diff --git a/AccordionLattice.ipynb b/AccordionLattice_20230801.ipynb
similarity index 97%
rename from AccordionLattice.ipynb
rename to AccordionLattice_20230801.ipynb
index 2ff37b1..a89c865 100644
--- a/AccordionLattice.ipynb
+++ b/AccordionLattice_20230801.ipynb
@@ -4050,12 +4050,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 126,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -5876,6 +5876,52 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "99.99999999999999 kHz\n",
+      "166.66666666666663 Hz\n"
+     ]
+    }
+   ],
+   "source": [
+    "dt = 5e-6\n",
+    "samplingFreq = 1/ dt\n",
+    "blockLength = 1200\n",
+    "df = samplingFreq / blockLength\n",
+    "print(str(samplingFreq / 2 / 1e3) + ' kHz')\n",
+    "print(str(df) + ' Hz')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.1 kHz\n",
+      "0.16666666666666666 Hz\n"
+     ]
+    }
+   ],
+   "source": [
+    "dt = 5e-3\n",
+    "samplingFreq = 1/ dt\n",
+    "blockLength = 1200\n",
+    "df = samplingFreq / blockLength\n",
+    "print(str(samplingFreq / 2 / 1e3) + ' kHz')\n",
+    "print(str(df) + ' Hz')"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/AccordionLattice_20230808.ipynb b/AccordionLattice_20230808.ipynb
new file mode 100644
index 0000000..a29500a
--- /dev/null
+++ b/AccordionLattice_20230808.ipynb
@@ -0,0 +1,2614 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Import supporting package"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import copy\n",
+    "import glob\n",
+    "from datetime import datetime\n",
+    "\n",
+    "# The package for data structure\n",
+    "import xarray as xr\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "# The packages for working with uncertainties\n",
+    "from uncertainties import ufloat\n",
+    "from uncertainties import unumpy as unp\n",
+    "from uncertainties import umath\n",
+    "\n",
+    "# The package for plotting\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.rcParams['font.size'] = 18 # Set the global font size\n",
+    "\n",
+    "# -------------- The modules written by us --------------\n",
+    "\n",
+    "# The packages for read data\n",
+    "from DataContainer.ReadData import read_hdf5_file, read_hdf5_global, read_hdf5_run_time, read_csv_file\n",
+    "\n",
+    "# The packages for data analysis\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",
+    "# Add errorbar plot to xarray package\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"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Start a client for parallel computing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "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 57804 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-dfee8bd5-35d5-11ee-960c-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://127.0.0.1:57804/status\" target=\"_blank\">http://127.0.0.1:57804/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;\">162c46da</p>\n",
+       "        <table style=\"width: 100%; text-align: left;\">\n",
+       "            <tr>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:57804/status\" target=\"_blank\">http://127.0.0.1:57804/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-b1db6959-375b-4ed2-9a77-02ce7ec59048</p>\n",
+       "            <table style=\"width: 100%; text-align: left;\">\n",
+       "                <tr>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Comm:</strong> tcp://127.0.0.1:57807\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://127.0.0.1:57804/status\" target=\"_blank\">http://127.0.0.1:57804/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://127.0.0.1:57846\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://127.0.0.1:57847/status\" target=\"_blank\">http://127.0.0.1:57847/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://127.0.0.1:57810\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-o0fweh8b\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://127.0.0.1:57840\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://127.0.0.1:57842/status\" target=\"_blank\">http://127.0.0.1:57842/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://127.0.0.1:57811\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-toa8_m6l\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://127.0.0.1:57834\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://127.0.0.1:57835/status\" target=\"_blank\">http://127.0.0.1:57835/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://127.0.0.1:57812\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-_6d2fpz3\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://127.0.0.1:57837\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://127.0.0.1:57838/status\" target=\"_blank\">http://127.0.0.1:57838/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://127.0.0.1:57813\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-jquysjj5\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://127.0.0.1:57849\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://127.0.0.1:57850/status\" target=\"_blank\">http://127.0.0.1:57850/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://127.0.0.1:57814\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-okg6p_4_\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://127.0.0.1:57841\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://127.0.0.1:57843/status\" target=\"_blank\">http://127.0.0.1:57843/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://127.0.0.1:57815\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-iszz7udd\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://127.0.0.1:57807' processes=6 threads=60, memory=55.88 GiB>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from dask.distributed import Client\n",
+    "client = Client(n_workers=6, threads_per_worker=10, processes=True, memory_limit='10GB')\n",
+    "client"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Load Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## With Modulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
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+       ".xr-wrap {\n",
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+       "\n",
+       ".xr-text-repr-fallback {\n",
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+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;CH1&#x27; (x: 6000000)&gt;\n",
+       "array([0.329, 0.317, 0.309, ..., 0.293, 0.305, 0.309])\n",
+       "Coordinates:\n",
