960 lines
2.3 MiB
Plaintext
960 lines
2.3 MiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [
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"import xarray as xr\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import copy\n",
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"\n",
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"import glob\n",
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"\n",
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"import xrft\n",
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"import finufft\n",
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"\n",
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"from uncertainties import ufloat\n",
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"from uncertainties import unumpy as unp\n",
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"from uncertainties import umath\n",
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"\n",
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"from datetime import datetime\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"\n",
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"plt.rcParams['font.size'] = 18\n",
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"\n",
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"from scipy.ndimage import gaussian_filter\n",
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"import matplotlib as mpl\n",
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"from scipy.optimize import curve_fit\n",
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"mpl.rc('xtick', labelsize=8)\n",
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"mpl.rc('ytick', labelsize=8)\n",
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"\n",
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"from DataContainer.ReadData import read_hdf5_file, read_hdf5_global, read_hdf5_run_time, read_csv_file\n",
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"from Analyser.ImagingAnalyser import ImageAnalyser\n",
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"from Analyser.FitAnalyser import FitAnalyser\n",
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"from Analyser.FitAnalyser import ThomasFermi2dModel, DensityProfileBEC2dModel, Polylog22dModel\n",
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"from Analyser.FFTAnalyser import fft, ifft, fft_nutou\n",
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"from ToolFunction.ToolFunction import *\n",
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"\n",
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"import time\n",
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"\n",
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"from ToolFunction.HomeMadeXarrayFunction import errorbar, dataarray_plot_errorbar\n",
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"xr.plot.dataarray_plot.errorbar = errorbar\n",
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"xr.plot.accessor.DataArrayPlotAccessor.errorbar = dataarray_plot_errorbar\n",
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"\n",
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"imageAnalyser = ImageAnalyser()\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"#dataSet"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Some functions"
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],
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"metadata": {
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"collapsed": false
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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": 253,
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"outputs": [],
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"source": [
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"# get center of thresholded image\n",
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"\n",
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"def calc_cen(thresh1):\n",
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" \"\"\"\n",
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" returns array: [Y_center,X_center]\n",
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" \"\"\"\n",
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" cen = np.zeros(2)\n",
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" (Y,X) = np.shape(thresh1)\n",
