.DATADICT[pulse][ichan][ipm][istack][0:itrunc] if plot: for ichan in self.DATADICT[pulse]["chan"]: canvas.ax2.plot( RSTIMES, 1e9*self.DATADICT[pulse][ichan][ipm][istack], \ label = pulse + " ipm=" + str(ipm) + " istack=" + str(istack) + " ichan=" + str(ichan)) for ichan in self.DATADICT[pulse]["rchan"]: canvas.ax1.plot( RSTIMES, 1e9*self.DATADICT[pulse][ichan][ipm][istack], \ label = pulse + " ipm=" + str(ipm) + " istack=" + str(istack) + " ichan=" + str(ichan)) canvas.ax1.legend(prop={'size':6}, loc='upper right') canvas.ax2.legend(prop={'size':6}, loc='upper right') canvas.draw() percent = (int)(1e2*((float)(iFID*self.DATADICT["nPulseMoments"]+(ipm))/( len(self.DATADICT["PULSES"])*self.nPulseMoments))) self.progressTrigger.emit(percent) iFID += 1 self.DATADICT[pulse]["TIMES"] = RSTIMES ##################################### # resample pulse data for pulse in self.DATADICT["PULSES"]: for ipm in range(self.DATADICT["nPulseMoments"]): for istack in self.DATADICT["stacks"]: ndi = np.shape(self.DATADICT[pulse]["CURRENT"][ipm][istack])[0]%dec if ndi: [self.DATADICT[pulse]["CURRENT"][ipm][istack], RSPTIMES] = signal.resample(self.DATADICT[pulse]["CURRENT"][ipm][istack][0:-ndi],\ len(self.DATADICT[pulse]["CURRENT"][ipm][istack][0:-ndi])//dec,\ self.DATADICT[pulse]["PULSE_TIMES"][0:-ndi], window='hamm') else: [self.DATADICT[pulse]["CURRENT"][ipm][istack], RSPTIMES] = signal.resample(self.DATADICT[pulse]["CURRENT"][ipm][istack],\ len(self.DATADICT[pulse]["CURRENT"][ipm][istack])//dec,\ self.DATADICT[pulse]["PULSE_TIMES"], window='hamm') self.DATADICT[pulse]["PULSE_TIMES"] = RSPTIMES self.doneTrigger.emit() self.updateProcTrigger.emit() def computeWindow(self, pulse, band, centre, ftype, canvas=None): # Compute window nd = len(self.DATADICT[pulse][self.DATADICT[pulse]["chan"][0]][0][self.DATADICT["stacks"][0]]) # num. data fft1 = np.fft.rfft(self.DATADICT[pulse][self.DATADICT[pulse]["chan"][0]][0][self.DATADICT["stacks"][0]]) freqs = np.fft.fftfreq(nd, self.dt) df = freqs[1] - freqs[0] N = int((round)(band/df)) if ftype == "Hamming": window = np.hamming(N) elif ftype == "Hanning": window = np.hanning(N) elif ftype == "Rectangular": window = np.ones(N) elif ftype == "Flat top": window = signal.flattop(N) else: print ("in windowFilter, window type undefined") WINDOW = np.zeros(len(fft1)) ifreq = int(round(centre/df)) istart = ifreq-len(window)//2 iend = 0 if N%2: WINDOW[ifreq-N//2:ifreq+N//2+1] = window iend = ifreq+N//2+1 else: WINDOW[ifreq-N//2:ifreq+N//2] = window iend = ifreq+N//2 self.WINDOW = WINDOW self.iWindowStart = istart self.iWindowEnd = iend self.FFTtimes = nd fft1 = np.fft.irfft(WINDOW) # calculate dead time self.windead = 0. for ift in np.arange(100,0,-1): #print( ift, fft1[ift] ) if (abs(fft1[ift])/abs(fft1[0])) > 1e-2: #print ("DEAD TIME", 1e3*self.DATADICT[pulse]["TIMES"][ift] - 1e3*self.DATADICT[pulse]["TIMES"][0] ) dead = 1e3*self.DATADICT[pulse]["TIMES"][ift] - 1e3*self.DATADICT[pulse]["TIMES"][0] self.windead = self.DATADICT[pulse]["TIMES"][ift] - self.DATADICT[pulse]["TIMES"][0] break if canvas != None: canvas.fig.clear() canvas.ax1 = canvas.fig.add_axes([.1, .6, .75, .35]) canvas.ax2 = canvas.fig.add_axes([.1, .1, .75, .35]) canvas.ax1.plot(WINDOW) canvas.ax2.plot( 1e3* self.DATADICT[pulse]["TIMES"][0:100] - 1e3*self.DATADICT[pulse]["TIMES"][0], fft1[0:100] ) canvas.ax2.set_xlabel("time (ms)") canvas.ax2.set_title("IFFT") canvas.draw() return [WINDOW, nd, istart, iend, dead, ift] def