275 lines
9.9 KiB
Python
275 lines
9.9 KiB
Python
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# -*- coding: iso-8859-1 -*-
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from numpy import *
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from scipy.signal import *
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from scipy.optimize import *
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from os import path, rename
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def result():
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the_experiment = None # current experiment's name
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measurements = {'satrec2_experiment': MeasurementResult('Saturation Recovery'),
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'ste_experiment': MeasurementResult('Stimulated Echo'),
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'hahn_experiment': MeasurementResult('Hahn Echo')}
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measurement_ranges = {'satrec2_experiment': [0.5e-6, 4.5e-6],
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'ste_experiment': [1.5e-6, 4.5e-6],
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'hahn_experiment': [2.5e-6, 4.5e-6]}
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measurement_ranging = True
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# loop over the incoming results:
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for timesignal in results:
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if not isinstance(timesignal,ADC_Result):
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continue
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# read experiment parameters:
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pars = timesignal.get_description_dictionary()
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# catch the actual experiment's name:
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if the_experiment != pars.get('PROG'):
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the_experiment = pars.get('PROG')
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suffix = '' # output file name's suffix
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counter = 1
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# ---------------- digital filter ------------------
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# get actual sampling rate of timesignal:
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sampling_rate = timesignal.get_sampling_rate()
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# get user-defined spectrum width:
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spec_width = pars['SW']
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# specify cutoff frequency, in relative units:
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cutoff = spec_width / sampling_rate
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# number of filter's coefficients:
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numtaps = 29
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if cutoff < 1: # otherwise no filter applied
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# use firwin to create a lowpass FIR filter:
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fir_coeff = firwin(numtaps, cutoff)
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# downsize x according to user-defined spectral window:
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skip = int(sampling_rate / spec_width)
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timesignal.x = timesignal.x[::skip]
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for i in range(2):
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# apply the filter to ith channel:
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timesignal.y[i] = lfilter(fir_coeff, 1.0, timesignal.y[i])
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# zeroize first N-1 "corrupted" samples:
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timesignal.y[i][:numtaps-1] = 0.0
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# circular left shift of y:
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timesignal.y[i] = roll(timesignal.y[i], -(numtaps-1))
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# downsize y to user-defined number of samples (SI):
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timesignal.y[i] = timesignal.y[i][::skip]
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# update the sampling_rate attribute of the signal's:
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timesignal.set_sampling_rate(spec_width)
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# ----------------------------------------------------
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# rotate timesignal according to current receiver's phase:
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timesignal.phase(pars['rec_phase'])
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# provide timesignal to the display tab:
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data['Current scan'] = timesignal
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# accumulate...
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if not locals().get('accu'):
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accu = Accumulation()
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# skip dummy scans, if any:
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if pars['run'] < 0: continue
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# add up:
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accu += timesignal
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# provide accumulation to the display tab:
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data['Accumulation'] = accu
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# check how many scans are done:
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if accu.n == pars['NS']: # accumulation is complete
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# make a copy:
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echo = accu + 0
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# compute the signal's phase:
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#phi0 = arctan2(echo.y[1][0], echo.y[0][0]) * 180 / pi
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#if not locals().get('ref'): ref = phi0
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#print 'phi0 = ', phi0
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# rotate the signal to maximize Re (optional):
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#echo.phase(-phi0)
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# check whether it is an arrayed experiment:
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var_key = pars.get('VAR_PAR')
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if var_key:
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# get variable parameter's value:
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var_value = pars.get(var_key)
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# store signals recorded for different var_values:
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data['Accumulation'+"/"+var_key+"=%e"%(var_value)] = accu
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# estimate noise level:
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if not locals().get('noise'):
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n = int(0.1*echo.x.size)
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noise = 3*std(echo.y[0][-n-numtaps:-1-numtaps])
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# measure echo intensity vs. var_value:
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if the_experiment in measurements.keys():
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# option a: sum over the time interval specified:
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if measurement_ranging == True:
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[start, stop] = echo.get_sampling_rate() * array(measurement_ranges[the_experiment])
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measurements[the_experiment][var_value] = sum(echo.y[0][int(start):int(stop)])
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# option b: sum of all samples above noise:
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else:
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measurements[the_experiment][var_value] = sum(echo.y[0][echo.y[0]>noise])
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# store a measurement:
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data[measurements[the_experiment].get_title()] = measurements[the_experiment]
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# update the file name suffix:
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suffix = '_' + str(counter)
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counter += 1
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else:
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print "Cannot recognize experiment: continue without measuring"
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# save accu if required:
