2022 lines
5.0 MiB
Plaintext
2022 lines
5.0 MiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"source": [
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"# Import supporting package"
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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": 3,
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"outputs": [],
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"source": [
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"import lmfit\n",
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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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"#test\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.interpolate import CubicSpline\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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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2023-08-01T13:58:29.507478500Z",
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"start_time": "2023-08-01T13:58:29.454111800Z"
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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": 23,
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"outputs": [],
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"source": [
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"# get center of thresholded image\n",
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"def calc_thresh(data):\n",
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" shape = np.shape(data)\n",
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" thresh = np.zeros(shape)\n",
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" sigma = 0.4\n",
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"\n",
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" if len(shape) == 4:\n",
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" blurred = gaussian_filter(data, sigma=sigma)\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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" thresh[i,j] = np.where(blurred[i,j] < np.max(blurred[i,j])*0.3,0,1)\n",
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"\n",
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" elif len(shape) == 3:\n",
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" blurred = gaussian_filter(data, sigma=sigma)\n",
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" for i in range(0,shape[0]):\n",
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" thresh[i] = np.where(blurred[i] < np.max(blurred[i])*0.3,0,1)\n",
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"\n",
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" else:\n",
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" print(\"Shape of data is wrong, output is empty\")\n",
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"\n",
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" return thresh\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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"def guess_BEC_width(thresh, center):\n",
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" \"\"\"\n",
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" returns width of thresholded area along both axis through the center with shape of thresh and [X_width, Y_width] for each image\n",
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" \"\"\"\n",
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" shape = np.shape(thresh)\n",
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" BEC_width_guess = np.zeros((shape[0], shape[1], 2))\n",
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"\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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" BEC_width_guess[i, j, 0] = np.sum(thresh[i, j, round(center[i,j,1]), :])\n",
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" BEC_width_guess[i, j, 1] = np.sum(thresh[i, j, :, round(center[i,j,0])])\n",
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"\n",
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" return BEC_width_guess\n",
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"\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 polylog_tab(pow, x):\n",
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" order = 100\n",
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" sum = 0\n",
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" for k in range(1,order):\n",
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" sum += x ** k /k **pow\n",
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" return sum\n",
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"\n",
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"x_int = np.linspace(0, 1.00001, 100000)\n",
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"\n",
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"poly_tab = polylog_tab(2,x_int)\n",
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"\n",
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"\n",
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"\n",
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"polylog_int = CubicSpline(x_int, poly_tab)\n",
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"\n",
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"def thermal(x, x0, amp, sigma):\n",
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" res = np.exp(-0.5 * (x-x0)**2 / sigma**2)\n",
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" return amp/1.643 * polylog_int(res)\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)\n",
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" res = np.where(res > 0, res, 0)\n",
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" res = res**(3/2)\n",
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" return amp * res\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):\n",
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" return thermal(x, x0_th, amp_th, sigma_th) + Thomas_Fermi_1d(x, x0_bec, amp_bec, sigma_bec)\n",
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"\n",
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"\n",
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"def polylog(pow, x):\n",
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" order = 15\n",
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" sum = 0\n",
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" for k in range(1,order):\n",
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" sum += x ** k /k **pow\n",
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" return sum\n",
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"\n",
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"\n",
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"def ThomasFermi_2d(x, y=0.0, centerx=0.0, centery=0.0, amplitude=1.0, sigmax=1.0, sigmay=1.0):\n",
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"\n",
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" res = (1- ((x-centerx)/(sigmax))**2 - ((y-centery)/(sigmay))**2)\n",
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" res = np.where(res > 0, res, 0)\n",
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" res = res**(3/2)\n",
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" return amplitude * res\n",
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" # return amplitude * 5 / 2 / np.pi / max(tiny, sigmax * sigmay) * np.where(res > 0, res, 0)\n",
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"\n",
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"\n",
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" # return amplitude / 2 / np.pi / 1.20206 / max(tiny, sigmax * sigmay) * polylog(2, np.exp( -((x-centerx)**2/(2 * (sigmax)**2))-((y-centery)**2/( 2 * (sigmay)**2)) ))\n",
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"# Set up table for polylog\n",
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"\n",
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"\n",
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"def polylog2_2d(x, y=0.0, centerx=0.0, centery=0.0, amplitude=1.0, sigmax=1.0, sigmay=1.0):\n",
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" ## Approximation of the polylog function with 2D gaussian as argument. -> discribes the thermal part of the cloud\n",
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" return amplitude/1.643 * polylog_int(np.exp( -((x-centerx)**2/(2 * sigmax**2))-((y-centery)**2/( 2 * sigmay**2)) ))\n",
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"\n",
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"\n",
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"\n",
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"def density_profile_BEC_2d(x, y=0.0, amp_bec=1.0, amp_th=1.0, x0_bec=0.0, y0_bec=0.0, x0_th=0.0, y0_th=0.0,\n",
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" sigmax_bec=1.0, sigmay_bec=1.0, sigma_th=1.0):\n",
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" return ThomasFermi_2d(x=x, y=y, centerx=x0_bec, centery=y0_bec,\n",
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" amplitude=amp_bec, sigmax=sigmax_bec, sigmay=sigmay_bec\n",
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" ) + polylog2_2d(x=x, y=y, centerx=x0_th, centery=y0_th,\n",
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" amplitude=amp_th, sigmax=sigma_th,sigmay=sigma_th)\n",
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"\n",
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"def cond_frac(results):\n",
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" bval = results.best_values\n",
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" tf_fit = ThomasFermi_2d(X,Y,centerx=bval['x0_bec'], centery=bval['y0_bec'], amplitude=bval['amp_bec'], sigmax=bval['sigmax_bec'], sigmay=bval['sigmay_bec'])\n",
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" N_bec = np.sum(tf_fit)\n",
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" fit = density_profile_BEC_2d(X,Y, **bval)\n",
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" N_ges = np.sum(fit)\n",
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" return N_bec/N_ges\n",
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"\n",
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"def print_bval(res_s):\n",
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" keys = res_s.best_values.keys()\n",
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" bval = res_s.best_values\n",
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" init = res_s.init_params\n",
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"\n",
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" for item in keys:\n",
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" print(f'{item}: {bval[item]:.3f}, (init = {init[item].value:.3f}), bounds = [{init[item].min:.2f} : {init[item].max :.2f}] ')\n",
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" print('')\n",