windowFilter(self, ftype, band, centre, trunc, canvas): ############################### # Window Filter (Ormsby filter http://www.xsgeo.com/course/filt.htm) # apply window iFID = 0 for pulse in self.DATADICT["PULSES"]: [WINDOW, nd, istart, iend, dead, idead] = self.computeWindow(pulse, band, centre, ftype) for istack in self.DATADICT["stacks"]: for ipm in range(self.DATADICT["nPulseMoments"]): for ichan in np.append(self.DATADICT[pulse]["chan"], self.DATADICT[pulse]["rchan"]): fft = np.fft.rfft( self.DATADICT[pulse][ichan][ipm][istack] ) fft *= WINDOW if trunc: self.DATADICT[pulse][ichan][ipm][istack] = np.fft.irfft(fft, nd)[idead:-idead] else: self.DATADICT[pulse][ichan][ipm][istack] = np.fft.irfft(fft, nd) percent = (int)(1e2*((float)(iFID*self.DATADICT["nPulseMoments"]+(ipm))/(len(self.DATADICT["PULSES"])*self.nPulseMoments))) self.progressTrigger.emit(percent) iFID += 1 if trunc: self.DATADICT[pulse]["TIMES"] = self.DATADICT[pulse]["TIMES"][idead:-idead] [WINDOWxx, ndxx, istart, iend, deadxx, ideadxx] = self.computeWindow(pulse, band, centre, ftype) self.plotFT(canvas, istart, iend) self.doneTrigger.emit() def bandpassFilter(self, canvas, blank, plot=True): if plot: canvas.reAx2() canvas.ax1.set_ylabel(r"signal [nV]", fontsize=8) canvas.ax2.set_xlabel(r"time [s]", fontsize=8) canvas.ax2.set_ylabel(r"signal [nV]", fontsize=8) ife = (int)( max(self.fe, self.windead) * self.samp ) # Data iFID = 0 for pulse in self.DATADICT["PULSES"]: self.DATADICT[pulse]["TIMES"] = self.DATADICT[pulse]["TIMES"][ife:-ife] for ipm in range(self.DATADICT["nPulseMoments"]): for istack in self.DATADICT["stacks"]: if plot: canvas.softClear() mmax = 0 for ichan in self.DATADICT[pulse]["rchan"]: canvas.ax1.plot( self.DATADICT[pulse]["TIMES"], 1e9*self.DATADICT[pulse][ichan][ipm][istack][ife:-ife], alpha=.5) mmax = max( mmax, np.max(1e9*self.DATADICT[pulse][ichan][ipm][istack][ife:-ife])) for ichan in self.DATADICT[pulse]["chan"]: canvas.ax2.plot( self.DATADICT[pulse]["TIMES"], 1e9*self.DATADICT[pulse][ichan][ipm][istack][ife:-ife], alpha=.5) mmax = max( mmax, np.max(1e9*self.DATADICT[pulse][ichan][ipm][istack][ife:-ife])) canvas.ax2.set_prop_cycle(None) canvas.ax1.set_prop_cycle(None) canvas.ax1.set_ylim(-mmax, mmax) for ichan in self.DATADICT[pulse]["rchan"]: # reflect signal back on itself to reduce gibbs effects on early times #nr = len( self.DATADICT[pulse][ichan][ipm][istack] ) - 1 + ife #refl = np.append( -1*self.DATADICT[pulse][ichan][ipm][istack][::-1][0:-1], self.DATADICT[pulse][ichan][ipm][istack] ) #reflfilt = signal.filtfilt( self.filt_b, self.filt_a, refl ) #self.DATADICT[pulse][ichan][ipm][istack] = reflfilt[nr:-ife] # don't reflect self.DATADICT[pulse][ichan][ipm][istack] = \ signal.filtfilt(self.filt_b, self.filt_a, self.DATADICT[pulse][ichan][ipm][istack])[ife:-ife] # plot if plot: canvas.ax1.plot( self.DATADICT[pulse]["TIMES"], 1e9*self.DATADICT[pulse][ichan][ipm][istack], \ label = pulse + " ipm=" + str(ipm) + " istack=" + str(istack) + " rchan=" + str(ichan)) for ichan in self.DATADICT[pulse]["chan"]: # reflect signal back on itself to reduce gibbs effects on early times #nr = len( self.DATADICT[pulse][ichan][ipm][istack] ) - 1 + ife #refl = np.append( -1*self.DATADICT[pulse][ichan][ipm][istack][::-1][0:-1], self.DATADICT[pulse][ichan][ipm][istack] ) #reflfilt = signal.filtfilt( self.filt_b, self.filt_a, refl ) #self.DATADICT[pulse][ichan][ipm][istack] = reflfilt[nr:-ife] # don't reflect self.DATADICT[pulse][ichan][ipm][istack] = \ scipy.signal.filtfilt(self.filt_b, self.filt_a, self.DATADICT[pulse][ichan][ipm][istack])[ife:-ife] # plot if plot: canvas.ax2.plot( self.DATADICT[pulse]["TIMES"], 1e9*self.DATADICT[pulse][ichan][ipm][istack], \ label = "data " + pulse + " ipm=" + str(ipm) + " istack=" + str(istack) + " chan=" + str(ichan)) if plot: canvas.ax1.legend(prop={'size':6}, loc='upper right') canvas.ax2.legend(prop={'size':6}, loc='upper right') canvas.draw() percent = (int)(1e2*((float)(iFID*self.DATADICT["nPulseMoments"]+(ipm))/(len(self.DATADICT["PULSES"])*self.nPulseMoments))) self.progressTrigger.emit(percent) iFID += 1 self.doneTrigger.emit() self.updateProcTrigger.emit() def loadGMRBinaryFID( self, rawfname, istack ): """ Reads a single binary GMR file and fills into DATADICT """ ################################################################################# # figure out key data indices # Pulse nps = (int)((self.prePulseDelay)*self.samp) npul = (int)(self.pulseLength[0]*self.samp) #+ 100 # Data nds = nps+npul+(int)((self.deadTime)*self.samp); # indice pulse 1 data starts nd1 = (int)(1.*self.samp) # samples in first pulse invGain = 1./self.RxGain invCGain = self.CurrentGain pulse = "Pulse 1" chan = self.DATADICT[pulse]["chan"] rchan = self.DATADICT[pulse]["rchan"] rawFile = open( rawfname, 'rb') for ipm in range(self.nPulseMoments): buf1 = rawFile.read(4) buf2 = rawFile.read(4) N_chan = struct.unpack('>i', buf1 )[0] N_samp = struct.unpack('>i', buf2 )[0] T = N_samp * self.dt TIMES = np.arange(0, T, self.dt) - .0002 # small offset in GMR DAQ? DATA = np.zeros([N_samp, N_chan+1]) for ichan in range(N_chan): DATADUMP = rawFile.read(4*N_samp) for irec in range(N_samp): DATA[irec,ichan] = struct.unpack('>f', DATADUMP[irec*4:irec*4+4])[0] # Save into Data Cube for ichan in chan: self.DATADICT["Pulse 1"][ichan][ipm][istack] = DATA[:,eval(ichan)+3][nds:nds+nd1] * invGain self.DATADICT["Pulse 1"]["TIMES"] = TIMES[nds:nds+nd1] self.DATADICT["Pulse 1"]["CURRENT"][ipm][istack] = DATA[:,1][nps:nps+npul] * invCGain self.DATADICT["Pulse 1"]["PULSE_TIMES"] = TIMES[nps:nps+npul] # reference channels? for ichan in rchan: self.DATADICT["Pulse 1"][ichan][ipm][istack] = DATA[:,eval(ichan)+3][nds:nds+nd1] * invGain self.DATADICT["Pulse 1"]["TIMES"] = TIMES[nds:nds+nd1] def loadGMRASCIIFID( self, rawfname, istack ): """Based on the geoMRI instrument manufactured by VistaClara. Imports a suite of raw .lvm files with the following format (on one line) time(s) DC_Bus/100(V) Current+/75(A) Curr-/75(A) Voltage+/200(V) \ Ch1(V) Ch2(V) Ch3(V) Ch4(V) Sampling rate is assumed at 50 kHz """ import pandas as pd ################################################################################# # figure out key data indices # Pulse nps = (int)((self.prePulseDelay)*self.samp) npul = (int)(self.pulseLength[0]*self.samp) #+ 100 # Data nds = nps+npul+(int)((self.deadTime)*self.samp); # indice pulse 1 data starts nd1 = (int)(1.*self.samp) - nds # samples in first pulse ndr = (int)(1.*self.samp) # samples in record invGain = 1./self.RxGain invCGain = self.CurrentGain pulse = "Pulse 1" chan = self.DATADICT[pulse]["chan"] rchan = self.DATADICT[pulse]["rchan"] T = 1.5 #N_samp * self.dt TIMES = np.arange(0, T, self.dt) - .0002 # small offset in GMR DAQ? self.DATADICT["Pulse 1"]["TIMES"] = TIMES[nds:nds+nd1] self.DATADICT["Pulse 1"]["PULSE_TIMES"] = TIMES[nps:nps+npul] # pandas is much faster than numpy for io #DATA = np.loadtxt(rawfname) DATA = pd.read_csv(rawfname, header=None, sep="\t").values for ipm in range(self.nPulseMoments): for