491 lines
18 KiB
Python
491 lines
18 KiB
Python
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import numpy as np
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import lmfit
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from lmfit.models import (ConstantModel, ComplexConstantModel, LinearModel, QuadraticModel,
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PolynomialModel, SineModel, GaussianModel, Gaussian2dModel, LorentzianModel,
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SplitLorentzianModel, VoigtModel, PseudoVoigtModel, MoffatModel,
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Pearson7Model, StudentsTModel, BreitWignerModel, LognormalModel,
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DampedOscillatorModel, ExponentialGaussianModel, SkewedGaussianModel,
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SkewedVoigtModel, ThermalDistributionModel, DoniachModel, PowerLawModel,
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ExponentialModel, StepModel, RectangleModel, ExpressionModel, DampedHarmonicOscillatorModel)
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from lmfit.models import (guess_from_peak, guess_from_peak2d, fwhm_expr, height_expr,
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update_param_vals)
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from lmfit.lineshapes import (not_zero, breit_wigner, damped_oscillator, dho, doniach,
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expgaussian, exponential, gaussian, gaussian2d,
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linear, lognormal, lorentzian, moffat, parabolic,
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pearson7, powerlaw, pvoigt, rectangle, sine,
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skewed_gaussian, skewed_voigt, split_lorentzian, step,
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students_t, thermal_distribution, tiny, voigt)
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from lmfit import Model
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import numpy as np
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from numpy import (arctan, copysign, cos, exp, isclose, isnan, log, pi, real,
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sin, sqrt, where)
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from scipy.special import erf, erfc
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from scipy.special import gamma as gamfcn
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from scipy.special import wofz
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from scipy.optimize import curve_fit
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import xarray as xr
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log2 = log(2)
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s2pi = sqrt(2*pi)
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s2 = sqrt(2.0)
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def gaussianWithOffset(x, amplitude=1.0, center=0.0, sigma=1.0, offset=0.0):
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"""Return a 1-dimensional Gaussian function with an offset.
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gaussian(x, amplitude, center, sigma) =
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(amplitude/(s2pi*sigma)) * exp(-(1.0*x-center)**2 / (2*sigma**2))
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"""
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return ((amplitude/(max(tiny, s2pi*sigma)))
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* exp(-(1.0*x-center)**2 / max(tiny, (2*sigma**2))) + offset)
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def lorentzianWithOffset(x, amplitude=1.0, center=0.0, sigma=1.0, offset=0.0):
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return ((amplitude/(1 + ((1.0*x-center)/max(tiny, sigma))**2))
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/ max(tiny, (pi*sigma)) + offset)
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def exponentialWithOffset(x, amplitude=1.0, decay=1.0, offset=0.0):
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decay = not_zero(decay)
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return amplitude * exp(-x/decay) + offset
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def expansion(x, amplitude=1.0, offset=0.0):
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return np.sqrt(amplitude*x*x + offset)
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def dampingOscillation(x, center=0, amplitude=1.0, frequency=1.0, decay=1.0, offset=0.0):
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return amplitude * np.exp(-decay*x)*np.sin(2*np.pi*frequency*(x-center)) + offset
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def double_structure(x, x1=0.25, x2=0.75, amplitude=1.0, center=0.0, sigma=1.0, a=-1.0, b=0, c=0):
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y = np.zeros(x.shape)
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return ((amplitude/(max(tiny, s2pi*sigma)))
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* exp(-(1.0*x-center)**2 / max(tiny, (2*sigma**2))))
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class GaussianWithOffsetModel(Model):
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fwhm_factor = 2*np.sqrt(2*np.log(2))
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height_factor = 1./np.sqrt(2*np.pi)
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def __init__(self, independent_vars=['x'], nan_policy='raise', prefix='', name=None, **kwargs):
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kwargs.update({'prefix': prefix, 'nan_policy': nan_policy,
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'independent_vars': independent_vars})
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super().__init__(gaussianWithOffset, **kwargs)
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self._set_paramhints_prefix()
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def _set_paramhints_prefix(self):
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self.set_param_hint('sigma', min=0)
