analyseScript/Joschka/Fitting_Data_Britta.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Import supporting package"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "import lmfit\n",
    "import xarray as xr\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import copy\n",
    "\n",
    "import glob\n",
    "\n",
    "import xrft\n",
    "import finufft\n",
    "\n",
    "from uncertainties import ufloat\n",
    "from uncertainties import unumpy as unp\n",
    "from uncertainties import umath\n",
    "\n",
    "from datetime import datetime\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#test\n",
    "plt.rcParams['font.size'] = 18\n",
    "\n",
    "from scipy.ndimage import gaussian_filter\n",
    "import matplotlib as mpl\n",
    "from scipy.interpolate import CubicSpline\n",
    "from scipy.optimize import curve_fit\n",
    "mpl.rc('xtick', labelsize=8)\n",
    "mpl.rc('ytick', labelsize=8)\n",
    "\n",
    "from DataContainer.ReadData import read_hdf5_file, read_hdf5_global, read_hdf5_run_time, read_csv_file\n",
    "from Analyser.ImagingAnalyser import ImageAnalyser\n",
    "from Analyser.FitAnalyser import FitAnalyser\n",
    "from Analyser.FitAnalyser import ThomasFermi2dModel, DensityProfileBEC2dModel, Polylog22dModel\n",
    "from Analyser.FFTAnalyser import fft, ifft, fft_nutou\n",
    "from ToolFunction.ToolFunction import *\n",
    "\n",
    "import time\n",
    "\n",
    "from ToolFunction.HomeMadeXarrayFunction import errorbar, dataarray_plot_errorbar\n",
    "xr.plot.dataarray_plot.errorbar = errorbar\n",
    "xr.plot.accessor.DataArrayPlotAccessor.errorbar = dataarray_plot_errorbar\n",
    "\n",
    "imageAnalyser = ImageAnalyser()\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [],
   "source": [
    "# get center of thresholded image\n",
    "def calc_thresh(data):\n",
    "    shape = np.shape(data)\n",
    "    thresh = np.zeros(shape)\n",
    "    sigma = 0.4\n",
    "\n",
    "    if len(shape) == 4:\n",
    "        blurred = gaussian_filter(data, sigma=sigma)\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])*0.3,0,1)\n",
    "\n",
    "    elif len(shape) == 3:\n",
    "        blurred = gaussian_filter(data, sigma=sigma)\n",
    "        for i in range(0,shape[0]):\n",
    "                thresh[i] = np.where(blurred[i] < np.max(blurred[i])*0.3,0,1)\n",
    "\n",
    "    else:\n",
    "        print(\"Shape of data is wrong, output is empty\")\n",
    "\n",
    "    return thresh\n",
    "\n",
    "def calc_cen(thresh1):\n",
    "    \"\"\"\n",
    "    returns array: [Y_center,X_center]\n",
    "    \"\"\"\n",
    "    cen = np.zeros(2)\n",
    "    (Y,X) = np.shape(thresh1)\n",
    "\n",
    "\n",
    "    thresh1 = thresh1 /np.sum(thresh1)\n",
    "\n",
    "    # marginal distributions\n",
    "    dx = np.sum(thresh1, 0)\n",
    "    dy = np.sum(thresh1, 1)\n",
    "\n",
    "    # expected values\n",
    "    cen[0] = np.sum(dx * np.arange(X))\n",
    "    cen[1] = np.sum(dy * np.arange(Y))\n",
    "    return cen\n",
    "\n",
    "def calc_cen_bulk(thresh):\n",
    "    \"\"\"\n",
    "    returns array in shape of input, containing array with [Y_center,X_center] for each image\n",
    "    \"\"\"\n",
    "    shape = np.shape(thresh)\n",
    "    cen = np.zeros((shape[0], shape[1], 2))\n",
    "    for i in range(0, shape[0]):\n",
    "        for j in range(0, shape[1]):\n",
    "            cen[i,j] = calc_cen(thresh[i,j])\n",
    "    return cen\n",
    "\n",
    "def guess_BEC_width(thresh, center):\n",
    "    \"\"\"\n",
    "    returns width of thresholded area along both axis through the center with shape of thresh and [X_width, Y_width] for each image\n",
    "    \"\"\"\n",
    "    shape = np.shape(thresh)\n",
    "    BEC_width_guess = np.zeros((shape[0], shape[1], 2))\n",
    "\n",
    "    for i in range(0, shape[0]):\n",
    "        for j in range(0, shape[1]):\n",
    "            BEC_width_guess[i, j, 0] = np.sum(thresh[i, j, round(center[i,j,1]), :])\n",
    "            BEC_width_guess[i, j, 1] = np.sum(thresh[i, j, :, round(center[i,j,0])])\n",
    "\n",
    "    return BEC_width_guess\n",
    "\n",
    "\n",
    "\n",
    "def gaussian(x, x0, sigma, A):\n",
    "    return A * np.exp(-0.5 * (x-x0)**2 / sigma**2)\n",
    "\n",
    "# def polylog(power, numerator, order = 15):\n",
    "#\n",
    "#     dataShape = numerator.shape\n",
    "#     numerator = np.tile(numerator, (order, 1))\n",
    "#     numerator = np.power(numerator.T, np.arange(1, order+1)).T\n",
    "#\n",
    "#     denominator = np.arange(1, order+1)\n",
    "#     denominator = np.tile(denominator, (dataShape[0], 1))\n",
    "#     denominator = denominator.T\n",
    "#\n",
    "#     data = numerator/ np.power(denominator, power)\n",
    "#\n",
    "#     return np.sum(data, axis=0)\n",
    "\n",
    "def polylog_tab(pow, x):\n",
    "    order = 100\n",
    "    sum = 0\n",
    "    for k in range(1,order):\n",
    "        sum  += x ** k /k **pow\n",
    "    return sum\n",
    "\n",
    "x_int = np.linspace(0, 1.00001, 100000)\n",
    "\n",
    "poly_tab = polylog_tab(2,x_int)\n",
    "\n",
    "\n",
    "\n",
    "polylog_int = CubicSpline(x_int, poly_tab)\n",
    "\n",
    "def thermal(x, x0, amp, sigma):\n",
    "    res = np.exp(-0.5 * (x-x0)**2 / sigma**2)\n",
    "    return amp/1.643 * polylog_int(res)\n",
    "\n",
    "def Thomas_Fermi_1d(x, x0, amp, sigma):\n",
    "    res = (1- ((x-x0)/sigma)**2)\n",
    "    res = np.where(res > 0, res, 0)\n",
    "    res = res**(3/2)\n",
    "    return amp * res\n",
    "\n",
    "def density_1d(x, x0_bec, x0_th, amp_bec, amp_th, sigma_bec, sigma_th):\n",
    "    return thermal(x, x0_th, amp_th, sigma_th) + Thomas_Fermi_1d(x, x0_bec, amp_bec, sigma_bec)\n",
    "\n",
    "\n",
    "def polylog(pow, x):\n",
    "    order = 15\n",
    "    sum = 0\n",
    "    for k in range(1,order):\n",
    "        sum  += x ** k /k **pow\n",
    "    return sum\n",
    "\n",
    "\n",
    "def ThomasFermi_2d(x, y=0.0, centerx=0.0, centery=0.0, amplitude=1.0, sigmax=1.0, sigmay=1.0):\n",
    "\n",
    "    res = (1- ((x-centerx)/(sigmax))**2 - ((y-centery)/(sigmay))**2)\n",
    "    res = np.where(res > 0, res, 0)\n",
    "    res = res**(3/2)\n",
    "    return amplitude * res\n",
    "    # return amplitude * 5 / 2 / np.pi / max(tiny, sigmax * sigmay) * np.where(res > 0, res, 0)\n",
    "\n",
    "\n",
    "    # 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",
    "# Set up table for polylog\n",
    "\n",
    "\n",
    "def polylog2_2d(x, y=0.0, centerx=0.0, centery=0.0, amplitude=1.0, sigmax=1.0, sigmay=1.0):\n",
    "    ## Approximation of the polylog function with 2D gaussian as argument. -> discribes the thermal part of the cloud\n",
    "    return amplitude/1.643  * polylog_int(np.exp( -((x-centerx)**2/(2 * sigmax**2))-((y-centery)**2/( 2 * sigmay**2)) ))\n",
    "\n",
    "\n",
    "\n",
    "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",
    "                           sigmax_bec=1.0, sigmay_bec=1.0, sigma_th=1.0):\n",
    "    return ThomasFermi_2d(x=x, y=y, centerx=x0_bec, centery=y0_bec,\n",
    "                          amplitude=amp_bec, sigmax=sigmax_bec, sigmay=sigmay_bec\n",
    "                          ) + polylog2_2d(x=x, y=y, centerx=x0_th, centery=y0_th,\n",
    "                                          amplitude=amp_th, sigmax=sigma_th,sigmay=sigma_th)\n",
    "\n",
    "def cond_frac(results):\n",
    "    bval = results.best_values\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",
    "    N_bec = np.sum(tf_fit)\n",
    "    fit = fit = density_profile_BEC_2d(X,Y, **bval)\n",
    "    N_ges = np.sum(fit)\n",
    "    return N_bec/N_ges\n",
    "\n",
    "def print_bval(res_s):\n",
    "    keys = res_s.best_values.keys()\n",
    "    bval = res_s.best_values\n",
    "    init = res_s.init_params\n",
    "\n",
    "    for item in keys:\n",
    "        print(f'{item}: {bval[item]:.3f}, (init = {init[item].value:.3f}), bounds = [{init[item].min:.2f} : {init[item].max :.2f}] ')\n",
    "    print('')\n",
    "\n",
    "def print_bval_bulk(res_):\n",
    "    shape = np.shape(res_)\n",
    "    if len(shape) == 2:\n",
    "        for i in range(shape[0]):\n",
    "            for j in range(shape[1]):\n",
    "                print(f'image: {i}, {j}')\n",
    "                print_bval(res_[i][j])\n",
    "\n",
    "    if len(shape) == 1:\n",
    "        for i in range(shape[0]):\n",
    "            print(f'image: {i}')\n",
    "            print_bval(res_[i])\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-01T15:20:49.853382Z",
     "start_time": "2023-08-01T15:20:49.405949500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
    "shape = np.shape(data)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-01T15:15:37.267480300Z",
     "start_time": "2023-08-01T15:15:36.671379300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "def guess_BEC_width(thresh, center):\n",
    "    \"\"\"\n",
    "    returns width of thresholded area along both axis through the center with shape of thresh and [X_width, Y_width] for each image\n",
    "    \"\"\"\n",
    "    shape = np.shape(thresh)\n",
    "    BEC_width_guess = np.zeros((shape[0], shape[1], 2))\n",
    "\n",
    "    for i in range(0, shape[0]):\n",
    "        for j in range(0, shape[1]):\n",
    "            BEC_width_guess[i, j, 0] = np.sum(thresh[i, j, round(center[i,j,1])-4:round(center[i,j,1])+5, :])/9\n",
    "            BEC_width_guess[i, j, 1] = np.sum(thresh[i, j, :, round(center[i,j,0])-4:round(center[i,j,0])+5])/9\n",
    "\n",
    "    return BEC_width_guess"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-01T15:15:37.268477600Z",
     "start_time": "2023-08-01T15:15:37.267480300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9, 250)\n"
     ]
    }
   ],
   "source": [
    "print(np.shape(thresh[i, j, round(center[i,j,1])-4:round(center[i,j,1])+5, :]))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-01T10:07:45.908882100Z",
     "start_time": "2023-08-01T10:07:45.795191900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [
    "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",
    "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": "2023-08-01T15:16:07.587125100Z",
     "start_time": "2023-08-01T15:16:03.874883800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1081.  481.]\n",
      "0.32106129452561555\n",
      "-0.03579800587577903\n"
     ]
    },
    {
     "ename": "type",
     "evalue": "could not broadcast input array from shape (244,250) into shape (250,250)",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mValueError\u001B[0m                                Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[6], line 17\u001B[0m\n\u001B[0;32m     15\u001B[0m         \u001B[38;5;28mprint\u001B[39m(np\u001B[38;5;241m.\u001B[39mmax(data[i,j]))\n\u001B[0;32m     16\u001B[0m         \u001B[38;5;28mprint\u001B[39m(data[i,j, \u001B[38;5;28mround\u001B[39m(center[i,j,\u001B[38;5;241m0\u001B[39m]), \u001B[38;5;28mround\u001B[39m(center[i,j,\u001B[38;5;241m1\u001B[39m]) ])\n\u001B[1;32m---> 17\u001B[0m         \u001B[43mcropOD\u001B[49m\u001B[43m[\u001B[49m\u001B[43mi\u001B[49m\u001B[43m,\u001B[49m\u001B[43mj\u001B[49m\u001B[43m]\u001B[49m \u001B[38;5;241m=\u001B[39m data[i,j, \u001B[38;5;28mround\u001B[39m(center[i,j,\u001B[38;5;241m0\u001B[39m]\u001B[38;5;241m-\u001B[39mcut_width\u001B[38;5;241m/\u001B[39m\u001B[38;5;241m2\u001B[39m):\u001B[38;5;28mround\u001B[39m(center[i,j,\u001B[38;5;241m0\u001B[39m]\u001B[38;5;241m+\u001B[39mcut_width\u001B[38;5;241m/\u001B[39m\u001B[38;5;241m2\u001B[39m), \u001B[38;5;28mround\u001B[39m(center[i,j,\u001B[38;5;241m1\u001B[39m]\u001B[38;5;241m-\u001B[39mcut_width\u001B[38;5;241m/\u001B[39m\u001B[38;5;241m2\u001B[39m):\u001B[38;5;28mround\u001B[39m(center[i,j,\u001B[38;5;241m1\u001B[39m]\u001B[38;5;241m+\u001B[39mcut_width\u001B[38;5;241m/\u001B[39m\u001B[38;5;241m2\u001B[39m)]\n\u001B[0;32m     19\u001B[0m thresh \u001B[38;5;241m=\u001B[39m calc_thresh(cropOD)\n\u001B[0;32m     20\u001B[0m center \u001B[38;5;241m=\u001B[39m calc_cen_bulk(thresh)\n",
      "\u001B[1;31mValueError\u001B[0m: could not broadcast input array from shape (244,250) into shape (250,250)"
     ]
    }
   ],
   "source": [
    "\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=0.4)\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)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-01T15:15:49.603304400Z",
     "start_time": "2023-08-01T15:15:45.139480Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[24. 22.]\n"
     ]
    }
   ],
   "source": [
    "print(BEC_width_guess[1,7])\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-01T10:12:50.194861600Z",
     "start_time": "2023-08-01T10:12:50.080033Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 2000x500 with 22 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 2000x500 with 22 Axes>",
      "image/png": "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
     },
     "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')\n",
    "        #ax[i,j].plot(center[i,j,0], center[i,j,1], markersize=15,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": "2023-08-01T15:16:18.076087600Z",
     "start_time": "2023-08-01T15:16:11.378249600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[58. 44.]\n",
      "  [36. 29.]\n",
      "  [35. 31.]\n",
      "  [34. 30.]\n",
      "  [32. 29.]\n",
      "  [35. 31.]\n",
      "  [35. 30.]\n",
      "  [35. 31.]\n",
      "  [35. 31.]\n",
      "  [35. 28.]\n",
      "  [39. 33.]]\n",
      "\n",
      " [[27. 27.]\n",
      "  [29. 29.]\n",
      "  [34. 17.]\n",
      "  [24. 25.]\n",
      "  [32. 31.]\n",
      "  [34. 24.]\n",
      "  [32. 27.]\n",
      "  [23. 23.]\n",
      "  [30. 22.]\n",
      "  [31. 26.]\n",
      "  [31. 27.]]]\n"
     ]
    }
   ],
   "source": [
    "print(BEC_width_guess)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-01T13:47:24.382090900Z",
     "start_time": "2023-08-01T13:47:24.263490300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image 0, 0\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = differential_evolution\n",
      "    # function evals   = 3567\n",
      "    # data points      = 250\n",
      "    # variables        = 6\n",
      "    chi-square         = 0.02337656\n",
      "    reduced chi-square = 9.5806e-05\n",
      "    Akaike info crit   = -2307.37055\n",
      "    Bayesian info crit = -2286.24179\n",
      "    R-squared          = 0.90568814\n",
      "##  Warning: uncertainties could not be estimated:\n",
      "    this fitting method does not natively calculate uncertainties\n",
      "    and numdifftools is not installed for lmfit to do this. Use\n",
      "    `pip install numdifftools` for lmfit to estimate uncertainties\n",
      "    with this fitting method.\n",
      "[[Variables]]\n",
      "    x0_bec:     153.441480 (init = 124.0604)\n",
      "    x0_th:      124.776093 (init = 124.0604)\n",
      "    amp_bec:    0.00432072 (init = 0.07733014)\n",
      "    amp_th:     0.09064429 (init = 0.03314149)\n",
      "    deltax:     33.1535731 (init = 174)\n",
      "    sigma_bec:  33.4779285 (init = 47.54098)\n",
      "    sigma_th:   38.3316017 == '0.632*sigma_bec + 0.518*deltax'\n",
      "\n",
      "\n",
      "image 0, 1\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = differential_evolution\n",
      "    # function evals   = 5187\n",
      "    # data points      = 250\n",
      "    # variables        = 6\n",
      "    chi-square         = 0.03635169\n",
      "    reduced chi-square = 1.4898e-04\n",
      "    Akaike info crit   = -2196.99384\n",
      "    Bayesian info crit = -2175.86508\n",
      "    R-squared          = 0.97313579\n",
      "##  Warning: uncertainties could not be estimated:\n",
      "    this fitting method does not natively calculate uncertainties\n",
      "    and numdifftools is not installed for lmfit to do this. Use\n",
      "    `pip install numdifftools` for lmfit to estimate uncertainties\n",
      "    with this fitting method.\n",
      "[[Variables]]\n",
      "    x0_bec:     125.297500 (init = 123.2258)\n",
      "    x0_th:      124.762485 (init = 123.2258)\n",
      "    amp_bec:    0.11703212 (init = 0.2043726)\n",
      "    amp_th:     0.16304627 (init = 0.08758826)\n",
