analyseScript/Joschka/Parameter guessing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "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",
    "import lmfit\n",
    "plt.rcParams['font.size'] = 18\n",
    "\n",
    "from scipy.ndimage import gaussian_filter\n",
    "import matplotlib as mpl\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",
    "\n",
    "\n",
    "\n",
    "#dataSet"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-27T08:59:09.268628700Z",
     "start_time": "2023-07-27T08:59:06.705558800Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Some functions"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [],
   "source": [
    "# get center of thresholded image\n",
    "\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])*0.5,0,1)\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])*0.5,0,1)\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",
    "\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(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",
    "def thermal(x, x0, amp, sigma, order = 15):\n",
    "    res = np.exp(-0.5 * (x-x0)**2 / sigma**2)\n",
    "    return amp/1.643 * polylog(2, res, order)\n",
    "\n",
    "def Thomas_Fermi_1d(x, x0, amp, sigma):\n",
    "    res = (1-(( x - x0 ) / sigma) **2) **(3/2)\n",
    "    return amp * np.where(res > 0, res, 0)\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, order=15) + Thomas_Fermi_1d(x, x0_bec, amp_bec, sigma_bec)\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-27T09:14:13.365949100Z",
     "start_time": "2023-07-27T09:14:13.270705500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-1,3, 1000)\n",
    "plt.plot(x, Thomas_Fermi_1d(x, 1, 1, 1))\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-25T10:28:41.148813400Z",
     "start_time": "2023-07-25T10:28:40.941625100Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Import Data"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "# import data\n",
    "img_dir = '//DyLabNAS/Data/'\n",
    "SequenceName = \"Evaporative_Cooling\" + \"/\"\n",
    "folderPath = img_dir + SequenceName + '2023/04/24'# get_date()\n",
    "\n",
    "\n",
    "shotNum = \"0009\"\n",
    "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n",
    "filePath = folderPath + \"/\" + shotNum + \"/2023-04-24_0009_Evaporative_Cooling_*0.h5\"\n",
    "\n",
    "# # load the data from HDF5 files\n",
    "# dataSetDict = {\n",
    "#     dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n",
    "#     for i in [0] # range(len(groupList))\n",
    "# }\n",
    "\n",
    "# selecte the data for centain camera\n",
    "dataSet = read_hdf5_file(filePath, \"images/MOT_3D_Camera/in_situ_absorption\")\n",
    "# flip the x and y axis\n",
    "dataSet = swap_xy(dataSet)\n",
    "\n",
    "# get the scan axis name of the shot\n",
    "scanAxis = get_scanAxis(dataSet)\n",
    "\n",
    "# rechunck the data for parallel computing\n",
    "dataSet = auto_rechunk(dataSet)\n",
    "\n",
    "# calculate the absorption imaging\n",
    "dataSet = imageAnalyser.get_absorption_images(dataSet)\n",
    "\n",
    "dataSet\n",
    "\n",
    "OD = dataSet[\"OD\"]\n",
    "\n",
    "OD_np = OD.to_numpy()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-27T08:59:16.033976200Z",
     "start_time": "2023-07-27T08:59:09.275760300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1000x900 with 10 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imageAnalyser.center = (960, 890)\n",
    "imageAnalyser.span = (150, 200)\n",
    "imageAnalyser.fraction = (0.1, 0.1)\n",
    "\n",
    "dataSet_cropOD = imageAnalyser.crop_image(dataSet.OD)\n",
    "dataSet_cropOD = imageAnalyser.substract_offset(dataSet_cropOD).load()\n",
    "cropOD = dataSet_cropOD.to_numpy()\n",
    "dataSet_cropOD.plot.pcolormesh(cmap='jet', vmin=0, col=scanAxis[1], row=scanAxis[0])\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-26T14:14:53.382898300Z",
     "start_time": "2023-07-26T14:14:47.878441800Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Guess center\n",
    "\n",
    "ToDo: Crop from center guess"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "# from opencv import moments\n",
    "data = OD_np\n",
    "\n",
    "thresh = OD_\n",
    "\n",
    "# thresh = gaussian_filter(thresh, sigma=0.1)\n",
    "# thresh = np.where(thresh<0.1,0,1)\n",
    "\n",
    "\n",
    "#M = moments(thresh)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-27T08:56:07.983013300Z",
     "start_time": "2023-07-27T08:56:06.995037700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [],
   "source": [
    "thresh = calc_thresh(OD_np)\n",
    "center = calc_cen_bulk(thresh)\n",
    "\n",
    "shape = np.shape(OD_np)\n",
    "cropOD = np.zeros((shape[0], shape[1], 150, 150))\n",
    "\n",
    "for i in range(0,shape[0]):\n",
    "    for j in range(0, shape[1]):\n",
