mirror of
https://github.com/nqrduck/nqr-blochsimulator.git
synced 2024-06-01 13:09:13 +00:00
Blochsimulator works and produces same results as matlab script.
This commit is contained in:
jupfi
parent
a0470a05e3
commit
52db21e337
3
.gitignore
vendored
3
.gitignore
vendored
|
|
@ -1 +1,2 @@
|
|||
venv/
|
||||
venv/
|
||||
__pycache__/
|
||||
47
src/nqr_blochsimulator/classes/pulse.py
Normal file
47
src/nqr_blochsimulator/classes/pulse.py
Normal file
|
|
@ -0,0 +1,47 @@
|
|||
import numpy as np
|
||||
|
||||
class PulseArray:
|
||||
"""A class to represent a pulsearray for a NQR sequence."""
|
||||
|
||||
def __init__(self, pulseamplitude, pulsephase, dwell_time) -> None:
|
||||
"""
|
||||
Constructs all the necessary attributes for the pulsearray object.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
pulseamplitude : float
|
||||
The amplitude of the pulse.
|
||||
pulsephase : float
|
||||
The phase of the pulse.
|
||||
dwell_time : float
|
||||
The dwell time of the pulse.
|
||||
"""
|
||||
self.pulseamplitude = pulseamplitude
|
||||
self.pulsephase = pulsephase
|
||||
self.dwell_time = dwell_time
|
||||
|
||||
def get_real_pulsepower(self) -> np.array:
|
||||
"""Returns the real part of the pulse power."""
|
||||
return self.pulseamplitude * np.cos(self.pulsephase)
|
||||
|
||||
def get_imag_pulsepower(self) -> np.array:
|
||||
"""Returns the imaginary part of the pulse power."""
|
||||
return self.pulseamplitude * np.sin(self.pulsephase)
|
||||
|
||||
@property
|
||||
def pulseamplitude(self) -> np.array:
|
||||
"""Amplitude of the pulse."""
|
||||
return self._pulseamplitude
|
||||
|
||||
@pulseamplitude.setter
|
||||
def pulseamplitude(self, pulseamplitude):
|
||||
self._pulseamplitude = pulseamplitude
|
||||
|
||||
@property
|
||||
def pulsephase(self) -> np.array:
|
||||
"""Phase of the pulse."""
|
||||
return self._pulsephase
|
||||
|
||||
@pulsephase.setter
|
||||
def pulsephase(self, pulsephase):
|
||||
self._pulsephase = pulsephase
|
||||
|
|
@ -24,6 +24,8 @@ class Sample:
|
|||
T2_star,
|
||||
atom_density=None,
|
||||
sample_volume=None,
|
||||
sample_length=None,
|
||||
sample_diameter=None
|
||||
):
|
||||
"""
|
||||
Constructs all the necessary attributes for the sample object.
|
||||
|
|
@ -58,6 +60,10 @@ class Sample:
|
|||
The atom density of the sample (atoms per cm^3). By default None.
|
||||
sample_volume : float, optional
|
||||
The volume of the sample (m^3). By default None.
|
||||
sample_length : float, optional
|
||||
The length of the sample (m). By default None.
|
||||
sample_diameter : float, optional
|
||||
The diameter of the sample (m). By default None.
|
||||
"""
|
||||
self.name = name
|
||||
self.density = density
|
||||
|
|
@ -73,11 +79,14 @@ class Sample:
|
|||
self.T2_star = T2_star
|
||||
self.atom_density = atom_density
|
||||
self.sample_volume = sample_volume
|
||||
self.sample_length = sample_length
|
||||
self.sample_diameter = sample_diameter
|
||||
self.calculate_atoms()
|
||||
|
||||
def calculate_atoms(self):
|
||||
"""
|
||||
Calculate the number of atoms in the sample per volume unit.
|
||||
Calculate the number of atoms in the sample per volume unit. This only works if the sample volume and atom density are provided.
|
||||
Also the sample should be cylindrical.
|
||||
|
||||
If atom density and sample volume are provided, use these to calculate the number of atoms.
|
||||
If not, use Avogadro's number, density, and molar mass to calculate the number of atoms.