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self.set_param_hint('fwhm', expr=fwhm_expr(self))
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self.set_param_hint('height', expr=height_expr(self))
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def guess(self, data, x, negative=False, **kwargs):
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offset = np.min(data)
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data = data - offset
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pars = guess_from_peak(self, data, x, negative)
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pars.add('offset', value=offset)
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return update_param_vals(pars, self.prefix, **kwargs)
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class LorentzianWithOffsetModel(Model):
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fwhm_factor = 2.0
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height_factor = 1./np.pi
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def __init__(self, independent_vars=['x'], prefix='', nan_policy='raise',
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**kwargs):
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kwargs.update({'prefix': prefix, 'nan_policy': nan_policy,
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'independent_vars': independent_vars})
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super().__init__(lorentzianWithOffset, **kwargs)
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self._set_paramhints_prefix()
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def _set_paramhints_prefix(self):
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self.set_param_hint('sigma', min=0)
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self.set_param_hint('fwhm', expr=fwhm_expr(self))
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self.set_param_hint('height', expr=height_expr(self))
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def guess(self, data, x, negative=False, **kwargs):
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"""Estimate initial model parameter values from data."""
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offset = np.min(data)
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data = data - offset
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pars = guess_from_peak(self, data, x, negative, ampscale=1.25)
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pars.add('offset', value=offset)
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return update_param_vals(pars, self.prefix, **kwargs)
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class ExponentialWithOffsetModel(Model):
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def __init__(self, independent_vars=['x'], prefix='', nan_policy='raise',
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**kwargs):
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kwargs.update({'prefix': prefix, 'nan_policy': nan_policy,
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'independent_vars': independent_vars})
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super().__init__(exponentialWithOffset, **kwargs)
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def guess(self, data, x, **kwargs):
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"""Estimate initial model parameter values from data."""
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offset = np.min(data)
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data = data - offset
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try:
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sval, oval = np.polyfit(x, np.log(abs(data)+1.e-15), 1)
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except TypeError:
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sval, oval = 1., np.log(abs(max(data)+1.e-9))
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pars = self.make_params(amplitude=np.exp(oval), decay=-1.0/sval)
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pars.add('offset', value=offset)
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return update_param_vals(pars, self.prefix, **kwargs)
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class ExpansionModel(Model):
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def __init__(self, independent_vars=['x'], prefix='', nan_policy='raise',
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**kwargs):
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kwargs.update({'prefix': prefix, 'nan_policy': nan_policy,
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'independent_vars': independent_vars})
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super().__init__(expansion, **kwargs)
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def guess(self, data, x, **kwargs):
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"""Estimate initial model parameter values from data."""
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popt1, pcov1 = curve_fit(expansion, x, data)
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pars = self.make_params(amplitude=popt1[0], offset=popt1[1])
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return update_param_vals(pars, self.prefix, **kwargs)
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class DampingOscillationModel(Model):
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def __init__(self, independent_vars=['x'], prefix='', nan_policy='raise',
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**kwargs):
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kwargs.update({'prefix': prefix, 'nan_policy': nan_policy,
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'independent_vars': independent_vars})
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super().__init__(dampingOscillation, **kwargs)
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def guess(self, data, x, **kwargs):
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"""Estimate initial model parameter values from data."""