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"\n",
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"def print_bval_bulk(res_):\n",
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" shape = np.shape(res_)\n",
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" if len(shape) == 2:\n",
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" for i in range(shape[0]):\n",
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" for j in range(shape[1]):\n",
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" print(f'image: {i}, {j}')\n",
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" print_bval(res_[i][j])\n",
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"\n",
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" if len(shape) == 1:\n",
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" for i in range(shape[0]):\n",
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" print(f'image: {i}')\n",
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" print_bval(res_[i])\n"
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],
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"metadata": {
|
||
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"collapsed": false,
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||
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"ExecuteTime": {
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"end_time": "2023-08-01T14:18:42.474015Z",
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"start_time": "2023-08-01T14:18:42.094488100Z"
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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": "code",
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"execution_count": 5,
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"outputs": [],
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"source": [
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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 + '2023/04/17'# get_date()\n",
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"\n",
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"\n",
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"shotNum = \"import\"\n",
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"filePath = folderPath + \"/\" + shotNum + \"/*.h5\""
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],
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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": "2023-08-01T13:58:51.807115400Z",
|
||
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"start_time": "2023-08-01T13:58:51.742005700Z"
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}
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||
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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": "code",
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||
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"execution_count": 6,
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"outputs": [],
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||
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"source": [
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"\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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"# 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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"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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||
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"metadata": {
|
||
|
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"collapsed": false,
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2023-08-01T13:59:04.135949400Z",
|
||
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"start_time": "2023-08-01T13:58:54.705356600Z"
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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": "code",
|
||
|
|
"execution_count": 12,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 1600x600 with 11 Axes>",
|
||
|
|
"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"imageAnalyser.center = (960, 890)\n",
|
||
|
|
"imageAnalyser.span = (150, 150)\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[0], row=scanAxis[1])\n",
|
||
|
|
"plt.show()"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2023-07-31T11:21:31.909385100Z",
|
||
|
|
"start_time": "2023-07-31T11:21:23.490780Z"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 11,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[875. 961.]\n",
|
||
|
|
"1.3862943611198906\n",
|
||
|
|
"0.3848458209054287\n",
|
||
|
|
"[875. 961.]\n",
|
||
|
|
"1.0986122886681098\n",
|
||
|
|
"0.5059356384717989\n",
|
||
|
|
"[875. 961.]\n",
|
||
|
|
"0.916290731874155\n",
|
||
|
|
"0.46357273891544515\n",
|
||
|
|
"[876. 962.]\n",
|
||
|
|
"0.706570200892086\n",
|
||
|
|
"0.37729423114146804\n",
|
||
|
|
"[876. 962.]\n",
|
||
|
|
"0.9019019944220554\n",
|
||
|
|
"0.750305594399894\n",
|
||
|
|
"[875. 961.]\n",
|
||
|
|
"0.6931471805599453\n",
|
||
|
|
"0.3483066942682157\n",
|
||
|
|
"[875. 961.]\n",
|
||
|
|
"1.3862943611198906\n",
|
||
|
|
"0.5520685823000397\n",
|
||
|
|
"[875. 961.]\n",
|
||
|
|
"0.842678914530909\n",
|
||
|
|
"0.5671064596645803\n",
|
||
|
|
"[876. 962.]\n",
|
||
|
|
"0.706570200892086\n",
|
||
|
|
"0.5768873744440833\n",
|
||
|
|
"[876. 962.]\n",
|
||
|
|
"0.916290731874155\n",
|
||
|
|
"0.5596157879354228\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"data = OD_np\n",
|
||
|
|
"cut_width = 250\n",
|
||
|
|
"thresh = calc_thresh(data)\n",
|
||
|
|
"center = calc_cen_bulk(thresh)\n",
|
||
|
|
"\n",
|
||
|
|
"shape = np.shape(data)\n",
|
||
|
|
"cropOD = np.zeros((shape[0], shape[1], cut_width, cut_width))\n",
|
||
|
|
"blurred = gaussian_filter(data, sigma=1.7)\n",
|
||
|
|
"\n",
|
||
|
|
"for i in range(0,shape[0]):\n",
|
||
|
|
" for j in range(0, shape[1]):\n",
|
||
|
|
" amax = np.argmax(blurred[i,j])\n",
|
||
|
|
"\n",
|
||
|
|
" center[i,j] = np.unravel_index(amax, (shape[2], shape[3]))\n",
|
||
|
|
" print(center[i,j])\n",
|
||
|
|
" print(np.max(data[i,j]))\n",
|
||
|
|
" print(data[i,j, round(center[i,j,0]), round(center[i,j,1]) ])\n",
|
||
|
|
" cropOD[i,j] = data[i,j, round(center[i,j,0]-cut_width/2):round(center[i,j,0]+cut_width/2), round(center[i,j,1]-cut_width/2):round(center[i,j,1]+cut_width/2)]\n",
|
||
|
|
"\n",
|
||
|
|
"thresh = calc_thresh(cropOD)\n",
|
||
|
|
"center = calc_cen_bulk(thresh)\n",
|
||
|
|
"# print(center)\n",
|
||
|
|
"BEC_width_guess = guess_BEC_width(thresh, center)\n"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2023-08-01T14:03:22.189914500Z",
|
||
|
|
"start_time": "2023-08-01T14:03:19.142721600Z"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 226,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[[[19. 15.]\n",
|
||
|
|
" [19. 18.]\n",
|
||
|
|
" [24. 20.]\n",
|
||
|
|
" [26. 23.]\n",
|
||
|
|
" [26. 19.]\n",
|
||
|
|
" [28. 23.]\n",
|
||
|
|
" [26. 22.]\n",
|
||
|
|
" [26. 25.]\n",
|
||
|
|
" [25. 22.]\n",
|
||
|
|
" [25. 20.]\n",
|
||
|
|
" [30. 25.]]\n",
|
||
|
|
"\n",
|
||
|
|
" [[ 5. 3.]\n",
|
||
|
|
" [ 9. 5.]\n",
|
||
|
|
" [ 7. 9.]\n",
|
||
|
|
" [10. 4.]\n",
|
||
|
|
" [15. 13.]\n",
|
||
|
|
" [22. 17.]\n",
|
||
|
|
" [15. 15.]\n",
|
||
|
|
" [ 7. 4.]\n",
|
||
|
|
" [13. 12.]\n",
|
||
|
|
" [19. 15.]\n",
|
||
|
|
" [14. 12.]]]\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(BEC_width_guess)\n"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2023-07-27T15:03:47.737360600Z",
|
||
|
|
"start_time": "2023-07-27T15:03:47.628926100Z"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 12,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 2000x500 with 10 Axes>",
|
||
|
|
"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 2000x500 with 10 Axes>",
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAABksAAAGsCAYAAAB9zZAnAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAABcfklEQVR4nO3dbYxc5X03/t+6SxbRsotqZDBhjUONkSq7LFBXCN39O0RFomlUqAiNVCXgisj0RYVyp5Viv4K+wURFINRUIi8qS40lizROo0pIVUmj4hspah0RikQVYqeg3VDAxJTdFhUXh/N/Qdgsw3mcOTNzHj6f1Wh35zxd1+zM19e5fj4zM0mSJAEAAAAAANBTm6bdAAAAAAAAgGlSLAEAAAAAAHpNsQQAAAAAAOg1xRIAAAAAAKDXFEsAAAAAAIBeUywBAAAAAAB6TbEEAAAAAADoNcUSAAAAAACg1xRLAAAAAACAXqtULHn77bfjtttui507d8Y111wTN998c5w6dSoiIj7+8Y/Hxz72sVhaWoqlpaV45JFH1rc7ffp03HLLLXHVVVfFrl274vjx4/X2AmACZCDQV/IP6DMZCPSZDAT6ZLbqBvv374/f/u3fjpmZmfjKV74Sn//85+Of/umfIiLikUceidtuu+1D2xw4cCBuuOGG+Pu///s4ceJE/N7v/V68+OKLcd55543afoCJkoFAX8k/oM9kINBnMhDoi0rFkvPPPz8++clPrv9+ww03xEMPPVS43de//vX1qvOePXvisssui6eeeip+67d+60Prnj17Ns6ePbv++7vvvhtvvPFGbN68OWZmZqo0F+iRJEniv/7rv+Kyyy6LTZvG8w6DMhBoqnFnoPwDmsoYEOgzGQj02VgyMBnBZz/72eTee+9NkiRJ9u7dm1x99dXJrl27kt///d9PfvSjHyVJkiQ/+clPko985CMf2O6OO+5I/uqv/ip1n/fdd18SEW5ubm5D3VZWVkaJtUpkoJubW9Nuk8pA+efm5ta0mzGgm5tbn28y0M3Nrc+3OjOw8ttwve+BBx6IU6dOxT/+4z9GRMTXvva1WFxcjCRJ4i//8i/jU5/6VPzbv/1b5f0ePHgwvvjFL67/vrq6Gtu2bYuI/xsRc8M2dyoOxIPxYByYdjOgJ85GxCNx4YUXTuRoMhBolslloPwDmsUYEOgzGQj0Wf0ZOFSx5KGHHopvfvOb8e1vfzsuuOCCiIhYXFyMiIiZmZn44z/+4/jTP/3TOHPmTGzevDlmZ2fj1VdfjUsvvTQiIl566aWfhd6Hzc3NxdxcWhDORcT5wzR3ah6M+6fdBOidSVyiKwOBphp3Bso/oKmMAYE+k4FAn9WZgZXfzOvhhx+Oo0ePxpNPPhkXXXRRREScO3cuXnvttfV1jh07Fpdcckls3rw5IiLuuOOOeOyxxyIi4sSJE/Hyyy/H3r17a2g+wGTJQKCv5B/QZzIQ6DMZCPRFpStLfvzjH8ef/MmfxJVXXhk33XRTRLxX/f3Od74Tv/M7vxNnz56NTZs2xcUXXxx/93d/t77dl7/85fjc5z4XV111VXzkIx+JI0eOxHnnnVdvTwDGTAYCfSX/gFHc/7OvtpKBQJ/JQKBPZpIkSabdiDxra2uxsLAQEQei7ZfeTeskoe0nJ1DO2xHxYKyursb8/Py0G1ObLmUgME7dy0D5B5TTvfyLkIFAWTIQ6LP6M7Dy23AxvHEXLLL2r1ACAAAAAADZFEs6RFGkffzNAAAAAACmr9fFEhPVTFvV52AX31Kta/0BAJrFWAMAACij0ge8d40TJ9qmi8/ZLvYJAGgOYw0AAKCMXl9ZAgAAAAAAoFhCozXtfwI2rT0AAAAAAIxOsYRGa1pxomntAQAAAABgdIolAAAAAABArymWAAAAAAAAvaZYwjpvMQUAAAAAQB8plrBOsQQAAAAAgD5SLAEAAAAAAHpNsQQAAAAAAOi13hdLvPXUeA3z+PqbAAAAAAAwSYolJubHSrEEAAAAAICm632xBAAAAAAA6DfFEiAiXNEDAAAAAPSXYknPmBAni+cGAAAAANBXiiU9Y0IcAAAAAAA+SLGkgOICXeb5DQAAAACgWFLIZHK9PJ7N4u8BAAAAAKBYwoS1dXK+re0GAAAAAKCYYglk2FggUSwBAAAAAOguxRIq60vhoC/9BAAAAADoO8WSKbv/Z1/TOO40tm2irvUHAAAAAIBqFEumrE3Fkq4WFbrUry71BQAAAABgUioVS95+++247bbbYufOnXHNNdfEzTffHKdOnYqIiNOnT8ctt9wSV111VezatSuOHz++vl3eMtrDRHzz+RuNlwwE+kr+AX0mA4E+k4FAn1S+smT//v3xwgsvxL/+67/GrbfeGp///OcjIuLAgQNxww03xMmTJ+Pw4cPxB3/wB/HOO+8ULmszE9PQPzIQ6Cv5B/SZDAT6TAYCfVGpWHL++efHJz/5yZiZmYmIiBtuuCFeeumliIj4+te/Hn/0R38UERF79uyJyy67LJ566qnCZYPOnj0ba2trH7g1lWIJ9IsMBPpK/gF9JgOBPpOBQJ+M9Jkljz76aNx6661x5syZeOedd+LSSy9dX7Z9+/ZYXl7OXZbm0KFDsbCwsH5bXFwcpYn02LQ+D4b+kIFAX8k/oM9kINBnMhDosqGLJQ888ECcOnUqDh06VGd74uDBg7G6urp+W1lZqXX/bWOyf3geO8ZJBgJ9Jf+APpOBQJ/JQKDrZofZ6KGHHopvfvOb8e1vfzsuuOCCuOCCC2J2djZeffXV9arxSy+9FNu2bYvNmzdnLkszNzcXc3NzQ3ane0z4j8bjxzjIQKCv5B/QZzIQ6DMZCPRB5StLHn744Th69Gg8+eSTcdFFF63ff8cdd8Rjjz0WEREnTpyIl19+Ofbu3Vu4jG5RnKDrZCDQV/IP6DMZCPSZDAT6YiZJkqTsyj/+8Y9jcXExrrzyyrjwwgsj4r3q7z//8z/Ha6+9Fp/73OfixRdfjI985CPxla98JW666aaIiNxlRdbW1mJhYSEiDkTE+ZU72HTvFxcUGWBUb0fEg7G6uhrz8/NjOYIMBJprvBko/4DmMgYE+kwGAn1WfwZWKpZMg4AEyhn/IHEaZCBQTvcyUP4B5XQv/yJkIFCWDAT6rP4MHPoD3oH+ciUUTJ/XIUD9ZCsAAPSXYgmt4yR2+vwNYPq8DgHqJ1sBAKC/FEtoHSexAAAAAADUSbGETlFIAQAAAACgKsWSHrv/Z19d0rX+AAAAAAAwfoolPdbFYsk0eAwBAAAAANpNsQRGpFgCAAAAANBuiiUAAAAAAECvKZbQWe9f8ZF15YcrQgAAAAAAiFAsoYMGiyRZRZOiYoliCgAAAABAPyiWTImJ+PrVfSXJKH8jf1+g6eQUAAAAwM8plmQY9ySSSarqiq4QadJj2qS2AKSRUwAAAAA/p1iSwSRS/UZ9TId9Oy0AAAAAAMijWDJGfZnEL/u2V2Ufj2lfKTLJt+sCAAAAAGD6OlcsadLEdZPaMk51fUbI4HbD7LdKgWbYdt//s6+y6wMAAAAA0GyKJYysyhUjdfx9Rrnyo44iR139AAAAAACgGTpXLGHyJlEsqfoWXnUcs2sm/Vh43AEAAACAtlAsYSiTnggvmui/P+Ura71xtK0NFEtgerwe3uNxAAAAAJpKsaSnhv3w9WHX37hNmc8E2fhz3u8b7yv7FlvDFg2yHrNpvLUY0C7jfI23KT/a1FYAAACgXxRLemrUYskwx8sqLuS9ddZgIaSoUFKmEFJ05UnWvqoUZKoaZV8mH6H9ZAAAAADAdHWyWGLiaDrSChpFxY7B9Qb3lbafvH0Oc7VH2r7KFnaG1ZRCC9AMg7k27PYAAAAADGd22g0YBxNGk5VX6ChzFUna/VnHKFN0KfN98L5xFUWyjGP/JkuhnUYtlLy/DwAAAACG18krS5isKldzlC1K5F1ZMkzb6mhT3cetm8lSaKe0166rzwAA+se4DQCmS7GEykYpWuQVJqosy2pT2pUkZQstZfvkCg5gEqoWostecQfQdfIOaCv5BQDT1cm34aI+eW9fVWUfVa/wyCt65G03eLz
