From 6f7c2bbf69690046ac48394d4c1f36906b5b964d Mon Sep 17 00:00:00 2001
From: Gao <gao@physi.uni-heidelberg.de>
Date: Thu, 29 Jun 2023 11:54:10 +0200
Subject: [PATCH] improve the fit

---
 20230620_Data_Analysis.ipynb | 3129 ++++++++++++++++++++++++++++++++++
 Analyser/FitAnalyser.py      |   67 +-
 magenticField.ipynb          | 2380 ++++++++++++++++++++++++++
 test.ipynb                   |  125 +-
 test_fit.ipynb               | 2498 +++++++++++++++++++++++++++
 5 files changed, 8182 insertions(+), 17 deletions(-)
 create mode 100644 20230620_Data_Analysis.ipynb
 create mode 100644 magenticField.ipynb
 create mode 100644 test_fit.ipynb

diff --git a/20230620_Data_Analysis.ipynb b/20230620_Data_Analysis.ipynb
new file mode 100644
index 0000000..921e55b
--- /dev/null
+++ b/20230620_Data_Analysis.ipynb
@@ -0,0 +1,3129 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Import supporting package"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr\n",
+    "import numpy as np\n",
+    "import copy\n",
+    "\n",
+    "from uncertainties import ufloat\n",
+    "from uncertainties import unumpy as unp\n",
+    "from uncertainties import umath\n",
+    "import random\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.rcParams['font.size'] = 12\n",
+    "\n",
+    "from DataContainer.ReadData import read_hdf5_file\n",
+    "from Analyser.ImagingAnalyser import ImageAnalyser\n",
+    "from Analyser.FitAnalyser import FitAnalyser\n",
+    "from Analyser.FitAnalyser import NewFitModel, DensityProfileBEC2dModel\n",
+    "from ToolFunction.ToolFunction import *\n",
+    "\n",
+    "from scipy.optimize import curve_fit\n",
+    "\n",
+    "from ToolFunction.HomeMadeXarrayFunction import errorbar, dataarray_plot_errorbar\n",
+    "xr.plot.dataarray_plot.errorbar = errorbar\n",
+    "xr.plot.accessor.DataArrayPlotAccessor.errorbar = dataarray_plot_errorbar\n",
+    "\n",
+    "imageAnalyser = ImageAnalyser()\n",
+    "\n",
+    "# %matplotlib notebook"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Start a client for parallel computing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n",
+       "    <div style=\"margin-left: 48px;\">\n",
+       "        <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
+       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-f69f7e77-14be-11ee-a66c-80e82ce2fa8e</p>\n",
+       "        <table style=\"width: 100%; text-align: left;\">\n",
+       "\n",
+       "        <tr>\n",
+       "        \n",
+       "            <td style=\"text-align: left;\"><strong>Connection method:</strong> Cluster object</td>\n",
+       "            <td style=\"text-align: left;\"><strong>Cluster type:</strong> distributed.LocalCluster</td>\n",
+       "        \n",
+       "        </tr>\n",
+       "\n",
+       "        \n",
+       "            <tr>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
+       "                </td>\n",
+       "                <td style=\"text-align: left;\"></td>\n",
+       "            </tr>\n",
+       "        \n",
+       "\n",
+       "        </table>\n",
+       "\n",
+       "        \n",
+       "\n",
+       "        \n",
+       "            <details>\n",
+       "            <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Cluster Info</h3></summary>\n",
+       "            <div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
+       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\">\n",
+       "    </div>\n",
+       "    <div style=\"margin-left: 48px;\">\n",
+       "        <h3 style=\"margin-bottom: 0px; margin-top: 0px;\">LocalCluster</h3>\n",
+       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">cbd5084a</p>\n",
+       "        <table style=\"width: 100%; text-align: left;\">\n",
+       "            <tr>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
+       "                </td>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Workers:</strong> 8\n",
+       "                </td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Total threads:</strong> 128\n",
+       "                </td>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Total memory:</strong> 149.01 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-0b555d69-83fd-43e1-9215-9c67aeb6d78c</p>\n",
+       "            <table style=\"width: 100%; text-align: left;\">\n",
+       "                <tr>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Comm:</strong> tcp://127.0.0.1:61752\n",
+       "                    </td>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Workers:</strong> 8\n",
+       "                    </td>\n",
+       "                </tr>\n",
+       "                <tr>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
+       "                    </td>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Total threads:</strong> 128\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> 149.01 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://127.0.0.1:61792\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 16\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:61794/status\" target=\"_blank\">http://127.0.0.1:61794/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 18.63 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:61755\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-vwm7x29k\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://127.0.0.1:61793\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 16\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:61798/status\" target=\"_blank\">http://127.0.0.1:61798/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 18.63 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:61756\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-dsm02jmm\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://127.0.0.1:61810\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 16\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:61811/status\" target=\"_blank\">http://127.0.0.1:61811/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 18.63 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:61757\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-kb6dkrm1\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://127.0.0.1:61775\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 16\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:61790/status\" target=\"_blank\">http://127.0.0.1:61790/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 18.63 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:61758\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-vtzwx9iy\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://127.0.0.1:61804\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 16\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:61806/status\" target=\"_blank\">http://127.0.0.1:61806/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 18.63 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:61759\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-a97vh11n\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://127.0.0.1:61796\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 16\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:61801/status\" target=\"_blank\">http://127.0.0.1:61801/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 18.63 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:61760\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-2jc_h5j9\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: 6</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:61797\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 16\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:61800/status\" target=\"_blank\">http://127.0.0.1:61800/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 18.63 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:61761\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-mvwb15rr\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: 7</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:61805\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 16\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:61808/status\" target=\"_blank\">http://127.0.0.1:61808/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 18.63 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:61762\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-co4d63m7\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://127.0.0.1:61752' processes=8 threads=128, memory=149.01 GiB>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from dask.distributed import Client\n",
+    "client = Client(n_workers=8, threads_per_worker=16, processes=True, memory_limit='20GB')\n",
+    "client"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Start a client for Mongo DB"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pymongo\n",
+    "import xarray_mongodb\n",
+    "\n",
+    "from DataContainer.MongoDB import MongoDB\n",
+    "\n",
+    "mongoClient = pymongo.MongoClient('mongodb://control:DyLab2021@127.0.0.1:27017/?authMechanism=DEFAULT')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Set global path for experiment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "groupList = [\n",
+    "    \"images/MOT_3D_Camera/in_situ_absorption\",\n",
+    "    \"images/ODT_1_Axis_Camera/in_situ_absorption\",\n",
+    "    \"images/ODT_2_Axis_Camera/in_situ_absorption\",\n",
+    "]\n",
+    "\n",
+    "dskey = {\n",
+    "    \"images/MOT_3D_Camera/in_situ_absorption\": \"camera_0\",\n",
+    "    \"images/ODT_1_Axis_Camera/in_situ_absorption\": \"camera_1\",\n",
+    "    \"images/ODT_2_Axis_Camera/in_situ_absorption\": \"camera_2\",\n",
+    "}\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img_dir = 'C:/Users/control/DyLab/Experiments/DyBEC/'\n",
+    "SequenceName = \"Repetition_scan\"\n",
+    "folderPath = img_dir + SequenceName + \"/\" + get_date()\n",
+    "\n",
+    "mongoDB = mongoClient[SequenceName]\n",
+    "\n",
+    "DB = MongoDB(mongoClient, mongoDB, date=get_date())"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Repetition Scans"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## scan MOT freq - Z Comp 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib notebook\n",
+    "shotNum = \"0001\"\n",
+    "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n",
+    "\n",
+    "dataSetDict = {\n",
+    "    dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n",
+    "    for i in range(len(groupList))\n",
+    "}\n",
+    "\n",
+    "dataSet = dataSetDict[\"camera_1\"]\n",
+    "\n",
+    "print_scanAxis(dataSet)\n",
+    "\n",
+    "scanAxis = get_scanAxis(dataSet)\n",
+    "\n",
+    "dataSet = auto_rechunk(dataSet)\n",
+    "\n",
+    "dataSet = imageAnalyser.get_absorption_images(dataSet)\n",
+    "\n",
+    "imageAnalyser.center = (310, 825)\n",
+    "imageAnalyser.span = (550, 1200)\n",
+    "imageAnalyser.fraction = (0.1, 0.1)\n",
+    "\n",
+    "dataSet_cropOD = imageAnalyser.crop_image(dataSet.OD)\n",
+    "dataSet_cropOD = imageAnalyser.substract_offset(dataSet_cropOD).load()\n",
+    "\n",
+    "Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n",
+    "Ncount_mean = Ncount.mean(dim='runs')\n",
+    "Ncount_std = Ncount.std(dim='runs')\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.gca()\n",
+    "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n",
+    "plt.xlabel('MOT AOM Freq (MHz)')\n",
+    "plt.ylabel('NCount')\n",
+    "plt.tight_layout()\n",
+    "plt.grid(visible=1)\n",
+    "plt.show()\n",
+    "\n",
+    "DB.create_global(shotNum, dataSet)\n",
+    "DB.add_data(shotNum, dataSet_cropOD, engine='xarray')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## scan Push freq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib notebook\n",
+    "shotNum = \"0002\"\n",
+    "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n",
+    "\n",
+    "dataSetDict = {\n",
+    "    dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n",
+    "    for i in range(len(groupList))\n",
+    "}\n",
+    "\n",
+    "dataSet = dataSetDict[\"camera_1\"]\n",
+    "\n",
+    "print_scanAxis(dataSet)\n",
+    "\n",
+    "scanAxis = get_scanAxis(dataSet)\n",
+    "\n",
+    "dataSet = auto_rechunk(dataSet)\n",
+    "\n",
+    "dataSet = imageAnalyser.get_absorption_images(dataSet)\n",
+    "\n",
+    "imageAnalyser.center = (310, 825)\n",
+    "imageAnalyser.span = (525, 1255)\n",
+    "imageAnalyser.fraction = (0.1, 0.1)\n",
+    "\n",
+    "dataSet_cropOD = imageAnalyser.crop_image(dataSet.OD)\n",
+    "dataSet_cropOD = imageAnalyser.substract_offset(dataSet_cropOD).load()\n",
+    "\n",
+    "Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n",
+    "Ncount_mean = calculate_mean(Ncount)\n",
+    "Ncount_std = calculate_std(Ncount)\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.gca()\n",
+    "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n",
+    "plt.xlabel('Push AOM Freq (MHz)')\n",
+    "plt.ylabel('NCount')\n",
+    "plt.tight_layout()\n",
+    "plt.grid(visible=1)\n",
+    "plt.show()\n",
+    "\n",
+    "DB.create_global(shotNum, dataSet)\n",
+    "DB.add_data(shotNum, dataSet_cropOD, engine='xarray')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## scan Z comp current"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib notebook\n",
+    "shotNum = \"0005\"\n",
+    "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n",
+    "\n",
+    "dataSetDict = {\n",
+    "    dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n",
+    "    for i in range(len(groupList))\n",
+    "}\n",
+    "\n",
+    "dataSet = dataSetDict[\"camera_1\"]\n",
+    "\n",
+    "print_scanAxis(dataSet)\n",
+    "\n",
+    "scanAxis = get_scanAxis(dataSet)\n",
+    "\n",
+    "dataSet = auto_rechunk(dataSet)\n",
+    "\n",
+    "dataSet = imageAnalyser.get_absorption_images(dataSet)\n",
+    "\n",
+    "imageAnalyser.center = (305, 875)\n",
+    "imageAnalyser.span = (400, 400)\n",
+    "imageAnalyser.fraction = (0.1, 0.1)\n",
+    "\n",
+    "dataSet_cropOD = imageAnalyser.crop_image(dataSet.OD)\n",
+    "dataSet_cropOD = imageAnalyser.substract_offset(dataSet_cropOD).load()\n",
+    "\n",
+    "Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n",
+    "Ncount_mean = calculate_mean(Ncount)\n",
+    "Ncount_std = calculate_std(Ncount)\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.gca()\n",
+    "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n",
+    "plt.xlabel('comp Z current (A)')\n",
+    "plt.ylabel('NCount')\n",
+    "plt.tight_layout()\n",
+    "plt.grid(visible=1)\n",
+    "plt.show()\n",
+    "\n",
+    "DB.create_global(shotNum, dataSet)\n",
+    "DB.add_data(shotNum, dataSet_cropOD, engine='xarray')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## scan cMOT final Amp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "shotNum = \"0006\"\n",
+    "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n",
+    "\n",
+    "dataSetDict = {\n",
+    "    dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n",
+    "    for i in range(len(groupList))\n",
+    "}\n",
+    "\n",
+    "dataSet = dataSetDict[\"camera_1\"]\n",
+    "\n",
+    "print_scanAxis(dataSet)\n",
+    "\n",
+    "scanAxis = get_scanAxis(dataSet)\n",
+    "\n",
+    "dataSet = auto_rechunk(dataSet)\n",
+    "\n",
+    "dataSet = imageAnalyser.get_absorption_images(dataSet)\n",
+    "\n",
+    "imageAnalyser.center = (305, 875)\n",
+    "imageAnalyser.span = (400, 400)\n",
+    "imageAnalyser.fraction = (0.1, 0.1)\n",
+    "\n",
+    "dataSet_cropOD = imageAnalyser.crop_image(dataSet.OD)\n",
+    "dataSet_cropOD = imageAnalyser.substract_offset(dataSet_cropOD).load()\n",
+    "\n",
+    "Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n",
+    "Ncount_mean = calculate_mean(Ncount)\n",
+    "Ncount_std = calculate_std(Ncount)\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.gca()\n",
+    "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n",
+    "plt.xlabel('cMOT final Amp (%)')\n",
+    "plt.ylabel('NCount')\n",
+    "#plt.ylim([0, 25000])\n",
+    "plt.tight_layout()\n",
+    "plt.grid(visible=1)\n",
+    "plt.show()\n",
+    "\n",
+    "DB.create_global(shotNum, dataSet)\n",
+    "DB.add_data(shotNum, dataSet_cropOD, engine='xarray')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib notebook\n",
+    "shotNum = \"0011\"\n",
+    "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n",
+    "\n",
+    "dataSetDict = {\n",
+    "    dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n",
+    "    for i in range(len(groupList))\n",
+    "}\n",
+    "\n",
+    "dataSet = dataSetDict[\"camera_1\"]\n",
+    "\n",
+    "print_scanAxis(dataSet)\n",
+    "\n",
+    "scanAxis = get_scanAxis(dataSet)\n",
+    "\n",
+    "dataSet = auto_rechunk(dataSet)\n",
+    "\n",
+    "dataSet = imageAnalyser.get_absorption_images(dataSet)\n",
+    "\n",
+    "imageAnalyser.center = (305, 875)\n",
+    "imageAnalyser.span = (400, 400)\n",
+    "imageAnalyser.fraction = (0.1, 0.1)\n",
+    "\n",
+    "dataSet_cropOD = imageAnalyser.crop_image(dataSet.OD)\n",
+    "dataSet_cropOD = imageAnalyser.substract_offset(dataSet_cropOD).load()\n",
+    "\n",
+    "Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n",
+    "Ncount_mean = calculate_mean(Ncount)\n",
+    "Ncount_std = calculate_std(Ncount)\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.gca()\n",
+    "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n",
+    "plt.xlabel('comp Z current (A)')\n",
+    "plt.ylabel('NCount')\n",
+    "plt.tight_layout()\n",
+    "plt.grid(visible=1)\n",
+    "plt.show()\n",
+    "\n",
+    "DB.create_global(shotNum, dataSet)\n",
+    "DB.add_data(shotNum, dataSet_cropOD, engine='xarray')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Evaporative Cooling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img_dir = '//DyLabNAS/Data/'\n",
+    "SequenceName = \"Evaporative_Cooling\"\n",
+    "#folderPath = img_dir + SequenceName + \"/\" + get_date()\n",
+    "folderPath = img_dir + SequenceName + \"/2023/06/20\"\n",
+    "\n",
+    "mongoDB = mongoClient[SequenceName]\n",
+    "\n",
+    "DB = MongoDB(mongoClient, mongoDB, date=get_date())"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Calibration of the magnetic fields"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Z Offset field =  0.119 A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib notebook\n",
+    "shotNum = \"0004\"\n",
+    "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n",
+    "\n",
+    "dataSetDict = {\n",
+    "    dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i], excludeAxis = ['sweep_start_freq', 'sweep_stop_freq'])\n",
+    "    for i in [0]\n",
+    "}\n",
+    "\n",
+    "dataSet = dataSetDict[\"camera_0\"]\n",
+    "\n",
+    "print_scanAxis(dataSet)\n",
+    "\n",
+    "scanAxis = get_scanAxis(dataSet)\n",
+    "\n",
+    "dataSet = auto_rechunk(dataSet)\n",
+    "\n",
+    "dataSet = imageAnalyser.get_absorption_images(dataSet)\n",
+    "\n",
+    "imageAnalyser.center = (160, 880)\n",
+    "imageAnalyser.span = (250, 250)\n",
+    "imageAnalyser.fraction = (0.1, 0.1)\n",
+    "\n",
+    "dataSet_cropOD = imageAnalyser.crop_image(dataSet.OD)\n",
+    "dataSet_cropOD = imageAnalyser.substract_offset(dataSet_cropOD).load()\n",
+    "\n",
+    "Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n",
+    "Ncount_mean = calculate_mean(Ncount)\n",
+    "Ncount_std = calculate_std(Ncount)\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.gca()\n",
+    "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n",
+    "\n",
+    "plt.ylabel('NCount')\n",
+    "plt.tight_layout()\n",
+    "#plt.ylim([0, 3500])\n",
+    "plt.grid(visible=1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Ncount_mean_1 = Ncount_mean\n",
+    "Ncount_std_1 = Ncount_std\n",
+    "\n",
+    "fitAnalyser_1 = FitAnalyser(\"Gaussian With Offset\", fitDim=1)\n",
+    "# params = fitAnalyser.guess(Ncount_mean_1, x=scanAxis[0], guess_kwargs=dict(negative=True), dask=\"parallelized\")\n",
+    "params = fitAnalyser_1.fitModel.make_params()\n",
+    "params.add(name=\"amplitude\", value=  -6000, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"center\", value=  2.9576, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"sigma\", value=  0.1, max=np.inf, min=  0, vary=True)\n",
+    "params.add(name=\"offset\", value=  6000, max=np.inf, min=-np.inf, vary=True)\n",
+    "\n",
+    "fitResult_1 = fitAnalyser_1.fit(Ncount_mean_1, params, x=scanAxis[0]).load()\n",
+    "freqdata = np.linspace(2.9445, 2.9601, 500)\n",
+    "fitCurve_1 = fitAnalyser_1.eval(fitResult_1, x=freqdata, dask=\"parallelized\").load()\n",
+    "fitCurve_1 = fitCurve_1.assign_coords({'x':np.array(freqdata)})\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.gca()\n",
+    "\n",
+    "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n",
+    "fitCurve_1.plot.errorbar(ax=ax, fmt='--g')\n",
+    "plt.xlabel('Center Frequency (MHz)')\n",
+    "plt.ylabel('NCount')\n",
+    "#plt.ylim([0, 3500])\n",
+    "plt.tight_layout()\n",
+    "plt.grid(visible=1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f_1 = fitAnalyser_1.get_fit_value(fitResult_1).center\n",
+    "df_1 = fitAnalyser_1.get_fit_std(fitResult_1).center\n",
+    "\n",
+    "print('f = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(f_1)* 1e3,df_1* 1e3]))\n",
+    "\n",
+    "s_1 = fitAnalyser_1.get_fit_value(fitResult_1).sigma\n",
+    "ds_1 = fitAnalyser_1.get_fit_std(fitResult_1).sigma\n",
+    "\n",
+    "fwhm_1 = 2.3548200*s_1 * 1e3\n",
+    "dfwhm_1 = 2.3548200*ds_1 * 1e3\n",
+    "\n",
+    "print('fwhm = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(fwhm_1),dfwhm_1]))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Z Offset field =  0.189 A, 0.25 Vpp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib notebook\n",
+    "shotNum = \"0008\"\n",
+    "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n",
+    "\n",
+    "dataSetDict = {\n",
+    "    dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i], maxFileNum = 100, excludeAxis = ['sweep_start_freq', 'sweep_stop_freq'])\n",
+    "    for i in [0]\n",
+    "}\n",
+    "\n",
+    "dataSet = dataSetDict[\"camera_0\"]\n",
+    "\n",
+    "print_scanAxis(dataSet)\n",
+    "\n",
+    "scanAxis = get_scanAxis(dataSet)\n",
+    "\n",
+    "dataSet = auto_rechunk(dataSet)\n",
+    "\n",
+    "dataSet = imageAnalyser.get_absorption_images(dataSet)\n",
+    "\n",
+    "imageAnalyser.center = (160, 880)\n",
+    "imageAnalyser.span = (250, 250)\n",
+    "imageAnalyser.fraction = (0.1, 0.1)\n",
+    "\n",
+    "dataSet_cropOD = imageAnalyser.crop_image(dataSet.OD)\n",
+    "dataSet_cropOD = imageAnalyser.substract_offset(dataSet_cropOD).load()\n",
+    "\n",
+    "Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n",
+    "Ncount_mean = calculate_mean(Ncount)\n",
+    "Ncount_std = calculate_std(Ncount)\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.gca()\n",
+    "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n",
+    "\n",
+    "plt.ylabel('NCount')\n",
+    "plt.tight_layout()\n",
+    "#plt.ylim([0, 3500])\n",
+    "plt.grid(visible=1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Ncount_mean_1 = Ncount_mean\n",
+    "Ncount_std_1 = Ncount_std\n",
+    "\n",
+    "fitAnalyser_1 = FitAnalyser(\"Gaussian With Offset\", fitDim=1)\n",
+    "# params = fitAnalyser.guess(Ncount_mean_1, x=scanAxis[0], guess_kwargs=dict(negative=True), dask=\"parallelized\")\n",
+    "params = fitAnalyser_1.fitModel.make_params()\n",
+    "params.add(name=\"amplitude\", value=  -6000, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"center\", value=  4.25, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"sigma\", value=  0.1, max=np.inf, min=  0, vary=True)\n",
+    "params.add(name=\"offset\", value=  6000, max=np.inf, min=-np.inf, vary=True)\n",
+    "\n",
+    "fitResult_1 = fitAnalyser_1.fit(Ncount_mean_1, params, x=scanAxis[0]).load()\n",
+    "freqdata = np.linspace(4.2375, 4.266, 500)\n",
+    "fitCurve_1 = fitAnalyser_1.eval(fitResult_1, x=freqdata, dask=\"parallelized\").load()\n",
+    "fitCurve_1 = fitCurve_1.assign_coords({'x':np.array(freqdata)})\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.gca()\n",
+    "\n",
+    "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n",
+    "fitCurve_1.plot.errorbar(ax=ax, fmt='--g')\n",
+    "plt.xlabel('Center Frequency (MHz)')\n",
+    "plt.ylabel('NCount')\n",
+    "#plt.ylim([0, 3500])\n",
+    "plt.tight_layout()\n",
+    "plt.grid(visible=1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f_1 = fitAnalyser_1.get_fit_value(fitResult_1).center\n",
+    "df_1 = fitAnalyser_1.get_fit_std(fitResult_1).center\n",
+    "\n",
+    "print('f = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(f_1)* 1e3,df_1* 1e3]))\n",
+    "\n",
+    "s_1 = fitAnalyser_1.get_fit_value(fitResult_1).sigma\n",
+    "ds_1 = fitAnalyser_1.get_fit_std(fitResult_1).sigma\n",
+    "\n",
+    "fwhm_1 = 2.3548200*s_1 * 1e3\n",
+    "dfwhm_1 = 2.3548200*ds_1 * 1e3\n",
+    "\n",
+    "print('fwhm = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(fwhm_1),dfwhm_1]))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Z Offset field =  0.189 A, 0.5 Vpp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib notebook\n",
+    "shotNum = \"0009\"\n",
+    "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n",
+    "\n",
+    "dataSetDict = {\n",
+    "    dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i], maxFileNum = 100, excludeAxis = ['sweep_start_freq', 'sweep_stop_freq'])\n",
+    "    for i in [0]\n",
+    "}\n",
+    "\n",
+    "dataSet = dataSetDict[\"camera_0\"]\n",
+    "\n",
+    "print_scanAxis(dataSet)\n",
+    "\n",
+    "scanAxis = get_scanAxis(dataSet)\n",
+    "\n",
+    "dataSet = auto_rechunk(dataSet)\n",
+    "\n",
+    "dataSet = imageAnalyser.get_absorption_images(dataSet)\n",
+    "\n",
+    "imageAnalyser.center = (160, 880)\n",
+    "imageAnalyser.span = (250, 250)\n",
+    "imageAnalyser.fraction = (0.1, 0.1)\n",
+    "\n",
+    "dataSet_cropOD = imageAnalyser.crop_image(dataSet.OD)\n",
+    "dataSet_cropOD = imageAnalyser.substract_offset(dataSet_cropOD).load()\n",
+    "\n",
+    "Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n",
+    "Ncount_mean = calculate_mean(Ncount)\n",
+    "Ncount_std = calculate_std(Ncount)\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.gca()\n",
+    "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n",
+    "\n",
+    "plt.ylabel('NCount')\n",
+    "plt.tight_layout()\n",
+    "#plt.ylim([0, 3500])\n",
+    "plt.grid(visible=1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Ncount_mean_1 = Ncount_mean\n",
+    "Ncount_std_1 = Ncount_std\n",
+    "\n",
+    "fitAnalyser_1 = FitAnalyser(\"Gaussian With Offset\", fitDim=1)\n",
+    "# params = fitAnalyser.guess(Ncount_mean_1, x=scanAxis[0], guess_kwargs=dict(negative=True), dask=\"parallelized\")\n",
+    "params = fitAnalyser_1.fitModel.make_params()\n",
+    "params.add(name=\"amplitude\", value=  -6000, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"center\", value=  4.25, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"sigma\", value=  0.1, max=np.inf, min=  0, vary=True)\n",
+    "params.add(name=\"offset\", value=  6000, max=np.inf, min=-np.inf, vary=True)\n",
+    "\n",
+    "fitResult_1 = fitAnalyser_1.fit(Ncount_mean_1, params, x=scanAxis[0]).load()\n",
+    "freqdata = np.linspace(4.23, 4.275, 500)\n",
+    "fitCurve_1 = fitAnalyser_1.eval(fitResult_1, x=freqdata, dask=\"parallelized\").load()\n",
+    "fitCurve_1 = fitCurve_1.assign_coords({'x':np.array(freqdata)})\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.gca()\n",
+    "\n",
+    "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n",
+    "fitCurve_1.plot.errorbar(ax=ax, fmt='--g')\n",
+    "plt.xlabel('Center Frequency (MHz)')\n",
+    "plt.ylabel('NCount')\n",
+    "#plt.ylim([0, 3500])\n",
+    "plt.tight_layout()\n",
+    "plt.grid(visible=1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f_1 = fitAnalyser_1.get_fit_value(fitResult_1).center\n",
+    "df_1 = fitAnalyser_1.get_fit_std(fitResult_1).center\n",
+    "\n",
+    "print('f = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(f_1)* 1e3,df_1* 1e3]))\n",
+    "\n",
+    "s_1 = fitAnalyser_1.get_fit_value(fitResult_1).sigma\n",
+    "ds_1 = fitAnalyser_1.get_fit_std(fitResult_1).sigma\n",
+    "\n",
+    "fwhm_1 = 2.3548200*s_1 * 1e3\n",
+    "dfwhm_1 = 2.3548200*ds_1 * 1e3\n",
+    "\n",
+    "print('fwhm = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(fwhm_1),dfwhm_1]))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Z Offset field =  0.189 A, 1 Vpp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib notebook\n",
+    "shotNum = \"0010\"\n",
+    "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n",
+    "\n",
+    "dataSetDict = {\n",
+    "    dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i], maxFileNum = 100, excludeAxis = ['sweep_start_freq', 'sweep_stop_freq'])\n",
+    "    for i in [0]\n",
+    "}\n",
+    "\n",
+    "dataSet = dataSetDict[\"camera_0\"]\n",
+    "\n",
+    "print_scanAxis(dataSet)\n",
+    "\n",
+    "scanAxis = get_scanAxis(dataSet)\n",
+    "\n",
+    "dataSet = auto_rechunk(dataSet)\n",
+    "\n",
+    "dataSet = imageAnalyser.get_absorption_images(dataSet)\n",
+    "\n",
+    "imageAnalyser.center = (160, 880)\n",
+    "imageAnalyser.span = (250, 250)\n",
+    "imageAnalyser.fraction = (0.1, 0.1)\n",
+    "\n",
+    "dataSet_cropOD = imageAnalyser.crop_image(dataSet.OD)\n",
+    "dataSet_cropOD = imageAnalyser.substract_offset(dataSet_cropOD).load()\n",
+    "\n",
+    "Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n",
+    "Ncount_mean = calculate_mean(Ncount)\n",
+    "Ncount_std = calculate_std(Ncount)\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.gca()\n",
+    "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n",
+    "\n",
+    "plt.ylabel('NCount')\n",
+    "plt.tight_layout()\n",
+    "#plt.ylim([0, 3500])\n",
+    "plt.grid(visible=1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Ncount_mean_1 = Ncount_mean\n",
+    "Ncount_std_1 = Ncount_std\n",
+    "\n",
+    "fitAnalyser_1 = FitAnalyser(\"Gaussian With Offset\", fitDim=1)\n",
+    "# params = fitAnalyser.guess(Ncount_mean_1, x=scanAxis[0], guess_kwargs=dict(negative=True), dask=\"parallelized\")\n",
+    "params = fitAnalyser_1.fitModel.make_params()\n",
+    "params.add(name=\"amplitude\", value=  -6000, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"center\", value=  4.25, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"sigma\", value=  0.1, max=np.inf, min=  0, vary=True)\n",
+    "params.add(name=\"offset\", value=  6000, max=np.inf, min=-np.inf, vary=True)\n",
+    "\n",
+    "fitResult_1 = fitAnalyser_1.fit(Ncount_mean_1, params, x=scanAxis[0]).load()\n",
+    "freqdata = np.linspace(4.23, 4.275, 500)\n",
+    "fitCurve_1 = fitAnalyser_1.eval(fitResult_1, x=freqdata, dask=\"parallelized\").load()\n",
+    "fitCurve_1 = fitCurve_1.assign_coords({'x':np.array(freqdata)})\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.gca()\n",
+    "\n",
+    "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n",
+    "fitCurve_1.plot.errorbar(ax=ax, fmt='--g')\n",
+    "plt.xlabel('Center Frequency (MHz)')\n",
+    "plt.ylabel('NCount')\n",
+    "#plt.ylim([0, 3500])\n",
+    "plt.tight_layout()\n",
+    "plt.grid(visible=1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f_1 = fitAnalyser_1.get_fit_value(fitResult_1).center\n",
+    "df_1 = fitAnalyser_1.get_fit_std(fitResult_1).center\n",
+    "\n",
+    "print('f = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(f_1)* 1e3,df_1* 1e3]))\n",
+    "\n",
+    "s_1 = fitAnalyser_1.get_fit_value(fitResult_1).sigma\n",
+    "ds_1 = fitAnalyser_1.get_fit_std(fitResult_1).sigma\n",
+    "\n",
+    "fwhm_1 = 2.3548200*s_1 * 1e3\n",
+    "dfwhm_1 = 2.3548200*ds_1 * 1e3\n",
+    "\n",
+    "print('fwhm = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(fwhm_1),dfwhm_1]))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Z Offset field =  0.189 A, 3 Vpp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib notebook\n",
+    "shotNum = \"0011\"\n",
+    "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n",
+    "\n",
+    "dataSetDict = {\n",
+    "    dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i], maxFileNum = 80, excludeAxis = ['sweep_start_freq', 'sweep_stop_freq'])\n",
+    "    for i in [0]\n",
+    "}\n",
+    "\n",
+    "dataSet = dataSetDict[\"camera_0\"]\n",
+    "\n",
+    "print_scanAxis(dataSet)\n",
+    "\n",
+    "scanAxis = get_scanAxis(dataSet)\n",
+    "\n",
+    "dataSet = auto_rechunk(dataSet)\n",
+    "\n",
+    "dataSet = imageAnalyser.get_absorption_images(dataSet)\n",
+    "\n",
+    "imageAnalyser.center = (160, 880)\n",
+    "imageAnalyser.span = (250, 250)\n",
+    "imageAnalyser.fraction = (0.1, 0.1)\n",
+    "\n",
+    "dataSet_cropOD = imageAnalyser.crop_image(dataSet.OD)\n",
+    "dataSet_cropOD = imageAnalyser.substract_offset(dataSet_cropOD).load()\n",
+    "\n",
+    "Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n",
+    "Ncount_mean = calculate_mean(Ncount)\n",
+    "Ncount_std = calculate_std(Ncount)\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.gca()\n",
+    "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n",
+    "\n",
+    "plt.ylabel('NCount')\n",
+    "plt.tight_layout()\n",
+    "#plt.ylim([0, 3500])\n",
+    "plt.grid(visible=1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Ncount_mean_1 = Ncount_mean\n",
+    "Ncount_std_1 = Ncount_std\n",
+    "\n",
+    "fitAnalyser_1 = FitAnalyser(\"Gaussian With Offset\", fitDim=1)\n",
+    "# params = fitAnalyser.guess(Ncount_mean_1, x=scanAxis[0], guess_kwargs=dict(negative=True), dask=\"parallelized\")\n",
+    "params = fitAnalyser_1.fitModel.make_params()\n",
+    "params.add(name=\"amplitude\", value=  -6000, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"center\", value=  4.25, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"sigma\", value=  0.1, max=np.inf, min=  0, vary=True)\n",
+    "params.add(name=\"offset\", value=  6000, max=np.inf, min=-np.inf, vary=True)\n",
+    "\n",
+    "fitResult_1 = fitAnalyser_1.fit(Ncount_mean_1, params, x=scanAxis[0]).load()\n",
+    "freqdata = np.linspace(4.22, 4.275, 500)\n",
+    "fitCurve_1 = fitAnalyser_1.eval(fitResult_1, x=freqdata, dask=\"parallelized\").load()\n",
+    "fitCurve_1 = fitCurve_1.assign_coords({'x':np.array(freqdata)})\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.gca()\n",
+    "\n",
+    "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n",
+    "fitCurve_1.plot.errorbar(ax=ax, fmt='--g')\n",
+    "plt.xlabel('Center Frequency (MHz)')\n",
+    "plt.ylabel('NCount')\n",
+    "#plt.ylim([0, 3500])\n",
+    "plt.tight_layout()\n",
+    "plt.grid(visible=1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f_1 = fitAnalyser_1.get_fit_value(fitResult_1).center\n",
+    "df_1 = fitAnalyser_1.get_fit_std(fitResult_1).center\n",
+    "\n",
+    "print('f = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(f_1)* 1e3,df_1* 1e3]))\n",
+    "\n",
+    "s_1 = fitAnalyser_1.get_fit_value(fitResult_1).sigma\n",
+    "ds_1 = fitAnalyser_1.get_fit_std(fitResult_1).sigma\n",
+    "\n",
+    "fwhm_1 = 2.3548200*s_1 * 1e3\n",
+    "dfwhm_1 = 2.3548200*ds_1 * 1e3\n",
+    "\n",
+    "print('fwhm = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(fwhm_1),dfwhm_1]))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Scan Z offset field during evaporation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The detected scaning axes and values are: \n",
+      "\n",
+      "{'compZ_current_sg': array([0.18 , 0.182, 0.184, 0.186, 0.188, 0.19 , 0.192, 0.194, 0.196,\n",
+      "       0.198, 0.2  , 0.202, 0.204, 0.206, 0.208, 0.21 , 0.212, 0.214,\n",
+      "       0.216, 0.218, 0.22 ]), 'runs': array([0., 1., 2.])}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "shotNum = \"0015\"\n",
+    "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n",
+    "\n",
+    "dataSetDict = {\n",
+    "    dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n",
+    "    for i in [0]\n",
+    "}\n",
+    "\n",
+    "dataSet = dataSetDict[\"camera_0\"]\n",
+    "\n",
+    "print_scanAxis(dataSet)\n",
+    "\n",
+    "scanAxis = get_scanAxis(dataSet)\n",
+    "\n",
+    "dataSet = auto_rechunk(dataSet)\n",
+    "\n",
+    "dataSet = imageAnalyser.get_absorption_images(dataSet)\n",
+    "\n",
+    "imageAnalyser.center = (890, 880)\n",
+    "imageAnalyser.span = (150, 150)\n",
+    "imageAnalyser.fraction = (0.1, 0.1)\n",
+    "\n",
+    "dataSet_cropOD = imageAnalyser.crop_image(dataSet.OD)\n",
+    "dataSet_cropOD = imageAnalyser.substract_offset(dataSet_cropOD).load()\n",
+    "\n",
+    "Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n",
+    "Ncount_mean = calculate_mean(Ncount)\n",
+    "Ncount_std = calculate_std(Ncount)\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.gca()\n",
+    "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='-ob')\n",
+    "plt.xlabel('comp Z current (A)')\n",
+    "plt.ylabel('NCount')\n",
+    "plt.tight_layout()\n",
+    "plt.grid(visible=1)\n",
+    "plt.show()\n",
+    "\n",
+    "# DB.create_global(shotNum, dataSet)\n",
+    "# DB.add_data(shotNum, dataSet_cropOD, engine='xarray')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure()\n",
