156 lines
4.8 KiB
Python
156 lines
4.8 KiB
Python
#!/usr/bin/python3
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import spintrum
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import math
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import matplotlib.pyplot as plt
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import numpy as np
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import scipy.optimize
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import multiprocessing
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import mpmath as mp
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mp.mp.dps = 25
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def filter_spectrum(spec, freq_lim):
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x_axis = np.array([])
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y_axis = np.array([])
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for i in range(len(freq_lim)):
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for j in range(len(spec["x"])):
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if freq_lim[i][0] <= spec["x"][j] <= freq_lim[i][1]:
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x_axis = np.append(x_axis,spec["x"][j])
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y_axis = np.append(y_axis,spec["y"][j])
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return {"x": x_axis, "y": y_axis}
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#defining initial parameters
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with open("../data/ethanolSignal.txt") as f:
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data = f.readlines()
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data = np.array(list(map(np.double,data)))
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data = data - np.mean(data)
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gammas = [4257.7e4,4257.7e4,4257.7e4,4257.7e4,4257.7e4,1070.8e4]
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multips = [2,2,2,2,2,2]
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gammah = 2*math.pi*4257.7e4
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BThermal = 1.8e0
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T2 = 1
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points = len(data)
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sampleRate = 2000
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spectrumRange = [[0,299]]
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jCouplings = \
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[
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[0,0,0,0,0,0],
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[0,0,0,0,0,0],
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[0,0,0,0,0,0],
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[0,0,0,0,0,0],
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[0,0,0,0,0,0],
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[0,0,0,0,0,0],
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]
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experimental_spectrum = filter_spectrum(spintrum.FFTSpectralDensity(data, 2000), spectrumRange)
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gen = spintrum.SpinSimulator(gyromagneticRatios=gammas,
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jCouplings=jCouplings,
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spinMultiplicities=multips,
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doPrint=False)
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spinOp = spintrum.SpinOperations()
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spinOp.add_operation(spintrum.SpinOperations.OPERATION__THERMAL_POPULATE,
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{'Bx': 0, 'By': 0, 'Bz': BThermal, 'T': 293.778})
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spinOp.add_operation(spintrum.SpinOperations.OPERATION__TIP_SPINS,
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{'direction': 'y', 'BVsTArea': 4*math.pi/gammah})
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spinOp.add_operation(spintrum.SpinOperations.OPERATION__SET_HAMILTONIAN,
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{'Bx': 0, 'By': 0, 'Bz': 0})
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spinOp.add_operation(spintrum.SpinOperations.OPERATION__INIT_TIME_INDEPENDENT_EVOLUTION,
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{'samplingRate': sampleRate, 'measurementDirection': 'z'})
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spinOp.add_operation(spintrum.SpinOperations.OPERATION__EVOLVE_TIME_INDEPENDENT,
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{'points': points, 'threads': multiprocessing.cpu_count()})
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gen.update_parameters(spinOperations=spinOp, jCouplings=jCouplings, gyromagneticRatios=gammas)
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def generate_spectrum(params):
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amplitude = params[3]
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T2 = params[4]
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jCouplings = \
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[
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[0, 0, 0, params[0],params[1],params[1]],
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[0, 0, 0, params[0],params[1],params[1]],
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[0, 0, 0, params[0],params[1],params[1]],
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[0, 0, 0, 0, 0, params[2]],
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[0, 0, 0, 0, 0, params[2]],
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[0, 0, 0, 0, 0, 0],
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]
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gen.update_parameters(jCouplings=jCouplings)
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signal = amplitude*gen.simulate()
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signal = signal - np.mean(signal)
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signal = [signal[i]*math.exp(-i/sampleRate/T2) for i in range(len(signal))]
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fft = filter_spectrum(spintrum.FFTSpectralDensity(signal, 2000), spectrumRange)
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# plt.plot(fft['x'],fft['y'],realSpectrum['x'],realSpectrum['y'])
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# plt.show()
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return fft
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def objective_func(params):
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print(params)
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spect = generate_spectrum(params)
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funcValue = np.sqrt(np.sum((spect['y'] - experimental_spectrum['y']) ** 2))
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print('Objective function value: ',funcValue)
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return funcValue
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pars = np.array([7.1,141.0,-4.65,0.00002,6])
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#pars = np.array([-1.06785235e+00,2.26791767e+02,4.26684847e+00,l4.53620118e-05,2.73284204e+00])
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# get_objective_func(pars)
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optimum_params = scipy.optimize.fmin(objective_func, pars, ftol = 5.0e-13, maxfun = 1)
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opt_j_couplings = mp.matrix([
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[0, 0, 0, optimum_params[0], optimum_params[1], optimum_params[1]],
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[0, 0, 0, optimum_params[0], optimum_params[1], optimum_params[1]],
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[0, 0, 0, optimum_params[0], optimum_params[1], optimum_params[1]],
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[0, 0, 0, 0, 0, optimum_params[2]],
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[0, 0, 0, 0, 0, optimum_params[2]],
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[0, 0, 0, 0, 0, 0],
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])
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CorrelationMatrix = spintrum.get_correlation_matrix(optimum_params, generate_spectrum, objective_func)
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print('Correlation matrix: ','\n',CorrelationMatrix)
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print('Parameters at minimum: ','\n', optimum_params)
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errorbars = spintrum.get_errorbars(CorrelationMatrix)
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print('Standard deviations of parameters: ','\n',errorbars)
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#writout to file
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np.set_printoptions(suppress=True,precision=20)
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fr = open("../data/ethanolFitResult.txt","w")
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fr.write('Minimum at J-couplings:\n')
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fr.write(str(opt_j_couplings) + '\n')
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fr.write('Minimum at paramters:\n')
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fr.write(str(optimum_params) + '\n')
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fr.write('Parameters standard deviations:\n')
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fr.write(str(errorbars)+'\n')
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fr.write('Parameters correlation matrix:\n')
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fr.write(str(CorrelationMatrix)+'\n')
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fr.write('Minimum objective function value: ' + str(objective_func(optimum_params)))
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fr.close()
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fft_final = generate_spectrum(optimum_params)
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plt.plot(fft_final['x'], fft_final['y'], experimental_spectrum['x'], experimental_spectrum['y'])
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plt.show()
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