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outfile = pars.get('OUTFILE')
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if outfile:
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datadir = pars.get('DATADIR')
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# write raw data in Simpson format:
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filename = datadir+outfile+suffix+'.dat'
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if path.exists(filename):
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rename(filename, datadir+'~'+outfile+suffix+'.dat')
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accu.write_to_simpson(filename)
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# write parameters in a text file:
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filename = datadir+outfile+suffix+'.par'
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if path.exists(filename):
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rename(filename, datadir+'~'+outfile+suffix+'.par')
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fileobject = open(filename, 'w')
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for key in sorted(pars.iterkeys()):
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if key=='run': continue
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if key=='rec_phase': continue
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fileobject.write('%s%s%s'%(key,'=', pars[key]))
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fileobject.write('\n')
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fileobject.close()
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# reset accumulation:
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del accu
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if var_key == 'TAU' or var_key == 'D2':
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# mono-exponential recovery fit:
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try:
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xdata = measurements['satrec2_experiment'].get_xdata()
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ydata = measurements['satrec2_experiment'].get_ydata()
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[amplitude, rate, offset] = fitfunc_recovery(xdata, ydata)
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print '%s%02g' % ('Amplitude = ', amplitude)
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print '%s%02g' % ('T1 [s] = ', 1./rate)
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# update display for fit:
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measurements['satrec2_experiment'].y = func_recovery([amplitude, rate, offset], xdata)
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data[measurements['satrec2_experiment'].get_title()] = measurements['satrec2_experiment']
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except:
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pass
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# mono-exponential decay fit to Hahn echoes:
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try:
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xdata = measurements['hahn_experiment'].get_xdata()
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ydata = measurements['hahn_experiment'].get_ydata()
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[amplitude, rate] = fitfunc_decay(xdata, ydata)
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print 'Mono-exponential fit to Hahn echo decay:'
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print '%s%02g' % ('Amplitude = ', amplitude)
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print '%s%02g' % ('T2 [s] = ', 1./rate)
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# update display for the fit:
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measurements['hahn_experiment'].y = func_decay([amplitude, rate], xdata)
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data[measurements['hahn_experiment'].get_title()] = measurements['hahn_experiment']
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except:
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pass
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try:
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# mono-exponential decay fit to stimulated echoes:
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xdata = measurements['ste_experiment'].get_xdata()
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ydata = measurements['ste_experiment'].get_ydata()
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[amplitude, rate] = fitfunc_decay(xdata, ydata)
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print 'Mono-exponential fit to stimulated echo decay:'
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print '%s%02g' % ('Amplitude = ', amplitude)
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print '%s%02g' % ('T2 [s] = ', 1./rate)
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# update display for the fit:
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measurements['ste_experiment'].y = func_decay([amplitude, rate], xdata)
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data[measurements['ste_experiment'].get_title()] = measurements['ste_experiment']
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except:
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pass
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# the fitting procedure for satrec:
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def fitfunc_recovery(xdata, ydata):
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# initialize variable parameters:
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try:
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# solve Az = b:
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A = array((ones(xdata.size/2), xdata[0:xdata.size/2]))
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b = log(abs(mean(ydata[-2:]) - ydata[0:xdata.size/2]))
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z = linalg.lstsq(transpose(A), b)
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amplitude = exp(z[0][0])
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rate = -z[0][1]
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except:
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amplitude = abs(ydata[-1] - ydata[0])
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rate = 1./(xdata[-1] - xdata[0])
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offset = min(ydata)
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p0 = [amplitude, rate, offset]
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# run least-squares optimization:
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plsq = leastsq(residuals_recovery, p0, args=(xdata, ydata))
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return plsq[0] # best-fit parameters
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def residuals_recovery(p, xdata, ydata):
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return ydata - func_recovery(p, xdata)
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# here is the function to fit
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def func_recovery(p, xdata):
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return p[0]*(1-exp(-p[1]*xdata)) + p[2]
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# the fitting procedure for hahn and ste:
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def fitfunc_decay(xdata, ydata):
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# initialize variable parameters:
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try:
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# solve Az = b:
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A = array((ones(xdata.size/2), xdata[0:xdata.size/2]))
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b = log(abs(ydata[0:xdata.size/2]))
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z = linalg.lstsq(transpose(A), b)
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amplitude = exp(z[0][0])
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rate = -z[0][1]
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except:
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amplitude = abs(ydata[0])
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rate = 1./(xdata[-1] - xdata[0])
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p0 = [amplitude, rate]
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# run least-squares optimization:
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plsq = leastsq(residuals_decay, p0, args=(xdata, ydata))
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return plsq[0] # best-fit parameters
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def residuals_decay(p, xdata, ydata):
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return ydata - func_decay(p, xdata)
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# here is the function to fit:
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def func_decay(p, xdata):
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return p[0]*exp(-p[1]*xdata)
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pass
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