+    "\n",
+    "data = dataSet_cropOD.sel(compZ_current_sg = 0.198, runs=0)\n",
+    "data = data.assign_coords(x=data.x * 2.352 * 5.86)\n",
+    "data = data.assign_coords(y=data.y * 2.352 * 5.86)\n",
+    "\n",
+    "data.plot.pcolormesh(cmap='jet', vmin=0, vmax=2)\n",
+    "\n",
+    "plt.title('')\n",
+    "plt.xlabel('x ($\\mu m$)')\n",
+    "plt.ylabel('y ($\\mu m$)')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "f:\\Jianshun\\analyseScript\\Analyser\\FitAnalyser.py:86: RuntimeWarning: invalid value encountered in power\n",
+      "  res = (1- ((x-centerx)/(sigmax))**2 - ((y-centery)/(sigmay))**2)**(3 / 2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = dataSet_cropOD.sel(compZ_current_sg = 0.198, runs=0)\n",
+    "\n",
+    "fitModel = DensityProfileBEC2dModel()\n",
+    "fitAnalyser_1 = FitAnalyser(fitModel, fitDim=2)\n",
+    "\n",
+    "params = fitAnalyser_1.guess(data, dask=\"parallelized\")\n",
+    "\n",
+    "fitResult_1 = fitAnalyser_1.fit(data, params).load()\n",
+    "\n",
+    "# x = np.linspace(2.7725, 2.822, 500)\n",
+    "# y = np.linspace(2.7725, 2.822, 500)\n",
+    "# fitCurve_1 = fitAnalyser_1.eval(fitResult_1, x=x, y=y, dask=\"parallelized\").load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\data\\AppData\\Roaming\\Python\\Python39\\site-packages\\numpy\\lib\\function_base.py:2246: RuntimeWarning: invalid value encountered in _get_fit_full_result_single (vectorized)\n",
+      "  outputs = ufunc(*inputs)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
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+       "<defs>\n",
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+       "</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.Dataset&gt;\n",
+       "Dimensions:              ()\n",
+       "Coordinates:\n",
+       "    compZ_current_sg     float64 0.198\n",
+       "    runs                 float64 0.0\n",
+       "Data variables:\n",
+       "    BEC_amplitude        object 792.7896024042066+/-nan\n",
+       "    thermal_amplitude    object 0.0+/-nan\n",
+       "    BEC_centerx          object 72.03322637975705+/-nan\n",
+       "    BEC_centery          object 74.15709273093088+/-nan\n",
+       "    thermal_centerx      object 73.1219837873983+/-nan\n",
+       "    thermal_centery      object 75.0675362391377+/-nan\n",
+       "    BEC_sigmax           object 25.92336716503899+/-nan\n",
+       "    BEC_sigmay           object 10.643305951751195+/-nan\n",
+       "    thermal_sigmax       object 17.18976684635309+/-nan\n",
+       "    thermal_sigmay       object 14.449859669689275+/-nan\n",
+       "    thermalAspectRatio   object 0.8406082408706373+/-nan\n",
+       "    condensate_fraction  object 1.0+/-nan\n",
+       "Attributes:\n",
+       "    IMAGE_SUBCLASS:       IMAGE_GRAYSCALE\n",
+       "    IMAGE_VERSION:        1.2\n",
+       "    IMAGE_WHITE_IS_ZERO:  0\n",
+       "    x_start:              815\n",
+       "    x_end:                965\n",
+       "    y_end:                955\n",
+       "    y_start:              805\n",
+       "    x_center:             890\n",
+       "    y_center:             880\n",
+       "    x_span:               150\n",
+       "    y_span:               150</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-ee805def-8782-4ff7-aaa5-0307542091fe' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ee805def-8782-4ff7-aaa5-0307542091fe' 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-bbeb3fd8-9d26-42ce-a0f6-9e9fccaac797' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bbeb3fd8-9d26-42ce-a0f6-9e9fccaac797' class='xr-section-summary' >Coordinates: <span>(2)</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>compZ_current_sg</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.198</div><input id='attrs-290bd221-ede4-4895-b984-6361188a7a61' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-290bd221-ede4-4895-b984-6361188a7a61' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0fc86fa5-f9e1-436b-96fd-ee93ac5da45c' class='xr-var-data-in' type='checkbox'><label for='data-0fc86fa5-f9e1-436b-96fd-ee93ac5da45c' 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(0.198)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>runs</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-0c115949-5d9f-4808-a73d-8486b09d9ef7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0c115949-5d9f-4808-a73d-8486b09d9ef7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7240579c-8983-46b8-8df4-f5586e5c5c9d' class='xr-var-data-in' type='checkbox'><label for='data-7240579c-8983-46b8-8df4-f5586e5c5c9d' 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.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1b80cf78-e245-4bdf-b9d3-38af0d71eae7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1b80cf78-e245-4bdf-b9d3-38af0d71eae7' class='xr-section-summary' >Data variables: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>BEC_amplitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>792.7896024042066+/-nan</div><input id='attrs-829f02b0-3ab6-4c29-a25b-a21789ce5b42' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-829f02b0-3ab6-4c29-a25b-a21789ce5b42' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ef932b3-726d-43a3-a5de-0dcf8f5a42f6' class='xr-var-data-in' type='checkbox'><label for='data-1ef932b3-726d-43a3-a5de-0dcf8f5a42f6' 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(792.7896024042066+/-nan, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thermal_amplitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0.0+/-nan</div><input id='attrs-325b3b78-a941-4d6a-b7d1-990d99338c66' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-325b3b78-a941-4d6a-b7d1-990d99338c66' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6a0ed95-0836-42a9-84a8-f21fafc8c87c' class='xr-var-data-in' type='checkbox'><label for='data-a6a0ed95-0836-42a9-84a8-f21fafc8c87c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0.0+/-nan, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>BEC_centerx</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>72.03322637975705+/-nan</div><input id='attrs-bba65f9c-d068-4d2a-997f-d59e73c00815' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bba65f9c-d068-4d2a-997f-d59e73c00815' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e47fe682-8729-48dd-84e3-8519bd55cdbd' class='xr-var-data-in' type='checkbox'><label for='data-e47fe682-8729-48dd-84e3-8519bd55cdbd' 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(72.03322637975705+/-nan, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>BEC_centery</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>74.15709273093088+/-nan</div><input id='attrs-f9cfbeac-597e-435f-b815-f22b515ccc6b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f9cfbeac-597e-435f-b815-f22b515ccc6b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dde4a815-c791-4da9-8a62-df7acf73d8eb' class='xr-var-data-in' type='checkbox'><label for='data-dde4a815-c791-4da9-8a62-df7acf73d8eb' 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(74.15709273093088+/-nan, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thermal_centerx</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>73.1219837873983+/-nan</div><input id='attrs-ecef8712-c5f6-4562-98c6-4f212d91bf5d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ecef8712-c5f6-4562-98c6-4f212d91bf5d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72ff6427-fdba-4430-bca9-7fb4a2a5123c' class='xr-var-data-in' type='checkbox'><label for='data-72ff6427-fdba-4430-bca9-7fb4a2a5123c' 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(73.1219837873983+/-nan, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thermal_centery</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>75.0675362391377+/-nan</div><input id='attrs-b0873bad-e216-468d-a0ab-d3618fee8275' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b0873bad-e216-468d-a0ab-d3618fee8275' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4c9ca1a-1373-4dd1-88ad-b2406ba71c9e' class='xr-var-data-in' type='checkbox'><label for='data-c4c9ca1a-1373-4dd1-88ad-b2406ba71c9e' 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(75.0675362391377+/-nan, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>BEC_sigmax</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>25.92336716503899+/-nan</div><input id='attrs-dc327619-c896-4a56-b50b-44666fe31227' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dc327619-c896-4a56-b50b-44666fe31227' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af3ece8f-8eb2-4e6a-9759-3e4df0202e65' class='xr-var-data-in' type='checkbox'><label for='data-af3ece8f-8eb2-4e6a-9759-3e4df0202e65' 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(25.92336716503899+/-nan, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>BEC_sigmay</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>10.643305951751195+/-nan</div><input id='attrs-a8adae5d-d924-4146-9c94-b613464e2415' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a8adae5d-d924-4146-9c94-b613464e2415' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca6acbbe-f9cb-4353-bbae-c96c6fe41958' class='xr-var-data-in' type='checkbox'><label for='data-ca6acbbe-f9cb-4353-bbae-c96c6fe41958' 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(10.643305951751195+/-nan, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thermal_sigmax</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>17.18976684635309+/-nan</div><input id='attrs-5e4e1667-afc4-48e4-a761-087c233df25c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5e4e1667-afc4-48e4-a761-087c233df25c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b67ab31-cd74-4079-8283-c63cfefa77a7' class='xr-var-data-in' type='checkbox'><label for='data-6b67ab31-cd74-4079-8283-c63cfefa77a7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(17.18976684635309+/-nan, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thermal_sigmay</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>14.449859669689275+/-nan</div><input id='attrs-cef60924-f084-4637-b29d-43379935d91a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cef60924-f084-4637-b29d-43379935d91a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-efca491e-3c05-49ff-971a-b41bc9bc4e15' class='xr-var-data-in' type='checkbox'><label for='data-efca491e-3c05-49ff-971a-b41bc9bc4e15' 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(14.449859669689275+/-nan, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thermalAspectRatio</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0.8406082408706373+/-nan</div><input id='attrs-4619e4bc-31c7-40d1-8a18-c8d99e8c9672' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4619e4bc-31c7-40d1-8a18-c8d99e8c9672' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e9287cd-6395-43e4-87e3-e53faf91bc3c' class='xr-var-data-in' type='checkbox'><label for='data-1e9287cd-6395-43e4-87e3-e53faf91bc3c' 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(0.8406082408706373+/-nan, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>condensate_fraction</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1.0+/-nan</div><input id='attrs-4697bed4-a3ab-43b8-849a-27cb1c294259' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4697bed4-a3ab-43b8-849a-27cb1c294259' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2667370e-34a2-4631-b03a-848a9dc9f74a' class='xr-var-data-in' type='checkbox'><label for='data-2667370e-34a2-4631-b03a-848a9dc9f74a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(1.0+/-nan, dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-85181b44-d4a0-4862-997d-3cbb8f6f9835' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-85181b44-d4a0-4862-997d-3cbb8f6f9835' 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-5d1bf8af-9415-4f31-90bb-4416117d730d' class='xr-section-summary-in' type='checkbox'  ><label for='section-5d1bf8af-9415-4f31-90bb-4416117d730d' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>IMAGE_SUBCLASS :</span></dt><dd>IMAGE_GRAYSCALE</dd><dt><span>IMAGE_VERSION :</span></dt><dd>1.2</dd><dt><span>IMAGE_WHITE_IS_ZERO :</span></dt><dd>0</dd><dt><span>x_start :</span></dt><dd>815</dd><dt><span>x_end :</span></dt><dd>965</dd><dt><span>y_end :</span></dt><dd>955</dd><dt><span>y_start :</span></dt><dd>805</dd><dt><span>x_center :</span></dt><dd>890</dd><dt><span>y_center :</span></dt><dd>880</dd><dt><span>x_span :</span></dt><dd>150</dd><dt><span>y_span :</span></dt><dd>150</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:              ()\n",
+       "Coordinates:\n",
+       "    compZ_current_sg     float64 0.198\n",
+       "    runs                 float64 0.0\n",
+       "Data variables:\n",
+       "    BEC_amplitude        object 792.7896024042066+/-nan\n",
+       "    thermal_amplitude    object 0.0+/-nan\n",
+       "    BEC_centerx          object 72.03322637975705+/-nan\n",
+       "    BEC_centery          object 74.15709273093088+/-nan\n",
+       "    thermal_centerx      object 73.1219837873983+/-nan\n",
+       "    thermal_centery      object 75.0675362391377+/-nan\n",
+       "    BEC_sigmax           object 25.92336716503899+/-nan\n",
+       "    BEC_sigmay           object 10.643305951751195+/-nan\n",
+       "    thermal_sigmax       object 17.18976684635309+/-nan\n",
+       "    thermal_sigmay       object 14.449859669689275+/-nan\n",
+       "    thermalAspectRatio   object 0.8406082408706373+/-nan\n",
+       "    condensate_fraction  object 1.0+/-nan\n",
+       "Attributes:\n",
+       "    IMAGE_SUBCLASS:       IMAGE_GRAYSCALE\n",
+       "    IMAGE_VERSION:        1.2\n",
+       "    IMAGE_WHITE_IS_ZERO:  0\n",
+       "    x_start:              815\n",
+       "    x_end:                965\n",
+       "    y_end:                955\n",
+       "    y_start:              805\n",
+       "    x_center:             890\n",
+       "    y_center:             880\n",
+       "    x_span:               150\n",
+       "    y_span:               150"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fitAnalyser_1.get_fit_full_result(fitResult_1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.1654007155341837"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "792.7896024042066 * 147 /1e5"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Calibration of the magnetic fields - after connecting the Z coils to the HiPPS"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Z Offset field =  0.119 A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib notebook\n",
+    "shotNum = \"0035\"\n",
+    "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n",
+    "\n",
+    "dataSetDict = {\n",
+    "    dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i], excludeAxis = ['sweep_start_freq', 'sweep_stop_freq'])\n",
+    "    for i in [0]\n",
+    "}\n",
+    "\n",
+    "dataSet = dataSetDict[\"camera_0\"]\n",
+    "\n",
+    "print_scanAxis(dataSet)\n",
+    "\n",
+    "scanAxis = get_scanAxis(dataSet)\n",
+    "\n",
+    "dataSet = auto_rechunk(dataSet)\n",
+    "\n",
+    "dataSet = imageAnalyser.get_absorption_images(dataSet)\n",
+    "\n",
+    "imageAnalyser.center = (160, 880)\n",
+    "imageAnalyser.span = (250, 250)\n",
+    "imageAnalyser.fraction = (0.1, 0.1)\n",
+    "\n",
+    "dataSet_cropOD = imageAnalyser.crop_image(dataSet.OD)\n",
+    "dataSet_cropOD = imageAnalyser.substract_offset(dataSet_cropOD).load()\n",
+    "\n",
+    "Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n",
+    "Ncount_mean = calculate_mean(Ncount)\n",
+    "Ncount_std = calculate_std(Ncount)\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.gca()\n",
+    "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n",
+    "\n",
+    "plt.ylabel('NCount')\n",
+    "plt.tight_layout()\n",
+    "#plt.ylim([0, 3500])\n",
+    "plt.grid(visible=1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Ncount_mean_1 = Ncount_mean\n",
+    "Ncount_std_1 = Ncount_std\n",
+    "\n",
+    "fitAnalyser_1 = FitAnalyser(\"Gaussian With Offset\", fitDim=1)\n",
+    "# params = fitAnalyser.guess(Ncount_mean_1, x=scanAxis[0], guess_kwargs=dict(negative=True), dask=\"parallelized\")\n",
+    "params = fitAnalyser_1.fitModel.make_params()\n",
+    "params.add(name=\"amplitude\", value=  -5000, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"center\", value=  2.8, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"sigma\", value=  0.1, max=np.inf, min=  0, vary=True)\n",
+    "params.add(name=\"offset\", value=  5000, max=np.inf, min=-np.inf, vary=True)\n",
+    "\n",
+    "fitResult_1 = fitAnalyser_1.fit(Ncount_mean_1, params, x=scanAxis[0]).load()\n",
+    "freqdata = np.linspace(2.7725, 2.822, 500)\n",
+    "fitCurve_1 = fitAnalyser_1.eval(fitResult_1, x=freqdata, dask=\"parallelized\").load()\n",
+    "fitCurve_1 = fitCurve_1.assign_coords({'x':np.array(freqdata)})\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.gca()\n",
+    "\n",
+    "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n",
+    "fitCurve_1.plot.errorbar(ax=ax, fmt='--g')\n",
+    "plt.xlabel('Center Frequency (MHz)')\n",
+    "plt.ylabel('NCount')\n",
+    "#plt.xlim([2.7828, 2.81625])\n",
+    "plt.tight_layout()\n",
+    "plt.grid(visible=1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f_1 = fitAnalyser_1.get_fit_value(fitResult_1).center\n",
+    "df_1 = fitAnalyser_1.get_fit_std(fitResult_1).center\n",
+    "\n",
+    "print('f = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(f_1)* 1e3,df_1* 1e3]))\n",
+    "\n",
+    "s_1 = fitAnalyser_1.get_fit_value(fitResult_1).sigma\n",
+    "ds_1 = fitAnalyser_1.get_fit_std(fitResult_1).sigma\n",
+    "\n",
+    "fwhm_1 = 2.3548200*s_1 * 1e3\n",
+    "dfwhm_1 = 2.3548200*ds_1 * 1e3\n",
+    "\n",
+    "print('fwhm = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(fwhm_1),dfwhm_1]))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Z Offset field =  0.140 A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib notebook\n",
+    "shotNum = \"0044\"\n",
+    "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n",
+    "\n",
+    "dataSetDict = {\n",
+    "    dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i], excludeAxis = ['sweep_start_freq', 'sweep_stop_freq'])\n",
+    "    for i in [0]\n",
+    "}\n",
+    "\n",
+    "dataSet = dataSetDict[\"camera_0\"]\n",
+    "\n",
+    "print_scanAxis(dataSet)\n",
+    "\n",
+    "scanAxis = get_scanAxis(dataSet)\n",
+    "\n",
+    "dataSet = auto_rechunk(dataSet)\n",
+    "\n",
+    "dataSet = imageAnalyser.get_absorption_images(dataSet)\n",
+    "\n",
+    "imageAnalyser.center = (160, 880)\n",
+    "imageAnalyser.span = (250, 250)\n",
+    "imageAnalyser.fraction = (0.1, 0.1)\n",
+    "\n",
+    "dataSet_cropOD = imageAnalyser.crop_image(dataSet.OD)\n",
+    "dataSet_cropOD = imageAnalyser.substract_offset(dataSet_cropOD).load()\n",
+    "\n",
+    "Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n",
+    "Ncount_mean = calculate_mean(Ncount)\n",
+    "Ncount_std = calculate_std(Ncount)\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.gca()\n",
+    "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n",
+    "\n",
+    "plt.ylabel('NCount')\n",
+    "plt.tight_layout()\n",
+    "#plt.ylim([0, 3500])\n",
+    "plt.grid(visible=1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Ncount_mean_1 = Ncount_mean\n",
+    "Ncount_std_1 = Ncount_std\n",
+    "\n",
+    "fitAnalyser_1 = FitAnalyser(\"Gaussian With Offset\", fitDim=1)\n",
+    "# params = fitAnalyser.guess(Ncount_mean_1, x=scanAxis[0], guess_kwargs=dict(negative=True), dask=\"parallelized\")\n",
+    "params = fitAnalyser_1.fitModel.make_params()\n",
+    "params.add(name=\"amplitude\", value=  -2500, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"center\", value=  3.177, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"sigma\", value=  0.1, max=np.inf, min=  0, vary=True)\n",
+    "params.add(name=\"offset\", value=  2500, max=np.inf, min=-np.inf, vary=True)\n",
+    "\n",
+    "fitResult_1 = fitAnalyser_1.fit(Ncount_mean_1, params, x=scanAxis[0]).load()\n",
+    "freqdata = np.linspace(3.158, 3.198, 500)\n",
+    "fitCurve_1 = fitAnalyser_1.eval(fitResult_1, x=freqdata, dask=\"parallelized\").load()\n",
+    "fitCurve_1 = fitCurve_1.assign_coords({'x':np.array(freqdata)})\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.gca()\n",
+    "\n",
+    "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n",
+    "fitCurve_1.plot.errorbar(ax=ax, fmt='--g')\n",
+    "plt.xlabel('Center Frequency (MHz)')\n",
+    "plt.ylabel('NCount')\n",
+    "#plt.xlim([2.7828, 2.81625])\n",
+    "plt.tight_layout()\n",
+    "plt.grid(visible=1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f_1 = fitAnalyser_1.get_fit_value(fitResult_1).center\n",
+    "df_1 = fitAnalyser_1.get_fit_std(fitResult_1).center\n",
+    "\n",
+    "print('f = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(f_1)* 1e3,df_1* 1e3]))\n",
+    "\n",
+    "s_1 = fitAnalyser_1.get_fit_value(fitResult_1).sigma\n",
+    "ds_1 = fitAnalyser_1.get_fit_std(fitResult_1).sigma\n",
+    "\n",
+    "fwhm_1 = 2.3548200*s_1 * 1e3\n",
+    "dfwhm_1 = 2.3548200*ds_1 * 1e3\n",
+    "\n",
+    "print('fwhm = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(fwhm_1),dfwhm_1]))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Z Offset field =  0.200 A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib notebook\n",
+    "shotNum = \"0048\"\n",
+    "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n",
+    "\n",
+    "dataSetDict = {\n",
+    "    dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i], excludeAxis = ['sweep_start_freq', 'sweep_stop_freq'])\n",
+    "    for i in [0]\n",
+    "}\n",
+    "\n",
+    "dataSet = dataSetDict[\"camera_0\"]\n",
+    "\n",
+    "print_scanAxis(dataSet)\n",
+    "\n",
+    "scanAxis = get_scanAxis(dataSet)\n",
+    "\n",
+    "dataSet = auto_rechunk(dataSet)\n",
+    "\n",
+    "dataSet = imageAnalyser.get_absorption_images(dataSet)\n",
+    "\n",
+    "imageAnalyser.center = (160, 880)\n",
+    "imageAnalyser.span = (250, 250)\n",
+    "imageAnalyser.fraction = (0.1, 0.1)\n",
+    "\n",
+    "dataSet_cropOD = imageAnalyser.crop_image(dataSet.OD)\n",
+    "dataSet_cropOD = imageAnalyser.substract_offset(dataSet_cropOD).load()\n",
+    "\n",
+    "Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n",
+    "Ncount_mean = calculate_mean(Ncount)\n",
+    "Ncount_std = calculate_std(Ncount)\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.gca()\n",
+    "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n",
+    "\n",
+    "plt.ylabel('NCount')\n",
+    "plt.tight_layout()\n",
+    "#plt.ylim([0, 3500])\n",
+    "plt.grid(visible=1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Ncount_mean_1 = Ncount_mean\n",
+    "Ncount_std_1 = Ncount_std\n",
+    "\n",
+    "fitAnalyser_1 = FitAnalyser(\"Gaussian With Offset\", fitDim=1)\n",
+    "# params = fitAnalyser.guess(Ncount_mean_1, x=scanAxis[0], guess_kwargs=dict(negative=True), dask=\"parallelized\")\n",
+    "params = fitAnalyser_1.fitModel.make_params()\n",
+    "params.add(name=\"amplitude\", value=  -4500, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"center\", value=  4.275, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"sigma\", value=  0.1, max=np.inf, min=  0, vary=True)\n",
+    "params.add(name=\"offset\", value=  4500, max=np.inf, min=-np.inf, vary=True)\n",
+    "\n",
+    "fitResult_1 = fitAnalyser_1.fit(Ncount_mean_1, params, x=scanAxis[0]).load()\n",
+    "freqdata = np.linspace(4.260, 4.289, 500)\n",
+    "fitCurve_1 = fitAnalyser_1.eval(fitResult_1, x=freqdata, dask=\"parallelized\").load()\n",
+    "fitCurve_1 = fitCurve_1.assign_coords({'x':np.array(freqdata)})\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.gca()\n",
+    "\n",
+    "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n",
+    "fitCurve_1.plot.errorbar(ax=ax, fmt='--g')\n",
+    "plt.xlabel('Center Frequency (MHz)')\n",
+    "plt.ylabel('NCount')\n",
+    "#plt.xlim([2.7828, 2.81625])\n",
+    "plt.tight_layout()\n",
+    "plt.grid(visible=1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f_1 = fitAnalyser_1.get_fit_value(fitResult_1).center\n",
+    "df_1 = fitAnalyser_1.get_fit_std(fitResult_1).center\n",
+    "\n",
+    "print('f = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(f_1)* 1e3,df_1* 1e3]))\n",
+    "\n",
+    "s_1 = fitAnalyser_1.get_fit_value(fitResult_1).sigma\n",
+    "ds_1 = fitAnalyser_1.get_fit_std(fitResult_1).sigma\n",
+    "\n",
+    "fwhm_1 = 2.3548200*s_1 * 1e3\n",
+    "dfwhm_1 = 2.3548200*ds_1 * 1e3\n",
+    "\n",
+    "print('fwhm = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(fwhm_1),dfwhm_1]))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Z Offset field =  0.259 A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib notebook\n",
+    "shotNum = \"0056\"\n",
+    "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n",
+    "\n",
+    "dataSetDict = {\n",
+    "    dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i], excludeAxis = ['sweep_start_freq', 'sweep_stop_freq'])\n",
+    "    for i in [0]\n",
+    "}\n",
+    "\n",
+    "dataSet = dataSetDict[\"camera_0\"]\n",
+    "\n",
+    "print_scanAxis(dataSet)\n",
+    "\n",
+    "scanAxis = get_scanAxis(dataSet)\n",
+    "\n",
+    "dataSet = auto_rechunk(dataSet)\n",
+    "\n",
+    "dataSet = imageAnalyser.get_absorption_images(dataSet)\n",
+    "\n",
+    "imageAnalyser.center = (160, 880)\n",
+    "imageAnalyser.span = (250, 250)\n",
+    "imageAnalyser.fraction = (0.1, 0.1)\n",
+    "\n",
+    "dataSet_cropOD = imageAnalyser.crop_image(dataSet.OD)\n",
+    "dataSet_cropOD = imageAnalyser.substract_offset(dataSet_cropOD).load()\n",
+    "\n",
+    "Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n",
+    "Ncount_mean = calculate_mean(Ncount)\n",
+    "Ncount_std = calculate_std(Ncount)\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.gca()\n",
+    "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n",
+    "\n",
+    "plt.ylabel('NCount')\n",
+    "plt.tight_layout()\n",
+    "#plt.ylim([0, 3500])\n",
+    "plt.grid(visible=1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Ncount_mean_1 = Ncount_mean\n",
+    "Ncount_std_1 = Ncount_std\n",
+    "\n",
+    "fitAnalyser_1 = FitAnalyser(\"Gaussian With Offset\", fitDim=1)\n",
+    "# params = fitAnalyser.guess(Ncount_mean_1, x=scanAxis[0], guess_kwargs=dict(negative=True), dask=\"parallelized\")\n",
+    "params = fitAnalyser_1.fitModel.make_params()\n",
+    "params.add(name=\"amplitude\", value=  -4500, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"center\", value=  5.3, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"sigma\", value=  0.1, max=np.inf, min=  0, vary=True)\n",
+    "params.add(name=\"offset\", value=  4500, max=np.inf, min=-np.inf, vary=True)\n",
+    "\n",
+    "fitResult_1 = fitAnalyser_1.fit(Ncount_mean_1, params, x=scanAxis[0]).load()\n",
+    "freqdata = np.linspace(5.34, 5.364, 500)\n",
+    "fitCurve_1 = fitAnalyser_1.eval(fitResult_1, x=freqdata, dask=\"parallelized\").load()\n",
+    "fitCurve_1 = fitCurve_1.assign_coords({'x':np.array(freqdata)})\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.gca()\n",
+    "\n",
+    "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n",
+    "fitCurve_1.plot.errorbar(ax=ax, fmt='--g')\n",
+    "plt.xlabel('Center Frequency (MHz)')\n",
+    "plt.ylabel('NCount')\n",
+    "#plt.xlim([2.7828, 2.81625])\n",
+    "plt.tight_layout()\n",
+    "plt.grid(visible=1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f_1 = fitAnalyser_1.get_fit_value(fitResult_1).center\n",
+    "df_1 = fitAnalyser_1.get_fit_std(fitResult_1).center\n",
+    "\n",
+    "print('f = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(f_1)* 1e3,df_1* 1e3]))\n",
+    "\n",
+    "s_1 = fitAnalyser_1.get_fit_value(fitResult_1).sigma\n",
+    "ds_1 = fitAnalyser_1.get_fit_std(fitResult_1).sigma\n",
+    "\n",
+    "fwhm_1 = 2.3548200*s_1 * 1e3\n",
+    "dfwhm_1 = 2.3548200*ds_1 * 1e3\n",
+    "\n",
+    "print('fwhm = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(fwhm_1),dfwhm_1]))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Z Offset field =  0.329 A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib notebook\n",
+    "shotNum = \"0059\"\n",
+    "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n",
+    "\n",
+    "dataSetDict = {\n",
+    "    dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i], excludeAxis = ['sweep_start_freq', 'sweep_stop_freq'])\n",
+    "    for i in [0]\n",
+    "}\n",
+    "\n",
+    "dataSet = dataSetDict[\"camera_0\"]\n",
+    "\n",
+    "print_scanAxis(dataSet)\n",
+    "\n",
+    "scanAxis = get_scanAxis(dataSet)\n",
+    "\n",
+    "dataSet = auto_rechunk(dataSet)\n",
+    "\n",
+    "dataSet = imageAnalyser.get_absorption_images(dataSet)\n",
+    "\n",
+    "imageAnalyser.center = (160, 880)\n",
+    "imageAnalyser.span = (250, 250)\n",
+    "imageAnalyser.fraction = (0.1, 0.1)\n",
+    "\n",
+    "dataSet_cropOD = imageAnalyser.crop_image(dataSet.OD)\n",
+    "dataSet_cropOD = imageAnalyser.substract_offset(dataSet_cropOD).load()\n",
+    "\n",
+    "Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n",
+    "Ncount_mean = calculate_mean(Ncount)\n",
+    "Ncount_std = calculate_std(Ncount)\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.gca()\n",
+    "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n",
+    "\n",
+    "plt.ylabel('NCount')\n",
+    "plt.tight_layout()\n",
+    "#plt.ylim([0, 3500])\n",
+    "plt.grid(visible=1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Ncount_mean_1 = Ncount_mean\n",
+    "Ncount_std_1 = Ncount_std\n",
+    "\n",
+    "fitAnalyser_1 = FitAnalyser(\"Gaussian With Offset\", fitDim=1)\n",
+    "# params = fitAnalyser.guess(Ncount_mean_1, x=scanAxis[0], guess_kwargs=dict(negative=True), dask=\"parallelized\")\n",
+    "params = fitAnalyser_1.fitModel.make_params()\n",
+    "params.add(name=\"amplitude\", value=  -4500, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"center\", value=  6.636, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"sigma\", value=  0.1, max=np.inf, min=  0, vary=True)\n",
+    "params.add(name=\"offset\", value=  4500, max=np.inf, min=-np.inf, vary=True)\n",
+    "\n",
+    "fitResult_1 = fitAnalyser_1.fit(Ncount_mean_1, params, x=scanAxis[0]).load()\n",
+    "freqdata = np.linspace(6.62, 6.655, 500)\n",
+    "fitCurve_1 = fitAnalyser_1.eval(fitResult_1, x=freqdata, dask=\"parallelized\").load()\n",
+    "fitCurve_1 = fitCurve_1.assign_coords({'x':np.array(freqdata)})\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.gca()\n",
+    "\n",
+    "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n",
+    "fitCurve_1.plot.errorbar(ax=ax, fmt='--g')\n",
+    "plt.xlabel('Center Frequency (MHz)')\n",
+    "plt.ylabel('NCount')\n",
+    "#plt.xlim([2.7828, 2.81625])\n",
+    "plt.tight_layout()\n",
+    "plt.grid(visible=1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f_1 = fitAnalyser_1.get_fit_value(fitResult_1).center\n",
+    "df_1 = fitAnalyser_1.get_fit_std(fitResult_1).center\n",
+    "\n",
+    "print('f = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(f_1)* 1e3,df_1* 1e3]))\n",
+    "\n",
+    "s_1 = fitAnalyser_1.get_fit_value(fitResult_1).sigma\n",
+    "ds_1 = fitAnalyser_1.get_fit_std(fitResult_1).sigma\n",
+    "\n",
+    "fwhm_1 = 2.3548200*s_1 * 1e3\n",
+    "dfwhm_1 = 2.3548200*ds_1 * 1e3\n",
+    "\n",
+    "print('fwhm = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(fwhm_1),dfwhm_1]))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Z Offset field =  0.419 A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib notebook\n",
+    "shotNum = \"0063\"\n",
+    "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n",
+    "\n",
+    "dataSetDict = {\n",
+    "    dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i], excludeAxis = ['sweep_start_freq', 'sweep_stop_freq'])\n",
+    "    for i in [0]\n",
+    "}\n",
+    "\n",
+    "dataSet = dataSetDict[\"camera_0\"]\n",
+    "\n",
+    "print_scanAxis(dataSet)\n",
+    "\n",
+    "scanAxis = get_scanAxis(dataSet)\n",
+    "\n",
+    "dataSet = auto_rechunk(dataSet)\n",
+    "\n",
+    "dataSet = imageAnalyser.get_absorption_images(dataSet)\n",
+    "\n",
+    "imageAnalyser.center = (160, 880)\n",
+    "imageAnalyser.span = (250, 250)\n",
+    "imageAnalyser.fraction = (0.1, 0.1)\n",
+    "\n",
+    "dataSet_cropOD = imageAnalyser.crop_image(dataSet.OD)\n",
+    "dataSet_cropOD = imageAnalyser.substract_offset(dataSet_cropOD).load()\n",
+    "\n",
+    "Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n",
+    "Ncount_mean = calculate_mean(Ncount)\n",
+    "Ncount_std = calculate_std(Ncount)\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.gca()\n",
+    "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n",
+    "\n",
+    "plt.ylabel('NCount')\n",
+    "plt.tight_layout()\n",
+    "#plt.ylim([0, 3500])\n",
+    "plt.grid(visible=1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Ncount_mean_1 = Ncount_mean\n",
+    "Ncount_std_1 = Ncount_std\n",
+    "\n",
+    "fitAnalyser_1 = FitAnalyser(\"Gaussian With Offset\", fitDim=1)\n",
+    "# params = fitAnalyser.guess(Ncount_mean_1, x=scanAxis[0], guess_kwargs=dict(negative=True), dask=\"parallelized\")\n",
+    "params = fitAnalyser_1.fitModel.make_params()\n",
+    "params.add(name=\"amplitude\", value=  -1500, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"center\", value=  8.286, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"sigma\", value=  0.1, max=np.inf, min=  0, vary=True)\n",
+    "params.add(name=\"offset\", value=  1500, max=np.inf, min=-np.inf, vary=True)\n",
+    "\n",
+    "fitResult_1 = fitAnalyser_1.fit(Ncount_mean_1, params, x=scanAxis[0]).load()\n",
+    "freqdata = np.linspace(8.27, 8.305, 500)\n",
+    "fitCurve_1 = fitAnalyser_1.eval(fitResult_1, x=freqdata, dask=\"parallelized\").load()\n",
+    "fitCurve_1 = fitCurve_1.assign_coords({'x':np.array(freqdata)})\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.gca()\n",
+    "\n",
+    "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n",
+    "fitCurve_1.plot.errorbar(ax=ax, fmt='--g')\n",
+    "plt.xlabel('Center Frequency (MHz)')\n",
+    "plt.ylabel('NCount')\n",
+    "#plt.xlim([2.7828, 2.81625])\n",
+    "plt.tight_layout()\n",
+    "plt.grid(visible=1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f_1 = fitAnalyser_1.get_fit_value(fitResult_1).center\n",
+    "df_1 = fitAnalyser_1.get_fit_std(fitResult_1).center\n",
+    "\n",
+    "print('f = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(f_1)* 1e3,df_1* 1e3]))\n",
+    "\n",
+    "s_1 = fitAnalyser_1.get_fit_value(fitResult_1).sigma\n",
+    "ds_1 = fitAnalyser_1.get_fit_std(fitResult_1).sigma\n",
+    "\n",
+    "fwhm_1 = 2.3548200*s_1 * 1e3\n",
+    "dfwhm_1 = 2.3548200*ds_1 * 1e3\n",
+    "\n",
+    "print('fwhm = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(fwhm_1),dfwhm_1]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img_dir = 'C:/Users/control/DyLab/Experiments/DyBEC/'\n",
+    "SequenceName = \"Evaporative_Cooling\"\n",
+    "folderPath = img_dir + SequenceName + \"/\" + get_date()\n",
+    "\n",
+    "mongoDB = mongoClient[SequenceName]\n",
+    "\n",
+    "DB = MongoDB(mongoClient, mongoDB, date=get_date())"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Z Offset field =  0.489 A"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib notebook\n",
+    "shotNum = \"0002\"\n",
+    "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n",
+    "\n",
+    "dataSetDict = {\n",
+    "    dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i], excludeAxis = ['sweep_start_freq', 'sweep_stop_freq'])\n",
+    "    for i in [0]\n",
+    "}\n",
+    "\n",
+    "dataSet = dataSetDict[\"camera_0\"]\n",
+    "\n",
+    "print_scanAxis(dataSet)\n",
+    "\n",
+    "scanAxis = get_scanAxis(dataSet)\n",
+    "\n",
+    "dataSet = auto_rechunk(dataSet)\n",
+    "\n",
+    "dataSet = imageAnalyser.get_absorption_images(dataSet)\n",
+    "\n",
+    "imageAnalyser.center = (160, 880)\n",
+    "imageAnalyser.span = (250, 250)\n",
+    "imageAnalyser.fraction = (0.1, 0.1)\n",
+    "\n",
+    "dataSet_cropOD = imageAnalyser.crop_image(dataSet.OD)\n",
+    "dataSet_cropOD = imageAnalyser.substract_offset(dataSet_cropOD).load()\n",
+    "\n",
+    "Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n",
+    "Ncount_mean = calculate_mean(Ncount)\n",
+    "Ncount_std = calculate_std(Ncount)\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.gca()\n",
+    "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n",
+    "\n",
+    "plt.ylabel('NCount')\n",
+    "plt.tight_layout()\n",
+    "#plt.ylim([0, 3500])\n",
+    "plt.grid(visible=1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Ncount_mean_1 = Ncount_mean\n",
+    "Ncount_std_1 = Ncount_std\n",
+    "\n",
+    "fitAnalyser_1 = FitAnalyser(\"Gaussian With Offset\", fitDim=1)\n",
+    "# params = fitAnalyser.guess(Ncount_mean_1, x=scanAxis[0], guess_kwargs=dict(negative=True), dask=\"parallelized\")\n",
+    "params = fitAnalyser_1.fitModel.make_params()\n",
+    "params.add(name=\"amplitude\", value=  -2500, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"center\", value=  9.575, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"sigma\", value=  0.2, max=np.inf, min=  0, vary=True)\n",
