regular backup

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',

DataContainer/ReadData.py

@@ -207,6 +207,7 @@ def _assign_scan_axis_partial_and_remove_everything(x, datesetOfGlobal, fullFile
         }
     )
    
+    
 def _read_run_time_from_hdf5(x):
     runTime = datetime.strptime(x.attrs['run time'], '%Y%m%dT%H%M%S')
     return runTime