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20230629_Data_Analysis.ipynb
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20230629_Data_Analysis.ipynb
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20230630_Data_Analysis.ipynb
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20230630_Data_Analysis.ipynb
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20230630_Data_Analysis_2.ipynb
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20230630_Data_Analysis_2.ipynb
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2D-MOT power.ipynb
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2D-MOT power.ipynb
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3DMOT optimize.ipynb
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3DMOT optimize.ipynb
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smooth-cool-warm-table-float-1024.csv
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smooth-cool-warm-table-float-1024.csv
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test_fit.ipynb
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test_fit.ipynb
@ -1021,37 +1021,154 @@
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"params.add(name=\"B_sigmax\", value= 80e-6, max=np.inf, min=-np.inf, vary=True)\n",
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"params.add(name=\"B_sigmay\", value= 80e-6, max=np.inf, min=-np.inf, vary=True)\n",
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"\n",
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"for i in range (11):\n",
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" print(i)\n",
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" sim_fT_flatfield = xr.DataArray(\n",
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" data = sim_fT_flatfield_np[i], \n",
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" dims = [\"x\", \"y\"],\n",
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" coords = dict(\n",
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" x = (\"x\", x),\n",
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" y = (\"y\", y),\n",
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"# for i in range (11):\n",
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"# print(i)\n",
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"# sim_fT_flatfield = xr.DataArray(\n",
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"# data = sim_fT_flatfield_np[i], \n",
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"# dims = [\"x\", \"y\"],\n",
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"# coords = dict(\n",
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"# x = (\"x\", x),\n",
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"# y = (\"y\", y),\n",
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"# )\n",
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"# )\n",
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"# # perform the fit for one simulation with flatfield\n",
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"# params = fitAnalyser.guess(sim_fT_flatfield, dask=\"parallelized\", guess_kwargs=dict(pureBECThreshold=1.2))\n",
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"# fitResult = fitAnalyser.fit(sim_fT_flatfield, params, dask=\"parallelized\").load()\n",
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"# fitCurve = fitAnalyser.eval(fitResult, x=x, y=y).load()\n",
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"# fitValue = fitAnalyser.get_fit_value(fitResult)\n",
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"# fitStd = fitAnalyser.get_fit_std(fitResult)\n",
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"# # store the results as numpy array\n",
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"# fit_fT_flatfield = fitCurve.to_numpy()\n",
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"# fitValue_array = fitValue.to_array()\n",
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"# fitStd_array = fitStd.to_array()\n",
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"# fit_fT_flatfield_result[i] = fitValue_array.to_numpy()\n",
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"# fit_fT_flatfield_std[i] = fitStd_array.to_numpy()\n",
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"# # plot fit\n",
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"# plt.figure(figsize=(12,8))\n",
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"# plt.errorbar(x, sim_fT_flatfield_np[i,100], fmt = '.', label = \"simulated data\")\n",
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"# plt.plot(x, fit_fT_flatfield[100], label = \"BEC fit\")\n",
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"# plt.xlabel(\"y axis [px]\")\n",
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"# plt.ylabel(\"OD\")\n",
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"# plt.title(\"OD distribution cross-section \\n f={}, flatfield lightsource\".format(str(f[i])))\n",
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"# plt.legend()\n",
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"# plt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"class DensityProfileBEC2dModel(Model):\n",
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"\n",
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" fwhm_factor = 2*np.sqrt(2*np.log(2))\n",
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" height_factor = 1./2*np.pi\n",
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"\n",
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" def __init__(self, independent_vars=['x', 'y'], prefix='', nan_policy='raise',\n",
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" **kwargs):\n",
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" kwargs.update({'prefix': prefix, 'nan_policy': nan_policy,\n",
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" 'independent_vars': independent_vars})\n",
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" super().__init__(density_profile_BEC_2d, **kwargs)\n",
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" self._set_paramhints_prefix()\n",
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"\n",
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" def _set_paramhints_prefix(self):\n",
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" # self.set_param_hint('BEC_sigmax', min=0)\n",