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"fig, ax = plt.subplots(shape[0],shape[1], figsize=(20,5))\n",
|
||
|
|
"\n",
|
||
|
|
"for i in range(0,shape[0]):\n",
|
||
|
|
" for j in range(0,shape[1]):\n",
|
||
|
|
" ax[i,j].pcolormesh(cropOD[i,j], cmap='jet',shading='auto')\n",
|
||
|
|
" #ax[i,j].plot(center[i,j,0], center[i,j,1], markersize=12, marker='x')\n",
|
||
|
|
"plt.show()\n",
|
||
|
|
"\n",
|
||
|
|
"fig, ax = plt.subplots(shape[0],shape[1], figsize=(20,5))\n",
|
||
|
|
"\n",
|
||
|
|
"for i in range(0,shape[0]):\n",
|
||
|
|
" for j in range(0,shape[1]):\n",
|
||
|
|
" ax[i,j].pcolormesh(thresh[i,j], cmap='jet')\n",
|
||
|
|
"plt.show()\n"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2023-08-01T14:03:30.344887500Z",
|
||
|
|
"start_time": "2023-08-01T14:03:26.884235300Z"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 43,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stderr",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"C:\\Users\\Jianshun Gao\\AppData\\Local\\Temp\\ipykernel_15584\\2817508691.py:91: RuntimeWarning: invalid value encountered in power\n",
|
||
|
|
" res = (1-(( x - x0 ) / sigma) **2) **(3/2)\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[[Fit Statistics]]\n",
|
||
|
|
" # fitting method = leastsq\n",
|
||
|
|
" # function evals = 94\n",
|
||
|
|
" # data points = 200\n",
|
||
|
|
" # variables = 6\n",
|
||
|
|
" chi-square = 0.02547745\n",
|
||
|
|
" reduced chi-square = 1.3133e-04\n",
|
||
|
|
" Akaike info crit = -1781.65579\n",
|
||
|
|
" Bayesian info crit = -1761.86588\n",
|
||
|
|
" R-squared = 0.98666724\n",
|
||
|
|
"[[Variables]]\n",
|
||
|
|
" x0_bec: 101.124647 +/- 0.79071563 (0.78%) (init = 101.8194)\n",
|
||
|
|
" x0_th: 102.711004 +/- 0.24982699 (0.24%) (init = 101.8194)\n",
|
||
|
|
" amp_bec: 0.03110320 +/- 0.00530709 (17.06%) (init = 0.2253557)\n",
|
||
|
|
" amp_th: 0.28871096 +/- 0.00350675 (1.21%) (init = 0.09658102)\n",
|
||
|
|
" deltax: 50.4263791 +/- 1.73159941 (3.43%) (init = 70)\n",
|
||
|
|
" sigma_bec: 10.3009445 +/- 1.47976427 (14.37%) (init = 23.77049)\n",
|
||
|
|
" sigma_th: 32.6310613 +/- 0.37432681 (1.15%) == '0.632*sigma_bec + 0.518*deltax'\n",
|
||
|
|
"[[Correlations]] (unreported correlations are < 0.100)\n",
|
||
|
|
" C(deltax, sigma_bec) = -0.9174\n",
|
||
|
|
" C(amp_bec, amp_th) = -0.5988\n",
|
||
|
|
" C(amp_th, sigma_bec) = -0.4403\n",
|
||
|
|
" C(amp_bec, deltax) = +0.2670\n",
|
||
|
|
" C(x0_bec, x0_th) = -0.2479\n",
|
||
|
|
" C(amp_th, deltax) = +0.1433\n",
|
||
|
|
"\n",
|
||
|
|
"[[Fit Statistics]]\n",
|
||
|
|
" # fitting method = leastsq\n",
|
||
|
|
" # function evals = 51\n",
|
||
|
|
" # data points = 200\n",
|
||
|
|
" # variables = 6\n",
|
||
|
|
" chi-square = 0.03558549\n",
|
||
|
|
" reduced chi-square = 1.8343e-04\n",
|
||
|
|
" Akaike info crit = -1714.82692\n",
|
||
|
|
" Bayesian info crit = -1695.03701\n",
|
||
|
|
" R-squared = 0.98531277\n",
|
||
|
|
"[[Variables]]\n",
|
||
|
|
" x0_bec: 100.212258 +/- 0.14784820 (0.15%) (init = 100.0973)\n",
|
||
|
|
" x0_th: 100.613249 +/- 0.27928393 (0.28%) (init = 100.0973)\n",
|
||
|
|
" amp_bec: 0.17198861 +/- 0.00693686 (4.03%) (init = 0.3243154)\n",
|
||
|
|
" amp_th: 0.27605495 +/- 0.00443533 (1.61%) (init = 0.1389923)\n",
|
||
|
|
" deltax: 42.6770155 +/- 0.77365507 (1.81%) (init = 70)\n",
|
||
|
|
" sigma_bec: 7.98709626 +/- 0.27695082 (3.47%) (init = 12.29508)\n",
|
||
|
|
" sigma_th: 27.1545389 +/- 0.41769075 (1.54%) == '0.632*sigma_bec + 0.518*deltax'\n",
|
||
|
|
"[[Correlations]] (unreported correlations are < 0.100)\n",
|
||
|
|
" C(amp_th, deltax) = -0.5892\n",
|
||
|
|
" C(amp_bec, amp_th) = -0.5722\n",
|
||
|
|
" C(amp_bec, deltax) = +0.4909\n",
|
||
|
|
" C(amp_th, sigma_bec) = -0.4443\n",
|
||
|
|
" C(x0_bec, x0_th) = -0.2350\n",
|
||
|
|
" C(deltax, sigma_bec) = -0.1196\n",
|
||
|
|
"\n",
|
||
|
|
"[[Fit Statistics]]\n",
|
||
|
|
" # fitting method = leastsq\n",
|
||
|
|
" # function evals = 57\n",
|
||
|
|
" # data points = 200\n",
|
||
|
|
" # variables = 6\n",
|
||
|
|
" chi-square = 0.03263910\n",
|
||
|
|
" reduced chi-square = 1.6824e-04\n",
|
||
|
|
" Akaike info crit = -1732.11231\n",
|
||
|
|
" Bayesian info crit = -1712.32240\n",
|
||
|
|
" R-squared = 0.98442486\n",
|
||
|
|
"[[Variables]]\n",
|
||
|
|
" x0_bec: 99.1918618 +/- 0.10311539 (0.10%) (init = 98.72093)\n",
|
||
|
|
" x0_th: 99.4469147 +/- 0.30510339 (0.31%) (init = 98.72093)\n",
|
||
|
|
" amp_bec: 0.23128532 +/- 0.00695683 (3.01%) (init = 0.3316982)\n",
|
||
|
|
" amp_th: 0.23031989 +/- 0.00457604 (1.99%) (init = 0.1421564)\n",
|
||
|
|
" deltax: 37.9768886 +/- 0.84525100 (2.23%) (init = 70)\n",
|
||
|
|
" sigma_bec: 7.56164208 +/- 0.19393023 (2.56%) (init = 7.377049)\n",
|
||
|
|
" sigma_th: 24.4509861 +/- 0.45968176 (1.88%) == '0.632*sigma_bec + 0.518*deltax'\n",
|
||
|
|
"[[Correlations]] (unreported correlations are < 0.100)\n",
|
||
|
|
" C(amp_th, deltax) = -0.6697\n",
|
||
|
|
" C(amp_bec, amp_th) = -0.5913\n",
|
||
|
|
" C(amp_bec, deltax) = +0.4948\n",
|
||
|
|
" C(amp_th, sigma_bec) = -0.4477\n",
|
||
|
|
" C(x0_bec, x0_th) = -0.2481\n",
|
||
|
|
"\n",
|
||
|
|
"[[Fit Statistics]]\n",
|
||
|
|
" # fitting method = leastsq\n",
|
||
|
|
" # function evals = 43\n",
|
||
|
|
" # data points = 200\n",
|
||
|
|
" # variables = 6\n",
|
||
|
|
" chi-square = 0.03426857\n",
|
||
|
|
" reduced chi-square = 1.7664e-04\n",
|
||
|
|
" Akaike info crit = -1722.36882\n",
|
||
|
|
" Bayesian info crit = -1702.57892\n",
|
||
|
|
" R-squared = 0.98184582\n",
|
||
|
|
"[[Variables]]\n",
|
||
|
|
" x0_bec: 98.1322103 +/- 0.11092152 (0.11%) (init = 97.92982)\n",
|
||
|
|
" x0_th: 99.4774933 +/- 0.34268907 (0.34%) (init = 97.92982)\n",
|
||
|
|
" amp_bec: 0.23569270 +/- 0.00749401 (3.18%) (init = 0.3080213)\n",
|
||
|
|
" amp_th: 0.20433844 +/- 0.00557015 (2.73%) (init = 0.1320091)\n",
|
||
|
|
" deltax: 31.8076800 +/- 0.96163189 (3.02%) (init = 70)\n",
|
||
|
|
" sigma_bec: 8.28907651 +/- 0.20848009 (2.52%) (init = 7.377049)\n",
|
||
|
|
" sigma_th: 21.7150746 +/- 0.52551865 (2.42%) == '0.632*sigma_bec + 0.518*deltax'\n",
|
||
|
|
"[[Correlations]] (unreported correlations are < 0.100)\n",
|
||
|
|
" C(amp_th, deltax) = -0.6978\n",
|
||
|
|
" C(amp_bec, amp_th) = -0.6787\n",
|
||
|
|
" C(amp_bec, deltax) = +0.5549\n",
|
||
|
|
" C(amp_th, sigma_bec) = -0.4786\n",
|
||
|
|
" C(x0_bec, x0_th) = -0.3105\n",
|
||
|
|
" C(x0_th, amp_bec) = +0.1248\n",
|
||
|
|
" C(x0_th, amp_th) = -0.1135\n",
|
||
|
|
"\n",
|
||
|
|
"[[Fit Statistics]]\n",
|
||
|
|
" # fitting method = leastsq\n",
|
||
|
|
" # function evals = 78\n",
|
||
|
|
" # data points = 200\n",
|
||
|
|
" # variables = 6\n",
|
||
|
|
" chi-square = 0.06101663\n",
|
||
|
|
" reduced chi-square = 3.1452e-04\n",
|
||
|
|
" Akaike info crit = -1606.98525\n",
|
||
|
|
" Bayesian info crit = -1587.19534\n",
|
||
|
|
" R-squared = 0.98228418\n",
|
||
|
|
"[[Variables]]\n",
|
||
|
|
" x0_bec: 100.367517 +/- 0.08259950 (0.08%) (init = 99.8617)\n",
|
||
|
|
" x0_th: 102.252499 +/- 0.39681117 (0.39%) (init = 99.8617)\n",
|
||
|
|
" amp_bec: 0.45283805 +/- 0.01437236 (3.17%) (init = 0.4733184)\n",
|
||
|
|
" amp_th: 0.21226194 +/- 0.01322887 (6.23%) (init = 0.2028507)\n",
|
||
|
|
" deltax: 16.6747948 +/- 1.14121471 (6.84%) (init = 70)\n",
|
||
|
|
" sigma_bec: 7.79102040 +/- 0.15115365 (1.94%) (init = 8.196721)\n",
|
||
|
|
" sigma_th: 13.5614686 +/- 0.62177649 (4.58%) == '0.632*sigma_bec + 0.518*deltax'\n",
|
||
|
|
"[[Correlations]] (unreported correlations are < 0.100)\n",
|
||
|
|
" C(amp_bec, amp_th) = -0.8435\n",
|
||
|
|
" C(amp_th, deltax) = -0.7880\n",
|
||
|
|
" C(amp_bec, deltax) = +0.6885\n",
|
||
|
|
" C(amp_th, sigma_bec) = -0.5636\n",
|
||
|
|
" C(x0_bec, x0_th) = -0.4165\n",
|
||
|
|
" C(x0_th, amp_bec) = +0.3364\n",
|
||
|
|
" C(x0_th, amp_th) = -0.2753\n",
|
||
|
|
" C(amp_bec, sigma_bec) = +0.2751\n",
|
||
|
|
" C(deltax, sigma_bec) = +0.2481\n",
|
||
|
|
" C(x0_bec, deltax) = +0.2081\n",
|
||
|
|
" C(x0_th, deltax) = +0.1708\n",
|
||
|
|
" C(x0_bec, amp_th) = -0.1648\n",
|
||
|
|
" C(x0_th, sigma_bec) = +0.1635\n",
|
||
|
|
"\n",
|
||
|
|
"[[Fit Statistics]]\n",
|
||
|
|
" # fitting method = leastsq\n",
|
||
|
|
" # function evals = 113\n",
|
||
|
|
" # data points = 200\n",
|
||
|
|
" # variables = 6\n",
|
||
|
|
" chi-square = 0.02600358\n",
|
||
|
|
" reduced chi-square = 1.3404e-04\n",
|
||
|
|
" Akaike info crit = -1777.56765\n",
|
||
|
|
" Bayesian info crit = -1757.77775\n",
|
||
|
|
" R-squared = 0.98775463\n",
|
||
|
|
"[[Variables]]\n",
|
||
|
|
" x0_bec: 117.605583 +/- 1.84230729 (1.57%) (init = 101.4468)\n",
|
||
|
|
" x0_th: 100.760389 +/- 0.39898453 (0.40%) (init = 101.4468)\n",
|
||
|
|
" amp_bec: 0.02307738 +/- 0.00557250 (24.15%) (init = 0.2281153)\n",
|
||
|
|
" amp_th: 0.30582941 +/- 0.00462296 (1.51%) (init = 0.0977637)\n",
|
||
|
|
" deltax: 36.3480965 +/- 4.00611948 (11.02%) (init = 70)\n",
|
||
|
|
" sigma_bec: 21.2886122 +/- 3.35779527 (15.77%) (init = 29.5082)\n",
|
||
|
|
" sigma_th: 32.2827169 +/- 0.35246280 (1.09%) == '0.632*sigma_bec + 0.518*deltax'\n",
|
||
|
|
"[[Correlations]] (unreported correlations are < 0.100)\n",
|
||
|
|
" C(deltax, sigma_bec) = -0.9861\n",
|
||
|
|
" C(x0_th, amp_bec) = -0.7647\n",
|
||
|
|
" C(amp_bec, amp_th) = -0.7386\n",
|
||
|
|
" C(x0_th, amp_th) = +0.6257\n",
|
||
|
|
" C(amp_th, sigma_bec) = -0.5995\n",
|
||
|
|
" C(amp_th, deltax) = +0.5101\n",
|
||
|
|
" C(x0_th, sigma_bec) = -0.4952\n",
|
||
|
|
" C(x0_th, deltax) = +0.4810\n",
|
||
|
|
" C(x0_bec, amp_th) = +0.4187\n",
|
||
|
|
" C(amp_bec, sigma_bec) = +0.3259\n",
|
||
|
|
" C(amp_bec, deltax) = -0.2933\n",
|
||
|
|
" C(x0_bec, sigma_bec) = -0.1676\n",
|
||
|
|
" C(x0_bec, amp_bec) = -0.1513\n",
|
||
|
|
"\n",
|
||
|
|
"[[Fit Statistics]]\n",
|
||
|
|
" # fitting method = leastsq\n",
|
||
|
|
" # function evals = 65\n",
|
||
|
|
" # data points = 200\n",
|
||
|
|
" # variables = 6\n",
|
||
|
|
" chi-square = 0.03796787\n",
|
||
|
|
" reduced chi-square = 1.9571e-04\n",
|
||
|
|
" Akaike info crit = -1701.86650\n",
|
||
|
|
