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2614
AccordionLattice_20230808.ipynb
Normal file
2614
AccordionLattice_20230808.ipynb
Normal file
File diff suppressed because one or more lines are too long
@ -25,11 +25,15 @@ 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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from scipy.interpolate import CubicSpline
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from scipy.ndimage import gaussian_filter
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import xarray as xr
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import copy
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import matplotlib.pyplot as plt
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log2 = log(2)
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s2pi = sqrt(2*pi)
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@ -109,6 +113,30 @@ def polylog2_2d(x, y=0.0, centerx=0.0, centery=0.0, amplitude=1.0, sigmax=1.0, s
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return amplitude / 2 / np.pi / 1.20206 / max(tiny, sigmax * sigmay) * polylog(2, np.exp( -((x-centerx)**2/(2 * (sigmax)**2))-((y-centery)**2/( 2 * (sigmay)**2)) ))
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def polylog_tab(pow, x, order=100):
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"""Calculationg the polylog sum_i(x^i/i^pow), up to the order-th element of the sum
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:param pow: power in denominator of sum
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:type pow: int (can be float)
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:param x: argument of Polylogarithm
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:type x: float or 1D numpy array
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:return: value of polylog(x)
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:rtype: same as x, float or 1D numpy array
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"""
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sum = 0
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for k in range(1,order):
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sum += x ** k /k **pow
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return sum
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# Creating array for interpolation
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x_int = np.linspace(0, 1.00001, 100000)
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poly_tab = polylog_tab(2,x_int)
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# Creating function interpolating between the Polylog values calculated before
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polylog_int = CubicSpline(x_int, poly_tab)
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def density_profile_BEC_2d(x, y=0.0, BEC_amplitude=1.0, thermal_amplitude=1.0, BEC_centerx=0.0, BEC_centery=0.0, thermal_centerx=0.0, thermal_centery=0.0,
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BEC_sigmax=1.0, BEC_sigmay=1.0, thermal_sigmax=1.0, thermal_sigmay=1.0):
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@ -318,115 +346,333 @@ class ThomasFermi2dModel(Model):
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return update_param_vals(pars, self.prefix, **kwargs)
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class DensityProfileBEC2dModel(Model):
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fwhm_factor = 2*np.sqrt(2*np.log(2))
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height_factor = 1./2*np.pi
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def __init__(self, independent_vars=['x', 'y'], prefix='', nan_policy='raise',
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**kwargs):
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class DensityProfileBEC2dModel(lmfit.Model):
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""" Fitting class to do 2D bimodal fit on OD of absorption images
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"""
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def __init__(self,
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independent_vars=['x', 'y'],
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prefix='',
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nan_policy='raise',
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atom_n_conv=144,
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is_debug=False,
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**kwargs
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):
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kwargs.update({'prefix': prefix, 'nan_policy': nan_policy,
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'independent_vars': independent_vars})
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self.atom_n_conv = atom_n_conv
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self.is_debug=is_debug
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super().__init__(density_profile_BEC_2d, **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('BEC_sigmax', min=0)
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self.set_param_hint('deltax', min=0)
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self.set_param_hint('BEC_sigmax', expr=f'3 * {self.prefix}thermal_sigmax - {self.prefix}deltax')
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self.set_param_hint('BEC_sigmay', min=0)
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self.set_param_hint('thermal_sigmax', min=0)
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# self.set_param_hint('thermal_sigmay', min=0)
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self.set_param_hint('BEC_amplitude', min=0)
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self.set_param_hint('thermal_amplitude', min=0)
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self.set_param_hint('thermalAspectRatio', min=0.8, max=1.2)
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self.set_param_hint('thermal_sigmay', expr=f'{self.prefix}thermalAspectRatio * {self.prefix}thermal_sigmax')
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# self.set_param_hint('betax', value=0)
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# self.set_param_hint('BEC_centerx', expr=f'{self.prefix}thermal_sigmax - {self.prefix}betax')
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self.set_param_hint('condensate_fraction', expr=f'{self.prefix}BEC_amplitude / ({self.prefix}BEC_amplitude + {self.prefix}thermal_amplitude)')
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self.set_param_hint('amp_bec', min=0)
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self.set_param_hint('amp_th', min=0)
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self.set_param_hint('x0_bec', min=0)
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self.set_param_hint('y0_bec', min=0)
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self.set_param_hint('x0_th', min=0)
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self.set_param_hint('y0_th', min=0)
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self.set_param_hint('sigmax_bec', min=0)
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self.set_param_hint('sigmay_bec', min=0)
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self.set_param_hint('sigma_th', min=0)
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def guess(self, data, x, y, negative=False, pureBECThreshold=0.5, noBECThThreshold=0.0, **kwargs):
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"""Estimate initial model parameter values from data."""