+    "params.add(name=\"offset\", value=  2500, max=np.inf, min=-np.inf, vary=True)\n",
+    "\n",
+    "fitResult_1 = fitAnalyser_1.fit(Ncount_mean_1, params, x=scanAxis[0]).load()\n",
+    "freqdata = np.linspace(9.555, 9.595, 500)\n",
+    "fitCurve_1 = fitAnalyser_1.eval(fitResult_1, x=freqdata, dask=\"parallelized\").load()\n",
+    "fitCurve_1 = fitCurve_1.assign_coords({'x':np.array(freqdata)})\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.gca()\n",
+    "\n",
+    "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n",
+    "fitCurve_1.plot.errorbar(ax=ax, fmt='--g')\n",
+    "plt.xlabel('Center Frequency (MHz)')\n",
+    "plt.ylabel('NCount')\n",
+    "#plt.xlim([2.7828, 2.81625])\n",
+    "plt.tight_layout()\n",
+    "plt.grid(visible=1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f_1 = fitAnalyser_1.get_fit_value(fitResult_1).center\n",
+    "df_1 = fitAnalyser_1.get_fit_std(fitResult_1).center\n",
+    "\n",
+    "print('f = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(f_1)* 1e3,df_1* 1e3]))\n",
+    "\n",
+    "s_1 = fitAnalyser_1.get_fit_value(fitResult_1).sigma\n",
+    "ds_1 = fitAnalyser_1.get_fit_std(fitResult_1).sigma\n",
+    "\n",
+    "fwhm_1 = 2.3548200*s_1 * 1e3\n",
+    "dfwhm_1 = 2.3548200*ds_1 * 1e3\n",
+    "\n",
+    "print('fwhm = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(fwhm_1),dfwhm_1]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f = [2798.24971, 3178.49790, 4275.39905, 5352.17283, 6637.80418, 8288.35264, 9573.59333]\n",
+    "df = [0.36873, 0.29413, 0.20667, 0.20818, 0.21978, 0.20285, 0.18495]\n",
+    "z_offset_current = [0.119, 0.140, 0.2, 0.259, 0.329, 0.419, 0.489]\n",
+    "\n",
+    "f_fit = f\n",
+    "df_fit = df\n",
+    "z_offset_current_fit = z_offset_current\n",
+    "\n",
+    "\n",
+    "x = np.array(z_offset_current_fit)\n",
+    "y = np.array(f_fit)\n",
+    "\n",
+    "# Degree of the fitting polynomial\n",
+    "deg = 1\n",
+    "# Parameters from the fit of the polynomial\n",
+    "p = np.polyfit(x, y, deg)\n",
+    "m = p[0]  # Gradient\n",
+    "c = p[1]  # y-intercept\n",
+    "\n",
+    "#print(f'The fitted straight line has equation y = {m:.1f}x {c:=+6.1f}')\n",
+    "\n",
+    "# Model the data using the parameters of the fitted straight line\n",
+    "y_model = np.polyval(p, x)\n",
+    "\n",
+    "# Create the linear (1 degree polynomial) model\n",
+    "model = np.poly1d(p)\n",
+    "# Fit the model\n",
+    "y_model = model(x)\n",
+    "\n",
+    "# Mean\n",
+    "y_bar = np.mean(y)\n",
+    "# Coefficient of determination, R²\n",
+    "R2 = np.sum((y_model - y_bar)**2) / np.sum((y - y_bar)**2)\n",
+    "\n",
+    "#print(f'R² = {R2:.2f}')\n",
+    "\n",
+    "fitted_SlopeInkHz = m\n",
+    "fitted_offsetInkHz = c\n",
+    "muB = 9.274e-24\n",
+    "hbar = 6.626e-34 / (2 * np.pi)\n",
+    "gJ = 1.24\n",
+    "Slope = (((2 * np.pi * fitted_SlopeInkHz * 1e3)*hbar) / (muB*gJ)) * 1e4\n",
+    "Offset  = (((2 * np.pi * fitted_offsetInkHz * 1e3)*hbar) / (muB*gJ)) * 1e4\n",
+    "\n",
+    "def calib_fit(x, B):\n",
+    "    alpha = ((2 * np.pi * fitted_SlopeInkHz * 1e3)*hbar) / (muB*gJ)\n",
+    "    beta  = ((2 * np.pi * fitted_offsetInkHz * 1e3)*hbar) / (muB*gJ)\n",
+    "    delta_nu = ((muB * gJ) / hbar) * np.sqrt((B**2-beta**2) + ((alpha * x) + beta)**2)\n",
+    "    return delta_nu / (2 * np.pi * 1e3)\n",
+    "\n",
+    "\n",
+    "popt, pcov = curve_fit(calib_fit, z_offset_current, f, np.array([0.1*1e-4]))\n",
+    "Boffset = popt[0] * 1e4\n",
+    "dBoffset = pcov[0][0]**0.5 * 1e4\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.gca()\n",
+    "plt.clf\n",
+    "#plt.scatter(z_offset_current, f, c='gray', marker='o', edgecolors='k', s=30)\n",
+    "plt.errorbar(z_offset_current, f, yerr=df, fmt='o')\n",
+    "xvals = np.linspace(0, 0.5, 500)\n",
+    "plt.plot(np.array(xvals), p[1] + p[0] * np.array(xvals), label=f'Line Fit')\n",
+    "plt.plot(xvals, calib_fit(xvals, *popt), label=f'Curve Fit')\n",
+    "plt.text(0.25, 2200, f'Line Slope  = {Slope:.3f} G/A', fontsize=12)\n",
+    "plt.text(0.25, 1500, f'Line Offset = {Offset:=.3f} G', fontsize=12)\n",
+    "plt.text(0.25, 800, f'Bo= {Boffset:=.3f} +/- {dBoffset:=.3f} G', fontsize=12)\n",
+    "plt.xlabel('Z Offset Coil Current (A)', fontsize=12)\n",
+    "plt.ylabel('Resonance Frequency (kHz)', fontsize=12)\n",
+    "plt.xticks(fontsize=12)\n",
+    "plt.yticks(fontsize=12)\n",
+    "plt.legend(fontsize=12)\n",
+    "#plt.xlim(-0.01, 0.04)\n",
+    "#plt.ylim(0, 2000)\n",
+    "plt.grid(visible=1)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "l = list(np.arange(9.555, 9.595, 0.002))\n",
+    "# l = np.logspace(np.log10(250e-6), np.log10(500e-3), num=15)\n",
+    "\n",
+    "l = [round(item, 7) for item in l]\n",
+    "#random.shuffle(l)\n",
+    "\n",
+    "print(l)\n",
+    "print(len(l))\n",
+    "np.mean(l)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pixel = 5.86e-6\n",
+    "M     = 0.6827\n",
+    "F = (1/(0.3725*8.4743e-14)) * (pixel / M)**2\n",
+    "NCount = 85000\n",
+    "AtomNumber = NCount * F / 1e8\n",
+    "print(AtomNumber)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "muB = 9.274e-24\n",
+    "hbar = 6.626e-34 / (2 * np.pi)\n",
+    "gJ = 1.24\n",
+    "Delta = 2 * np.pi * 9573.59333  * 1e3\n",
+    "\n",
+    "Bz = (Delta*hbar) / (muB*gJ)\n",
+    "print(Bz * 1e4)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ODT 1 Calibration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v_high = 2.7\n",
+    "\"\"\"High Power\"\"\"\n",
+    "P_arm1_high = 5.776 * v_high - 0.683\n",
+    "\n",
+    "v_mid = 0.2076\n",
+    "\"\"\"Intermediate Power\"\"\"\n",
+    "P_arm1_mid = 5.815 * v_mid - 0.03651\n",
+    "\n",
+    "v_low = 0.0587\n",
+    "\"\"\"Low Power\"\"\"\n",
+    "P_arm1_low = 5271 * v_low - 27.5\n",
+    "\n",
+    "print(round(P_arm1_high, 3))\n",
+    "print(round(P_arm1_mid, 3))\n",
+    "print(round(P_arm1_low, 3))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ODT 2 Power Calibration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v = 0.7607\n",
+    "P_arm2 = 2.302 * v - 0.06452\n",
+    "print(round(P_arm2, 3))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "c05913ad4f24fdc6b2418069394dc5835b1981849b107c9ba6df693aafd66650"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Analyser/FitAnalyser.py b/Analyser/FitAnalyser.py
index 4c96afc..23ed248 100644
--- a/Analyser/FitAnalyser.py
+++ b/Analyser/FitAnalyser.py
@@ -331,7 +331,10 @@ class DensityProfileBEC2dModel(Model):
         self._set_paramhints_prefix()
 
     def _set_paramhints_prefix(self):
-        self.set_param_hint('BEC_sigmax', min=0)
+        # 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)
@@ -341,12 +344,26 @@ class DensityProfileBEC2dModel(Model):
         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)')
 
     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
         
@@ -356,25 +373,59 @@ class DensityProfileBEC2dModel(Model):
         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), BEC_sigmay=(pars_guess['A_sigmay'].value / 2.355), 
+                                # 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)
                                 )
         
-        if BEC_amplitude / (thermal_amplitude + BEC_amplitude) > pureBECThreshold:
-            if np.abs(1 - pars_guess['A_sigmax'].value / pars_guess['A_sigmay'].value) < 0.1:   
-                pars[f'{self.prefix}BEC_amplitude'].set(value=0)
-                pars[f'{self.prefix}thermal_amplitude'].set(value=(thermal_amplitude + BEC_amplitude))
+        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)
             else:
-                pars[f'{self.prefix}thermal_amplitude'].set(value=0)
-                pars[f'{self.prefix}BEC_amplitude'].set(value=(thermal_amplitude + BEC_amplitude))
+                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)
     
 
diff --git a/magenticField.ipynb b/magenticField.ipynb
new file mode 100644
index 0000000..c5d5bca
--- /dev/null
+++ b/magenticField.ipynb
@@ -0,0 +1,2380 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Import supporting package"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr\n",
+    "import numpy as np\n",
+    "import copy\n",
+    "\n",
+    "from uncertainties import ufloat\n",
+    "from uncertainties import unumpy as unp\n",
+    "from uncertainties import umath\n",
+    "import random\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.rcParams['font.size'] = 12\n",
+    "\n",
+    "from DataContainer.ReadData import read_hdf5_file\n",
+    "from Analyser.ImagingAnalyser import ImageAnalyser\n",
+    "from Analyser.FitAnalyser import FitAnalyser\n",
+    "from Analyser.FitAnalyser import NewFitModel, DensityProfileBEC2dModel\n",
+    "from ToolFunction.ToolFunction import *\n",
+    "\n",
+    "from scipy.optimize import curve_fit\n",
+    "\n",
+    "from ToolFunction.HomeMadeXarrayFunction import errorbar, dataarray_plot_errorbar\n",
+    "xr.plot.dataarray_plot.errorbar = errorbar\n",
+    "xr.plot.accessor.DataArrayPlotAccessor.errorbar = dataarray_plot_errorbar\n",
+    "\n",
+    "imageAnalyser = ImageAnalyser()\n",
+    "\n",
+    "# %matplotlib notebook"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Start a client for parallel computing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n",
+       "    <div style=\"margin-left: 48px;\">\n",
+       "        <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
+       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-6b96bd98-14cc-11ee-84cc-80e82ce2fa8e</p>\n",
+       "        <table style=\"width: 100%; text-align: left;\">\n",
+       "\n",
+       "        <tr>\n",
+       "        \n",
+       "            <td style=\"text-align: left;\"><strong>Connection method:</strong> Cluster object</td>\n",
+       "            <td style=\"text-align: left;\"><strong>Cluster type:</strong> distributed.LocalCluster</td>\n",
+       "        \n",
+       "        </tr>\n",
+       "\n",
+       "        \n",
+       "            <tr>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
+       "                </td>\n",
+       "                <td style=\"text-align: left;\"></td>\n",
+       "            </tr>\n",
+       "        \n",
+       "\n",
+       "        </table>\n",
+       "\n",
+       "        \n",
+       "\n",
+       "        \n",
+       "            <details>\n",
+       "            <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Cluster Info</h3></summary>\n",
+       "            <div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
+       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\">\n",
+       "    </div>\n",
+       "    <div style=\"margin-left: 48px;\">\n",
+       "        <h3 style=\"margin-bottom: 0px; margin-top: 0px;\">LocalCluster</h3>\n",
+       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">4e95eff3</p>\n",
+       "        <table style=\"width: 100%; text-align: left;\">\n",
+       "            <tr>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
+       "                </td>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Workers:</strong> 8\n",
+       "                </td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Total threads:</strong> 128\n",
+       "                </td>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Total memory:</strong> 149.01 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-06ee832c-dd0b-4c48-a7b6-ae1064b4a1bf</p>\n",
+       "            <table style=\"width: 100%; text-align: left;\">\n",
+       "                <tr>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Comm:</strong> tcp://127.0.0.1:62877\n",
+       "                    </td>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Workers:</strong> 8\n",
+       "                    </td>\n",
+       "                </tr>\n",
+       "                <tr>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
+       "                    </td>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Total threads:</strong> 128\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> 149.01 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://127.0.0.1:62921\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 16\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:62923/status\" target=\"_blank\">http://127.0.0.1:62923/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 18.63 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:62880\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-he_941fu\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://127.0.0.1:62922\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 16\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:62924/status\" target=\"_blank\">http://127.0.0.1:62924/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 18.63 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:62881\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-3sii11m3\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://127.0.0.1:62916\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 16\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:62919/status\" target=\"_blank\">http://127.0.0.1:62919/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 18.63 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:62882\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-fs_kjhm5\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://127.0.0.1:62931\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 16\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:62934/status\" target=\"_blank\">http://127.0.0.1:62934/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 18.63 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:62883\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-bc9tduav\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://127.0.0.1:62930\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 16\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:62932/status\" target=\"_blank\">http://127.0.0.1:62932/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 18.63 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:62884\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-xjws4_zw\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://127.0.0.1:62915\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 16\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:62917/status\" target=\"_blank\">http://127.0.0.1:62917/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 18.63 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:62885\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-8j774v71\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: 6</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:62898\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 16\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:62913/status\" target=\"_blank\">http://127.0.0.1:62913/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 18.63 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:62886\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-yeltpw2b\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: 7</h4>\n",
+       "                </summary>\n",
+       "                <table style=\"width: 100%; text-align: left;\">\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Comm: </strong> tcp://127.0.0.1:62927\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 16\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:62928/status\" target=\"_blank\">http://127.0.0.1:62928/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 18.63 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:62887\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-z6tlv47i\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://127.0.0.1:62877' processes=8 threads=128, memory=149.01 GiB>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from dask.distributed import Client\n",
+    "client = Client(n_workers=8, threads_per_worker=16, processes=True, memory_limit='20GB')\n",
+    "client"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Start a client for Mongo DB"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pymongo\n",
+    "import xarray_mongodb\n",
+    "\n",
+    "from DataContainer.MongoDB import MongoDB\n",
+    "\n",
+    "mongoClient = pymongo.MongoClient('mongodb://control:DyLab2021@127.0.0.1:27017/?authMechanism=DEFAULT')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Set global path for experiment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "groupList = [\n",
+    "    \"images/MOT_3D_Camera/in_situ_absorption\",\n",
+    "    \"images/ODT_1_Axis_Camera/in_situ_absorption\",\n",
+    "    \"images/ODT_2_Axis_Camera/in_situ_absorption\",\n",
+    "]\n",
+    "\n",
+    "dskey = {\n",
+    "    \"images/MOT_3D_Camera/in_situ_absorption\": \"camera_0\",\n",
+    "    \"images/ODT_1_Axis_Camera/in_situ_absorption\": \"camera_1\",\n",
+    "    \"images/ODT_2_Axis_Camera/in_situ_absorption\": \"camera_2\",\n",
+    "}\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# img_dir = 'C:/Users/control/DyLab/Experiments/DyBEC/'\n",
+    "# SequenceName = \"Repetition_scan\"\n",
+    "# folderPath = img_dir + SequenceName + \"/\" + get_date()\n",
+    "\n",
+    "# mongoDB = mongoClient[SequenceName]\n",
+    "\n",
+    "# DB = MongoDB(mongoClient, mongoDB, date=get_date())"
+   ]
+  },
+  {
+   "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": [
+    "fx = [751.71168, 736.56597, 885.97705, 1118.95297, 1538.68901, 3085.17389, 4700.81524, 6434.98779, 8223.48942]\n",
+    "dfx = [1.32859, 1.28606, 0.99619, 0.99134, 1.03403, 0.35958, 0.33758, 0.38790, 0.31241]\n",
+    "x_offset_current = [0, 0.03, 0.05, 0.07, 0.1, 0.2, 0.3, 0.4, 0.515]\n",
+    "\n",
+    "fy = [751.71168, 918.67701, 1129.74687, 1429.82911, 1749.75310, 3089.87897, 4314.90128, 5387.64638, 6666.03670]\n",
+    "dfy = [1.32859, 0.90271, 1.33691, 1.77735, 1.42421, 1.58909, 1.73638, 1.40175, 1.00526]\n",
+    "y_offset_current = [0.0, 0.015, 0.03, 0.05, 0.07, 0.15, 0.22, 0.28, 0.35]\n",
+    "\n",
+    "fz = [751.71168, 1223.54627, 1739.13344, 2364.93284, 2798.24971, 3178.49790, 4275.39905, 5352.17283, 6637.80418, 8288.35264, 9573.59333]\n",
+    "dfz = [1.32859, 0.35554, 0.48471, 0.69762, 0.36873, 0.29413, 0.20667, 0.20818, 0.21978, 0.20285, 0.18495]\n",
+    "z_offset_current = [0.0, 0.03, 0.06, 0.095, 0.119, 0.140, 0.2, 0.259, 0.329, 0.419, 0.489]\n",
+    "\n",
+    "f = fy\n",
+    "df = dfy\n",
+    "offset_current = y_offset_current\n",
+    "\n",
+    "f_fit =  fy[4:]\n",
+    "df_fit = dfy[4:]\n",
+    "offset_current_fit = y_offset_current[4:]\n",
+    "\n",
+    "x = np.array(offset_current_fit)\n",
+    "y = np.array(f_fit)\n",
+    "\n",
+    "# Degree of the fitting polynomial\n",
+    "deg = 1\n",
+    "# Parameters from the fit of the polynomial\n",
+    "p = np.polyfit(x, y, deg)\n",
+    "m = p[0]  # Gradient\n",
+    "c = p[1]  # y-intercept\n",
+    "\n",
+    "#print(f'The fitted straight line has equation y = {m:.1f}x {c:=+6.1f}')\n",
+    "\n",
+    "# Model the data using the parameters of the fitted straight line\n",
+    "y_model = np.polyval(p, x)\n",
+    "\n",
+    "# Create the linear (1 degree polynomial) model\n",
+    "model = np.poly1d(p)\n",
+    "# Fit the model\n",
+    "y_model = model(x)\n",
+    "\n",
+    "# Mean\n",
+    "y_bar = np.mean(y)\n",
+    "# Coefficient of determination, R²\n",
+    "R2 = np.sum((y_model - y_bar)**2) / np.sum((y - y_bar)**2)\n",
+    "\n",
+    "#print(f'R² = {R2:.2f}')\n",
+    "\n",
+    "fitted_SlopeInkHz = m\n",
+    "fitted_offsetInkHz = c\n",
+    "muB = 9.274e-24\n",
+    "hbar = 6.626e-34 / (2 * np.pi)\n",
+    "gJ = 1.24\n",
+    "Slope = (((2 * np.pi * fitted_SlopeInkHz * 1e3)*hbar) / (muB*gJ)) * 1e4\n",
+    "Offset  = (((2 * np.pi * fitted_offsetInkHz * 1e3)*hbar) / (muB*gJ)) * 1e4\n",
+    "\n",
+    "def calib_fit(x, B):\n",
+    "    alpha = ((2 * np.pi * fitted_SlopeInkHz * 1e3)*hbar) / (muB*gJ)\n",
+    "    beta  = ((2 * np.pi * fitted_offsetInkHz * 1e3)*hbar) / (muB*gJ)\n",
+    "    delta_nu = ((muB * gJ) / hbar) * np.sqrt((B**2-beta**2) + ((alpha * x) + beta)**2)\n",
+    "    return delta_nu / (2 * np.pi * 1e3)\n",
+    "\n",
+    "\n",
+    "popt, pcov = curve_fit(calib_fit, offset_current, f, np.array([0.1*1e-4]))\n",
+    "Boffset = popt[0] * 1e4\n",
+    "dBoffset = pcov[0][0]**0.5 * 1e4\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.gca()\n",
+    "plt.clf\n",
+    "plt.errorbar(offset_current, f, yerr=df, fmt='o')\n",
+    "xvals = np.linspace(0, 0.55, 500)\n",
+    "plt.plot(np.array(xvals), p[1] + p[0] * np.array(xvals), label=f'Line Fit')\n",
+    "plt.plot(xvals, calib_fit(xvals, *popt), label=f'Curve Fit')\n",
+    "plt.text(0.25, 2200, f'Line Slope  = {Slope:.3f} G/A', fontsize=12)\n",
+    "plt.text(0.25, 1500, f'Line Offset = {Offset:=.3f} G', fontsize=12)\n",
+    "plt.text(0.25, 800, f'Bo= {Boffset:=.3f} +/- {dBoffset:=.3f} G', fontsize=12)\n",
+    "plt.xlabel('Y Offset Coil Current (A)', fontsize=12)\n",
+    "plt.ylabel('Resonance Frequency (kHz)', fontsize=12)\n",
+    "plt.xticks(fontsize=12)\n",
+    "plt.yticks(fontsize=12)\n",
+    "plt.legend(fontsize=12)\n",
+    "#plt.xlim(-0.01, 0.04)\n",
+    "plt.ylim(0, 10000)\n",
+    "plt.grid(visible=1)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# X-comp coil"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fx = [751.71168, 736.56597, 885.97705, 1118.95297, 1538.68901, 3085.17389, 4700.81524, 6434.98779, 8223.48942]\n",
+    "dfx = [1.32859, 1.28606, 0.99619, 0.99134, 1.03403, 0.35958, 0.33758, 0.38790, 0.31241]\n",
+    "x_offset_current = [0, 0.03, 0.05, 0.07, 0.1, 0.2, 0.3, 0.4, 0.515]\n",
+    "\n",
+    "# fx = [736.56597, 885.97705, 1118.95297, 1538.68901, 3085.17389, 4700.81524, 6434.98779, 8223.48942]\n",
+    "# dfx = [1.28606, 0.99619, 0.99134, 1.03403, 0.35958, 0.33758, 0.38790, 0.31241]\n",
+    "# x_offset_current = [0.03, 0.05, 0.07, 0.1, 0.2, 0.3, 0.4, 0.515]\n",
+    "\n",
+    "data = xr.DataArray(\n",
+    "    data=fx,\n",
+    "    dims=['x'],\n",
+    "    coords={\n",
+    "        'x': x_offset_current\n",
+    "    } \n",
+    ")\n",
+    "\n",
+    "data_std = xr.DataArray(\n",
+    "    data=dfx,\n",
+    "    dims=['x'],\n",
+    "    coords={\n",
+    "        'x': x_offset_current\n",
+    "    } \n",
+    ")\n",
+    "\n",
+    "data.plot.errorbar(fmt='ob', yerr=data_std)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def magnetic_field_func(x, b0=0, by0=0, alpha=1):\n",
+    "    return 1 / (1e3 * 6.626e-34) * (9.273e-24 * 1.24) * np.sqrt( (b0**2 - by0**2) + (alpha * x + by0)**2 )\n",
+    "\n",
+    "data_quadratic = data\n",
+    "\n",
+    "fitModel_quadratic = NewFitModel(magnetic_field_func)\n",
+    "fitAnalyser_quadratic = FitAnalyser(fitModel_quadratic, fitDim=1)\n",
+    "params_quadratic = fitAnalyser_quadratic.fitModel.make_params()\n",
+    "params_quadratic.add(name=\"b0\", value=  0.3, max=np.inf, min=-np.inf, vary=True)\n",
+    "params_quadratic.add(name=\"by0\", value=  0.07, max=np.inf, min=-np.inf, vary=True)\n",
+    "params_quadratic.add(name=\"alpha\", value=  100, max=np.inf, min=-np.inf, vary=True)\n",
+    "fitResult_quadratic = fitAnalyser_quadratic.fit(data_quadratic, params_quadratic).load()\n",
+    "\n",
+    "fitCurve_quadratic = fitAnalyser_quadratic.eval(fitResult_quadratic, x=np.linspace(0, 0.6, 100), dask=\"parallelized\").load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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tiMbvZx8BAD5sVh2h79WHsaGBniurWhjeiIiIqFh5ecCxY0BcHGDlkIvf7p3D6XtJMJAAM9/1wfBWNSGRcEZpWWN4IyIioiLCw4GJE4HHjwEj+ww494mCsX0WpAZGWDm0EYK9nPVdYpXF8EZERESFhIcDffsCQgCmNRLg2OscDE0VUKSYIS68CZIDrQAvfVdZdfEiNREREank5eWfcRMCsPR/AOf3z8DQVAHZYzvE/dYK8gQrTJqU3470g+GNiIiIVI4dAx7HKmHX4Soc3omGxEAgI9oDzzY3gzJLCiGAR4/y25F+8LIpERERqdx7LIdznwswq/0cAJB8xAtpp2oDKDwxIS5OD8URAIY3IiIi+p9HSVn49X4UzGpnQCk3QOJf/si64VZsW7fiN1MZYHgjIiIinL2fhI9+O4ekzFyILCnitwYiJ862SDuJBPD0BNq0KfsaKR/veSMiIqriws8/xoe/nEZSZi7qu1tjTstWyH1qi5eXcCt4vWQJYGhY5mXS/1So8HbhwgX06tUL7u7uMDc3h7e3N7766itkZWUVanf+/Hl07NgRlpaWsLW1RUhICO7evVtsn8uWLYO3tzekUilq1aqF0NBQyOXyIu3i4+MxbNgwODo6wtzcHC1atMChQ4d08jmJiIjKglIp8O2+GHy+5RJy85ToUt8Vf3zcAiP6m2HrVsDDo3B7T09g61YgJEQ/9VK+ChPerl27hpYtW+L+/ftYsmQJdu/ejf79++Orr77CgAEDVO1iYmLQrl075ObmYsuWLVi9ejVu3ryJNm3a4Pnz54X6/OabbzBx4kSEhIRg//79GDt2LObPn49x48YVapeTk4MOHTrg0KFDWLp0KXbu3AkXFxd06dIFR44cKZPPT0REpE1ZuQqM23geKw7fAQCMC66NFQMbw9wk/46qkBDg/n0gMhLYuDH/f+/dY3ArDyrMPW8bN26ETCbDtm3bULt2bQBA+/btERcXh59//hnJycmws7PD7NmzIZVKsXv3blhbWwMAAgICULduXSxevBiLFi0CACQmJmLevHkYPXo05s+fDwBo164d5HI5Zs6ciUmTJsHHxwcAsGrVKkRHR+PEiRNo0aIFACA4OBgNGzbElClTcPr06bIeDiIiIo2l5AAfrorC1SfpMDE0wMI+vghp7FmknaEh0K5d2ddHr1dhzrwZGxsDAGxsbAptt7W1hYGBAUxMTKBQKLB792706dNHFdwAoEaNGggODsb27dtV2/bt2weZTIbhw4cX6m/48OEQQmDHjh2qbdu3b4eXl5cquAGAkZERBg0ahDNnziA2NlabH5WIiEhnomPT8N0VQ1x9kg57CxNsGN2s2OBG5VeFOfM2dOhQLFmyBJ988gkWLVoEJycnHDlyBD/99BPGjRsHCwsL3LhxA9nZ2fDz8ytyvJ+fHyIiIiCTyWBqaoro6GgAgK+vb6F2bm5ucHR0VO0HgOjoaLQpZlpNwftcvXoVHi/fGPA/OTk5yMnJUb1OS0sDAMjl8mLvrSutgj510Xdlx7HTHMdOcxw7zXHs1Lfv6jN8sfUKZAoJajuZ45fBjVHNzpxjWEK6/s6VtN8KE95q1qyJkydPonfv3qrLpgAwYcIELFmyBED+pVAAsLe3L3K8vb09hBBITk6Gm5sbEhMTIZVKYWFhUWzbgr4K+n1Vny++b3EWLFiA0NDQItsPHDgAc3PzVx5XWhERETrru7Lj2GmOY6c5jp3mOHZvJgQQESvBX4/yp4jWs1ViaM00XDl5GFf0XFtFpKvv3MsTMF9Fo/CWlpaGU6dOITY2FtnZ2XB0dISPjw8aNGigSXclcv/+ffTo0QMuLi7YunUrnJyccPr0acybNw8ZGRlYtWqVqq3k5bnNL3hxX0nbqdv2RdOnT8fnn3+uep2WloZq1aqhc+fOhS7taotcLkdERAQ6deqkutRMJcOx0xzHTnMcO81x7EomR6HEzB1X8dej/EciDGrqicYG99GlM8dNXbr+zhVcnXuTEoc3hUKBrVu3YuXKlTh+/DiUSiWEEKr9EokEDg4OGDhwIMaOHYu6deuqX/VrTJs2DWlpabh48aLqbFnbtm3h6OiIESNGYMiQIXB1dQVQ/JmwpKQkSCQS2NraAgAcHBwgk8mQlZVV5AxYUlISAgICVK8dHBxe2SdQ/Jm+AlKpFFKptMh2Y2Njnf7S6Lr/yoxjpzmOneY4dprj2L1aQkYOxvx2DuceJMPQQILQ9+rjgwB37Nlzn+NWCroau5L2WaIJC3/++Sd8fHwwZMgQWFhYYP78+Thw4AAuXbqEGzdu4OTJkwgLC0P//v2xY8cO+Pj44OOPP0ZCQkKpPsSLLl68CB8fnyKXOZs0aQIg/7602rVrw8zMDFeuFD0JfOXKFdSpUwempqYA/rnX7eW2T58+RUJCQqGziL6+vq/sE4BOzzgSERFp4sbTdPRafhznHiTD2tQI64Y3xaDmNfRdFmlBicLb0KFDMWjQIDx+/Bh//fUXvvjiC3To0AG+vr6oW7cumjVrhgEDBuD777/HvXv3sG/fPty8eRMrVqzQWqHu7u64evUqMjIyCm0/efIkAMDT0xNGRkbo0aMHwsPDkZ6ermrz8OFDREZGIuSFxWm6dOkCU1NTrF27tlB/a9euhUQiQa9evVTbevfujZiYmEJLgigUCoSFhaFZs2Zwd3fX2uckIiIqrciYePT58QQeJ2ejpoM5to9rhdZ1HfVdFmlJiS6b3rt3T3W5sSQ6dOiADh06ICUlRcOyipo0aRJ69eqFTp064bPPPoOjoyNOnTqFBQsWwMfHB127dgUAhIaGokmTJujevTumTZsGmUyG2bNnw9HREZMnT1b1Z29vj5kzZ2LWrFmwt7dH586dERUVhblz52LUqFGqNd4AYMSIEVi+fDn69euHhQsXwtnZGStWrMCNGzdw8OBBrX1GIiKi0hBCYPXx+/jmr2tQCqD5W/b4cWAA7CxM9F0aaVGJzrypE9y0cVxx3nvvPRw6dAjW1taYOHEiunfvjnXr1mHMmDE4evQoTEzyv5je3t44fPgwjI2N0bdvXwwbNgx16tTB0aNH4eTkVKjPGTNmYMmSJdi6dSs6d+6MZcuWYdq0aVi+fHmhdlKpFIcOHUJwcDDGjx+PHj16IC4uDnv37kVQUJDWPiMREZGm5HlKfLk9Gl/vzg9uHwRWw/oRzRjcKiG1Z5uOGDECDRo0KDSDssDdu3cxb948rF69WivFvSw4OBjBwcFvbBcQEFDiM2ITJkzAhAkT3tjOxcUF69atK1GfREREZSklKxdjN5zHiTuJkEiAGd3qYWTrWq9dDYEqLrXDW8E9YZcuXcKqVatgZPRPF8+fP8e6det0Ft6IiIiosLvPMzBy3VncS8iEhYkhvh/QCB3quei7LNIhjR6P9X//93/4448/0K1bt0ITA4iIiKjsnLidgN4rTuBeQiY8bM2w9ZOWDG5VgEbhrU+fPjhw4ADOnTuHtm3bIi4uTtt1ERER0WtsPP0QQ1afQWq2HI2q22LHuFao56b9xd+p/NH4wfStW7fGf//7XyQlJaFFixa4fv26NusiIiKiYuQpBb7adQ1fbr8ChVKgp787No1uDierogvCU+WkcXgDgHr16uHkyZOwtbVFq1atcPjwYS2VRURERC9Ll8kxal0UVh+/BwCY3OltLPnAH6bGhnqujMpSqR9M7+7ujmPHjqFXr16YPn06Z7YQERHpwKOkLIxadxY3nqVDamSAf7/fEN39uEh8VaR2eJszZw48PT0LbbOyssK+ffswadIkXLt2TWvFEREREXDuQRI+Wn8OiZm5cLaS4pchgWhYzVbfZZGeaBTeimNsbFxkcVsiIiIqne0XHmPq1ivIzVPCx80aq4YFws3GTN9lkR6V+rIpERERaZ9SKfBdxE38EHkbANDZxwVL+vvD3IR/dVd1JfoGvPXWWyXuUCKR4M6dOxoXREREVNVl5+Zh8h8XsefKUwDAJ+1q44vOXjAw4H3lVMLw5uPjU2gighACe/bsQevWrWFjY6Oz4oiIiKqap6kyjF5/FldiU2FsKMH83r7oF1hN32VROVKi8LZ79+5CrxUKBUxMTLBkyRI0btxYJ4URERFVNVcep2LU+ig8S8uBnbkxfhociKa17PVdFpUzGl0453IgRERE2rUvOg6Tfr8ImVyJus6WWDW0Cao7mOu7LCqHeNcjERGRHgkhsOLwHfxr/w0AQNDbTlj2YSNYmxrruTIqrxjeiIiI9CRHkYfp264g/EIsAGBYy5qY+W49GBmW6gFIVMkxvBEREelBQkYOxvx2DuceJMPQQILQ9+pjUPMa+i6LKoAShbfz588Xep2XlwcAiImJKbY9JzEQERG92o2n6Ri5LgqPk7NhbWqEFQMD0Lquo77LogqiROEtMDCw2EkKgwcPLvRaCAGJRKIKd0RERFRYZEw8xm+6gIwcBWo6mGPVsCao7WSp77KoAilReFuzZo2u6yAiIqrUhBBY9d97mL/nOpQCaP6WPX4cGAA7CxN9l0YVTInC29ChQ3VdBxERUaUlz1Ni9s5obDrzCADQv0k1fNWzAUyMODGB1Kf2t+bs2bOv3b969WqNiyEiIqpsUrJyMWTVGWw68wgSCTDz3XpYEOLL4EYaU/ub06NHDzx48KDYfZs3b8aYMWNKXRQREVFlcPd5BnqvOIGTdxNhYWKIX4cEYlSbt7jYPZWK2uHNz88P3bp1Q0pKSqHtf/75J4YMGYJx48ZpqzYiIqIK6/jtBPRafhz3EjLhYWuGrZ+0RId6LvouiyoBtcPb1q1bYWhoiN69e0MulwMAIiIi8MEHH2Dw4MFYsmSJtmskIiKqUDacfoAhq88gTaZA4+q22DGuFeq5Weu7LKok1A5vVlZW2LNnD27duoXhw4fjv//9L3r37o2ePXti1apVuqiRiIioQshTCoTuuooZ26ORpxTo5e+OjaObw8lKqu/SqBLR6AkLnp6e2L17N9q2bYstW7agS5cu2LBhg7ZrIyIiqjDSZXKM33QBh288BwD8X+e3MS64Du9vI60rUXgLDw8vdnv//v2xc+dO1f8WCAkJ0U51REREFcCjpCyMXBeFm88yYGpsgO/e90c3Xzd9l0WVVInCW9++fSGRSCCEKHb/4MGDVfv4hAUiIqpKzt5Pwke/nUNSZi5crKX4ZUgg/Dxt9V0WVWIlCm+RkZG6roOIiKjCCT//GNO2XUFunhINPKzx65AmcLUx1XdZVMmVKLwFBQXpug4iIqIKQ6kUWHzgBlYcvgMA6FLfFd990BDmJhrdSk6kFn7LiIiI1JCVq8Bnv1/E/qvPAABj29XG/3X2goEBJyZQ2SjRUiHdunXDhQsXStxpTk4OvvvuOyxfvlzjwoiIiMqbp6ky9Ft5EvuvPoOJoQG+e78hpnTxZnCjMlWiM2+urq5o0qQJmjVrhiFDhqBdu3bw8vIq1CY9PR2nT5/Gzp07sWnTJtjY2OC3337TSdFERERl7fLjFIxadxbx6TlwsDDBT4MDEFjTXt9lURVUovC2evVqjB8/HgsXLsSECROgUChgZmYGJycnmJqaIikpCYmJiRBCoEaNGvjyyy8xbtw4SKVclJCIiCq+PVfi8PmWi5DJlXjbxRKrhjZBNXtzfZdFVVSJ73lr1KgRfv/9d8THx2P//v04deoUnjx5guzsbAQEBMDb2xvt2rVDq1atuCAhERFVCkII/PD3bfw74iYAoJ2XE5YNaAQrU2M9V0ZVmdoTFpydnTF48GAMHjxYF/UQERGVCzJ5HqZuu4ydF58AAEa0qoUZ79aDIe9vIz3jbFMiIqKXPE/PwUe/ncWFhykwMpDgq54N8GGz6vouiwgAwxsREVEh1+PSMHJtFJ6kymBjZowfBzZGyzqO+i6LSIXhjYiI6H8OXnuGCZsvICs3D285WuDXoYF4y8lS32URFcLwRkREVZ4QAr8cu4sFe2MgBNCqjgNWfBgAG3NOTKDyh+GNiIiqtFyFEjN3XMGWs48BAAObVcfc9+rD2LBE69gTlTm1w1tubi5MTEx0UQsREVGZSsrMxcdh53DmXhIMJMCs7j4Y1rIml7yick3tf1Z4eHhg+vTpePjwoS7qISIiKhO349PRa/lxnLmXBEupEVYNa4LhrWoxuFG5p3Z469GjB77//nvUrl0bvXv3xqFDh3RRFxERkc4cufkcvZefwMOkLFSzN0P42JYI9nLWd1lEJaJ2eFu9ejUeP36Mb775BpcuXULnzp1Rr149/PDDD0hPT9dFjURERFqz7sR9DF9zBuk5CjSpaYcdY1vhbRcrfZdFVGIa3Y1pZ2eHKVOm4M6dO9i+fTuqVauGiRMnwsPDA59++iliYmK0XScREVGpKPKUmLUjGnP+vAqlAPo09kTYqGZwsORzuKliKdVUGolEgvfeew+LFi1CUFAQMjIysGLFCtSvXx99+vRBfHy8tuokIiLSWGq2HMPXRuG3Uw8gkQBTu3hjcT8/SI0M9V0akdo0Dm8KhQKbNm1C69atERgYiLt372LRokW4f/8+lixZgmPHjmHIkCHarJWIiEht9xMyEbLiOI7dSoCZsSFWDgrAJ+1qc2ICVVhqLxUSGxuLn376Cb/88guePXuGNm3aYMuWLejduzcMDPKz4Pjx4+Hh4YFBgwZpvWAiIqKSOnU3ER+HnUNKlhxuNqb4dWgg6rvb6LssolJRO7zVrFkTRkZG6N+/PyZOnAh/f/9i27311ltwcXEpbX1EREQa+T3qIWbuiIY8T6BhNVv8MjgAztam+i6LqNTUDm9z5szBmDFj4OTk9Np2/v7+uHfvnsaFERERvUpeHnDkiARHj3rAwkKC4GDA8H+3r+UpBRbuvY5fjuX/HdTdzw2L+zWEqTHvb6PKQe3wNnPmTF3UQUREVCLh4cDEicDjx0YAAvHdd4CnJ7B0KdD5XQUmbrqAQzH5E+YmdayLiR3q8v42qlTUDm9r1qzBgwcPMHfu3CL7QkNDUatWLU5UICIinQgPB/r2BYQovD02FvhgRBYCPzuLuOx0SI0MsLhfQ/Ro6K6fQol0SO3Zpt9//z3s7OyK3efg4IDvv/++1EURERG9LC8v/4zby8ENAEzck+A6+DjistPhaCnF72NaMLhRpaX2mbfbt2+jQYMGxe7z8fHBrVu3Sl0UERHRy44dAx4/LrrdwucxHLpegcRIidxn1pgeFAj/amZlXyBRGVE7vAFAamrqK7crFIpSFURERFScuLiXtwjYtrkBm5Z3AABZN12QsNsfuZ01+quNqMJQ+7Kpr68vNm/eXOy+TZs2wdfXt9RFERERvczN7Z//LzHKg2Ov86rglnqqNp5vD4CQGxVqR1QZqR3ePv30U2zduhVDhw7F6dOnERsbi9OnT2PYsGHYtm0bxo8fr4s6iYioimvTJn9WqZGVDC4DT8DC6ylEngQJfzVEyhFvSCQSVKuW346oMlP73PKHH36ImJgYLFiwAGFhYartBgYGmDlzJgYOHKjVAomIiID8ddz+75sU/OvMWRhZ5iAvywTPwwOQE2uPgpVAliz5Z703ospKoxsDvvrqK4wYMQIRERF4/vw5nJyc0LlzZ9SoUUPb9REREQEA/rochx9vXYSRpRIixRJPNzeBItUcQP4ZuSVLgJAQ/dZIVBY0vquzZs2aGD16tDZrISIiKkIIgWV/38Z3ETcBAO28nLDk/UY421eCvXvPomtXfwQHG/GMG1UZGoe3+Ph4PHjwANnZ2UX2tW3btlRFERERAYBMnoep2y5j58UnAIARrWrhy27eMDI0QFCQHJmZsQgKasjgRlWK2uEtLi4OgwcPRmRkJID8fxEBgEQigRACEokEeXl52q2SiIiqnPh0GT5afw4XH6XAyECC0J71MbAZb88hUju8ffrpp7hw4QIWLVoEPz8/SKVSXdRFRERV2PW4NIxcG4UnqTLYmBnjx4GN0bKOo77LIioX1A5vR44cweLFizF8+HBd1ENERFXcwWvPMGHzBWTl5uEtRwv8OjQQbzlZ6rssonJD7fCWv45ONV3UQkREVZgQAj8fvYuF+2IgBNCqjgNWfBgAG3NjfZdGVK6ovUhvv379sHv3bl3UQkREVVSuQokpWy9jwd784PZhs+pYO7wpgxtRMdQOb++//z7++usvTJgwARERETh//nyRH13673//i27dusHOzg5mZmaoW7cuvv7660Jtzp8/j44dO8LS0hK2trYICQnB3bt3i+1v2bJl8Pb2hlQqRa1atRAaGgq5XF6kXXx8PIYNGwZHR0eYm5ujRYsWOHTokE4+IxFRVZKUmYtBq07jj3OPYSAB5vbwwTe9GsDYUO2/ooiqBLUvm7Zv3x4A8MMPP2D58uWF9ul6tunGjRsxePBgvP/++1i/fj0sLS1x584dPHnyRNUmJiYG7dq1g7+/P7Zs2QKZTIbZs2ejTZs2uHjxIpycnFRtv/nmG8yaNQvTpk1D586dERUVhZkzZyI2NhY///yzql1OTg46dOiAlJQULF26FM7Ozli+fDm6dOmCgwcPIigoSCefl4iosrsdn44Ra8/iYVIWrKRGWPZhI7TzctZ3WUTlmtrhbc2aNbqo441iY2Px0UcfYcyYMVixYoVqe3BwcKF2s2fPhlQqxe7du2FtbQ0ACAgIQN26dbF48WIsWrQIAJCYmIh58+Zh9OjRmD9/PgCgXbt2kMvlmDlzJiZNmgQfHx8AwKpVqxAdHY0TJ06gRYsWqvdt2LAhpkyZgtOnT+v88xMRVTaHb8Rj/MYLSM9RoLq9OVYNDURdFyt9l0VU7qkd3oYOHaqLOt7o119/RWZmJqZOnfrKNgqFArt378aQIUNUwQ0AatSogeDgYGzfvl0V3vbt2weZTFZk1uzw4cMxY8YM7NixQxXetm/fDi8vL1VwAwAjIyMMGjQIX375JWJjY+Hh4aHNj0tEVGkJIbDuxH18tfsalAJoWtMeKwcHwN7CRN+lEVUIGj9hAQBu3LiBhIQE+Pv7w8LCQls1Fevo0aOwt7dHTEwMevbsiejoaNjb2yMkJATffvstrK2tcefOHWRnZ8PPz6/I8X5+foiIiIBMJoOpqSmio6MBAL6+voXaubm5wdHRUbUfAKKjo9GmTZti+wSAq1evvjK85eTkICcnR/U6LS0NACCXy4u9t660CvrURd+VHcdOcxw7zVW1sZPnKfH1XzHYFPUYANCnsTtCe/hAaiRRewyq2thpC8dNc7oeu5L2q1F4W79+Pb788kvExcUBAKKiotC4cWO8//776NSpk06eeRobG4usrCz069cP06dPx5IlSxAVFYU5c+YgOjoax44dQ2JiIgDA3t6+yPH29vYQQiA5ORlubm5ITEyEVCotNnTa29ur+gLyL7G+qs+C/a+yYMEChIaGFtl+4MABmJubv/mDaygiIkJnfVd2HDvNcew0VxXGLksBrLlpgJupBpBA4L0aSrQxeYhDBx6Wqt+qMHa6wHHTnK7GLisrq0Tt1A5vf/zxB4YNG4bu3buja9euGDdunGpf48aNsWXLFp2EN6VSCZlMhjlz5mDatGkA8u9RMzExwaRJk3Do0CFVGJJIJK/s58V9JW2nbtsXTZ8+HZ9//rnqdVpaGqpVq4bOnTsXurSrLXK5HBEREejUqROMjTnFXh0cO81x7DRXVcbuXkImxoRdwL3ULJibGOK7vr7oUK90ExOqythpG8dNc7oeu4Krc2+idnhbsGABhg8fjlWrViEvL69QeKtXrx6WLVumbpcl4uDggFu3buGdd94ptL1r166YNGkSzp8/j549ewIo/kxYUlISJBIJbG1tVf3JZDJkZWUVOQOWlJSEgICAQu/9qj6B4s/0FZBKpcU+QszY2FinvzS67r8y49hpjmOnuco8diduJ+CTDeeRmi2Hh60ZfhkSCB937f3jtTKPnS5x3DSnq7EraZ9qL6Jz/fp19O/fv9h9L19u1Kbi7mMD8m98BQADAwPUrl0bZmZmuHLlSpF2V65cQZ06dWBqagrgn3vdXm779OlTJCQkoEGDBqptvr6+r+wTQKG2RET0jw2nH2DI6jNIzZajUXVb7BjXSqvBjagqUju8mZubIzU1tdh9sbGxsLOzK3VRxenTpw8AYO/evYW279mzBwDQvHlzGBkZoUePHggPD0d6erqqzcOHDxEZGYmQkBDVti5dusDU1BRr164t1N/atWshkUjQq1cv1bbevXsjJiam0JIgCoUCYWFhaNasGdzd3bX1MYmIKgVFnhKhu65ixvZoKJQCvfzdsWl0czhZFb0SQUTqUfuyaatWrfDDDz+owtSL1q5di3bt2mmjriI6d+6MHj164KuvvoJSqUTz5s1x9uxZhIaGonv37mjdujUAIDQ0FE2aNEH37t0xbdo01SK9jo6OmDx5sqo/e3t7zJw5E7NmzYK9vb1qkd65c+di1KhRqmVCAGDEiBFYvnw5+vXrh4ULF8LZ2RkrVqzAjRs3cPDgQZ18XiKiiipNJsf4jRdw5OZzAMD/dX4b44LrvPb+YCIqObXD2+zZs9G6dWs0bdoUH374ISQSCcLDwzFnzhwcPXoUZ86c0UWdAIDff/8doaGh+PnnnxEaGgp3d3d89tlnmDNnjqqNt7c3Dh8+jKlTp6Jv374wMjJC+/btsXjx4kJPVwCAGTNmwMrKCsuXL8fixYvh6uqKadOmYcaMGYXaSaVSHDp0CFOmTMH48eORlZUFf39/7N27l09XICJ6wcPELIxYF4Xb8RkwNTbAd+/7o5uvm77LIqpU1A5vgYGB2Lt3L8aOHas6kzV//nzUrVsXe/bs0en9X2ZmZli4cCEWLlz42nYBAQElPiM2YcIETJgw4Y3tXFxcsG7duhL1SURUFZ2+m4iPw84hOUsOF2spfh3SBL6eNvoui6jS0Widt+DgYFy/fh137tzBs2fP4OjoiLffflvbtRERUQWx5ewjzNh+BfI8AV8PG/w6NBAu1qb6LouoUirVExZq166N2rVra6sWIiKqYPKUAov2xeDno3cBAN18XfHvfv4wMzHUc2VElZfa4W39+vVvbDNkyBCNiiEiooojI0eBSZsv4uD1ZwCACe3rYFLHt2FgwIkJRLqkdngbNmxYsdtfnEXE8EZEVLk9Ts7CqHVnEfM0HSZGBvhXXz/09C/+Gc9EpF1qh7d79+4V2ZaQkICdO3fi999/x+bNm7VSGBERlU/nHybjo/VnkZCRC0dLKX4ZEoBG1XWzxicRFaV2eKtRo0ax2wICAiCXy7F06dIiC98SEVHlsONCLKZsu4xchRL13Kzx69BAeNia6bssoiqlVBMWXtahQwe8//772uySiIjKAaVS4LuIm/gh8jYAoJOPC5Z84A8LqVb/GiGiEtDqb92DBw9gaMgZRkRElUlWrgKf/34J+64+BQB80q42vujsxYkJRHqidng7evRokW05OTm4fPkyFixYgA4dOmilMCIi0r+nqTKMWh+F6Ng0mBgaYEGIL/oEeOq7LKIqTe3w1q5duyLPpxNCAAA6duyIZcuWaacyIiLSq0uPUjB6/VnEp+fAwcIEPw0OQGBNe32XRVTlqR3eIiMji2wzNTVFzZo14eLiopWiiIhIv3ZffoLJWy4hR6GEl4sVfh0aiGr25voui4igQXjjg9iJiCovIQSWHrqFJQdvAQDaeztjaX9/WJka67kyIirAaUJERAQAkMnz8H9/XMLuy3EAgNFtamFa13ow5MQEonJF7fBWq1atIve8vYpEIsGdO3fULoqIiMpWfJoMo9efxaXHqTAykOCb3g3wQZPq+i6LiIqh0WXTyMhIPH36FC1btoSrqyuePn2KEydOwM3NDcHBwbqok4iIdCQ6NhWj1p3F0zQZbM2N8ePAALSo7aDvsojoFdQObx06dMCJEydw69YtVK/+z7/KHjx4gE6dOqFdu3YYOnSoVoskIiLd2Bcdh89+v4RseR5qO1lg9bAmqOFgoe+yiOg1DNQ9YOHChQgNDS0U3ID8R2TNmTMHCxcu1FpxRESkG0IILI+8jY/DziNbnoe2bzth+7hWDG5EFYDaZ97u3LkDGxubYvfZ2dnh/v37pa2JiIh0SCbPw7Rtl7Hj4hMAwLCWNTHz3XowMlT73/NEpAdq/6bWrFkTq1atKnbfL7/8UuyD64mIqHx4np6DD385hR0Xn8DQQIKvezXA3PfqM7gRVSBqn3mbNm0aRowYgaZNm2LAgAGqCQubNm3CuXPn8Ouvv+qiTiIiKqXrcWkYte4sYlOyYW1qhB8HBaBVHUd9l0VEalI7vA0bNgwAMHPmTEyePFm13c3NDb/88guGDx+uteKIiEg7Iq49w8TNF5CVm4dajhZYNTQQbzlZ6rssItKARov0Dhs2DEOHDsWNGzeQmJgIBwcHeHl5lXj9NyIiKhtCCPx09C4W7YuBEECrOg5Y8WEAbMz5xASiikrjJyxIJBJ4e3trsxYiItKiHEUeZmyPxtZzjwEAA5tVx9z36sOY97cRVWga/QbHxMRgwIABcHNzg4mJCc6fPw8ACA0NLfbB9UREVLYSM3Iw6NfT2HruMQwkQOh79TGvVwMGN6JKQO3f4osXL6JJkyY4cuQI2rVrh7y8PNW+jIwMrFy5UqsFEhGRem4+S0fP5ccRdT8ZVqZGWDO8KYa2rMlbW4gqCbXD27Rp0+Dn54fbt2/jt99+gxBCta9p06aIiorSaoFERFRykTHxCFlxAo+Ts1HDwRzbx7ZE0NtO+i6LiLRI7Xvejh8/jrCwMJibmxc66wYALi4uePr0qdaKIyKikhFCYNV/72H+nutQCqBZLXusHBQAOwsTfZdGRFqmdngTQsDEpPg/DJKTkyGVSktdFBERlVyuQonZO6OxOeoRAGBA02oIfa8BTIx4fxtRZaT2b7afnx+2b99e7L59+/YhICCg1EUREVHJJGfmYvCq09gc9QgGEmBWdx/M7+3L4EZUial95m3ixIn48MMPYWFhgcGDBwMAHj58iL///hurV6/G1q1btV4kEREVdTs+HSPXncWDxCxYSo2wbEAjBHs767ssItIxtcPbBx98gDt37mDu3Ln4/vvvAQB9+vSBkZERQkND0aNHD60XSUREhR2+EY/xGy8gPUeBavZmWDW0Cd52sdJ3WURUBtQOb7m5uZg2bRqGDBmC/fv349mzZ3B0dMQ777zDh9ITEemYEAJrT9zH17uvQSmApjXt8eOgxnCw5P3GRFWFWuFNJpPBwsICW7duRe/evTFy5Ehd1UVERC+R5ykx58+r2Hj6IQCgX4An5vVuAKmRoZ4rI6KypFZ4MzU1hYODAywsLHRVDxERFSMlKxefhJ3HybuJkEiA6V29MbrNW1x4l6gKUns6Uo8ePV4525SIiLTvdnwGei0/jpN3E2FhYohfBgfio7a1GdyIqii173nr378/Ro4ciREjRiAkJARubm5F/gBp3Lix1gokIqrKjt16jrEbziNdpoCHrRlWDQuEt6u1vssiIj1SO7y98847AIC1a9di3bp1hfYJISCRSIo8eYGIiNS3/uR9hO66hjylQGANO6wcHABHTkwgqvLUDm9r1qzRRR1ERPQ/8jwlQnddRdip/IkJfRp7Yn4IJyYQUb4ShbcVK1agX79+cHJywtChQ3VdExFRlZWaJcfYjedw/Hb+xISpXbwxpi0nJhDRP0o0YWH8+PG4d++e6rVSqUT16tURHR2ts8KIiKqau88z0HvFcRy/nQhzE0P8PDgQHwdxYgIRFVaiM29CiCKvHz9+jNzcXJ0URURUFeTlAUeOSHD0qAce5iThl2sXkfa/iQm/Dg1EPTdOTCCiotS+542IiEovPByYOBF4/NgIlo0cYW94DhIDgZqWtvhjXCCcrDgxgYiKx/BGRFTGwsOBvn0BASXsOl6DdcADAEDmVQ8c3eeLYz6GCAnRc5FEVG6VOLylpaUhKSkJAKBQKIpse5G9vb2WyiMiqlzy8vLPuElM5HDqeR5mtRIgBJByxAtpp/Pvb5s0CejZEzDk5FIiKkaJw1vB+m4v6tChQ7Ftuc4bEVHxjh0DnmZmwnVwFIwdMqHMNUTCbn9k33IFAAgBPHqU365dO/3WSkTlU4nC25w5c3RdBxFRlfDfmwlwHXwehmZyKNJMEb+1CeTPi05MiIvTQ3FEVCEwvBERlZGwUw+w+t5VGJoJ5DyxRXx4AJSZpsW2dXMr4+KIqMLghAUiIh1T5Cnx9e5rWHcyf2KC8p47noX7QSiK3tQmkQCenkCbNmVdJRFVFAxvREQ6lJotx6cbz+PYrQQAwBfveME1qTb6/SGBRJJ/j1uBgrV4lyzhZAUiejWGNyIiHbmXkImR66Jw93kmzIwN8Z8PGqJLg/zroVu3Fqzz9k97T8/84MZlQojodRjeiIh04MTtBHyy4TxSs+VwszHFL0MC0cDDRrU/JCR/OZDISAX27r2Irl39ERxsxDNuRPRGDG9ERFq24fQDzNl5FQqlgH81W/w8OADO1kUnJhgaAkFBApmZsQgKasjgRkQlwvBGRKQlijwl5v11HWtP3AcA9PR3x6I+fjA1ZiojIu0x0OSgnJwc/PTTTxgwYAA6deqEW7duAQB27tyJu3fvarVAIqKKIDVbjuFro1TB7Yt3vLDkA38GNyLSOrXPvCUkJCA4OBhXr16Fq6srnj17hvT0dADAjh07sH//fqxYsULrhRIRlVevm5hARKRtap95mzJlClJSUnD27Fk8fPgQ4oV57sHBwThy5IhWCyQiKs9O3E5Ar+XHcfd5JtxsTPHHxy0Y3IhIp9Q+87Z7924sWrQIjRs3LvIMU09PTzx+cd47EVElFnbqAeb++cLEhCEBcLYq/okJRETaonZ4S0tLQ40aNYrdJ5fLoVAoSl0UEVF59vITE3r5u2MhJyYQURlR+7JprVq1cPLkyWL3nTlzBl5eXqUuioiovErNyp+YUBDcvnjHC//hxAQiKkNqh7eBAwdi0aJF2Llzp+p+N4lEgqioKCxduhSDBw/WepFEROXBvYRM9F5xHMduJcDM2BArBwVgXHAdSAqea0VEVAbUvmw6depUHD9+HL1794adnR0A4J133kFiYiK6dOmCiRMnar1IIiJ9O347AWP/98QEdxtT/DI0EPXdbd58IBGRlqkd3oyNjbFnzx78/vvv+Ouvv/Ds2TM4Ojqie/fu6N+/PwwMNFo6joio3Prt5H3M3XUNeUqBRtVt8dNgTkwgIv3R6AkLEokE/fv3R//+/bVdDxFRuaHIU+Kr3dewnhMTiKgcUfs02c2bN1+5ltuRI0dUT1sgIqrIUrPkGLYmCutPPoBEAkzpwokJRFQ+qH3m7fPPP8fbb7+NoKCgIvt27dqFmzdv4s8//9RKcURE+nD3eQZGrTuLuwmZMDcxxJIP/NG5vqu+yyIiAqDBmbeoqCi0bdu22H1BQUGIiooqdVFERPpy7Nbz/CcmJGTCw9YMWz9uyeBGROWK2mfeUlNTYWlpWew+MzMzJCcnl7ooIqKyJoTA+pMP8NXu/IkJATXssHJQAJyspPoujYioELXPvHl4eODMmTPF7jtz5gzc3PhMPyKqWOR5SszcEY05f15FnlIgpLEHNo5uxuBGROWS2uGtV69eWLhwISIjIwttP3z4MBYtWoTevXtrrbg3+fXXXyGRSIo9E3j+/Hl07NgRlpaWsLW1RUhICO7evVtsP8uWLYO3tzekUilq1aqF0NBQyOXyIu3i4+MxbNgwODo6wtzcHC1atMChQ4e0/rmIqOwkZ+ZiyKoz2HD6ISQSYHpXb/y7X0NIjTgxgYjKJ7XD2+zZs1G9enV07NgR9erVQ6dOnVCvXj106NAB1atXx9y5c3VQZlGxsbH4v//7P7i7uxfZFxMTg3bt2iE3NxdbtmzB6tWrcfPmTbRp0wbPnz8v1Pabb77BxIkTERISgv3792Ps2LGYP38+xo0bV6hdTk4OOnTogEOHDmHp0qXYuXMnXFxc0KVLl1fOviWi8u12fDp6rTiOk3cTYWFiiJ8HB2JMUG0+MYGIyjW173mzsbHBqVOn8J///Af79u3DgwcP4OTkhNDQUEyaNOmV98Np28cff4y2bdvC3t4eW7duLbRv9uzZkEql2L17N6ytrQEAAQEBqFu3LhYvXoxFixYBABITEzFv3jyMHj0a8+fPBwC0a9cOcrkcM2fOxKRJk+Dj4wMAWLVqFaKjo3HixAm0aNECABAcHIyGDRtiypQpOH36dJl8biLSjsM34jF+4wWk5yjgYWuGVcMC4e1qre+yiIjeSKPHIVhaWmLWrFk4fvw4bt68iePHj2PmzJllFtzCwsJw5MgRrFixosg+hUKB3bt3o0+fPqrgBgA1atRAcHAwtm/frtq2b98+yGQyDB8+vFAfw4cPhxACO3bsUG3bvn07vLy8VMENAIyMjDBo0CCcOXMGsbGxWvyERKQrQgis/u89jFgbhfQcBZrUtMOfn7ZicCOiCkOjJyzoU3x8PCZNmoSFCxfC09OzyP47d+4gOzsbfn5+Rfb5+fkhIiICMpkMpqamiI6OBgD4+voWaufm5gZHR0fVfgCIjo5GmzZtiu0TAK5evQoPD48i+3NycpCTk6N6nZaWBgCQy+XF3ldXWgV96qLvyo5jp7mKMna5CiW++us6fj+b/4+tvo09ENqjHkyMDPRWe0UZu/KIY6cZjpvmdD12Je1Xo/AWFhaGjRs34sGDB8jOzi60TyKR4M6dO5p0WyJjx46Fl5cXPvnkk2L3JyYmAgDs7e2L7LO3t4cQAsnJyXBzc0NiYiKkUiksLCyKbVvQV0G/r+rzxfd92YIFCxAaGlpk+4EDB2Bubl7sMdoQERGhs74rO46d5srz2GXIgdU3DHEnXQIJBHrWUKK1yQMcPPBA36UBKN9jV95x7DTDcdOcrsYuKyurRO3UDm+LFi3C9OnT4ePjg4YNG0IqLbup9Nu2bcOuXbtw4cKFN95Q/Lr9L+4raTt12xaYPn06Pv/8c9XrtLQ0VKtWDZ07dy50WVdb5HI5IiIi0KlTJxgbG2u9/8qMY6e58j52t55l4KMNF/A4PRuWUiP8531ftHvbSd9lASj/Y1eecew0w3HTnK7HruDq3JuoHd5+/vlnjBs3DsuWLVO7qNLIyMjAuHHjMH78eLi7uyMlJQUAkJubCwBISUmBsbExHBwcABR/JiwpKQkSiQS2trYAAAcHB8hkMmRlZRU5C5aUlISAgADVawcHh1f2CRR/pg8ApFJpsQHX2NhYp780uu6/MuPYaa48jt3fMc8wYdNFZOQoUN3eHKuGBqKui5W+yyqiPI5dRcGx0wzHTXO6GruS9qn2hIWnT5+W6VpuBRISEvDs2TP8+9//hp2dnepn06ZNyMzMhJ2dHQYOHIjatWvDzMwMV65cKdLHlStXUKdOHZiamgL45163l9s+ffoUCQkJaNCggWqbr6/vK/sEUKgtEemfEAI/H72DkevOIiNHgeZv2WPnuFblMrgREalD7fAWEBCg03vaXsXV1RWRkZFFft555x2YmpoiMjIS8+bNg5GREXr06IHw8HCkp6erjn/48CEiIyMREhKi2talSxeYmppi7dq1hd5r7dq1kEgk6NWrl2pb7969ERMTU2hJEIVCgbCwMDRr1qzY9eaISD9yFHn4vz8uY/6eGAgBfNisOn4b2Qx2Fib6Lo2IqNTUvmz63XffYdCgQWjcuHGhy4q6Zmpqinbt2hXZvnbtWhgaGhbaFxoaiiZNmqB79+6YNm0aZDIZZs+eDUdHR0yePFnVzt7eHjNnzsSsWbNgb2+Pzp07IyoqCnPnzsWoUaNUa7wBwIgRI7B8+XL069cPCxcuhLOzM1asWIEbN27g4MGDuvzoRKSGhIwcjPntHM49SIahgQSz3q2HoS1rcuFdIqo01A5vw4cPR2JiIpo2bQpXV1fVPWYFJBIJLl26pLUCNeHt7Y3Dhw9j6tSp6Nu3L4yMjNC+fXssXrwYTk6Fb1KeMWMGrKyssHz5cixevBiurq6YNm0aZsyYUaidVCrFoUOHMGXKFIwfPx5ZWVnw9/fH3r17ERQUVJYfj4he4dqTNIxefxaxKdmwMjXCioGN0aZu+ZiYQESkLWqHNwcHBzg6OuqiFo2sXbu2yGVPIP/ybknPiE2YMAETJkx4YzsXFxesW7dO3RKJqAzsv/oUn/1+EVm5eXjL0QK/DA1EbaeyWTiciKgsqR3eDh8+rIMyiIg0I4TAisN38K/9NwAAres4YvmHjWFjzll0RFQ5VbgnLBARFZDJ8zBl62X8eekJAGBoixqY1d0HRoYaPfmPiKhC0Di8paam4ubNm0WesAAAbdu2LVVRRERv8ixNho/Wn8Wlx6kwMpAgtGd9DGxWQ99lERHpnNrhTaFQ4OOPP8b69euRl5dXbJtXbSci0obLj1Mwev1ZPEvLga25MVYMbIyWtcvPvbhERLqk9rWF//znP9i1axdWr14NIQR++OEH/PTTTwgMDETdunWxd+9eXdRJRAQA2HXpCfqtPIlnaTmo42yJneNaMbgRUZWidnj77bffMGPGDAwYMAAA0KxZM4waNQqnT59GjRo1EBkZqfUiiYiUSoHvDtzA+E0XkKNQop2XE8LHtkQNBwt9l0ZEVKbUDm93795Fw4YNYWCQf6hMJlPt+/jjj7FhwwbtVUdEBCArV4GxG87j+79vAwBGt6mFVUObwNqUM0qJqOpRO7xZWFggNzcXEokE9vb2ePDggWqfmZlZsQ9vJyLSVGxKNvr+eBL7rj6FiaEB/tXXDzPe9YGhAZ+YQERVk9oTFry9vXHv3j0AQMuWLfHdd9+hTZs2MDExwbfffgsvLy+tF0lEVdO5B0kY89s5JGTkwtHSBCsHBSCwpr2+yyIi0iu1w9sHH3yAmzdvAsh/hmjbtm1Ro0b+9HxjY2OEh4drt0IiqpK2nnuML8OvIDdPiXpu1vhlSAA87cz1XRYRkd6pHd7Gjh2r+v+NGjXCtWvXsH37dhgYGKBTp04880ZEpZKnFFi0LwY/H70LAHinvgu+e98fFlKuKU5EBGjhCQvVqlUr0XNBiYjeJF0mx8TNF/F3TDwAYEL7OpjU8W0Y8P42IiKVUoW358+fF/uEherVq5emWyKqgu4nZGLU+rO4HZ8BqZEBFvdriB4N3fVdFhFRuaN2eEtPT8dnn32GTZs2FVom5EV8wgIRqePE7QSM3XgeKVlyuFhL8cuQQPh52uq7LCKicknt8DZp0iRs3LgRI0eOhJ+fH6RSqS7qIqIq4reT9zF31zXkKQUaVrPFz4MD4GJtqu+yiIjKLbXD219//YWFCxdi4sSJuqiHiKoIeZ4SobuuIuzUQwBAL393LOzjB1NjQz1XRkRUvqkd3mQyGXx9fXVRCxFVEcmZuRi74TxO3k2ERAJ88Y4XPgmqDYmEExOIiN5E7ScsdOvWDceOHdNFLURUBdx8lo6ey4/j5N1EWJgY4ufBgRjbrg6DGxFRCal95m3mzJno27cvrKys0KNHDzg4OBRpY2/PFdCJqKhD159h4uaLyMhRoJq9GX4d0gRerlb6LouIqEJRO7w1aNAAAPDFF1/giy++KLYNZ5sS0YuEEPjp6F0s2hcDIYDmb9ljxcAA2FuY6Ls0IqIKR+3wNnv2bF7eIKISk8nzMD38CrZfiAUADGxWHXPfqw9jQ7Xv2iAiImgQ3ubOnauDMoioMopPk2H0b+dw6VEKDA0kmNvDB4Nb1NR3WUREFVqpnrAgk8mQnJwMOzs7mJpyXSYi+sflxykYvf4snqXlwNbcGCs+bIyWdRz1XRYRUYWn0XWLEydOoE2bNrCysoKnpyesrKwQFBSEkydPars+IqqAdl6MRb+VJ/EsLQd1nC2xc1wrBjciIi1R+8zbqVOn0L59e9ja2uKjjz6Cu7s7YmNjER4ejvbt2+Pw4cNo1qyZLmolonIoLw84ckSCo0c9YGYOXFDE4McjdwAA7b2dsbS/P6xMjfVcJRFR5aHRhAU/Pz9ERkbCwsJCtf1f//oXgoODMXv2bOzfv1+rRRJR+RQeDkycCDx+bASJiT9+u38B5nXjAQBjgt7ClHe8YWjACU5ERNqk0Zm31atXFwpuAGBhYYEvvvgCI0eO1FpxRFR+hYcDffsCQgBGNllw6hMFE6cMCIUBEvf5wqupJzihlIhI+9T+ozUvL++VD6M3NTXlGm9EVUBeXv4ZNyEAafUEuA79L0ycMqBIl+LpxhbIuuaJSZPy2xERkXapHd4aNmyIH3/8sdh9P/30Exo2bFjqooiofDt2DHj8GLBsdB8uH5yBoZkcOU9s8HR9a+TG2UII4NGj/HZERKRdal82nTZtGnr16oVGjRph0KBBcHNzQ1xcHDZu3IiLFy9ix44dOiiTiMqTR7FK2He+CqtGDwEAmVfdkbjPD0JhWKhdXJw+qiMiqtzUDm/vvfcewsLCMGXKlEKPx/Lw8EBYWBh69Oih1QKJqHxJysxF2ONzsGqUBCGAlCPeSDv9FoCiExPc3Mq+PiKiyk6jRXo//PBDDBgwADdu3EBiYiIcHBzg5eXFx2YRVXIxT9Mwat1ZPE7OhpAb4fmf/si+7VKknUQCeHoCbdrooUgiokpO4ycsSCQSeHt7q17LZDI+ZYGoEtsX/RSfb7mIrNw81HAwxwfugfj0P1aQSPInLhQo+DfckiWAoWGxXRERUSmoPWHh999/x4oVK1Svb9++DR8fH1hYWKBNmzZITk7WaoFEpF9CCHx/6BY+DjuHrNw8tKrjgJ3jWmHsQCts3Qp4eBRu7+kJbN0KhITop14iospO7fC2ePFiZGZmql5/8cUXSE5OxsSJExETE4P58+drtUAi0p+sXAXGbTyP7yJuAgCGtayJdcObwtbcBEB+QLt/H4iIUODzz88iIkKBe/cY3IiIdEnty6Z3795FgwYNAORfKt2/fz9WrlyJIUOGwMvLC4sXL8a//vUvrRdKRGXrcXIWPlp/Dtfi0mBsKMHXPRugf9PqRdoZGgJBQQKZmbEICmrIS6VERDqmdnjLyspSPV3h9OnTyMnJQdeuXQEAPj4+iI2N1W6FRFTmztxLwidh55CYmQsHCxOsHByAJjXt9V0WERFBg8umbm5uuHjxIgBg37598PLygpOTEwAgOTkZ5ubmWi2QiMrWpjMPMfDXU0jMzEV9d2v8Ob41gxsRUTmi9pm3kJAQzJgxA0eOHMHevXsxdepU1b7Lly+jdu3aWi2QiMqGPE+JebuvYd3JBwCAd/3csLhvQ5iZ8DooEVF5onZ4+/rrr5GRkYETJ07gww8/xJQpU1T7du/ejY4dO2q1QCLSvaTMXIzbcB4n7yYCAL54xwtj29Xm2o1EROWQ2uHNzMwMK1euLHbfqVOnSl0QEZWtFxfetTAxxH8+8Efn+q76LouIiF5B40V6AeDGjRtISEiAv7+/ahIDEVUc+6Lj8PmWS6qFd38ZEoi3Xaz0XRYREb2G2hMWAGD9+vXw9PSEj48P2rZtixs3bgAA3n//ffzyyy9aLZCItE+pFPhPxE18HHYeWbl5aF3HETvHtWJwIyKqANQOb3/88QeGDRuGxo0b44cffoB44bk4jRs3xpYtW7RaIBFpV2aOAp9sOIelh24BAEa0qoW1w5uoFt4lIqLyTe3wtmDBAgwfPhx//vknPvroo0L76tWrh2vXrmmtOCLSroeJWQhZcQL7rz6DiaEBvu3rh9k9fGBkqNFJeCIi0gO173m7fv06Fi1aVOw+e3t7JCYmlrooItK+E7cTMHbjeaRkyeFkJcXKQQEIqGGn77KIiEhNaoc3c3NzpKamFrsvNjYWdnb8y4CoPBFCYN2J+/j6r+vIUwo09LTBT4MD4Wpjqu/SiIhIA2pfK2nVqlWRe90KrF27Fu3atdNGXUSkBTmKPEzddhlzd11DnlIgpJEHfh/TgsGNiKgCU/vM2+zZs9G6dWs0bdoUH374ISQSCcLDwzFnzhwcPXoUZ86c0UWdRKSm+DQZxoSdw4WHKTCQANO71sOoNrW48C4RUQWn9pm3wMBA7N27FxkZGZg8eTKEEJg/fz5u3ryJPXv2oEGDBrqok4jUcPFRCnr88F9ceJgCa1MjrB3eFKPbvsXgRkRUCWi0SG9wcDCuX7+OO3fu4NmzZ3B0dMTbb78NIP/+Gv4FQaQ/2849xvTtV5CrUKKusyV+GRKImo5cRJuIqLIo1foAtWvXRsuWLVXBbePGjahXr55WCiMi9SjylPhq1zVM/uMSchVKdPJxwfZxrRjciIgqmRKfeUtNTcWOHTvw7NkzvP3223jvvfdgYJCf/cLDwzF79mxcu3YNNWrU0FmxRFS85MxcjNt4Hifu5C/VM6FDXUzqUBcGBjwLTkRU2ZQovN2+fRtt2rRBfHy86rJoUFAQduzYgQEDBmDfvn2wtbXFt99+i/Hjx+u6ZiJ6wfW4NHz021k8SsqGuYkhvnu/Ibo0cNN3WUREpCMlCm+zZs1CWloa5s6di8DAQNy9exfffPMNWrZsiWvXrmHUqFH49ttvYWtrq+NyiehFe67EYfKWS8iW56G6vTl+HhIAb1drfZdFREQ6VKLwduTIEcycORPTp09XbatTpw66du2Kjz/+GCtWrNBZgURUVJ5S4LuIG1geeQcA0LqOI374sBGfT0pEVAWUKLw9f/4crVq1KrStdevWAIAPPvhA+1UR0SulyeSYtPki/o6JBwCMal0L07p68/mkRERVRInCW15eHkxNC6/IXvDayspK+1URUbFux2fgo/VncTchE1IjAyzs44vejTz1XRYREZWhEs82vXHjBoyM/mmel5cHAIiJiSnStnHjxloojYhedPDaM0z6/SIychRwtzHFT4MD4etpo++yiIiojJU4vA0bNqzY7YMHD1b9/4KZqAXBjohKT6kUWPb3bfzn4E0AQNNa9lgxsDEcLaV6royIiPShROFtzZo1uq6DiIqRLpNj8pZLOHDtGQBgaIsamNndB8a8v42IqMoqUXgbOnSorusgopfcfZ6Bj347h9vxGTAxNMC83g3wfmA1fZdFRER6ptGzTYlIt/6OeYaJmy8iXaaAq7UpVg4OgH81W32XRURE5QDDG1E5olQKLI+8je8O3oQQQGANO6wY1BjOVqZvPpiIiKoEhjeiciIjR4HJWy5i/9X8+9sGNa+O2d3rw8SI97cREdE/GN6IyoGX72/7uld9fNCkur7LIiKicojhjUjPDl1/hkmbLyI9RwEXaylWDgpAo+p2+i6LiIjKqQpzPebvv//GiBEj4O3tDQsLC3h4eKBnz544d+5ckbbnz59Hx44dYWlpCVtbW4SEhODu3bvF9rts2TJ4e3tDKpWiVq1aCA0NhVwuL9IuPj4ew4YNg6OjI8zNzdGiRQscOnRI65+Tqg6lUmDJwZsYue4s0nMUCKxhh13jWzO4ERHRa1WY8Pbjjz/i/v37mDhxIvbs2YOlS5ciPj4ezZs3x99//61qFxMTg3bt2iE3NxdbtmzB6tWrcfPmTbRp0wbPnz8v1Oc333yDiRMnIiQkBPv378fYsWMxf/58jBs3rlC7nJwcdOjQAYcOHcLSpUuxc+dOuLi4oEuXLjhy5EiZfH6qXNJkcnz021ksOXgLADCkRQ1sHN2cExOIiOiNKsxl0+XLl8PZ2bnQti5duqBOnTqYP38+2rdvDwCYPXs2pFIpdu/eDWtrawBAQEAA6tati8WLF2PRokUAgMTERMybNw+jR4/G/PnzAQDt2rWDXC7HzJkzMWnSJPj4+AAAVq1ahejoaJw4cQItWrQAAAQHB6Nhw4aYMmUKTp8+XSZjQJXDrWfpGPPbOdxNyISJkQG+6dUA/bh+GxERlVCFOfP2cnADAEtLS/j4+ODRo0cAAIVCgd27d6NPnz6q4AYANWrUQHBwMLZv367atm/fPshkMgwfPrxQn8OHD4cQAjt27FBt2759O7y8vFTBDQCMjIwwaNAgnDlzBrGxsdr6mFTJ7bkSh57Lj+NuQibcbUyx9eMWDG5ERKSWCnPmrTipqak4f/686qzbnTt3kJ2dDT8/vyJt/fz8EBERAZlMBlNTU0RHRwMAfH19C7Vzc3ODo6Ojaj8AREdHo02bNsX2CQBXr16Fh4dHsTXm5OQgJydH9TotLQ0AIJfLi723rrQK+tRF35WdLsdOkafEdwdv45f/3gcANK9lhyUfNISDhUml+G/F753mOHaa49hphuOmOV2PXUn7rdDhbdy4ccjMzMSMGTMA5F8KBQB7e/sibe3t7SGEQHJyMtzc3JCYmAipVAoLC4ti2xb0VdDvq/p88X2Ls2DBAoSGhhbZfuDAAZibm7/hE2ouIiJCZ31Xdtoeuww5sO6WAW6m5p/obu+mRHeX5zh95KBW36c84PdOcxw7zXHsNMNx05yuxi4rK6tE7SpseJs1axY2bNiAZcuWISAgoNA+iUTyyuNe3FfSduq2fdH06dPx+eefq16npaWhWrVq6Ny5c6FLu9oil8sRERGBTp06wdjYWOv9V2a6GLvo2DR8uvkiYlNlMDcxxIJe9dHN11UrfZcn/N5pjmOnOY6dZjhumtP12BVcnXuTChneQkNDMW/ePHzzzTf49NNPVdsdHBwAFH8mLCkpCRKJBLa2tqq2MpkMWVlZRc6AJSUlFQqEDg4Or+wTKP5MXwGpVAqpVFpku7GxsU5/aXTdf2WmrbHbEvUIM3dGI1ehRE0Hc/w0OBBerlZaqLD84vdOcxw7zXHsNMNx05yuxq6kfVaYCQsFQkNDMXfuXMydOxdffvlloX21a9eGmZkZrly5UuS4K1euoE6dOjA1zV+KoeBet5fbPn36FAkJCWjQoIFqm6+v7yv7BFCoLVGOIg/Tw69gyrbLyFUo0bGeM3Z+2rrSBzciIiobFSq8ff3115g7dy5mzpyJOXPmFNlvZGSEHj16IDw8HOnp6artDx8+RGRkJEJCQlTbunTpAlNTU6xdu7ZQH2vXroVEIkGvXr1U23r37o2YmJhCS4IoFAqEhYWhWbNmcHd3196HpArtSUo23l95EpvOPIREAkzu9DZ+HhwIGzP+65aIiLSjwlw2/fe//43Zs2ejS5cuePfdd3Hq1KlC+5s3bw4g/8xckyZN0L17d0ybNg0ymQyzZ8+Go6MjJk+erGpvb2+PmTNnYtasWbC3t0fnzp0RFRWFuXPnYtSoUao13gBgxIgRWL58Ofr164eFCxfC2dkZK1aswI0bN3DwYOW76Zw0c/x2AsZvuoCkzFzYmBljaX9/tPMqusQNERFRaVSY8LZr1y4A+euz7du3r8h+IQQAwNvbG4cPH8bUqVPRt29fGBkZoX379li8eDGcnJwKHTNjxgxYWVlh+fLlWLx4MVxdXTFt2jTV7NUCUqkUhw4dwpQpUzB+/HhkZWXB398fe/fuRVBQkI4+MVUUSqXAj0fu4N8HbkApAB83a/w0OADV7HU3m5iIiKquChPeDh8+XOK2AQEBJT4jNmHCBEyYMOGN7VxcXLBu3boS10BVQ2q2HJO3XMLB688AAP0CPPF1rwYwNTbUc2VERFRZVZjwRlTeXI9Lwydh53A/MQsmhgYI7Vkf/ZtUe+3SMURERKXF8EakgW3nHmPGjiuQyZXwsDXDj4Maw8/TVt9lERFRFcDwRqQGmTwPobuuYdOZhwCANnUdsbR/I9hbmOi5MiIiqioY3ohK6FFSFsZuOI8rsamQSIAJ7etiQoe6MDTgZVIiIio7DG9EJfB3zDN89vslpGbLYWtujCUfcBkQIiLSD4Y3otdQ5CnxXcRNrDh8BwDQsJotVgxsDA9bMz1XRkREVRXDGxGAvDzgyBEJjh71gIWFBMHBQGKmDOM3XcDpe/nPsB3cvAZmdq8HqRGXASEiIv1heKMqLzwcmDgRePzYCEAgvvsO8GycCNtuF5Auz4G5iSEW9vHDew35GDQiItI/hjeq0sLDgb59gf89oAOAgHXzOzBocwPpcsDVzAphnzRGHWdLfZZJRESkwvBGVVZeXv4Zt4LgZmCWA8ceF2FWKwEAkHHFE0+uNUCtmbxMSkRE5QfDG1VZx44Bjx/n/3+pZxIc3zsPI6scKOUGSIpogMwrnkiEBMeOAe3a6bVUIiIiFYY3qrLi4oCCy6S2bW5CYiAgT7TA8x0BkCdYvdSOiIiofGB4oyrLwj4Hzv0uweyt5wCAjGgPJB1oACEv/Gvh5qaP6oiIiIrH8EZV0vHbCfg66iLM3sq/TJp8sD4yLlcD8M/TEiQSwNMTaNNGf3USERG9jOGNqhRFnhJLDt7C8sO3IQTgamaJC6saQ55oVaid5H8ZbskSwJDzFYiIqBwx0HcBRGUlNiUbA345hR8i84PbgKbVEDm9NTb9ZAUPj8JtPT2BrVuBkBD91EpERPQqPPNGVcLeK3GYuu0y0mQKWEqNsCDEFz3+t+huSAjQsycQGanA3r0X0bWrP4KDjXjGjYiIyiWGN6rUsnPz8NXuq9h05hGA/GeTft/fHzUcLAq1MzQEgoIEMjNjERTUkMGNiIjKLYY3qrSuPUnDhM0XcDs+AxIJ8HFQbXze6W0YG/JuASIiqrgY3qjSUSoFVh+/h2/33UBunhLOVlL85wN/tKrjqO/SiIiISo3hjSqVuNRs/N8fl3D8diIAoGM9Z3zbtyHsLUz0XBkREZF2MLxRpfHX5Th8uf0KUrPlMDU2wKzuPviwaXVIJJI3H0xERFRBMLxRhZeaLUforqsIPx8LAPDztMF/PvBHbSdLPVdGRESkfQxvVCHk5eU/SD4uLv9xVW3a5M8Q/e+tBHyx9RLiUmUwkABj29XBxI51OSmBiIgqLYY3KvfCw4GJE4HHj//Z5llDgbYTYnA8/gEAoIaDOf7dryECa9rrqUoiIqKywfBG5Vp4ONC3LyDEP9ukHklQvnMJx+OzAACDm9fAtK7esJDy60xERJUf/7ajcisvL/+MW0FwkxgrYNv2BqwC7kMiARTppjCI8sPcb5y4qC4REVUZDG9Ubh079s+lUtPqCbDvehnGttkAgIzLnkj62wcixxjHjgHt2umvTiIiorLE8EblVlwcIDGRwy44Blb+DwEAijRTJO7zg+yeU6F2REREVQXDG5VLQgg8QhzcR1+DkWUOACD9fHUkH/GGyDUu1NbNTR8VEhER6QfDG5U7j5KyMHNHNI7cfA4jS0CeZIHE/Q2Q87Dw460kEsDTM3/ZECIioqqC4Y3KjRxFHlb99x6+P3QLMrkSJoYGaOdSG7/+uzaQV3hGQsFDE5YsAScrEBFRlcLwRmXmVQvtCiHwd0w8vt59DfcT85f/aP6WPb7p7YvaTpbo4lnMOm+e+cEtJEQ/n4WIiEhfGN6oTBS70K4n8OXCDJzKuYYjN58DABwtpZje1RshjT1UzyQNCQF69iw++BEREVU1DG+kc8UttGtgloNMrztYePE+JIYCxoYSjGhdC58G14GVqXGRPgwNuRwIERERwPBGOlZkoV0TBayb3IV1k7swkOYBAESsM/Yu9kEdFws9VkpERFQxMLyRTqkW2jXMg1Wjh7BpcRuG5rkAgJyn1kg54g3ZfSc8HgnUcdFvrURERBUBwxvp1L3Hclg3fQirwHswsspfr02eZIGUo17IuuEKIP++Ni60S0REVDIMb5VIXh5w5IgER496wMJCguBg7d7U/6rZosV5np6DNcfvYU3MA9gFKwDkPx0h9URdZFzxBJQGhdpzoV0iIqKSYXirJP6ZzWkEIBDffZc/m3PpUu0sp/Gq2aIv9i+EwOl7SdgS9Qi7r8QhV6HM355qgaTjtZFx1aNIaONCu0REROpheKsEipvNCQCxsfnbt24tXYB7U/+rN8qQ7RqLLWcf4V5Cpmp/o+q2+CSoNtJiXPD+TxJIALzYBRfaJSIiUh/DWwX38mxOu47RUMpMILvniJw4W0iEASZNyl8nTZOA9HL/+QSMHTNgVvsZzOrEY875ZEj+d0LNwsQQ7/m74/3AavCvZpu/Vlv9/ADJhXaJiIhKj+GtglPN5gQgMVbAyv8hJIYCaHULyhxDyB45IOW+I9buskW/LpawLmYNtTf2H5cHE5cMmLimwsQlFaa1nsPYNrtQu9o2thjTsTre9XODhbTo14oL7RIREWkHw1sF9/IszaQDDWBaMwGmNRJhaJ4L8zrxMK8Tj69PAV+fApytpKjtZInazhawNTOBmYkhzIwNYW5iCCNDA6Rk5SIpMxfJWblIzMjFlbvZqP5Zen4gfIFQGED2wAFZt12QfccZC34yw/tNXl8rF9olIiIqPYa3Cu7FWZpCboSMy9WRcbk6AAFj5zSY/S/IudVLQ0pODuLT839O3k0s8XtIDIG8bGPkPrNB7lNr5MTaQfbAEUL+z9eHs0WJiIjKBsNbBdemTf69Y7GxL9+XJoE83gaK5zawiauNc5uATLkcd59n4nZ8Bu4nZCIjR4GsXAWy5Upk5yqQmydga2YMewsT2JmbwN7SBE4WUozua4PYW6YQQlLk/TlblIiIqGwxvFVwhob5y3X07ZsfpF4McC/P5rQ2NIZ/NVv4V7NV6z2WLihZ/0RERKR7Bm9uQuVdSEj+bE4Pj8LbPT1Lv0xIWfRPREREJcczb5VEwWzOyEgF9u69iK5d/REcbKS1M2KcLUpERFQ+MLxVIoaGQFCQQGZmLIKCGmo9WHG2KBERkf7xsikRERFRBcLwRkRERFSBMLwRERERVSAMb0REREQVCMMbERERUQXC8EZERERUgTC8EREREVUgDG9EREREFQjDGxEREVEFwvBGREREVIEwvBERERFVIAxvRERERBUIH0xfxoQQAIC0tDSd9C+Xy5GVlYW0tDQYGxvr5D0qK46d5jh2muPYaY5jpxmOm+Z0PXYF2aAgK7wKw1sZS09PBwBUq1ZNz5UQERFReZSeng4bG5tX7peIN8U70iqlUoknT57AysoKEolE6/2npaWhWrVqePToEaytrbXef2XGsdMcx05zHDvNcew0w3HTnK7HTgiB9PR0uLu7w8Dg1Xe28cxbGTMwMICnp6fO38fa2pq/lBri2GmOY6c5jp3mOHaa4bhpTpdj97ozbgU4YYGIiIioAmF4IyIiIqpAGN4qGalUijlz5kAqleq7lAqHY6c5jp3mOHaa49hphuOmufIydpywQERERFSB8MwbERERUQXC8EZERERUgTC8EREREVUgDG8VREZGBiZNmgR3d3eYmprC398fmzdvLtGx8fHxGDZsGBwdHWFubo4WLVrg0KFDOq64/NB07B4/foxJkyYhKCgItra2kEgkWLt2re4LLkc0Hbvw8HAMGDAAderUgZmZGWrWrImBAwfi1q1bZVB1+aDp2B08eBCdOnWCu7s7pFIpnJ2d0b59e+zZs6cMqta/0vxZ96KZM2dCIpGgQYMGOqiyfNJ07NauXQuJRFLsz9OnT8ugcv0r7fdu586dCAoKgrW1NSwsLFC/fn38/PPPuitYUIXQqVMnYWtrK1auXCn+/vtvMWrUKAFAbNiw4bXHyWQy0aBBA+Hp6SnCwsLEgQMHRM+ePYWRkZE4fPhwGVWvX5qOXWRkpHB0dBQdO3YUAwYMEADEmjVryqbockLTsWvatKl47733xOrVq8Xhw4fFb7/9JurVqycsLS1FdHR0GVWvX5qO3ebNm8XEiRPF5s2bxeHDh0V4eLjo3LmzACB+++23MqpefzQdtxdduHBBSKVS4eLiIurXr6/DassXTcduzZo1qj/fTp48WegnNze3jKrXr9J87xYsWCAMDAzE2LFjxd69e8XBgwfFDz/8IJYtW6azehneKoC//vpLABAbN24stL1Tp07C3d1dKBSKVx67fPlyAUCcOHFCtU0ulwsfHx/RtGlTndVcXpRm7PLy8lT/PyoqqsqFt9KM3bNnz4psi42NFcbGxmLkyJFar7W8Kc3YFSc3N1d4eHiINm3aaLPMckcb4yaXy4W/v7+YMGGCCAoKqjLhrTRjVxDeoqKidF1muVSasTt79qwwMDAQixYt0nWZhfCyaQWwfft2WFpaol+/foW2Dx8+HE+ePMHp06dfe6yXlxdatGih2mZkZIRBgwbhzJkziI2N1Vnd5UFpxu51z5WrCkozds7OzkW2ubu7w9PTE48ePdJ6reVNacauOMbGxrC1tYWRUeV+oqE2xm3hwoVISkrCN998o6syyyVtf+eqktKM3Q8//ACpVIrx48frusxCqvbfThVEdHQ06tWrV+QPbj8/P9X+1x1b0K64Y69evarFSsuf0oxdVaftsbt79y4ePHiA+vXra63G8kobY6dUKqFQKPDkyRPMmTMHN2/exOTJk3VSb3lR2nG7du0a5s2bhx9//BGWlpY6q7M80sZ3rnv37jA0NIS9vT1CQkKqzJ+PpRm7o0ePol69eti2bRu8vLxgaGgIT09PTJs2Dbm5uTqruXL/M66SSExMxFtvvVVku729vWr/644taKfusZVBacauqtPm2CkUCowcORKWlpb47LPPtFZjeaWNsevWrRv2798PIP8h2L///jveffdd7RZazpRm3JRKJUaMGIGQkBB069ZNZzWWV6UZO1dXV8yYMQPNmzeHtbU1rly5goULF6J58+Y4fvw4GjZsqLO6y4PSjF1sbCyeP3+OCRMm4Ouvv4aPjw8OHTqEhQsX4tGjR9iwYYNOamZ4qyAkEolG+0p7bGVQ1T9/aWhj7IQQGDlyJI4dO4Zt27ahWrVq2iqvXCvt2C1btgwpKSmIi4tDWFgYPvjgA6xbtw4DBgzQZpnljqbj9t133+HWrVv4888/dVFWhaDp2HXp0gVdunRRvW7bti3effdd+Pr6Yvbs2di5c6dW6yyPNB07pVKJ9PR0bNq0Cf379wcABAcHIzMzE0uWLEFoaCjq1Kmj9Xp52bQCcHBwKDb5JyUlAUCxZ9a0cWxlUNU/f2loY+yEEBg1ahTCwsKwdu1a9OzZU+t1lkfaGLu6deuiSZMmeO+997BlyxZ06NAB48aNg1Kp1Hq95YWm4/bw4UPMnj0bc+bMgYmJCVJSUpCSkgKFQgGlUomUlBRkZ2frtHZ90/afdTVr1kTr1q1x6tQprdRXnpX271gAeOeddwpt79q1KwDg/Pnz2iqzEIa3CsDX1xfXr1+HQqEotP3KlSsA8Np1jHx9fVXt1D22MijN2FV1pR27guC2Zs0a/Prrrxg0aJDOai1vdPG9a9q0KZKTk/H8+XOt1FgeaTpud+/eRXZ2NiZOnAg7OzvVz/Hjx3H9+nXY2dlh+vTpOq9fn3TxnRNCVImJW6UZu+LuKQfyxw7Q4cS3Mp3bShrZs2ePACA2b95caHuXLl3eOI15xYoVAoA4deqUaptcLhf169cXzZo101nN5UVpxu5FVXGpkNKMnVKpFCNHjhQSiUT8/PPPui613NHW966AUqkUQUFBwtbWVsjlcm2WWq5oOm7JyckiMjKyyE/Dhg1FzZo1RWRkpLh161ZZfAS90fZ37u7du8LS0lL06tVLm2WWS6UZu59++qnY9eAmTJggDAwMxP3793VSM8NbBdGpUydhZ2cnfv75Z/H333+L0aNHCwAiLCxM1WbEiBHC0NCw0JdFJpOJ+vXri2rVqokNGzaIiIgI0bt37yq3SK8mYyeEEH/88Yf4448/xKJFiwQAMW7cONW2qkDTsfv0008FADFixIgii36eP39eHx+lzGk6du+9956YNWuW2LZtmzh8+LDYuHGjapHe5cuX6+OjlKnS/L6+rCqt8yaE5mPXoUMHERoaKrZv3y4OHToklixZItzd3YWVlZW4cuWKPj5KmdN07HJzc0Xjxo2FjY2NWLp0qYiIiBBTp04VhoaG4tNPP9VZvQxvFUR6erqYMGGCcHV1FSYmJsLPz09s2rSpUJuhQ4cKAOLevXuFtj99+lQMGTJE2NvbC1NTU9G8eXMRERFRhtXrV2nGDsArf6oCTceuRo0arxy3GjVqlO2H0BNNx27RokWiSZMmws7OThgaGgoHBwfxzjvviN27d5fxJ9CP0vy+vqyqhTdNx27SpEnCx8dHWFlZCSMjI+Hu7i4GDRokbty4UcafQH9K871LTEwUY8aMES4uLsLY2Fi8/fbb4l//+lehhd61TSLE/y7MEhEREVG5V/nvRCQiIiKqRBjeiIiIiCoQhjciIiKiCoThjYiIiKgCYXgjIiIiqkAY3oiIiIgqEIY3IiIiogqE4Y2IiIioAmF4I6qCRowYAalUqnrw8osWLlwIiUSCXbt2vbEfuVyOH3/8ES1atICNjQ3MzMxQr149TJs2DYmJiUXa5+bm4uOPP4abmxsMDQ3h7+8PAEhKSkL//v3h7OwMiUSCXr16lfYjFpKVlYW5c+fi8OHDah337NkzTJs2Db6+vrC0tISpqSnq1q2LiRMn4tatW2rXsXbtWkgkEty/f1+1bdiwYahZs2aJ+9i1axd69OgBFxcXmJiYwN7eHh06dMCGDRsgl8vVrqm82LhxI5YsWaLWMXK5HN7e3li4cGGx+7///ntIJJJXPlg8OTkZtra22LFjh5rVEumZzp7dQETlVmpqqqhevbpo1KiRyM3NVW2/fPmyMDExEcOGDXtjH5mZmSIoKEgYGhqKTz75RPz111/i77//Ft98842ws7MT1apVEzExMYWOWbJkiQAgli1bJk6cOCEuX74shMh/PI+JiYkICwsTJ0+e1PpjeZ4/fy4AiDlz5pT4mNOnTwsnJyfh6Ogo5s6dK/bv3y8iIyPFypUrRevWrYWtra3adcTHx4uTJ08KmUym2jZ06NASPTJMqVSKYcOGCQCiW7duIiwsTBw5ckT8+eef4rPPPhPW1tZiyZIlatdUXrz77rtqPzptyZIlwtnZWWRkZBS7v2HDhqrHsp06darYNnPnzhV16tQROTk56pZMpDcMb0RVVEREhJBIJGL27NlCiPwHLDds2FBUq1ZNpKSkvPH4jz76SAAQmzdvLrLvxo0bwsbGRtSvX18oFArV9lGjRgkzM7Mi7Tt27Cjq1atXik/zeuqGt9TUVOHq6iqqVasmHj16VGybP/74Qyu1lTS8LVq0SAAQoaGhxe6Pi4sTx44d00pNmZmZxW5XKpUiKytLK+/xMnXDm1wuFx4eHmLatGnF7o+KihIAxLvvvisAiNGjRxfb7unTp8LIyEhs2LBBk7KJ9ILhjagK++STT4SRkZE4e/as+PLLLwUAceDAgTceFxcXJ4yMjMQ777zzyjbz588XAMTWrVuFEKLYh9SvWbOm2O2RkZFCCCFWrFgh/Pz8hIWFhbC0tBReXl5i+vTpRWr56KOPhIeHhzA2NhY1a9YUc+fOFXK5XAghxL1794p9j6FDh76y9sWLFwsARR5M/To7d+4UzZs3F2ZmZsLS0lJ07NhRnDhxolCbgs/74oOtSxLecnNzhb29vfD29hZKpfKNtURGRhYaxwIFY7FmzZpC729hYSEuX74sOnXqJCwtLUXz5s2FEPn/zcaNGyd+/PFH4e3tLYyNjcWPP/4ohBDi5s2bYsCAAcLJyUmYmJgIb29v8cMPPxRbx8aNG8WXX34p3NzchJWVlejQoUOhs7JBQUHF/jd6nW3btgkA4urVq8Xu//jjjwUAceXKFdGyZUthZWX1ylDatWtX0aZNm9e+H1F5wvBGVIVlZGSIt956S9SsWVMYGhqKjz/+uETHbdy4UQBQ/UVenGvXrgkAYsyYMUIIIU6ePCm6desmzMzMxMmTJ8XJkyfF06dPxcmTJ0WjRo3EW2+9pdqempoqNm3aJACI8ePHiwMHDoiDBw+KlStXigkTJqjeIy4uTlSrVk3UqFFD/PTTT+LgwYPi66+/FlKpVHXpVyaTiX379gkAYuTIkar3uH379itr79y5szA0NHzl5biXbdiwQQAQnTt3Fjt27BC///67CAgIECYmJoXOhmka3k6cOCEAiKlTp5aoHnXDW0HoXbBggTh06JDYv3+/ECI/vHl4eAg/Pz+xceNG8ffff4vo6Ghx9epVYWNjI3x9fcX69evFgQMHxOTJk4WBgYGYO3dukTpq1qwpBg4cKP766y+xadMmUb16dVG3bl3VWdmrV6+KVq1aCVdXV9V/n5MnT772M44YMUI4OzsXuy8rK0vY2NiIJk2aCCGE+PXXXwUAsXbt2mLbL1q0SBgYGIjk5OTXvidRecHwRlTFFQQxV1dXkZ6eXqJjFi5cKACIffv2vbJNdna2ACC6du2q2lZwludlQUFBon79+oW2ffrpp2+8r2zMmDHC0tJSPHjwoND2gjNnBWdl1L1s6u3tLVxdXUvUNi8vT7i7uwtfX1+Rl5en2p6eni6cnZ1Fy5YtVds0DW+bN28WAMTKlStLVJO64Q2AWL16dZF+AAgbGxuRlJRUaPs777wjPD09RWpqaqHtn376qTA1NVW1L6ijW7duhdpt2bJFACgU0NS9bFqvXj3RpUuXYvetX7++0Hilp6cLS0vLV55di4iIEADE3r17S/z+RPrE2aZEVZhSqcSyZctgYGCA+Ph4XLp0SevvIZFINDquadOmSElJwYABA7Bz504kJCQUabN7924EBwfD3d0dCoVC9dO1a1cAwJEjR0pVe0ncuHEDT548weDBg2Fg8M8fqZaWlujTpw9OnTqFrKwsnddRWn369Cl2e/v27WFnZ6d6LZPJcOjQIfTu3Rvm5uaFxr1bt26QyWQ4depUoT7ee++9Qq/9/PwAAA8ePNC43idPnsDZ2bnYfatWrYKZmRn69+8PIP+/Rb9+/XDs2LFiZwkX9BMbG6txPURlieGNqApbvHgxTp48iY0bN6Ju3boYMWIEsrOz33hc9erVAQD37t17ZZuCfdWqVdOotsGDB2P16tV48OAB+vTpA2dnZzRr1gwRERGqNs+ePcOuXbtgbGxc6Kd+/foAUGzgK4nq1avj+fPnyMzMfGPbgiVR3Nzciuxzd3eHUqlEcnKyRnW8WA/w+vEuDXNzc1hbWxe77+XPlZiYCIVCgWXLlhUZ927dugEoOu4ODg6FXkulUgAo0XftVbKzs2Fqalpk++3bt3H06FG8++67EEIgJSUFKSkp6Nu3LwBg9erVRY4p6Kc09RCVJYY3oirq2rVrmD17NoYMGYIPPvgAa9euxe3btzFjxow3HhscHAwjI6PXro9VsK9Tp04a1zh8+HCcOHECqamp+OuvvyCEQPfu3VVnbBwdHdG5c2dERUUV+zNy5EiN3vedd95BXl5eida6KwgmcXFxRfY9efIEBgYGhc5caSIwMBD29vbYuXMnhBBvbF8QRnJycgptf1WYfd3Z0Zf32dnZwdDQEMOGDXvluBeEOF1ydHREUlJSke2rV6+GEAJbt26FnZ2d6ufdd98FAKxbtw55eXmFjinox9HRUed1E2kDwxtRFaRQKDB06FA4Ojpi6dKlAIDmzZvj888/x9KlS3H8+PHXHu/q6ooRI0Zg//79+P3334vsv3nzJhYtWoT69etrZcFdCwsLdO3aFTNmzEBubi6uXr0KAOjevTuio6NRu3ZtBAYGFvlxd3cHoP6ZnpEjR8LV1RVTpkx55aW08PBwAICXlxc8PDywcePGQsEqMzMT27ZtQ4sWLWBubq7xZwcAY2NjTJ06FTExMfj666+LbRMfH6/671aw6O/ly5cLtfnzzz9LVQeQf5YuODgYFy5cgJ+fX7Hj/vKZtpKQSqVqnfny9vbGnTt3Cm3Ly8vDunXrULt2bURGRhb5mTx5MuLi4rB3795Cx929excA4OPjo3bdRPpgpO8CiKjsLViwAGfPnsXevXtha2ur2v71119j165dGDFiBC5evAgzM7NX9vHdd9/hxo0bGDRoEI4ePYoePXpAKpXi1KlTWLx4MaysrLBt2zYYGhpqVOPo0aNhZmaGVq1awc3NDU+fPsWCBQtgY2ODJk2aAAC++uorREREoGXLlpgwYQK8vLwgk8lw//597NmzBytXroSnpyesrKxQo0YN7Ny5Ex06dIC9vT0cHR1f+WQDGxsb7Ny5E927d0ejRo3w6aefokWLFjAxMcGtW7cQFhaGS5cuISQkBAYGBvj2228xcOBAdO/eHWPGjEFOTg7+9a9/ISUl5ZWr/6vriy++wPXr1zFnzhycOXMGH374IapVq4bU1FQcPXoUP//8M0JDQ9GqVSu4urqiY8eOWLBgAezs7FCjRg0cOnRIFThLa+nSpWjdujXatGmDTz75BDVr1kR6ejpu376NXbt24e+//1a7T19fX4SHh+PHH39EQEAADAwMEBgY+Mr27dq1w1dffYWsrCxVON67dy+ePHmCRYsWoV27dkWOadCgAX744QesWrUK3bt3V20/deoUHBwc4Ovrq3bdRHqh1+kSRFTmLl68KIyNjV+5aOnJkyeFgYGB+Oyzz97YV25urli+fLlo1qyZsLS0FFKpVHh5eYkpU6aIhISEIu3VmW26bt06ERwcLFxcXISJiYlwd3cX77//vuqpDAWeP38uJkyYIGrVqiWMjY2Fvb29CAgIEDNmzCi01MfBgwdFo0aNhFQqfeM6bwWePn0qpk6dKurXry/Mzc2FVCoVderUEWPGjBFXrlwp1HbHjh2iWbNmwtTUVFhYWIgOHTqI48ePF2qj6WzTF+3cuVO8++67wsnJSRgZGQk7OzsRHBwsVq5cWegpAXFxcaJv377C3t5e2NjYiEGDBomzZ8++cp234uB/67wV5969e2LEiBGq9fWcnJxEy5Ytxbx581RtCmabvrygcXGzXpOSkkTfvn2Fra2tkEgkb1zn7fbt20IikYgtW7aotvXq1UuYmJiI+Pj4Vx7Xv39/YWRkJJ4+fSqEyF94uEaNGmL8+PGvfT+i8kQiRAluoCAiIipnevToAYVCUeQyqDoOHTqEzp074+rVq/D29tZidUS6w/BGREQVUnR0NBo1aoQTJ06oLqWrKzg4GHXq1MEvv/yi5eqIdIf3vBERUYXUoEEDrFmzBk+fPtXo+OTkZAQFBWHs2LFaroxIt3jmjYiIiKgC4VIhRERERBUIwxsRERFRBcLwRkRERFSBMLwRERERVSAMb0REREQVCMMbERERUQXC8EZERERUgTC8EREREVUg/w+IQutE2oBodgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data_linear = data[3:]\n",
+    "\n",
+    "fitAnalyser_linear = FitAnalyser('Linear', fitDim=1)\n",
+    "params_linear = fitAnalyser_linear.guess(data_linear, dask=\"parallelized\")\n",
+    "fitResult_linear = fitAnalyser_linear.fit(data_linear, params_linear).load()\n",
+    "\n",
+    "fitCurve_linear = fitAnalyser_linear.eval(fitResult_linear, x=np.linspace(0, 0.5, 100), dask=\"parallelized\").load()\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.gca()\n",
+    "\n",
+    "data.plot.errorbar(ax=ax, fmt='ob', yerr=data_std)\n",
+    "# fitCurve_linear.plot.errorbar(ax=ax)\n",
+    "fitCurve_quadratic.plot.errorbar(ax=ax)\n",
+    "\n",
+    "plt.xlabel('X Offset Coil Current (A)', fontsize=12)\n",
+    "plt.ylabel('Resonance Frequency (kHz)', fontsize=12)\n",
+    "plt.xticks(fontsize=12)\n",
+    "plt.yticks(fontsize=12)\n",
+    "# plt.legend(fontsize=12)\n",
+    "#plt.xlim(-0.01, 0.04)\n",
+    "# plt.ylim(0, 10000)\n",
+    "plt.grid(visible=1)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "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 id=\"icon-file-text2\" 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.Dataset&gt;\n",
+       "Dimensions:  ()\n",
+       "Data variables:\n",
+       "    b0       object 0.435+/-0.016\n",
+       "    by0      object -0.164+/-0.017\n",
+       "    alpha    object 9.52+/-0.05</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-05cbf367-574f-45e5-98fd-f2532de532ce' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-05cbf367-574f-45e5-98fd-f2532de532ce' 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-2cac1ee7-d44a-409e-a1d6-5fb3b81d7410' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2cac1ee7-d44a-409e-a1d6-5fb3b81d7410' 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-61f8ea8a-7171-490c-aba2-cefa0f0a3af7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-61f8ea8a-7171-490c-aba2-cefa0f0a3af7' class='xr-section-summary' >Data variables: <span>(3)</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>b0</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0.435+/-0.016</div><input id='attrs-f7be1e84-7c68-4419-ad87-ba1f353393d0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f7be1e84-7c68-4419-ad87-ba1f353393d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e10443e6-b5c8-4635-808f-afc18505a93d' class='xr-var-data-in' type='checkbox'><label for='data-e10443e6-b5c8-4635-808f-afc18505a93d' 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(0.4354862392709215+/-0.016241255661794147, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>by0</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>-0.164+/-0.017</div><input id='attrs-a24f0b66-670b-43b8-994a-d9e43280d6d9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a24f0b66-670b-43b8-994a-d9e43280d6d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da2b7bea-f86b-4433-b147-cf1694cf90cb' class='xr-var-data-in' type='checkbox'><label for='data-da2b7bea-f86b-4433-b147-cf1694cf90cb' 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(-0.16389847359556292+/-0.017148268573828836, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>alpha</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>9.52+/-0.05</div><input id='attrs-15c77479-0068-4c20-958e-2dc56241d9d5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-15c77479-0068-4c20-958e-2dc56241d9d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-551fa910-8da8-4f36-9eb0-5c44fc299dce' class='xr-var-data-in' type='checkbox'><label for='data-551fa910-8da8-4f36-9eb0-5c44fc299dce' 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(9.524149639955006+/-0.051341904930731916, dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e93f96ec-145f-404a-9ffb-86fbe74e7aa0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e93f96ec-145f-404a-9ffb-86fbe74e7aa0' 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-dcc7c765-2e68-4874-b7ad-2614fd53b950' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-dcc7c765-2e68-4874-b7ad-2614fd53b950' 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",
+       "    b0       object 0.435+/-0.016\n",
+       "    by0      object -0.164+/-0.017\n",
+       "    alpha    object 9.52+/-0.05"
+      ]
+     },
+     "execution_count": 129,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fitAnalyser_quadratic.get_fit_full_result(fitResult_quadratic) * 1e4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "val_x = fitAnalyser_quadratic.get_fit_value(fitResult_quadratic) * 1e4\n",
+    "std_x = fitAnalyser_quadratic.get_fit_std(fitResult_quadratic) * 1e4\n",
+    "res_x = fitAnalyser_quadratic.get_fit_full_result(fitResult_quadratic) * 1e4"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Y-comp coil"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fy = [751.71168, 918.67701, 1129.74687, 1429.82911, 1749.75310, 3089.87897, 4314.90128, 5387.64638, 6666.03670]\n",
+    "dfy = [1.32859, 0.90271, 1.33691, 1.77735, 1.42421, 1.58909, 1.73638, 1.40175, 1.00526]\n",
+    "y_offset_current = [0.0, 0.015, 0.03, 0.05, 0.07, 0.15, 0.22, 0.28, 0.35]\n",
+    "\n",
+    "# fy = [918.67701, 1129.74687, 1429.82911, 1749.75310, 3089.87897, 4314.90128, 5387.64638, 6666.03670]\n",
+    "# dfy = [0.90271, 1.33691, 1.77735, 1.42421, 1.58909, 1.73638, 1.40175, 1.00526]\n",
+    "# y_offset_current = [0.015, 0.03, 0.05, 0.07, 0.15, 0.22, 0.28, 0.35]\n",
+    "\n",
+    "data = xr.DataArray(\n",
+    "    data=fy,\n",
+    "    dims=['x'],\n",
+    "    coords={\n",
+    "        'x': y_offset_current\n",
+    "    } \n",
+    ")\n",
+    "\n",
+    "data_std = xr.DataArray(\n",
+    "    data=dfy,\n",
+    "    dims=['x'],\n",
+    "    coords={\n",
+    "        'x': y_offset_current\n",
+    "    } \n",
+    ")\n",
+    "\n",
+    "data.plot.errorbar(fmt='ob', yerr=data_std)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def magnetic_field_func(x, b0=0, by0=0, alpha=1):\n",
+    "    return 1 / (1e3 * 6.626e-34) * (9.273e-24 * 1.24) * np.sqrt( (b0**2 - by0**2) + (alpha * x + by0)**2 )\n",
+    "\n",
+    "data_quadratic = data\n",
+    "\n",
+    "fitModel_quadratic = NewFitModel(magnetic_field_func)\n",
+    "fitAnalyser_quadratic = FitAnalyser(fitModel_quadratic, fitDim=1)\n",
+    "params_quadratic = fitAnalyser_quadratic.fitModel.make_params()\n",
+    "params_quadratic.add(name=\"b0\", value=  0.3, max=np.inf, min=-np.inf, vary=True)\n",
+    "params_quadratic.add(name=\"by0\", value=  0.07, max=np.inf, min=-np.inf, vary=True)\n",
+    "params_quadratic.add(name=\"alpha\", value=  100, max=np.inf, min=-np.inf, vary=True)\n",
+    "fitResult_quadratic = fitAnalyser_quadratic.fit(data_quadratic, params_quadratic).load()\n",
+    "\n",
+    "fitCurve_quadratic = fitAnalyser_quadratic.eval(fitResult_quadratic, x=np.linspace(0, 0.6, 100), dask=\"parallelized\").load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data_linear = data[3:]\n",
+    "\n",
+    "fitAnalyser_linear = FitAnalyser('Linear', fitDim=1)\n",
+    "params_linear = fitAnalyser_linear.guess(data_linear, dask=\"parallelized\")\n",
+    "fitResult_linear = fitAnalyser_linear.fit(data_linear, params_linear).load()\n",
+    "\n",
+    "fitCurve_linear = fitAnalyser_linear.eval(fitResult_linear, x=np.linspace(0, 0.5, 100), dask=\"parallelized\").load()\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.gca()\n",
+    "\n",
+    "data.plot.errorbar(ax=ax, fmt='ob', yerr=data_std)\n",
+    "# fitCurve_linear.plot.errorbar(ax=ax)\n",
+    "fitCurve_quadratic.plot.errorbar(ax=ax)\n",
+    "\n",
+    "plt.xlabel('Y Offset Coil Current (A)', fontsize=12)\n",
+    "plt.ylabel('Resonance Frequency (kHz)', fontsize=12)\n",
+    "plt.xticks(fontsize=12)\n",
+    "plt.yticks(fontsize=12)\n",
+    "# plt.legend(fontsize=12)\n",
+    "#plt.xlim(-0.01, 0.04)\n",
+    "# plt.ylim(0, 10000)\n",
+    "plt.grid(visible=1)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "<defs>\n",
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+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
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+       "  --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",
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+       "  --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",
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+       "  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.Dataset&gt;\n",
+       "Dimensions:  ()\n",
+       "Data variables:\n",
+       "    b0       object 0.440+/-0.009\n",
+       "    by0      object 0.202+/-0.015\n",
+       "    alpha    object 10.30+/-0.05</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-65c281c6-2c36-4496-9e23-a700b90819ce' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-65c281c6-2c36-4496-9e23-a700b90819ce' 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-8318d64d-dd05-491e-a6f6-07f38de2619e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8318d64d-dd05-491e-a6f6-07f38de2619e' 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-ab018521-2dc5-4f64-a759-8c951922b69c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ab018521-2dc5-4f64-a759-8c951922b69c' class='xr-section-summary' >Data variables: <span>(3)</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>b0</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0.440+/-0.009</div><input id='attrs-909853d3-0cbb-4b52-a8da-d71ba069a7fb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-909853d3-0cbb-4b52-a8da-d71ba069a7fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4546b020-7e06-402a-9ac2-83db067732a4' class='xr-var-data-in' type='checkbox'><label for='data-4546b020-7e06-402a-9ac2-83db067732a4' 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(0.44046053036292887+/-0.008743037526448819, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>by0</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0.202+/-0.015</div><input id='attrs-90c38744-62fc-47b9-bb03-795b31d99a9a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-90c38744-62fc-47b9-bb03-795b31d99a9a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f6d9ce67-e2b6-4721-8ddc-5840cd33eec8' class='xr-var-data-in' type='checkbox'><label for='data-f6d9ce67-e2b6-4721-8ddc-5840cd33eec8' 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(0.2022134511996896+/-0.014697906285531557, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>alpha</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>10.30+/-0.05</div><input id='attrs-3524648c-1496-4088-bbe9-765d7f05351b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3524648c-1496-4088-bbe9-765d7f05351b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0666538c-5594-4f58-af8b-f4d05c7a1744' class='xr-var-data-in' type='checkbox'><label for='data-0666538c-5594-4f58-af8b-f4d05c7a1744' 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(10.299175733301144+/-0.05309408600386392, dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-17e1d337-1022-4f3a-9ee0-024f4e0c751d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-17e1d337-1022-4f3a-9ee0-024f4e0c751d' 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-ddbb00eb-f357-4b00-9a75-5bb0fa47d4ea' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ddbb00eb-f357-4b00-9a75-5bb0fa47d4ea' 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",
+       "    b0       object 0.440+/-0.009\n",
+       "    by0      object 0.202+/-0.015\n",
+       "    alpha    object 10.30+/-0.05"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fitAnalyser_quadratic.get_fit_full_result(fitResult_quadratic) * 1e4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "val_y = fitAnalyser_quadratic.get_fit_value(fitResult_quadratic) * 1e4\n",
+    "std_y = fitAnalyser_quadratic.get_fit_std(fitResult_quadratic) * 1e4\n",
