1099 lines
7.2 MiB
Plaintext
1099 lines
7.2 MiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": true,
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"ExecuteTime": {
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"end_time": "2023-08-02T12:36:40.928831300Z",
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"start_time": "2023-08-02T12:36:40.905797Z"
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}
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},
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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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},
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{
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"cell_type": "code",
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"execution_count": 40,
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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, thresh_val=0.3, sigma=0.4):\n",
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" \"\"\"Returns thresholded binary image after blurring to guess BEC size\n",
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"\n",
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" :param data: 2d image or 1D or 2D array containing 2d images\n",
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" :type data: 2d, 3d or 4d numpy array\n",
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" :param thresh_val: relative threshhold value for binarization with respect to maximum of blurred image\n",
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" :param sigma: sigma of gaussian blur filter (see scipy.ndimage.gaussian_filter\n",
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" :return: binary 2d image or 1D or 2D array containing 2d images\n",
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" :rtype: 2d, 3d or 4d numpy array\n",
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" \"\"\"\n",
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" shape = np.shape(data)\n",
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" thresh = np.zeros(shape)\n",
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"\n",
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" blurred = gaussian_filter(data, sigma=sigma)\n",
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"\n",
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" if len(shape) == 4:\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])*thresh_val, 0, 1)\n",
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"\n",
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" elif len(shape) == 3:\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])*thresh_val, 0, 1)\n",
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"\n",
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" elif len(shape) == 2:\n",
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" thresh = np.where(blurred < np.max(blurred)*thresh_val, 0, 1)\n",
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"\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: [X_center,Y_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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"\n",
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" if len(shape) == 2:\n",
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" BEC_width_guess = np.array([np.sum(thresh[round(center[1]), :]), np.sum(thresh[:, round(center[0])]) ])\n",
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"\n",
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" elif len(shape) == 3:\n",
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" BEC_width_guess = np.zeros((shape[0], 2))\n",
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" for i in range(0, shape[0]):\n",
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" BEC_width_guess[i, 0] = np.sum(thresh[i, round(center[i,j,1]), :])\n",
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" BEC_width_guess[i, 1] = np.sum(thresh[i, :, round(center[i,j,0])])\n",
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"\n",
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" elif len(shape) == 4:\n",
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" BEC_width_guess = 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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" 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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" 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 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 = 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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"\n",