ichan in np.append(chan,rchan): self.DATADICT["Pulse 1"][ichan][ipm][istack] = DATA[:, eval(ichan)+4][nds:(nds+nd1)] * invGain self.DATADICT["Pulse 1"]["CURRENT"][ipm][istack] = DATA[:,2][nps:nps+npul] * invCGain nds += ndr nps += ndr def loadGMRASCIIT1( self, rawfname, istack ): """Based on the geoMRI instrument manufactured by VistaClara. Imports a suite of raw .lvm files with the following format (on one line) time(s) DC_Bus/100(V) Current+/75(A) Curr-/75(A) Voltage+/200(V) \ Ch1(V) Ch2(V) Ch3(V) Ch4(V) Sampling rate is assumed at 50 kHz """ import pandas as pd ################################################################################# # figure out key data indices # Pulse nps = (int)((self.prePulseDelay)*self.samp) npul = (int)(self.pulseLength[0]*self.samp) #+ 100 # phase cycling # Older T1 GMR data had a curious phase cycling npc = 2 #(int)( self.samp / self.transFreq / 6 ) #print("npc", npc) # Data nds = nps+npul+(int)((self.deadTime)*self.samp); # indice pulse 1 data starts nd1 = (int)( (self.interpulseDelay) * self.samp) - nds # samples in first pulse ndr = (int)( (self.interpulseDelay) * self.samp) # samples in record invGain = 1./self.RxGain invCGain = self.CurrentGain pulse = "Pulse 1" chan = self.DATADICT[pulse]["chan"] rchan = self.DATADICT[pulse]["rchan"] T = 1.5 #N_samp * self.dt TIMES = np.arange(0, T, self.dt) - .0002 # small offset in GMR DAQ? self.DATADICT["Pulse 1"]["TIMES"] = TIMES[nds:nds+nd1] self.DATADICT["Pulse 1"]["PULSE_TIMES"] = TIMES[nps:nps+npul] # pandas is much faster than numpy for io #DATA = np.loadtxt(rawfname) DATA = pd.read_csv(rawfname, header=None, sep="\t").values for ipm in range(self.nPulseMoments): for ichan in np.append(chan,rchan): if ipm%2: self.DATADICT["Pulse 1"][ichan][ipm][istack] = DATA[:, eval(ichan)+4][(nds+npc):(nds+nd1+npc)] * invGain #self.DATADICT["Pulse 1"][ichan][ipm][istack] = DATA[:, eval(ichan)+4][nds:(nds+nd1)] * invGain self.DATADICT["Pulse 1"]["CURRENT"][ipm][istack] = DATA[:,2][nps+npc:nps+npul+npc] * invCGain else: self.DATADICT["Pulse 1"][ichan][ipm][istack] = DATA[:, eval(ichan)+4][nds:(nds+nd1)] * invGain self.DATADICT["Pulse 1"]["CURRENT"][ipm][istack] = DATA[:,2][nps:nps+npul] * invCGain nds += ndr nps += ndr def loadFIDData(self, base, procStacks, chanin, rchanin, FIDProc, canvas, deadTime, plot): ''' Loads a GMR FID dataset, reads binary and ASCII format files ''' canvas.reAx3(True,False) chan = [] for ch in chanin: chan.append(str(ch)) rchan = [] for ch in rchanin: rchan.append(str(ch)) self.deadTime = deadTime # instrument dead time before measurement self.samp = 50000. # in case this is a reproc, these might have self.dt = 1./self.samp # changed ################################################################################# # Data structures PULSES = [FIDProc] PULSES = ["Pulse 1"] self.DATADICT = {} self.DATADICT["nPulseMoments"] = self.nPulseMoments self.DATADICT["stacks"] = procStacks self.DATADICT["PULSES"] = PULSES for pulse in PULSES: self.DATADICT[pulse] = {} self.DATADICT[pulse]["chan"] = chan # TODO these should not be a subet of pulse! for GMR all self.DATADICT[pulse]["rchan"] = rchan # data are consistent self.DATADICT[pulse]["CURRENT"] = {} for ichan in np.append(chan,rchan): self.DATADICT[pulse][ichan] = {} for ipm in range(self.nPulseMoments): self.DATADICT[pulse][ichan][ipm] = {} self.DATADICT[pulse]["CURRENT"][ipm] = {} for istack in procStacks: