{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import mpmath as mp\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def polylog(power, numerator):\n",
    "    \n",
    "    order = 100\n",
    "    \n",
    "    dataShape = numerator.shape\n",
    "    numerator = np.tile(numerator, (order, 1))\n",
    "    numerator = np.power(numerator.T, np.arange(1, order+1)).T\n",
    "\n",
    "    denominator = np.arange(1, order+1)\n",
    "    denominator = np.tile(denominator, (dataShape[0], 1))\n",
    "    denominator = denominator.T\n",
    "\n",
    "    data = numerator/ np.power(denominator, power)\n",
    "\n",
    "    return np.sum(data, axis=0)\n",
    "\n",
    "x = np.linspace(0, 1, 51)\n",
    "y1 = polylog(2, x)\n",
    "y2 = [float(mp.polylog(2, i).real) for i in x]\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "plt.plot(x, y1, 'r')\n",
    "plt.plot(x, y2, 'b')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lmfit.lineshapes import (not_zero, breit_wigner, damped_oscillator, dho, doniach,\n",
    "                              expgaussian, exponential, gaussian, gaussian2d,\n",
    "                              linear, lognormal, lorentzian, moffat, parabolic,\n",
    "                              pearson7, powerlaw, pvoigt, rectangle, sine,\n",
    "                              skewed_gaussian, skewed_voigt, split_lorentzian, step,\n",
    "                              students_t, thermal_distribution, tiny, voigt)\n",
    "\n",
    "def polylog(power, numerator):\n",
    "    \n",
    "    order = 100\n",
    "    \n",
    "    dataShape = numerator.shape\n",
    "    numerator = np.tile(numerator, (order, 1))\n",
    "    numerator = np.power(numerator.T, np.arange(1, order+1)).T\n",
    "\n",
    "    denominator = np.arange(1, order+1)\n",
    "    denominator = np.tile(denominator, (dataShape[0], 1))\n",
    "    denominator = denominator.T\n",
    "\n",
    "    data = numerator/ np.power(denominator, power)\n",
    "\n",
    "    return np.sum(data, axis=0)\n",
    "\n",
    "def polylog2_2d(x, y=0.0, centerx=0.0, centery=0.0, amplitude=1.0, sigmax=1.0, sigmay=1.0): \n",
    "    ## Approximation of the polylog function with 2D gaussian as argument. -> discribes the thermal part of the cloud\n",
    "    return amplitude / 2 / 5.403642092095097 / max(tiny, sigmax * sigmay) * polylog(2, np.exp( -((x-centerx)**2/(2 * (sigmax)**2))-((y-centery)**2/( 2 * (sigmay)**2)) ))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.403642092095097\n"
     ]
    }
   ],
   "source": [
    "from scipy import special\n",
    "\n",
    "sum = 0\n",
    "for i in range(1,20000):\n",
    "    sum += 1/i**4 * special.gamma(1/2/i)**2\n",
    "    \n",
    "print(sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.0"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[1] - x[0] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8405962721688879"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.linspace(-100, 100, 101)\n",
    "y = np.linspace(-100, 100, 101)\n",
    "\n",
    "X, Y = np.meshgrid(x, y)\n",
    "X = X.flatten()\n",
    "Y = Y.flatten()\n",
    "Z = polylog2_2d(x=X, y=Y).reshape(101, 101)\n",
    "\n",
    "np.sum(Z)"
   ]
  },
  {
   "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 glob\n",
    "\n",
    "import xrft\n",
    "import finufft\n",
    "\n",
    "from uncertainties import ufloat\n",
    "from uncertainties import unumpy as unp\n",
    "from uncertainties import umath\n",
    "\n",
    "from datetime import datetime\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.size'] = 18\n",
    "\n",
    "from DataContainer.ReadData import read_hdf5_file, read_hdf5_global, read_hdf5_run_time, read_csv_file\n",
    "from Analyser.ImagingAnalyser import ImageAnalyser\n",
    "from Analyser.FitAnalyser import FitAnalyser\n",
    "from Analyser.FitAnalyser import ThomasFermi2dModel, DensityProfileBEC2dModel, Polylog22dModel\n",
    "from Analyser.FFTAnalyser import fft, ifft, fft_nutou\n",
    "from ToolFunction.ToolFunction import *\n",
    "\n",
    "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": [
    "# Import supporting package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "\n",
    "from uncertainties import ufloat\n",
    "from uncertainties import unumpy as unp\n",
    "from uncertainties import umath\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from DataContainer.ReadData import read_hdf5_file\n",
    "from Analyser.ImagingAnalyser import ImageAnalyser\n",
    "from Analyser.FitAnalyser import FitAnalyser\n",
    "from Analyser.FitAnalyser import ThomasFermi2dModel, DensityProfileBEC2dModel, Polylog22dModel\n",
    "from Analyser.FitAnalyser import NewFitModel\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": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\data\\AppData\\Roaming\\Python\\Python39\\site-packages\\distributed\\node.py:182: UserWarning: Port 8787 is already in use.\n",
      "Perhaps you already have a cluster running?\n",
      "Hosting the HTTP server on port 52475 instead\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "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-5ac7f000-261b-11ee-9f6c-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://127.0.0.1:52475/status\" target=\"_blank\">http://127.0.0.1:52475/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;\">c80cced4</p>\n",
       "        <table style=\"width: 100%; text-align: left;\">\n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:52475/status\" target=\"_blank\">http://127.0.0.1:52475/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> 60\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-0363aa80-9ca7-4bd2-8096-e563f44991c3</p>\n",
       "            <table style=\"width: 100%; text-align: left;\">\n",
       "                <tr>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Comm:</strong> tcp://127.0.0.1:52478\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://127.0.0.1:52475/status\" target=\"_blank\">http://127.0.0.1:52475/status</a>\n",
       "                    </td>\n",
       "                    <td style=\"text-align: left;\">\n",
       "                        <strong>Total threads:</strong> 60\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://127.0.0.1:52506\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 10\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:52509/status\" target=\"_blank\">http://127.0.0.1:52509/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://127.0.0.1:52481\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-bwtzfry2\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://127.0.0.1:52512\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 10\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:52513/status\" target=\"_blank\">http://127.0.0.1:52513/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://127.0.0.1:52482\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-mp7n6m76\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://127.0.0.1:52515\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 10\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:52516/status\" target=\"_blank\">http://127.0.0.1:52516/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://127.0.0.1:52483\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-co90qahq\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://127.0.0.1:52518\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 10\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:52519/status\" target=\"_blank\">http://127.0.0.1:52519/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://127.0.0.1:52484\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-4p3qclmm\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://127.0.0.1:52521\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 10\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:52522/status\" target=\"_blank\">http://127.0.0.1:52522/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://127.0.0.1:52485\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-jl88pafx\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://127.0.0.1:52507\n",
       "                        </td>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Total threads: </strong> 10\n",
       "                        </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <td style=\"text-align: left;\">\n",
       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:52508/status\" target=\"_blank\">http://127.0.0.1:52508/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://127.0.0.1:52486\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-lczd_jce\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://127.0.0.1:52478' processes=6 threads=60, memory=55.88 GiB>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dask.distributed import Client\n",
    "client = Client(n_workers=6, threads_per_worker=10, processes=True, memory_limit='10GB')\n",
    "client"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set global path for experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filepath = \"//DyLabNAS/Data/Evaporative_Cooling/2023/05/03/0043/*.h5\"\n",
    "# filepath = \"//DyLabNAS/Data/Evaporative_Cooling/2023/04/18/0003/2023-04-18_0003_Evaporative_Cooling_000.h5\"\n",
    "\n",
    "# filepath = \"//DyLabNAS/Data/Repetition_scan/2023/04/21/0002/*.h5\"\n",
    "\n",
    "# filepath = r\"./testData/0002/*.h5\"\n",
    "\n",
    "# filepath = r\"./testData/0002/2023-04-21_0002_Evaporative_Cooling_0.h5\"\n",
    "\n",
    "# filepath = r'd:/Jianshun Gao/Simulations/analyseScripts/testData/0002/2023-04-21_0002_Evaporative_Cooling_0.h5'\n",
    "\n",
