def fid(spin_system):
sys_hamiltonian = pg.Hcs(spin_system) + pg.HJ(spin_system)
read = pg.Fm(spin_system, "1H")
ACQ = pg.acquire1D(pg.gen_op(read), sys_hamiltonian, 0.000001)
sigma = pg.sigma_eq(spin_system)
sigma0 = pg.Ixpuls(spin_system, sigma, "1H", 90.0)
return pg.TTable1D(ACQ.table(sigma0))
def fid(sys, b0=123.23):
sys.OmegaAdjust(b0)
H = pg.Hcs(sys) + pg.HJ(sys)
D = pg.Fm(sys)
ACQ = pg.acquire1D(pg.gen_op(D), H, 0.1)
sigma = pg.sigma_eq(sys)
sigma0 = pg.Ixpuls(sys, sigma, 90.0)
mx = pg.TTable1D(ACQ.table(sigma0))
return mx
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))
specfreq = sys.Omega()
# read_pulse replaces ReadPulse() and is a static member of row_vector
pulse = pg.row_vector.read_pulse(inpulse180file, pg.row_vector.ASCII_MT_DEG)
ptime = pg.row_vector(pulse.size())
#need to use pg.complex() so it can find correct function to call.
for j in range(pulse.size()):
ptime.put(pg.complex(pulsestep, 0), j)
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
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))
def megapress(spin_system, te=68, omega=OMEGA, high_ppm=-7.5, low_ppm=1):
# ----------------------------------------------------------------
# This is an example megapress PyGAMMA pulse sequence
# for use in Vespa-Simulation
#
# It expects that the editing and localization pulses
# have been designed or read in from file via Vespa-RFPulse
#
# ----------------------------------------------------------------
# order of pulse objects from VeSPA
# so lets load the pulses, and import them as pulse object
# - these were exported directly from vespa as pickled objects
pulse_names = [
'siemens_hsinc_400_8750', 'siemens_mao_400_4', 'siemens_rf_gauss_44hz'
]
rf_pulses = []
for ps_name in pulse_names:
with open('./Simulators/PyGamma/pulses/' + ps_name + '.vps',
'rb') as ps_filename:
rf_pulses.append(pickle.load(ps_filename))
# spectrometer frequency in MHz
specfreq = omega
# initial evolution time after 90,before 1st 180
tinitial = float(6.6) / 1000.0 # 6600 for svs_edit
# echo time
te = float(te) / 1000.0
# Pulse centers in ppm for editing pulses
PulseCenterPPM_local = float(3) # 3.0
PulseCenterPPM_edit_on = float(1.9) # 1.9
PulseCenterPPM_edit_off = float(7.4) # 7.5
bw_pulse_bottom = float(
2000) # 2600.0 - Bandwidth of the pulse at the bottom
min_peak_hz = (
-low_ppm) * specfreq # 224 Hz at 3T the lowest spin ~1.8 ppm
max_peak_hz = (
-high_ppm
) * specfreq # 570 Hz at 3T the highest frequency spin (~4.6 ppm*spefreq)
freqoff1 = min_peak_hz # this is the start of the sweep
freqoff2 = -(bw_pulse_bottom / 2.0) + min_peak_hz
freqoff3 = freqoff2
freqfinal = (bw_pulse_bottom / 2.0) + max_peak_hz
# number of spatial points (for both x,y)
numxy_points = int(5)
# Number of Points in the z,x,y directions for spatial simulation
Points1 = 5
Points2 = numxy_points
Points3 = numxy_points
Step = (Points2 * specfreq) / (
freqfinal - freqoff2
) # may make is (freqfinal-freqoff2)/Points2 and remove specfreq from eqn.
# Read Pulse Files, Initialize Waveforms --------------------------
# PulWaveform expects Hz amplitude and angle in degrees, while
# Simulation gives us mT amplitude and angle in radians.
# We need to use the appropriate gyromagnetic ratio to do the
# conversion. This depends on our observe_isotope, since the pulse
# is not isotope specific, but the spins we expect it to affect,
# is.
# (e.g. we use 42576.0 for 1H to covert mT and RAD2DEG for phase)
obs_iso = '1H'
