analyseScript/backupScript/20230509_Data_Analysis.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import supporting package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import copy\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",
    "plt.rcParams['font.size'] = 18\n",
    "\n",
    "from DataContainer.ReadData import read_hdf5_file, read_hdf5_global, read_hdf5_run_time\n",
    "from Analyser.ImagingAnalyser import ImageAnalyser\n",
    "from Analyser.FitAnalyser import FitAnalyser\n",
    "from Analyser.FFTAnalyser import fft, ifft, fft_nutou\n",
    "from ToolFunction.ToolFunction import *\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()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Start a client for parallel computing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n",
       "    <div style=\"margin-left: 48px;\">\n",
       "        <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-ce3d6de8-f653-11ed-8a44-80e82ce2fa8e</p>\n",
       "        <table style=\"width: 100%; text-align: left;\">\n",
       "\n",
       "        <tr>\n",
       "        \n",
       "            <td style=\"text-align: left;\"><strong>Connection method:</strong> Cluster object</td>\n",
       "            <td style=\"text-align: left;\"><strong>Cluster type:</strong> distributed.LocalCluster</td>\n",
       "        \n",
       "        </tr>\n",
       "\n",
       "        \n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
       "                </td>\n",
       "                <td style=\"text-align: left;\"></td>\n",
       "            </tr>\n",
       "        \n",
       "\n",
       "        </table>\n",
       "\n",
       "        \n",
       "\n",
       "        \n",
       "            <details>\n",
       "            <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Cluster Info</h3></summary>\n",
       "            <div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\">\n",
       "    </div>\n",
       "    <div style=\"margin-left: 48px;\">\n",
       "        <h3 style=\"margin-bottom: 0px; margin-top: 0px;\">LocalCluster</h3>\n",
       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">b4aeb775</p>\n",
       "        <table style=\"width: 100%; text-align: left;\">\n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
       "                </td>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Workers:</strong> 6\n",
       "                </td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Total threads:</strong> 24\n",
       "                </td>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Total memory:</strong> 55.88 GiB\n",
       "                </td>\n",
       "            </tr>\n",
       "            \n",
       "            <tr>\n",
       "    <td style=\"text-align: left;\"><strong>Status:</strong> running</td>\n",
       "    <td style=\"text-align: left;\"><strong>Using processes:</strong> True</td>\n",
       "</tr>\n",
       "\n",
       "            \n",
       "        </table>\n",
       "\n",
       "        <details>\n",
       "            <summary style=\"margin-bottom: 20px;\">\n",
       "                <h3 style=\"display: inline;\">Scheduler Info</h3>\n",
       "            </summary>\n",
       "\n",
       "            <div style=\"\">\n",
       "    <div>\n",
       "        <div style=\"width: 24px; height: 24px; background-color: #FFF7E5; border: 3px solid #FF6132; border-radius: 5px; position: absolute;\"> </div>\n",
       "        <div style=\"margin-left: 48px;\">\n",
       "            <h3 style=\"margin-bottom: 0px;\">Scheduler</h3>\n",
       "            <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-7292c9f7-a659-455f-b744-b366f71faa4e</p>\n",
       "            <table style=\"width: 100%; text-align: left;\">\n",
       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Comm:</strong> tcp://127.0.0.1:59304\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Workers:</strong> 6\n",
       "                    </td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Total threads:</strong> 24\n",
       "                    </td>\n",
       "                </tr>\n",
       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Started:</strong> Just now\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Total memory:</strong> 55.88 GiB\n",
       "                    </td>\n",
       "                </tr>\n",
       "            </table>\n",
       "        </div>\n",
       "    </div>\n",
       "\n",
       "    <details style=\"margin-left: 48px;\">\n",
       "        <summary style=\"margin-bottom: 20px;\">\n",
       "            <h3 style=\"display: inline;\">Workers</h3>\n",
       "        </summary>\n",
       "\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 0</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:59336\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 4\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:59339/status\" target=\"_blank\">http://127.0.0.1:59339/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 9.31 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:59307\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-73y3n_r9\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 1</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:59345\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 4\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:59347/status\" target=\"_blank\">http://127.0.0.1:59347/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 9.31 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:59308\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-z72eyh5g\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 2</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:59332\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 4\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:59333/status\" target=\"_blank\">http://127.0.0.1:59333/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 9.31 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:59309\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-q12q3qsa\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 3</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:59344\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 4\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:59346/status\" target=\"_blank\">http://127.0.0.1:59346/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 9.31 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:59310\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-75c6pwd0\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 4</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:59335\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 4\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:59337/status\" target=\"_blank\">http://127.0.0.1:59337/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 9.31 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:59311\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-vs73ymq0\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "        <div style=\"margin-bottom: 20px;\">\n",
       "            <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
       "            <div style=\"margin-left: 48px;\">\n",
       "            <details>\n",
       "                <summary>\n",
       "                    <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 5</h4>\n",
       "                </summary>\n",
       "                <table style=\"width: 100%; text-align: left;\">\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Comm: </strong> tcp://127.0.0.1:59341\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 4\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:59342/status\" target=\"_blank\">http://127.0.0.1:59342/status</a>\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Memory: </strong> 9.31 GiB\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Nanny: </strong> tcp://127.0.0.1:59312\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\"></td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td colspan=\"2\" style=\"text-align: left;\">\n",
       "                            <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-6pd88xs6\n",
       "                        </td>\n",
       "                    </tr>\n",
       "\n",
       "                    \n",
       "\n",
       "                    \n",
       "\n",
       "                </table>\n",
       "            </details>\n",
       "            </div>\n",
       "        </div>\n",
       "        \n",
       "\n",
       "    </details>\n",
       "</div>\n",
       "\n",
       "        </details>\n",
       "    </div>\n",
       "</div>\n",
       "            </details>\n",
       "        \n",
       "\n",
       "    </div>\n",
       "</div>"
      ],
      "text/plain": [
       "<Client: 'tcp://127.0.0.1:59304' processes=6 threads=24, memory=55.88 GiB>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dask.distributed import Client\n",
    "client = Client(n_workers=6, threads_per_worker=4, processes=True, memory_limit='10GB')\n",
    "client"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set global path for experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "groupList = [\n",
    "    \"images/MOT_3D_Camera/in_situ_absorption\",\n",
    "    \"images/ODT_1_Axis_Camera/in_situ_absorption\",\n",
    "    \"images/ODT_2_Axis_Camera/in_situ_absorption\",\n",
    "]\n",
    "\n",
    "dskey = {\n",
    "    \"images/MOT_3D_Camera/in_situ_absorption\": \"camera_0\",\n",
    "    \"images/ODT_1_Axis_Camera/in_situ_absorption\": \"camera_1\",\n",
    "    \"images/ODT_2_Axis_Camera/in_situ_absorption\": \"camera_2\",\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_dir = '//DyLabNAS/Data/'\n",
    "SequenceName = \"Evaporative_Cooling\" + \"/\"\n",
    "folderPath = img_dir + SequenceName + \"2023/05/09\" # get_date()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check the stability of our BEC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The detected scaning axes and values are: \n",
      "\n",
      "{'runs': array([  0.,   1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.,\n",