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"\n",
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"# model = DensityProfileBEC2dModel()\n",
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"#\n",
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"# init_params = model.guess(data, x, y)\n",
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"#\n",
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"# res = model.fit(data, x=x, y=y, params = init_params)\n",
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"#\n",
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"# if not res makes sense:\n",
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"# params update --> A_th = 0\n",
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"# res = model.fit\n"
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],
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||
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"metadata": {
|
||
|
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"collapsed": false,
|
||
|
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"ExecuteTime": {
|
||
|
|
"end_time": "2023-08-02T14:34:56.808273100Z",
|
||
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"start_time": "2023-08-02T14:34:56.376294600Z"
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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": 198,
|
||
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"outputs": [],
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||
|
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"source": [
|
||
|
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"import lmfit\n",
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"\n",
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"\n",
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"\n",
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"class DensityProfileBEC2dModel(lmfit.Model):\n",
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"\n",
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" def __init__(self, independent_vars=['x', 'y'], prefix='', nan_policy='raise',\n",
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" **kwargs):\n",
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|
|
" kwargs.update({'prefix': prefix, 'nan_policy': nan_policy,\n",
|
||
|
|
" 'independent_vars': independent_vars})\n",
|
||
|
|
" super().__init__(density_profile_BEC_2d, **kwargs)\n",
|
||
|
|
" self._set_paramhints_prefix()\n",
|
||
|
|
"\n",
|
||
|
|
" def _set_paramhints_prefix(self):\n",
|
||
|
|
" # self.set_param_hint('BEC_sigmax', min=0)\n",
|
||
|
|
" self.set_param_hint('amp_bec', min=0)\n",
|
||
|
|
" self.set_param_hint('amp_th', min=0)\n",
|
||
|
|
" self.set_param_hint('x0_bec', min=0)\n",
|
||
|
|
" self.set_param_hint('y0_bec', min=0)\n",
|
||
|
|
" self.set_param_hint('x0_th', min=0)\n",
|
||
|
|
" self.set_param_hint('y0_th', min=0)\n",
|
||
|
|
" self.set_param_hint('sigmax_bec', min=0)\n",
|
||
|
|
" self.set_param_hint('sigmay_bec', min=0)\n",
|
||
|
|
" self.set_param_hint('sigma_th', min=0)\n",
|
||
|
|
"\n",
|
||
|
|
" def guess(self, data, x, y, negative=False, pureBECThreshold=0.5, noBECThThreshold=0.0, **kwargs):\n",
|
||
|
|
" \"\"\"Estimate initial model parameter values from data.\"\"\"\n",
|
||
|
|
" #\n",
|
||
|
|
" # global X_guess\n",
|
||
|
|
" # global bval_1d\n",
|
||
|
|
" shape = np.shape(data)\n",
|
||
|
|
" cut_width = np.max(shape)\n",
|
||
|
|
" thresh = calc_thresh(data)\n",
|
||
|
|
" center = calc_cen(thresh)\n",
|
||
|
|
"\n",
|
||
|
|
" BEC_width_guess = guess_BEC_width(thresh, center)\n",
|
||
|
|
"\n",
|
||
|
|
" X_guess = np.sum(data[round(center[1] - BEC_width_guess[1]/2) : round(center[1] + BEC_width_guess[1]/2) , :], 0) / len(data[round(center[1] - BEC_width_guess[1]/2) : round(center[1] + BEC_width_guess[1]/2),0])\n",
|
||
|
|
"\n",
|
||
|
|
" x = np.linspace(0, len(X_guess), len(X_guess))\n",
|
||
|
|
"\n",
|
||
|
|
" max_val = np.max(X_guess)\n",
|
||
|
|
"\n",
|
||
|
|
" # ToDo: check if image x and y dimension is right\n",
|
||
|
|
" # ToDo: Fit along short axis not x\n",
|
||
|
|
" fitmodel_1d = lmfit.Model(density_1d, independent_vars=['x'])\n",
|
||
|
|
" params_1d = lmfit.Parameters()\n",
|
||
|
|
" params_1d.add_many(\n",
|
||
|
|
" ('x0_bec', center[0], True,0, 200),\n",
|
||
|
|
" ('x0_th',center[0], True,0, 200),\n",
|
||
|
|
" ('amp_bec', 0.7 * max_val, True, 0, 1.3 * max_val),\n",