gyratio = 42576.0
# ------ Pulse Setup Section ---------- #
#
# - set up RF Pulse containers and read pulse values into them.
# - format values to units that GAMMA expects
# - copy into row_vector objects
# - apply phase roll for off-resonance pulse locations
# - set up gamma waveform object
# - set up composite pulse object
# - get propagator for all steps of the shaped pulse
# Pulse offsets in HZ (should be negative or 0)
B1_offset_local = (0 - PulseCenterPPM_local) * specfreq
B1_offset_edit_on = (0 - PulseCenterPPM_edit_on) * specfreq
B1_offset_edit_off = (0 - PulseCenterPPM_edit_off) * specfreq
# # ------ Excite Pulse
# vol1, excite_length = pulse2op(rf_pulses[0],
# gyratio,
# "Excite_90",
# spin_system,
# obs_iso,
# offset=B1_offset_local)
# print(excite_length)
# # ------ Refocus Pulse
vol2, refocus_length = pulse2op(rf_pulses[1],
gyratio,
"Refocus_180",
spin_system,
obs_iso,
offset=B1_offset_local)
vol4 = vol2
# print(refocus_length)
# ------ Editing Pulses
edit1_on, edit1_length = pulse2op(rf_pulses[2],
gyratio,
"siemens_gauss44hz_on",
spin_system,
obs_iso,
offset=B1_offset_edit_on)
edit1_off, edit1_length = pulse2op(rf_pulses[2],
gyratio,
"siemens_gauss44hz_off",
spin_system,
obs_iso,
offset=B1_offset_edit_off)
# edit2_off, edit2_on, edit2_length = edit1_off, edit1_on, edit1_length
edit2_on, edit2_length = pulse2op(rf_pulses[2],
gyratio,
"siemens_gauss44hz_on",
spin_system,
obs_iso,
offset=B1_offset_edit_on)
edit2_off, edit2_length = pulse2op(rf_pulses[2],
gyratio,
"siemens_gauss44hz_off",
spin_system,
obs_iso,
offset=B1_offset_edit_off)
# ------ Sequence Timings and GAMMA Initialization ---------- #
excite_length = 0.0026 # if using Ideal 90y for excite
# refocus_length = 0.003 # if using ideal 180y refocusing pulses
# initial evolution time after 90 and before first localization 180
t1 = tinitial - excite_length / 2 - refocus_length / 2
te2 = (te - tinitial * 2.0) / 2.0
t2 = tinitial + te2 - edit1_length - 0.002 - refocus_length
t3 = 0.002
t4 = 0.002
t5 = te2 - refocus_length / 2 - 0.002 - edit2_length
print('Megapress pulse seuqence timing:')
print(' te: ' + str(te))
print(' Tall: ' +
str(t1 + t2 + t3 + t4 + t5 + edit1_length + edit2_length +
(refocus_length * 2) + (excite_length / 2.0)))
print(' t1: ' + str(t1))
print(' t2: ' + str(t2))
print(' t3: ' + str(t3))
print(' t4: ' + str(t4))
print(' t5: ' + str(t5))
# set up steady state and observation variables
H = pg.Hcs(spin_system) + pg.HJ(spin_system)
D = pg.Fm(spin_system, obs_iso)
# Set up acquisition
ac = pg.acquire1D(pg.gen_op(D), H, 0.000001)
ACQ = ac
# Calculate delays here (before spatial loop)
Udelay1 = pg.prop(H, t1) # First evolution time
Udelay2 = pg.prop(H, t2) # Second evolution time
Udelay3 = pg.prop(H, t3) # Third evolution time
Udelay4 = pg.prop(H, t4) # Fourth evolution time
Udelay5 = pg.prop(H, t5) # Fifth evolution time
# -- Do common first steps outside of spatial loop
# Equilibrium density matrix
sigma0 = pg.sigma_eq(spin_system)
sigma_total = pg.gen_op(sigma0) # create a copy
mx_tables = []
for edit_flag in [False, True]:
loopcounter = 0
local_scale = 1.0 / float(Points1 * Points2 * Points3)
for nss1 in range(Points1): # slice
# Apply an ideal 90y pulse
sigma1 = pg.Ixpuls(spin_system, sigma0, obs_iso, 90.0)
# Apply a shaped 90 pulse
# sigma1 = vol1.evolve(sigma0)
# Evolve for t1
sigma0 = pg.evolve(sigma1, Udelay1)
for nss2 in range(Points2):
for nss3 in range(Points3):
# First 180 volume selection pulse - with gradient crushers
offsethz2 = freqoff2 + nss2 * specfreq / Step
spin_system.offsetShifts(offsethz2) # note: arg. in Hz