+       "  * x        (x) float64 0.0 2e-06 4e-06 6e-06 8e-06 ... 12.0 12.0 12.0 12.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'CH1'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>x</span>: 6000000</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-cd69e8af-adb0-4d58-8d60-e88ea56db5b0' class='xr-array-in' type='checkbox' checked><label for='section-cd69e8af-adb0-4d58-8d60-e88ea56db5b0' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.329 0.317 0.309 0.305 0.309 0.317 ... 0.273 0.281 0.293 0.305 0.309</span></div><div class='xr-array-data'><pre>array([0.329, 0.317, 0.309, ..., 0.293, 0.305, 0.309])</pre></div></div></li><li class='xr-section-item'><input id='section-bc920d53-f2bd-40af-bc4d-8f591ba63893' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bc920d53-f2bd-40af-bc4d-8f591ba63893' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 2e-06 4e-06 ... 12.0 12.0 12.0</div><input id='attrs-acbf94cc-4894-45e9-8a54-c2bd6c8d627b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-acbf94cc-4894-45e9-8a54-c2bd6c8d627b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40334779-fbfa-4a07-a197-7deb87a48270' class='xr-var-data-in' type='checkbox'><label for='data-40334779-fbfa-4a07-a197-7deb87a48270' 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([0.000000e+00, 2.000000e-06, 4.000000e-06, ..., 1.199999e+01,\n",
+       "       1.200000e+01, 1.200000e+01])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-95913ac9-6209-44de-a908-d3343b013a35' class='xr-section-summary-in' type='checkbox'  ><label for='section-95913ac9-6209-44de-a908-d3343b013a35' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2e83bee0-b8fc-40ad-9a4f-f16a2f330b4a' class='xr-index-data-in' type='checkbox'/><label for='index-2e83bee0-b8fc-40ad-9a4f-f16a2f330b4a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([                  0.0,                 2e-06,\n",
+       "                              4e-06,                 6e-06,\n",
+       "                              8e-06, 9.999999999999999e-06,\n",
+       "                            1.2e-05,               1.4e-05,\n",
+       "                            1.6e-05,               1.8e-05,\n",
+       "              ...\n",
+       "                 11.999979999999999,             11.999982,\n",
+       "                          11.999984,             11.999986,\n",
+       "                          11.999988,    11.999989999999999,\n",
+       "                 11.999991999999999,             11.999994,\n",
+       "                          11.999996,             11.999998],\n",
+       "             dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=6000000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ba5d9fa6-1249-46a1-897b-98e029355046' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ba5d9fa6-1249-46a1-897b-98e029355046' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'CH1' (x: 6000000)>\n",
+       "array([0.329, 0.317, 0.309, ..., 0.293, 0.305, 0.309])\n",
+       "Coordinates:\n",
+       "  * x        (x) float64 0.0 2e-06 4e-06 6e-06 8e-06 ... 12.0 12.0 12.0 12.0"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filePath = r'F:\\Jianshun\\AccordionLatticeShorTermStability\\20230808\\withModulation.csv'\n",
+    "\n",
+    "data = read_csv_file(filePath, csvEngine='pandas', csvKwargs=dict(header=[0], skiprows=[1], encoding = \"ISO-8859-1\",))\n",
+    "dataWithModulation = xr.DataArray(\n",
+    "    data=data.CH1[0,:],\n",
+    "    dims=['x'],\n",
+    "    coords=dict(\n",
+    "        x=data.X[0,:].to_numpy() * 2e-6\n",
+    "    )\n",
+    ")\n",
+    "dataWithModulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dataWithModulation.plot.errorbar()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataWithModulation = dataWithModulation - dataWithModulation.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "ax = fig.gca()\n",
+    "\n",
+    "dataWithModulation.plot.errorbar(ax=ax)\n",
+    "# ax.set_xlim([0.5, 1])\n",
+    "# ax.set_ylim([0, 0.25])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataWithModulationFFT = fft(dataWithModulation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "ax = fig.gca()\n",
+    "\n",
+    "abs(dataWithModulationFFT).plot.errorbar(ax=ax, x='freq_x')\n",
+    "ax.set_xlim([0, 500])\n",
+    "# ax.set_ylim([0, 0.25])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Without Modulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;CH1&#x27; (x: 6000000)&gt;\n",
+       "array([0.501, 0.485, 0.473, ..., 0.501, 0.485, 0.477])\n",
+       "Coordinates:\n",
+       "  * x        (x) float64 0.0 2e-06 4e-06 6e-06 8e-06 ... 12.0 12.0 12.0 12.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'CH1'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>x</span>: 6000000</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-fe3719a9-f72c-43e4-a4f2-36d213e72f04' class='xr-array-in' type='checkbox' checked><label for='section-fe3719a9-f72c-43e4-a4f2-36d213e72f04' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.501 0.485 0.473 0.465 0.469 0.477 ... 0.509 0.513 0.501 0.485 0.477</span></div><div class='xr-array-data'><pre>array([0.501, 0.485, 0.473, ..., 0.501, 0.485, 0.477])</pre></div></div></li><li class='xr-section-item'><input id='section-5397a53a-6e8e-42d7-9c49-a622d357f6f6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5397a53a-6e8e-42d7-9c49-a622d357f6f6' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 2e-06 4e-06 ... 12.0 12.0 12.0</div><input id='attrs-424b853b-629f-4f96-ae98-8b9c32ea38de' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-424b853b-629f-4f96-ae98-8b9c32ea38de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3805d3f8-8130-48f7-a818-fedd5f92f304' class='xr-var-data-in' type='checkbox'><label for='data-3805d3f8-8130-48f7-a818-fedd5f92f304' 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([0.000000e+00, 2.000000e-06, 4.000000e-06, ..., 1.199999e+01,\n",
+       "       1.200000e+01, 1.200000e+01])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-21c608c9-9a05-41d1-b33b-fde7446732f7' class='xr-section-summary-in' type='checkbox'  ><label for='section-21c608c9-9a05-41d1-b33b-fde7446732f7' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c1452f30-9046-4fb4-9e30-f4bc2670a046' class='xr-index-data-in' type='checkbox'/><label for='index-c1452f30-9046-4fb4-9e30-f4bc2670a046' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([                  0.0,                 2e-06,\n",
+       "                              4e-06,                 6e-06,\n",
+       "                              8e-06, 9.999999999999999e-06,\n",
+       "                            1.2e-05,               1.4e-05,\n",
+       "                            1.6e-05,               1.8e-05,\n",
+       "              ...\n",
+       "                 11.999979999999999,             11.999982,\n",
+       "                          11.999984,             11.999986,\n",
+       "                          11.999988,    11.999989999999999,\n",
+       "                 11.999991999999999,             11.999994,\n",
+       "                          11.999996,             11.999998],\n",
+       "             dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=6000000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2eb331e4-d81c-48b8-8452-b948b64c7a6f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2eb331e4-d81c-48b8-8452-b948b64c7a6f' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'CH1' (x: 6000000)>\n",
+       "array([0.501, 0.485, 0.473, ..., 0.501, 0.485, 0.477])\n",
+       "Coordinates:\n",