|
||
|
|
" ('amp_th', 0.3 * max_val, True, 0, 1.3 * max_val),\n",
|
||
|
|
" ('deltax', 3*BEC_width_guess[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[0]/1.22, True, 0, 50)\n",
|
||
|
|
" )\n",
|
||
|
|
" params_1d.add('sigma_th', 3*BEC_width_guess[0], min=0, expr=f'0.632*sigma_bec + 0.518*deltax')\n",
|
||
|
|
"\n",
|
||
|
|
" res_1d = fitmodel_1d.fit(X_guess, x=x, params=params_1d)\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
" bval_1d = res_1d.best_values\n",
|
||
|
|
"\n",
|
||
|
|
" S = np.max(gaussian_filter(data, sigma=1)) / (bval_1d['amp_bec'] + bval_1d['amp_th'])\n",
|
||
|
|
" max_val = np.max(data)\n",
|
||
|
|
"\n",
|
||
|
|
" params = self.make_params()\n",
|
||
|
|
"\n",
|
||
|
|
" if bval_1d['amp_th']/bval_1d['amp_bec'] > 3:\n",
|
||
|
|
" print('Image seems to be purely thermal (guessed from 1d fit amplitude)')\n",
|
||
|
|
"\n",
|
||
|
|
" params[f'{self.prefix}amp_bec'].set(value=0, vary=False)\n",
|
||
|
|
" params[f'{self.prefix}amp_th'].set(value= S * bval_1d['amp_th'], max= 1.3 * max_val, vary=True)\n",
|
||
|
|
" params[f'{self.prefix}x0_bec'].set(value=1, vary=False)\n",
|
||
|
|
" params[f'{self.prefix}y0_bec'].set(value=1, vary=False)\n",
|
||
|
|
" params[f'{self.prefix}x0_th'].set(value=center[0], min=center[0] -10, max=center[0] + 10, vary=True)\n",
|
||
|
|
" params[f'{self.prefix}y0_th'].set(value=center[1], min=center[1] -10, max=center[1] + 10, vary=True)\n",
|
||
|
|
" params[f'{self.prefix}sigmax_bec'].set(value=1, vary=False)\n",
|
||
|
|
" params[f'{self.prefix}sigmay_bec'].set(value=1, vary=False)\n",
|
||
|
|
" params[f'{self.prefix}sigma_th'].set(value=bval_1d['sigma_th'], max=cut_width, vary=True)\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",
|
||
|
|
"\n",
|
||
|
|
" params[f'{self.prefix}amp_bec'].set(value= S * bval_1d['amp_bec'], max= 1.3 * max_val, vary=True)\n",
|
||
|
|
" params[f'{self.prefix}amp_th'].set(value=0, vary=False)\n",
|
||
|
|
" params[f'{self.prefix}x0_bec'].set(value=center[0], min=center[0] -10, max=center[0] + 10, vary=True)\n",
|
||
|
|
" params[f'{self.prefix}y0_bec'].set(value=center[1], min=center[1] -10, max=center[1] + 10, vary=True)\n",
|
||
|
|
" params[f'{self.prefix}x0_th'].set(value=1, vary=False)\n",
|
||
|
|
" params[f'{self.prefix}y0_th'].set(value=1, vary=False)\n",
|
||
|
|
" params[f'{self.prefix}sigmax_bec'].set(value=bval_1d['sigma_bec'], max= 2*BEC_width_guess[0], vary=True)\n",
|
||
|
|
" params[f'{self.prefix}sigmay_bec'].set(value=BEC_width_guess[1]/1.22, max= 2*BEC_width_guess[1], vary=True)\n",
|
||
|
|
" params[f'{self.prefix}sigma_th'].set(value=1, vary=False)\n",
|
||
|
|
"\n",
|
||
|
|
" else:\n",
|
||
|
|
" params[f'{self.prefix}amp_bec'].set(value= S * bval_1d['amp_bec'], max= 1.3 * max_val, vary=True)\n",
|
||
|
|
" params[f'{self.prefix}amp_th'].set(value= S * bval_1d['amp_th'], max= 1.3 * max_val, vary=True)\n",
|
||
|
|
" params[f'{self.prefix}x0_bec'].set(value=center[0], min=center[0] -10, max=center[0] + 10, vary=True)\n",
|
||
|
|
" params[f'{self.prefix}y0_bec'].set(value=center[1], min=center[1] -10, max=center[1] + 10, vary=True)\n",
|
||
|
|
" params[f'{self.prefix}x0_th'].set(value=center[0], min=center[0] -10, max=center[0] + 10, vary=True)\n",
|
||
|
|
" params[f'{self.prefix}y0_th'].set(value=center[1], min=center[1] -10, max=center[1] + 10, vary=True)\n",
|
||
|
|
" params[f'{self.prefix}sigmax_bec'].set(value=bval_1d['sigma_bec'], max= 2*BEC_width_guess[0], vary=True)\n",
|
||
|
|
" params[f'{self.prefix}sigmay_bec'].set(value=BEC_width_guess[1]/1.22, max= 2*BEC_width_guess[1], vary=True)\n",
|
||
|
|
" params[f'{self.prefix}sigma_th'].set(value=bval_1d['sigma_th'], max=cut_width, vary=True)\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
" return lmfit.models.update_param_vals(params, self.prefix, **kwargs)\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
" def fit(self, data, **kwargs):\n",
|
||
|
|
"\n",
|
||
|
|
" data1d = data.flatten()\n",
|
||
|
|
"\n",
|
||
|
|
" res = super().fit(data1d, **kwargs)\n",
|
||
|
|
"\n",
|
||
|
|
" if res.params['amp_bec'].vary and res.params['amp_th'].vary:\n",
|
||
|
|
" bval = res.best_values\n",
|
||
|
|
" sigma_cut = max(bval['sigmay_bec'], bval['sigmax_bec'])\n",
|
||
|
|
" 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",
|
||
|
|
" 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",
|
||
|
|
" if check_value < 45:\n",
|
||
|
|
" print('No thermal part detected, performing fit without thermal function')\n",
|
||
|
|
" params = res.params\n",
|
||
|
|