+    "res_y = fitAnalyser_quadratic.get_fit_full_result(fitResult_quadratic) * 1e4"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Z-comp coil"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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WRhgCAACWRhgCAACWRhgCAACWRhgCAACWRhgCAACWRhgCAACWRhgCAACWRhgCAACWRhgCAACWRhgCAACWRhgCAACWRhgCAACWFtnaEwCsqK5OKiiQSkulhAQpLU2KiGjtWQGANRGGgBBzuaTMTOnUqW9rSUlSTo7kdLbevADAqnibDAghl0uaMsU3CElSSYmn7nK1zrwAwMoIQ0CI1NV5rggZ43+svpaV5ekDAIQOYQgIkYIC/ytCDRkjFRd7+gAAoUMYAkKktDSwfQCAwCAMASGSkBDYPgBAYBCGgBBJS/PcNWazNX7cZpOSkz19AIDQIQwBIRIR4bl9XvIPRPU/L1/OfkMAEGqEISCEnE5pwwapZ0/felKSp84+QwAQemy6CISY0ylNmMAO1ABwuyAMAa0gIkJKT2/tWQAAJN4mAwAAFkcYAgAAlkYYAgAAlsZnhoAbVFfHh54BoC0iDAE3wOXyfMlqw+8WS0ry7BvE7fAAEN54mwy4DpdLmjLF/0tWS0o8dZerdeYFAAgMwhDQjLo6zxUhY/yP1deysjx9AIDwRBgCmlFQ4H9FqCFjpOJiTx8AIDwRhoBmlJYGtg8AcPshDAHNSEgIbB8A4PZDGAKakZbmuWvs2m+Zr2ezScnJnj4AQHgiDAHNiIjw3D4v+Qei+p+XL2e/IQAIZ4Qh4DqcTmnDBqlnT996UpKnzj5DABDe2HQRuAFOpzRhAjtQA0BbRBgCblBEhJSe3tqzAAAEGm+TAQAASyMMAQAASyMMAQAASyMMAQAAS+MD1Ghz6uq46wsAcOMIQ2hTXC7Pt8w3/HLVpCTPxonsBwQAaAxvk6HNcLmkKVP8v2W+pMRTd7laZ14AgNsbYQhtQl2d54qQMf7H6mtZWZ4+AAAaIgyhTSgo8L8i1JAxUnGxpw8AgIYIQ2gTSksD2wcAsA7CENqEhITA9gEArIMwhDYhLc1z15jN1vhxm01KTvb0AQDQEGEIbUJEhOf2eck/ENX/vHw5+w0BAPwRhtBmOJ3Shg1Sz56+9aQkT519hgAAjWHTRbQpTqc0YQI7UAMAbhxhCG1ORISUnt7aswAAhAveJgMAAJZGGAIAAJYWtDD00UcfadasWRowYIA6duyonj17asKECfq///s/v94DBw5o7Nixio6OVmxsrJxOp44fP97ouCtWrNCAAQNkt9t19913a/HixaqpqfHrO3PmjGbMmKH4+HhFRUUpNTVVeXl5AT9PAAAQ3oIWhn7961/rxIkTyszM1JYtW5STk6MzZ85o5MiR+uijj7x9RUVFSk9P15UrV7R+/XqtXbtWR44cUVpams6ePeszZnZ2tjIzM+V0OrVt2zbNnj1bS5Ys0Zw5c3z6qqurNWbMGOXl5SknJ0ebNm1S9+7dlZGRoV27dgXrlAEAQDgyQfL111/71aqqqkz37t3NmDFjvLWpU6ea+Ph4U1FR4a2dOHHCtGvXzrzwwgve2rlz54zD4TBPP/20z5jZ2dnGZrOZw4cPe2srV640kszu3bu9tZqaGjNw4EAzfPjwFp1HRUWFkeQzPwAAcHtryet30K4MdevWza8WHR2tgQMHqri4WJJUW1urzZs3a/LkyYqJifH29e7dW6NHj9bGjRu9ta1bt8rtdmvmzJk+Y86cOVPGGL333nve2saNG9W/f3+lpqZ6a5GRkZo2bZr27t2rkpKSQJ0mAAAIcyH9AHVFRYUOHDigQYMGSZKOHTumy5cva8iQIX69Q4YM0dGjR+V2uyVJhw4dkiSlpKT49CUkJCg+Pt57vL63qTEl6fDhw03Osbq6WpWVlT4PAADQdoU0DM2ZM0cXL17Uyy+/LEkqKyuTJMXFxfn1xsXFyRij8+fPe3vtdrs6duzYaG/9WPW9TY3Z8Pc2ZunSperUqZP3kZyc3IIzBAAA4SZkYWjBggV688039atf/UpDhw71OWZr6ts1rzl2o30t7W1o3rx5qqio8D7q39IDAABtU0h2oF68eLFee+01ZWdn69lnn/XWu3TpIqnxKzXl5eWy2WyKjY319rrdbl26dElRUVF+vQ0DVpcuXZocU2r8SlQ9u90uu91+4ycHAADCWtCvDC1evFivvPKKXnnlFb300ks+x/r06aMOHTqosLDQ73mFhYXq27evHA6HpG8/K3Rt7+nTp3Xu3DkNHjzYW0tJSWlyTEk+vQAAwNqCGoZeffVVvfLKK5o/f74WLVrkdzwyMlLjx4+Xy+VSVVWVt37y5Enl5+fL2eBrxjMyMuRwOJSbm+szRm5urmw2myZOnOitTZo0SUVFRdqzZ4+3Vltbq3Xr1mnEiBFKTEwM3EkCAICwZjPGmGAM/Mtf/lI///nPlZGR0WgQGjlypCTPpovDhg3T/fffr7lz58rtdmvhwoUqLy/XwYMH1bVrV+9zsrOztWDBAs2bN0/jxo3Tvn37NH/+fE2fPl2rV6/29lVXV2vo0KGqrKzUsmXL1K1bN61atUoffPCBduzYoVGjRt3weVRWVqpTp06qqKjwuf0fTaur41vjAQCtq0Wv38Ha7GjUqFFGUpOPhvbv32/GjBljoqKiTExMjJk4caI5evRoo+Pm5OSYfv36mfbt25tevXqZRYsWmStXrvj1nT592kyfPt3ExcUZh8NhRo4cabZv397i82DTxZZ5911jkpKMkb59JCV56gAAhEpLXr+DdmWoreDK0I1zuaQpUzwRqKH6m/c2bJAavPMJAEDQtOT1m2+tR0DU1UmZmf5BSPq2lpXl6QMA4HZCGEJAFBRIp041fdwYqbjY0wcAwO2EMISAKC0NbB8AAKFCGEJAJCQEtg8AgFAhDCEg0tKkpKRvPyx9LZtNSk729AEAcDshDCEgIiKknBzPf18biOp/Xr6c/YYAALcfwhACxun03D7fs6dvPSmJ2+oBALevkHxRK6zD6ZQmTGAHagBA+CAMIeAiIqT09NaeBQAAN4a3yQAAgKURhgAAgKURhgAAgKURhgAAgKURhgAAgKURhgAAgKURhgAAgKURhgAAgKWx6aIF1NWxIzQAAE0hDLVxLpeUmSmdOvVtLSnJ86WqfFcYAAC8TdamuVzSlCm+QUiSSko8dZerdeYFAMDthDDURtXVea4IGeN/rL6WleXpAwDAyghDbVRBgf8VoYaMkYqLPX0AAFgZYaiNKi0NbB8AAG0VYaiNSkgIbB8AAG0VYaiNSkvz3DVmszV+3GaTkpM9fQAAWBlhqI2KiPDcPi/5B6L6n5cvZ78hAAAIQ22Y0ylt2CD17OlbT0ry1NlnCAAANl1s85xOacIEdqAGAKAphCELiIiQ0tNbexYAANyeeJsMAABYGmEIAABYGmEIAABYGmEIAABYGmEIAABYGmEIAABYGrfWt4K6Ovb9AQDgdkEYCjGXS8rMlE6d+raWlOT56gx2hAYAIPR4myyEXC5pyhTfICRJJSWeusvVOvMCAMDKCEMhUlfnuSJkjP+x+lpWlqcPAACEDmEoRAoK/K8INWSMVFzs6QMAAKFDGAqR0tLA9gEAgMAgDIVIQkJg+wAAQGAQhkIkLc1z15jN1vhxm01KTvb0AQCA0CEMhUhEhOf2eck/ENX/vHw5+w0BABBqhKEQcjqlDRuknj1960lJnjr7DAEAEHpsuhhiTqc0YQI7UAMAcLsgDLWCiAgpPb21ZwEAACTeJgMAABZHGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJZGGAIAAJbGDtTXYYyRJFVWVrbyTAAAwI2qf92ufx1vDmHoOqqqqiRJycnJrTwTAADQUlVVVerUqVOzPTZzI5HJwq5evaqvvvpKd955p2w2W8DGraysVHJysoqLixUTExOwcdE01jy0WO/QYr1Di/UOrZtZb2OMqqqqlJiYqDvuaP5TQVwZuo477rhDSUlJQRs/JiaG/yGFGGseWqx3aLHeocV6h1ZL1/t6V4Tq8QFqAABgaYQhAABgaYShVmK327Vo0SLZ7fbWnoplsOahxXqHFusdWqx3aAV7vfkANQAAsDSuDAEAAEsjDAEAAEsjDAEAAEsjDAXYN998o6ysLCUmJsrhcOi+++7TO++8c0PPPXPmjGbMmKH4+HhFRUUpNTVVeXl5QZ5xeLvZ9T516pSysrI0atQoxcbGymazKTc3N/gTbgNuds1dLpeeeOIJ9e3bVx06dNBdd92lJ598Ul988UUIZh2+bna9d+zYoQcffFCJiYmy2+3q1q2b/vmf/1lbtmwJwazD1638G97Q/PnzZbPZNHjw4CDMsu242fXOzc2VzWZr9HH69OmWT8QgoB588EETGxtr/vM//9N89NFH5oc//KGRZN58881mn+d2u83gwYNNUlKSWbdunfnTn/5kJkyYYCIjI83OnTtDNPvwc7PrnZ+fb+Lj483YsWPNE088YSSZN954IzSTDnM3u+bDhw83jz76qFm7dq3ZuXOn+cMf/mC+853vmOjoaHPo0KEQzT783Ox6v/POOyYzM9O88847ZufOncblcplx48YZSeYPf/hDiGYffm52vRv67LPPjN1uN927dzeDBg0K4mzD382u9xtvvOH9d/uTTz7xeVy5cqXF8yAMBdD//M//GEnmrbfe8qk/+OCDJjEx0dTW1jb53JUrVxpJZvfu3d5aTU2NGThwoBk+fHjQ5hzObmW96+rqvP+9b98+wtANupU1//rrr/1qJSUlpl27duapp54K+FzbgltZ78ZcuXLF9OzZ06SlpQVymm1GINa7pqbG3Hfffea5554zo0aNIgw141bWuz4M7du3LyBz4W2yANq4caOio6M1depUn/rMmTP11Vdfac+ePc0+t3///kpNTfXWIiMjNW3aNO3du1clJSVBm3e4upX1vt731KBxt7Lm3bp186slJiYqKSlJxcXFAZ9rW3Ar692Ydu3aKTY2VpGRfBNTYwKx3suWLVN5ebmys7ODNc02I9B/v28FrwgBdOjQIX3nO9/x+4dmyJAh3uPNPbe+r7HnHj58OIAzbRtuZb1xcwK95sePH9ff/vY3DRo0KGBzbEsCsd5Xr15VbW2tvvrqKy1atEhHjhzRz372s6DMN9zd6nr/5S9/0WuvvaZf//rXio6ODto824pA/P3+/ve/r4iICMXFxcnpdN70v/v834MAKisr09/93d/51ePi4rzHm3tufV9Ln2tVt7LeuDmBXPPa2lo99dRTio6O1vPPPx+wObYlgVjvRx55RNu2bZPk+ZLL//7v/9b3vve9wE60jbiV9b569apmzZolp9OpRx55JGhzbEtuZb179Oihl19+WSNHjlRMTIwKCwu1bNkyjRw5Uh9//LHuvffeFs2FMBRgNpvtpo7d6nOtijULvUCsuTFGTz31lAoKCvTuu+8qOTk5UNNrc251vVesWKELFy6otLRU69at0+OPP67f/e53euKJJwI5zTbjZtf7P/7jP/TFF1/o/fffD8a02qybXe+MjAxlZGR4f37ggQf0ve99TykpKVq4cKE2bdrUonkQhgKoS5cujSbZ8vJySWr0yk8gnmtVrFnoBWLNjTH64Q9/qHXr1ul3v/udJkyYEPB5thWBWO977rnH+9+PPvqoHn74Yc2ZM0ePP/44n527xs2u98mTJ7Vw4UItW7ZM7du314ULFyR5rn5evXpVFy5ckN1uV4cOHYI293AU6H/D77rrLv3TP/2TPv300xbPhf8lBFBKSoo+//xz1dbW+tQLCwslqdn9JlJSUrx9LX2uVd3KeuPm3Oqa1wehN954Q2vWrNG0adOCNte2IBh/x4cPH67z58/r7NmzAZljW3Kz6338+HFdvnxZmZmZ6ty5s/fx8ccf6/PPP1fnzp01b968oM8/3ATj77cx5uZCfkDuSYMxxpgtW7YYSeadd97xqWdkZFz3NsFVq1YZSebTTz/11mpqasygQYPMiBEjgjbncHYr690Qt9bfuFtZ86tXr5qnnnrK2Gw2s3r16mBPtU0I1N/xelevXjWjRo0ysbGxpqamJpBTbRNudr3Pnz9v8vPz/R733nuvueuuu0x+fr754osvQnEKYSXQf7+PHz9uoqOjzcSJE1s8F8JQgD344IOmc+fOZvXq1eajjz4yP/rRj4wks27dOm/PrFmzTEREhDlx4oS35na7zaBBg0xycrJ58803zfbt282kSZPYdPE6bna9jTHmj3/8o/njH/9oXn/9dSPJzJkzx1tD0252zZ999lkjycyaNctvk7QDBw60xqmEhZtd70cffdQsWLDAvPvuu2bnzp3mrbfe8m66uHLlytY4lbBwK/+mXIt9hq7vZtd7zJgxZvHixWbjxo0mLy/PLF++3CQmJpo777zTFBYWtngehKEAq6qqMs8995zp0aOHad++vRkyZIh5++23fXp+8IMfGEnmyy+/9KmfPn3aTJ8+3cTFxRmHw2FGjhxptm/fHsLZh59bWW9JTT7QtJtd8969eze53r179w7tSYSRm13v119/3QwbNsx07tzZREREmC5dupiHHnrIbN68OcRnEF5u5d+UaxGGru9m1zsrK8sMHDjQ3HnnnSYyMtIkJiaaadOmmb/+9a83NQ+bMca0/M01AACAtoEPUAMAAEsjDAEAAEsjDAEAAEsjDAEAAEsjDAEAAEsjDAEAAEsjDAEAAEsjDAEAAEsjDAEAAEsjDAEAAEsjDAGwDLfbrb//+79X3759VVFR4a2fPn1aPXr0UHp6uurq6lpxhgBaA2EIgGU4HA6tX79eZ86c0axZsyRJV69e1ZNPPiljjN5++21FRES08iwBhFpka08AAELpnnvu0Zo1a/T4448rJydH5eXl2rlzp7Zu3aqEhITWnh6AVsC31gOwpNmzZ2vNmjWqq6vTSy+9pFdffbW1pwSglRCGAFjS/v37NWzYMLVv316nTp1S165dW3tKAFoJYQiA5Vy8eFH/8A//oKtXr+rrr7/WqFGjtGnTptaeFoBWwgeoAVjOj3/8Y508eVIul0u//e1v9f777+tXv/pVa08LQCshDAGwlDVr1mjdunVauXKlBg0apMmTJ+vZZ5/Viy++qL1797b29AC0At4mA2AZhYWFGjFihB577DHl5uZ669XV1frHf/xHlZWV6bPPPlNsbGyrzRFA6BGGAACApfE2GQAAsDTCEAAAsDTCEAAAsDTCEAAAsDTCEAAAsDTCEAAAsDTCEAAAsDTCEAAAsDTCEAAAsDTCEAAAsDTCEAAAsDTCEAAAsLT/BzdvrG45uIGKAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fz = [751.71168, 1223.54627, 1739.13344, 2364.93284, 2798.24971, 3178.49790, 4275.39905, 5352.17283, 6637.80418, 8288.35264, 9573.59333]\n",
+    "dfz = [1.32859, 0.35554, 0.48471, 0.69762, 0.36873, 0.29413, 0.20667, 0.20818, 0.21978, 0.20285, 0.18495]\n",
+    "z_offset_current = [0.0, 0.03, 0.06, 0.095, 0.119, 0.140, 0.2, 0.259, 0.329, 0.419, 0.489]\n",
+    "\n",
+    "data = xr.DataArray(\n",
+    "    data=fz,\n",
+    "    dims=['x'],\n",
+    "    coords={\n",
+    "        'x': z_offset_current\n",
+    "    } \n",
+    ")\n",
+    "\n",
+    "data_std = xr.DataArray(\n",
+    "    data=dfz,\n",
+    "    dims=['x'],\n",
+    "    coords={\n",
+    "        'x': z_offset_current\n",
+    "    } \n",
+    ")\n",
+    "\n",
+    "data.plot.errorbar(fmt='ob', yerr=data_std)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def magnetic_field_func(x, b0=0, by0=0, alpha=1):\n",
+    "    return 1 / (1e3 * 6.626e-34) * (9.273e-24 * 1.24) * np.sqrt( (b0**2 - by0**2) + (alpha * x + by0)**2 )\n",
+    "\n",
+    "data_quadratic = data\n",
+    "\n",
+    "fitModel_quadratic = NewFitModel(magnetic_field_func)\n",
+    "fitAnalyser_quadratic = FitAnalyser(fitModel_quadratic, fitDim=1)\n",
+    "params_quadratic = fitAnalyser_quadratic.fitModel.make_params()\n",
+    "params_quadratic.add(name=\"b0\", value=  0.3, max=np.inf, min=-np.inf, vary=True)\n",
+    "params_quadratic.add(name=\"by0\", value=  0.07, max=np.inf, min=-np.inf, vary=True)\n",
+    "params_quadratic.add(name=\"alpha\", value=  100, max=np.inf, min=-np.inf, vary=True)\n",
+    "fitResult_quadratic = fitAnalyser_quadratic.fit(data_quadratic, params_quadratic).load()\n",
+    "\n",
+    "fitCurve_quadratic = fitAnalyser_quadratic.eval(fitResult_quadratic, x=np.linspace(0, 0.6, 100), dask=\"parallelized\").load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data_linear = data[3:]\n",
+    "\n",
+    "fitAnalyser_linear = FitAnalyser('Linear', fitDim=1)\n",
+    "params_linear = fitAnalyser_linear.guess(data_linear, dask=\"parallelized\")\n",
+    "fitResult_linear = fitAnalyser_linear.fit(data_linear, params_linear).load()\n",
+    "\n",
+    "fitCurve_linear = fitAnalyser_linear.eval(fitResult_linear, x=np.linspace(0, 0.5, 100), dask=\"parallelized\").load()\n",
+    "\n",
+    "fig = plt.figure()\n",
+    "ax = fig.gca()\n",
+    "\n",
+    "data.plot.errorbar(ax=ax, fmt='ob', yerr=data_std)\n",
+    "# fitCurve_linear.plot.errorbar(ax=ax)\n",
+    "fitCurve_quadratic.plot.errorbar(ax=ax)\n",
+    "\n",
+    "plt.xlabel('Y Offset Coil Current (A)', fontsize=12)\n",
+    "plt.ylabel('Resonance Frequency (kHz)', fontsize=12)\n",
+    "plt.xticks(fontsize=12)\n",
+    "plt.yticks(fontsize=12)\n",
+    "# plt.legend(fontsize=12)\n",
+    "#plt.xlim(-0.01, 0.04)\n",
+    "# plt.ylim(0, 10000)\n",
+    "plt.grid(visible=1)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       ":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.Dataset&gt;\n",
+       "Dimensions:  ()\n",
+       "Data variables:\n",
+       "    b0       object 0.4333+/-0.0007\n",
+       "    by0      object 0.3254+/-0.0008\n",
+       "    alpha    object 10.6021+/-0.0022</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-f77d168d-c7f1-4cbb-be7f-2cef6dfaf365' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f77d168d-c7f1-4cbb-be7f-2cef6dfaf365' 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-57ddfb08-9489-4068-b143-f0306231c01c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-57ddfb08-9489-4068-b143-f0306231c01c' 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-cc75bd83-4893-4666-8e8c-e9d9cbec9fb9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cc75bd83-4893-4666-8e8c-e9d9cbec9fb9' class='xr-section-summary' >Data variables: <span>(3)</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>b0</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0.4333+/-0.0007</div><input id='attrs-d69271e3-d2be-4615-b6e9-16971b1d9bee' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d69271e3-d2be-4615-b6e9-16971b1d9bee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9859a199-e6b6-4124-869a-40487a3bd516' class='xr-var-data-in' type='checkbox'><label for='data-9859a199-e6b6-4124-869a-40487a3bd516' 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(0.43327107997401876+/-0.0007013859839914253, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>by0</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0.3254+/-0.0008</div><input id='attrs-001a69b2-d62f-4955-a882-ed50e4b925ea' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-001a69b2-d62f-4955-a882-ed50e4b925ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-637572eb-5cc6-461d-bf12-6de7cd87d0f5' class='xr-var-data-in' type='checkbox'><label for='data-637572eb-5cc6-461d-bf12-6de7cd87d0f5' 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(0.32538643700542086+/-0.000794288942625618, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>alpha</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>10.6021+/-0.0022</div><input id='attrs-36ac86c4-9b53-49ad-9820-b0daabbf0894' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-36ac86c4-9b53-49ad-9820-b0daabbf0894' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03225bdf-14a6-42bc-9f5d-9f12aa4b7152' class='xr-var-data-in' type='checkbox'><label for='data-03225bdf-14a6-42bc-9f5d-9f12aa4b7152' 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(10.602080670806222+/-0.0022465278174072264, dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4476f9ef-4a00-4571-a023-46646534028c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4476f9ef-4a00-4571-a023-46646534028c' 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-2e492b41-c758-4893-b7ac-09be25884b14' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2e492b41-c758-4893-b7ac-09be25884b14' 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",
+       "    b0       object 0.4333+/-0.0007\n",
+       "    by0      object 0.3254+/-0.0008\n",
+       "    alpha    object 10.6021+/-0.0022"
+      ]
+     },
+     "execution_count": 139,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fitAnalyser_quadratic.get_fit_full_result(fitResult_quadratic) * 1e4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "val_z = fitAnalyser_quadratic.get_fit_value(fitResult_quadratic) * 1e4\n",
+    "std_z = fitAnalyser_quadratic.get_fit_std(fitResult_quadratic) * 1e4\n",
+    "res_z = fitAnalyser_quadratic.get_fit_full_result(fitResult_quadratic) * 1e4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.416688520216393+/-0.009836437571434223"
+      ]
+     },
+     "execution_count": 145,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "umath.sqrt(res_x['by0'].item()**2 + res_y['by0'].item()**2 + res_z['by0'].item()**2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.43640594986928966+/-0.006152786477679057"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(res_x['b0'].item() + res_y['b0'].item() + res_z['b0'].item())/3"
+   ]
+  },
+  {
+   "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/test.ipynb b/test.ipynb
index 755fdf4..f46ff34 100644
--- a/test.ipynb
+++ b/test.ipynb
@@ -164,7 +164,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2291,23 +2291,130 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6417497231450753+/-0.01090681927109203"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(-ufloat(99.835,0.018) + ufloat(99.969,0.014))/15*1e3\n",
+    "(-ufloat(99.835,0.018) + ufloat(100.994,0.008))/1.29/1.4"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6267995570321101+/-0.01750984307955913"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(ufloat(99.835,0.018) - ufloat(98.703,0.026))/1.29/1.4"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6342746400885927+/-0.010314471766443609"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "((ufloat(99.835,0.018) - ufloat(98.703,0.026))/1.29/1.4 + (-ufloat(99.835,0.018) + ufloat(100.994,0.008))/1.29/1.4) /2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.642+/-0.011\n",
+      "0.627+/-0.018\n",
+      "0.634+/-0.010\n",
+      "0.0444+/-0.0007\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = (-ufloat(99.835,0.018) + ufloat(100.994,0.008))/1.29/1.4\n",
+    "b = (ufloat(99.835,0.018) - ufloat(98.703,0.026))/1.29/1.4\n",
+    "\n",
+    "print(a)\n",
+    "print(b)\n",
+    "print((a+b)/2)\n",
+    "print((a+b)/2 * (1.29-1.24)*1.4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.637+/-0.011\n",
+      "0.641+/-0.018\n",
+      "0.639+/-0.010\n",
+      "0.0447+/-0.0007\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = (-ufloat(99.969,0.018) + ufloat(101.120,0.008))/1.29/1.4\n",
+    "b = (ufloat(99.969,0.018) - ufloat(98.811,0.026))/1.29/1.4\n",
+    "\n",
+    "print(a)\n",
+    "print(b)\n",
+    "print((a+b)/2)\n",
+    "print((a+b)/2 * (1.29-1.24)*1.4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0447+/-0.0007\n"
+     ]
+    }
+   ],
    "source": []
   },
   {
diff --git a/test_fit.ipynb b/test_fit.ipynb
new file mode 100644
index 0000000..c3b0052
--- /dev/null
+++ b/test_fit.ipynb
@@ -0,0 +1,2498 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Import stuff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import scipy.constants as const\n",
+    "import scipy.integrate as integrate\n",
+    "import mpmath as mp\n",
+    "import copy\n",
+    "import xarray\n",
+    "\n",
+    "# import xrft\n",
+    "# import finufft\n",
+    "\n",
+    "from uncertainties import ufloat\n",
+    "from uncertainties import unumpy as unp\n",
+    "from uncertainties import umath\n",
+    "\n",
+    "from datetime import datetime\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.rcParams['font.size'] = 18\n",
+    "\n",
+    "from DataContainer.ReadData import read_hdf5_file, read_hdf5_global, read_hdf5_run_time\n",
+    "from Analyser.ImagingAnalyser import ImageAnalyser\n",
+    "from Analyser.FitAnalyser import FitAnalyser\n",
+    "from ToolFunction.ToolFunction import *\n",
+    "from Analyser.FitAnalyser import Gaussian2dModel, DensityProfileBEC2dModel\n",
+    "from lmfit.lineshapes import gaussian2d\n",
+    "\n",
+    "\n",
+    "from ToolFunction.HomeMadeXarrayFunction import errorbar, dataarray_plot_errorbar\n",
+    "xr.plot.dataarray_plot.errorbar = errorbar\n",
+    "xr.plot.accessor.DataArrayPlotAccessor.errorbar = dataarray_plot_errorbar\n",
+    "\n",
+    "imageAnalyser = ImageAnalyser()\n",
+    "\n",
+    "Li2_vec = np.vectorize(lambda x: np.array(mp.fp.polylog(2, x), dtype=np.complex128))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n",
+       "    <div style=\"margin-left: 48px;\">\n",
+       "        <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
+       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-32175c6f-1597-11ee-bde8-80e82ce2fa8e</p>\n",
+       "        <table style=\"width: 100%; text-align: left;\">\n",
+       "\n",
+       "        <tr>\n",
+       "        \n",
+       "            <td style=\"text-align: left;\"><strong>Connection method:</strong> Cluster object</td>\n",
+       "            <td style=\"text-align: left;\"><strong>Cluster type:</strong> distributed.LocalCluster</td>\n",
+       "        \n",
+       "        </tr>\n",
+       "\n",
+       "        \n",
+       "            <tr>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
+       "                </td>\n",
+       "                <td style=\"text-align: left;\"></td>\n",
+       "            </tr>\n",
+       "        \n",
+       "\n",
+       "        </table>\n",
+       "\n",
+       "        \n",
+       "\n",
+       "        \n",
+       "            <details>\n",
+       "            <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Cluster Info</h3></summary>\n",
+       "            <div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
+       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\">\n",
+       "    </div>\n",
+       "    <div style=\"margin-left: 48px;\">\n",
+       "        <h3 style=\"margin-bottom: 0px; margin-top: 0px;\">LocalCluster</h3>\n",
+       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">d2bb20ef</p>\n",
+       "        <table style=\"width: 100%; text-align: left;\">\n",
+       "            <tr>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
+       "                </td>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Workers:</strong> 6\n",
+       "                </td>\n",
+       "            </tr>\n",
+       "            <tr>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Total threads:</strong> 24\n",
+       "                </td>\n",
+       "                <td style=\"text-align: left;\">\n",
+       "                    <strong>Total memory:</strong> 44.70 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-5e76ff5a-863e-458c-94c5-8655e950d00c</p>\n",
+       "            <table style=\"width: 100%; text-align: left;\">\n",
+       "                <tr>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Comm:</strong> tcp://127.0.0.1:61965\n",
+       "                    </td>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Workers:</strong> 6\n",
+       "                    </td>\n",
+       "                </tr>\n",
+       "                <tr>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
+       "                    </td>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Total threads:</strong> 24\n",
+       "                    </td>\n",
+       "                </tr>\n",
+       "                <tr>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Started:</strong> Just now\n",
+       "                    </td>\n",
+       "                    <td style=\"text-align: left;\">\n",
+       "                        <strong>Total memory:</strong> 44.70 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://127.0.0.1:61998\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 4\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:62001/status\" target=\"_blank\">http://127.0.0.1:62001/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 7.45 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:61968\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-tq4qoxbr\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://127.0.0.1:62000\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 4\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:62004/status\" target=\"_blank\">http://127.0.0.1:62004/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 7.45 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:61969\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-bhx86y4h\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://127.0.0.1:61995\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 4\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:61996/status\" target=\"_blank\">http://127.0.0.1:61996/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 7.45 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:61970\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-b8rqi6f7\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://127.0.0.1:62007\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 4\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:62008/status\" target=\"_blank\">http://127.0.0.1:62008/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 7.45 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:61971\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-y967j_78\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://127.0.0.1:61999\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 4\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:62003/status\" target=\"_blank\">http://127.0.0.1:62003/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 7.45 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:61972\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-s6j4tmqp\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://127.0.0.1:61982\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Total threads: </strong> 4\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:61993/status\" target=\"_blank\">http://127.0.0.1:61993/status</a>\n",
+       "                        </td>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Memory: </strong> 7.45 GiB\n",
+       "                        </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <td style=\"text-align: left;\">\n",
+       "                            <strong>Nanny: </strong> tcp://127.0.0.1:61973\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-dpv1awpr\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://127.0.0.1:61965' processes=6 threads=24, memory=44.70 GiB>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from dask.distributed import Client\n",
+    "client = Client(n_workers=6, threads_per_worker=4, processes=True, memory_limit='8GB')\n",
+    "client"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Different f/T\n",
+    "The simulation was peformed for 11 different values for teh condensate fraction f from 0 to 10 and the input temperature was changed according to the formular $f = 1-(T/T_c)^3$ <br>\n",
+    "N = 3.21e5<br>\n",
+    "omgx = 2pi 200 Hz<br>\n",
+    "omgy = 2pi 100 Hz<br>\n",
+    "omgz = 2pi 200 Hz<br>\n",
+    "a = 100 a0 <br>\n",
+    "tof = 20 ms <br>\n",
+    "The simulation was done for a flatfield lightsource and a manual light source."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Flatfield"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Get the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load the simulated data\n",
+    "sim_fT_flatfield_np = np.load(\"F:/Jianshun/analyseScript/testData/OD_fT_flatfield.npy\")\n",
+    "sim_fT_flatfield_np = np.nan_to_num(sim_fT_flatfield_np)\n",
+    "'''\n",
+    "sim_fT_flatfield_x10_np = np.load(\"C:/Users/QFBri/Code/analyseScript-Master III/Data/Simulations/OD_fT_flatfield_x10.npy\")\n",
+    "sim_fT_flatfield_x10_np = np.nan_to_num(sim_fT_flatfield_x10_np)\n",
+    "sim_fT_flatfield_x100_np = np.load(\"C:/Users/QFBri/Code/analyseScript-Master III/Data/Simulations/OD_fT_flatfield_x100.npy\")\n",
+    "sim_fT_flatfield_x100_np = np.nan_to_num(sim_fT_flatfield_x100_np)\n",
+    "sim_fT_flatfield_x1000_np = np.load(\"C:/Users/QFBri/Code/analyseScript-Master III/Data/Simulations/OD_fT_flatfield_x1000.npy\")\n",
+    "sim_fT_flatfield_x1000_np = np.nan_to_num(sim_fT_flatfield_x1000_np)\n",
+    "sim_fT_manual_x1000_np = np.load(\"C:/Users/QFBri/Code/analyseScript-Master III/Data/Simulations/OD_fT_manual_x1000.npy\")\n",
+    "sim_fT_manual_x1000_np = np.nan_to_num(sim_fT_manual_x1000_np)\n",
+    "sim_fT_manual_x10000_np = np.load(\"C:/Users/QFBri/Code/analyseScript-Master III/Data/Simulations/OD_fT_manual_x10000.npy\")\n",
+    "sim_fT_manual_x10000_np = np.nan_to_num(sim_fT_manual_x10000_np)\n",
+    "'''\n",
+    "\n",
+    "\n",
+    "# define axis\n",
+    "x = np.linspace(0, 1201, 1200)\n",
+    "y = np.linspace(0, 1921, 1920)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# crop the data around the center to get manly the area of the cloud\n",
+    "sim_fT_flatfield_np = sim_fT_flatfield_np[0:12, 500:-500, 860:-860]\n",
+    "'''\n",
+    "sim_fT_flatfield_x10_np = sim_fT_flatfield_x10_np[0:12, 550:-550, 910:-910]\n",
+    "sim_fT_flatfield_x100_np = sim_fT_flatfield_x100_np[0:12, 550:-550, 910:-910]\n",
+    "sim_fT_flatfield_x1000_np = sim_fT_flatfield_x1000_np[0:12, 550:-550, 910:-910]\n",
+    "sim_fT_manual_x10000_np = sim_fT_manual_x10000_np[0:12, 550:-550, 910:-910]\n",
+    "'''\n",
+    "\n",
+    "f = np.array([0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0])\n",
+    "x = x[500:-500]\n",
+    "y = y[860:-860]"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Perform the fit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\n## perform the BEC fit\\n\\n# set the fit Model\\nfitModel = DensityProfileBEC2dModel()\\nfitAnalyser = FitAnalyser(fitModel, fitDim=2)\\n\\nfit_fT_flatfield_x1000_result = np.empty((11,12))\\nfit_fT_flatfield_x1000_std = np.empty((11,12))\\n\\n# tansfer the data into an xarray\\nfor i in range (11):\\n    print(i)\\n    sim_fT_flatfield_x1000 = xr.DataArray(\\n    data = sim_fT_flatfield_x1000_np[i], \\n    dims = [\"x\", \"y\"],\\n    coords = dict(\\n        x = (\"x\", x),\\n        y = (\"y\", y),\\n        )\\n    )\\n    # perform the fit for one simulation with flatfield\\n    params = fitAnalyser.guess(sim_fT_flatfield_x1000, dask=\"parallelized\")\\n    fitResult = fitAnalyser.fit(sim_fT_flatfield_x1000, params, dask=\"parallelized\").load()\\n    fitCurve = fitAnalyser.eval(fitResult, x=x, y=y).load()\\n    fitValue = fitAnalyser.get_fit_value(fitResult)\\n    fitStd = fitAnalyser.get_fit_std(fitResult)\\n    # store the results as numpy array\\n    fit_fT_flatfield_x1000 = fitCurve.to_numpy()\\n    fitValue_array = fitValue.to_array()\\n    fitStd_array = fitStd.to_array()\\n    fit_fT_flatfield_x1000_result[i] = fitValue_array.to_numpy()\\n    fit_fT_flatfield_x1000_std[i] = fitStd_array.to_numpy()\\n'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "'''\n",
+    "## perform the BEC fit\n",
+    "\n",
+    "# set the fit Model\n",
+    "fitModel = DensityProfileBEC2dModel()\n",
+    "fitAnalyser = FitAnalyser(fitModel, fitDim=2)\n",
+    "\n",
+    "fit_fT_flatfield_x1000_result = np.empty((11,12))\n",
+    "fit_fT_flatfield_x1000_std = np.empty((11,12))\n",
+    "\n",
+    "# tansfer the data into an xarray\n",
+    "for i in range (11):\n",
+    "    print(i)\n",
+    "    sim_fT_flatfield_x1000 = xr.DataArray(\n",
+    "    data = sim_fT_flatfield_x1000_np[i], \n",
+    "    dims = [\"x\", \"y\"],\n",
+    "    coords = dict(\n",
+    "        x = (\"x\", x),\n",
+    "        y = (\"y\", y),\n",
+    "        )\n",
+    "    )\n",
+    "    # perform the fit for one simulation with flatfield\n",
+    "    params = fitAnalyser.guess(sim_fT_flatfield_x1000, dask=\"parallelized\")\n",
+    "    fitResult = fitAnalyser.fit(sim_fT_flatfield_x1000, params, dask=\"parallelized\").load()\n",
+    "    fitCurve = fitAnalyser.eval(fitResult, x=x, y=y).load()\n",
+    "    fitValue = fitAnalyser.get_fit_value(fitResult)\n",
+    "    fitStd = fitAnalyser.get_fit_std(fitResult)\n",
+    "    # store the results as numpy array\n",
+    "    fit_fT_flatfield_x1000 = fitCurve.to_numpy()\n",
+    "    fitValue_array = fitValue.to_array()\n",
+    "    fitStd_array = fitStd.to_array()\n",
+    "    fit_fT_flatfield_x1000_result[i] = fitValue_array.to_numpy()\n",
+    "    fit_fT_flatfield_x1000_std[i] = fitStd_array.to_numpy()\n",
+    "'''\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "0.3092462503676215\n",
+      "0.028120653587753792\n",
+      "<Parameter 'BEC_amplitude', value=36.39788888382145, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_sigmay', value=6.253359378774255, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_sigmax', value=5.939879881569993, bounds=[-inf:inf], expr='3 * thermal_sigmax - deltax'>\n",
+      "<Parameter 'thermal_amplitude', value=636.0835195600501, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_amplitude', value=36.39788888382145, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_amplitude', value=36.39788888382145, bounds=[0:inf]>\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "f:\\Jianshun\\analyseScript\\Analyser\\FitAnalyser.py:86: RuntimeWarning: invalid value encountered in power\n",
+      "  res = (1- ((x-centerx)/(sigmax))**2 - ((y-centery)/(sigmay))**2)**(3 / 2)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1\n",
+      "0.5528817557540787\n",
+      "0.28214712300392425\n",
+      "<Parameter 'BEC_amplitude', value=98.41779453626728, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_sigmay', value=3.3467229538363825, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_sigmax', value=2.9910144173151707, bounds=[-inf:inf], expr='3 * thermal_sigmax - deltax'>\n",
+      "<Parameter 'thermal_amplitude', value=571.8366157932207, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_amplitude', value=98.41779453626728, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_amplitude', value=98.41779453626728, bounds=[0:inf]>\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "f:\\Jianshun\\analyseScript\\Analyser\\FitAnalyser.py:86: RuntimeWarning: invalid value encountered in power\n",
+      "  res = (1- ((x-centerx)/(sigmax))**2 - ((y-centery)/(sigmay))**2)**(3 / 2)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2\n",
+      "0.6676448877490091\n",
+      "0.45280279486430974\n",
+      "<Parameter 'BEC_amplitude', value=204.6134675755257, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_sigmay', value=3.858167192178399, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_sigmax', value=3.3611224858332776, bounds=[-inf:inf], expr='3 * thermal_sigmax - deltax'>\n",
+      "<Parameter 'thermal_amplitude', value=498.1775441243017, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_amplitude', value=204.6134675755257, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_amplitude', value=204.6134675755257, bounds=[0:inf]>\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "f:\\Jianshun\\analyseScript\\Analyser\\FitAnalyser.py:86: RuntimeWarning: invalid value encountered in power\n",
+      "  res = (1- ((x-centerx)/(sigmax))**2 - ((y-centery)/(sigmay))**2)**(3 / 2)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3\n",
+      "0.8209805520698302\n",
+      "0.5698729694548221\n",
+      "<Parameter 'BEC_amplitude', value=314.62838536232505, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_sigmay', value=4.261130378826574, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_sigmax', value=3.7182217550401617, bounds=[-inf:inf], expr='3 * thermal_sigmax - deltax'>\n",
+      "<Parameter 'thermal_amplitude', value=367.6158038782502, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_amplitude', value=314.62838536232505, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_amplitude', value=314.62838536232505, bounds=[0:inf]>\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "f:\\Jianshun\\analyseScript\\Analyser\\FitAnalyser.py:86: RuntimeWarning: invalid value encountered in power\n",
+      "  res = (1- ((x-centerx)/(sigmax))**2 - ((y-centery)/(sigmay))**2)**(3 / 2)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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o27cvACA+Ph6nT5/WWVYul2Pq1Kk69/fr10/avnjxorFhAwBOnz6Nq1evAlBPzjhgwIAK1VOSvpjz8/Oxfft2AIC7uztefPFFnWX9/Pzw9NNPA1BPtvho93rN5+Pu3btQKpVGxag5FkC5k/tVVmU/xytXrgSgXk3lmWee0Vu2b9++qF+/PgCUOV87duyQvqPHjRtn8s93RZT8nL/77rs6y8nlckyfPl3rcdq89tprsLGx0brPxcUFnTt3BqBODpU3gSEREZMCRETVTNQzXrmySq44YMgM9o+6d++e9J/69u3bl0oyaKO5+Kuoxx57DIA67vDwcCxdutTkiRJdKjrfgj4lL2K16dy5M1xcXADAomYHP3nypLRtyEV1yTL6xqs3b95c799YgwYNpO309PRy29XmyJEj0rYp5nFo2LCh3rvLZ8+eRX5+PgB1EkLXhZuGvnOl+XykpqYiLCwMa9euRVZWlkFxtmnTRrp4Xrp0KSZNmoQTJ06YZFWSR1Xmc5yZmYlz584BUCcFtm7dis2bN+v90fTEuXLlSqm6TP1eV5YoitLn3MPDAx07dtRbfuDAgdJ2efM8dO3aVe9+zWdHFEWTr+BBRLUPJxokIjKCm5ubtF3R/2iVvOPn7u5e2ZBKKRlTyVgNdffuXWm7adOm5ZY3pIw+L774ItatW4cDBw4gNjYWkyZNwuTJk9GmTRuEhoYiPDwcjz/+OBQKRaXa0UZzwWRK5Z0PQRDQuHFjxMTEIC0tDfn5+bC1tTV5HKaWlJQkbRsyaVnJMiWPfVR5kx2WPDcVvdtZcshPq1atKlRHSeX93ZjyXL333nvYtm0bLl26hLNnz2Ls2LGQy+Vo3749unfvjj59+mDQoEFlJigE1HeeFy9ejFGjRqGgoABLly7F0qVL4erqim7duqFnz54YOHCgdEf5Ubt379a7TOGIESOk7cp8jhMSEqRExZkzZ/Dkk0/qPV8lley5BZj+va6szMxM6Rw2a9as3PKenp5QKBRQKpV6PzdA9Xx2iKjuYE8BIiIjNGzYUNqOjY1FYWGh0XVoujIDpe+EmkLJ9bM9PT2NPl4z8zdQuvuxLhWdpV/DxsYGu3btwoIFCxAQEABAfWfrwoUL+Pnnn/HMM8/A29sbU6ZMQWZmZqXaepS2C6nKMvacPXz40OQxVIWSd6cNec9Lzqmg7862TFb1/w0p+Xejba4HY5X3d2PKc1WvXj0cP34cM2bMgLe3NwCguLgYp0+fxrfffouRI0fC29sbs2bNkuYBKemJJ57AyZMnMWLECGn+k4yMDOzcuRMzZ85ESEgIgoODsXPnzjLHvvTSS3jyySd1/pRUmc+xscMiSioqKir12NTvdWUZ+7cA/Bt3eT1CquOzQ0R1B79RiIiM0LJlS+kOfH5+Ps6cOWN0HceOHZO2e/ToYbLYAOD48ePS9qMTjxmi5H9c9d0l1CiZRKgoGxsbTJ8+HbGxsbh48SJ++uknTJgwQUrA5Ofn4/vvv0fv3r2Rm5tb6faqkrHnrDIXLlXRDVyXkkvtGfKelyxT8lhz0AzXAKonCWPqc+Xs7IxPPvkEd+/exZkzZ/Dtt98iIiJCulOclZWFTz75BMOGDdM6NKldu3bYtGkTUlNTsWPHDsyaNQthYWFSkuDChQsYMmQIfvvtN6Nfa0kV/RyX/AxMnDgRoiga9VNSdb/X5TH2b6FkOXN/boiobmFSgIjICIIglBo3/uuvvxp1fFJSEvbt2yc91jazf0WlpqaWmngrLCzM6DpKdo3WzCiujyFljNG6dWtMnjwZy5cvR2JiIiIjI6U7j2fPnjXJigdVqbzzIYoibt26BUA9vOPRoQMlH2u781tSSkpKBaM0nq+vr7R9/fr1cstfu3ZN2q6KYRrGKNm7p6on3AOq7lzJZDJ06NABU6dOxZo1a3Dv3j1s2rRJSlLu2rULf/31l87jnZ2dMWjQIHz00Uc4cOAAkpKS8NZbbwFQ/12+/fbbpVaKiIuLM/hi/FHGfI5LvuYLFy7orbc81f1el8fFxUXqPWTI38KDBw+kIWDm/twQUd3CpAARkZHeeOMNaXv58uWluuyX5+OPP5a6vD722GMmHff6xRdfSGNHW7dujW7duhldh7e3Nxo1agQAiImJKXfehP379xvdhjH69OmDRYsWSY9LTiQGlO5CW5UTOBoqMjJS7/7Tp09LXZxDQkLK7C856Z6+5SozMjJKXUxqY8pzU7LXyZ49e8otX7JMRXqsmFKvXr2k7a1bt1Z5e+3atZOSOwcOHCh3iFFFz5VMJsOIESPw0UcfSc89+vnQx93dHV999ZU0p8D9+/cNunCtCH2fY09PT+l78PTp09LShBVhivfalJ8bQRCkz3lKSgpiYmL0lq9JnxsiqluYFCAiMpJmgi5A3dXzueeeM6jb+JYtW/Djjz8CUE8CNmfOHJPFtGHDBixYsEB6PGvWrAqPOdVMIFZQUIDvv/9eZ7ndu3dXeJk4Y2juMAJlxxCX7HpsiqEMlbVq1So8ePBA5/6vvvpK2ta2zn3JJR71JVy+//77Und1tTHluQkICJBmTj979myZpeBKOnXqlNQbxt/fH506dapU25XVqVMntGzZEgBw8ODBKl+33dbWFo8//jgA9YXg8uXLdZZNTEzE77//DkA9dKciyyXq+3xUx/GmaGf8+PEA1Bfh7733XoXbGDx4sNRzYvXq1Th//rzRdZj6O2XUqFHSdsnv6EcVFxdj4cKFWo8jIqpqTAoQEVXAihUr4OPjA0B91+uxxx7TeZdNpVLhu+++w5gxY6Q7T7Nnz0b37t0rHUdycjLeeecdPPXUU1LdL774IsaOHVvhOqdMmSKNN/7444+13v2+deuW3vXXDfXOO++UmmNBmx9++EHabteuXal9bm5u0ozmMTExZu8tkJGRgaefflrrxcSPP/6I1atXAwB8fHwwbty4MmUGDx4sbc+bN0/rMnw7duwodXdYl8DAQGm7InNfPKrkGusTJ04sNWGmRkJCAsaOHSvNd/Cf//wHcrm80m1XhiAI+Pjjj6XHTz/9NA4cOKCz/I0bNyqd7PrPf/4jJeXeeecd/P3332XKpKenY/To0dLfyquvvgpXV9dSZSZPnqz3wraoqAg///yz9Ljk52PXrl343//+p7e3z40bN6S7005OTmjSpEm5r02byn6Op06dKvVQWrNmDd544w1pWUdtlEolvv76a+zdu7fU846OjlJSoaCgAEOHDtV7/mJiYsr0TDD1d8rEiRPh5eUFAPj999/xzTfflClTXFyMqVOnIjo6GoC6F1F5y5sSEZkSlyQkIqoAb29v7NmzB0OGDEFiYiKOHj2KoKAgDBo0CH369IGvry9ycnJw9epVbNy4sVTC4O2338bMmTMNaufKlSvYvHmz9LigoAAZGRlISEjA8ePHcfjw4VJjzydNmqT37r4hWrRogQ8//BCzZs1CXl4eBgwYgHHjxqFfv36wtrbGqVOnsGTJEmRlZWH48OHYsmVLhdvasGEDvvrqKwQGBqJ///5o27YtPD09UVBQgMTERPzxxx/Sf5Td3d3x0ksvlamjb9++2LRpE27evImIiAiMHDmy1MVVWFhYlaw0oM2IESOwefNmBAUF4cUXX0SzZs2QkZGBTZs2SXeoBUHA4sWLtU4yGBISgrCwMBw8eBA3btxAx44d8corr6Bx48ZIS0vDzp07sWXLFjRv3hwODg7SudGmV69esLa2RmFhIRYsWACZTIa2bdvCxsYGgPrix5guymPGjMHmzZuxevVqJCUloUOHDpg4cSJCQ0Mhk8lw6tQp/PLLL9LwiAEDBuC1114z5vRVmdGjR2Pq1KlYtGgR0tPTpaX8Bg4ciPr166OgoAA3b97E/v37cfjwYSxduhRt2rSpcHvdunXDu+++i3nz5iErKwthYWEYN24c+vTpA3t7e5w/fx5Lly7FvXv3AABt27bVmuhZsmQJlixZgjZt2qBPnz5o06YN3N3dkZ2djdjYWKxevVr6bmnevDlGjx4tHauZM+Ddd99Fnz590LVrVzRu3BgODg5ISUlBVFQU1q1bJyUlpk2bVuHPSWU/x05OTti8eTPCw8ORlZWFb7/9Fn/88QfGjBmDdu3awcXFBVlZWYiNjcXJkycRGRmJgoICrXO6TJ8+HUeOHMHWrVsRHx+PDh064Mknn0R4eDi8vLyQm5uLq1evYvfu3Th16hT2798Pf3//UnWY8jvF2dkZy5Ytw7Bhw1BcXIw333wTmzdvxqhRo+Dp6YmEhAT8+uuvOHfunFR+5cqVxpx+IqLKE4mIqMLu3r0rRkREiIIgiAD0/jRo0EBcuXJluXXOnj273Loe/enWrZv4119/mfS1TZs2TWd7MplMnDdvnrhs2TLpuWXLlmmtR7M/LCyszL7AwECDXp+/v794+vRprfVHR0eL9vb2Oo+NjY2VyhoS76P8/f2lGLSZMGGCVOfVq1fFESNG6IzFxsZG/Pnnn/W2FxcXJwYEBOiso0WLFuK1a9fEsLAw6Tld3n//fZ31PPp+lHwdJc9ZSYWFheKkSZPKfb9Gjx4t5uTk6IxL399EZcqW56OPPhJtbGzKjX/FihUmiWPWrFmiXC7X21ZYWJiYkpKi9XhDvlcAiG3bti3znq1YscKgYwVBEN98802xuLjYmFNZiik+x6IoipcuXRLbtWtnUF22trbijh07tNZTUFAgvvbaa6JMJiu3noMHD5Y53pjvlP3790vPz549W+dr27x5s6hQKPTG4ufnp/f8GPIZrUhZIiL2FCAiqgRfX1+sWbMGM2bMwPr167Fnzx4kJCQgJSUF9vb28PLyQseOHTFkyBA89dRTlbpjLQgCHB0d4eLiAnd3dwQHB6Njx44YPHhwqbHopvLf//4XQ4cOxbfffoujR48iIyMD3t7e6N69O15//XX06NFD73hpQ5w5cwYHDhxAZGQkTpw4gVu3bkGpVEImk8HT0xNt27bFsGHDMH78eJ3nrn379jh9+jS++uorHD58GImJiQbN8VAVbGxssHHjRvz+++9YtmwZLly4gPT0dPj4+KB///545513yn2v/P39ER0djYULF2LTpk2IjY2FtbU1mjRpgjFjxmDq1KkGL2X42WefoW3btlixYgViYmKQlpZW7qoG+lhZWeHnn3/Giy++iCVLluDgwYNISkqCSqWCj48PevTogeeffx59+/atcBtVadasWRg/fjwWL16M3bt3IzY2FkqlEg4ODggICEDXrl0xcuRIac6Qyvroo48wduxY/Pjjj9i3bx8SExNRUFAAT09PdO3aFePGjdM6t4RGcnIyIiMjERkZidOnTyM2NhaZmZmwsbGBt7c3OnTogNGjRyMiIqLMMI3nnnsO7du3x759+3Dw4EFcvHgRSUlJyMvLg5OTEwIDA9GzZ0+88MIL6NChQ6Vepyk+xwDQqlUrREdHY+vWrdi4cSOOHTuGe/fuITs7G87OzvD390e7du3Qt29fDBs2rNTEnCVZW1vju+++w6uvvoolS5YgMjISiYmJyMrKgrOzM5o0aYLu3bvjqaeeKjU5oUZVfKcMHz4cN2/exPfff4+//voLN27cQGZmJurVq4egoCAMHz4ckydPrrZeTUREJQmiWAOmayYiIiIiIiKiaseJBomIiIiIiIjqKCYFiIiIiIiIiOooJgWIiIiIiIiI6igmBYiIiIiIiIjqKCYFiIiIiIiIiOooJgWIiIiIiIiI6igmBYiIiIiIiIjqKCYFiIiIiIiIiOooJgWIiIiIiIiI6igmBYiIiIiIiIjqKCYFiIgs0PHjx/H000/D398fdnZ2EAQBgiBg2rRp5g6tTktLS8OMGTPQoUMHODs7QyaTQRAEuLq6SmXCw8Ol96smqmmvISAgAIIgICAgoMbUpXnt4eHhWvdPnDhRKhMXF1eptsqzfPlyqa3ly5dXaVtERFQ7WZk7ACIiMs7q1avx7LPPQqVSmTsUiSiKWLduHX799VfExMTgwYMHcHNzQ+vWrfH0009j4sSJsLKq/D85OTk5OHToEKKiohAVFYVbt24hJSUFaWlpsLW1hY+PDzp27IjRo0dj5MiRkMvlJnh1hrl37x66detW5ReBhsrIyMD//vc/AED79u0xYsSIco+paa+BqldMTAw2b94MABgxYgTat29v1niIiKh6MClARGRB8vPz8eabb0KlUsHKygqvvPIKunTpAmdnZwBAkyZNqj2m9PR0jB49GpGRkaWeT05ORnJyMiIjI/HDDz9g06