IPKM/damaris-script-library
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PFG STE added

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Markus Rosenstihl 2018-11-27 18:29:29 +01:00
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26
README.md
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@ -7,4 +7,30 @@ You can get a clone of this repository via:
Details of the pulse programs can be found [[https://chaos3.fkp.physik.tu-darmstadt.de/diffusion/DSL/browse/master/The%20DAMARIS%20Script%20Library.pdf | here]].
## For people working with this and thy want to revert the local changes:
(This is from [[https://stackoverflow.com/questions/1146973/how-do-i-revert-all-local-changes-in-git-managed-project-to-previous-state|from StackOverflow]])
If you want to revert changes made to your working copy, do this:
git checkout .
If you want to revert changes made to the index (i.e., that you have added), do this. Warning this will reset all of your unpushed commits to master!:
git reset
If you want to revert a change that you have committed, do this:
git revert <commit 1> <commit 2>
If you want to remove untracked files (e.g., new files, generated files):
git clean -f
Or untracked directories (e.g., new or automatically generated directories):
git clean -fd
Current maintainer of this library is @markusro.

174
Scripts/PFG/Stimulated_Echo/pfg_ste_exp.py Normal file
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# -*- coding: iso-8859-1 -*-
TXEnableDelay = 2e-6 # test
TXEnableValue = 0b0001 # TTL line enabling RF amplifier (bit 0)
TXPulseValue = 0b0010 # TTL line triggering RF pulses (bit 1)
ADCSensitivity = 2 # voltage span for ADC
DAC_conv = 6.32e-5 # T/dac_value
def experiment(): # stimulated echo experiment
# set up acquisition parameters:
pars = {}
pars['P90'] = 3.2e-6 # 90-degree pulse length (s)
pars['SF'] = 338.7e6 # spectrometer frequency (Hz)
pars['O1'] = -87e3 # offset from SF (Hz)
pars['SW'] = 500e3 # spectrum width (Hz)
pars['SI'] = 8*1024 # number of acquisition points
pars['NS'] = 16*2 # number of scans
pars['DS'] = 0 # number of dummy scans
pars['RD'] = 3*7.7 # delay between scans (s)
pars['D1'] = 1e-3 # delay after first pulse (short tau) (s)
pars['D2'] = 50e-3 # delay after second pulse (long tau) (s)
pars['D4'] = 0e-6 # echo pre-acquisition delay (s)
pars["DAC1"] = 1000 # DAC value (PFG)
pars["D5"] = 0.9e-3 # PFG pulse length
pars['PHA'] = 30+180 # receiver phase (degree)
pars['DATADIR'] = '/home/markusro/STE' # data directory
pars['OUTFILE'] = "" # output file name
# specify a variable parameter (optional):
pars['VAR_PAR'] = "DAC1" # variable parameter name (a string)
start = 0 # starting value
stop = int(5/DAC_conv) #1.5e5 # end value
steps = 21 # number of values
log_scale = False # log scale flag
stag_range = False # staggered range flag
# check parameters for safety:
if pars['PHA'] < 0:
pars['PHA'] = 360 + pars['PHA']
if pars['P90'] > 20e-6:
raise Exception("Pulse too long!!!")
# check whether a variable parameter is named:
var_key = pars.get('VAR_PAR')
if var_key == 'P90' and (start > 20e-6 or stop > 20e-6):
raise Exception("Pulse too long!!!")
if pars['NS']%16 != 0:
pars['NS'] = int(round(pars['NS'] / 16) + 1) * 16
print 'Number of scans changed to ',pars['NS'],' due to phase cycling'
# start the experiment:
# check if a variable parameter is named:
var_key = pars.get('VAR_PAR')
if var_key:
# this is an arrayed experiment:
if log_scale:
array = log_range(start,stop,steps)
else:
array = lin_range(start,stop,steps)
if stag_range:
array = staggered_range(array, size = 2)
# estimate the experiment time:
if var_key == 'D1':
seconds = (sum(array)*2 + (pars['D2'] + pars['RD']) * steps) * (pars['NS'] + pars['DS'])
elif var_key == 'D2':
seconds = (sum(array) + (pars['D1']*2 + pars['RD']) * steps) * (pars['NS'] + pars['DS'])
elif var_key == 'RD':