    "        cropOD[i,j] = OD_np[i,j, round(center[i,j,1]-75):round(center[i,j,1]+75), round(center[i,j,0]-75):round(center[i,j,0]+75)]\n",
    "\n",
    "thresh = calc_thresh(cropOD)\n",
    "center = calc_cen_bulk(thresh)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-27T09:15:01.128146800Z",
     "start_time": "2023-07-27T09:14:59.438557900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "145\n",
      "145\n",
      "\n",
      "216\n",
      "216\n",
      "\n",
      "170\n",
      "170\n",
      "\n",
      "116\n",
      "116\n",
      "\n",
      "124\n",
      "124\n",
      "\n",
      "174\n",
      "174\n",
      "\n",
      "148\n",
      "150\n",
      "\n",
      "98\n",
      "99\n",
      "\n",
      "127\n",
      "131\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in range(0, shape[0]):\n",
    "    for j in range(0,shape[1]):\n",
    "        print(np.count_nonzero(thresh[i,j, 815:965, 885:1035]))\n",
    "        print(np.count_nonzero(thresh[i,j]))\n",
    "        print()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-26T14:39:38.163745700Z",
     "start_time": "2023-07-26T14:39:38.083533200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1200x800 with 9 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fsize = (12,8)\n",
    "\n",
    "nr_plots = 3\n",
    "\n",
    "# %matplotlib notebook\n",
    "# fig, ax = plt.subplots(nr_plots,nr_plots,figsize=fsize)\n",
    "\n",
    "# for i in range(0,nr_plots):\n",
    "#     for j in range(0,nr_plots):\n",
    "#         # ax[i][j].pcolormesh(blurred[i][j], cmap='jet', vmin=0, vmax=1.5, alpha=1)\n",
    "#         #ax[i][j].pcolormesh(thresh[i,j, 815:965, 885:1035], cmap='jet', vmin=0, vmax=1.5, alpha=1)\n",
    "#         ax[i][j].pcolormesh(thresh[i,j], cmap='jet', vmin=0, vmax=1.5, alpha=1)\n",
    "#         #ax[i][j].pcolormesh(cropOD[i][j], cmap='hot', vmin=0, vmax=1, alpha=1)\n",
    "#         ax[i][j].plot(center[i,j,0],center[i,j,1], marker='x', markersize=12)\n",
    "# plt.show()\n",
    "\n",
    "# fig, ax = plt.subplots(nr_plots,nr_plots,figsize=fsize)\n",
    "# for i in range(0,nr_plots):\n",
    "#     for j in range(0,nr_plots):\n",
    "#         ax[i][j].pcolormesh(blurred[i][j], cmap='jet', vmin=0, vmax=1.5, alpha=1)\n",
    "#         #ax[i][j].pcolormesh(thresh[i][j], cmap='jet', vmin=0, vmax=1.5, alpha=1)\n",
    "#         #ax[i][j].pcolormesh(cropOD[i][j], cmap='hot', vmin=0, vmax=1, alpha=1)\n",
    "#         ax[i][j].plot(center[i,j,0],center[i,j,1], marker='x', markersize=12)\n",
    "# plt.show()\n",
    "#\n",
    "fig, ax = plt.subplots(nr_plots,nr_plots,figsize=fsize)\n",
    "for i in range(0,nr_plots):\n",
    "    for j in range(0,nr_plots):\n",
    "        ax[i][j].pcolormesh(cropOD[i][j], cmap='jet', vmin=0, vmax=1.5)\n",
    "        #ax[i][j].plot(max[i,j,1],max[i,j,0], marker='x', markersize=12)\n",
    "        ax[i][j].plot(center[i,j,0],center[i,j,1], marker='x', color='g', markersize=12)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-27T09:16:10.257586100Z",
     "start_time": "2023-07-27T09:16:08.375571100Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Guess width"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[ 7. 25.]\n",
      "  [ 8. 26.]\n",
      "  [ 8. 26.]]\n",
      "\n",
      " [[ 8. 20.]\n",
      "  [ 7. 21.]\n",
      "  [ 7. 28.]]\n",
      "\n",
      " [[10. 19.]\n",
      "  [ 9. 18.]\n",
      "  [ 9. 18.]]]\n"
     ]
    }
   ],
   "source": [
    "def guess_BEC_width(thresh, center):\n",
    "    \"\"\"\n",
    "    returns width of thresholded area along both axis through the center with shape of thresh and [X_width, Y_width] for each image\n",
    "    \"\"\"\n",
    "    shape = np.shape(thresh)\n",
    "    BEC_width_guess = np.zeros((shape[0], shape[1], 2))\n",
    "\n",
    "    for i in range(0, shape[0]):\n",
    "        for j in range(0, shape[1]):\n",
    "            BEC_width_guess[i, j, 0] = np.sum(thresh[i, j, round(center[i,j,1]), :])\n",
    "            BEC_width_guess[i, j, 1] = np.sum(thresh[i, j, :, round(center[i,j,0])])\n",
    "\n",
    "    return BEC_width_guess\n",
    "\n",
    "BEC_width_guess = guess_BEC_width(thresh, center)\n",
    "\n",
    "print(BEC_width_guess)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-27T09:16:12.879369800Z",
     "start_time": "2023-07-27T09:16:12.803068500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1200x800 with 9 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# %matplotlib notebook\n",
    "fig, ax = plt.subplots(nr_plots,nr_plots,figsize=fsize)\n",
    "\n",
    "for i in range(0,nr_plots):\n",
    "    for j in range(0,nr_plots):\n",
    "        # ax[i][j].pcolormesh(blurred[i][j], cmap='jet', vmin=0, vmax=1.5, alpha=1)\n",
    "        ax[i][j].pcolormesh(thresh[i][j], cmap='jet', vmin=0, vmax=1.5, alpha=1)\n",
    "        #ax[i][j].pcolormesh(cropOD[i][j], cmap='hot', vmin=0, vmax=1, alpha=1)\n",