      "    deltax:     34.4497537 (init = 108)\n",
      "    sigma_bec:  15.6081798 (init = 29.5082)\n",
      "    sigma_th:   27.7093420 == '0.632*sigma_bec + 0.518*deltax'\n",
      "\n",
      "\n",
      "image 0, 2\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = differential_evolution\n",
      "    # function evals   = 5761\n",
      "    # data points      = 250\n",
      "    # variables        = 6\n",
      "    chi-square         = 0.03801295\n",
      "    reduced chi-square = 1.5579e-04\n",
      "    Akaike info crit   = -2185.82230\n",
      "    Bayesian info crit = -2164.69353\n",
      "    R-squared          = 0.98428608\n",
      "##  Warning: uncertainties could not be estimated:\n",
      "    this fitting method does not natively calculate uncertainties\n",
      "    and numdifftools is not installed for lmfit to do this. Use\n",
      "    `pip install numdifftools` for lmfit to estimate uncertainties\n",
      "    with this fitting method.\n",
      "[[Variables]]\n",
      "    x0_bec:     125.044918 (init = 124.1987)\n",
      "    x0_th:      125.036027 (init = 124.1987)\n",
      "    amp_bec:    0.21041411 (init = 0.2749891)\n",
      "    amp_th:     0.16877035 (init = 0.1178525)\n",
      "    deltax:     29.3582083 (init = 105)\n",
      "    sigma_bec:  18.1900164 (init = 28.68852)\n",
      "    sigma_th:   26.7036423 == '0.632*sigma_bec + 0.518*deltax'\n",
      "\n",
      "\n",
      "image 0, 3\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = differential_evolution\n",
      "    # function evals   = 7264\n",
      "    # data points      = 250\n",
      "    # variables        = 6\n",
      "    chi-square         = 0.03910902\n",
      "    reduced chi-square = 1.6028e-04\n",
      "    Akaike info crit   = -2178.71578\n",
      "    Bayesian info crit = -2157.58701\n",
      "    R-squared          = 0.98976751\n",
      "##  Warning: uncertainties could not be estimated:\n",
      "    this fitting method does not natively calculate uncertainties\n",
      "    and numdifftools is not installed for lmfit to do this. Use\n",
      "    `pip install numdifftools` for lmfit to estimate uncertainties\n",
      "    with this fitting method.\n",
      "[[Variables]]\n",
      "    x0_bec:     124.937119 (init = 124.2885)\n",
      "    x0_th:      125.005157 (init = 124.2885)\n",
      "    amp_bec:    0.26835003 (init = 0.348317)\n",
      "    amp_th:     0.20693134 (init = 0.1492787)\n",
      "    deltax:     17.5801548 (init = 102)\n",
      "    sigma_bec:  19.5988377 (init = 27.86885)\n",
      "    sigma_th:   21.4929856 == '0.632*sigma_bec + 0.518*deltax'\n",
      "\n",
      "\n",
      "image 0, 4\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = differential_evolution\n",
      "    # function evals   = 7777\n",
      "    # data points      = 250\n",
      "    # variables        = 6\n",
      "    chi-square         = 0.06306629\n",
      "    reduced chi-square = 2.5847e-04\n",
      "    Akaike info crit   = -2059.25745\n",
      "    Bayesian info crit = -2038.12868\n",
      "    R-squared          = 0.98860374\n",
      "##  Warning: uncertainties could not be estimated:\n",
      "    this fitting method does not natively calculate uncertainties\n",
      "    and numdifftools is not installed for lmfit to do this. Use\n",
      "    `pip install numdifftools` for lmfit to estimate uncertainties\n",
      "    with this fitting method.\n",
      "[[Variables]]\n",
      "    x0_bec:     115.616389 (init = 124.8436)\n",
      "    x0_th:      126.137165 (init = 124.8436)\n",
      "    amp_bec:    0.07905547 (init = 0.4125246)\n",
      "    amp_th:     0.58134074 (init = 0.1767962)\n",
      "    deltax:     14.0284362 (init = 96)\n",
      "    sigma_bec:  9.68587168 (init = 26.22951)\n",
      "    sigma_th:   13.3882008 == '0.632*sigma_bec + 0.518*deltax'\n",
      "\n",
      "\n",
      "image 0, 5\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = differential_evolution\n",
      "    # function evals   = 5811\n",
      "    # data points      = 250\n",
      "    # variables        = 6\n",
      "    chi-square         = 0.03483722\n",
      "    reduced chi-square = 1.4278e-04\n",
      "    Akaike info crit   = -2207.63249\n",
      "    Bayesian info crit = -2186.50372\n",
      "    R-squared          = 0.99458794\n",
      "##  Warning: uncertainties could not be estimated:\n",
      "    this fitting method does not natively calculate uncertainties\n",
      "    and numdifftools is not installed for lmfit to do this. Use\n",
      "    `pip install numdifftools` for lmfit to estimate uncertainties\n",
      "    with this fitting method.\n",
      "[[Variables]]\n",
      "    x0_bec:     125.026522 (init = 124.4716)\n",
      "    x0_th:      125.174821 (init = 124.4716)\n",
      "    amp_bec:    0.36935323 (init = 0.4431705)\n",
      "    amp_th:     0.24363800 (init = 0.1899302)\n",
      "    deltax:     6.66148730 (init = 105)\n",
      "    sigma_bec:  21.7020486 (init = 28.68852)\n",
      "    sigma_th:   17.1663451 == '0.632*sigma_bec + 0.518*deltax'\n",
      "\n",
      "\n",
      "image 0, 6\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = differential_evolution\n",
      "    # function evals   = 7956\n",
      "    # data points      = 250\n",
      "    # variables        = 6\n",
      "    chi-square         = 0.04184668\n",
      "    reduced chi-square = 1.7150e-04\n",
      "    Akaike info crit   = -2161.80093\n",
      "    Bayesian info crit = -2140.67217\n",
      "    R-squared          = 0.99485713\n",
      "##  Warning: uncertainties could not be estimated:\n",
      "    this fitting method does not natively calculate uncertainties\n",
      "    and numdifftools is not installed for lmfit to do this. Use\n",
      "    `pip install numdifftools` for lmfit to estimate uncertainties\n",
      "    with this fitting method.\n",
      "[[Variables]]\n",
      "    x0_bec:     124.897553 (init = 124.226)\n",
      "    x0_th:      125.376350 (init = 124.226)\n",
      "    amp_bec:    0.46053971 (init = 0.4840948)\n",
      "    amp_th:     0.22040635 (init = 0.2074692)\n",
      "    deltax:     3.11293969 (init = 105)\n",
      "    sigma_bec:  22.9411327 (init = 28.68852)\n",
      "    sigma_th:   16.1112986 == '0.632*sigma_bec + 0.518*deltax'\n",
      "\n",
      "\n",
      "image 0, 7\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = differential_evolution\n",
      "    # function evals   = 7452\n",
      "    # data points      = 250\n",
      "    # variables        = 6\n",
      "    chi-square         = 0.02845081\n",
      "    reduced chi-square = 1.1660e-04\n",
      "    Akaike info crit   = -2258.25987\n",
      "    Bayesian info crit = -2237.13111\n",
      "    R-squared          = 0.99692969\n",
      "##  Warning: uncertainties could not be estimated:\n",
      "    this fitting method does not natively calculate uncertainties\n",
      "    and numdifftools is not installed for lmfit to do this. Use\n",
      "    `pip install numdifftools` for lmfit to estimate uncertainties\n",
      "    with this fitting method.\n",
      "[[Variables]]\n",
      "    x0_bec:     125.032801 (init = 124.3744)\n",
      "    x0_th:      124.841135 (init = 124.3744)\n",
      "    amp_bec:    0.56271619 (init = 0.5310693)\n",
      "    amp_th:     0.16185082 (init = 0.2276011)\n",
      "    deltax:     0.00000000 (init = 105)\n",
      "    sigma_bec:  23.2491118 (init = 28.68852)\n",
      "    sigma_th:   14.6934387 == '0.632*sigma_bec + 0.518*deltax'\n",
      "\n",
      "\n",
      "image 0, 8\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = differential_evolution\n",
      "    # function evals   = 3499\n",
      "    # data points      = 250\n",
      "    # variables        = 6\n",
      "    chi-square         = 0.04803040\n",
      "    reduced chi-square = 1.9685e-04\n",
      "    Akaike info crit   = -2127.34550\n",
      "    Bayesian info crit = -2106.21673\n",
      "    R-squared          = 0.99523669\n",
      "##  Warning: uncertainties could not be estimated:\n",
      "    this fitting method does not natively calculate uncertainties\n",
      "    and numdifftools is not installed for lmfit to do this. Use\n",
      "    `pip install numdifftools` for lmfit to estimate uncertainties\n",
      "    with this fitting method.\n",
      "[[Variables]]\n",
      "    x0_bec:     125.187040 (init = 124.4929)\n",
      "    x0_th:      103.882350 (init = 124.4929)\n",
      "    amp_bec:    0.73222993 (init = 0.5422824)\n",
      "    amp_th:     0.01624567 (init = 0.2324067)\n",
      "    deltax:     0.12250819 (init = 105)\n",
      "    sigma_bec:  23.6013336 (init = 28.68852)\n",
      "    sigma_th:   14.9795021 == '0.632*sigma_bec + 0.518*deltax'\n",
      "\n",
      "\n",
      "image 0, 9\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = differential_evolution\n",
      "    # function evals   = 7693\n",
      "    # data points      = 250\n",
      "    # variables        = 6\n",
      "    chi-square         = 0.05382463\n",
      "    reduced chi-square = 2.2059e-04\n",
      "    Akaike info crit   = -2098.87128\n",
      "    Bayesian info crit = -2077.74251\n",
      "    R-squared          = 0.99533610\n",
      "##  Warning: uncertainties could not be estimated:\n",
      "    this fitting method does not natively calculate uncertainties\n",
      "    and numdifftools is not installed for lmfit to do this. Use\n",
      "    `pip install numdifftools` for lmfit to estimate uncertainties\n",
      "    with this fitting method.\n",
      "[[Variables]]\n",
      "    x0_bec:     125.117746 (init = 124.4059)\n",
      "    x0_th:      124.544314 (init = 124.4059)\n",
      "    amp_bec:    0.57923202 (init = 0.581434)\n",
      "    amp_th:     0.23327823 (init = 0.249186)\n",
      "    deltax:     0.00000000 (init = 105)\n",
      "    sigma_bec:  23.1733612 (init = 28.68852)\n",
      "    sigma_th:   14.6455643 == '0.632*sigma_bec + 0.518*deltax'\n",
      "\n",
      "\n",
      "image 0, 10\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = differential_evolution\n",
      "    # function evals   = 10166\n",
      "    # data points      = 250\n",
      "    # variables        = 6\n",
      "    chi-square         = 0.05277411\n",
      "    reduced chi-square = 2.1629e-04\n",
      "    Akaike info crit   = -2103.79887\n",
      "    Bayesian info crit = -2082.67011\n",
      "    R-squared          = 0.99394216\n",
      "##  Warning: uncertainties could not be estimated:\n",
      "    this fitting method does not natively calculate uncertainties\n",
      "    and numdifftools is not installed for lmfit to do this. Use\n",
      "    `pip install numdifftools` for lmfit to estimate uncertainties\n",
      "    with this fitting method.\n",
      "[[Variables]]\n",
      "    x0_bec:     133.724792 (init = 124.6719)\n",
      "    x0_th:      118.917613 (init = 124.6719)\n",
      "    amp_bec:    0.42527821 (init = 0.4817168)\n",
      "    amp_th:     0.61356960 (init = 0.2064501)\n",
      "    deltax:     0.00000000 (init = 117)\n",
      "    sigma_bec:  15.5658200 (init = 31.96721)\n",
      "    sigma_th:   9.83759826 == '0.632*sigma_bec + 0.518*deltax'\n",
      "\n",
      "\n",
      "image 1, 0\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = differential_evolution\n",
      "    # function evals   = 2238\n",
      "    # data points      = 250\n",
      "    # variables        = 6\n",
      "    chi-square         = 0.33913708\n",
      "    reduced chi-square = 0.00138991\n",
      "    Akaike info crit   = -1638.70295\n",
      "    Bayesian info crit = -1617.57418\n",
      "    R-squared          = 0.35479840\n",
      "##  Warning: uncertainties could not be estimated:\n",
      "    this fitting method does not natively calculate uncertainties\n",
      "    and numdifftools is not installed for lmfit to do this. Use\n",
      "    `pip install numdifftools` for lmfit to estimate uncertainties\n",
      "    with this fitting method.\n",
      "[[Variables]]\n",
      "    x0_bec:     109.225323 (init = 122.4314)\n",
      "    x0_th:      127.195567 (init = 122.4314)\n",
      "    amp_bec:    0.01684626 (init = 0.115638)\n",
      "    amp_th:     0.07190229 (init = 0.04955914)\n",
      "    deltax:     44.3174542 (init = 81)\n",
      "    sigma_bec:  38.9759514 (init = 22.13115)\n",
      "    sigma_th:   47.5892426 == '0.632*sigma_bec + 0.518*deltax'\n",
      "\n",
      "\n",
      "image 1, 1\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = differential_evolution\n",
      "    # function evals   = 4578\n",
      "    # data points      = 250\n",
      "    # variables        = 6\n",
      "    chi-square         = 0.27270940\n",
      "    reduced chi-square = 0.00111766\n",
      "    Akaike info crit   = -1693.20236\n",
      "    Bayesian info crit = -1672.07360\n",
      "    R-squared          = 0.83115399\n",
      "##  Warning: uncertainties could not be estimated:\n",
      "    this fitting method does not natively calculate uncertainties\n",
      "    and numdifftools is not installed for lmfit to do this. Use\n",
      "    `pip install numdifftools` for lmfit to estimate uncertainties\n",
      "    with this fitting method.\n",
      "[[Variables]]\n",
      "    x0_bec:     125.650627 (init = 123.7637)\n",
      "    x0_th:      125.603005 (init = 123.7637)\n",
      "    amp_bec:    0.14297581 (init = 0.2335334)\n",
      "    amp_th:     0.14659021 (init = 0.1000857)\n",
      "    deltax:     43.7665291 (init = 87)\n",
      "    sigma_bec:  14.7954170 (init = 23.77049)\n",
      "    sigma_th:   32.0217656 == '0.632*sigma_bec + 0.518*deltax'\n",
      "\n",
      "\n",
      "image 1, 2\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = differential_evolution\n",
      "    # function evals   = 6309\n",
      "    # data points      = 250\n",
      "    # variables        = 6\n",
      "    chi-square         = 0.47729229\n",
      "    reduced chi-square = 0.00195612\n",
      "    Akaike info crit   = -1553.27178\n",
      "    Bayesian info crit = -1532.14301\n",
      "    R-squared          = 0.87474842\n",
      "##  Warning: uncertainties could not be estimated:\n",
      "    this fitting method does not natively calculate uncertainties\n",
      "    and numdifftools is not installed for lmfit to do this. Use\n",
      "    `pip install numdifftools` for lmfit to estimate uncertainties\n",
      "    with this fitting method.\n",
      "[[Variables]]\n",
      "    x0_bec:     89.8231472 (init = 123.6648)\n",
      "    x0_th:      125.374090 (init = 123.6648)\n",
      "    amp_bec:    0.09613615 (init = 0.3626067)\n",
      "    amp_th:     0.48481876 (init = 0.1554029)\n",
      "    deltax:     20.4998269 (init = 102)\n",
      "    sigma_bec:  4.89952002 (init = 27.86885)\n",
      "    sigma_th:   13.7154070 == '0.632*sigma_bec + 0.518*deltax'\n",
      "\n",
      "\n",
      "image 1, 3\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = differential_evolution\n",
      "    # function evals   = 5464\n",
      "    # data points      = 250\n",
      "    # variables        = 6\n",
      "    chi-square         = 0.34785756\n",
      "    reduced chi-square = 0.00142565\n",
      "    Akaike info crit   = -1632.35577\n",
      "    Bayesian info crit = -1611.22701\n",
      "    R-squared          = 0.92340765\n",
      "##  Warning: uncertainties could not be estimated:\n",
      "    this fitting method does not natively calculate uncertainties\n",
      "    and numdifftools is not installed for lmfit to do this. Use\n",
      "    `pip install numdifftools` for lmfit to estimate uncertainties\n",
      "    with this fitting method.\n",
      "[[Variables]]\n",
      "    x0_bec:     124.278073 (init = 123.0047)\n",
      "    x0_th:      126.131819 (init = 123.0047)\n",
      "    amp_bec:    0.29775761 (init = 0.3912519)\n",
      "    amp_th:     0.20374123 (init = 0.1676794)\n",
      "    deltax:     18.7921671 (init = 72)\n",
      "    sigma_bec:  20.1430739 (init = 19.67213)\n",
      "    sigma_th:   22.4647652 == '0.632*sigma_bec + 0.518*deltax'\n",
      "\n",
      "\n",