|
||||
|
|
@ -87,7 +96,7 @@ class Sample:
|
|||
self.atom_density
|
||||
* self.sample_volume
|
||||
/ 1e-6
|
||||
/ (self.sample_volume * 6 / 3)
|
||||
/ (self.sample_volume * self.sample_length / self.sample_diameter)
|
||||
)
|
||||
else:
|
||||
self.atoms = self.avogadro * self.density / self.molar_mass
|
||||
|
|
|
|||
|
|
@ -1,148 +1,114 @@
|
|||
import numpy as np
|
||||
from numpy import pi
|
||||
import logging
|
||||
from scipy import signal
|
||||
from .sample import Sample
|
||||
from .pulse import PulseArray
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
logger.setLevel(logging.DEBUG)
|
||||
logger.addHandler(logging.StreamHandler())
|
||||
|
||||
class Simulation:
|
||||
def __init__(self) -> None:
|
||||
pass
|
||||
"""Class for the simulation of the Bloch equations."""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
sample : Sample,
|
||||
number_isochromats : int,
|
||||
initial_magnetization : float,
|
||||
gradient : float,
|
||||
noise : float,
|
||||
length_coil : float,
|
||||
diameter_coil : float,
|
||||
number_turns : float,
|
||||
power_amplifier_power : float,
|
||||
pulse : PulseArray
|
||||
):
|
||||
"""
|
||||
Constructs all the necessary attributes for the simulation object.
|
||||
|
||||
def blochsim(sim_points, sim_time, reference, isochrom, sample, pulse):
|
||||
# PRE-SETTINGS
|
||||
d = {"M0c": 1} # initial mag
|
||||
NISO = 100
|
||||
if isochrom > 0:
|
||||
NISO = isochrom # number of isochromates
|
||||
nsamples = (
|
||||
sim_points # number of sample/rasterization points for the calculation
|
||||
)
|
||||
sim_length = sim_time # in s; Not larger than the repetition time!
|
||||
modulation = "OFF" # select a optional modulation of the pulse ['OFF','SIN']
|
||||
# Replace by the NWA power later
|
||||
B1c_calc = np.sqrt(2 * 500 / 50) * pi * 4e-7 * 9 / 6e-3 # 17 od 8.5
|
||||
d["B1c"] = 17.3e-3 # for Peak B1 T %12.5%14.3
|
||||
|
||||
# SAMPLE SETTINGS
|
||||
d["T1"] = sample["T1"] # in s; T1, T2
|
||||
d["T2"] = sample["T2"] #
|
||||
T2STAR = sample["T2s"] # only used for some calculations
|
||||
d["gamma"] = (
|
||||
sample["gamma"] / (2 * pi) / 1e6
|
||||
) # gamma in MHz/T eg 5e6 % sample.gamma in rad/(T s) eg 0.8
|
||||
d["relax"] = 1 # Flag 1: with Relax, 0 without Relax
|
||||
|
||||
# Parameter preparation
|
||||
|
||||
# clear up some unit problems
|
||||
# DO NOT CHANGE
|
||||
d["B1c"] = d["B1c"] * 1e3
|
||||
d["T1"] = d["T1"] * 1e3
|
||||
d["T2"] = d["T2"] * 1e3
|
||||
d["gamma"] = d["gamma"] * 2 * pi
|
||||
|
||||
d["Nx"] = NISO
|
||||
d["M0"] = np.array(
|
||||
[np.zeros(NISO), np.zeros(NISO), np.ones(NISO)]
|
||||
) # initial magnetization
|
||||
d["dt"] = sim_length / nsamples # time step width
|
||||
d["dt"] = (
|
||||
d["dt"] * 1e3
|
||||
) # again unit correction. could be changed if necessary, but other time factors
|
||||
# in later calculations would have to be changed too
|
||||
|
||||
# Pulse Designer
|
||||
u = np.zeros((nsamples, 1))
|
||||
v = np.zeros((nsamples, 1))
|
||||
w = np.ones((nsamples, 1))
|
||||
|
||||
tt = (np.array(range(1, nsamples + 1)) * d["dt"] - d["dt"]).reshape(