+       "  * x        (x) float64 0.0 2e-06 4e-06 6e-06 8e-06 ... 12.0 12.0 12.0 12.0"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filePath = r'F:\\Jianshun\\AccordionLatticeShorTermStability\\20230808\\withoutModulation.csv'\n",
+    "\n",
+    "data = read_csv_file(filePath, csvEngine='pandas', csvKwargs=dict(header=[0], skiprows=[1], encoding = \"ISO-8859-1\",))\n",
+    "dataWithoutModulation = xr.DataArray(\n",
+    "    data=data.CH1[0,:],\n",
+    "    dims=['x'],\n",
+    "    coords=dict(\n",
+    "        x=data.X[0,:].to_numpy() * 2e-6\n",
+    "    )\n",
+    ")\n",
+    "dataWithoutModulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dataWithoutModulation.plot.errorbar()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataWithoutModulation = dataWithoutModulation - dataWithoutModulation.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "ax = fig.gca()\n",
+    "\n",
+    "dataWithoutModulation.plot.errorbar(ax=ax)\n",
+    "# ax.set_xlim([0.5, 1])\n",
+    "# ax.set_ylim([0, 0.25])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataWithoutModulationFFT = fft(dataWithoutModulation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "ax = fig.gca()\n",
+    "\n",
+    "abs(dataWithoutModulationFFT).plot.errorbar(ax=ax, x='freq_x')\n",
+    "ax.set_xlim([0, 50e3])\n",
+    "# ax.set_ylim([0, 0.25])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data analyse"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Transform intensity to phase"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from Analyser.FitAnalyser import NewFitModel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sine_with_offset(x, amplitude=1.0, frequency=1.0, shift=0.0, offset=0.0):\n",
+    "    \"\"\"Return a sinusoidal function.\n",
+    "\n",
+    "    sine(x, amplitude, frequency, shift) =\n",
+    "        amplitude * sin(x*frequency + shift)\n",
+    "\n",
+    "    \"\"\"\n",
+    "    return amplitude*np.sin(x*frequency + shift) + offset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fitModel = NewFitModel(sine_with_offset)\n",
+    "fitAnalyser = FitAnalyser(fitModel, fitDim=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "params = fitAnalyser.fitModel.make_params()\n",
+    "params.add(name=\"amplitude\", value=  0.2, max=np.inf, min=0, vary=True)\n",
+    "params.add(name=\"frequency\", value=  1*2*np.pi, max=np.inf, min=0, vary=True)\n",
+    "params.add(name=\"shift\", value=  np.pi/2, max=2*np.pi, min=0, vary=True)\n",
+    "params.add(name=\"offset\", value=  0, max=np.inf, min=-np.inf, vary=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# param = fitAnalyser.guess(dataWithModulation)\n",
+    "fitResult = fitAnalyser.fit(\n",
+    "    dataWithModulation.where(-0.1<dataWithModulation).where(dataWithModulation<0.1), \n",
+    "    params\n",
+    ")\n",
+    "fitCurve = fitAnalyser.eval(fitResult, x=dataWithModulation.x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "ax = fig.gca()\n",
+    "dataWithModulation.plot.errorbar(ax=ax)\n",
+    "fitCurve.plot.errorbar(ax=ax)\n",
+    "ax.plot([0, 12.5], [0.1, 0.1], 'r--')\n",
+    "ax.plot([0, 12.5], [-0.1, -0.1], 'r--')\n",
+    "plt.xlabel('t (s)')\n",
+    "plt.ylabel('PD signal (V)')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
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+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
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+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
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+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
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+       "  grid-column: 1 / -1;\n",
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+       "\n",
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+       ".xr-attrs dt,\n",
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+       "  padding-right: 10px;\n",
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+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:    ()\n",
+       "Data variables:\n",
+       "    amplitude  float64 0.2026\n",
+       "    frequency  float64 6.283\n",
+       "    shift      float64 3.003\n",
+       "    offset     float64 2.599e-06</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-0e946e25-39d4-48d4-b84f-54b7aa323331' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0e946e25-39d4-48d4-b84f-54b7aa323331' 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-11404388-0b91-434d-b3e5-b176cb78aa6d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-11404388-0b91-434d-b3e5-b176cb78aa6d' 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-b374b4d9-7237-410f-968c-42131949e58b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b374b4d9-7237-410f-968c-42131949e58b' 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>amplitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.2026</div><input id='attrs-1d120eea-f2d7-4e0a-b0b7-b9d724874cec' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1d120eea-f2d7-4e0a-b0b7-b9d724874cec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ecd60395-5201-408b-996d-a7f4f2ff4c7e' class='xr-var-data-in' type='checkbox'><label for='data-ecd60395-5201-408b-996d-a7f4f2ff4c7e' 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(0.20263455)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>frequency</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.283</div><input id='attrs-a15c60eb-d28b-4a3b-9bc3-c5c266189e35' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a15c60eb-d28b-4a3b-9bc3-c5c266189e35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5829198a-541a-4384-a9d2-50d6d6a4ab16' class='xr-var-data-in' type='checkbox'><label for='data-5829198a-541a-4384-a9d2-50d6d6a4ab16' 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(6.28264352)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>shift</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.003</div><input id='attrs-34209ee8-ce22-4624-8824-91c47595ea3b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-34209ee8-ce22-4624-8824-91c47595ea3b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-61002a13-5e14-442e-9ae4-5521942dc934' class='xr-var-data-in' type='checkbox'><label for='data-61002a13-5e14-442e-9ae4-5521942dc934' 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(3.00276275)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>offset</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.599e-06</div><input id='attrs-eafbafea-face-430d-a066-fb5f4fb8324e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eafbafea-face-430d-a066-fb5f4fb8324e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed2c4f8f-00ef-41c8-bbbc-880bc20503f4' class='xr-var-data-in' type='checkbox'><label for='data-ed2c4f8f-00ef-41c8-bbbc-880bc20503f4' 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(2.59883314e-06)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6ef0af21-7cf8-4336-8e8f-97ff24117555' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6ef0af21-7cf8-4336-8e8f-97ff24117555' 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-dda01715-8400-4e27-b5f8-c538ddb841c3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-dda01715-8400-4e27-b5f8-c538ddb841c3' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:    ()\n",