self.DATADICT[pulse][ichan][ipm][istack] = np.zeros(3) self.DATADICT[pulse]["CURRENT"][ipm][istack] = np.zeros(3) ############################################## # Read in binary (.lvm) data iistack = 0 for istack in procStacks: if self.nDAQVersion <= 1.0: try: self.loadGMRASCIIFID( base + "_" + str(istack), istack ) except: self.loadGMRASCIIFID( base + "_" + str(istack) + ".lvm", istack ) elif self.nDAQVersion < 2.3: self.loadGMRASCIIFID( base + "_" + str(istack), istack ) else: self.loadGMRBinaryFID( base + "_" + str(istack) + ".lvm", istack ) if plot: for ipm in range(self.nPulseMoments): canvas.softClear() for ichan in chan: canvas.ax1.plot(self.DATADICT["Pulse 1"]["PULSE_TIMES"], self.DATADICT["Pulse 1"]["CURRENT"][ipm][istack] , color='black') 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') for ichan in rchan: 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') canvas.ax3.legend(prop={'size':6}, loc='upper right') canvas.ax2.legend(prop={'size':6}, loc='upper right') canvas.ax1.set_ylabel("Current [A]", fontsize=8) canvas.ax1.ticklabel_format(style='sci', scilimits=(0,0), axis='y') canvas.ax1.set_xlabel("time [s]", fontsize=8) canvas.ax2.set_title("stack "+str(istack)+" pulse index " + str(ipm), fontsize=8) canvas.ax2.set_ylabel("RAW signal [V]", fontsize=8) canvas.ax3.set_ylabel("RAW signal [V]", fontsize=8) canvas.ax2.tick_params(axis='both', which='major', labelsize=8) canvas.ax2.tick_params(axis='both', which='minor', labelsize=6) canvas.draw() percent = (int) (1e2*((float)((iistack*self.nPulseMoments+ipm+1)) / (len(procStacks)*self.nPulseMoments))) self.progressTrigger.emit(percent) iistack += 1 # percent = (int) (1e2*((float)((iistack*self.nPulseMoments+ipm+1)) / (len(procStacks)*self.nPulseMoments))) # self.progressTrigger.emit(percent) # iistack += 1 self.enableDSP() self.doneTrigger.emit() def loadT1Data(self, base, procStacks, chanin, rchanin, FIDProc, canvas, deadTime, plot): ''' Loads a GMR T1 dataset, reads binary and ASCII format files ''' canvas.reAx3(True,False) chan = [] for ch in chanin: chan.append(str(ch)) rchan = [] for ch in rchanin: rchan.append(str(ch)) # not in any headers but this has changed, NOT the place to do this. MOVE #self.prePulseDelay = 0.01 # delay before pulse self.deadTime = deadTime # instrument dead time before measurement self.samp = 50000. # in case this is a reproc, these might have self.dt = 1./self.samp # changed ################################################################################# # Data structures PULSES = [FIDProc] self.DATADICT = {} self.DATADICT["nPulseMoments"] = self.nPulseMoments self.DATADICT["stacks"] = procStacks self.DATADICT["PULSES"] = PULSES for pulse in PULSES: self.DATADICT[pulse] = {} self.DATADICT[pulse]["chan"] = chan # TODO these should not be a subet of pulse! for GMR all self.DATADICT[pulse]["rchan"] = rchan # data are consistent self.DATADICT[pulse]["CURRENT"] = {} for ichan in np.append(chan,rchan): self.DATADICT[pulse][ichan] = {} for ipm in range(self.nPulseMoments): self.DATADICT[pulse][ichan][ipm] = {} self.DATADICT[pulse]["CURRENT"][ipm] = {} for istack in procStacks: self.DATADICT[pulse][ichan][ipm][istack] = np.zeros(3) self.DATADICT[pulse]["CURRENT"][ipm][istack] = np.zeros(3) ############################################## # Read in binary (.lvm) data iistack = 0 fnames = [] for istack in procStacks: if self.nDAQVersion < 2.3: #rawfname = base + "_" + str(istack) #self.loadGMRASCIIFID( base + "_" + str(istack), istack ) self.loadGMRASCIIT1( base + "_" + str(istack), istack ) else: self.loadGMRBinaryFID( base + "_" + str(istack) + ".lvm", istack ) #fnames.append( base + "_" + str(istack) + ".lvm" ) percent = (int) (1e2*((float)((iistack*self.nPulseMoments+ipm+1)) / (len(procStacks)*self.nPulseMoments))) self.progressTrigger.emit(percent) iistack += 1 # multiprocessing load data #info = {} #info["prePulseDelay"] = self.prePulseDelay #info["samp"] = self.samp #with multiprocessing.Pool() as pool: # results = pool.starmap( xxloadGMRBinaryFID, ( fnames, zip(itertools.repeat(info)) ) ) # Plotting if plot: iistack = 0 for istack in procStacks: #for ipm in range(0,7,1): for ipm in range(self.nPulseMoments): canvas.ax1.clear() canvas.ax2.clear() canvas.ax3.clear() #canvas.fig.patch.set_facecolor('blue') for ichan in chan: canvas.ax1.plot(self.DATADICT["Pulse 1"]["PULSE_TIMES"], self.DATADICT["Pulse 1"]["CURRENT"][ipm][istack] , color='black') 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') for ichan in rchan: 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') canvas.ax3.legend(prop={'size':6}, loc='upper right') canvas.ax2.legend(prop={'size':6}, loc='upper right') canvas.ax1.set_title("stack "+str(istack)+" pulse index " + str(ipm), fontsize=8) canvas.ax1.set_xlabel("time [s]", fontsize=8) canvas.ax1.set_ylabel("Current [A]", fontsize=8) canvas.ax1.ticklabel_format(style='sci', scilimits=(0,0), axis='y') canvas.ax2.set_ylabel("RAW signal [V]", fontsize=8) canvas.ax2.tick_params(axis='both', which='major', labelsize=8) canvas.ax2.tick_params(axis='both', which='minor', labelsize=6) canvas.ax2.set_xlabel("time [s]", fontsize=8) canvas.ax2.ticklabel_format(style='sci', scilimits=(0,0), axis='y') canvas.ax3.ticklabel_format(style='sci', scilimits=(0,0), axis='y') canvas.draw() #canvas.draw() percent = (int) (1e2*((float)((iistack*self.nPulseMoments+ipm+1)) / (len(procStacks)*self.nPulseMoments))) self.progressTrigger.emit(percent) iistack += 1 self.enableDSP() self.doneTrigger.emit() def load4PhaseT1Data(self, base, procStacks, chan, rchan, FIDProc, canvas, deadTime, plot): """ Designed to load GMR 4-phase data which use the following convention for phase cycles P1 P2 Stack 1 -> 0 0 <-- <-- Stack 2 -> 0 pi/2 | <-- <-- Stack 3 -> pi/2 0 <-- | <-- Stack 4 -> pi/2 pi/2 <-- <-- The cycle is determined by stack indice. Walbrecker proposes for pulse2 data (Stack2 - Stack1) / 2 equivalently (Stack 4 - Stack3) will yield the same voltage response wrt. the second pulse. 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 Then there are just the two phase cycles that can be stacked like normal. Unfortunately, we need to stack each cycle first, then perform corrections for phase cycling. The reason for this is that otherwise, 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, if there is an uneven number of a certain phase cycle. We could, I suppose impose this condition, but I think I would rather not? + more samples for std. deviation calculation + single spikes will have less residual effect - can no longer do normality tests etc. and remove data that are suspect. - requires a dumb stack, and may also require removal of entire stacks of data 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 ... This however, is altered by the above convention. It gets a little complicated... """ import struct canvas.reAx3() # not in any headers but this has changed, NOT the place to do this. MOVE self.prePulseDelay = 0.01 # delay before pulse self.deadTime = deadTime # instrument dead time before measurement self.samp = 50000. # in case this is