    "# filepath = \"//DyLabNAS/Data/Evaporative_Cooling/2023/04/18/0003/*.h5\"\n",
    "\n",
    "filepath = \"//DyLabNAS/Data/Evaporative_Cooling/2023/05/04/0000/*.h5\"\n",
    "\n",
    "# filepath = './result_from_experiment/2023-04-24/0013/2023-04-24_0013_Evaporative_Cooling_13.h5'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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_1\",\n",
    "    \"images/ODT_1_Axis_Camera/in_situ_absorption\": \"camera_2\",\n",
    "    \"images/ODT_2_Axis_Camera/in_situ_absorption\": \"camera_3\",\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_dir = '//DyLabNAS/Data/'\n",
    "SequenceName = \"Evaporative_Cooling\" + \"/\"\n",
    "folderPath = img_dir + SequenceName + '2023/05/23'# get_date()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# An example for one experimental run"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "f:\\Jianshun\\analyseScript\\DataContainer\\ReadData.py:234: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if not key in datesetOfGlobal.scanAxis\n"
     ]
    },
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:     (y: 1200, x: 1920)\n",
       "Dimensions without coordinates: y, x\n",
       "Data variables:\n",
       "    atoms       (y, x) uint16 dask.array&lt;chunksize=(1200, 1920), meta=np.ndarray&gt;\n",
       "    background  (y, x) uint16 dask.array&lt;chunksize=(1200, 1920), meta=np.ndarray&gt;\n",
       "    dark        (y, x) uint16 dask.array&lt;chunksize=(1200, 1920), meta=np.ndarray&gt;\n",
       "    shotNum     &lt;U2 &#x27;11&#x27;\n",
       "    OD          (y, x) float64 dask.array&lt;chunksize=(1200, 1920), meta=np.ndarray&gt;\n",
       "Attributes: (12/96)\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:                          0\n",
       "    y_offset_img:                      0\n",
       "    z_offset:                          0.189\n",
       "    z_offset_img:                      0.189\n",
       "    scanAxis:                          []\n",
       "    scanAxisLength:                    []</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-ec5b96ec-3fbd-41ed-93ff-44307cde3390' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ec5b96ec-3fbd-41ed-93ff-44307cde3390' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>y</span>: 1200</li><li><span>x</span>: 1920</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-baf349f6-ffc6-4810-a176-13e9f16ba25b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-baf349f6-ffc6-4810-a176-13e9f16ba25b' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-61a52fcb-e121-4c08-b890-1be565dbae2f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-61a52fcb-e121-4c08-b890-1be565dbae2f' class='xr-section-summary' >Data variables: <span>(5)</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>atoms</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1200, 1920), meta=np.ndarray&gt;</div><input id='attrs-25c95ac6-37ef-4466-8fba-b9d0998dec05' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-25c95ac6-37ef-4466-8fba-b9d0998dec05' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-13ef18e5-221b-48af-93de-6c0a04ad61e5' class='xr-var-data-in' type='checkbox'><label for='data-13ef18e5-221b-48af-93de-6c0a04ad61e5' 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'><dt><span>IMAGE_SUBCLASS :</span></dt><dd>IMAGE_GRAYSCALE</dd><dt><span>IMAGE_VERSION :</span></dt><dd>1.2</dd><dt><span>IMAGE_WHITE_IS_ZERO :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 4.39 MiB </td>\n",
       "                        <td> 4.39 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1200, 1920) </td>\n",
       "                        <td> (1200, 1920) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> uint16 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"125\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>background</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1200, 1920), meta=np.ndarray&gt;</div><input id='attrs-d2313912-491a-48a5-8d85-3992f8816257' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d2313912-491a-48a5-8d85-3992f8816257' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-14c4d7f0-6ed5-43f8-ac87-7dade8ec08b2' class='xr-var-data-in' type='checkbox'><label for='data-14c4d7f0-6ed5-43f8-ac87-7dade8ec08b2' 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'><dt><span>IMAGE_SUBCLASS :</span></dt><dd>IMAGE_GRAYSCALE</dd><dt><span>IMAGE_VERSION :</span></dt><dd>1.2</dd><dt><span>IMAGE_WHITE_IS_ZERO :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 4.39 MiB </td>\n",
       "                        <td> 4.39 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1200, 1920) </td>\n",
       "                        <td> (1200, 1920) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> uint16 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"125\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dark</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1200, 1920), meta=np.ndarray&gt;</div><input id='attrs-ffd44c81-646b-4564-b1fe-05a154f3a96b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ffd44c81-646b-4564-b1fe-05a154f3a96b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eade5bec-ce4b-4846-8277-603280226d6f' class='xr-var-data-in' type='checkbox'><label for='data-eade5bec-ce4b-4846-8277-603280226d6f' 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'><dt><span>IMAGE_SUBCLASS :</span></dt><dd>IMAGE_GRAYSCALE</dd><dt><span>IMAGE_VERSION :</span></dt><dd>1.2</dd><dt><span>IMAGE_WHITE_IS_ZERO :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 4.39 MiB </td>\n",
       "                        <td> 4.39 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1200, 1920) </td>\n",
       "                        <td> (1200, 1920) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> uint16 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"125\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"75\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"75\" style=\"stroke-width:2\" />\n",
       "\n",
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       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,75.0 0.0,75.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
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       "  <text x=\"60.000000\" y=\"95.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1920</text>\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>shotNum</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>&#x27;11&#x27;</div><input id='attrs-c809af6a-bccf-45f5-bbe1-aa233c8fcfab' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c809af6a-bccf-45f5-bbe1-aa233c8fcfab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f84c873-2e2c-466f-8ba9-0b2fdde93380' class='xr-var-data-in' type='checkbox'><label for='data-9f84c873-2e2c-466f-8ba9-0b2fdde93380' 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;11&#x27;, dtype=&#x27;&lt;U2&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>OD</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1200, 1920), meta=np.ndarray&gt;</div><input id='attrs-7f76e601-6ec1-48c3-b28a-de60fb1da3a6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7f76e601-6ec1-48c3-b28a-de60fb1da3a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80ae0948-0fc2-4a48-8232-d9376f5ac77b' class='xr-var-data-in' type='checkbox'><label for='data-80ae0948-0fc2-4a48-8232-d9376f5ac77b' 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'><dt><span>IMAGE_SUBCLASS :</span></dt><dd>IMAGE_GRAYSCALE</dd><dt><span>IMAGE_VERSION :</span></dt><dd>1.2</dd><dt><span>IMAGE_WHITE_IS_ZERO :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 17.58 MiB </td>\n",
       "                        <td> 17.58 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1200, 1920) </td>\n",
       "                        <td> (1200, 1920) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 16 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"125\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "  <!-- Horizontal lines -->\n",
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       "  <text x=\"60.000000\" y=\"95.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1920</text>\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-af699151-4f5c-49c7-bb0d-93998f824fa5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-af699151-4f5c-49c7-bb0d-93998f824fa5' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-6a5d30f7-7f90-46f2-b6a2-e6898a477881' class='xr-section-summary-in' type='checkbox'  ><label for='section-6a5d30f7-7f90-46f2-b6a2-e6898a477881' class='xr-section-summary' >Attributes: <span>(96)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>TOF_free :</span></dt><dd>0.02</dd><dt><span>abs_img_freq :</span></dt><dd>110.858</dd><dt><span>absorption_imaging_flag :</span></dt><dd>True</dd><dt><span>backup_data :</span></dt><dd>True</dd><dt><span>blink_off_time :</span></dt><dd>nan</dd><dt><span>blink_on_time :</span></dt><dd>nan</dd><dt><span>c_duration :</span></dt><dd>0.2</dd><dt><span>cmot_final_current :</span></dt><dd>0.65</dd><dt><span>cmot_hold :</span></dt><dd>0.06</dd><dt><span>cmot_initial_current :</span></dt><dd>0.18</dd><dt><span>compX_current :</span></dt><dd>0.005</dd><dt><span>compX_current_sg :</span></dt><dd>0</dd><dt><span>compX_final_current :</span></dt><dd>0.005</dd><dt><span>compX_initial_current :</span></dt><dd>0.005</dd><dt><span>compY_current :</span></dt><dd>0</dd><dt><span>compY_current_sg :</span></dt><dd>0</dd><dt><span>compY_final_current :</span></dt><dd>0.0</dd><dt><span>compY_initial_current :</span></dt><dd>0</dd><dt><span>compZ_current :</span></dt><dd>0</dd><dt><span>compZ_current_sg :</span></dt><dd>0.189</dd><dt><span>compZ_final_current :</span></dt><dd>0.2812</dd><dt><span>compZ_initial_current :</span></dt><dd>0</dd><dt><span>default_camera :</span></dt><dd>0</dd><dt><span>evap_1_arm_1_final_pow :</span></dt><dd>0.35</dd><dt><span>evap_1_arm_1_mod_depth_final :</span></dt><dd>0</dd><dt><span>evap_1_arm_1_mod_depth_initial :</span></dt><dd>1.0</dd><dt><span>evap_1_arm_1_mod_ramp_duration :</span></dt><dd>1.15</dd><dt><span>evap_1_arm_1_pow_ramp_duration :</span></dt><dd>1.65</dd><dt><span>evap_1_arm_1_start_pow :</span></dt><dd>7</dd><dt><span>evap_1_arm_2_final_pow :</span></dt><dd>5</dd><dt><span>evap_1_arm_2_ramp_duration :</span></dt><dd>0.5</dd><dt><span>evap_1_arm_2_start_pow :</span></dt><dd>0</dd><dt><span>evap_1_mod_ramp_trunc_value :</span></dt><dd>1</dd><dt><span>evap_1_pow_ramp_trunc_value :</span></dt><dd>1.0</dd><dt><span>evap_1_rate_constant_1 :</span></dt><dd>0.525</dd><dt><span>evap_1_rate_constant_2 :</span></dt><dd>0.51</dd><dt><span>evap_2_arm_1_final_pow :</span></dt><dd>0.037</dd><dt><span>evap_2_arm_1_start_pow :</span></dt><dd>0.35</dd><dt><span>evap_2_arm_2_final_pow :</span></dt><dd>0.09</dd><dt><span>evap_2_arm_2_start_pow :</span></dt><dd>5</dd><dt><span>evap_2_ramp_duration :</span></dt><dd>1.0</dd><dt><span>evap_2_ramp_trunc_value :</span></dt><dd>1</dd><dt><span>evap_2_rate_constant_1 :</span></dt><dd>0.37</dd><dt><span>evap_2_rate_constant_2 :</span></dt><dd>0.71</dd><dt><span>evap_3_arm_1_final_pow :</span></dt><dd>0.1038</dd><dt><span>evap_3_arm_1_mod_depth_final :</span></dt><dd>0.43</dd><dt><span>evap_3_arm_1_mod_depth_initial :</span></dt><dd>0</dd><dt><span>evap_3_arm_1_start_pow :</span></dt><dd>0.037</dd><dt><span>evap_3_ramp_duration :</span></dt><dd>0.1</dd><dt><span>evap_3_ramp_trunc_value :</span></dt><dd>1</dd><dt><span>evap_3_rate_constant_1 :</span></dt><dd>-0.879</dd><dt><span>evap_3_rate_constant_2 :</span></dt><dd>-0.297</dd><dt><span>final_amp :</span></dt><dd>8e-05</dd><dt><span>final_freq :</span></dt><dd>104.0</dd><dt><span>gradCoil_current :</span></dt><dd>0.18</dd><dt><span>gradCoil_current_sg :</span></dt><dd>0</dd><dt><span>imaging_method :</span></dt><dd>in_situ_absorption</dd><dt><span>imaging_pulse_duration :</span></dt><dd>2.5e-05</dd><dt><span>imaging_wavelength :</span></dt><dd>4.21291e-07</dd><dt><span>initial_amp :</span></dt><dd>0.62</dd><dt><span>initial_freq :</span></dt><dd>102.13</dd><dt><span>mod_depth_initial :</span></dt><dd>1.0</dd><dt><span>mot_3d_amp :</span></dt><dd>0.62</dd><dt><span>mot_3d_camera_exposure_time :</span></dt><dd>0.002</dd><dt><span>mot_3d_camera_trigger_duration :</span></dt><dd>0.00025</dd><dt><span>mot_3d_freq :</span></dt><dd>102.13</dd><dt><span>mot_load_duration :</span></dt><dd>4</dd><dt><span>odt_axis_camera_trigger_duration :</span></dt><dd>0.002</dd><dt><span>odt_hold_time_1 :</span></dt><dd>0.01</dd><dt><span>odt_hold_time_2 :</span></dt><dd>0.1</dd><dt><span>odt_hold_time_3 :</span></dt><dd>0.1</dd><dt><span>odt_hold_time_4 :</span></dt><dd>1</dd><dt><span>pow_arm_1 :</span></dt><dd>7</dd><dt><span>pow_arm_2 :</span></dt><dd>0</dd><dt><span>pulse_delay :</span></dt><dd>8e-05</dd><dt><span>push_amp :</span></dt><dd>0.16</dd><dt><span>push_freq :</span></dt><dd>102.25</dd><dt><span>ramp_duration :</span></dt><dd>1</dd><dt><span>recomp_ramp_duration :</span></dt><dd>0.5</dd><dt><span>recomp_ramp_pow_fin_arm_1 :</span></dt><dd>0.1038</dd><dt><span>recomp_ramp_pow_fin_arm_2 :</span></dt><dd>0.09</dd><dt><span>recomp_ramp_pow_ini_arm_1 :</span></dt><dd>0.1038</dd><dt><span>recomp_ramp_pow_ini_arm_2 :</span></dt><dd>0.09</dd><dt><span>runs :</span></dt><dd>1</dd><dt><span>save_results :</span></dt><dd>False</dd><dt><span>stern_gerlach_duration :</span></dt><dd>0.001</dd><dt><span>wait_after_2dmot_off :</span></dt><dd>0</dd><dt><span>wait_time_between_images :</span></dt><dd>0.22</dd><dt><span>x_offset :</span></dt><dd>0</dd><dt><span>x_offset_img :</span></dt><dd>0</dd><dt><span>y_offset :</span></dt><dd>0</dd><dt><span>y_offset_img :</span></dt><dd>0</dd><dt><span>z_offset :</span></dt><dd>0.189</dd><dt><span>z_offset_img :</span></dt><dd>0.189</dd><dt><span>scanAxis :</span></dt><dd>[]</dd><dt><span>scanAxisLength :</span></dt><dd>[]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:     (y: 1200, x: 1920)\n",
       "Dimensions without coordinates: y, x\n",
       "Data variables:\n",
       "    atoms       (y, x) uint16 dask.array<chunksize=(1200, 1920), meta=np.ndarray>\n",
       "    background  (y, x) uint16 dask.array<chunksize=(1200, 1920), meta=np.ndarray>\n",
       "    dark        (y, x) uint16 dask.array<chunksize=(1200, 1920), meta=np.ndarray>\n",
       "    shotNum     <U2 '11'\n",
       "    OD          (y, x) float64 dask.array<chunksize=(1200, 1920), meta=np.ndarray>\n",
       "Attributes: (12/96)\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:                          0\n",
       "    y_offset_img:                      0\n",
       "    z_offset:                          0.189\n",
       "    z_offset_img:                      0.189\n",
       "    scanAxis:                          []\n",
       "    scanAxisLength:                    []"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shotNum = \"0069\"\n",
    "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n",
    "# filePath = \"//DyLabNAS/Data/Evaporative_Cooling/2023/05/12/0065/*.h5\"\n",
    "filePath = './result_from_experiment/2023-04-24/0013/2023-04-24_0013_Evaporative_Cooling_11.h5'\n",
    "\n",
    "dataSetDict = {\n",
    "    dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n",
    "    for i in [0] # range(len(groupList))\n",
    "}\n",
    "\n",
    "dataSet = dataSetDict[\"camera_1\"]\n",
    "dataSet = swap_xy(dataSet)\n",
    "\n",
    "scanAxis = get_scanAxis(dataSet)\n",
    "\n",
    "dataSet = auto_rechunk(dataSet)\n",
    "\n",
    "dataSet = imageAnalyser.get_absorption_images(dataSet)\n",
    "\n",
    "dataSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected = np.zeros((2, 2))\n",
    "a =  xr.DataArray(\n",
    "                data=selected,\n",
    "                dims=[\"x\", \"y\"],\n",
    "                coords=dict(\n",
    "                    x=np.arange(np.shape(selected)[0]),\n",
    "                    y=np.arange(np.shape(selected)[1]),\n",
    "                )\n",
    "            )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:           (fileNum: 1, phony_dim_0: 59, phony_dim_1: 0)\n",
       "Dimensions without coordinates: fileNum, phony_dim_0, phony_dim_1\n",
       "Data variables:\n",
       "    connection table  (fileNum, phony_dim_0) [(&#x27;name&#x27;, &#x27;S256&#x27;), (&#x27;class&#x27;, &#x27;S256&#x27;), (&#x27;parent&#x27;, &#x27;S256&#x27;), (&#x27;parent port&#x27;, &#x27;S256&#x27;), (&#x27;unit conversion class&#x27;, &#x27;S256&#x27;), (&#x27;unit conversion params&#x27;, &#x27;O&#x27;), (&#x27;BLACS_connection&#x27;, &#x27;S9&#x27;), (&#x27;properties&#x27;, &#x27;O&#x27;)] dask.array&lt;chunksize=(1, 59), meta=np.ndarray&gt;\n",
       "    script            (fileNum) &lt;U7544 &#x27;from labscript import *\\nfrom labscri...\n",
       "    time_markers      (fileNum, phony_dim_1) [(&#x27;label&#x27;, &#x27;S256&#x27;), (&#x27;time&#x27;, &#x27;&lt;f8&#x27;), (&#x27;color&#x27;, &#x27;&lt;i4&#x27;, (1, 3))] dask.array&lt;chunksize=(1, 0), meta=np.ndarray&gt;\n",
       "    waits             (fileNum, phony_dim_1) [(&#x27;label&#x27;, &#x27;S256&#x27;), (&#x27;time&#x27;, &#x27;&lt;f8&#x27;), (&#x27;timeout&#x27;, &#x27;&lt;f8&#x27;)] dask.array&lt;chunksize=(1, 0), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    n_runs:           12\n",
       "    run number:       11\n",
       "    run time:         20230424T183333\n",
       "    script_basename:  Evaporative_Cooling\n",
       "    sequence_date:    2023-04-24\n",
       "    sequence_id:      20230424T183145_Evaporative_Cooling\n",
       "    sequence_index:   13</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-df0fb99b-2e32-443a-8abc-37d1cfce576a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-df0fb99b-2e32-443a-8abc-37d1cfce576a' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>fileNum</span>: 1</li><li><span>phony_dim_0</span>: 59</li><li><span>phony_dim_1</span>: 0</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d7da5bd2-8200-4a86-bebc-3bdd2d5c29d0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d7da5bd2-8200-4a86-bebc-3bdd2d5c29d0' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-90f2bb5c-fcfd-431e-8352-bce55b32b20d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-90f2bb5c-fcfd-431e-8352-bce55b32b20d' class='xr-section-summary' >Data variables: <span>(4)</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>connection table</span></div><div class='xr-var-dims'>(fileNum, phony_dim_0)</div><div class='xr-var-dtype'>[(&#x27;name&#x27;, &#x27;S256&#x27;), (&#x27;class&#x27;, &#x27;S256&#x27;), (&#x27;parent&#x27;, &#x27;S256&#x27;), (&#x27;parent port&#x27;, &#x27;S256&#x27;), (&#x27;unit conversion class&#x27;, &#x27;S256&#x27;), (&#x27;unit conversion params&#x27;, &#x27;O&#x27;), (&#x27;BLACS_connection&#x27;, &#x27;S9&#x27;), (&#x27;properties&#x27;, &#x27;O&#x27;)]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 59), meta=np.ndarray&gt;</div><input id='attrs-3ab7a604-2517-460d-a0e1-481bd0381665' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3ab7a604-2517-460d-a0e1-481bd0381665' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c5dbfe0-052d-42b1-9858-b77b261205b8' class='xr-var-data-in' type='checkbox'><label for='data-5c5dbfe0-052d-42b1-9858-b77b261205b8' 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'><dt><span>master_pseudoclock :</span></dt><dd>prawn</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 75.19 kiB </td>\n",