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"\n",
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"\n",
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" thresh1 = thresh1 /np.sum(thresh1)\n",
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"\n",
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" # marginal distributions\n",
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" dx = np.sum(thresh1, 0)\n",
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" dy = np.sum(thresh1, 1)\n",
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"\n",
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" # expected values\n",
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" cen[0] = np.sum(dx * np.arange(X))\n",
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" cen[1] = np.sum(dy * np.arange(Y))\n",
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" return cen\n",
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"\n",
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"def calc_cen_bulk(thresh):\n",
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" \"\"\"\n",
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" returns array in shape of input, containing array with [Y_center,X_center] for each image\n",
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" \"\"\"\n",
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" shape = np.shape(thresh)\n",
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" cen = np.zeros((shape[0], shape[1], 2))\n",
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" for i in range(0, shape[0]):\n",
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" for j in range(0, shape[1]):\n",
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" cen[i,j] = calc_cen(thresh[i,j])\n",
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" return cen\n",
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"\n",
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"\n",
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"def gaussian(x, x0, sigma, A):\n",
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" return A * np.exp(-0.5 * (x-x0)**2 / sigma**2)\n",
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"\n",
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"def polylog(power, numerator, order = 15):\n",
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"\n",
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" dataShape = numerator.shape\n",
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" numerator = np.tile(numerator, (order, 1))\n",
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" numerator = np.power(numerator.T, np.arange(1, order+1)).T\n",
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"\n",
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" denominator = np.arange(1, order+1)\n",
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" denominator = np.tile(denominator, (dataShape[0], 1))\n",
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" denominator = denominator.T\n",
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"\n",
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" data = numerator/ np.power(denominator, power)\n",
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"\n",
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" return np.sum(data, axis=0)\n",
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"\n",
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"def thermal(x, x0, amp, sigma, order = 15):\n",
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" res = np.exp(-0.5 * (x-x0)**2 / sigma**2)\n",
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" return amp * polylog(2, res, order)\n",
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"\n",
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"def Thomas_Fermi_1d(x, x0, amp, sigma):\n",
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" res = (1-(( x - x0 ) / sigma) **2) **3/2\n",
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" return amp * np.where(res > 0, res, 0)\n",
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"\n",
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"def density_1d(x, x0_bec, x0_th, amp_bec, amp_th, sigma_bec, sigma_th, polyorder=15):\n",