# sigma1 = apply_crushed_180_rf(spin_system, sigma0, dephase_ang=[0.0, 90.0], type='crusher')
sigma1 = apply_crushed_rf(spin_system,
sigma0,
vol2,
type='crusher')
spin_system.offsetShifts(-offsethz2)
# Evolve for t2
sigma2 = pg.evolve(sigma1, Udelay2)
# First BASING bipolar gradient+editing pulse
if edit_flag == 0:
sigma1 = apply_crushed_rf(spin_system,
sigma2,
edit1_off,
type='bipolar')
else:
sigma1 = apply_crushed_rf(spin_system,
sigma2,
edit1_on,
type='bipolar')
pg.zero_mqc(spin_system, sigma1, 2, 1) # Keep ZQC and SQC
# Evolve for t3
sigma2 = pg.evolve(sigma1, Udelay3)
# Second 180 volume selection - with gradient crushers
offsethz3 = freqoff3 + nss3 * specfreq / Step
spin_system.offsetShifts(offsethz3) # note: arg. in Hz
# sigma1 = apply_crushed_180_rf(spin_system, sigma2, type='crusher')
sigma1 = apply_crushed_rf(spin_system,
sigma2,
vol4,
type='crusher')
spin_system.offsetShifts(-offsethz3) # note: arg. in Hz
# Evolve for t4
sigma2 = pg.evolve(sigma1, Udelay4)
# Second BASING bipolar gradient+editing pulse
if edit_flag == 0:
sigma1 = apply_crushed_rf(spin_system,
sigma2,
edit2_off,
type='bipolar')
else:
sigma1 = apply_crushed_rf(spin_system,
sigma2,
edit2_on,
type='bipolar')
pg.zero_mqc(spin_system, sigma1, 2, 1) # Keep ZQC and SQC
# Evolve for t5
sigma2 = pg.evolve(sigma1, Udelay5)
sigma2 *= local_scale
print("System loop index is: " + str(loopcounter) +
" offsethz2 = " + str(offsethz2) +
" offsethz3 = " + str(offsethz3))
loopcounter += 1
if nss1 + nss2 + nss3 == 0:
sigma_total = sigma2
else:
sigma_total += sigma2
mx_tables.append(pg.TTable1D(ACQ.table(sigma_total)))
return mx_tables
def simulate(self):
self.postToConsole.emit(' | Simulating ... ' + self.insysfile)
print(' | Simulating ...' + self.insysfile)
metab_name = self.insysfile.replace('.sys', '')
if self.sim_experiment.b0 == 123.3:
self.insysfile = 'pints/metabolites/3T_' + self.insysfile
elif self.sim_experiment.b0 == 297.2:
self.insysfile = 'pints/metabolites/7T_' + self.insysfile
elif self.sim_experiment.b0 == 400.2:
self.insysfile = 'pints/metabolites/9.4T_' + self.insysfile
if self.sim_experiment.name == "semi-LASER (Bruker)":
spin_system = pg.spin_system()
spin_system.read(self.insysfile)
for i in range(spin_system.spins()):
spin_system.PPM(
i,
spin_system.PPM(i) - self.sim_experiment.RF_OFFSET)
TE = self.sim_experiment.TE * 1E-3
TE1 = self.sim_experiment.TE1 * 1E-3
TE2 = self.sim_experiment.TE2 * 1E-3
# build 90 degree pulse
inpulse90file = self.sim_experiment.inpulse90file
A_90 = self.sim_experiment.A_90
PULSE_90_LENGTH = self.sim_experiment.PULSE_90_LENGTH
gyratio = self.sim_experiment.getGyratio()
pulse90 = Pulse(inpulse90file, PULSE_90_LENGTH, 'bruker')
n_old = np.linspace(0, PULSE_90_LENGTH, sp.size(pulse90.waveform))
n_new = np.linspace(0, PULSE_90_LENGTH,
sp.size(pulse90.waveform) + 1)
waveform_real = sp.interpolate.InterpolatedUnivariateSpline(
n_old,
np.real(pulse90.waveform) * A_90)(n_new)
waveform_imag = sp.interpolate.InterpolatedUnivariateSpline(
n_old,
np.imag(pulse90.waveform) * A_90)(n_new)
pulse90.waveform = waveform_real + 1j * (waveform_imag)
ampl_arr = np.abs(pulse90.waveform)
phas_arr = np.unwrap(np.angle(pulse90.waveform)) * 180.0 / math.pi
pulse = pg.row_vector(len(pulse90.waveform))
ptime = pg.row_vector(len(pulse90.waveform))
for j, val in enumerate(zip(ampl_arr, phas_arr)):
pulse.put(pg.complex(val[0], val[1]), j)
ptime.put(pg.complex(pulse90.pulsestep, 0), j)
pulse_dur_90 = pulse.size() * pulse90.pulsestep
pwf_90 = pg.PulWaveform(pulse, ptime, "90excite")
pulc_90 = pg.PulComposite(pwf_90, spin_system,
self.sim_experiment.obs_iso)
