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joschka_dev
Jianshun Gao 1 year ago
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6f7c2bbf69
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7 changed files with 14729 additions and 30 deletions
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  1. 2219
      20230629_Data_Analysis.ipynb
  2. 3676
      20230630_Data_Analysis.ipynb
  3. 3217
      20230630_Data_Analysis_2.ipynb
  4. 3666
      2D-MOT power.ipynb
  5. 779
      3DMOT optimize.ipynb
  6. 1025
      smooth-cool-warm-table-float-1024.csv
  7. 175
      test_fit.ipynb

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

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