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fitModel = TwoGaussian2dModel()
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pars = fitModel.guess(data, x=x, y=y, negative=negative)
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pars['A_amplitude'].set(min=0)
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pars['B_amplitude'].set(min=0)
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pars['A_centerx'].set(min=pars['A_centerx'].value - 3 * pars['A_sigmax'],
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max=pars['A_centerx'].value + 3 * pars['A_sigmax'],)
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pars['A_centery'].set(min=pars['A_centery'].value - 3 * pars['A_sigmay'],
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max=pars['A_centery'].value + 3 * pars['A_sigmay'],)
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pars['B_centerx'].set(min=pars['B_centerx'].value - 3 * pars['B_sigmax'],
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max=pars['B_centerx'].value + 3 * pars['B_sigmax'],)
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pars['B_centery'].set(min=pars['B_centery'].value - 3 * pars['B_sigmay'],
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max=pars['B_centery'].value + 3 * pars['B_sigmay'],)
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fitResult = fitModel.fit(data, x=x, y=y, params=pars, **kwargs)
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pars_guess = fitResult.params
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BEC_amplitude = pars_guess['A_amplitude'].value
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thermal_amplitude = pars_guess['B_amplitude'].value
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pars = self.make_params(BEC_amplitude=BEC_amplitude,
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thermal_amplitude=thermal_amplitude,
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BEC_centerx=pars_guess['A_centerx'].value, BEC_centery=pars_guess['A_centery'].value,
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# BEC_sigmax=(pars_guess['A_sigmax'].value / 2.355),
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deltax = 3 * (pars_guess['B_sigmax'].value * s2) - (pars_guess['A_sigmax'].value / 2.355),
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BEC_sigmay=(pars_guess['A_sigmay'].value / 2.355),
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thermal_centerx=pars_guess['B_centerx'].value, thermal_centery=pars_guess['B_centery'].value,
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thermal_sigmax=(pars_guess['B_sigmax'].value * s2),
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thermalAspectRatio=(pars_guess['B_sigmax'].value * s2) / (pars_guess['B_sigmay'].value * s2)
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# thermal_sigmay=(pars_guess['B_sigmay'].value * s2)
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)
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nBEC = pars[f'{self.prefix}BEC_amplitude'] / 2 / np.pi / 5.546 / pars[f'{self.prefix}BEC_sigmay'] / pars[f'{self.prefix}BEC_sigmax']
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if (pars[f'{self.prefix}condensate_fraction']>0.95) and (np.max(data) > 1.05 * nBEC):
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temp = ((np.max(data) - nBEC) * s2pi * pars[f'{self.prefix}thermal_sigmay'] / pars[f'{self.prefix}thermal_sigmax'])
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if temp > pars[f'{self.prefix}BEC_amplitude']:
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pars[f'{self.prefix}thermal_amplitude'].set(value=pars[f'{self.prefix}BEC_amplitude'] / 2)
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def guess(self, data, x, y, **kwargs):
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"""
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Estimate and create initial model parameters for 2d fit
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:param data: Flattened 2d array, flattened from array with (x,y) -->
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:param x:
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:param y:
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:param kwargs:
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:return:
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"""
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#
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# global X_guess
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# global bval_1d
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x_width = len(np.unique(x))
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y_width = len(np.unique(y))
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data = np.reshape(data, (y_width, x_width))
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data = data.T
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shape = np.shape(data)
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cut_width = np.max(shape)
