damaris-script-library/Scripts/Steady_Gradient_Spin_Echo/sgse_res.py
2018-09-14 17:43:02 +02:00

201 lines
6.6 KiB
Python

# -*- 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 = True
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)
# ----------------------------------------------------
# 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:
echo = accu + 0
# compute the signal's phase:
phi0 = arctan2(echo.y[1][0], echo.y[0][0]) * 180 / pi
if not locals().get('ref'): ref = phi0
print 'phi0 = ', phi0
# rotate the signal to maximize Re (optional):
#echo.phase(-phi0)
# specify noise level:
if not locals().get('noise'):
noise = 0.1*max(abs(echo.y[0]))
samples = echo.y[0] > noise
# 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] = echo.get_sampling_rate() * array(measurement_range)
measurement[var_value] = sum(echo.y[0][int(start):int(stop)])
else:
measurement[var_value] = sum(echo.y[0][samples])
# provide the measurement result 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] = 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], 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])
p0 = [amplitude, rate]
# 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)
pass