" params[f'{self.prefix}amp_th'].set(value=0, vary=False)\n",
|
||
|
|
" params[f'{self.prefix}x0_th'].set(value=1, vary=False)\n",
|
||
|
|
" params[f'{self.prefix}y0_th'].set(value=1, vary=False)\n",
|
||
|
|
" params[f'{self.prefix}sigma_th'].set(value=1, vary=False)\n",
|
||
|
|
"\n",
|
||
|
|
" res = super().fit(data1d, x=kwargs['x'], y=kwargs['y'], params=params)\n",
|
||
|
|
"\n",
|
||
|
|
" return res\n",
|
||
|
|
"\n",
|
||
|
|
" return res\n"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2023-08-02T17:00:15.869774100Z",
|
||
|
|
"start_time": "2023-08-02T17:00:15.823391200Z"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 95,
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# load Brittas data\n",
|
||
|
|
"\n",
|
||
|
|
"data = np.zeros((2,11, 1200, 1920))\n",
|
||
|
|
"data[0] = np.load('Data_Britta/OD_ft_flatfield.npy')\n",
|
||
|
|
"data[1] = np.load('Data_Britta/OD_ft_manual.npy')\n",
|
||
|
|
"\n",
|
||
|
|
"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",
|
||
|
|
"\n",
|
||
|
|
"for i in range(0,shape[0]):\n",
|
||
|
|
" for j in range(0, shape[1]):\n",
|
||
|
|
" cropOD[i,j] = data[i,j, round(center[i,j,1]-cut_width/2):round(center[i,j,1]+cut_width/2), round(center[i,j,0]-cut_width/2):round(center[i,j,0]+cut_width/2)]\n",
|
||
|
|
"\n",
|
||
|
|
"shape = np.shape(cropOD)"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2023-08-02T14:52:34.335544Z",
|
||
|
|
"start_time": "2023-08-02T14:52:30.850396700Z"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 199,
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"Fitmodel = DensityProfileBEC2dModel()"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2023-08-02T17:00:19.756790200Z",
|
||
|
|
"start_time": "2023-08-02T17:00:19.728358700Z"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 200,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"image: 0, 0\n",
|
||
|
|
"Image seems to be purely thermal (guessed from 1d fit amplitude)\n",
|
||
|
|
" time = 0.336 s\n",
|
||
|
|
"image: 0, 1\n",
|
||
|
|
" time = 0.430 s\n",
|
||
|
|
"image: 0, 2\n",
|
||
|
|
" time = 0.515 s\n",
|
||
|
|
"image: 0, 3\n",
|
||
|
|
" time = 0.484 s\n",
|
||
|
|
"image: 0, 4\n",
|
||
|
|
" time = 0.370 s\n",
|
||
|
|
"image: 0, 5\n",
|
||
|
|
" time = 0.572 s\n",
|
||
|
|
"image: 0, 6\n",
|
||
|
|
" time = 0.321 s\n",
|
||
|
|
"image: 0, 7\n",
|
||
|
|
"No thermal part detected, performing fit without thermal function\n",
|
||
|
|
" time = 0.467 s\n",
|
||
|
|
"image: 0, 8\n",
|
||
|
|
"No thermal part detected, performing fit without thermal function\n",
|
||
|
|
" time = 0.788 s\n",
|
||
|
|
"image: 0, 9\n",
|
||
|
|
"No thermal part detected, performing fit without thermal function\n",
|
||
|
|
" time = 0.763 s\n",
|
||
|
|
"image: 0, 10\n",
|
||
|
|
"Image seems to be pure BEC (guessed from 1d fit amplitude)\n",
|
||
|
|
" time = 0.238 s\n",
|
||
|
|
"image: 1, 0\n",
|
||
|
|
"Image seems to be purely thermal (guessed from 1d fit amplitude)\n",
|
||
|
|
" time = 0.273 s\n",
|
||
|
|
"image: 1, 1\n",
|
||
|
|
" time = 1.558 s\n",
|
||
|
|
"image: 1, 2\n",
|
||
|
|
" time = 0.419 s\n",
|
||
|
|
"image: 1, 3\n",
|
||
|
|
" time = 0.350 s\n",
|
||
|
|
"image: 1, 4\n",
|
||
|
|
" time = 0.335 s\n",
|
||
|
|
"image: 1, 5\n",
|
||
|
|
" time = 0.379 s\n",
|
||
|
|
"image: 1, 6\n",
|
||
|
|
" time = 0.372 s\n",
|
||
|
|
"image: 1, 7\n",
|
||
|
|
"No thermal part detected, performing fit without thermal function\n",
|
||
|
|
" time = 0.543 s\n",
|
||
|
|
"image: 1, 8\n",
|
||
|
|
"No thermal part detected, performing fit without thermal function\n",
|
||
|
|
" time = 0.556 s\n",
|
||
|
|
"image: 1, 9\n",
|
||
|
|
"Image seems to be pure BEC (guessed from 1d fit amplitude)\n",
|
||
|
|
" time = 0.188 s\n",
|
||
|
|
"image: 1, 10\n",
|
||
|
|
"Image seems to be pure BEC (guessed from 1d fit amplitude)\n",
|
||
|
|
" time = 0.189 s\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"x = np.linspace(0,shape[3],cut_width)\n",
|
||
|
|
"y = np.linspace(0,shape[2], cut_width)\n",
|
||
|
|
"\n",
|
||
|
|
"X,Y = np.meshgrid(x, y)\n",
|
||
|
|
"X_1d = X.flatten()\n",
|
||
|
|
"Y_1d = Y.flatten()\n",
|
||
|
|
"result = []\n",
|
||
|
|
"for i in range(0, shape[0]):\n",
|
||
|
|
" t_res_arr = []\n",
|
||
|
|
" for j in range(0, shape[1]):\n",
|
||
|
|
" print(f'image: {i}, {j}')\n",
|
||
|
|
" start = time.time()\n",
|
||
|
|
" init = Fitmodel.guess(cropOD[i][j], X_1d, Y_1d)\n",
|
||
|
|
" # init.pretty_print()\n",
|
||
|
|
" res = Fitmodel.fit(cropOD[i][j], x=X_1d, y=Y_1d, params=init)\n",
|
||
|
|
" stop = time.time()\n",
|
||
|
|
