def press(spin_system, te1=34, te2=34):
#----------------------------------------------------------------------
# This is an example PyGAMMA pulse sequence for use in Vespa-Simulation
#
# A timing diagram for this pulse sequence can be found in the Appendix
# of the Simulation User Manual.
#----------------------------------------------------------------------
# the isotope string used to sort/select the metabolites of interest is passed
# in the sim_desc object so the user can tailor other object within their
# code to be nuclei specific, such as the observe operator or pulses
obs_iso = '1H'
# extract the dynamically changing variable from loop 1 and 2 for 'te1' and
# 'te2', divide by 1000.0 because the GUI states that values are entered in
# [ms], but PyGAMMA wants [sec]
te1 = te1 / 1000.0
te2 = te2 / 1000.0
# set up steady state and observation variables
H = pg.Hcs(spin_system) + pg.HJ(spin_system)
D = pg.Fm(spin_system, obs_iso)
ac = pg.acquire1D(pg.gen_op(D), H, 0.000001)
ACQ = ac
sigma0 = pg.sigma_eq(spin_system)
# excite, propagate, refocus and acquire the data
sigma1 = pg.Iypuls(spin_system, sigma0, obs_iso, 90.0)
Udelay = pg.prop(H, te1 * 0.5)
sigma0 = pg.evolve(sigma1, Udelay)
sigma1 = pg.Iypuls(spin_system, sigma0, obs_iso, 180.0)
Udelay = pg.prop(H, (te1 + te2) * 0.5)
sigma0 = pg.evolve(sigma1, Udelay)
sigma1 = pg.Iypuls(spin_system, sigma0, obs_iso, 180.0)
Udelay = pg.prop(H, te2 * 0.5)
sigma0 = pg.evolve(sigma1, Udelay)
# instantiate and save transition table of simulation results
# note. this step copies the TTable1D result from the ACQ into
# a TTable1D object in the sim_desc object. Thus, when
# we return from this function and the ACQ variable gets
# garbage collected, our copy of the results in not affected
return pg.TTable1D(ACQ.table(sigma0))
def apply_crushed_180_rf(sys,
sigma,
dephase_ang=[0.0, 90.0, 180.0, 270.0],
type='crusher'):
sigma_mult = []
for i in dephase_ang:
sigma_mult.append(pg.gen_op(sigma))
for i, angle in enumerate(dephase_ang):
riz = pg.gen_op(pg.Rz(sys, angle))
sigma_mult[i] = pg.evolve(sigma_mult[i], riz)
sigma_mult[i] = pg.Iypuls(sys, sigma_mult[i], '1H', 180.0)
if type == 'bipolar':
riz = pg.gen_op(pg.Rz(sys, -1 * angle))
sigma_mult[i] = pg.evolve(sigma_mult[i], riz)
sigma_mult[i] *= 0.25
if i == 0:
sigma_res = pg.gen_op(sigma_mult[i])
else:
sigma_res += sigma_mult[i]
return sigma_res
pwf = pg.PulWaveform(pulse, ptime, "TestPulse") pulc = pg.PulComposite(pwf, sys, "1H") H = pg.Hcs(sys) + pg.HJ(sys) D = pg.Fm(sys) Udelay1 = pg.prop(H, t1) Udelay2 = pg.prop(H, t2) # Neet to effectively typecast D as a gen_op. ac = pg.acquire1D(pg.gen_op(D), H, 0.001) ACQ = ac sigma0 = pg.sigma_eq(sys) sigma1 = pg.Iypuls(sys, sigma0, 90.0) #Apply a 90y pulse sigma0 = pg.evolve(sigma1, Udelay1) #Evolve through T1 Ureal180 = pulc.GetUsum(-1) #Get the propagator for steps of 180 sigma1 = Ureal180.evolve(sigma0) #Evolve through pulse sigma0 = pg.evolve(sigma1, Udelay2) #Evolve through T2 mx = ACQ.table(sigma0) #Transitions table (no lb) mx.dbwrite(outfile, out_name, specfreq, sys.spins(), 0, header)
def steam(spin_system, te=20, tm=10):