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" return thermal(x, x0_th, amp_th, sigma_th, polyorder) + Thomas_Fermi_1d(x, x0_bec, amp_bec, sigma_bec)\n"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2023-07-19T12:36:06.445703Z",
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"start_time": "2023-07-19T12:36:06.423639600Z"
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}
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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": 252,
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"outputs": [
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{
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"data": {
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"text/plain": "<Figure size 640x480 with 1 Axes>",
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"image/png": "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},
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"metadata": {},
|
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"output_type": "display_data"
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}
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],
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"source": [
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"x = np.linspace(-1,3, 1000)\n",
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"plt.plot(x, Thomas_Fermi_1d(x, 1, 1, 1))\n",
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"plt.show()"
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],
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"metadata": {
|
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2023-07-19T12:27:53.792186700Z",
|
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"start_time": "2023-07-19T12:27:53.612653200Z"
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}
|
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|
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"## Import Data"
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],
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"metadata": {
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"collapsed": false
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}
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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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"outputs": [],
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"source": [
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"# import data\n",
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"img_dir = '//DyLabNAS/Data/'\n",
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"SequenceName = \"Evaporative_Cooling\" + \"/\"\n",
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"folderPath = img_dir + SequenceName + '2023/04/24'# get_date()\n",
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"\n",
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"\n",
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"shotNum = \"0009\"\n",
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"filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n",
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"filePath = folderPath + \"/\" + shotNum + \"/2023-04-24_0009_Evaporative_Cooling_*0.h5\"\n",
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"\n",
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"# # load the data from HDF5 files\n",
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"# dataSetDict = {\n",
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"# dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n",
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"# for i in [0] # range(len(groupList))\n",
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"# }\n",
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"\n",
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"# selecte the data for centain camera\n",
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"dataSet = read_hdf5_file(filePath, \"images/MOT_3D_Camera/in_situ_absorption\")\n",
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"# flip the x and y axis\n",
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"dataSet = swap_xy(dataSet)\n",
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"\n",
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"# get the scan axis name of the shot\n",