       "                        <td> 75.19 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1, 59) </td>\n",
       "                        <td> (1, 59) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> [('name', 'S256'), ('class', 'S256'), ('parent', 'S256'), ('parent port', 'S256'), ('unit conversion class', 'S256'), ('unit conversion params', 'O'), ('BLACS_connection', 'S9'), ('properties', 'O')] numpy.ndarray </td>\n",
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       "                </tbody>\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>script</span></div><div class='xr-var-dims'>(fileNum)</div><div class='xr-var-dtype'>&lt;U7544</div><div class='xr-var-preview xr-preview'>&#x27;from labscript import *\\nfrom l...</div><input id='attrs-a5f506c9-a282-44ca-b34b-38a5510ec73d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a5f506c9-a282-44ca-b34b-38a5510ec73d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea2e991d-94bd-40ca-a7e9-8724e449bc2b' class='xr-var-data-in' type='checkbox'><label for='data-ea2e991d-94bd-40ca-a7e9-8724e449bc2b' 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'><dt><span>name :</span></dt><dd>Evaporative_Cooling.py</dd><dt><span>path :</span></dt><dd>C:\\Users\\control\\labscript-suite\\userlib\\labscriptlib\\DyBEC\\Sequences</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;from labscript import *\\nfrom labscript_utils import import_or_reload\\nimport_or_reload(\\&#x27;labscriptlib.DyBEC.connection_table\\&#x27;)\\n\\nfrom labscriptlib.DyBEC.Subsequences.MOT import *\\nfrom labscriptlib.DyBEC.Subsequences.ODT import *\\nfrom labscriptlib.DyBEC.Subsequences.SequenceControl import *\\nfrom labscriptlib.DyBEC.Subsequences.Manipulations import *\\n\\n&quot;&quot;&quot;\\ndifferent parameters:\\nTOF: Duration of time-of-flight\\nimaging_pulse_duration: Duration of imaging pulse (controlled by the RF switch)\\nmot_3d_camera_trigger_duration: trigger duration for the camera\\nwait_time_between_images: lapsed time between taking two successive images (e.g. \\&#x27;with atoms\\&#x27; and \\&#x27;without atoms\\&#x27;)\\npulse_delay: delay for the imaging pulse (with respect to the exposure trigger)\\n&quot;&quot;&quot;\\n\\nstart()\\n\\nt0 = 1e-3\\n\\nt  = reinitialise(t0)\\n\\n&quot;&quot;&quot;Switch on the 2D MOT, 3D MOT and push beam&quot;&quot;&quot;\\nMOT_2D_Shutter.go_high(t)\\nMOT_3D_Shutter.go_high(t)\\nPush_Beam_Red_Shutter.go_high(t) ## red push shutter on \\n\\nMOT_Grad_Coil_Switch.go_high(t)\\nMOT_CompX_Coil_Switch.go_high(t)\\nMOT_CompY_Coil_Switch.go_high(t)\\nMOT_CompZ_Coil_Switch.go_high(t)\\n\\nt += 5e-3\\n\\n&quot;&quot;&quot;Load 3D MOT from 2D MOT with push&quot;&quot;&quot;\\nMOT_3D_RF_Switch.go_high(t) \\nPush_Beam_Red_Switch.go_high(t) ## red push switch on\\n\\nt += load_mot3d(t)\\n\\n&quot;&quot;&quot;Switch off the 2D MOT and the push beam&quot;&quot;&quot;\\nPush_Beam_Red_Switch.go_low(t) ## red push switch off\\nPush_Beam_Red_Shutter.go_low(t) ## red push shutter off\\nMOT_2D_Shutter.go_low(t)\\n\\n&quot;&quot;&quot;Set power ODT1, ODT2, and modulation of ODT1&quot;&quot;&quot;\\nset_ODT_power(t)\\nset_modulation(t)\\n\\n&quot;&quot;&quot;MOT Compression&quot;&quot;&quot;\\nt += compress_mot3d(t)\\n\\n&quot;&quot;&quot;Switch on ODT1&quot;&quot;&quot;\\nCDT1_Switch.go_high(t)\\n\\nt += cmot_hold\\n\\nMOT_3D_RF_Switch.go_low(t)\\nMOT_3D_Shutter.go_low(t)\\nMOT_Grad_Coil_Switch.go_low(t)\\n\\n&quot;&quot;&quot;Set the offset magnetic field in x, y and z directions&quot;&quot;&quot;\\nt += odt_hold_time_1\\nset_offset_mag_field(t, x_offset, y_offset, z_offset)\\nt += odt_hold_time_2\\n\\n&quot;&quot;&quot;EVAPORATION 1&quot;&quot;&quot;\\n&quot;&quot;&quot;Ramp down modulation and power of ODT1, switch on and ramp up power of ODT2&quot;&quot;&quot;\\nCDT2_Switch.go_high(t)\\nramp_on_arm_2(t + 1, evap_1_arm_2_ramp_duration, evap_1_arm_2_start_pow, evap_1_arm_2_final_pow)\\nt += ramp_modulation_and_power(t, evap_1_arm_1_mod_ramp_duration, evap_1_arm_1_pow_ramp_duration, evap_1_arm_1_mod_depth_initial,\\n                               evap_1_arm_1_mod_depth_final, evap_1_arm_1_start_pow, evap_1_arm_1_final_pow, evap_1_rate_constant_1, \\n                               evap_1_rate_constant_2, evap_1_mod_ramp_trunc_value, evap_1_pow_ramp_trunc_value)\\n\\n&quot;&quot;&quot;EVAPORATION 2&quot;&quot;&quot;\\n&quot;&quot;&quot;Evaporation in the crossed ODT: ramp down the power of ODT1 &amp; ODT2 simultaneously&quot;&quot;&quot;\\n# t += odt_hold_time_3\\nt += ramp_cODT(t, evap_2_ramp_duration, evap_2_arm_1_start_pow, evap_2_arm_1_final_pow, evap_2_arm_2_start_pow, evap_2_arm_2_final_pow,\\n               evap_2_rate_constant_1, evap_2_rate_constant_2, evap_2_ramp_trunc_value)\\n\\n&quot;&quot;&quot;EVAPORATION 3&quot;&quot;&quot;\\n&quot;&quot;&quot;Ramp back on modulation and power of ODT1&quot;&quot;&quot;\\n# t += ramp_modulation_and_power(t, evap_3_ramp_duration, evap_3_ramp_duration, evap_3_arm_1_mod_depth_initial, evap_3_arm_1_mod_depth_final,\\n#                                evap_3_arm_1_start_pow, evap_3_arm_1_final_pow, evap_3_rate_constant_1, evap_3_rate_constant_2,\\n#                                evap_3_ramp_trunc_value, evap_3_ramp_trunc_value)\\n\\n&quot;&quot;&quot; Recompress in cODT to measure lifetime &quot;&quot;&quot;\\n# t1 = t\\n# ramp_on_arm_1(t1, recomp_ramp_duration, recomp_ramp_pow_ini_arm_1, recomp_ramp_pow_fin_arm_1)\\n# t += ramp_on_arm_2(t1, recomp_ramp_duration, recomp_ramp_pow_ini_arm_2, recomp_ramp_pow_fin_arm_2)\\n\\nt += odt_hold_time_4\\n\\n&quot;&quot;&quot; ODT blinking for measuring the trapping frequencies &quot;&quot;&quot;\\n# CDT1_Switch.go_low(t)\\n# CDT2_Switch.go_low(t)\\n# t += blink_off_time\\n# CDT1_Switch.go_high(t)\\n# CDT2_Switch.go_high(t)\\n# t += blink_on_time\\n\\n&quot;&quot;&quot; Time of Flight &quot;&quot;&quot;\\nif TOF_free &lt; 12e-3:\\n    set_offset_mag_field(t, x_offset_img, y_offset_img, z_offset_img)\\n    Imaging_Shutter.go_high(t)\\n    t += 20e-3\\n    CDT1_Switch.go_low(t)\\n    CDT2_Switch.go_low(t)\\n    t += 20e-6 # Additional wait to ensure all other beams are off when imaging beam is on so as to not have light shifts\\n    t += TOF_free\\nelse:\\n    CDT1_Switch.go_low(t)\\n    CDT2_Switch.go_low(t)\\n    t += 5e-3\\n    set_offset_mag_field(t, x_offset_img, y_offset_img, z_offset_img)\\n    Imaging_Shutter.go_high(t)\\n    t += TOF_free - 5e-3\\n\\n\\&#x27;\\&#x27;\\&#x27;first image: with atoms\\&#x27;\\&#x27;\\&#x27;\\nMOT_3D_Camera.expose(t, imaging_method, \\&#x27;atoms\\&#x27;, trigger_duration = mot_3d_camera_trigger_duration)\\n# ODT_1_Axis_Camera.expose(t, imaging_method, \\&#x27;atoms\\&#x27;, trigger_duration = odt_axis_camera_trigger_duration)\\n# ODT_2_Axis_Camera.expose(t, imaging_method, \\&#x27;atoms\\&#x27;, trigger_duration = odt_axis_camera_trigger_duration)\\n\\nt += pulse_delay\\nImaging_RF_Switch.go_high(t)\\nImaging_RF_Switch.go_low(t + imaging_pulse_duration)   ## first imaging pulse with duration set by \\&#x27;imaging_pulse_duration\\&#x27;\\n\\nt += wait_time_between_images   ## wait time between images\\n\\n\\&#x27;\\&#x27;\\&#x27;second image: without atoms\\&#x27;\\&#x27;\\&#x27;\\nMOT_3D_Camera.expose(t, imaging_method, \\&#x27;background\\&#x27;, trigger_duration = mot_3d_camera_trigger_duration)\\n# ODT_1_Axis_Camera.expose(t, imaging_method, \\&#x27;background\\&#x27;, trigger_duration = odt_axis_camera_trigger_duration)\\n# ODT_2_Axis_Camera.expose(t, imaging_method, \\&#x27;background\\&#x27;, trigger_duration = odt_axis_camera_trigger_duration)\\n\\nt += pulse_delay\\nImaging_RF_Switch.go_high(t)\\nImaging_RF_Switch.go_low(t + imaging_pulse_duration)   ## second imaging pulse with duration set by \\&#x27;imaging_pulse_duration\\&#x27;\\n\\nt += wait_time_between_images    ## wait time between images\\n\\n\\&#x27;\\&#x27;\\&#x27;third image: dark\\&#x27;\\&#x27;\\&#x27;\\nMOT_3D_Camera.expose(t, imaging_method, \\&#x27;dark\\&#x27;, trigger_duration = mot_3d_camera_trigger_duration)\\n# ODT_1_Axis_Camera.expose(t, imaging_method, \\&#x27;dark\\&#x27;, trigger_duration = odt_axis_camera_trigger_duration)\\n# ODT_2_Axis_Camera.expose(t, imaging_method, \\&#x27;dark\\&#x27;, trigger_duration = odt_axis_camera_trigger_duration)\\n\\nMOT_CompX_Coil_Switch.go_low(t)\\nMOT_CompY_Coil_Switch.go_low(t)\\nMOT_CompZ_Coil_Switch.go_low(t)\\nt += 100e-3\\n\\nImaging_Shutter.go_low(t+1e-3)\\nt = reinitialise(t)\\nt += 100e-3\\n\\nstop(t)\\n\\n########## Deprecated ############\\n\\n&quot;&quot;&quot;EVAPORATION 1&quot;&quot;&quot;\\n&quot;&quot;&quot;Ramp down modulation and power of ODT1 exponentially, switch on and ramp up power of ODT2&quot;&quot;&quot;\\n# t += lin_ramp_modulation_and_power(t, evap_1_ramp_duration_1, mod_depth_1_ini, mod_depth_1_fin, evap_1_start_pow_1, evap_1_final_pow_1)\\n# t += lin_ramp_modulation_and_power(t, evap_1_ramp_duration_2, mod_depth_2_ini, mod_depth_2_fin, evap_1_start_pow_2, evap_1_final_pow_2)\\n# t += lin_ramp_modulation_and_power(t, evap_1_ramp_duration_3, mod_depth_3_ini, mod_depth_3_fin, evap_1_start_pow_3, evap_1_final_pow_3)\\n# CDT2_Switch.go_high(t)\\n# t0 = t \\n# ramp_on_arm_2(t0, evap_1_ramp_duration_4, pow_ini_arm_2, pow_fin_arm_2)\\n# t += lin_ramp_modulation_and_power(t0, evap_1_ramp_duration_4, mod_depth_4_ini, mod_depth_4_fin, evap_1_start_pow_4, evap_1_final_pow_4)\\n\\n&quot;&quot;&quot;EVAPORATION 2&quot;&quot;&quot;\\n&quot;&quot;&quot;evaporation in the ODT1: ramp down the power of ODT1 only&quot;&quot;&quot;\\n# t += lin_ramp_arm_1(t, evap_2_ramp_duration_1, evap_2_start_pow_1, evap_2_final_pow_1)\\n\\n&quot;&quot;&quot;EVAPORATION 3&quot;&quot;&quot;\\n&quot;&quot;&quot;evaporation in the CDT: ramp down the power of ODT1 &amp; ODT2 simultaneously&quot;&quot;&quot;\\n# t += odt_hold_time_3\\n# t += ramp_cODT(t, evap_3_ramp_duration, evap_3_arm_1_start_pow, evap_3_arm_1_final_pow, evap_3_arm_2_start_pow, evap_3_arm_2_final_pow, evap_3_rate_constant_1, evap_3_rate_constant_2, evap_3_truncation_value)\\n\\n&quot;&quot;&quot;EVAPORATION 4&quot;&quot;&quot;\\n&quot;&quot;&quot;ramp back on modulation and power of ODT1&quot;&quot;&quot;\\n# t += lin_ramp_modulation_and_power(t, evap_ramp_4_duration, mod_depth_on_initial, mod_depth_on_final, evap_4_arm_1_start_pow, evap_4_arm_1_final_pow)\\n\\n########## Deprecated ############&#x27;],\n",