      "image 1, 4\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = differential_evolution\n",
      "    # function evals   = 4523\n",
      "    # data points      = 250\n",
      "    # variables        = 6\n",
      "    chi-square         = 0.30733891\n",
      "    reduced chi-square = 0.00125959\n",
      "    Akaike info crit   = -1663.31628\n",
      "    Bayesian info crit = -1642.18751\n",
      "    R-squared          = 0.94464764\n",
      "##  Warning: uncertainties could not be estimated:\n",
      "    this fitting method does not natively calculate uncertainties\n",
      "    and numdifftools is not installed for lmfit to do this. Use\n",
      "    `pip install numdifftools` for lmfit to estimate uncertainties\n",
      "    with this fitting method.\n",
      "[[Variables]]\n",
      "    x0_bec:     124.969451 (init = 121.5974)\n",
      "    x0_th:      126.703300 (init = 121.5974)\n",
      "    amp_bec:    0.44330500 (init = 0.3992703)\n",
      "    amp_th:     0.10684316 (init = 0.1711158)\n",
      "    deltax:     26.0476983 (init = 96)\n",
      "    sigma_bec:  21.9421230 (init = 26.22951)\n",
      "    sigma_th:   27.3601294 == '0.632*sigma_bec + 0.518*deltax'\n",
      "\n",
      "\n",
      "image 1, 5\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = differential_evolution\n",
      "    # function evals   = 6343\n",
      "    # data points      = 250\n",
      "    # variables        = 6\n",
      "    chi-square         = 0.36323407\n",
      "    reduced chi-square = 0.00148866\n",
      "    Akaike info crit   = -1621.54219\n",
      "    Bayesian info crit = -1600.41342\n",
      "    R-squared          = 0.95552477\n",
      "##  Warning: uncertainties could not be estimated:\n",
      "    this fitting method does not natively calculate uncertainties\n",
      "    and numdifftools is not installed for lmfit to do this. Use\n",
      "    `pip install numdifftools` for lmfit to estimate uncertainties\n",
      "    with this fitting method.\n",
      "[[Variables]]\n",
      "    x0_bec:     123.945322 (init = 125.6102)\n",
      "    x0_th:      127.165697 (init = 125.6102)\n",
      "    amp_bec:    0.38581784 (init = 0.4918795)\n",
      "    amp_th:     0.28603710 (init = 0.2108055)\n",
      "    deltax:     4.89169118 (init = 102)\n",
      "    sigma_bec:  22.4910822 (init = 27.86885)\n",
      "    sigma_th:   16.7482600 == '0.632*sigma_bec + 0.518*deltax'\n",
      "\n",
      "\n",
      "image 1, 6\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = differential_evolution\n",
      "    # function evals   = 5942\n",
      "    # data points      = 250\n",
      "    # variables        = 6\n",
      "    chi-square         = 0.32298403\n",
      "    reduced chi-square = 0.00132371\n",
      "    Akaike info crit   = -1650.90333\n",
      "    Bayesian info crit = -1629.77456\n",
      "    R-squared          = 0.96675892\n",
      "##  Warning: uncertainties could not be estimated:\n",
      "    this fitting method does not natively calculate uncertainties\n",
      "    and numdifftools is not installed for lmfit to do this. Use\n",
      "    `pip install numdifftools` for lmfit to estimate uncertainties\n",
      "    with this fitting method.\n",
      "[[Variables]]\n",
      "    x0_bec:     123.954692 (init = 123)\n",
      "    x0_th:      127.162708 (init = 123)\n",
      "    amp_bec:    0.46356390 (init = 0.5517363)\n",
      "    amp_th:     0.28052674 (init = 0.2364584)\n",
      "    deltax:     0.00000000 (init = 96)\n",
      "    sigma_bec:  23.3349116 (init = 26.22951)\n",
      "    sigma_th:   14.7476641 == '0.632*sigma_bec + 0.518*deltax'\n",
      "\n",
      "\n",
      "image 1, 7\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = differential_evolution\n",
      "    # function evals   = 4170\n",
      "    # data points      = 250\n",
      "    # variables        = 6\n",
      "    chi-square         = 0.36801150\n",
      "    reduced chi-square = 0.00150824\n",
      "    Akaike info crit   = -1618.27550\n",
      "    Bayesian info crit = -1597.14673\n",
      "    R-squared          = 0.97141166\n",
      "##  Warning: uncertainties could not be estimated:\n",
      "    this fitting method does not natively calculate uncertainties\n",
      "    and numdifftools is not installed for lmfit to do this. Use\n",
      "    `pip install numdifftools` for lmfit to estimate uncertainties\n",
      "    with this fitting method.\n",
      "[[Variables]]\n",
      "    x0_bec:     124.437977 (init = 124.8898)\n",
      "    x0_th:      129.277304 (init = 124.8898)\n",
      "    amp_bec:    0.67483443 (init = 0.618981)\n",
      "    amp_th:     0.17462262 (init = 0.2652776)\n",
      "    deltax:     0.00000000 (init = 69)\n",
      "    sigma_bec:  23.2066942 (init = 18.85246)\n",
      "    sigma_th:   14.6666308 == '0.632*sigma_bec + 0.518*deltax'\n",
      "\n",
      "\n",
      "image 1, 8\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = differential_evolution\n",
      "    # function evals   = 6185\n",
      "    # data points      = 250\n",
      "    # variables        = 6\n",
      "    chi-square         = 0.53609694\n",
      "    reduced chi-square = 0.00219712\n",
      "    Akaike info crit   = -1524.22530\n",
      "    Bayesian info crit = -1503.09653\n",
      "    R-squared          = 0.96187022\n",
      "##  Warning: uncertainties could not be estimated:\n",
      "    this fitting method does not natively calculate uncertainties\n",
      "    and numdifftools is not installed for lmfit to do this. Use\n",
      "    `pip install numdifftools` for lmfit to estimate uncertainties\n",
      "    with this fitting method.\n",
      "[[Variables]]\n",
      "    x0_bec:     135.571193 (init = 124.2619)\n",
      "    x0_th:      124.773959 (init = 124.2619)\n",
      "    amp_bec:    0.21252338 (init = 0.648623)\n",
      "    amp_th:     0.95601004 (init = 0.2779813)\n",
      "    deltax:     22.4402001 (init = 90)\n",
      "    sigma_bec:  1.61326322 (init = 24.59016)\n",
      "    sigma_th:   12.6436060 == '0.632*sigma_bec + 0.518*deltax'\n",
      "\n",
      "\n",
      "image 1, 9\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = differential_evolution\n",
      "    # function evals   = 2897\n",
      "    # data points      = 250\n",
      "    # variables        = 6\n",
      "    chi-square         = 0.36752607\n",
      "    reduced chi-square = 0.00150625\n",
      "    Akaike info crit   = -1618.60549\n",
      "    Bayesian info crit = -1597.47672\n",
      "    R-squared          = 0.97165517\n",
      "##  Warning: uncertainties could not be estimated:\n",
      "    this fitting method does not natively calculate uncertainties\n",
      "    and numdifftools is not installed for lmfit to do this. Use\n",
      "    `pip install numdifftools` for lmfit to estimate uncertainties\n",
      "    with this fitting method.\n",
      "[[Variables]]\n",
      "    x0_bec:     124.670819 (init = 124.2739)\n",
      "    x0_th:      152.268084 (init = 124.2739)\n",
      "    amp_bec:    0.81785867 (init = 0.6808652)\n",
      "    amp_th:     0.02689453 (init = 0.2917994)\n",
      "    deltax:     14.9118820 (init = 93)\n",
      "    sigma_bec:  23.4840073 (init = 25.40984)\n",
      "    sigma_th:   22.5662475 == '0.632*sigma_bec + 0.518*deltax'\n",
      "\n",
      "\n",
      "image 1, 10\n",
      "[[Fit Statistics]]\n",
      "    # fitting method   = differential_evolution\n",
      "    # function evals   = 9555\n",
      "    # data points      = 250\n",
      "    # variables        = 6\n",
      "    chi-square         = 0.43920688\n",
      "    reduced chi-square = 0.00180003\n",
      "    Akaike info crit   = -1574.06141\n",
      "    Bayesian info crit = -1552.93264\n",
      "    R-squared          = 0.96007591\n",
      "##  Warning: uncertainties could not be estimated:\n",
      "    this fitting method does not natively calculate uncertainties\n",
      "    and numdifftools is not installed for lmfit to do this. Use\n",
      "    `pip install numdifftools` for lmfit to estimate uncertainties\n",
      "    with this fitting method.\n",
      "[[Variables]]\n",
      "    x0_bec:     116.033383 (init = 125.2517)\n",
      "    x0_th:      131.505534 (init = 125.2517)\n",
      "    amp_bec:    0.47031236 (init = 0.5626007)\n",
      "    amp_th:     0.66708963 (init = 0.2411146)\n",
      "    deltax:     2.7715e-05 (init = 93)\n",
      "    sigma_bec:  16.0906185 (init = 25.40984)\n",
      "    sigma_th:   10.1692852 == '0.632*sigma_bec + 0.518*deltax'\n",
      "\n",
      "\n",
      "total time: 39506.48522377014 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",
    "\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",
    "        print(f'image {i}, {j}')\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",
    "        # params.pretty_print()\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",
    "\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": "2023-08-02T16:09:49.075495200Z",
     "start_time": "2023-08-02T16:09:09.547382400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.11047163542693593\n"
     ]
    }
   ],
   "source": [
    "print(np.max(X_guess_og[0][0]))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-02T15:35:15.416425700Z",
     "start_time": "2023-08-02T15:35:15.372503500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image: 0, 0\n",
      "x0_bec: 142.004, (init = 124.060), bounds = [0.00 : 200.00] \n",
      "x0_th: 124.284, (init = 124.060), bounds = [0.00 : 200.00] \n",
      "amp_bec: 0.007, (init = 0.077), bounds = [0.00 : 0.14] \n",
      "amp_th: 0.088, (init = 0.033), bounds = [0.00 : 0.14] \n",
      "sigma_bec: 46.836, (init = 47.541), bounds = [0.00 : 50.00] \n",
      "sigma_th: 38.731, (init = 120.178), bounds = [0.00 : inf] \n",
      "\n",
      "image: 0, 1\n",
      "x0_bec: 125.256, (init = 123.226), bounds = [0.00 : 200.00] \n",
      "x0_th: 124.785, (init = 123.226), bounds = [0.00 : 200.00] \n",
      "amp_bec: 0.122, (init = 0.204), bounds = [0.00 : 0.38] \n",
      "amp_th: 0.164, (init = 0.088), bounds = [0.00 : 0.38] \n",
      "sigma_bec: 15.416, (init = 29.508), bounds = [0.00 : 50.00] \n",
      "sigma_th: 27.656, (init = 74.593), bounds = [0.00 : inf] \n",
      "\n",
      "image: 0, 2\n",
      "x0_bec: 125.039, (init = 124.199), bounds = [0.00 : 200.00] \n",
      "x0_th: 125.046, (init = 124.199), bounds = [0.00 : 200.00] \n",
      "amp_bec: 0.214, (init = 0.275), bounds = [0.00 : 0.51] \n",
      "amp_th: 0.170, (init = 0.118), bounds = [0.00 : 0.51] \n",
      "sigma_bec: 18.047, (init = 28.689), bounds = [0.00 : 50.00] \n",
      "sigma_th: 26.639, (init = 72.521), bounds = [0.00 : inf] \n",
      "\n",
      "image: 0, 3\n",
      "x0_bec: 124.926, (init = 124.288), bounds = [0.00 : 200.00] \n",
      "x0_th: 125.027, (init = 124.288), bounds = [0.00 : 200.00] \n",
      "amp_bec: 0.273, (init = 0.348), bounds = [0.00 : 0.65] \n",
      "amp_th: 0.207, (init = 0.149), bounds = [0.00 : 0.65] \n",
      "sigma_bec: 19.490, (init = 27.869), bounds = [0.00 : 50.00] \n",
      "sigma_th: 21.472, (init = 70.449), bounds = [0.00 : inf] \n",
      "\n",
      "image: 0, 4\n",
      "x0_bec: 125.124, (init = 124.844), bounds = [0.00 : 200.00] \n",
      "x0_th: 125.193, (init = 124.844), bounds = [0.00 : 200.00] \n",
      "amp_bec: 0.371, (init = 0.413), bounds = [0.00 : 0.77] \n",
      "amp_th: 0.199, (init = 0.177), bounds = [0.00 : 0.77] \n",
      "sigma_bec: 20.901, (init = 26.230), bounds = [0.00 : 50.00] \n",
      "sigma_th: 20.335, (init = 66.305), bounds = [0.00 : inf] \n",
      "\n",
      "image: 0, 5\n",
      "x0_bec: 125.059, (init = 124.472), bounds = [0.00 : 200.00] \n",
      "x0_th: 125.087, (init = 124.472), bounds = [0.00 : 200.00] \n",
      "amp_bec: 0.362, (init = 0.443), bounds = [0.00 : 0.82] \n",
      "amp_th: 0.257, (init = 0.190), bounds = [0.00 : 0.82] \n",
      "sigma_bec: 21.613, (init = 28.689), bounds = [0.00 : 50.00] \n",
      "sigma_th: 16.823, (init = 72.521), bounds = [0.00 : inf] \n",
      "\n",
      "image: 0, 6\n",
      "x0_bec: 124.891, (init = 124.226), bounds = [0.00 : 200.00] \n",
      "x0_th: 125.353, (init = 124.226), bounds = [0.00 : 200.00] \n",
      "amp_bec: 0.446, (init = 0.484), bounds = [0.00 : 0.90] \n",
      "amp_th: 0.241, (init = 0.207), bounds = [0.00 : 0.90] \n",
      "sigma_bec: 22.882, (init = 28.689), bounds = [0.00 : 50.00] \n",
      "sigma_th: 15.696, (init = 72.521), bounds = [0.00 : inf] \n",
      "\n",
      "image: 0, 7\n",
      "x0_bec: 125.045, (init = 124.374), bounds = [0.00 : 200.00] \n",
      "x0_th: 124.786, (init = 124.374), bounds = [0.00 : 200.00] \n",
      "amp_bec: 0.561, (init = 0.531), bounds = [0.00 : 0.99] \n",
      "amp_th: 0.167, (init = 0.228), bounds = [0.00 : 0.99] \n",
      "sigma_bec: 23.194, (init = 28.689), bounds = [0.00 : 50.00] \n",
      "sigma_th: 14.658, (init = 72.521), bounds = [0.00 : inf] \n",
      "\n",
      "image: 0, 8\n",
      "x0_bec: 125.082, (init = 124.493), bounds = [0.00 : 200.00] \n",
      "x0_th: 124.756, (init = 124.493), bounds = [0.00 : 200.00] \n",
      "amp_bec: 0.593, (init = 0.542), bounds = [0.00 : 1.01] \n",
      "amp_th: 0.162, (init = 0.232), bounds = [0.00 : 1.01] \n",
      "sigma_bec: 23.255, (init = 28.689), bounds = [0.00 : 50.00] \n",
      "sigma_th: 14.697, (init = 72.521), bounds = [0.00 : inf] \n",
      "\n",
      "image: 0, 9\n",
      "x0_bec: 125.148, (init = 124.406), bounds = [0.00 : 200.00] \n",
      "x0_th: 124.448, (init = 124.406), bounds = [0.00 : 200.00] \n",
      "amp_bec: 0.578, (init = 0.581), bounds = [0.00 : 1.08] \n",
      "amp_th: 0.239, (init = 0.249), bounds = [0.00 : 1.08] \n",
      "sigma_bec: 23.096, (init = 28.689), bounds = [0.00 : 50.00] \n",
      "sigma_th: 14.596, (init = 72.521), bounds = [0.00 : inf] \n",
      "\n",
      "image: 0, 10\n",
      "x0_bec: 125.177, (init = 124.672), bounds = [0.00 : 200.00] \n",
      "x0_th: 33.642, (init = 124.672), bounds = [0.00 : 200.00] \n",
      "amp_bec: 0.667, (init = 0.482), bounds = [0.00 : 0.89] \n",
      "amp_th: 0.001, (init = 0.206), bounds = [0.00 : 0.89] \n",
      "sigma_bec: 25.278, (init = 31.967), bounds = [0.00 : 50.00] \n",
      "sigma_th: 49.148, (init = 80.809), bounds = [0.00 : inf] \n",
      "\n",
      "image: 1, 0\n",
      "x0_bec: 115.300, (init = 122.431), bounds = [0.00 : 200.00] \n",
      "x0_th: 124.861, (init = 122.431), bounds = [0.00 : 200.00] \n",
      "amp_bec: 0.017, (init = 0.116), bounds = [0.00 : 0.21] \n",
      "amp_th: 0.079, (init = 0.050), bounds = [0.00 : 0.21] \n",
      "sigma_bec: 15.995, (init = 22.131), bounds = [0.00 : 50.00] \n",
      "sigma_th: 46.606, (init = 55.945), bounds = [0.00 : inf] \n",
      "\n",
      "image: 1, 1\n",
      "x0_bec: 125.641, (init = 123.764), bounds = [0.00 : 200.00] \n",
      "x0_th: 125.645, (init = 123.764), bounds = [0.00 : 200.00] \n",
      "amp_bec: 0.147, (init = 0.234), bounds = [0.00 : 0.43] \n",
      "amp_th: 0.147, (init = 0.100), bounds = [0.00 : 0.43] \n",
      "sigma_bec: 14.760, (init = 23.770), bounds = [0.00 : 50.00] \n",
      "sigma_th: 31.996, (init = 60.089), bounds = [0.00 : inf] \n",
      "\n",
      "image: 1, 2\n",
      "x0_bec: 125.476, (init = 123.665), bounds = [0.00 : 200.00] \n",
      "x0_th: 121.336, (init = 123.665), bounds = [0.00 : 200.00] \n",
      "amp_bec: 0.349, (init = 0.363), bounds = [0.00 : 0.67] \n",
      "amp_th: 0.115, (init = 0.155), bounds = [0.00 : 0.67] \n",
      "sigma_bec: 19.502, (init = 27.869), bounds = [0.00 : 50.00] \n",
      "sigma_th: 31.308, (init = 70.449), bounds = [0.00 : inf] \n",
      "\n",
      "image: 1, 3\n",
      "x0_bec: 124.301, (init = 123.005), bounds = [0.00 : 200.00] \n",
      "x0_th: 126.112, (init = 123.005), bounds = [0.00 : 200.00] \n",
      "amp_bec: 0.301, (init = 0.391), bounds = [0.00 : 0.73] \n",
      "amp_th: 0.205, (init = 0.168), bounds = [0.00 : 0.73] \n",
      "sigma_bec: 20.053, (init = 19.672), bounds = [0.00 : 50.00] \n",
      "sigma_th: 22.374, (init = 49.729), bounds = [0.00 : inf] \n",
      "\n",
      "image: 1, 4\n",
      "x0_bec: 124.986, (init = 121.597), bounds = [0.00 : 200.00] \n",
      "x0_th: 126.549, (init = 121.597), bounds = [0.00 : 200.00] \n",