a reproc, these might have self.dt = 1./self.samp # changed invGain = 1./self.RxGain invCGain = self.CurrentGain ################################################################################# # figure out key data indices # Pulse nps = (int)((self.prePulseDelay)*self.samp) nps2 = (int)((self.prePulseDelay+self.interpulseDelay)*self.samp) npul = (int)(self.pulseLength[0]*self.samp) #+ 100 np2 = (int)(self.pulseLength[1]*self.samp) #+ 100 # Data nds = nps+npul+(int)((self.deadTime)*self.samp); # indice pulse 1 data starts nd1 = (int)((self.interpulseDelay)*self.samp) # samples in first pulse nd2s = nps+npul+nd1+(int)((self.deadTime)*self.samp); # indice pulse 2 data starts nd2 = (int)((1.)*self.samp) # samples in first pulse nd1 -= (int)((.028)*self.samp) + nps # some time to get ready for next pulse ################################################################################# # Data structures PULSES = [FIDProc] if FIDProc == "Both": PULSES = ["Pulse 1","Pulse 2"] self.DATADICT = {} self.DATADICT["nPulseMoments"] = self.nPulseMoments self.DATADICT["stacks"] = procStacks self.DATADICT["PULSES"] = PULSES for pulse in PULSES: self.DATADICT[pulse] = {} self.DATADICT[pulse]["chan"] = chan self.DATADICT[pulse]["rchan"] = rchan self.DATADICT[pulse]["CURRENT"] = {} for ichan in np.append(chan,rchan): self.DATADICT[pulse][ichan] = {} for ipm in range(self.nPulseMoments): self.DATADICT[pulse][ichan][ipm] = {} self.DATADICT[pulse]["CURRENT"][ipm] = {} for istack in procStacks: self.DATADICT[pulse][ichan][ipm][istack] = np.zeros(3) self.DATADICT[pulse]["CURRENT"][ipm][istack] = np.zeros(3) ############################################## # Read in binary data iistack = 0 for istack in procStacks: rawFile = open(base + "_" + str(istack) + ".lvm", 'rb') for ipm in range(self.nPulseMoments): N_chan = struct.unpack('>i', rawFile.read(4))[0] N_samp = struct.unpack('>i', rawFile.read(4))[0] T = N_samp * self.dt TIMES = np.arange(0, T, self.dt) - .0002 # small offset in GMR DAQ? DATA = np.zeros([N_samp, N_chan+1]) for ichan in range(N_chan): DATADUMP = rawFile.read(4*N_samp) for irec in range(N_samp): DATA[irec,ichan] = struct.unpack('>f', DATADUMP[irec*4:irec*4+4])[0] if plot: #canvas.ax1.clear() #canvas.ax2.clear() canvas.softClear() li = np.shape( DATA[:,4][nd2s:nd2s+nd2] )[0] ###################################### # save into DATA cube # TODO, changing iFID to 'Pulse 1' or 'Pulse 2' for ichan in chan: if FIDProc == "Pulse 1": self.DATADICT["Pulse 1"][ichan][ipm][istack] = DATA[:,ichan+3][nds:nds+nd1] * invGain self.DATADICT["Pulse 1"]["TIMES"] = TIMES[nds:nds+nd1] self.DATADICT["Pulse 1"]["CURRENT"][ipm][istack] = DATA[:,1][nps:nps+npul] * invCGain self.DATADICT["Pulse 1"]["PULSE_TIMES"] = TIMES[nps:nps+npul] if plot: 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') canvas.ax1.plot(self.DATADICT["Pulse 1"]["PULSE_TIMES"], self.DATADICT["Pulse 1"]["CURRENT"][ipm][istack] , color='black') elif FIDProc == "Pulse 2": print("TODO fix y scale") self.DATADICT["Pulse 2"][ichan][ipm][istack] = DATA[:,ichan+3][nd2s:nd2s+nd2] *invGain self.DATADICT["Pulse 2"]["TIMES"] = TIMES[nd2s:nd2s+nd2] self.DATADICT["Pulse 2"]["CURRENT"][ipm][istack] = DATA[:,1][nps2:nps2+np2] * invCGain self.DATADICT["Pulse 2"]["PULSE_TIMES"] = TIMES[nps2:nps2+np2] if plot: canvas.ax3.plot(self.DATADICT["Pulse 2"]["TIMES"], self.DATADICT["Pulse 2"][ichan][ipm][istack], label="Pulse 2 FID data ch. "+str(ichan)) #, color='blue') canvas.ax1.plot( self.DATADICT["Pulse 