ZN8PPzq1Rbhw4dwuDBg7XuKywsxI0bN3Djxg2sW7cObdu2xdq1a9GyZctKtWmoTz/9VLqY7tGjB5599ll4e3tDEARYW1tXSwwlZWRkYO7cuQCACRMmGJQUqGmvgapXTEyM9DcTEBDApAARUR3BpAARkQWJiorCgwcPAAAvvvgivv32W7PGU1BQgOHDh+Pw4cMAgEaNGuGll15C06ZNcfv2bfzyyy+4fPkyzpw5g8GDB+PYsWNwcXGpdLutW7dGly5dEBAQAF9fX7i5uUGpVCI6Ohpr1qxBamoqzp07h7CwMJw9exY+Pj6VbrM827dvBwDUq1cPu3fvhoODQ5W3aWqGvoYDBw5UY1SWZ/ny5ezKT0REFoNJASIiC5KYmChtd+jQwYyRqP3www9SQqBjx47Yu3cv6tWrJ+2fOnUqRowYgV27duHSpUv4+OOPsWDBggq3FxISgrt378LX11dnmU8++QSPP/44jh49ivv372Pu3Ln44YcfKtymoTTvTYsWLSwyIQDUjtdARERExuFEg0REFiQ/P1/atrW1NWMkQFFRET799FMA6onXVq5cWSohAAB2dnZYuXIlHB0dAQDffvstUlNTK9ymu7u73oQAALi6uuK7776THv/1118Vbs8YBQUFAMz/vlRGbXgNREREZBwmBYiILIBm1vTnn39eeu7555+XZh031ezsxoiMjJSGMvTr1w9t2rTRWs7Lywtjx44FoE5qbNmypcpja9WqlbR97969Kmtnzpw5ZWbhP3jwYKn3RRAEo7vbJyQkYNGiRXjqqafQokULODk5wcbGBl5eXggPD8fnn38OpVKp9di4uDgIgoDAwEDpuRUrVpSJSTMzfkVegzGrDyQkJGDGjBno0qULPD09YWNjAx8fHzz22GP44YcfpEREZWVmZmLu3Llo27YtnJycUK9ePXTq1Amff/45srOzTdKGoQxdfaC4uBg//fQTevXqBTc3Nzg6OqJly5Z45513EB8fb1RdJV26dAkvv/wymjRpAjs7O7i7u6N///5Yu3at1vKaFQz0fb/o+o65e/cuPvzwQ4SGhsLNzQ3W1tZwc3NDy5Yt0a9fP3z88ceIiorSG29aWho+/vhjhIaGSn8jvr6+6N+/P7799lvk5eXpPd6YVSXKK6vtfK9ZswZDhgxBw4YNYW1tXWoljpIiIyPx0ksvoVWrVnB1dYW1tTU8PT3Rq1cvzJw5E5cuXdIbW2pqKj777DP06tULPj4+sLGxkY7/4osvkJWVVe7rIyKqEJGIiGo8f39/EYDeH39//2qN6Z133pHaXrhwod6y69evl8o+9dRTVR7bhQsXpPb8/PyqrJ3Zs2eX+74AEPfv3y8dExYWJj2vzf79+0VBEMqt09PTUzx8+HCZ42NjYw2KCYAYGxtbJa9B4/PPPxdtbW311tusWTPx6tWrOuvQ/O3r+/u+cOGC2LBhQ51ttGrVSoyLizOoLkNo6g0LC9O6f8KECaXOsTbp6eli9+7ddcZcr149ce/eveXWtWzZMmn/smXLxF9++UXvOX/hhRf01mHMd8y2bdtEJyenco9TKBQ6z+XmzZtFV1dXvcf7+fmJZ86c0VmHMe9reWVLnu8rV66IQ4cOLff13L9/X+zfv79B51CXFStWiC4uLnqP9fb2Fo8ePVruayQiMhbnFCAisgA//fQTcnJyEBkZKU0u+Prrr6Nv375SmeoeA37hwgVpu1OnTnrLdu7cWetxVSE7Oxtvvvmm9HjUqFFV1tbYsWOlGdqffPJJAECbNm3wySeflCoXFBRkcJ15eXkQRRFt2rRBnz590KpVK7i7uyMvLw+JiYnYvHkzTp8+jQcPHuCJJ55ATExMqbueXl5e2LRpE+7fv4+XX34ZANCnTx+88cYbZdrS9OIw9WsAgOnTp+PLL78EADg7O2Ps2LHo0qULFAoFkpOTsXnzZkRGRuL69evo3bs3YmJiKjQh5IMHD9C/f38kJycDAJo2bYoXXngBjRs3xv3797F69WocO3YMTz31FAoLC42uvyqIoojhw4fj6NGjAAA3NzdMmjQJ7dq1Q35+PiIjI/H7779jzJgxRq0AsGPHDvzxxx9QKBSYMmUKOnToAEEQcPDgQSxbtgxFRUX45Zdf0Lt3b0yYMEE6rm/fvti0aZPe7xeg9HfMnTt3MHbsWDx8+BCA+m9syJAh8PHxgbW1Ne7fv4+zZ89iz549Onu1bN++HaNGjUJxcTEAICwsDKNGjYKXlxfi4+OxatUqnD9/HgkJCQgLC8PJkyerbTURAJg2bRp27tyJZs2a4bnnnkOzZs2QnZ2NEydOSGUePHiArl27IjY2FgCgUCjw9NNPIyQkBC4uLkhNTUVMTAy2bduG27dva21n0aJFeP311wGoh+6MGjUKvXr1gru7O9LS0rBz505s2bIF9+7dQ//+/XHy5EmdPbOIiCrE3FkJIiIy3KN3BfXZtWuXuGnTpkr/7Nq1S2v9gYGB5d4N1SgsLBTlcrkIQLSyshJVKlUFz8C/srKypBg3btwoLl++XHz77bdFX19fKa5OnTqJSqWy0m0ZQtOmrrvHGuXdZY+LixPPnTunt47Vq1dL53PixIlay5TsMTBhwgRDXoLJXsPWrVul/d27dxeTkpK0llu8eLFUTlcPEmPu7D7++ONibm5uqf0qlUp899139d7xNlZ556m8u/s//fSTtL958+bi3bt3y5TZu3evaGdnVyru8noKABDbt28v3rt3r0y5kr112rRpozVuY75fFixYIJWdP3++znIqlUo8dOhQmeczMzNFLy8vqY4vv/yyTJnCwkJx8uTJUpnOnTtrbaOqegoAEMeOHSvm5+frrG/w4MFS2X79+ompqalay6lUKnHjxo1lnj99+rRobW0tAhBbtGghXrt2Tevx27Ztk8qFhISU+zqJiIzBpAARkQUx5j/thgw5MORH13+e69WrJ5XJysoqN3Zjy5fn8uXLOmN2c3MT3377bTEnJ6fS7RjKVBfUhtJcvNjb24sFBQVl9pszKdC+fXsRgOjh4aHzIknjueeeEwGIMplMjI+PL7Nf30VccnKydKHk4eEhpqena21DpVKJoaGhNSYp0KZNG2n/yZMndbbz0UcfGZUUsLa2Fm/evKmzvpLDFRITE/XWVd73y8svvyyVLe891ubrr7+Wjh8zZozOckVFRWK7du2ksnv27ClTpqqSAn5+fnq/Q44ePSqVbdq0qfjw4cNy23/UsGHDRACira2teP36db1lZ82aJbV35MgRo9siItKFEw0SEVGFaLoNA+pVBspjb28vbVf1hFk9e/ZEeHh4qTZrm9DQUABAbm4uzp07Z+Zo/nX27FnExMQAAF544QW4ubnpLf/ss88CAFQqFfbt22dUW3/99Zc0JGDixIk6J4ATBAFvvfWWUXVXlZs3b+LixYsAgG7duiEkJERn2VdffRVWVoaP9Bw6dCgaN26sc3+/fv2kbU0MFVVyKEF5E+hps3HjRmn73Xff1VlOLpdj+vTpWo+rai+88ILe75BVq1ZJ2zNnzpRWWTFUeno6tm3bBgAYPnw4mjZtqre85rMCALt37zaqLSIifTinABFRLWXoTOWWqmXLlhBFEYB6FvfU1FScPHkSixYtwtatW7F161aMHj0ay5cvN/o/6zXBiRMnsGrVKhw/fhy3bt1CVlaWzjHxt2/fLndeh+py+PBhaVulUmHz5s16y9+5c0favnLlilFtlZzV/tHx748qeUFsTqdOnZK2w8PD9Zb18PBAmzZtcPbsWYPq7tq1q979DRo0kLbT09MNqlOXxx57DP/9738BACNHjsSMGTMwevToUm3oIoqi9N55eHigY8eOessPHDhQ2i45nr+q9ezZU+/+I0eOAFAnnYYOHWp0/X///TdUKhUAdWK1vM9Kyc+/sZ8VIiJ9mBQgIqIKcXJyki4s8vLy4OTkpLd8bm6utO3s7GzSWORyOby8vPDEE0/giSeewKxZs/DJJ5/gjz/+gCAIWLdunUnbq0oFBQWYNGkSfv31V4OPyczMrMKIjFMyGbVw4UIsXLjQ4GPT0tKMauvu3bvSdnl3Wd3c3ODq6oqMjAyj2jC1pKQkabvkspG6NG7c2OCkgIeHh979tra20nZ5y/yVZ/DgwRg3bhx+//13PHjwANOmTcO0adPQrFkzhIaGIiwsDI8//ji8vb3LHJuZmYmcnBwAQLNmzcpty9PTEwqFAkqlstT5q2r169fXu18zcaCXl1e5PWK0KflZWblyJVauXGnwscZ+VoiI9GFSgIiIKsTV1VVKCqSmpupNChQVFUkXrlZWVlV+537u3LlYt24drl27hvXr1+PSpUto3bp1lbZpKlOmTJESAra2thgyZAhCQkLQoEEDODo6Qi6XA0CpmeI1s7fXBLpmmjeEsasDZGdnS9uGrL7h6Oho9qRAyWE3hnwOjPmsyGTVOyp01apV6Nu3L/773/9KwxGuX7+O69evY+XKlZDL5RgzZgy+/PJL+Pr6SseVHD5k6OtzcnKCUqms8qFHJZU3/EjznVZeQlSX6vysEBHpw6QAEVEttXv3buluXGU4ODhgwIABZZ5v3ry5tAxXXFwc/P39ddZx+/Zt6cK1WbNmEASh0nHpI5PJ8Nhjj+HatWsAgAMHDlhEUiA+Ph5Lly4FADRs2BAHDx7UOUa8ZLf7mqTkBdKBAwcQFhZWZW2VvKA05G+9ZBLBXIyN2RSf4aoiCAJefPFFvPjii7h16xaOHDmCo0ePSktNFhcXY/Xq1Thy5AiioqKkXgMlewoZ+p5oylW2l5Gmu74puLi4IC0trVSixxglPyvLly8vtUwkEVF1YlKAiKiWeumllxAfH1/pevz9/bXOTxAUFIRdu3YBUI+T1nfxV3IctbHr3VdUyYuHytyRq0779u2T5kl477339E4aZ4r3tiqUHFN+4cKFKk0KlOzefePGDb1d0dPS0szeSwBAqTvmmqSaPrdu3arKcEymcePGaNy4McaPHw8AOHPmDCZNmoTo6GgkJiZiwYIF0lASFxcXODg4ICcnB9evXy+37gcPHkjvnbYu/ZphEQUFBXrrEUXRpN3uGzZsiLS0NNy7dw+pqalwd3c36vhHPytERObC1QeIiKhCSk7+pUkO6LJz505pe9CgQVUWU0k3btyQtj09Paulzcq6d++etN2kSRO9ZcubfbxkV3JNoqE69O7dW9qu6pniS87cv3//fr1lIyMjqzQWQ3Xu3FnaPnDggN6yqamp1XqxaMq/mY4dO5aaF0MzKR+g7mGgee9SUlKk1Sp02bNnj7TdpUuXMvvr1asHQJ080JcYuHDhgkl7i/Tq1Uva/vPPP40+vnfv3lKvqS1btpi0FwMRkTGYFCAiqqXi4uIgimKlf3StYtCnTx/pYnvv3r06lzi7f/8+1qxZA0A9w/bw4cOr5PWWdPv2bWzfvl163L179ypv0xRKjou/efOmznJbtmwpd/K5kl2Tq7PbfOfOndGqVSsA6gvx8hJGlfH444/D2toagLr7tb4eIZqZ8s2tadOm0lCWY8eOlVpB4VHff/89ioqKqis0k//NBAQESNuPvo5Ro0ZJ2wsWLNBZR3FxcanJKksep6E5n0VFRaWSD4/SzMFhKiWXCPz000+NPmdeXl7S0Kzr16/j559/Nml8RESGYlKAiIgqxMrKCjNmzACgvqs4fvz4Msuc5eXlYcKECdJ/lqdOnaqzi+3EiRMhCAIEQcCcOXO0lnn33XfL7TZ/8+ZNPPHEE9JY7LCwMIuYTwAofRd04cKFWpeNO3nyJF544YVy63Jzc4NCoQAAxMTEVFtvAZlMhnnz5kmPx44dix07dug95uLFi3j11VeNbsvb2xtPP/00APVd4ueeew75+fllys2YMQNHjx41uv6qMm3aNGn72Wef1Tqj/r59+/DZZ59VY1SlV0M4c+aM3rIfffQRdu/erffu9vfffy9tt2vXrtS+iRMnwsvLCwDw+++/45tvvilzfHFxMaZOnYro6GgA6p4h2paWHDx4sLQ9a9YsrX8DS5cuxZIlS/S+JmN169YNQ4YMAaDumTRixAidwxNEUcTWrVvLPP/JJ59Iia033ngDK1as0NtmfHw8pk+fjvv371cyeiKif3FOASIiqrBXX30VGzZswOHDh3HmzBm0a9cOL7/8Mpo2bYrbt29j6dKluHz5MgD13byZM2dWqr3FixdjwYIF6NatG3r06IEWLVpAoVCgqKgId+/exZEjR7B9+3apC7GPj4/eC4GJEydK/wmfPXu2zmREdQkNDUVISAiioqIQFxeHli1b4pVXXkGLFi2Qm5uL/fv3Y82aNRBFUVoOTp++ffti06ZNuHnzJiIiIjBy5Ei4urpK+8PCwsqdYb0ihg8fjg8++ACfffYZMjIyMGTIEPTs2RNDhgyBv78/rKyskJaWhosXL+LAgQO4cOEC5HI5fvjhB6PbWrhwIXbv3o3k5GT8+eefCA4OxgsvvIDGjRtLvVT+/vtvhISE4M6dO6WWMTSXF198EatWrcKhQ4dw7do1BAUFYdKkSWjfvj3y8/Oxb98+/P7771AoFOjevbs09KGqVxcIDg6Gl5cX7t+/j1WrVsHT0xPdunWT/kbs7e2lOSIiIyMxe/Zs+Pj4YODAgWjfvj28vb0hiiLu3r2LrVu34vDhwwDUY/7ffvvtUm05Oztj2bJlGDZsGIqLi/Hmm29i8+bNGDVqFDw9PZGQkIBff/0V586dk8rrWrJv+PDhaN68Oa5du4ajR48iJCQEL774IurXr4/k5GRs3rwZkZGR6NWrF27evGnSv4EVK1agS5cuiI2Nxd69e9GkSROMHTsWISEhcHFxQXp6Os6dO4c///wT8fHxZZJznTt3xqJFi/DKK6+goKAAEydOxH//+1+MGDECTZs2ha2tLTIyMnDlyhUcOXIEJ0+eBFA6sUREVGkiERFZjGXLlokARADismXLzB2OKIqimJaWJvbt21eKS9tPx44dxfj4eL31TJgwQSo/e/ZsrWUUCoXedkr+hIeHizdu3Kh0m4bS1BMWFqa3XFhYmFRWm5s3b4r+/v46X5etra24ZMkSg/4WoqOjRXt7e511xcbGVslr0Pjxxx9FZ2dng94vf39/rXVozoWu/aIoihcuXBAbNmyos+5WrVqJcXFxBtVliPLOU8m/q0fPsUZ6eroYGhqqM2ZXV1dx79694jPPPCM9l5aWVqYeY74TDCm7ePFig96jPn36GPS+enh4iLt27dIZ0+bNm8v9XPv5+YmnT5/W+9rOnDkjurm56ayja9eu4v3798v9GzDkvXtUcnJyqc+Erh9BEPSeB09PT4POqbu7u/jgwQODYiMiMgSHDxARUaXUq1cPe/fuxZo1a/D444+jfv36sLGxgbe3N/r27YuffvoJJ06cgJ+fX6XbunDhAn799Ve8+OKL6NKlC/z9/WFvbw9bW1t4eXkhNDQUb7zxBo4cOYL9+/eXO1lfyeXeaspkhI0bN8aZM2fwwQcfoHXr1rCzs4OTkxNatGiBqVOn4syZM3jxxRcNqqt9+/Y4ffo0Jk2ahBYtWpSas6A6vPzyy4iPj8eCBQswYMAA1K9fH7a2trC1tYWPjw969+6N//znP9i3b1+lZtlv06YNLl68iNmzZyMoKAgODg5QKBTo2LEj5s+fj6ioKL1LZpqDq6srDh8+jB9//BE9evSAq6srHBwc0Lx5c7z11luIiYlBv379kJqaCgCQy+VwcXGp8rheeukl7Ny5EyNGjEDDhg2lmf0ftW3bNuzcuRP/+c9/0LNnT/j4+MDa2ho2Njbw8fFBv379sHDhQly/fl3rkqYaw4cPx82bN/HRRx+ha9eucHd3h7W1Nby8vNC3b198/fXXuHLlCjp27Kg37g4dOuD8+fN444030LRpU9jZ2aFevXoIDQ3Fd999h8OHD1fZZ9zb2xsHDhzA9u3b8dxzz6Fx48ZwdHSUXkdYWBjmzJmDq1ev6qxj+PDhiIuLww8//IBhw4ahUaNGsLe3h42NDTw9PREaGorXX38df/75J+7evQsPD48qeS1EVDcJoliNUxITERHVIPXr10dSUhJ8fX1x48aNar9oJtJHpVLB19cX9+/fR3BwsNSVnoiIyJTYU4CIiOqkixcvShO8ffjhh0wIUI2zYcMGaUK58PBw8wZDRES1FpMCRERUJ+3duxeAeom4SZMmmTkaqmsuXLigc6Z6QL3KxGuvvQYAEAQBkydPrq7QiIiojuHqA0REVCft27cPAPDxxx/Dyor/HFL12rx5Mz799FM89thj6N69O/z9/SGXy5GUlIT9+/fjzz//lJb7mzZtGoKDg80cMRER1Vb8XxAREdVJ2tYMJ6pOeXl5+PPPP/Hnn3/qLDNlyhQsWLCgGqMiIqK6hhMNEhEREVWz+/fvY9u2bdi9ezcuXLiABw8eID09HQ4ODmjYsCF69eqFyZMnlzvrPhERUWUxKUBERERERERUR3H4QBVTqVS4e/cunJ2dIQiCucMhIiIiIiKiWk4URWRlZaF+/fqQyfSvL8CkQBW7e/cuGjVqZO4wiIiIiIiIqI5JTExEw4YN9ZZhUqCKOTs7A1C/GS4uLmaOhoiIiIiIiGq7zMxMNGrUSLoe1YdJgSqmGTLg4uLCpAARERERERFVG0OGsOsfXFADZWVlYc6cOQgODoaTkxMUCgVCQkLw5ZdfoqCgoFJ1//HHHxg6dCjq168PGxsbODo6okWLFpg8eTJiYmJM8wKIiIiIiIiIagiLWn0gPj4e4eHhiIuLAwA4ODiguLgY+fn5AIAOHTpg3759qFevnlH15ufn46mnniq1TrCTkxMKCgqkRINMJsPChQvx1ltvGVV3ZmYmFAoFlEolewoQERERERFRlTPmOtRiegoUFxdj6NChiIuLg6+vL/bs2YPs7Gzk5ORgzZo1cHZ2RnR0NJ555hmj6/7ss8+khMBrr72G27dvIysrC7m5uTh16hR69uwJlUqFd955B6dOnTL1SyMiIiIiIiIyC4tJCixfvhznz58HAGzYsAH9+/cHoL6DHxERgcWLFwMAduzYgX379hlV98qVKwEAYWFh+O6779CgQQOp7k6dOmHbtm1wcnKCKIrYsGGDqV4SERERERERkVlZTFJgxYoVAIA+ffogNDS0zP6xY8ciMDAQwL8X+YZKSkoCAHTu3FnrfoVCgebNmwMAHj58aFTdRERERERERDWVRSQFcnJy8PfffwMABg8erLWMIAgYNGgQAGD37t1G1d+4cWMAwOnTp7XuVyqVuHbtGgDdiQMiIiIiIiIiS2MRSYHLly9DpVIBAIKCgnSW0+xLTk5GWlqawfW/+uqrAIADBw5gypQpuHPnDgBAFEWcOXMGTzzxBB4+fIhu3bpVaM4CIiIiIiIioprIIpICd+/elbY14/21Kbmv5DHlmTJlCv7v//4PMpkM33//PRo2bAhnZ2fY2dmhU6dOuHHjBt577z1ERkbCysqqYi+CiIiIiIiIqIaxiCvcrKwsadvBwUFnuZL7Sh5THplMhnnz5qF169aYOnUqHj58WGrugLy8PCiVSmRnZ8Pe3l5vXfn5+dISiYB6KQgiIiIiqrtEUURhYaHU85WIyBAymQzW1tYQBKFK27GIpEBVS0lJwVNPPYUDBw7gsccew+zZsxEUFITc3FwcO3YM7777Ln744Qfs2rULhw4d0ttbYd68eZg7d241Rk9ERERENVFxcTFSUlKQlZWFwsJCc4dDRBbI2toazs7O8PDwgFwur5I2LCIp4OzsLG3n5OToLFdyX8ljyjNhwgQcOHAAYWFh2LVrl5SJUSgUePLJJ9GjRw+0adMGt27dwnvvvYdff/1VZ13vv/8+3n77belxZmYmGjVqZHAsRERERGT5iouLkZiYiPz8fCgUCjg5OUEul1f5HT8iqh1EUURxcTEePnyIjIwM5ObmolGjRlWSGLCIpED9+vWl7Tt37qBt27Zay2kmCHz0GH0uX76M7du3AwDeeecdrV/UXl5eGD9+PL766its3LgRK1eu1PmFbmtrC1tbW4PaJiIiIqLaKSUlBfn5+fDz8yt3+CkRkS5OTk5QKBRISEhASkoKvL29Td6GRUw02KpVK8hk6lAvXLigs5xmn4+PD9zc3Ayq+9KlS9J2kyZNdJZr1qwZAHVvhPv37xtUNxERERHVPaIoIisrCwqFggkBIqo0e3t7uLi4ICsrC6Iomrx+i0gKODg4oEePHgCAnTt3ai0jiiJ27doFABgwYIDBdWuSDQAQHx+vs9y9e/ekbScnJ4PrJyIiIqK6pbCwEIWFhfw/IxGZjLOzs/TdYmoWkRQA1OP+AWD//v04ceJEmf3r16/HrVu3AADjx483uN6OHTtK2z/88IPWMtnZ2Vi5ciUAoG3btnB0dDS4fiIiIiKqWzSrDFTVpGBEVPdovk+qYhUTi0oKBAcHQxRFjBo1Cvv27QOgPinr16/H5MmTAQCDBw9Gv379Sh07Z84cCIIAQRAQFxdXap+/vz+GDh0KAPjzzz/x3HPP4ebNm9LSMUePHkV4eLiUcHjnnXeq+JUSERERUW3ASQWJyFSq8vvEIiYaBAArKyts3boVffr0QVxcHPr37w8HBweoVCrk5eUBADp06IDffvvN6Lp/+eUXDBo0CKdPn8aqVauwatUqODg4oKCgAEVFRVK56dOnG9ULgYiIiIiIiKgms5ieAgAQEBCAc+fO4cMPP0RQUBAEQYC1tTU6deqEhQsX4vjx46hXr57R9Xp4eOD48eNYsmQJBg4cCG9vbxQWFsLKygqNGzfGs88+i8OHD2PBggVV8KqIiIiIiIiIzEMQq2L6QpJkZmZCoVBAqVTCxcXF3OEQERERURXLy8tDbGwsAgMDYWdnZ+5wiKgWMPZ7xZjrUIvqKUBERERERFRbnT9/HmPGjIGvry+srKwgCALat28PADhw4IA0TxqRKTEpQERERERENVLJCcMf/XFwcECzZs0wYcIEHD16VGcdJS+mDfk5cOCA3ph27NiBl156CW3atIGbmxusra3h7u6OLl26YNq0aVpXSjNEbGwsevTogfXr1yM5ORkKhQLe3t7w8PAo99iYmBjMmTMH//vf/yrUNtVtFjPRIBERERER1V3e3t7StkqlQlpaGm7cuIEbN25g5cqVmD17NubMmaO3jnr16sHGxkZvGV37r127hmeeeQanTp2SnpPL5VIX7aioKERFReHrr79Gnz59sG7dOoMu6DUWL16MrKwsNG3aFPv370fDhg1L7XdwcECLFi20HhsTE4O5c+fC398f06ZNM7hNIoBJASIiIqphkpS5iE3JRqCHI3wV9uYOh4hqiOTk5FKPi4uLcfz4cbz55ps4ffo05s6diwEDBqB79+4669i4cSPCw8ONbjsqKgoDBgxARkYGHB0d8cYbb2Ds2LEIDg6GIAhQqVS4cuUKNm/ejG+++Qb79+/H7du3jUoKnD9/HgAwfPjwMgkBAOjSpQuuXLlidOxE5WFSgIiIiGqMtVEJeH/jeahEQCYA80YGIyLEz9xhEVENJJfL0aNHD2zevBmNGjUCAGzZskVvUqAiUlNTMXLkSGRkZKB+/frYvXs32rRpU6qMTCZD69at0bp1a0ybNg1vvfWW0WP/c3JyAABOTk4mi53IEJxTgIiIiGqEJGWulBAAAJUIfLDxApKUueYNjIhqtIYNG8Ld3R0A8PDhQ5PX/8UXX+D27dsAgNWrV5dJCDzKwcEBixcvRnBwsEH1BwQElJrLYO7cuVrnONA10aAgCHj++ecBAPHx8WXmSChvSAURewoQERFRjRCbki0lBDSKRRFxKTkcRkBEOt25cwepqakAoHPMfUUVFRVh8eLFAIB+/fqhd+/eBh8rkxl2/9XT0xN5eXlIS0tDYWEhHB0dS/UWKG8OBG9vb+Tm5iIzMxMymQyenp6l9rPnAZWHSQEiIiKqEQI9HCETUCoxIBcEBHg4mC8oIqqxiouLcfLkSbz55psAAC8vL4wfP96kbZw6dQpKpRIA8OSTT5q0bo2oqCgAQHh4OA4ePIjp06cbdXc/OTkZy5cvx/PPP49GjRohLi6uSuKk2otJASIiIqoRfBX2mDcyGB9svIBiUYRcEPDZyCD2EqDaSRSBwhxzR2Fa1g6AkePojeHj4yNta1YfKC4uhouLC5555hl8+umncHV11VvHyJEj9d55b9SokXSRDgAXL16Utjt06FDx4IlqMCYFiIiIqMaICPFD7+aeiEvJQYCHAxMCVHsV5gCf1Td3FKb1wV3AxrHKqr93757W53NycqBUKnHv3j34+/vrrSM9PV3vfjs7u1KPNcMSAMDNzc3ASIksCycaJCIiohrFV2GP0CbuTAgQUSmiKJb6yc3NRXR0NCZMmIBt27ahd+/e2Lx5s9469u/fX6aekj+Pdr0XxX/HMxm7mgCRpWBPASIiIiKi6mbtoL6zXptYV+/8H3Z2dmjfvj2WLFmCtLQ0bNq0CRMnTkRCQgJcXFxM0oaHh4e0XbLXAFFtwqQAEREREVF1E4Qq7Wpf10yePBmbNm2CUqnE9u3bMXbsWJPUW3L5wejoaHTv3t0k9RLVJBw+QEREREREFq3kXAKxsbEmq7dz585QKBQAgE2bNpmsXqKahEkBIiIiIiKyaLdv35a2HR1N1wPDysoKL730EgBg3759OHTokMHHqlQqk8VRHplMfVlXcg4EIkMxKUBERERERBbt999/l7Y7d+5s0rr/7//+D/Xrq1eKePrpp0stU6hNbm4uXnvtNZw/f96kceijmUMhIyOj2tqk2oNJASIiIiIiskjJycmYOXMmVqxYAQDo1q0bQkNDTdqGh4cHNmzYABcXF9y9exddu3bFBx98gAsXLkh35kVRxJUrV/DFF1+gSZMm+OGHH6r1rn1QUBAAIDMzE+vWrau2dql24ESDRERERERU4/n4+JR6nJeXB6VSKT0ODg7Ghg0b9C4dOHLkSNjY2OhtZ/r06Zg+fXqp57p164bjx4/j2WefxZkzZzBv3jzMmzcPVlZWcHFxQWZmJoqKiqTyAwcORKNGjYx5eZXStGlT9OvXD/v27UNERAQmTZoENzc3AMC0adMwbdq0aouFLA+TAkREREREVOPdu3ev1GNra2v4+PigXbt2GD16NMaPH1/uBX96enq57Tx8+FDr861atcLp06fx119/YdOmTfj777+RnJyMzMxMuLi4oEmTJujZsyeeffZZdOzY0fAXZiJ//PEHPvroI/z1119ISEhAfHw8AA4poPIJImejqFKZmZlQKBRQKpUmWy+ViIiIiGquvLw8xMbGIjAwEHZ2duYOh4hqAWO/V4y5DuWcAkRERERERER1FJMCRERERERERHUUkwJEREREREREdRSTAkRERERERER1FJMCRERERERERHUUkwJEREREREREdRSTAkRERERERER1FJMCRERERERERHUUkwJEREREREREdRSTAkRERERERER1FJMCRERERERERHUUkwJEREREREREdRSTAkRERERERER1FJMCRERERERERHUUkwJEREREREREdRSTAkRERERERER1FJMCRERERERERHUUkwJERERERFRrzJkzB4IgIDw83NyhGO3AgQMQBAGCIJg7lCoTHh4OQRAwZ86cKqk/ICAAgiBg+fLlVVJ/bWRl7gCIiIiIiIio6mRkZOB///sfAGDatGlwdXU1azyW6H//+x8yMjIwYsQItG/f3tzhmBSTAkREREREVGt4eHigRYsW8PPzM3coNUZGRgbmzp0LAJg4cSKTAhXwv//9D/Hx8QgICGBSgIiIiIiIqKaaOnUqpk6dau4wiCwG5xQgIiIiIiIiqqOYFCAiIiIiohpr3bp1GDx4MLy9vWFtbQ1XV1c0a9YMw4YNw3fffYe8vLxS5fVNNDhx4kQIgoCJEycCAJYvX47Q0FAoFAq4ubmhf//+OHTokFS+qKgI3377LTp16gQXFxcoFAoMGTIEZ86c0Rrr8uXLIQgCAgICdL6euLg4aTLBuLg4g8+DSqXC33//jffeew/dunVDw4YNYWNjA3d3d4SFheHHH39EYWFhmePCw8MRGBgoPQ4MDJTa13WeiouLsXz5cgwcOBDe3t6wsbGBp6cnBg4ciDVr1kAURZ1xFhcXY9GiRejYsSMcHR3h5uaG8PBw/PHHHwa/Vn1yc3PxySefoHXr1rC3t4eXlxeGDBmCffv2lXvs1atXsWDBAvTv3x9NmjSBvb09XFxc0KFDB8ycORMpKSlljtH8PcXHxwMAnn/++VLn79FJISvShrlx+AAREREREdVIL774In755RfpsZOTEwoLC3Hjxg3cuHEDf/75Jx5//HG9F+G6TJw4EStWrICVlRXs7e2Rnp6Offv24eDBg9i0aRMee+wxDBs2DLt374aNjQ2sra2RnZ2NHTt24ODBgzh06BA6depkwlerX0JCAnr27Ck9trKygoODA9LS0nDo0CEcOnQIv//+O3bt2gV7e3upnJubGzw8PKSLUQ8PD8jl8lL7S7p37x6GDx+OEydOSM8pFAqkpKRg9+7d2L17N1avXo3169fDxsam1LH5+fkYPnw4du3aBQCQyWSwsbHBoUOHcPDgQbz77ruVOgdpaWno378/oqOjpXNQWFiIHTt2YOfOnfjuu+/0Hj9w4EDp4l4QBCgUCiiVSsTExCAmJgbLly/Hvn370KJFC+kYJycneHt748GDB1CpVHBxcSl1fk3RhrmxpwARERERUS2VpMzF0ZspSFLmmjsUox05cgS//PILZDIZPv/8c6SmpiIrKwvZ2dlISUnBrl27MGHChDIXpobYsmUL1q1bh8WLFyMzMxOZmZm4cuUKOnXqhKKiIrz++uuYPn06Tp06hXXr1uHhw4fIysrCqVOn0KRJE+Tk5ODNN9+sgletm5WVFYYPH461a9fizp07yM/Ph1KpRFZWFpYtW4b69evj8OHDmDFjRqnjNm7ciKioKOlxVFQUkpOTpZ+NGzdK+woKCjB06FCcOHECHTt2xF9//YXs7GxkZGTg4cOHWLFiBby8vLB161atF/jvv/8+du3aBUEQ8MknnyA9PR3p6elITk7Gq6++is8//xwxMTEVPgeTJk1CdHQ0bG1t8eOPPyIrKwvp6emIi4vDiBEj8Oabb+LBgwc6j+/WrRu+/fZb3LhxA3l5eUhPT0deXh727t2LLl264M6dOxg3blypY6ZPn47k5GQ0atQIAPD111+XOn/JycmVbsPsRKpSSqVSBCAqlUpzh0JERERE1SA3N1e8dOmSmJuba9Y41pyMFwPf2yb6v7tNDHxvm7jmZLxZ4zHW559/LgIQBwwYYNRxs2fPFgGIYWFhZfZNmDBBBCACEFetWlVm/82bN0VBEKQyhw8fLlNm37590v7ExMRS+5YtWyYCEP39/XXGFxsbKx0fGxtbat/+/fulfcaKiooSAYiOjo5l/vb0tVnSokWLRABimzZtxMzMTK1lTp06JQqCINrY2Ij37t2Tnr9z545oZWUlAhBnzZql9dinn35aimP27NlGvb4TJ05Ixy5durTM/qKiIrFnz55SmWXLlhlVf1ZWlujt7a3zfff3969Qvca0oY+x3yvGXIeypwARERERUS2TpMzF+xvPQ/XP0G+VCHyw8YJF9RjQLJv34MEDFBcXm7RuPz8/rXdrGzdujCZNmgAAevXqVaq7vkZYWBhsbW0BAOfOnTNpXJXRuXNneHl5ITs7u8J345csWQIAeO211+Ds7Ky1TKdOndCmTRsUFBRg//790vN//PEHioqKYG9vj+nTp2s9ds6cORWKCwDWrFkDAGjUqBGef/75MvvlcjlmzZpV4fqdnJwQFhYGQN1LpSpURxsVwaQAEREREVEtE5uSLSUENIpFEXEpOeYJqAL69+8POzs7REdHo1evXli6dCliY2NNUnfnzp3LTBCn4e3tDQAICQnRul8ul8PDwwMAkJ6ebpJ4DFVQUIAff/wRAwYMQP369WFnZ1dqwrv79+8DAG7fvm103VlZWVKSY9asWfDx8dH5c/XqVQCQxs4DwKlTpwCoz62Li4vWNpo3b44GDRoYHVvJ+sPDw3W+d71794aVlf5p87Zt24aIiAg0btwYjo6Opc7funXrAFTs/FV3G6bEiQaJiIjI4iUpcxGbko1AD0f4KnRPAEVUVwR6OEImoFRiQC4ICPBwMF9QRmrcuDGWLFmCV155BceOHcOxY8cAAJ6enujTpw/GjRuHYcOG6bxA1EfXXXAA0kWlIWW0zfZfVe7fv4/+/fvj/Pnz0nN2dnalJg7UTIaXnZ1tdP3JyclQqVQA1BP6GSIn598kkyYhUd5Ff8OGDXHnzh2j4zOkfjs7O7i7u+PevXtl9qlUKjz77LNYvXq19JyVlRXq1asnzUuhVCqRl5dXofNXXW1UBfYUICIiIou2NioBPeZHYtzPJ9BjfiTWRiWYOyQis/NV2GPeyGDI/7lglgsCPhsZZHFJs2eeeQbx8fH48ccfERERgUaNGuHBgwdYt24dRowYgbCwMGRmZpo7zGrx1ltv4fz583B3d8cvv/yCpKQk5Obm4sGDB9KEd/Xr1wcAvUsG6lJyiMbx48chimK5P9qGA1QkSWOMita/dOlSrF69GnK5HB9++CGuX7+O/Px8pKWlSedv9OjRACp2/qqrjarApAARERFZrNowbpqoqkSE+OHIe32wenI3HHmvDyJC/MwdUoW4ubnh5Zdfxpo1a5CQkIAbN27gvffegyAIOHz4cKXGqZuapgdBXl6ezjJKpdLoegsLC6VVAhYtWoTnn38ePj4+pcoUFxdLyw5WhGbYBIBSvREM5eXlBaD8bvEV6SVgaP35+flITU3Vuk8zJ8GkSZMwd+5cNG3aFDJZ6cvhR1cSMFZ1tFEVmBQgIiIii1Ubxk0TVSVfhT1Cm7hbXA8BfZo0aYJ58+ZJEwXu2bPHzBH9q169egDUXd3z8/O1ljlx4oTR9T548EBKNHTo0EFrmSNHjuhMRpS8MNV1h7pevXpo3bo1gH8vbo3RuXNnAOqx/1lZWVrLXL9+vcJj6TX1Hzx4UOdrOHToEIqKirTuS0xMBKD7/D18+FDve6M5h/ru8Fe2DXNhUoCIiIgslmbcdEmWNm6aiLTTdVGtYW+vTnRoxtPXBO3atQOgvnDctGlTmf25ubn473//a3S9Li4uUrf5s2fPltlfVFSEGTNm6D1eIyMjQ2e5l156CQCwb9++chMDj847MGrUKFhZWSE3Nxdffvml1mM++ugjvXXqExERAQBISEjAihUryuxXqVT45JNPdB6vUCgAaD9/APDxxx/rTGYA/55Dfeevsm2YC5MCREREVGWSlLk4ejOlyrrz15Zx00RU1tSpUzFmzBhs2LBBmmQOUN9t/fHHH7Fy5UoAwJAhQ8wVYhkNGzaUljF8++23sXfvXmms/unTp9G/f/9Sr8VQTk5O6NGjh1RvZGSkNCnghQsXMGTIEJw6dQqOjo5aj3d1dZUm6Fu2bJnOu+mvvPIKunbtCgB47rnnMHPmTOnuN6CeWPDAgQOYOnWqtHSjRoMGDfDaa68BUF/8zps3T7oAfvDgAaZOnYpVq1ZJF87G6tq1K4YNGwYAePXVV/Hzzz9LiaOEhARERETg2LFjcHDQnhQeNGgQAODnn3/GTz/9hIKCAgDq7vxvvfUWvvjiC7i7u+tsPygoCIB66UVdq05Utg2zEalKKZVKEYCoVCrNHQoREVG1WnMyXgx8b5vo/+42MfC9beKak/FV1tbdjBzx6I0U8W5GTpW1QWSo3Nxc8dKlS2Jubq65Q7FoEyZMEAFIP05OTqKrq2up53r27Ck+fPiw1HGzZ88WAYhhYWE665wwYYLOdsPCwkQA4uzZs3WW8ff3FwGIy5YtK7MvOjpadHZ2lmK0s7MTHR0dRQCit7e3+Ndff0n7YmNjSx27f/9+ad+jTp06JdUDQLS1tZXasbKyEleuXKk3ro8//rjUsY0aNRL9/f3FiIiIUuUePHgg9u3bt9R5dnFxEV1dXUVBEKTnrKysyrSRm5sr9u/fXyojl8vFevXqSce9++67Bp1fXVJSUsR27dpJ9VtbW0t/E4IgiN99953Oc5Ceni62bNlSOlYmk5V6TS+//LLev4+DBw9KZeVyuejr6yv6+/uL/v7+JmtDH2O/V4y5DmVPASIiIjK56p4AsDaOmyaq62bNmoVvvvkGTz75JFq2bAkrKys8fPgQXl5eeOyxx/DLL7/gwIEDOu+Om0v79u1x8uRJjB07Fl5eXlCpVPDw8MCUKVMQExMjjds3VqdOnXDy5EmMGTMGHh4eUKlUcHZ2xpgxY3D06FE899xzeo//4IMP8PXXX6Nz586wtrbG7du3ER8fX2biOw8PD+zduxdbtmzB6NGj0ahRI+Tn5yM3NxcNGjTA4MGDsWjRIsTFxZVpw87ODjt27MDXX3+N9u3bw8bGBqIoolevXli3bh3mz59fodeu4e7ujqNHj2Lu3Llo2bIlZDIZrKysMGjQIOzZs0fqqaCNq6srjh49imnTpiEgIAByuRxWVlYIDw/H6tWr8eOPP+ptu3fv3vjrr7/Qv39/KBQK3Lt3D/Hx8YiPjzdZG+YiiGINWguhFsrMzIRCoYBSqSw1loeIiKg2O3ozBeN+LjuZ0urJ3RDapAZ2nSQyoby8PMTGxiIwMBB2dnbmDoeIagFjv1eMuQ5lTwEiIiIyOU4ASEREZBmYFCAiIiKT4wSARERElsHK3AEQERFR7RQR4ofezT0Rl5KDAA8HJgSIiIhqICYFiIiIqMr4KuyZDCAiIqrBOHyAiIiIiIiIqI5iUoCIiIiIiIiojmJSgIiIiIiIiKiOYlKAiIiIiIiIqI5iUoCIiIiIiIiojmJSgIiIiIioCoiiaO4QiKiWqMrvEyYFiIiIiIhMSCZT/xe7uLjYzJEQUW2h+T7RfL+YEpMCREREREQmZG1tDWtrazx8+NDcoRBRLZGVlSV9t5gakwJERERERCYkCAKcnZ2hVCqRm5tr7nCIyMLl5uYiMzMTzs7OEATB5PVbmbxGIiIiIqI6zsPDA7m5uUhISICLiwucnZ0hl8ur5D/0RFT7iKKI4uJiZGVlITMzE7a2tvDw8KiStpgUICIiIiIyMblcjkaNGiElJQVZWVnIyMgwd0hEZIGsra3h6uoKDw8PyOXyKmmDSQEiIiIioiogl8vh7e0NLy8vFBYWQqVSmTskIrIgMpkM1tbWVd7DiEkBIiIiIqIqJAgCbGxszB0GEZFWnGiQiIiIiIiIqI5iUoCIiIiIiIiojmJSgIiIiIiIiKiOYlKAiIiIiIiIqI5iUoCIiIiIiIiojmJSgIiIiIiIiKiOYlKAiIiIiIiIqI5iUoCIiIiIiIiojmJSgIiIiIiIiKiOYlKAiIiIiIiIqI5iUoCIiIiIiIiojmJSgIiIiIiIiKiOYlKAiIiItEpS5uLozRQkKXPNHQoRERFVEStzB0BEREQ1z9qoBLy/8TxUIiATgHkjgxER4mfusIiIiMjE2FOAiIiISklS5koJAQBQicAHGy+wxwAREVEtxKQAERERlRKbki0lBDSKRRFxKTnmCYiIiIiqDJMCREREVEqghyNkQunn5IKAAA8H8wREREREVYZJASIiIirFV2GPeSODIRfUmQG5IOCzkUHwVdibOTIiIiIyNU40SERERGVEhPihd3NPxKXkIMDDofoSAioVcGs/kHkXsLYHrOwAazvANQDwaFo9MRAREdUhTAoQERGRVr4K++pLBogicGMvEPkxkHS21K4k0Q2xKh8EBoXC9/H3ACev6omJiIioDmBSgIiIiMwr/iiw7yMg4Zj6sY0T4BcKFOdjbWpjvH+/P1SQQRatwrwrryNiYDgQMgmQW5s1bCIiotpAEEVRLL8YVVRmZiYUCgWUSiVcXFzMHQ4REVHNcvwHYOd76m0rO/XFfs+3AUd3JClz0WN+ZKmVEOQoxhHbN+Hr7QOM+ZVDCoiIiLQw5jqUEw0SERGReUSv+jch0P5Z4I1oYOCngKM7AB1LI0KOOOtmwP1LwKqRwMP71Rw0ERFR7cKkABEREVW/S1uAra+rt0OnAsMXAS71SxXRuTTiC78A9QKBjHjg9wigILuagiYiIqp9mBQgIiKi6nUzEtgwCRBVQIfngAGfAIJQppjOpRHrNwSe+QOwdwPungH+eBFQFZc6NkmZi6M3U5CkzK2Wl0RERGSpOKdAFeOcAkRERCXcPg2seAIozAFaDwdGLwNkcr2HJClztS+NmHACWDkMKMpTz0UwZCEgCFgblYD3N56HSgRkAjBvZDAiQvyq+IURERHVHJxTgIiIiGqewjxg42R1QqBJX2Dkz+UmBAB1j4HQJu5ll0f06wqM/AmAAEQtAU4sRpIyV0oIAIBKBD7YeIE9BoiIiHRgUoCIiIiqx5GvgLSbgJMP8NRywMq28nW2Hq4efgAA++YiNj6+7OSEooi4lJzKt0VERFQLMSlAREREVe/BNeDwV+rtwZ8DdgrT1R06BfALBQpzEHj+a+2TE3o4mK49IiKiWoRJASIiIqpaoghsewtQFQLNBqjv7puSIABDFgCCDL7Xf8e8XjZlJyd8dOgBERERAQCszB0AERER1XIxvwPxRwAre2kyQJPzCVZPNnjyJ0TcmoHe0/ciLqOw7OSEREREVIrF9RTIysrCnDlzEBwcDCcnJygUCoSEhODLL79EQUFBpetPTk7GrFmz0KlTJ7i5ucHe3h7+/v4YNGgQ5s+fj8LCQhO8CiIiojoiOxXYPVO93ed9oJ5/1bXVZwbg4AGkXIXvleXaJyckIiKiUixqScL4+HiEh4cjLi4OAODg4IDi4mLk5+cDADp06IB9+/ahXr16Fap/7dq1eOmll5CZmQkAsLGxgb29PZRKpVQmPT0drq6uBtfJJQmJiKhO2zwFiFkFeLUBXj4IyK2rtr3oVcCWKYCNEzD1FODiW7XtERER1UC1cknC4uJiDB06FHFxcfD19cWePXuQnZ2NnJwcrFmzBs7OzoiOjsYzzzxTofrXr1+PcePGITMzExEREYiOjkZ+fj4yMjKQlZWFw4cP46233oK1dRX/Z4aIiKi2SL0JnP1dvT30f1WfEACAduOAhiFAwUNgz6yqb4+IiMjCWUxPgaVLl2LSpEkAgKNHjyI0NLTU/tWrV2PcuHEAgL1796Jfv34G152UlIQ2bdogPT0db731Fr766iuTxc2eAkREVGdtfQM4s0I9ueAz66uv3bvRwE/hAARgahTg0az62iYiIqoBamVPgRUrVgAA+vTpUyYhAABjx45FYGAgAGDlypVG1f3NN98gPT0dDRs2xPz58ysfLBERkQVKUubi6M0UJClzK19ZZhJwdrV6u+fbla/PGPU7AM0HAxCBo99Wb9tEREQWxiKSAjk5Ofj7778BAIMHD9ZaRhAEDBo0CACwe/duo+rXJBGeffZZ2NjYVCJSIiIiy7Q2KgE95kdi3M8n0GN+JNZGJVSuwmOLgOICwC8U8C+bzK9yPd5Q/z67Bnh4v/rbJyIishAWkRS4fPkyVCoVACAoKEhnOc2+5ORkpKWlGVR3bGws7t69CwAICwtDdHQ0IiIi4OPjA1tbWzRq1Ahjx47FsWPHKvkqiIiIaqYkZS7e33geqn8GFKpE4IONFyreYyAnDTi1TL1d3b0ENPxCgQadgeJ84MRi88RARERkASwiKaC5aAeABg0a6CxXcl/JY/S5du2atH3y5El07doV69atg1KphL29PW7fvo21a9eiR48emDdvXrn15efnIzMzs9QPERFRTRabki0lBDSKRRFxKTkVq/Dkz0BhNuAdDDR7rPIBVoQgAD3eVG9HLQHyH5onDiIiohrOIpICWVlZ0raDg4POciX3lTxGn/T0dGl77ty58Pb2xs6dO5GdnY2MjAxcvnwZ/fr1gyiK+OCDD7B582a99c2bNw8KhUL6adSokUFxEBERmUughyNkQunn5IKAAA/d/+bqVJANnPhRvd1zmvri3FxaPg64NQbyMtRLFRIREVEZFpEUqEqaYQma7fXr12PgwIGQydSnpmXLltiyZQvq168PAJgzZ47e+t5//30olUrpJzExscpiJyIiMgVfhT3mjQyG/J8LeLkg4LORQfBV2Btf2ekVQG4aUC8QaD3CtIEaSyYHQqeqt49/BxQXmTceIiKiGsjK3AEYwtnZWdrOydHdlbHkvpLHGFp3z5490a1btzJlHB0d8dprr2HmzJk4e/Ys7t27B29vb6312drawtbW1qC2iYiIaoqIED/0bu6JuJQcBHg4VCwhUFSgnmAQUHfdl9eA/2a0Hwfs/wzISAAubQaCR5s7IiIiohrFInoKaO7SA8CdO3d0liu5r+Qx+pSch6BVq1Y6y5XcFx8fb1DdRERElsRXYY/QJu46EwLlLll4bSeQeQdw9FJfjNcE1vZAl5fU20e/AURRf3kiIqI6xiKSAq1atZK681+4cEFnOc0+Hx8fuLm5GVR369atIZfLAaiXNdRFLPGfCH3liIiIaiODlizUjNtvPw6wqkG95kImAVb2QNJZIPGEuaMhIiKqUSwiKeDg4IAePXoAAHbu3Km1jCiK2LVrFwBgwIABBtdtZ2eH3r17AwAuXbqks9zly5cBqBMCAQEBBtdPRERk6QxasjDzLnBjj3q7w3PVH6Q+ju5A0Ej1dszv5o2FiIiohrGIpAAATJgwAQCwf/9+nDhRNsu/fv163Lp1CwAwfvx4o+p+/vnnAQBHjhzBsWPHyuzPycnBDz/8AADo2rUrPD09jaqfiIjIkhm0ZOHZ1YCoAvxCAY+m1RugITTDGS5uAgoquNQiERFRLWRRSYHg4GCIoohRo0Zh3759AP5dMWDy5MkAgMGDB6Nfv36ljp0zZw4EQYAgCIiLiytT9zPPPIMuXboAACIiIrBr1y5pVYIrV65g2LBhuHv3LmQyGT799NMqfJVEREQ1T7lLForiv0MHOjxbvcEZyq874OoH5GcCV/4ydzREREQ1hsUkBaysrLB161YEBATgzp076N+/PxwdHeHo6IgxY8YgMzMTHTp0wG+//WZ03TKZDFu2bEHr1q2RmJiIQYMGwcnJCa6urmjVqhX27dsHa2tr/Pjjj+jbt28VvDoiIqKaq9wlC+OPAmm3ABsn8y9DqItMBrT7p7dAjPH/VyAiIqqtasBaQYYLCAjAuXPnsHDhQmzcuBGxsbGwtrZGmzZt8PTTT+P111+HjY1Nher28fHBmTNnsGjRIqxduxbXrl1Dbm4uAgIC0LdvX7z11lsICgoy8SsiIiKyDHqXLNT0EmjzJGDrZJ4ADdFuLHBwPnDrAKC8AygalHsIERFRbSeIItfmqUqZmZlQKBRQKpVwcXExdzhERESmlZcJfNkCKMwBXtgN+HU1d0T6LXsciD8C9PsQ6PWOuaMhIiKqEsZch1rM8AEiIiKqgS5uVCcEPJoDjbpUqIokZS6O3kwpvZpBVWn/tPp3zO/quRCIiIjqOCYFiIiIqOJKTjAoCPrLarE2KgE95kdi3M8n0GN+JNZGJZg4wEe0Hg5YOwCpN4DbUVXbFhERkQVgUoCIiIgq5v4V9YW1IAfajjX68CRlLt7feF5a7lAlAh9svFC1PQZsndWJAUDdW4CIiKiOY1KAiIiIKubCH+rfzQYAzt5GHx6bki0lBDSKRRFxKTkmCE6P9v+sQnBhI1BYDUMWiIiIajAmBYiIiKhiLm1R/w4aWaHDAz0cIXtkxIFcEBDg4VDJwMrh3xNQ+AH5SuDq9qpti4iIqIZjUoCIiIiMd/8KkHINkNsAzQdWqApfhT3mjQyG/J+5COSCgM9GBpVe7rAqyGRA8Gj1tiaxQUREVEdZmTsAIiIiskCai+kmfQE7RYWriQjxQ+/mnohLyUGAh0PVJwQ0Wg8DjnwFXN+jHkJgXU3tEhER1TDsKUBERETG0yQFWg2rdFW+CnuENnGvvoQAAPi2BxSN1Msp3oysvnaJiIhqGCYFiIiIyDgpN4D7FwGZFdBisLmjqRhBAFoNVW9f/tO8sRAREZkRkwJERERknMv/9BIIDAMc3MwbS2W0fEL9++p2oLjQvLEQERGZCZMCREREZBzN0IHWw80bR2X5dQMcPIA8JRB3xNzREBERmQWTAkRERGS4tFgg6SwgyICWj5s7msqRyf99DRxCQEREdRSTAkRERGS4y1vVvwN6Ao4e5o3FFDQTJV7ZBqhU5o2FiIjIDJgUICIiIsNd+icpYOlDBzQCewO2LsDDe8DtKHNHQ0REVO2YFCAiIiLDZCQCd04BEICWQ80djWlY2QDNB6m3Nb0giIiI6hAmBYiIiMgwmnH3fqGAs7d5YzGlkksTiqJ5YyEiIqpmTAoQERGRYa5uV/9uPcy8cZha036AlR2QEQ8knzd3NERERNWKSQEiIiIqX14mkHBMvd1sgHljMTUbR6Bpf/U2VyEgIqI6hkkBIiIiKl/sQUBVBLg1AdybmDsa02v5hPr3tZ3mjYOIiKiaMSlARERE5bu+W/37kV4CScpcHL2ZgiRlrtbHFqNpPySJbjh6pwhJdxLNHQ0REVG1sTJ3AERERFTDiSJwfa96u1l/6em1UQl4f+N5qERAJgBPdmiATdF3pMfzRgYjIsTPTEHrl6TMRWxKNgI9HOGrsMfay3l4P/8bqCCD7NuzmDdKrLGxExERmRKTAkRERKTfvYtA1l3Ayh7w7wlAfVGtSQgAgEoENpy5Ix2iEoEPNl5A7+ae8FXYmyNqnR5NZrw7qCU+33kFqn86UKog1NjYiYiITI3DB4iIiEg/zdCBxmGAtR0AIDYlW0oI6FIsiohLyani4IyjLZnx+Y4rZV5LTYydiIioKjApQERERPrd+GfoQNN/hw4EejhCJug/TC4ICPBwqMLAjKctmaECIDzyWuQCKhS7xc6pQEREdRaTAkRERKRbbgaQcFy93ewx6WlfhT3mjQyG/J+rabkgYFTHBqUefzYyqEz3e3NfNGtLZsgFAe8Nbvlv7CjGZ21uGz10YG1UAnrMj8S4n0+gx/xIrI1KMFXYREREVUYQRbGczn9UGZmZmVAoFFAqlXBxcTF3OERERMa5uAlYPxHwaAFMPVlmd5IyF3EpOQjwcICvwr7M45IeHctvrokI10Yl4IONF1AsilLyIiLETx37sU0IODYDvo2aAJP2GlxnkjIXPeZHluqFIBcEHHmvD+clICKiamfMdSgnGiQiIiLdpFUHHtO621dhX+qi99HHGtrG8ptrMr+IED/0bu5ZJnnhq7CHb2gYcDwNuJ0O5KQBDm4G1altWIJmXgImBYiIqCbj8AEiIiLSTqUCbuxRb+tIChhK30WzsUwxBMFXYY/QJu5lL9gVDQCv1gBE4GakwfXpGpZQ0+ZUICIiehSTAkRERKTdvfPAw3uAjRPgF1qpqkx10Vwt4/Y1Eyre2GfwIdrmWNA2pwIREVFNw+EDREREpJ1mKcLAMMDKtlJVaS6aHx3Lb8xFc7UNQWjaHzj6jXrVBZUKkBl2D0XXsAQiIqKajEkBIiIi0u66aYYOaFT2ornaxu37dQOsHYHs++reEr7tDD5U15wKRERENRWHDxAREVFZeZnA7VPq7ab9TFatzrH8Bqi2cftWtkBgb/X2DcNXICAiIrJETAoQERFRWQnHALEYcGsMuFb/soHaVOu4/WbGzytARERkiTh8gIiIiMqKPaT+rbljXkNU27j9Jv/0jkg8AeQ/BGydqqYdIiIiM2NPASIiIirr1kH17xqWFAAqNwTBYG6BgMIPUBUBicerrh0iIiIzY1KAiIiISstOVU+wBwABNS8pUG0Ce6l/xx42bxxERERViEkBIiIiKi3un4tgrzaAk6d5YzEnTS8JzVAKIiKiWohJASIiIiottuYOHahWAf/0FEiKAfKUZg2FiIioqjApQERERKXV0EkGKyNJmYujN1OQpMw1/CBFA/XqC6IKiD9WdcERERGZEVcfICIion8p7wCpNwBBBgT0MHc0JrE2KgHvbzwPlQjIBGDeyGBEhBi4zGJALyDtlnpIRYtBVRsoERGRGbCnABEREf1LM59A/Q6AncK8sZhAkjJXSggAgEoEPth4wfAeA5xXgIiIajkmBYiIiOhfNXgpwoqITcmWEgIaxaKIuJQcwyrQzCuQfB7ISTNtcERERDUAkwJERESkJoq1bj6BQA9HyITSz8kFAQEeDoZV4OwNeLQAIALxf5s8PiIiInNjUoCIiIjU0m4BmbcBuQ3QqJu5ozEJX4U95o0MhlxQZwbkgoDPRgbBV2FveCWB//QWiD1cBRESERGZFycaJCIiIjXNUoQNuwA2Bt5JtwARIX7o3dwTcSk5CPBwMC4hAKiHEEQt+Xe+BSIiolqESQEiIiJSq2VDB0ryVdgbnwzQ0MwrcP8SkJ0COHqYLjAiIiIz4/ABIiIiAlSqf7vHNw4zbyw1jaM74NVGvc3eAkREVMswKUBERETAgytATgpg7QDU72juaGoeLk1IRES1FJMCREREBCQcVf9u1AWwsjFvLDVRYC8kiW44eiURScpcc0dDRERkMpxTgIiIiID4Y+rffqHmjaOGWpvaBO/nfwNVvgyy+ZGYNzIYvZt7IjYlG4EejhWfr4CIiMjMmBQgIiKq60QRSGBSQJckZS7e33YTqn86WKpE4L0N5yEI6m2ZAMwbGYyIED8zR0pERGQ8Dh8gIiKq65SJQOYdQGYFNAwxdzQ1TmxKNlRi6edEQHpOJQIfbLzAYQVERGSRmBQgIiKq6zRDB3zbAzYOZg2lJgr0cIRM0F+mWBQRl5JTPQERERGZEJMCREREdZ1mkkF/Dh3Qxldhj3kjgyH/JzEgQzEezRHIBQEBHkyoEBGR5WFSgIiIqK7jJIPligjxw5H3+mJ1vZ/wt+2bmN/LCnJBnRqQCwI+GxnEyQaJiMgicaJBIiKiuiw7FUi5qt5mUkAvX4U9fJv5AOcOIML+FHq/9zbiUnIQ4OHAhAAREVks9hQgIiKqyzSrDni2BBzczBuLJfDrpv6dcAy+CnuENnFnQoCIiCwakwJERER1GZciNI7mPN0+BRQXmjcWIiIiE2BSgIiIqC7TJAX8u5s3Dkvh0RywcwWKcoGkc+aOhoiIqNKYFCAiIqqrCrKBpLPqbU23eJIkKXNx9GYKkpS5/z4pk5UaQkBERGTpmBQgIiKqq25HAaoiwKUh4Opn7mhqlLVRCegxPxLjfj6BHvMjsTYq4d+dTAoQEVEtwqQAERFRXaVZitCf8wmUlKTMxfsbz0Mlqh+rROCDjRf+7TGgmVcg4TggiuYJkoiIyESYFCAiIqqrEo6qf3OSwVJiU7KlhIBGsSgiLiVH/aB+B0BuC+SkAKk3qz9AIiIiE2JSgIiIqC4qLlTPoA9wksFHBHo4QiaUfk4uCAjwcFA/sLIFGnRUb3MIARERWTgmBYiIiOqipLNAYY56Jn2PFuaOpkbxVdhj3shgyAV1ZkAuCPhsZBB8Ffb/FtLMK5B43AwREhERmY6VuQMgIiIiM0j452LWr5t6Rn0qJSLED72beyIuJQcBHg6lEwLAP0Mu/vvveSQiIrJQTAoQERHVRbej1L8bdTFvHDWYr8K+bDJAQ3PeUm8ADx8ATp7VFxgREZEJ8dYAERFRXaRJCjRkUqBC7OsBXq3V25xXgIiILBiTAkRERHWN8g6QeQcQZP9OmEfGa9RV/fv2SfPGQUREVAlMChAREdU1motY7zaAjaN5Y7FkDUPUvzWrOBAREVkgJgWIiIjqGs1FLIcOVI4mKXA3Wr3EIxERkQViUoCIiKiuSfynpwAnGawc96aAnQIoygPuXTB3NERERBXCpAAREVFdUpQPJMWotzV3uqliZDKgQWf1NocQEBGRhWJSgIiIqC5JOgcUFwAO7oBbY3NHY/mkeQWizBsHERFRBTEpQEREVJdoJhlsGAIIgnljqQ2YFCAiIgvHpAAREVFdorl45dAB09As6Zh2C8hONW8sREREFcCkABERUV2S+E9SgJMMmoaDG+DeTL19h/MKEBGR5WFSgIiIqK7IvAtk3gYEGVC/o7mjqT00CRYOISAiIgvEpAAREVFdoVmK0KsNYOtk3lhqk4aaFQiYFCAiIsvDpAAREVFdoblobcT5BExKmmzwNKAqNm8sRERERmJSgIiIqK7gJINVw7MVYO0IFGQBKdfMHQ0REZFRmBQgIiKqC4oKgLsx6u2GnGTQpORW/65CwCEERERkYZgUICIiqguSzwHF+YC9G+DexNzR1D6cV4CIiCwUkwJERER1QcmhA4Jg3lhqI2leAS5LSEREloVJASIiorqA8wlUrQb/9BS4fxnIy5SeTlLm4ujNFCQpc80UGBERkX5W5g6AiIiIqsGd0+rfDTuZN47aytkbcPUDMhKAu2eAxuFYG5WA9zeeh0oEZAIwb2QwIkL8zB0pERFRKewpQEREVAuVukOdkwakx6l31O9g1rhqNWkIQRSSlLlSQgAAVCLwwcYL7DFAREQ1DnsKEBER1TJl7lD3sEIEALg1AezrmTu82qthCHBhA3D7FGIbZEsJAY1iUURcSg58FfbmiY+IiEgL9hQgIiKqRbTeoT5SiCTRDWjAoQNVqv4/yxLeOYNAdwfIHpnPUS4ICPBwqP64iIiI9GBSgIiIqBaJTdFyhxoC4lTeQIOO5gmqrvAJBgQ5kH0fvkIa5o0MhvyflR7kgoDPRgaxlwAREdU4HD5ARERUiwR6OEImoFRiQA4VAmT32FOgqtk4AF6tgXvngTtnEBEyDL2beyIuJQcBHg5MCBARUY3EngJERES1iK/C/pE71MBnVkvgK89U38mmqqXpjXH3DAD1+xHaxJ0JASIiqrHYU4CIiKiWiQjx+/cOdeph+G4/AHi1Bax5YVrlGnQEzqz4dwlIIiKiGo5JASIiolrIV2Gvvjt985+LUw4dqB6ayQbvxgAqFSBjp0wiIqrZ+C8VERFRbXY3Wv2bkwxWD69WgJUdkJ8JpN00dzRERETlYlKAiIiotlIVl0gKsKdAtZBbAz5t1dt3zpg3FiIiIgMwKUBERFRbpVwDCh4C1o6AZ0tzR1N3PDLZIBERUU3GpAAREVFtpblT7dsOkMnNG0tdoplXgD0FiIjIAjApQEREVFtpZsDnfALVS3O+k88BxYXmjYWIiKgcTAoQERHVVpru65xPoHq5NQFsXYCiPOD+ZXNHQ0REpBeTAkRERLVRYR6QfEG9zZ4C1UsmA+q3V29zXgEiIqrhmBQgIiKqje5dAFSFgIM74Opv7mjqHs4rQEREFoJJASIiotroTomhA4Jg3ljqIq5AQEREFoJJASIiotpIM8lgfQ4dMAvNPA73LgGFueaNhYiISA8mBYiIiGojaZJBJgXMwqUB4OgFiMVA0jlzR0NERKSTxSUFsrKyMGfOHAQHB8PJyQkKhQIhISH48ssvUVBQYNK2XnnlFQiCAEEQEBAQYNK6iYiIqkx+FpByXb2tp6dAkjIXR2+mIEnJO9kmJwgcQkBERBbBytwBGCM+Ph7h4eGIi4sDADg4OCA/Px+nTp3CqVOn8Ntvv2Hfvn2oV69epds6cOAAfvrpp0rXQ0REVO2SzgEQ1XernTy1FlkblYD3N56HSgRkAjBvZDAiQvyqN87arn5H4NpOTjZIREQ1msX0FCguLsbQoUMRFxcHX19f7NmzB9nZ2cjJycGaNWvg7OyM6OhoPPPMM5VuKycnB5MmTYKVlRU6d+5sguiJiIiq0d1o9e/6HbTuTlLmSgkBAFCJwAcbL7DHgKmxpwAREVkAi0kKLF++HOfPnwcAbNiwAf379wcAyGQyREREYPHixQCAHTt2YN++fZVqa8aMGbh58yb+7//+D23atKlc4ERERNUtKUb927e91t2xKdlSQkCjWBQRl5JTpWHVOZqkTOoNIC/TvLEQERHpYDFJgRUrVgAA+vTpg9DQ0DL7x44di8DAQADAypUrK9zO8ePH8c0336B58+aYOXNmheshIiIym7sx6t/122vdHejhCNkjqxTKBQEBHg5VGlad4+gBuDRUbydzskEiIqqZLCIpkJOTg7///hsAMHjwYK1lBEHAoEGDAAC7d++uUDv5+fl44YUXIIoiFi9eDDs7u4oFTEREZC55mUDqP5MM6ugp4Kuwx7yRwZAL6syAXBDw2cgg+CrsqynIOkSTmNEkaoiIiGoYi5ho8PLly1CpVACAoKAgneU0+5KTk5GWlgY3Nzej2vnoo49w+fJlTJo0CeHh4RWOl4iIyGw0d6RdGuqcZBAAIkL80Lu5J+JSchDg4cCEQFXxbQ9c2fbvkA4iIqIaxiKSAnfv3pW2GzRooLNcyX137941KikQHR2NL774At7e3vjiiy8qFijUvQ3y8/Olx5mZHENIRETVqJyhAyX5KuyZDKhqj/QUSFLmIjYlG4Eejjz3RERUI1hEUiArK0vadnDQPd6x5L6Sx5SnqKgIL7zwAoqKivDNN99UaknDefPmYe7cuRU+noiIqFKklQfamzUM+odmCEfqDaw9eg3v/3mdy0ASEVGNYhFzClS1+fPnIyYmBk888QTGjBlTqbref/99KJVK6ScxMdFEURIRERlAWnlA+3KEVM2cPAGXBkgS60kJAYDLQBIRUc1hET0FnJ2dpe2cHN3LJZXcV/IYfS5duoSPP/4YTk5O+P777yse5D9sbW1ha2tb6XqIiIiMlpepXv4OYE+BmsS3PWLTY3UuA8lhBEREZE4W0VOgfv360vadO3d0liu5r+Qx+kyZMgUFBQWYMWMG6tWrh4cPH5b6KSoqAgCIoig9V1hYWMFXQkREVIWSzqp/Kxqpl8OjmqF+ewTKkiFD6awAl4EkIqKawCKSAq1atYJMpg71woULOstp9vn4+Bg8yWBsbCwAdbd/Z2fnMj+//fYbACAhIUF67rvvvqvMyyEiIqoa0tCBdmYNgx7h2x6+Qhrm1dvKZSCJiKjGsYikgIODA3r06AEA2Llzp9Yyoihi165dAIABAwZUW2xEREQ1hhErD1A1+uf9iMhdhyNvdcHqyd1w5L0+nGSQiIhqBItICgDAhAkTAAD79+/HiRMnyuxfv349bt26BQAYP368wfXGxcVBFEWdP5p2/f39peemTZtW+RdERERkQknKXByNzUCS6AbU5ySDNYqTF+BcH4AI39xrCG3izh4CRERUY1hUUiA4OBiiKGLUqFHYt28fAEClUmH9+vWYPHkyAGDw4MHo169fqWPnzJkDQRAgCALi4uKqO3QiIqIqtTYqAT3mR2Jc6gvokf8N1iY3MHdI9ChN7w1Nbw4iIqIawmKSAlZWVti6dSsCAgJw584d9O/fH46OjnB0dMSYMWOQmZmJDh06SHMAEBER1QVJyly8v/H8v0vdQYYP/orlUnc1jW979W/NZJBEREQ1hMUkBQAgICAA586dw4cffoigoCAIggBra2t06tQJCxcuxPHjx1GvXj1zh0lERFRtYlOydS51RzWIpqeAZjJIIiKiGkIQRVEsvxhVVGZmJhQKBZRKJVxcXMwdDhER1TJJylz0mB9ZKjEgFwQcea8Px63XJFn3gC+bA4IMeP82YONo7oiIiKgWM+Y61KJ6ChAREVFpvgp7zBsZDDlUAAC5AC51VxM5ewPOvoCoApLPmzsaIiIiiZW5AyAiIqLKiQhWoPe2NxCn8kbAaxvh61vf3CGRNr7tgawk9WSDft3MHQ0REREA9hQgIiKyfMnn4SukIdTtIRMCNRnnFSAiohqISQEiIiJLl3RO/dunrXnjIP00KxBwWUIiIqpBmBQgIiKydMn/JAV8dScFkpS5OHozhUsVmpOmp0DKVaAg26yhEBERaXBOASIiIktXTk+BtVEJeH/jeahEQCYA80YGIyLErxoDJACAsw/g5AM8TAbuXQQadTF3REREROwpQEREZNEK84AHV9TbWnoKJClzpYQAAKhE4IONF9hjwFw071HSWfPGQURE9A8mBYiIiCzZ/UuAWAzYuwEuDcrsjk3JlhICGsWiiLiUnGoKkErR9ObQDPkgIiIyMyYFiIiILFnJ+QQEoczuQA9HyB55Wi4ICPBwqIbgqAyppwCTAkREVDMwKUBERGTJyplPwFdhj3kjgyH/J2EgFwR8NjIIvgr76oqQStK8T/cvAcWF5o2FiIgInGiQiIjIskk9BdrpLBIR4ofezT0Rl5KDAA8HJgTMqV4AYKsA8pXAg6uAT5C5IyIiojqOPQWIiIgslapYPYs9oLOngIavwh6hTdyZEDA3QQB8gtXbnGyQiIhqACYFiIiILFXqDaAwB7B2ANybmDsaMpQvJxskIqKag0kBIiIiS6WZT8A7CJDJzRsLGc6Hkw0SEVHNwaQAERGRpUr+p/u5r/6hA1TDSD0FzgMqlXljISKiOo9JASIiIktVzsoDVEN5NAfktkBBFpAea+5oiIiojmNSgIiIyBKJYomVB5gUsChya8C7tXqb8woQEZGZMSlARERkiZS3gdx0QGYFeLU2dzRkLM4rQERENQSTAkRERJZIc4fZsyVgZWveWMh4XIGAiIhqCCYFiIiILBHnE7BsPu3Uv9lTgIiIzIxJASIiIkvE+QQsm3cbQJAB2feBrGRzR0NERHUYkwJERESWiD0FLJuNA+DeTL3N3gJERGRGVpWtID8/H8ePH8fNmzeRlpYGAHB3d0eTJk3QrVs32NjYVDpIIiIiKiEnDci8rd72CTJvLFRxvm2BlKtA8lmg+QBzR0NERHVUhZMCN2/exEcffYR169ahoKBAaxlbW1s8/fTTmDlzJgIDAyscJBEREZWQdFb9u14AYKcwayhUCT5tgfPr2VOAiIjMqkLDB7Zv344OHTpg1apVyM/PhyiKWn/y8vKwfPlytGvXDrt37zZ17ERERHVTMocO1ApcgYCIiGoAo3sKnD9/HiNHjkRBQQEEQcDjjz+Oxx9/HO3atYObmxtEUUR6ejrOnj2Lv/76C9u3b8fDhw8xfPhwnDlzBq1ataqK10FERFR3JHGSwVpBk9RJjwPylOz1QUREZmF0UuDll19GQUEB/Pz8sH79eoSEhGgtFxoaildeeQUnT57EU089hcTERLz88ss4dOhQpYMmIiKq06SeAu3MGwdVjoMboGgEKBOB5PNAQE9zR0RERHWQUcMHzp49i+PHj8POzg7btm3TmRAoqUuXLti2bRvs7Ozw999/4/z58xUOloiIqM4ryAZSrqu32VPA8vn+k9jhvAJERGQmRiUFNm7cCAAYP348goIMn+04ODgYzz33HABgw4YNxjRJREREJd27CEAEHL0AZx9zR0OV5cN5BYiIyLyMSgqcOXMGgiDg6aefNrqhcePGQRRFnDlzxuhjiYiI6B/JnE+gVtG8j+wpQEREZmJUUuDy5csAgE6dOhndkOYYTR1ERERUAUlceaBW0byPD64AhXnmjYWIiOoko5ICGRkZsLOzg5OTk9ENOTk5wcHBAenp6UYfS0RERP9gT4HaxaU+4OAOiMXA/YvmjoaIiOogo5ICmZmZcHFxqXBjTk5OyMrKqvDxREREdVpxIXDvknqbPQVqB0H4973kEAIiIjIDo5ICRUVFEAShwo0JgoCioqIKH09ERFSnpVwDivMBG2egXqC5oyFT8eVkg0REZD5GJQWIiIjIjKT5BIIBGf8JrzXYU4CIiMzIytgDlEolXnjhhQo1plQqK3QcERERgfMJ1Fa+7dS/710EVMWATG7eeIiIqE4xOimQl5eHFStWVKgxURQrNfyAiIioTuPKA7WTWxPA2hEozAZSrgNeLc0dERER1SFGJQX8/Px4UU9ERGRiScpcxKZkI9DDEb4Ke+2FRBFIPq/eZk+B2kUmA3yCgMQT6t4gTAoQEVE1MiopEBcXV0VhEBER1U1roxLw/sbzUImATADmjQxGRIhf2YLpcUC+EpDbAJ68aKx1fNqqkwJJZ4G2Y8wdDRER1SGcpYiIiMhMkpS5UkIAAFQi8MHGC0hS5pYtrJlPwKsVILeuviCpenAFAiIiMhOj5xTQRqVSIT4+HqmpqQAAd3d3+Pv7Q8aZkYmIiHSKTcmWEgIaxaKIuJScssMIOJ9A7VZyBQJRBEoM1zRoeAkREVEFVSopsGPHDnz33Xc4fPgwHj58WGqfk5MTevXqhSlTpmDw4MGVCpKIiKg2CvRwhExAqcSAXBAQ4OFQtrC08kC76gmOqpdXK0BmBeRlAMpEwFU9hMTg4SVEREQVVKFb+SkpKRgwYACeeOIJ7NixA1lZWRBFsdRPVlYWduzYgSeeeAKPPfYY7t+/b+rYiYiILJqvwh7zRgZD/s9dYbkg4LORQdrvBrOnQO1mZQt4tlJv//NeGzW8hIiIqIKM7imQlpaGHj164MaNGxBFEc7OzhgwYADat28PDw8PiKKI1NRUREdHY8+ePcjKykJkZCR69uyJY8eOwd3dvSpeBxERkUWKCPFD7+aeiEvJQYCHg/aEwMP7wMNkAALg3abaY6Rq4tsWuHde3Suk1RPGDS8hIiKqIKOTAs899xyuX78OGxsbzJw5E2+99RYcHR21ls3OzsZXX32FTz/9FDdv3sRzzz2H7du3VzpoIiKi2sRXYV/qIq/MGHJNLwH3poCtk5mipCrn0xbAb9L7bdTwEiIiogoyavjAgQMHsGPHDlhbW2Pz5s2YOXOmzoQAADg6OmLWrFnYtGkT5HI5du3ahf3791c6aCIiotpqbVQCesyPxLifT6DH/EisjUoAks+qd/py6ECt9sgKBEYNLyEiIqogo5ICq1evBgBMnToVgwYNMvi4wYMHY+rUqRBFUaqDiIiIStM5hjz+uvoJzidQu3kHqX9n3gGy1Ss6RYT44ch7fbB6cjccea8PJxkkIiKTMyopcPjwYQiCgJdfftnohl599VWpDiIiIipL5xjypH8m62VPgdrNzgVwa6ze1vQOgbrHQGgTd/YQICKiKmFUUuDu3buwtbVF8+bNjW6oWbNmsLOzQ1JSktHHEhER1QWaMeQlyQUg4GG0+oEPlyOs9TS9QTTzSBAREVUxo5ICBQUFsLW1rXBjtra2KCgoqPDxREREtZnWMeRhDvAV/r+9O4+Pqrz7Pv6dyUZCFgJEEtYgiIqgoqKlKErBhdpWQS2L+0Krrd7VLk8rti7P3QL62LuLS21RcatCFbTWDZXlFqSlQF3YRIWEYAhoWJJAQiAz5/njZIaEZJLZzzlzPu/Xa16ZzNmumTNnrnN+57p+1x4pv4/UlRF8Ut5ReQUAAEi0iEYfKCoq0hdffKGamhoVFBREtKGamhrV1NSob9++ES0HAICbtBmicNPT5gTyCbhDoDUILQUAAEkSUUuBU04xK6qXX3454g0tXLhQknTyyZzUAADQkVZ9yAN3jMkn4A6B/bz7c6lxv7VlAQC4QkRBgYsvvliGYejuu+/Wnj17wl5u9+7duueee+TxePTtb3874kICAOBagTvGtBRwh9xjpNxiSYa0a4PVpQEAuEBEQYHrrrtOffr0UWVlpcaNG6fPP/+802U+++wzjRs3Tl988YV69+6t6667LtqyAgDgLk2N0lebzOe0FHAP8goAAJIooqBAVlaWnnzySaWlpenjjz/WySefrJtuuklvvPGGqqqqdOjQIR06dEhVVVV6/fXXdcMNN+iUU07Rxx9/rPT0dD3xxBMxJSoEAMBVvtwk+ZukLt2kgn5WlwZJUtXtNK30DVXVts1WFwUA4AIRJRqUpPPPP1/PPfecbrzxRh04cEBz587V3LlzQ85vGIays7P1+OOP64ILLoipsAAAuErLfAIeT8fzIiXMX12hO5cPl18ny7vWr1mlFZo8sr/VxQIApLCIWgoEfPe739WaNWs0ceJEeTweGYbR7sPj8WjixIlavXq1pk6dGu+yAwCQ2sgn4CpVNQ26c+E6+WUGgPzyasbCdaqqabC4ZACAVBZxS4GA448/XgsWLNDOnTu1bNkybdiwQbt375Yk9ejRQ0OHDtXYsWNVXFwct8ICAOAqwZYCp1hbDiRFWfUB+Y3Wr/kMqby63hyJAgCABIg6KBBQXFysKVOmxKMsAAAgwO+Tdq43n9NSwBUG9uwqr0etAgNpHkOlPXOsKxQAIOVF1X0AAAAk2J6t0uEDUnq21PM4q0uDJCgpyNasScOV1pw/Ik0+zRzyeaetBKpqGrRySzXdDAAAUYm5pQAAAEiAqo/Mv71Okrxp1pYFSTN5ZH+NGVKk8n+9qtKVv1CJhkq6PeT881dXmHkIDMnrkWZNGk5iQgBARGgpAACAHe1cZ/4toeuA25QUZGvUKUNV4tljfg8Mo935gokJmyf7DWnGwvW0GAAARISgAAAAdrSTkQdcregEKS1TaqyR9pa3O0v7iQkNlVfXJ758AICUQVAAAAC7MYwjwxHSUsCd0jKkY040nwdajRwlkJiw1WIeD4kJAQARISgAAIDd1FVJ9dWSJ0065iSrSwOrFA83/wZajRylTWJCj0czJw1j+EIAQERINAgAgN0EWgkUHS9ldGl/lpoGlVUf0MCeXbkITFXFp0h67sj3oR3BxITV9SrtmcN3AQAQMYICAADYTSf5BMg47xKBriMhWgoEZyvIJhgAAIga3QcAALCbwHCE7eQTIOO8i/Q6SZLH7E6y/yurSwMASFEEBQAAsJsOWgqQcd5FsvKk7seazztpLQAAQLQICgAAYCcNe6V9FebzQKK5Fsg47zJhdiEAACBaBAUAALCTwPBz3QZI2d3aTCbjvMsEWot0kGwQAIBYkGgQAAA7CVz8tZNPIICM8y5CSwEAQIIRFAAAwE6C+QRO6XA2Ms67RKClwO4tUuN+KSvX2vIAAFIO3QcAALCTMFoKwEVyj5FyiyUZ0q4NVpcGAJCCCAoAAGAXhxuk6k/N5+2MPACXogsBACCBCAoAAGAXuzZKhk/qWiTlFVtdGthFMNngR9aWAwCQkggKAABgFzubL/qKT5Y8no7nhXvQUgAAkEAEBQAAsAvyCaA9gZYCX26SfIetLQsAIOUQFAAAwC6CIw8Mt7YcsJduA6SsfMl3SPpqs9WlAQCkGIICAADYga/pSHb5ToYjhMt4vUcCRXQhAADEGUEBAADsYPdnUtNBKTNX6n6s1aWB3QSTDRIUAADEF0EBAADsIHCx12uYeWcYaIlkgwCABOGsAwAAO9hJkkF0INBSYOc6ye+3tiwAgJRCUAAAADuoajEcIXC0ouOltEypsVbat83q0gAAUghBAQAArGYYtBRAx9IypGNONJ/ThQAAEEcEBQAAsNq+CulgjeTNkIpOtLo0sCuSDQIAEoCgAAAAVgvc+T3mBCk909qywL5KmoeqpKUAACCOCAoAAGC1wJ3f4lOsLQfsjZYCAIAEICgAAIDVyCeAcPQ6SZJH2r9T2v+l1aUBAKQIggIAAFgt2FKAoAA6kJUr9RhkPqcLAQAgTggKAABgpQPVUt0O83nxMGvLAvujCwEAIM4ICgAAYKWqD82/PQZLWXmWFgUOEOhiQksBAECcEBQAAMBKVR+Zf0tOtbQYcIji4eZfWgoAAOKEoAAAAFYKBgUYecCNqmoatHJLtapqGsJbIDBCxZ6tUmNd4goGAHCNdKsLAACAq+340PxLUMB15