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thresh = self.calc_thresh(data)
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center = self.calc_cen(thresh)
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BEC_width_guess = self.guess_BEC_width(thresh, center)
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if BEC_width_guess[0] < BEC_width_guess[1]:
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if self.is_debug:
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print(f'x smaller y')
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s_width_ind = 0
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X_guess = np.sum(data[:, round(center[1] - BEC_width_guess[1]/2) : round(center[1] + BEC_width_guess[1]/2)], 1) / len(data[0,round(center[1] - BEC_width_guess[1]/2) : round(center[1] + BEC_width_guess[1]/2)])
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else:
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if self.is_debug:
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print(f'y smaller x')
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s_width_ind = 1
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X_guess = np.sum(data[round(center[0] - BEC_width_guess[0]/2) : round(center[0] + BEC_width_guess[0]/2), :], 0) / len(data[0,round(center[0] - BEC_width_guess[0]/2) : round(center[0] + BEC_width_guess[0]/2)])
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if self.is_debug:
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print(f'center = {center}')
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print(f'BEC widths: {BEC_width_guess}')
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plt.plot(X_guess)
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plt.show()
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x = np.linspace(0, len(X_guess), len(X_guess))
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max_val = np.max(X_guess)
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fitmodel_1d = lmfit.Model(density_1d, independent_vars=['x'])
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params_1d = lmfit.Parameters()
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params_1d.add_many(
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('x0_bec', center[s_width_ind], True,0, 200),
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('x0_th',center[s_width_ind], True,0, 200),
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('amp_bec', 0.7 * max_val, True, 0, 1.3 * max_val),
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('amp_th', 0.3 * max_val, True, 0, 1.3 * max_val),
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('deltax', 3*BEC_width_guess[s_width_ind], True, 0,cut_width),
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# ('sigma_bec',BEC_width_guess[i,j,0]/1.22, True, 0, 50)
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('sigma_bec',BEC_width_guess[s_width_ind]/1.22, True, 0, 50)
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)
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params_1d.add('sigma_th', 3*BEC_width_guess[0], min=0, expr=f'0.632*sigma_bec + 0.518*deltax')
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res_1d = fitmodel_1d.fit(X_guess, x=x, params=params_1d)
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if self.is_debug:
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params_1d.pretty_print()
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self.print_bval(res_1d)
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bval_1d = res_1d.best_values
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amp_conv_1d_2d = np.max(gaussian_filter(data, sigma=1)) / (bval_1d['amp_bec'] + bval_1d['amp_th'])
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max_val = np.max(data)
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params = self.make_params()
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if bval_1d['amp_th']/bval_1d['amp_bec'] > 3:
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print(f'Image seems to be purely thermal (guessed from 1d fit amplitude)')
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params[f'{self.prefix}amp_bec'].set(value=0, vary=False)
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params[f'{self.prefix}amp_th'].set(value=amp_conv_1d_2d * bval_1d['amp_th'], max=1.3 * max_val, vary=True)
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params[f'{self.prefix}x0_bec'].set(value=1, vary=False)
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params[f'{self.prefix}y0_bec'].set(value=1, vary=False)
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params[f'{self.prefix}x0_th'].set(value=center[0], min=center[0] -10, max=center[0] + 10, vary=True)
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params[f'{self.prefix}y0_th'].set(value=center[1], min=center[1] -10, max=center[1] + 10, vary=True)
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params[f'{self.prefix}sigmax_bec'].set(value=1, vary=False)
