Spintrum/spintrum/examples/TimeDependent/MagneticResonance.py

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#!/usr/bin/python3
import spintrum
import math
import matplotlib.pyplot as plt
import numpy as np
import matplotlib as mpl
from mpl_toolkits.mplot3d import Axes3D
def run(dirs):
gammas = [10] #Hz/T
jCouplings = [[0]]
BThermal = 10e0
T2 = 10
sampleRate = 1e4
T= 1/sampleRate
B0=10.
B1=0.3e0
points = 1650
f = B0*gammas[0]
bx = np.array([np.sin(2*math.pi*f*t/sampleRate)*B1 for t in range(points)])
by = np.array([np.cos(2*math.pi*f*t/sampleRate)*B1 for t in range(points)])
spinOp = spintrum.SpinOperations()
spinOp.add_operation(spintrum.SpinOperations.OPERATION__THERMAL_POPULATE,
{'Bx': 0, 'By': 0, 'Bz': BThermal, 'T': 293.778})
spinOp.add_operation(spintrum.SpinOperations.OPERATION__SET_HAMILTONIAN,
{'Bx': 0, 'By': 0, 'Bz': B0})
spinOp.add_operation(spintrum.SpinOperations.OPERATION__EVOLVE_TIME_DEPENDENT,
{'threads': 4, 'samplingRate': sampleRate, 'store': dirs,
'input': {'Bx': bx, 'By': by}})
signal = spintrum.simulate(gyromagneticRatios=gammas,
jCouplings=jCouplings,
spinOperations=spinOp)
return signal
#this will result in points in the form xyzxyzxyz (order doesn't make a difference)
data = run('xyz')
mpl.rcParams['legend.fontsize'] = 10
fig = plt.figure()
ax = fig.gca(projection='3d')
datanp = np.transpose(np.split(np.array(data),len(data)/3))
ax.plot(datanp[0], datanp[1], datanp[2], label='parametric curve')
ax.legend()
plt.show()