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https://github.com/nqrduck/nqr-blochsimulator.git
synced 2024-06-27 07:49:07 +00:00
Formatting.
This commit is contained in:
jupfi
parent
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commit
5b2b3645d1
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@ -1,5 +1,6 @@
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import numpy as np
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class PulseArray:
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"""A class to represent a pulsearray for a NQR sequence."""
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@ -25,7 +25,7 @@ class Sample:
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atom_density=None,
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sample_volume=None,
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sample_length=None,
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sample_diameter=None
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sample_diameter=None,
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):
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"""
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Constructs all the necessary attributes for the sample object.
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@ -9,6 +9,7 @@ logger = logging.getLogger(__name__)
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logger.setLevel(logging.DEBUG)
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logger.addHandler(logging.StreamHandler())
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class Simulation:
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"""Class for the simulation of the Bloch equations."""
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@ -29,7 +30,6 @@ class Simulation:
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temperature: float,
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loss_TX: float = 0,
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loss_RX: float = 0,
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) -> None:
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"""
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Constructs all the necessary attributes for the simulation object.
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@ -87,7 +87,9 @@ class Simulation:
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def simulate(self):
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reference_voltage = self.calculate_reference_voltage()
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B1 = self.calc_B1() * 1e3 # I think this is multiplied by 1e3 because everything is in mT
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B1 = (
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self.calc_B1() * 1e3
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) # I think this is multiplied by 1e3 because everything is in mT
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B1 = 17.3 # Something might be wrong with the calculation of the B1 field. This has to be checked.
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self.sample.gamma = self.sample.gamma * 1e-6 # We need our gamma in MHz / T
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self.sample.T1 = self.sample.T1 * 1e3 # We need our T1 in ms
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@ -104,7 +106,9 @@ class Simulation:
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imag_pulsepower = imag_pulsepower * (1 - 10 ** (-self.loss_TX / 20))
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# Calculate the magnetization
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M_sy1 = self.bloch_symmetric_strang_splitting(B1, xdis, real_pulsepower, imag_pulsepower)
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M_sy1 = self.bloch_symmetric_strang_splitting(
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B1, xdis, real_pulsepower, imag_pulsepower
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)
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# Z-Component
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Mlong = np.squeeze(M_sy1[2, :, :]) # Indices start at 0 in Python
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@ -112,7 +116,9 @@ class Simulation:
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Mlong_avg = np.delete(Mlong_avg, -1) # Remove the last element
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# XY-Component
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Mtrans = np.squeeze(M_sy1[0,:,:] + 1j*M_sy1[1,:,:]) # Indices start at 0 in Python
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Mtrans = np.squeeze(
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M_sy1[0, :, :] + 1j * M_sy1[1, :, :]
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) # Indices start at 0 in Python
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Mtrans_avg = np.mean(Mtrans, axis=0)
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Mtrans_avg = np.delete(Mtrans_avg, -1) # Remove the last element
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@ -129,8 +135,9 @@ class Simulation:
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return timedomain_signal
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def bloch_symmetric_strang_splitting(self, B1, xdis, real_pulsepower, imag_pulsepower, relax = 1):
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def bloch_symmetric_strang_splitting(
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self, B1, xdis, real_pulsepower, imag_pulsepower, relax=1
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):
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"""This method simulates the Bloch equations using the symmetric strang splitting method.