seconds = (sum(array) + (pars['D1']*2 + pars['D2']) * steps) * (pars['NS'] + pars['DS'])
else:
seconds = (pars['D1']*2 + pars['D2'] + pars['RD']) * steps * (pars['NS']+ pars['DS'])
m, s = divmod(seconds, 60)
h, m = divmod(m, 60)
print '%s%02d:%02d:%02d' % ('Experiment time estimated: ', h, m, s)
# loop for a variable parameter:
for index, pars[var_key] in enumerate(array):
print 'Arrayed experiment for '+var_key+': run = '+str(index+1)+\
' out of '+str(array.size)+': value = '+str(pars[var_key])
# loop for accumulation:
for run in xrange(pars['NS']+pars['DS']):
yield ste_experiment(pars, run)
synchronize()
else:
# estimate the experiment time:
seconds = (pars['D1']*2 + pars['D2'] + pars['RD']) * (pars['NS']+ pars['DS'])
m, s = divmod(seconds, 60)
h, m = divmod(m, 60)
print '%s%02d:%02d:%02d' % ('Experiment time estimated: ', h, m, s)
# loop for accumulation:
for run in xrange(pars['NS']+pars['DS']):
yield ste_experiment(pars, run)
# the pulse program:
def ste_experiment(pars, run):
e=Experiment()
dummy_scans = pars.get('DS')
if dummy_scans:
run -= dummy_scans
pars['PROG'] = 'ste_experiment'
# phase lists [16-phase cycle from JMR 157, 31 (2002)]:
pars['PH1'] = [0, 180, 0, 180, 0, 180, 0, 180, 90, 270, 90, 270, 90, 270, 90, 270] # 1st 90-degree pulse
pars['PH3'] = [0, 0, 180, 180, 0, 0, 180, 180, 0, 0, 180, 180, 0, 0, 180, 180] # 2nd 90-degree pulse
pars['PH4'] = [0, 0, 0, 0, 180, 180, 180, 180, 0, 0, 0, 0, 180, 180, 180, 180] # 3nd 90-degree pulse
pars['PH2'] = [0, 180, 180, 0, 180, 0, 0, 180, 270, 90, 90, 270, 90, 270, 270, 90] # receiver
# read in variables:
P90 = pars['P90']
SF = pars['SF']
O1 = pars['O1']
RD = pars['RD']
D1 = pars['D1']
D2 = pars['D2']
D4 = pars['D4']
D5 = pars['D5']
DAC1 = pars['DAC1']
PH1 = pars['PH1'][run%len(pars['PH1'])]
PH3 = pars['PH3'][run%len(pars['PH3'])]
PH4 = pars['PH4'][run%len(pars['PH4'])]
PH2 = pars['PH2'][run%len(pars['PH2'])]
PHA = pars['PHA']
# set sampling parameters:
SI = pars['SI']
SW = pars['SW']
while SW <= 10e6 and SI < 256*1024:
SI *= 2
SW *= 2
# run the pulse sequence:
e.wait(RD) # delay between scans
e.set_frequency(SF+O1, phase=PH1)
e.ttl_pulse(TXEnableDelay, value=TXEnableValue)
e.ttl_pulse(P90, value=TXEnableValue|TXPulseValue) # 90-degree pulse
e.set_phase(PH3)
e.set_pfg(dac_value=DAC1, length=D5, shape=("rec",100e-6))
e.wait(D1-P90/2-TXEnableDelay - D5) # 'short tau'
e.ttl_pulse(TXEnableDelay, value=TXEnableValue)
e.ttl_pulse(P90, value=TXEnableValue|TXPulseValue) # 90-degree pulse
e.wait(D2-P90/2-TXEnableDelay) # 'long tau'
e.set_phase(PH4)
e.ttl_pulse(TXEnableDelay, value=TXEnableValue)
e.ttl_pulse(P90, value=TXEnableValue|TXPulseValue) # 90-degree pulse
e.set_phase(PHA)
e.set_pfg(dac_value=DAC1, length=D5, shape=("rec",100e-6))
e.wait(D1-P90/2-TXEnableDelay+D4-D5) # 'short tau'
e.record(SI, SW, sensitivity=ADCSensitivity) # acquisition
# write experiment parameters:
for key in pars.keys():
e.set_description(key, pars[key]) # acquisition parameters
e.set_description('run', run) # current scan
e.set_description('rec_phase', -PH2) # current receiver phase
return e

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Scripts/PFG/Stimulated_Echo/pfg_ste_res.py Normal file
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# -*- 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()
# 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:
echo = accu + 0
# compute the initial phase of the signal:
phi0 = arctan2(accu.y[1][0], accu.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)
# do FFT:
echo.exp_window(line_broadening=10)
spectrum = echo.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 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 == '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' % ('T1 [s] = ', 1./rate)
print '%s%02g' % ('Offset = ', offset)
# 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