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try:
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popt1, pcov1 = curve_fit(dampingOscillation, x, data, np.array(0, 5, 5e2, 1e3, 16))
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pars = self.make_params(center=popt1[0], amplitude=popt1[1], frequency=popt1[2], decay=popt1[3], offset=popt1[4])
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except:
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pars = self.make_params(center=0, amplitude=5.0, frequency=5e2, decay=1.0e3, offset=16.0)
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return update_param_vals(pars, self.prefix, **kwargs)
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lmfit_models = {'Constant': ConstantModel,
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'Complex Constant': ComplexConstantModel,
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'Linear': LinearModel,
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'Quadratic': QuadraticModel,
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'Polynomial': PolynomialModel,
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'Gaussian': GaussianModel,
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'Gaussian-2D': Gaussian2dModel,
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'Lorentzian': LorentzianModel,
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'Split-Lorentzian': SplitLorentzianModel,
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'Voigt': VoigtModel,
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'PseudoVoigt': PseudoVoigtModel,
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'Moffat': MoffatModel,
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'Pearson7': Pearson7Model,
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'StudentsT': StudentsTModel,
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'Breit-Wigner': BreitWignerModel,
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'Log-Normal': LognormalModel,
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'Damped Oscillator': DampedOscillatorModel,
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'Damped Harmonic Oscillator': DampedHarmonicOscillatorModel,
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'Exponential Gaussian': ExponentialGaussianModel,
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'Skewed Gaussian': SkewedGaussianModel,
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'Skewed Voigt': SkewedVoigtModel,
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'Thermal Distribution': ThermalDistributionModel,
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'Doniach': DoniachModel,
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'Power Law': PowerLawModel,
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'Exponential': ExponentialModel,
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'Step': StepModel,
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'Rectangle': RectangleModel,
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'Expression': ExpressionModel,
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'Gaussian With Offset':GaussianWithOffsetModel,
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'Lorentzian With Offset':LorentzianWithOffsetModel,
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'Expansion':ExpansionModel,
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'Damping Oscillation Model':DampingOscillationModel
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}
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class FitAnalyser():
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def __init__(self, fitModel, fitDim=1, **kwargs) -> None:
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if isinstance(fitModel, str):
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self.fitModel = lmfit_models[fitModel](**kwargs)
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else:
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self.fitModel = fitModel
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self.fitDim = fitDim
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def _guess_1D(self, data, x):
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return self.fitModel.guess(data=data, x=x)
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def _guess_2D(self, data, x, y):
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return self.fitModel.guess(data=data, x=x, y=y)
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def guess(self, dataArray, x=None, y=None, input_core_dims=None, dask='parallelized', vectorize=True, **kwargs):
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kwargs.update(
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{
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"dask": dask,
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"vectorize": vectorize,
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"input_core_dims": input_core_dims
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}
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)
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if input_core_dims is None:
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kwargs.update(
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{
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"input_core_dims": [['x']],
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}