" Bayesian info crit = -1682.07659\n",
|
||
|
|
" R-squared = 0.98342267\n",
|
||
|
|
"[[Variables]]\n",
|
||
|
|
" x0_bec: 99.6392368 +/- 0.18357929 (0.18%) (init = 99.63265)\n",
|
||
|
|
" x0_th: 100.417176 +/- 0.27776134 (0.28%) (init = 99.63265)\n",
|
||
|
|
" amp_bec: 0.13767284 +/- 0.00733007 (5.32%) (init = 0.3014446)\n",
|
||
|
|
" amp_th: 0.28131017 +/- 0.00459721 (1.63%) (init = 0.1291905)\n",
|
||
|
|
" deltax: 41.4699041 +/- 0.78035414 (1.88%) (init = 70)\n",
|
||
|
|
" sigma_bec: 7.45495653 +/- 0.34274281 (4.60%) (init = 10.65574)\n",
|
||
|
|
" sigma_th: 26.1929428 +/- 0.41287001 (1.58%) == '0.632*sigma_bec + 0.518*deltax'\n",
|
||
|
|
"[[Correlations]] (unreported correlations are < 0.100)\n",
|
||
|
|
" C(amp_bec, amp_th) = -0.5594\n",
|
||
|
|
" C(amp_th, deltax) = -0.5267\n",
|
||
|
|
" C(amp_bec, deltax) = +0.4832\n",
|
||
|
|
" C(amp_th, sigma_bec) = -0.4416\n",
|
||
|
|
" C(deltax, sigma_bec) = -0.2276\n",
|
||
|
|
" C(x0_bec, x0_th) = -0.2253\n",
|
||
|
|
"\n",
|
||
|
|
"[[Fit Statistics]]\n",
|
||
|
|
" # fitting method = leastsq\n",
|
||
|
|
" # function evals = 64\n",
|
||
|
|
" # data points = 200\n",
|
||
|
|
" # variables = 6\n",
|
||
|
|
" chi-square = 0.04751327\n",
|
||
|
|
" reduced chi-square = 2.4491e-04\n",
|
||
|
|
" Akaike info crit = -1657.01274\n",
|
||
|
|
" Bayesian info crit = -1637.22283\n",
|
||
|
|
" R-squared = 0.98002766\n",
|
||
|
|
"[[Variables]]\n",
|
||
|
|
" x0_bec: 99.5850923 +/- 0.10908572 (0.11%) (init = 99.13918)\n",
|
||
|
|
" x0_th: 100.444273 +/- 0.36500118 (0.36%) (init = 99.13918)\n",
|
||
|
|
" amp_bec: 0.26299997 +/- 0.00825821 (3.14%) (init = 0.352122)\n",
|
||
|
|
" amp_th: 0.23693805 +/- 0.00531433 (2.24%) (init = 0.1509094)\n",
|
||
|
|
" deltax: 39.8361886 +/- 1.00607476 (2.53%) (init = 70)\n",
|
||
|
|
" sigma_bec: 7.62911099 +/- 0.20319081 (2.66%) (init = 9.016393)\n",
|
||
|
|
" sigma_th: 25.4567439 +/- 0.54512266 (2.14%) == '0.632*sigma_bec + 0.518*deltax'\n",
|
||
|
|
"[[Correlations]] (unreported correlations are < 0.100)\n",
|
||
|
|
" C(amp_th, deltax) = -0.6783\n",
|
||
|
|
" C(amp_bec, amp_th) = -0.5805\n",
|
||
|
|
" C(amp_bec, deltax) = +0.4805\n",
|
||
|
|
" C(amp_th, sigma_bec) = -0.4397\n",
|
||
|
|
" C(x0_bec, x0_th) = -0.2370\n",
|
||
|
|
"\n",
|
||
|
|
"[[Fit Statistics]]\n",
|
||
|
|
" # fitting method = leastsq\n",
|
||
|
|
" # function evals = 71\n",
|
||
|
|
" # data points = 200\n",
|
||
|
|
" # variables = 6\n",
|
||
|
|
" chi-square = 0.04531517\n",
|
||
|
|
" reduced chi-square = 2.3358e-04\n",
|
||
|
|
" Akaike info crit = -1666.48617\n",
|
||
|
|
" Bayesian info crit = -1646.69626\n",
|
||
|
|
" R-squared = 0.97443891\n",
|
||
|
|
"[[Variables]]\n",
|
||
|
|
" x0_bec: 99.6356695 +/- 0.12340247 (0.12%) (init = 98.87047)\n",
|
||
|
|
" x0_th: 99.7343179 +/- 0.38022916 (0.38%) (init = 98.87047)\n",
|
||
|
|
" amp_bec: 0.22842555 +/- 0.00866289 (3.79%) (init = 0.3240541)\n",
|
||
|
|
" amp_th: 0.20700460 +/- 0.00620962 (3.00%) (init = 0.1388803)\n",
|
||
|
|
" deltax: 32.3995231 +/- 1.06874338 (3.30%) (init = 70)\n",
|
||
|
|
" sigma_bec: 7.48523838 +/- 0.23274658 (3.11%) (init = 6.557377)\n",
|
||
|
|
" sigma_th: 21.5136236 +/- 0.58585189 (2.72%) == '0.632*sigma_bec + 0.518*deltax'\n",
|
||
|
|
"[[Correlations]] (unreported correlations are < 0.100)\n",
|
||
|
|
" C(amp_th, deltax) = -0.6911\n",
|
||
|
|
" C(amp_bec, amp_th) = -0.6447\n",
|
||
|
|
" C(amp_bec, deltax) = +0.5326\n",
|
||
|
|
" C(amp_th, sigma_bec) = -0.4885\n",
|
||
|
|
" C(x0_bec, x0_th) = -0.2939\n",
|
||
|
|
"\n",
|
||
|
|
"[[Fit Statistics]]\n",
|
||
|
|
" # fitting method = leastsq\n",
|
||
|
|
" # function evals = 50\n",
|
||
|
|
" # data points = 200\n",
|
||
|
|
" # variables = 6\n",
|
||
|
|
" chi-square = 0.05567171\n",
|
||
|
|
" reduced chi-square = 2.8697e-04\n",
|
||
|
|
" Akaike info crit = -1625.32009\n",
|
||
|
|
" Bayesian info crit = -1605.53019\n",
|
||
|
|
" R-squared = 0.97892132\n",
|
||
|
|
"[[Variables]]\n",
|
||
|
|
" x0_bec: 100.548635 +/- 0.09308962 (0.09%) (init = 100.0176)\n",
|
||
|
|
" x0_th: 102.267967 +/- 0.39603164 (0.39%) (init = 100.0176)\n",
|
||
|
|
" amp_bec: 0.35611272 +/- 0.01122725 (3.15%) (init = 0.3972421)\n",
|
||
|
|
" amp_th: 0.20822200 +/- 0.00932906 (4.48%) (init = 0.1702466)\n",
|
||
|
|
" deltax: 22.8802225 +/- 1.12445938 (4.91%) (init = 70)\n",
|
||
|
|
" sigma_bec: 7.69352924 +/- 0.17453799 (2.27%) (init = 9.016393)\n",
|
||
|
|
" sigma_th: 16.7142657 +/- 0.61358538 (3.67%) == '0.632*sigma_bec + 0.518*deltax'\n",
|
||
|
|
"[[Correlations]] (unreported correlations are < 0.100)\n",
|
||
|
|
" C(amp_bec, amp_th) = -0.7591\n",
|
||
|
|
" C(amp_th, deltax) = -0.7491\n",
|
||
|
|
" C(amp_bec, deltax) = +0.6139\n",
|
||
|
|
" C(amp_th, sigma_bec) = -0.5303\n",
|
||
|
|
" C(x0_bec, x0_th) = -0.3668\n",
|
||
|
|
" C(x0_th, amp_bec) = +0.2320\n",
|
||
|
|
" C(x0_th, amp_th) = -0.1995\n",
|
||
|
|
" C(deltax, sigma_bec) = +0.1949\n",
|
||
|
|
" C(amp_bec, sigma_bec) = +0.1502\n",
|
||
|
|
" C(x0_th, sigma_bec) = +0.1339\n",
|
||
|
|
" C(x0_th, deltax) = +0.1174\n",
|
||
|
|
" C(x0_bec, deltax) = +0.1151\n",
|
||
|
|
"\n",
|
||
|
|
"total time: 317.14844703674316 ms\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# from opencv import moments\n",
|
||
|
|
"start = time.time()\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",
|
||
|
|
"center = calc_cen_bulk(thresh)\n",
|
||
|
|
"\n",
|
||
|
|
"BEC_width_guess = guess_BEC_width(thresh, center)\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(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])\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(cropOD[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)])\n",
|
||
|
|
"\n",
|
||
|
|
"#[nr images x, nr images y, X / Y, center / sigma]\n",
|
||
|
|
"x = np.linspace(0,shape[3],shape[3])\n",
|
||
|
|
"y = np.linspace(0,shape[2], shape[2])\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",
|
||
|
|
"# Fitting x without math constr\n",
|
||
|
|
"fitmodel = lmfit.Model(density_1d,independent_vars=['x'])\n",
|
||
|
|
"\n",
|
||
|
|
"result_x = []\n",
|
||
|
|
"\n",
|
||
|
|
"for i in range(0, shape[0]):\n",
|
||
|
|
" temp_res = []\n",
|
||
|
|
" for j in range(0, shape[1]):\n",
|
||
|
|
" t1 = time.time()\n",
|
||
|
|
" params = lmfit.Parameters()\n",
|
||
|
|
" params.add_many(\n",
|
||
|
|
" ('x0_bec', center[i,j,0], True,0, 200),\n",
|
||
|
|
" ('x0_th',center[i,j,0], True,0, 200),\n",
|
||
|
|
" ('amp_bec', 0.7 * max[i,j], True, 0, 1.3 * np.max(X_guess_og[i,j])),\n",
|
||
|
|
" ('amp_th', 0.3 * max[i,j], True, 0, 1.3 * np.max(X_guess_og[i,j])),\n",
|
||
|
|
" ('deltax', 70, True, 0,150),\n",
|
||
|
|
" ('sigma_bec',BEC_width_guess[i,j,0]/1.22, True, 0, 50)\n",
|
||
|
|
" )\n",
|
||
|
|
" params.add('sigma_th', 3*BEC_width_guess[i,j,0], min=0, expr=f'0.632*sigma_bec + 0.518*deltax')\n",
|
||
|
|
"\n",
|
||
|
|
" # params.add('sigma_th', 3*BEC_width_guess[i,j,0], True, min=0,max=150)\n",
|
||
|
|
" t2 = time.time()\n",
|
||
|
|
" res = fitmodel.fit(X_guess_og[i,j], x=x, params=params)\n",
|
||
|
|
" t3 = time.time()\n",
|
||
|
|
" temp_res.append(res)\n",
|
||
|
|
" t4 = time.time()\n",
|
||
|
|
" # print(t2 - t1)\n",
|
||
|
|
" # print(t3 - t2)\n",
|
||
|
|
" # print(t4 - t3)\n",
|
||
|
|
" # print(\"\")\n",
|
||
|
|
" lmfit.report_fit(res)\n",
|
||
|
|
"\n",
|
||
|
|
" print()\n",
|
||
|
|
" result_x.append(temp_res)\n",
|
||
|
|
"stop = time.time()\n",
|
||
|
|
"\n",
|
||
|
|
"print(f'total time: {(stop-start)*1e3} ms')"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2023-07-31T11:40:34.358010300Z",
|
||
|
|
"start_time": "2023-07-31T11:40:33.866807300Z"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 13,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stderr",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"C:\\Users\\Jianshun Gao\\AppData\\Local\\Temp\\ipykernel_30284\\141522514.py:91: RuntimeWarning: invalid value encountered in power\n",
|
||
|
|
" res = (1-(( x - x0 ) / sigma) **2) **(3/2)\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"image 0, 0\n",
|
||
|
|
"[[Fit Statistics]]\n",
|
||
|
|
" # fitting method = leastsq\n",
|
||
|
|
" # function evals = 143\n",
|
||
|
|
" # data points = 250\n",
|
||
|
|
" # variables = 6\n",
|
||
|
|
" chi-square = 0.02046362\n",
|
||
|
|
" reduced chi-square = 8.3867e-05\n",
|
||
|
|
" Akaike info crit = -2340.64189\n",
|
||
|
|
" Bayesian info crit = -2319.51313\n",
|
||
|
|
" R-squared = 0.98804264\n",
|
||
|
|
"[[Variables]]\n",
|
||
|
|
" x0_bec: 118.969515 +/- 1.9798e+10 (16640885617.16%) (init = 125.2444)\n",
|
||
|
|
" x0_th: 126.291765 +/- 0.46890175 (0.37%) (init = 125.2444)\n",
|
||
|
|
" amp_bec: 5.0309e-13 +/- 0.00535605 (1064634965786.03%) (init = 0.1857203)\n",
|
||
|
|
" amp_th: 0.25828629 +/- 0.01294854 (5.01%) (init = 0.0795944)\n",
|
||
|
|
" deltax: 1.67647827 +/- 152948.188 (9123183.47%) (init = 183)\n",
|
||
|
|
" sigma_bec: 49.9995295 +/- 8259069.00 (16518293.43%) (init = 50)\n",
|
||
|
|
" sigma_th: 32.4681184 +/- 5298958.79 (16320498.53%) == '0.632*sigma_bec + 0.518*deltax'\n",
|
||
|
|
"[[Correlations]] (unreported correlations are < 0.100)\n",
|
||
|
|
" C(deltax, sigma_bec) = +1.0000\n",
|
||
|
|
" C(amp_bec, amp_th) = +0.9874\n",
|
||
|
|
" C(x0_th, amp_bec) = -0.8663\n",
|
||
|
|
" C(x0_th, amp_th) = -0.8518\n",
|
||
|
|
" C(amp_bec, sigma_bec) = +0.4070\n",
|
||
|
|
" C(amp_bec, deltax) = +0.4070\n",
|
||
|
|
" C(x0_th, sigma_bec) = -0.3466\n",
|
||
|
|
" C(x0_th, deltax) = -0.3466\n",
|
||
|
|
" C(amp_th, sigma_bec) = +0.3335\n",
|
||
|
|
" C(amp_th, deltax) = +0.3335\n",
|
||
|
|
" C(x0_bec, x0_th) = -0.2184\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"image 0, 1\n",
|
||
|
|
"[[Fit Statistics]]\n",
|
||
|
|
" # fitting method = leastsq\n",
|
||
|
|
" # function evals = 107\n",
|
||
|
|
" # data points = 250\n",
|
||
|
|
" # variables = 6\n",
|
||
|
|
" chi-square = 0.02379560\n",
|
||
|
|
" reduced chi-square = 9.7523e-05\n",
|
||
|
|
" Akaike info crit = -2302.92888\n",
|
||
|
|
" Bayesian info crit = -2281.80012\n",
|
||
|
|
" R-squared = 0.98746106\n",
|
||
|
|
"[[Variables]]\n",
|
||
|
|
" x0_bec: 124.102991 +/- 0.17630481 (0.14%) (init = 124.3687)\n",
|
||
|
|
" x0_th: 124.635809 +/- 0.22674888 (0.18%) (init = 124.3687)\n",
|
||
|
|
" amp_bec: 0.10230528 +/- 0.00511417 (5.00%) (init = 0.2449757)\n",
|
||
|
|
" amp_th: 0.24670886 +/- 0.00310912 (1.26%) (init = 0.1049896)\n",
|
||
|
|
" deltax: 43.3496466 +/- 0.65892469 (1.52%) (init = 114)\n",
|
||
|
|
" sigma_bec: 7.53889215 +/- 0.33126562 (4.39%) (init = 31.14754)\n",
|
||
|
|
" sigma_th: 27.2196968 +/- 0.33523134 (1.23%) == '0.632*sigma_bec + 0.518*deltax'\n",
|
||
|
|
"[[Correlations]] (unreported correlations are < 0.100)\n",
|
||
|
|
" C(amp_bec, amp_th) = -0.5451\n",
|
||
|
|
" C(amp_bec, deltax) = +0.4833\n",
|
||
|
|
" C(amp_th, deltax) = -0.4735\n",
|
||
|
|
" C(amp_th, sigma_bec) = -0.4155\n",
|
||
|
|
" C(deltax, sigma_bec) = -0.3355\n",
|
||
|
|
" C(x0_bec, x0_th) = -0.2113\n",
|
||
|
|