      "amp_bec: 0.443, (init = 0.399), bounds = [0.00 : 0.74] \n",
      "amp_th: 0.109, (init = 0.171), bounds = [0.00 : 0.74] \n",
      "sigma_bec: 21.878, (init = 26.230), bounds = [0.00 : 50.00] \n",
      "sigma_th: 27.009, (init = 66.305), bounds = [0.00 : inf] \n",
      "\n",
      "image: 1, 5\n",
      "x0_bec: 123.630, (init = 125.610), bounds = [0.00 : 200.00] \n",
      "x0_th: 127.392, (init = 125.610), bounds = [0.00 : 200.00] \n",
      "amp_bec: 0.368, (init = 0.492), bounds = [0.00 : 0.91] \n",
      "amp_th: 0.313, (init = 0.211), bounds = [0.00 : 0.91] \n",
      "sigma_bec: 22.582, (init = 27.869), bounds = [0.00 : 50.00] \n",
      "sigma_th: 16.015, (init = 70.449), bounds = [0.00 : inf] \n",
      "\n",
      "image: 1, 6\n",
      "x0_bec: 123.995, (init = 123.000), bounds = [0.00 : 200.00] \n",
      "x0_th: 126.963, (init = 123.000), bounds = [0.00 : 200.00] \n",
      "amp_bec: 0.454, (init = 0.552), bounds = [0.00 : 1.02] \n",
      "amp_th: 0.295, (init = 0.236), bounds = [0.00 : 1.02] \n",
      "sigma_bec: 23.228, (init = 26.230), bounds = [0.00 : 50.00] \n",
      "sigma_th: 14.680, (init = 66.305), bounds = [0.00 : inf] \n",
      "\n",
      "image: 1, 7\n",
      "x0_bec: 124.486, (init = 124.890), bounds = [0.00 : 200.00] \n",
      "x0_th: 129.053, (init = 124.890), bounds = [0.00 : 200.00] \n",
      "amp_bec: 0.675, (init = 0.619), bounds = [0.00 : 1.15] \n",
      "amp_th: 0.176, (init = 0.265), bounds = [0.00 : 1.15] \n",
      "sigma_bec: 23.196, (init = 18.852), bounds = [0.00 : 50.00] \n",
      "sigma_th: 14.660, (init = 47.657), bounds = [0.00 : inf] \n",
      "\n",
      "image: 1, 8\n",
      "x0_bec: 125.882, (init = 124.262), bounds = [0.00 : 200.00] \n",
      "x0_th: 122.209, (init = 124.262), bounds = [0.00 : 200.00] \n",
      "amp_bec: 0.660, (init = 0.649), bounds = [0.00 : 1.20] \n",
      "amp_th: 0.231, (init = 0.278), bounds = [0.00 : 1.20] \n",
      "sigma_bec: 22.940, (init = 24.590), bounds = [0.00 : 50.00] \n",
      "sigma_th: 14.498, (init = 62.161), bounds = [0.00 : inf] \n",
      "\n",
      "image: 1, 9\n",
      "x0_bec: 124.657, (init = 124.274), bounds = [0.00 : 200.00] \n",
      "x0_th: 151.041, (init = 124.274), bounds = [0.00 : 200.00] \n",
      "amp_bec: 0.817, (init = 0.681), bounds = [0.00 : 1.26] \n",
      "amp_th: 0.028, (init = 0.292), bounds = [0.00 : 1.26] \n",
      "sigma_bec: 23.461, (init = 25.410), bounds = [0.00 : 50.00] \n",
      "sigma_th: 22.517, (init = 64.233), bounds = [0.00 : inf] \n",
      "\n",
      "image: 1, 10\n",
      "x0_bec: 124.870, (init = 125.252), bounds = [0.00 : 200.00] \n",
      "x0_th: 167.237, (init = 125.252), bounds = [0.00 : 200.00] \n",
      "amp_bec: 0.729, (init = 0.563), bounds = [0.00 : 1.04] \n",
      "amp_th: 0.007, (init = 0.241), bounds = [0.00 : 1.04] \n",
      "sigma_bec: 26.148, (init = 25.410), bounds = [0.00 : 50.00] \n",
      "sigma_th: 16.526, (init = 64.233), bounds = [0.00 : inf] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print_bval_bulk(result_x)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-01T13:47:36.165187200Z",
     "start_time": "2023-08-01T13:47:36.030016100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x500 with 22 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 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()\n",
    "\n",
    "bval = result_x[0][0].best_values\n",
    "plt.plot(x, X_guess_og[0,0])\n",
    "plt.plot(x, density_1d(x, **result_x[0][0].best_values))\n",
    "plt.plot(x, thermal(x, bval['x0_th'], bval['amp_th'], bval['sigma_th']))\n",
    "plt.show()\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-02T16:09:57.213069400Z",
     "start_time": "2023-08-02T16:09:54.252121900Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 2D Fit without mathematical constraint"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "outputs": [
    {
     "data": {
      "text/plain": "0.4358651483519299"
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(gaussian_filter(cropOD[1][1], sigma=1))\n",
    "S = np.max(gaussian_filter(data, sigma=1))/(bval_1d['amp_bec'] + bval_1d['amp_th'])\n",
    "print(S)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-01T13:14:57.791180400Z",
     "start_time": "2023-08-01T13:14:57.674401100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "image 0,0\n",
      "{'x0_bec': 142.00445054598697, 'x0_th': 124.28433053144651, 'amp_bec': 0.007373864322594913, 'amp_th': 0.0875248828973439, 'sigma_bec': 46.835962030049785, 'sigma_th': 38.73088055278765}\n",
      "Image seems to be purely thermal (guessed from 1d fit amplitude)\n",
      "Name           Value      Min      Max   Stderr     Vary     Expr Brute_Step\n",
      "amp_bec            0        0    0.402     None    False     None     None\n",
      "amp_th        0.1352        0    0.402     None     True     None     None\n",
      "sigma_th       38.73        0      250     None     True     None     None\n",
      "sigmax_bec         1     -inf      inf     None    False     None     None\n",
      "sigmay_bec         1     -inf      inf     None    False     None     None\n",
      "x0_bec             1     -inf      inf     None    False     None     None\n",
      "x0_th          124.1    114.1    134.1     None     True     None     None\n",
      "y0_bec             1     -inf      inf     None    False     None     None\n",
      "y0_th          124.3    114.3    134.3     None     True     None     None\n",
      "time 1st fit: 0.242 s\n",
      "\n",
      "image 0,1\n",
      "{'x0_bec': 125.25577180118816, 'x0_th': 124.78468527561685, 'amp_bec': 0.12152950655479954, 'amp_th': 0.1637417913764779, 'sigma_bec': 15.415704344918188, 'sigma_th': 27.655652447556896}\n",
      "Name           Value      Min      Max   Stderr     Vary     Expr Brute_Step\n",
      "amp_bec       0.1698        0   0.7187     None     True     None     None\n",
      "amp_th        0.2287        0   0.7187     None     True     None     None\n",
      "sigma_th       27.66        0      250     None     True     None     None\n",
      "sigmax_bec     15.42        0    59.02     None     True     None     None\n",
      "sigmay_bec     23.77        0    47.54     None     True     None     None\n",
      "x0_bec         123.2    113.2    133.2     None     True     None     None\n",
      "x0_th          123.2    113.2    133.2     None     True     None     None\n",
      "y0_bec         126.1    116.1    136.1     None     True     None     None\n",
      "y0_th          126.1    116.1    136.1     None     True     None     None\n",
      "time 1st fit: 0.493 s\n",
      "\n",
      "image 0,2\n",
      "{'x0_bec': 125.03930894042861, 'x0_th': 125.04635162070355, 'amp_bec': 0.2144277721612532, 'amp_th': 0.1696419781395752, 'sigma_bec': 18.04728438783922, 'sigma_th': 26.6394342289486}\n",
      "Name           Value      Min      Max   Stderr     Vary     Expr Brute_Step\n",
      "amp_bec       0.3083        0   0.8679     None     True     None     None\n",
      "amp_th        0.2439        0   0.8679     None     True     None     None\n",
      "sigma_th       26.64        0      250     None     True     None     None\n",
      "sigmax_bec     18.05        0    57.38     None     True     None     None\n",
      "sigmay_bec     25.41        0    50.82     None     True     None     None\n",
      "x0_bec         124.2    114.2    134.2     None     True     None     None\n",
      "x0_th          124.2    114.2    134.2     None     True     None     None\n",
      "y0_bec         125.1    115.1    135.1     None     True     None     None\n",
      "y0_th          125.1    115.1    135.1     None     True     None     None\n",
      "time 1st fit: 0.371 s\n",
      "\n",
      "image 0,3\n",
      "{'x0_bec': 124.92625233577346, 'x0_th': 125.02682757068777, 'amp_bec': 0.27265942749051736, 'amp_th': 0.20738200828334746, 'sigma_bec': 19.489535904921766, 'sigma_th': 21.471525793182366}\n",
      "Name           Value      Min      Max   Stderr     Vary     Expr Brute_Step\n",
      "amp_bec       0.3762        0    1.067     None     True     None     None\n",
      "amp_th        0.2861        0    1.067     None     True     None     None\n",
      "sigma_th       21.47        0      250     None     True     None     None\n",
      "sigmax_bec     19.49        0    55.74     None     True     None     None\n",
      "sigmay_bec     24.59        0    49.18     None     True     None     None\n",
      "x0_bec         124.3    114.3    134.3     None     True     None     None\n",
      "x0_th          124.3    114.3    134.3     None     True     None     None\n",
      "y0_bec         125.4    115.4    135.4     None     True     None     None\n",
      "y0_th          125.4    115.4    135.4     None     True     None     None\n",
      "time 1st fit: 0.313 s\n",
      "\n",
      "image 0,4\n",
      "{'x0_bec': 125.12441109737169, 'x0_th': 125.19345014170287, 'amp_bec': 0.3714727519389241, 'amp_th': 0.19908084523127406, 'sigma_bec': 20.901397005132623, 'sigma_th': 20.33510418067732}\n",
      "Name           Value      Min      Max   Stderr     Vary     Expr Brute_Step\n",
      "amp_bec       0.4801        0    1.238     None     True     None     None\n",
      "amp_th        0.2573        0    1.238     None     True     None     None\n",
      "sigma_th       20.34        0      250     None     True     None     None\n",
      "sigmax_bec      20.9        0    52.46     None     True     None     None\n",
      "sigmay_bec     23.77        0    47.54     None     True     None     None\n",
      "x0_bec         124.8    114.8    134.8     None     True     None     None\n",
      "x0_th          124.8    114.8    134.8     None     True     None     None\n",
      "y0_bec         125.6    115.6    135.6     None     True     None     None\n",
      "y0_th          125.6    115.6    135.6     None     True     None     None\n",
      "time 1st fit: 0.292 s\n",
      "\n",
      "image 0,5\n",
      "{'x0_bec': 125.05880935383358, 'x0_th': 125.08710237316461, 'amp_bec': 0.3619755295023772, 'amp_th': 0.25678414606161054, 'sigma_bec': 21.61282768969963, 'sigma_th': 16.823200155520258}\n",
      "Name           Value      Min      Max   Stderr     Vary     Expr Brute_Step\n",
      "amp_bec       0.5038        0    1.258     None     True     None     None\n",
      "amp_th        0.3574        0    1.258     None     True     None     None\n",
      "sigma_th       16.82        0      250     None     True     None     None\n",
      "sigmax_bec     21.61        0    57.38     None     True     None     None\n",
      "sigmay_bec     25.41        0    50.82     None     True     None     None\n",
      "x0_bec         124.5    114.5    134.5     None     True     None     None\n",
      "x0_th          124.5    114.5    134.5     None     True     None     None\n",
      "y0_bec         125.6    115.6    135.6     None     True     None     None\n",
      "y0_th          125.6    115.6    135.6     None     True     None     None\n",
      "time 1st fit: 0.335 s\n",
      "\n",
      "image 0,6\n",
      "{'x0_bec': 124.8909916649399, 'x0_th': 125.35264471205264, 'amp_bec': 0.44592093863560234, 'amp_th': 0.24131310850250906, 'sigma_bec': 22.881675505502773, 'sigma_th': 15.696093137012516}\n",
      "Name           Value      Min      Max   Stderr     Vary     Expr Brute_Step\n",
      "amp_bec       0.5892        0    1.431     None     True     None     None\n",
      "amp_th        0.3189        0    1.431     None     True     None     None\n",
      "sigma_th        15.7        0      250     None     True     None     None\n",
      "sigmax_bec     22.88        0    57.38     None     True     None     None\n",
      "sigmay_bec     24.59        0    49.18     None     True     None     None\n",
      "x0_bec         124.2    114.2    134.2     None     True     None     None\n",
      "x0_th          124.2    114.2    134.2     None     True     None     None\n",
      "y0_bec         125.5    115.5    135.5     None     True     None     None\n",
      "y0_th          125.5    115.5    135.5     None     True     None     None\n",
      "time 1st fit: 0.294 s\n",
      "\n",
      "image 0,7\n",
      "{'x0_bec': 125.04469492751748, 'x0_th': 124.7857884621788, 'amp_bec': 0.5608043784706194, 'amp_th': 0.16682391910241443, 'sigma_bec': 23.193603880722023, 'sigma_th': 14.658357655151772}\n",
      "Name           Value      Min      Max   Stderr     Vary     Expr Brute_Step\n",
      "amp_bec       0.7446        0    1.465     None     True     None     None\n",
      "amp_th        0.2215        0    1.465     None     True     None     None\n",
      "sigma_th       14.66        0      250     None     True     None     None\n",
      "sigmax_bec     23.19        0    57.38     None     True     None     None\n",
      "sigmay_bec     25.41        0    50.82     None     True     None     None\n",
      "x0_bec         124.4    114.4    134.4     None     True     None     None\n",
      "x0_th          124.4    114.4    134.4     None     True     None     None\n",
      "y0_bec         125.4    115.4    135.4     None     True     None     None\n",
      "y0_th          125.4    115.4    135.4     None     True     None     None\n",
      "time 1st fit: 0.246 s\n",
      "No thermal part detected, performing fit without thermal function\n",
      "time pure bec fit: 0.116 s\n",
      "\n",
      "\n",
      "image 0,8\n",
      "{'x0_bec': 125.08153671213292, 'x0_th': 124.7558768216271, 'amp_bec': 0.5927708914537864, 'amp_th': 0.16249800996749553, 'sigma_bec': 23.254998019081764, 'sigma_th': 14.697158756687998}\n",
      "Name           Value      Min      Max   Stderr     Vary     Expr Brute_Step\n",
      "amp_bec         0.84        0    1.622     None     True     None     None\n",
      "amp_th        0.2303        0    1.622     None     True     None     None\n",
      "sigma_th        14.7        0      250     None     True     None     None\n",
      "sigmax_bec     23.25        0    57.38     None     True     None     None\n",
      "sigmay_bec     25.41        0    50.82     None     True     None     None\n",
      "x0_bec         124.5    114.5    134.5     None     True     None     None\n",
      "x0_th          124.5    114.5    134.5     None     True     None     None\n",
      "y0_bec         125.5    115.5    135.5     None     True     None     None\n",
      "y0_th          125.5    115.5    135.5     None     True     None     None\n",
      "time 1st fit: 0.273 s\n",
      "No thermal part detected, performing fit without thermal function\n",
      "time pure bec fit: 0.132 s\n",
      "\n",
      "\n",
      "image 0,9\n",
      "{'x0_bec': 125.14832036671892, 'x0_th': 124.44756415270118, 'amp_bec': 0.5775981297541561, 'amp_th': 0.23938122478229795, 'sigma_bec': 23.095694571065323, 'sigma_th': 14.596478970725943}\n",
      "Name           Value      Min      Max   Stderr     Vary     Expr Brute_Step\n",
      "amp_bec       0.7835        0    1.651     None     True     None     None\n",
      "amp_th        0.3247        0    1.651     None     True     None     None\n",
      "sigma_th        14.6        0      250     None     True     None     None\n",
      "sigmax_bec      23.1        0    57.38     None     True     None     None\n",
      "sigmay_bec     22.95        0     45.9     None     True     None     None\n",
      "x0_bec         124.4    114.4    134.4     None     True     None     None\n",
      "x0_th          124.4    114.4    134.4     None     True     None     None\n",
      "y0_bec         125.5    115.5    135.5     None     True     None     None\n",
      "y0_th          125.5    115.5    135.5     None     True     None     None\n",
      "time 1st fit: 0.349 s\n",
      "No thermal part detected, performing fit without thermal function\n",
      "time pure bec fit: 0.127 s\n",
      "\n",
      "\n",
      "image 0,10\n",
      "{'x0_bec': 125.17723569835653, 'x0_th': 33.64171959660166, 'amp_bec': 0.6670196979301563, 'amp_th': 0.001172093233604589, 'sigma_bec': 25.278067756513355, 'sigma_th': 49.148384211550834}\n",
      "Image seems to be pure BEC (guessed from 1d fit amplitude)\n",
      "Name           Value      Min      Max   Stderr     Vary     Expr Brute_Step\n",