+       "Data variables:\n",
+       "    amplitude  float64 0.2026\n",
+       "    frequency  float64 6.283\n",
+       "    shift      float64 3.003\n",
+       "    offset     float64 2.599e-06"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "val = fitAnalyser.get_fit_value(fitResult)\n",
+    "val"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def voltage_to_phase(voltage):\n",
+    "    # voltage = (voltage - val.offset)\n",
+    "    # voltage = voltage / val.amplitude\n",
+    "    voltage_frac, b= np.modf(voltage.to_numpy())\n",
+    "    voltage = xr.where(np.abs(voltage) > 1, np.arcsin(voltage_frac) + np.trunc(voltage) * np.pi, np.arcsin(voltage))\n",
+    "    voltage = np.arcsin(voltage)\n",
+    "    return voltage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def autocorr(x):\n",
+    "    result = np.correlate(x, x, mode='full')\n",
+    "    return result[len(result)//2:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_noise_spectrum(data):\n",
+    "    data = data - data.mean()\n",
+    "    dataAutoCorr = autocorr(data)\n",
+    "    dataAutoCorr = xr.DataArray(\n",
+    "        data=dataAutoCorr,\n",
+    "        dims=['x'],\n",
+    "        coords=dict(\n",
+    "            x=data.x\n",
+    "        )\n",
+    "    )\n",
+    "    \n",
+    "    dataNoiseSpec = fft(dataAutoCorr)\n",
+    "    \n",
+    "    return dataNoiseSpec\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "phaseWithoutModulation = voltage_to_phase(dataWithoutModulation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "ax = fig.gca()\n",
+    "\n",
+    "(phaseWithoutModulation/np.pi).plot.errorbar(ax=ax)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "noiseWithoutModulation = get_noise_spectrum(phaseWithoutModulation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "<defs>\n",
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+       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
+       "</symbol>\n",
+       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
+       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
+       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
+       ":root {\n",
+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
+       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
+       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (freq_x: 6000000)&gt;\n",
+       "array([0.00146663-2.99062180e-13j, 0.00146904+2.91230736e-06j,\n",
+       "       0.00146276+5.40701190e-06j, ..., 0.00146716+7.80786951e-06j,\n",
+       "       0.00146276-5.40701190e-06j, 0.00146904-2.91230736e-06j])\n",
+       "Coordinates:\n",
+       "  * freq_x   (freq_x) float64 -2.5e+05 -2.5e+05 -2.5e+05 ... 2.5e+05 2.5e+05</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>freq_x</span>: 6000000</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-8af54559-f573-490b-a117-c7aa63419946' class='xr-array-in' type='checkbox' checked><label for='section-8af54559-f573-490b-a117-c7aa63419946' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>(0.0014666294599858997-2.9906217954251947e-13j) ... (0.001469035917...</span></div><div class='xr-array-data'><pre>array([0.00146663-2.99062180e-13j, 0.00146904+2.91230736e-06j,\n",
+       "       0.00146276+5.40701190e-06j, ..., 0.00146716+7.80786951e-06j,\n",
+       "       0.00146276-5.40701190e-06j, 0.00146904-2.91230736e-06j])</pre></div></div></li><li class='xr-section-item'><input id='section-f9cdbaba-091e-43bc-9be8-5d807bf3e21c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f9cdbaba-091e-43bc-9be8-5d807bf3e21c' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>freq_x</span></div><div class='xr-var-dims'>(freq_x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-2.5e+05 -2.5e+05 ... 2.5e+05</div><input id='attrs-c95594e6-a8a2-40ed-82c2-baa1acc0597d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c95594e6-a8a2-40ed-82c2-baa1acc0597d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec813544-e010-40ae-8dd8-bedc0ddbd5b1' class='xr-var-data-in' type='checkbox'><label for='data-ec813544-e010-40ae-8dd8-bedc0ddbd5b1' 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>spacing :</span></dt><dd>0.08333333334303461</dd><dt><span>direct_lag :</span></dt><dd>6.0</dd></dl></div><div class='xr-var-data'><pre>array([-250000.      , -249999.916667, -249999.833333, ...,  249999.75    ,\n",
+       "        249999.833333,  249999.916667])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b98ca7ef-738d-46b9-9d88-6de529f88bbb' class='xr-section-summary-in' type='checkbox'  ><label for='section-b98ca7ef-738d-46b9-9d88-6de529f88bbb' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>freq_x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3af2a7d6-d077-46e3-a60c-a7b786e143b8' class='xr-index-data-in' type='checkbox'/><label for='index-3af2a7d6-d077-46e3-a60c-a7b786e143b8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([          -250000.0, -249999.91666666666,  -249999.8333333333,\n",
+       "                       -249999.75, -249999.66666666666,  -249999.5833333333,\n",
+       "                        -249999.5, -249999.41666666666,  -249999.3333333333,\n",
+       "                       -249999.25,\n",
+       "              ...\n",
+       "               249999.16666666666,           249999.25,   249999.3333333333,\n",
+       "               249999.41666666666,            249999.5,   249999.5833333333,\n",
+       "               249999.66666666666,           249999.75,   249999.8333333333,\n",
+       "               249999.91666666666],\n",
+       "             dtype=&#x27;float64&#x27;, name=&#x27;freq_x&#x27;, length=6000000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3e3ebfd1-e26b-45b5-9eb4-c87263ca6f2a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3e3ebfd1-e26b-45b5-9eb4-c87263ca6f2a' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray (freq_x: 6000000)>\n",
+       "array([0.00146663-2.99062180e-13j, 0.00146904+2.91230736e-06j,\n",
+       "       0.00146276+5.40701190e-06j, ..., 0.00146716+7.80786951e-06j,\n",
+       "       0.00146276-5.40701190e-06j, 0.00146904-2.91230736e-06j])\n",
+       "Coordinates:\n",
+       "  * freq_x   (freq_x) float64 -2.5e+05 -2.5e+05 -2.5e+05 ... 2.5e+05 2.5e+05"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "noiseWithoutModulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "ax = fig.gca()\n",
+    "\n",
+    "abs(noiseWithoutModulation).plot.errorbar(ax=ax)\n",
+    "\n",
+    "# plt.xlim([25, 100e3])\n",
+    "plt.xlim([0, 250e3])\n",
+    "plt.ylim([0, 4])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "250.0\n",
+      "0.08333333333333333\n"
+     ]
+    }
+   ],
+   "source": [
+    "dt = 2e-6\n",
+    "samplingFreq = 1/ dt\n",
+    "blockLength = 6e6\n",
+    "df = samplingFreq / blockLength\n",
+    "print(samplingFreq / 2 / 1e3)\n",
