From 591ba03f963f750d27550b4d2b2f1533f75f43c8 Mon Sep 17 00:00:00 2001 From: Joschka Date: Thu, 3 Aug 2023 17:16:07 +0200 Subject: [PATCH] Save --- Joschka/Implementing model.ipynb | 227 ++++++++++++++++++++++++------- 1 file changed, 180 insertions(+), 47 deletions(-) diff --git a/Joschka/Implementing model.ipynb b/Joschka/Implementing model.ipynb index fbcb42f..e8041ac 100644 --- a/Joschka/Implementing model.ipynb +++ b/Joschka/Implementing model.ipynb @@ -2,12 +2,12 @@ "cells": [ { "cell_type": "code", - "execution_count": 3, + "execution_count": 12, "metadata": { "collapsed": true, "ExecuteTime": { - "end_time": "2023-08-02T12:36:40.928831300Z", - "start_time": "2023-08-02T12:36:40.905797Z" + "end_time": "2023-08-03T13:22:27.512026500Z", + "start_time": "2023-08-03T13:22:26.423413100Z" } }, "outputs": [], @@ -60,7 +60,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 13, "outputs": [], "source": [ "# get center of thresholded image\n", @@ -279,14 +279,14 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-08-02T14:34:56.808273100Z", - "start_time": "2023-08-02T14:34:56.376294600Z" + "end_time": "2023-08-03T13:22:29.428319700Z", + "start_time": "2023-08-03T13:22:28.935877100Z" } } }, { "cell_type": "code", - "execution_count": 198, + "execution_count": 18, "outputs": [], "source": [ "import lmfit\n", @@ -314,7 +314,7 @@ " self.set_param_hint('sigmay_bec', min=0)\n", " self.set_param_hint('sigma_th', min=0)\n", "\n", - " def guess(self, data, x, y, negative=False, pureBECThreshold=0.5, noBECThThreshold=0.0, **kwargs):\n", + " def guess(self, data, x, y, **kwargs):\n", " \"\"\"Estimate initial model parameter values from data.\"\"\"\n", " #\n", " # global X_guess\n", @@ -399,12 +399,19 @@ " return lmfit.models.update_param_vals(params, self.prefix, **kwargs)\n", "\n", "\n", - " def fit(self, data, **kwargs):\n", + " def fit(self, data, imaging_props: dict[str, any] = {'magn' : 2.352, 'eff_pix' : 2.493e-6, 'eta' : 0.5, 'sig_cross' : 8.4743e-14}, **kwargs):\n", + " expected_keys = {'magn', 'eff_pix', 'eta', 'sig_cross'}\n", + "\n", + " if not set(imaging_props.keys()) == expected_keys:\n", + " raise ValueError(f\"Invalid dictionary structure, expected keys: {expected_keys}\")\n", "\n", " data1d = data.flatten()\n", "\n", " res = super().fit(data1d, **kwargs)\n", "\n", + " # conversion N_count to Pixels\n", + " F = 1 /imaging_props['eta'] /imaging_props['sigma_cross'] * imaging_props['eff_pix']**2 /imaging_props['magn']\n", + "\n", " if res.params['amp_bec'].vary and res.params['amp_th'].vary:\n", " bval = res.best_values\n", " sigma_cut = max(bval['sigmay_bec'], bval['sigmax_bec'])\n", @@ -415,9 +422,12 @@ " #mask[i,j] = gaussian_filter(mask[i,j], sigma = 0.4)\n", " mask = np.where(tf_fit_2 > 0, mask, np.nan)\n", "\n", - " check_value = np.nansum(mask)\n", + " N_c = np.nansum(mask)\n", + " # conversion N_count to Pixels\n", + " N_a = F * N_c\n", "\n", - " if check_value < 45:\n", + "\n", + " if N_a < 2806:\n", " print('No thermal part detected, performing fit without thermal function')\n", " params = res.params\n", " params[f'{self.prefix}amp_th'].set(value=0, vary=False)\n", @@ -434,19 +444,76 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-08-02T17:00:15.869774100Z", - "start_time": "2023-08-02T17:00:15.823391200Z" + "end_time": "2023-08-03T13:23:18.655080200Z", + "start_time": "2023-08-03T13:23:18.592113500Z" } } }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 54, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3300.298608734645\n" + ] + } + ], + "source": [ + "imaging_props = {'magn' : 2.352, 'eff_pix' : 2.493e-6, 'eta' : 0.5, 'sigma_cross' : 8.4743e-14}\n", + "\n", + "# conversion N_count to Pixels\n", + "F = 1 /imaging_props['eta'] /imaging_props['sigma_cross'] * imaging_props['eff_pix']**2 /imaging_props['magn']**2\n", + "\n", + "# F = imaging_props['eff_pix']**2/imaging_props['sigma_cross']\n", + "print(F*45)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-08-03T14:09:56.837034Z", + "start_time": "2023-08-03T14:09:56.734306500Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Success\n" + ] + } + ], + "source": [ + "test_d = {'magn' : 1, 'eff_pix' : 7, 'sig_cross' : 10e-7}\n", + "\n", + "\n", + "test_func(test_d)\n", + "\n", + "test_func()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-08-03T13:13:06.489486500Z", + "start_time": "2023-08-03T13:13:06.363567400Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 29, "outputs": [], "source": [ "# load Brittas data\n", "\n", - "data = np.zeros((2,11, 1200, 1920))\n", + "data = np.empty((2,11, 1200, 1920))\n", "data[0] = np.load('Data_Britta/OD_ft_flatfield.npy')\n", "data[1] = np.load('Data_Britta/OD_ft_manual.npy')\n", "\n", @@ -466,29 +533,71 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-08-02T14:52:34.335544Z", - "start_time": "2023-08-02T14:52:30.850396700Z" + "end_time": "2023-08-03T13:56:36.782758200Z", + "start_time": "2023-08-03T13:56:33.250915300Z" } } }, { "cell_type": "code", - "execution_count": 199, - "outputs": [], + "execution_count": 57, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "49415.86534245327\n", + "-inf\n", + "-inf\n", + "(1084, 483)\n" + ] + } + ], "source": [ - "Fitmodel = DensityProfileBEC2dModel()" + "i = 1\n", + "j = 7\n", + "print(F *np.sum(cropOD[i,j]))\n", + "print(F*np.sum(data[i,j]))\n", + "print(np.min(data[i,j]))\n", + "print( np.unravel_index(np.argmin(data[i,j]),(1200,1920)) )" ], "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-08-02T17:00:19.756790200Z", - "start_time": "2023-08-02T17:00:19.728358700Z" + "end_time": "2023-08-03T14:14:56.861214500Z", + "start_time": "2023-08-03T14:14:56.766975800Z" } } }, { "cell_type": "code", - "execution_count": 200, + "execution_count": 52, + "outputs": [ + { + "data": { + "text/plain": "
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "im = plt.pcolormesh(gaussian_filter(data[i,j,1050:1100, 450:500], sigma=0.5), cmap='jet')\n", + "plt.colorbar(im)\n", + "plt.show()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-08-03T14:04:27.370933200Z", + "start_time": "2023-08-03T14:04:27.161778400Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 19, "outputs": [ { "name": "stdout", @@ -496,62 +605,86 @@ "text": [ "image: 0, 0\n", "Image seems to be purely thermal (guessed from 1d fit amplitude)\n", - " time = 0.336 s\n", + "{'magn': 2.352, 'eff_pix': 2.493e-06, 'sig_cross': 8.4743e-14}\n", + " time = 0.252 s\n", "image: 0, 1\n", - " time = 0.430 s\n", + "{'magn': 2.352, 'eff_pix': 2.493e-06, 'sig_cross': 8.4743e-14}\n", + " time = 0.410 s\n", "image: 0, 2\n", - " time = 0.515 s\n", + "{'magn': 2.352, 'eff_pix': 2.493e-06, 'sig_cross': 8.4743e-14}\n", + " time = 0.342 s\n", "image: 0, 3\n", - " time = 0.484 s\n", + "{'magn': 2.352, 'eff_pix': 2.493e-06, 'sig_cross': 8.4743e-14}\n", + " time = 0.381 s\n", "image: 0, 4\n", - " time = 0.370 s\n", + "{'magn': 2.352, 'eff_pix': 2.493e-06, 'sig_cross': 8.4743e-14}\n", + " time = 0.233 s\n", "image: 0, 5\n", - " time = 0.572 s\n", + "{'magn': 2.352, 'eff_pix': 2.493e-06, 'sig_cross': 8.4743e-14}\n", + " time = 0.397 s\n", "image: 0, 6\n", - " time = 0.321 s\n", + "{'magn': 2.352, 'eff_pix': 2.493e-06, 'sig_cross': 8.4743e-14}\n", + " time = 0.351 s\n", "image: 0, 7\n", + "{'magn': 2.352, 'eff_pix': 2.493e-06, 'sig_cross': 8.4743e-14}\n", "No thermal part detected, performing fit without thermal function\n", - " time = 0.467 s\n", + " time = 0.478 s\n", "image: 0, 8\n", + "{'magn': 2.352, 'eff_pix': 2.493e-06, 'sig_cross': 8.4743e-14}\n", "No thermal part detected, performing fit without thermal function\n", - " time = 0.788 s\n", + " time = 0.441 s\n", "image: 0, 9\n", + "{'magn': 2.352, 'eff_pix': 2.493e-06, 'sig_cross': 8.4743e-14}\n", "No thermal part detected, performing fit without thermal function\n", - " time = 0.763 s\n", + " time = 0.561 s\n", "image: 0, 10\n", "Image seems to be pure BEC (guessed from 1d fit amplitude)\n", - " time = 0.238 s\n", + "{'magn': 2.352, 'eff_pix': 2.493e-06, 'sig_cross': 8.4743e-14}\n", + " time = 0.224 s\n", "image: 1, 0\n", "Image seems to be purely thermal (guessed from 1d fit amplitude)\n", - " time = 0.273 s\n", + "{'magn': 2.352, 'eff_pix': 2.493e-06, 'sig_cross': 8.4743e-14}\n", + " time = 0.267 s\n", "image: 1, 1\n", - " time = 1.558 s\n", + "{'magn': 2.352, 'eff_pix': 2.493e-06, 'sig_cross': 8.4743e-14}\n", + " time = 1.173 s\n", "image: 1, 2\n", - " time = 0.419 s\n", + "{'magn': 2.352, 'eff_pix': 2.493e-06, 'sig_cross': 8.4743e-14}\n", + " time = 0.577 s\n", "image: 1, 3\n", - " time = 0.350 s\n", + "{'magn': 2.352, 'eff_pix': 2.493e-06, 'sig_cross': 8.4743e-14}\n", + " time = 0.646 s\n", "image: 1, 4\n", - " time = 0.335 s\n", + "{'magn': 2.352, 'eff_pix': 2.493e-06, 'sig_cross': 8.4743e-14}\n", + " time = 0.300 s\n", "image: 1, 5\n", - " time = 0.379 s\n", + "{'magn': 2.352, 'eff_pix': 2.493e-06, 'sig_cross': 8.4743e-14}\n", + " time = 0.361 s\n", "image: 1, 6\n", - " time = 0.372 s\n", + "{'magn': 2.352, 'eff_pix': 2.493e-06, 'sig_cross': 8.4743e-14}\n", + " time = 0.346 s\n", "image: 1, 7\n", + "{'magn': 2.352, 'eff_pix': 2.493e-06, 'sig_cross': 8.4743e-14}\n", "No thermal part detected, performing fit without thermal function\n", - " time = 0.543 s\n", + " time = 0.475 s\n", "image: 1, 8\n", + "{'magn': 2.352, 'eff_pix': 2.493e-06, 'sig_cross': 8.4743e-14}\n", "No thermal part detected, performing fit without thermal function\n", - " time = 0.556 s\n", + " time = 0.577 s\n", "image: 1, 9\n", "Image seems to be pure BEC (guessed from 1d fit amplitude)\n", - " time = 0.188 s\n", + "{'magn': 2.352, 'eff_pix': 2.493e-06, 'sig_cross': 8.4743e-14}\n", + " time = 0.205 s\n", "image: 1, 10\n", "Image seems to be pure BEC (guessed from 1d fit amplitude)\n", - " time = 0.189 s\n" + "{'magn': 2.352, 'eff_pix': 2.493e-06, 'sig_cross': 8.4743e-14}\n", + " time = 0.219 s\n" ] } ], "source": [ + "Fitmodel = DensityProfileBEC2dModel()\n", + "\n", "x = np.linspace(0,shape[3],cut_width)\n", "y = np.linspace(0,shape[2], cut_width)\n", "\n", @@ -576,8 +709,8 @@ "metadata": { "collapsed": false, "ExecuteTime": { - "end_time": "2023-08-02T17:00:30.391591100Z", - "start_time": "2023-08-02T17:00:19.933806300Z" + "end_time": "2023-08-03T13:23:36.915552300Z", + "start_time": "2023-08-03T13:23:27.669543300Z" } } },