|
||||
-1, 1
|
||||
) # time axis in ms
|
||||
|
||||
# PULSE TEMPLATES
|
||||
pulse_dur_pow_pha = pulse
|
||||
num_pulses, _ = pulse_dur_pow_pha.shape
|
||||
# loop through every pulse
|
||||
for count in range(num_pulses):
|
||||
pulse_begin = pulse_dur_pow_pha[count, 0]
|
||||
pulse_end = pulse_dur_pow_pha[count, 1]
|
||||
pha = pulse_dur_pow_pha[count, 3] * (2 * pi / 360) # phase in rad
|
||||
|
||||
ind_begin = np.argmin(np.abs(tt * 1e-3 - pulse_begin)) # minValue is unused
|
||||
ind_end = np.argmin(np.abs(tt * 1e-3 - pulse_end))
|
||||
ind_end = ind_end - 1
|
||||
|
||||
u_pow, v_pow = np.pol2cart(
|
||||
pha, pulse_dur_pow_pha[count, 2]
|
||||
) # theta angle; rho abs
|
||||
|
||||
if modulation == "OFF":
|
||||
u[ind_begin:ind_end, 0] = u_pow # set real pulse power
|
||||
v[ind_begin:ind_end, 0] = v_pow # set imag pulse power
|
||||
elif modulation == "SIN":
|
||||
u[ind_begin:ind_end, 0] = u_pow * np.sin(
|
||||
(pi * 1e-3 / (pulse_end - pulse_begin)) * tt[ind_begin:ind_end, 0]
|
||||
)
|
||||
v[ind_begin:ind_end, 0] = v_pow * np.sin(
|
||||
(pi * 1e-3 / (pulse_end - pulse_begin)) * tt[ind_begin:ind_end, 0]
|
||||
)
|
||||
|
||||
# Some sidenotes that can be ignored
|
||||
for count in range(1):
|
||||
d["G3"] = 1 # mT/m, fhwm of 2mm Gradient scaling
|
||||
w = w * d["G3"] # Gradient in mT/m
|
||||
|
||||
# Isochromatic simulaten by modeling with Lorentz distribution
|
||||
Df = 1 / pi / T2STAR # FWHF of Lorentzian in Hz
|
||||
foffr = []
|
||||
uu = np.random.rand(NISO, 1) - 0.5
|
||||
foffr = Df / 2 * np.tan(pi * uu) # cauchy distributed frequency offset
|
||||
|
||||
d["xdis"] = np.linspace(-1, 1, NISO) # in m spatial resolution
|
||||
d["xdis"] = (
|
||||
np.array(foffr) * 1e-6 / (d["gamma"] / 2 / pi) / (d["G3"] * 1e-3)
|
||||
) # Conversion factors: foffr from Hz/T to MHz/T as required for d.gamma/2/pi, conversion from Hz-Gamma to radian gamma, and gradient must be scaled from mT/m to T/m
|
||||
|
||||
# USE BLOCH EQUATIONS
|
||||
# M_sy1 = bloch_symmetric_strang_splitting_vectorised(u, v, w, d) # This function would need to be defined or imported
|
||||
|
||||
# Z-Component
|
||||
# Mlong = np.squeeze(M_sy1[2, :, :]) # Coordinates M: space components - location(isochromat) - time
|
||||
# Mlong_avg = np.mean(Mlong, 1)
|
||||
# Mlong_avg = Mlong_avg[:-1]
|
||||
# siglong = np.abs(Mlong_avg)
|
||||
|
||||
# XY-Component
|
||||
# Mtrans = np.squeeze(M_sy1[0, :, :] + 1j*M_sy1[1, :, :]) # Coordinates M: space components - location(isochromat) - time
|
||||
# Mtrans_avg = np.mean(Mtrans, 1)
|
||||
# Mtrans_avg = Mtrans_avg[:-1]
|
||||
# sigtrans = Mtrans_avg * reference
|
||||
|
||||
# return sigtrans
|
||||
|
||||
def bloch_symmetric_strang_splitting_vectorised(u, v, w, d):
|
||||
"""Vectorised version of bloch_symmetric_strang_splitting
|
||||
|
||||
Parameters
|
||||
----------
|
||||
u : array_like
|
||||
Real part of pulse
|
||||
v : array_like
|
||||
Imaginary part of pulse
|
||||
w : array_like
|
||||
Gradient
|
||||
d : dict
|
||||
sample : Sample
|
||||
The sample that is used for the simulation.