" print(f' time = {stop-start:.3f} s')\n",
|
||
|
|
" t_res_arr.append(res)\n",
|
||
|
|
" result.append((t_res_arr))\n",
|
||
|
|
"\n"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2023-08-02T17:00:30.391591100Z",
|
||
|
|
"start_time": "2023-08-02T17:00:19.933806300Z"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 201,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 1400x8800 with 110 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]),dpi = 100)\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",
|
||
|
|
" cen_x = round(max(bval['x0_th'], bval['x0_bec']))\n",
|
||
|
|
" cen_y = round(max(bval['y0_th'], bval['y0_bec']))\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, cen_y, :])\n",
|
||
|
|
" ax.plot(x, fit[cen_y, :])\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, :, cen_x])\n",
|
||
|
|
" ax.plot(y, fit[:, cen_x])\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 {i}, {j}, 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()\n"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2023-08-02T17:00:48.249460Z",
|
||
|
|
"start_time": "2023-08-02T17:00:32.908096300Z"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 157,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"image: 0, 0\n",
|
||
|
|
"Image seems to be purely thermal (guessed from 1d fit amplitude)\n",
|
||
|
|
"{'x0_bec': 141.9388495930993, 'x0_th': 124.26348801310331, 'amp_bec': 0.007803691687802039, 'amp_th': 0.0858750681615077, 'sigma_bec': 46.86928315350596, 'sigma_th': 39.30783070539829}\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
||
|
|
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|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"image: 0, 1\n",
|
||
|
|
"{'x0_bec': 125.38163142125995, 'x0_th': 124.49885958144816, 'amp_bec': 0.12141877271402701, 'amp_th': 0.15821401051357287, 'sigma_bec': 15.843973367955948, 'sigma_th': 28.209064417127983}\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
||
|
|
"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"image: 0, 2\n",
|
||
|
|
"{'x0_bec': 125.062939389457, 'x0_th': 124.90856747839467, 'amp_bec': 0.19969242281440205, 'amp_th': 0.16311134280165113, 'sigma_bec': 18.298255963211897, 'sigma_th': 27.040711199511588}\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
||
|
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"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"image: 0, 3\n",
|
||
|
|
"{'x0_bec': 124.93716396139996, 'x0_th': 125.00497547044476, 'amp_bec': 0.2683546088669588, 'amp_th': 0.20692658648004425, 'sigma_bec': 19.59891280345783, 'sigma_th': 21.4931782316055}\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
||
|
|
"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"image: 0, 4\n",
|
||
|
|
"{'x0_bec': 125.11874376585588, 'x0_th': 125.14617962680515, 'amp_bec': 0.3772186215371362, 'amp_th': 0.20019282028651214, 'sigma_bec': 21.023211358748846, 'sigma_th': 20.353545926805133}\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
||
|
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|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"image: 0, 5\n",
|
||
|
|
"{'x0_bec': 125.02653078661412, 'x0_th': 125.17486768691222, 'amp_bec': 0.36938295522800974, 'amp_th': 0.2436025582693044, 'sigma_bec': 21.70321615610075, 'sigma_th': 17.165663035342554}\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
||
|
|
"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"image: 0, 6\n",
|
||
|
|
"{'x0_bec': 124.89822161181937, 'x0_th': 125.37729750375459, 'amp_bec': 0.46158795707906725, 'amp_th': 0.21923439478678494, 'sigma_bec': 22.939154769975083, 'sigma_th': 16.138144354700508}\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
||
|
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"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"image: 0, 7\n",
|
||
|
|
"{'x0_bec': 125.03279653115828, 'x0_th': 124.84110050371334, 'amp_bec': 0.5627533395292672, 'amp_th': 0.16180945390692278, 'sigma_bec': 23.249266183667334, 'sigma_th': 14.69353622979438}\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
||
|
|
"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"image: 0, 8\n",
|
||
|
|
"{'x0_bec': 125.00429964824527, 'x0_th': 125.04731652051468, 'amp_bec': 0.6062908065559611, 'amp_th': 0.15812192425042074, 'sigma_bec': 23.410202515558918, 'sigma_th': 14.795247990074596}\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
||
|
|
"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"image: 0, 9\n",
|
||
|
|