    "        ax[i][j].plot(center[i,j,0],center[i,j,1], marker='x', markersize=12)\n",
    "plt.show()\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-25T10:28:56.450487400Z",
     "start_time": "2023-07-25T10:28:55.296612200Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Mask array"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "(3, 3, 200, 150)"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "masked = np.where(thresh==0, cropOD, np.nan)\n",
    "np.shape(masked)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-25T10:28:56.553864600Z",
     "start_time": "2023-07-25T10:28:56.442739600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1100x1100 with 9 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nr_plots,nr_plots,figsize=(11,11))\n",
    "\n",
    "cut_factor = 1\n",
    "\n",
    "for i in range(0,nr_plots):\n",
    "    for j in range(0,nr_plots):\n",
    "        #print(np.nanmax(masked[i,j]))\n",
    "        # ax[i][j].pcolormesh(blurred[i][j], cmap='jet', vmin=0, vmax=1.5, alpha=1)\n",
    "        #ax[i][j].pcolormesh(masked[i][j], cmap='jet', vmin=0, vmax=0.5, alpha=1)\n",
    "        ax[i][j].pcolormesh(cropOD[i][j], cmap='jet', vmin=0, vmax=0.7, alpha=1)\n",
    "        #ax[i][j].pcolormesh(thresh[i][j], cmap='jet', vmin=0, vmax=1.5, alpha=1)\n",
    "        #ax[i][j].pcolormesh(cropOD[i][j], cmap='hot', vmin=0, vmax=1, alpha=1)\n",
    "        ax[i][j].plot(center[i,j,0],center[i,j,1], marker='x', markersize=12)\n",
    "        alpha=1\n",
    "        ax[i][j].hlines(center[i,j,1] - cut_factor * BEC_width_guess[i,j,1]/2, 0, 150, color='r',alpha=alpha,linestyles='dotted')\n",
    "        ax[i][j].hlines(center[i,j,1] + cut_factor * BEC_width_guess[i,j,1]/2, 0, 150, color='r',alpha=alpha,linestyles='dotted')\n",
    "\n",
    "        ax[i][j].vlines(center[i,j,0] - cut_factor * BEC_width_guess[i,j,0]/2, 0, 200, color='r',alpha=alpha,linestyles='dotted')\n",
    "        ax[i][j].vlines(center[i,j,0] + cut_factor * BEC_width_guess[i,j,0]/2, 0, 200, color='r',alpha=alpha,linestyles='dotted')\n",
    "\n",
    "        ax[i][j].set_xlim(25,125)\n",
    "        ax[i][j].set_ylim(45,145)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-25T10:28:57.597197600Z",
     "start_time": "2023-07-25T10:28:56.497285Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Cut out region of interest for thermal fitting guess"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [],
   "source": [
    "shape = np.shape(masked)\n",
    "\n",
    "X_guess = np.zeros((shape[0], shape[1], shape[3]))\n",
    "Y_guess = np.zeros((shape[0], shape[1], shape[2]))\n",
    "for i in range(0, shape[0]):\n",
    "    for j in range(0, shape[1]):\n",
    "        X_guess[i,j] = np.sum(masked[i,j,round(center[i,j,1] - cut_factor * BEC_width_guess[i,j,1]/2) : round(center[i,j,1] + cut_factor * BEC_width_guess[i,j,1]/2) , :], 0) / len(masked[i,j,round(center[i,j,1] - cut_factor * BEC_width_guess[i,j,1]/2) : round(center[i,j,1] + cut_factor * BEC_width_guess[i,j,1]/2),0])\n",
    "\n",
    "        Y_guess[i,j] = np.sum(masked[i,j, :, round(center[i,j,0] - cut_factor * BEC_width_guess[i,j,0]/2) : round(center[i,j,0] + cut_factor * BEC_width_guess[i,j,0]/2)], 1) / len(masked[i,j,0,round(center[i,j,0] - cut_factor * BEC_width_guess[i,j,0]/2) : round(center[i,j,0] + cut_factor * BEC_width_guess[i,j,0]/2)])\n",
    "\n",
    "\n",
    "#print(X_guess)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-25T10:28:57.612204400Z",
     "start_time": "2023-07-25T10:28:57.602187Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1200x800 with 9 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nr_plots,nr_plots,figsize=fsize)\n",
    "\n",
    "for i in range(0, shape[0]):\n",
    "    for j in range(0, shape[1]):\n",
    "        ax[i,j].plot(X_guess[i,j], label = 'x_axis')\n",
    "        ax[i,j].plot(Y_guess[i,j], label = 'y_axis')\n",
    "        ax[i,j].legend(fontsize=10)\n",
    "plt.show()\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-25T10:28:58.656642600Z",
     "start_time": "2023-07-25T10:28:57.615406200Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Fitting 1D gaussian"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [],
   "source": [
    "#[nr images x, nr images y, X / Y, center / sigma]\n",
    "x = np.linspace(0,149,150)\n",
    "y = np.linspace(0,199, 200)\n",
    "\n",
    "popt = np.zeros((shape[0], shape[1], 2, 3))\n",
    "\n",
    "p0 = np.ones((shape[0], shape[1], 2, 3))\n",
    "\n",
    "p0[:, :, :, 0] = center\n",
    "p0[:, :, :, 1] = BEC_width_guess / 2.355\n",
    "p0[:, :, :, 2] = 0.1\n",
    "\n",
    "\n",
    "for i in range(0, shape[0]):\n",
    "    for j in range(0, shape[1]):\n",
    "        popt[i,j, 0], pcov = curve_fit(gaussian, x, X_guess[i,j] , p0[i, j, 0], nan_policy='omit')\n",
    "        popt[i,j, 1], pcov = curve_fit(gaussian, y, Y_guess[i,j] , p0[i, j, 1], nan_policy='omit')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-25T10:28:58.831141900Z",