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params[f'{self.prefix}sigmay_bec'].set(value=1, vary=False)
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params[f'{self.prefix}sigma_th'].set(value=bval_1d['sigma_th'], max=cut_width, vary=True)
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elif bval_1d['amp_bec']/bval_1d['amp_th'] > 10:
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print('Image seems to be pure BEC (guessed from 1d fit amplitude)')
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params[f'{self.prefix}amp_bec'].set(value=amp_conv_1d_2d * bval_1d['amp_bec'], max=1.3 * max_val, vary=True)
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params[f'{self.prefix}amp_th'].set(value=0, vary=False)
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params[f'{self.prefix}x0_bec'].set(value=center[0], min=center[0] -10, max=center[0] + 10, vary=True)
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params[f'{self.prefix}y0_bec'].set(value=center[1], min=center[1] -10, max=center[1] + 10, vary=True)
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params[f'{self.prefix}x0_th'].set(value=1, vary=False)
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params[f'{self.prefix}y0_th'].set(value=1, vary=False)
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params[f'{self.prefix}sigma_th'].set(value=1, vary=False)
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if s_width_ind == 0:
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params[f'{self.prefix}sigmax_bec'].set(value=bval_1d['sigma_bec'], max= 2*BEC_width_guess[0], vary=True)
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params[f'{self.prefix}sigmay_bec'].set(value=BEC_width_guess[1]/1.22, max= 2*BEC_width_guess[1], vary=True)
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elif s_width_ind == 1:
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params[f'{self.prefix}sigmax_bec'].set(value=BEC_width_guess[0]/1.22, max= 2*BEC_width_guess[0], vary=True)
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params[f'{self.prefix}sigmay_bec'].set(value=bval_1d['sigma_bec'], max= 2*BEC_width_guess[1], vary=True)
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else:
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pars[f'{self.prefix}thermal_amplitude'].set(value=temp * 10)
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if BEC_amplitude / (thermal_amplitude + BEC_amplitude) > pureBECThreshold:
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pars[f'{self.prefix}thermal_amplitude'].set(value=0)
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pars[f'{self.prefix}BEC_amplitude'].set(value=(thermal_amplitude + BEC_amplitude))
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if BEC_amplitude / (thermal_amplitude + BEC_amplitude) < noBECThThreshold:
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pars[f'{self.prefix}BEC_amplitude'].set(value=0)
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pars[f'{self.prefix}thermal_amplitude'].set(value=(thermal_amplitude + BEC_amplitude))
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pars[f'{self.prefix}BEC_centerx'].set(
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min=pars[f'{self.prefix}BEC_centerx'].value - 10 * pars[f'{self.prefix}BEC_sigmax'].value,
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max=pars[f'{self.prefix}BEC_centerx'].value + 10 * pars[f'{self.prefix}BEC_sigmax'].value,
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)
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pars[f'{self.prefix}thermal_centerx'].set(
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min=pars[f'{self.prefix}thermal_centerx'].value - 3 * pars[f'{self.prefix}thermal_sigmax'].value,
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max=pars[f'{self.prefix}thermal_centerx'].value + 3 * pars[f'{self.prefix}thermal_sigmax'].value,
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)
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pars[f'{self.prefix}BEC_centery'].set(
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min=pars[f'{self.prefix}BEC_centery'].value - 10 * pars[f'{self.prefix}BEC_sigmay'].value,
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max=pars[f'{self.prefix}BEC_centery'].value + 10 * pars[f'{self.prefix}BEC_sigmay'].value,
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)
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pars[f'{self.prefix}thermal_centery'].set(
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min=pars[f'{self.prefix}thermal_centery'].value - 3 * pars[f'{self.prefix}thermal_sigmay'].value,
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max=pars[f'{self.prefix}thermal_centery'].value + 3 * pars[f'{self.prefix}thermal_sigmay'].value,
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)
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pars[f'{self.prefix}BEC_sigmay'].set(
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max=5 * pars[f'{self.prefix}BEC_sigmay'].value,