      "        11.,  12.,  13.,  14.,  15.,  16.,  17.,  18.,  19.,  20.,  21.,\n",
      "        22.,  23.,  24.,  25.,  26.,  27.,  28.,  29.,  30.,  31.,  32.,\n",
      "        33.,  34.,  35.,  36.,  37.,  38.,  39.,  40.,  41.,  42.,  43.,\n",
      "        44.,  45.,  46.,  47.,  48.,  49.,  50.,  51.,  52.,  53.,  54.,\n",
      "        55.,  56.,  57.,  58.,  59.,  60.,  61.,  62.,  63.,  64.,  65.,\n",
      "        66.,  67.,  68.,  69.,  70.,  71.,  72.,  73.,  74.,  75.,  76.,\n",
      "        77.,  78.,  79.,  80.,  81.,  82.,  83.,  84.,  85.,  86.,  87.,\n",
      "        88.,  89.,  90.,  91.,  92.,  93.,  94.,  95.,  96.,  97.,  98.,\n",
      "        99., 100., 101., 102., 103., 104., 105., 106., 107., 108., 109.,\n",
      "       110., 111., 112., 113., 114., 115., 116., 117., 118., 119., 120.,\n",
      "       121., 122., 123., 124., 125., 126., 127., 128., 129., 130., 131.,\n",
      "       132., 133., 134., 135., 136., 137., 138., 139., 140., 141., 142.,\n",
      "       143., 144., 145., 146., 147., 148., 149., 150., 151., 152., 153.,\n",
      "       154., 155., 156., 157., 158., 159., 160., 161., 162., 163., 164.,\n",
      "       165., 166., 167., 168., 169., 170., 171., 172., 173., 174., 175.,\n",
      "       176., 177., 178., 179., 180., 181., 182., 183., 184., 185., 186.,\n",
      "       187., 188., 189., 190., 191., 192., 193., 194., 195., 196., 197.,\n",
      "       198., 199., 200., 201., 202., 203., 204., 205., 206., 207., 208.,\n",
      "       209., 210., 211., 212., 213., 214., 215., 216., 217., 218., 219.,\n",
      "       220., 221., 222., 223., 224., 225., 226., 227., 228., 229., 230.,\n",
      "       231., 232., 233., 234., 235., 236., 237., 238., 239., 240., 241.,\n",
      "       242., 243., 244., 245., 246., 247., 248., 249., 250., 251., 252.,\n",
      "       253., 254., 255., 256., 257., 258., 259., 260., 261., 262., 263.,\n",
      "       264., 265., 266., 267., 268., 269., 270., 271., 272., 273., 274.,\n",
      "       275., 276., 277., 278., 279., 280., 281., 282., 283., 284., 285.,\n",
      "       286., 287., 288., 289., 290., 291., 292., 293., 294., 295., 296.,\n",
      "       297., 298., 299., 300., 301., 302., 303., 304., 305., 306., 307.,\n",
      "       308., 309., 310., 311., 312., 313., 314., 315., 316., 317., 318.,\n",
      "       319., 320., 321., 322., 323., 324., 325., 326., 327., 328., 329.,\n",
      "       330., 331., 332., 333., 334., 335., 336., 337., 338., 339., 340.,\n",
      "       341., 342., 343., 344., 345., 346., 347., 348., 349., 350., 351.,\n",
      "       352., 353., 354., 355., 356., 357., 358., 359., 360., 361., 362.,\n",
      "       363., 364., 365., 366., 367., 368., 369., 370., 371., 372., 373.,\n",
      "       374., 375., 376., 377., 378., 379., 380., 381., 382., 383., 384.,\n",
      "       385., 386., 387., 388., 389., 390., 391., 392., 393., 394., 395.,\n",
      "       396., 397., 398., 399., 400., 401., 402., 403., 404., 405., 406.,\n",
      "       407., 408., 409., 410., 411., 412., 413., 414., 415., 416., 417.,\n",
      "       418., 419., 420., 421., 422., 423., 424., 425., 426., 427., 428.,\n",
      "       429., 430., 431., 432., 433., 434., 435., 436., 437., 438., 439.,\n",
      "       440., 441., 442., 443., 444., 445., 446., 447., 448., 449., 450.,\n",
      "       451., 452., 453., 454., 455., 456., 457., 458., 459., 460., 461.,\n",
      "       462., 463., 464., 465., 466., 467., 468., 469., 470., 471., 472.,\n",
      "       473., 474., 475., 476., 477., 478., 479., 480., 481., 482., 483.,\n",
      "       484., 485., 486., 487., 488., 489., 490., 491., 492., 493., 494.,\n",
      "       495., 496., 497., 498., 499., 500., 501., 502., 503., 504., 505.,\n",
      "       506., 507., 508., 509., 510., 511., 512., 513., 514., 515., 516.,\n",
      "       517., 518., 519., 520., 521., 522., 523., 524., 525., 526., 527.,\n",
      "       528., 529., 530., 531., 532., 533., 534., 535., 536., 537., 538.,\n",
      "       539., 540., 541., 542., 543., 544., 545., 546., 547., 548., 549.])}\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shotNum = \"0007\"\n",
    "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n",
    "\n",
    "dataSetOfGlobalDict = {\n",
    "    dskey[groupList[i]]: read_hdf5_global(filePath, groupList[i])\n",
    "    for i in [0]\n",
    "}\n",
    "\n",
    "dataSetDict = {\n",
    "    dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i], datesetOfGlobal=dataSetOfGlobalDict[dskey[groupList[i]]])\n",
    "    for i in [0]\n",
    "}\n",
    "\n",
    "dataSet = dataSetDict[\"camera_0\"]\n",
    "\n",
    "print_scanAxis(dataSet)\n",
    "\n",
    "scanAxis = get_scanAxis(dataSet)\n",
    "\n",
    "dataSet = auto_rechunk(dataSet)\n",
    "dataSet = swap_xy(dataSet)\n",
    "\n",
    "dataSet = imageAnalyser.get_absorption_images(dataSet)\n",
    "\n",
    "imageAnalyser.center = (959, 876)\n",
    "imageAnalyser.span = (100, 100)\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",
    "\n",
    "Ncount = imageAnalyser.get_Ncount(dataSet_cropOD).load()\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.gca()\n",
    "\n",
    "Ncount.plot.errorbar(ax=ax, fmt='ob')\n",
    "\n",
    "plt.ylabel('NCount')\n",
    "plt.tight_layout()\n",
    "plt.grid(visible=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataSet_cropOD = auto_rechunk(dataSet_cropOD)\n",
    "\n",
    "fitAnalyser = FitAnalyser(\"Two Gaussian-2D\", fitDim=2)\n",
    "params = fitAnalyser.guess(dataSet_cropOD, dask=\"parallelized\")\n",
    "fitResult = fitAnalyser.fit(dataSet_cropOD, params, dask=\"parallelized\").load()\n",
    "\n",
    "fitValue = fitAnalyser.get_fit_value(fitResult)\n",
    "fitStd = fitAnalyser.get_fit_std(fitResult)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "BEC_Ncount_val = fitValue['A_amplitude']\n",
    "BEC_Ncount_std = fitStd['A_amplitude']\n",
    "\n",
    "thermal_Ncount_val = fitValue['B_amplitude']\n",
    "thermal_Ncount_std = fitStd['B_amplitude']\n",
    "\n",
    "BEC_width_x_val = fitValue['A_sigmax']\n",
    "BEC_width_x_std = fitStd['A_sigmax']\n",
    "BEC_width_y_val = fitValue['A_sigmay']\n",
    "BEC_width_y_std = fitStd['A_sigmay']\n",
    "\n",
    "thermal_width_x_val = fitValue['B_sigmax']\n",
    "thermal_width_x_std = fitStd['B_sigmax']\n",
    "thermal_width_y_val = fitValue['B_sigmay']\n",
    "thermal_width_y_std = fitStd['B_sigmay']\n",
    "\n",
    "BEC_center_x_val = fitValue['A_centerx']\n",
    "BEC_center_x_std = fitStd['A_centerx']\n",
    "BEC_center_y_val = fitValue['A_centery']\n",
    "BEC_center_y_std = fitStd['A_centery']\n",
    "\n",
    "thermal_center_x_val = fitValue['B_centerx']\n",
    "thermal_center_x_std = fitStd['B_centerx']\n",
    "thermal_center_y_val = fitValue['B_centery']\n",
    "thermal_center_y_std = fitStd['B_centery']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray ()>\n",
      "array(853.42940839)\n"
     ]
    }
   ],
   "source": [
    "total_Ncount_val = BEC_Ncount_val + thermal_Ncount_val\n",
    "total_Ncount_std = BEC_Ncount_std + thermal_Ncount_std\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.gca()\n",
    "\n",
    "total_Ncount_val.plot.errorbar(ax=ax, yerr=total_Ncount_std, fmt='ob')\n",
    "# plt.ylim([0, 1100])\n",
    "plt.ylabel('Ncount from fit')\n",
    "plt.tight_layout()\n",
    "plt.grid(visible=1)\n",
    "plt.show()\n",
    "\n",
    "print(total_Ncount_val.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.gca()\n",
    "\n",
    "BEC_Ncount_val.plot.errorbar(ax=ax, yerr=BEC_Ncount_std, fmt='ob')\n",
    "plt.ylim([0, 750])\n",
    "plt.ylabel('Ncount of BEC part')\n",
    "plt.tight_layout()\n",
    "plt.grid(visible=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.gca()\n",
    "\n",
    "thermal_Ncount_val.plot.errorbar(ax=ax, yerr=thermal_Ncount_std, fmt='or')\n",
    "plt.ylim([0, 500])\n",
    "plt.ylabel('Ncount of thermal part')\n",
    "plt.tight_layout()\n",
    "plt.grid(visible=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.gca()\n",
    "\n",
    "BEC_width_x_val.plot.errorbar(ax=ax, yerr=BEC_width_x_std, fmt='ob')\n",
    "\n",
    "plt.ylabel('X-axis Width of BEC part')\n",
    "plt.tight_layout()\n",
    "plt.grid(visible=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAl4AAAG+CAYAAABCjQqZAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACsaElEQVR4nO2dd3gVVfrHv5ObkJCEFEIQSELRFRusFVaECChlibJobKgo6q5YfhakqihtFWVBxF07KoiAFBNhZSNFDFIURV1XQcVCDyAtpBLSzu+PcW7mzp1yzszclryf55knuVPOnJk5c84773mLxBhjIAiCIAiCIAJOVKgrQBAEQRAE0VQgwYsgCIIgCCJIkOBFEARBEAQRJEjwIgiCIAiCCBIkeBEEQRAEQQQJErwIgiAIgiCCBAleBEEQBEEQQYIEL4IgCIIgiCBBghdBEARBEESQiA51BQhz6uvrceDAAbRo0QKSJIW6OgRBEARBaGCMoaysDO3atUNUlIVOi0UIFRUVrKCggP39739n1157LWvfvj0DwACwSZMmmR67f/9+9tJLL7Hrr7+enXHGGSwuLo7FxcWxjh07sqFDh7J169a5UsdffvmFjRgxgnXs2JHFxsay9PR0NmDAAPbee+/ZLnPfvn3e66SFFlpooYUWWsJ32bdvn+W4HjEary+++AI5OTnCx+3btw8dOnQAU6WkjI+PB2MMu3fvxu7du7F48WLcddddeP311+HxeGzVr6CgADfccAMqKysBAElJSTh27BjWrFmDNWvW4M4778Sbb74prLVq0aKF9zqSkpJs1U2PmpoarFmzBgMGDEBMTIxr5RKEKNQWiXCB2iJhl9LSUmRlZXnHbDMiRvACgNTUVFx00UXe5ZFHHsGhQ4dMj6mrqwNjDFdeeSVuv/129OvXD+3atUN9fT1+/PFHPP7441ixYgXeeusttGvXDn//+9+F67Vr1y7ceOONqKysRM+ePfHWW2+hc+fOKC8vx4wZMzB16lTMnTsXZ599NsaNGydUtiKoJSUluS54xcfHIykpiToYIqRQWyTCBWqLhFN4lCsRY1yfnZ2N48eP46OPPsI//vEPDB06FLGxsZbHpaam4quvvsJHH32E22+/He3atQMAREVF4dxzz8X777+PP//5zwCA2bNno6qqSrhuEydOREVFBdq0aYOVK1eic+fOAIDExERMmTIFI0aMAAA8/fTTKC4uFi6fIAiCIIjGQcQIXnanAJOTk3HRRRcZbpckCXfddRcAoLy8HD/88INQ+RUVFcjLywMA3HfffUhJSfHb57HHHgMgqyKXL18uVD5BEARBEI2HiBG8AklcXJz3/7q6OqFjN23ahJMnTwIABg0apLtPx44dcc455wAA1qxZY7OWBEEQBEFEOiR4AVi/fj0AoFmzZt5pQl62bdvm/f+8884z3K9Lly4AgO3bt4tXkCAIgiCIRkFEGdcHgl27duHVV18FANx0003CBuwHDhwAINuSxcfHG+6XkZHhs78Rp06dwqlTp7y/S0tLAchGnzU1NUJ1M0Mpy80yCcIO1BaJcIHaImEXkTbTpAWvkydPekNApKWl4ZlnnhEuo6ysDABMhS71dmV/I5555hlMmTLFb/2aNWssz2GHtWvXul4mQdiB2iIRLlBbJERRQknx0GQFr9raWtxyyy346quvEBMTg0WLFnm1UqHksccew6hRo7y/ldggAwYMcD2cxNq1a9G/f39ymyZCCrVFIlygtkjYRZmd4qFJCl51dXUYNmwYli9fjujoaCxatAgDBgywVZYSLM1K2lW2WwVXi42N1Q2TERMTE5COIFDlEoQo1BaJcIHaIiGKSHtpcsb1itC1ZMkSeDweLFiwANdff73t8pS4YMXFxabCV1FRkc/+BEEQBEE0PZqU4FVXV4dbb70Vixcv9gpdN910k6MyFW9FwNxjUfF+NPN8JAiCIAiicdNkBC9F6FJruoYOHeq43F69eqF58+YAgFWrVunus2fPHm9gVrtTmgRBEARBRD5NQvCqq6vDLbfcgiVLliA6OhoLFy50RegCgISEBFx33XUAgFdeeQUlJSV++0yfPh2AbN91zTXXuHJegiAIgiAij4gSvIqLi3H06FHvUl9fD0A2XFevLy8v9x5TV1eH2267DUuXLvUa0otOL95xxx2QJMkw+eXUqVORkJCAgwcPYvDgwfj5558ByOmEpk6d6o0T9sQTTyA1NdXOpRMEQRAE0QiIKMHrwgsvRHp6unfZt28fAGDGjBk+6x944AHvMZs3b8a7774LQM7L+OCDD6JNmzaGy5IlS4Tr1alTJyxduhTx8fHYuHEjOnfujJSUFCQnJ2PSpElgjOGOO+7A2LFj3bkRBEEQRKOkogKQJHmpqAh1bYhA0OjDSShaMUCO0fLbb7+Z7q/kXRQlJycH3377LaZPn461a9fiwIEDSElJwUUXXYR77rnHOx1JEARBEETTJaIEr927dwsf06dPHzDGHJ133rx5mDdvnuV+Z5xxBl5//XVH5yIIgiAIovESUVONBEEQBEEQkQwJXgRBEARBEEGCBC+CIAiCCBPq6hr+37DB9zfROCDBiyAIgiDCgPx84NxzG37n5AAdO8rricYDCV4EQRAEEWLy84Hrrwd+T+vrpahIXk/CV+OBBC+CIAiCCCF1dcDDDwN6DvjKupEjadqxsUCCF0EQBEGEkI0bgf37jbczBuzbJ+9HRD4keBEEQRBECDl40N39iPCGBC+CIIhGCqWfiQzatnV3PyK8IcGLIAiCIEJIdjaQmSkLyHpIEpCVJe9HRD4keBEEQRBECPF4gBdekP/XCl/K79mz5f2IyIcEL4IgCIIIMbm5wHvvAe3a+a7PyJDX5+aGpl6E+9gWvKZOnYpZs2Zx7//Pf/4TU6dOtXs6giCIiIdsrggzcnOB77/3Xbd9OwldjQ3bgtfkyZMxc+ZM7v2ff/55TJkyxe7pCIIgCEEo/UzkoZ1OpOnFxgdNNRIEQYQhTrVjlH6mcUACc+MjaILX8ePHERcXF6zTEQRBNFmaYvqZxjKNu2KF728SmBsfQRG8li1bhrKyMrRv3z4YpyMIgghLgjH1R+lnIpf8fGDYMP/1jVlgbopE8+74wgsv4AXF3/V3jhw5gtNPP93wGMYYTpw4gdLSUkiShKuuusp+TQmCICKY/HzgoYcafufkyLGbXnjBXeNpkfQzffq4d17CGVYCsyTJAvOQIWT3FelwC14nTpzA7t27fdbV1dX5rTPiyiuvxMSJE0XqRhAE0ShQpv60g6qiyXAzXACln4lMSGBuOnALXtdccw06duwIQNZk3XXXXUhOTsbs2bMNj4mKikJSUhK6dOmCM844w2ldCYIgIg67mgzttOSAAXyajqaafsbu/QoXSGBuOnALXueffz7OP/987++77roLzZs3x/DhwwNSMYIgiMaAHU2Gk2lJJf1MUZG+sCdJ8vbGlH5G734BwMKFwC23hKZOojRVgbkpYtu4vrCwEIsXL8aJEydcrA5BEETjQlST4dQjsamlnzG6X4BsqB4pBumUr7HpYFvw6tu3L6644gowvU8qgiAIAoCYJsMtj0Sj9DOZmY0r/YzZ/VKIFA/OpiYwN2VsC17JyclISUlBamqqm/UhCCLINJb4Rwrhdj0imgyRaUkrtOlnCgqAXbsaj9AFuHu/woGmIjA3dWwLXn/4wx9QVlaGU6dOuVkfgiCIRoWIJsNtA2u1duTyyxuftqQxGqQ3BYG5qWNb8Bo6dChqamqwdOlSN+tDEAQRMgKlLePVZESigXUoNYyRcL/s3J/GLjA3dWwLXg8//DB69OiBBx54AAUFBW7WiSAIotHBo8kgA2sZXmHF6n4BstBy9Kj7dSQIu3CHk9Aybdo0XH755fjuu+8wePBgnHfeeejZsydat24Nj4l4TkFUCYJoqlhpMpRpyeuvl4UJrdE4Y8Bzz/FrQBISzA3PIx31/TKirg648UaykSLCB9uC1+TJkyFJktercdu2bdi+fbvlcSR4EUR4EemBJ0WoqAASE+X/y8tlwSTcUKYlH3pIP0TCqFHy8yEhQiY3F1iyBBg6FKi
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.gca()\n",
    "\n",
    "BEC_width_y_val.plot.errorbar(ax=ax, yerr=BEC_width_y_std, fmt='ob')\n",
    "\n",
    "plt.ylabel('Y-axis Width of BEC part')\n",
    "plt.tight_layout()\n",
    "plt.grid(visible=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.gca()\n",
    "\n",
    "thermal_width_x_val.plot.errorbar(ax=ax, yerr=thermal_width_x_std, fmt='or')\n",
    "\n",
    "plt.ylabel('X-axis width of thermal part')\n",
    "plt.tight_layout()\n",
    "plt.grid(visible=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.gca()\n",
    "\n",
    "thermal_width_y_val.plot.errorbar(ax=ax, yerr=thermal_width_y_std, fmt='or')\n",
    "\n",
    "plt.ylabel('Y-axis width of thermal part')\n",
    "plt.tight_layout()\n",
    "plt.grid(visible=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.gca()\n",
    "\n",
    "BEC_center_x_val.plot.errorbar(ax=ax, yerr=BEC_center_x_std, fmt='ob')\n",
    "\n",
    "plt.ylabel('X-axis center of BEC part')\n",
    "plt.tight_layout()\n",
    "plt.grid(visible=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.gca()\n",
    "\n",
    "BEC_center_y_val.plot.errorbar(ax=ax, yerr=BEC_center_y_std, fmt='ob')\n",
    "\n",
    "plt.ylabel('Y-axis center of BEC part')\n",
    "plt.tight_layout()\n",
    "plt.grid(visible=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.gca()\n",
    "\n",
    "thermal_center_x_val.plot.errorbar(ax=ax, yerr=thermal_center_x_std, fmt='or')\n",
    "\n",
    "plt.ylabel('X-axis center of thermal part')\n",
    "plt.tight_layout()\n",
    "plt.grid(visible=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.gca()\n",
    "\n",
    "thermal_center_y_val.plot.errorbar(ax=ax, yerr=thermal_center_y_std, fmt='or')\n",
    "\n",
    "plt.ylabel('Y-axis center of thermal part')\n",
    "plt.tight_layout()\n",
    "plt.grid(visible=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fitFullResult = fitAnalyser.get_fit_full_result(fitResult)\n",
    "condensateFraction = fitFullResult['A_amplitude'] / (fitFullResult['A_amplitude'] + fitFullResult['B_amplitude'])\n",
    "condensateFraction_value, condensateFraction_std = seperate_uncertainty(condensateFraction)\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.gca()\n",
    "\n",
    "condensateFraction_value.plot.errorbar(ax=ax, yerr=condensateFraction_std, fmt='ob')\n",
    "\n",
    "plt.ylabel('Condensate Fraction')\n",
    "plt.tight_layout()\n",
    "plt.grid(visible=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The total Ncount is:  849.84 ±  73.69\n",
      "The total Ncount from fit is:  853.43 ±  66.18\n",
      "The Ncount of the BEC part is:  528.79 ±  65.37\n",
      "The Ncount of the thermal part is:  324.64 ±  35.62\n",
      "The x-axis width of the BEC part is:  4.06 ±  0.28\n",
      "The y-axis width of the BEC part is:  11.03 ±  0.36\n",
      "The x-axis width of the thermal part is:  15.30 ±  0.91\n",
      "The y-axis width of the thermal part is:  12.99 ±  0.61\n",
      "The x-axis center of the BEC part is:  47.44 ±  1.82\n",
      "The y-axis center of the BEC part is:  51.13 ±  1.83\n",
      "The x-axis center of the thermal part is:  49.62 ±  1.54\n",
      "The y-axis center of the thermal part is:  51.17 ±  1.37\n",
      "The condensate fraction is:  0.6180 ±  0.0464\n"
     ]
    }
   ],
   "source": [
    "val = Ncount.mean().item()\n",
    "std = Ncount.std().item()\n",
    "print(f'The total Ncount is: {val: .2f} \\u00B1 {std: .2f}')\n",
    "\n",
    "val = total_Ncount_val.mean().item()\n",
    "std = total_Ncount_val.std().item()\n",
    "print(f'The total Ncount from fit is: {val: .2f} \\u00B1 {std: .2f}')\n",
    "\n",
    "val = BEC_Ncount_val.mean().item()\n",
    "std = BEC_Ncount_val.std().item()\n",
    "print(f'The Ncount of the BEC part is: {val: .2f} \\u00B1 {std: .2f}')\n",
    "\n",
    "val = thermal_Ncount_val.mean().item()\n",
    "std = thermal_Ncount_val.std().item()\n",
    "print(f'The Ncount of the thermal part is: {val: .2f} \\u00B1 {std: .2f}')\n",
    "\n",
    "val = BEC_width_x_val.mean().item()\n",
    "std = BEC_width_x_val.std().item()\n",
    "print(f'The x-axis width of the BEC part is: {val: .2f} \\u00B1 {std: .2f}')\n",
    "\n",
    "val = BEC_width_y_val.mean().item()\n",
    "std = BEC_width_y_val.std().item()\n",
    "print(f'The y-axis width of the BEC part is: {val: .2f} \\u00B1 {std: .2f}')\n",
    "\n",
    "val = thermal_width_x_val.mean().item()\n",
    "std = thermal_width_x_val.std().item()\n",
    "print(f'The x-axis width of the thermal part is: {val: .2f} \\u00B1 {std: .2f}')\n",
    "\n",
    "val = thermal_width_y_val.mean().item()\n",
    "std = thermal_width_y_val.std().item()\n",
    "print(f'The y-axis width of the thermal part is: {val: .2f} \\u00B1 {std: .2f}')\n",
    "\n",
    "val = BEC_center_x_val.mean().item()\n",
    "std = BEC_center_x_val.std().item()\n",
    "print(f'The x-axis center of the BEC part is: {val: .2f} \\u00B1 {std: .2f}')\n",
    "\n",
    "val = BEC_center_y_val.mean().item()\n",
    "std = BEC_center_y_val.std().item()\n",
    "print(f'The y-axis center of the BEC part is: {val: .2f} \\u00B1 {std: .2f}')\n",
    "\n",
    "val = thermal_center_x_val.mean().item()\n",
    "std = thermal_center_x_val.std().item()\n",
    "print(f'The x-axis center of the thermal part is: {val: .2f} \\u00B1 {std: .2f}')\n",
    "\n",
    "val = thermal_center_y_val.mean().item()\n",
    "std = thermal_center_y_val.std().item()\n",
    "print(f'The y-axis center of the thermal part is: {val: .2f} \\u00B1 {std: .2f}')\n",
    "\n",
    "val = condensateFraction_value.mean().item()\n",
    "std = condensateFraction_value.std().item()\n",
    "print(f'The condensate fraction is: {val: .4f} \\u00B1 {std: .4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       " */\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "  padding: 0;\n",
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       "\n",
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       "\n",
       ".xr-dim-list:after {\n",
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       "\n",
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       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
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       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
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       "\n",
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       ".xr-index-name div,\n",
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       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
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       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (runs: 550)\n",
       "Coordinates:\n",
       "  * runs     (runs) float64 0.0 1.0 2.0 3.0 4.0 ... 546.0 547.0 548.0 549.0\n",
       "Data variables:\n",
       "    runTime  (runs) datetime64[ns] 2023-05-09T14:30:03 ... 2023-05-09T15:56:53\n",
       "Attributes: (12/101)\n",
       "    TOF_free:                          0.02\n",
       "    abs_img_freq:                      110.858\n",
       "    absorption_imaging_flag:           True\n",
       "    backup_data:                       True\n",
       "    blink_off_time:                    nan\n",
       "    blink_on_time:                     nan\n",
       "    ...                                ...\n",
       "    y_offset_img:                      0\n",
       "    z_offset:                          0.189\n",
       "    z_offset_img:                      0.189\n",
       "    runs:                              [  0.   1.   2.   3.   4.   5.   6.   ...\n",
       "    scanAxis:                          [&#x27;runs&#x27;]\n",