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"scanAxis = get_scanAxis(dataSet)\n",
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"\n",
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"# rechunck the data for parallel computing\n",
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"dataSet = auto_rechunk(dataSet)\n",
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"\n",
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"# calculate the absorption imaging\n",
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"dataSet = imageAnalyser.get_absorption_images(dataSet)\n",
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"\n",
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"dataSet\n",
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"\n",
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"OD = dataSet[\"OD\"]\n",
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"\n",
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"OD_np = OD.to_numpy()"
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],
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|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 211,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 1000x900 with 10 Axes>",
|
||
|
|
"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"imageAnalyser.center = (960, 890)\n",
|
||
|
|
"imageAnalyser.span = (150, 200)\n",
|
||
|
|
"imageAnalyser.fraction = (0.1, 0.1)\n",
|
||
|
|
"\n",
|
||
|
|
"dataSet_cropOD = imageAnalyser.crop_image(dataSet.OD)\n",
|
||
|
|
"dataSet_cropOD = imageAnalyser.substract_offset(dataSet_cropOD).load()\n",
|
||
|
|
"cropOD = dataSet_cropOD.to_numpy()\n",
|
||
|
|
"dataSet_cropOD.plot.pcolormesh(cmap='jet', vmin=0, col=scanAxis[1], row=scanAxis[0])\n",
|
||
|
|
"plt.show()"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2023-07-19T12:09:08.446295800Z",
|
||
|
|
"start_time": "2023-07-19T12:09:00.896322400Z"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"source": [
|
||
|
|
"## Guess center\n",
|
||
|
|
"\n",
|
||
|
|
"ToDo: Crop from center guess"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 214,
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# from opencv import moments\n",
|
||
|
|
"shape = np.shape(cropOD)\n",
|
||
|
|
"sigma = 0.4\n",
|
||
|
|
"blurred = gaussian_filter(cropOD, sigma=sigma)\n",
|
||
|
|
"\n",
|
||
|
|
"thresh = np.zeros(shape)\n",
|
||
|
|
"for i in range(0,shape[0]):\n",
|
||
|
|
" for j in range(0, shape[1]):\n",
|
||
|
|
" thresh[i,j] = np.where(blurred[i,j] < np.max(blurred[i,j])*0.5,0,1)\n",
|
||
|
|
"\n",
|
||
|
|
"# thresh = gaussian_filter(thresh, sigma=0.1)\n",
|
||
|
|
"# thresh = np.where(thresh<0.1,0,1)\n",
|
||
|
|
"\n",
|
||
|
|
"#M = moments(thresh)"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2023-07-19T12:09:26.336425100Z",
|
||
|
|
"start_time": "2023-07-19T12:09:26.282568400Z"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 215,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[[[75.69178082 88.5 ]\n",
|
||
|
|
" [77.85648148 83.31944444]\n",
|
||
|
|
" [74.20467836 83.28654971]]\n",
|
||
|
|
"\n",
|
||
|
|
" [[77.33333333 84.52136752]\n",
|
||
|
|
" [74.9516129 84.98387097]\n",
|
||
|
|
" [78.32758621 90.42528736]]\n",
|
||
|
|
"\n",
|
||
|
|
" [[76.02054795 84.00684932]\n",
|
||
|
|
" [76.22058824 83.63970588]\n",
|
||
|
|
" [75.00787402 84.74015748]]]\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"center = calc_cen_bulk(thresh)\n",
|
||
|
|
"print(center)"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2023-07-19T12:09:26.898612200Z",
|
||
|
|
"start_time": "2023-07-19T12:09:26.833267600Z"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 216,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 1200x800 with 9 Axes>",
|
||
|
|
"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 1200x800 with 9 Axes>",
|
||
|
|
"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 1200x800 with 9 Axes>",
|
||
|
|
"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"fsize = (12,8)\n",
|
||
|
|
"\n",
|
||
|
|
"nr_plots = 3\n",
|
||
|
|
"\n",
|
||
|
|
"fig, ax = plt.subplots(nr_plots,nr_plots,figsize=fsize)\n",
|
||
|
|
"\n",
|
||
|
|
"for i in range(0,nr_plots):\n",
|
||
|
|
" for j in range(0,nr_plots):\n",
|
||
|
|
" # ax[i][j].pcolormesh(blurred[i][j], cmap='jet', vmin=0, vmax=1.5, alpha=1)\n",
|
||
|
|
" ax[i][j].pcolormesh(thresh[i][j], cmap='jet', vmin=0, vmax=1.5, alpha=1)\n",