Ureal90 = pulc_90.GetUsum(-1)
# build 180 degree pulse
inpulse180file = self.sim_experiment.inpulse180file
A_180 = self.sim_experiment.A_180
PULSE_180_LENGTH = self.sim_experiment.PULSE_180_LENGTH
gyratio = self.sim_experiment.getGyratio()
pulse180 = Pulse(inpulse180file, PULSE_180_LENGTH, 'bruker')
n_old = np.linspace(0, PULSE_180_LENGTH,
sp.size(pulse180.waveform))
n_new = np.linspace(0, PULSE_180_LENGTH,
sp.size(pulse180.waveform) + 1)
waveform_real = sp.interpolate.InterpolatedUnivariateSpline(
n_old,
np.real(pulse180.waveform) * A_180)(n_new)
waveform_imag = sp.interpolate.InterpolatedUnivariateSpline(
n_old,
np.imag(pulse180.waveform) * A_180)(n_new)
pulse180.waveform = waveform_real + 1j * (waveform_imag)
ampl_arr = np.abs(pulse180.waveform)
phas_arr = np.unwrap(np.angle(pulse180.waveform)) * 180.0 / math.pi
freq_arr = np.gradient(phas_arr)
pulse = pg.row_vector(len(pulse180.waveform))
ptime = pg.row_vector(len(pulse180.waveform))
for j, val in enumerate(zip(ampl_arr, phas_arr)):
pulse.put(pg.complex(val[0], val[1]), j)
ptime.put(pg.complex(n_new[1], 0), j)
pulse_dur_180 = pulse.size() * pulse180.pulsestep
pwf_180 = pg.PulWaveform(pulse, ptime, "180afp")
pulc_180 = pg.PulComposite(pwf_180, spin_system,
self.sim_experiment.obs_iso)
Ureal180 = pulc_180.GetUsum(-1)
H = pg.Hcs(spin_system) + pg.HJ(spin_system)
D = pg.Fm(spin_system, self.sim_experiment.obs_iso)
ac = pg.acquire1D(pg.gen_op(D), H, self.sim_experiment.dwell_time)
ACQ = ac
delay1 = TE1 / 2.0 - pulse_dur_90 / 2.0 - pulse_dur_180 / 2.0
delay2 = TE1 / 2.0 + TE2 / 2.0 - pulse_dur_180
delay3 = TE2 - pulse_dur_180
delay4 = delay2
delay5 = TE1 / 2.0 - pulse_dur_180 + self.sim_experiment.DigShift
Udelay1 = pg.prop(H, delay1)
Udelay2 = pg.prop(H, delay2)
Udelay3 = pg.prop(H, delay3)
Udelay4 = pg.prop(H, delay4)
Udelay5 = pg.prop(H, delay5)
sigma0 = pg.sigma_eq(spin_system) # init
sigma1 = Ureal90.evolve(sigma0) # apply 90-degree pulse
sigma0 = pg.evolve(sigma1, Udelay1)
sigma1 = Ureal180.evolve(sigma0) # apply AFP1
sigma0 = pg.evolve(sigma1, Udelay2)
sigma1 = Ureal180.evolve(sigma0) # apply AFP2
sigma0 = pg.evolve(sigma1, Udelay3)
sigma1 = Ureal180.evolve(sigma0) # apply AFP3
sigma0 = pg.evolve(sigma1, Udelay4)
sigma1 = Ureal180.evolve(sigma0) # apply AFP4
sigma0 = pg.evolve(sigma1, Udelay5)
elif self.sim_experiment.name == "semi-LASER":
spin_system = pg.spin_system()
spin_system.read(self.insysfile)
for i in range(spin_system.spins()):
spin_system.PPM(
i,
spin_system.PPM(i) - self.sim_experiment.RF_OFFSET)
TE = self.sim_experiment.TE
TE1 = float((TE * 0.31) / 1000.0)
TE3 = float((TE * 0.31) / 1000.0)
TE2 = float(TE / 1000.0 - TE1 - TE3)
TE_fill = TE / 1000.0 - TE1 - TE2 - TE3
# build 90 degree pulse
inpulse90file = self.sim_experiment.inpulse90file
A_90 = self.sim_experiment.A_90
PULSE_90_LENGTH = self.sim_experiment.PULSE_90_LENGTH
gyratio = self.sim_experiment.getGyratio()
pulse90 = Pulse(inpulse90file, PULSE_90_LENGTH)
n_old = np.linspace(0, PULSE_90_LENGTH, sp.size(pulse90.waveform))
n_new = np.linspace(0, PULSE_90_LENGTH,
sp.size(pulse90.waveform) + 1)
waveform_real = sp.interpolate.InterpolatedUnivariateSpline(
n_old,
np.real(pulse90.waveform) * A_90)(n_new)
waveform_imag = sp.interpolate.InterpolatedUnivariateSpline(
n_old,
np.imag(pulse90.waveform) * A_90)(n_new)
pulse90.waveform = waveform_real + 1j * (waveform_imag)
ampl_arr = np.abs(pulse90.waveform) * gyratio
phas_arr = np.unwrap(np.angle(pulse90.waveform)) * 180.0 / math.pi
pulse = pg.row_vector(len(pulse90.waveform))
ptime = pg.row_vector(len(pulse90.waveform))
for j, val in enumerate(zip(ampl_arr, phas_arr)):