# ------------------------------------------------------------------------
# This is an example PyGAMMA pulse sequence for use in Vespa-Simulation
#
# A timing diagram for this pulse sequence can be found in the Appendix
# of the Simulation User Manual.
# ------------------------------------------------------------------------
# the isotope string used to sort/select the metabolites of interest is passed
# in the sim_desc object so the user can tailor other object within their
# code to be nuclei specific, such as the observe operator or pulses
obs_iso = '1H'
# extract the dynamically changing variable from loops 1 and 2 for 'te'
# and 'tm', divide by 1000.0 because the GUI states that values are
# entered in [ms], but PyGAMMA wants [sec]
te = te / 1000.0
tm = tm / 1000.0
# set up steady state and observation variables
H = pg.Hcs(spin_system) + pg.HJ(spin_system)
D = pg.Fm(spin_system, obs_iso)
ac = pg.acquire1D(pg.gen_op(D), H, 0.000001)
ACQ = ac
# excite, propagate, refocus and acquire the data
#
# for the case of STEAM, we need to simulate crusher gradients around the
# second and third 90 pulses. We do this by creating 4 copies of the
# operator matrix at that point, rotate respectively by 0, 90, 180 and 270
# degrees to each other, apply the 90 pulses and TM period, and then
# add the four back into one normalized matrix.
dephase_ang = [0.0, 90.0, 180.0, 270.0]
Udelay1 = pg.prop(H, te * 0.5)
Udelay2 = pg.prop(H, tm)
sigma0 = pg.sigma_eq(spin_system)
# first 90 pulse, excite spins
sigma0 = pg.Iypuls(spin_system, sigma0, obs_iso, 90.0)
# nutate TE/2
sigma0 = pg.evolve(sigma0, Udelay1)
# Now we need to create the effect of crushers around the 2nd and 3rd
# 90 pulses. This is done by creating 4 copies of spin state and repeating
# the rest of the sequence for four different rotations around z-axis
sigma_mult = []
for i in dephase_ang:
sigma_mult.append(pg.gen_op(sigma0))
for i, angle in enumerate(dephase_ang):
# calculate and apply rotation around z-axis
riz = pg.gen_op(pg.Rz(spin_system, angle))
sigma_mult[i] = pg.evolve(sigma_mult[i], riz)
# second 90 pulse
sigma_mult[i] = pg.Ixpuls(spin_system, sigma_mult[i], obs_iso, 90.0)
# this function removes all coherences still in transverse plane
# this removes all stimulated echos from first and second 90 pulse
pg.zero_mqc(spin_system, sigma_mult[i], 0, -1)
# third 90 pulse
sigma_mult[i] = pg.Ixpuls(spin_system, sigma_mult[i], obs_iso, 90.0)
# undo rotation around z-axis
sigma_mult[i] = pg.evolve(sigma_mult[i], riz)
# scale results based on the number of phase angles
sigma_mult[i] *= 1.0 / float(len(dephase_ang))
# sum up each rotated/unrotated results
if i == 0:
sigma_res = pg.gen_op(sigma_mult[i])
else:
sigma_res += sigma_mult[i]
# last TE/2 nutation
sigma0 = pg.evolve(sigma_res, Udelay1)
# instantiate and save transition table of simulation results
# note. this step copies the TTable1D result from the ACQ into
# a TTable1D object in the sim_desc object. Thus, when
# we return from this function and the ACQ variable gets
# garbage collected, our copy of the results in not affected
return pg.TTable1D(ACQ.table(sigma0))
'car': 0,
'size': t2pts,
'label': '1H',
'encoding': 'direct',
'time': False,
'freq': True
}
}
fid = pg.row_vector(t2pts) #block_1D tmp(t2pts); // 1D-data block storage
H = pg.Hcs(sys)+ pg.HJw(sys) # // Hamiltonian, weak coupling
detect = pg.gen_op(pg.Fp(sys)) # // F+ for detection operator
sigma0 = pg.sigma_eq(sys) # // equilibrium density matrix
sigma1 = pg.Iypuls(sys, sigma0, 90)
pg.FID(sigma1,detect,H,dt2,t2pts,fid)
pg.exponential_multiply(fid,-5)
spec = fid.FFT()
npspec = spec.toNParray()
uc0 = ng.fileiobase.unit_conversion(udic[0]['size'],
udic[0]['complex'],
udic[0]['sw'],
udic[0]['obs'],
udic[0]['car'])
##################################################################################
# Plot 1D spectrum
header = (s1, s2) sys = pg.sys_dynamic() sys.read(infile) specfreq = sys.Omega() mx = pg.TTable1D() H = pg.Ho(sys) detect = pg.Fm(sys) sigma0 = pg.sigma_eq(sys) sigmap = pg.Iypuls(sys, sigma0, 90.) L = pg.Hsuper(H) L *= pg.complex(0,1) L += pg.Kex(sys, H.get_basis()); ACQ1 = pg.acquire1D(pg.gen_op(detect), L) mx = ACQ1.table(sigmap); #mx.dbwrite_old(outfile, "test_lines", -10, 10, specfreq, .1, 0, header) mx.dbwrite(outfile, runname, specfreq, sys.spins(), 0, header)