initial check-in
This commit is contained in:
Markus Rosenstihl
169
Scripts/T1Q/op_t1q_exp.py
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169
Scripts/T1Q/op_t1q_exp.py
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# -*- coding: iso-8859-1 -*-
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TXEnableDelay = 2e-6
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TXEnableValue = 0b0001 # TTL line blanking RF amplifier (bit 0)
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TXPulseValue = 0b0010 # TTL line triggering RF pulses (bit 1)
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ADCSensitivity = 2 # voltage span for ADC
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def experiment(): # Jeener-Broekaert sequence with solid-echo detection to measure T1Q [JMR 43, 213 (1981)].
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# set up acquisition parameters:
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pars = {}
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pars['P90'] = 2.3e-6 # 90-degree pulse length (s)
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pars['SF'] = 46.7e6 # spectrometer frequency (Hz)
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pars['O1'] = 5.6e3 # offset from SF (Hz)
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pars['SW'] = 200e3 # spectrum width (Hz)
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pars['SI'] = 1*256 # number of acquisition points
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pars['NS'] = 32 # number of scans
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pars['DS'] = 0 # number of dummy scans
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pars['RD'] = .5 # delay between scans (s)
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pars['D1'] = 30e-6 # delay after first pulse or 'short tau' (s)
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pars['D2'] = 30e-6 # delay after second pulse or 'long tau' (s)
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pars['PHA'] = -27 # receiver phase (degree)
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pars['DATADIR'] = '/home/fprak/Students/' # data directory
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pars['OUTFILE'] = None # output file name
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# specify a variable parameter (optional):
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pars['VAR_PAR'] = 'D2' # variable parameter name (a string)
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start = 30e-6 # starting value
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stop = 2e-0 # end value
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steps = 16 # number of values
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log_scale = True # log scale flag
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stag_range = False # staggered range flag
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# check parameters for safety:
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if pars['PHA'] < 0:
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pars['PHA'] = 360 + pars['PHA']
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if pars['P90'] > 20e-6:
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raise Exception("Pulse too long!!!")
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# check whether a variable parameter is named:
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var_key = pars.get('VAR_PAR')
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if var_key == 'P90' and (start > 20e-6 or stop > 20e-6):
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raise Exception("Pulse too long!!!")
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if pars['NS']%8 != 0:
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pars['NS'] = int(round(pars['NS'] / 8) + 1) * 8
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print 'Number of scans changed to ',pars['NS'],' due to phase cycling'
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# start the experiment:
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if var_key:
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# this is an arrayed experiment:
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if log_scale:
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array = log_range(start,stop,steps)
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else:
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array = lin_range(start,stop,steps)
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if stag_range:
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array = staggered_range(array, size = 2)
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# estimate the experiment time:
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if var_key == 'D1':
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seconds = (sum(array)*2 + (pars['D2'] + pars['RD']) * steps) * (pars['NS'] + pars['DS'])
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elif var_key == 'D2':
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seconds = (sum(array) + (pars['D1']*2 + pars['RD']) * steps) * (pars['NS'] + pars['DS'])
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elif var_key == 'RD':
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seconds = (sum(array) + (pars['D1']*2 + pars['D2']) * steps) * (pars['NS'] + pars['DS'])
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else:
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seconds = (pars['D1']*2 + pars['D2'] + pars['RD']) * steps * (pars['NS']+ pars['DS'])
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m, s = divmod(seconds, 60)
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h, m = divmod(m, 60)
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print '%s%02d:%02d:%02d' % ('Experiment time estimated: ', h, m, s)
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# loop for a variable parameter:
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for index, pars[var_key] in enumerate(array):
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print 'Arrayed experiment for '+var_key+': run = '+str(index+1)+\
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' out of '+str(array.size)+': value = '+str(pars[var_key])
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# loop for accumulation:
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for run in xrange(pars['NS']+pars['DS']):
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yield t1q_experiment(pars, run)
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synchronize()
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else:
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# estimate the experiment time:
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seconds = (pars['D1']*2 + pars['D2'] + pars['RD']) * (pars['NS']+ pars['DS'])
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m, s = divmod(seconds, 60)
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h, m = divmod(m, 60)
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print '%s%02d:%02d:%02d' % ('Experiment time estimated: ', h, m, s)
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# loop for accumulation:
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for run in xrange(pars['NS']+pars['DS']):
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yield t1q_experiment(pars, run)
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# the pulse program:
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def t1q_experiment(pars, run):
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e=Experiment()
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dummy_scans = pars.get('DS')
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if dummy_scans:
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run -= dummy_scans
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pars['PROG'] = 't1q_experiment'
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# phase cycle from J. Magn. Reson. 43, 213-223 (1981) extended for 90-deg refocusing pulse which also eliminates echo(es) after 3rd 45-deg pulse:
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pars['PH1'] = [0, 0, 90, 90, 180, 180, 270, 270] # 90-deg pulse
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pars['PH3'] = [90, 90, 180, 180, 270, 270, 0, 0] # 1st 45-deg pulse
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pars['PH4'] = [0, 180, 0, 180, 180, 0, 180, 0] # 2nd 45-deg pulse
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pars['PH5'] = [0, 0, 180, 180, 270, 90, 90, 270] # refocucing 90-deg pulse