      "amp_bec        0.896        0      1.4     None     True     None     None\n",
      "amp_th             0     -inf      inf     None    False     None     None\n",
      "sigma_th       49.15        0       50     None    False     None     None\n",
      "sigmax_bec     25.28        0    63.93     None     True     None     None\n",
      "sigmay_bec     27.05        0    63.93     None     True     None     None\n",
      "x0_bec         124.7    114.7    134.7     None     True     None     None\n",
      "x0_th              1     -inf      inf     None    False     None     None\n",
      "y0_bec         125.5    115.5    135.5     None     True     None     None\n",
      "y0_th              1     -inf      inf     None    False     None     None\n",
      "time 1st fit: 0.171 s\n",
      "\n",
      "image 1,0\n",
      "{'x0_bec': 115.2999768678062, 'x0_th': 124.8610881345612, 'amp_bec': 0.017319670376729462, 'amp_th': 0.07888591648540823, 'sigma_bec': 15.994939453828422, 'sigma_th': 46.60622154086924}\n",
      "Image seems to be purely thermal (guessed from 1d fit amplitude)\n",
      "Name           Value      Min      Max   Stderr     Vary     Expr Brute_Step\n",
      "amp_bec            0        0    1.225     None    False     None     None\n",
      "amp_th         0.209        0    1.225     None     True     None     None\n",
      "sigma_th       46.61        0      250     None     True     None     None\n",
      "sigmax_bec         1     -inf      inf     None    False     None     None\n",
      "sigmay_bec         1     -inf      inf     None    False     None     None\n",
      "x0_bec             1     -inf      inf     None    False     None     None\n",
      "x0_th          122.4    112.4    132.4     None     True     None     None\n",
      "y0_bec             1     -inf      inf     None    False     None     None\n",
      "y0_th          127.4    117.4    137.4     None     True     None     None\n",
      "time 1st fit: 0.207 s\n",
      "\n",
      "image 1,1\n",
      "{'x0_bec': 125.64091007896643, 'x0_th': 125.64514313918502, 'amp_bec': 0.14742375240111238, 'amp_th': 0.14686523357432518, 'sigma_bec': 14.7603215515683, 'sigma_th': 31.99614649538574}\n",
      "Name           Value      Min      Max   Stderr     Vary     Expr Brute_Step\n",
      "amp_bec       0.2183        0    1.255     None     True     None     None\n",
      "amp_th        0.2175        0    1.255     None     True     None     None\n",
      "sigma_th          32        0      250     None     True     None     None\n",
      "sigmax_bec     14.76        0    47.54     None     True     None     None\n",
      "sigmay_bec     23.77        0    47.54     None     True     None     None\n",
      "x0_bec         123.8    113.8    133.8     None     True     None     None\n",
      "x0_th          123.8    113.8    133.8     None     True     None     None\n",
      "y0_bec         126.1    116.1    136.1     None     True     None     None\n",
      "y0_th          126.1    116.1    136.1     None     True     None     None\n",
      "time 1st fit: 0.752 s\n",
      "\n",
      "image 1,2\n",
      "{'x0_bec': 125.47573499952959, 'x0_th': 121.3360031869806, 'amp_bec': 0.3488782243097099, 'amp_th': 0.11461950154784539, 'sigma_bec': 19.50209844607982, 'sigma_th': 31.308229448246607}\n",
      "Name           Value      Min      Max   Stderr     Vary     Expr Brute_Step\n",
      "amp_bec       0.4859        0    1.481     None     True     None     None\n",
      "amp_th        0.1596        0    1.481     None     True     None     None\n",
      "sigma_th       31.31        0      250     None     True     None     None\n",
      "sigmax_bec      19.5        0    55.74     None     True     None     None\n",
      "sigmay_bec     13.93        0    27.87     None     True     None     None\n",
      "x0_bec         123.7    113.7    133.7     None     True     None     None\n",
      "x0_th          123.7    113.7    133.7     None     True     None     None\n",
      "y0_bec           129      119      139     None     True     None     None\n",
      "y0_th            129      119      139     None     True     None     None\n",
      "time 1st fit: 0.463 s\n",
      "\n",
      "image 1,3\n",
      "{'x0_bec': 124.30117026347911, 'x0_th': 126.11199574611234, 'amp_bec': 0.30069680977837876, 'amp_th': 0.20541314543839723, 'sigma_bec': 20.05319776888755, 'sigma_th': 22.374354426220144}\n",
      "Name           Value      Min      Max   Stderr     Vary     Expr Brute_Step\n",
      "amp_bec       0.4567        0    1.973     None     True     None     None\n",
      "amp_th         0.312        0    1.973     None     True     None     None\n",
      "sigma_th       22.37        0      250     None     True     None     None\n",
      "sigmax_bec     20.05        0    39.34     None     True     None     None\n",
      "sigmay_bec     20.49        0    40.98     None     True     None     None\n",
      "x0_bec           123      113      133     None     True     None     None\n",
      "x0_th            123      113      133     None     True     None     None\n",
      "y0_bec         127.8    117.8    137.8     None     True     None     None\n",
      "y0_th          127.8    117.8    137.8     None     True     None     None\n",
      "time 1st fit: 0.499 s\n",
      "\n",
      "image 1,4\n",
      "{'x0_bec': 124.9862517955858, 'x0_th': 126.54880752038085, 'amp_bec': 0.44349528057747933, 'amp_th': 0.10946111445269097, 'sigma_bec': 21.87846319656666, 'sigma_th': 27.00891763857539}\n",
      "Name           Value      Min      Max   Stderr     Vary     Expr Brute_Step\n",
      "amp_bec       0.6429        0    1.761     None     True     None     None\n",
      "amp_th        0.1587        0    1.761     None     True     None     None\n",
      "sigma_th       27.01        0      250     None     True     None     None\n",
      "sigmax_bec     21.88        0    52.46     None     True     None     None\n",
      "sigmay_bec     25.41        0    50.82     None     True     None     None\n",
      "x0_bec         121.6    111.6    131.6     None     True     None     None\n",
      "x0_th          121.6    111.6    131.6     None     True     None     None\n",
      "y0_bec         125.8    115.8    135.8     None     True     None     None\n",
      "y0_th          125.8    115.8    135.8     None     True     None     None\n",
      "time 1st fit: 0.514 s\n",
      "\n",
      "image 1,5\n",
      "{'x0_bec': 123.62961045437524, 'x0_th': 127.39212821302428, 'amp_bec': 0.36843957335640354, 'amp_th': 0.3129914230267123, 'sigma_bec': 22.581842629500287, 'sigma_th': 16.01451287267635}\n",
      "Name           Value      Min      Max   Stderr     Vary     Expr Brute_Step\n",
      "amp_bec       0.4673        0    1.731     None     True     None     None\n",
      "amp_th        0.3969        0    1.731     None     True     None     None\n",
      "sigma_th       16.01        0      250     None     True     None     None\n",
      "sigmax_bec     22.58        0    55.74     None     True     None     None\n",
      "sigmay_bec     19.67        0    39.34     None     True     None     None\n",
      "x0_bec         125.6    115.6    135.6     None     True     None     None\n",
      "x0_th          125.6    115.6    135.6     None     True     None     None\n",
      "y0_bec         129.1    119.1    139.1     None     True     None     None\n",
      "y0_th          129.1    119.1    139.1     None     True     None     None\n",
      "time 1st fit: 0.429 s\n",
      "\n",
      "image 1,6\n",
      "{'x0_bec': 123.99541994176477, 'x0_th': 126.96289225537845, 'amp_bec': 0.45407626835677517, 'amp_th': 0.2954323696551742, 'sigma_bec': 23.228122284311485, 'sigma_th': 14.680173311781266}\n",
      "Name           Value      Min      Max   Stderr     Vary     Expr Brute_Step\n",
      "amp_bec       0.5913        0    2.092     None     True     None     None\n",
      "amp_th        0.3847        0    2.092     None     True     None     None\n",
      "sigma_th       14.68        0      250     None     True     None     None\n",
      "sigmax_bec     23.23        0    52.46     None     True     None     None\n",
      "sigmay_bec     22.13        0    44.26     None     True     None     None\n",
      "x0_bec           123      113      133     None     True     None     None\n",
      "x0_th            123      113      133     None     True     None     None\n",
      "y0_bec         124.9    114.9    134.9     None     True     None     None\n",
      "y0_th          124.9    114.9    134.9     None     True     None     None\n",
      "time 1st fit: 0.444 s\n",
      "\n",
      "image 1,7\n",
      "{'x0_bec': 124.48568621006982, 'x0_th': 129.05342476497512, 'amp_bec': 0.675384310916434, 'amp_th': 0.1756796886879987, 'sigma_bec': 23.19598463923248, 'sigma_th': 14.659862294155865}\n",
      "Name           Value      Min      Max   Stderr     Vary     Expr Brute_Step\n",
      "amp_bec       0.9392        0    2.832     None     True     None     None\n",
      "amp_th        0.2443        0    2.832     None     True     None     None\n",
      "sigma_th       14.66        0      250     None     True     None     None\n",
      "sigmax_bec      23.2        0     37.7     None     True     None     None\n",
      "sigmay_bec     18.85        0     37.7     None     True     None     None\n",
      "x0_bec         124.9    114.9    134.9     None     True     None     None\n",
      "x0_th          124.9    114.9    134.9     None     True     None     None\n",
      "y0_bec         125.4    115.4    135.4     None     True     None     None\n",
      "y0_th          125.4    115.4    135.4     None     True     None     None\n",
      "time 1st fit: 0.643 s\n",
      "No thermal part detected, performing fit without thermal function\n",
      "time pure bec fit: 0.341 s\n",
      "\n",
      "\n",
      "image 1,8\n",
      "{'x0_bec': 125.88235659033784, 'x0_th': 122.20851997777476, 'amp_bec': 0.6599688637476858, 'amp_th': 0.23095084283480255, 'sigma_bec': 22.94039969781213, 'sigma_th': 14.498332644737234}\n",
      "Name           Value      Min      Max   Stderr     Vary     Expr Brute_Step\n",
      "amp_bec       0.8676        0    2.488     None     True     None     None\n",
      "amp_th        0.3036        0    2.488     None     True     None     None\n",
      "sigma_th        14.5        0      250     None     True     None     None\n",
      "sigmax_bec     22.94        0    49.18     None     True     None     None\n",
      "sigmay_bec     18.03        0    36.07     None     True     None     None\n",
      "x0_bec         124.3    114.3    134.3     None     True     None     None\n",
      "x0_th          124.3    114.3    134.3     None     True     None     None\n",
      "y0_bec         125.8    115.8    135.8     None     True     None     None\n",
      "y0_th          125.8    115.8    135.8     None     True     None     None\n",
      "time 1st fit: 0.408 s\n",
      "No thermal part detected, performing fit without thermal function\n",
      "time pure bec fit: 0.301 s\n",
      "\n",
      "\n",
      "image 1,9\n",
      "{'x0_bec': 124.65703878309688, 'x0_th': 151.0411000719266, 'amp_bec': 0.8167723325112359, 'amp_th': 0.028188745783018408, 'sigma_bec': 23.461295260810388, 'sigma_th': 22.516913844745105}\n",
      "Image seems to be pure BEC (guessed from 1d fit amplitude)\n",
      "Name           Value      Min      Max   Stderr     Vary     Expr Brute_Step\n",
      "amp_bec        1.175        0     2.53     None     True     None     None\n",
      "amp_th             0     -inf      inf     None    False     None     None\n",
      "sigma_th       22.52        0       50     None    False     None     None\n",
      "sigmax_bec     23.46        0    50.82     None     True     None     None\n",
      "sigmay_bec     21.31        0    50.82     None     True     None     None\n",
      "x0_bec         124.3    114.3    134.3     None     True     None     None\n",
      "x0_th              1     -inf      inf     None    False     None     None\n",
      "y0_bec         125.5    115.5    135.5     None     True     None     None\n",
      "y0_th              1     -inf      inf     None    False     None     None\n",
      "time 1st fit: 0.147 s\n",
      "\n",
      "image 1,10\n",
      "{'x0_bec': 124.86950626446966, 'x0_th': 167.2366912201061, 'amp_bec': 0.7288161256417663, 'amp_th': 0.007497160034535551, 'sigma_bec': 26.14829032722183, 'sigma_th': 16.525719532048736}\n",
      "Image seems to be pure BEC (guessed from 1d fit amplitude)\n",
      "Name           Value      Min      Max   Stderr     Vary     Expr Brute_Step\n",
      "amp_bec        1.087        0    2.192     None     True     None     None\n",
      "amp_th             0     -inf      inf     None    False     None     None\n",
      "sigma_th       16.53        0       50     None    False     None     None\n",
      "sigmax_bec     26.15        0    50.82     None     True     None     None\n",
      "sigmay_bec     22.13        0    50.82     None     True     None     None\n",
      "x0_bec         125.3    115.3    135.3     None     True     None     None\n",
      "x0_th              1     -inf      inf     None    False     None     None\n",
      "y0_bec         126.4    116.4    136.4     None     True     None     None\n",
      "y0_th              1     -inf      inf     None    False     None     None\n",
      "time 1st fit: 0.116 s\n",
      "fitting time = 0.413 +- 0.207\n",
      "max fitting time = 0.988s\n",
      "[0.24640846 0.49725819 0.37406635 0.31478739 0.29485202 0.33671474\n",
      " 0.29728603 0.36642241 0.40815687 0.48236537 0.17365742 0.20959353\n",
      " 0.75546026 0.46600842 0.50232697 0.51688814 0.4309833  0.44705105\n",
      " 0.98757935 0.71261954 0.14972115 0.11826086]\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",
    "X,Y = np.meshgrid(x, y)\n",
    "X_1d = X.flatten()\n",
    "Y_1d = Y.flatten()\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",
    "        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",
    "        print(bval_1d)\n",
    "\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",
    "\n",
    "        params.pretty_print()\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": "2023-08-02T14:56:03.655488900Z",
     "start_time": "2023-08-02T14:55:54.444880200Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Plotting"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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",
    "\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 {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()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-01T15:21:11.108359900Z",
     "start_time": "2023-08-01T15:20:54.022000300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image 0, 0\n",
      "FWHM_x BEC: 32.75, FWHM_x thermal: 74.38\n",
      "FWHM_y BEC: 25.59\n",
      "Ratio fwhm_th/fwhm_bec: 2.91\n",
      "\n",
      "image 0, 1\n",
      "FWHM_x BEC: 20.62, FWHM_x thermal: 54.05\n",
      "FWHM_y BEC: 18.09\n",
      "Ratio fwhm_th/fwhm_bec: 2.99\n",
      "\n",
      "image 0, 2\n",
      "FWHM_x BEC: 23.84, FWHM_x thermal: 49.32\n",
      "FWHM_y BEC: 20.18\n",
      "Ratio fwhm_th/fwhm_bec: 2.44\n",
      "\n",
      "image 0, 3\n",
      "FWHM_x BEC: 25.95, FWHM_x thermal: 41.75\n",
      "FWHM_y BEC: 22.19\n",
      "Ratio fwhm_th/fwhm_bec: 1.88\n",
      "\n",
      "image 0, 4\n",
      "FWHM_x BEC: 27.86, FWHM_x thermal: 37.63\n",
      "FWHM_y BEC: 23.50\n",
      "Ratio fwhm_th/fwhm_bec: 1.60\n",
      "\n",
      "image 0, 5\n",
      "FWHM_x BEC: 28.81, FWHM_x thermal: 34.44\n",
      "FWHM_y BEC: 24.27\n",
      "Ratio fwhm_th/fwhm_bec: 1.42\n",
      "\n",
      "image 0, 6\n",
      "FWHM_x BEC: 30.15, FWHM_x thermal: 29.43\n",
      "FWHM_y BEC: 25.28\n",
      "Ratio fwhm_th/fwhm_bec: 1.16\n",
      "\n",
      "image 0, 7\n",
      "FWHM_x BEC: 30.70, FWHM_x thermal: 25.68\n",
      "FWHM_y BEC: 26.31\n",
      "Ratio fwhm_th/fwhm_bec: 0.98\n",
      "\n",
      "image 0, 8\n",
      "FWHM_x BEC: 32.13, FWHM_x thermal: 19.48\n",
      "FWHM_y BEC: 27.06\n",