     "start_time": "2023-07-25T10:28:58.664738400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1200x800 with 9 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nr_plots,nr_plots,figsize=fsize)\n",
    "for i in range(0, shape[0]):\n",
    "    for j in range(0, shape[1]):\n",
    "        ax[i,j].plot(x, X_guess[i,j], label = 'x_axis')\n",
    "        ax[i,j].plot(x, gaussian(x, *popt[i,j,0]), linestyle='dotted')\n",
    "\n",
    "        ax[i,j].plot(y, Y_guess[i,j], label = 'y_axis')\n",
    "        ax[i,j].plot(y, gaussian(y, *popt[i,j,1]), linestyle='dotted')\n",
    "        ax[i,j].legend(fontsize=10)\n",
    "        ax[i,j].set_facecolor('#FFFFFF')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-25T10:28:59.763890800Z",
     "start_time": "2023-07-25T10:28:58.705502800Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Try with not masked array\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [],
   "source": [
    "shape = np.shape(masked)\n",
    "\n",
    "X_guess_og = np.zeros((shape[0], shape[1], shape[3]))\n",
    "Y_guess_og = np.zeros((shape[0], shape[1], shape[2]))\n",
    "\n",
    "for i in range(0, shape[0]):\n",
    "    for j in range(0, shape[1]):\n",
    "        X_guess_og[i,j] = np.sum(cropOD[i,j,round(center[i,j,1] - BEC_width_guess[i,j,1]/2) : round(center[i,j,1] + BEC_width_guess[i,j,1]/2) , :], 0) / len(masked[i,j,round(center[i,j,1] - BEC_width_guess[i,j,1]/2) : round(center[i,j,1] + BEC_width_guess[i,j,1]/2),0])\n",
    "\n",
    "        Y_guess_og[i,j] = np.sum(cropOD[i,j, :, round(center[i,j,0] - BEC_width_guess[i,j,0]/2) : round(center[i,j,0] + BEC_width_guess[i,j,0]/2)], 1) / len(masked[i,j,0,round(center[i,j,0] - BEC_width_guess[i,j,0]/2) : round(center[i,j,0] + BEC_width_guess[i,j,0]/2)])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-25T10:28:59.792176200Z",
     "start_time": "2023-07-25T10:28:59.769873600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1200x800 with 9 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nr_plots,nr_plots,figsize=fsize)\n",
    "\n",
    "for i in range(0, shape[0]):\n",
    "    for j in range(0, shape[1]):\n",
    "        ax[i,j].plot(x, X_guess[i,j], label = 'x_axis masked',zorder=5)\n",
    "        ax[i,j].plot(x, X_guess_og[i,j], label = 'x_axis original')\n",
    "#         ax[i,j].legend(fontsize=10)\n",
    "# plt.show()\n",
    "#\n",
    "# fig, ax = plt.subplots(nr_plots,nr_plots,figsize=fsize)\n",
    "#\n",
    "# for i in range(0, shape[0]):\n",
    "#     for j in range(0, shape[1]):\n",
    "        ax[i,j].plot(y, Y_guess[i,j], label = 'y_axis masked',zorder=5)\n",
    "        ax[i,j].plot(y, Y_guess_og[i,j], label = 'y_axis original')\n",
    "        ax[i,j].legend(fontsize=8)\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-25T10:29:00.992680400Z",
     "start_time": "2023-07-25T10:28:59.782041400Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Bimodal 1d Fit"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fitting time: 106.31179809570312 ms\n"
     ]
    }
   ],
   "source": [
    "#[nr images x, nr images y, X / Y, center / sigma]\n",
    "x = np.linspace(0,149,150)\n",
    "y = np.linspace(0,199, 200)\n",
    "\n",
    "popt = np.zeros((shape[0], shape[1], 6))\n",
    "\n",
    "p0 = np.ones((shape[0], shape[1], 6))\n",
    "\n",
    "max = np.zeros((shape[0], shape[1]))\n",
    "\n",
    "for i in range(0, shape[0]):\n",
    "    max[i] = np.ndarray.max(X_guess_og[i],axis=1)\n",
    "\n",
    "\n",
    "p0[:, :, 0] = center[:, :, 0]  # center BEC\n",
    "p0[:, :, 1] = center[:, :, 0]   # center th\n",
    "p0[:, :, 2] = 0.7 * max          # amp BEC\n",
    "p0[:, :, 3] = 0.3 * max         # amp th\n",
    "p0[:, :, 4] = BEC_width_guess[:, :, 0]   # sigma BEC\n",
    "p0[:, :, 5] = BEC_width_guess[:, :, 0] * 3          # sigma th\n",
    "\n",
    "start = time.time()\n",
    "for i in range(0, shape[0]):\n",
    "    for j in range(0, shape[1]):\n",
    "        popt[i,j], pcov = curve_fit(density_1d, x, X_guess_og[i,j] , p0[i, j], nan_policy='omit')\n",
    "stop = time.time()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "print(f'fitting time: {(stop-start)*1e3} ms')\n",
    "        #popt[i,j, 1], pcov = curve_fit(density_1d, y, Y_guess_og[i,j] , p0[i, j, 1], nan_policy='omit')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-25T10:29:01.123984600Z",
     "start_time": "2023-07-25T10:29:00.997834600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [],
   "source": [
    "# Fitting x without math constr\n",
    "fitmodel = lmfit.Model(density_1d,independent_vars=['x'])\n",
    "\n",
    "result_x = []\n",
    "start = time.time()\n",
    "for i in range(0, shape[0]):\n",
    "    temp_res = []\n",
    "    for j in range(0, shape[1]):\n",
    "        params = lmfit.Parameters()\n",