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pars['PH2'] = [0, 180, 0, 180, 180, 0, 180, 0] # receiver
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# read in variables:
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P90 = pars['P90']
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P45 = pars['P90']*0.5
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SF = pars['SF']
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O1 = pars['O1']
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RD = pars['RD']
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D1 = pars['D1']
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D2 = pars['D2']
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PH1 = pars['PH1'][run%len(pars['PH1'])]
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PH3 = pars['PH3'][run%len(pars['PH3'])]
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PH4 = pars['PH4'][run%len(pars['PH4'])]
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PH5 = pars['PH5'][run%len(pars['PH5'])]
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PH2 = pars['PH2'][run%len(pars['PH2'])]
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PHA = pars['PHA']
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# set sampling parameters:
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SI = pars['SI']
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SW = pars['SW']
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while SW <= 10e6 and SI < 256*1024:
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SI *= 2
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SW *= 2
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# run the pulse sequence:
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e.wait(RD) # delay between scans
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e.set_frequency(SF+O1, phase=PH1)
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e.ttl_pulse(TXEnableDelay, value=TXEnableValue)
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e.ttl_pulse(P90, value=TXEnableValue|TXPulseValue) # 90-degree pulse
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e.wait(D1-P90/2-TXEnableDelay) # 'short tau'
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e.set_phase(PH3)
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e.ttl_pulse(TXEnableDelay, value=TXEnableValue)
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e.ttl_pulse(P45, value=TXEnableValue|TXPulseValue) # 45-degree pulse
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e.wait(D2-P45/2-TXEnableDelay) # 'long tau'
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e.set_phase(PH4)
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e.ttl_pulse(TXEnableDelay, value=TXEnableValue)
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e.ttl_pulse(P45, value=TXEnableValue|TXPulseValue) # 45-degree pulse
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e.wait(10e-6-TXEnableDelay)
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e.set_phase(PH5)
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e.ttl_pulse(TXEnableDelay, value=TXEnableValue)
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e.ttl_pulse(P90, value=TXEnableValue|TXPulseValue) # 90-degree pulse
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e.set_phase(PHA)
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e.wait(13e-6)
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e.record(SI, SW, sensitivity=ADCSensitivity) # acquisition
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# write experiment attributes:
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for key in pars.keys():
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e.set_description(key, pars[key]) # acqusition parameters
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e.set_description('run', run) # current scan
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e.set_description('rec_phase', -PH2) # current receiver phase
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return e
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209
Scripts/T1Q/op_t1q_res.py
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209
Scripts/T1Q/op_t1q_res.py
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# -*- coding: iso-8859-1 -*-
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from numpy import *
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from scipy.signal import *
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from scipy.optimize import *
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from scipy.fftpack import rfft
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from os import path, rename
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def result():
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measurement = MeasurementResult('Magnetization')
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suffix = '' # output file name's suffix and...
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counter = 1 # counter for arrayed experiments
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var_key = '' # variable parameter name
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# loop over the incoming results:
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for timesignal in results:
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if not isinstance(timesignal,ADC_Result):
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continue
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# read experiment parameters:
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pars = timesignal.get_description_dictionary()
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# ---------------- digital filter ------------------
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# get actual sampling rate of timesignal:
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sampling_rate = timesignal.get_sampling_rate()
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# get user-defined spectrum width:
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spec_width = pars['SW']
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# specify cutoff frequency, in relative units:
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cutoff = spec_width / sampling_rate
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if cutoff < 1: # otherwise no filter applied
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# number of filter's coefficients:
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numtaps = 29
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# use firwin to create a lowpass FIR filter:
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fir_coeff = firwin(numtaps, cutoff)
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# downsize x according to user-defined spectral window:
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skip = int(sampling_rate / spec_width)
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timesignal.x = timesignal.x[::skip]
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for i in range(2):
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# apply the filter to ith channel:
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timesignal.y[i] = lfilter(fir_coeff, 1.0, timesignal.y[i])
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# zeroize first N-1 "corrupted" samples:
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timesignal.y[i][:numtaps-1] = 0.0
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# circular left shift of y:
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timesignal.y[i] = roll(timesignal.y[i], -(numtaps-1))
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# downsize y to user-defined number of samples (SI):
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timesignal.y[i] = timesignal.y[i][::skip]
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# update the sampling_rate attribute of the signal's:
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timesignal.set_sampling_rate(spec_width)
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# ----------------------------------------------------
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# phase timesignal according to current rec_phase:
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timesignal.phase(pars['rec_phase'])
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# provide timesignal to the display tab:
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data['Current scan'] = timesignal
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# accumulate...