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" self.set_param_hint('deltax', min=0)\n",
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" self.set_param_hint('BEC_sigmax', expr=f'3 * {self.prefix}thermal_sigmax - {self.prefix}deltax')\n",
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" \n",
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" self.set_param_hint('BEC_sigmay', min=0)\n",
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" self.set_param_hint('thermal_sigmax', min=0)\n",
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" # self.set_param_hint('thermal_sigmay', min=0)\n",
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" self.set_param_hint('BEC_amplitude', min=0)\n",
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" self.set_param_hint('thermal_amplitude', min=0)\n",
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" \n",
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" self.set_param_hint('thermalAspectRatio', min=0.8, max=1.2)\n",
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" self.set_param_hint('thermal_sigmay', expr=f'{self.prefix}thermalAspectRatio * {self.prefix}thermal_sigmax')\n",
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" \n",
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" # self.set_param_hint('betax', value=0)\n",
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" # self.set_param_hint('BEC_centerx', expr=f'{self.prefix}thermal_sigmax - {self.prefix}betax')\n",
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" \n",
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" self.set_param_hint('condensate_fraction', expr=f'{self.prefix}BEC_amplitude / ({self.prefix}BEC_amplitude + {self.prefix}thermal_amplitude)')\n",
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"\n",
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" def guess(self, data, x, y, negative=False, pureBECThreshold=0.5, noBECThThreshold=0.0, **kwargs):\n",
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" \"\"\"Estimate initial model parameter values from data.\"\"\"\n",
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" fitModel = TwoGaussian2dModel()\n",
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" pars = fitModel.guess(data, x=x, y=y, negative=negative)\n",
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" pars['A_amplitude'].set(min=0)\n",
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" pars['B_amplitude'].set(min=0)\n",
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" pars['A_centerx'].set(min=pars['A_centerx'].value - 3 * pars['A_sigmax'], \n",
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" max=pars['A_centerx'].value + 3 * pars['A_sigmax'],)\n",
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" pars['A_centery'].set(min=pars['A_centery'].value - 3 * pars['A_sigmay'], \n",
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" max=pars['A_centery'].value + 3 * pars['A_sigmay'],)\n",
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" pars['B_centerx'].set(min=pars['B_centerx'].value - 3 * pars['B_sigmax'], \n",
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" max=pars['B_centerx'].value + 3 * pars['B_sigmax'],)\n",
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" pars['B_centery'].set(min=pars['B_centery'].value - 3 * pars['B_sigmay'], \n",
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" max=pars['B_centery'].value + 3 * pars['B_sigmay'],)\n",
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" \n",
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" fitResult = fitModel.fit(data, x=x, y=y, params=pars, **kwargs)\n",
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" pars_guess = fitResult.params\n",
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" \n",
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" BEC_amplitude = pars_guess['A_amplitude'].value\n",
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" thermal_amplitude = pars_guess['B_amplitude'].value\n",
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" \n",
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" pars = self.make_params(BEC_amplitude=BEC_amplitude,\n",
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" thermal_amplitude=thermal_amplitude, \n",
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" BEC_centerx=pars_guess['A_centerx'].value, BEC_centery=pars_guess['A_centery'].value,\n",
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" # BEC_sigmax=(pars_guess['A_sigmax'].value / 2.355), \n",
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" deltax = 3 * (pars_guess['B_sigmax'].value * s2) - (pars_guess['A_sigmax'].value / 2.355),\n",
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" BEC_sigmay=(pars_guess['A_sigmay'].value / 2.355), \n",
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" thermal_centerx=pars_guess['B_centerx'].value, thermal_centery=pars_guess['B_centery'].value,\n",
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" thermal_sigmax=(pars_guess['B_sigmax'].value * s2), \n",
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" thermalAspectRatio=(pars_guess['B_sigmax'].value * s2) / (pars_guess['B_sigmay'].value * s2)\n",
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" # thermal_sigmay=(pars_guess['B_sigmay'].value * s2)\n",
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" )\n",
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" \n",
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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']\n",
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" if (pars[f'{self.prefix}condensate_fraction']>0.95) and (np.max(data) > 1.05 * nBEC):\n",
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" temp = ((np.max(data) - nBEC) * s2pi * pars[f'{self.prefix}thermal_sigmay'] / pars[f'{self.prefix}thermal_sigmax'])\n",
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" if temp > pars[f'{self.prefix}BEC_amplitude']:\n",
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" pars[f'{self.prefix}thermal_amplitude'].set(value=pars[f'{self.prefix}BEC_amplitude'] / 2)\n",
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" else:\n",
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" pars[f'{self.prefix}thermal_amplitude'].set(value=temp * 10)\n",