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)
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pars[f'{self.prefix}thermal_sigmax'].set(
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max=5 * pars[f'{self.prefix}thermal_sigmax'].value,
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)
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return update_param_vals(pars, self.prefix, **kwargs)
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print('Error in small width BEC recogintion, s_width_ind should be 0 or 1')
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else:
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params[f'{self.prefix}amp_bec'].set(value=amp_conv_1d_2d * bval_1d['amp_bec'], max=1.3 * max_val, vary=True)
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params[f'{self.prefix}amp_th'].set(value=amp_conv_1d_2d * bval_1d['amp_th'], max=1.3 * max_val, vary=True)
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params[f'{self.prefix}x0_bec'].set(value=center[0], min=center[0] -10, max=center[0] + 10, vary=True)
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params[f'{self.prefix}y0_bec'].set(value=center[1], min=center[1] -10, max=center[1] + 10, vary=True)
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params[f'{self.prefix}x0_th'].set(value=center[0], min=center[0] -10, max=center[0] + 10, vary=True)
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params[f'{self.prefix}y0_th'].set(value=center[1], min=center[1] -10, max=center[1] + 10, vary=True)
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params[f'{self.prefix}sigma_th'].set(value=bval_1d['sigma_th'], max=cut_width, vary=True)
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if s_width_ind == 0:
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params[f'{self.prefix}sigmax_bec'].set(value=bval_1d['sigma_bec'], max= 2*BEC_width_guess[0], vary=True)
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params[f'{self.prefix}sigmay_bec'].set(value=BEC_width_guess[1]/1.22, max= 2*BEC_width_guess[1], vary=True)
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elif s_width_ind == 1:
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params[f'{self.prefix}sigmax_bec'].set(value=BEC_width_guess[0]/1.22, max= 2*BEC_width_guess[0], vary=True)
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params[f'{self.prefix}sigmay_bec'].set(value=bval_1d['sigma_bec'], max= 2*BEC_width_guess[1], vary=True)
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else:
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print('Error in small width BEC recogintion, s_width_ind should be 0 or 1')
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if self.is_debug:
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print('')
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print('Init Params')
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params.pretty_print()
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return lmfit.models.update_param_vals(params, self.prefix, **kwargs)
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def fit(self, data_1d, **kwargs):
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res = super().fit(data_1d, **kwargs)
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if self.is_debug:
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print('bval first fit')
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self.print_bval(res)
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if res.params['amp_bec'].vary and res.params['amp_th'].vary:
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bval = res.best_values
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sigma_cut = max(bval['sigmay_bec'], bval['sigmax_bec'])
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tf_fit = ThomasFermi_2d(kwargs['x'],kwargs['y'],centerx=bval['x0_bec'], centery=bval['y0_bec'], amplitude=bval['amp_bec'], sigmax=bval['sigmax_bec'], sigmay=bval['sigmay_bec'])
|
||||
tf_fit_2 = ThomasFermi_2d(kwargs['x'],kwargs['y'],centerx=bval['x0_bec'], centery=bval['y0_bec'], amplitude=bval['amp_bec'], sigmax=1.5 * sigma_cut, sigmay=1.5*sigma_cut)
|
||||
mask = np.where(tf_fit > 0, np.nan, data_1d)
|
||||
#mask[i,j] = gaussian_filter(mask[i,j], sigma = 0.4)
|
||||
mask = np.where(tf_fit_2 > 0, mask, np.nan)
|
||||
|
||||
N_c = np.nansum(mask)
|
||||
# conversion N_count to Pixels
|
||||
N_a = self.atom_n_conv * N_c
|
||||
|
||||
if N_a < 6615:
|
||||
print('No thermal part detected, performing fit without thermal function')
|
||||
params = res.params
|
||||
params[f'{self.prefix}amp_th'].set(value=0, vary=False)
|
||||
params[f'{self.prefix}x0_th'].set(value=1, vary=False)
|
||||
params[f'{self.prefix}y0_th'].set(value=1, vary=False)
|
||||
params[f'{self.prefix}sigma_th'].set(value=1, vary=False)
|
||||
|
||||
res = super().fit(data_1d, x=kwargs['x'], y=kwargs['y'], params=params)
|
||||
|
||||
return res
|
||||
|
||||
return res
|
||||
|
||||
def calc_thresh(self, data, thresh_val=0.3, sigma=0.4):