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if not locals().get('accu'):
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accu = Accumulation()
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# skip dummy scans, if any:
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if pars['run'] < 0: continue
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# add up:
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accu += timesignal
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# provide accumulation to the display tab:
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data['Accumulation'] = accu
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# check how many scans are done:
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if accu.n == pars['NS']: # accumulation is complete
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# make a copy:
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fid = accu + 0
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# compute the signal's phase:
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phi0 = arctan2(fid.y[1][0], fid.y[0][0]) * 180 / pi
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if not 'ref' in locals(): ref = phi0
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print 'phi0 = ', phi0
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# do FFT:
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fid.exp_window(line_broadening=10)
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spectrum = fid.fft(samples=2*pars['SI'])
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spectrum.baseline()
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# provide spectrum to the display tab:
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data['Spectrum'] = spectrum
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# check whether it is an arrayed experiment:
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var_key = pars.get ('VAR_PAR')
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if var_key:
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# get variable parameter's value:
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var_value = pars.get(var_key)
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# provide signal recorded with the var_value to the display tab:
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data['Accumulation'+"/"+var_key+"=%e"%(var_value)] = accu
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# measure signal intensity vs. var_value:
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signal = (accu + 0).y[1]
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# -*- discrete cosine transform of Im -*-
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N = len(signal)
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y = empty(2*N, float)
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y[:N] = signal[:]
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y[N:] = signal[::-1]
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c = rfft(y)
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phi = exp(-1j*pi*arange(N)/(2*N))
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dct = real(phi*c[:N])
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# ---------------------------------------
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measurement[var_value] = sum(dct[0:9])
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# provide measurement to the display tab:
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data[measurement.get_title()] = measurement
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# update the file name suffix:
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suffix = '_' + str(counter)
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counter += 1
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# save accu if required:
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outfile = pars.get('OUTFILE')
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if outfile:
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datadir = pars.get('DATADIR')
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# write raw data in Simpson format:
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filename = datadir+outfile+suffix+'.dat'
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if path.exists(filename):
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rename(filename, datadir+'~'+outfile+suffix+'.dat')
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accu.write_to_simpson(filename)
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# write raw data in Tecmag format:
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# filename = datadir+outfile+'.tnt'
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# accu.write_to_tecmag(filename, nrecords=20)
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# write parameters in a text file:
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filename = datadir+outfile+suffix+'.par'
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if path.exists(filename):
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rename(filename, datadir+'~'+outfile+suffix+'.par')
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fileobject = open(filename, 'w')
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for key in sorted(pars.iterkeys()):
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if key=='run': continue
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if key=='rec_phase': continue
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fileobject.write('%s%s%s'%(key,'=', pars[key]))
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fileobject.write('\n')
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fileobject.close()
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# reset accumulation:
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del accu
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if var_key == 'D2':
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# mono-exponential decay fit:
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xdata = measurement.get_xdata()
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ydata = measurement.get_ydata()
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[amplitude, rate, offset] = fitfunc(xdata, ydata)
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print '%s%02g' % ('Amplitude = ', amplitude)
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print '%s%02g' % ('T1Q [s] = ', 1./rate)
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# update display for the fit:
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measurement.y = func([amplitude, rate, offset], xdata)
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data[measurement.get_title()] = measurement
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# the fitting procedure:
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def fitfunc(xdata, ydata):
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# initialize variable parameters:
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try:
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# solve Az = b:
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A = array((ones(xdata.size/2), xdata[0:xdata.size/2]))
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b = log(abs(ydata[0:xdata.size/2]))
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z = linalg.lstsq(transpose(A), b)
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amplitude = exp(z[0][0])
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rate = -z[0][1]
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except:
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amplitude = abs(ydata[0])
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rate = 1./(xdata[-1] - xdata[0])
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offset = min(ydata)
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p0 = [amplitude, rate, offset]
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# run least-squares optimization:
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plsq = leastsq(residuals, p0, args=(xdata, ydata))
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return plsq[0] # best-fit parameters
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def residuals(p, xdata, ydata):
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return ydata - func(p, xdata)
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# here is the function to fit:
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def func(p, xdata):
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return p[0]*exp(-p[1]*xdata)+p[2]
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pass
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