" C(amp_bec, sigma_bec) = -0.1365\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"image 0, 2\n",
|
||
|
|
"[[Fit Statistics]]\n",
|
||
|
|
" # fitting method = leastsq\n",
|
||
|
|
" # function evals = 78\n",
|
||
|
|
" # data points = 250\n",
|
||
|
|
" # variables = 6\n",
|
||
|
|
" chi-square = 0.02855995\n",
|
||
|
|
" reduced chi-square = 1.1705e-04\n",
|
||
|
|
" Akaike info crit = -2257.30273\n",
|
||
|
|
" Bayesian info crit = -2236.17397\n",
|
||
|
|
" R-squared = 0.98458722\n",
|
||
|
|
"[[Variables]]\n",
|
||
|
|
" x0_bec: 125.183186 +/- 0.10809287 (0.09%) (init = 125.0292)\n",
|
||
|
|
" x0_th: 125.593427 +/- 0.26291811 (0.21%) (init = 125.0292)\n",
|
||
|
|
" amp_bec: 0.18259048 +/- 0.00586579 (3.21%) (init = 0.2788634)\n",
|
||
|
|
" amp_th: 0.22031642 +/- 0.00387218 (1.76%) (init = 0.1195129)\n",
|
||
|
|
" deltax: 37.0211023 +/- 0.72778295 (1.97%) (init = 84)\n",
|
||
|
|
" sigma_bec: 7.42225741 +/- 0.20409097 (2.75%) (init = 22.95082)\n",
|
||
|
|
" sigma_th: 23.8677977 +/- 0.39626497 (1.66%) == '0.632*sigma_bec + 0.518*deltax'\n",
|
||
|
|
"[[Correlations]] (unreported correlations are < 0.100)\n",
|
||
|
|
" C(amp_th, deltax) = -0.6428\n",
|
||
|
|
" C(amp_bec, amp_th) = -0.5921\n",
|
||
|
|
" C(amp_bec, deltax) = +0.5008\n",
|
||
|
|
" C(amp_th, sigma_bec) = -0.4495\n",
|
||
|
|
" C(x0_bec, x0_th) = -0.2494\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"image 0, 3\n",
|
||
|
|
"[[Fit Statistics]]\n",
|
||
|
|
" # fitting method = leastsq\n",
|
||
|
|
" # function evals = 85\n",
|
||
|
|
" # data points = 250\n",
|
||
|
|
" # variables = 6\n",
|
||
|
|
" chi-square = 0.03198083\n",
|
||
|
|
" reduced chi-square = 1.3107e-04\n",
|
||
|
|
" Akaike info crit = -2229.01991\n",
|
||
|
|
" Bayesian info crit = -2207.89114\n",
|
||
|
|
" R-squared = 0.98037328\n",
|
||
|
|
"[[Variables]]\n",
|
||
|
|
" x0_bec: 123.136221 +/- 0.11814214 (0.10%) (init = 123.438)\n",
|
||
|
|
" x0_th: 124.617766 +/- 0.30979342 (0.25%) (init = 123.438)\n",
|
||
|
|
" amp_bec: 0.18750833 +/- 0.00636413 (3.39%) (init = 0.2690883)\n",
|
||
|
|
" amp_th: 0.19630744 +/- 0.00464469 (2.37%) (init = 0.1153236)\n",
|
||
|
|
" deltax: 32.9999725 +/- 0.86043579 (2.61%) (init = 63)\n",
|
||
|
|
" sigma_bec: 8.14335191 +/- 0.22218028 (2.73%) (init = 17.21311)\n",
|
||
|
|
" sigma_th: 22.2405842 +/- 0.47201439 (2.12%) == '0.632*sigma_bec + 0.518*deltax'\n",
|
||
|
|
"[[Correlations]] (unreported correlations are < 0.100)\n",
|
||
|
|
" C(amp_th, deltax) = -0.6705\n",
|
||
|
|
" C(amp_bec, amp_th) = -0.6617\n",
|
||
|
|
" C(amp_bec, deltax) = +0.5411\n",
|
||
|
|
" C(amp_th, sigma_bec) = -0.4828\n",
|
||
|
|
" C(x0_bec, x0_th) = -0.2977\n",
|
||
|
|
" C(x0_th, amp_bec) = +0.1288\n",
|
||
|
|
" C(x0_th, amp_th) = -0.1174\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"image 0, 4\n",
|
||
|
|
"[[Fit Statistics]]\n",
|
||
|
|
" # fitting method = leastsq\n",
|
||
|
|
" # function evals = 64\n",
|
||
|
|
" # data points = 250\n",
|
||
|
|
" # variables = 6\n",
|
||
|
|
" chi-square = 0.05487637\n",
|
||
|
|
" reduced chi-square = 2.2490e-04\n",
|
||
|
|
" Akaike info crit = -2094.03332\n",
|
||
|
|
" Bayesian info crit = -2072.90455\n",
|
||
|
|
" R-squared = 0.97990259\n",
|
||
|
|
"[[Variables]]\n",
|
||
|
|
" x0_bec: 125.437069 +/- 0.07853394 (0.06%) (init = 125.3496)\n",
|
||
|
|
" x0_th: 127.166144 +/- 0.39753185 (0.31%) (init = 125.3496)\n",
|
||
|
|
" amp_bec: 0.39898058 +/- 0.01165945 (2.92%) (init = 0.4155771)\n",
|
||
|
|
" amp_th: 0.17879507 +/- 0.01053873 (5.89%) (init = 0.1781045)\n",
|
||
|
|
" deltax: 17.6761229 +/- 1.15472056 (6.53%) (init = 45)\n",
|
||
|
|
" sigma_bec: 7.86397815 +/- 0.14425228 (1.83%) (init = 12.29508)\n",
|
||
|
|
" sigma_th: 14.1262658 +/- 0.62636465 (4.43%) == '0.632*sigma_bec + 0.518*deltax'\n",
|
||
|
|
"[[Correlations]] (unreported correlations are < 0.100)\n",
|
||
|
|
" C(amp_bec, amp_th) = -0.8321\n",
|
||
|
|
" C(amp_th, deltax) = -0.7877\n",
|
||
|
|
" C(amp_bec, deltax) = +0.6817\n",
|
||
|
|
" C(amp_th, sigma_bec) = -0.5456\n",
|
||
|
|
" C(x0_bec, x0_th) = -0.4202\n",
|
||
|
|
" C(x0_th, amp_bec) = +0.2985\n",
|
||
|
|
" C(x0_th, amp_th) = -0.2469\n",
|
||
|
|
" C(amp_bec, sigma_bec) = +0.2413\n",
|
||
|
|
" C(deltax, sigma_bec) = +0.2406\n",
|
||
|
|
" C(x0_bec, deltax) = +0.1730\n",
|
||
|
|
" C(x0_th, deltax) = +0.1542\n",
|
||
|
|
" C(x0_th, sigma_bec) = +0.1471\n",
|
||
|
|
" C(x0_bec, amp_th) = -0.1332\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"image 1, 0\n",
|
||
|
|
"[[Fit Statistics]]\n",
|
||
|
|
" # fitting method = leastsq\n",
|
||
|
|
" # function evals = 145\n",
|
||
|
|
" # data points = 250\n",
|
||
|
|
" # variables = 6\n",
|
||
|
|
" chi-square = 0.01357162\n",
|
||
|
|
" reduced chi-square = 5.5621e-05\n",
|
||
|
|
" Akaike info crit = -2443.30890\n",
|
||
|
|
" Bayesian info crit = -2422.18014\n",
|
||
|
|
" R-squared = 0.99213486\n",
|
||
|
|
"[[Variables]]\n",
|
||
|
|
" x0_bec: 141.980857 +/- 2.43599848 (1.72%) (init = 124.94)\n",
|
||
|
|
" x0_th: 125.812254 +/- 0.34610895 (0.28%) (init = 124.94)\n",
|
||
|
|
" amp_bec: 0.01340870 +/- 0.00410283 (30.60%) (init = 0.1823071)\n",
|
||
|
|
" amp_th: 0.25139708 +/- 0.00370473 (1.47%) (init = 0.07813161)\n",
|
||
|
|
" deltax: 33.1360950 +/- 4.95259194 (14.95%) (init = 210)\n",
|
||
|
|
" sigma_bec: 24.7716948 +/- 4.17307378 (16.85%) (init = 50)\n",
|
||
|
|
" sigma_th: 32.8202083 +/- 0.30767404 (0.94%) == '0.632*sigma_bec + 0.518*deltax'\n",
|
||
|
|
"[[Correlations]] (unreported correlations are < 0.100)\n",
|
||
|
|
" C(deltax, sigma_bec) = -0.9934\n",
|
||
|
|
" C(amp_bec, amp_th) = -0.8258\n",
|
||
|
|
" C(x0_th, amp_bec) = -0.8111\n",
|
||
|
|
" C(x0_th, amp_th) = +0.6701\n",
|
||
|
|
" C(amp_th, sigma_bec) = -0.6408\n",
|
||
|
|
" C(amp_th, deltax) = +0.5789\n",
|
||
|
|
" C(x0_th, sigma_bec) = -0.5011\n",
|
||
|
|
" C(x0_th, deltax) = +0.4893\n",
|
||
|
|
" C(x0_bec, amp_th) = +0.4668\n",
|
||
|
|
" C(amp_bec, sigma_bec) = +0.4307\n",
|
||
|
|
" C(amp_bec, deltax) = -0.3984\n",
|
||
|
|
" C(x0_bec, sigma_bec) = -0.2154\n",
|
||
|
|
" C(x0_bec, amp_bec) = -0.2125\n",
|
||
|
|
" C(x0_bec, deltax) = +0.1484\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"image 1, 1\n",
|
||
|
|
"[[Fit Statistics]]\n",
|
||
|
|
" # fitting method = leastsq\n",
|
||
|
|
" # function evals = 80\n",
|
||
|
|
" # data points = 250\n",
|
||
|
|
" # variables = 6\n",
|
||
|
|
" chi-square = 0.03407079\n",
|
||
|
|
" reduced chi-square = 1.3963e-04\n",
|
||
|
|
" Akaike info crit = -2213.19391\n",
|
||
|
|
" Bayesian info crit = -2192.06515\n",
|
||
|
|
" R-squared = 0.98320626\n",
|
||
|
|
"[[Variables]]\n",
|
||
|
|
" x0_bec: 124.539710 +/- 0.23579865 (0.19%) (init = 124.5766)\n",
|
||
|
|
" x0_th: 125.101779 +/- 0.25466467 (0.20%) (init = 124.5766)\n",
|
||
|
|
" amp_bec: 0.09094370 +/- 0.00619316 (6.81%) (init = 0.2455857)\n",
|
||
|
|
" amp_th: 0.25828614 +/- 0.00391761 (1.52%) (init = 0.105251)\n",
|
||
|
|
" deltax: 41.0719061 +/- 0.76640562 (1.87%) (init = 108)\n",
|
||
|
|
" sigma_bec: 7.53580971 +/- 0.43882438 (5.82%) (init = 29.5082)\n",
|
||
|
|
" sigma_th: 26.0378791 +/- 0.37974263 (1.46%) == '0.632*sigma_bec + 0.518*deltax'\n",
|
||
|
|
"[[Correlations]] (unreported correlations are < 0.100)\n",
|
||
|
|
" C(amp_bec, amp_th) = -0.5675\n",
|
||
|
|
" C(amp_bec, deltax) = +0.4752\n",
|
||
|
|
" C(amp_th, sigma_bec) = -0.4406\n",
|
||
|
|
" C(deltax, sigma_bec) = -0.4102\n",
|
||
|
|
" C(amp_th, deltax) = -0.4089\n",
|
||
|
|
" C(x0_bec, x0_th) = -0.2296\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"image 1, 2\n",
|
||
|
|
"[[Fit Statistics]]\n",
|
||
|
|
" # fitting method = leastsq\n",
|
||
|
|
" # function evals = 71\n",
|
||
|
|
" # data points = 250\n",
|
||
|
|
" # variables = 6\n",
|
||
|
|
" chi-square = 0.03992012\n",
|
||
|
|
" reduced chi-square = 1.6361e-04\n",
|
||
|
|
" Akaike info crit = -2173.58391\n",
|
||
|
|
" Bayesian info crit = -2152.45515\n",
|
||
|
|
" R-squared = 0.98170832\n",
|
||
|
|
"[[Variables]]\n",
|
||
|
|
" x0_bec: 125.671131 +/- 0.10728573 (0.09%) (init = 125.4114)\n",
|
||
|
|
" x0_th: 126.551089 +/- 0.30855469 (0.24%) (init = 125.4114)\n",
|
||
|
|
" amp_bec: 0.21804959 +/- 0.00677440 (3.11%) (init = 0.316436)\n",
|
||
|
|
" amp_th: 0.22801736 +/- 0.00437620 (1.92%) (init = 0.1356154)\n",
|
||
|
|
" deltax: 39.4015337 +/- 0.84855618 (2.15%) (init = 63)\n",
|
||
|
|
" sigma_bec: 7.58235403 +/- 0.20031392 (2.64%) (init = 17.21311)\n",
|
||
|
|
" sigma_th: 25.2020422 +/- 0.46096462 (1.83%) == '0.632*sigma_bec + 0.518*deltax'\n",
|
||
|
|
"[[Correlations]] (unreported correlations are < 0.100)\n",
|
||
|
|
" C(amp_th, deltax) = -0.6618\n",
|
||
|
|
" C(amp_bec, amp_th) = -0.5814\n",
|
||
|
|
" C(amp_bec, deltax) = +0.4857\n",
|
||
|
|
" C(amp_th, sigma_bec) = -0.4429\n",
|
||
|
|
" C(x0_bec, x0_th) = -0.2390\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"image 1, 3\n",
|
||
|
|
"[[Fit Statistics]]\n",
|
||
|
|
" # fitting method = leastsq\n",
|
||
|
|
" # function evals = 92\n",
|
||
|
|
" # data points = 250\n",
|
||
|
|
" # variables = 6\n",
|
||
|
|
" chi-square = 0.04259267\n",
|
||
|
|
" reduced chi-square = 1.7456e-04\n",
|
||
|
|
" Akaike info crit = -2157.38350\n",
|
||
|
|
" Bayesian info crit = -2136.25473\n",
|
||
|
|
" R-squared = 0.97345327\n",
|
||
|
|
"[[Variables]]\n",
|
||
|
|
" x0_bec: 124.548630 +/- 0.12302562 (0.10%) (init = 124.4211)\n",
|
||
|
|
" x0_th: 124.867634 +/- 0.34241704 (0.27%) (init = 124.4211)\n",
|
||
|
|
" amp_bec: 0.19465845 +/- 0.00744247 (3.82%) (init = 0.2883304)\n",
|
||
|
|
" amp_th: 0.19908971 +/- 0.00522647 (2.63%) (init = 0.1235702)\n",
|
||
|
|
" deltax: 33.2106499 +/- 0.95386489 (2.87%) (init = 72)\n",
|
||
|
|
" sigma_bec: 7.24444398 +/- 0.23450025 (3.24%) (init = 19.67213)\n",
|
||
|
|
" sigma_th: 21.7816053 +/- 0.52460231 (2.41%) == '0.632*sigma_bec + 0.518*deltax'\n",
|
||
|
|
"[[Correlations]] (unreported correlations are < 0.100)\n",
|
||
|
|
" C(amp_th, deltax) = -0.6711\n",
|
||
|
|
" C(amp_bec, amp_th) = -0.6225\n",
|
||
|
|
" C(amp_bec, deltax) = +0.5184\n",
|
||
|
|
" C(amp_th, sigma_bec) = -0.4918\n",
|
||
|
|
" C(x0_bec, x0_th) = -0.2805\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"image 1, 4\n",
|
||
|
|
"[[Fit Statistics]]\n",
|
||
|
|
" # fitting method = leastsq\n",
|
||
|
|
" # function evals = 50\n",
|
||
|
|
" # data points = 250\n",
|
||
|
|
" # variables = 6\n",
|
||
|
|
" chi-square = 0.05280479\n",
|
||
|
|
" reduced chi-square = 2.1641e-04\n",
|
||
|
|
" Akaike info crit = -2103.65357\n",
|
||
|
|
" Bayesian info crit = -2082.52480\n",
|
||
|
|
" R-squared = 0.97611358\n",
|
||
|
|
"[[Variables]]\n",
|
||
|
|
" x0_bec: 125.550350 +/- 0.09146206 (0.07%) (init = 125.3887)\n",
|
||
|
|
" x0_th: 127.196908 +/- 0.37774559 (0.30%) (init = 125.3887)\n",
|
||
|
|
" amp_bec: 0.31158831 +/- 0.00960934 (3.08%) (init = 0.3444)\n",
|
||
|
|
" amp_th: 0.18968669 +/- 0.00790889 (4.17%) (init = 0.1476)\n",
|
||
|
|
" deltax: 23.5195069 +/- 1.07252976 (4.56%) (init = 51)\n",