    "        params.add_many(\n",
    "            ('x0_bec', center[i,j,0], True,0, 200),\n",
    "            ('x0_th',center[i,j,0], True,0, 200),\n",
    "            ('amp_bec', 0.7 * max[i,j], True, 0, 1.3 * np.max(X_guess_og[i,j])),\n",
    "            ('amp_th', 0.3 * max[i,j], True, 0, 1.3*np.max(X_guess_og[i,j])),\n",
    "            ('sigma_bec',BEC_width_guess[i,j,0], True, 0, 50),\n",
    "            ('sigma_th', 3*BEC_width_guess[i,j,0], True, 0, 50)\n",
    "        )\n",
    "        res = fitmodel.fit(X_guess_og[i,j], x=x, params=params)\n",
    "        temp_res.append(res)\n",
    "    result_x.append(temp_res)\n",
    "stop = time.time()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-25T10:29:04.899029200Z",
     "start_time": "2023-07-25T10:29:04.583135300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [],
   "source": [
    "# Fitting x with math constraint on width\n",
    "fitmodel = lmfit.Model(density_1d,independent_vars=['x'])\n",
    "\n",
    "result_x = []\n",
    "start = time.time()\n",
    "for i in range(0, shape[0]):\n",
    "    temp_res = []\n",
    "    for j in range(0, shape[1]):\n",
    "        params = lmfit.Parameters()\n",
    "        params.add_many(\n",
    "            ('x0_bec', center[i,j,0], True,0, 200),\n",
    "            ('x0_th',center[i,j,0], True,0, 200),\n",
    "            ('amp_bec', 0.7 * max[i,j], True, 0, 1.3 * np.max(Y_guess_og[i,j])),\n",
    "            ('amp_th', 0.3 * max[i,j], True, 0, 1.3*np.max(Y_guess_og[i,j])),\n",
    "            ('deltax', 2* BEC_width_guess[i,j,0], True,0,50),\n",
    "            ('sigma_bec',BEC_width_guess[i,j,0], True, 0, 50)\n",
    "        )\n",
    "        params.add('sigma_th', min=0, expr=f'sigma_bec+deltax'\n",
    "        )\n",
    "        res = fitmodel.fit(X_guess_og[i,j], x=x, params=params)\n",
    "        temp_res.append(res)\n",
    "    result_x.append(temp_res)\n",
    "stop = time.time()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-25T10:29:20.291311900Z",
     "start_time": "2023-07-25T10:29:19.914352200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.69218233 0.66625539 0.61833837]\n",
      " [0.58760028 0.5772473  0.61120507]\n",
      " [0.69197339 0.7760333  0.6094938 ]]\n"
     ]
    }
   ],
   "source": [
    "S = np.zeros((shape[0], shape[1]))\n",
    "for i in range(0, shape[0]):\n",
    "    for j in range(0, shape[1]):\n",
    "        S[i,j] = np.max(Y_guess_og[i,j])/(popt[i,j,2] + popt[i,j,3])\n",
    "print(S)\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-20T12:19:14.076672600Z",
     "start_time": "2023-07-20T12:19:14.026039900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "outputs": [],
   "source": [
    "#fitting Y on popt\n",
    "# math constr\n",
    "\n",
    "fitmodel = lmfit.Model(density_1d,independent_vars=['x'])\n",
    "\n",
    "p0_y = np.ones((shape[0], shape[1], 6))\n",
    "popt_y = np.zeros((shape[0], shape[1], 6))\n",
    "\n",
    "result_y = []\n",
    "start = time.time()\n",
    "for i in range(0, shape[0]):\n",
    "    temp_res = []\n",
    "    for j in range(0, shape[1]):\n",
    "        params = lmfit.Parameters()\n",
    "        params.add_many(\n",
    "            ('x0_bec', center[i,j,1], True,0, 200),\n",
    "            ('x0_th',center[i,j,1], True,0, 200),\n",
    "            ('amp_bec', S[i,j]* popt[i,j,2], True, 0, 1.3 * np.max(Y_guess_og[i,j])),\n",
    "            ('amp_th', S[i,j]* popt[i,j,3], True, 0, 1.3*np.max(Y_guess_og[i,j])),\n",
    "            ('deltax', 0, True, 0,50),\n",
    "            ('sigma_bec',BEC_width_guess[i,j,0], True, 0, 15)\n",
    "        )\n",
    "        params.add('sigma_th',popt[i,j,5], min=0, expr=f'sigma_bec + deltax'\n",
    "        )\n",
    "        res = fitmodel.fit(Y_guess_og[i,j], x=y, params=params)\n",
    "        temp_res.append(res)\n",
    "    result_y.append(temp_res)\n",
    "stop = time.time()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-20T13:19:22.545609300Z",
     "start_time": "2023-07-20T13:19:22.258075800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "outputs": [
    {
     "ename": "type",
     "evalue": "list indices must be integers or slices, not tuple",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mTypeError\u001B[0m                                 Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[227], line 12\u001B[0m\n\u001B[0;32m     10\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m i \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(\u001B[38;5;241m0\u001B[39m, shape[\u001B[38;5;241m0\u001B[39m]):\n\u001B[0;32m     11\u001B[0m     \u001B[38;5;28;01mfor\u001B[39;00m j \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(\u001B[38;5;241m0\u001B[39m, shape[\u001B[38;5;241m1\u001B[39m]):\n\u001B[1;32m---> 12\u001B[0m         S[i,j] \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39mmax(Y_guess_og[i,j])\u001B[38;5;241m/\u001B[39m(\u001B[43mresult_x\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[39mbest_values[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mamp_bec\u001B[39m\u001B[38;5;124m'\u001B[39m] \u001B[38;5;241m+\u001B[39m result_x[i,j]\u001B[38;5;241m.