       "    scanAxisLength:                    [550.]</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-88084dc8-d188-4cb7-9f08-7bbc1ecd5214' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-88084dc8-d188-4cb7-9f08-7bbc1ecd5214' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>runs</span>: 550</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-244c7b76-f615-490a-b022-10578f28ae3f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-244c7b76-f615-490a-b022-10578f28ae3f' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>runs</span></div><div class='xr-var-dims'>(runs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0 ... 547.0 548.0 549.0</div><input id='attrs-e024ad9f-479d-49ad-b632-e22673b877a2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e024ad9f-479d-49ad-b632-e22673b877a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af447ba5-c806-4fd5-a4af-329fa037168e' class='xr-var-data-in' type='checkbox'><label for='data-af447ba5-c806-4fd5-a4af-329fa037168e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0.,   1.,   2., ..., 547., 548., 549.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d8431900-c013-4df4-8c03-ff6ae292a2ae' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d8431900-c013-4df4-8c03-ff6ae292a2ae' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>runTime</span></div><div class='xr-var-dims'>(runs)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2023-05-09T14:30:03 ... 2023-05-...</div><input id='attrs-107aa3fc-177d-4d23-8a4b-b024a6afe3ad' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-107aa3fc-177d-4d23-8a4b-b024a6afe3ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e7b9479-5510-47e8-903a-8638060c107d' class='xr-var-data-in' type='checkbox'><label for='data-4e7b9479-5510-47e8-903a-8638060c107d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2023-05-09T14:30:03.000000000&#x27;, &#x27;2023-05-09T14:30:11.000000000&#x27;,\n",
       "       &#x27;2023-05-09T14:30:19.000000000&#x27;, &#x27;2023-05-09T14:30:27.000000000&#x27;,\n",
       "       &#x27;2023-05-09T14:30:35.000000000&#x27;, &#x27;2023-05-09T14:30:43.000000000&#x27;,\n",
       "       &#x27;2023-05-09T14:30:52.000000000&#x27;, &#x27;2023-05-09T14:31:00.000000000&#x27;,\n",
       "       &#x27;2023-05-09T14:31:08.000000000&#x27;, &#x27;2023-05-09T14:31:16.000000000&#x27;,\n",
       "       &#x27;2023-05-09T14:31:24.000000000&#x27;, &#x27;2023-05-09T14:31:33.000000000&#x27;,\n",
       "       &#x27;2023-05-09T14:31:41.000000000&#x27;, &#x27;2023-05-09T14:31:49.000000000&#x27;,\n",
       "       &#x27;2023-05-09T14:31:57.000000000&#x27;, &#x27;2023-05-09T14:32:05.000000000&#x27;,\n",
       "       &#x27;2023-05-09T14:32:13.000000000&#x27;, &#x27;2023-05-09T14:32:22.000000000&#x27;,\n",
       "       &#x27;2023-05-09T14:32:30.000000000&#x27;, &#x27;2023-05-09T14:32:38.000000000&#x27;,\n",
       "       &#x27;2023-05-09T14:32:46.000000000&#x27;, &#x27;2023-05-09T14:32:54.000000000&#x27;,\n",
       "       &#x27;2023-05-09T14:33:03.000000000&#x27;, &#x27;2023-05-09T14:33:11.000000000&#x27;,\n",
       "       &#x27;2023-05-09T14:33:19.000000000&#x27;, &#x27;2023-05-09T14:33:27.000000000&#x27;,\n",
       "       &#x27;2023-05-09T14:33:35.000000000&#x27;, &#x27;2023-05-09T14:33:44.000000000&#x27;,\n",
       "       &#x27;2023-05-09T14:33:52.000000000&#x27;, &#x27;2023-05-09T14:34:00.000000000&#x27;,\n",
       "       &#x27;2023-05-09T14:34:08.000000000&#x27;, &#x27;2023-05-09T14:34:16.000000000&#x27;,\n",
       "       &#x27;2023-05-09T14:34:24.000000000&#x27;, &#x27;2023-05-09T14:34:32.000000000&#x27;,\n",
       "       &#x27;2023-05-09T14:34:41.000000000&#x27;, &#x27;2023-05-09T14:34:49.000000000&#x27;,\n",
       "       &#x27;2023-05-09T14:34:57.000000000&#x27;, &#x27;2023-05-09T14:35:05.000000000&#x27;,\n",
       "       &#x27;2023-05-09T14:35:13.000000000&#x27;, &#x27;2023-05-09T14:35:21.000000000&#x27;,\n",
       "...\n",
       "       &#x27;2023-05-09T15:51:57.000000000&#x27;, &#x27;2023-05-09T15:52:05.000000000&#x27;,\n",
       "       &#x27;2023-05-09T15:52:13.000000000&#x27;, &#x27;2023-05-09T15:52:21.000000000&#x27;,\n",
       "       &#x27;2023-05-09T15:52:29.000000000&#x27;, &#x27;2023-05-09T15:52:37.000000000&#x27;,\n",
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       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ef465c85-8b4c-441b-9e1e-fded7ea4f456' class='xr-section-summary-in' type='checkbox'  ><label for='section-ef465c85-8b4c-441b-9e1e-fded7ea4f456' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>runs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f7d107bc-6e17-40b5-a03b-62a880f79946' class='xr-index-data-in' type='checkbox'/><label for='index-f7d107bc-6e17-40b5-a03b-62a880f79946' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([  0.0,   1.0,   2.0,   3.0,   4.0,   5.0,   6.0,   7.0,   8.0,\n",
       "                9.0,\n",
       "              ...\n",
       "              540.0, 541.0, 542.0, 543.0, 544.0, 545.0, 546.0, 547.0, 548.0,\n",
       "              549.0],\n",
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       "  14.  15.  16.  17.  18.  19.  20.  21.  22.  23.  24.  25.  26.  27.\n",
       "  28.  29.  30.  31.  32.  33.  34.  35.  36.  37.  38.  39.  40.  41.\n",
       "  42.  43.  44.  45.  46.  47.  48.  49.  50.  51.  52.  53.  54.  55.\n",
       "  56.  57.  58.  59.  60.  61.  62.  63.  64.  65.  66.  67.  68.  69.\n",
       "  70.  71.  72.  73.  74.  75.  76.  77.  78.  79.  80.  81.  82.  83.\n",
       "  84.  85.  86.  87.  88.  89.  90.  91.  92.  93.  94.  95.  96.  97.\n",
       "  98.  99. 100. 101. 102. 103. 104. 105. 106. 107. 108. 109. 110. 111.\n",
       " 112. 113. 114. 115. 116. 117. 118. 119. 120. 121. 122. 123. 124. 125.\n",
       " 126. 127. 128. 129. 130. 131. 132. 133. 134. 135. 136. 137. 138. 139.\n",
       " 140. 141. 142. 143. 144. 145. 146. 147. 148. 149. 150. 151. 152. 153.\n",
       " 154. 155. 156. 157. 158. 159. 160. 161. 162. 163. 164. 165. 166. 167.\n",
       " 168. 169. 170. 171. 172. 173. 174. 175. 176. 177. 178. 179. 180. 181.\n",
       " 182. 183. 184. 185. 186. 187. 188. 189. 190. 191. 192. 193. 194. 195.\n",
       " 196. 197. 198. 199. 200. 201. 202. 203. 204. 205. 206. 207. 208. 209.\n",
       " 210. 211. 212. 213. 214. 215. 216. 217. 218. 219. 220. 221. 222. 223.\n",
       " 224. 225. 226. 227. 228. 229. 230. 231. 232. 233. 234. 235. 236. 237.\n",
       " 238. 239. 240. 241. 242. 243. 244. 245. 246. 247. 248. 249. 250. 251.\n",
       " 252. 253. 254. 255. 256. 257. 258. 259. 260. 261. 262. 263. 264. 265.\n",
       " 266. 267. 268. 269. 270. 271. 272. 273. 274. 275. 276. 277. 278. 279.\n",
       " 280. 281. 282. 283. 284. 285. 286. 287. 288. 289. 290. 291. 292. 293.\n",
       " 294. 295. 296. 297. 298. 299. 300. 301. 302. 303. 304. 305. 306. 307.\n",
       " 308. 309. 310. 311. 312. 313. 314. 315. 316. 317. 318. 319. 320. 321.\n",
       " 322. 323. 324. 325. 326. 327. 328. 329. 330. 331. 332. 333. 334. 335.\n",
       " 336. 337. 338. 339. 340. 341. 342. 343. 344. 345. 346. 347. 348. 349.\n",
       " 350. 351. 352. 353. 354. 355. 356. 357. 358. 359. 360. 361. 362. 363.\n",
       " 364. 365. 366. 367. 368. 369. 370. 371. 372. 373. 374. 375. 376. 377.\n",
       " 378. 379. 380. 381. 382. 383. 384. 385. 386. 387. 388. 389. 390. 391.\n",
       " 392. 393. 394. 395. 396. 397. 398. 399. 400. 401. 402. 403. 404. 405.\n",
       " 406. 407. 408. 409. 410. 411. 412. 413. 414. 415. 416. 417. 418. 419.\n",
       " 420. 421. 422. 423. 424. 425. 426. 427. 428. 429. 430. 431. 432. 433.\n",
       " 434. 435. 436. 437. 438. 439. 440. 441. 442. 443. 444. 445. 446. 447.\n",
       " 448. 449. 450. 451. 452. 453. 454. 455. 456. 457. 458. 459. 460. 461.\n",
       " 462. 463. 464. 465. 466. 467. 468. 469. 470. 471. 472. 473. 474. 475.\n",
       " 476. 477. 478. 479. 480. 481. 482. 483. 484. 485. 486. 487. 488. 489.\n",
       " 490. 491. 492. 493. 494. 495. 496. 497. 498. 499. 500. 501. 502. 503.\n",
       " 504. 505. 506. 507. 508. 509. 510. 511. 512. 513. 514. 515. 516. 517.\n",
       " 518. 519. 520. 521. 522. 523. 524. 525. 526. 527. 528. 529. 530. 531.\n",
       " 532. 533. 534. 535. 536. 537. 538. 539. 540. 541. 542. 543. 544. 545.\n",
       " 546. 547. 548. 549.]</dd><dt><span>scanAxis :</span></dt><dd>[&#x27;runs&#x27;]</dd><dt><span>scanAxisLength :</span></dt><dd>[550.]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (runs: 550)\n",
       "Coordinates:\n",
       "  * runs     (runs) float64 0.0 1.0 2.0 3.0 4.0 ... 546.0 547.0 548.0 549.0\n",
       "Data variables:\n",
       "    runTime  (runs) datetime64[ns] 2023-05-09T14:30:03 ... 2023-05-09T15:56:53\n",
       "Attributes: (12/101)\n",
       "    TOF_free:                          0.02\n",
       "    abs_img_freq:                      110.858\n",
       "    absorption_imaging_flag:           True\n",
       "    backup_data:                       True\n",
       "    blink_off_time:                    nan\n",
       "    blink_on_time:                     nan\n",
       "    ...                                ...\n",
       "    y_offset_img:                      0\n",
       "    z_offset:                          0.189\n",
       "    z_offset_img:                      0.189\n",
       "    runs:                              [  0.   1.   2.   3.   4.   5.   6.   ...\n",
       "    scanAxis:                          ['runs']\n",
       "    scanAxisLength:                    [550.]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i=0\n",
    "runTime = read_hdf5_run_time(filePath, datesetOfGlobal=dataSetOfGlobalDict[dskey[groupList[i]]])\n",
    "runTime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "time = runTime.runTime.to_numpy()\n",
    "time0 = int(time[0])\n",
    "time = np.array(\n",
    "    [\n",
    "        float(value) - time0\n",
    "        for value in time\n",
    "    ]\n",
    ")\n",
    "time = time / time.max() * 2 * np.pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;OD&#x27; (time: 550)&gt;\n",
       "array([ 750.47641876,  738.34281204,  784.41476569,  796.02169322,\n",
       "        952.51855344,  882.92079597,  863.59651678,  866.57709198,\n",
       "        941.99125428,  783.16551019,  946.27689189,  918.33176133,\n",
       "        941.81141492,  947.74774665,  892.61913887,  977.17520626,\n",
       "        945.34126351,  956.52682689,  804.78165476,  939.49484698,\n",
       "        953.56682753,  879.61475127,  846.05592616,  830.90774024,\n",
       "        910.80224254,  839.43361196,  863.23083974,  873.50170576,\n",