q+u0J0L18lvSF6PNGvScE0e2b/jhXKLpLwSqa5K2rleGjAqOYUFAKQsWgoAAGCVhr3Svm3mc4ICrlJV0xAMCEiS35BmLFwfXouBQFeTQD4KAABiQFAAAACrBLoOFJZK2d2sLAmSrKz6QDAgEOAzDJVX13e+cO9Tzb+BViYAAMSAoAAAAFYhyaBrDezZVV5P69fSPB6V9szpfOFgS4GP4l4uAID7EBQAAMAqJBl0rZKCbM2aNFxpHjMykObxaOakYSopyA5j4ebvS/Vm6dCBBJYSAOAGJBoEAMAqJBl0tckj+2vMkCKVV9ertGdOeAEBScovkXJ7Sft3mckG+5+V2IICAFIaLQUAALDCwVppzxbzOd0HXKukIFujBvUIPyAQXPBU828Ckw1GPFwiAMCRaCkAAIAVdn5s/i3oJ3XtYW1Z4Dy9T5U+W5SwZINRDZcIAHAkWgoAAGAF8gkgFglsKRDTcIkAAMchKAAAgBWC+QROtbIUcKrAsIRffSIdCmMYwwjENFwiAMBxHBcUqKur07333qvhw4crNzdXBQUFGjlypH7729/q0KFDUa2zsrJSjz76qK644goNHjxY2dnZys7O1sCBAzV16lQtWbIkzu8CAOB6EbQUoG832sgrkboWSYZf2rUhrquOabhEAIDjeAzDMDqfzR62bdum8847T+Xl5ZKknJwc+Xw+NTY2SpJGjBihxYsXq7CwMOx1bt++XQMGDFDLjyEnJ0eGYaih4cjJ1w033KC//OUvSktLi6jMtbW1KigoUE1NjfLz8yNaFgCQog4dkGb2kWRIP/1Myj0m5Kz07UZIz10uff6O9M0HpTOnx3XV81dXaMbC9fIZRnC4RL53AOAckVyHOqalgM/n07e//W2Vl5erpKRE77zzjg4cOKD6+nrNmzdPeXl5+uCDD3TllVdGvF7DMDRu3Dg9/fTTqqys1IEDB7R//35t2LBBl1xyiSTpySef1L333puAdwYAcJ2d6yQZ5t3eDgIC9O1GhwJdCBKQbHDyyP5a8YuxemH617TiF2MJCABACnNMUOCpp57SunXrJEkLFizQ+PHjJUler1eTJ0/Wn//8Z0nSm2++qcWLF4e93sLCQq1du1bvvvuurrnmGvXu3Tu43qFDh+rll1/WRRddJEn6/e9/r4MHD8bzbQEA3CjMrgP07UaHEjwsYdTDJQIAHMUxQYGnn35akjR27FiNGjWqzfQpU6Zo4MCBkqRnnnkm7PUWFBTotNNOCznd4/HohhtukCTt379fmzZtiqTYAACXazcfQJhJBunbjQ4FWgp8uUk6TOsRAEB0HBEUqK+v1/vvvy9JmjBhQrvzeDye4B39t99+O67b79KlS/C5z+eL67oBAKlr/uoKjZ69RNPmrNLo2Us0f3WFOSHMlgIlBdmaNWm40jxmZCDQt5s7t5Ak5feRcnpIhk/atdHq0gAAHCrd6gKEY9OmTfL7/ZKkYcOGhZwvMG3nzp3as2ePunfvHpftL1u2TJKUmZmpIUOGxGWdAIDUFiofwJiBeSr56hPzxTBGHpg8sr/GDClSeXW9SnvmEBDAER6P2dpky2Kp6gOp7+lWlwgA4ECOaCmwY8eO4PM+ffqEnK/ltJbLxKKsrEyPPfaYJGny5MmMIAAACEvIfACfbzLv7HYtkvJ7h7Uu+nYjpAQmGwQAuIMjWgrU1dUFn+fkhO5H2XJay2Wi1dDQoCuuuEL19fXq0aOHZs2a1ekyjY2NwSESJXMoCABA6qiqaVBZ9QEN7Nm1w4v0QD6AloGBNI9HpQebc9OUnGLe6QVikeBkgx0J91gAANibI4ICVmhqatK0adO0du1aZWRk6Pnnn++wlULArFmzdN999yWhhACAZJu/uiLYJcDrkWZNGh5yqLZAPoCjx3ov+eIf5gy9Qye5BcLWKtngQSmjS4ezx0skxwIAwN4c0X0gLy8v+Ly+PvQwTC2ntVwmUj6fT1dddZVeeeUVpaen6/nnn9cFF1wQ1rJ33nmnampqgo/t27dHXQ4AgH2EyhHQalSBo7Q71vuO/5gT+xAUQBwU9JOyu0v+JunLDUnZZDTHAgDAvhwRFOjd+0ify8rKypDztZzWcplIBAIC8+fPV1pamp577jldfvnlYS+flZWl/Pz8Vg8AgPOFzBFQHTpYLR2VD6CxTvpqszmBlgKIB4/nSMLKJOUViPZYQHy0O8wpAMTAEUGBE088UV6vWdT169eHnC8wrbi4OKqRB3w+n6688krNmzcvGBCYPHlydIUGAKSUQI6AltI8HpX2DJ3rpo2qjyQZUn5fKa9XXMsHF+vTPOpAoBVKgsXlWEBUQg5zCgAxcERQICcnR6NHj5YkvfXWW+3OYxiGFi1aJElhN/VvKRAQaNlCYMqUKdEXGgCQUgI5AtKakwMGcwREkmCtcq0kqaro69zpQ/wEggJfrE3K5uJyLCBidNsAkCiOSTR47bXXavny5Vq6dKlWrVqls846q9X0F198UVu3bpUkXXPNNRGt2+fzadq0afrb3/6m9PR0WggAANo1eWR/jRlSpPLqepX2zIn8IqjyP5rfdJ7u3HCJ/BtWkaAN8RHIT/HVJ2YXlazo8yqFK+ZjARHrqNsGnz+AWDiipYBkBgWGDx8uwzB02WWXafHixZIkv9+vF198UdOnT5ckTZgwQePGjWu17L333iuPxyOPx6Py8vJW03w+n66++upgQOD5558nIAAACKlVjoAIVW3/XHc23SS/zDus3OlDXOQVm11SZCQtr4AU27GAyNFtA0CiOCYokJ6erldffVWlpaWqrKzU+PHj1bVrV3Xt2lXf/e53VVtbqxEjRuivf/1rROt9//339cILL0iSPB6PbrvtNhUXF4d8zJ8/PxFvDwCQ6g5Uq2yfX/6jql4StCEu+jZ3IahMThcCJB/dNgAkimO6D0hSaWmpPv74Yz344INauHChysrKlJGRoZNOOklTp07VbbfdpszMzIjW6ff7g88PHz6sXbt2dTh/QwN3cwAAUaj8jwZ6d8qr1oEB7vQhLvqcLm38O0GBFEe3DQCJ4DEMw+h8NkSrtrZWBQUFqqmpYXhCAHCzZbOlZbM0v+RnmlF+mnyGEbzTR04BxKx8hfTUxWY3gh9vsLo0AACLRXId6qiWAgAAOFalOVzc5FOLNGbKWO70Ib5KTpU8Xqn2C6lup5lnAACAMDgmpwAAAI5lGEfGkO99GgnaEH9ZuVLRCeZzuhAAACJAUAAAgESr2S4d+ErypkvFw60uDVJVH5INAgAiR1AAAIBEa+46oGOGShldrC0LUhdBAQBAFAgKAACQaIGuA4GLNiARgkGBD6QWoysBANARggIAADSrqmnQyi3VqqqJ8/CzgZYCfU6L73qBlo45UUrPlhprpN2fW10aAIBDMPoAAACS5q+u0J0L18lvSF6PNGvS8PgMFej3Szs+NJ/3JiiABErLkEpOkbb/y+xCUDQkOKmqpkFl1Qc0sGdXElwCAFqhpQAAwPWqahqCAQFJ8hvSjIXr49NiYPdn0qE6KSPnSHZ4IFH6nmH+bZFXYP7qCo2evUTT5qzS6NlLNH91hUWFAwDYEUEBAIDrlVUfCAYEAnyGofLq+thXHug6UHKKlEYDPSRYoItKc1AgoQEvAEBKICgAAHC9gT27yutp/Vqax6PSnjmxrzxwx5auA0iGQLLBneukpsbEBrwAACmBoAAAwPVKCrI1a9JwpXnMyECax6OZk4bFp+/19lXm30CzbiCRug2QcnpI/sPSzvVxDXglLBEnAMBStGMEAEDS5JH9NWZIkcqr61XaMyc+AYHGOmnXevN5/6/Fvj6gMx6P1OcM6bNFUuUalZx1umZNGq4ZC9fLZxhRB7wSlogTAGA5ggIAADQrKciOb2b2L9ZIhl8q6C/l947feoGO9DndDAp8sUY66/sxB7xC5SUYM6SIkQwAIAUQFAAAIFECXQf6n2VtOeAu/Uaafyv+FXwploBXR3kJCAoAgPORUwAAgEQJXJT1IyiAJOp7puRJk2oqpJovYl5dQhNxAgAsR1AAAIBE8PvM5tsS+QSQXFm5UsnJ5vMWrQWildBEnAAAy9F9AICqahpUVn1AA3t25SQPiJddG6RDdVJWvnTMUKtLA7fp/3VpxwfStpXS8MtjXl1CEnFGiToLAOKLoADgcmSUBhKk5VCE3jRrywL36f816V+PSBX/jNsq456IMwrUWQAQf3QfAFwsVEZpxqAG4iCYT4CuA7BA/1Hm3y83SvV7rC1LnFBnAUBiEBQAXKyjjNIAYsTIA7BSbpHU4zjz+fZ/W1uWOKHOAoDEICgAuBgZpYEEqamUarZLHq85ZjxghQHNrQUqVlpbjjihzgKAxCAoALgYGaWBBNne3HWg1zApK8/assC9Al0ItsUvr4CVqLMAIDFINAi4nJ0ySgMpI9Bcm6EIYaVAUGDHB9LhBikjsb/vyRgVgDoLAOKPoAAAW2SUBlJKMMkg+QRgocJSKa9EqquSvlgjDTwnYZtK5qgA1FkAEF90HwAAII6qvtytlZVNqjK601IA1vJ4jrQWCASqEoBRAQDA2QgKAAAQJ/NXV2j07/6laYdmaHTjHzX/U7/VRYLbDfi6+TeByQYZFQAAnI2gAAAAcdDmbqm83C2F9QKtVbb/W/I1JWQTjAoAAM5GUAAAgDjgbils6ZihUlaBdGi/tGtdQjbBqAAA4GwkGgQAIA4Cd0tbBga4WwrLedOk/mdJn71t5hXoPSIhm2FUAABwLloKAAAQByUF2Zo1Jktp8kmS0jzibinsIZBscFvi8gpI5jEwalAPvvMA4DC0FAAAIE4mpy/XmKxnVD5wqkon/oqLI9hDMCjwvuT3S17uCQEAjqBWAAAgXj5frBLPHo067VQCAkiYqpoGrdxSHX4Sy75nSFn5Uv1uqXJtYgsHAHAcWgoAABAPdbuaE7l5pEHfsLo0SFHzV1cER7nweqRZk4Zr8sj+HS+UliENHidteFn69C2p38jkFBYA4Ai0FAAAIAbBu7brlpovlJwide1pbaGQktoMe2ko/GEvh0ww/376VuIKCABwJFoKAAAQpVZ3bZWrWennafLg060uFlJUR8Nedtpd5bjzJY9X2rVe2rdd6tav1eSqmgaVVR/QwJ5d6foCAC5DSwEAAKLQ5q6tPJrRdKOqiuk6gMQIDHvZUtjDXuZ0l/qdZT4/qrXA/NUVGj17iabNWaXRs5do/uqKOJUYAOAEBAUAAIhCu3dtlabyzOOsKRBSXklBtmZNGq40jxkZSPN4Ihv2csiF5t9PFwVfiqlLAgAgJdB9AACAKATu2rYMDKTJr9Jj8q0rFFLe5JH9NWZIkcqr61XaMyeypv5DJkjv3iuVvScdOiBldo2tSwIAICXQUgAAgCi0uWsrn2aeXseFFBKupCBbowb1iPy7VnS8VFgq+RqlrcskxdglAQCQEggKAIBDRDw2ORJu8sj+WnH7GXoh8zdakfUjTR4/2uoiAaF5PNKQi8znm9+UFIcuCQAAx6P7AAA4QFRjkyMpSnb/SyXeDVKPwVLhAKuLA3RsyEXSqsfMvAJ+v+T1xtYlAQDgeLQUAACbIxGYzX3+rvl38HhrywGEY8BoKTNPOvClVPVB8OWouyQAAByPoAAA2FxHicBgMcOQtiwxnw8aZ21ZgHCkZ0qDm4fN3PxWx/MCAFyBoAAA2ByJwGys7D2pZruUliWVkk8ADhHIK/ApQQEAAEEB2AyJ1IC2SARmU4YhLflv8/lp10iZXa0tDxCu4y6Q5JF2fiztXG91aQC4AOf49kaiQdgGidSA0EgEZkOfLpK+WC2lZ0tjfmp1aYDwde0pnfhtadOr0is3SzctMbsVAEACcI5vf7QUgC2QSA3oHInAbMTvl5b82nx+5nQpr9ja8gCR+uaDUnZ3aec66b0HrC5NEHcTgdTCOb4zEBSALZBIDYCjbHxF2rXOzOJ+9h1WlwaIXF4v6Vu/M58v/x/pi7XWlkfm3cTRs5do2pxVGj17ieavrkjYtgg+AMnBOb4zEBSALZBIDYBj+JqkpTPN56N+KOV0t7Y8QLROulQafoVk+KSXvy8dtu4COZl3E5MZfIB7EGhqH+f4zkBQALZAIjUAjvHxfGn3Z1J2oRkUAJxswgNSbrH5nX73PsuKkay7iTRlRiIQaAotFc/xUzEARKJB2AaJ1ADYXtMh6X9nm89H3y51ybe0OEDMcrpLlzws/fVyadWfpJJTpFOnJr0YgbuJLQMDibib2FHwgfMORCNUoGnMkCK+U81S6Rw/VZMm0lIAtkIiNQC2dbBWmn+ltK9Cyu0lnfk9q0sExMdx50sjp5vPX7lZeuduye9LahGSdTeRpsyIN/rMhycVzvFTuaURLQUQUlVNg8qqD2hgz66OPoABIGZ7yqQXpkhffSKld5G+85CUyUUEUsiEB8yWL8t/K73/B+nLT6TLHk9qa5hk3E0MBB9mLFwvn2GkRFNmWCtZrVxgvVRuaURQAO1K1aYxdkcgBrCh8hXS/Kulhj1SXok05Xmpz2lWlwqIL69XGne3dMxQ6e8/lD5bJD1xvjT1Ban7sUkrRklBdsLrv1RqygzrEWhyj1QOAHkMwzA6nw3Rqq2tVUFBgWpqapSf74y+p1U1DRo9e0mbL/yKX4zlBy6BCMRYg0AMOrRmrvTGzyT/Yan3CDMgkN/b6lIBiVW5Vpp3pVRXZSbUvOJp6dhzrS4VYGtVNQ0Emlxg/uqKNgEgu56vR3IdSksBtJHKTWPsiiQ11iAQg5AOH5Te/Jn0n2fM/0+aKF3yKF0G4A59Tpe+t0yaN80MEDw7UZpwv3TmdKtLBthWMlq5wHqp2tKIRINogyQ8yUeSmuRL5WQxiFHNF9JT35T+84yq1FMrT5mtqvMJCMBl8oql696QTp4sGT7pjZ9Kr90h+Q5bXTIAsFQqJE08GkEBtJHM8URTcZzPaCQyEMNn3D4CMWhX2XLpz+dKlWs13/tNjW78o6at6q/R9y9l3Gm4T0YXaeKfpfH3SfJIa56Unv6OVLfT6pIBAOKI7gNoVzKaxtB0+4hEJanhMw4tlZPFIAqGIf3rUentX0mGT1U9v647K6+iSw/g8Uhn3y4VnSAtuEmqWCk9do50xVyp9GyrSwcAiAMSDSaYExMNJgPJDNsXzyQ1fMadc1KyGCTQoXrpH/8lrXvR/P/kyVo59B5Ne+rDNrO+MP1rGjWoR3LLB9hF9efS366WvtwoedLM0QpG/8gMHCQQCWEBIHIkGoTtkcywffFMUsNn3LlUTRaDCOwpk+ZfJe1aL3nTpQtnSmd+TwNrD9KSBDhaz8HSTe9Kr/1Y+nie9O490vZ/S5c+KmV3S8gmafEGAIlHTgFYgmSGicdnHJ5UTBbjBNHkuoh7foxN/5D+cq4ZEOhaJF3zqnTW9yWPJ6m5VQBHyewqTXxM+tbvpbRMafPr0l/Ok6o+jvumSAgLAMlBSwFYIlF96HFEsj9jmnciXNHc+Yvr3cLDB6W375JWP27+33ek9N1npPzerWajJQkQgscjnXG91PtU6W/XSHvLpMfHSxc/KJ12TUSr6qjuoMUbACQHOQUSjJwCHYtnH3q0LxmfMc07Ea5ocl2Eu0xYgamvPpVeut5sHSCZ/aG/8SspLSPWtwa4U/0e6eWbpc8Wmf+fepX0zf8X1hCendUd5MYBgOhFch1K9wFYiqbbiZfoz5jmnYhENENBhrPM/NUVGj17iabNWaXRs5do/uqK1t0NDEP64K9Hugvk9JSuXCCd/38JCACxyOkuTZ1nBtc8XunD58zjrOqjDhcLp+6gGw8AJAfdBwDEhOadiEQ0Q0F2tkx7Fxe/WLBOnuZlvB5p1oAPNHnn/2te4Rhp0hwprzjebw9wJ69XGvNTsyvOwu9J1Z9Kc8aZoxOMutWcfpRw6w668TjX0a23UqmbIe8FqYagAICYRHORB/eKJtdFZ8u0d3FhyGwcIDXfgSw/WWO69FTJN26Wzv6x5E1L0DsEXOzYc6VbVppDfH7ymvTOr6TP35Em/rlNzo5I6o54jsyD5Di6a8jEEX308geVKdHNMJW6TKbSe0FsyCmQYOQUgBvMX13R5oKNSgUdiSbXRahl2ut33J4XvpOnUV8fE0uxAYTDMKT/PC29dad0uF7q0k36zh+loZe0mi1RdQd3Pq0Vzm+yU3NDpFKei1R6L2hfJNehtBQAEDOadyJS0dz5C7XM0S0JvJIMGTJ0ZEzONI9UetLIWIsNIBwej3T6ddKA0dKCm6SqD81RCkZcLV00W8rKlZSYuoM7n9YHRdprvXU0p3YzTKUuk6n0XhA7Eg0CiAuSRsJKk0f214r/c65eOPsrvd/1Z5qdPkdp8kkyAwIzJw3nuwkkW8/jpBvfkc6+Q5JH+uBZ6c/nSF+sDc4Sz7qDxLftJ12NRqtErREKdA3piFO7Gbb33ngviRPL9zAVy5FItBQAADjfvgqVvPpDlZS9J0mafNwgjRl7ksoPd6P1CmCl9Exp/L3SoHHSy9+X9myVnhhvJiAcO0PKiN+x6fY7n6GCImOGFHX6/lu2Lnjv069iam3RXh6YS0f01isf7Ag7l4xdRZMXx67s/l6S1eqns5Y1bml9RE6BBEvlnAJWN08DAHOowWelt2ZIh+qkjBxzmMEzbmw34zkACzXslV7/qbT+JfP/7oOkSx6WBnw9Lqt3ex/plVuqNW3OqjavvzD9axo1qEfI5Vpe9ARuHLe8OIj2Mzw6D0w0uWTsiveSWMk6lju74Hf6bwo5BZBwbomaAbCx2iozy/lnb5v/9/uadOmjUo9B1pYLQPuyC6XLn5CGXy69doe0Z4s0d4I0cro0/h4pKy+m1dv9zmeiRTMa0NGtC9q7Uxhta4uj88Ck0igSvJfEimern1A3McNpWeOm1kcEBRCxWJqnITJWtcagFQhsze+X1s6VFt8nHayR0rKkb/xSGvVDhhoEnOD4CVL/UeaQhf95Rlo9R/r0Lenbf5AGj4tp1W5OfBtNUCScpIB262eO1Bev4a47uokZzgW/m4bdJiiAiIU6iNaW71X3XC4k48Wq1hi0AoGtVX1s3mGsXGP+33uEdOlj0jEnWFsuAJHJ7iZ95yHppElmi599FdJzk6RTr5Qu/I3ZqiBKdrzzmSyRBkXau+jxyBxAwm/Ida0tYA/xaPXT2U3McC743dT6iJwCCZaKOQXa61/TsgLhQjJ2VvVhcnrfKavQsiIJGuukpbOkVX+SDL+UmSeNu1saeSOtAwCna9wvLfm1tOoxSYaU20u64DdmNwNPJ2nsEbP5qyvaXPS4tbUFopOo86BY8h2Ek2Ojve9+e9cvdsy7EA5yCiAhWh7wbccEF90J4siqPkxu6jsVL7SsSDDDkD55TXrz51JtpfnaSROlC2dJ+SXWlg1AfGTlShNmm8f2338o7f5MWniT2U1owgNS8TCrS5jSQrUuoN5HOBJ5HhRLq59wWgKE27LGDa2PCAogLO0d8Ct+MVbl1fXafaBRtz7/Qav5uZCMjVV9mJK53VS4u05+jQTbu0168/+YfY0lqbBU+uZvpePGW1osAAnS/yzp5hXSPx+S3vuttO196c/nmIkIx84wuxwgIdxw0WM3Tj4PCpS9a2aabc+Dwm36z3ffRFAAnQp14bPiF2M1alAPVdU0uCYJR7JY1YcpntvtqLJLlbvrtKwwxf3E5lC99M+HpeX/IzU1SN4MafSPpDE/bTWmuZNPqACEkNFFGvMz6eQp0tt3SRv/Lv37z9L6BdL4e82cAww3Codz8nnQ0UNYHt0P3U7nQW5OPBopggLoVGcXPm5KwpFMVv2QxWO7HVV2Vt9dj+eFpJuy0oYS1xMbv1/6eJ60+L+luh3mawPOlr71P1LR8YnbLgD76dZP+u4z0palZouh6k+lV281uxR880Gpz2lJLxKBSMSD1edBsQhnCEu7nQfREiA8BAXQqXj2yUFkrPohi2W7nVV2Vt5dj/eFpNsDYnE9sSl7T1p0l7TzY/P/gv7muOXDLmuTaMzJJ1QAIjRorHTz+2ZrgWWzpcq10pxvmC0Gxt4pFfRNSjEIRCJenNzKMNQQll5Gq3A8ggLoFH1yEInOKjur7q4n6kLSzQGxuJzYVH8mvXO3tPkN8/+sfOmcn0hn3Ww2I07UdgE4R3qm9PXbpOFXmL8XH8+XPnxOWveidNb3pLN/LOV0T9jmCUQinpzcyjBU2Rf+YJTqD/lbnQfRssZZCAogLG6+8EFkOqvsrLq7nsgLSbcGxCI5sWlzcnBgt/S/s6U1T0r+JsmTJp1xvXTenVLXnnHbLoAUklcsTfqLNPIm6Z17pIqV0sqHpLXPSGffbgYTM+P/O0AgEvHk5FaGocp+Sr/CVvPRssZ5PIZhtNcdBHESyfiQQKoIZ9zXZI/5WlXToNGzl7S5kFzxi7GOqIjtKpx93ebk4MQKTa6cKTXWmjMMuUg6//+2yRsQ63YBpDDDkD57W3r3PunLDeZreSXSuT+XRlwtpcXvvpfd6w/uyDpTss+D4qmjstv9eHGTSK5DCQokGEEBuJUdKzsuJBMj4pMD+bQi60cqKe4jXfhr6djz4r5dAB1LmQtJv8/sRrDkN1JNhflaj8HSN34lnfiduI1UYNf6gzuy7uKE43bllmpNm7OqzesvTP+aRg3qYUGJ3IuggI0QFAASJ5rKkQvJ5Fq5sVzTntnQ5vUXLvBp1HnfYmgxwAIpeSHZ1Gh2R3rv/0n1u83Xjhlq5hs4aWJcWg7Yrf7gjmx0nHBh3R6nHLd8L+0jkutQzsYAONL81RUaPXuJps1ZpdGzl2j+6oqwlispyNaoQT2omBLtYI20bLYGvvwdeeVvNSnNI5Wefj4BAcACoZLmVdU0JG37K7dUx3976VnS126R/utDswtBVr705UZp4U3Sw2dIa58yAwcxsFv90VGuA7Qv2nMHq8Vy3CbsmAshkHcgrXnkICflTHAzEg0CcBwyQdtY3U7p33+RVj8uHaxRiaRZha9rxr5vyWd4ODkALJZKw8IGtL7zmy+NnSF97QfS6jnSPx+V9pZJ//iRtOx+cxSD06+VMrvG4R1Zi6SrkXHyuUO0x61VrQtIUO48BAWABHFq8zQnIBO0De1cL/3zEbNvr/+w+VrRCdK5P9fkoZdqTF1jhycHHC9AcqTasLAhL3qyu0ljfmYGB9Y+La38o1S3Q1p0p7T8QbNVwcjp5nwOFW4W+1T6fY3lvTj53CGa49bqIEgyRmZKpe+21QgKAAnglH5fTsXdkfiJqUI1DOnzxdI/H5K2Ljvyer+vSaN+KJ1wseRNk9TxyQHHC5A8qTQsbFgXPZldpVE/kEbeKH30grTid9LecmnJr6Xlv5NOmSyd+X3pmBOif3MW6uyObCr9vsb6Xpx87hDJcRuo1/ccOOTYIEg4Uum7bQckGkwwEg26j9UJVtwSNbVrJugAu+yHjsoRdYV6+KC07m9my4CvPjFf83iloZdIo26V+p4RUflISAQkXyoMCxtVlnNfk7ThZTM48GWLJKgDx5jBgeMnBIOZVohn3ZFKv6/xei92P3foTGfHbct63dP8WssLPafu/6Ol0nc7kSK5DqWlABBnqdhf047s3F/NLvuho3KEusN2QnGeDhzytX9C+tWn0n+eNu+2BbJ7Z+ZJp10jnfV9qXBAxGV0cnNOwMmS0bT36O3Fu4VCVHd+09Klk6+Qhl8ula+Q/v1n6ZPXpbL3zEdBP2nkjaoaPEVlBzKSGtiNd92RSr+vnSVVDDeQYudzh3B0dNweXa8bMgMDgWMklXL6pNJ32y4ICgBxFslJSrzvCCSq75hd7nofLdknteGwug9fuOUIVaFe+uhKGS1PSE8tkjb+3eyTW7HyyMz5faWv3WwGBLoURF1OJzfnBBCZeF+QxRRo8HikgeeYj33bpTVPmL9zNds1/61lurNpsPzymr+FE4dr8pmJDewmou5Ipd/XUO/l48p9uvLxf0UUSLHjuUNAvHMmGJIemjJCPXKzHBkECSWVvtt2QVAAiLNwT1KcckfALne9ncIu0evOytFehSqZaQKk5hPSBR9rzDszVHJom/mixysdd6GZuXvw+XEZ99uq/s0ArBHvC7K4BBq69ZPG3yud+3NV/ftl3fmPPPmbR+02L84/0pi9C1Ry1uXmvAmQiLojlX5f23sv/+ei43X/m59YHoSPl0TlTDi9tNCRn0dHUum7bRcEBYAE6OwkxSl3BOxy1zsSVrdqsEv0urNyHF2heiX5j1qHTx6VN+SopEd/acQ10ogrpfzecS+r05tzArBW3AINGdkqK75AfrXOU+CTV+Ur5qvkn/dJx54rnTJNOvHbUmb8ftcTVXek0u/r0e/FLkH4eIjH+ZbbLpRT6bttBwQFgATp6CTFKXcEnFbh2qFVg10q5XDKMXlkf43pn6XyD5cqp+xdTSy/JHh3TJLS5Ffp5b+Whn9D8nrb20xcy9uybFYHdwC4U/sX54ZK+w+QKjeZI61sXSa9nieddKl08nelAaNjTk6YyLojWc3lk/G7ffR7sUMQPh7idb7ltgtlO3cFcRqCAoAFnHJHwC53vcNhp1YNdqmUQ5bj0AFp85vS+gUq+fxdlfgOSZJmpX+lGU03ySev0jzSzEmnqOQUeyVIBIBEav/ifLhKRs43hzL8aL704V+lfdukD541Hzk9pRO/JZ34HXMUg7SMqLZtl7ojGlb8btslCB8P8Tzf4kIZ0WBIwgRjSEKE4pRhcZxSzqiGprJQ0u+E1++RPntH+vRN6dNF0uH6I9OOGSoNmyQNu0xVaSWWnpAyzBAAO+hw6De/X6r4p/TR8+bIBQ17j0zr0k064VvS0O9Ix54npWcls9iWsPp3O9nDayaKU8634BwMSQg4gFPuCDilnE5q1ZCUOyqGIVV/ZgYBNr8lbf+XZLTIGlBYKg27XBp2mdRraPDlEsnSfey0LisAOubUrkAd3m31eqXS0ebjW7+XypdLG1+VNv1Dqq+WPnzOfGTlS0Muko67QBr0Damr/QLU8WD173aq3Bl3yvkWUhNBAcBCTqnInFBOpzQjTGg3h6ZD5sX/5rfMYMCera2nH3OSdPxF0gkXS71PM4fkshknBXcAdMwVXYHSMswL/kHfkC7+rdmCYOPfzQBBXZW07m/mQx6pz2nS4PHm6C19Tos5D4Fd8LsdP04430JqovtAgtF9AKnKrnd/7N6MMK7dHPw+qepDqew987Htn1JTw5HpaZlS6TnS8ROkIRdK3ZxxMk4TSsD5rG5Sbjm/X/pitbT5denzJdKuda2nZxeagYTB481uBgkY2SWZ+N0G7IfuAwASys53f+weZY/pjophSF9uOhIEKF8hNda0nqdrkdlUdchF0qCxUlZefN9AEtCEEnA+q5uUHy3pgWyvV+p/lvk4//9KtVXSlsXS5+9KW5aYeQjWLzAfktmla8BoacDXzb+FpbZszRUKv9uAs9FSIMFoKYBUk8y7P1a1Rkj0dsO+o9K4X6pcK33xb+mLNeZdp/rdrefJKpBKzzYzXh97rlR0gqNOJJPt6H1r1xYvgNPZqaWA7QLZvibzt/3zd81H1Yetc75IUl7v5gBB86PnkJTpbgAgOSK5DiUokGAEBVKXWy8mkpXl36qTuGRtt003B7/fzAHwxb+l7f82AwBfbmx7opieLQ0YZQYBBp4rlZwSPFFM1HcyVb7rR+/biSP66OUPKu1zoQCkGDs0KbdTcCKkg7Xm7/6296VtK82Agf9w63kyc6WSU6U+I8ycML1HOK41AYDkovsAkGC2u+uQRMlIKJTQZHx22O7hBpXs36SSfeukT9ZJO9dJu9ZLh/a3nbegn9R3pPnod6ZUfLKUntlmtkR9J1Plu97evl3wn8rg9GR9xwA3sUOTcrt1Y2hXl3zpuPHmQ5ION5iB4W0rzUDBF2vM+mHbCvMRkN3dDA70Oc2sG3qdZAYKaFEAIEKOCwrU1dXpt7/9rRYsWKCysjKlpaVpyJAhmjJlim677TZlZrY9WQ7Xrl279MADD+i1115TRUWFsrOzddJJJ+naa6/VjTfeKA/RWCi2C8dUuOOajCz/Vp3ExX27viZpb7lU/alUvVnatdEMAFR/Khm+tvOndzFP8PqeIfU90wwE5Jd0uplEBTOsCs4kQnv79mi2u1AAUoDVeV4cmRk/I7u5NdgY839fk1mHVP5H2vGBtOM/0s71UsMeM0/BlsVHlk3PloqONwMExwyVjjnRfJ7by5atClLhvAhIBY4KCmzbtk3nnXeeysvLJUk5OTlqbGzUmjVrtGbNGv31r3/V4sWLVVhYGPG6165dqwsvvFC7d5v9dXNzc1VXV6cVK1ZoxYoVevHFF/Xqq68qKysrnm8JDhTthWOq3HGVEn/3x6qTuHC32+okJr+LmTBqb5lU/VlzAOBT6atPze4ARzcBDcjpYd7ZKR5+5G+PwVJa5D/LiQqihFrv2vK96p7rrJO49vbt0Wx/oQAgYk4ZrrZDaenmhX2vk6TTrjZfa2o0W5gFAgW7NkhffWKOQFP1ofloKbvQrGO6D5J6ND8Czy1KSJtK50WA0zkmp4DP59OIESO0bt06lZSU6JlnntH48ePl9/v14osvavr06aqrq9OECRP0xhtvRLTumpoanXDCCdq5c6dOOOEEPfvsszrjjDN06NAhzZkzR3fccYcOHz6sW265RY8++mhE6yanQOqJpn+iI/o02oxVfVHb3e6pRVLNF9Lecs3/4Evd+Z8C+eWRV37Nyv6rJhtvhl5hRo7U8zgzSVTR8VLxKWYAIK84bndtEvX9am+9HpnFduJJ3NH79tIRvfXKBzsYQgtwAbsPVxsXfp/ZOm3XBjMnzZcbzRZqe7a0zU/TUm4vM0BQWCp162d2Wwv8Legrpcf/hhjnRUDipWSiwSeeeEI33XSTJGnlypUaNWpUq+kvvPCCpk2bJkl69913NW7cuLDX/atf/Uq//vWvlZ2drQ0bNmjgwIGtps+aNUszZsxQWlqaNm7cqCFDhoS9boICzhBp87VIL1iTlZwv1ST8JM7vlw7uk/bvkmp3SHVVUu0OVX1VrfLdDSo99LlK6j8xm2hKqjK6a3TjH+WXN7iKNPm0IutHKsnLMC/8AwGAwCO/j+T12mdEgxjW65VkND8CnHYSd/R3yhUXCgDc7fBBafdn0u4tZoBgd+DxuVRf3cnCHjNo0K05QJBXYga1c4vNv4FHVn5EgW7Oi4DES8mgwJgxY7R8+XKNHTtWS5YsaTPdMAwNGjRIZWVluuaaa/T000+Hve4BAwaooqJC119/vZ588sk20/fv36+SkhLt379fd999t+67776w101QwP6ibb4WycVEPCPi9L/rwOGD5kV+wz6zSf/B5r/1u6X9X0oHvmr++6V0oNr8398U3rozc7WyyxhN+/KqNpNeuP5UjTq+T8hFLRvRIM7r3X2gUbc+/0Gb6ZzEAYBDHaw5EiTYt02q2S7t237kb1NDeOtJzzaDA12LzO5xOT2knMIWz5sf2d2lnB6qOtRFox9YRksBIIFSbvSB+vp6vf/++5KkCRMmtDuPx+PRRRddpD/96U96++23w1735s2bVVFR0eG6c3Nzdc455+jNN9/U22+/HVFQAPYWSyK1SJInxatPY8r2v/P7zMzKhw5Ijfubn4f6v8680D/6wr9hX/gnL0frUmDe0c8rkfJ7m8/zS5r/9jYfXbppYO1BedsJ7pQWdw+56mQm60tUQq/AeqtqGiLPucDJHQDYV5cCc/SCPqe1nWYYZlB9X4UZJKj5Qqrbabauq6syn9ftkhprzPp3b5n5CEOJPJqV/U3NqJ8qn7xKk18z+61WydLXzRwHWXnmqAyB51n5zY88KStXyuhqJmRM7yJ5vZ1vEGjGOUr7HBEU2LRpk/x+sy/UsGHDQs4XmLZz507t2bNH3buHPlEPWL9+fZvlQ637zTff1MaNG8MtNhwgmVnuY03OF5eLS7/fTHznO2z+9ftaPG8yMxwHp/taPG9qnn7UvE0HzWRHTQ3Nfw+ad+s7fb35+eEG80I/2ov59ni8UpduUnY3M7FSl27m3YncY6SuPaWux7R+3rWo3SH+2hNNcMcRw2GFKZz3n7KBKwBwG4+nua7s2X7QIOBQvbR/pxkkOFBtBhIa9kj1e8zn9btbPN9jBhFkaLL/dY3J+qfK/b1U6t2lki/3SF9GUc70bCkzx8zhk5Hd/Mhp/Te9i5kbIS3zyCM98Dyr9fO0jNbzpje/lpYpedMlT5o57KM3vcXfdPP8I/A88LoNR3xwM85RQnNEUGDHjh3B5336hG6i23Lajh07wgoKRLru2tpa7d+/X7m5ue3O19jYqMbGxuD/tbW1nZbBFt7/o5kxXVKrHsOtLmZavm448HW1eX3goWx5delRfcT9Kl3+M2nVgbiXp8QwVGL4zYQ/htH8N9TDMIeta/6/7OAA+Y3rW70Nn2Go/NHLVZLxmTmf3xdi3c0X/wrxediFJ828A5DZfCcgs6uUmWs+Av9n5TVf9Be2vvAP/J+Zl9C7BpEGdxw5HFYHOnr/qTSEIQAgTJk5UvdjzUc4fIeDXftK6nerpLHObAV4sMb821gnNdYeeX6wtsX/zX99h46sr6mh+ebC7oS8vWhVGd1VZpRooPcrlaTXNQcLWgQOPIHAgcyAgjzmX4/XDCa0es0T4rVQy6njdcnTImDRInARKojR6vX25g+1jvBfrzqUrbKDuRrYZb9Ksho7nDe8crR6A6pq7KI7/3Oe/M0T/IY0Y8HHGlP2R5VkHVTEug2Qzv1Z5MvZlCOCAnV1dcHnOTmhT6RbTmu5TCLWHSooMGvWLGd2L/jsbal8udWlSLoSSbPSqzWj6Ub5lKY0+TQz/QmVlC+zumhtDDSq5dW1bZLclR7cKDXuiX7FHq/kzTCj4IGKKi2j+bVAxDvwPKPF9HQz8p7R5UgEPj27+W/z/xkt/285X4vlMruaF/KZXc1pDoiqW9F1xE5Cvf9UahUBAEiQtAyzxV7uMdGvw+8zWxsebpAO17d4tHytofVrvkPmo6n5r6/RDFA0NbaY1vyar7HtfL5Dza0ofebNlkArynZyE81vOk93Nt0kv7zmSEXpj2ty+rLo36/FqozuKvMXa6B3p0o8MZxzhpCMz6vMN1R+jW31mk8ela9boZK0TZGvsORUggII7c4779SPf/zj4P+1tbXq16+fhSUK02nXSoNaHiixR/ziv64wIplRrGuyx6Mx9ftUvj9Npbl+leRMkjQpce8vGLnt7NEc0fWmSR6vSjxezdp0SDP+94B8hpTmkWZ+o7tKhr1y1HJprZcPPAIX+t60Fs/T6YuXBLF2HXGKVGsVAQAdoW+yhbzNLQuz2r9Jl3R+vxkcMHyq2ntAd/5ulQKDQPrl1Qzf9zRm+gMq6eptDiwEAgo+SUbrFp4y2rb6DL6mMOdrsU61bD3a4rWAYOvW9lu8zi/r0mIoZkOzTqvR5NL61u8/hha1VQ1e3fl23yN38OXVDN90jRl7oUqyfTGvP/D6wIZ0eZcYwe1IUprHUOk5U6XsMJNOt5TbK/JlbMwRQYG8vLzg8/r6+pDztZzWcplI1h0qM2O4687KylJWVvzHc024k6+wugSWKml+BNi1op88QBoziiHUnChRSQDtJBVbRQBAe+ibjFa8Xslr5icqq2uv1ZxU3tRTJT2dNVKP2S1wSYsAh0czPijUmAsnxa1uL9tSLb9aD0/pMzwqL52skjiObFQiaVbh0cM3D1fJyG/FbRtO5oigQO/evYPPKysrdfLJJ7c7X2VlZbvLRLLuUEGBwLrz8/NDdh1A5+x6sd2S3St6N1xc2o0Tvrd24ZZWEQDci/wp6EgqtZpLRrfAZH5enKOE5oh2wyeeeKK8zU2cW44WcLTAtOLi4rCSDEqtRxwIZ91Dhw4Na71oa/7qCo2evUTT5qzS6NlLNH91hdVFaiNURV9VE8fs+HAUJ3xv7aakIFujBvWgsgWQkjq6UEJqqqpp0Mot1WGdDwZazaU1dx11cqu5wAV7S/G+YE/258U5Svsc0VIgJydHo0eP1vLly/XWW2/pZz9rm9TBMAwtWrRIknTBBReEve7jjz9e/fv3V0VFhd566y1dcUXbZvQHDhzQ8uXLI143jnBKVJ1EaWjJKd9bAEDyxHJnk5ZnzhNNC9JUuSOdrG6BqfJ5OZkjWgpI0rXXXitJWrp0qVatWtVm+osvvqitW7dKkq655pqI1h2Yf968eSovL28z/ZFHHtH+/fuVlpamK6+8MsKSQ3JOVD0ZEVE4h1O+twCA5In2ziYtz5wnlhakqXJHevLI/lrxi7F6YfrXtOIXYxPWpTZVPi+nclRQYPjw4TIMQ5dddpkWL14sSfL7/XrxxRc1ffp0SdKECRM0bty4Vsvee++98ng88ng87V70//SnP1VxcbHq6+t18cUXa+3atZKkQ4cO6U9/+pN+9atfSZK+973vaciQIQl8l6nLKRfbqdTkC7Gz+/c2kuaMAID4ifRCie6JzsTNARMX7KnPEd0HJCk9PV2vvvqqxo4dq/Lyco0fP145OTny+/06ePCgJGnEiBH661//GvG6CwoK9Nprr+nCCy/Uxo0bdcYZZygvL08HDx7U4cOHJZndBn73u9/F9T25iZOyktOECQF2/t7aPSEmAKS6SBL/0j3RmVIpaSDQEccEBSSptLRUH3/8sR588EEtXLhQZWVlysjI0EknnaSpU6fqtttuU2ZmZlTrPv3007Vhwwbdf//9eu2117R9+3Z17dpVw4YN07XXXqsbbrghmOwQ0XHSxTYZ/hFgx+8tuQ4AwFm4uHQmO98cAOLJYxiG0flsiFZtba0KCgpUU1MTcrhDAIjEyi3VmjanbW6VF6Z/TaPiOKYvACB+5q8+eoz0YbTwcoiqmgZb3RwAwhHJdaijWgoAdkZGYSQLd5wAwHns2PIM4aEFKVIdQQEgDujfjWSiOSOAzhCoticuLgHYEd0HEozuA8mX7BOhqpoGjZ69pM1d2xW/GEvFj4SiOSOA9hCoBgDQfQCuZcWJEBmFYRXuOAE4GolIAQCRIp0+UoZVYwDbfSx7t6uqadDKLdWMBQ3AFRhXHQAQKYICcJxQF3lWnQgF+neneczIAP277WP+6gqNnr1E0+as0ujZSzR/dYXVRQKAhCJQjWQh6A6kDroPwFE66h5gZUZ2MgrbD01oAbgRiUiRDOStAFILQQE4RmcXeVafCNG/217I9QDArQhUI5EIugOph6AAHCOcizxOhBBgZcsRALAagWokCkF3IPWQUwCOEW4/yZKCbI0a1IOKqQNu6AdIrgcAAOKPvBVA6qGlABzD6u4BqcLO/QCrahpUVn1AA3t2jct+peUIAABtxVLfcj4GpB6PYRhG57MhWrW1tSooKFBNTY3y8/OtLk5I8b4YS6SqmgYu8qJUVdOg0bOXtGlSv+IXYy3/LO0crAAAIFXEq77lfAywt0iuQ2kpAMddjNFPsrVIAjp27QdI0iIAABIvnvUt52NA6iCngMuFqhxSua95Kpm/ukKjZy/RtDmrNHr2Es1fXdHh/HbtB9hRsMIt2svz4IbcDwDgZsn+nae+BdAeWgq4nF3vHKNz0UT77doP0O0jBbTXWkeSo1rwAAAiY0VLTbfXtwDaR1DA5agcnCvagI4dk+/ZNViRDO0Fd+5csE5qcVzSnQIAUotV3ebcXN8CCI2ggMtROThXLAEdO/YDDCdY4aSEmOFqL7jjlyRa8ABAyrKypaYdbw4AsBZBAVA5OFQqBnQ6ClY4LSFmuNoL7nilVi0FJFrwAEAqsbqlph1vDgCwDkMSJphThiSEc7lhSCA7D6UYD/NXV7QJ7khq81oqBEEAAKb2fvv5nQcQLwxJCLiIG6L9qZ4QM1RrHVrwAEDqoqVmW6nYTdBuwvmM2Q/uQ1AACcWPCuLB6maWydBecMcNAR8AcDN+549I1W6CsYj3eXQ4nzH7wZ28VhcAqWv+6gqNnr1E0+as0ujZSzR/dYXVRYJDBfInpHk8kpQS+RMAAPZRVdOglVuqVVXTYHVRbCnRn0+o0RjcvD/ifR4dzmfMfnAvWgogIawaaieRaPVgLZpZAgASgTujHUvG55Pq3QQjlYjz6HA+Y/aDe9FSAAnR0Y+KE9HqwR5KCrI1alAPKiYAQFxwZ7Rjyfp8At0EW0q1boKRSMR5dDifsd33Ay16EoegABLC7j8qkeCEAQCA1JRqNzHiLVmfTzy7CabChWMizqPD+Yzt3F2TG3SJRfcBJETgR+XooXbs8KMiRdYVgKZUAACkJjckso1FMj+feHQTTJWuIIk6jw7nM7Zjd81U7JZsNwQFkDB2/FGRIq8wOGEAACA12f0mhtWS/fnEMhpDql04Juo8OpzP2G6jYnCDLvEICiCh7PajEk2FwQkDAACpy643MezCKZ9PKl442u082ircoEs8ggJwlWgrDKdUiAAAIHJcfHXMCZ8PF46pixt0iUdQAK4QyCHQNTMt6grDCRUiAACAG3HhmNrCuUHH8OHRIyiAlHd0DoGJI/rolQ92UGEAAACkkGhbdnIx6Qwd3aBLlSSTVvEYhmF0PhuiVVtbq4KCAtXU1Cg/P9/q4rhOVU2DRs9e0qZlwMIfjFL9IT9dAQAAAFyMi0nnC3W+v+IXY119nh/Jdag3SWUCLBEqh0D9Ib9GDerh6h8KAAAANwuVgLqqpsHagiEiHeUMQ3gICiClBZLOtETSGQAAAHAxmRrieb5fVdOglVuqXRcYIiiAlBZIOpPmMX8pyCEAAAAAiZtHqSJe5/vzV1do9OwlmjZnlUbPXqL5qysSUVxbIqdAgpFTwB6qahoYThAAAMBlOksiOH91RZsRC8gp4EyxnO+nYl6CSK5DGX0ArsBwgtYjsy8AAEimcJIIRjtiAewnlvP9jrqSuOE7QVAAQMKR2RcAgMgRUI9eqCSCY4YUtfksuXmEQFeSo1sKuKUrCTkFACQUmX0BAIicm/s3xwNJBBEJt+cho6UAgIRye3MsAAAiFcldbrTP7Xd+7c6OrWDc3JWEoACAhKJSBgAgMgTUYxe483t0EkE+P+vZuVupW7uSEBQAkFBUygAARIaAeny4+c6vXdEKxp4ICgBIOCplAADCR0A9fuxy59eOzeWtQCsYeyIoACAp7FIpAwDgBATUU4edm8snG61g7InRBwAAAAAbKinI1qhBPQgIOBijMLXm9iz/dkVLAQAAAABIAJrLt0UrGPshKAAAAAAACUBz+fbRrdRe6D4AAAAAAAlAc3k4AS0FAAAAgBiRXR6h0FwedkdQAAAAAIgB2eXRGZrLw87oPgAAAABEiezyAJyOoABsr6qmQSu3VFO5AgAA2+kouzwAOAHdB2Br4TTHow8fAACwCtnlATgdLQVgW+E0x5u/ukKjZy/RtDmrNHr2Es1fXWFRaQEAgBuRXR6A09FSALbVUXO8koLskEGDMUOKqIgBAEDSkF0egJMRFIBtddYcr7OgAQAAQLKQXR6AU9F9ALbVWXO8QNCgJfrwAQAAAED4aCkAW+uoOV4gaDBj4Xr5DIM+fAAAAAAQIYICsL2OmuPRhw8AAAAAokdQAI5HHz4AAAAAiA45BQAAAACgWVVNg1ZuqW41DLYdOaWcsD9aCgAAAACApPmrK4JDXns90qxJwzV5ZH+ri9WGU8oJZ6ClAAAAAADXq6ppCF5oS+aw2DMWrrfdnXinlBPOQVAAAAAAgOuVVR8IXmgH+AxD5dX11hQoBKeUE85BUAAAAACA6w3s2VVeT+vX0jwelfbMsaZAITilnHAOggIAAAAAbCfZifRKCrI1a9JwpXnMK+40j0czJw2z3ShXTiknnMNjGIbR+WyIVm1trQoKClRTU6P8/HyriwMAAADYnpWJ9KpqGlReXa/Snjm2vtB2SjlhjUiuQxl9AAAAAIizqpoGlVUf0MCeXblgi1CoRHpjhhQl5bMsKch2xD5zSjlhfwQFAMBFOEkFgMRjuLjYdJRIj7oLiD+CAgDgEpykAkDiWX2XOxUEEum1DAyQSA9IHBINAoALMKYxACQHw8XFjkR6QHLRUgAAXICmmACQHNzljo/JI/trzJAiEukBSUBLAQBwAcY0BoDk4C53/JQUZGvUoB58dkCC0VIAAFwgcJI6Y+F6+QyDk1QASCDucgNwEoICAOASnKQCQPIwXBwApyAoAAAuwkkqAAAAWiKnAAAAAAAALkVQAAAAAAAAlyIoAAAAAACASxEUAAAAAADApQgKAAAAAADgUgQFAAAAAABwKYICAAAAAAC4FEEBAAAAAABciqAAAAAAAAAuRVAAAAAAAACXIigAAAAAAIBLERQAAAAAAMClCAoAAAAAAOBSBAUAAAAAAHApggIAAAAAALgUQQEAAAAAAFyKoAAAAAAAAC5FUAAAAAAAAJciKAAAAAAAgEsRFAAAAAAAwKUICgAAAAAA4FIEBQAAAAAAcKl0qwuQ6gzDkCTV1tZaXBIAAAAAgBsErj8D16MdISiQYHV1dZKkfv36WVwSAAAAAICb1NXVqaCgoMN5PEY4oQNEze/3a8eOHcrLy5PH47G6OK3U1taqX79+2r59u/Lz860uDkJgPzkD+8kZ2E/OwH5yBvaTM7CfnIH95AxO2k+GYaiurk69e/eW19tx1gBaCiSY1+tV3759rS5Gh/Lz823/pQb7ySnYT87AfnIG9pMzsJ+cgf3kDOwnZ3DKfuqshUAAiQYBAAAAAHApggIAAAAAALgUQQEXy8rK0j333KOsrCyri4IOsJ+cgf3kDOwnZ2A/OQP7yRnYT87AfnKGVN1PJBoEAAAAAMClaCkAAAAAAIBLERQAAAAAAMClCAoAAAAAAOBSBAUAAAAAAHApggIO8tRTT8nj8XT6ePfdd0OuY9euXfrJT36i448/XtnZ2erevbvOOeccPf744won5+SWLVv0/e9/XwMHDlSXLl10zDHH6MILL9SCBQvi+VYdLZb9VFlZqUcffVRXXHGFBg8erOzsbGVnZ2vgwIGaOnWqlixZ0uG2r7vuurC23dTUlKi37xix7Kd77703rGU///zzDsvA8dS5aPdTeXl5WMsFHtdff32bbXM8Rae2tlb333+/vv71r6uoqEhZWVnq27evxo4dq3vvvVf79u1rdznqp+SKdD9RP1kj0v1E/WSNSPYT9VPyRPI5jx07NuR63FA/pVtdAETO6/WqqKgo5PRQQ2SsXbtWF154oXbv3i1Jys3NVV1dnVasWKEVK1boxRdf1Kuvvhpy+TfeeENXXHGF6uvrJUn5+fnavXu33n77bb399tu6/vrr9cQTT8jj8cT4DlNDpPtp+/btGjBgQKsfl5ycHBmGofLycpWXl2vevHm64YYb9Je//EVpaWkh192lSxcVFBSEnM4+OiLa40mSMjIy1L1795DT09ND/8RyPEUm0v2UlpamXr16dbjOgwcPqqamRpI0cuTIkPNxPIVv6dKlmjp1qnbt2iXJPAZyc3NVWVmpyspKLVu2TJdeeqlOPfXUVstRPyVXpPuJ+ska0R5PEvVTMkW6n6ifkqezz/nw4cPas2ePpNCfs2vqJwOOMXfuXEOSMWDAgIiX3bdvn1FcXGxIMk444QRj9erVhmEYRmNjo/Hwww8bGRkZhiTjlltuaXf5rVu3Gl27djUkGaNHjzY2b95sGIZh1NXVGXfffbchyZBk3H///VG/v1QR7X4qKyszJBnjxo0znn76aaOystIwDMPw+XzGhg0bjEsuuST4Of/yl79sdx3XXnutIcm49tprY3wXqS+W4+mee+4xJBnnnntuVNvmeApfLPupM7feeqshycjOzjb27t3bZjrHU2RWrFhhZGdnG5KM8ePHGytWrDB8Pp9hGIZRX19vrFmzxrjrrruMrVu3tlqO+im5otlP1E/JF+3xRP2UXNHup85QPyXHgw8+GPxOf/LJJ22mu6l+IijgILGcHP/yl78M/ri098M0c+ZMQ5KRlpYW/MK2dNVVVxmSjOLi4nZ/nL73ve8Zkoz8/Hxjz549EZcvlUS7n/bt22esXbs25HS/329cdNFFhiQjNzfXaGhoaDMPlUT4rAwKcDyFL1FBgYaGBqOwsNCQZFx11VXtzsPxFL4DBw4Yxx57rCHJuOyyy4InxeGgfkqeaPcT9VNyxXI8UT8lTyz7qSPUT8lz4oknGpKMs88+u93pbqqfyCngEs8884wkacqUKRo4cGCb6bfddptyc3Pl8/n017/+tdW0AwcOBPu83HLLLerWrVub5e+8805JZp+qV155Jb6Fd4mCggKddtppIad7PB7dcMMNkqT9+/dr06ZNySoa4ojjyR4WLlyovXv3SpJuuukmi0vjfM8++6y2bt2q7OxsPfbYY/J6wz+9oH5Knmj3E/VTcsVyPMWC4ykyidpP1E/JsXLlyuBvVajP2U31E0EBF9i8ebMqKiokSRMmTGh3ntzcXJ1zzjmSpLfffrvVtBUrVqihoaHD5UtLS3XiiSe2uzzip0uXLsHnPp/PwpIgWhxP9vDEE09Iko477jide+65FpfG+QInTpdccol69uwZ9nLUT8kV7X4KB/VT/CRyP3WE4ykyidpP1E/JEfic8/PzdcUVV7SZ7rb6iaCAA3311Vc6/fTTlZubq+zsbB177LG66qqrtGzZsnbnX79+ffD5sGHDQq43MG3jxo0hlz/ppJM6XX7Dhg2dvgc3iHQ/hSOwbGZmpoYMGRJyvsWLF2vIkCHq0qWL8vPzNXz4cN1+++367LPPot52qoplP23YsEHDhg1Tdna2cnNzdfzxx2v69On64IMPQi7D8RSdeB5PW7du1dKlSyVJN954Y6fzczx1rLGxUWvWrJEknXvuudq6datuvPFG9e3bV1lZWSouLtYll1yiN998s82y1E/JE8t+Cgf1U3zEaz9RPyVWoo4n6qfk2L9/v/72t79JkqZNm6acnJw287iufrK08wIiEuhbG3gUFhYamZmZrV67/vrrjcOHD7da7o9//GNwek1NTcj1//73vw/OV1dXF3z9xz/+cXB7Hbn99tsNSUaPHj1ie6MOF+1+6szWrVuNnJwcQ5Jx9dVXtztPoI+Zmvs3FRYWGmlpacHXMjMzjUcffTQeb9PxYtlPgT6bkgyv12t0797dSE9PD77m8XiMu+66q93tcjxFJhHH01133WVIMtLT042qqqqQ83E8heeTTz4JfiZ33XVXMKlSZmam0a1bt1b76uabb261LPVT8sSynzpD/RQ/se4n6qfkSNTxRP2UHHPmzAl+VmvWrGl3HrfVT7QUcJDevXvrnnvu0UcffaSDBw9qz549qq+v1/vvv6/x48dLkubOnas77rij1XJ1dXXB5+1Fwtqb1nKZwPOOlm05veWybhTtfupIQ0NDcDiTHj16aNasWe3Od9ppp+nhhx9WeXm5GhsbtWfPHtXW1mrBggUaNGiQDh06pB/84Ad66aWX4vJenSyW/XTcccfpgQce0ObNm3Xw4EHt3r1bBw4c0KJFi3T66afLMAz95je/0W9/+9s2y3I8RSbex5PP59NTTz0lSbr44otVXFwccl6Op/AE+r5K0qxZs5SRkaEXXnhB+/fv1969e1VRUaEpU6ZIkh577DH94Q9/CM5P/ZQ8seynjlA/xVes+4n6KTkScTxRPyXP448/Lkk65ZRTdPrpp7c7j+vqJ0tDEogbn88XHBLI6/Uan376aXDab37zm2AEq6O7aX/5y1+C8+3YsSP4+vTp0w1JRp8+fTosw4wZM4LRSbSvo/0UyuHDh41LL73UkGRkZGQYixYtimrb1dXVRmlpqSGZmdz9fn9U63GDaPZTQENDgzFy5EhDzVm49+3b12o6x1P8RLOfXnvtteDv3D/+8Y+ot83xdMT777/f6q7YvHnz2szj8/mMESNGGJKMnj17Busi6qfkiWU/hUL9FH+J2E8B1E/xk4j9RP2UHOvXrw9+zg899FDI+dxWP9FSIEV4vV49+OCDkiS/369//OMfwWl5eXnB5/X19SHX0XJay2UCzztatuX0lsuitY72U3t8Pp+uuuoqvfLKK0pPT9fzzz+vCy64IKpt9+jRQ3fddZckadu2bR32K3S7SPdTS126dNHMmTMlmX3WFi9e3Go6x1P8RLOfAncH+vTpEzLxTzg4no5o+R3t16+fJk+e3GYer9ern/zkJ5Kk6upqrV27ts2y1E+JFct+ag/1U2LEez+1RP0UP4nYT9RPyRH4nLt06aIrr7wy5Hxuq58ICqSQwYMHB7Ofbt26Nfh67969g88rKytDLh+Ylp+fr9zc3DbL7927t8MvdmD5lttDW6H209ECJ1zz589XWlqannvuOV1++eUxbXvUqFHB5x1tG+Hvp/Z09DlzPMVXJPvpyy+/1Ouvvy5Juu6665SWlhbTtjmeTH369Ak+P+GEE0LOF8iwLJknqhL1UzLFsp+ORv2UOPHcT+2hfoqPeO8n6qfkOHTokJ577jlJ0mWXXabCwsKQ87qtfiIo4AItM2a2zIR5tMC0oUOHhly+o8yYgeU7yrCJ8Ph8Pl155ZWaN29e8ISrvSg0nIfjyTpPP/20Dh8+3GpMdcSue/fuwRNkj8cTcj7DMILPA/NRPyVPLPupJeqnxIrXfooGx1P44r2fqJ+S4+9//7uqq6slSTfddFOH87qtfiIokEK2bNkS/KIPHDgw+Prxxx+v/v37S5Leeuutdpc9cOCAli9fLkltmv+dffbZys7O7nD5bdu2adOmTe0uj9ZC7aeAwAlXyzswgWQ1sfrXv/4VfN7etnFEZ/upIx19zhxP8RXJfgqMSTx27Fgde+yxMW+b4+mIwPd006ZNrU6CWwp8p6Ujnxf1U3JFu58CqJ+SI9b91BHqp/iJ536ifkqOQNeBwYMH69xzz+1wXtfVT9alM0AkOksS4vf7jYkTJwYTbn3yySetpv/yl780JBk5OTlGWVlZm+Xvv//+4LAmmzdvbjP9qquuMiQZJSUlbRLTGIZh3HLLLYYkIy8vz9izZ09kby6FxLqfmpqajO9+97vB4WjaS1wT7bZ3795tHHvssYYko2/fvobP5wt73akmlv3U2bIHDx40zjrrLEOS0bVrV2Pv3r1t5uF4Ck+sx1NLy5cvDyYCev7552PeNsdTa++9917w833hhRfaTG+ZcKtPnz6tPi/qp+SJZT9RPyVPtPuJ+im5YjmeWqJ+So5t27YZXq/XkGTMnDkzrGXcVD8RFHCIsrIyY+TIkcZjjz1mbNmyJfiD4PP5jH/+85/GhRdeGPxBueWWW9osv2/fPqO4uNiQZAwdOjQ4JmdjY6Px6KOPBsf9bm9ZwzDHIA6MwXrOOecEs3zv37/fuO+++wyPx2NIMu6///4EfQLOEMt+ampqMqZOnRo84frb3/4W0bafeeYZY+LEicZLL71k7Nq1K/h6fX298fLLLxvHHXdch1ly3SSW/bRs2TJj3LhxxrPPPmts3749+PqhQ4eMd999N5jZuaPjgeMpPLH+7rUUGNO5e/fuxsGDBzvdNsdT5C6//HJDktGtWzdj3rx5xqFDhwzDMIyKigpjypQpwc/rqaeearUc9VNyRbOfqJ+SL5r9RP2UfNH+7rVE/ZQc99xzT/A3rOUoAR1xU/1EUMAhysrKWg19kpWVZfTs2dPIyspq9fr1118fctiMNWvWGD169AjOm5eXZ2RkZAT/v+CCCzr8MXr99deNnJyc4PwFBQVGWlpa8P/rrrvO9cOexLKf/vd//zc4PSMjw+jVq1eHj6N/6OfOndtqG127djV69OjRah9lZWUZjzzySDI/EluKZT8tXbq01TzZ2dlGz549Wx1LXq/XmDFjRodl4HjqXDx+9wzDMGpra4Of9X/913+FtW2Op8jt37/fGDNmTKvPp7CwsNXnePfdd7e7LPVT8kSzn6ifki+a/UT9lHyx/O4ZBvVTsvh8PmPAgAGGJOM73/lORMu6pX4iKOAQ9fX1xkMPPWRMmzbNGDp0qFFUVGSkp6cbubm5xgknnGDccMMNxooVKzpdz86dO4077rjDOO6444wuXboY3bp1M84++2xjzpw5YTUv+vzzz43p06cbpaWlRmZmptGjRw/j/PPPN1566aV4vE3Hi2U/HV2Zd/aYO3duq+XLy8uN3/zmN8a3vvUtY9CgQUa3bt2M9PR0o7Cw0Bg5cqTx85//3Ni6dWsSPgX7i2U/VVdXGw8++KBx2WWXGUOGDDG6d+9upKenG/n5+cYpp5xi3HrrrcbHH38cVjk4njoWr9+9P//5z8HjJtx9w/EUHZ/PZ8yZM8cYM2aM0b17dyMjI8Po06ePMWXKFOP999/vcFnqp+SJdD9RP1kj0v1E/WSNWH73qJ+SY9GiRcHP+dVXX414eTfUTx7DCJEZAwAAAAAApDRGHwAAAAAAwKUICgAAAAAA4FIEBQAAAAAAcCmCAgAAAAAAuBRBAQAAAAAAXIqgAAAAAAAALkVQAAAAAAAAlyIoAAAAAACASxEUAAAAAADApQgKAACApCstLZXH49FTTz2VlO15PJ42jw8//DAp2w7HK6+80qZ8paWlVhcLAOAC6VYXAAAAIFkKCwuVmZkpScrIyLC4NEd06dJFvXr1kiQ1NDSotrbW4hIBANyClgIAACDpBg0apOOPP14FBQVJ3e7ChQu1c+dO7dy5UyeddFJSt92Riy66KFiuP/zhD1YXBwDgIrQUAAAASbd48WKriwAAAERLAQAAAAAAXIugAAAANrV3717l5OTI4/Hob3/7W4fz/upXv5LH49Gxxx4rwzDC3kZFRYUeeeQRXXzxxRoyZIi6du2q3NxcDR06VLfffrsqKiraXe7++++Xx+NRZmam/v3vf7c7zxtvvCGv1yuPx6Pnn3++1bSOEg02NDTowQcf1KhRo1RYWKiMjAwVFRVp6NChuvbaa7VgwYKw318kysvLg0n+ysvL9dlnn+m6665T3759lZWVpf79++vmm29WZWVlm2V3796tvn37yuPxaOLEie2u3+fzafTo0fJ4PDr55JN18ODBhLwPAAAiYgAAANu69tprDUnGuHHjQs7T1NRk9OnTx5Bk/OY3v4lo/eeee64hKfgoKCgwvF5vq/+XL1/eZjm/32+MHz/ekGQce+yxRm1tbavpO3bsMIqKigxJxjXXXNNm+QEDBhiSjLlz57Z6vba21jjllFOC2/d4PEa3bt2M9PT04GsDBgyI6D0ahhFcdunSpSHnKSsrC843b948Iy8vz5Bk5ObmGtnZ2cFp3bt3N9auXdtm+WXLlgU/u4cffrjN9LvuusuQZGRnZxvr168PWY65c+dG/T4BAIgULQUAALCxW265RZK0ZMkSbd26td153njjDVVWVio9PV033HBDROsfNmyYZs+erY0bN6q+vl779u1TY2OjVq1apYsuukg1NTWaPHmyGhoaWi3n8Xj07LPP6phjjtHWrVt18803B6cZhqFrrrlGX331lQYPHqxHHnkk7PL84Q9/0EcffaTu3btrwYIFamho0N69e9XY2KjKyko988wzuuCCCyJ6j9H4/ve/r4EDB2rVqlWqq6vTgQMHtGjRIvXv31979uzRxIkTVVdX12qZc889V3fddZck6ac//anWrVsXnLZs2TLNmjVLkvS73/3OVkkOAQDuRlAAAAAbO+usszRixAgZhqE5c+a0O89f/vIXSdJ3vvMdFRcXR7T+hx9+WD//+c914oknKjs7W5KUnp6uM888U6+99ppOPvlk7dixo90m+8XFxXrqqaeC3QOefvppSWbXgnfffVcZGRl64YUXlJubG3Z5Vq5cKcm8qJ40aZKysrIkSV6vV71799bVV18dfL+JlJ6ernfeeUdnnnmmJDMIcsEFF+itt95SZmamKioq9Nhjj7VZ7p577tHo0aN18OBBTZkyRQ0NDdq9e7euuuoq+f1+TZo0Sd///vcTXn4AAMJFUAAAAJsL3IWfO3euDh8+3GpaZWWl3nzzTUmK+8VmWlqaLrroIknSihUr2p1nwoQJuuOOOyRJt956q5577jndfffdkqSZM2fqjDPOiGib3bp1kyRVVVVFWer4uPnmm3XMMce0ef3EE0/U5ZdfLkmaN29em+lpaWl6/vnnVVhYqI0bN+pHP/qRbrjhBlVWVqpfv356/PHHE152AAAiQVAAAACbmzZtmvLz87Vr1y794x//aDXtySeflM/n08CBA3X++edHtf7ly5fruuuu0wknnKDc3Nxgsj2Px6MHHnhAkvTFF1+EXH7WrFk6/fTTtX//fl199dU6fPiwLrjgAv3kJz+JuCzf+ta3JJktGKZOnapXXnlF1dXVUb2vWHzjG9/odNrHH3/cJkgjSf379w+26pgzZ45effVVeb1ePffccyosLExMgQEAiBJBAQAAbC43N1dXXnmlJLVqOu/3+/XEE09IkqZPny6PxxPxun/+859rzJgxevrpp7V582YdPHhQhYWF6tWrl3r16qWuXbtKkg4cOBByHZmZma1GESgoKNDTTz8dVXmmTZumH/3oR/J4PJo3b54mTpyooqIiHXfccfrhD3+otWvXRrzOaPTp06fTaU1NTdqzZ0+781x22WW67LLLgv//7Gc/05gxY+JbSAAA4oCgAAAADhBIOPjOO++ovLxckvT2229r27ZtSk9P1/XXXx/xOt95551gS4Af/OAHWrdunRobG7Vnzx7t3LlTO3fuDHYNMDoZ5rBlsKK2tlYffvhhxOUJ+P3vf6/Nmzdr5syZmjBhgrp166bPP/9cjz76qM444wzdfvvtUa87XNEENFoqLy/Xu+++G/z//fffl8/ni7VYAADEHUEBAAAcYPjw4fr617/eqnVAoIn6JZdcEnGCQelIn/gLL7xQjzzyiIYNG6a0tLRW8+zcubPT9bz22mt66KGHJEknn3yyDMPQtddeq127dkVcpoDBgwfrzjvv1BtvvKHdu3frn//8py699FJJ5ggFr776atTrDkdH3SUqKyslmckIu3fv3mZ6U1OTpk6dqpqaGg0ZMkRZWVlasWKF/vu//zth5QUAIFoEBQAAcIhAa4Enn3xSlZWVwfwC3/ve96Ja3/bt2yVJI0aMaHe6YRhasmRJh+uoqqoKtlK4/vrr9d5776m0tFRffvmlrr322k5bGITD6/Xqa1/7ml566SX1799fktnKIZGWLl3a6bSTTz5ZGRkZbabfc889+te//qWcnBy98soruv/++yVJv/71r0MmbAQAwCoEBQAAcIgrrrhCPXr00I4dOzRt2jQdPnw4pgSDBQUFkqSPPvqo3emPPfaYtm7dGnJ5v9+vq6++WtXV1TruuOP00EMPqaCgQM8//7zS09O1aNEi/c///E9EZWpsbAw5LS0tTZmZmcHnifTYY4+1m+Bw8+bNeumllyRJkydPbjN96dKlmj17tiTpd7/7nU488UT96Ec/0sUXXyyfz6crr7xSe/fuTWjZAQCIBEEBAAAcIisrS9ddd50k6b333pMUfYJBScHhBt98803993//dzCZ4L59+zRz5kzddttt6tGjR8jlH3jgAS1evFgZGRl64YUXgkkJR40apXvuuUeSNGPGDP3nP/8Ju0xnnXWW/uu//kvLli1rldxwx44duu222/T5559Lkr75zW9G9mYjdPjwYZ1//vlavXq1JLPVxLvvvqsLL7xQjY2N6tevX3CoyIDdu3fr6quvlt/v16RJk1q14Jg7d65KSkpUUVGh6dOnJ7TsAABEgqAAAAAOcvPNNweDANEmGAy45pprdM4550iS7r77buXl5al79+7q0aOH7rrrLl100UXBLgtH+/e//627775bkjRz5kydfvrprabPmDFD5513ng4dOqSpU6d2OHpBS/v27dNDDz2ksWPHKi8vT4WFhcrNzVWfPn308MMPS5LuuOMOXXDBBdG+7bD8+c9/1pYtW3TmmWcqLy9Pubm5Ov/887Vt2zZ169ZNCxcuVH5+fqtlbrzxRlVWVqpfv356/PHHW00rKirSM888I4/HowULFgTzQQAAYDWCAgAAOMjgwYN16qmnSoo+wWBARkaG3n77bd1zzz0aMmSIMjIyZBiGzjzzTP3pT3/Sq6++2m4z/bq6Ok2dOjV4N/0nP/lJm3m8Xq+effZZde/eXZ9++qluvfXWsMo0b9483XfffRo3bpwGDhyoQ4cO6fDhwxowYIAmT56sxYsXR9wlIRpnnXWW1qxZo2uuuUYFBQVqampSnz59NH36dK1bt05nnHFGq/kfeeQR/f3vf5fX69Vzzz2nwsLCNuscP368fvazn0mSbr/9dm3atCnh7wMAgM54jHhkAAIAAEmxc+dO9evXT01NTVq0aFHC75inikDriqVLl+q8885rd57y8nINHDhQklRWVqbS0tIkla61p556Stdff70GDBgQHH4SAIBEoaUAAAAO8thjj6mpqUmDBw+OOsEgAABAAEEBAAAcYs2aNfrtb38rSfrxj38cdYJBNxs7dqw8Ho88Ho8+/PBDq4sT9MorrwTLFUueCAAAIpVudQEAAEDHSktL1djYqJ07d0qSRowYoZtuusniUjlLr1692ryWkZFhQUna16VLlzZlLCoqsqg0AAA3IacAAAA2F2gRUFxcrIsuukizZ89u9yIXsbFLTgEAAJKJoAAAAAAAAC5FTgEAAAAAAFyKoAAAAAAAAC5FUAAAAAAAAJciKAAAAAAAgEsRFAAAAAAAwKUICgAAAAAA4FIEBQAAAAAAcCmCAgAAAAAAuBRBAQAAAAAAXOr/A7+1K32I14cjAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4\n",
+      "0.9524451336942912\n",
+      "0.7133580209950589\n",
+      "<Parameter 'BEC_amplitude', value=449.95823450592684, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_sigmay', value=4.531159303422342, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_sigmax', value=3.9948050534083563, bounds=[-inf:inf], expr='3 * thermal_sigmax - deltax'>\n",
+      "<Parameter 'thermal_amplitude', value=234.346332071024, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_amplitude', value=449.95823450592684, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_amplitude', value=449.95823450592684, bounds=[0:inf]>\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "f:\\Jianshun\\analyseScript\\Analyser\\FitAnalyser.py:86: RuntimeWarning: invalid value encountered in power\n",
+      "  res = (1- ((x-centerx)/(sigmax))**2 - ((y-centery)/(sigmay))**2)**(3 / 2)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5\n",
+      "0.9679921062510455\n",
+      "0.8353970604407345\n",
+      "<Parameter 'BEC_amplitude', value=556.8529128920721, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_sigmay', value=4.676859970607908, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_sigmax', value=4.090095537635108, bounds=[-inf:inf], expr='3 * thermal_sigmax - deltax'>\n",
+      "<Parameter 'thermal_amplitude', value=138.73314738692025, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_amplitude', value=556.8529128920721, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_amplitude', value=556.8529128920721, bounds=[0:inf]>\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "f:\\Jianshun\\analyseScript\\Analyser\\FitAnalyser.py:86: RuntimeWarning: invalid value encountered in power\n",
+      "  res = (1- ((x-centerx)/(sigmax))**2 - ((y-centery)/(sigmay))**2)**(3 / 2)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6\n",
+      "111\n",
+      "<Parameter 'thermal_amplitude', value=18.11559956556029, bounds=[0:inf]>\n",
+      "111\n",
+      "1.1007065302712244\n",
+      "0.9474072638704876\n",
+      "<Parameter 'BEC_amplitude', value=676.9280696811649, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_sigmay', value=4.847622882969768, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_sigmax', value=4.229774867478724, bounds=[-inf:inf], expr='3 * thermal_sigmax - deltax'>\n",
+      "<Parameter 'thermal_amplitude', value=4.6111713076833265, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_amplitude', value=676.9280696811649, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_amplitude', value=676.9280696811649, bounds=[0:inf]>\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "f:\\Jianshun\\analyseScript\\Analyser\\FitAnalyser.py:86: RuntimeWarning: invalid value encountered in power\n",
+      "  res = (1- ((x-centerx)/(sigmax))**2 - ((y-centery)/(sigmay))**2)**(3 / 2)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7\n",
+      "111\n",
+      "<Parameter 'thermal_amplitude', value=1.5964361357756474e-05, bounds=[0:inf]>\n",
+      "111\n",
+      "1.1271856611121658\n",
+      "1.0443499897922286\n",
+      "<Parameter 'BEC_amplitude', value=715.9101065602428, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_sigmay', value=4.712712244300954, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_sigmax', value=4.174281856530882, bounds=[-inf:inf], expr='3 * thermal_sigmax - deltax'>\n",
+      "<Parameter 'thermal_amplitude', value=2.4916588305431375, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_amplitude', value=715.9101065602428, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_amplitude', value=715.9101065602428, bounds=[0:inf]>\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "f:\\Jianshun\\analyseScript\\Analyser\\FitAnalyser.py:86: RuntimeWarning: invalid value encountered in power\n",
+      "  res = (1- ((x-centerx)/(sigmax))**2 - ((y-centery)/(sigmay))**2)**(3 / 2)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8\n",
+      "111\n",
+      "<Parameter 'thermal_amplitude', value=6.152406713200165e-06, bounds=[0:inf]>\n",
+      "111\n",
+      "1.24761097358594\n",
+      "1.1007144514442881\n",
+      "<Parameter 'BEC_amplitude', value=731.6475562621214, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_sigmay', value=4.684134961245081, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_sigmax', value=4.072284625627418, bounds=[-inf:inf], expr='3 * thermal_sigmax - deltax'>\n",
+      "<Parameter 'thermal_amplitude', value=4.418579710142679, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_amplitude', value=731.6475562621214, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_amplitude', value=731.6475562621214, bounds=[0:inf]>\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "f:\\Jianshun\\analyseScript\\Analyser\\FitAnalyser.py:86: RuntimeWarning: invalid value encountered in power\n",
