diff --git a/Analyser/FitAnalyser.py b/Analyser/FitAnalyser.py index 4fd7591..47c85d1 100644 --- a/Analyser/FitAnalyser.py +++ b/Analyser/FitAnalyser.py @@ -80,6 +80,48 @@ def two_gaussian2d(x, y=0.0, A_amplitude=1.0, A_centerx=0.0, A_centery=0.0, A_si return z +def ThomasFermi_2d(x, y=0.0, centerx=0.0, centery=0.0, amplitude=1.0, sigmax=1.0, sigmay=1.0): + res = (1- ((x-centerx)/(sigmax))**2 - ((y-centery)/(sigmay))**2)**(3 / 2) + return amplitude * 5 / 2 / np.pi / max(tiny, sigmax * sigmay) * np.where(res > 0, res, 0) + + +def polylog(power, numerator): + + dataShape = numerator.shape + numerator = np.tile(numerator, (20, 1)) + + denominator = np.arange(1, 21) + denominator = np.tile(denominator, (dataShape[0], 1)) + denominator = denominator.T + + data = numerator / denominator + + return np.sum(np.power(data, power), axis=0) + + +def polylog2_2d(x, y=0.0, centerx=0.0, centery=0.0, amplitude=1.0, sigmax=1.0, sigmay=1.0): + ## Approximation of the polylog function with 2D gaussian as argument. -> discribes the thermal part of the cloud + return amplitude / np.pi / 1.59843 / max(tiny, sigmax * sigmay) * polylog(2, np.exp( -((x-centerx)**2/(2 * (sigmax)**2))-((y-centery)**2/( 2 * (sigmay)**2)) )) + + +def density_profile_BEC_2d(x, y=0.0, amplitude=1.0, condensateFraction=1.0, BEC_centerx=0.0, BEC_centery=0.0, thermal_centerx=0.0, thermal_centery=0.0, + BEC_sigmax=1.0, BEC_sigmay=1.0, thermal_sigmax=1.0, thermal_sigmay=1.0): + + return ThomasFermi_2d(x=x, y=y, centerx=BEC_centerx, centery=BEC_centery, + amplitude=amplitude*condensateFraction, sigmax=BEC_sigmax, sigmay=BEC_sigmay + ) + polylog2_2d(x=x, y=y, centerx=thermal_centerx, centery=thermal_centery, + amplitude=amplitude * (1 - condensateFraction), sigmax=thermal_sigmax, sigmay=thermal_sigmay) + + +# 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, +# BEC_sigmax=1.0, BEC_sigmay=1.0, thermal_sigmax=1.0, thermal_sigmay=1.0): + +# return ThomasFermi_2d(x=x, y=y, centerx=BEC_centerx, centery=BEC_centery, +# amplitude=BEC_amplitude, sigmax=BEC_sigmax, sigmay=BEC_sigmay +# ) + polylog2_2d(x=x, y=y, centerx=thermal_centerx, centery=thermal_centery, +# amplitude=thermal_amplitude, sigmax=thermal_sigmax, sigmay=thermal_sigmay) + + class GaussianWithOffsetModel(Model): fwhm_factor = 2*np.sqrt(2*np.log(2)) @@ -224,6 +266,124 @@ class TwoGaussian2dModel(Model): return pars +class Polylog22dModel(Model): + + fwhm_factor = 2*np.sqrt(2*np.log(2)) + height_factor = 1./2*np.pi + + def __init__(self, independent_vars=['x', 'y'], prefix='', nan_policy='raise', + **kwargs): + kwargs.update({'prefix': prefix, 'nan_policy': nan_policy, + 'independent_vars': independent_vars}) + super().__init__(polylog2_2d, **kwargs) + self._set_paramhints_prefix() + + def _set_paramhints_prefix(self): + self.set_param_hint('Rx', min=0) + self.set_param_hint('Ry', min=0) + + def guess(self, data, x, y, negative=False, **kwargs): + """Estimate initial model parameter values from data.""" + pars = guess_from_peak2d(self, data, x, y, negative) + return update_param_vals(pars, self.prefix, **kwargs) + + +class ThomasFermi2dModel(Model): + + fwhm_factor = 1 + height_factor = 0.5 + + def __init__(self, independent_vars=['x', 'y'], prefix='', nan_policy='raise', + **kwargs): + kwargs.update({'prefix': prefix, 'nan_policy': nan_policy, + 'independent_vars': independent_vars}) + super().