damaris-script-library/Scripts/T1Q/t1q_res.py

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2015-06-26 12:17:24 +00:00
# -*- coding: iso-8859-1 -*-
from numpy import *
from scipy.signal import *
from scipy.optimize import *
from scipy.fftpack import rfft
from os import path, rename
def result():
measurement = MeasurementResult('Magnetization')
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()
# get user-defined spectrum width:
spec_width = pars['SW']
# specify cutoff frequency, in relative units:
cutoff = spec_width / sampling_rate
if cutoff < 1: # otherwise no filter applied
# number of filter's coefficients:
numtaps = 29
# use firwin to create a lowpass FIR filter:
fir_coeff = firwin(numtaps, cutoff)
# downsize x according to user-defined spectral window:
skip = int(sampling_rate / spec_width)
timesignal.x = timesignal.x[::skip]
for i in range(2):
# apply the filter to ith channel:
timesignal.y[i] = lfilter(fir_coeff, 1.0, timesignal.y[i])
# zeroize first N-1 "corrupted" samples:
timesignal.y[i][:numtaps-1] = 0.0
# circular left shift of y:
timesignal.y[i] = roll(timesignal.y[i], -(numtaps-1))
# downsize y to user-defined number of samples (SI):
timesignal.y[i] = timesignal.y[i][::skip]
# update the sampling_rate attribute of the signal's:
timesignal.set_sampling_rate(spec_width)
# ----------------------------------------------------
# phase timesignal according to current rec_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 signal's phase:
phi0 = arctan2(fid.y[1][0], fid.y[0][0]) * 180 / pi
if not 'ref' in locals(): ref = phi0
print 'phi0 = ', phi0
# do FFT:
fid.exp_window(line_broadening=10)
spectrum = fid.fft(samples=2*pars['SI'])
spectrum.baseline()
# 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 the var_value to the display tab:
data['Accumulation'+"/"+var_key+"=%e"%(var_value)] = accu
# measure signal intensity vs. var_value:
signal = (accu + 0).y[1]
# -*- discrete cosine transform of Im -*-
N = len(signal)
y = empty(2*N, float)
y[:N] = signal[:]
y[N:] = signal[::-1]
c = rfft(y)
phi = exp(-1j*pi*arange(N)/(2*N))
dct = real(phi*c[:N])
# ---------------------------------------
measurement[var_value] = sum(dct[0:9])
# 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 == 'D2':
# mono-exponential decay fit:
xdata = measurement.get_xdata()
ydata = measurement.get_ydata()
[amplitude, rate, offset] = fitfunc(xdata, ydata)
print '%s%02g' % ('Amplitude = ', amplitude)
print '%s%02g' % ('T1Q [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(ydata[0:xdata.size/2]))
z = linalg.lstsq(transpose(A), b)
amplitude = exp(z[0][0])
rate = -z[0][1]
except:
amplitude = abs(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]*exp(-p[1]*xdata)+p[2]
pass