|
||
|
|
" sigma_bec: 7.63218924 +/- 0.17190803 (2.25%) (init = 13.93443)\n",
|
||
|
|
" sigma_th: 17.0066482 +/- 0.58611511 (3.45%) == '0.632*sigma_bec + 0.518*deltax'\n",
|
||
|
|
"[[Correlations]] (unreported correlations are < 0.100)\n",
|
||
|
|
" C(amp_bec, amp_th) = -0.7499\n",
|
||
|
|
" C(amp_th, deltax) = -0.7447\n",
|
||
|
|
" C(amp_bec, deltax) = +0.6070\n",
|
||
|
|
" C(amp_th, sigma_bec) = -0.5314\n",
|
||
|
|
" C(x0_bec, x0_th) = -0.3625\n",
|
||
|
|
" C(x0_th, amp_bec) = +0.2159\n",
|
||
|
|
" C(deltax, sigma_bec) = +0.1911\n",
|
||
|
|
" C(x0_th, amp_th) = -0.1864\n",
|
||
|
|
" C(amp_bec, sigma_bec) = +0.1423\n",
|
||
|
|
" C(x0_th, sigma_bec) = +0.1273\n",
|
||
|
|
" C(x0_th, deltax) = +0.1089\n",
|
||
|
|
" C(x0_bec, deltax) = +0.1061\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"total time: 419.1107749938965 ms\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# from opencv import moments\n",
|
||
|
|
"start = time.time()\n",
|
||
|
|
"\n",
|
||
|
|
"shape = np.shape(cropOD)\n",
|
||
|
|
"thresh = calc_thresh(cropOD)\n",
|
||
|
|
"center = calc_cen_bulk(thresh)\n",
|
||
|
|
"# print(center)\n",
|
||
|
|
"BEC_width_guess = guess_BEC_width(thresh, center)\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(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])\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(cropOD[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)])\n",
|
||
|
|
"\n",
|
||
|
|
"#[nr images x, nr images y, X / Y, center / sigma]\n",
|
||
|
|
"x = np.linspace(0,shape[3],shape[3])\n",
|
||
|
|
"y = np.linspace(0,shape[2], shape[2])\n",
|
||
|
|
"\n",
|
||
|
|
"popt = np.zeros((shape[0], shape[1], 6))\n",
|
||
|
|
"\n",
|
||
|
|
"p0 = np.ones((shape[0], shape[1], 6))\n",
|
||
|
|
"\n",
|
||
|
|
"max_val = np.zeros((shape[0], shape[1]))\n",
|
||
|
|
"\n",
|
||
|
|
"for i in range(0, shape[0]):\n",
|
||
|
|
" max_val[i] = np.ndarray.max(X_guess_og[i], axis=1)\n",
|
||
|
|
"\n",
|
||
|
|
"# Fitting x without math constr\n",
|
||
|
|
"fitmodel = lmfit.Model(density_1d,independent_vars=['x'])\n",
|
||
|
|
"\n",
|
||
|
|
"result_x = []\n",
|
||
|
|
"\n",
|
||
|
|
"for i in range(0, shape[0]):\n",
|
||
|
|
" temp_res = []\n",
|
||
|
|
" for j in range(0, shape[1]):\n",
|
||
|
|
" t1 = time.time()\n",
|
||
|
|
" params = lmfit.Parameters()\n",
|
||
|
|
" params.add_many(\n",
|
||
|
|
" ('x0_bec', center[i,j,0], True,0, 200),\n",
|
||
|
|
" ('x0_th',center[i,j,0], True,0, 200),\n",
|
||
|
|
" ('amp_bec', 0.7 * max_val[i,j], True, 0, 1.3 * np.max(X_guess_og[i,j])),\n",
|
||
|
|
" ('amp_th', 0.3 * max_val[i,j], True, 0, 1.3 * np.max(X_guess_og[i,j])),\n",
|
||
|
|
" ('deltax', 3*BEC_width_guess[i,j,0], True, 0,cut_width),\n",
|
||
|
|
" # ('sigma_bec',BEC_width_guess[i,j,0]/1.22, True, 0, 50)\n",
|
||
|
|
" ('sigma_bec',BEC_width_guess[i,j,0]/1.22, True, 0, 50)\n",
|
||
|
|
" )\n",
|
||
|
|
" params.add('sigma_th', 3*BEC_width_guess[i,j,0], min=0, expr=f'0.632*sigma_bec + 0.518*deltax')\n",
|
||
|
|
"\n",
|
||
|
|
" # params.add('sigma_th', 3*BEC_width_guess[i,j,0], True, min=0,max=150)\n",
|
||
|
|
" t2 = time.time()\n",
|
||
|
|
" res = fitmodel.fit(X_guess_og[i,j], x=x, params=params)\n",
|
||
|
|
" t3 = time.time()\n",
|
||
|
|
" temp_res.append(res)\n",
|
||
|
|
" t4 = time.time()\n",
|
||
|
|
" # print(t2 - t1)\n",
|
||
|
|
" # print(t3 - t2)\n",
|
||
|
|
" # print(t4 - t3)\n",
|
||
|
|
" # print(\"\")\n",
|
||
|
|
" print(f'image {i}, {j}')\n",
|
||
|
|
" lmfit.report_fit(res)\n",
|
||
|
|
" print()\n",
|
||
|
|
"\n",
|
||
|
|
" print()\n",
|
||
|
|
" result_x.append(temp_res)\n",
|
||
|
|
"stop = time.time()\n",
|
||
|
|
"\n",
|
||
|
|
"print(f'total time: {(stop-start)*1e3} ms')"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2023-08-01T14:05:13.963843900Z",
|
||
|
|
"start_time": "2023-08-01T14:05:13.506865400Z"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 1,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"ename": "NameError",
|
||
|
|
"evalue": "name 'shape' is not defined",
|
||
|
|
"output_type": "error",
|
||
|
|
"traceback": [
|
||
|
|
"\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
|
||
|
|
"\u001B[1;31mNameError\u001B[0m Traceback (most recent call last)",
|
||
|
|
"Cell \u001B[1;32mIn[1], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m i \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(\u001B[38;5;241m0\u001B[39m, \u001B[43mshape\u001B[49m[\u001B[38;5;241m0\u001B[39m]):\n\u001B[0;32m 2\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m j \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(\u001B[38;5;241m0\u001B[39m, shape[\u001B[38;5;241m1\u001B[39m]):\n\u001B[0;32m 3\u001B[0m \u001B[38;5;28mprint\u001B[39m(result_x[i][j]\u001B[38;5;241m.\u001B[39mbest_values)\n",
|
||
|
|
"\u001B[1;31mNameError\u001B[0m: name 'shape' is not defined"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"for i in range(0, shape[0]):\n",
|
||
|
|
" for j in range(0, shape[1]):\n",
|
||
|
|
" print(result_x[i][j].best_values)"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2023-08-01T13:02:43.356422900Z",
|
||
|
|
"start_time": "2023-08-01T13:02:43.051468800Z"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 25,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 1000x500 with 10 Axes>",
|
||
|
|
"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"fsize= (10,5)\n",
|
||
|
|
"fig, ax = plt.subplots(shape[0],shape[1],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",
|
||
|
|
" bval = result_x[i][j].best_values\n",
|
||
|
|
" ax[i,j].plot(x, X_guess_og[i,j])\n",
|
||
|
|
" ax[i,j].plot(x, density_1d(x, **result_x[i][j].best_values))\n",
|
||
|
|
" ax[i,j].plot(x, thermal(x, bval['x0_th'], bval['amp_th'], bval['sigma_th']))\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"plt.show()"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2023-08-01T15:03:41.983964700Z",
|
||
|
|
"start_time": "2023-08-01T15:03:41.172805200Z"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"source": [
|
||
|
|
"## 2D Fit without mathematical constraint"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 64,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"image 0,0\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stderr",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"C:\\Users\\Jianshun Gao\\AppData\\Local\\Temp\\ipykernel_15584\\686923892.py:107: RuntimeWarning: invalid value encountered in power\n",
|
||
|
|
" res = (1- ((x-centerx)/(sigmax))**2 - ((y-centery)/(sigmay))**2)**(3 / 2)\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"2.446171998977661\n",
|
||
|
|
"image 0,1\n",
|
||
|
|
"0.42479395866394043\n",
|
||
|
|
"image 0,2\n",
|
||
|
|
"0.3388192653656006\n",
|
||
|
|
"image 0,3\n",
|
||
|
|
"1.0576553344726562\n",
|
||
|
|
"image 0,4\n",
|
||
|
|
"0.4093611240386963\n",
|
||
|
|
"image 1,0\n",
|
||
|
|
"1.2626025676727295\n",
|
||
|
|
"image 1,1\n",
|
||
|
|
"0.5342316627502441\n",
|
||
|
|
"image 1,2\n",
|
||
|
|
"0.40906715393066406\n",
|
||
|
|
"image 1,3\n",
|
||
|
|
"0.4411289691925049\n",
|
||
|
|
"image 1,4\n",
|
||
|
|
"0.33938121795654297\n",
|
||
|
|
"fitting time = 0.7710072994232178 +- 0.670787745643047\n",
|
||
|
|
"[2.45115781 0.42978144 0.34380507 1.06264186 0.41334987 1.26659203\n",
|
||
|
|
" 0.53821802 0.41405177 0.44610786 0.34436727]\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"\n",
|
||
|
|
"result = []\n",
|
||
|
|
"times = []\n",
|
||
|
|
"x = np.linspace(0,shape[3],cut_width)\n",
|
||
|
|
"y = np.linspace(0,shape[2], cut_width)\n",
|
||
|
|
"\n",
|
||
|
|
"for i in range(0,shape[0]):\n",
|
||
|
|
" temp_res_arr = []\n",
|
||
|
|
" for j in range(0,shape[1]):\n",
|
||
|
|
" print(f'image {i},{j}')\n",
|
||
|
|
" data = cropOD[i,j]\n",
|
||
|
|
" fitModel = lmfit.Model(density_profile_BEC_2d, independent_vars=['x','y'])\n",
|
||
|
|
" #fitModel.set_param_hint('deltax', value=5)\n",
|
||
|
|
"\n",
|
||
|
|
" bval_1d = result_x[i][j].best_values\n",
|
||
|
|
" S = np.max(blurred[i,j])/(bval_1d['amp_bec'] + bval_1d['amp_th'])\n",
|
||
|
|
"\n",
|
||
|
|
" params = lmfit.Parameters()\n",
|
||
|
|
" #print(bval['sigma_th'])\n",
|
||
|
|
" params.add_many(\n",
|
||
|
|
" ('amp_bec', S * bval_1d['amp_bec'], True, 0, 1.3 * np.max(data)),\n",
|
||
|
|
" ('amp_th',S * bval_1d['amp_th'], True, 0, 1.3 * np.max(data)),\n",
|
||
|
|
" ('x0_bec',center[i,j,0], True, 0, 150),\n",
|
||
|
|
" ('y0_bec',center[i,j,1], True, 0, 150),\n",
|
||
|
|
" ('x0_th',center[i,j,0], True, 0, 150),\n",
|
||
|
|
" ('y0_th',center[i,j,1], True, 0, 150),\n",
|
||
|
|
" ('sigmax_bec',bval_1d['sigma_bec'], True, 0, 100),\n",
|
||
|
|
" ('sigmay_bec',BEC_width_guess[i,j,1], True, 0, 100),\n",
|
||
|
|
" ('sigma_th',bval_1d['sigma_th'], True, 0, 50)\n",
|
||
|
|
" )\n",
|
||
|
|
"\n",
|
||
|
|
" X,Y = np.meshgrid(x, y)\n",
|
||
|
|
" X_1d = X.flatten()\n",
|
||
|
|
" Y_1d = Y.flatten()\n",
|
||
|
|
"\n",
|
||
|
|
" data1d = data.flatten()\n",
|
||
|
|
" start = time.time()\n",
|
||
|
|
" res = fitModel.fit(data1d, x=X_1d, y=Y_1d, params=params)\n",
|
||
|
|
" stop = time.time()\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
" # Check if there is an thermal part\n",
|
||
|
|
" bval = res.best_values\n",
|
||
|
|
" sigma_cut = max(bval['sigmay_bec'],bval['sigmax_bec'])\n",
|
||
|
|
" tf_fit = ThomasFermi_2d(X,Y,centerx=bval['x0_bec'], centery=bval['y0_bec'], amplitude=bval['amp_bec'], sigmax=bval['sigmax_bec'], sigmay=bval['sigmay_bec'])\n",
|
||
|
|
" tf_fit_2 = ThomasFermi_2d(X,Y,centerx=bval['x0_bec'], centery=bval['y0_bec'], amplitude=bval['amp_bec'], sigmax=1.5 * sigma_cut, sigmay=1.5*sigma_cut)\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
" mask = np.where(tf_fit > 0, np.nan, data)\n",
|
||
|
|
" #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",
|
||
|
|
" print(stop-start)\n",
|
||
|
|
"\n",
|
||
|
|
" # if (check_value < 45) or ((check_value > 200) and (bval['sigma_th'] < min(bval['sigmax_bec'], bval['sigmay_bec']))):\n",
|
||
|
|
" # print('No thermal part detected, performing fit without thermal function')\n",
|
||
|
|
" # if check_value > 200:\n",
|
||
|
|
" # print('Sigma Thermal smaller than BEC, but still strong part around masked region --> BEC guessed wrong')\n",
|
||
|
|
" #\n",
|
||
|
|
" # params = lmfit.Parameters()\n",
|
||
|
|
" # #print(bval['sigma_th'])\n",
|
||
|
|
" # params.add_many(\n",
|
||
|
|
" # ('amp_bec', S * bval_1d['amp_bec'], True, 0, 1.3 * np.max(data)),\n",
|
||
|
|
" # ('amp_th',0, False, 0, 1.3 * np.max(data)),\n",
|
||
|
|
" # ('x0_bec',center[i,j,0], True, 0, 150),\n",
|
||
|
|
" # ('y0_bec',center[i,j,1], True, 0, 150),\n",
|
||
|
|
" # ('x0_th', 1, False, 0, 150),\n",
|
||
|
|
" # ('y0_th', 1, False, 0, 150),\n",
|
||
|
|
" # ('sigmax_bec',bval_1d['sigma_bec'], True, 0, 50),\n",
|
||
|
|
" # ('sigmay_bec',BEC_width_guess[i,j,1], True, 0, 50),\n",
|
||
|
|
" # ('sigma_th',1, False, 0, 50)\n",
|
||
|
|
" # )\n",
|
||
|
|
" #\n",
|
||
|
|
" # start2 = time.time()\n",
|
||
|
|