       "      dtype=&#x27;&lt;U7544&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_markers</span></div><div class='xr-var-dims'>(fileNum, phony_dim_1)</div><div class='xr-var-dtype'>[(&#x27;label&#x27;, &#x27;S256&#x27;), (&#x27;time&#x27;, &#x27;&lt;f8&#x27;), (&#x27;color&#x27;, &#x27;&lt;i4&#x27;, (1, 3))]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 0), meta=np.ndarray&gt;</div><input id='attrs-96366a15-b8a7-4819-a1af-91c14a407a87' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-96366a15-b8a7-4819-a1af-91c14a407a87' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d986513c-da4d-44ce-8e39-d31ef1e1f487' class='xr-var-data-in' type='checkbox'><label for='data-d986513c-da4d-44ce-8e39-d31ef1e1f487' 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'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 0 B </td>\n",
       "                        <td> 0 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1, 0) </td>\n",
       "                        <td> (1, 0) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> [('label', 'S256'), ('time', '<f8'), ('color', '<i4', (1, 3))] numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        \n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>waits</span></div><div class='xr-var-dims'>(fileNum, phony_dim_1)</div><div class='xr-var-dtype'>[(&#x27;label&#x27;, &#x27;S256&#x27;), (&#x27;time&#x27;, &#x27;&lt;f8&#x27;), (&#x27;timeout&#x27;, &#x27;&lt;f8&#x27;)]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 0), meta=np.ndarray&gt;</div><input id='attrs-229d9531-b917-48a2-8c2c-d33d13f0c3aa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-229d9531-b917-48a2-8c2c-d33d13f0c3aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a30f2d1-a3a8-4f41-9e11-d9480b1e4980' class='xr-var-data-in' type='checkbox'><label for='data-7a30f2d1-a3a8-4f41-9e11-d9480b1e4980' 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'><dt><span>wait_monitor_acquisition_connection :</span></dt><dd>internal</dd><dt><span>wait_monitor_acquisition_device :</span></dt><dd>prawn_internal_wait_monitor_outputs</dd><dt><span>wait_monitor_timeout_connection :</span></dt><dd>internal</dd><dt><span>wait_monitor_timeout_device :</span></dt><dd>prawn_internal_wait_monitor_outputs</dd><dt><span>wait_monitor_timeout_trigger_type :</span></dt><dd>rising</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 0 B </td>\n",
       "                        <td> 0 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1, 0) </td>\n",
       "                        <td> (1, 0) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> [('label', 'S256'), ('time', '<f8'), ('timeout', '<f8')] numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        \n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-8a09c761-42d2-4d22-9690-2553848a07ba' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8a09c761-42d2-4d22-9690-2553848a07ba' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-44b83dfc-4a6d-432c-b8e7-61cdcc75a383' class='xr-section-summary-in' type='checkbox'  checked><label for='section-44b83dfc-4a6d-432c-b8e7-61cdcc75a383' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>n_runs :</span></dt><dd>12</dd><dt><span>run number :</span></dt><dd>11</dd><dt><span>run time :</span></dt><dd>20230424T183333</dd><dt><span>script_basename :</span></dt><dd>Evaporative_Cooling</dd><dt><span>sequence_date :</span></dt><dd>2023-04-24</dd><dt><span>sequence_id :</span></dt><dd>20230424T183145_Evaporative_Cooling</dd><dt><span>sequence_index :</span></dt><dd>13</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:           (fileNum: 1, phony_dim_0: 59, phony_dim_1: 0)\n",
       "Dimensions without coordinates: fileNum, phony_dim_0, phony_dim_1\n",
       "Data variables:\n",
       "    connection table  (fileNum, phony_dim_0) [('name', 'S256'), ('class', 'S256'), ('parent', 'S256'), ('parent port', 'S256'), ('unit conversion class', 'S256'), ('unit conversion params', 'O'), ('BLACS_connection', 'S9'), ('properties', 'O')] dask.array<chunksize=(1, 59), meta=np.ndarray>\n",
       "    script            (fileNum) <U7544 'from labscript import *\\nfrom labscri...\n",
       "    time_markers      (fileNum, phony_dim_1) [('label', 'S256'), ('time', '<f8'), ('color', '<i4', (1, 3))] dask.array<chunksize=(1, 0), meta=np.ndarray>\n",
       "    waits             (fileNum, phony_dim_1) [('label', 'S256'), ('time', '<f8'), ('timeout', '<f8')] dask.array<chunksize=(1, 0), meta=np.ndarray>\n",
       "Attributes:\n",
       "    n_runs:           12\n",
       "    run number:       11\n",
       "    run time:         20230424T183333\n",
       "    script_basename:  Evaporative_Cooling\n",
       "    sequence_date:    2023-04-24\n",
       "    sequence_id:      20230424T183145_Evaporative_Cooling\n",
       "    sequence_index:   13"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xr.open_mfdataset(filePath, \n",
    "            concat_dim=\"fileNum\", \n",
    "            combine=\"nested\", \n",
    "            engine=\"h5netcdf\", \n",
    "            phony_dims=\"access\", \n",
    "            parallel=True, )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['F:',\n",
       " 'Jianshun',\n",
       " 'analyseScript',\n",
       " 'testData',\n",
       " '0002',\n",
       " '2023-04-21_0002_Evaporative_Cooling_0.h5']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = 'F:\\\\Jianshun/analyseScript/testData/0002/2023-04-21_0002_Evaporative_Cooling_0.h5'\n",
    "a.replace('\\\\', '/').split('/')[-1].split('_')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate an plot OD images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# imageAnalyser.center = (960, 1040)\n",
    "# imageAnalyser.span = (100, 100)\n",
    "# imageAnalyser.fraction = (0.1, 0.1)\n",
    "\n",
    "imageAnalyser.center = (960, 875)\n",
    "imageAnalyser.span = (300, 300)\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",
    "dataSet_cropOD.plot.pcolormesh(cmap='jet', vmin=0, col=scanAxis[0], row=scanAxis[1])\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Do a 2D two-peak gaussian fit to the OD images"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Do the fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "f:\\Jianshun\\analyseScript\\Analyser\\FitAnalyser.py:86: RuntimeWarning: invalid value encountered in power\n",
      "  res = (1- ((x-centerx)/(sigmax))**2 - ((y-centery)/(sigmay))**2)**(3 / 2)\n"
     ]
    }
   ],
   "source": [
    "from Analyser.FitAnalyser import ThomasFermi2dModel, DensityProfileBEC2dModel, polylog2_2d\n",
    "\n",
    "fitModel = DensityProfileBEC2dModel()\n",
    "# fitModel = ThomasFermi2dModel()\n",
    "\n",
    "fitAnalyser = FitAnalyser(fitModel, fitDim=2)\n",
    "\n",
    "# fitAnalyser = FitAnalyser(\"Gaussian-2D\", fitDim=2)\n",
    "\n",
    "# dataSet_cropOD = dataSet_cropOD.chunk((1,1,100,100))\n",
    "\n",
    "params = fitAnalyser.guess(dataSet_cropOD, guess_kwargs=dict(pureBECThreshold=0.3), dask=\"parallelized\")\n",
    "fitResult = fitAnalyser.fit(dataSet_cropOD, params).load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table><tr><th> name </th><th> value </th><th> initial value </th><th> min </th><th> max </th><th> vary </th><th> expression </th></tr><tr><td> BEC_amplitude </td><td>  366.888620 </td><td> None </td><td>  0.00000000 </td><td>         inf </td><td> True </td><td>  </td></tr><tr><td> thermal_amplitude </td><td>  0.00000000 </td><td> None </td><td>  0.00000000 </td><td>         inf </td><td> True </td><td>  </td></tr><tr><td> BEC_centerx </td><td>  152.087577 </td><td> None </td><td>        -inf </td><td>         inf </td><td> True </td><td>  </td></tr><tr><td> BEC_centery </td><td>  156.309927 </td><td> None </td><td>        -inf </td><td>         inf </td><td> True </td><td>  </td></tr><tr><td> thermal_centerx </td><td>  169.884333 </td><td> None </td><td>        -inf </td><td>         inf </td><td> True </td><td>  </td></tr><tr><td> thermal_centery </td><td>  157.547034 </td><td> None </td><td>        -inf </td><td>         inf </td><td> True </td><td>  </td></tr><tr><td> BEC_sigmax </td><td>  1.63603577 </td><td> None </td><td>  0.00000000 </td><td>         inf </td><td> True </td><td>  </td></tr><tr><td> BEC_sigmay </td><td>  3.42894901 </td><td> None </td><td>  0.00000000 </td><td>         inf </td><td> True </td><td>  </td></tr><tr><td> thermal_sigmax </td><td>  158.415970 </td><td> None </td><td>  0.00000000 </td><td>         inf </td><td> True </td><td>  </td></tr><tr><td> thermal_sigmay </td><td>  190.099163 </td><td> None </td><td>        -inf </td><td>         inf </td><td> False </td><td> thermalAspectRatio * thermal_sigmax </td></tr><tr><td> thermalAspectRatio </td><td>  1.20000000 </td><td> None </td><td>  0.80000000 </td><td>  1.20000000 </td><td> True </td><td>  </td></tr><tr><td> condensate_fraction </td><td>  1.00000000 </td><td> None </td><td>        -inf </td><td>         inf </td><td> False </td><td> BEC_amplitude / (BEC_amplitude + thermal_amplitude) </td></tr></table>"