      "Ratio fwhm_th/fwhm_bec: 0.72\n",
      "\n",
      "image 0, 9\n",
      "FWHM_x BEC: 32.91, FWHM_x thermal: 17.25\n",
      "FWHM_y BEC: 27.48\n",
      "Ratio fwhm_th/fwhm_bec: 0.63\n",
      "\n",
      "image 0, 10\n",
      "FWHM_x BEC: 33.10, FWHM_x thermal: 1.21\n",
      "FWHM_y BEC: 28.28\n",
      "Ratio fwhm_th/fwhm_bec: 0.04\n",
      "\n",
      "image 1, 0\n",
      "FWHM_x BEC: 29.53, FWHM_x thermal: 79.98\n",
      "FWHM_y BEC: 3.75\n",
      "Ratio fwhm_th/fwhm_bec: 21.31\n",
      "\n",
      "image 1, 1\n",
      "FWHM_x BEC: 19.91, FWHM_x thermal: 56.26\n",
      "FWHM_y BEC: 18.48\n",
      "Ratio fwhm_th/fwhm_bec: 3.04\n",
      "\n",
      "image 1, 2\n",
      "FWHM_x BEC: 23.70, FWHM_x thermal: 48.49\n",
      "FWHM_y BEC: 19.98\n",
      "Ratio fwhm_th/fwhm_bec: 2.43\n",
      "\n",
      "image 1, 3\n",
      "FWHM_x BEC: 26.07, FWHM_x thermal: 42.63\n",
      "FWHM_y BEC: 21.63\n",
      "Ratio fwhm_th/fwhm_bec: 1.97\n",
      "\n",
      "image 1, 4\n",
      "FWHM_x BEC: 27.87, FWHM_x thermal: 36.96\n",
      "FWHM_y BEC: 23.97\n",
      "Ratio fwhm_th/fwhm_bec: 1.54\n",
      "\n",
      "image 1, 5\n",
      "FWHM_x BEC: 29.86, FWHM_x thermal: 33.99\n",
      "FWHM_y BEC: 24.15\n",
      "Ratio fwhm_th/fwhm_bec: 1.41\n",
      "\n",
      "image 1, 6\n",
      "FWHM_x BEC: 30.65, FWHM_x thermal: 35.69\n",
      "FWHM_y BEC: 25.63\n",
      "Ratio fwhm_th/fwhm_bec: 1.39\n",
      "\n",
      "image 1, 7\n",
      "FWHM_x BEC: 16.17, FWHM_x thermal: 20.71\n",
      "FWHM_y BEC: 25.97\n",
      "Ratio fwhm_th/fwhm_bec: 1.28\n",
      "\n",
      "image 1, 8\n",
      "FWHM_x BEC: 31.36, FWHM_x thermal: 18.54\n",
      "FWHM_y BEC: 28.22\n",
      "Ratio fwhm_th/fwhm_bec: 0.66\n",
      "\n",
      "image 1, 9\n",
      "FWHM_x BEC: 31.84, FWHM_x thermal: 16.81\n",
      "FWHM_y BEC: 27.11\n",
      "Ratio fwhm_th/fwhm_bec: 0.62\n",
      "\n",
      "image 1, 10\n",
      "FWHM_x BEC: 33.73, FWHM_x thermal: 0.04\n",
      "FWHM_y BEC: 27.85\n",
      "Ratio fwhm_th/fwhm_bec: 0.00\n",
      "\n"
     ]
    }
   ],
   "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(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": "2023-07-31T14:30:32.405769500Z",
     "start_time": "2023-07-31T14:30:32.206822Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image 0, 0\n"
     ]
    },
    {
     "ename": "type",
     "evalue": "'numpy.ndarray' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mTypeError\u001B[0m                                 Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[188], line 12\u001B[0m\n\u001B[0;32m     10\u001B[0m arr \u001B[38;5;241m=\u001B[39m []\n\u001B[0;32m     11\u001B[0m bval \u001B[38;5;241m=\u001B[39m result[i][j]\u001B[38;5;241m.\u001B[39mbest_values\n\u001B[1;32m---> 12\u001B[0m sigma_cut \u001B[38;5;241m=\u001B[39m \u001B[43mmax_val\u001B[49m\u001B[43m(\u001B[49m\u001B[43mbval\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43msigmay_bec\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mbval\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43msigmax_bec\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m     13\u001B[0m tf_fit \u001B[38;5;241m=\u001B[39m ThomasFermi_2d(X,Y,centerx\u001B[38;5;241m=\u001B[39mbval[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mx0_bec\u001B[39m\u001B[38;5;124m'\u001B[39m], centery\u001B[38;5;241m=\u001B[39mbval[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124my0_bec\u001B[39m\u001B[38;5;124m'\u001B[39m], amplitude\u001B[38;5;241m=\u001B[39mbval[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mamp_bec\u001B[39m\u001B[38;5;124m'\u001B[39m], sigmax\u001B[38;5;241m=\u001B[39mbval[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124msigmax_bec\u001B[39m\u001B[38;5;124m'\u001B[39m]\u001B[38;5;241m/\u001B[39m\u001B[38;5;241m1.22\u001B[39m, sigmay\u001B[38;5;241m=\u001B[39mbval[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124msigmay_bec\u001B[39m\u001B[38;5;124m'\u001B[39m]\u001B[38;5;241m/\u001B[39m\u001B[38;5;241m1.22\u001B[39m)\n\u001B[0;32m     14\u001B[0m tf_fit_2 \u001B[38;5;241m=\u001B[39m ThomasFermi_2d(X,Y,centerx\u001B[38;5;241m=\u001B[39mbval[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mx0_bec\u001B[39m\u001B[38;5;124m'\u001B[39m], centery\u001B[38;5;241m=\u001B[39mbval[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124my0_bec\u001B[39m\u001B[38;5;124m'\u001B[39m], amplitude\u001B[38;5;241m=\u001B[39mbval[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mamp_bec\u001B[39m\u001B[38;5;124m'\u001B[39m], sigmax\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m1.5\u001B[39m \u001B[38;5;241m*\u001B[39m sigma_cut\u001B[38;5;241m/\u001B[39m\u001B[38;5;241m1.22\u001B[39m, sigmay\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m1.5\u001B[39m\u001B[38;5;241m*\u001B[39m sigma_cut\u001B[38;5;241m/\u001B[39m\u001B[38;5;241m1.22\u001B[39m)\n",
      "\u001B[1;31mTypeError\u001B[0m: 'numpy.ndarray' object is not callable"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x1000 with 22 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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_val(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[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",
    "\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": "2023-08-01T13:28:57.973001500Z",
     "start_time": "2023-08-01T13:28:54.940256400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image: 0, 0\n",
      "amp_bec: 0.000, (init = 0.000), bounds = [0.00 : 0.40] \n",
      "amp_th: 0.103, (init = 0.135), bounds = [0.00 : 0.40] \n",
      "x0_bec: 114.060, (init = 114.060), bounds = [114.06 : 134.06] \n",
      "y0_bec: 114.291, (init = 114.291), bounds = [114.29 : 134.29] \n",
      "x0_th: 125.454, (init = 124.060), bounds = [114.06 : 134.06] \n",
      "y0_th: 125.676, (init = 124.291), bounds = [114.29 : 134.29] \n",
      "sigmax_bec: 1.000, (init = 1.000), bounds = [0.00 : 95.08] \n",
      "sigmay_bec: 1.000, (init = 1.000), bounds = [0.00 : 72.13] \n",
      "sigma_th: 37.876, (init = 38.731), bounds = [0.00 : 250.00] \n",
      "\n",
      "image: 0, 1\n",
      "amp_bec: 0.217, (init = 0.170), bounds = [0.00 : 0.72] \n",
      "amp_th: 0.169, (init = 0.229), bounds = [0.00 : 0.72] \n",
      "x0_bec: 125.205, (init = 123.226), bounds = [113.23 : 133.23] \n",
      "y0_bec: 126.138, (init = 126.090), bounds = [116.09 : 136.09] \n",
      "x0_th: 124.613, (init = 123.226), bounds = [113.23 : 133.23] \n",
      "y0_th: 126.036, (init = 126.090), bounds = [116.09 : 136.09] \n",
      "sigmax_bec: 16.903, (init = 15.416), bounds = [0.00 : 59.02] \n",
      "sigmay_bec: 14.835, (init = 23.770), bounds = [0.00 : 47.54] \n",
      "sigma_th: 28.007, (init = 27.656), bounds = [0.00 : 250.00] \n",
      "\n",
      "image: 0, 2\n",
      "amp_bec: 0.351, (init = 0.308), bounds = [0.00 : 0.87] \n",
      "amp_th: 0.186, (init = 0.244), bounds = [0.00 : 0.87] \n",
      "x0_bec: 124.756, (init = 124.199), bounds = [114.20 : 134.20] \n",
      "y0_bec: 125.997, (init = 125.119), bounds = [115.12 : 135.12] \n",
      "x0_th: 125.359, (init = 124.199), bounds = [114.20 : 134.20] \n",
      "y0_th: 125.902, (init = 125.119), bounds = [115.12 : 135.12] \n",
      "sigmax_bec: 19.537, (init = 18.047), bounds = [0.00 : 57.38] \n",
      "sigmay_bec: 16.545, (init = 25.410), bounds = [0.00 : 50.82] \n",
      "sigma_th: 25.553, (init = 26.639), bounds = [0.00 : 250.00] \n",
      "\n",
      "image: 0, 3\n",
      "amp_bec: 0.417, (init = 0.376), bounds = [0.00 : 1.07] \n",
      "amp_th: 0.221, (init = 0.286), bounds = [0.00 : 1.07] \n",
      "x0_bec: 124.875, (init = 124.288), bounds = [114.29 : 134.29] \n",
      "y0_bec: 126.008, (init = 125.419), bounds = [115.42 : 135.42] \n",
      "x0_th: 125.107, (init = 124.288), bounds = [114.29 : 134.29] \n",
      "y0_th: 125.814, (init = 125.419), bounds = [115.42 : 135.42] \n",
      "sigmax_bec: 21.269, (init = 19.490), bounds = [0.00 : 55.74] \n",
      "sigmay_bec: 18.190, (init = 24.590), bounds = [0.00 : 49.18] \n",
      "sigma_th: 21.630, (init = 21.472), bounds = [0.00 : 250.00] \n",
      "\n",
      "image: 0, 4\n",
      "amp_bec: 0.506, (init = 0.480), bounds = [0.00 : 1.24] \n",
      "amp_th: 0.228, (init = 0.257), bounds = [0.00 : 1.24] \n",
      "x0_bec: 125.172, (init = 124.844), bounds = [114.84 : 134.84] \n",
      "y0_bec: 125.980, (init = 125.632), bounds = [115.63 : 135.63] \n",
      "x0_th: 125.047, (init = 124.844), bounds = [114.84 : 134.84] \n",
      "y0_th: 125.947, (init = 125.632), bounds = [115.63 : 135.63] \n",
      "sigmax_bec: 22.839, (init = 20.901), bounds = [0.00 : 52.46] \n",
      "sigmay_bec: 19.261, (init = 23.770), bounds = [0.00 : 47.54] \n",
      "sigma_th: 19.498, (init = 20.335), bounds = [0.00 : 250.00] \n",
      "\n",
      "image: 0, 5\n",
      "amp_bec: 0.578, (init = 0.504), bounds = [0.00 : 1.26] \n",
      "amp_th: 0.238, (init = 0.357), bounds = [0.00 : 1.26] \n",
      "x0_bec: 124.950, (init = 124.472), bounds = [114.47 : 134.47] \n",
      "y0_bec: 125.948, (init = 125.613), bounds = [115.61 : 135.61] \n",
      "x0_th: 125.548, (init = 124.472), bounds = [114.47 : 134.47] \n",
      "y0_th: 126.312, (init = 125.613), bounds = [115.61 : 135.61] \n",
      "sigmax_bec: 23.612, (init = 21.613), bounds = [0.00 : 57.38] \n",
      "sigmay_bec: 19.890, (init = 25.410), bounds = [0.00 : 50.82] \n",
      "sigma_th: 17.846, (init = 16.823), bounds = [0.00 : 250.00] \n",
      "\n",
      "image: 0, 6\n",
      "amp_bec: 0.624, (init = 0.589), bounds = [0.00 : 1.43] \n",
      "amp_th: 0.273, (init = 0.319), bounds = [0.00 : 1.43] \n",
      "x0_bec: 124.841, (init = 124.226), bounds = [114.23 : 134.23] \n",
      "y0_bec: 126.087, (init = 125.493), bounds = [115.49 : 135.49] \n",
      "x0_th: 125.518, (init = 124.226), bounds = [114.23 : 134.23] \n",
      "y0_th: 125.328, (init = 125.493), bounds = [115.49 : 135.49] \n",
      "sigmax_bec: 24.716, (init = 22.882), bounds = [0.00 : 57.38] \n",
      "sigmay_bec: 20.723, (init = 24.590), bounds = [0.00 : 49.18] \n",
      "sigma_th: 15.245, (init = 15.696), bounds = [0.00 : 250.00] \n",
      "\n",
      "image: 0, 7\n",
      "amp_bec: 0.921, (init = 0.745), bounds = [0.00 : 1.47] \n",
      "amp_th: 0.000, (init = 0.000), bounds = [0.00 : 1.47] \n",
      "x0_bec: 124.991, (init = 124.374), bounds = [114.37 : 134.37] \n",
      "y0_bec: 126.028, (init = 125.431), bounds = [115.43 : 135.43] \n",
      "x0_th: 1.000, (init = 1.000), bounds = [0.00 : 150.00] \n",
      "y0_th: 1.000, (init = 1.000), bounds = [0.00 : 150.00] \n",
      "sigmax_bec: 25.377, (init = 23.194), bounds = [0.00 : 57.38] \n",
      "sigmay_bec: 22.347, (init = 25.410), bounds = [0.00 : 57.38] \n",
      "sigma_th: 14.658, (init = 14.658), bounds = [0.00 : 50.00] \n",
      "\n",
      "image: 0, 8\n",
      "amp_bec: 0.961, (init = 0.840), bounds = [0.00 : 1.62] \n",
      "amp_th: 0.000, (init = 0.000), bounds = [0.00 : 1.62] \n",
      "x0_bec: 125.003, (init = 124.493), bounds = [114.49 : 134.49] \n",
      "y0_bec: 125.923, (init = 125.501), bounds = [115.50 : 135.50] \n",
      "x0_th: 1.000, (init = 1.000), bounds = [0.00 : 150.00] \n",
      "y0_th: 1.000, (init = 1.000), bounds = [0.00 : 150.00] \n",
      "sigmax_bec: 25.550, (init = 23.255), bounds = [0.00 : 57.38] \n",
      "sigmay_bec: 22.058, (init = 25.410), bounds = [0.00 : 57.38] \n",
      "sigma_th: 14.697, (init = 14.697), bounds = [0.00 : 50.00] \n",
      "\n",
      "image: 0, 9\n",
      "amp_bec: 0.979, (init = 0.783), bounds = [0.00 : 1.65] \n",
      "amp_th: 0.000, (init = 0.000), bounds = [0.00 : 1.65] \n",
      "x0_bec: 124.977, (init = 124.406), bounds = [114.41 : 134.41] \n",
      "y0_bec: 126.070, (init = 125.502), bounds = [115.50 : 135.50] \n",
      "x0_th: 1.000, (init = 1.000), bounds = [0.00 : 150.00] \n",
      "y0_th: 1.000, (init = 1.000), bounds = [0.00 : 150.00] \n",
      "sigmax_bec: 25.456, (init = 23.096), bounds = [0.00 : 57.38] \n",
      "sigmay_bec: 21.924, (init = 22.951), bounds = [0.00 : 57.38] \n",
      "sigma_th: 14.596, (init = 14.596), bounds = [0.00 : 50.00] \n",
      "\n",
      "image: 0, 10\n",
      "amp_bec: 0.876, (init = 0.896), bounds = [0.00 : 1.40] \n",
      "amp_th: 0.000, (init = 0.000), bounds = [0.00 : 1.40] \n",
      "x0_bec: 125.124, (init = 124.672), bounds = [114.67 : 134.67] \n",
      "y0_bec: 125.952, (init = 125.545), bounds = [115.54 : 135.54] \n",
      "x0_th: 1.000, (init = 1.000), bounds = [0.00 : 150.00] \n",
      "y0_th: 1.000, (init = 1.000), bounds = [0.00 : 150.00] \n",
      "sigmax_bec: 27.134, (init = 25.278), bounds = [0.00 : 63.93] \n",
      "sigmay_bec: 23.177, (init = 27.049), bounds = [0.00 : 63.93] \n",
      "sigma_th: 49.148, (init = 49.148), bounds = [0.00 : 50.00] \n",
      "\n",
      "image: 1, 0\n",
      "amp_bec: 0.000, (init = 0.000), bounds = [0.00 : 1.22] \n",
      "amp_th: 0.094, (init = 0.209), bounds = [0.00 : 1.22] \n",
      "x0_bec: 112.431, (init = 112.431), bounds = [112.43 : 132.43] \n",
      "y0_bec: 117.409, (init = 117.409), bounds = [117.41 : 137.41] \n",
      "x0_th: 123.481, (init = 122.431), bounds = [112.43 : 132.43] \n",
      "y0_th: 125.563, (init = 127.409), bounds = [117.41 : 137.41] \n",
      "sigmax_bec: 1.000, (init = 1.000), bounds = [0.00 : 44.26] \n",
      "sigmay_bec: 1.000, (init = 1.000), bounds = [0.00 : 44.26] \n",
      "sigma_th: 41.492, (init = 46.606), bounds = [0.00 : 250.00] \n",
      "\n",
      "image: 1, 1\n",
      "amp_bec: 0.222, (init = 0.218), bounds = [0.00 : 1.25] \n",
      "amp_th: 0.167, (init = 0.218), bounds = [0.00 : 1.25] \n",
      "x0_bec: 125.679, (init = 123.764), bounds = [113.76 : 133.76] \n",
      "y0_bec: 126.265, (init = 126.074), bounds = [116.07 : 136.07] \n",
      "x0_th: 125.736, (init = 123.764), bounds = [113.76 : 133.76] \n",
      "y0_th: 126.341, (init = 126.074), bounds = [116.07 : 136.07] \n",
      "sigmax_bec: 16.318, (init = 14.760), bounds = [0.00 : 47.54] \n",
      "sigmay_bec: 15.153, (init = 23.770), bounds = [0.00 : 47.54] \n",
      "sigma_th: 29.155, (init = 31.996), bounds = [0.00 : 250.00] \n",
      "\n",
      "image: 1, 2\n",
      "amp_bec: 0.350, (init = 0.486), bounds = [0.00 : 1.48] \n",
      "amp_th: 0.186, (init = 0.160), bounds = [0.00 : 1.48] \n",
      "x0_bec: 125.699, (init = 123.665), bounds = [113.66 : 133.66] \n",
      "y0_bec: 125.724, (init = 129.025), bounds = [119.02 : 139.02] \n",
      "x0_th: 123.987, (init = 123.665), bounds = [113.66 : 133.66] \n",
      "y0_th: 125.451, (init = 129.025), bounds = [119.02 : 139.02] \n",
      "sigmax_bec: 19.430, (init = 19.502), bounds = [0.00 : 55.74] \n",
      "sigmay_bec: 16.378, (init = 13.934), bounds = [0.00 : 27.87] \n",
      "sigma_th: 25.125, (init = 31.308), bounds = [0.00 : 250.00] \n",
      "\n",
      "image: 1, 3\n",
      "amp_bec: 0.406, (init = 0.457), bounds = [0.00 : 1.97] \n",
      "amp_th: 0.221, (init = 0.312), bounds = [0.00 : 1.97] \n",
      "x0_bec: 124.571, (init = 123.005), bounds = [113.00 : 133.00] \n",
      "y0_bec: 125.526, (init = 127.835), bounds = [117.83 : 137.83] \n",
      "x0_th: 125.097, (init = 123.005), bounds = [113.00 : 133.00] \n",
      "y0_th: 125.680, (init = 127.835), bounds = [117.83 : 137.83] \n",
      "sigmax_bec: 21.363, (init = 20.053), bounds = [0.00 : 39.34] \n",
      "sigmay_bec: 17.720, (init = 20.492), bounds = [0.00 : 40.98] \n",
      "sigma_th: 22.086, (init = 22.374), bounds = [0.00 : 250.00] \n",
      "\n",
      "image: 1, 4\n",
      "amp_bec: 0.530, (init = 0.643), bounds = [0.00 : 1.76] \n",
      "amp_th: 0.222, (init = 0.159), bounds = [0.00 : 1.76] \n",
      "x0_bec: 124.518, (init = 121.597), bounds = [111.60 : 131.60] \n",
      "y0_bec: 126.389, (init = 125.829), bounds = [115.83 : 135.83] \n",
      "x0_th: 127.371, (init = 121.597), bounds = [111.60 : 131.60] \n",
      "y0_th: 126.405, (init = 125.829), bounds = [115.83 : 135.83] \n",
      "sigmax_bec: 22.847, (init = 21.878), bounds = [0.00 : 52.46] \n",
      "sigmay_bec: 19.646, (init = 25.410), bounds = [0.00 : 50.82] \n",
      "sigma_th: 19.150, (init = 27.009), bounds = [0.00 : 250.00] \n",
      "\n",
      "image: 1, 5\n",
      "amp_bec: 0.584, (init = 0.467), bounds = [0.00 : 1.73] \n",
      "amp_th: 0.230, (init = 0.397), bounds = [0.00 : 1.73] \n",