+    "print(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[5, 4, 3, 2, 1, 0]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(list(range(5, -1 ,-1)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Analyser/FitAnalyser.py b/Analyser/FitAnalyser.py
index 58a1707..2472d69 100644
--- a/Analyser/FitAnalyser.py
+++ b/Analyser/FitAnalyser.py
@@ -25,11 +25,15 @@ from scipy.special import erf, erfc
 from scipy.special import gamma as gamfcn
 from scipy.special import wofz
 from scipy.optimize import curve_fit
+from scipy.interpolate import CubicSpline
+from scipy.ndimage import gaussian_filter
 
 import xarray as xr
 
 import copy
 
+import matplotlib.pyplot as plt
+
 
 log2 = log(2)
 s2pi = sqrt(2*pi)
@@ -109,6 +113,30 @@ def polylog2_2d(x, y=0.0, centerx=0.0, centery=0.0, amplitude=1.0, sigmax=1.0, s
     return amplitude / 2 / np.pi / 1.20206 / max(tiny, sigmax * sigmay) * polylog(2, np.exp( -((x-centerx)**2/(2 * (sigmax)**2))-((y-centery)**2/( 2 * (sigmay)**2)) ))
 
 
+def polylog_tab(pow, x, order=100):
+    """Calculationg the polylog sum_i(x^i/i^pow), up to the order-th element of the sum
+
+    :param pow: power in denominator of sum
+    :type pow: int (can be float)
+    :param x: argument of Polylogarithm
+    :type x: float or 1D numpy array
+    :return: value of polylog(x)
+    :rtype: same as x, float or 1D numpy array
+    """
+
+    sum = 0
+    for k in range(1,order):
+        sum  += x ** k /k **pow
+    return sum
+
+# Creating array for interpolation
+x_int = np.linspace(0, 1.00001, 100000)
+poly_tab = polylog_tab(2,x_int)
+
+# Creating function interpolating between the Polylog values calculated before
+polylog_int = CubicSpline(x_int, poly_tab)
+
+
 def density_profile_BEC_2d(x, y=0.0, BEC_amplitude=1.0, thermal_amplitude=1.0, BEC_centerx=0.0, BEC_centery=0.0, thermal_centerx=0.0, thermal_centery=0.0, 
                         BEC_sigmax=1.0, BEC_sigmay=1.0, thermal_sigmax=1.0, thermal_sigmay=1.0):
     
@@ -318,115 +346,333 @@ class ThomasFermi2dModel(Model):
         return update_param_vals(pars, self.prefix, **kwargs)
     
 
-class DensityProfileBEC2dModel(Model):
-
-    fwhm_factor = 2*np.sqrt(2*np.log(2))
-    height_factor = 1./2*np.pi
-
-    def __init__(self, independent_vars=['x', 'y'], prefix='', nan_policy='raise',
-                 **kwargs):
+class DensityProfileBEC2dModel(lmfit.Model):
+    """ Fitting class to do 2D bimodal fit on OD of absorption images
+    """
+    def __init__(self,
+                independent_vars=['x', 'y'],
+                prefix='',
+                nan_policy='raise',
+                atom_n_conv=144,
+                is_debug=False,
+                **kwargs
+        ):
         kwargs.update({'prefix': prefix, 'nan_policy': nan_policy,
                        'independent_vars': independent_vars})
+
+        self.atom_n_conv = atom_n_conv
+        self.is_debug=is_debug
         super().__init__(density_profile_BEC_2d, **kwargs)
         self._set_paramhints_prefix()
 
     def _set_paramhints_prefix(self):
         # self.set_param_hint('BEC_sigmax', min=0)
-        self.set_param_hint('deltax', min=0)
-        self.set_param_hint('BEC_sigmax', expr=f'3 * {self.prefix}thermal_sigmax - {self.prefix}deltax')
-        
-        self.set_param_hint('BEC_sigmay', min=0)
-        self.set_param_hint('thermal_sigmax', min=0)
-        # self.set_param_hint('thermal_sigmay', min=0)
-        self.set_param_hint('BEC_amplitude', min=0)
-        self.set_param_hint('thermal_amplitude', min=0)
-        
-        self.set_param_hint('thermalAspectRatio', min=0.8, max=1.2)
-        self.set_param_hint('thermal_sigmay', expr=f'{self.prefix}thermalAspectRatio * {self.prefix}thermal_sigmax')
-        
-        # self.set_param_hint('betax', value=0)
-        # self.set_param_hint('BEC_centerx', expr=f'{self.prefix}thermal_sigmax - {self.prefix}betax')
-        
-        self.set_param_hint('condensate_fraction', expr=f'{self.prefix}BEC_amplitude / ({self.prefix}BEC_amplitude + {self.prefix}thermal_amplitude)')
+        self.set_param_hint('amp_bec', min=0)
+        self.set_param_hint('amp_th', min=0)
+        self.set_param_hint('x0_bec', min=0)
+        self.set_param_hint('y0_bec', min=0)
+        self.set_param_hint('x0_th', min=0)
+        self.set_param_hint('y0_th', min=0)
+        self.set_param_hint('sigmax_bec', min=0)
+        self.set_param_hint('sigmay_bec', min=0)
+        self.set_param_hint('sigma_th', min=0)
+
+
+    def guess(self, data, x, y, **kwargs):
+        """
+        Estimate and create initial model parameters for 2d fit
+        :param data: Flattened 2d array, flattened from array with (x,y) -->
+        :param x:
+        :param y:
+        :param kwargs:
+        :return:
+        """
+        #
+        # global X_guess
+        # global bval_1d
+        x_width = len(np.unique(x))
+        y_width = len(np.unique(y))
+        data = np.reshape(data, (y_width, x_width))
+        data = data.T
+
+        shape = np.shape(data)
+        cut_width = np.max(shape)
+
+        thresh = self.calc_thresh(data)
+        center = self.calc_cen(thresh)
+
+        BEC_width_guess = self.guess_BEC_width(thresh, center)
+
+        if BEC_width_guess[0] < BEC_width_guess[1]:
+            if self.is_debug:
+                print(f'x smaller y')
+            s_width_ind = 0
+            X_guess = np.sum(data[:, round(center[1] - BEC_width_guess[1]/2) : round(center[1] + BEC_width_guess[1]/2)], 1) / len(data[0,round(center[1] - BEC_width_guess[1]/2) : round(center[1] + BEC_width_guess[1]/2)])
+        else:
+            if self.is_debug:
+                print(f'y smaller x')
+            s_width_ind = 1
+            X_guess = np.sum(data[round(center[0] - BEC_width_guess[0]/2) : round(center[0] + BEC_width_guess[0]/2), :], 0) / len(data[0,round(center[0] - BEC_width_guess[0]/2) : round(center[0] + BEC_width_guess[0]/2)])
+
+        if self.is_debug:
+            print(f'center = {center}')
+            print(f'BEC widths: {BEC_width_guess}')
+            plt.plot(X_guess)
+            plt.show()
+
+        x = np.linspace(0, len(X_guess), len(X_guess))
+
+        max_val = np.max(X_guess)
+
+        fitmodel_1d = lmfit.Model(density_1d, independent_vars=['x'])
+        params_1d = lmfit.Parameters()
+        params_1d.add_many(
+            ('x0_bec', center[s_width_ind], True,0, 200),
+            ('x0_th',center[s_width_ind], True,0, 200),
+            ('amp_bec', 0.7 * max_val, True, 0, 1.3 * max_val),
+            ('amp_th', 0.3 * max_val, True, 0, 1.3 * max_val),
+            ('deltax', 3*BEC_width_guess[s_width_ind], True, 0,cut_width),
+            # ('sigma_bec',BEC_width_guess[i,j,0]/1.22, True, 0, 50)
+            ('sigma_bec',BEC_width_guess[s_width_ind]/1.22, True, 0, 50)
+        )
+        params_1d.add('sigma_th', 3*BEC_width_guess[0], min=0, expr=f'0.632*sigma_bec + 0.518*deltax')
 
-    def guess(self, data, x, y, negative=False, pureBECThreshold=0.5, noBECThThreshold=0.0, **kwargs):
-        """Estimate initial model parameter values from data."""