      ],
      "text/plain": [
       "Parameters([('BEC_amplitude', <Parameter 'BEC_amplitude', value=366.8886196688306, bounds=[0:inf]>), ('thermal_amplitude', <Parameter 'thermal_amplitude', value=0, bounds=[0:inf]>), ('BEC_centerx', <Parameter 'BEC_centerx', value=152.08757708603804, bounds=[-inf:inf]>), ('BEC_centery', <Parameter 'BEC_centery', value=156.30992724959418, bounds=[-inf:inf]>), ('thermal_centerx', <Parameter 'thermal_centerx', value=169.88433327212883, bounds=[-inf:inf]>), ('thermal_centery', <Parameter 'thermal_centery', value=157.5470340720574, bounds=[-inf:inf]>), ('BEC_sigmax', <Parameter 'BEC_sigmax', value=1.636035766171183, bounds=[0:inf]>), ('BEC_sigmay', <Parameter 'BEC_sigmay', value=3.4289490138791647, bounds=[0:inf]>), ('thermal_sigmax', <Parameter 'thermal_sigmax', value=158.41596952074627, bounds=[0:inf]>), ('thermal_sigmay', <Parameter 'thermal_sigmay', value=190.09916342489552, bounds=[-inf:inf], expr='thermalAspectRatio * thermal_sigmax'>), ('thermalAspectRatio', <Parameter 'thermalAspectRatio', value=1.2, bounds=[0.8:1.2]>), ('condensate_fraction', <Parameter 'condensate_fraction', value=1.0, bounds=[-inf:inf], expr='BEC_amplitude / (BEC_amplitude + thermal_amplitude)'>)])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params.compute().item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x1e0e84006d0>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fitCurve = fitAnalyser.eval(fitResult, x=np.arange(300), y=np.arange(300), dask=\"parallelized\").load()\n",
    "\n",
    "fitCurve.plot.pcolormesh(cmap='jet', vmin=0, col=scanAxis[0], row=scanAxis[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "fitModel2 = Polylog22dModel(prefix='thermal_')\n",
    "fitAnalyser2 = FitAnalyser(fitModel2, fitDim=2)\n",
    "fitCurve2 = fitAnalyser2.eval(fitResult, x=np.arange(100), y=np.arange(100), dask=\"parallelized\").load()\n",
    "\n",
    "fitModel3 = ThomasFermi2dModel(prefix='BEC_')\n",
    "fitAnalyser3 = FitAnalyser(fitModel3, fitDim=2)\n",
    "fitCurve3 = fitAnalyser3.eval(fitResult, x=np.arange(100), y=np.arange(100), dask=\"parallelized\").load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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": [
    "fig = plt.figure()\n",
    "ax = fig.gca()\n",
    "\n",
    "dataSet_cropOD.sum(dim='x').plot(ax=ax, col=scanAxis[0], row=scanAxis[1])\n",
    "fitCurve.sum(dim='x').plot(ax=ax, col=scanAxis[0], row=scanAxis[1])\n",
    "fitCurve2.sum(dim='x').plot(ax=ax, col=scanAxis[0], row=scanAxis[1])\n",
    "fitCurve3.sum(dim='x').plot(ax=ax, col=scanAxis[0], row=scanAxis[1])\n",
    "\n",
    "plt.show()\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.gca()\n",
    "\n",
    "dataSet_cropOD.sum(dim='y').plot(ax=ax, col=scanAxis[0], row=scanAxis[1])\n",
    "fitCurve.sum(dim='y').plot(ax=ax, col=scanAxis[0], row=scanAxis[1])\n",
    "fitCurve2.sum(dim='y').plot(ax=ax, col=scanAxis[0], row=scanAxis[1])\n",
    "fitCurve3.sum(dim='y').plot(ax=ax, col=scanAxis[0], row=scanAxis[1])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\data\\AppData\\Roaming\\Python\\Python39\\site-packages\\numpy\\lib\\function_base.py:2246: RuntimeWarning: invalid value encountered in _get_fit_full_result_single (vectorized)\n",
      "  outputs = ufunc(*inputs)\n"
     ]
    }
   ],
   "source": [
    "value = fitAnalyser.get_fit_full_result(fitResult)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       ".xr-attrs dd {\n",
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       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
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       "\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",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:              ()\n",
       "Data variables:\n",
       "    BEC_amplitude        object 0.0+/-nan\n",
       "    thermal_amplitude    object 2104.548431645919+/-nan\n",
       "    BEC_centerx          object 146.94301032591366+/-nan\n",
       "    BEC_centery          object 147.47224593536436+/-nan\n",
       "    thermal_centerx      object 146.27287010988167+/-nan\n",
       "    thermal_centery      object 148.78153517037947+/-nan\n",
       "    BEC_sigmax           object 17.155488681677085+/-nan\n",
       "    BEC_sigmay           object 18.315601451967396+/-nan\n",
       "    thermal_sigmax       object 42.999686622150065+/-nan\n",
       "    thermal_sigmay       object 51.599623946580074+/-nan\n",
       "    thermalAspectRatio   object 1.2+/-nan\n",
       "    condensate_fraction  object 0.0+/-nan\n",
       "Attributes:\n",
       "    IMAGE_SUBCLASS:       IMAGE_GRAYSCALE\n",
       "    IMAGE_VERSION:        1.2\n",
       "    IMAGE_WHITE_IS_ZERO:  0\n",
       "    x_start:              810\n",
       "    x_end:                1110\n",
       "    y_end:                1025\n",
       "    y_start:              725\n",
       "    x_center:             960\n",
       "    y_center:             875\n",
       "    x_span:               300\n",
       "    y_span:               300</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-53523a01-2c07-4165-91ae-60ce98257240' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-53523a01-2c07-4165-91ae-60ce98257240' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-448573c2-6f7b-479e-ae18-5c693d5ebbdb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-448573c2-6f7b-479e-ae18-5c693d5ebbdb' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-f81fa7c4-b59b-456c-929b-d5fdf0999ba7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f81fa7c4-b59b-456c-929b-d5fdf0999ba7' class='xr-section-summary' >Data variables: <span>(12)</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>BEC_amplitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0.0+/-nan</div><input id='attrs-ae17e55d-accd-49e8-9a73-373506561892' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ae17e55d-accd-49e8-9a73-373506561892' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d309f58-8d37-475d-9462-884f4ad37e77' class='xr-var-data-in' type='checkbox'><label for='data-4d309f58-8d37-475d-9462-884f4ad37e77' 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(0.0+/-nan, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thermal_amplitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2104.548431645919+/-nan</div><input id='attrs-ce8230fc-40ed-4545-8712-ec500a361b66' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ce8230fc-40ed-4545-8712-ec500a361b66' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4d45705-76ae-4681-a822-8d9af1dbbece' class='xr-var-data-in' type='checkbox'><label for='data-c4d45705-76ae-4681-a822-8d9af1dbbece' 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(2104.548431645919+/-nan, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>BEC_centerx</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>146.94301032591366+/-nan</div><input id='attrs-d3679e45-9660-4dc8-9900-6ff9b96700a5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d3679e45-9660-4dc8-9900-6ff9b96700a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d54cdcf-9b44-4720-b151-cdd4cfffd6be' class='xr-var-data-in' type='checkbox'><label for='data-2d54cdcf-9b44-4720-b151-cdd4cfffd6be' 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(146.94301032591366+/-nan, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>BEC_centery</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>147.47224593536436+/-nan</div><input id='attrs-bdd1b243-37f9-4c5b-b281-cad8518f539e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bdd1b243-37f9-4c5b-b281-cad8518f539e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9fe3035-bc63-408d-93e5-50cffe8d1a60' class='xr-var-data-in' type='checkbox'><label for='data-d9fe3035-bc63-408d-93e5-50cffe8d1a60' 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(147.47224593536436+/-nan, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thermal_centerx</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>146.27287010988167+/-nan</div><input id='attrs-7fb7dc26-82ad-49eb-83ae-bafb0b3f876a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7fb7dc26-82ad-49eb-83ae-bafb0b3f876a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-08bdb556-ba2d-473d-8b0f-8955e6e3124e' class='xr-var-data-in' type='checkbox'><label for='data-08bdb556-ba2d-473d-8b0f-8955e6e3124e' 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(146.27287010988167+/-nan, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thermal_centery</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>148.78153517037947+/-nan</div><input id='attrs-3da43036-1dea-4a3f-a01f-e5389fa02de0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3da43036-1dea-4a3f-a01f-e5389fa02de0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b9d40d4-ace6-44fa-8f39-f640f11e6976' class='xr-var-data-in' type='checkbox'><label for='data-4b9d40d4-ace6-44fa-8f39-f640f11e6976' 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(148.78153517037947+/-nan, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>BEC_sigmax</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>17.155488681677085+/-nan</div><input id='attrs-783a8786-d131-48bc-b94c-6301659c7cf1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-783a8786-d131-48bc-b94c-6301659c7cf1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba8ac7d5-06cf-43de-b1f3-06cdd30e4412' class='xr-var-data-in' type='checkbox'><label for='data-ba8ac7d5-06cf-43de-b1f3-06cdd30e4412' 