"{'x0_bec': 125.03656964618455, 'x0_th': 124.76187778970447, 'amp_bec': 0.5769223246245394, 'amp_th': 0.2327501634748024, 'sigma_bec': 23.160544703046963, 'sigma_th': 14.637464253443392}\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
||
|
|
"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"image: 0, 10\n",
|
||
|
|
"Image seems to be pure BEC (guessed from 1d fit amplitude)\n",
|
||
|
|
"{'x0_bec': 125.17664256918006, 'x0_th': 0.08189463384382645, 'amp_bec': 0.6670109105975315, 'amp_th': 0.0012688907842482594, 'sigma_bec': 25.27841271967096, 'sigma_th': 66.64693647236199}\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
||
|
|
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|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"image: 1, 0\n",
|
||
|
|
"{'x0_bec': 115.13147723692292, 'x0_th': 125.05103983194897, 'amp_bec': 0.02112511198925298, 'amp_th': 0.07395402501713118, 'sigma_bec': 15.723788570332712, 'sigma_th': 48.1198044449804}\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAi8AAAGZCAYAAABBr9GxAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAADoFUlEQVR4nOx9d5gkVbn+W1UdJs/ubM6RXTLLApIRUZSLAZWLguIVlOA1X6+/68UM4gWveo0oIIKBoAioYAJJsuASlswum3Oc3Z3dST0dKvz+qD6nvnPqVHX3TPfO9Ox5n2efnenprtRV57zn/d7v+wzP8zxoaGhoaGhoaNQJzOE+AA0NDQ0NDQ2NSqDJi4aGhoaGhkZdQZMXDQ0NDQ0NjbqCJi8aGhoaGhoadQVNXjQ0NDQ0NDTqCpq8aGhoaGhoaNQVNHnR0NDQ0NDQqCto8qKhoaGhoaFRV0gM9wHUAq7rYvv27WhtbYVhGMN9OBoaGhoaGhplwPM89Pb2YurUqTDNaH1lVJKX7du3Y8aMGcN9GBoaGhoaGhqDwJYtWzB9+vTIv49K8tLa2grAP/m2trZhPhoNDQ0NDQ2NctDT04MZM2bweTwKo5K8sFBRW1ubJi8aGhoaGhp1hlKWD23Y1dDQ0NDQ0KgraPKioaGhoaGhUVfQ5EVDQ0NDQ0OjrqDJi4aGhoaGhkZdQZMXDQ0NDQ0NjbqCJi8aGhoaGhoadQVNXjQ0NDQ0NDTqCpq8aGhoaGhoaNQVNHnR0NDQ0NDQqCto8qKhoaGhoaFRV9DkRUNDQ0NDQ6OuoMmLhoaGhoaGRl1BkxcNDY26xmvbuvHzJzfAcb3hPhQNDY0DhFHZVVpDQ+PgwTf+tALPbOjCEVPbcNLcccN9OBoaGgcAWnnR0NCoa/RmbQBAz0BhmI9EQ0PjQEGTFw0NjbqG6/nhIluHjTQ0Dhpo8qKhoVHXYOSl4LjDfCQaGhoHCjUhL2vWrMEpp5yCBQsW4IQTTsDy5ctD79m4cSPOPPNMtLe3Y9GiRcLfHn/8cTQ2NmLRokX838DAQC0OVUNDo87BjLq2o5UXDY2DBTUhL1deeSWuuOIKrF69Gl/4whdwySWXhN7T1taGa6+9FnfeeadyGwsXLsRLL73E/zU2NtbiUDU0NOocLFqks400NA4eVJ28dHZ2YtmyZbj44osBAOeffz62bNmCtWvXCu/r6OjAaaedhubm5iHvM5fLoaenR/inoaFxcICRloKrw0YaGgcLqk5etmzZgilTpiCR8LOwDcPAzJkzsXnz5oq2s27dOixevBgnnHACfvKTn8S+97rrrkN7ezv/N2PGjEEfv4aGRn1Bh400NA4+jMg6L4sXL8bWrVvR3t6OrVu34txzz8X48ePxvve9T/n+q666Cp/73Of47z09PZrAaGgcJPC0YVdD46BD1ZWXGTNmYMeOHbBtv/aC53nYvHkzZs6cWfY22tra0N7eDgCYPn06LrroIixZsiTy/el0Gm1tbcI/DQ2NgwOOTpXW0DjoUHXyMnHiRCxevBi33347AODee+/F9OnTMX/+/LK3sWPHDrjF+HVvby/+9Kc/4dhjj632oWpoaIwCMMHFroHy4rgevvW3lfjH6t1V37aGhsbgUZNso5tuugk33XQTFixYgOuvvx633XYbAOCyyy7D/fffDwDIZDKYPn06LrjgAqxYsQLTp0/HVVddBcAnPEcddRSOOeYYnHTSSTj77LNx6aWX1uJQNTQ06hxBnZfqKy8vbt6Hnz6+Dv/7t5VV37aGhsbgURPPy8KFC7F06dLQ67fccgv/uampCVu3blV+/pOf/CQ++clP1uLQNDQ0Rhm4YbcG2UaZvAMAyBacqm9bQ0Nj8NAVdjU0NOoatWwPwAiRttNoaIwsaPKioaFR13BrmCrNQlGMIGloaIwMaPKioaFR1+DZRjUy7AKavGhojDRo8qKhoVHXYFaXQg1iO6x2jC7eq6ExsqDJi4aGRl2jlsqLrcNGGhojEpq8aGho1DW4YbcGnhcdNtLQGJnQ5EVDQ6Nu4XkeGK+oSdioGC/SnQc0NEYWNHnR0NCoWziEsNQybORp5UVDY0RBkxcNDY26hUNIRW3qvOiwkYbGSIQmLxoaGnULmgVUG+WFhY00edHQGEnQ5EVDQ6Nu4R4g5UULLxoaIwuavGhoaNQtaNioUAPlhdd50exFQ2NEQZMXDQ2NuoUrGHZrlyrtaPKioTGioMmLhoZG3YJ6UWpTYZcZdqu+aQ0NjSFAkxcNDY26hZBtVEPDrk6V1tAYWdDkRUNDo25BOUUtMoKYYVdnG2lojCxo8qKhoRGLlTt78LU/vobdvbnhPpQQhLBRLZQXlxl2q75pDQ2NIUCTFw0NjVj8fMkG/HLpJjzw8vbhPpQQhAq7tVBeiAlYh440NEYONHnR0NCIRV/OBgD0Zu1hPpIwhDovNcg2ooSIEqW/vbYD/33vK8jbuumRhsZwQJMXDQ2NWOSKE3TWdob5SMKoediIbJMKOz96dC1+89wWLNvUVfV9amholIYmLxoaGrHIFUnLQH7kkRdKKGoRNqLp11TlYYROKy8aGsMDTV40NDRikS34E3RuBCovbo0r7DqOmry4OgtJQ2NYocmLhoZGLEay8uLUuMKu7arDRkzlqYXao6GhURqavGhoaMQiV1ReBgojm7zUQgUpOOrtO1p50dAYVmjyoqGhEQtu2C2MPH+HEDZyaxA2ctWp0my/WnnR0BgeaPKioaERCx42GoHKC+UOnld9JaQQkW0UVN4deYROQ+NggCYvGhoasWDKS24EkheZrFTbtBtV54UZdmvhs9HQ0CgNTV40NDRikS2MZOVFJA/VDuPQOi80bMQaQmrPi4bG8ECTFw0NjUh4njeiPS8yeah2Z2lbqPNC9utoz4uGxnBCkxcNDY1IFByPd24ekcpLKGxUbeUlrLbQn7XyoqExPNDkRUNDIxK0MF12BJIXJxQ2qq7yQjOYXIX/RSsvGhrDA01eNDQ0IpEj5e9HInmRuUO1DbRiqnT4dZ1tpKExPNDkRUNDIxKUvBQcr+qekqFCDhtV37AbHzbSyouGxvBAkxcNDY1IyGpLdoQ1Iqy1YVes8+Lvy3UDH5BMnjQ0NA4MNHnR0NCIRE7KMBppoSPZ81Jtw66qwi7dp1ZeNDSGB5q8aGhoRELuJD3SmjOGw0a1U17Yj7Xup6ShoVEamrxoaGhEIieFiWQyM1R4noeP3/E8vvKH1wb1eZk7VD1VWqjzEk6P1sqLhsbwQJMXDQ2NSMjkZSBfXWVjd28Of3l1J3799KZB+VVCqdI1LVIXDhtp5UVDY3igyYuGhkYk5H5G1S5UR8lRf67ybctho2qTCUqGWESK7lP3NtLQGB5o8qKhoREJObuo2oZdSl56c4WKPx9qzFiCvOzoHsCK7T1lbdt1PSEs5SrSo3WdFw2N4YEmL3WIrfsyOOf7T+B3y7YM96Fo1BF+9sR6/OHFbRV9ptbKS56Ql76cXfHnKw0bfejnz+JdP34SXf35ktsuSMSEpkrz/emwkYbGsECTlzrE0+u7sHJnLx54ZcdwH4pGnaCzJ4tv/uX1io2xsuel2soLzebpy1ZOXrwKU6U3d2Vgux66+nMlty2rOtrzoqExcqDJSx1CtQLU0IhDppjiXKlyUmvykifkpXcQ5EUWWuJSpQuOy5Wech4dmQixz1Cfi1ZeNDSGB5q81CE83dFWo0KwSbbSyVZOjc4WquvxyAuel2qEjaLPL0Nq1Lhe6esgh6DYYsHVyouGxrBDk5c6BC+WVcYArKEBiIpEJYqdXGG3pp6XQSgv8rkUYjwvmXyw/XJ8tjIxcXSdFw2NEQNNXuoQOmykUSkGG+rIhpSX2oWN+qqQbRR3bpUqL3LmEvuIo7ONNDSGHZq81CFUPVY0NOIgpveOIuVFDhvFkRdSR6acRycUNlL1NtJ1XjQ0hgWavNQh2PislReNckEVgnJI7+OrOrGlK8MNu4bhvy6TmUogZwYBQ/e8hMhLTNion4aNyvG8RBTAo4RFe140NIYHmrzUIVQrQA2NONDMGaeEWvD6jh5ccttz+NzdL3HDbltDEsD