|
||||
"""Returns thresholded binary image after blurring to guess BEC size
|
||||
|
||||
:param data: 2d image or 1D or 2D array containing 2d images
|
||||
:type data: 2d, 3d or 4d numpy array
|
||||
:param thresh_val: relative threshhold value for binarization with respect to maximum of blurred image
|
||||
:param sigma: sigma of gaussian blur filter (see scipy.ndimage.gaussian_filter
|
||||
:return: binary 2d image or 1D or 2D array containing 2d images
|
||||
:rtype: 2d, 3d or 4d numpy array
|
||||
"""
|
||||
shape = np.shape(data)
|
||||
thresh = np.zeros(shape)
|
||||
|
||||
blurred = gaussian_filter(data, sigma=sigma)
|
||||
|
||||
if len(shape) == 4:
|
||||
for i in range(0,shape[0]):
|
||||
for j in range(0, shape[1]):
|
||||
thresh[i,j] = np.where(blurred[i,j] < np.max(blurred[i,j])*thresh_val, 0, 1)
|
||||
|
||||
elif len(shape) == 3:
|
||||
for i in range(0,shape[0]):
|
||||
thresh[i] = np.where(blurred[i] < np.max(blurred[i])*thresh_val, 0, 1)
|
||||
|
||||
elif len(shape) == 2:
|
||||
thresh = np.where(blurred < np.max(blurred)*thresh_val, 0, 1)
|
||||
|
||||
|
||||
else:
|
||||
print("Shape of data is wrong, output is empty")
|
||||
|
||||
return thresh
|
||||
|
||||
def calc_cen(self, thresh1):
|
||||
"""
|
||||
returns array: [X_center,Y_center]
|
||||
"""
|
||||
cen = np.zeros(2)
|
||||
(Y,X) = np.shape(thresh1)
|
||||
|
||||
|
||||
thresh1 = thresh1 /np.sum(thresh1)
|
||||
|
||||
# marginal distributions
|
||||
dx = np.sum(thresh1, 0)
|
||||
dy = np.sum(thresh1, 1)
|
||||
|
||||
# expected values
|
||||
cen[0] = np.sum(dx * np.arange(X))
|
||||
cen[1] = np.sum(dy * np.arange(Y))
|
||||
return cen
|
||||
|
||||
def guess_BEC_width(self, thresh, center):
|
||||
"""
|
||||
returns width of thresholded area along both axis through the center with shape of thresh and [X_width, Y_width] for each image
|
||||
"""
|
||||
shape = np.shape(thresh)
|
||||
|
||||
if len(shape) == 2:
|
||||
BEC_width_guess = np.array([np.sum(thresh[round(center[1]), :]), np.sum(thresh[:, round(center[0])]) ])
|
||||
|
||||
elif len(shape) == 3:
|
||||
BEC_width_guess = np.zeros((shape[0], 2))
|
||||
for i in range(0, shape[0]):
|
||||
BEC_width_guess[i, 0] = np.sum(thresh[i, round(center[i,j,1]), :])
|
||||
BEC_width_guess[i, 1] = np.sum(thresh[i, :, round(center[i,j,0])])
|
||||
|
||||
elif len(shape) == 4:
|
||||
BEC_width_guess = np.zeros((shape[0], shape[1], 2))
|
||||
for i in range(0, shape[0]):
|
||||
for j in range(0, shape[1]):
|
||||
BEC_width_guess[i, j, 0] = np.sum(thresh[i, j, round(center[i,j,1]), :])
|
||||
BEC_width_guess[i, j, 1] = np.sum(thresh[i, j, :, round(center[i,j,0])])
|
||||
else:
|
||||
print("Shape of data is wrong, output is empty")
|
||||
|
||||
return BEC_width_guess
|
||||
|
||||
def cond_frac(self, results):
|
||||
"""Returns condensate fraction"""
|
||||
bval = results.best_values
|
||||
tf_fit = ThomasFermi_2d(X, Y, centerx=bval['x0_bec'], centery=bval['y0_bec'], amplitude=bval['amp_bec'], sigmax=bval['sigmax_bec'], sigmay=bval['sigmay_bec'])
|
||||
N_bec = np.sum(tf_fit)
|
||||
fit = density_profile_BEC_2d(X, Y, **bval)
|
||||
N_ges = np.sum(fit)
|
||||
return N_bec/N_ges
|
||||
|
||||
def return_atom_number(self, result, X, Y, is_print=True):
|
||||
"""Printing fitted atom number in bec + thermal state"""
|
||||
bval = result.best_values
|
||||
tf_fit = ThomasFermi_2d(X,Y,centerx=bval['x0_bec'], centery=bval['y0_bec'], amplitude=bval['amp_bec'], sigmax=bval['sigmax_bec'], sigmay=bval['sigmay_bec'])
|
||||
N_bec = self.atom_n_conv * np.sum(tf_fit)
|
||||
|
||||
th_fit = polylog2_2d(X, Y, centerx=bval['x0_th'], centery=bval['y0_th'], amplitude=bval['amp_th'], sigmax=bval['sigma_th'], sigmay=bval['sigma_th'])
|
||||
N_th = self.atom_n_conv * np.sum(th_fit)
|
||||
|
||||
# fit = density_profile_BEC_2d(X,Y, **bval)
|
||||
# N_ges = self.atom_n_conv * np.sum(fit)
|
||||
|
||||
if is_print:
|
||||
print()
|
||||
print('Atom numbers:')
|
||||
print(f' N_bec: {N_bec :.0f}')
|
||||
print(f' N_th: {N_th :.0f}')
|
||||
print(f' N_ges: {N_bec + N_th :.0f}')
|
||||
print(f' Cond. frac: {N_bec/(N_bec + N_th):.2f}')
|
||||
print('')
|
||||
|
||||
|
||||
def return_temperature(self, result, omg, tof, is_print=True, eff_pix=2.472e-6):
|
||||
"""Returns temperature of thermal cloud"""
|
||||
R_th = result.best_values['sigma_th'] * eff_pix * np.sqrt(2)
|
||||
print(R_th)
|
||||
T = R_th**2 * 164*const.u/const.k * (1/omg**2 + tof**2)**(-1)
|
||||
if is_print:
|
||||
print(f'Temperature: {T*1e9:.2f} nK')
|
||||
return T
|
||||
|
||||
def print_bval(self, res_s):
|
||||
"""nicely print best fitted values + init values + bounds """
|
||||
keys = res_s.best_values.keys()
|
||||
bval = res_s.best_values
|
||||
init = res_s.init_params
|
||||
|
||||
for item in keys:
|
||||
print(f'{item}: {bval[item]:.3f}, (init = {init[item].value:.3f}), bounds = [{init[item].min:.2f} : {init[item].max :.2f}] ')
|
||||
print('')
|
||||
|
||||
|
||||
class NewFitModel(Model):
|
||||
|
||||
Loading…
Reference in New Issue
Block a user