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Parameters
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@ -155,7 +162,9 @@ class Simulation:
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# Bloch simulation in magnetization domain
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gadt = self.sample.gamma * dt / 2
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B1 = np.tile((gadt * (real_pulsepower - 1j * imag_pulsepower) * B1).reshape(-1, 1), Nx)
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B1 = np.tile(
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(gadt * (real_pulsepower - 1j * imag_pulsepower) * B1).reshape(-1, 1), Nx
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)
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K = gadt * xdis * w * self.gradient
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phi = -np.sqrt(np.abs(B1) ** 2 + K**2)
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@ -180,21 +189,44 @@ class Simulation:
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M = np.zeros((3, Nx, Nu + 1))
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M[:, :, 0] = M0
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Mt = M0
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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)])
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b = np.array([0, 0, self.initial_magnetization]) - np.array([0, 0, self.initial_magnetization * np.exp(-1 / self.sample.T1 * relax * dt)])
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D = np.diag(
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[
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np.exp(-1 / self.sample.T2 * relax * dt),
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np.exp(-1 / self.sample.T2 * relax * dt),
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np.exp(-1 / self.sample.T1 * relax * dt),
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]
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)
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b = np.array([0, 0, self.initial_magnetization]) - np.array(
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[
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0,
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0,
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self.initial_magnetization * np.exp(-1 / self.sample.T1 * relax * dt),
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]
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)
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for n in range(Nu): # time loop
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Mrot = np.zeros((3, Nx))
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Mrot[0,:] = Bd1.T[:,n]*Mt[0,:] + Bd2.T[:,n]*Mt[1,:] + Bd3.T[:,n]*Mt[2,:]
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Mrot[1,:] = Bd4.T[:,n]*Mt[0,:] + Bd5.T[:,n]*Mt[1,:] + Bd6.T[:,n]*Mt[2,:]
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Mrot[2,:] = Bd7.T[:,n]*Mt[0,:] + Bd8.T[:,n]*Mt[1,:] + Bd9.T[:,n]*Mt[2,:]
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Mrot[0, :] = (
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Bd1.T[:, n] * Mt[0, :] + Bd2.T[:, n] * Mt[1, :] + Bd3.T[:, n] * Mt[2, :]
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)
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Mrot[1, :] = (
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Bd4.T[:, n] * Mt[0, :] + Bd5.T[:, n] * Mt[1, :] + Bd6.T[:, n] * Mt[2, :]
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)
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Mrot[2, :] = (
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Bd7.T[:, n] * Mt[0, :] + Bd8.T[:, n] * Mt[1, :] + Bd9.T[:, n] * Mt[2, :]
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)
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Mt = np.dot(D, Mrot) + np.tile(b, (Nx, 1)).T
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Mrot[0,:] = Bd1.T[:,n]*Mt[0,:] + Bd2.T[:,n]*Mt[1,:] + Bd3.T[:,n]*Mt[2,:]
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Mrot[1,:] = Bd4.T[:,n]*Mt[0,:] + Bd5.T[:,n]*Mt[1,:] + Bd6.T[:,n]*Mt[2,:]
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Mrot[2,:] = Bd7.T[:,n]*Mt[0,:] + Bd8.T[:,n]*Mt[1,:] + Bd9.T[:,n]*Mt[2,:]
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Mrot[0, :] = (
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Bd1.T[:, n] * Mt[0, :] + Bd2.T[:, n] * Mt[1, :] + Bd3.T[:, n] * Mt[2, :]
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)
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Mrot[1, :] = (
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Bd4.T[:, n] * Mt[0, :] + Bd5.T[:, n] * Mt[1, :] + Bd6.T[:, n] * Mt[2, :]
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)
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Mrot[2, :] = (
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Bd7.T[:, n] * Mt[0, :] + Bd8.T[:, n] * Mt[1, :] + Bd9.T[:, n] * Mt[2, :]
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)
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Mt = Mrot
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M[:, :, n + 1] = Mrot
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@ -209,7 +241,13 @@ class Simulation:
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B1 : float
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The B1 field of the solenoid coil in T."""
