201 lines
6.6 KiB
Python
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
|