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)
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if x is None:
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if 'x' in dataArray.dims:
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x = dataArray['x'].to_numpy()
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else:
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if isinstance(x, str):
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x = dataArray[x].to_numpy()
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if input_core_dims is None:
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kwargs.update(
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{
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"input_core_dims": [[x]],
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}
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)
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if self.fitDim == 1:
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return xr.apply_ufunc(self._guess_1D, dataArray, kwargs={'x':x},
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output_dtypes=[type(self.fitModel.make_params())],
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**kwargs
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)
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if self.fitDim == 2:
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if y is None:
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if 'y' in dataArray.dims:
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y = dataArray['y'].to_numpy()
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if input_core_dims is None:
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kwargs.update(
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{
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"input_core_dims": [['x', 'y']],
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}
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)
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else:
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if isinstance(y, str):
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y = dataArray[y].to_numpy()
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kwargs["input_core_dims"][0] = np.append(kwargs["input_core_dims"][0], y)
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elif input_core_dims is None:
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kwargs.update(
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{
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"input_core_dims": [['x', 'y']],
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}
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)
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_x, _y = np.meshgrid(x, y)
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_x = _x.flatten()
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_y = _y.flatten()
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dataArray = dataArray.stack(_z=(kwargs["input_core_dims"][0][0], kwargs["input_core_dims"][0][1]))
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kwargs["input_core_dims"][0] = ['_z']
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return xr.apply_ufunc(self._guess_2D, dataArray, kwargs={'x':_x, 'y':_y},
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output_dtypes=[type(self.fitModel.make_params())],
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**kwargs
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)
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def _fit_1D(self, data, params, x):
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# try:
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return self.fitModel.fit(data=data, x=x, params=params)
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def _fit_2D(self, data, params, x, y):
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return self.fitModel.fit(data=data, x=x, y=y, params=params)
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def fit(self, dataArray, paramsArray, x=None, y=None, input_core_dims=None, dask='parallelized', vectorize=True, **kwargs):
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kwargs.update(
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{
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"dask": dask,
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"vectorize": vectorize,
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"input_core_dims": input_core_dims,
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}
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)
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if input_core_dims is None:
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kwargs.update(
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{
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"input_core_dims": [['x'], []],
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}
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)
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if x is None:
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if 'x' in dataArray.dims:
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x = dataArray['x'].to_numpy()
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else:
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if isinstance(x, str):
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x = dataArray[x].to_numpy()