2"]["PULSE_TIMES"], self.DATADICT["Pulse 2"]["CURRENT"][ipm][istack], color='black' ) else: self.DATADICT["Pulse 1"][ichan][ipm][istack] = DATA[:,ichan+3][nds:nds+nd1] * invGain self.DATADICT["Pulse 2"][ichan][ipm][istack] = DATA[:,ichan+3][nd2s:nd2s+nd2] * invGain self.DATADICT["Pulse 1"]["TIMES"] = TIMES[nds:nds+nd1] self.DATADICT["Pulse 2"]["TIMES"] = TIMES[nd2s:nd2s+nd2] self.DATADICT["Pulse 1"]["CURRENT"][ipm][istack] = DATA[:,1][nps:nps+npul] * invCGain self.DATADICT["Pulse 1"]["PULSE_TIMES"] = TIMES[nps:nps+npul] self.DATADICT["Pulse 2"]["CURRENT"][ipm][istack] = DATA[:,1][nps2:nps2+np2] * invCGain self.DATADICT["Pulse 2"]["PULSE_TIMES"] = TIMES[nps2:nps2+np2] if plot: 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') canvas.ax3.plot(self.DATADICT["Pulse 2"]["TIMES"], self.DATADICT["Pulse 2"][ichan][ipm][istack], label="Pulse 2 FID data ch. "+str(ichan)) #, color='blue') canvas.ax1.plot( self.DATADICT["Pulse 1"]["PULSE_TIMES"], self.DATADICT["Pulse 1"]["CURRENT"][ipm][istack] , color='black' ) canvas.ax1.plot( self.DATADICT["Pulse 2"]["PULSE_TIMES"], self.DATADICT["Pulse 2"]["CURRENT"][ipm][istack] , color='black') for ichan in rchan: if FIDProc == "Pulse 1": self.DATADICT["Pulse 1"][ichan][ipm][istack] = DATA[:,ichan+3][nds:nds+nd1] * invGain self.DATADICT["Pulse 1"]["TIMES"] = TIMES[nds:nds+nd1] if plot: 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') elif FIDProc == "Pulse 2": self.DATADICT["Pulse 2"][ichan][ipm][istack] = DATA[:,ichan+3][nd2s:nd2s+nd2] * invGain self.DATADICT["Pulse 2"]["TIMES"] = TIMES[nd2s:nd2s+nd2] if plot: canvas.ax2.plot(self.DATADICT["Pulse 2"]["TIMES"], self.DATADICT["Pulse 2"][ichan][ipm][istack], label="Pulse 2 FID ref ch. "+str(ichan)) #, color='blue') else: self.DATADICT["Pulse 1"][ichan][ipm][istack] = DATA[:,ichan+3][nds:nds+nd1] * invGain self.DATADICT["Pulse 2"][ichan][ipm][istack] = DATA[:,ichan+3][nd2s:nd2s+nd2] * invGain self.DATADICT["Pulse 1"]["TIMES"] = TIMES[nds:nds+nd1] self.DATADICT["Pulse 2"]["TIMES"] = TIMES[nd2s:nd2s+nd2] if plot: 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') canvas.ax2.plot(self.DATADICT["Pulse 2"]["TIMES"], self.DATADICT["Pulse 2"][ichan][ipm][istack], label="Pulse 2 FID ref ch. "+str(ichan)) #, color='blue') if plot: canvas.ax3.legend(prop={'size':6}, loc='upper right') canvas.ax2.legend(prop={'size':6}, loc='upper right') canvas.ax1.set_title("stack "+str(istack)+" pulse index " + str(ipm), fontsize=8) canvas.ax1.set_xlabel("time [s]", fontsize=8) canvas.ax3.set_ylabel("RAW signal [V]", fontsize=8) canvas.ax2.set_ylabel("RAW signal [V]", fontsize=8) canvas.ax1.set_ylabel("Current [A]", fontsize=8) #canvas.ax2.tick_params(axis='both', which='major', labelsize=8) #canvas.ax2.tick_params(axis='both', which='minor', labelsize=6) #canvas.ax2.ticklabel_format(style='sci', scilimits=(0,0), axis='y') #canvas.ax1.ticklabel_format(style='sci', scilimits=(0,0), axis='y') canvas.draw() # update GUI of where we are percent = (int) (1e2*((float)((iistack*self.nPulseMoments+ipm+1)) / (len(procStacks)*self.nPulseMoments))) self.progressTrigger.emit(percent) iistack += 1 self.enableDSP() self.doneTrigger.emit() if __name__ == "__main__": if len(sys.argv) < 4: print( "mrsurvey path/to/header ") exit() GMR = GMRDataProcessor() GMR.readHeaderFile(sys.argv[1]) GMR.Print() if GMR.pulseType == "FID": GMR.loadFIDData(sys.argv[1], sys.argv[2], sys.argv[3], 5) if GMR.pulseType == "4PhaseT1": GMR.load4PhaseT1Data(sys.argv[1], sys.argv[2], sys.argv[3], 5) pylab.show()