" # res = fitModel.fit(data1d, x=X_1d, y=Y_1d, params=params)\n",
|
||
|
|
" # stop2 = time.time()\n",
|
||
|
|
" #\n",
|
||
|
|
" # print(stop2-start2)\n",
|
||
|
|
" # print('')\n",
|
||
|
|
" stop2 = time.time()\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
" times.append(stop2-start)\n",
|
||
|
|
" temp_res_arr.append(res)\n",
|
||
|
|
" result.append(temp_res_arr)\n",
|
||
|
|
"times = np.array(times)\n",
|
||
|
|
"print(f\"fitting time = {np.mean(times)} +- {np.std(times, ddof=1)}\")\n",
|
||
|
|
"print(times)\n",
|
||
|
|
"\n"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2023-07-31T16:18:46.439989800Z",
|
||
|
|
"start_time": "2023-07-31T16:18:38.694610600Z"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 15,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"\n",
|
||
|
|
"image 0,0\n",
|
||
|
|
"Image seems to be purely thermal (guessed from 1d fit amplitude)\n",
|
||
|
|
"time 1st fit: 0.229 s\n",
|
||
|
|
"\n",
|
||
|
|
"image 0,1\n",
|
||
|
|
"time 1st fit: 0.487 s\n",
|
||
|
|
"\n",
|
||
|
|
"image 0,2\n",
|
||
|
|
"time 1st fit: 0.422 s\n",
|
||
|
|
"\n",
|
||
|
|
"image 0,3\n",
|
||
|
|
"time 1st fit: 0.969 s\n",
|
||
|
|
"\n",
|
||
|
|
"image 0,4\n",
|
||
|
|
"time 1st fit: 0.334 s\n",
|
||
|
|
"\n",
|
||
|
|
"image 1,0\n",
|
||
|
|
"Image seems to be purely thermal (guessed from 1d fit amplitude)\n",
|
||
|
|
"time 1st fit: 0.136 s\n",
|
||
|
|
"\n",
|
||
|
|
"image 1,1\n",
|
||
|
|
"time 1st fit: 1.682 s\n",
|
||
|
|
"\n",
|
||
|
|
"image 1,2\n",
|
||
|
|
"time 1st fit: 0.440 s\n",
|
||
|
|
"\n",
|
||
|
|
"image 1,3\n",
|
||
|
|
"time 1st fit: 0.252 s\n",
|
||
|
|
"\n",
|
||
|
|
"image 1,4\n",
|
||
|
|
"time 1st fit: 0.359 s\n",
|
||
|
|
"fitting time = 0.534 +- 0.463\n",
|
||
|
|
"max fitting time = 1.685s\n",
|
||
|
|
"[0.23147106 0.49030185 0.42403865 0.97199774 0.33587432 0.13781762\n",
|
||
|
|
" 1.68479657 0.44243336 0.25544643 0.36216903]\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"\n",
|
||
|
|
"result = []\n",
|
||
|
|
"result_1 = []\n",
|
||
|
|
"times = []\n",
|
||
|
|
"x = np.linspace(0,shape[3],cut_width)\n",
|
||
|
|
"y = np.linspace(0,shape[2], cut_width)\n",
|
||
|
|
"\n",
|
||
|
|
"fitModel = lmfit.Model(density_profile_BEC_2d, independent_vars=['x','y'])\n",
|
||
|
|
"\n",
|
||
|
|
"for i in range(0,shape[0]):\n",
|
||
|
|
" temp_res_arr = []\n",
|
||
|
|
" temp_res_arr_1 = []\n",
|
||
|
|
" for j in range(0,shape[1]):\n",
|
||
|
|
" print()\n",
|
||
|
|
" print(f'image {i},{j}')\n",
|
||
|
|
" data = cropOD[i,j]\n",
|
||
|
|
"\n",
|
||
|
|
" #fitModel.set_param_hint('deltax', value=5)\n",
|
||
|
|
"\n",
|
||
|
|
" bval_1d = result_x[i][j].best_values\n",
|
||
|
|
" S = np.max(gaussian_filter(data, sigma=1))/(bval_1d['amp_bec'] + bval_1d['amp_th'])\n",
|
||
|
|
" params = lmfit.Parameters()\n",
|
||
|
|
" #print(bval['sigma_th'])\n",
|
||
|
|
" do_fit_2 = True\n",
|
||
|
|
" if bval_1d['amp_th']/bval_1d['amp_bec'] > 4:\n",
|
||
|
|
" print('Image seems to be purely thermal (guessed from 1d fit amplitude)')\n",
|
||
|
|
" do_fit_2 = False\n",
|
||
|
|
" params.add_many(\n",
|
||
|
|
" ('amp_bec', 0, False, 0, 1.3 * np.max(data)),\n",
|
||
|
|
" ('amp_th',S * bval_1d['amp_th'], True, 0, 1.3 * np.max(data)),\n",
|
||
|
|
" ('x0_bec',1, False),\n",
|
||
|
|
" ('y0_bec',1, False),\n",
|
||
|
|
" ('x0_th',center[i,j,0], True, center[i,j,0] -10, center[i,j,0] + 10),\n",
|
||
|
|
" ('y0_th',center[i,j,1], True, center[i,j,1] -10, center[i,j,1] + 10),\n",
|
||
|
|
" ('sigmax_bec', 1, False),\n",
|
||
|
|
" ('sigmay_bec', 1, False),\n",
|
||
|
|
" ('sigma_th',bval_1d['sigma_th'], True, 0, cut_width)\n",
|
||
|
|
" )\n",
|
||
|
|
"\n",
|
||
|
|
" elif bval_1d['amp_bec']/bval_1d['amp_th'] > 10:\n",
|
||
|
|
" print('Image seems to be pure BEC (guessed from 1d fit amplitude)')\n",
|
||
|
|
" do_fit_2 = False\n",
|
||
|
|
" params.add_many(\n",
|
||
|
|
" ('amp_bec', S * bval_1d['amp_bec'], True, 0, 1.3 * np.max(data)),\n",
|
||
|
|
" ('amp_th',0, False),\n",
|
||
|
|
" ('x0_bec',center[i,j,0], True, center[i,j,0] -10, center[i,j,0] + 10),\n",
|
||
|
|
" ('y0_bec',center[i,j,1], True, center[i,j,1] - 10, center[i,j,1] + 10),\n",
|
||
|
|
" ('x0_th', 1, False),\n",
|
||
|
|
" ('y0_th', 1, False),\n",
|
||
|
|
" ('sigmax_bec',bval_1d['sigma_bec'], True, 0, 2/1.22 * BEC_width_guess[i,j,0]),\n",
|
||
|
|
" ('sigmay_bec',BEC_width_guess[i,j,1]/1.22, True, 0, 2/1.22 * BEC_width_guess[i,j,0]),\n",
|
||
|
|
" ('sigma_th',bval_1d['sigma_th'], False, 0, 50)\n",
|
||
|
|
" )\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
" else:\n",
|
||
|
|
" params.add_many(\n",
|
||
|
|
" ('amp_bec', S * bval_1d['amp_bec'], True, 0, 1.3 * np.max(data)),\n",
|
||
|
|
" ('amp_th',S * bval_1d['amp_th'], True, 0, 1.3 * np.max(data)),\n",
|
||
|
|
" ('x0_bec',center[i,j,0], True, center[i,j,0] -10, center[i,j,0] + 10),\n",
|
||
|
|
" ('y0_bec',center[i,j,1], True, center[i,j,1] -10, center[i,j,1] + 10),\n",
|
||
|
|
" ('x0_th',center[i,j,0], True, center[i,j,0] -10, center[i,j,0] + 10),\n",
|
||
|
|
" ('y0_th',center[i,j,1], True, center[i,j,1] -10, center[i,j,1] + 10),\n",
|
||
|
|
" ('sigmax_bec',bval_1d['sigma_bec'], True, 0, 2/1.22 * BEC_width_guess[i,j,0]),\n",
|
||
|
|
" ('sigmay_bec',BEC_width_guess[i,j,1]/1.22, True, 0, 2/1.22 * BEC_width_guess[i,j,1]),\n",
|
||
|
|
" ('sigma_th',bval_1d['sigma_th'], True, 0, cut_width)\n",
|
||
|
|
" )\n",
|
||
|
|
"\n",
|
||
|
|
" X,Y = np.meshgrid(x, y)\n",
|
||
|
|
" X_1d = X.flatten()\n",
|
||
|
|
" Y_1d = Y.flatten()\n",
|
||
|
|
"\n",
|
||
|
|
" data1d = data.flatten()\n",
|
||
|
|
" start = time.time()\n",
|
||
|
|
" res = fitModel.fit(data1d, x=X_1d, y=Y_1d, params=params)\n",
|
||
|
|
" stop = time.time()\n",
|
||
|
|
" temp_res_arr_1.append(res)\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
" # Check if there is an thermal part\n",
|
||
|
|
" bval = res.best_values\n",
|
||
|
|
" sigma_cut = max(bval['sigmay_bec'], bval['sigmax_bec'])\n",
|
||
|
|
" tf_fit = ThomasFermi_2d(X,Y,centerx=bval['x0_bec'], centery=bval['y0_bec'], amplitude=bval['amp_bec'], sigmax=bval['sigmax_bec'], sigmay=bval['sigmay_bec'])\n",
|
||
|
|
" tf_fit_2 = ThomasFermi_2d(X,Y,centerx=bval['x0_bec'], centery=bval['y0_bec'], amplitude=bval['amp_bec'], sigmax=1.5 * sigma_cut, sigmay=1.5*sigma_cut)\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
" mask = np.where(tf_fit > 0, np.nan, data)\n",
|
||
|
|
" #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",
|
||
|
|
" print(f'time 1st fit: {stop-start :.3f} s')\n",
|
||
|
|
"\n",
|
||
|
|
" # if (check_value < 45) or ((check_value > 10000) and (bval['sigma_th'] < min(bval['sigmax_bec'], bval['sigmay_bec']))):\n",
|
||
|
|
" #check_value = 200\n",
|
||
|
|
" if check_value < 45 and do_fit_2:\n",
|
||
|
|
" print('No thermal part detected, performing fit without thermal function')\n",
|
||
|
|
" # if check_value > 200:\n",
|
||
|
|
" # print('Sigma Thermal smaller than BEC, but still strong part around masked region --> BEC guessed wrong')\n",
|
||
|
|
"\n",
|
||
|
|
" params = lmfit.Parameters()\n",
|
||
|
|
" #print(bval['sigma_th'])\n",
|
||
|
|
" params.add_many(\n",
|
||
|
|
" ('amp_bec', S * bval_1d['amp_bec'], True, 0, 1.3 * np.max(data)),\n",
|
||
|
|
" ('amp_th',0, False),\n",
|
||
|
|
" ('x0_bec',center[i,j,0], True, center[i,j,0] -10, center[i,j,0] + 10),\n",
|
||
|
|
" ('y0_bec',center[i,j,1], True, center[i,j,1] - 10, center[i,j,1] + 10),\n",
|
||
|
|
" ('x0_th', 1, False),\n",
|
||
|
|
" ('y0_th', 1, False),\n",
|
||
|
|
" ('sigmax_bec',bval_1d['sigma_bec'], True, 0, 2/1.22 * BEC_width_guess[i,j,0]),\n",
|
||
|
|
" ('sigmay_bec',BEC_width_guess[i,j,1]/1.22, True, 0, 2/1.22 * BEC_width_guess[i,j,0]),\n",
|
||
|
|
" ('sigma_th',bval_1d['sigma_th'], False, 0, 50)\n",
|
||
|
|
" )\n",
|
||
|
|
"\n",
|
||
|
|
" start2 = time.time()\n",
|
||
|
|
" res = fitModel.fit(data1d, x=X_1d, y=Y_1d, params=params)\n",
|
||
|
|
" stop2 = time.time()\n",
|
||
|
|
"\n",
|
||
|
|
" print(f'time pure bec fit: {stop2-start2 :.3f} s')\n",
|
||
|
|
" print('')\n",
|
||
|
|
" stop2 = time.time()\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
" times.append(stop2-start)\n",
|
||
|
|
" temp_res_arr.append(res)\n",
|
||
|
|
" result_1.append(temp_res_arr_1)\n",
|
||
|
|
" result.append(temp_res_arr)\n",
|
||
|
|
"times = np.array(times)\n",
|
||
|
|
"print(f\"fitting time = {np.mean(times):.3f} +- {np.std(times, ddof=1):.3f}\")\n",
|
||
|
|
"print(f\"max fitting time = {np.max(times) :.3f}s\")\n",
|
||
|
|
"print(times)\n",
|
||
|
|
"\n"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2023-08-01T14:07:36.161088100Z",
|
||
|
|
"start_time": "2023-08-01T14:07:30.769703500Z"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"source": [
|
||
|
|
"## Plotting"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 26,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 1400x4000 with 50 Axes>",
|
||
|
|
"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"fig, axs = plt.subplots(shape[0] * shape[1], 5, figsize=(14, 4 * shape[0] * shape[1]))\n",
|
||
|
|
"\n",
|
||
|
|
"ii = 0\n",
|
||
|
|
"for i in range(0,shape[0]):\n",
|
||
|
|
"\n",
|
||
|
|
" for j in range(0,shape[1]):\n",
|
||
|
|
" axs[ii,0].set_title(f'image {i}, {j}, cond. frac = {cond_frac(result[i][j]) :.2f}')\n",
|
||
|
|
" lmfit.fit_report(result[i][j])\n",
|
||
|
|
" bval = result[i][j].best_values\n",
|
||
|
|
" fit = density_profile_BEC_2d(X,Y, **bval)\n",
|
||
|
|
" vmax = np.max(fit)\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
" ax = axs[ii,0]\n",
|
||
|
|
" ax.pcolormesh(X, Y, cropOD[i,j], vmin=0, vmax=vmax, cmap='jet', shading='auto')\n",
|
||
|
|
" #plt.colorbar(art, ax=ax, label='z')\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
" # Plot gaussian 2d Fit + legend including Width parameters\n",
|
||
|
|
" ax = axs[ii,1]\n",
|
||
|
|
"\n",
|
||
|
|
" ax.pcolormesh(X, Y, fit, vmin=0, vmax=vmax, cmap='jet', shading='auto')\n",
|
||
|
|
" #plt.colorbar(art, ax=ax, label='z')\n",
|
||
|
|
"\n",
|
||
|
|
" ax = axs[ii,2]\n",
|
||
|
|
"\n",
|
||
|
|
" ax.pcolormesh(X, Y, fit-cropOD[i,j], vmin=0, vmax=0.2, cmap='jet', shading='auto')\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
" ax = axs[ii,3]\n",
|
||
|
|
"\n",
|
||
|
|
" ax.plot(x, cropOD[i,j, round(center[i,j,1]), :])\n",
|
||
|
|
" ax.plot(x, fit[round(center[i,j,1]), :])\n",
|
||
|
|
" ax.plot(x, thermal(x, bval['x0_th'], bval['amp_th'], bval['sigma_th']))\n",
|
||
|
|
"\n",
|
||
|
|
" ax = axs[ii,4]\n",
|
||
|
|
"\n",
|
||
|
|
" ax.plot(y, cropOD[i,j, :, round(center[i,j,0])])\n",
|
||
|
|
" ax.plot(y, fit[:, round(center[i,j,0])])\n",
|
||
|
|
" ax.plot(x, thermal(y, bval['y0_th'], bval['amp_th'], bval['sigma_th']))\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
" ii += 1\n",
|
||
|
|
"\n",
|
||
|
|
"axs[0,0].set_title(f'Data \\n \\n image {0}, {0}, cond. frac = {cond_frac(result[0][0]) :.2f}')\n",
|
||
|
|
"axs[0,1].set_title('Fit \\n \\n')\n",
|
||
|
|
"axs[0,2].set_title('Data - Fit \\n \\n')\n",
|
||
|
|
"axs[0,3].set_title('cut along x \\n \\n')\n",