\u001B[39mbest_values[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mamp_th\u001B[39m\u001B[38;5;124m'\u001B[39m])\n\u001B[0;32m     13\u001B[0m \u001B[38;5;28mprint\u001B[39m(S[\u001B[38;5;241m0\u001B[39m,\u001B[38;5;241m0\u001B[39m]\u001B[38;5;241m*\u001B[39m popt[\u001B[38;5;241m0\u001B[39m,\u001B[38;5;241m0\u001B[39m,\u001B[38;5;241m3\u001B[39m]\u001B[38;5;241m+\u001B[39mS[\u001B[38;5;241m0\u001B[39m,\u001B[38;5;241m0\u001B[39m]\u001B[38;5;241m*\u001B[39m popt[\u001B[38;5;241m0\u001B[39m,\u001B[38;5;241m0\u001B[39m,\u001B[38;5;241m2\u001B[39m])\n\u001B[0;32m     15\u001B[0m result_y \u001B[38;5;241m=\u001B[39m []\n",
      "\u001B[1;31mTypeError\u001B[0m: list indices must be integers or slices, not tuple"
     ]
    }
   ],
   "source": [
    "# Fitting Y\n",
    "# without mathematical constraint\n",
    "# Fix most\n",
    "fitmodel = lmfit.Model(density_1d,independent_vars=['x'])\n",
    "\n",
    "p0_y = np.ones((shape[0], shape[1], 6))\n",
    "popt_y = np.zeros((shape[0], shape[1], 6))\n",
    "\n",
    "S = np.zeros((shape[0], shape[1]))\n",
    "for i in range(0, shape[0]):\n",
    "    for j in range(0, shape[1]):\n",
    "        S[i,j] = np.max(Y_guess_og[i,j])/(result_x[i,j].best_values['amp_bec'] + result_x[i,j].best_values['amp_th'])\n",
    "print(S[0,0]* popt[0,0,3]+S[0,0]* popt[0,0,2])\n",
    "\n",
    "result_y = []\n",
    "start = time.time()\n",
    "for i in range(0, shape[0]):\n",
    "    temp_res = []\n",
    "    for j in range(0, shape[1]):\n",
    "        params = lmfit.Parameters()\n",
    "\n",
    "        bval = result_x[i][j].best_values\n",
    "        params.add_many(\n",
    "            ('x0_bec', center[i,j,1], False,0, 200),\n",
    "            ('x0_th',center[i,j,1], False,0, 200),\n",
    "            ('amp_bec', S[i,j]* bval['amp_bec'], True, 0, 1.3 * np.max(Y_guess_og[i,j])),\n",
    "            ('amp_th', S[i,j]* bval['amp_th'], False),\n",
    "            ('sigma_bec',BEC_width_guess[i,j,1], True, 0, 50),\n",
    "            ('sigma_th',bval['sigma_th'], False)\n",
    "        )\n",
    "        res = fitmodel.fit(Y_guess_og[i,j], x=y, params=params)\n",
    "        temp_res.append(res)\n",
    "    result_y.append(temp_res)\n",
    "stop = time.time()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-20T13:57:46.171454600Z",
     "start_time": "2023-07-20T13:57:46.075934300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[75.41570333 77.33388115  2.56706786  0.32435386  9.02534473 21.78913789]\n",
      "{'x0_bec': 75.41570631345125, 'x0_th': 77.33387669882647, 'amp_bec': 2.5670677717602537, 'amp_th': 0.32435362458962513, 'sigma_bec': 9.025351656015761, 'sigma_th': 21.789148834558127}\n",
      "25.0\n",
      "\n",
      "[77.87651631 81.11230043  2.46976259  0.33019881  9.77967464 21.30093181]\n",
      "{'x0_bec': 77.87414667926795, 'x0_th': 81.00942140743041, 'amp_bec': 2.4251361988169013, 'amp_th': 0.336733100911304, 'sigma_bec': 9.771032230037038, 'sigma_th': 21.0114608336347}\n",
      "26.0\n",
      "\n",
      "[73.89357632 76.27664646  2.4312179   0.27440553  9.98643754 20.45775545]\n",
      "{'x0_bec': 73.87690614810727, 'x0_th': 75.75396443024098, 'amp_bec': 2.1748880271879374, 'amp_th': 0.32117568442009115, 'sigma_bec': 9.814809230807409, 'sigma_th': 18.256517221101156}\n",
      "26.0\n",
      "\n",
      "[77.11393592 81.40236184  2.88921236  0.26104054  8.62447632 18.73504104]\n",
      "{'x0_bec': 77.08861286232403, 'x0_th': 79.69001135621787, 'amp_bec': 2.4064163373141456, 'amp_th': 0.34799624740698715, 'sigma_bec': 8.351489369586584, 'sigma_th': 15.16726157787356}\n",
      "19.0\n",
      "\n",
      "[74.59729659 78.58410424  2.78343705  0.22861603  9.03257479 18.55393163]\n",
      "{'x0_bec': 74.56347324160527, 'x0_th': 76.67698294957175, 'amp_bec': 2.2603093432491317, 'amp_th': 0.338441239554665, 'sigma_bec': 8.542756926651688, 'sigma_th': 13.927244795040806}\n",
      "21.0\n",
      "\n",
      "[78.50292483 82.40258606  2.35517079  0.20782915  9.17053985 15.99073945]\n",
      "{'x0_bec': 78.4482746738924, 'x0_th': 81.06222964804594, 'amp_bec': 2.0364741307301464, 'amp_th': 0.27923725023708695, 'sigma_bec': 8.802233429669542, 'sigma_th': 13.147732761193325}\n",
      "26.0\n",
      "\n",
      "[75.51767384 76.75506578  0.87440112  0.14660177  8.96571527  5.96931421]\n",
      "{'x0_bec': 75.74475709382901, 'x0_th': 76.32144038563793, 'amp_bec': 0.9184588823685144, 'amp_th': 0.12427203265282463, 'sigma_bec': 8.275865193835182, 'sigma_th': 8.275865193840495}\n",
      "20.0\n",
      "\n",
      "[76.17323785 76.98707429  0.61643944  0.23667638  9.4043337   5.34970959]\n",