-        fitModel = TwoGaussian2dModel()
-        pars = fitModel.guess(data, x=x, y=y, negative=negative)
-        pars['A_amplitude'].set(min=0)
-        pars['B_amplitude'].set(min=0)
-        pars['A_centerx'].set(min=pars['A_centerx'].value - 3 * pars['A_sigmax'], 
-                              max=pars['A_centerx'].value + 3 * pars['A_sigmax'],)
-        pars['A_centery'].set(min=pars['A_centery'].value - 3 * pars['A_sigmay'], 
-                              max=pars['A_centery'].value + 3 * pars['A_sigmay'],)
-        pars['B_centerx'].set(min=pars['B_centerx'].value - 3 * pars['B_sigmax'], 
-                              max=pars['B_centerx'].value + 3 * pars['B_sigmax'],)
-        pars['B_centery'].set(min=pars['B_centery'].value - 3 * pars['B_sigmay'], 
-                              max=pars['B_centery'].value + 3 * pars['B_sigmay'],)
-        
-        fitResult = fitModel.fit(data, x=x, y=y, params=pars, **kwargs)
-        pars_guess = fitResult.params
-        
-        BEC_amplitude = pars_guess['A_amplitude'].value
-        thermal_amplitude = pars_guess['B_amplitude'].value
-                
-        pars = self.make_params(BEC_amplitude=BEC_amplitude,
-                                thermal_amplitude=thermal_amplitude, 
-                                BEC_centerx=pars_guess['A_centerx'].value, BEC_centery=pars_guess['A_centery'].value,
-                                # BEC_sigmax=(pars_guess['A_sigmax'].value / 2.355), 
-                                deltax = 3 * (pars_guess['B_sigmax'].value * s2) - (pars_guess['A_sigmax'].value / 2.355),
-                                BEC_sigmay=(pars_guess['A_sigmay'].value / 2.355), 
-                                thermal_centerx=pars_guess['B_centerx'].value, thermal_centery=pars_guess['B_centery'].value,
-                                thermal_sigmax=(pars_guess['B_sigmax'].value * s2), 
-                                thermalAspectRatio=(pars_guess['B_sigmax'].value * s2) / (pars_guess['B_sigmay'].value * s2)
-                                # thermal_sigmay=(pars_guess['B_sigmay'].value * s2)
-                                )
-        
-        nBEC = pars[f'{self.prefix}BEC_amplitude'] / 2 / np.pi / 5.546 / pars[f'{self.prefix}BEC_sigmay'] / pars[f'{self.prefix}BEC_sigmax']
-        if (pars[f'{self.prefix}condensate_fraction']>0.95) and (np.max(data) > 1.05 * nBEC):
-            temp = ((np.max(data) - nBEC) * s2pi * pars[f'{self.prefix}thermal_sigmay'] / pars[f'{self.prefix}thermal_sigmax'])
-            if temp > pars[f'{self.prefix}BEC_amplitude']:
-                pars[f'{self.prefix}thermal_amplitude'].set(value=pars[f'{self.prefix}BEC_amplitude'] / 2)
+
+        res_1d = fitmodel_1d.fit(X_guess, x=x, params=params_1d)
+
+        if self.is_debug:
+            params_1d.pretty_print()
+            self.print_bval(res_1d)
+
+
+        bval_1d = res_1d.best_values
+
+        amp_conv_1d_2d = np.max(gaussian_filter(data, sigma=1)) / (bval_1d['amp_bec'] + bval_1d['amp_th'])
+        max_val = np.max(data)
+
+        params = self.make_params()
+
+        if bval_1d['amp_th']/bval_1d['amp_bec'] > 3:
+            print(f'Image seems to be purely thermal (guessed from 1d fit amplitude)')
+
+            params[f'{self.prefix}amp_bec'].set(value=0, vary=False)
+            params[f'{self.prefix}amp_th'].set(value=amp_conv_1d_2d * bval_1d['amp_th'], max=1.3 * max_val, vary=True)
+            params[f'{self.prefix}x0_bec'].set(value=1, vary=False)
+            params[f'{self.prefix}y0_bec'].set(value=1, vary=False)
+            params[f'{self.prefix}x0_th'].set(value=center[0], min=center[0] -10, max=center[0] + 10, vary=True)
+            params[f'{self.prefix}y0_th'].set(value=center[1], min=center[1] -10, max=center[1] + 10, vary=True)
+            params[f'{self.prefix}sigmax_bec'].set(value=1, vary=False)
+            params[f'{self.prefix}sigmay_bec'].set(value=1, vary=False)
+            params[f'{self.prefix}sigma_th'].set(value=bval_1d['sigma_th'], max=cut_width, vary=True)
+
+        elif bval_1d['amp_bec']/bval_1d['amp_th'] > 10:
+            print('Image seems to be pure BEC (guessed from 1d fit amplitude)')
+
+            params[f'{self.prefix}amp_bec'].set(value=amp_conv_1d_2d * bval_1d['amp_bec'], max=1.3 * max_val, vary=True)
+            params[f'{self.prefix}amp_th'].set(value=0, vary=False)
+            params[f'{self.prefix}x0_bec'].set(value=center[0], min=center[0] -10, max=center[0] + 10, vary=True)
+            params[f'{self.prefix}y0_bec'].set(value=center[1], min=center[1] -10, max=center[1] + 10, vary=True)
+            params[f'{self.prefix}x0_th'].set(value=1, vary=False)
+            params[f'{self.prefix}y0_th'].set(value=1, vary=False)
+            params[f'{self.prefix}sigma_th'].set(value=1, vary=False)
+
+            if s_width_ind == 0:
+                params[f'{self.prefix}sigmax_bec'].set(value=bval_1d['sigma_bec'], max= 2*BEC_width_guess[0], vary=True)
+                params[f'{self.prefix}sigmay_bec'].set(value=BEC_width_guess[1]/1.22, max= 2*BEC_width_guess[1], vary=True)
+            elif s_width_ind == 1:
+                params[f'{self.prefix}sigmax_bec'].set(value=BEC_width_guess[0]/1.22, max= 2*BEC_width_guess[0], vary=True)
+                params[f'{self.prefix}sigmay_bec'].set(value=bval_1d['sigma_bec'], max= 2*BEC_width_guess[1], vary=True)