pulse.put(pg.complex(val[0], val[1]), j)
ptime.put(pg.complex(pulse90.pulsestep, 0), j)
pulse_dur_90 = pulse.size() * pulse90.pulsestep
peak_to_end_90 = pulse_dur_90 - (
209 + self.sim_experiment.fudge_factor) * pulse90.pulsestep
pwf_90 = pg.PulWaveform(pulse, ptime, "90excite")
pulc_90 = pg.PulComposite(pwf_90, spin_system,
self.sim_experiment.obs_iso)
Ureal90 = pulc_90.GetUsum(-1)
# build 180 degree pulse
inpulse180file = self.sim_experiment.inpulse180file
A_180 = self.sim_experiment.A_180
PULSE_180_LENGTH = self.sim_experiment.PULSE_180_LENGTH
gyratio = self.sim_experiment.getGyratio()
pulse180 = Pulse(inpulse180file, PULSE_180_LENGTH)
n_old = np.linspace(0, PULSE_180_LENGTH,
sp.size(pulse180.waveform))
n_new = np.linspace(0, PULSE_180_LENGTH,
sp.size(pulse180.waveform) + 1)
waveform_real = sp.interpolate.InterpolatedUnivariateSpline(
n_old,
np.real(pulse180.waveform) * A_180)(n_new)
waveform_imag = sp.interpolate.InterpolatedUnivariateSpline(
n_old,
np.imag(pulse180.waveform) * A_180)(n_new)
pulse180.waveform = waveform_real + 1j * (waveform_imag)
ampl_arr = np.abs(pulse180.waveform) * gyratio
phas_arr = np.unwrap(np.angle(pulse180.waveform)) * 180.0 / math.pi
freq_arr = np.gradient(phas_arr)
pulse = pg.row_vector(len(pulse180.waveform))
ptime = pg.row_vector(len(pulse180.waveform))
for j, val in enumerate(zip(ampl_arr, phas_arr)):
pulse.put(pg.complex(val[0], val[1]), j)
ptime.put(pg.complex(n_new[1], 0), j)
pulse_dur_180 = pulse.size() * pulse180.pulsestep
pwf_180 = pg.PulWaveform(pulse, ptime, "180afp")
pulc_180 = pg.PulComposite(pwf_180, spin_system,
self.sim_experiment.obs_iso)
Ureal180 = pulc_180.GetUsum(-1)
H = pg.Hcs(spin_system) + pg.HJ(spin_system)
D = pg.Fm(spin_system, self.sim_experiment.obs_iso)
ac = pg.acquire1D(pg.gen_op(D), H, self.sim_experiment.dwell_time)
ACQ = ac
delay1 = TE1 / 2.0 + TE_fill / 8.0 - pulse_dur_180 / 2.0 - peak_to_end_90
delay2 = TE1 / 2.0 + TE_fill / 8.0 + TE2 / 4.0 + TE_fill / 8.0 - pulse_dur_180
delay3 = TE2 / 4.0 + TE_fill / 8.0 + TE2 / 4.0 + TE_fill / 8.0 - pulse_dur_180
delay4 = TE2 / 4.0 + TE_fill / 8.0 + TE3 / 2.0 + TE_fill / 8.0 - pulse_dur_180
delay5 = TE3 / 2.0 + TE_fill / 8.0 - pulse_dur_180 / 2.0
Udelay1 = pg.prop(H, delay1)
Udelay2 = pg.prop(H, delay2)
Udelay3 = pg.prop(H, delay3)
Udelay4 = pg.prop(H, delay4)
Udelay5 = pg.prop(H, delay5)
sigma0 = pg.sigma_eq(spin_system) # init
sigma1 = Ureal90.evolve(sigma0) # apply 90-degree pulse
sigma0 = pg.evolve(sigma1, Udelay1)
sigma1 = Ureal180.evolve(sigma0) # apply AFP1
sigma0 = pg.evolve(sigma1, Udelay2)
sigma1 = Ureal180.evolve(sigma0) # apply AFP2
sigma0 = pg.evolve(sigma1, Udelay3)
sigma1 = Ureal180.evolve(sigma0) # apply AFP3
sigma0 = pg.evolve(sigma1, Udelay4)
sigma1 = Ureal180.evolve(sigma0) # apply AFP4
sigma0 = pg.evolve(sigma1, Udelay5)
elif self.sim_experiment.name == "LASER":
spin_system = pg.spin_system()
spin_system.read(self.insysfile)
for i in range(spin_system.spins()):
spin_system.PPM(
i,
spin_system.PPM(i) - self.sim_experiment.RF_OFFSET)
# build 90 degree AHP pulse
inpulse90file = self.sim_experiment.inpulse90file
A_90 = self.sim_experiment.A_90
PULSE_90_LENGTH = self.sim_experiment.PULSE_90_LENGTH
gyratio = self.sim_experiment.getGyratio()
pulse90 = Pulse(inpulse90file, PULSE_90_LENGTH, 'varian')
n_new = np.linspace(0, PULSE_90_LENGTH, 256)
waveform_real = np.real(pulse90.waveform) * A_90
waveform_imag = np.imag(pulse90.waveform) * A_90
pulse90.waveform = waveform_real + 1j * (waveform_imag)
ampl_arr = np.abs(pulse90.waveform) * gyratio
phas_arr = np.unwrap(np.angle(pulse90.waveform)) * 180.0 / math.pi