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if input_core_dims is None:
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kwargs.update(
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{
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"input_core_dims": [[x], []],
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}
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)
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if isinstance(paramsArray, type(self.fitModel.make_params())):
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if self.fitDim == 1:
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return xr.apply_ufunc(self._fit_1D, dataArray, kwargs={'params':paramsArray,'x':x},
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output_dtypes=[type(lmfit.model.ModelResult(self.fitModel, self.fitModel.make_params()))],
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**kwargs)
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if self.fitDim == 2:
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if y is None:
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if 'y' in dataArray.dims:
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y = dataArray['y'].to_numpy()
|
||
|
|
if input_core_dims is None:
|
||
|
|
kwargs.update(
|
||
|
|
{
|
||
|
|
"input_core_dims": [['x', 'y'], []],
|
||
|
|
}
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
if isinstance(y, str):
|
||
|
|
y = dataArray[y].to_numpy()
|
||
|
|
kwargs["input_core_dims"][0] = np.append(kwargs["input_core_dims"][0], y)
|
||
|
|
elif input_core_dims is None:
|
||
|
|
kwargs.update(
|
||
|
|
{
|
||
|
|
"input_core_dims": [['x', 'y'], []],
|
||
|
|
}
|
||
|
|
)
|
||
|
|
|
||
|
|
_x, _y = np.meshgrid(x, y)
|
||
|
|
_x = _x.flatten()
|
||
|
|
_y = _y.flatten()
|
||
|
|
|
||
|
|
dataArray = dataArray.stack(_z=(kwargs["input_core_dims"][0][0], kwargs["input_core_dims"][0][1]))
|
||
|
|
|
||
|
|
kwargs["input_core_dims"][0] = ['_z']
|
||
|
|
|
||
|
|
return xr.apply_ufunc(self._fit_2D, dataArray, kwargs={'params':paramsArray,'x':_x, 'y':_y},
|
||
|
|
output_dtypes=[type(lmfit.model.ModelResult(self.fitModel, self.fitModel.make_params()))],
|
||
|
|
**kwargs)
|
||
|
|
|
||
|
|
else:
|
||
|
|
if self.fitDim == 1:
|
||
|
|
return xr.apply_ufunc(self._fit_1D, dataArray, paramsArray, kwargs={'x':x},
|
||
|
|
output_dtypes=[type(lmfit.model.ModelResult(self.fitModel, self.fitModel.make_params()))],
|
||
|
|
**kwargs)
|
||
|
|
|
||
|
|
if self.fitDim == 2:
|
||
|
|
|
||
|
|
if input_core_dims is None:
|
||
|
|
kwargs.update(
|
||
|
|
{
|
||
|
|
"input_core_dims": [['x', 'y'], []],
|
||
|
|
}
|
||
|
|
)
|
||
|
|
|
||
|
|
if y is None:
|
||
|
|
if 'y' in dataArray.dims:
|
||
|
|
y = dataArray['y'].to_numpy()
|
||
|
|
else:
|
||
|
|
if isinstance(y, str):
|
||
|
|
y = dataArray[y].to_numpy()
|
||
|
|
kwargs["input_core_dims"][0] = np.append(kwargs["input_core_dims"][0], y)
|
||
|
|
|
||
|
|
_x, _y = np.meshgrid(x, y)
|
||
|
|
_x = _x.flatten()
|
||
|
|
_y = _y.flatten()
|
||
|
|
|
||
|
|
dataArray = dataArray.stack(_z=(kwargs["input_core_dims"][0][0], kwargs["input_core_dims"][0][1]))
|
||
|
|
|
||
|
|
kwargs["input_core_dims"][0] = ['_z']
|
||
|
|
|
||
|
|
return xr.apply_ufunc(self._fit_2D, dataArray, paramsArray, kwargs={'x':_x, 'y':_y},
|
||
|
|
output_dtypes=[type(lmfit.model.ModelResult(self.fitModel, self.fitModel.make_params()))],
|
||
|
|
**kwargs)
|
||
|
|
|
||
|
|
|
||
|
|
def _eval_1D(self, fitResult, x):
|
||
|
|
return self.fitModel.eval(x=x, **fitResult.best_values)
|
||
|
|
|
||
|
|
def _eval_2D(self, fitResult, x, y, shape):
|
||
|
|
res = self.fitModel.eval(x=x, y=y, **fitResult.best_values)
|
||
|
|
return res.reshape(shape)
|
||
|
|
|
||
|
|
def eval(self, fitResultArray, x=None, y=None, output_core_dims=None, prefix="", dask='parallelized', vectorize=True, **kwargs):
|
||
|
|
|
||
|
|
kwargs.update(
|
||
|
|
{
|
||
|
|
"dask": dask,
|
||
|
|
"vectorize": vectorize,
|
||
|
|
"output_core_dims": output_core_dims,
|
||
|
|
}
|
||
|
|
)
|
||
|
|
|
||
|
|
if self.fitDim == 1:
|
||
|
|
|
||
|
|
if output_core_dims is None:
|
||
|
|
kwargs.update(
|
||
|
|
{
|
||
|
|
"output_core_dims": [[prefix+'x']],
|
||
|
|
"output_dtypes": float,
|
||
|
|
}
|
||
|
|
)
|
||
|
|
output_core_dims = [prefix+'x']
|
||
|
|
|
||
|
|
kwargs.update(
|
||
|
|
{
|
||
|
|
"dask_gufunc_kwargs": {
|
||
|
|
'output_sizes': {
|
||
|
|
output_core_dims[0]: np.size(x),
|
||
|
|
},
|
||
|
|
},
|
||
|
|
}
|
||
|
|
)
|
||
|
|
|
||
|
|
return xr.apply_ufunc(self._eval_1D, fitResultArray, kwargs={"x":x}, **kwargs)
|
||
|
|
|
||
|
|
if self.fitDim == 2:
|
||
|
|
if output_core_dims is None:
|
||
|
|
kwargs.update(
|
||
|
|
{
|
||
|
|
"output_core_dims": [[prefix+'x', prefix+'y']],
|
||
|
|
"output_dtypes": float,
|
||
|
|
}
|
||
|
|
)
|
||
|
|
output_core_dims = [prefix+'x', prefix+'y']
|
||
|
|
|
||
|
|
kwargs.update(
|
||
|
|
{
|
||
|
|
"dask_gufunc_kwargs": {
|
||
|
|
'output_sizes': {
|
||
|
|
output_core_dims[0]: np.size(x),
|
||
|
|
output_core_dims[1]: np.size(y),
|
||
|
|
},
|
||
|
|
# 'output_dtypes': {
|
||
|
|
# output_core_dims[0]: float,
|
||
|
|
# output_core_dims[1]: float,
|
||
|
|
# },
|
||
|
|
},
|
||
|
|
}
|
||
|
|
)
|
||
|
|
|
||
|
|
_x, _y = np.meshgrid(x, y)
|
||
|
|
_x = _x.flatten()
|
||
|
|
_y = _y.flatten()
|
||
|
|
|
||
|
|
return xr.apply_ufunc(self._eval_2D, fitResultArray, kwargs={"x":_x, "y":_y, "shape":(len(x), len(y))}, **kwargs)
|
||
|
|
|
||
|
|
|