       "        850.29285459,  949.59349556,  707.93266373,  946.74069024,\n",
       "        941.71185143,  946.57095286,  914.32343568,  947.09283187,\n",
       "        954.03294364,  784.23261906,  786.97273688,  832.62952621,\n",
       "        903.46885276,  794.84132388,  987.33131008,  920.97693631,\n",
       "        982.49210229,  790.82171889,  796.04783468,  672.41580595,\n",
       "        726.07270248,  709.64654892,  820.34697312,  839.24755133,\n",
       "        830.20821813,  905.60581009,  832.01909227,  614.3819873 ,\n",
       "        723.89815083,  930.88065587,  825.30243762,  842.16853182,\n",
       "        960.03822443,  970.87588969,  867.93951095,  796.77918204,\n",
       "        715.07236109,  867.86554561,  949.15778283,  938.56330193,\n",
       "        857.52360377,  880.71776388,  856.94886599,  923.54732893,\n",
       "        840.56332593,  934.82056594,  938.21743126,  841.27262899,\n",
       "        935.776538  ,  810.94173848,  926.17365109,  746.68729357,\n",
       "...\n",
       "        865.51482127,  833.61692314,  821.20906768,  933.87516973,\n",
       "        810.80092789,  824.63722508,  859.85285532,  913.23783203,\n",
       "        789.32182143,  814.52479359,  843.87902457,  857.31154799,\n",
       "        896.47897516,  872.95758519,  761.01860691,  806.85333498,\n",
       "        947.18607913,  882.95786654,  660.90304299,  779.06534297,\n",
       "        824.68260644,  960.00725562,  931.83023265,  925.32091745,\n",
       "        876.67147414,  808.28701944,  865.12927984,  907.22865863,\n",
       "        849.53390823,  827.70871779,  726.90703872,  878.79705242,\n",
       "        960.28888691,  750.46295033,  903.46216093,  862.60511899,\n",
       "        956.07697944,  881.35524969,  837.32695128,  791.87607618,\n",
       "        811.78036383,  902.4373154 ,  942.28581666,  874.3906838 ,\n",
       "        896.64409276,  787.28302139,  963.13514734,  877.87315412,\n",
       "        833.86614596,  826.5946265 ,  735.16788438,  922.53477054,\n",
       "        880.6268579 ,  867.12639832,  852.01398293,  828.11720597,\n",
       "        891.6310036 ,  807.47838578,  895.25022758,  822.18630467,\n",
       "        943.8055441 ,  845.66585589,  729.57792525,  884.88667118,\n",
       "        796.64506694,  855.18595889,  803.11938466,  832.46778894,\n",
       "        858.2150589 ,  937.40605043,  853.13728532,  910.90015676,\n",
       "        780.99561864,  883.83375992,  804.26394636,  978.32360651,\n",
       "        901.75651529,  884.02352999])\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2023-05-09T14:30:03 ... 2023-05-09T15:56:53</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'OD'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 550</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-f0a04964-e324-4797-8c9b-8976d3db2498' class='xr-array-in' type='checkbox' checked><label for='section-f0a04964-e324-4797-8c9b-8976d3db2498' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>750.5 738.3 784.4 796.0 952.5 882.9 ... 883.8 804.3 978.3 901.8 884.0</span></div><div class='xr-array-data'><pre>array([ 750.47641876,  738.34281204,  784.41476569,  796.02169322,\n",
       "        952.51855344,  882.92079597,  863.59651678,  866.57709198,\n",
       "        941.99125428,  783.16551019,  946.27689189,  918.33176133,\n",
       "        941.81141492,  947.74774665,  892.61913887,  977.17520626,\n",
       "        945.34126351,  956.52682689,  804.78165476,  939.49484698,\n",
       "        953.56682753,  879.61475127,  846.05592616,  830.90774024,\n",
       "        910.80224254,  839.43361196,  863.23083974,  873.50170576,\n",
       "        850.29285459,  949.59349556,  707.93266373,  946.74069024,\n",
       "        941.71185143,  946.57095286,  914.32343568,  947.09283187,\n",
       "        954.03294364,  784.23261906,  786.97273688,  832.62952621,\n",
       "        903.46885276,  794.84132388,  987.33131008,  920.97693631,\n",
       "        982.49210229,  790.82171889,  796.04783468,  672.41580595,\n",
       "        726.07270248,  709.64654892,  820.34697312,  839.24755133,\n",
       "        830.20821813,  905.60581009,  832.01909227,  614.3819873 ,\n",
       "        723.89815083,  930.88065587,  825.30243762,  842.16853182,\n",
       "        960.03822443,  970.87588969,  867.93951095,  796.77918204,\n",
       "        715.07236109,  867.86554561,  949.15778283,  938.56330193,\n",
       "        857.52360377,  880.71776388,  856.94886599,  923.54732893,\n",
       "        840.56332593,  934.82056594,  938.21743126,  841.27262899,\n",
       "        935.776538  ,  810.94173848,  926.17365109,  746.68729357,\n",
       "...\n",
       "        865.51482127,  833.61692314,  821.20906768,  933.87516973,\n",
       "        810.80092789,  824.63722508,  859.85285532,  913.23783203,\n",
       "        789.32182143,  814.52479359,  843.87902457,  857.31154799,\n",
       "        896.47897516,  872.95758519,  761.01860691,  806.85333498,\n",
       "        947.18607913,  882.95786654,  660.90304299,  779.06534297,\n",
       "        824.68260644,  960.00725562,  931.83023265,  925.32091745,\n",
       "        876.67147414,  808.28701944,  865.12927984,  907.22865863,\n",
       "        849.53390823,  827.70871779,  726.90703872,  878.79705242,\n",
       "        960.28888691,  750.46295033,  903.46216093,  862.60511899,\n",
       "        956.07697944,  881.35524969,  837.32695128,  791.87607618,\n",
       "        811.78036383,  902.4373154 ,  942.28581666,  874.3906838 ,\n",
       "        896.64409276,  787.28302139,  963.13514734,  877.87315412,\n",
       "        833.86614596,  826.5946265 ,  735.16788438,  922.53477054,\n",
       "        880.6268579 ,  867.12639832,  852.01398293,  828.11720597,\n",
       "        891.6310036 ,  807.47838578,  895.25022758,  822.18630467,\n",
       "        943.8055441 ,  845.66585589,  729.57792525,  884.88667118,\n",
       "        796.64506694,  855.18595889,  803.11938466,  832.46778894,\n",
       "        858.2150589 ,  937.40605043,  853.13728532,  910.90015676,\n",
       "        780.99561864,  883.83375992,  804.26394636,  978.32360651,\n",
       "        901.75651529,  884.02352999])</pre></div></div></li><li class='xr-section-item'><input id='section-48b7aec8-eb02-45cc-a32b-cfda791bcf2f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-48b7aec8-eb02-45cc-a32b-cfda791bcf2f' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2023-05-09T14:30:03 ... 2023-05-...</div><input id='attrs-8c7d8736-257d-4634-a606-ae183ed97285' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8c7d8736-257d-4634-a606-ae183ed97285' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-172876be-c3c8-47bd-81c3-5517ed8455e5' class='xr-var-data-in' type='checkbox'><label for='data-172876be-c3c8-47bd-81c3-5517ed8455e5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2023-05-09T14:30:03.000000000&#x27;, &#x27;2023-05-09T14:30:11.000000000&#x27;,\n",
       "       &#x27;2023-05-09T14:30:19.000000000&#x27;, ..., &#x27;2023-05-09T15:56:37.000000000&#x27;,\n",
       "       &#x27;2023-05-09T15:56:45.000000000&#x27;, &#x27;2023-05-09T15:56:53.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d7fccb17-8668-433e-9732-ee85659ac075' class='xr-section-summary-in' type='checkbox'  ><label for='section-d7fccb17-8668-433e-9732-ee85659ac075' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-51443323-7ed6-43b0-8d07-b7f5c6291202' class='xr-index-data-in' type='checkbox'/><label for='index-51443323-7ed6-43b0-8d07-b7f5c6291202' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2023-05-09 14:30:03&#x27;, &#x27;2023-05-09 14:30:11&#x27;,\n",
       "               &#x27;2023-05-09 14:30:19&#x27;, &#x27;2023-05-09 14:30:27&#x27;,\n",
       "               &#x27;2023-05-09 14:30:35&#x27;, &#x27;2023-05-09 14:30:43&#x27;,\n",
       "               &#x27;2023-05-09 14:30:52&#x27;, &#x27;2023-05-09 14:31:00&#x27;,\n",
       "               &#x27;2023-05-09 14:31:08&#x27;, &#x27;2023-05-09 14:31:16&#x27;,\n",
       "               ...\n",
       "               &#x27;2023-05-09 15:55:41&#x27;, &#x27;2023-05-09 15:55:49&#x27;,\n",
       "               &#x27;2023-05-09 15:55:57&#x27;, &#x27;2023-05-09 15:56:05&#x27;,\n",
       "               &#x27;2023-05-09 15:56:13&#x27;, &#x27;2023-05-09 15:56:21&#x27;,\n",
       "               &#x27;2023-05-09 15:56:29&#x27;, &#x27;2023-05-09 15:56:37&#x27;,\n",
       "               &#x27;2023-05-09 15:56:45&#x27;, &#x27;2023-05-09 15:56:53&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=550, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-619055c1-9dbb-4e1d-9f14-0b7293d15f72' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-619055c1-9dbb-4e1d-9f14-0b7293d15f72' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'OD' (time: 550)>\n",
       "array([ 750.47641876,  738.34281204,  784.41476569,  796.02169322,\n",
       "        952.51855344,  882.92079597,  863.59651678,  866.57709198,\n",
       "        941.99125428,  783.16551019,  946.27689189,  918.33176133,\n",
       "        941.81141492,  947.74774665,  892.61913887,  977.17520626,\n",
       "        945.34126351,  956.52682689,  804.78165476,  939.49484698,\n",
       "        953.56682753,  879.61475127,  846.05592616,  830.90774024,\n",
       "        910.80224254,  839.43361196,  863.23083974,  873.50170576,\n",
       "        850.29285459,  949.59349556,  707.93266373,  946.74069024,\n",
       "        941.71185143,  946.57095286,  914.32343568,  947.09283187,\n",
       "        954.03294364,  784.23261906,  786.97273688,  832.62952621,\n",
       "        903.46885276,  794.84132388,  987.33131008,  920.97693631,\n",
       "        982.49210229,  790.82171889,  796.04783468,  672.41580595,\n",
       "        726.07270248,  709.64654892,  820.34697312,  839.24755133,\n",
       "        830.20821813,  905.60581009,  832.01909227,  614.3819873 ,\n",
       "        723.89815083,  930.88065587,  825.30243762,  842.16853182,\n",
       "        960.03822443,  970.87588969,  867.93951095,  796.77918204,\n",