|
||
|
|
"axs[0,4].set_title('cut along y \\n \\n')\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"plt.show()"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2023-08-01T15:06:33.159195600Z",
|
||
|
|
"start_time": "2023-08-01T15:06:25.850200700Z"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 51,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"ename": "type",
|
||
|
|
"evalue": "'sigma_th'",
|
||
|
|
"output_type": "error",
|
||
|
|
"traceback": [
|
||
|
|
"\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
|
||
|
|
"\u001B[1;31mKeyError\u001B[0m Traceback (most recent call last)",
|
||
|
|
"Cell \u001B[1;32mIn[51], line 5\u001B[0m\n\u001B[0;32m 3\u001B[0m sx \u001B[38;5;241m=\u001B[39m result[i][j]\u001B[38;5;241m.\u001B[39mbest_values[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124msigmax_bec\u001B[39m\u001B[38;5;124m'\u001B[39m]\n\u001B[0;32m 4\u001B[0m sy \u001B[38;5;241m=\u001B[39m result[i][j]\u001B[38;5;241m.\u001B[39mbest_values[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124msigmay_bec\u001B[39m\u001B[38;5;124m'\u001B[39m]\n\u001B[1;32m----> 5\u001B[0m s_th \u001B[38;5;241m=\u001B[39m \u001B[43mresult\u001B[49m\u001B[43m[\u001B[49m\u001B[43mi\u001B[49m\u001B[43m]\u001B[49m\u001B[43m[\u001B[49m\u001B[43mj\u001B[49m\u001B[43m]\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mbest_values\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43msigma_th\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m]\u001B[49m\n\u001B[0;32m 7\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mimage \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mi\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m, \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mj\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m'\u001B[39m)\n\u001B[0;32m 8\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;28mmin\u001B[39m(sx\u001B[38;5;241m*\u001B[39m\u001B[38;5;241m1.22\u001B[39m, sy\u001B[38;5;241m*\u001B[39m\u001B[38;5;241m1.22\u001B[39m))\n",
|
||
|
|
"\u001B[1;31mKeyError\u001B[0m: 'sigma_th'"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"for i in range(0,shape[0]):\n",
|
||
|
|
" for j in range(0,shape[1]):\n",
|
||
|
|
" sx = result[i][j].best_values['sigmax_bec']\n",
|
||
|
|
" sy = result[i][j].best_values['sigmay_bec']\n",
|
||
|
|
" s_th = result[i][j].best_values['sigma_th']\n",
|
||
|
|
"\n",
|
||
|
|
" print(f'image {i}, {j}')\n",
|
||
|
|
" print(min(sx*1.22, sy*1.22))\n",
|
||
|
|
" print(f'FWHM_x BEC: { sx*1.22:.2f}, FWHM_x thermal: { s_th*1.93:.2f}')\n",
|
||
|
|
" print(f'FWHM_y BEC: { sy*1.22:.2f}')\n",
|
||
|
|
" print(f'Ratio fwhm_th/fwhm_bec: { 1/min(sx,sy)/1.22 * s_th *1.93 :.2f}')\n",
|
||
|
|
" print('')"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2023-07-31T14:31:51.354781600Z",
|
||
|
|
"start_time": "2023-07-31T14:31:51.116754900Z"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"source": [],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 68,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"image 0, 0\n",
|
||
|
|
"check val, 996.1322515049512\n",
|
||
|
|
"image 0, 1\n",
|
||
|
|
"check val, 503.70403248392944\n",
|
||
|
|
"image 0, 2\n",
|
||
|
|
"check val, 404.5014209391836\n",
|
||
|
|
"image 0, 3\n",
|
||
|
|
"check val, 204.52155001627648\n",
|
||
|
|
"image 0, 4\n",
|
||
|
|
"check val, 117.05256423462238\n",
|
||
|
|
"image 1, 0\n",
|
||
|
|
"check val, 607.2031498159346\n",
|
||
|
|
"image 1, 1\n",
|
||
|
|
"check val, 569.9239240506499\n",
|
||
|
|
"image 1, 2\n",
|
||
|
|
"check val, 423.4129240784928\n",
|
||
|
|
"image 1, 3\n",
|
||
|
|
"check val, 251.18077589127904\n",
|
||
|
|
"image 1, 4\n",
|
||
|
|
"check val, 151.64045224955777\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stderr",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"C:\\Users\\Jianshun Gao\\AppData\\Local\\Temp\\ipykernel_15584\\686923892.py:107: RuntimeWarning: invalid value encountered in power\n",
|
||
|
|
" res = (1- ((x-centerx)/(sigmax))**2 - ((y-centery)/(sigmay))**2)**(3 / 2)\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 1000x1000 with 10 Axes>",
|
||
|
|
"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"\n",
|
||
|
|
"mask = np.zeros(shape)\n",
|
||
|
|
"mask2 = np.zeros(shape)\n",
|
||
|
|
"mask3 = []\n",
|
||
|
|
"fig, ax = plt.subplots(shape[0], shape[1], figsize=(10, 10))\n",
|
||
|
|
"\n",
|
||
|
|
"for i in range(0, shape[0]):\n",
|
||
|
|
" temp_arr = []\n",
|
||
|
|
" for j in range(0, shape[1]):\n",
|
||
|
|
" print(f'image {i}, {j}')\n",
|
||
|
|
" arr = []\n",
|
||
|
|
" bval = result[i][j].best_values\n",
|
||
|
|
" sigma_cut = max(bval['sigmay_bec'], bval['sigmax_bec'])\n",
|
||
|
|
" tf_fit = ThomasFermi_2d(X, Y, centerx=bval['x0_bec'], centery=bval['y0_bec'], amplitude=bval['amp_bec'],\n",
|
||
|
|
" sigmax=bval['sigmax_bec'], sigmay=bval['sigmay_bec'])\n",
|
||
|
|
" tf_fit_2 = ThomasFermi_2d(X, Y, centerx=bval['x0_bec'], centery=bval['y0_bec'], amplitude=bval['amp_bec'],\n",
|
||
|
|
" sigmax=1.5 * sigma_cut, sigmay=1.5 * sigma_cut)\n",
|
||
|
|
"\n",
|
||
|
|
" mask[i, j] = np.where(tf_fit > 0, np.nan, cropOD[i, j])\n",
|
||
|
|
" #mask[i,j] = gaussian_filter(mask[i,j], sigma = 0.4)\n",
|
||
|
|
" #mask[i,j] = np.where(tf_fit_2 > 0, mask[i,j], np.nan)\n",
|
||
|
|
" mask2[i, j] = np.where(tf_fit_2 > 0, mask[i, j], np.nan)\n",
|
||
|
|
" # print(f'max = {np.nanmax(mask[i,j])}, {np.nanmax(mask[i,j]) / np.nanmin(mask[i,j])}')\n",
|
||
|
|
"\n",
|
||
|
|
" check_value = np.nanmean(mask2[i, j]) / (bval[\"amp_bec\"] + bval[\"amp_th\"])\n",
|
||
|
|
"\n",
|
||
|
|
" print(f'check val, {np.nansum(mask2[i, j])}')\n",
|
||
|
|
"\n",
|
||
|
|
" ax[i, j].pcolormesh(mask2[i, j], cmap='jet', vmin=0, vmax=0.5)\n",
|
||
|
|
"\n",
|
||
|
|
"plt.show()"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2023-07-31T16:24:30.214087200Z",
|
||
|
|
"start_time": "2023-07-31T16:24:27.819111500Z"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 66,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<lmfit.model.ModelResult at 0x26135f2cd90>",
|
||
|
|
"text/html": "<h2> Model</h2> Model(density_profile_BEC_2d) <h2>Fit Statistics</h2><table><tr><td>fitting method</td><td>leastsq</td><td></td></tr><tr><td># function evals</td><td>187</td><td></td></tr><tr><td># data points</td><td>40000</td><td></td></tr><tr><td># variables</td><td>9</td><td></td></tr><tr><td>chi-square</td><td> 169.276560</td><td></td></tr><tr><td>reduced chi-square</td><td> 0.00423287</td><td></td></tr><tr><td>Akaike info crit.</td><td>-218586.036</td><td></td></tr><tr><td>Bayesian info crit.</td><td>-218508.667</td><td></td></tr><tr><td>R-squared</td><td> 0.52992868</td><td></td></tr></table><h2>Variables</h2><table><tr><th> name </th><th> value </th><th> standard error </th><th> relative error </th><th> initial value </th><th> min </th><th> max </th><th> vary </th></tr><tr><td> amp_bec </td><td> 0.02336412 </td><td> 0.00390198 </td><td> (16.70%) </td><td> 0.034266049998156745 </td><td> 0.00000000 </td><td> 0.66407331 </td><td> True </td></tr><tr><td> amp_th </td><td> 0.34124925 </td><td> 0.00616591 </td><td> (1.81%) </td><td> 0.4541054784032772 </td><td> 0.00000000 </td><td> 0.66407331 </td><td> True </td></tr><tr><td> x0_bec </td><td> 105.141495 </td><td> 1.75047827 </td><td> (1.66%) </td><td> 101.44676258992803 </td><td> 0.00000000 </td><td> 150.000000 </td><td> True </td></tr><tr><td> y0_bec </td><td> 127.593699 </td><td> 5.28013452 </td><td> (4.14%) </td><td> 99.38848920863309 </td><td> 0.00000000 </td><td> 150.000000 </td><td> True </td></tr><tr><td> x0_th </td><td> 101.488658 </td><td> 0.21913615 </td><td> (0.22%) </td><td> 101.44676258992803 </td><td> 0.00000000 </td><td> 150.000000 </td><td> True </td></tr><tr><td> y0_th </td><td> 98.1618593 </td><td> 0.31096970 </td><td> (0.32%) </td><td> 99.38848920863309 </td><td> 0.00000000 </td><td> 150.000000 </td><td> True </td></tr><tr><td> sigmax_bec </td><td> 37.0890339 </td><td> 3.20093427 </td><td> (8.63%) </td><td> 21.28861216515048 </td><td> 0.00000000 </td><td> 100.000000 </td><td> True </td></tr><tr><td> sigmay_bec </td><td> 61.3773595 </td><td> 6.14919207 </td><td> (10.02%) </td><td> 44.0 </td><td> 0.00000000 </td><td> 100.000000 </td><td> True </td></tr><tr><td> sigma_th </td><td> 32.4009422 </td><td> 0.24468440 </td><td> (0.76%) </td><td> 32.282716889121275 </td><td> 0.00000000 </td><td> 50.0000000 </td><td> True </td></tr></table><h2>Correlations (unreported correlations are < 0.100)</h2><table><tr><td>amp_th</td><td>y0_bec</td><td>+0.8618</td></tr><tr><td>amp_th</td><td>sigma_th</td><td>-0.8152</td></tr><tr><td>y0_bec</td><td>sigma_th</td><td>-0.7673</td></tr><tr><td>amp_bec</td><td>amp_th</td><td>-0.6667</td></tr><tr><td>amp_bec</td><td>y0_th</td><td>-0.6569</td></tr><tr><td>x0_bec</td><td>x0_th</td><td>-0.5454</td></tr><tr><td>y0_bec</td><td>sigmay_bec</td><td>-0.5361</td></tr><tr><td>sigmay_bec</td><td>sigma_th</td><td>+0.4854</td></tr><tr><td>amp_th</td><td>sigmay_bec</td><td>-0.4785</td></tr><tr><td>y0_th</td><td>sigmax_bec</td><td>-0.4698</td></tr><tr><td>amp_bec</td><td>y0_bec</td><td>-0.4444</td></tr><tr><td>y0_th</td><td>sigmay_bec</td><td>+0.4099</td></tr><tr><td>amp_bec</td><td>sigma_th</td><td>+0.3226</td></tr><tr><td>y0_bec</td><td>x0_th</td><td>+0.2463</td></tr><tr><td>amp_th</td><td>x0_th</td><td>+0.2452</td></tr><tr><td>amp_bec</td><td>sigmax_bec</td><td>+0.2383</td></tr><tr><td>amp_bec</td><td>x0_th</td><td>-0.2263</td></tr><tr><td>sigmax_bec</td><td>sigmay_bec</td><td>-0.2073</td></tr><tr><td>amp_th</td><td>y0_th</td><td>+0.1766</td></tr><tr><td>amp_th</td><td>sigmax_bec</td><td>-0.1624</td></tr><tr><td>x0_th</td><td>sigma_th</td><td>-0.1609</td></tr><tr><td>x0_th</td><td>sigmay_bec</td><td>-0.1543</td></tr><tr><td>sigmax_bec</td><td>sigma_th</td><td>-0.1412</td></tr><tr><td>y0_th</td><td>sigma_th</td><td>+0.1321</td></tr></table>"
|
||
|
|
},
|
||
|
|
"execution_count": 66,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"result[1][0]"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2023-07-31T16:19:06.602394400Z",
|
||
|
|
"start_time": "2023-07-31T16:19:06.568485700Z"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 19,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "[<matplotlib.lines.Line2D at 0x24d4ab0e4c0>]"
|
||
|
|
},
|
||
|
|
"execution_count": 19,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
||
|
|
"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"i = 0\n",
|
||
|
|
"j = 0\n",
|
||
|
|
"bval = result[i][j].best_values\n",
|
||
|
|
"plt.plot(x, cropOD[i,j, round(center[i,j,1]), :])\n",
|
||
|
|
"plt.plot(x, fit[round(center[i,j,1]), :])\n",
|
||
|
|
"plt.plot(x, thermal(x, bval['x0_th'], bval['amp_th'], bval['sigma_th']))"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2023-08-01T14:15:06.902035Z",
|
||
|
|
"start_time": "2023-08-01T14:15:06.632129700Z"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"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
|
||
|
|
}
|