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"image: 1, 1\n",
|
||
|
|
"{'x0_bec': 125.98266418134926, 'x0_th': 125.10607266718432, 'amp_bec': 0.15076792988288587, 'amp_th': 0.14032268056966216, 'sigma_bec': 15.58363774828232, 'sigma_th': 32.91110196057714}\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
||
|
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"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"image: 1, 2\n",
|
||
|
|
"{'x0_bec': 126.1172887092211, 'x0_th': 121.91922044593235, 'amp_bec': 0.22750238845860385, 'amp_th': 0.17565334199821572, 'sigma_bec': 18.273136515366595, 'sigma_th': 23.539294758943058}\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
||
|
|
"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"image: 1, 3\n",
|
||
|
|
"{'x0_bec': 124.54686569481797, 'x0_th': 125.30784821887502, 'amp_bec': 0.3198846898793186, 'amp_th': 0.22188704166381057, 'sigma_bec': 20.01394955278246, 'sigma_th': 22.416678235965882}\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
||
|
|
"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"image: 1, 4\n",
|
||
|
|
"{'x0_bec': 124.94088540695563, 'x0_th': 126.05838860646763, 'amp_bec': 0.4854770484380169, 'amp_th': 0.10120909270389601, 'sigma_bec': 21.984988170628352, 'sigma_th': 29.08919919364982}\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
||
|
|
"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"image: 1, 5\n",
|
||
|
|
"{'x0_bec': 124.13202436253795, 'x0_th': 127.1284100236026, 'amp_bec': 0.3692513342418664, 'amp_th': 0.25168044523752053, 'sigma_bec': 22.086974645622263, 'sigma_th': 17.83800603647807}\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAigAAAGZCAYAAACjc8rIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAACS9ElEQVR4nO2dd5wkdZn/39V5Uk+e2dkJO5tzhCUsIEFATKACp5yAoAjmOzl/p+iZ7lTw9PQMJ6yiqKCAEldUJOeFDWzOaXZmd2Z2cu7c9fujuqqre3py93TP7PN+vea1O93V1d+u6frWp57n8zxfRVVVFUEQBEEQhAzCku4BCIIgCIIgxCMCRRAEQRCEjEMEiiAIgiAIGYcIFEEQBEEQMg4RKIIgCIIgZBwiUARBEARByDhEoAiCIAiCkHGIQBEEQRAEIeOwpXsA4yUcDtPY2EheXh6KoqR7OIIgCIIgjAJVVent7WXmzJlYLEPHSaasQGlsbKS6ujrdwxAEQRAEYRw0NDRQVVU15PNTVqDk5eUB2gd0u91pHo0gCIIgCKOhp6eH6upq4zo+FFNWoOhpHbfbLQJFEARBEKYYI9kzxCQrCIIgCELGIQJFEARBEISMQwSKIAiCIAgZhwgUQRAEQRAyDhEogiAIgiBkHCJQBEEQBEHIOESgCIIgCIKQcYhAEQRBEAQh4xCBIgiCIAhCxiECRRAEQRCEjEMEiiAIgiAIGYcIFEEQBEEQMg4RKIIgZAyqqvL7jXVsPd6R7qEIgpBmRKAIgpAx7Gns4RtP7uFrj+9O91AEQUgzIlAEQcgYWvt8ALT3+9M8EkEQ0o0IFEEQMoY+bxCAAV8wzSMRBCHdiEARBCFj6NUFSiCEqqppHo0gCOlEBIogCBlDny8AgKqCNxBO82gEQUgnIlAEQcgY9BQPQL9f0jyCcDojAkUQhIyhxyRQPP5QGkciCEK6EYEiCELG0OeTCIogCBoiUARByBhiUjw+iaAIwumMCBRBECaFrgE/7//Za/zqlaNDbmOOoEiKRxBOb0SgCIIwKWyr72LXyW4e3FQ/5Da93oDxf0nxCMLpjQgUQRAmBV9Qi4i09vqG3KbXFEEZEIEiCKc1IlAEQZgUfEGtr0mvLzhk+sbsQRmQFI8gnNaIQBEEYVIIhKKdYYeKovSaBYqYZAXhtEYEiiAIk4I/GO0M29LrHfR8MBTGE4iKEomgCMLpjQgUQRAmBX8wKjhaEkRQ4suKxYMiCKc3IlAEQZgU/KFoBCVRiqfHVMEDUsUjCKc7IlAEQZgURkrxmHuggJbi6fYEeGpnI96ApHsE4XRDBIogCJOCWaAkiqAMEii+EP/34mE+98dtPDRM7xRBEKYntnQPQBCE0wNfyBxBSSBQvGaBopLff4SToUoADpzqS/XwBEHIMESgCIIwKcSkeHqG9qCU0sX37L/msuat9Cs5nGVbxxsdn520cQqCkBmIQBEEYVIImE2yfYlTPJW0ssH5dYqVHgBy1H4+ZnuW2uY+4OLJGqogCBmAeFAEQZgUzBGU9j4fobAa83yfJ8B/2n9LsdLDwXAlH3f9mC/w7wRUKxcGN6LueWKSRywIQjoRgSIIwqRgFihhFdr7Y6MoMxqf5Z3WbQSx8enAv7LNX8UG7yruCb0fAPWvXwJP12QOWRCENJISgXLo0CHWrVvHggULWLt2LXv27Em43a5du7joootYvHgxixcv5rHHHkvFcARByADMfVAgzocS9HHxsf8B4Nmi6ziiVtI5oHlSfh78AEfCFVgGWmHHg5M2XkEQ0ktKBMptt93GrbfeysGDB/nyl7/MTTfdNGibgYEBrrrqKr7zne+wb98+du/ezQUXXJCK4QiCkAGYIygQV2q87y+4A600q4Vsr/14zHY+HPwudLn2y/Y/pnqYgiBkCEkXKC0tLWzZsoXrr78egKuvvpqGhgYOHz4cs90f//hHzjnnHM4//3wArFYrpaWlQ+7X5/PR09MT8yMIwtTBN5xA2fxrAB4MXkJxQf6g1/4ldC4hxQbNO+FU4oisIAjTi6QLlIaGBioqKrDZtAIhRVGoqamhvj620dLevXtxOp28733vY9WqVdx44420trYOud8777yT/Px846e6ujrZQxcEIYXoEZQchxUwdZM9tRfq3yCEhYdCF1Oa5xz02k7cHMg7F4Anfvc/MRVBgiBMT9Jmkg0Ggzz33HOsX7+ebdu2UVlZyac//ekht7/jjjvo7u42fhoaGiZxtIIgTBTdg1JVmA2YmrVt+Q0AbznO4RRFuF12suzWQa+/b0ATKOv6n6e+TSKogjDdSbpAqa6upqmpiWBQ6wqpqir19fXU1NTEbFdTU8PFF19MZWUliqJw/fXX8+abbw65X6fTidvtjvkRBGHqoEdQZha4AGjv90MoCLsfAeBxy7sAyHPZyXFGBcqckhwAnuhbRreaTZnShadu62QOXRCENJB0gVJWVsaaNWt44IEHAHj00Uepqqpi3rx5Mdv90z/9E5s3bza8JH/7299YuXJlsocjCEKGoKdlZuRrAqWjzw/1b4CnE5+jkJf9iwDIddrIckQFyooqzZMSwMbr4WUAWI+/PJlDFwQhDaQkxbN+/XrWr1/PggULuOuuu7jvvvsAuOWWW9iwYQOgRVC++tWvsm7dOlasWMELL7zAPffck4rhCIKQAegRlHJ3RKD0+2H/XwF4cmAFLQPaisV5Lhs5jmiT62WVUdOsLlDym16flDELgpA+UtLqfuHChWzcuHHQ4/fee2/M7zfccAM33HBDKoYgCEKGoQuUGYZA8RkC5R/hM43t4iMoMwuyKMtz0tLr47WIQCnr2gH+fnDkTNbwBUGYZKSTrCAIk4I/LsUz03MQuhsYUJ28Fl7OquoCLlpYSkG2PSaCUpTjYHVNARYF5i9czgm1BJsagPrBN0GCIEwfZLFAQRAmBb0Pii5QLlE2A/ByeAWq1cXjn1mHoigAMRGUklwHP7h2JS09XnY0dPPa4WV8xPYSHH0J5l06qZ9BEITJQyIogiBMCnqKJ9dpI9dp40LLTgCeC51BSa7DECcQ7ZUCUJTjxO2yM68sj9I8p+FD4chLkzZ2QRAmHxEogiCkHFVVjRSPw2ahOtvPcuUoAK+Hlw5qzpbt1IK7FgUKsuzG46V5Tt4ML9F+ObUbfL2TMHpBENKBCBRBEFJOMKyiqtr/nVYr59sPYlVUmm1VNFNMSW6cQIk0aivMdmCxRCMrZXlOWinghFoCqNC4bbI+giAIk4wIFEEQUo65Nb3DZmGtuguAN1kKMFigRCIoxbmOmMcLsx3YLArbw3O1B05sSdWQBUFIMyJQBEFIOeaVjO1WhWX+HQA8M7AQgJK8WCGSHfGgFOXEPm6xKJTkOtke1ho/hk5sRdVDM4IgTCtEoAiCkHJ0gWJRwOZpZ6ZP85/ofpLSuAiK3itlVtHgPieleU4jgtK2/3X++x8HAGjoGODPWxoIykKCgjAtkDJjQRBSjl5i7LBZoO5VAPaFa+hAW1OrJM4k+57lFVgsCuvmFg/aV2mekzfU2QRVC+VKJ7v27oUrFvGNJ3fz4oFWinMdXLKoPMWfSBCEVCMRFEEQUo5RwWO1GA3W3gwvNp6P96A4bBauXDlz0OOgGWW9ODmgVgNQ3LULVVXZ36xV9LT1+VPyGQRBmFxEoAiCkHL8RgTFCg2bANgaXmA8n0iIDIVekqz7UBaHD3Ki00NTtxcAbyCUlDELgpBeRKAIgpBydIHitga0/iXA2+H5xvPxfVCGo0wXKKrmQ1mpHOXVQ23G8yJQBGF6IAJFEISUo6d4llmOQjhIMLucRjR/icNqwe0avR1OFzN7w7UALLYc5+UDp4znvQExyQrCdEAEiiAIKUePoKxQDwKgVq0FtAZs8W3uR6I0T6vwOazOJISFAqWfI0cOGs97JIIiCNMCESiCIKQcPYKyNLQfANuss7FFOsSOJb0DsKTCzYLyXN61spau7NkA1ASOGs9LikcQpgciUARBSBktvV72N/dEIigqi0JazxKlai2FkSZsYzHIgrbS8TNfvJCfXrcab7FWCbRYqTeeF4EiCNMDESiCIKSMm+/
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"image: 1, 6\n",
|
||
|
|
"{'x0_bec': 124.03965908195778, 'x0_th': 127.34721368748414, 'amp_bec': 0.4855471995353272, 'amp_th': 0.2562779233501634, 'sigma_bec': 23.62210984602798, 'sigma_th': 14.929173432250373}\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
||
|
|
"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"image: 1, 7\n",
|
||
|
|
"{'x0_bec': 124.3102516408376, 'x0_th': 129.41550511341939, 'amp_bec': 0.6796952456245661, 'amp_th': 0.1804669142499331, 'sigma_bec': 23.20386857434493, 'sigma_th': 14.664844940958337}\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
||
|
|
"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"image: 1, 8\n",
|
||
|
|
"{'x0_bec': 125.61877194001123, 'x0_th': 123.4006658543455, 'amp_bec': 0.6593565560433862, 'amp_th': 0.2342058441345835, 'sigma_bec': 23.13540932172452, 'sigma_th': 14.621578817427519}\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
||
|
|
"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"image: 1, 9\n",
|
||
|
|
"Image seems to be pure BEC (guessed from 1d fit amplitude)\n",
|
||
|
|
"{'x0_bec': 124.67090198215693, 'x0_th': 152.27820518157057, 'amp_bec': 0.8178652575015817, 'amp_th': 0.026889421982387324, 'sigma_bec': 23.48422463948881, 'sigma_th': 22.56373662024177}\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
||
|
|
"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"image: 1, 10\n",
|
||
|
|
"Image seems to be pure BEC (guessed from 1d fit amplitude)\n",
|
||
|
|
"{'x0_bec': 124.90950013701135, 'x0_th': 52.01141356926877, 'amp_bec': 0.751254779373083, 'amp_th': 0.009191897095241318, 'sigma_bec': 26.370801991139352, 'sigma_th': 16.666346903716047}\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "<Figure size 640x480 with 1 Axes>",
|
||
|
|
"image/png": "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
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"for i in range(0, shape[0]):\n",
|
||
|
|
" for j in range(0, shape[1]):\n",
|
||
|
|
" print(f'image: {i}, {j}')\n",
|
||
|
|
" init = Fitmodel.guess(cropOD[i][j], X_1d, Y_1d)\n",
|
||
|
|
" # init.pretty_print()\n",
|
||
|
|
" print(bval_1d)\n",
|
||
|
|
" plt.plot(x, X_guess)\n",
|
||
|
|
" plt.plot(x, density_1d(x, **bval_1d))\n",
|
||
|
|
" plt.plot(x, thermal(x, bval_1d['x0_th'], bval_1d['amp_th'], bval_1d['sigma_th']))\n",
|
||
|
|
" plt.show()\n"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2023-08-02T16:00:35.147608400Z",
|
||
|
|
"start_time": "2023-08-02T16:00:31.348813400Z"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 190,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": "False"
|
||
|
|
},
|
||
|
|
"execution_count": 190,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"result[0][0].params['amp_bec'].vary"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"collapsed": false,
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2023-08-02T16:56:01.170829Z",
|
||
|
|
"start_time": "2023-08-02T16:56:01.045641800Z"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"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
|
||
|
|
}
|