pulse = pg.row_vector(len(pulse90.waveform))
ptime = pg.row_vector(len(pulse90.waveform))
for j, val in enumerate(zip(ampl_arr, phas_arr)):
pulse.put(pg.complex(val[0], val[1]), j)
ptime.put(pg.complex(pulse90.pulsestep, 0), j)
pulse_dur_90 = pulse.size() * pulse90.pulsestep
pwf_90 = pg.PulWaveform(pulse, ptime, "90excite")
pulc_90 = pg.PulComposite(pwf_90, spin_system,
self.sim_experiment.obs_iso)
Ureal90 = pulc_90.GetUsum(-1)
# build 180 degree pulse
inpulse180file = self.sim_experiment.inpulse180file
A_180 = self.sim_experiment.A_180
PULSE_180_LENGTH = self.sim_experiment.PULSE_180_LENGTH
gyratio = self.sim_experiment.getGyratio()
pulse180 = Pulse(inpulse180file, PULSE_180_LENGTH, 'varian')
n_new = np.linspace(0, PULSE_180_LENGTH, 512)
waveform_real = np.real(pulse180.waveform) * A_180
waveform_imag = np.imag(pulse180.waveform) * A_180
pulse180.waveform = waveform_real + 1j * (waveform_imag)
ampl_arr = np.abs(pulse180.waveform) * gyratio
phas_arr = np.unwrap(np.angle(pulse180.waveform)) * 180.0 / math.pi
freq_arr = np.gradient(phas_arr)
pulse = pg.row_vector(len(pulse180.waveform))
ptime = pg.row_vector(len(pulse180.waveform))
for j, val in enumerate(zip(ampl_arr, phas_arr)):
pulse.put(pg.complex(val[0], val[1]), j)
ptime.put(pg.complex(n_new[1], 0), j)
pulse_dur_180 = pulse.size() * pulse180.pulsestep
pwf_180 = pg.PulWaveform(pulse, ptime, "180afp")
pulc_180 = pg.PulComposite(pwf_180, spin_system,
self.sim_experiment.obs_iso)
Ureal180 = pulc_180.GetUsum(-1)
# calculate pulse timings
ROF1 = 100E-6 #sec
ROF2 = 10E-6 #sec
TCRUSH1 = 0.0008 #sec
TCRUSH2 = 0.0008 #sec
ss_grad_rfDelayFront = 0 #TCRUSH1 - ROF1
ss_grad_rfDelayBack = 0 #TCRUSH2 - ROF2
ro_grad_atDelayFront = 0
ro_grad_atDelayBack = 0
TE = self.sim_experiment.TE / 1000.
ipd = (TE - pulse_dur_90 \
- 6*(ss_grad_rfDelayFront + pulse_dur_180 + ss_grad_rfDelayBack) \
- ro_grad_atDelayFront) / 12
delay1 = ipd + ss_grad_rfDelayFront
delay2 = ss_grad_rfDelayBack + 2 * ipd + ss_grad_rfDelayFront
delay3 = ss_grad_rfDelayBack + 2 * ipd + ss_grad_rfDelayFront
delay4 = ss_grad_rfDelayBack + 2 * ipd + ss_grad_rfDelayFront
delay5 = ss_grad_rfDelayBack + 2 * ipd + ss_grad_rfDelayFront
delay6 = ss_grad_rfDelayBack + 2 * ipd + ss_grad_rfDelayFront
delay7 = ss_grad_rfDelayBack + ipd + ro_grad_atDelayFront
# print A_90, A_180, pulse_dur_90, pulse_dur_180
# print TE, ipd, pulse_dur_90+6*pulse_dur_180, delay1+delay2+delay3+delay4+delay5+delay6+delay7, pulse_dur_90+6*pulse_dur_180+delay1+delay2+delay3+delay4+delay5+delay6+delay7
# print ''
# initialize acquisition
H = pg.Hcs(spin_system) + pg.HJ(spin_system)
D = pg.Fm(spin_system, self.sim_experiment.obs_iso)
ac = pg.acquire1D(pg.gen_op(D), H, self.sim_experiment.dwell_time)
ACQ = ac
Udelay1 = pg.prop(H, delay1)
Udelay2 = pg.prop(H, delay2)
Udelay3 = pg.prop(H, delay3)
Udelay4 = pg.prop(H, delay4)
Udelay5 = pg.prop(H, delay5)
Udelay6 = pg.prop(H, delay6)
Udelay7 = pg.prop(H, delay7)
sigma0 = pg.sigma_eq(spin_system) # init
sigma1 = Ureal90.evolve(sigma0) # apply 90-degree pulse
sigma0 = pg.evolve(sigma1, Udelay1)
sigma1 = Ureal180.evolve(sigma0) # apply AFP1
sigma0 = pg.evolve(sigma1, Udelay2)
sigma1 = Ureal180.evolve(sigma0) # apply AFP2
sigma0 = pg.evolve(sigma1, Udelay3)
sigma1 = Ureal180.evolve(sigma0) # apply AFP3
sigma0 = pg.evolve(sigma1, Udelay4)
sigma1 = Ureal180.evolve(sigma0) # apply AFP4
sigma0 = pg.evolve(sigma1, Udelay5)
sigma1 = Ureal180.evolve(sigma0) # apply AFP5
sigma0 = pg.evolve(sigma1, Udelay6)
sigma1 = Ureal180.evolve(sigma0) # apply AFP6
sigma0 = pg.evolve(sigma1, Udelay7)
# acquire
mx = pg.TTable1D(ACQ.table(sigma0))