             else:
-                pars[f'{self.prefix}thermal_amplitude'].set(value=temp * 10)
-        
-        if BEC_amplitude / (thermal_amplitude + BEC_amplitude) > pureBECThreshold:
-            pars[f'{self.prefix}thermal_amplitude'].set(value=0)
-            pars[f'{self.prefix}BEC_amplitude'].set(value=(thermal_amplitude + BEC_amplitude))
-        
-        if BEC_amplitude / (thermal_amplitude + BEC_amplitude) < noBECThThreshold:
-            pars[f'{self.prefix}BEC_amplitude'].set(value=0)
-            pars[f'{self.prefix}thermal_amplitude'].set(value=(thermal_amplitude + BEC_amplitude))
-        
-        pars[f'{self.prefix}BEC_centerx'].set(
-            min=pars[f'{self.prefix}BEC_centerx'].value - 10 * pars[f'{self.prefix}BEC_sigmax'].value,
-            max=pars[f'{self.prefix}BEC_centerx'].value + 10 * pars[f'{self.prefix}BEC_sigmax'].value,  
-        )
-        
-        pars[f'{self.prefix}thermal_centerx'].set(
-            min=pars[f'{self.prefix}thermal_centerx'].value - 3 * pars[f'{self.prefix}thermal_sigmax'].value,
-            max=pars[f'{self.prefix}thermal_centerx'].value + 3 * pars[f'{self.prefix}thermal_sigmax'].value,  
-        )
-        
-        pars[f'{self.prefix}BEC_centery'].set(
-            min=pars[f'{self.prefix}BEC_centery'].value - 10 * pars[f'{self.prefix}BEC_sigmay'].value,
-            max=pars[f'{self.prefix}BEC_centery'].value + 10 * pars[f'{self.prefix}BEC_sigmay'].value,  
-        )
-        
-        pars[f'{self.prefix}thermal_centery'].set(
-            min=pars[f'{self.prefix}thermal_centery'].value - 3 * pars[f'{self.prefix}thermal_sigmay'].value,
-            max=pars[f'{self.prefix}thermal_centery'].value + 3 * pars[f'{self.prefix}thermal_sigmay'].value,  
-        )
-        
-        pars[f'{self.prefix}BEC_sigmay'].set(
-            max=5 * pars[f'{self.prefix}BEC_sigmay'].value,  
-        )
-        
-        pars[f'{self.prefix}thermal_sigmax'].set(
-            max=5 * pars[f'{self.prefix}thermal_sigmax'].value,   
-        )
-        
-        return update_param_vals(pars, self.prefix, **kwargs)
+                print('Error in small width BEC recogintion, s_width_ind should be 0 or 1')
+
+        else:
+            params[f'{self.prefix}amp_bec'].set(value=amp_conv_1d_2d * bval_1d['amp_bec'], max=1.3 * max_val, vary=True)
+            params[f'{self.prefix}amp_th'].set(value=amp_conv_1d_2d * bval_1d['amp_th'], max=1.3 * max_val, vary=True)
+            params[f'{self.prefix}x0_bec'].set(value=center[0], min=center[0] -10, max=center[0] + 10, vary=True)
+            params[f'{self.prefix}y0_bec'].set(value=center[1], min=center[1] -10, max=center[1] + 10, vary=True)
+            params[f'{self.prefix}x0_th'].set(value=center[0], min=center[0] -10, max=center[0] + 10, vary=True)
+            params[f'{self.prefix}y0_th'].set(value=center[1], min=center[1] -10, max=center[1] + 10, vary=True)
+            params[f'{self.prefix}sigma_th'].set(value=bval_1d['sigma_th'], max=cut_width, vary=True)
+
+            if s_width_ind == 0:
+                params[f'{self.prefix}sigmax_bec'].set(value=bval_1d['sigma_bec'], max= 2*BEC_width_guess[0], vary=True)
+                params[f'{self.prefix}sigmay_bec'].set(value=BEC_width_guess[1]/1.22, max= 2*BEC_width_guess[1], vary=True)
+            elif s_width_ind == 1:
+                params[f'{self.prefix}sigmax_bec'].set(value=BEC_width_guess[0]/1.22, max= 2*BEC_width_guess[0], vary=True)
+                params[f'{self.prefix}sigmay_bec'].set(value=bval_1d['sigma_bec'], max= 2*BEC_width_guess[1], vary=True)
+            else:
+                print('Error in small width BEC recogintion, s_width_ind should be 0 or 1')
+
+        if self.is_debug:
+            print('')
+            print('Init Params')
+            params.pretty_print()
+        return lmfit.models.update_param_vals(params, self.prefix, **kwargs)
+
+
+    def fit(self, data_1d, **kwargs):
+
+        res = super().fit(data_1d, **kwargs)
+
+        if self.is_debug:
+            print('bval first fit')
+            self.print_bval(res)
+
+
+        if res.params['amp_bec'].vary and res.params['amp_th'].vary:
+            bval = res.best_values
+            sigma_cut = max(bval['sigmay_bec'], bval['sigmax_bec'])
+            tf_fit = ThomasFermi_2d(kwargs['x'],kwargs['y'],centerx=bval['x0_bec'], centery=bval['y0_bec'], amplitude=bval['amp_bec'], sigmax=bval['sigmax_bec'], sigmay=bval['sigmay_bec'])
+            tf_fit_2 = ThomasFermi_2d(kwargs['x'],kwargs['y'],centerx=bval['x0_bec'], centery=bval['y0_bec'], amplitude=bval['amp_bec'], sigmax=1.5 * sigma_cut, sigmay=1.5*sigma_cut)
+            mask = np.where(tf_fit > 0, np.nan, data_1d)
+            #mask[i,j] = gaussian_filter(mask[i,j], sigma = 0.4)
+            mask = np.where(tf_fit_2 > 0, mask, np.nan)
+
+            N_c = np.nansum(mask)
+            # conversion N_count to Pixels
+            N_a = self.atom_n_conv * N_c
+
+            if N_a < 6615:
+                print('No thermal part detected, performing fit without thermal function')
+                params = res.params
+                params[f'{self.prefix}amp_th'].set(value=0, vary=False)
+                params[f'{self.prefix}x0_th'].set(value=1, vary=False)
+                params[f'{self.prefix}y0_th'].set(value=1, vary=False)
+                params[f'{self.prefix}sigma_th'].set(value=1, vary=False)