       "        715.07236109,  867.86554561,  949.15778283,  938.56330193,\n",
       "        857.52360377,  880.71776388,  856.94886599,  923.54732893,\n",
       "        840.56332593,  934.82056594,  938.21743126,  841.27262899,\n",
       "        935.776538  ,  810.94173848,  926.17365109,  746.68729357,\n",
       "...\n",
       "        865.51482127,  833.61692314,  821.20906768,  933.87516973,\n",
       "        810.80092789,  824.63722508,  859.85285532,  913.23783203,\n",
       "        789.32182143,  814.52479359,  843.87902457,  857.31154799,\n",
       "        896.47897516,  872.95758519,  761.01860691,  806.85333498,\n",
       "        947.18607913,  882.95786654,  660.90304299,  779.06534297,\n",
       "        824.68260644,  960.00725562,  931.83023265,  925.32091745,\n",
       "        876.67147414,  808.28701944,  865.12927984,  907.22865863,\n",
       "        849.53390823,  827.70871779,  726.90703872,  878.79705242,\n",
       "        960.28888691,  750.46295033,  903.46216093,  862.60511899,\n",
       "        956.07697944,  881.35524969,  837.32695128,  791.87607618,\n",
       "        811.78036383,  902.4373154 ,  942.28581666,  874.3906838 ,\n",
       "        896.64409276,  787.28302139,  963.13514734,  877.87315412,\n",
       "        833.86614596,  826.5946265 ,  735.16788438,  922.53477054,\n",
       "        880.6268579 ,  867.12639832,  852.01398293,  828.11720597,\n",
       "        891.6310036 ,  807.47838578,  895.25022758,  822.18630467,\n",
       "        943.8055441 ,  845.66585589,  729.57792525,  884.88667118,\n",
       "        796.64506694,  855.18595889,  803.11938466,  832.46778894,\n",
       "        858.2150589 ,  937.40605043,  853.13728532,  910.90015676,\n",
       "        780.99561864,  883.83375992,  804.26394636,  978.32360651,\n",
       "        901.75651529,  884.02352999])\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2023-05-09T14:30:03 ... 2023-05-09T15:56:53"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ncount_time = xr.DataArray(\n",
    "    data=Ncount,\n",
    "    dims=[\"time\"],\n",
    "    coords={\n",
    "        \"time\": runTime.runTime.to_numpy(),\n",
    "    }\n",
    ")\n",
    "Ncount_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1d0be120160>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Ncount_time.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 70000.0)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Ncount_time_interp = Ncount_time.interp(time=pd.date_range(\"2023-05-09T14:30:03.000000000\", \"2023-05-09T15:56:53.000000000\", periods=500))\n",
    "da_fft = xrft.fft(Ncount_time_interp)\n",
    "da_fft_amp = np.abs(da_fft)\n",
    "# da_fft_amp.isel(freq_time=range(251,370)).plot()\n",
    "da_fft_amp.plot()\n",
    "# plt.xlim([-0.05, 0.05])\n",
    "plt.ylim([0, 7e4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 70000.0)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# da_test2.isel(time=range(300)).plot()\n",
    "da_fft = xrft.fft(\n",
    "    Ncount_time.isel(time=range(300)).interp(\n",
    "        time=pd.date_range(\n",
    "            Ncount_time.time[0].item(), Ncount_time.time[299].item(), periods=300\n",
    "            # \"2023-05-09T14:30:03.000000000\", \"2023-05-09T15:10:06.000000000\", periods=300\n",
    "        )\n",
    "    )\n",
    ")\n",
    "# np.abs(da_fft).isel(freq_time=range(151,300)).plot()\n",
    "np.abs(da_fft).plot()\n",
    "# plt.xlim([0, 0.003])\n",
    "plt.ylim([0, 7e4])\n",
    "# plt.yscale(\"log\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "analyserDataArray = Ncount\n",
    "\n",
    "analyserDataArray_time = xr.DataArray(\n",
    "    data=analyserDataArray,\n",
    "    dims=[\"time\"],\n",
    "    coords={\n",
    "        \"time\": runTime.runTime.to_numpy(),\n",
    "    }\n",
    ")\n",
    "\n",
    "# desired number of Fourier modes (uniform outputs)\n",
    "N = 1001\n",
    "\n",
    "# calculate the transform\n",
    "analyserDataArray_time_array = analyserDataArray_time.to_numpy()\n",
    "analyserDataArray_time_array = np.array(analyserDataArray_time_array, dtype=complex)\n",
    "f = xr.DataArray(\n",
    "    data=finufft.nufft1d1(time, analyserDataArray_time_array, N),\n",
    "    dims=['time_freq'],\n",
    "    coords={\n",
    "        \"time_freq\":np.linspace(-0.125/2,0.125/2,N)\n",
    "    }\n",
    ")\n",
    "\n",
    "value = np.abs(f)\n",
    "value[int((N-1)/2)] = np.nan\n",
    "value.where(value.time_freq>0).plot()\n",
    "plt.xlim([0, 0.01])\n",
    "# plt.ylim([0, 2000])\n",
    "plt.xlabel('frequency (Hz)')\n",
    "plt.show()\n",
    "\n",
    "mask = xr.DataArray(\n",
    "    data = np.full(runTime.runTime.shape,fill_value=False, dtype=bool),\n",
    "    dims = [\"time\"],\n",
    "    coords = {\n",
    "        \"time\":runTime.runTime.to_numpy()\n",
    "    }\n",
    ")\n",
    "\n",
    "for i in range(len(mask)):\n",
    "    if (int(mask.time[i]) - 1683642540000000000) % 5.4e11 > 3.6e11:\n",
    "        mask[i] = True\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.gca()\n",
    "\n",
    "xr.where(mask, np.nan, analyserDataArray_time).plot.errorbar(fmt='ob')\n",
    "analyserDataArray_time.where(mask).plot.errorbar(fmt='or')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "analyserDataArray = BEC_Ncount_val\n",
    "\n",
    "analyserDataArray_time = xr.DataArray(\n",
    "    data=analyserDataArray,\n",
    "    dims=[\"time\"],\n",
    "    coords={\n",
    "        \"time\": runTime.runTime.to_numpy(),\n",
    "    }\n",
    ")\n",
    "\n",
    "# desired number of Fourier modes (uniform outputs)\n",
    "N = 1001\n",
    "\n",
    "# calculate the transform\n",
    "analyserDataArray_time_array = analyserDataArray_time.to_numpy()\n",
    "analyserDataArray_time_array = np.array(analyserDataArray_time_array, dtype=complex)\n",
    "f = xr.DataArray(\n",
    "    data=finufft.nufft1d1(time, analyserDataArray_time_array, N),\n",
    "    dims=['time_freq'],\n",
    "    coords={\n",
    "        \"time_freq\":np.linspace(-0.125/2,0.125/2,N)\n",
    "    }\n",
    ")\n",
    "\n",
    "value = np.abs(f)\n",
    "value[int((N-1)/2)] = np.nan\n",
    "value.where(value.time_freq>0).plot()\n",
    "plt.xlim([0, 0.01])\n",
    "# plt.ylim([0, 2000])\n",
    "plt.show()\n",
    "\n",
    "mask = xr.DataArray(\n",
    "    data = np.full(runTime.runTime.shape,fill_value=False, dtype=bool),\n",
    "    dims = [\"time\"],\n",
    "    coords = {\n",
    "        \"time\":runTime.runTime.to_numpy()\n",
    "    }\n",
    ")\n",
    "\n",
    "for i in range(len(mask)):\n",
    "    if (int(mask.time[i]) - 1683642540000000000) % 5.4e11 > 3.6e11:\n",
    "        mask[i] = True\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.gca()\n",
    "\n",
    "xr.where(mask, np.nan, analyserDataArray_time).plot.errorbar(fmt='ob')\n",
    "analyserDataArray_time.where(mask).plot.errorbar(fmt='or')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "analyserDataArray = BEC_width_x_val\n",
    "\n",
    "analyserDataArray_time = xr.DataArray(\n",
    "    data=analyserDataArray,\n",
    "    dims=[\"time\"],\n",
    "    coords={\n",
    "        \"time\": runTime.runTime.to_numpy(),\n",
    "    }\n",
    ")\n",
    "\n",
    "# desired number of Fourier modes (uniform outputs)\n",
    "N = 1001\n",
    "\n",
    "# calculate the transform\n",
    "analyserDataArray_time_array = analyserDataArray_time.to_numpy()\n",
    "analyserDataArray_time_array = np.array(analyserDataArray_time_array, dtype=complex)\n",
    "f = xr.DataArray(\n",
    "    data=finufft.nufft1d1(time, analyserDataArray_time_array, N),\n",
    "    dims=['time_freq'],\n",
    "    coords={\n",
    "        \"time_freq\":np.linspace(-0.125/2,0.125/2,N)\n",
    "    }\n",
    ")\n",
    "\n",
    "value = np.abs(f)\n",
    "value[int((N-1)/2)] = np.nan\n",
    "value.where(value.time_freq>0).plot()\n",
    "plt.xlim([0, 0.01])\n",
    "# plt.ylim([0, 2000])\n",
    "plt.show()\n",
    "\n",
    "mask = xr.DataArray(\n",
    "    data = np.full(runTime.runTime.shape,fill_value=False, dtype=bool),\n",
    "    dims = [\"time\"],\n",
    "    coords = {\n",
    "        \"time\":runTime.runTime.to_numpy()\n",
    "    }\n",
    ")\n",
    "\n",
    "for i in range(len(mask)):\n",
    "    if (int(mask.time[i]) - 1683642540000000000) % 5.4e11 > 3.6e11:\n",
    "        mask[i] = True\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.gca()\n",
    "\n",
    "xr.where(mask, np.nan, analyserDataArray_time).plot.errorbar(fmt='ob')\n",
    "analyserDataArray_time.where(mask).plot.errorbar(fmt='or')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "analyserDataArray = thermal_width_y_val\n",
    "\n",
    "analyserDataArray_time = xr.DataArray(\n",
    "    data=analyserDataArray,\n",
    "    dims=[\"time\"],\n",
    "    coords={\n",
    "        \"time\": runTime.runTime.to_numpy(),\n",
    "    }\n",
    ")\n",
    "\n",
    "# desired number of Fourier modes (uniform outputs)\n",
    "N = 1001\n",
    "\n",
    "# calculate the transform\n",
    "analyserDataArray_time_array = analyserDataArray_time.to_numpy()\n",
    "analyserDataArray_time_array = np.array(analyserDataArray_time_array, dtype=complex)\n",
    "f = xr.DataArray(\n",
    "    data=finufft.nufft1d1(time, analyserDataArray_time_array, N),\n",
    "    dims=['time_freq'],\n",
    "    coords={\n",
    "        \"time_freq\":np.linspace(-0.125/2,0.125/2,N)\n",
    "    }\n",
    ")\n",
    "\n",
    "np.abs(f).plot()\n",
    "# plt.xlim([0, 0.01])\n",
    "# plt.ylim([0, 2000])\n",
    "plt.show()\n",
    "\n",
    "mask = xr.DataArray(\n",
    "    data = np.full(runTime.runTime.shape,fill_value=False, dtype=bool),\n",
    "    dims = [\"time\"],\n",
    "    coords = {\n",
    "        \"time\":runTime.runTime.to_numpy()\n",
    "    }\n",
    ")\n",
    "\n",
    "for i in range(len(mask)):\n",
    "    if (int(mask.time[i]) - 1683642540000000000) % 5.4e11 > 3.6e11:\n",
    "        mask[i] = True\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.gca()\n",
    "\n",
    "xr.where(mask, np.nan, analyserDataArray_time).plot.errorbar(fmt='ob')\n",
