damaris-script-library/Scripts/Saturation_Recovery_with_Solid_Echo_Detection/satrec2_res.py

# -*- coding: iso-8859-1 -*-

from numpy import *
from scipy.signal import *
from scipy.optimize import *
from os import path, rename

def result():

    measurement = MeasurementResult('Magnetization')
    
    measurement_range = [0.0, 10e-6]
    measurement_ranging = False    

    suffix = ''		# output file name's suffix and...
    counter = 1		# counter for arrayed experiments
    var_key = ''	# variable parameter name

    # loop over the incoming results: 
    for timesignal in results:
        if not isinstance(timesignal,ADC_Result): 
            continue   

        # read experiment parameters:
        pars = timesignal.get_description_dictionary()

        # ---------------- digital filter ------------------ 
             
        # get actual sampling rate of timesignal:
        sampling_rate = timesignal.get_sampling_rate()

        # ----------------------------------------------------

        # rotate timesignal according to current receiver's phase:
        timesignal.phase(pars['rec_phase'])

        # provide timesignal to the display tab:
        data['Current Scan'] = timesignal

        # accumulate...
        if not locals().get('accu'):
            accu = Accumulation()

        # skip dummy scans, if any:
        if pars['run'] < 0: continue

        # add up:
        accu += timesignal

        # provide accumulation to the display tab:
        data['Accumulation'] = accu

        # check how many scans are done:
        if accu.n == pars['NS']: # accumulation is complete

            # make a copy:
            fid = accu + 0

            # compute the initial phase of FID:
            phi0 = arctan2(fid.y[1][0], fid.y[0][0]) * 180 / pi
            if not locals().get('ref'): ref = phi0 
            print 'phi0 = ', phi0  

            # rotate FID to maximize Re (optional):     
            #fid.phase(-phi0) 

            # do FFT: 
            fid.exp_window(line_broadening=10)
            spectrum = fid.fft(samples=2*pars['SI'])

            # try zero-order phase correction:
            spectrum.phase(-phi0)

            # provide spectrum to the display tab:
            data['Spectrum'] = spectrum

            # check whether it is an arrayed experiment: 
            var_key = pars.get('VAR_PAR')
            if var_key:
                # get variable parameter's value:
                var_value = pars.get(var_key)

                # provide signal recorded with this var_value to the display tab:
                data['Accumulation'+"/"+var_key+"=%e"%(var_value)] = accu

                # measure signal intensity vs. var_value:
                if measurement_ranging == True:
                   [start, stop] = accu.get_sampling_rate() * array(measurement_range)
                   measurement[var_value] = sum(accu.y[0][int(start):int(stop)]) 
                        
                else:
                   measurement[var_value] = sum(accu.y[0][0:31])

                # provide measurement to the display tab:
                data[measurement.get_title()] = measurement

                # update the file name suffix:
                suffix = '_' + str(counter)
                counter += 1

            # save accu if required:   
            outfile = pars.get('OUTFILE')
            if outfile: 
                datadir = pars.get('DATADIR')

                # write raw data in Simpson format:
                filename = datadir+outfile+suffix+'.dat'
                if path.exists(filename):
                    rename(filename, datadir+'~'+outfile+suffix+'.dat')
                accu.write_to_simpson(filename)

                # write raw data in Tecmag format:
                # filename = datadir+outfile+'.tnt'
                # accu.write_to_tecmag(filename, nrecords=20)

               # write parameters in a text file:
                filename = datadir+outfile+suffix+'.par' 
                if path.exists(filename):
                    rename(filename, datadir+'~'+outfile+suffix+'.par')

                fileobject = open(filename, 'w')
                for key in sorted(pars.iterkeys()):
                    if key=='run': continue
                    if key=='rec_phase': continue
                    fileobject.write('%s%s%s'%(key,'=', pars[key]))
                    fileobject.write('\n')
                fileobject.close()

            # reset accumulation:
            del accu

    if var_key == 'TAU':
        # mono-exponential recovery fit:
        xdata = measurement.get_xdata()
        ydata = measurement.get_ydata()
        [amplitude, rate, offset] = fitfunc(xdata, ydata)
        print '%s%02g' % ('Amplitude = ', amplitude)
        print '%s%02g' % ('T1 [s] = ', 1./rate)

        # update display for the fit:
        measurement.y = func([amplitude, rate, offset], xdata)
        data[measurement.get_title()] = measurement

# the fitting procedure:
def fitfunc(xdata, ydata):

    # initialize variable parameters:
    try: 
        # solve Az = b:
        A = array((ones(xdata.size/2), xdata[0:xdata.size/2]))
        b = log(abs(mean(ydata[-2:]) - ydata[0:xdata.size/2]))     
        z = linalg.lstsq(transpose(A), b)
        amplitude = exp(z[0][0])
        rate = -z[0][1]
    except:
        amplitude = abs(ydata[-1] - ydata[0])
        rate = 1./(xdata[-1] - xdata[0])
    offset = min(ydata)
    p0 = [amplitude, rate, offset]

    # run least-squares optimization:
    plsq = leastsq(residuals, p0, args=(xdata, ydata))

    return plsq[0] # best-fit parameters

def residuals(p, xdata, ydata):
    return ydata - func(p, xdata)

# here is the function to fit:
def func(p, xdata):
    return p[0]*(1-exp(-p[1]*xdata)) + p[2]


pass