|
||
|
|
" #ax[i][j].pcolormesh(cropOD[i][j], cmap='hot', vmin=0, vmax=1, alpha=1)\n",
|
||
|
|
" ax[i][j].plot(center[i,j,0],center[i,j,1], marker='x', markersize=12)\n",
|
||
|
|
"plt.show()\n",
|
||
|
|
"\n",
|
||
|
|
"fig, ax = plt.subplots(nr_plots,nr_plots,figsize=fsize)\n",
|
||
|
|
"for i in range(0,nr_plots):\n",
|
||
|
|
" for j in range(0,nr_plots):\n",
|
||
|
|
" ax[i][j].pcolormesh(blurred[i][j], cmap='jet', vmin=0, vmax=1.5, alpha=1)\n",
|
||
|
|
" #ax[i][j].pcolormesh(thresh[i][j], cmap='jet', vmin=0, vmax=1.5, alpha=1)\n",
|
||
|
|
" #ax[i][j].pcolormesh(cropOD[i][j], cmap='hot', vmin=0, vmax=1, alpha=1)\n",
|
||
|
|
" ax[i][j].plot(center[i,j,0],center[i,j,1], marker='x', markersize=12)\n",
|
||
|
|
"plt.show()\n",
|
||
|
|
"\n",
|
||
|
|
"fig, ax = plt.subplots(nr_plots,nr_plots,figsize=fsize)\n",
|
||
|
|
"for i in range(0,nr_plots):\n",
|
||
|
|
" for j in range(0,nr_plots):\n",
|
||
|
|
" ax[i][j].pcolormesh(cropOD[i][j], cmap='jet', vmin=0, vmax=1.5)\n",
|
||
|
|
" #ax[i][j].plot(max[i,j,1],max[i,j,0], marker='x', markersize=12)\n",
|
||
|
|
" ax[i][j].plot(center[i,j,0],center[i,j,1], marker='x', color='g', markersize=12)\n",
|
||
|
|
"plt.show()"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2023-07-19T12:09:32.770081200Z",
|
||
|
|
"start_time": "2023-07-19T12:09:27.890651400Z"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"source": [
|
||
|
|
"## Guess width"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 217,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[[[ 8. 25.]\n",
|
||
|
|
" [ 8. 26.]\n",
|
||
|
|
" [ 9. 26.]]\n",
|
||
|
|
"\n",
|
||
|
|
" [[ 7. 19.]\n",
|
||
|
|
" [ 7. 21.]\n",
|
||
|
|
" [ 8. 26.]]\n",
|
||
|
|
"\n",
|
||
|
|
" [[ 8. 20.]\n",
|
||
|
|
" [ 9. 18.]\n",
|
||
|
|
" [ 8. 20.]]]\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"def guess_BEC_width(thresh, center):\n",
|
||
|
|
" \"\"\"\n",
|
||
|
|
" returns width of thresholded area along both axis through the center with shape of thresh and [X_width, Y_width] for each image\n",
|
||
|
|
" \"\"\"\n",
|
||
|
|
" shape = np.shape(thresh)\n",
|
||
|
|
" BEC_width_guess = np.zeros((shape[0], shape[1], 2))\n",
|
||
|
|
"\n",
|
||
|
|
" for i in range(0, shape[0]):\n",
|
||
|
|
" for j in range(0, shape[1]):\n",
|
||
|
|
" BEC_width_guess[i, j, 0] = np.sum(thresh[i, j, round(center[i,j,1]), :])\n",
|
||
|
|
" BEC_width_guess[i, j, 1] = np.sum(thresh[i, j, :, round(center[i,j,0])])\n",
|
||
|
|
"\n",
|
||
|
|
" return BEC_width_guess\n",
|
||
|
|
"\n",
|
||
|
|
"BEC_width_guess = guess_BEC_width(thresh, center)\n",
|
||
|
|
"\n",
|
||
|
|
"print(BEC_width_guess)"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2023-07-19T12:09:37.533687900Z",
|
||
|
|
"start_time": "2023-07-19T12:09:37.510728500Z"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 199,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 1200x800 with 9 Axes>",
|
||
|
|
"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# %matplotlib notebook\n",
|
||
|
|
"fig, ax = plt.subplots(nr_plots,nr_plots,figsize=fsize)\n",
|
||
|
|
"\n",
|
||
|
|
"for i in range(0,nr_plots):\n",
|
||
|
|
" for j in range(0,nr_plots):\n",
|
||
|
|
" # ax[i][j].pcolormesh(blurred[i][j], cmap='jet', vmin=0, vmax=1.5, alpha=1)\n",
|
||
|
|
" ax[i][j].pcolormesh(thresh[i][j], cmap='jet', vmin=0, vmax=1.5, alpha=1)\n",
|
||
|
|
" #ax[i][j].pcolormesh(cropOD[i][j], cmap='hot', vmin=0, vmax=1, alpha=1)\n",
|
||
|
|
" ax[i][j].plot(center[i,j,0],center[i,j,1], marker='x', markersize=12)\n",
|
||
|
|
"plt.show()\n"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2023-07-19T12:02:40.167162700Z",
|
||
|
|
"start_time": "2023-07-19T12:02:38.840555800Z"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"source": [
|
||
|
|
"## Mask array"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 200,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "(3, 3, 200, 150)"
|
||
|
|
},
|
||
|
|
"execution_count": 200,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"masked = np.where(thresh==0, cropOD, np.nan)\n",
|
||
|
|
"np.shape(masked)"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2023-07-19T12:02:42.006475600Z",
|
||
|
|
"start_time": "2023-07-19T12:02:41.962010400Z"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 279,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 1100x1100 with 9 Axes>",
|