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(17.155488681677085+/-nan, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>BEC_sigmay</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>18.315601451967396+/-nan</div><input id='attrs-1763da2e-7acf-4050-9254-12f20f086c8f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1763da2e-7acf-4050-9254-12f20f086c8f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f63fc78c-630c-49b7-8e38-508a140bfc7a' class='xr-var-data-in' type='checkbox'><label for='data-f63fc78c-630c-49b7-8e38-508a140bfc7a' 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(18.315601451967396+/-nan, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thermal_sigmax</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>42.999686622150065+/-nan</div><input id='attrs-853c9056-d1e5-4cd9-9651-c55696182c46' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-853c9056-d1e5-4cd9-9651-c55696182c46' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c7da350c-983c-4a4a-8c2b-3e3088dc3c45' class='xr-var-data-in' type='checkbox'><label for='data-c7da350c-983c-4a4a-8c2b-3e3088dc3c45' 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(42.999686622150065+/-nan, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thermal_sigmay</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>51.599623946580074+/-nan</div><input id='attrs-e39f9e6f-232b-4100-818e-8c04aa7ef1eb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e39f9e6f-232b-4100-818e-8c04aa7ef1eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-341b0d11-b102-48d6-bfce-9c50de3a8e63' class='xr-var-data-in' type='checkbox'><label for='data-341b0d11-b102-48d6-bfce-9c50de3a8e63' 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(51.599623946580074+/-nan, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thermalAspectRatio</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1.2+/-nan</div><input id='attrs-b22b238b-7313-415e-81fa-884f46a6ae01' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b22b238b-7313-415e-81fa-884f46a6ae01' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-692f659a-4ea0-4826-88fa-a9d75ebaffa3' class='xr-var-data-in' type='checkbox'><label for='data-692f659a-4ea0-4826-88fa-a9d75ebaffa3' 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(1.2+/-nan, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>condensate_fraction</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0.0+/-nan</div><input id='attrs-0114f61a-f370-4847-88b5-e2a6f07684d9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0114f61a-f370-4847-88b5-e2a6f07684d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3f12cda-3bd6-4dbd-92ca-e121db995872' class='xr-var-data-in' type='checkbox'><label for='data-d3f12cda-3bd6-4dbd-92ca-e121db995872' 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(0.0+/-nan, dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cf8bd205-992c-4301-b898-328e8b399d33' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cf8bd205-992c-4301-b898-328e8b399d33' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-8e60218d-87c4-4a98-9aad-43c0fba800f1' class='xr-section-summary-in' type='checkbox'  ><label for='section-8e60218d-87c4-4a98-9aad-43c0fba800f1' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>IMAGE_SUBCLASS :</span></dt><dd>IMAGE_GRAYSCALE</dd><dt><span>IMAGE_VERSION :</span></dt><dd>1.2</dd><dt><span>IMAGE_WHITE_IS_ZERO :</span></dt><dd>0</dd><dt><span>x_start :</span></dt><dd>810</dd><dt><span>x_end :</span></dt><dd>1110</dd><dt><span>y_end :</span></dt><dd>1025</dd><dt><span>y_start :</span></dt><dd>725</dd><dt><span>x_center :</span></dt><dd>960</dd><dt><span>y_center :</span></dt><dd>875</dd><dt><span>x_span :</span></dt><dd>300</dd><dt><span>y_span :</span></dt><dd>300</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:              ()\n",
       "Data variables:\n",
       "    BEC_amplitude        object 0.0+/-nan\n",
       "    thermal_amplitude    object 2104.548431645919+/-nan\n",
       "    BEC_centerx          object 146.94301032591366+/-nan\n",
       "    BEC_centery          object 147.47224593536436+/-nan\n",
       "    thermal_centerx      object 146.27287010988167+/-nan\n",
       "    thermal_centery      object 148.78153517037947+/-nan\n",
       "    BEC_sigmax           object 17.155488681677085+/-nan\n",
       "    BEC_sigmay           object 18.315601451967396+/-nan\n",
       "    thermal_sigmax       object 42.999686622150065+/-nan\n",
       "    thermal_sigmay       object 51.599623946580074+/-nan\n",
       "    thermalAspectRatio   object 1.2+/-nan\n",
       "    condensate_fraction  object 0.0+/-nan\n",
       "Attributes:\n",
       "    IMAGE_SUBCLASS:       IMAGE_GRAYSCALE\n",
       "    IMAGE_VERSION:        1.2\n",
       "    IMAGE_WHITE_IS_ZERO:  0\n",
       "    x_start:              810\n",
       "    x_end:                1110\n",
       "    y_end:                1025\n",
       "    y_start:              725\n",
       "    x_center:             960\n",
       "    y_center:             875\n",
       "    x_span:               300\n",
       "    y_span:               300"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "unable to infer dtype on variable 'OD'; xarray cannot serialize arbitrary Python objects",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32mf:\\Jianshun\\analyseScript\\test.ipynb Cell 25\u001b[0m in \u001b[0;36m1\n\u001b[1;32m----> <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/test.ipynb#Y216sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m fitResult\u001b[39m.\u001b[39;49mto_netcdf(\u001b[39m\"\u001b[39;49m\u001b[39msaved_on_disk.nc\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\core\\dataarray.py:3959\u001b[0m, in \u001b[0;36mDataArray.to_netcdf\u001b[1;34m(self, path, mode, format, group, engine, encoding, unlimited_dims, compute, invalid_netcdf)\u001b[0m\n\u001b[0;32m   3955\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m   3956\u001b[0m     \u001b[39m# No problems with the name - so we're fine!\u001b[39;00m\n\u001b[0;32m   3957\u001b[0m     dataset \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mto_dataset()\n\u001b[1;32m-> 3959\u001b[0m \u001b[39mreturn\u001b[39;00m to_netcdf(  \u001b[39m# type: ignore  # mypy cannot resolve the overloads:(\u001b[39;49;00m\n\u001b[0;32m   3960\u001b[0m     dataset,\n\u001b[0;32m   3961\u001b[0m     path,\n\u001b[0;32m   3962\u001b[0m     mode\u001b[39m=\u001b[39;49mmode,\n\u001b[0;32m   3963\u001b[0m     \u001b[39mformat\u001b[39;49m\u001b[39m=\u001b[39;49m\u001b[39mformat\u001b[39;49m,\n\u001b[0;32m   3964\u001b[0m     group\u001b[39m=\u001b[39;49mgroup,\n\u001b[0;32m   3965\u001b[0m     engine\u001b[39m=\u001b[39;49mengine,\n\u001b[0;32m   3966\u001b[0m     encoding\u001b[39m=\u001b[39;49mencoding,\n\u001b[0;32m   3967\u001b[0m     unlimited_dims\u001b[39m=\u001b[39;49munlimited_dims,\n\u001b[0;32m   3968\u001b[0m     compute\u001b[39m=\u001b[39;49mcompute,\n\u001b[0;32m   3969\u001b[0m     multifile\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[0;32m   3970\u001b[0m     invalid_netcdf\u001b[39m=\u001b[39;49minvalid_netcdf,\n\u001b[0;32m   3971\u001b[0m )\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\backends\\api.py:1216\u001b[0m, in \u001b[0;36mto_netcdf\u001b[1;34m(dataset, path_or_file, mode, format, group, engine, encoding, unlimited_dims, compute, multifile, invalid_netcdf)\u001b[0m\n\u001b[0;32m   1211\u001b[0m \u001b[39m# TODO: figure out how to refactor this logic (here and in save_mfdataset)\u001b[39;00m\n\u001b[0;32m   1212\u001b[0m \u001b[39m# to avoid this mess of conditionals\u001b[39;00m\n\u001b[0;32m   1213\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m   1214\u001b[0m     \u001b[39m# TODO: allow this work (setting up the file for writing array data)\u001b[39;00m\n\u001b[0;32m   1215\u001b[0m     \u001b[39m# to be parallelized with dask\u001b[39;00m\n\u001b[1;32m-> 1216\u001b[0m     dump_to_store(\n\u001b[0;32m   1217\u001b[0m         dataset, store, writer, encoding\u001b[39m=\u001b[39;49mencoding, unlimited_dims\u001b[39m=\u001b[39;49munlimited_dims\n\u001b[0;32m   1218\u001b[0m     )\n\u001b[0;32m   1219\u001b[0m     \u001b[39mif\u001b[39;00m autoclose:\n\u001b[0;32m   1220\u001b[0m         store\u001b[39m.\u001b[39mclose()\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\backends\\api.py:1263\u001b[0m, in \u001b[0;36mdump_to_store\u001b[1;34m(dataset, store, writer, encoder, encoding, unlimited_dims)\u001b[0m\n\u001b[0;32m   1260\u001b[0m \u001b[39mif\u001b[39;00m encoder:\n\u001b[0;32m   1261\u001b[0m     variables, attrs \u001b[39m=\u001b[39m encoder(variables, attrs)\n\u001b[1;32m-> 1263\u001b[0m store\u001b[39m.\u001b[39;49mstore(variables, attrs, check_encoding, writer, unlimited_dims\u001b[39m=\u001b[39;49munlimited_dims)\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\backends\\common.py:269\u001b[0m, in \u001b[0;36mAbstractWritableDataStore.store\u001b[1;34m(self, variables, attributes, check_encoding_set, writer, unlimited_dims)\u001b[0m\n\u001b[0;32m    266\u001b[0m \u001b[39mif\u001b[39;00m writer \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m    267\u001b[0m     writer \u001b[39m=\u001b[39m ArrayWriter()\n\u001b[1;32m--> 269\u001b[0m variables, attributes \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mencode(variables, attributes)\n\u001b[0;32m    271\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mset_attributes(attributes)\n\u001b[0;32m    272\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mset_dimensions(variables, unlimited_dims\u001b[39m=\u001b[39munlimited_dims)\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\backends\\common.py:358\u001b[0m, in \u001b[0;36mWritableCFDataStore.encode\u001b[1;34m(self, variables, attributes)\u001b[0m\n\u001b[0;32m    355\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mencode\u001b[39m(\u001b[39mself\u001b[39m, variables, attributes):\n\u001b[0;32m    356\u001b[0m     \u001b[39m# All NetCDF files get CF encoded by default, without this attempting\u001b[39;00m\n\u001b[0;32m    357\u001b[0m     \u001b[39m# to write times, for example, would fail.