+
+                res = super().fit(data_1d, x=kwargs['x'], y=kwargs['y'], params=params)
+
+                return res
+
+        return res
+
+    def calc_thresh(self, data, thresh_val=0.3, sigma=0.4):
+        """Returns thresholded binary image after blurring to guess BEC size
+
+        :param data: 2d image or 1D or 2D array containing 2d images
+        :type data: 2d, 3d or 4d numpy array
+        :param thresh_val: relative threshhold value for binarization with respect to maximum of blurred image
+        :param sigma: sigma of gaussian blur filter (see scipy.ndimage.gaussian_filter
+        :return: binary 2d image or 1D or 2D array containing 2d images
+        :rtype: 2d, 3d or 4d numpy array
+        """
+        shape = np.shape(data)
+        thresh = np.zeros(shape)
+
+        blurred = gaussian_filter(data, sigma=sigma)
+
+        if len(shape) == 4:
+            for i in range(0,shape[0]):
+                for j in range(0, shape[1]):
+                    thresh[i,j] = np.where(blurred[i,j] < np.max(blurred[i,j])*thresh_val, 0, 1)
+
+        elif len(shape) == 3:
+            for i in range(0,shape[0]):
+                    thresh[i] = np.where(blurred[i] < np.max(blurred[i])*thresh_val, 0, 1)
+
+        elif len(shape) == 2:
+            thresh = np.where(blurred < np.max(blurred)*thresh_val, 0, 1)
+
+
+        else:
+            print("Shape of data is wrong, output is empty")
+
+        return thresh
+
+    def calc_cen(self, thresh1):
+        """
+        returns array: [X_center,Y_center]
+        """
+        cen = np.zeros(2)
+        (Y,X) = np.shape(thresh1)
+
+
+        thresh1 = thresh1 /np.sum(thresh1)
+
+        # marginal distributions
+        dx = np.sum(thresh1, 0)
+        dy = np.sum(thresh1, 1)
+
+        # expected values
+        cen[0] = np.sum(dx * np.arange(X))
+        cen[1] = np.sum(dy * np.arange(Y))
+        return cen
+
+    def guess_BEC_width(self, thresh, center):
+        """
+        returns width of thresholded area along both axis through the center with shape of thresh and [X_width, Y_width] for each image
+        """
+        shape = np.shape(thresh)
+
+        if len(shape) == 2:
+            BEC_width_guess = np.array([np.sum(thresh[round(center[1]), :]), np.sum(thresh[:, round(center[0])]) ])
+
+        elif len(shape) == 3:
+            BEC_width_guess = np.zeros((shape[0], 2))
+            for i in range(0, shape[0]):
+                BEC_width_guess[i, 0] = np.sum(thresh[i, round(center[i,j,1]), :])
+                BEC_width_guess[i, 1] = np.sum(thresh[i, :, round(center[i,j,0])])
+
+        elif len(shape) == 4:
+            BEC_width_guess = np.zeros((shape[0], shape[1], 2))
+            for i in range(0, shape[0]):
+                for j in range(0, shape[1]):
+                    BEC_width_guess[i, j, 0] = np.sum(thresh[i, j, round(center[i,j,1]), :])
+                    BEC_width_guess[i, j, 1] = np.sum(thresh[i, j, :, round(center[i,j,0])])
+        else:
+            print("Shape of data is wrong, output is empty")
+
+        return BEC_width_guess
+
+    def cond_frac(self, results):
+        """Returns condensate fraction"""
+        bval = results.best_values
+        tf_fit = ThomasFermi_2d(X, Y, centerx=bval['x0_bec'], centery=bval['y0_bec'], amplitude=bval['amp_bec'], sigmax=bval['sigmax_bec'], sigmay=bval['sigmay_bec'])
+        N_bec = np.sum(tf_fit)
+        fit = density_profile_BEC_2d(X, Y, **bval)
+        N_ges = np.sum(fit)
+        return N_bec/N_ges
+
+    def return_atom_number(self, result, X, Y, is_print=True):
+        """Printing fitted atom number in bec + thermal state"""
+        bval = result.best_values
+        tf_fit = ThomasFermi_2d(X,Y,centerx=bval['x0_bec'], centery=bval['y0_bec'], amplitude=bval['amp_bec'], sigmax=bval['sigmax_bec'], sigmay=bval['sigmay_bec'])
+        N_bec = self.atom_n_conv * np.sum(tf_fit)
+
+        th_fit = polylog2_2d(X, Y, centerx=bval['x0_th'], centery=bval['y0_th'], amplitude=bval['amp_th'], sigmax=bval['sigma_th'], sigmay=bval['sigma_th'])
+        N_th = self.atom_n_conv * np.sum(th_fit)
+
+        # fit = density_profile_BEC_2d(X,Y, **bval)
+        # N_ges = self.atom_n_conv * np.sum(fit)
+
+        if is_print:
+            print()
+            print('Atom numbers:')
+            print(f'   N_bec: {N_bec :.0f}')
+            print(f'   N_th: {N_th :.0f}')
+            print(f'   N_ges: {N_bec + N_th :.0f}')
+            print(f'   Cond. frac: {N_bec/(N_bec + N_th):.2f}')
+            print('')
+
+
+    def return_temperature(self, result, omg, tof, is_print=True, eff_pix=2.472e-6):
+        """Returns temperature of thermal cloud"""
+        R_th = result.best_values['sigma_th'] * eff_pix * np.sqrt(2)
+        print(R_th)
+        T = R_th**2 * 164*const.u/const.k * (1/omg**2 + tof**2)**(-1)
+        if is_print:
+            print(f'Temperature: {T*1e9:.2f} nK')
+        return T
+
+    def print_bval(self, res_s):
+        """nicely print best fitted values + init values + bounds """
+        keys = res_s.best_values.keys()
+        bval = res_s.best_values
+        init = res_s.init_params
+
+        for item in keys:
+            print(f'{item}: {bval[item]:.3f}, (init = {init[item].value:.3f}), bounds = [{init[item].min:.2f} : {init[item].max :.2f}] ')
+        print('')
     
 
 class NewFitModel(Model):