__init__(ThomasFermi_2d, **kwargs) + self._set_paramhints_prefix() + + def _set_paramhints_prefix(self): + self.set_param_hint('Rx', min=0) + self.set_param_hint('Ry', min=0) + + def guess(self, data, x, y, negative=False, **kwargs): + """Estimate initial model parameter values from data.""" + pars = guess_from_peak2d(self, data, x, y, negative) + + # amplitude = pars['amplitude'].value + # simgax = pars['sigmax'].value + # sigmay = pars['sigmay'].value + + # pars['amplitude'].set(value=amplitude/s2pi/simgax/sigmay) + + simgax = pars['sigmax'].value + sigmay = pars['sigmay'].value + pars['simgax'].set(value=simgax / 2.355) + pars['sigmay'].set(value=sigmay / 2.355) + + return update_param_vals(pars, self.prefix, **kwargs) + + +class DensityProfileBEC2dModel(Model): + + fwhm_factor = 2*np.sqrt(2*np.log(2)) + height_factor = 1./2*np.pi + + def __init__(self, independent_vars=['x', 'y'], prefix='', nan_policy='raise', + **kwargs): + kwargs.update({'prefix': prefix, 'nan_policy': nan_policy, + 'independent_vars': independent_vars}) + super().__init__(density_profile_BEC_2d, **kwargs) + self._set_paramhints_prefix() + + def _set_paramhints_prefix(self): + self.set_param_hint('BEC_sigmax', min=0) + self.set_param_hint('BEC_sigmay', min=0) + self.set_param_hint('thermal_sigmax', min=0) + self.set_param_hint('thermal_sigmay', min=0) + + def guess(self, data, x, y, negative=False, **kwargs): + """Estimate initial model parameter values from data.""" + fitModel = TwoGaussian2dModel() + pars = fitModel.guess(data, x=x, y=y, negative=negative) + fitResult = fitModel.fit(data, x=x, y=y, params=pars, **kwargs) + pars_guess = fitResult.params + + amplitude = (pars_guess['A_amplitude'].value + pars_guess['B_amplitude'].value) + # amplitude = amplitude / amplitude * np.max(data) + condensateFraction = pars_guess['A_amplitude'].value / (pars_guess['A_amplitude'].value + pars_guess['B_amplitude'].value) + + pars = self.make_params(amplitude=amplitude, + condensateFraction=condensateFraction, + BEC_centerx=pars_guess['A_centerx'].value, BEC_centery=pars_guess['A_centery'].value, + BEC_sigmax=(pars_guess['A_sigmax'].value / 2.355), BEC_sigmay=(pars_guess['A_sigmay'].value / 2.355), + thermal_centerx=pars_guess['B_centerx'].value, thermal_centery=pars_guess['B_centery'].value, + thermal_sigmax=(pars_guess['B_sigmax'].value * s2), thermal_sigmay=(pars_guess['B_sigmay'].value * s2)) + + # BEC_amplitude = pars_guess['A_amplitude'].value + # thermal_amplitude = pars_guess['B_amplitude'].value + + # pars = self.make_params(BEC_amplitude=BEC_amplitude, + # thermal_amplitude=thermal_amplitude, + # BEC_centerx=pars_guess['A_centerx'].value, BEC_centery=pars_guess['A_centery'].value, + # BEC_sigmax=(pars_guess['A_sigmax'].value / 2.355), BEC_sigmay=(pars_guess['A_sigmay'].value / 2.355), + # thermal_centerx=pars_guess['B_centerx'].value, thermal_centery=pars_guess['B_centery'].value, + # thermal_sigmax=(pars_guess['B_sigmax'].value * s2), thermal_sigmay=(pars_guess['B_sigmay'].value * s2)) + + # pars[f'{self.prefix}BEC_sigmax'].set(min=0.0) + # pars[f'{self.prefix}BEC_sigmay'].set(min=0.0) + # pars[f'{self.prefix}thermal_sigmax'].set(min=0.0) + # pars[f'{self.prefix}thermal_sigmay'].set(min=0.0) + + if condensateFraction < 0.3: + pars[f'{self.prefix}condensateFraction'].set(max=condensateFraction*1.5) + if condensateFraction > 0.5: + pars[f'{self.prefix}condensateFraction'].set(min=0.9) + + # pars[f'{self.prefix}condensateFraction'].set(max=condensateFraction*1.5) + # pars[f'{self.prefix}condensateFraction'].set(min=condensateFraction*0.75) + + return update_param_vals(pars, self.prefix, **kwargs) + + class NewFitModel(Model): def __init__(self, func, independent_vars=['x'], prefix='', nan_policy='raise', diff --git a/DataContainer/ReadData.py b/DataContainer/ReadData.py index 6928cb8..1a4ebfc 100644 --- a/DataContainer/ReadData.py +++ b/DataContainer/ReadData.py @@ -206,6 +206,7 @@ def _assign_scan_axis_partial_and_remove_everything(x, datesetOfGlobal, fullFile for key in scanAxis } ) + def _read_run_time_from_hdf5(x): runTime = datetime.strptime(x.attrs['run time'], '%Y%m%dT%H%M%S')