    "analyserDataArray_time.where(mask).plot.errorbar(fmt='or')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "analyserDataArray = BEC_center_y_val\n",
    "\n",
    "analyserDataArray_time = xr.DataArray(\n",
    "    data=analyserDataArray,\n",
    "    dims=[\"time\"],\n",
    "    coords={\n",
    "        \"time\": runTime.runTime.to_numpy(),\n",
    "    }\n",
    ")\n",
    "\n",
    "# desired number of Fourier modes (uniform outputs)\n",
    "N = 1001\n",
    "\n",
    "# calculate the transform\n",
    "analyserDataArray_time_array = analyserDataArray_time.to_numpy()\n",
    "analyserDataArray_time_array = np.array(analyserDataArray_time_array, dtype=complex)\n",
    "f = xr.DataArray(\n",
    "    data=finufft.nufft1d1(time, analyserDataArray_time_array, N),\n",
    "    dims=['time_freq'],\n",
    "    coords={\n",
    "        \"time_freq\":np.linspace(-0.125/2,0.125/2,N)\n",
    "    }\n",
    ")\n",
    "\n",
    "np.abs(f).plot()\n",
    "# plt.xlim([0, 0.01])\n",
    "# plt.ylim([0, 2000])\n",
    "plt.show()\n",
    "\n",
    "mask = xr.DataArray(\n",
    "    data = np.full(runTime.runTime.shape,fill_value=False, dtype=bool),\n",
    "    dims = [\"time\"],\n",
    "    coords = {\n",
    "        \"time\":runTime.runTime.to_numpy()\n",
    "    }\n",
    ")\n",
    "\n",
    "for i in range(len(mask)):\n",
    "    if (int(mask.time[i]) - 1683642540000000000) % 5.4e11 > 3.6e11:\n",
    "        mask[i] = True\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.gca()\n",
    "\n",
    "xr.where(mask, np.nan, analyserDataArray_time).plot.errorbar(fmt='ob')\n",
    "analyserDataArray_time.where(mask).plot.errorbar(fmt='or')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "analyserDataArray = thermal_center_y_val\n",
    "\n",
    "analyserDataArray_time = xr.DataArray(\n",
    "    data=analyserDataArray,\n",
    "    dims=[\"time\"],\n",
    "    coords={\n",
    "        \"time\": runTime.runTime.to_numpy(),\n",
    "    }\n",
    ")\n",
    "\n",
    "# desired number of Fourier modes (uniform outputs)\n",
    "N = 1001\n",
    "\n",
    "# calculate the transform\n",
    "analyserDataArray_time_array = analyserDataArray_time.to_numpy()\n",
    "analyserDataArray_time_array = np.array(analyserDataArray_time_array, dtype=complex)\n",
    "f = xr.DataArray(\n",
    "    data=finufft.nufft1d1(time, analyserDataArray_time_array, N),\n",
    "    dims=['time_freq'],\n",
    "    coords={\n",
    "        \"time_freq\":np.linspace(-0.125/2,0.125/2,N)\n",
    "    }\n",
    ")\n",
    "\n",
    "np.abs(f).plot()\n",
    "# plt.xlim([0, 0.01])\n",
    "# plt.ylim([0, 2000])\n",
    "plt.show()\n",
    "\n",
    "mask = xr.DataArray(\n",
    "    data = np.full(runTime.runTime.shape,fill_value=False, dtype=bool),\n",
    "    dims = [\"time\"],\n",
    "    coords = {\n",
    "        \"time\":runTime.runTime.to_numpy()\n",
    "    }\n",
    ")\n",
    "\n",
    "for i in range(len(mask)):\n",
    "    if (int(mask.time[i]) - 1683642540000000000) % 5.4e11 > 3.6e11:\n",
    "        mask[i] = True\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.gca()\n",
    "\n",
    "xr.where(mask, np.nan, analyserDataArray_time).plot.errorbar(fmt='ob')\n",
    "analyserDataArray_time.where(mask).plot.errorbar(fmt='or')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "analyserDataArray = condensateFraction_value\n",
    "\n",
    "analyserDataArray_time = xr.DataArray(\n",
    "    data=analyserDataArray,\n",
    "    dims=[\"time\"],\n",
    "    coords={\n",
    "        \"time\": runTime.runTime.to_numpy(),\n",
    "    }\n",
    ")\n",
    "\n",
    "# desired number of Fourier modes (uniform outputs)\n",
    "N = 1001\n",
    "\n",
    "# calculate the transform\n",
    "analyserDataArray_time_array = analyserDataArray_time.to_numpy()\n",
    "analyserDataArray_time_array = np.array(analyserDataArray_time_array, dtype=complex)\n",
    "f = xr.DataArray(\n",
    "    data=finufft.nufft1d1(time, analyserDataArray_time_array, N),\n",
    "    dims=['time_freq'],\n",
    "    coords={\n",
    "        \"time_freq\":np.linspace(-0.125/2,0.125/2,N)\n",
    "    }\n",
    ")\n",
    "\n",
    "np.abs(f).plot()\n",
    "# plt.xlim([0, 0.01])\n",
    "# plt.ylim([0, 2000])\n",
    "plt.show()\n",
    "\n",
    "mask = xr.DataArray(\n",
    "    data = np.full(runTime.runTime.shape,fill_value=False, dtype=bool),\n",
    "    dims = [\"time\"],\n",
    "    coords = {\n",
    "        \"time\":runTime.runTime.to_numpy()\n",
    "    }\n",
    ")\n",
    "\n",
    "for i in range(len(mask)):\n",
    "    if (int(mask.time[i]) - 1683642540000000000) % 5.4e11 > 3.6e11:\n",
    "        mask[i] = True\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.gca()\n",
    "\n",
    "xr.where(mask, np.nan, analyserDataArray_time).plot.errorbar(fmt='ob')\n",
    "analyserDataArray_time.where(mask).plot.errorbar(fmt='or')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Close to the BEC transition point, in evaporative cooling 2 with truncation value = 0.77"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "shotNum = \"0015\"\n",
    "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n",
    "\n",
    "dataSetDict = {\n",
    "    dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n",
    "    for i in [0]\n",
    "}\n",
    "\n",
    "dataSet = dataSetDict[\"camera_0\"]\n",
    "\n",
    "print_scanAxis(dataSet)\n",
    "\n",
    "scanAxis = get_scanAxis(dataSet)\n",
    "\n",
    "dataSet = auto_rechunk(dataSet)\n",
    "\n",
    "dataSet = imageAnalyser.get_absorption_images(dataSet)\n",
    "\n",
    "imageAnalyser.center = (879, 956)\n",
    "imageAnalyser.span = (200, 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",
    "\n",
    "Ncount = imageAnalyser.get_Ncount(dataSet_cropOD).load()\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.gca()\n",
    "\n",
    "Ncount.plot.errorbar(ax=ax, fmt='ob')\n",
    "\n",
    "plt.ylabel('NCount')\n",
    "plt.tight_layout()\n",
    "plt.grid(visible=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.gca()\n",
    "\n",
    "Ncount.plot.errorbar(ax=ax, fmt='ob')\n",
    "plt.ylim([0, 3000])\n",
    "plt.ylabel('NCount')\n",
    "plt.tight_layout()\n",
    "plt.grid(visible=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataSet_cropOD = auto_rechunk(dataSet_cropOD)\n",
    "\n",
    "fitAnalyser = FitAnalyser(\"Gaussian-2D\", fitDim=2)\n",
    "params = fitAnalyser.guess(dataSet_cropOD, dask=\"parallelized\")\n",
    "fitResult = fitAnalyser.fit(dataSet_cropOD, params, dask=\"parallelized\").load()\n",
    "\n",
    "fitValue = fitAnalyser.get_fit_value(fitResult)\n",
    "fitStd = fitAnalyser.get_fit_std(fitResult)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "thermal_Ncount_val = fitValue['amplitude']\n",
    "thermal_Ncount_std = fitStd['amplitude']\n",
    "\n",
    "thermal_width_x_val = fitValue['sigmax']\n",
    "thermal_width_x_std = fitStd['sigmax']\n",
    "thermal_width_y_val = fitValue['sigmay']\n",
    "thermal_width_y_std = fitStd['sigmay']\n",
    "\n",
    "thermal_center_x_val = fitValue['centerx']\n",
    "thermal_center_x_std = fitStd['centerx']\n",
    "thermal_center_y_val = fitValue['centery']\n",
    "thermal_center_y_std = fitStd['centery']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_Ncount_val = thermal_Ncount_val\n",
    "total_Ncount_std = thermal_Ncount_std\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.gca()\n",
    "\n",
    "total_Ncount_val.plot.errorbar(ax=ax, yerr=total_Ncount_std, fmt='ob')\n",
    "plt.ylim([0, 3000])\n",
    "plt.ylabel('Ncount from fit')\n",
    "plt.tight_layout()\n",
    "plt.grid(visible=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.gca()\n",
    "\n",
    "thermal_width_x_val.plot.errorbar(ax=ax, yerr=thermal_width_x_std, fmt='or')\n",
    "\n",
    "plt.ylabel('X-axis width of thermal part')\n",
    "plt.tight_layout()\n",
    "plt.grid(visible=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.gca()\n",
    "\n",
    "thermal_width_y_val.plot.errorbar(ax=ax, yerr=thermal_width_y_std, fmt='or')\n",
    "\n",
    "plt.ylabel('Y-axis width of thermal part')\n",
    "plt.tight_layout()\n",
    "plt.grid(visible=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.gca()\n",
    "\n",
    "thermal_center_x_val.plot.errorbar(ax=ax, yerr=thermal_center_x_std, fmt='or')\n",
    "\n",
    "plt.ylabel('X-axis center of thermal part')\n",
    "plt.tight_layout()\n",
    "plt.grid(visible=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.gca()\n",
    "\n",
    "thermal_center_y_val.plot.errorbar(ax=ax, yerr=thermal_center_y_std, fmt='or')\n",
    "\n",
    "plt.ylabel('Y-axis center of thermal part')\n",
    "plt.tight_layout()\n",
    "plt.grid(visible=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "val = Ncount.mean().item()\n",
    "std = Ncount.std().item()\n",
    "print(f'The total Ncount is: {val: .2f} \\u00B1 {std: .2f}')\n",
    "\n",
    "val = total_Ncount_val.mean().item()\n",
    "std = total_Ncount_val.std().item()\n",
    "print(f'The total Ncount from fit is: {val: .2f} \\u00B1 {std: .2f}')\n",
    "\n",
    "val = thermal_width_x_val.mean().item()\n",
    "std = thermal_width_x_val.std().item()\n",
    "print(f'The x-axis width of the thermal part is: {val: .2f} \\u00B1 {std: .2f}')\n",
    "\n",
    "val = thermal_width_y_val.mean().item()\n",
    "std = thermal_width_y_val.std().item()\n",
    "print(f'The y-axis width of the thermal part is: {val: .2f} \\u00B1 {std: .2f}')\n",
    "\n",
    "val = thermal_center_x_val.mean().item()\n",
    "std = thermal_center_x_val.std().item()\n",
    "print(f'The x-axis center of the thermal part is: {val: .2f} \\u00B1 {std: .2f}')\n",
    "\n",
    "val = thermal_center_y_val.mean().item()\n",
    "std = thermal_center_y_val.std().item()\n",
    "print(f'The y-axis center of the thermal part is: {val: .2f} \\u00B1 {std: .2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "i=0\n",
    "runTime = read_hdf5_run_time(filePath, datesetOfGlobal=dataSetOfGlobalDict[dskey[groupList[i]]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "time = runTime.runTime.to_numpy()\n",
    "time0 = int(time[0])\n",
    "time = np.array(\n",
    "    [\n",
    "        float(value) - time0\n",
    "        for value in time\n",