|
||||
number_isochromats : int
|
||||
The number of isochromats used for the simulation.
|
||||
initial_magnetization : float
|
||||
The initial magnetization of the sample.
|
||||
gradient : float
|
||||
The gradient of the magnetic field in mt/M.
|
||||
noise : float
|
||||
The RMS Noise of the measurement setup in Volts.
|
||||
length_coil : float
|
||||
The length of the coil in meters.
|
||||
diameter_coil : float
|
||||
The diameter of the coil in meters.
|
||||
number_turns : float
|
||||
The number of turns of the coil.
|
||||
power_amplifier_power : float
|
||||
The power of the power amplifier in Watts.
|
||||
puslse: PulseArray
|
||||
The pulse that is used for the simulation.
|
||||
"""
|
||||
xdis = d["xdis"]
|
||||
Nx = d["Nx"]
|
||||
Nu = len(u)
|
||||
M0 = d["M0"]
|
||||
dt = d["dt"]
|
||||
self.sample = sample
|
||||
self.number_isochromats = number_isochromats
|
||||
self.initial_magnetization = initial_magnetization
|
||||
self.gradient = gradient
|
||||
self.noise = noise
|
||||
self.length_coil = length_coil
|
||||
self.diameter_coil = diameter_coil
|
||||
self.number_turns = number_turns
|
||||
self.power_amplifier_power = power_amplifier_power
|
||||
self.pulse = pulse
|
||||
|
||||
gadt = d["gamma"] * dt / 2
|
||||
B1 = np.tile(gadt * np.transpose(u - 1j * v) * d["B1c"], (Nx, 1))
|
||||
K = gadt * xdis * np.transpose(w) * d["G3"]
|
||||
phi = -np.sqrt(np.abs(B1) ** 2 + K**2)
|
||||
def simulate(self):
|
||||
B1 = self.calc_B1()
|
||||
xdis = self.calc_xdis()
|
||||
|
||||
real_pulsepower = self.pulse.get_real_pulsepower()
|
||||
imag_pulsepower = self.pulse.get_imag_pulsepower()
|
||||
M_sy1 = self.bloch_symmetric_strang_splitting(B1, xdis, real_pulsepower, imag_pulsepower)
|
||||
logger.debug("Shape of Msy1: %s", M_sy1.shape)
|
||||
|
||||
|
||||
# Z-Component
|
||||
Mlong = np.squeeze(M_sy1[2,:,:]) # Indices start at 0 in Python
|
||||
Mlong_avg = np.mean(Mlong, axis=0)
|
||||
Mlong_avg = np.delete(Mlong_avg, -1) # Remove the last element
|
||||
siglong = np.abs(Mlong_avg)
|
||||
|
||||
# XY-Component
|
||||
Mtrans = np.squeeze(M_sy1[0,:,:] + 1j*M_sy1[1,:,:]) # Indices start at 0 in Python
|
||||
Mtrans_avg = np.mean(Mtrans, axis=0)
|
||||
Mtrans_avg = np.delete(Mtrans_avg, -1) # Remove the last element
|
||||
reference = 4.5502
|
||||
sigtrans = Mtrans_avg * reference
|
||||
return sigtrans
|
||||
|
||||
|
||||
def bloch_symmetric_strang_splitting(self, B1, xdis, real_pulsepower, imag_pulsepower, relax = 1):
|
||||
"""This method simulates the Bloch equations using the symmetric strang splitting method.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
B1 : float
|
||||
The B1 field of the solenoid coil.
|
||||
xdis : np.array
|
||||
The x distribution of the isochromats.