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" \n",
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" if BEC_amplitude / (thermal_amplitude + BEC_amplitude) > pureBECThreshold:\n",
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" pars[f'{self.prefix}thermal_amplitude'].set(value=0)\n",
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" pars[f'{self.prefix}BEC_amplitude'].set(value=(thermal_amplitude + BEC_amplitude))\n",
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" \n",
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" if BEC_amplitude / (thermal_amplitude + BEC_amplitude) < noBECThThreshold:\n",
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" pars[f'{self.prefix}BEC_amplitude'].set(value=0)\n",
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" pars[f'{self.prefix}thermal_amplitude'].set(value=(thermal_amplitude + BEC_amplitude))\n",
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" \n",
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" pars[f'{self.prefix}BEC_centerx'].set(\n",
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" min=pars[f'{self.prefix}BEC_centerx'].value - 10 * pars[f'{self.prefix}BEC_sigmax'].value,\n",
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" max=pars[f'{self.prefix}BEC_centerx'].value + 10 * pars[f'{self.prefix}BEC_sigmax'].value, \n",
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" )\n",
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" )\n",
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" # perform the fit for one simulation with flatfield\n",
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" params = fitAnalyser.guess(sim_fT_flatfield, dask=\"parallelized\", guess_kwargs=dict(pureBECThreshold=1.2))\n",
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" fitResult = fitAnalyser.fit(sim_fT_flatfield, params, dask=\"parallelized\").load()\n",
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" fitCurve = fitAnalyser.eval(fitResult, x=x, y=y).load()\n",
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" fitValue = fitAnalyser.get_fit_value(fitResult)\n",
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" fitStd = fitAnalyser.get_fit_std(fitResult)\n",
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" # store the results as numpy array\n",
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" fit_fT_flatfield = fitCurve.to_numpy()\n",
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" fitValue_array = fitValue.to_array()\n",
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" fitStd_array = fitStd.to_array()\n",
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" fit_fT_flatfield_result[i] = fitValue_array.to_numpy()\n",
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" fit_fT_flatfield_std[i] = fitStd_array.to_numpy()\n",
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" # plot fit\n",
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" plt.figure(figsize=(12,8))\n",
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" plt.errorbar(x, sim_fT_flatfield_np[i,100], fmt = '.', label = \"simulated data\")\n",
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" plt.plot(x, fit_fT_flatfield[100], label = \"BEC fit\")\n",
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" plt.xlabel(\"y axis [px]\")\n",
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" plt.ylabel(\"OD\")\n",
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" plt.title(\"OD distribution cross-section \\n f={}, flatfield lightsource\".format(str(f[i])))\n",
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" plt.legend()\n",
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" plt.show()"
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" \n",
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" pars[f'{self.prefix}thermal_centerx'].set(\n",
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" min=pars[f'{self.prefix}thermal_centerx'].value - 3 * pars[f'{self.prefix}thermal_sigmax'].value,\n",
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" max=pars[f'{self.prefix}thermal_centerx'].value + 3 * pars[f'{self.prefix}thermal_sigmax'].value, \n",
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" )\n",
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" \n",
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" pars[f'{self.prefix}BEC_centery'].set(\n",
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" min=pars[f'{self.prefix}BEC_centery'].value - 10 * pars[f'{self.prefix}BEC_sigmay'].value,\n",
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" max=pars[f'{self.prefix}BEC_centery'].value + 10 * pars[f'{self.prefix}BEC_sigmay'].value, \n",
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" )\n",
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" \n",
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" pars[f'{self.prefix}thermal_centery'].set(\n",
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" min=pars[f'{self.prefix}thermal_centery'].value - 3 * pars[f'{self.prefix}thermal_sigmay'].value,\n",
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" max=pars[f'{self.prefix}thermal_centery'].value + 3 * pars[f'{self.prefix}thermal_sigmay'].value, \n",
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" )\n",
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" \n",
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" pars[f'{self.prefix}BEC_sigmay'].set(\n",
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" max=5 * pars[f'{self.prefix}BEC_sigmay'].value, \n",
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" )\n",
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" \n",
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" pars[f'{self.prefix}thermal_sigmax'].set(\n",
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" max=5 * pars[f'{self.prefix}thermal_sigmax'].value, \n",
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" )\n",
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" \n",
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" return update_param_vals(pars, self.prefix, **kwargs)"
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]
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},
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{
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