      "x0_bec: 124.242, (init = 125.610), bounds = [115.61 : 135.61] \n",
      "y0_bec: 126.095, (init = 129.130), bounds = [119.13 : 139.13] \n",
      "x0_th: 127.539, (init = 125.610), bounds = [115.61 : 135.61] \n",
      "y0_th: 126.423, (init = 129.130), bounds = [119.13 : 139.13] \n",
      "sigmax_bec: 24.476, (init = 22.582), bounds = [0.00 : 55.74] \n",
      "sigmay_bec: 19.799, (init = 19.672), bounds = [0.00 : 39.34] \n",
      "sigma_th: 17.612, (init = 16.015), bounds = [0.00 : 250.00] \n",
      "\n",
      "image: 1, 6\n",
      "amp_bec: 0.749, (init = 0.591), bounds = [0.00 : 2.09] \n",
      "amp_th: 0.137, (init = 0.385), bounds = [0.00 : 2.09] \n",
      "x0_bec: 125.001, (init = 123.000), bounds = [113.00 : 133.00] \n",
      "y0_bec: 126.174, (init = 124.917), bounds = [114.92 : 134.92] \n",
      "x0_th: 125.603, (init = 123.000), bounds = [113.00 : 133.00] \n",
      "y0_th: 123.634, (init = 124.917), bounds = [114.92 : 134.92] \n",
      "sigmax_bec: 25.120, (init = 23.228), bounds = [0.00 : 52.46] \n",
      "sigmay_bec: 21.012, (init = 22.131), bounds = [0.00 : 44.26] \n",
      "sigma_th: 18.497, (init = 14.680), bounds = [0.00 : 250.00] \n",
      "\n",
      "image: 1, 7\n",
      "amp_bec: 0.942, (init = 0.939), bounds = [0.00 : 2.83] \n",
      "amp_th: 0.000, (init = 0.000), bounds = [0.00 : 2.83] \n",
      "x0_bec: 125.129, (init = 124.890), bounds = [114.89 : 134.89] \n",
      "y0_bec: 126.026, (init = 125.362), bounds = [115.36 : 135.36] \n",
      "x0_th: 1.000, (init = 1.000), bounds = [0.00 : 150.00] \n",
      "y0_th: 1.000, (init = 1.000), bounds = [0.00 : 150.00] \n",
      "sigmax_bec: 25.268, (init = 23.196), bounds = [0.00 : 37.70] \n",
      "sigmay_bec: 22.227, (init = 18.852), bounds = [0.00 : 37.70] \n",
      "sigma_th: 14.660, (init = 14.660), bounds = [0.00 : 50.00] \n",
      "\n",
      "image: 1, 8\n",
      "amp_bec: 0.962, (init = 0.868), bounds = [0.00 : 2.49] \n",
      "amp_th: 0.000, (init = 0.000), bounds = [0.00 : 2.49] \n",
      "x0_bec: 124.986, (init = 124.262), bounds = [114.26 : 134.26] \n",
      "y0_bec: 126.237, (init = 125.803), bounds = [115.80 : 135.80] \n",
      "x0_th: 1.000, (init = 1.000), bounds = [0.00 : 150.00] \n",
      "y0_th: 1.000, (init = 1.000), bounds = [0.00 : 150.00] \n",
      "sigmax_bec: 24.950, (init = 22.940), bounds = [0.00 : 49.18] \n",
      "sigmay_bec: 22.706, (init = 18.033), bounds = [0.00 : 49.18] \n",
      "sigma_th: 14.498, (init = 14.498), bounds = [0.00 : 50.00] \n",
      "\n",
      "image: 1, 9\n",
      "amp_bec: 1.004, (init = 1.175), bounds = [0.00 : 2.53] \n",
      "amp_th: 0.000, (init = 0.000), bounds = [0.00 : 2.53] \n",
      "x0_bec: 124.837, (init = 124.274), bounds = [114.27 : 134.27] \n",
      "y0_bec: 126.150, (init = 125.475), bounds = [115.48 : 135.48] \n",
      "x0_th: 1.000, (init = 1.000), bounds = [0.00 : 150.00] \n",
      "y0_th: 1.000, (init = 1.000), bounds = [0.00 : 150.00] \n",
      "sigmax_bec: 25.198, (init = 23.461), bounds = [0.00 : 50.82] \n",
      "sigmay_bec: 21.781, (init = 21.311), bounds = [0.00 : 50.82] \n",
      "sigma_th: 22.517, (init = 22.517), bounds = [0.00 : 50.00] \n",
      "\n",
      "image: 1, 10\n",
      "amp_bec: 0.877, (init = 1.087), bounds = [0.00 : 2.19] \n",
      "amp_th: 0.000, (init = 0.000), bounds = [0.00 : 2.19] \n",
      "x0_bec: 124.955, (init = 125.252), bounds = [115.25 : 135.25] \n",
      "y0_bec: 126.100, (init = 126.356), bounds = [116.36 : 136.36] \n",
      "x0_th: 1.000, (init = 1.000), bounds = [0.00 : 150.00] \n",
      "y0_th: 1.000, (init = 1.000), bounds = [0.00 : 150.00] \n",
      "sigmax_bec: 27.646, (init = 26.148), bounds = [0.00 : 50.82] \n",
      "sigmay_bec: 22.830, (init = 22.131), bounds = [0.00 : 50.82] \n",
      "sigma_th: 16.526, (init = 16.526), bounds = [0.00 : 50.00] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print_bval_bulk(result)\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-01T13:54:57.594806500Z",
     "start_time": "2023-08-01T13:54:57.466504700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.40202012547790794\n",
      "amp_bec: 0.01, (init = 0.01), bounds = [0.00 : 0.40] \n",
      "amp_th: 0.10, (init = 0.24), bounds = [0.00 : 0.40] \n",
      "x0_bec: 124.50, (init = 124.06), bounds = [114.06 : 134.06] \n",
      "y0_bec: 115.07, (init = 124.29), bounds = [114.29 : 134.29] \n",
      "x0_th: 125.46, (init = 124.06), bounds = [114.06 : 134.06] \n",
      "y0_th: 125.96, (init = 124.29), bounds = [114.29 : 134.29] \n",
      "sigmax_bec: 79.34, (init = 47.78), bounds = [0.00 : 95.99] \n",
      "sigmay_bec: 3.97, (init = 48.09), bounds = [0.00 : 96.17] \n",
      "sigma_th: 38.05, (init = 38.77), bounds = [0.00 : 250.00] \n"
     ]
    }
   ],
   "source": [
    "res = result[0][0]\n",
    "print(res.init_params['amp_bec'].max)\n",
    "\n",
    "\n",
    "def print_bval(result):\n",
    "    keys = result.best_values.keys()\n",
    "    bval = result.best_values\n",
    "    init = result.init_params\n",
    "\n",
    "    for item in keys:\n",
    "        print(f'{item}: {bval[item]:.2f}, (init = {init[item].value:.2f}), bounds = [{init[item].min:.2f} : {init[item].max :.2f}] ')\n",
    "\n",
    "print_bval(res)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-01T11:52:39.509257700Z",
     "start_time": "2023-08-01T11:52:39.395834200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image 0, 0\n",
      "sigmax = 26.809654363723393, sigmay = 20.984865705907357 \n",
      "49.68707529159811\n",
      "[47.99635701 48.08743169]\n",
      "image 0, 1\n",
      "sigmax = 16.902431605565432, sigmay = 14.827716571560885 \n",
      "15.654987743234889\n",
      "[28.50637523 25.50091075]\n",
      "image 0, 2\n",
      "sigmax = 19.537062472446536, sigmay = 16.54490141361424 \n",
      "18.026074303206485\n",
      "[27.32240437 23.67941712]\n",
      "image 0, 3\n",
      "sigmax = 21.26943188775151, sigmay = 18.18988358733591 \n",
      "19.556767700963444\n",
      "[27.23132969 23.86156648]\n",
      "image 0, 4\n",
      "sigmax = 22.839337165618247, sigmay = 19.260967695867635 \n",
      "20.90144486963491\n",
      "[27.77777778 23.86156648]\n",
      "image 0, 5\n",
      "sigmax = 23.611867762365737, sigmay = 19.890208925283233 \n",
      "21.630994243513612\n",
      "[28.77959927 24.49908925]\n",
      "image 0, 6\n",
      "sigmax = 24.715898892965207, sigmay = 20.72301092040907 \n",
      "22.830805825494014\n",
      "[28.50637523 24.7723133 ]\n",
      "image 0, 7\n",
      "sigmax = 25.37740108909912, sigmay = 22.34732657140245 \n",
      "23.78148211266294\n",
      "[28.68852459 25.31876138]\n",
      "image 0, 8\n",
      "sigmax = 25.54988097917328, sigmay = 22.05819714419466 \n",
      "24.633548951333726\n",
      "[28.1420765  24.68123862]\n",
      "image 0, 9\n",
      "sigmax = 25.455752389974258, sigmay = 21.92384918530349 \n",
      "25.16924937651061\n",
      "[28.23315118 24.04371585]\n",
      "image 0, 10\n",
      "sigmax = 27.13351753291331, sigmay = 23.17650478721828 \n",
      "25.428970385518063\n",
      "[29.96357013 26.04735883]\n",
      "image 1, 0\n",
      "sigmax = 32.130698329287455, sigmay = 21.301370060449216 \n",
      "38.991763920546184\n",
      "[25.68306011 21.03825137]\n",
      "image 1, 1\n",
      "sigmax = 16.311826068846873, sigmay = 15.161785017907176 \n",
      "15.035048897926897\n",
      "[25.50091075 24.40801457]\n",
      "image 1, 2\n",
      "sigmax = 19.429204152163635, sigmay = 16.38000535679499 \n",
      "18.382290279191036\n",
      "[21.58469945 20.21857923]\n",
      "image 1, 3\n",
      "sigmax = 21.366923260200572, sigmay = 17.727126413466063 \n",
      "19.81456970617587\n",
      "[18.9435337  17.03096539]\n",
      "image 1, 4\n",
      "sigmax = 22.846174800426063, sigmay = 19.64564917108202 \n",
      "22.056975277842138\n",
      "[26.6848816  21.76684882]\n",
      "image 1, 5\n",
      "sigmax = 24.47493848239252, sigmay = 19.79849230949584 \n",
      "21.90523925207991\n",
      "[26.04735883 22.04007286]\n",
      "image 1, 6\n",
      "sigmax = 25.120085496179822, sigmay = 21.01142726725265 \n",
      "24.103158393287867\n",
      "[24.49908925 22.22222222]\n",
      "image 1, 7\n",
      "sigmax = 25.268375215655595, sigmay = 22.22675806585392 \n",
      "23.646253048054273\n",
      "[19.67213115 18.03278689]\n",
      "image 1, 8\n",
      "sigmax = 24.949462122653742, sigmay = 22.706400518903973 \n",
      "23.217749345311454\n",
      "[23.49726776 20.94717668]\n",
      "image 1, 9\n",
      "sigmax = 25.19797063692771, sigmay = 21.781054343748057 \n",
      "24.053321531664213\n",
      "[23.3151184  20.30965392]\n",
      "image 1, 10\n",
      "sigmax = 27.644788514841196, sigmay = 22.830162756976627 \n",
      "26.373230050836366\n",
      "[25.13661202 20.856102  ]\n"
     ]
    }
   ],
   "source": [
    "for i in range(0,shape[0]):\n",
    "    for j in range(0, shape[1]):\n",
    "        print(f'image {i}, {j}')\n",
    "        bval = result[i][j].best_values\n",
    "\n",
    "        print(f\"sigmax = {bval['sigmax_bec']}, sigmay = {bval['sigmay_bec']} \")\n",
    "        print(result_x[i][j].best_values['sigma_bec'])\n",
    "        print(BEC_width_guess[i,j] /1.22)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-01T10:25:48.757434900Z",
     "start_time": "2023-08-01T10:25:48.698502800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image: 0, 0\n",
      "amp_bec: 0.000, (init = 0.000), bounds = [0.00 : 0.40] \n",
      "amp_th: 0.103, (init = 0.138), bounds = [0.00 : 0.40] \n",
      "x0_bec: 114.060, (init = 114.060), bounds = [114.06 : 134.06] \n",
      "y0_bec: 114.291, (init = 114.291), bounds = [114.29 : 134.29] \n",
      "x0_th: 125.454, (init = 124.060), bounds = [114.06 : 134.06] \n",
      "y0_th: 125.676, (init = 124.291), bounds = [114.29 : 134.29] \n",
      "sigmax_bec: 1.000, (init = 1.000), bounds = [0.00 : 95.99] \n",
      "sigmay_bec: 1.000, (init = 1.000), bounds = [0.00 : 96.17] \n",
      "sigma_th: 37.876, (init = 38.769), bounds = [0.00 : 250.00] \n",
      "\n",
      "image: 0, 1\n",
      "amp_bec: 0.217, (init = 0.168), bounds = [0.00 : 0.72] \n",
      "amp_th: 0.169, (init = 0.230), bounds = [0.00 : 0.72] \n",
      "x0_bec: 125.205, (init = 123.226), bounds = [113.23 : 133.23] \n",
      "y0_bec: 126.138, (init = 126.090), bounds = [116.09 : 136.09] \n",
      "x0_th: 124.613, (init = 123.226), bounds = [113.23 : 133.23] \n",
      "y0_th: 126.037, (init = 126.090), bounds = [116.09 : 136.09] \n",
      "sigmax_bec: 16.902, (init = 15.655), bounds = [0.00 : 57.01] \n",
      "sigmay_bec: 14.834, (init = 25.501), bounds = [0.00 : 51.00] \n",
      "sigma_th: 28.006, (init = 28.067), bounds = [0.00 : 250.00] \n",
      "\n",
      "image: 0, 2\n",
      "amp_bec: 0.351, (init = 0.314), bounds = [0.00 : 0.87] \n",
      "amp_th: 0.186, (init = 0.238), bounds = [0.00 : 0.87] \n",
      "x0_bec: 124.756, (init = 124.199), bounds = [114.20 : 134.20] \n",
      "y0_bec: 125.997, (init = 125.119), bounds = [115.12 : 135.12] \n",
      "x0_th: 125.359, (init = 124.199), bounds = [114.20 : 134.20] \n",
      "y0_th: 125.902, (init = 125.119), bounds = [115.12 : 135.12] \n",
      "sigmax_bec: 19.537, (init = 18.026), bounds = [0.00 : 54.64] \n",
      "sigmay_bec: 16.545, (init = 23.679), bounds = [0.00 : 47.36] \n",
      "sigma_th: 25.553, (init = 26.395), bounds = [0.00 : 250.00] \n",
      "\n",
      "image: 0, 3\n",
      "amp_bec: 0.417, (init = 0.378), bounds = [0.00 : 1.07] \n",
      "amp_th: 0.221, (init = 0.284), bounds = [0.00 : 1.07] \n",
      "x0_bec: 124.875, (init = 124.288), bounds = [114.29 : 134.29] \n",
      "y0_bec: 126.008, (init = 125.419), bounds = [115.42 : 135.42] \n",
      "x0_th: 125.107, (init = 124.288), bounds = [114.29 : 134.29] \n",
      "y0_th: 125.814, (init = 125.419), bounds = [115.42 : 135.42] \n",
      "sigmax_bec: 21.269, (init = 19.557), bounds = [0.00 : 54.46] \n",
      "sigmay_bec: 18.190, (init = 23.862), bounds = [0.00 : 47.72] \n",
      "sigma_th: 21.630, (init = 21.469), bounds = [0.00 : 250.00] \n",
      "\n",
      "image: 0, 4\n",
      "amp_bec: 0.506, (init = 0.480), bounds = [0.00 : 1.24] \n",
      "amp_th: 0.228, (init = 0.257), bounds = [0.00 : 1.24] \n",
      "x0_bec: 125.172, (init = 124.844), bounds = [114.84 : 134.84] \n",
      "y0_bec: 125.980, (init = 125.632), bounds = [115.63 : 135.63] \n",
      "x0_th: 125.047, (init = 124.844), bounds = [114.84 : 134.84] \n",
      "y0_th: 125.947, (init = 125.632), bounds = [115.63 : 135.63] \n",
      "sigmax_bec: 22.839, (init = 20.901), bounds = [0.00 : 55.56] \n",
      "sigmay_bec: 19.261, (init = 23.862), bounds = [0.00 : 47.72] \n",
      "sigma_th: 19.498, (init = 20.335), bounds = [0.00 : 250.00] \n",
      "\n",
      "image: 0, 5\n",
      "amp_bec: 0.578, (init = 0.501), bounds = [0.00 : 1.26] \n",
      "amp_th: 0.238, (init = 0.360), bounds = [0.00 : 1.26] \n",
      "x0_bec: 124.950, (init = 124.472), bounds = [114.47 : 134.47] \n",
      "y0_bec: 125.948, (init = 125.613), bounds = [115.61 : 135.61] \n",
      "x0_th: 125.549, (init = 124.472), bounds = [114.47 : 134.47] \n",
      "y0_th: 126.311, (init = 125.613), bounds = [115.61 : 135.61] \n",
      "sigmax_bec: 23.612, (init = 21.631), bounds = [0.00 : 57.56] \n",
      "sigmay_bec: 19.890, (init = 24.499), bounds = [0.00 : 49.00] \n",
      "sigma_th: 17.844, (init = 16.789), bounds = [0.00 : 250.00] \n",
      "\n",
      "image: 0, 6\n",
      "amp_bec: 0.624, (init = 0.582), bounds = [0.00 : 1.43] \n",
      "amp_th: 0.273, (init = 0.326), bounds = [0.00 : 1.43] \n",
      "x0_bec: 124.841, (init = 124.226), bounds = [114.23 : 134.23] \n",
      "y0_bec: 126.087, (init = 125.493), bounds = [115.49 : 135.49] \n",
      "x0_th: 125.518, (init = 124.226), bounds = [114.23 : 134.23] \n",
      "y0_th: 125.329, (init = 125.493), bounds = [115.49 : 135.49] \n",
      "sigmax_bec: 24.716, (init = 22.831), bounds = [0.00 : 57.01] \n",
      "sigmay_bec: 20.723, (init = 24.772), bounds = [0.00 : 49.54] \n",
      "sigma_th: 15.245, (init = 15.560), bounds = [0.00 : 250.00] \n",
      "\n",
      "image: 0, 7\n",
      "amp_bec: 0.726, (init = 0.745), bounds = [0.00 : 1.47] \n",
      "amp_th: 0.246, (init = 0.221), bounds = [0.00 : 1.47] \n",
      "x0_bec: 124.976, (init = 124.374), bounds = [114.37 : 134.37] \n",
      "y0_bec: 126.191, (init = 125.431), bounds = [115.43 : 135.43] \n",
      "x0_th: 125.065, (init = 124.374), bounds = [114.37 : 134.37] \n",
      "y0_th: 125.186, (init = 125.431), bounds = [115.43 : 135.43] \n",
      "sigmax_bec: 25.165, (init = 23.194), bounds = [0.00 : 57.38] \n",
      "sigmay_bec: 21.561, (init = 25.319), bounds = [0.00 : 50.64] \n",
      "sigma_th: 13.309, (init = 14.658), bounds = [0.00 : 250.00] \n",
      "\n",
      "image: 0, 8\n",
      "amp_bec: 0.761, (init = 0.840), bounds = [0.00 : 1.62] \n",
      "amp_th: 0.285, (init = 0.230), bounds = [0.00 : 1.62] \n",
      "x0_bec: 124.933, (init = 124.493), bounds = [114.49 : 134.49] \n",
      "y0_bec: 125.930, (init = 125.501), bounds = [115.50 : 135.50] \n",
      "x0_th: 125.286, (init = 124.493), bounds = [114.49 : 134.49] \n",
      "y0_th: 125.894, (init = 125.501), bounds = [115.50 : 135.50] \n",
      "sigmax_bec: 26.333, (init = 23.255), bounds = [0.00 : 56.28] \n",
      "sigmay_bec: 22.180, (init = 24.681), bounds = [0.00 : 49.36] \n",
      "sigma_th: 10.090, (init = 14.697), bounds = [0.00 : 250.00] \n",
      "\n",
      "image: 0, 9\n",
      "amp_bec: 0.720, (init = 0.779), bounds = [0.00 : 1.65] \n",
      "amp_th: 0.404, (init = 0.330), bounds = [0.00 : 1.65] \n",
      "x0_bec: 125.067, (init = 124.406), bounds = [114.41 : 134.41] \n",
      "y0_bec: 126.004, (init = 125.502), bounds = [115.50 : 135.50] \n",
      "x0_th: 124.710, (init = 124.406), bounds = [114.41 : 134.41] \n",
      "y0_th: 126.249, (init = 125.502), bounds = [115.50 : 135.50] \n",
      "sigmax_bec: 26.971, (init = 23.021), bounds = [0.00 : 56.47] \n",
      "sigmay_bec: 22.523, (init = 24.044), bounds = [0.00 : 48.09] \n",
      "sigma_th: 8.937, (init = 14.549), bounds = [0.00 : 250.00] \n",
      "\n",
      "image: 0, 10\n",
      "amp_bec: 0.876, (init = 0.893), bounds = [0.00 : 1.40] \n",
      "amp_th: 0.296, (init = 0.004), bounds = [0.00 : 1.40] \n",