# binning to remove degenerate peaks
# BINNING
# Note: Metabolite Peak Normalization and Blending
# The transition tables calculated by the GAMMA density matrix simulations frequently contain a
# large number of transitions caused by degenerate splittings and other processes. At the
# conclusion of each simulation run a routine is called to extract lines from the transition table.
# These lines are then normalized using a closed form calculation based on the number of spins.
# To reduce the number of lines required for display, multiple lines are blended by binning them
# together based on their PPM locations and phases. The following parameters are used to
# customize these procedures:
# Peak Search Range -- Low/High (PPM): the range in PPM that is searched for lines from the
# metabolite simulation.
# Peak Blending Tolerance (PPM and Degrees): the width of the bins (+/- in PPM and +/- in
# PhaseDegrees) that are used to blend the lines in the simulation. Lines that are included in the
# same bin are summed using complex addition based on Amplitude and Phase.
b0 = self.sim_experiment.b0
obs_iso = self.sim_experiment.obs_iso
tolppm = self.sim_experiment.tolppm
tolpha = self.sim_experiment.tolpha
ppmlo = self.sim_experiment.ppmlo
ppmhi = self.sim_experiment.ppmhi
rf_off = self.sim_experiment.RF_OFFSET
field = b0
nspins = spin_system.spins()
nlines = mx.size()
tmp = pg.Isotope(obs_iso)
obs_qn = tmp.qn()
qnscale = 1.0
for i in range(nspins):
qnscale *= 2 * spin_system.qn(i) + 1
qnscale = qnscale / (2.0 * (2.0 * obs_qn + 1))
freqs = []
outf = []
outa = []
outp = []
nbin = 0
found = False
PI = 3.14159265358979323846
RAD2DEG = 180.0 / PI
indx = mx.Sort(0, -1, 0)
for i in range(nlines):
freqs.append(-1 * mx.Fr(indx[i]) / (2.0 * PI * field))
for i in range(nlines):
freq = freqs[i]
if (freq > ppmlo) and (freq < ppmhi):
val = mx.I(indx[i])
tmpa = np.sqrt(val.real()**2 + val.imag()**2) / qnscale
tmpp = -RAD2DEG * np.angle(val.real() + 1j * val.imag())
if nbin == 0:
outf.append(freq)
outa.append(tmpa)
outp.append(tmpp)
nbin += 1
else:
for k in range(nbin):
if (freq >= outf[k] - tolppm) and (freq <=
outf[k] + tolppm):
if (tmpp >= outp[k] - tolpha) and (tmpp <=
outp[k] + tolpha):
ampsum = outa[k] + tmpa
outf[k] = (outa[k] * outf[k] +
tmpa * freq) / ampsum
outp[k] = (outa[k] * outp[k] +
tmpa * tmpp) / ampsum
outa[k] += tmpa
found = True
if not found:
outf.append(freq)
outa.append(tmpa)
outp.append(tmpp)
nbin += 1
found = False
for i, item in enumerate(outf):
outf[i] = item + rf_off
outp[i] = outp[i] - 90.0
metab = Metabolite()
metab.name = metab_name
metab.var = 0.0
for i in range(sp.size(outf)):
if outf[i] <= 5:
metab.ppm.append(outf[i])
metab.area.append(outa[i])
metab.phase.append(-1.0 * outp[i])
insysfile = self.insysfile.replace('pints/metabolites/3T_', '')
insysfile = self.insysfile.replace('pints/metabolites/7T_', '')
insysfile = self.insysfile.replace('pints/metabolites/9.4T_', '')
if insysfile == 'alanine.sys': #
metab.A_m = 0.078
metab.T2 = (87E-3)
elif insysfile == 'aspartate.sys':
metab.A_m = 0.117
metab.T2 = (87E-3)
elif insysfile == 'choline_1-CH2_2-CH2.sys': #
metab.A_m = 0.165
metab.T2 = (87E-3)
elif insysfile == 'choline_N(CH3)3_a.sys' or insysfile == 'choline_N(CH3)3_b.sys': #
metab.A_m = 0.165