||
|
|
"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"fig, ax = plt.subplots(nr_plots,nr_plots,figsize=(11,11))\n",
|
||
|
|
"\n",
|
||
|
|
"cut_factor = 1\n",
|
||
|
|
"\n",
|
||
|
|
"for i in range(0,nr_plots):\n",
|
||
|
|
" for j in range(0,nr_plots):\n",
|
||
|
|
" #print(np.nanmax(masked[i,j]))\n",
|
||
|
|
" # ax[i][j].pcolormesh(blurred[i][j], cmap='jet', vmin=0, vmax=1.5, alpha=1)\n",
|
||
|
|
" #ax[i][j].pcolormesh(masked[i][j], cmap='jet', vmin=0, vmax=0.5, alpha=1)\n",
|
||
|
|
" ax[i][j].pcolormesh(cropOD[i][j], cmap='jet', vmin=0, vmax=2.5, alpha=1)\n",
|
||
|
|
" #ax[i][j].pcolormesh(thresh[i][j], cmap='jet', vmin=0, vmax=1.5, alpha=1)\n",
|
||
|
|
" #ax[i][j].pcolormesh(cropOD[i][j], cmap='hot', vmin=0, vmax=1, alpha=1)\n",
|
||
|
|
" ax[i][j].plot(center[i,j,0],center[i,j,1], marker='x', markersize=12)\n",
|
||
|
|
" alpha=1\n",
|
||
|
|
" ax[i][j].hlines(center[i,j,1] - cut_factor * BEC_width_guess[i,j,1]/2, 0, 150, color='r',alpha=alpha,linestyles='dotted')\n",
|
||
|
|
" ax[i][j].hlines(center[i,j,1] + cut_factor * BEC_width_guess[i,j,1]/2, 0, 150, color='r',alpha=alpha,linestyles='dotted')\n",
|
||
|
|
"\n",
|
||
|
|
" ax[i][j].vlines(center[i,j,0] - cut_factor * BEC_width_guess[i,j,0]/2, 0, 200, color='r',alpha=alpha,linestyles='dotted')\n",
|
||
|
|
" ax[i][j].vlines(center[i,j,0] + cut_factor * BEC_width_guess[i,j,0]/2, 0, 200, color='r',alpha=alpha,linestyles='dotted')\n",
|
||
|
|
"\n",
|
||
|
|
" ax[i][j].set_xlim(25,125)\n",
|
||
|
|
" ax[i][j].set_ylim(45,145)\n",
|
||
|
|
"plt.show()"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2023-07-19T13:06:22.111658300Z",
|
||
|
|
"start_time": "2023-07-19T13:06:19.953844600Z"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"source": [
|
||
|
|
"## Cut out region of interest for thermal fitting guess"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 243,
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"shape = np.shape(masked)\n",
|
||
|
|
"\n",
|
||
|
|
"X_guess = np.zeros((shape[0], shape[1], shape[3]))\n",
|
||
|
|
"Y_guess = np.zeros((shape[0], shape[1], shape[2]))\n",
|
||
|
|
"for i in range(0, shape[0]):\n",
|
||
|
|
" for j in range(0, shape[1]):\n",
|
||
|
|
" X_guess[i,j] = np.sum(masked[i,j,round(center[i,j,1] - cut_factor * BEC_width_guess[i,j,1]/2) : round(center[i,j,1] + cut_factor * BEC_width_guess[i,j,1]/2) , :], 0) / len(masked[i,j,round(center[i,j,1] - cut_factor * BEC_width_guess[i,j,1]/2) : round(center[i,j,1] + cut_factor * BEC_width_guess[i,j,1]/2),0])\n",
|
||
|
|
"\n",
|
||
|
|
" Y_guess[i,j] = np.sum(masked[i,j, :, round(center[i,j,0] - cut_factor * BEC_width_guess[i,j,0]/2) : round(center[i,j,0] + cut_factor * BEC_width_guess[i,j,0]/2)], 1) / len(masked[i,j,0,round(center[i,j,0] - cut_factor * BEC_width_guess[i,j,0]/2) : round(center[i,j,0] + cut_factor * BEC_width_guess[i,j,0]/2)])\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"#print(X_guess)"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2023-07-19T12:17:04.400265300Z",
|
||
|
|
"start_time": "2023-07-19T12:17:04.364847Z"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 244,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 1200x800 with 9 Axes>",
|
||
|
|
"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"fig, ax = plt.subplots(nr_plots,nr_plots,figsize=fsize)\n",
|
||
|
|
"\n",
|
||
|
|
"for i in range(0, shape[0]):\n",
|
||
|
|
" for j in range(0, shape[1]):\n",
|
||
|
|
" ax[i,j].plot(X_guess[i,j], label = 'x_axis')\n",
|
||
|
|
" ax[i,j].plot(Y_guess[i,j], label = 'y_axis')\n",
|
||
|
|
" ax[i,j].legend(fontsize=10)\n",
|
||
|
|
"plt.show()\n"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2023-07-19T12:17:06.852598Z",
|
||
|
|
"start_time": "2023-07-19T12:17:05.052365900Z"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"source": [
|
||
|
|
"## Fitting 1D gaussian"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 245,
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"#[nr images x, nr images y, X / Y, center / sigma]\n",
|
||
|
|
"x = np.linspace(0,149,150)\n",
|
||
|
|
"y = np.linspace(0,199, 200)\n",
|
||
|
|
"\n",
|
||
|
|
"popt = np.zeros((shape[0], shape[1], 2, 3))\n",
|
||
|
|
"\n",
|
||
|
|
"p0 = np.ones((shape[0], shape[1], 2, 3))\n",
|
||
|
|
"\n",
|
||
|
|
"p0[:, :, :, 0] = center\n",
|
||
|
|
"p0[:, :, :, 1] = BEC_width_guess / 2.355\n",
|
||
|
|