      "x0_bec: 125.119, (init = 124.672), bounds = [114.67 : 134.67] \n",
      "y0_bec: 125.956, (init = 125.545), bounds = [115.54 : 135.54] \n",
      "x0_th: 134.433, (init = 124.672), bounds = [114.67 : 134.67] \n",
      "y0_th: 116.927, (init = 125.545), bounds = [115.54 : 135.54] \n",
      "sigmax_bec: 27.134, (init = 25.429), bounds = [0.00 : 59.93] \n",
      "sigmay_bec: 23.176, (init = 26.047), bounds = [0.00 : 52.09] \n",
      "sigma_th: 0.445, (init = 16.071), bounds = [0.00 : 250.00] \n",
      "\n",
      "image: 1, 0\n",
      "amp_bec: 0.000, (init = 0.000), bounds = [0.00 : 1.22] \n",
      "amp_th: 0.094, (init = 0.215), bounds = [0.00 : 1.22] \n",
      "x0_bec: 112.431, (init = 112.431), bounds = [112.43 : 132.43] \n",
      "y0_bec: 117.409, (init = 117.409), bounds = [117.41 : 137.41] \n",
      "x0_th: 123.480, (init = 122.431), bounds = [112.43 : 132.43] \n",
      "y0_th: 125.563, (init = 127.409), bounds = [117.41 : 137.41] \n",
      "sigmax_bec: 1.000, (init = 1.000), bounds = [0.00 : 51.37] \n",
      "sigmay_bec: 1.000, (init = 1.000), bounds = [0.00 : 42.08] \n",
      "sigma_th: 41.492, (init = 46.919), bounds = [0.00 : 250.00] \n",
      "\n",
      "image: 1, 1\n",
      "amp_bec: 0.222, (init = 0.221), bounds = [0.00 : 1.25] \n",
      "amp_th: 0.167, (init = 0.215), bounds = [0.00 : 1.25] \n",
      "x0_bec: 125.676, (init = 123.764), bounds = [113.76 : 133.76] \n",
      "y0_bec: 126.273, (init = 126.074), bounds = [116.07 : 136.07] \n",
      "x0_th: 125.738, (init = 123.764), bounds = [113.76 : 133.76] \n",
      "y0_th: 126.340, (init = 126.074), bounds = [116.07 : 136.07] \n",
      "sigmax_bec: 16.309, (init = 15.038), bounds = [0.00 : 51.00] \n",
      "sigmay_bec: 15.156, (init = 24.408), bounds = [0.00 : 48.82] \n",
      "sigma_th: 29.161, (init = 32.054), bounds = [0.00 : 250.00] \n",
      "\n",
      "image: 1, 2\n",
      "amp_bec: 0.350, (init = 0.383), bounds = [0.00 : 1.48] \n",
      "amp_th: 0.186, (init = 0.262), bounds = [0.00 : 1.48] \n",
      "x0_bec: 125.700, (init = 123.665), bounds = [113.66 : 133.66] \n",
      "y0_bec: 125.723, (init = 129.025), bounds = [119.02 : 139.02] \n",
      "x0_th: 123.986, (init = 123.665), bounds = [113.66 : 133.66] \n",
      "y0_th: 125.452, (init = 129.025), bounds = [119.02 : 139.02] \n",
      "sigmax_bec: 19.432, (init = 18.382), bounds = [0.00 : 43.17] \n",
      "sigmay_bec: 16.380, (init = 20.219), bounds = [0.00 : 40.44] \n",
      "sigma_th: 25.127, (init = 23.158), bounds = [0.00 : 250.00] \n",
      "\n",
      "image: 1, 3\n",
      "amp_bec: 0.406, (init = 0.458), bounds = [0.00 : 1.97] \n",
      "amp_th: 0.221, (init = 0.311), bounds = [0.00 : 1.97] \n",
      "x0_bec: 124.575, (init = 123.005), bounds = [113.00 : 133.00] \n",
      "y0_bec: 125.522, (init = 127.835), bounds = [117.83 : 137.83] \n",
      "x0_th: 125.093, (init = 123.005), bounds = [113.00 : 133.00] \n",
      "y0_th: 125.685, (init = 127.835), bounds = [117.83 : 137.83] \n",
      "sigmax_bec: 21.369, (init = 19.815), bounds = [0.00 : 37.89] \n",
      "sigmay_bec: 17.727, (init = 17.031), bounds = [0.00 : 34.06] \n",
      "sigma_th: 22.086, (init = 23.094), bounds = [0.00 : 250.00] \n",
      "\n",
      "image: 1, 4\n",
      "amp_bec: 0.530, (init = 0.664), bounds = [0.00 : 1.76] \n",
      "amp_th: 0.222, (init = 0.138), bounds = [0.00 : 1.76] \n",
      "x0_bec: 124.518, (init = 121.597), bounds = [111.60 : 131.60] \n",
      "y0_bec: 126.389, (init = 125.829), bounds = [115.83 : 135.83] \n",
      "x0_th: 127.372, (init = 121.597), bounds = [111.60 : 131.60] \n",
      "y0_th: 126.405, (init = 125.829), bounds = [115.83 : 135.83] \n",
      "sigmax_bec: 22.846, (init = 22.057), bounds = [0.00 : 53.37] \n",
      "sigmay_bec: 19.648, (init = 21.767), bounds = [0.00 : 43.53] \n",
      "sigma_th: 19.152, (init = 28.619), bounds = [0.00 : 250.00] \n",
      "\n",
      "image: 1, 5\n",
      "amp_bec: 0.584, (init = 0.517), bounds = [0.00 : 1.73] \n",
      "amp_th: 0.230, (init = 0.347), bounds = [0.00 : 1.73] \n",
      "x0_bec: 124.245, (init = 125.610), bounds = [115.61 : 135.61] \n",
      "y0_bec: 126.094, (init = 129.130), bounds = [119.13 : 139.13] \n",
      "x0_th: 127.537, (init = 125.610), bounds = [115.61 : 135.61] \n",
      "y0_th: 126.430, (init = 129.130), bounds = [119.13 : 139.13] \n",
      "sigmax_bec: 24.480, (init = 21.905), bounds = [0.00 : 52.09] \n",
      "sigmay_bec: 19.802, (init = 22.040), bounds = [0.00 : 44.08] \n",
      "sigma_th: 17.618, (init = 17.856), bounds = [0.00 : 250.00] \n",
      "\n",
      "image: 1, 6\n",
      "amp_bec: 0.749, (init = 0.591), bounds = [0.00 : 2.09] \n",
      "amp_th: 0.137, (init = 0.385), bounds = [0.00 : 2.09] \n",
      "x0_bec: 125.001, (init = 123.000), bounds = [113.00 : 133.00] \n",
      "y0_bec: 126.174, (init = 124.917), bounds = [114.92 : 134.92] \n",
      "x0_th: 125.603, (init = 123.000), bounds = [113.00 : 133.00] \n",
      "y0_th: 123.634, (init = 124.917), bounds = [114.92 : 134.92] \n",
      "sigmax_bec: 25.120, (init = 23.228), bounds = [0.00 : 49.00] \n",
      "sigmay_bec: 21.012, (init = 22.222), bounds = [0.00 : 44.44] \n",
      "sigma_th: 18.497, (init = 14.680), bounds = [0.00 : 250.00] \n",
      "\n",
      "image: 1, 7\n",
      "amp_bec: 0.756, (init = 0.936), bounds = [0.00 : 2.83] \n",
      "amp_th: 0.270, (init = 0.247), bounds = [0.00 : 2.83] \n",
      "x0_bec: 124.566, (init = 124.890), bounds = [114.89 : 134.89] \n",
      "y0_bec: 126.524, (init = 125.362), bounds = [115.36 : 135.36] \n",
      "x0_th: 127.625, (init = 124.890), bounds = [114.89 : 134.89] \n",
      "y0_th: 123.574, (init = 125.362), bounds = [115.36 : 135.36] \n",
      "sigmax_bec: 25.560, (init = 23.201), bounds = [0.00 : 39.34] \n",
      "sigmay_bec: 21.976, (init = 18.033), bounds = [0.00 : 36.07] \n",
      "sigma_th: 10.918, (init = 14.663), bounds = [0.00 : 250.00] \n",
      "\n",
      "image: 1, 8\n",
      "amp_bec: 0.752, (init = 0.836), bounds = [0.00 : 2.49] \n",
      "amp_th: 0.313, (init = 0.336), bounds = [0.00 : 2.49] \n",
      "x0_bec: 124.766, (init = 124.262), bounds = [114.26 : 134.26] \n",
      "y0_bec: 126.339, (init = 125.803), bounds = [115.80 : 135.80] \n",
      "x0_th: 125.826, (init = 124.262), bounds = [114.26 : 134.26] \n",
      "y0_th: 125.887, (init = 125.803), bounds = [115.80 : 135.80] \n",
      "sigmax_bec: 25.700, (init = 22.730), bounds = [0.00 : 46.99] \n",
      "sigmay_bec: 23.133, (init = 20.947), bounds = [0.00 : 41.89] \n",
      "sigma_th: 9.610, (init = 14.365), bounds = [0.00 : 250.00] \n",
      "\n",
      "image: 1, 9\n",
      "amp_bec: 0.822, (init = 0.886), bounds = [0.00 : 2.53] \n",
      "amp_th: 0.291, (init = 0.330), bounds = [0.00 : 2.53] \n",
      "x0_bec: 124.518, (init = 124.274), bounds = [114.27 : 134.27] \n",
      "y0_bec: 126.183, (init = 125.475), bounds = [115.48 : 135.48] \n",
      "x0_th: 126.405, (init = 124.274), bounds = [114.27 : 134.27] \n",
      "y0_th: 125.945, (init = 125.475), bounds = [115.48 : 135.48] \n",
      "sigmax_bec: 26.098, (init = 22.764), bounds = [0.00 : 46.63] \n",
      "sigmay_bec: 22.223, (init = 20.310), bounds = [0.00 : 40.62] \n",
      "sigma_th: 8.711, (init = 14.387), bounds = [0.00 : 250.00] \n",
      "\n",
      "image: 1, 10\n",
      "amp_bec: 0.875, (init = 1.085), bounds = [0.00 : 2.19] \n",
      "amp_th: 2.190, (init = 0.013), bounds = [0.00 : 2.19] \n",
      "x0_bec: 124.948, (init = 125.252), bounds = [115.25 : 135.25] \n",
      "y0_bec: 126.115, (init = 126.356), bounds = [116.36 : 136.36] \n",
      "x0_th: 128.909, (init = 125.252), bounds = [115.25 : 135.25] \n",
      "y0_th: 118.954, (init = 126.356), bounds = [116.36 : 136.36] \n",
      "sigmax_bec: 27.665, (init = 26.371), bounds = [0.00 : 50.27] \n",
      "sigmay_bec: 22.836, (init = 20.856), bounds = [0.00 : 41.71] \n",
      "sigma_th: 0.365, (init = 16.667), bounds = [0.00 : 250.00] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print_bval_bulk(result_1)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-01T13:23:38.538388800Z",
     "start_time": "2023-08-01T13:23:38.428939500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [
    {
     "data": {
      "text/plain": "111.52339962118164"
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_x[1][0].best_values['sigma_th']"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-01T09:35:39.865148600Z",
     "start_time": "2023-08-01T09:35:39.777868400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image 0, 0\n",
      "check val, 0.0\n",
      "image 0, 1\n",
      "check val, 202.81899317348075\n",
      "image 0, 2\n",
      "check val, 171.66227694315586\n",
      "image 0, 3\n",
      "check val, 137.86010387483958\n",
      "image 0, 4\n",
      "check val, 112.63334115900692\n",
      "image 0, 5\n",
      "check val, 80.32146811618618\n",
      "image 0, 6\n",
      "check val, 47.14137119721544\n",
      "image 0, 7\n",
      "check val, 20.13939399629303\n",
      "image 0, 8\n",
      "check val, 7.729201970644159\n",
      "image 0, 9\n",
      "check val, -0.24707647603264393\n",
      "image 0, 10\n",
      "check val, -10.485648818860035\n",
      "image 1, 0\n",
      "check val, 0.0\n",
      "image 1, 1\n",
      "check val, 155.40174729200504\n",
      "image 1, 2\n",
      "check val, 267.84199180102325\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Jianshun Gao\\AppData\\Local\\Temp\\ipykernel_36888\\2144014449.py:109: RuntimeWarning: invalid value encountered in power\n",
      "  res = np.where(res > 0, res**(3/2), 0)\n",
      "C:\\Users\\Jianshun Gao\\AppData\\Local\\Temp\\ipykernel_36888\\737382478.py:25: RuntimeWarning: Mean of empty slice\n",
      "  check_value = np.nanmean(mask2[i,j]) / (bval[\"amp_bec\"] + bval[\"amp_th\"])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image 1, 3\n",
      "check val, 134.5222938744657\n",
      "image 1, 4\n",
      "check val, 90.07549048132266\n",
      "image 1, 5\n",
      "check val, 109.52459360079988\n",
      "image 1, 6\n",
      "check val, 63.76264252044428\n",
      "image 1, 7\n",
      "check val, 75.0924950611463\n",
      "image 1, 8\n",
      "check val, 29.827565402107858\n",
      "image 1, 9\n",
      "check val, -0.20590658874586376\n",
      "image 1, 10\n",
      "check val, 3.7193052125950476\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x1000 with 22 Axes>",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0QAAAMtCAYAAAC/4rG8AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAADdtElEQVR4nOzde3xU1b3//9e2URoUKEhBLuEiAaSHawBvaIHT2EJ7UkUtVKkFK6fK19IW9UdFTs9mtwUsohybWvAUjlKLVspFTS22RsULpSIGC7VQiQUNFEEMJNSGS8r6/bH2TBLITDKTSeay388+0kxm9mzWfFyzZz57r/VZjjHGICIiIiIiEkBnJbsBIiIiIiIiyaKESEREREREAksJkYiIiIiIBJYSIhERERERCSwlRCIiIiIiElhKiEREREREJLCUEImIiIiISGApIRIRERERkcBSQiQiIiIiIoEVU0J07NgxrrnmGvr168eQIUO46qqrKC0tBWDMmDH07t2boUOHMnToUBYvXhx+3sGDBxk3bhx9+/Zl4MCBvPLKK4l9FSlAsYlO8YlMsYlO8YlO8YlMsYlO8YlMsYlO8YlMsUlTJgZVVVXm2WefNadOnTLGGFNYWGhGjx5tjDFm9OjRZt26dfU+7+abbzau6xpjjNm8ebPp1q2bOXHiRCz/dMpTbKJTfCJTbKJTfKJTfCJTbKJTfCJTbKJTfCJTbNJTVizJ0yc/+Um++MUvhv++9NJLWbRoUYPPW7VqVTg7HjlyJF27duXll18mPz//jG2PHz/O8ePHw3+fOnWK8vJyzj//fBzHiaW5Le6KK67g6NGjAAwcOJD77ruPyspK/vWvf/HPf/6TysrKM56zatUqtm7dSmVlJf369aNjx4689NJLfP7znz9j23SODSg+0TQ1Nv3796dz586sXr2aSZMmcdZZdS/+pnNsQH2nIYpPZIpNdIpPZIpNdIpPZIpN8zLGcPToUbp27XrG952m7DRuX/va18y3v/1tY4zNevv3728GDhxoJk6caN59911jjDGHDh0y55xzTp3nfeUrXzHLly+vd5+u6xog0D/33XefYqP4xP1TVlam2ET4Ud9RfBQbxUexSa0fxUexifenvu878XKMMYY4zJ8/n6KiIl544QVat25NWVkZOTk5GGN46KGH+NnPfsZf/vIXPvroI7p27Vonk504cSLjxo3jG9/4xhn7PT3rraiooEePHpSVldG2bdt4mtriFi1axHPPPcczzzxD69at2bt3L927d8cYw89//nOWLVvG5s2bKS8vp3///nz44YcAVFZWkpOTQ2FhId/61rfO2G8mxAYUn2jijQ3A5MmT+c1vfsORI0do165dnf1mQmxAfachik9kik10ik9kik10ik9kik3zCMWnvu87cYsni7rvvvvM8OHDzeHDhyNu06pVK3Po0CFjjDGtW7c2+/fvDz82cuRI8/zzzzfq36qoqDCAqaioiKepLa4psQm91qeeeqpR/1a6xcYYxSeapr6v8vLyGv160y02xqjvNETxiUyxiU7xiUyxiU7xiUyxaT7N8XpjHnj3wAMP8MQTT/D888/zqU99CoDq6moOHDgQ3mbNmjV07tyZ888/H4CvfOUrLF26FIA33niDffv2MXr06Fj/6ZTX1Ni8+eabgB17mokUn8gS8b7av39/i7e7pajvRKf4RKbYRKf4RKbYRKf4RKbYpJ+Yhszt3buXnJwcLrzwQtq0aQNAq1atePHFFxk9ejTHjx/nrLPOomPHjjzwwAMMGTIEgAMHDnDTTTexe/duzjnnHH76058yduzYRv2blZWVtGvXjoqKipS+DJiI2GRlZbFz585Gv9Z0iQ0oPtEk6n314x//mIKCgka93nSJDajvNETxiUyxiU7xiUyxiU7xiUyxaX7N8XrjnkPUUoL0HznW1xqk2IDi05BYXq9ik9jt053iE5liE53iE5liE53iE5liE11zvN4E1aoTERERERFJP0qIREREREQksJQQiYiIiIhIYCkhEhERERGRwFJCJCIiIiIigaWESEREREREAksJkYiIiIiIBJYSIhERERERCSwlRCIiIiIiElhKiEREREREJLCUEImIiIiISGApIRIRERERkcBSQiQiIiIiIoGlhEhERERERAJLCZGIiIiIiASWEiIREREREQksJUQiIiIiIhJYSohERERERCSwlBCJiIiIiEhgKSESEREREZHAUkIkIiIiIiKBpYRIREREREQCSwmRiIiIiIgElhIiEREREREJLCVEIiIiIiISWEqIREREREQksJQQiYiIiIhIYCkhEhERERGRwFJCJCIiIiIigaWESEREREREAksJkYiIiIiIBJYSIhERERERCSwlRCIiIiIiElhKiEREREREJLCUEImIiIiISGApIRIRERERkcBSQiQiIiIiIoGlhEhERERERAJLCZGIiIiIiASWEiIREREREQksJUQiIiIiIhJYSohERERERCSwlBCJiIiIiEhgKSESEREREZHAUkIkIiIiIiKBpYRIREREREQCSwmRiIiIiIgElhIiEREREREJLCVEIiIiIiISWEqIREREREQksJQQiYiIiIhIYCkhEhERERGRwFJCJCIiIiIigaWESEREREREAksJkYiIiIiIBJYSIhERERERCSwlRCIiIiIiElhKiEREREREJLCUEImIiIiISGApIRIRERERkcBSQiQiIiIiIoEVU0J07NgxrrnmGvr168eQIUO46qqrKC0tBeDgwYOMGzeOvn37MnDgQF555ZXw86I9likSEZtLL700Wc1vdopPZIl6X23cuDFZL6FZqe9Ep/hEpthEp/hEpthEp/hEptikKRODqqoq8+yzz5pTp04ZY4wpLCw0o0ePNsYYc/PNNxvXdY0xxmzevNl069bNnDhxosHHGlJRUWEAU1FREUtTW1wiYvPiiy8awBw6dKhR/2a6xMYYxSeaRL2vunbt2ujXmy6xMUZ9pyGKT2SKTXSKT2SKTXSKT2SKTfNrjtcbU0J0ujfeeMP07NnTGGPMueeea/bv3x9+bOTIkeb5559v8LHTHTt2zFRUVIR/ysrK0vI/cjyxCf0Hfuqpp+rdZ6bExhjFJ5p431d5eXkRX2+mxMYY9Z2GKD6RKTbRKT6RKTbRKT6RKTaJ1xwJUZPmED344INcffXVfPTRR5w8eZILLrgg/FivXr14//33oz5WnwULFtCuXbvwT05OTlOamDTxxgZg79699e4zU2IDik808camR48eEfeZKbEB9Z2GKD6RKTbRKT6RKTbRKT6RKTbpIe6EaP78+ZSWlrJgwYJEtofZs2dTUVER/ikrK0vo/luCYhOd4hOZYhOd4hOd4hOZYhOd4hOZYhOd4hOZYpM+suJ50qJFi1i7di3FxcW0bt2a1q1bk5WVxQcffBDObvfs2UOPHj04//zzIz5Wn1atWtGqVas4X07yNTU2AN27d6933+keG1B8omlqbCJddYX0jw2o7zRE8YlMsYlO8YlMsYlO8YlMsUkzsY6xu//++01eXp4pLy+vc/+UKVPOmOAdmigW7bGGpNNEsabGJtMn0Sk+kSXifdWlS5eMLKpgjPpOQxSfyBSb6BSfyBSb6BSfyBSb5pX0ogqhSVsXXnihGTJkiBkyZIi5+OKLjTHGfPDBB+aqq64yubm55jOf+Yx58cUXw8+L9lhD0uU/ciJic9FFF8X0WtMlNsYoPtEk6n1VVFSUkQmR+k50ik9kik10ik9kik10ik9kik3za47X6xhjTAIvOCVcZWUl7dq1o6KigrZt2ya7Oc0q1tcapNiA4tOQWF6vYpPY7dOd4hOZYhOd4hOZYhOd4hOZYhNdc7zeJlWZExERERERSWdKiEREREREJLCUEImIiIiISGApIRIRERERkcBSQiQiIiIiIoGlhEhERERERAJLCZGIiIiIiASWEiIREREREQksJUQiIiIiIhJYSohERERERCSwlBCJiIiIiEhgKSESEREREZHAUkIkIiIiIiKBpYRIREREREQCSwmRiIiIiIgElhIiEREREREJLCVEIiIiIiISWEqIREREREQ
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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(BEC_width_guess[i,j,0], BEC_width_guess[i,j,1])\n",
    "        tf_fit = ThomasFermi_2d(X,Y,centerx=bval['x0_bec'], centery=bval['y0_bec'], amplitude=bval['amp_bec'], sigmax=BEC_width_guess[i,j,0]/1.22, sigmay=BEC_width_guess[i,j,1]/1.22)\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/1.22, sigmay=1.5* sigma_cut/1.22)\n",
    "\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",
    "\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": "2023-08-01T13:29:36.744796500Z",
     "start_time": "2023-08-01T13:29:33.948301900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[ 0.07268155, -0.09124867,  0.11122564, ...,  0.09485779,\n         0.04357125,  0.02711324],\n       [ 0.09737416,  0.01287571,  0.04367506, ...,  0.04072961,\n        -0.04108686, -0.02136833],\n       [ 0.17652024,  0.05190786,  0.02469261, ...,  0.06313304,\n        -0.01336323, -0.02586351],\n       ...,\n       [-0.11633899, -0.0156079 , -0.01670185, ...,  0.03050345,\n         0.01282069,  0.06573208],\n       [ 0.07503519, -0.00413224, -0.00858374, ..., -0.02333086,\n         0.06368782,  0.03050345],\n       [-0.08058049,  0.01200014,  0.02309571, ...,  0.05032508,\n        -0.10199917, -0.00209424]])"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cropOD[0][0]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-02T14:46:21.508554600Z",
     "start_time": "2023-08-02T14:46:21.329176100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
 ],
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   "language": "python",
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  "language_info": {
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    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
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