      "{'x0_bec': 76.46913757414158, 'x0_th': 76.83078679310978, 'amp_bec': 0.8606601665782735, 'amp_th': 0.1553960250745048, 'sigma_bec': 8.072403404223405, 'sigma_th': 8.072403404877084}\n",
      "18.0\n",
      "\n",
      "[74.88995354 73.93092826  0.90810872  0.09114657  9.42569183  7.98461584]\n",
      "{'x0_bec': 74.84814609706852, 'x0_th': 74.34947866579368, 'amp_bec': 0.7917518736101876, 'amp_th': 0.12935441772036824, 'sigma_bec': 8.30427046215347, 'sigma_th': 8.304270463041027}\n",
      "20.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "for i in range(0, shape[0]):\n",
    "    for j in range(0, shape[1]):\n",
    "        print(popt[i,j])\n",
    "        print(result_x[i][j].best_values)\n",
    "        #print(result_y[i][j].best_values)\n",
    "        print(BEC_width_guess[i,j,1])\n",
    "        print(\"\")\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-25T10:29:51.620810400Z",
     "start_time": "2023-07-25T10:29:51.571566300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1200x800 with 9 Axes>",
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA9EAAAKPCAYAAABqw0lAAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3zU9f3A8df39iW5XPaAJAQIGwSCKC5cdStatdZdVBCr1rbW2vKztmpbsXW0WgeoqHXUhdriqHuBVgQRRJARIHvPu6xb3+/vj28SQBLIuORG3s/H4x5yd9/73vvO483n/f0sRdM0DSGEEEIIIYQQQhyUIdQBCCGEEEIIIYQQkUKKaCGEEEIIIYQQopekiBZCCCGEEEIIIXpJimghhBBCCCGEEKKXpIgWQgghhBBCCCF6SYpoIYQQQgghhBCil6SIFkIIIYQQQggheskU6gC+T1VVysvLcTgcKIoS6nCEEBFI0zTcbjcjRozAYIiua4WSI4UQAyH5UQghuteX/Bh2RXR5eTnZ2dmhDkMIEQVKSkrIysoKdRhBJTlSCBEMkh+FEKJ7vcmPYVdEOxwOQA8+Pj4+xNEIISKRy+UiOzu7K59EE8mRQoiBkPwohBDd60t+DLsiunP4TXx8vCRAIcSARONwPsmRQohgkPwohBDd601+jK7JMEIIIYQQQgghxCCSIloIIYQQQgghhOglKaKFEEIIIYQQQohekiJaCCGEEEIIIYToJSmiRcQKqBovri1mR5U71KEIIURYavcFePKz3RTXtYY6FDEM3HDDDeTm5qIoChs2bOj2mCeffJIZM2Z03VJSUjj33HMBKCwsxGg07vP8zp07h/ATiGj22telrC2sD3UYIkpIES0i1pOf7eY3r2xi8aubQh2KEEKEpZUby7n99S386c0toQ5FDAPnn38+q1evZtSoUT0ec8UVV7Bhw4auW0ZGBpdccknX8w6HY5/nx44dOxShiyi3vcrNL1/cyHXPrQ91KCJKhN0WV0L0hqvdx4MfFQCwpcKFqmoYDNG3XYcQQgzErpoWAL4pbQpxJGI4mDt3bp+OX7NmDdXV1cybN2+QIhJC15kDq90emlp9OGPMIY5IRDrpiRYRadknO2ls9QHQ6g1Q3tQW4oiEECL8lDXqubHS1U5dsyfE0Qixr+XLl3PZZZdhNu8paFpaWpg9ezb5+fnccccdBAKBHl/v8XhwuVz73ITozncVe34bRfUtIYxERAspokXE0TSNpz8vAsBi1H/CO6qbQxmSEEKEpbKGPXOhv6uQ9SNE+GhpaeGFF17gqquu6nosMzOTsrIy1q5dy/vvv8+qVau49957ezzHkiVLcDqdXbfs7OyhCF1EoH2KaFkjQgSBFNEi4tS3eDnN/x53m5Zye/qnpNDETimihRBiP5090TlKFeq6JyDgD3FEQuhefvllpkyZwuTJk7ses1qtpKWlAZCUlMSVV17JqlWrejzH4sWLaWpq6rqVlJQMetwi8mia9r0iWnqixcDJnGgRcSpr67nTtByTokL9p2SZp/JG1dRQhyWEEGHF61epdntQUHncfA/jt5XBl3FwxLWhDk0Ili9fvk8vNEB1dTWJiYmYzWY8Hg+vvvoqM2fO7PEcVqsVq9U62KGKCFfpaqehYwogQKH0RIsgkJ5oEXHair7CpKj4Oq4B5Rt2sLOqMbRBCSFEmKloakPT4HTDl4w3lOkPrnlEeqPFoFm0aBFZWVmUlpZyyimnkJeXB8CCBQtYuXJl13Hbtm1jw4YN/PjHP97n9atXr2bmzJlMnz6d/Px8MjIyuOWWW4b0M4jos3cvNCBb/omgkJ5oEXnKvwJgc8xsDvF+Tay/HW9NAZp2DIoiK3QLIQRAWUMbCiq/tLy258HGYtj6Bkw5J2Rxiei1bNmybh9//PHH97k/YcIE3O795+ife+65XXtGCxEsnetBjE6JZXdtC4UynFsEgfREi4gTV/MNALXOQyBjGgC53gKq3bLyrBBCdCptbOM4w0byKMFNDE/6T9Gf+OLh0AYmhBBDaEtHT/SpUzMAfZurVq+MyBEDI0W0iDip7s0AtKbNwJA5A4CphkIKZHExIYToUtrQRr5hBwAb4o7lYf/Z+hMla6C1PoSRCSHE0NlRpfdEHzY6iYSO/aGL62VItxgYKaJFZGmpJdlXAYBx5EzInA7AVGW3FNFCCLGXsoY2xin6XOhJM+agxqZRqqUAoNVsDWVoQggxZGqbvQBkOm2MSo4FoLBWimgxMFJEi8hSth6AnWomKSlpe4poQyEVHVu5CCGEgLLGVsYppQCkjD6Ef155GLu0EQC4SzaHMjQhhBgSAVWjsdWLAZWsbx7kHNMXgGxzJQZOimgRUbQyfVGxDdpYMp12SJtEQDETr7Tir9sd4uiEECJ8VDe4GKVU6XdSJzJ1pJNaWy4AjcXfhi4wIYQYIq42H6oGPzSsJu7zv3BZ5RJSaaCkQXqixcBIES0iiq9C7z3ZouaS7rSC0YzLOR4AR4P0rAghBOi9L1ZXISZFRbXEgyMTAH/SOADUmu2hDE8IIYZEfasXCz5+ZVkBgEnzMd/0DnUdQ7yF6C8pokVE8TfqQxObrJlYTUYAvMmTAHC27ApZXEIIEU7qWjyM1UoAUNImQMf2f/YRkwGIc+8MWWxCCDFU6lu8XGz8gBHUgtEKwKXG92lxN4U4MhHppIgWEcXgLgcg0NGrAmBKyAIgxlMdkpiEECLc1Ld4GWfQFxVTUid2PZ6ZNwOAFH8VeGQxRiFEdKtv8XKp8X39zil/ps2Ri1Np5bCm/4Y2MBHxpIgWkSPgx9JWA4DRmdX1sD0lB4DkQB3tvkBIQhNCiHBS3+Ilr2NRMfYqosePGUWtFg+Aq/S7UIQmhBBDxuVyMUbRd3Vh0jzcky8CYGL7NyGMSkQDKaJF5GiuwoCKTzMSm5TR9bA9WS+oM5R6ql2eUEUnhBBho77F27W91d5FdLzNTKkxG4DKnRtDEZoQQgwZrXYbBkWjxeiEuDQsI/VdXUapJaiqFuLoRCSTIlpEDpc+lLuKRNITYrseVuJHAnoRXeVuD0loQggRThrdLYxWKvU7aRP3ec4dNwaA5jJZjFEIEd1s9dsAqI0ZA4pCbNYUAHKVShqbZZsr0X9SRIvI4dKHJlZoSWQlxux5PF7f9zRZcVPTIAtFCCGEt6EUsxLAp1ig40Jjl1R9RwNjXUEIIhNCiKET59oBgMuh70xgTsymGTtmJUBz+dZQhiYinBTRInJ09ERXaklkJdr3PG5PxKtYAGipKQ1FZEIIEVa0Jj1ftlhSu1bm7hSbrvdEx7RXDnlcQggxlFJa9J0IPEkT9AcUhWKDPqXFUyHrQoj+kyJaRIxAoz6/r0JLJnvvnmhFwW1JA8DTUBKK0IQQIqwozfpCOu329P2ei+tYjNHprx3SmIQQYqhlegsBUNMmdz1WYckFQKmWIlr0nxTRImK01xUDUKskkxJn2ec5T0dDUe3ofRFCiOHM0loFgD82Y7/nkjJHAZCsNeDzeYc0LhHdbrjhBnJzc1EUhQ0bNnR7zMcff4zdbmfGjBldt7a2tq7nly9fzrhx4xg7diwLFy7E5/MNUfQi6rQ3karqu7pYMvYU0XUxowEwN+wISVgiOkgRLSJGoFEfqu2NzUT53vBEf5y+b7SppWLI4xJCiHBjb68GQHNk7vdcUupIfJoRo6JRW1E81KGJKHb++eezevVqRo0adcDjJkyYwIYNG7pudrs+RWv37t3ceuutrFq1ioKCAqqqqnj00UeHInQRjar1Oc/lWhLxiSldD7s75kfHuWRdCNF/UkSLiGHsGJ5oSBi533OGjsXFrG1VQxqTEHsLRi+MEMEQ79OLaFN3+dJkot6QCEBDVdGQxiWi29y5c8nKyur361esWMG8efPIyMhAURSuueYann/++SBGKIYTX8W3AGxXs0mK3TOC0ZOkF9EJrUUQkJEOon+kiBaRQQ1gb9eH5FiTsvd72pqk/6Pt8FYPaVhC7G2gvTBCBIOmaST66wCwJu1fRAM0mfRemeZq6YkWQ2/nzp3k5+cze/ZsHn744a7Hi4uL98mfubm5FBf