"p0[:, :, :, 2] = 0.1\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"for i in range(0, shape[0]):\n",
|
||
|
|
" for j in range(0, shape[1]):\n",
|
||
|
|
" popt[i,j, 0], pcov = curve_fit(gaussian, x, X_guess[i,j] , p0[i, j, 0], nan_policy='omit')\n",
|
||
|
|
" popt[i,j, 1], pcov = curve_fit(gaussian, y, Y_guess[i,j] , p0[i, j, 1], nan_policy='omit')"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2023-07-19T12:17:08.014466200Z",
|
||
|
|
"start_time": "2023-07-19T12:17:07.958106900Z"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 246,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 1200x800 with 9 Axes>",
|
||
|
|
"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"fig, ax = plt.subplots(nr_plots,nr_plots,figsize=fsize)\n",
|
||
|
|
"for i in range(0, shape[0]):\n",
|
||
|
|
" for j in range(0, shape[1]):\n",
|
||
|
|
" ax[i,j].plot(x, X_guess[i,j], label = 'x_axis')\n",
|
||
|
|
" ax[i,j].plot(x, gaussian(x, *popt[i,j,0]), linestyle='dotted')\n",
|
||
|
|
"\n",
|
||
|
|
" ax[i,j].plot(y, Y_guess[i,j], label = 'y_axis')\n",
|
||
|
|
" ax[i,j].plot(y, gaussian(y, *popt[i,j,1]), linestyle='dotted')\n",
|
||
|
|
" ax[i,j].legend(fontsize=10)\n",
|
||
|
|
" ax[i,j].set_facecolor('#FFFFFF')\n",
|
||
|
|
"plt.show()"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2023-07-19T12:17:10.027028300Z",
|
||
|
|
"start_time": "2023-07-19T12:17:08.582932400Z"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"source": [
|
||
|
|
"## Try with not masked array\n"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 209,
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"shape = np.shape(masked)\n",
|
||
|
|
"\n",
|
||
|
|
"X_guess_og = np.zeros((shape[0], shape[1], shape[3]))\n",
|
||
|
|
"Y_guess_og = np.zeros((shape[0], shape[1], shape[2]))\n",
|
||
|
|
"\n",
|
||
|
|
"for i in range(0, shape[0]):\n",
|
||
|
|
" for j in range(0, shape[1]):\n",
|
||
|
|
" X_guess_og[i,j] = np.sum(cropOD[i,j,round(center[i,j,1] - BEC_width_guess[i,j,1]/2) : round(center[i,j,1] + BEC_width_guess[i,j,1]/2) , :], 0) / len(masked[i,j,round(center[i,j,1] - BEC_width_guess[i,j,1]/2) : round(center[i,j,1] + BEC_width_guess[i,j,1]/2),0])\n",
|
||
|
|
"\n",
|
||
|
|
" Y_guess_og[i,j] = np.sum(cropOD[i,j, :, round(center[i,j,0] - BEC_width_guess[i,j,0]/2) : round(center[i,j,0] + BEC_width_guess[i,j,0]/2)], 1) / len(masked[i,j,0,round(center[i,j,0] - BEC_width_guess[i,j,0]/2) : round(center[i,j,0] + BEC_width_guess[i,j,0]/2)])"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2023-07-19T12:03:10.212862800Z",
|
||
|
|
"start_time": "2023-07-19T12:03:10.178939400Z"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 265,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 1200x800 with 9 Axes>",
|
||
|
|
"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"fig, ax = plt.subplots(nr_plots,nr_plots,figsize=fsize)\n",
|
||
|
|
"\n",
|
||
|
|
"for i in range(0, shape[0]):\n",
|
||
|
|
" for j in range(0, shape[1]):\n",
|
||
|
|
" ax[i,j].plot(x, X_guess[i,j], label = 'x_axis masked',zorder=5)\n",
|
||
|
|
" ax[i,j].plot(x, X_guess_og[i,j], label = 'x_axis original')\n",
|
||
|
|
"# ax[i,j].legend(fontsize=10)\n",
|
||
|
|
"# plt.show()\n",
|
||
|
|
"#\n",
|
||
|
|
"# fig, ax = plt.subplots(nr_plots,nr_plots,figsize=fsize)\n",
|
||
|
|
"#\n",
|
||
|
|
"# for i in range(0, shape[0]):\n",
|
||
|
|
"# for j in range(0, shape[1]):\n",
|
||
|
|
" ax[i,j].plot(y, Y_guess[i,j], label = 'y_axis masked',zorder=5)\n",
|
||
|
|
" ax[i,j].plot(y, Y_guess_og[i,j], label = 'y_axis original')\n",
|
||
|
|
" ax[i,j].legend(fontsize=8)\n",
|
||
|
|
"plt.show()"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2023-07-19T12:52:13.040993700Z",
|
||
|
|
"start_time": "2023-07-19T12:52:11.105524Z"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"source": [
|
||
|
|
"## Bimodal 1d Fit"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 293,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"fitting time: 189.01324272155762 ms\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"#[nr images x, nr images y, X / Y, center / sigma]\n",
|
||
|
|
"x = np.linspace(0,149,150)\n",
|
||
|
|
"y = np.linspace(0,199, 200)\n",
|
||
|
|
"\n",
|
||
|
|
"popt = np.zeros((shape[0], shape[1], 6))\n",
|
||
|
|
"\n",
|
||
|
|
"p0 = np.ones((shape[0], shape[1], 6))\n",
|
||
|
|
"\n",
|
||
|
|
"max = np.zeros((shape[0], shape[1]))\n",
|
||
|
|
"\n",
|
||
|
|