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B1 = np.sqrt(2 * self.power_amplifier_power / 50) * np.pi * 4e-7 * self.number_turns / self.length_coil
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B1 = (
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np.sqrt(2 * self.power_amplifier_power / 50)
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* np.pi
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* 4e-7
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* self.number_turns
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/ self.length_coil
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)
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return B1
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def calc_xdis(self) -> np.array:
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@ -229,7 +267,9 @@ class Simulation:
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# 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
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xdis = np.linspace(-1, 1, self.number_isochromats)
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xdis = (foffr.T * 1e-6) / (self.sample.gamma / 2 / np.pi) / (self.gradient * 1e-3)
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xdis = (
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(foffr.T * 1e-6) / (self.sample.gamma / 2 / np.pi) / (self.gradient * 1e-3)
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)
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return xdis
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@ -243,11 +283,36 @@ class Simulation:
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"""
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u = 4 * np.pi * 1e-7 # permeability of free space
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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.spin_factor
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magnetization = (
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self.sample.gamma
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* 2
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* self.sample.atoms
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/ (2 * self.sample.nuclear_spin + 1)
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* h**2
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* self.sample.resonant_frequency
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/ Boltzmann
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/ self.temperature
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* self.sample.spin_factor
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)
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coil_crossection = np.pi * (self.diameter_coil / 2) ** 2
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reference_voltage = self.number_turns * coil_crossection * u * self.sample.gamma * 2 * self.sample.atoms / (2 * self.sample.nuclear_spin +1) * h**2 * self.sample.resonant_frequency **2 / Boltzmann / self.temperature * self.sample.spin_factor
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reference_voltage = reference_voltage * self.sample.powder_factor * self.sample.filling_factor
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reference_voltage = (
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self.number_turns
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* coil_crossection
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* u
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* self.sample.gamma
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* 2
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* self.sample.atoms
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/ (2 * self.sample.nuclear_spin + 1)
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* h**2
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* self.sample.resonant_frequency**2
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/ Boltzmann
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/ self.temperature
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* self.sample.spin_factor
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)
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reference_voltage = (
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reference_voltage * self.sample.powder_factor * self.sample.filling_factor
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)
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return reference_voltage
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def calculate_noise(self, timedomain_signal: np.array) -> np.array:
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@ -263,7 +328,9 @@ class Simulation:
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noise_data : np.array
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The noise array that is added to the signal."""
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n_timedomain_points = timedomain_signal.shape[0]
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noise_data = (self.noise * np.random.randn(self.averages, n_timedomain_points) + 1j * self.noise * np.random.randn(self.averages, n_timedomain_points))
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noise_data = self.noise * np.random.randn(
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self.averages, n_timedomain_points
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) + 1j * self.noise * np.random.randn(self.averages, n_timedomain_points)
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noise_data = np.sum(noise_data, axis=0) # Sum along the first axis
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return noise_data
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@ -5,9 +5,9 @@ from nqr_blochsimulator.classes.sample import Sample
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from nqr_blochsimulator.classes.simulation import Simulation
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from nqr_blochsimulator.classes.pulse import PulseArray
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class TestSimulation(unittest.TestCase):
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def setUp(self):
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self.sample = Sample(
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"BiPh3",
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density=1.585e6, # g/m^3
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@ -27,6 +27,7 @@ class TestSimulation(unittest.TestCase):
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dwell_time = 1e-6
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self.time_array = np.arange(0, simulation_length, dwell_time)
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pulse_length = 3e-6
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# Simple FID sequence with pulse length of 3µs
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pulse_amplitude_array = np.zeros(int(simulation_length / dwell_time))
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pulse_amplitude_array[: int(pulse_length / dwell_time)] = 1
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@ -35,7 +36,7 @@ class TestSimulation(unittest.TestCase):
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self.pulse = PulseArray(
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pulseamplitude=pulse_amplitude_array,
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pulsephase=pulse_phase_array,
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dwell_time=dwell_time
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dwell_time=dwell_time,
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)
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self.simulation = Simulation(
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@ -53,7 +54,7 @@ class TestSimulation(unittest.TestCase):
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gain=6000,
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temperature=77,
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loss_TX=12,
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loss_RX=12
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loss_RX=12,
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)
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def test_simulation(self):
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