Spintrum/spintrum/examples/ZULF/ethanol_fit.py

156 lines
4.8 KiB
Python

#!/usr/bin/python3
import spintrum
import math
import matplotlib.pyplot as plt
import numpy as np
import scipy.optimize
import multiprocessing
import mpmath as mp
mp.mp.dps = 25
def filter_spectrum(spec, freq_lim):
x_axis = np.array([])
y_axis = np.array([])
for i in range(len(freq_lim)):
for j in range(len(spec["x"])):
if freq_lim[i][0] <= spec["x"][j] <= freq_lim[i][1]:
x_axis = np.append(x_axis,spec["x"][j])
y_axis = np.append(y_axis,spec["y"][j])
return {"x": x_axis, "y": y_axis}
#defining initial parameters
with open("../data/ethanolSignal.txt") as f:
data = f.readlines()
data = np.array(list(map(np.double,data)))
data = data - np.mean(data)
gammas = [4257.7e4,4257.7e4,4257.7e4,4257.7e4,4257.7e4,1070.8e4]
multips = [2,2,2,2,2,2]
gammah = 2*math.pi*4257.7e4
BThermal = 1.8e0
T2 = 1
points = len(data)
sampleRate = 2000
spectrumRange = [[0,299]]
jCouplings = \
[
[0,0,0,0,0,0],
[0,0,0,0,0,0],
[0,0,0,0,0,0],
[0,0,0,0,0,0],
[0,0,0,0,0,0],
[0,0,0,0,0,0],
]
experimental_spectrum = filter_spectrum(spintrum.FFTSpectralDensity(data, 2000), spectrumRange)
gen = spintrum.SpinSimulator(gyromagneticRatios=gammas,
jCouplings=jCouplings,
spinMultiplicities=multips,
doPrint=False)
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__TIP_SPINS,
{'direction': 'y', 'BVsTArea': 4*math.pi/gammah})
spinOp.add_operation(spintrum.SpinOperations.OPERATION__SET_HAMILTONIAN,
{'Bx': 0, 'By': 0, 'Bz': 0})
spinOp.add_operation(spintrum.SpinOperations.OPERATION__INIT_TIME_INDEPENDENT_EVOLUTION,
{'samplingRate': sampleRate, 'measurementDirection': 'z'})
spinOp.add_operation(spintrum.SpinOperations.OPERATION__EVOLVE_TIME_INDEPENDENT,
{'points': points, 'threads': multiprocessing.cpu_count()})
gen.update_parameters(spinOperations=spinOp, jCouplings=jCouplings, gyromagneticRatios=gammas)
def generate_spectrum(params):
amplitude = params[3]
T2 = params[4]
jCouplings = \
[
[0, 0, 0, params[0],params[1],params[1]],
[0, 0, 0, params[0],params[1],params[1]],
[0, 0, 0, params[0],params[1],params[1]],
[0, 0, 0, 0, 0, params[2]],
[0, 0, 0, 0, 0, params[2]],
[0, 0, 0, 0, 0, 0],
]
gen.update_parameters(jCouplings=jCouplings)
signal = amplitude*gen.simulate()
signal = signal - np.mean(signal)
signal = [signal[i]*math.exp(-i/sampleRate/T2) for i in range(len(signal))]
fft = filter_spectrum(spintrum.FFTSpectralDensity(signal, 2000), spectrumRange)
# plt.plot(fft['x'],fft['y'],realSpectrum['x'],realSpectrum['y'])
# plt.show()
return fft
def objective_func(params):
print(params)
spect = generate_spectrum(params)
funcValue = np.sqrt(np.sum((spect['y'] - experimental_spectrum['y']) ** 2))
print('Objective function value: ',funcValue)
return funcValue
pars = np.array([7.1,141.0,-4.65,0.00002,6])
#pars = np.array([-1.06785235e+00,2.26791767e+02,4.26684847e+00,l4.53620118e-05,2.73284204e+00])
# get_objective_func(pars)
optimum_params = scipy.optimize.fmin(objective_func, pars, ftol = 5.0e-13, maxfun = 1)
opt_j_couplings = mp.matrix([
[0, 0, 0, optimum_params[0], optimum_params[1], optimum_params[1]],
[0, 0, 0, optimum_params[0], optimum_params[1], optimum_params[1]],
[0, 0, 0, optimum_params[0], optimum_params[1], optimum_params[1]],
[0, 0, 0, 0, 0, optimum_params[2]],
[0, 0, 0, 0, 0, optimum_params[2]],
[0, 0, 0, 0, 0, 0],
])
CorrelationMatrix = spintrum.get_correlation_matrix(optimum_params, generate_spectrum, objective_func)
print('Correlation matrix: ','\n',CorrelationMatrix)
print('Parameters at minimum: ','\n', optimum_params)
errorbars = spintrum.get_errorbars(CorrelationMatrix)
print('Standard deviations of parameters: ','\n',errorbars)
#writout to file
np.set_printoptions(suppress=True,precision=20)
fr = open("../data/ethanolFitResult.txt","w")
fr.write('Minimum at J-couplings:\n')
fr.write(str(opt_j_couplings) + '\n')
fr.write('Minimum at paramters:\n')
fr.write(str(optimum_params) + '\n')
fr.write('Parameters standard deviations:\n')
fr.write(str(errorbars)+'\n')
fr.write('Parameters correlation matrix:\n')
fr.write(str(CorrelationMatrix)+'\n')
fr.write('Minimum objective function value: ' + str(objective_func(optimum_params)))
fr.close()
fft_final = generate_spectrum(optimum_params)
plt.plot(fft_final['x'], fft_final['y'], experimental_spectrum['x'], experimental_spectrum['y'])
plt.show()