metab.T2 = (121E-3)
elif insysfile == 'creatine_N(CH3).sys':
metab.A_m = 0.296
metab.T2 = (90E-3)
elif insysfile == 'creatine_X.sys':
metab.A_m = 0.296
metab.T2 = (81E-3)
elif insysfile == 'd-glucose-alpha.sys': #
metab.A_m = 0.049
metab.T2 = (87E-3)
elif insysfile == 'd-glucose-beta.sys': #
metab.A_m = 0.049
metab.T2 = (87E-3)
elif insysfile == 'eth.sys': #
metab.A_m = 0.320
metab.T2 = (87E-3)
elif insysfile == 'gaba.sys': #
metab.A_m = 0.155
metab.T2 = (82E-3)
elif insysfile == 'glutamate.sys':
metab.A_m = 0.898
metab.T2 = (88E-3)
elif insysfile == 'glutamine.sys':
metab.A_m = 0.427
metab.T2 = (87E-3)
elif insysfile == 'glutathione_cysteine.sys':
metab.A_m = 0.194
metab.T2 = (87E-3)
elif insysfile == 'glutathione_glutamate.sys':
metab.A_m = 0.194
metab.T2 = (87E-3)
elif insysfile == 'glutathione_glycine.sys':
metab.A_m = 0.194
metab.T2 = (87E-3)
elif insysfile == 'glycine.sys':
metab.A_m = 0.068
metab.T2 = (87E-3)
elif insysfile == 'gpc_7-CH2_8-CH2.sys': #
metab.A_m = 0.097
metab.T2 = (87E-3)
elif insysfile == 'gpc_glycerol.sys': #
metab.A_m = 0.097
metab.T2 = (87E-3)
elif insysfile == 'gpc_N(CH3)3_a.sys': #
metab.A_m = 0.097
metab.T2 = (121E-3)
elif insysfile == 'gpc_N(CH3)3_b.sys': #
metab.A_m = 0.097
metab.T2 = (121E-3)
elif insysfile == 'lactate.sys': #
metab.A_m = 0.039
metab.T2 = (87E-3)
elif insysfile == 'myoinositol.sys':
metab.A_m = 0.578
metab.T2 = (87E-3)
elif insysfile == 'naa_acetyl.sys':
metab.A_m = 1.000
metab.T2 = (130E-3)
elif insysfile == 'naa_aspartate.sys':
metab.A_m = 1.000
metab.T2 = (69E-3)
elif insysfile == 'naag_acetyl.sys':
metab.A_m = 0.160
metab.T2 = (130E-3)
elif insysfile == 'naag_aspartyl.sys':
metab.A_m = 0.160
metab.T2 = (87E-3)
elif insysfile == 'naag_glutamate.sys':
metab.A_m = 0.160
metab.T2 = (87E-3)
elif insysfile == 'pcho_N(CH3)3_a.sys': #
metab.A_m = 0.058
metab.T2 = (121E-3)
elif insysfile == 'pcho_N(CH3)3_b.sys': #
metab.A_m = 0.058
metab.T2 = (121E-3)
elif insysfile == 'pcho_X.sys': #
metab.A_m = 0.058
metab.T2 = (87E-3)
elif insysfile == 'pcr_N(CH3).sys':
metab.A_m = 0.422
metab.T2 = (90E-3)
elif insysfile == 'pcr_X.sys':
metab.A_m = 0.422
metab.T2 = (81E-3)
elif insysfile == 'peth.sys':
metab.A_m = 0.126
metab.T2 = (87E-3)
elif insysfile == 'scyllo-inositol.sys':
metab.A_m = 0.044
metab.T2 = (87E-3)
elif insysfile == 'taurine.sys':
metab.A_m = 0.117
metab.T2 = (85E-3)
elif insysfile == 'water.sys':
metab.A_m = 1.000
metab.T2 = (43.60E-3)
# Send save data signal
self.outputResults.emit(metab)
self.postToConsole.emit(' | Simulation completed for ... ' +
self.insysfile)
self.finished.emit(self.thread_num)
0: { 'sw': 1/dt2,
'dw': dt2,
'complex': True,
'obs': 400.0,
'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'],
printsep(iso, f)
# Basics.IsotopeData
iso = pg.IsotopeData()
print(iso, file=f)
iso = pg.IsotopeData('1H')
printsep(iso, f)
sys = pg.spin_system()
print(sys, file=f) # empty
sys.read(infile)
printsep(sys, f)
# HSLib.GenOp
# nb. empty gen_op() throws exception
gop = pg.Hcs(sys)
printsep(gop, f)
gop_list = gop.toList()
printsep(gop_list, f)
gop_np = gop.toNParray()
printsep(gop_np, f)
# HSLib.SpinOp
ax = pg.spin_sys(2)
sop = pg.spin_op()
printsep(sop, f)
sop = pg.Fx(ax)
printsep(sop, f)
sop = pg.Fx(ax) + pg.Fy(ax)
printsep(sop, f)
mx = pg.matrix()