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nr_plots,nr_plots,figsize=fsize)\n",
    "for i in range(0, shape[0]):\n",
    "    for j in range(0, shape[1]):\n",
    "        lab = f\"A$_{{BEC}}$ = {popt[i,j,0]:.1f} \\n A$_{{th}}$ = {popt[i,j,1]:.1f} \"\n",
    "        ax[i,j].plot(x, X_guess_og[i,j])\n",
    "        ax[i,j].plot(x, density_1d(x, *popt[i,j]), label = lab)\n",
    "        ax[i,j].plot(x, thermal(x, popt[i,j,1], popt[i,j, 3], popt[i,j, 5]))\n",
    "\n",
    "\n",
    "        #ax[i,j].legend(fontsize=10)\n",
    "        ax[i,j].set_facecolor('#FFFFFF')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-25T10:29:32.565187100Z",
     "start_time": "2023-07-25T10:29:31.085329400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22.774\n",
      "22.814\n"
     ]
    }
   ],
   "source": [
    "print(11.8*1.93)\n",
    "print(18.7*1.22)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-25T13:42:22.372434400Z",
     "start_time": "2023-07-25T13:42:22.299475700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1200x800 with 9 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nr_plots,nr_plots,figsize=fsize)\n",
    "for i in range(0, shape[0]):\n",
    "    for j in range(0, shape[1]):\n",
    "        #lab = f\"A$_{{BEC}}$ = {popt[i,j,0]:.1f} \\n A$_{{th}}$ = {popt[i,j,1]:.1f} \"\n",
    "        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), label = lab)\n",
    "        ax[i,j].plot(x, thermal(x, bval['x0_th'], bval['amp_th'], bval['sigma_th']))\n",
    "\n",
    "\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-20T13:30:21.491998700Z",
     "start_time": "2023-07-20T13:30:19.773425Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1200x800 with 9 Axes>",
      "image/png": "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
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nr_plots,nr_plots,figsize=fsize)\n",
    "for i in range(0, shape[0]):\n",
    "    for j in range(0, shape[1]):\n",
    "        #lab = f\"A$_{{BEC}}$ = {popt[i,j,0]:.1f} \\n A$_{{th}}$ = {popt[i,j,1]:.1f} \"\n",
    "        bval = result_y[i][j].best_values\n",
    "        ax[i,j].plot(y, Y_guess_og[i,j])\n",
    "        ax[i,j].plot(y, density_1d(y, **result_y[i][j].best_values), label = lab)\n",
    "        ax[i,j].plot(y, thermal(y, bval['x0_th'], bval['amp_th'], bval['sigma_th']))\n",
    "        #ax[i,j].plot(y, thermal(y, **result[i][j].best_values))\n",
    "\n",
    "\n",
    "        #ax[i,j].legend(fontsize=10)\n",
    "        ax[i,j].set_facecolor('#FFFFFF')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-20T13:32:23.044548800Z",
     "start_time": "2023-07-20T13:32:21.525149900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[88.5        88.5         2.36427288  0.29873033 25.         21.78913789]\n",
      "[0. 0. 0. 0. 0. 0.]\n",
      "\n",
      "[83.31944444 83.31944444  2.00635046  0.26824219 26.         21.30093181]\n",
      "[0. 0. 0. 0. 0. 0.]\n",
      "\n",
      "[83.28654971 83.28654971  2.14473074  0.24207043 26.         20.45775545]\n",
      "[0. 0. 0. 0. 0. 0.]\n",
      "\n",
      "[84.52136752 84.52136752  2.35846552  0.21308753 19.         18.73504104]\n",
      "[0. 0. 0. 0. 0. 0.]\n",
      "\n",
      "[84.98387097 84.98387097  2.21373578  0.18182394 21.         18.55393163]\n",
      "[0. 0. 0. 0. 0. 0.]\n",
      "\n",
      "[90.42528736 90.42528736  1.79170364  0.15810669 26.         15.99073945]\n",
      "[0. 0. 0. 0. 0. 0.]\n",
      "\n",
      "[84.00684932 84.00684932  0.76816174  0.12878971 20.          5.96931421]\n",
      "[0. 0. 0. 0. 0. 0.]\n",
      "\n",
      "[83.63970588 83.63970588  0.66093309  0.25375932 18.          5.34970959]\n",
      "[0. 0. 0. 0. 0. 0.]\n",
      "\n",
      "[8.47401575e+01 8.47401575e+01 7.75240700e-01 7.78106534e-02\n",
      " 2.00000000e+01 7.98461584e+00]\n",
      "[0. 0. 0. 0. 0. 0.]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in range(0,3):\n",
    "    for j in range(0,3):\n",
    "        print(p0_y[i,j])\n",
    "        print(popt_y[i,j])\n",
    "        print(\"\")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-20T09:38:31.270317900Z",
     "start_time": "2023-07-20T09:38:31.227819Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'x0_bec': 88.71878723899935, 'x0_th': 87.86994848219054, 'amp_bec': 2.601808340467902, 'amp_th': 0.29982321034674164, 'sigma_bec': 28.01890078868497, 'sigma_th': 28.018901081653368}\n"
     ]
    }
   ],
   "source": [
    "print(result[0][0].best_values)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-07-20T12:06:33.767462300Z",
     "start_time": "2023-07-20T12:06:33.710814Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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