|
||||
"""
|
||||
Nx = self.number_isochromats
|
||||
Nu = real_pulsepower.shape[0]
|
||||
M0 = np.array([np.zeros(Nx), np.zeros(Nx), np.ones(Nx)])
|
||||
dt = self.pulse.dwell_time
|
||||
|
||||
w = np.ones((Nu, 1)) * self.gradient
|
||||
|
||||
# Bloch simulation in magnetization domain
|
||||
gadt = self.sample.gamma * dt /2 * 1e-3
|
||||
B1 = np.tile((gadt * (real_pulsepower - 1j * imag_pulsepower) * B1).reshape(-1, 1), Nx)
|
||||
K = gadt * xdis * w * self.gradient
|
||||
phi = -np.sqrt(np.abs(B1) ** 2 + K ** 2)
|
||||
|
||||
cs = np.cos(phi)
|
||||
si = np.sin(phi)
|
||||
|
|
@ -162,40 +128,148 @@ class Simulation:
|
|||
Bd8 = n3 * n2 * (1 - cs) + n1 * si
|
||||
Bd9 = n3 * n3 * (1 - cs) + cs
|
||||
|
||||
M = np.zeros((3, Nx, Nu))
|
||||
M = np.zeros((3, Nx, Nu+1))
|
||||
M[:, :, 0] = M0
|
||||
Mt = M0
|
||||
D = np.diag(
|
||||
[
|
||||
np.exp(-1 / d["T2"] * d["relax"] * dt),
|
||||
np.exp(-1 / d["T2"] * d["relax"] * dt),
|
||||
np.exp(-1 / d["T1"] * d["relax"] * dt),
|
||||
]
|
||||
)
|
||||
b = np.array([0, 0, d["M0c"]]) - np.array(
|
||||
[0, 0, d["M0c"] * np.exp(-1 / d["T1"] * d["relax"] * dt)]
|
||||
)
|
||||
D = np.diag([np.exp(-1 / self.sample.T2 * relax * dt), np.exp(-1 / self.sample.T2 * relax * dt), np.exp(-1 / self.sample.T1 * relax * dt)])
|
||||
b = np.array([0, 0, self.initial_magnetization]) - np.array([0, 0, self.initial_magnetization * np.exp(-1 / self.sample.T1 * relax * dt)])
|
||||
|
||||
for n in range(Nu): # Time loop
|
||||
Mrot = np.array(
|
||||
[
|
||||
Bd1[:, n] * Mt[0, :] + Bd2[:, n] * Mt[1, :] + Bd3[:, n] * Mt[2, :],
|
||||
Bd4[:, n] * Mt[0, :] + Bd5[:, n] * Mt[1, :] + Bd6[:, n] * Mt[2, :],
|
||||
Bd7[:, n] * Mt[0, :] + Bd8[:, n] * Mt[1, :] + Bd9[:, n] * Mt[2, :],
|
||||
]
|
||||
)
|
||||
for n in range(Nu): # time loop
|
||||
|
||||
Mt = np.dot(D, Mrot) + np.tile(b, (Nx, 1)).transpose()
|
||||
Mrot = np.zeros((3, Nx))
|
||||
Mrot[0,:] = Bd1.T[:,n]*Mt[0,:] + Bd2.T[:,n]*Mt[1,:] + Bd3.T[:,n]*Mt[2,:]
|
||||
Mrot[1,:] = Bd4.T[:,n]*Mt[0,:] + Bd5.T[:,n]*Mt[1,:] + Bd6.T[:,n]*Mt[2,:]
|
||||
Mrot[2,:] = Bd7.T[:,n]*Mt[0,:] + Bd8.T[:,n]*Mt[1,:] + Bd9.T[:,n]*Mt[2,:]
|
||||
|
||||
Mrot = np.array(
|
||||
[
|
||||
Bd1[:, n] * Mt[0, :] + Bd2[:, n] * Mt[1, :] + Bd3[:, n] * Mt[2, :],
|
||||
Bd4[:, n] * Mt[0, :] + Bd5[:, n] * Mt[1, :] + Bd6[:, n] * Mt[2, :],
|
||||
Bd7[:, n] * Mt[0, :] + Bd8[:, n] * Mt[1, :] + Bd9[:, n] * Mt[2, :],
|
||||
]
|
||||
)
|
||||
Mt = np.dot(D, Mrot) + np.tile(b, (Nx, 1)).T
|
||||
|
||||
Mrot[0,:] = Bd1.T[:,n]*Mt[0,:] + Bd2.T[:,n]*Mt[1,:] + Bd3.T[:,n]*Mt[2,:]
|
||||
Mrot[1,:] = Bd4.T[:,n]*Mt[0,:] + Bd5.T[:,n]*Mt[1,:] + Bd6.T[:,n]*Mt[2,:]
|
||||
Mrot[2,:] = Bd7.T[:,n]*Mt[0,:] + Bd8.T[:,n]*Mt[1,:] + Bd9.T[:,n]*Mt[2,:]
|
||||
|
||||
Mt = Mrot
|
||||
M[:, :, n + 1] = Mrot
|
||||
M[:, :,n+1] = Mrot
|
||||
|
||||
return M
|
||||
|
||||
def calc_B1(self) -> float:
|
||||
"""This method calculates the B1 field of our solenoid coil based on the coil parameters and the power amplifier power.
