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Adapted units.

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jupfi 2024-06-02 20:31:51 +02:00
parent c89dda1813
commit f845135dc2
2 changed files with 46 additions and 35 deletions
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24
src/nqr_blochsimulator/classes/sample.py
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@ -39,9 +39,9 @@ class Sample:
molar_mass : float
The molar mass of the sample (g/mol or kg/mol).
resonant_frequency : float
The resonant frequency of the sample in Hz.
The resonant frequency of the sample in MHz.
gamma : float
The gamma value of the sample in Hz/T.
The gamma value of the sample in MHz/T.
nuclear_spin : float
The nuclear spin quantum number of the sample.
spin_factor : float
@ -51,32 +51,32 @@ class Sample:
filling_factor : float
The filling factor of the sample.
T1 : float
The spin-lattice relaxation time of the sample in seconds.
The spin-lattice relaxation time of the sample in microseconds.
T2 : float
The spin-spin relaxation time of the sample in seconds.
The spin-spin relaxation time of the sample in microseconds.
T2_star : float
The effective spin-spin relaxation time of the sample in seconds.
The effective spin-spin relaxation time of the sample in microseconds.
atom_density : float, optional
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.
The length of the sample (mm). By default None.
sample_diameter : float, optional
The diameter of the sample (m). By default None.
The diameter of the sample m(m). By default None.
"""
self.name = name
self.density = density
self.molar_mass = molar_mass
self.resonant_frequency = resonant_frequency
self.gamma = gamma
self.resonant_frequency = resonant_frequency * 1e6
self.gamma = gamma * 1e6
self.nuclear_spin = nuclear_spin
self.spin_factor = spin_factor
self.powder_factor = powder_factor
self.filling_factor = filling_factor
self.T1 = T1
self.T2 = T2
self.T2_star = T2_star
self.T1 = T1 * 1e-6
self.T2 = T2 * 1e-6
self.T2_star = T2_star * 1e-6
self.atom_density = atom_density
self.sample_volume = sample_volume
self.sample_length = sample_length

57
src/nqr_blochsimulator/classes/simulation.py
View file

@ -23,8 +23,8 @@ class Simulation:
length_coil: float,
diameter_coil: float,
number_turns: float,
q_factor_transmit:float,
q_factor_receive:float,
q_factor_transmit: float,
q_factor_receive: float,
power_amplifier_power: float,
pulse: PulseArray,
averages: int,
@ -82,8 +82,8 @@ class Simulation:
self.initial_magnetization = initial_magnetization
self.gradient = gradient
self.noise = noise
self.length_coil = length_coil
self.diameter_coil = diameter_coil
self.length_coil = length_coil * 1e-3 # We need our length in meters
self.diameter_coil = diameter_coil * 1e-3 # We need our diameter in meters
self.number_turns = number_turns
self.q_factor_transmit = q_factor_transmit
self.q_factor_receive = q_factor_receive
@ -134,7 +134,6 @@ class Simulation:
Mtrans_avg = np.mean(Mtrans, axis=0)
Mtrans_avg = np.delete(Mtrans_avg, -1) # Remove the last element
# Scale the signal according to the reference voltage, averages and gain
timedomain_signal = Mtrans_avg * reference_voltage
@ -144,7 +143,9 @@ class Simulation:
# Add noise to the signal
noise_data = self.calculate_noise(timedomain_signal)
timedomain_signal = (timedomain_signal * self.averages * self.gain) + (noise_data * self.gain)
timedomain_signal = (timedomain_signal * self.averages * self.gain) + (
noise_data * self.gain
)
# print(abs(timedomain_signal))
timedomain_signal = timedomain_signal
@ -223,25 +224,37 @@ class Simulation:
for n in range(Nu): # time loop
Mrot = np.zeros((3, Nx))
Mrot[0, :] = (
Bd1.conj().T[:, n] * Mt[0, :] + Bd2.conj().T[:, n] * Mt[1, :] + Bd3.conj().T[:, n] * Mt[2, :]
Bd1.conj().T[:, n] * Mt[0, :]
+ Bd2.conj().T[:, n] * Mt[1, :]
+ Bd3.conj().T[:, n] * Mt[2, :]
)
Mrot[1, :] = (
Bd4.conj().T[:, n] * Mt[0, :] + Bd5.conj().T[:, n] * Mt[1, :] + Bd6.conj().T[:, n] * Mt[2, :]
Bd4.conj().T[:, n] * Mt[0, :]
+ Bd5.conj().T[:, n] * Mt[1, :]
+ Bd6.conj().T[:, n] * Mt[2, :]
)
Mrot[2, :] = (
Bd7.conj().T[:, n] * Mt[0, :] + Bd8.conj().T[:, n] * Mt[1, :] + Bd9.conj().T[:, n] * Mt[2, :]
Bd7.conj().T[:, n] * Mt[0, :]
+ Bd8.conj().T[:, n] * Mt[1, :]
+ Bd9.conj().T[:, n] * Mt[2, :]
)
Mt = np.dot(D, Mrot) + np.tile(b, (Nx, 1)).T
Mrot[0, :] = (
Bd1.conj().T[:, n] * Mt[0, :] + Bd2.conj().T[:, n] * Mt[1, :] + Bd3.conj().T[:, n] * Mt[2, :]
Bd1.conj().T[:, n] * Mt[0, :]
+ Bd2.conj().T[:, n] * Mt[1, :]
+ Bd3.conj().T[:, n] * Mt[2, :]
)
Mrot[1, :] = (
Bd4.conj().T[:, n] * Mt[0, :] + Bd5.conj().T[:, n] * Mt[1, :] + Bd6.conj().T[:, n] * Mt[2, :]
Bd4.conj().T[:, n] * Mt[0, :]
+ Bd5.conj().T[:, n] * Mt[1, :]
+ Bd6.conj().T[:, n] * Mt[2, :]
)
Mrot[2, :] = (
Bd7.conj().T[:, n] * Mt[0, :] + Bd8.conj().T[:, n] * Mt[1, :] + Bd9.conj().T[:, n] * Mt[2, :]
Bd7.conj().T[:, n] * Mt[0, :]
+ Bd8.conj().T[:, n] * Mt[1, :]
+ Bd9.conj().T[:, n] * Mt[2, :]
)
Mt = Mrot
@ -301,14 +314,12 @@ class Simulation:
u = 4 * np.pi * 1e-7 # permeability of free space
magnetization = (
((self.sample.gamma
* 2
* self.sample.atoms)
/ (2 * self.sample.nuclear_spin + 1))
* (h**2
* self.sample.resonant_frequency)
/ (Boltzmann
* self.temperature)
(
(self.sample.gamma * 2 * self.sample.atoms)
/ (2 * self.sample.nuclear_spin + 1)
)
* (h**2 * self.sample.resonant_frequency)
/ (Boltzmann * self.temperature)
* self.sample.spin_factor
)
@ -425,16 +436,16 @@ class Simulation:
def q_factor_transmit(self) -> float:
"""Q-factor of the transmit path of the probe coil."""
return self._q_factor_transmit
@q_factor_transmit.setter
def q_factor_transmit(self, q_factor_transmit):
def q_factor_transmit(self, q_factor_transmit):
self._q_factor_transmit = q_factor_transmit
@property
def q_factor_receive(self) -> float:
"""Q-factor of the receive path of the probe coil."""
return self._q_factor_receive
@q_factor_receive.setter
def q_factor_receive(self, q_factor_receive):
self._q_factor_receive = q_factor_receive