    "    ]\n",
    ")\n",
    "time = time / time.max() * 2 * np.pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "analyserDataArray = Ncount\n",
    "\n",
    "analyserDataArray_time = xr.DataArray(\n",
    "    data=analyserDataArray,\n",
    "    dims=[\"time\"],\n",
    "    coords={\n",
    "        \"time\": runTime.runTime.to_numpy(),\n",
    "    }\n",
    ")\n",
    "\n",
    "# desired number of Fourier modes (uniform outputs)\n",
    "N = 701\n",
    "\n",
    "# calculate the transform\n",
    "analyserDataArray_time_array = analyserDataArray_time.to_numpy()\n",
    "analyserDataArray_time_array = np.array(analyserDataArray_time_array, dtype=complex)\n",
    "f = xr.DataArray(\n",
    "    data=finufft.nufft1d1(time, analyserDataArray_time_array, N),\n",
    "    dims=['time_freq'],\n",
    "    coords={\n",
    "        \"time_freq\":np.linspace(-0.125/2,0.125/2,N)\n",
    "    }\n",
    ")\n",
    "\n",
    "value = np.abs(f)\n",
    "value[int((N-1)/2)] = np.nan\n",
    "value.where(value.time_freq>0).plot()\n",
    "# plt.xlim([0, 0.02])\n",
    "# plt.ylim([0, 2000])\n",
    "plt.xlabel('frequency (Hz)')\n",
    "plt.show()\n",
    "\n",
    "mask = xr.DataArray(\n",
    "    data = np.full(runTime.runTime.shape,fill_value=False, dtype=bool),\n",
    "    dims = [\"time\"],\n",
    "    coords = {\n",
    "        \"time\":runTime.runTime.to_numpy()\n",
    "    }\n",
    ")\n",
    "\n",
    "for i in range(len(mask)):\n",
    "    if (int(mask.time[i]) - 1683642540000000000) % 5.4e11 > 3.6e11:\n",
    "        mask[i] = True\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.gca()\n",
    "\n",
    "xr.where(mask, np.nan, analyserDataArray_time).plot.errorbar(fmt='ob')\n",
    "analyserDataArray_time.where(mask).plot.errorbar(fmt='or')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## At the end of ODT loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "shotNum = \"0020\"\n",
    "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n",
    "\n",
    "dataSetDict = {\n",
    "    dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n",
    "    for i in [0]\n",
    "}\n",
    "\n",
    "dataSet = dataSetDict[\"camera_0\"]\n",
    "\n",
    "print_scanAxis(dataSet)\n",
    "\n",
    "scanAxis = get_scanAxis(dataSet)\n",
    "\n",
    "dataSet = auto_rechunk(dataSet)\n",
    "\n",
    "dataSet = imageAnalyser.get_absorption_images(dataSet)\n",
    "\n",
    "imageAnalyser.center = (550, 800)\n",
    "imageAnalyser.span = (900, 1600)\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",
    "\n",
    "Ncount = imageAnalyser.get_Ncount(dataSet_cropOD).load()\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.gca()\n",
    "\n",
    "Ncount.plot.errorbar(ax=ax, fmt='ob')\n",
    "\n",
    "plt.ylabel('NCount')\n",
    "plt.tight_layout()\n",
    "plt.grid(visible=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.gca()\n",
    "\n",
    "Ncount.plot.errorbar(ax=ax, fmt='ob')\n",
    "plt.ylim([0, 150000])\n",
    "plt.ylabel('NCount')\n",
    "plt.tight_layout()\n",
    "plt.grid(visible=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataSet_cropOD = dataSet_cropOD.chunk((1, 900, 1600))\n",
    "dataSet_cropOD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# dataSet_cropOD = auto_rechunk(dataSet_cropOD)\n",
    "\n",
    "fitAnalyser = FitAnalyser(\"Gaussian-2D\", fitDim=2)\n",
    "params = fitAnalyser.guess(dataSet_cropOD, dask=\"parallelized\")\n",
    "fitResult = fitAnalyser.fit(dataSet_cropOD, params, dask=\"parallelized\").load()\n",
    "\n",
    "fitValue = fitAnalyser.get_fit_value(fitResult)\n",
    "fitStd = fitAnalyser.get_fit_std(fitResult)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "thermal_Ncount_val = fitValue['amplitude']\n",
    "thermal_Ncount_std = fitStd['amplitude']\n",
    "\n",
    "thermal_width_x_val = fitValue['sigmax']\n",
    "thermal_width_x_std = fitStd['sigmax']\n",
    "thermal_width_y_val = fitValue['sigmay']\n",
    "thermal_width_y_std = fitStd['sigmay']\n",
    "\n",
    "thermal_center_x_val = fitValue['centerx']\n",
    "thermal_center_x_std = fitStd['centerx']\n",
    "thermal_center_y_val = fitValue['centery']\n",
    "thermal_center_y_std = fitStd['centery']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_Ncount_val = thermal_Ncount_val\n",
    "total_Ncount_std = thermal_Ncount_std\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.gca()\n",
    "\n",
    "total_Ncount_val.plot.errorbar(ax=ax, yerr=total_Ncount_std, fmt='ob')\n",
    "plt.ylim([0, 160000])\n",
    "plt.ylabel('Ncount from fit')\n",
    "plt.tight_layout()\n",
    "plt.grid(visible=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.gca()\n",
    "\n",
    "thermal_width_x_val.plot.errorbar(ax=ax, yerr=thermal_width_x_std, fmt='or')\n",
    "\n",
    "plt.ylabel('Y-axis width of thermal part')\n",
    "plt.tight_layout()\n",
    "plt.grid(visible=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.gca()\n",
    "\n",
    "thermal_width_y_val.plot.errorbar(ax=ax, yerr=thermal_width_y_std, fmt='or')\n",
    "\n",
    "plt.ylabel('X-axis width of thermal part')\n",
    "plt.tight_layout()\n",
    "plt.grid(visible=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.gca()\n",
    "\n",
    "thermal_center_x_val.plot.errorbar(ax=ax, yerr=thermal_center_x_std, fmt='or')\n",
    "\n",
    "plt.ylabel('Y-axis center of thermal part')\n",
    "plt.tight_layout()\n",
    "plt.grid(visible=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.gca()\n",
    "\n",
    "thermal_center_y_val.plot.errorbar(ax=ax, yerr=thermal_center_y_std, fmt='or')\n",
    "\n",
    "plt.ylabel('X-axis center of thermal part')\n",
    "plt.tight_layout()\n",
    "plt.grid(visible=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "val = Ncount.mean().item()\n",
    "std = Ncount.std().item()\n",
    "print(f'The total Ncount is: {val: .2f} \\u00B1 {std: .2f}')\n",
    "\n",
    "val = total_Ncount_val.mean().item()\n",
    "std = total_Ncount_val.std().item()\n",
    "print(f'The total Ncount from fit is: {val: .2f} \\u00B1 {std: .2f}')\n",
    "\n",
    "val = thermal_width_x_val.mean().item()\n",
    "std = thermal_width_x_val.std().item()\n",
    "print(f'The y-axis width of the thermal part is: {val: .2f} \\u00B1 {std: .2f}')\n",
    "\n",
    "val = thermal_width_y_val.mean().item()\n",
    "std = thermal_width_y_val.std().item()\n",
    "print(f'The x-axis width of the thermal part is: {val: .2f} \\u00B1 {std: .2f}')\n",
    "\n",
    "val = thermal_center_x_val.mean().item()\n",
    "std = thermal_center_x_val.std().item()\n",
    "print(f'The y-axis center of the thermal part is: {val: .2f} \\u00B1 {std: .2f}')\n",
    "\n",
    "val = thermal_center_y_val.mean().item()\n",
    "std = thermal_center_y_val.std().item()\n",
    "print(f'The x-axis center of the thermal part is: {val: .2f} \\u00B1 {std: .2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "i=0\n",
    "runTime = read_hdf5_run_time(filePath, datesetOfGlobal=dataSetOfGlobalDict[dskey[groupList[i]]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "time = runTime.runTime.to_numpy()\n",
    "time0 = int(time[0])\n",
    "time = np.array(\n",
    "    [\n",
    "        float(value) - time0\n",
    "        for value in time\n",
    "    ]\n",
    ")\n",
    "time = time / time.max() * 2 * np.pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "analyserDataArray = Ncount\n",
    "\n",
    "analyserDataArray_time = xr.DataArray(\n",
    "    data=analyserDataArray,\n",
    "    dims=[\"time\"],\n",
    "    coords={\n",
    "        \"time\": runTime.runTime.to_numpy(),\n",
    "    }\n",
    ")\n",
    "\n",
    "# desired number of Fourier modes (uniform outputs)\n",
    "N = 701\n",
    "\n",
    "# calculate the transform\n",
    "analyserDataArray_time_array = analyserDataArray_time.to_numpy()\n",
    "analyserDataArray_time_array = np.array(analyserDataArray_time_array, dtype=complex)\n",
    "f = xr.DataArray(\n",
    "    data=finufft.nufft1d1(time, analyserDataArray_time_array, N),\n",
    "    dims=['time_freq'],\n",
    "    coords={\n",
    "        \"time_freq\":np.linspace(-0.125/2,0.125/2,N)\n",
    "    }\n",
    ")\n",
    "\n",
    "value = np.abs(f)\n",
    "value[int((N-1)/2)] = np.nan\n",
    "value.where(value.time_freq>0).plot()\n",
    "# plt.xlim([0, 0.02])\n",
    "# plt.ylim([0, 2000])\n",
    "plt.xlabel('frequency (Hz)')\n",
    "plt.show()\n",
    "\n",
    "mask = xr.DataArray(\n",
    "    data = np.full(runTime.runTime.shape,fill_value=False, dtype=bool),\n",
    "    dims = [\"time\"],\n",
    "    coords = {\n",
    "        \"time\":runTime.runTime.to_numpy()\n",
    "    }\n",
    ")\n",
    "\n",
    "for i in range(len(mask)):\n",
    "    if (int(mask.time[i]) - 1683642540000000000) % 5.4e11 > 3.6e11:\n",
    "        mask[i] = True\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.gca()\n",
    "\n",
    "xr.where(mask, np.nan, analyserDataArray_time).plot.errorbar(fmt='ob')\n",
    "analyserDataArray_time.where(mask).plot.errorbar(fmt='or')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "l = list(np.arange(0.001, 0.025, 0.0005))\n",
    "# l = np.logspace(np.log10(100e-3), np.log10(20), num=20)\n",
    "\n",
    "l = [round(item, 7) for item in l]\n",
    "#random.shuffle(l)\n",
    "\n",
    "print(l)\n",
    "print(len(l))\n",
    "np.mean(l)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ODT 1 Calibration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "v_high = 2.7\n",
    "\"\"\"High Power\"\"\"\n",
    "P_arm1_high = 5.776 * v_high - 0.683\n",
    "\n",
    "v_mid = 0.2076\n",
    "\"\"\"Intermediate Power\"\"\"\n",
    "P_arm1_mid = 5.815 * v_mid - 0.03651\n",
    "\n",
    "v_low = 0.0587\n",
    "\"\"\"Low Power\"\"\"\n",
    "P_arm1_low = 5271 * v_low - 27.5\n",
    "\n",
    "print(round(P_arm1_high, 3))\n",
    "print(round(P_arm1_mid, 3))\n",
    "print(round(P_arm1_low, 3))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ODT 2 Power Calibration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "v = 0.7607\n",
    "P_arm2 = 2.302 * v - 0.06452\n",
    "print(round(P_arm2, 3))"
   ]
  }
 ],
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