|
||||
|
||||
Returns
|
||||
-------
|
||||
B1 : float
|
||||
The B1 field of the solenoid coil."""
|
||||
|
||||
B1 = np.sqrt(2 * self.power_amplifier_power / 50) * np.pi * 4e-7 * self.number_turns / self.length_coil
|
||||
return B1
|
||||
|
||||
def calc_xdis(self) -> np.array:
|
||||
""" Calculates the x distribution of the isochromats.
|
||||
|
||||
Returns
|
||||
-------
|
||||
xdis : np.array
|
||||
The x distribution of the isochromats.
|
||||
"""
|
||||
# Df is the Full Width at Half Maximum (FWHM) of Lorentzian in Hz
|
||||
Df = 1 / np.pi / self.sample.T2_star
|
||||
logger.debug("Df: %s", Df)
|
||||
|
||||
# Randomly generating frequency offset using Cauchy distribution
|
||||
uu = np.random.rand(self.number_isochromats, 1) - 0.5
|
||||
foffr = Df / 2 * np.tan(np.pi * uu)
|
||||
|
||||
# xdis is a spatial function, but it is being repurposed here to convert through the gradient to a phase difference per time -> T2 dispersion of the isochromats
|
||||
xdis = np.linspace(-1, 1, self.number_isochromats)
|
||||
xdis = (foffr.T) / (self.sample.gamma / (2 * np.pi)) / (self.gradient * 1e-3)
|
||||
return xdis
|
||||
|
||||
@property
|
||||
def sample(self) -> Sample:
|
||||
"""Sample that is used for the simulation."""
|
||||
return self._sample
|
||||
|
||||
@sample.setter
|
||||
def sample(self, sample):
|
||||
self._sample = sample
|
||||
|
||||
@property
|
||||
def number_isochromats(self) -> int:
|
||||
"""Number of isochromats used for the simulation."""
|
||||
return self._number_isochromats
|
||||
|
||||
@number_isochromats.setter
|
||||
def number_isochromats(self, number_isochromats):
|
||||
self._number_isochromats = number_isochromats
|
||||
|
||||
@property
|
||||
def initial_magnetization(self) -> float:
|
||||
"""Initial magnetization of the sample."""
|
||||
return self._initial_magnetization
|
||||
|
||||
@initial_magnetization.setter
|
||||
def initial_magnetization(self, initial_magnetization):
|
||||
self._initial_magnetization = initial_magnetization
|
||||
|
||||
@property
|
||||
def gradient(self) -> float:
|
||||
"""Gradient of the magnetic field in mt/M."""
|
||||
return self._gradient
|
||||
|
||||
@gradient.setter
|
||||
def gradient(self, gradient):
|
||||
self._gradient = gradient
|
||||
|
||||
@property
|
||||
def noise(self) -> float:
|
||||
""" RMS Noise of the measurement setup in Volts"""
|
||||
return self._noise
|
||||
|
||||
@noise.setter
|
||||
def noise(self, noise):
|
||||
self._noise = noise
|
||||
|
||||
@property
|
||||
def length_coil(self) -> float:
|
||||
"""Length of the coil in meters."""
|
||||
return self._length_coil
|
||||
|
||||
@length_coil.setter
|
||||
def length_coil(self, length_coil):
|
||||
self._length_coil = length_coil
|
||||
|
||||
@property
|
||||
def diameter_coil(self) -> float:
|
||||
"""Diameter of the coil in meters."""