\u001b[39;00m\n\u001b[1;32m--> 358\u001b[0m     variables, attributes \u001b[39m=\u001b[39m cf_encoder(variables, attributes)\n\u001b[0;32m    359\u001b[0m     variables \u001b[39m=\u001b[39m {k: \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mencode_variable(v) \u001b[39mfor\u001b[39;00m k, v \u001b[39min\u001b[39;00m variables\u001b[39m.\u001b[39mitems()}\n\u001b[0;32m    360\u001b[0m     attributes \u001b[39m=\u001b[39m {k: \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mencode_attribute(v) \u001b[39mfor\u001b[39;00m k, v \u001b[39min\u001b[39;00m attributes\u001b[39m.\u001b[39mitems()}\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\conventions.py:775\u001b[0m, in \u001b[0;36mcf_encoder\u001b[1;34m(variables, attributes)\u001b[0m\n\u001b[0;32m    772\u001b[0m \u001b[39m# add encoding for time bounds variables if present.\u001b[39;00m\n\u001b[0;32m    773\u001b[0m _update_bounds_encoding(variables)\n\u001b[1;32m--> 775\u001b[0m new_vars \u001b[39m=\u001b[39m {k: encode_cf_variable(v, name\u001b[39m=\u001b[39mk) \u001b[39mfor\u001b[39;00m k, v \u001b[39min\u001b[39;00m variables\u001b[39m.\u001b[39mitems()}\n\u001b[0;32m    777\u001b[0m \u001b[39m# Remove attrs from bounds variables (issue #2921)\u001b[39;00m\n\u001b[0;32m    778\u001b[0m \u001b[39mfor\u001b[39;00m var \u001b[39min\u001b[39;00m new_vars\u001b[39m.\u001b[39mvalues():\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\conventions.py:775\u001b[0m, in \u001b[0;36m<dictcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    772\u001b[0m \u001b[39m# add encoding for time bounds variables if present.\u001b[39;00m\n\u001b[0;32m    773\u001b[0m _update_bounds_encoding(variables)\n\u001b[1;32m--> 775\u001b[0m new_vars \u001b[39m=\u001b[39m {k: encode_cf_variable(v, name\u001b[39m=\u001b[39;49mk) \u001b[39mfor\u001b[39;00m k, v \u001b[39min\u001b[39;00m variables\u001b[39m.\u001b[39mitems()}\n\u001b[0;32m    777\u001b[0m \u001b[39m# Remove attrs from bounds variables (issue #2921)\u001b[39;00m\n\u001b[0;32m    778\u001b[0m \u001b[39mfor\u001b[39;00m var \u001b[39min\u001b[39;00m new_vars\u001b[39m.\u001b[39mvalues():\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\conventions.py:189\u001b[0m, in \u001b[0;36mencode_cf_variable\u001b[1;34m(var, needs_copy, name)\u001b[0m\n\u001b[0;32m    186\u001b[0m     var \u001b[39m=\u001b[39m coder\u001b[39m.\u001b[39mencode(var, name\u001b[39m=\u001b[39mname)\n\u001b[0;32m    188\u001b[0m \u001b[39m# TODO(kmuehlbauer): check if ensure_dtype_not_object can be moved to backends:\u001b[39;00m\n\u001b[1;32m--> 189\u001b[0m var \u001b[39m=\u001b[39m ensure_dtype_not_object(var, name\u001b[39m=\u001b[39;49mname)\n\u001b[0;32m    191\u001b[0m \u001b[39mfor\u001b[39;00m attr_name \u001b[39min\u001b[39;00m CF_RELATED_DATA:\n\u001b[0;32m    192\u001b[0m     pop_to(var\u001b[39m.\u001b[39mencoding, var\u001b[39m.\u001b[39mattrs, attr_name)\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\conventions.py:145\u001b[0m, in \u001b[0;36mensure_dtype_not_object\u001b[1;34m(var, name)\u001b[0m\n\u001b[0;32m    143\u001b[0m     data[missing] \u001b[39m=\u001b[39m fill_value\n\u001b[0;32m    144\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> 145\u001b[0m     data \u001b[39m=\u001b[39m _copy_with_dtype(data, dtype\u001b[39m=\u001b[39m_infer_dtype(data, name))\n\u001b[0;32m    147\u001b[0m \u001b[39massert\u001b[39;00m data\u001b[39m.\u001b[39mdtype\u001b[39m.\u001b[39mkind \u001b[39m!=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mO\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mor\u001b[39;00m data\u001b[39m.\u001b[39mdtype\u001b[39m.\u001b[39mmetadata\n\u001b[0;32m    148\u001b[0m var \u001b[39m=\u001b[39m Variable(dims, data, attrs, encoding, fastpath\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\conventions.py:77\u001b[0m, in \u001b[0;36m_infer_dtype\u001b[1;34m(array, name)\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[39mif\u001b[39;00m dtype\u001b[39m.\u001b[39mkind \u001b[39m!=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mO\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[0;32m     75\u001b[0m     \u001b[39mreturn\u001b[39;00m dtype\n\u001b[1;32m---> 77\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m     78\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39munable to infer dtype on variable \u001b[39m\u001b[39m{!r}\u001b[39;00m\u001b[39m; xarray \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m     79\u001b[0m     \u001b[39m\"\u001b[39m\u001b[39mcannot serialize arbitrary Python objects\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(name)\n\u001b[0;32m     80\u001b[0m )\n",
      "\u001b[1;31mValueError\u001b[0m: unable to infer dtype on variable 'OD'; xarray cannot serialize arbitrary Python objects"
     ]
    }
   ],
   "source": [
    "fitResult.to_netcdf(\"saved_on_disk.nc\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get the Ncount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Ncount = dataSet_crop.OD.sum(dim=(scanAxis[0], 'x', 'y'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Ncount.load()\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.gca()\n",
    "Ncount.plot(ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fitAnalyser = FitAnalyser(\"Lorentzian With Offset\")\n",
    "params = fitAnalyser.guess(Ncount, x='runs', dask=\"parallelized\", guess_kwargs=dict(negative=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fitResult = fitAnalyser.fit(Ncount, params, x='runs', dask=\"parallelized\")\n",
    "fitCurve = fitAnalyser.eval(fitResult, x=np.arange(40), dask=\"parallelized\").load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.gca()\n",
    "plt.errorbar([1], [1], yerr=[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fitCurve.plot.errorbar(yerr=fitCurve)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.ufunc(fitCurve)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# filePath = 'Z:/Dy_Lab/Data/Measurements/Experiments/DyBEC/BEC Stability Check/20230509-0007/*.csv'\n",
    "\n",
    "# filePath = np.sort(glob.glob(filePath))\n",
    "\n",
    "# read_csv_file(filePath, maxFileNum=5, csvEngine='pandas', csvKwargs=dict(header=[0,1], na_filter=False, index_col=0))\n",
    "# read_csv_file(filePath, csvEngine='dask')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "filePath = 'Z:/Dy_Lab/Data/Measurements/Experiments/DyBEC/BEC Stability Check/20230509-0007/*.csv'\n",
    "\n",
    "filePath = np.sort(glob.glob(filePath))\n",
    "\n",
    "data = np.empty(filePath.shape,dtype=object)\n",
    "\n",
    "i = 0\n",
    "for fp in filePath:\n",
    "    data_single = pd.read_csv(fp)\n",
    "    data_single = xr.Dataset.from_dataframe(data_single)\n",
    "    data_single = data_single.drop_isel(index=0)\n",
    "    # data_single = data_single.expand_dims(dim='runs')\n",
    "    data[i] = data_single\n",
    "    i = i + 1\n",
    "\n",
    "data = xr.concat(data, 'runs')\n",
    "\n",
    "data = data.assign_coords(dict(index=data.Time.isel(runs=0).astype(float))).rename(dict(index='time')).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "arm2_mean = data['Channel A'].mean(dim='runs')\n",
    "arm2_std = data['Channel A'].std(dim='runs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "arm2_mean.plot.errorbar(yerr=arm2_std, fmt='ob')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "arm2_std.plot.errorbar(fmt='ob')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Channel A'].sel(time=4.55, method='nearest').plot.errorbar(fmt='ob')\n",
    "\n",
    "plt.ylim([0, 0.15])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6417497231450753+/-0.01090681927109203"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(-ufloat(99.835,0.018) + ufloat(99.969,0.014))/15*1e3\n",
    "(-ufloat(99.835,0.018) + ufloat(100.994,0.008))/1.29/1.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6267995570321101+/-0.01750984307955913"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(ufloat(99.835,0.018) - ufloat(98.703,0.026))/1.29/1.4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6342746400885927+/-0.010314471766443609"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((ufloat(99.835,0.018) - ufloat(98.703,0.026))/1.29/1.4 + (-ufloat(99.835,0.018) + ufloat(100.994,0.008))/1.29/1.4) /2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.642+/-0.011\n",
      "0.627+/-0.018\n",
      "0.634+/-0.010\n",
      "0.0444+/-0.0007\n"
     ]
    }
   ],
   "source": [
    "a = (-ufloat(99.835,0.018) + ufloat(100.994,0.008))/1.29/1.4\n",
    "b = (ufloat(99.835,0.018) - ufloat(98.703,0.026))/1.29/1.4\n",
    "\n",
    "print(a)\n",
    "print(b)\n",
    "print((a+b)/2)\n",
    "print((a+b)/2 * (1.29-1.24)*1.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.637+/-0.011\n",
      "0.641+/-0.018\n",
      "0.639+/-0.010\n",
      "0.0447+/-0.0007\n"
     ]
    }
   ],
   "source": [
    "a = (-ufloat(99.969,0.018) + ufloat(101.120,0.008))/1.29/1.4\n",
    "b = (ufloat(99.969,0.018) - ufloat(98.811,0.026))/1.29/1.4\n",
    "\n",
    "print(a)\n",
    "print(b)\n",
    "print((a+b)/2)\n",
    "print((a+b)/2 * (1.29-1.24)*1.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0447+/-0.0007\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "file_extension": ".py",
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