176 lines
6.2 KiB
Python
176 lines
6.2 KiB
Python
# -*- 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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measurement = MeasurementResult('Magnetization')
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suffix = '' # output file name's suffix and...
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counter = 1 # counter for arrayed experiments
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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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# rotate timesignal by 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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# get number of echoes:
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num_echoes = pars['NECH']
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# downsize accu to one point per echo:
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echodecay = accu + 0
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echodecay.x = resize(echodecay.x, int(num_echoes))
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echodecay.y[0] = resize(echodecay.y[0], int(num_echoes))
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echodecay.y[1] = resize(echodecay.y[1], int(num_echoes))
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# specify noise level:
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if not locals().get('noise'):
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echo = accu.get_accu_by_index(0)
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noise = 0.1*max(abs(echo.y[0]))
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samples = abs(echo.y[0]) > noise
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# set echo times and intensities:
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for i in range(num_echoes):
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# get ith echo:
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echo = accu.get_accu_by_index(i)
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# set echo timing:
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echodecay.x[i] = i*2*pars['TAU']
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# set echo value:
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echodecay.y[0][i] = sum(echo.y[0][samples]) # the sum of echo points that exeed noise
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echodecay.y[1][i] = sum(echo.y[1][samples])
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#echodecay.y[0][i] = sum(echo.y[0]) # the sum of all echo points
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#echodecay.y[1][i] = sum(echo.y[1])
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#echodecay.y[0][i] = echo.y[0][echo.x.size/2] # a middle echo point
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#echodecay.y[1][i] = echo.y[1][echo.x.size/2]
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# compute a signal's phase:
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phi0 = arctan2(echodecay.y[1][0], echodecay.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 signal to maximize Re (optional):
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#echodecay.phase(-phi0)
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# provide echo decay to the display tab:
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data['Echo Decay'] = echodecay
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# fit a mono-exponential function to the echo decay:
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[amplitude, rate] = fitfunc(echodecay.x, echodecay.y[0])
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print '%s%02g' % ('Amplitude = ', amplitude)
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print '%s%02g' % ('T2 [s] = ', 1./rate)
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# provide the fit to the display tab:
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fit = MeasurementResult('Mono-Exponential Fit')
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for i, key in enumerate(echodecay.x):
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fit[key] = echodecay.y[0][i]
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fit.y = func([amplitude, rate], echodecay.x)
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data[fit.get_title()] = fit
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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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# provide data recorded with different var_value's to the display tab:
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data['Accumulation'+"/"+var_key+"=%e"%(var_value)] = accu
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data['Echo Decay'+"/"+var_key+"=%e"%(var_value)] = echodecay
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data[fit.get_title()+"/"+var_key+"=%e"%(var_value)] = fit
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# measure a signal parameter vs. var_value:
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measurement[var_value] = amplitude
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#measurement[var_value] = sum(echodecay.y[0][:])
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#measurement[var_value] = 1./rate
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# provide measurement to the display tab:
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data[measurement.get_title()] = measurement
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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 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 data in Tecmag format:
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# filename = datadir+outfile+'.tnt'
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# accu.write_to_tecmag(filename, nrecords=20)
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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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# the fitting procedure:
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def fitfunc(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, p0, args=(xdata, ydata))
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return plsq[0] # best-fit parameters
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def residuals(p, xdata, ydata):
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return ydata - func(p, xdata)
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# here is the function to fit:
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def func(p, xdata):
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return p[0]*exp(-p[1]*xdata)
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pass |