|
||||
return self._diameter_coil
|
||||
|
||||
@diameter_coil.setter
|
||||
def diameter_coil(self, diameter_coil):
|
||||
self._diameter_coil = diameter_coil
|
||||
|
||||
@property
|
||||
def number_turns(self) -> float:
|
||||
"""Number of turns of the coil."""
|
||||
return self._number_turns
|
||||
|
||||
@number_turns.setter
|
||||
def number_turns(self, number_turns):
|
||||
self._number_turns = number_turns
|
||||
|
||||
@property
|
||||
def power_amplifier_power(self) -> float:
|
||||
"""Power of the power amplifier in Watts."""
|
||||
return self._power_amplifier_power
|
||||
|
||||
@power_amplifier_power.setter
|
||||
def power_amplifier_power(self, power_amplifier_power):
|
||||
self._power_amplifier_power = power_amplifier_power
|
||||
|
||||
@property
|
||||
def pulse(self) -> PulseArray:
|
||||
"""Pulse that is used for the simulation."""
|
||||
return self._pulse
|
||||
|
||||
@pulse.setter
|
||||
def pulse(self, pulse):
|
||||
self._pulse = pulse
|
||||
|
|
|
|||
|
|
@ -1,25 +1,59 @@
|
|||
import unittest
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from nqr_blochsimulator.classes.sample import Sample
|
||||
from nqr_blochsimulator.classes.simulation import Simulation
|
||||
from nqr_blochsimulator.classes.pulse import PulseArray
|
||||
|
||||
class TestSimulation(unittest.TestCase):
|
||||
def setUp(self):
|
||||
self.sample = Sample(
|
||||
"Ammonium nitrate",
|
||||
1720,
|
||||
80.0433
|
||||
* 1e-3
|
||||
/ Simulation.avogadro, # molar mass in kg/mol
|
||||
1.945e6,
|
||||
2 * 3.436e8,
|
||||
1.5,
|
||||
0.5,
|
||||
1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
0.1,
|
||||
)
|
||||
self.simulation = Simulation(self.sample, 1e-6, 1e-6, 1e-6, 1e-6, 1e-6)
|
||||
|
||||
|
||||
self.sample = Sample(
|
||||
"BiPh3",
|
||||
density=1.585e6 ,#g/m^3
|
||||
molar_mass=440.3, #g/mol
|
||||
resonant_frequency=83.56e6, #Hz
|
||||
gamma=4.342e7, #Hz/T
|
||||
nuclear_spin=9/2,
|
||||
spin_factor=2,
|
||||
powder_factor=1,
|
||||
filling_factor=0.7,
|
||||
T1=82.6e-5, #s
|
||||
T2=396e-6, #s
|
||||
T2_star=50e-6, #s
|
||||
)
|
||||
|
||||
simulation_length = 300e-6
|
||||
dwell_time = 1e-6
|
||||
self.time_array = np.arange(0, simulation_length, dwell_time)
|
||||
pulse_length = 3e-6
|
||||
# Simple FID sequence with pulse length of 3µs
|
||||
pulse_amplitude_array = np.zeros(int(simulation_length/dwell_time))
|
||||
pulse_amplitude_array[:int(pulse_length/dwell_time)] = 1
|
||||
pulse_phase_array = np.zeros(int(simulation_length/dwell_time))
|
||||
|
||||
self.pulse = PulseArray(
|
||||
pulseamplitude=pulse_amplitude_array,
|
||||
pulsephase=pulse_phase_array,
|
||||
dwell_time=dwell_time
|
||||
)
|
||||
|
||||
self.simulation = Simulation(
|
||||
sample=self.sample,
|
||||
number_isochromats=1000,
|
||||
initial_magnetization=1,
|
||||
gradient=1,
|
||||
noise=0,
|
||||
length_coil=6e-3,
|
||||
diameter_coil=3e-3,
|
||||
number_turns=9,
|
||||
power_amplifier_power=500,
|
||||
pulse = self.pulse
|
||||
)
|
||||
|
||||
def test_simulation(self):
|
||||
M = self.simulation.simulate()
|
||||
|
||||
# Plotting the results
|
||||
plt.plot(self.time_array, abs(M))
|
||||
plt.show()
|
||||
Loading…
Reference in a new issue