"for i in range(0, shape[0]):\n",
|
||
|
|
" max[i] = np.ndarray.max(X_guess_og[i],axis=1)\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"p0[:, :, 0] = center[:, :, 0] # center BEC\n",
|
||
|
|
"p0[:, :, 1] = center[:, :, 0] # center th\n",
|
||
|
|
"p0[:, :, 2] = 0.7 * max # amp BEC\n",
|
||
|
|
"p0[:, :, 3] = 0.3 * max # amp th\n",
|
||
|
|
"p0[:, :, 4] = BEC_width_guess[:, :, 0] # sigma BEC\n",
|
||
|
|
"p0[:, :, 5] = BEC_width_guess[:, :, 0] * 3 # sigma th\n",
|
||
|
|
"\n",
|
||
|
|
"start = time.time()\n",
|
||
|
|
"for i in range(0, shape[0]):\n",
|
||
|
|
" for j in range(0, shape[1]):\n",
|
||
|
|
" popt[i,j], pcov = curve_fit(density_1d, x, X_guess_og[i,j] , p0[i, j], nan_policy='omit')\n",
|
||
|
|
"stop = time.time()\n",
|
||
|
|
"\n",
|
||
|
|
"print(f'fitting time: {(stop-start)*1e3} ms')\n",
|
||
|
|
" #popt[i,j, 1], pcov = curve_fit(density_1d, y, Y_guess_og[i,j] , p0[i, j, 1], nan_policy='omit')"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2023-07-19T14:22:21.518302700Z",
|
||
|
|
"start_time": "2023-07-19T14:22:21.265419Z"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 294,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 1200x800 with 9 Axes>",
|
||
|
|
"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"fig, ax = plt.subplots(nr_plots,nr_plots,figsize=fsize)\n",
|
||
|
|
"for i in range(0, shape[0]):\n",
|
||
|
|
" for j in range(0, shape[1]):\n",
|
||
|
|
" lab = f\"A$_{{BEC}}$ = {popt[i,j,0]:.1f} \\n A$_{{th}}$ = {popt[i,j,1]:.1f} \"\n",
|
||
|
|
" ax[i,j].plot(x, X_guess_og[i,j])\n",
|
||
|
|
" ax[i,j].plot(x, density_1d(x, *popt[i,j]), label = lab)\n",
|
||
|
|
" ax[i,j].plot(x, thermal(x, popt[i,j,1], popt[i,j, 3], popt[i,j, 5]))\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
" #ax[i,j].legend(fontsize=10)\n",
|
||
|
|
" ax[i,j].set_facecolor('#FFFFFF')\n",
|
||
|
|
"plt.show()"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2023-07-19T14:22:25.505279900Z",
|
||
|
|
"start_time": "2023-07-19T14:22:21.896071700Z"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 295,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[75.69178082 75.69178082 1.28576093 0.5510404 8. 24. ]\n",
|
||
|
|
"[75.41570333 77.33388115 2.56706786 0.32435386 9.02534473 21.78913789]\n",
|
||
|
|
"\n",
|
||
|
|
"[77.85648148 77.85648148 1.26012635 0.54005415 8. 24. ]\n",
|
||
|
|
"[77.87651631 81.11230043 2.46976259 0.33019881 9.77967464 21.30093181]\n",
|
||
|
|
"\n",
|
||
|
|
"[74.20467836 74.20467836 1.24055688 0.53166723 9. 27. ]\n",
|
||
|
|
"[73.89357632 76.27664646 2.4312179 0.27440553 9.98643754 20.45775545]\n",
|
||
|
|
"\n",
|
||
|
|
"[77.33333333 77.33333333 1.34319384 0.5756545 7. 21. ]\n",
|
||
|
|
"[77.11393592 81.40236184 2.88921236 0.26104054 8.62447632 18.73504104]\n",
|
||
|
|
"\n",
|
||
|
|
"[74.9516129 74.9516129 1.37694159 0.59011782 7. 21. ]\n",
|
||
|
|
"[74.59729659 78.58410424 2.78343705 0.22861603 9.03257479 18.55393163]\n",
|
||
|
|
"\n",
|
||
|
|
"[78.32758621 78.32758621 1.11153483 0.47637207 8. 24. ]\n",
|
||
|
|
"[78.50292483 82.40258606 2.35517079 0.20782915 9.17053985 15.99073945]\n",
|
||
|
|
"\n",
|
||
|
|
"[76.02054795 76.02054795 0.47105793 0.20188197 8. 24. ]\n",
|
||
|
|
"[75.51767384 76.75506578 0.87440112 0.14660177 8.96571527 5.96931421]\n",
|
||
|
|
"\n",
|
||
|
|
"[76.22058824 76.22058824 0.47981987 0.20563709 9. 27. ]\n",
|
||
|
|
"[76.17323785 76.98707429 0.61643944 0.23667638 9.4043337 5.34970959]\n",
|
||
|
|
"\n",
|
||
|
|
"[75.00787402 75.00787402 0.43698764 0.18728042 8. 24. ]\n",
|
||
|
|
"[74.88995354 73.93092826 0.90810872 0.09114657 9.42569183 7.98461584]\n",
|
||
|
|
"\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"for i in range(0,3):\n",
|
||
|
|
" for j in range(0,3):\n",
|
||
|
|
" print(p0[i,j])\n",
|
||
|
|
" print(popt[i,j])\n",
|
||
|
|
" print(\"\")"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2023-07-19T14:22:25.522303500Z",
|
||
|
|
"start_time": "2023-07-19T14:22:25.510339Z"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"outputs": [],
|
||
|
|
"source": [],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
}
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": {
|
||
|
|
"name": "ipython",
|
||
|
|
"version": 2
|
||
|
|
},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython2",
|
||
|
|
"version": "2.7.6"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 0
|
||
|
|
}
|