+      "  res = (1- ((x-centerx)/(sigmax))**2 - ((y-centery)/(sigmay))**2)**(3 / 2)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "9\n",
+      "111\n",
+      "<Parameter 'thermal_amplitude', value=1.0471999798422615e-06, bounds=[0:inf]>\n",
+      "111\n",
+      "1.2696766909374297\n",
+      "1.1316765124630221\n",
+      "<Parameter 'BEC_amplitude', value=736.2192048064755, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_sigmay', value=4.644189572770608, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_sigmax', value=4.019899236706962, bounds=[-inf:inf], expr='3 * thermal_sigmax - deltax'>\n",
+      "<Parameter 'thermal_amplitude', value=2.767321194144596, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_amplitude', value=736.2192048064755, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_amplitude', value=736.2192048064755, bounds=[0:inf]>\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "f:\\Jianshun\\analyseScript\\Analyser\\FitAnalyser.py:86: RuntimeWarning: invalid value encountered in power\n",
+      "  res = (1- ((x-centerx)/(sigmax))**2 - ((y-centery)/(sigmay))**2)**(3 / 2)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10\n",
+      "111\n",
+      "<Parameter 'thermal_amplitude', value=2.0170076719949748e-09, bounds=[0:inf]>\n",
+      "111\n",
+      "1.0770639524619068\n",
+      "0.9921090880797887\n",
+      "<Parameter 'BEC_amplitude', value=746.360298383822, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_sigmay', value=5.030614372702362, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_sigmax', value=4.291492331580912, bounds=[-inf:inf], expr='3 * thermal_sigmax - deltax'>\n",
+      "<Parameter 'thermal_amplitude', value=2.379912245174944, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_amplitude', value=746.360298383822, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_amplitude', value=746.360298383822, bounds=[0:inf]>\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "f:\\Jianshun\\analyseScript\\Analyser\\FitAnalyser.py:86: RuntimeWarning: invalid value encountered in power\n",
+      "  res = (1- ((x-centerx)/(sigmax))**2 - ((y-centery)/(sigmay))**2)**(3 / 2)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "## perform the BEC fit\n",
+    "\n",
+    "# set the fit Model\n",
+    "fitModel = DensityProfileBEC2dModel()\n",
+    "fitAnalyser = FitAnalyser(fitModel, fitDim=2)\n",
+    "\n",
+    "fit_fT_flatfield_result = np.empty((11,13))\n",
+    "fit_fT_flatfield_std = np.empty((11,13))\n",
+    "\n",
+    "# params = fitAnalyser.guess(data_sim, dask=\"parallelized\")\n",
+    "params = fitAnalyser.fitModel.make_params()\n",
+    "params.add(name=\"A_amplitude\", value=  3000, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"A_centerx\", value=  0, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"A_centery\", value=  0, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"A_sigmax\", value= 50e-6, max=  0, min=-np.inf, vary=True)\n",
+    "params.add(name=\"A_sigmay\", value=  30e-6, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"B_amplitude\", value=  27000, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"B_centerx\", value=  0, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"B_centery\", value=  0, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"B_sigmax\", value=  80e-6, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"B_sigmay\", value=  80e-6, max=np.inf, min=-np.inf, vary=True)\n",
+    "\n",
+    "for i in range (11):\n",
+    "    print(i)\n",
+    "    sim_fT_flatfield = xr.DataArray(\n",
+    "    data = sim_fT_flatfield_np[i], \n",
+    "    dims = [\"x\", \"y\"],\n",
+    "    coords = dict(\n",
+    "        x = (\"x\", x),\n",
+    "        y = (\"y\", y),\n",
+    "        )\n",
+    "    )\n",
+    "    # perform the fit for one simulation with flatfield\n",
+    "    params = fitAnalyser.guess(sim_fT_flatfield, dask=\"parallelized\", guess_kwargs=dict(pureBECThreshold=1.2))\n",
+    "    fitResult = fitAnalyser.fit(sim_fT_flatfield, params, dask=\"parallelized\").load()\n",
+    "    fitCurve = fitAnalyser.eval(fitResult, x=x, y=y).load()\n",
+    "    fitValue = fitAnalyser.get_fit_value(fitResult)\n",
+    "    fitStd = fitAnalyser.get_fit_std(fitResult)\n",
+    "    # store the results as numpy array\n",
+    "    fit_fT_flatfield = fitCurve.to_numpy()\n",
+    "    fitValue_array = fitValue.to_array()\n",
+    "    fitStd_array = fitStd.to_array()\n",
+    "    fit_fT_flatfield_result[i] = fitValue_array.to_numpy()\n",
+    "    fit_fT_flatfield_std[i] = fitStd_array.to_numpy()\n",
+    "    # plot fit\n",
+    "    plt.figure(figsize=(12,8))\n",
+    "    plt.errorbar(x, sim_fT_flatfield_np[i,100], fmt = '.', label = \"simulated data\")\n",
+    "    plt.plot(x, fit_fT_flatfield[100], label = \"BEC fit\")\n",
+    "    plt.xlabel(\"y axis [px]\")\n",
+    "    plt.ylabel(\"OD\")\n",
+    "    plt.title(\"OD distribution cross-section \\n f={}, flatfield lightsource\".format(str(f[i])))\n",
+    "    plt.legend()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4\n",
+      "0.9524451336942912\n",
+      "0.7133580209950589\n",
+      "<Parameter 'BEC_amplitude', value=449.95823450592684, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_sigmay', value=4.531159303422342, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_sigmax', value=3.9948050534083563, bounds=[-inf:inf], expr='3 * thermal_sigmax - deltax'>\n",
+      "<Parameter 'thermal_amplitude', value=234.346332071024, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_amplitude', value=449.95823450592684, bounds=[0:inf]>\n",
+      "<Parameter 'BEC_amplitude', value=449.95823450592684, bounds=[0:inf]>\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "f:\\Jianshun\\analyseScript\\Analyser\\FitAnalyser.py:86: RuntimeWarning: invalid value encountered in power\n",
+      "  res = (1- ((x-centerx)/(sigmax))**2 - ((y-centery)/(sigmay))**2)**(3 / 2)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Parameters([('BEC_amplitude', <Parameter 'BEC_amplitude', value=449.95823450592684, bounds=[0:inf]>), ('thermal_amplitude', <Parameter 'thermal_amplitude', value=234.346332071024, bounds=[0:inf]>), ('BEC_centerx', <Parameter 'BEC_centerx', value=600.4901284357314, bounds=[560.5420779016479:640.438178969815]>), ('BEC_centery', <Parameter 'BEC_centery', value=960.6538995040197, bounds=[915.3423064697963:1005.9654925382431]>), ('thermal_centerx', <Parameter 'thermal_centerx', value=600.3018356023492, bounds=[499.70580257202505:700.8978686326734]>), ('thermal_centery', <Parameter 'thermal_centery', value=960.5575103290612, bounds=[858.7135761857239:1062.4014444723985]>), ('BEC_sigmax', <Parameter 'BEC_sigmax', value=3.9948050534083563, bounds=[-inf:inf], expr='3 * thermal_sigmax - deltax'>), ('BEC_sigmay', <Parameter 'BEC_sigmay', value=4.531159303422342, bounds=[0:22.65579651711171]>), ('thermal_sigmax', <Parameter 'thermal_sigmax', value=33.53201101010806, bounds=[0:167.66005505054028]>), ('thermal_sigmay', <Parameter 'thermal_sigmay', value=33.94797804777908, bounds=[-inf:inf], expr='thermalAspectRatio * thermal_sigmax'>), ('deltax', <Parameter 'deltax', value=96.60122797691582, bounds=[0:inf]>), ('thermalAspectRatio', <Parameter 'thermalAspectRatio', value=1.012405072798814, bounds=[0.8:1.2]>), ('condensate_fraction', <Parameter 'condensate_fraction', value=0.6575408911220945, bounds=[-inf:inf], expr='BEC_amplitude / (BEC_amplitude + thermal_amplitude)'>)])\n"
+     ]
+    }
+   ],
+   "source": [
+    "i = 4\n",
+    "print(i)\n",
+    "sim_fT_flatfield = xr.DataArray(\n",
+    "data = sim_fT_flatfield_np[i], \n",
+    "dims = [\"x\", \"y\"],\n",
+    "coords = dict(\n",
+    "    x = (\"x\", x),\n",
+    "    y = (\"y\", y),\n",
+    "    )\n",
+    ")\n",
+    "# perform the fit for one simulation with flatfield\n",
+    "params = fitAnalyser.guess(sim_fT_flatfield, dask=\"parallelized\", guess_kwargs=dict(pureBECThreshold=20))\n",
+    "fitResult = fitAnalyser.fit(sim_fT_flatfield, params, dask=\"parallelized\").load()\n",
+    "fitCurve = fitAnalyser.eval(fitResult, x=x, y=y).load()\n",
+    "fitValue = fitAnalyser.get_fit_value(fitResult)\n",
+    "fitStd = fitAnalyser.get_fit_std(fitResult)\n",
+    "# store the results as numpy array\n",
+    "fit_fT_flatfield = fitCurve.to_numpy()\n",
+    "fitValue_array = fitValue.to_array()\n",
+    "fitStd_array = fitStd.to_array()\n",
+    "fit_fT_flatfield_result[i] = fitValue_array.to_numpy()\n",
+    "fit_fT_flatfield_std[i] = fitStd_array.to_numpy()\n",
+    "# plot fit\n",
+    "plt.figure(figsize=(12,8))\n",
+    "plt.errorbar(x, sim_fT_flatfield_np[i,100], fmt = '.', label = \"simulated data\")\n",
+    "plt.plot(x, fit_fT_flatfield[100], label = \"BEC fit\")\n",
+    "plt.xlabel(\"y axis [px]\")\n",
+    "plt.ylabel(\"OD\")\n",
+    "plt.title(\"OD distribution cross-section \\n f={}, flatfield lightsource\".format(str(f[i])))\n",
+    "plt.legend()\n",
+    "plt.show()\n",
+    "\n",
+    "print(params.item())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12,8))\n",
+    "plt.errorbar(x, np.sum(sim_fT_flatfield_np[i], axis=0), fmt = '.', label = \"simulated data\")\n",
+    "plt.plot(x, np.sum(fit_fT_flatfield, axis=0), label = \"BEC fit\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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0MmKNt+4v4hOv5RMKhbBnzx40NjZixowZ7MiDTJKNlk8s5IyM4MaL0t1GhYpP+qBut0j6nPgQN5vf72fTSIW+XGq1moqPCG63G9u2bQPDMCgrK8t4jUtfWhz5IyMcDgc6OjrQ1taGQ4cOpX1kBJ3nkz7iER8yVoSKT5bDr92RKhrVaDRZM2gtVcQT82lpacH27dsxePBgTJw4MWtSiPtigohKpYLNZoPNZsPw4cMRDAYFR0ZwU7qTHS+ilk/6oJZPJH1CfLhzd8iXKdYXqr+43eS+RoZhcPDgQdTW1mLSpEkoKSlJ8dkpp6+JDx+NRiM6MmL//v3wer1RLYAS3RxQ8UkfiRSZ0phPFsJvkSO3U0F/EB+5lo/P50N1dTXcbjcWLFgAm82WhrNTRn9cIMVGRrS3tydtZAQVn/QRT5Epcbtl43cyUXq1+HBrd1QqlaIPtr/EfGJZC52dndi2bRtyc3NRVlaWVWnAfPq65RMLqZERBw4cgF6vj2gBJCclnopP+ojH7eb1ehEMBqn4ZAvJmLvT3y0fhmFw9OhR7Nu3D2PGjEFpaWlWL0LZfG6ZQGpkRF1dHXbt2iVrZAQVn/QRj9vN6XQCAHW7ZQPJmrvTH8RHzPIJBALYtWsX2tvbMXv2bBQUFGTg7JTT3y0fKeIdGUHFJ33EY/k4HA628W1fo1eJT6zaHSX0h2w3oVRrh8OBbdu2QafToaysLOWpvMmCLpDKkBoZUVdXB5VKhYKCAoRCIXi93j65s84miLdGacyHZLr1xeu/V4iP3NodJWg0Gni93iSdYXbCd7s1NjZix44dGD58OMaOHZs1adRyoZZPfEiNjACA6urqPjsyIlsg38N43G5ms5mKTyZI1Xjr/pRwEAqFsG/fPtTX12PatGkYNGhQpk9NMX3xy5cpyMiInJwc1NTUYP78+XC5XIIjIwoKCpCbm9vrNirZRiLi01et0qwWn1SOt+4vbrdAIIDNmzcjGAxi4cKFvfpCppZPciHvp9TIiF27dvXqkRHZAnfzrASHw5H2lkvpIivFJx3jrftDwoHT6UR3dzeGDBmCSZMmxVXgli30xS9fpiHiw39v+SMjnE4nm7zQ20ZGZAsk3qP0Ou6r3Q2ALBQfUrtDrJJUmft9WXwYhkFNTQ1qampgMBgwZcqUPrF4U8snuYiJDxeVSgWr1Qqr1So5MoKIUbaNjMgWEmmt0xcz3YAsEh9ui5xUuNn49FXx8fv92LFjB+x2O8aNG4djx471CeHpC68h25AjPnx628iIbIH2dYsmK8QnVUkFUvTFhIPu7m5UVVXBarVi4cKF6O7uzuq4lpLPuC82Fs008YgPHyUjIwoKCvps5lYsEunrRsUnRXBb5KRDdAh9zfIhXZBHjRqFUaNGse2Gsn3BVlLkmOnX8n3BHPyovSKj55BMkiE+fOSOjCBuut5SZ5Yo8dT4AFR8UkIyWuQkQl/JdgsGg9izZw+ampowc+ZMNmMJSN4Y7VShRHjSvVuuXnQypq//n+BtfUWEyLWRqvdWaGREV1cXOjo6cPToUezevTvlIyOyhUTcbrm5uSk4o8yTkU86E242Pn3B8nG5XNi2bRvUajVOOumkqKyj3mD5KCGdr8VR407bc2WKdLfW0Wg0rNWTrpER2UK84uNyubJyvEkyyIj4kAte7viDVEBiPr21t1VzczO2b9+OkpISjB8/XvBLmsgY7WyjN35G2U6mr/10jIzIFuKN+ZA6n75IxmzcTO/KyYUQ70WRKRiGwYEDB1BXV4cpU6aguLhY9L59LUifDa/l+4I5mT6FpJFp8eGTipER2UK8MR+Xy0XFp6/RG8XH6/WiuroaXq8XCxcujBmIpJYPRYpsEx8uSkZGkBZA2fw9pqnW0fR78QkGg72iiWJHRwe2bduG/Px8zJo1S1ZglliX2bjIKD2fTFhxP5TMw0n1mwGELR6NSY2gu2+IOZDd4sNHaGQESenes2eP6MiIbIGKTzQZE59MXxgkFTnbkw4YhkFdXR0OHDiAcePGYfjw4YozxLJ1kcnGc+JSNDMv06eQUrL1upCDXq/H4MGDMXjwYDAMwzZGJSMj1Go123GBxIsySTAYVOwmJK+rL04xBfqx5QNkf8ZbIBDAzp070dHRgTlz5iA/P1/R8cTHTFLZezOZil9xrZ++Rm8WHy4qlQoWiwUWiyVqZERjYyP2798Po9HIClEmRkbQhINo+rX4ZLPlY7fbsW3bNhiNRpx00klxBVfJwtJX4j7pYt+SMzJ9Cmmhr4gPHzIyIjc3FyNHjkQgEEBnZyfbGDUTIyNokWk0/dbtBmRvoenx48exa9culJaWYsyYMXG/V+Riz4YssURJh+XDvPQ7qK5fCSDS5fZDyTz256nXT8aOl3al9DzSBRnM2NdRMjKioKAgJSMM4on5kI7iVHz6INnmdguFQti7dy8aGhowffp0tv4hXvqa5ZNK8aledDIAwPyfs1P2HNlGX7V8YpGJkRHxiI/b7QbDMFR8+iLZJD5utxvbtm0DwzBYuHBhUtqokyLeviA+/XGRTDX9VXy4CI2MIC2AkjkyIp6Yj9PpBAAqPskmGy76bBGf1tZWVFdXY9CgQZg4cWJS6xUyXcybTLLpdXQ/cANyHnkx06eREFR8ouFmyY0aNQp+v5+NF5GRETk5OaxVJHdkRDwxH4fDAY1G02eH9fVryyfTCQcMw+DQoUOoqanBxIkTMXTo0KQ/R18pNE3lIklcbmIUzcxD04b2lD1/pqDiExudTic4MqK9vR319fWyR0bE43Yj3Q36alyuX4tPJhMOfD4ftm/fDpfLhfnz5yMnJyclz9OXWuxk4nX05VofKj7KERoZ0d7ezo6M0Ol0ESndZGREPG63vpxmDVC3W0Ysn66uLlRVVSEnJwcLFy5Mac0BtXzkYS4yw9XqEr1dY+p7u08qPonBHRkxYsQI0ZER+fn5ca0zTqeTik9fJd3iwzAMjh49in379mHMmDEoLS1N+Ze/ryQcANkV8+kLUPFJLmIjI9ra2gAAmzdvVjQygohPX/2MMio+mXYJpTPmEwwGsWvXLrS2tmLWrFlsj6pUk80JB0o+/776BcwkoVCIvq8phIyMyMvLQ0NDA+bOnYuurq6IkRH8FkDcz6OvWz59z5eggHTFfJxOJ8rLy+F2u1FWVpY24QF6v9vtlsfsAFKzUam/7oKov4UC8jYjrfsa0P3ADUk9n3RDLZ/0QDa4FosFJSUlmDp1KhYvXowZM2bAZrOhubkZmzZtQnl5Ofbs2YOGhgY0NDQoLjC12+1YsWIFpk6dCqvVitzcXMydOxdPPfUUfD5fXOe+YsUKtmRD6t/BgwcVPzZ1u6XY8mlqasKOHTswdOhQjBs3Lu2ZK73d7ebscgIIN1ZMpvgwL/0u7mOnXj8ZHbWtSTuXTEHFJz2QZAPuey02MqK9vR0//PADbrjhBowYMQJGoxFr1qzBSSedJNkcta6uDqeccgpqa2sBAGazGV6vFxUVFaioqMCbb76JtWvXKu4PSdDpdCgoKBC9PZ7ap4xaPpm+8FMpPqRbwY4dOzBlyhRMmDAhIymT2ex2U8JTbw/CfX/X4voVbSl7DnNR4oW9vQkqPulBTo0PGRkxduxYXHjhhdi5cycWLFgAn8+Ha6+9Fvn5+Tj99NNht9sFH/+8885DbW0tiouL8fXXX8PpdMLlcuHtt9+GzWZDVVUVLr/88rhfQ1lZGRobG0X/lZaWKn7Mfu92S4X4eDwebNmyBa2trVi4cCEGDx6c9OeQS7ZaPtet6P2WQ2+Hik96iKfGZ+jQoSgqKsKPf/xjHDlyBNXV1bjssssE3XCvvvoqduzYAQBYvXo1Tj/9dADhjecll1yC5557DgDw+eefY+3atQm+muTRr8UnFQkH7e3t2LBhA0wmExYsWJDxgGFvsnxu+n0H+/O1D7TIOuaqextTdTp9Hio+6SHeQXIOhwNWqxUqlQrjx4/H9ddfL/h5vfbaawCAU089FQsXLoy6/dJLL8XIkSMBAK+//rri80gV/d7tliyrgGEY1NTUoLKyEmPGjMHUqVPj8oMmm2xLOLjxkZ5OAXV1dWhubkYgEJA8RsrVpjNkbgpt676GjD13MqDikx7ineUjJ+HA5XLhhx9+AACcfbZwU1yVSoWzzjoLAPDVV18pPo9U0a8tn2S53fx+P7Zt24a6ujrMmzdP0bTRVJNt4qNSqeB2uwEAT75bhJqaGqxbtw6VlZUAwhk7xFKTa/0kglDGW3+Bik96iHeWD2mvI8WePXvY7/eUKVNE70dua2xsRHu78lZRu3btwpQpU2AymWC1WjF+/HjceOONqKqqUvxYhMxvzTNIMsTHbrejqqoKZrMZZWVlcQ19SyWZrqUSory8HMBUAMD8+fPh9XrZQrxbH3PBYJS2hAhX3duYUcsHAFpuuwwAMOCvb2X0POKhv8zzyTTxut3k1PkcP36c/bmkpET0ftzbjh8/Lpm5JkRrayva29uRl5eH7u5u7N+/H/v378dLL72E++67D4888oiixwOo5ZOQ+NTX12Pjxo0YMmQIZs+enXXCA2SX5UMK68aMGcP+jWEYGI1GyS8OHzkZb9c9lNyEhlg93jQf/Enw779+2p3U80gm1PJJD4mITyy3Gzf7TWoMC/c2oYw5McaOHYsnnngC+/btg8fjQVtbG5xOJ7788kvMnj0bDMPg0UcfxVNPPSX7MQn9OuYTb8JBMBjEzp07sXfvXsyYMSOhaaOpJhuy3UKhEHbv3o39+/cDAIYPHw4A8Li8AIAbVrbj6vubFD1mQ4O8eIscEVKSYr3733tk3zfboeKTHlIpPqnm8ssvx29/+1uMGzeO7UGp1+txxhlnYP369Zg7dy6AcDFqV1eXosfu95ZPKBRS5JZyuVzYtGkT7HY7ysrK2Fbr2Uqms91uerQDFRUV6OjoEMzEIej0ytxn9fX17M+HDh1CZ2enLJG98u7enSSQTKj4pId4Eg4YhoHL5YopPjabjf3Z5RJvjMu9jXtMIhiNRvzhD38AEM7MU5rG3e/FB5A/ZrqlpQXl5eXIy8vD/PnzJSuOs4VMut26u7sBhHdKT781WNAt8Kunwm6poD8Iv1derAcA5syZw/7s9XqxY8cOrF+/Hjt27Ijwg/Mx55jj6m7QWtWp+Jhsh4pPeog34UCO5TNkyBD2Z+6GjA/3Nu4xicLdUB4+fFjRsf3a7UbEJ5brjWEYHDhwANu2bcPEiRMxadKkXhOoTXfCwbW/C2eoNTY2YtOmTQCA6dOno2Cw/LYeSkTI7XDjqXcHYdGiRWyvrAeeDcfeiJVzw8p2VoCfHfy07MfmMulnE+M6DsjeuA8Vn/SQSrfbxIkT2bVo586dovcjtw0ePFhxskGq6B0raIogH5qU+Ph8PlRUVKCxsRELFixI6q4hHaTb8nF2OXHgwAHs3LkT06ZNAwC2+aBSlMTjLv9tPX75qFO0zcdFtx7G9u3bAQC+dmW+abmIJR1kK1R80kM84hMKhWS53cxmM0466SQAwBdffCF4H4Zh8OWXXwIAzjjjDEXnEYuNGzeyP5NCVrn0a/FRqVSSSQednZ3YsGEDdDodFi5cmDRfaTpJZ8IBKRZtaGjA/PnzMWjQoIjbiVWUKKFA9OuRk3L99/+MSMrz9xWo+KSHeGI+LpcLDMPISji4+uqrAQDffvst623g8t5777Eusauuukr2OcTymHi9Xtx///0Awh27TzvtNNmPDfRztxsg3OWAYRjU1dVhy5YtKC0txfTp07OiW0E8pCvhwOVysbugBQsWCAp1d1s4BsTtcqAUn9uHEBOKSLf2nsiaEyMUDOHlByPFiVg/rnbxIG2iZKu7jUDFJz3EE/NxOp0AIFt8pk6dCoZhcMEFF7CB/1AohPfeew833ngjgHAHBL5AcEcmkI7YhO+//x6nn3463njjDRw7doz9u9/vx9q1a7F48WJW7B566CHk5eUpeo29c0VNIvxan0AggJ07d6KjowNz5syJuwV5tpAOt1trayuqq6tZl6RYvVNhcdjXrFKpEBSZm2PLC3/Zjh+qhzU/R/Q5dXodnF0OqGXsKH0eH4p2fI2X5wK+ozHvHsHAicVo3iOdITdw3hSEnC6oLb2rKzYdJpce4nG7OZ1O6HQ6GAyGmPfVarX45JNPcOqpp6K2thann346zGYzQqEQPB4PAGDmzJl48803FZ0DwzBYu3YtK2YmkwkWiwVdXV3w+/0AwuvLPffcg7vuukvRYwNUfCLEx+FwoKqqCgaDAWVlZbI++GwnlQkHxEI8cOAAJk2adKJQtFbWsUF/EBqdBi67GzqDDlqdVtSCcXY5oDeGPwutTosQEy2mfq8fRrMRV97dEOGCC/hjJy9M+Nn/hX/QarH75c9F7zfpZxNpnQ9FMfG43ZxOJ8xms+zPp7S0FNu3b8eqVavwwQcfoKamBjqdDpMnT8Zll12G2267TXER/NSpU7Fq1SqUl5djx44daG1tRWdnJ8xmMyZNmoTFixfjpptuwtSpUxU9LiHjY7QzDRGfhoYG7Ny5EyNGjMCYMWN6TTZbLFJl+YRCIXYs+Ny5cxWb3EA4ThPLZQYA1rywC8/n8QkKD/f1EeFRqWNfW8PPWoC9//5G9PZJ152N1vJtMR+Hz21PufDX5dlvBVHxSQ/xWD4Oh0NxR3ybzYaVK1di5cqVso9ZsWIFVqxYIXhbYWEhli9frugclNA3VtgEUKlUOHr0KHbt2oXp06dnZNpoKkmF+Fz8m1ps2rQJDocDCxcuZIXn4t/URtzvinvE622EMJh7LM2BwwdHWC0abfSXN29Afq/4rDzu6BHG2VDsSsUnPcTrdst0d4NUk/FvbiYvfo/HA6fTCYfDgbKyMgwcODBj55Iqkp3t1tnZiSGjBsFisWDevHkwGo0Rt6s1PZeUzqBDZ4t0WjNXcLhodfK+rJbcnt0h192m1WllWT98Jl0n3JaevZ1T72MbHB2T2r17N4Bwin62Q8UnPcSbcGCxWPr055Nx8ckUbW1t2LBhA7RaLUaMGCHZlK83k8xst/r6emzZsgVA2B8stpsjKdXOLqf4eWl7Lj0iNHIFRw5uh3SmmXlEuJEpG+9JEpOZcI+65X+NjDVlW2dxIHxO9/8js13B+zoMw8Qd88n0IMpU0+/Eh2EYHDp0CFu3bsW4ceOQn5+flQtDskiG5RMKhbB3717s3bsXM2fOBAD85EbxwLvb4Y6o6eHX9zi7XIK1OnyMZmPM+8Ti9luGwO04kU5tskA/boKs46QsoIJp4wStHgAI6YVbLt38SCd27tyJ6x5qhdcbO86VDvrydZ8tkO8eFZ9oMi4+6TQr/X4/tm7dimPHjmH+/PkYOnRoSkZpZxOJWj5+vx+VlZVoaWnBggULUFRUBJc9nL553g27cfld4v2kiAvOx4l5OLui62pCocjzc9ndcNmjLRezzQyTxQStTjxPxtnliPh9mm8TLLmJ+c4HTiwWv21ezwCvkDP82jQa4a+VxWIBE2LYyZOHDh1CR0dHxnrvUfFJPWRtUep2IyO0+zIZF5900dXVhQ0bNgAAysrKkJMT3rkmc5R2NpJIwoHD4UB5eTnUajUWLlwYsRMbPU1ZKw0lGMwGeFxhgTMKxISkxCeZqIvEY4Bc0eHz1JIdgn//4xt5AIBXvgzHjbxeL3bu3Il169Zhx44dqK+vZ+sy0gEVn9QTr/jIaa3T2+kX4nPs2DFs3rwZw4YNw6xZs9i5FEDyRmlnK/HW+TQ3N2Pjxo0oLi7GrFmz8MsnIy0KYv0AkLR+5I5KEEsOCPilPxtiOQdl1PMkg4Hnnwvt6LGit2t3lAv+vbu9G15PjwXo9/kwadIkLFq0CLNmzYLNZsPyVQFce18LNm3ahAMHDuDKuxtSujGi4pN6SKabUg8PqfPpy2RcfFLpdgsGg9ixYwf279+PWbNmYdSoUVHP19fFR6nlQ2Ji1dXVmDJlCsaOHSv6GeUNFO/+ECvgT/A4hHf6g0cIWx3cBZP7s60gMgbDtY5uvyWzzWCZUAgGkfiVSqWCzWaLaIg6cuRItk/e999/j+rqahw7dixiJstV9zZGPM5v/hx+H2/6fQf7t0tvr5M+Lyo+KSeeZAOgf6Ra99kOBy6XC1VVVdBoNCgrK4tKCSZQ8emBtBbq7OzE/PnzWdekGLY8C7pa7TCYDVE1PkoJBUOisRKC2GJpy7dFtOtxdjki4jzTfNHNFqWIlW5NkHK9ERzdLlldFiIed+DAE2n/DZgzZw7a29vR0tKCAwcOwGg0orCwEEBOVP0IESC58N/Pax9owSuPZPdwxN5GvLN8HA4HRo0alYIzyh4ybvmkgqamJmzYsAEFBQWCtShc+nrCgVy3m9vtxqZNm+D1eiNiYoC0W41Akgu4Fgd/xydU86M36eFxeKDWqOH3SS/SXGuKLOj816aWOaMpHgaep7wdfUdjm+TtN6wMN1m94q5wQS4/nmW1WjF8+HDMnDkT726ag7Fjx7Kved26ddi2bRuOHDmi+LxufdIZ9d4pFUlKbOKd5eN2u/u82y3jlk8y3W6hUAgHDhzAkSNHMGXKFBQXi2cpEWjCAdDe3o5t27Zh0KBBEcOpgEjhufmPnQj4g4IWit/rZ7sQaHXaqIWssa4RRrNwGrLepEcoGIJao4beqIPbGZmKrNFq4Ha4I+JCUoJqtCZ/wqwc4eE3Fu1ud8BWmBt1P61OC71Jj2sfCKegE+EhXHl3A/71eM+1S263FdhQVFSEu/+mAuDHvHnzcNsTbty27CiAgbB3OqHRqNHS0iLYEPfWJ8O3/+WO8PtD3W6pJ17xodluvQiv14uKigq0tLRg4cKFsoQH6Ptut1h1PkeOHEFlZSXGjBmDyZMni7oIOpuVDWDjZ6nZ8qRdeEDY9SYE1+IhP4tlvOUUxn4eJcRj7SSDWPEaAOzO+NnPIt0zj75pw7p16wCEhczhcAgKDRWf1JPIFNPeOD9MCX1CfDo6OrBhwwYYDAYsWLBA0Y6hr4sPqfPhLzSkMejBgwcxe/ZsDB8+POHnyh+UB71RvHOu0SLs/lTzLCmvyxtl/XCRm8yQLGIJkFT2Gx+3M7rOyWgxRrUZMtt60tr93shWPaRA9+d/CM9H8vv8Ebe77G58XL2Q/b2yshK3rQo/bzAYYi2uFz8fJ/u8KfERb8KBy+WiRaapJhG3G8MwqK2tRUVFBUaPHo1p06YpHvrWH8QHiNzler1ebNmyBV1dXVi4cGHETPfrVrQCCLvbrrwvdvPL3KLw7mzQ8J5AtZA46AxaBGOkTfPJKQhvIiy5FhQWR7uRVCoVWw8khNnW+3zmepMeelOPgPNdcrc8Zlf8mC9+Ji0y1AJKHfEkHDAM0y+y3TIuPvESCASwbds21NbWYu7cuRg+fHhcQtYfEg6AnjYf3d3dKC8vh8FgwPz582Ey9cRHiPBct6KV7b0mtzmnx+WD3xs7YK0zaKEzaOFxedg0a6mxClqdBkazsDWl4fSH43a9jmenqZRY1s6jP2uOskgIQtYP0DM6QgidXgd7u3LhkcP69euxa9cuAJENUa99oIW1kuQix12YKNwpttlOIm43Kj5ZiN1ux4YNGxAIBFBWVhbXLBlCf0g4AMK7qYaGBmzatAnDhg3D9OnTcdny8FjPC391GBf+6jC627uhUqsiFnUh+AkH/J1zblFkkF1nUJ7X4rJLj7e2d0QuxHzXHRfj4WrFzy9E98g5iu5P3IxqVeS5cV1qBJ1eFxXHsuRaYLaZMGDoABitRpht4okU9s7IJq6OLhdyCuTFv6ZOncpmhK5fvx5btmzB4cOHZR3bG+DXRKUTKj7i9DrxOX78OFt5P2fOHMXT+fj0F7fbwYMHceefQpg+fTpGjx4taSWGQoxgWxsCdz6NxyU8OkDMCmpvao/6G3cUglKsuUnyiyt010qhZsLX09M3hWMyKrWKXYBIKngoEILRGh0D43eE4MfQhIpyNRp1xHFmmwk+jw+eGIP6yKYgLy8Po0ePBgAsWrQIf3mvBL9/uccK27lzJy689RDbEJXEmsTgzipKdG5RMqwcJpQ5t2I8MZ9gMAi3201jPqlGrquMBMj37NmDGTNmSFbeK4FYPn3V7x0MBvHvdTPQ3NwMAKIzi/IH5kX9Ter9jWX96E16+H1+UdcTANg7oxcxsYw3Pol2vFYXD43625FF18X1WK0j50f8zm2xw134+IuQzyMs3EyIgd/rh9vZEztT4nLzesTfc0JjXY81wHWtCW3myCK4YcMGbN68GSq1SnZDVK1OK8t1d91DrYJ/52+CepPLDYh/lg8Amu2WDZACSBIgHzAgeVXY5MLoi9aP0+nExo0bAQAzZsxQdGx3W3ix42ZhtRzrWUSEpnMKYRGxTPwJDltz2XvcTFyRdHVLu+syjZiVx3czqrV8V12Pyy0VxaDEQhGyVB5+Kez+WbRoEV78Mhzruu85LdatW4ft27ejvr4ebrc7IStHym3am4l3iikA6nbLNC0tLdiwYQNycnKwYMGCpFf9kgujr8V9WltbUV5ezgo19wsgd7y1x+lhkwGEFk0xl5sYJHsNEI57SNHWEO5Z5vN44fN4oVJFX7r8BYxrOXCxLpiPPWN/yv6+70e/kn0e3cOmC/49tOA0WcdLdeQmApSIuEjFhQCgqzVsbRYNCV8X3HicJdci6QHgNuQFgFmzZiEnJwdNTU3YuHFjxIaCv5m77I6j8l4Ah18+4Yh9pywnXvExGAxR73dfI+PiI+baYRgGBw8exLZt2zBhwgTJAshE6GuWD8MwqKmpQVVVFSZOnIiVL4UX+ZsfDheJSgkP3/UmVpeTKKoT73lhcZHg7T6PD/kDojsD+HjWFn8OECBuWXhGCYuGGFLjFITotsoraiYIxXsAoKOpXTBGESsuFgoG4ff2uNtyCsIuG2t+9O45xIQ3WmrOoqjWKp/7dOtjLjz4TwNmzZqFxYsXh8/zhNtu3bp1qK6OnejBTyXva8Q7xdRsNvfpEdpAFoiPED6fD5WVlTh+/DgWLFiAkpKSlD2XSqXqM0kHpIt3bW0t5s2bx75v3LgLE2LY2hgSzBYLyPIXvMLiQgDirq2OE8kEXpdXcoR2LDS8cdrOLleE71/I6uGjNMgcT7xHKN06MHVh1N9W/DJaaLjp5dzYj1BroCAnDsa1ipxdTlaQNRIWFbFIiNUTD7lFefjZnceizletVeNndx6Lqq976fPxUS1+9u3bh9bWVva7dsVdxyPcsjc/2iUa++mtxGP5OByOPp9sAGSJ+HAVngx902g0WLhwYVqCbn1BfDweDzZv3gyn04mFCxciNzfacuDCtYBi1fKI3S62UzZajWxVfmdzh+B9xHAITDqNlbUFRMdM5NYn8Tmy6DocOunmuI4VEp5HftNjeQhdY26niy2GVepu48eFiLUDhOujopqU8qwgg7Fnc6G0AFgK8jqEumbs378f33//PaqqqljhSTTZ5+r7mxI6Ph7kFvvGm3BgtVqp5ZMuGIbBkSNHsHnzZowYMQIzZsxIm8+ztxeadnZ2ory8HFarFfPnzxfs4s23JqTgum/4dPBSpV12FwoGim8QSGzH7fDITlIgacN8ARlcKu4KM1mS20z0wPjzRW8Ti/vEQmqwntflERyIZ+90sJmFbpHZRwSuRcLPeGs9HrYouALk43QRjxUrEoO0+iEQ4fH7fGzHbsL48eNRVlaG+fPno6iox+VKJgxzrzuhrDahv4nFhW5+VFkvwlQRj+XTH1rrAFkiPoFAADt27MChQ4cwe/ZsjBw5Mq2q35stnyvva8CWLVswcuRITJkyhd1lXfbbo7jst9JBXuKayi3KRXtjtIUSK+3Z1e1iv1hEgIS6FXS19iwEJM5jirPzdKxEBdLuh4vSeI8QYgWmSvq6GUzx1aTlDwgLRk6hjRVk8tmZRGJHQE+CR/6ggqjbuMJOYkBcuFaYUJEwP02cn+lGujUIbXrMZjOeeLcQ+hOW14QJEyJu37p1a9QxvZV4Yj7U7ZYmGIZBRUUF3G43ysrKIvqMpYve2OUgFAphz549AIDXv52C0tJSVrAvuPVQzOOZEAONToMLbj3E7ji5AhSP20pniFxc8wflAQAKTsSKkoXJaorqoiBWM6MUP5Maa1tpDMqWJ5xmq1KrWHcb3+ow20yyanykMJgN0Bv18J6IC3KFRypTT+nmjWvF3fds5OMOGjQo4vfdu3eL1vc4JGKL3KmuSrj2gZakJULE63aj4pMGVCoVJk+ejLlz58JgEK+qTyW9zfIhCRltbfEV3IU4r5WIDPG/EyHSG/UIBoLhIW8Sbjg+3PobALDmW9iFt7st/oA3F6HdfvhLHn4tJFHimVul3VRKUqwTRarFUHe78Psi1hhVo9FExHv47jhHhwOODmF3lJgIkgxEKZHkF3wqcXWarCbWOuK74/gp8g8+F/k8z3wUXRDc3d0tGCviJyzEU3skVRitBIZh4s526+s1PkAWiA8A5OTkpCSNWi69SXzsdjvKy8uh0WiwYMGChB7L0RkZNM0T6HIACKfrEpJZ8NjV2gkgfF6NdU2wd0QKmU/mrt5sS02KeCpQCyxM5FrUaNSKOnNrOS4ug9kgagWZLCYwIQZMiIHX4xO1aJxd0nU2RDSlFtdwm5+wOKq16ojkgFgxLC78eNm2bdtwzQPNPc/j82VdplzP56hcfKjlkyYyndXRW8SHFPMNGTIEM2fOjEhvlTP+IF64cRziyw8GhN+vUVOGRwXOubt+ssvlfuYBf4AVMWeXE7lFeextKhmbEjkut2266Ey0bIKbYi0VD4uVlSbHSuVmuXldHskZTJ3Nnehs7hSdoWTJtZxogBoWyZDC75HYdRSLdzbOivh9/fr17M+dnZ1xPaZc5Ga6EVc+FR9hskJ8Mo2cUdOZhBTcbt++HVOnTsWq9wfg549FZvMIZStxXSihYCiilb9QIFhq4SK38RMKuO13uI8pN85DrB2CSq2C3+uH2RZdbW/vcMBkNcIu4lLicsUVo2LeBwi73kxq8R24WNab0oy3Sy4K11wNGz1AMJ4m14U1cFgR8gbEP62VKzxaBRmQQpht5oguEtY8G/RGadc5sWA0Wg37j/wuBUlaMZoN0Ol1UVbTokWL2J937Ngh/0XI4PZnYqf6CxEMBqFSqRRvrqnbrR+RzZYPmVtUX1+PBQsWYPDgwextsbLZpOC73JIBt9kn3/rJG5gb5dZzO9zIGxAuRBRy8eQW2mDJ6VmUpTptC2F1Nkf8vnH4lYqOTwSbI9y4My8U6QrKyw+/BpVaJeqydJwYF+Gyu6HXC7vESEKAyx5tkQwaHs4otLfbJTMW3U4PtCLlDPmDC5A/uABaic4KicZGTJaez1NOTz6Sci408oM0RGVCTIQQETZt2oSDBw+KNkS99cn4i6LFIMkGSsXH5XJR8UkX1O0WzU9/eQAulwubNm2C3+9PWsGt2CAzKRIZeSCFzxPeUTq7HFHpwAWDctkEAq4Fp+MtxkItdjKFkhY7QsIzbFzYOuK64HIKYsd8Av4ALAJxLrH2SNz6Hp87/BmYLAaYbUboDLqoz9uab0soJhv0BxAMBiXrnIDwdeZ2uKNcfLHmS7HPc+I7zF9P9CY9Xlk7EX6/H7t27cK6detw2R1H2YaogLCIKx2kJ3Q+8c7yoW63fkI2FpkOHTME5eXlyM/PT2huEXGXuR1utu+WFPwiUoJWp2U7XQPhpqNiFA6W7q5AFhfvicfwSjwW0LOTBwCTRS8YIOfO9TnnxwWYl7db8jGzAa1OC2uuhe2pR9roEBecVA2P0tRtoVofACgeGbakhVyB3O4UUnEhfgNXhglFuGdNOWEB5VpKDYfrAfTEAAP+AGv9WHItrFAFAyEEAyHWqvZ7/TETFbgFptyR5BMnTsRJJ52EWbPC8SISQyWd39va2th1YMDQxMsD4hUfh8NBLZ/+QjZZPgzDoK4uPIp43LhxmDRpEtRqtWRCgdlmgoOXGUYueu5CHQwGRRd6El/hBvt9Hh80Wg18bl/UTj2WtcpfILiZatx+XiarAfmDw190ocwvQJ51IycOpBQS71E6wVSozQ4XR4cdYyb19Cv0eyPnHjk6Y78WuTE1Z5czQqjamzrBhBg23hOIM+DPjQ/y642AcGYb17Wr0WjC9XQibkCj2QiD2RDhmlWpVJJxKSJQoUAoZqab3+fHlXc34Kp7GlkPAmmISgbp7d27Fzf8LnLzxY87xhqkxyWeNGsgbPn09Vk+QJaITza43bIh4YAMzDt0KFwkOmzYMPa2WC4LIFxceuGvIscfh4IhNkPK3a3cr83vJA2EK+0BoGSUss7PfPQmAwwGLZuKG4vCwbnwegIYNCy8ix83Kfz8uUU2DB87WOrQrIAs2PxiXKAnniG0kMul6UjsVGOx+UqxIBmFZCMgpwhZbzSIWkyFxdEzuXQGnaBFxxcg7vVizbVAre3xXCgd2a7VanH3X8OvpaysLOr28vJyBPxBtLa2KhIeIL4CUyAc80n26JhsJCvEJ9Ok0/K56v5GXHV/9Ex5r9eLzZs3o7u7GwsX9uycr1txoidXrnA2FD/LTc5Oy2Qxw2QJX9wmqymqKadcGiQWO24RZKw2PaRbQW5RLmz54js+r6fH+mo9HvbHG4w6WKwGnLwwvKim0+W2seRniu4vFTuz5IY/D7LTZhhGsmce0JN0oHSqq1angTuGq5Nb+Onscsge9iYlSuQ7FgqGItogCY0G5+LkNJsN+INsKrrBpIeXtznySqTd6wy6CDdc1LkLbILHjRsHADhw4EDUbbEaosbjdmMYhlo+/YlMu926urpQXl4Ok8mE+fPnw2SKFBQiQELw3W1KEMt04otFwB+I2DHbck1RVo+jO3oBCQVDCPiDCPBqU8ju1npCaPIKY+/G+W68nALpuBIQru1JR33PQd9oydt3t0a2i5GyYgePkJ7Sy3Vf6k3R2X95vN52JP3d5/HJbkHEzzyz8jYEYp6KoD8oKD5anTbCouEOFRSCG090drng9XhhPpH1KOSGs8qw5oTqlK6+vymi6PWqe6M3hStfC28KuBtCwoYNG7Bnzx40NzcjEIhOIKEJB9Jkhfhkg9stE+Lz6z+7cPz4cWzevBnDhw/HtGnToi5Wa65J0OohAV2hppChYDBm5wF+1hv/MwgFQ+wO05JriehWLIRQQ1ExiGtEbBGWs0gKLWA//nE42+ygb3RMQVBChSfxxqRj88Np35NHq1krRyh1XK1RY8ioQREp5kC4cSv3M9Jo1MjjDNxra+iM+B8IC5FWp4Wzyxnhzotl9RQW98zhkernRjCIpMBzXx9//EMsxOJeQQEr2u/1I+gPRliAjAw3uloVbh1FBI1894SuS25hKRHSiRMnQqvVoqamBuvWrUNlZSVqa2tht9vjbq0DhN1u/cHyUeYg7aNkItuNmOy7d+/GjBkz2HHXXH71J2GrpqOpM8rdFvAH4LY7YCvIi/i7x+WG0RwtXiaLmQ1ym6wm+Dy+KP+8ULxHCqH729vtMeMMQn5+j8sHo1ncRWKy9Nw2aaINgPhik0rrh+t6+7ZxCpZaxRND7r4a2MeJZxtMeqhUKkGxNRgz99Xkfx5el5d1u3mcHsEUbm7nBSbEQC0y8I//PdNo1OFWQAo2L3xIHIovONzWQdZcCwL+IJgQw8Zu3E6XYJJL3sCcKGudDxNiUFBQwDZCdrvdaG9vR3t7O+rq6qDRaKDT6aDT6eD3+2WPhwkEAvB4PP3C8qHig8wkHPzmL2E3wIIFC3D/i2r85Texj7luRStCwaDgTjMYDKKwZKBiwUgkwA2ERbS5voMNmMeLs8sZIVJyu2rz7+f0qCGjNCYjHOiIdFXmF5jR0e6KEB+/LxBVy8RFZ9Ci/uBxDBtXAr9X3Lrlu996Hl+4MNTn8SPgDwq6tfxeHwwicSWhjUPewHzR61BoPDrDMJKxGGLR6Aw6BHkipVarEAoxMecRhds0hQVFqjiWe5scESKYTCaUlJSgpKQEoVAIXV1dOHjwIJxOJ9atW4ecnBwUFhaioKAAOTk5ot4ehyNs8VHLp5+QyZhPOJ9fOODv6HILutwC/gA0mp4va1tDdG2Oy+5kZ994XNI1PkKvPdxWP77d6PHacDKA1Nhmr8sr6q7h4vP4oTfqMGh4EUIhBm6nFwajFgMHhV9bMl22UuMUiOttjrE64ecpGiy9sPh9Afh9AVhzBIYCatQYMrqnmFXMddnd7ohyTeYPyEFHC2kGqmZdWGqOgOv0WnhcPkHxFyrE1Bv18Pv8rKUjVX8kJDyxMFqNktehWBq+VOIBQW80SLqndXqtbPHholarkZ+fD5vNhoKCAgwdOhTt7e1oa2vD0aPhriQFBQUoLCxEYWFhRA2f0xn2dtA6nzTRX2I+LS3iFdM//WV0No3YF0OsuaRQbzYhn73bGQ7i8nFKzEYBoosexVwrBKnpqdZ8eW4Ffofq3HwzCouyz7RREmMiLXaAsPUjB7dTvkXLjfsAPdZhW0Mn/F6/oJUqZ5FVn3CRSRHvkMBUEKvJqlThLAC4ndKbr1ses+OWx+yiY7xJzMdgMKC4uBhTpkzBokWLMH36dJjNZhw7dgzr16/H5s2bcejQIWzduhVdXV0wGo2yY0V2ux0rVqzA1KlTYbVakZubi7lz5+Kpp56CTyQ+K5empiYsX74c48ePh8lkQkFBARYvXowXX3wx4dHnQJaID5BZAUq1+DAMg5qaGmzbti3mfS+5vU7w752t4YAn8VFzd6FSnYSFLB7iavO5vWwjTzmIzZ3hQqwegpUzFK2tPnYdCvdcSJxBrxcaOyB88Y8ojJ39t2fsT9mfpZqKCkEsoN2hyYqOk0IoeUKr1bAZhNxMQqdd/vm2NykfJe1xhReszubOqHgIyYIUWniCwWBE8ovepBe0SoSsJy5EKAP+nuuAbHq4a0Ss6zavMAcDhsjvUqDVaSRrhGKlgwshlO2mVquRm5uLUaNGYe7cuVi0aBGGDx8Oj8eDq6++GqeddhpsNhtefvllHDt2TPLx6+rqMG3aNKxcuRI7d+4EwzDwer2oqKjAnXfeiQULFqCjI76BepWVlZg8eTKefvpp7N+/H1qtFna7HevXr8eNN96Is846C15v/HE6IIvEJ5Oo1Wo2OyXZBINBbN++HXV1dZg3b57o/QYMDScc8LPJiOgA0ZaJwahH0B+U1TZHCNI0Mpm7VSFriLtocNO4uV/2gkF5MBh1CPgDKBycD6U4Pem7lOUKz4b2xAXKaU/sCy6EnDEV5JoIBUMw2ywRi6jS1j5c+Nlq8U6gFWsyKyftmqSSCz3GkFGDov5GULrbl1NkqtfrMXjwYEyePBnbtm3Do48+CpVKhVdffRWlpaWYNm0atm/fLvjY5513Hmpra1FcXIyvv/4aTqcTLpcLb7/9Nmw2G6qqqnD55ZcrOmcgXPqxZMkStLW1YcKECdiyZQvsdjucTif+9re/QafT4auvvsLtt9+u+LG5UPFBT4plssXH4/Fg06ZNcLvdWLhwIXJzc/H6o7Er8YVccEK47G74vT1fXq6bTijDzSDSaFIprY2RO2o5yQYdjcI94/j1QQWD8iLScknoIeAPxixWzWb4yQbxIJRmTMgpsLIu1lh1NHLgCow5R3pzQp5XrNhV6Ppw2d1RVhD5fLlWD4FsYEwWo6BocBuiGk16dLfHblHU3hSfVSAXpXU+Go0GQ4YMQVFREdavX4+WlhY8+OCDGD58eNR9X331VXZ0xOrVq3H66acDCG+kL7nkEjz33HMAgM8//xxr165VdN6rVq1CY2MjTCYTPvvsM8yZE24vpdfr8ctf/hIrV64EADz//PPYv3+/osfmkjXik2m3G6B8Dr0UHR0d2LBhA3JycvDn94aIjgi/4NZDMR9LzB/vcYm7MHxuL9s1uqfPm06wtUs81Ow+qmiKqa0wPINGTISk4KZVcxFzvWWajx0/lrx9YKGyztwel08ys03KXSQVrLflxbYScoty4HF5o+p0tDqt7BlEfEuLKzpi/fz4dLfb0d0e9gJIvV9et48VHjkCJIRQtqGc5Bg+8dT5cFvr5Ofn46KLLkJeXl7U/V577TUAwKmnnipYAHvppZdi5MiRAIDXX39d0TmQ+3Mfg8ttt90Gq9WKYDCIN998U9Fjc8ka8ckkZOBTssTn2LFjqKiowOjRo/HIK9KDv4aOKUZDbTN8Hj9u/mMncgoi72/NjQ5IK+nP5Xa6otwkBcWFyCnIgYEz+IvrP+cHYjtboneIao2GFTeCzqCFzqCF4cTxBt7jWPNtGFQq7tYQwiux6AKAmSNMcuI96eJjx4/h8fV8vfzBxDZXYskbKrUKJo5Fq8TqaW/qVHwe3GuJuKHkFKJqdVo4TrTJIV0vVGp1QvU93HMgxErGkeoUDgDHDwsnD8RDPL3d5HS0drlc+OGHHwAAZ599tuB9VCoVzjrrLADAV199Jfv59+3bhyNHjkg+ttVqxeLFixU/Nh8qPgh/UMkoNA2FQti9ezf27duHWbNmYcSIEext/H5uJOXV4/bBkmthLT+D2SDa/l4MIgL8AW7qE52ECWRksVDiAPHx2/Is6G7vlvRvx8qKE4O7cHldXtbN0tkm/nh5vGwwoznsWiHfaQNn11/XZkHVYensMZJssOlwkeT9koWcuI+YO5EJMQlnFfHb9SQrvscXHJ1Bx9b2CFm3YmO45SDazof3vnkEhIwJMYpiVLHqhZQQT3sdOa119uzZw4YIpkyZIno/cltjYyPa2+V5HHbu3Bl1vNRj794dfx9FKj4nSLTQ1OfzoaKiAu3t7Vi4cCEKC8UzbWI2JPQH4LR7ou7n9/kirBBSx0OO4cNfIExWEyy5kbsqvVEPv9ePgD8gawcrF2L1SI0HsHFqmEiWVcS5CWS5AYDD0XNfq028Nqe6xohdRyMXkx3HUys6sVxuQhQO6PlMCmT0uZNb0KvVaWK6nuKpYxGCb8HIaW8DQDDlP164BdMk887j8iTFoxHPJiAe8ZEzxfT48ePszyUlJaL3497GPSaZj93d3c0Wxiola8SnN9f62O12lJeXQ6fTYcGCBTCbzaJJA1fcE/5whS5mbtNIITGRQm+U9knL6Rgg5/UrzXQymA0RqeCODie0Og3b30wuRpMuYpEmDBrQI5jHm6UXvCNt8na1n1dLN/cUonx3YrG0gkILQoEQXIIirIWjU9g6DKc4M3HHNwBpAdLptex0Uam4h8FsiEwU0WjYzYzb4YZKrYq45rljEbguX6FhhiTWw4V7jJCrLVYNmhRCrje9MXKTI+f7FE/MR47lY7f3vB9Soxe4t3GPydRj88ka8ck08YpPY2MjNm7ciJKSEsyYMQNarbD1QAZrcd0P9k7x5p5CmU18q4ULd2GIJURKMVnMsMuo8XHw3HFSixq3TX66qN6V2ZjQF9/0vIc6vZr9RzDbIj83JsQgFAhBo9NAI2KVDhiizEVry5Mv+jqDDm0NHcgpyGFjkWLfEf7mMV7XLB++lcd1rXE3Qm6HJ6Yg8F1/3NfV2dyZ4JlGEm9j0f4yxRSg4sOiRHwuuPUQLrj1EA4cOIAdO3Zg2rRpGDNmjCLrLVZhpxzXisfhhkanZRcmqUy2AO/5uG6SeAeMyUHIUnJ2CTd05MLvMdbV4ULoxM45GGTQ3OiMcL8BgNEQ+f5rtT2/y7V6UonfJ2zN8jfp3GwrvVEneS14PfIKhAHpjhNCBPyBiIxGqbiNRqu8ezOBfBeCnKmqYr3k+HVB3O+clFVushqTFu969j7pJCKgR6SVJhzIsXy4fd9cLvFNHPc2ub3iUvnYfLJGfDLtdlOScPDOn8OJBMePH8eCBQswaJBwBpfQ0DhAvJlnwB+Qlf0jleIs1zfN9bXLGSaXN1C48DOZAVoh/L4gWhp6LAbudZKTExbbxmb5CzAQHff57uBQfHdwaMTfpFxv/DiSEtQqeZ+Px+1jxZaLNcfECg4/xZq0LJIamcAVq9yi2IsoF34mphABfyDmiO9Yk2sNZiN0el2Uuy0UDLGuvGAwBJc90royCmSy5XPGcgsh1h2kQ0Em4GuPRn//yVoST8wnlvgMGTKE/bm+vl70ftzbuMck87FzcnLittSyRnwyjVzLx+VyYePGjQDCA6aEVP/i39QKHkviPUJwBUVOBbqUtcL9Usp1wZlt0u6YYCAIa54NthMLkCXXwv4sB67gklRuoQF0UjQe62R/dnR70NwYfp2DB4onHRw8JB4L2XS4KEp0+BztVubWikV5Vc/O3eMJoKsz/B6QWI+S2iUxq4ef9RUKhiS7Rkc8psLUZ67FQ65ht7PHQpLqYMAdWscVJLFmqfziU2tez3dPKB5lzjEhGAix9UgkzVpn0LHTc4VIxoYqGAyyWbRKkDPFdOLEiezjcrPT+JDbBg8ezI5+iAU3w03OY0+aNEnW4wpBxecE/Gw3oYSBtrY2lJeXs5lseoVtbfg7tViYLMaIQHJ4omjkLkNq7DQgfzSBFPyFiwifkF+fLAJS1fix+nsB4pYwabAp1PFZjLf/q7wvlxg1TfLmsvCp2C6+qHd3eWGxxr6WrDE6DUjBdWkBPRsckkwgRt7AvAhBMtvMEXVg/MdNFEuuNe6WO3xIkgB3TEIwEBJtzRMPYl6DeAfJyXG7mc1mnHTSSQCAL774QvA+DMPgyy+/BACcccYZsp9//PjxbEcFsccmYyKUPjafrBGfTLvdpCwfhmFQV1eHrVu3Yvz48Zg4caLg/W56tAOXLg8XaHH9z1K+Zq557+xyRnyxhYLyfJebki4DfMTSXANeP+wdPS4PsdksRIS4I66JsHBfP9eSi5XlRlKu5boPO7ujRe6rTdKX9e79scUomd0TKrZ70dokbIFZrHq2e3J3h/wkDGL1SCVukE4ASmN6RGwiAvpON2vRcMVIaPHlu5XJBkin18HV7YpohQOA/dnv8/fEf04kq/BfH9nccM9NSSzL5/HJ7qqQCPEUmALyp5heffXVAIBvv/0WmzZtirr9vffew+HDhwEAV111laJzIPd/++23UVtbG3X73//+dzgcDmg0mrh6xxGyRnwyjZj4hEIh7Ny5E4cPH8a/vpuKoUOl3TQ5BeELhzQKBXoCqqFAiHVTSPUpExsmZraZ2f5ZAX8AOQKuA7/XB5M1dkYT+cLL6VTNhSsKPo8Pnc2xOydzkweS0YqdD1eAmprD4hVLgIR49Sv5vmuxuM93/4vs6s3tckA4XNtjaXS2R1sdPgVJBELD32JBLALu8+QWWmVbMbF6vZlzhK+/UDDIWtFEoExWU4QQxQM/1Vpv0st2M6aKeGp8gHC2m1SKM+Hqq6/G1KlTwTAMLrjgArZ/WygUwnvvvYcbb7wRQLhLwWmnnRZx7IoVK9iuLkLicuedd2Lw4MFwuVw499xzUVlZCSBcy/jPf/4TDz74IADgpptuwrhx4xS/RgIVnxMIJRxct6IVmzdvhsPhiOifJJZIwIekVxP0JoPsLzjXAmpv6oSF54fmWorx7LC4eBxuNvDKD2Jzg7h8wRTKLnI73KzLLVZVe8AfZFvyJIvuLk/E/3yGF7rZlj37DkRaI51tkbvsZMd7+Ph88q6Frjb5dRQNNfKuTTlwLSahmCD3WiY/Ky1UjriOZdbmtDe0xbwPfw6U2+GJsNCThVCyARCf+DAMIyvmAwBarRaffPIJSktLUV9fj9NPPx0WiwUWiwUXX3wxuru7MXPmzLh6r+Xm5uLTTz9FYWEhdu/ejTlz5rCJBb/4xS/g8/lwxhln4E9/+pPix+aSNeKTDW43oQ4HZrMZ8+bNg9EYeTFzv5hX3HNcMpmAICQ8/L8JTSUFwm4WKWupuzVsgeQW5UYETIlbTix1VawFPckSstiMilN0uZCdOf8xiAUkWQvk8Arezm2y2dDgQleXH5squnDgQNiKazwWfi8qNzcLPm4oyLDCU1Ul3nak+qAa1QfT8xXp7op2gZLXLlbjI0Ss3nldrd3wefwRGwN+Cx6lkGQBsUSBnvtFv0biCZA6VkjQiPtNyO2nTnCkO5fjh8S/11JZovFaPnJiPoTS0lJs374dDz30EKZMmQKVSgWdTofZs2dj1apV2LhxI/LzlY8nAYDZs2dj165duP322zF27Fj4/X5YLBYsWrQIL7zwAj7//HPRZslyyRrxyTRctxu3xcTUqVMjLqLLfnuU/fna34VdLGqNWtEFL5RqynVHRfzMsS7E4jsGswF6kyFivEIs8k50mTZZjMg7ITT8tNTC4sjfY71GISEjcQetTivprtFo1VFJBI21zYKNRdube1yFRIi8ngCbPSeUokz4cG38MbJYyQYVVcIWypGD4hNsrTnCX2AiPB63L8K1Rqwg/i6e/1lJEW+fNb5lEqu2R0mhaTx1QkKLP9/dRpIN9EY9+y8dhEKhuGM+SlKXbTYbVq5ciR07dsDhcKC7uxsVFRVYvny5aELUihUrwDDhvoGlpaWijz1o0CB2mJzb7UZHRwfWrVuHG264IWFvC0DFh0Wj0SAQCGDv3r1ss7zOlg5cvzLSxOfv0IgAGSM6DPR86GTXxg3Cki+a1+VlUz65aaeubuHFQUlyAQny8gVBTjr5gGHKd8LcxZAIZjKK+kgCgtx+ZgR+Gvfwwuj31OUQFmuxZIP649E79+ffiHYB8eM+SuDHe7iWX0dLN7wimWBSxaZ+b9hqJpYz15Kw5QsvdMRSZUIMa2VwXbBSsSa5fd3IfaXuz71e+W7eZCyAQghlY/51ubJ2UPFYPj6fDz6fL+6izd5G1ohPpt1uDMOgo6MDLS0tgvMx5MZ5xLCKpESL1VUQ6ydWcFfO+2bNt7FBXa1Oy/4sNmZYq9WIzkyJJQJCXzivSLacEPHGf8S6ByQDKdebz+3D15vlf42O1US6+bo7peMQ8SQUSD9ez/ubSKdpKeTUqQHyZuSIbbjsAv3egOhYD59kpXED4vEeIP6O1gBoe53+hMPhQE1NDQBgwYIFsn2uXIilIXRxi9X3cOsPoo45Yf3wraBgIAirSDYRF4PZyFpgUTtGlTqqEaXYjppPrMFnaq14pwi+CGl4A8o6WqWbY1psxgiXWzDIyE6JjqfW59iRngVOSoCaGuw4dKADTod0gSa3c3d3d+R9uRaP3JY5JA3Z7fDIqp3iIhRHSTTrjEus+jK1Vi3palNr1fB7/Yob2Qp1R08F/7xH2jqJt68bgLjWn95IVolPJqyf5uZmbNy4EXl5eTCZTNDplH8BPSfamfg8Pji6nOxuNbcoDz6Pjx19IFZvIeVOU2vV7KLAnRzJ/+IazAbJjgNMiBEVBZPAeG0LZwcpNqCLj1RhKSCdCRWKaBIpvIg77R5Fwffo42MvTM9+FLkb5woQl83liWeVuew9r1MonkY2Mskae8CFVPvrjTpRq1GuGHFd0cSaMllNCaU7k9oynUEnaP3pjXr2eYXa6hASGRMilWwQi3jHKZjN5pS5E7ON/vEqBWAYBocPH0Z1dTUmT56MYcOGxT3PJ39QnuzaDL5ouJ2uiHiPFFIixRdufhU3350ltTBw73u8pkm0Fxe/Nxh3LLOYtScWz9LqNFHZUEJibTRHn7fLJe+9Nxi16BIo5OSnXHMR23i7nZ64e7zxz1esrov7mcp1v/EtILkWLcFoNcJoNcZctN0OT0RzXLVaBY/TE3HdKY3TEaSeu/HwiZEknA+mvaGNdcPJcatJ3ef5B8PZYfmD8uScqijxTjG1WCwZD0Gki34lPtc8GJ7TEQwGUV1djUdez8G8efNQXFwcs7cb/wsh5DLg+7qb6xqjMtCksm0S6QwsB6GdmEdBPIaLXGuID1dAuTt6bhNJnUGLrlb5tS3+GKO2+bicibtmmBCDnTs7RW/nJx0IucViWYpAz3skx/px2d3sgu/1+GA0G6DT62C2maKube4mwN4hPytNqRtM+rF6Xr/f62c7buiNesFx1zqDHjkD8hAMBmHvDLtfTVaTYDzVZffAZY/tauWPASEoaSwqRKqmmPYlskp80qH4brcbmzZtgtcbvtBzc8PZZhqNBn9dPUzwmPNv2Q+gx70mZ+EluyuSzSb3S6vRathYkFor/vEYT1guckVAzF0lZU21HI+ugYn1fPkDeroukPeAX58kJsBCnz83IYNr9XBjPdxiTa4rS4qQQKzIaOpxH7W29ixcx4+LWHGcxc3vDQh2KzfqhQXG3hlfsD+nwIau1uiuFDkFVuQUWFlBY0KMqDstt8iGAUMLRC0Mcq0KXbN8C8yWb4vaNDGhUNR1Zc21QKVWiRaSxnLxObscousDOU+uRRNvrY/fL7+7hBSJDJKjlk8fpby8HDk5OXju89ERf1er1WBCDC781WHRY4OBYNTi6/f6FbVDIXALQW35Vmi0GvZvYoszN1MtGTQfawXQY/2I7QJVahVbs8HPUMotyolaxEiLIS7WXHEXldD7R2IHQhlZGp2WzQZkGCY6kSGOhYfreju4T7zwdOfOTsHiVa1OExVk/+5/LVj7v9jthwhi7z+Bm6AiVnCcUyCdKZVbZENnc7R48eHW0JisyosJyWdgy7ey4kCExyByfYtd2x6XF7lFeYrPAUis92EiUMsnNv1GfI4eDReHjhkzJqJtOIFcKEyIiSgk5cO9mLvblPVF48PdnQm1cZdrLXF3SlJZT2KPx43pEGun/lD8AXXyPEaLUXCR5FozbodHcIFI5YA7OfBb7QBAh0AftkQRG38tFJcgVg9f5NSc38WyvTwON5gQI0t4lMK1hoQ2TmK1RHz4LXy4mxq1Ri0rNVsJQrN8+IWZb/15OJ66TQufL/y+/v1ui6zYcCIxn/5CVolPKszNUCiE3bt3Y//+sOuMtAvno9FoMHRMMYaOKcbAEvFhWDqDDiq1SnQSqVGkjQ0X8gWNmEMvw/8PyAuocgXI4/JG+Pv9Xj/UWjW74IvtDKUSEuScA8nwA6TjXPFAMvGkmpSSnXcXR0RipXJz0Rt1CAYZVkjFXG9StMuIG6hFUpKlvgtC15izyyUoLInGEblWj6Mj/P6J9Uiz5FoSbhzLTViwyhQtpWh1GtEhcnzBMBgM0Gq17N+DwSACgQD8fj+CwaCoEMVr+fSXGh8gy8Qn2fh8PmzZsgUdHR0RhaNCrjWhC0UrM+1aaiS2o8sZc3KjEPzFn7Sz56a1kp2hUCxKSMy4WWzxZiIB0hYZGQ/AFRzugjRoeJGo4BG3kpKalViB+LCIRL4XQm6+mt09abVmm/QOO94kDSmI67CjpUtS3LXsyPTE3K9H9x9HblGke9Tv9cPR4YDL7kLRkOimqmIbklgj4Qlci03M9cb9PH0en+wuGYlscF5aURTxPxe1Wg2dTsf2MdPr9exaQYTI5/MhEAggFAqxYhRPzEfOFNO+RJ8Vn+7ubmzYsAEGgwHz58+P2ab8ynsbov7m9/rg6Azv9oT6VHG/GN1t3eyXkOu7zynIER0/bMm1wGA2RmTJJTKcS6ywL1mWB+nSbcm1xJzLI8Wg4dFfciCxmgwhhBqiCrmtCO3N3QgGGfi9yj4DOVlVBIORWxMT+ziuEHW1dkdYsQazIaEaoMbayNhVrHhRLMRSxpVArlXuBkTq+hUbSS+EnHHxXD56bnzU3zQaDfR6PYxGY4RVxDAMAoEAaxUFAgHFnhxq+fQBGhsbsWnTJgwbNgzTp0+HVtvzpSDp1rEYNrZE9Dahnb/epGcXhtyi3JjBY0Dc5SIXqZHHUreJWT2eONutBPwBqLVq1uohSHXhjrgfZwERWpC7RVqpBIMhdpFSq1SCr6tdZN5QZ5vzxGPLd8UR+JYbf9iekLuNn20ot72/5MLLeX+lBqRpNGoMGDpA0pXa3tTzPpHnJK1qpLoGkHhPd3t3lMutq1VesoXX40NXa2fE39LVAFQJLz4UaQ1yrSK9Xg+dTgeNRoPu7m74/X6o1eoIqygWDoeDik+mSDTmwzAMDhw4gB07dmD69OkYPXp00uNIPo9PsnWIUK0Qd7ES+lIRt5ycOThys3f0Rn3C4qaklifWLBZ7Z+Suk+uKlKqvMpqN0Gg0ohM7pSywWOJHhKe9uUeAOlqEA/LOLg+8ZMpqjEQQJsSgraEDRrMhYhaPWJwhGZDH9sQ5syZeq4frcmutb424TW8Mu6qkhNbeIU/8pVpRcWEb+QZDsr777/xphODf33pKuOxCDLVaDa1WC5fLhZ07d2LcuHGw2WwRVpHP55OMFVG3Wy8lEAigqqoKDQ0NWLBgAQYOHCjruOtWtMa+Ewcht5izK/oLJBQUFloMxXzfYmh1WnS0RO8oSdwHUDZWWAiS8abT62A4sYAwTM+5x+rbJYcBnJiCLd8qOlco4A9E7diJSAsF2bnjFEidSygYgouzALodHkUNJrluuNbjHbKPiwX3HMQ6P7C3i7iMtDoN/N5AzEJbe6e8RV6otkxKxLkuYyI2fMRiQnKFB4jPJfvCg5GzbN58IuzNEIrtJIOuri5UVVVh7NixGD58eIRVpNVqJWNFAE217pW4XC5s3LgRwWAQCxYsiGpJHsvVJpZaPXx8z8hsn0fcjWUUCYqacpRfSDmF4v3ZpOaj6PS6nr5anB5w8bov7G3y61PExiYD0kIllogR8AeiXDhi/bv4Xb+5rjey8yUiJFZ4KDTkLBZc4bDmR3/O1nxrzD503MyuWN28dfpwlqWcbEohbHlhy4a0PcopzEFOYQ5ajvWMhOC6IFmrUCJjj5/0oDfpWSuHuNxa61ujhIPEqeT0fuN+ZnKsnxcfKsDz9+eJ3k46Ub+0oiipItTV1YWtW7di9OjRGDYs0moi7jm9Xs/+02g0UVZRY2NjXL0leytZJT7xuMhaW1tRXl6OoqIizJ49W3SAUiqJZ2KhFESAgsFghItHquOBFNyFnJu+KiYaQsKji/G+8hcSbsyJ7Hz5cSgmxETtqskCQ44htR1y4kck/uNx+SIC8fGIC9+1Zu90wiDQV45A2gG1Nci3jPgNXZVawUBPLEnM2uUKvE6vRd7AfFbIyWZGKHPO6/KywqTke8l3GRNrSKgrAxBbgHwen+z07WQlrCh1uXV3d2Pr1q0YNWqUaCkHQa1Ws0kLXKuouroaFRUVaGiITnzqq2SV+CiBYRjU1taiqqoKEyZMwIQJExQVdSn1v3PrVoD4elzFa4UIPRdxvbGLNW9XyH0ulUrF7tSDvJgR/7FJ0oHO0HO80OIUDIbAMAxUahX0RvHdmkqlglqjDneHCAbRfGJBa2voFLw/V8ilFh25dVFykErOOHqgHt0ivc/cTnnxFXKuRCi62yNdaB1N8q3MeNL2ufAz0ownBLD5aGuUa4+72eG7KfnXTczNCe/aT1an7mS4gBOhu7sblZWVGDlyJEaMEI4fSaFWq7Fr1y5ccMEF+OMf/4hVq1al4Cyzk14pPsFgEDt27EBNTQ3mzp2LkhLxzDQxHF1OdLfHrvYuLpUXO+JDguhCrjIx94/S4V6JpsZqNGqYbWZodT3+6LyB0TPf49mRs88hUeRInpM7ToG8b1KFvMmGJHEIBcZ1en1EnI+72Aml+cqZFMtfMInrUKVWR7jVNFpNxHvAdad6XB72vKz5FsFGnHIQs3qkMNtMUd0GuBaMVCcCMUvHEKc7kQs/Gy3V2O12bN26FSNHjpQcRy3Fzp07cd555+G3v/0tli9f3m/6ugFAcgsrEkTOG+/xeFBVVQUAWLhwIYzGxC5aUrwpB6PZKHvnGTXvXqdJSp8p7u6UWD+WXItkPEgJ/LRhLnkDooWJTzAQZHfXBrMhprss4A/AgBOuNZk1G6FASNAF6XZ4BNsUEcRcPwSyEDfXt2FgSaFomrZYsoKSTtyWE+dJnrOxtgGDS4uhUqsRCoSg0WkQDASjxJs/xj0UDAFxJJhIXS9i1fn851YKea1MiIkS4GS7rrm8+vv4NpBS2O12VFZWYsSIEXELz549e7BkyRLceuutuPfee/uV8AC9zPLp7OxEeXk5LBYL5s2bl7DwyEUsoSARyG6fn/EjR6BYiyEYSkoPNL6FMWBYOCgbYkIIcbLc+Dvibk4asVAWoNK0X7nptFIoibnw0Rl07GLfWNcS495hSLIB93OLZf0IZaYNLi1mf+a+5/wFSavTRvxNaMHyebySCTIAkMfpPk4gHdjJ/0Cke01sppBwN40TtUISySgklZ8raj6PL6mjrlOBw+FAZWUlhg8fjpEjR8b1GPv378eSJUtw/fXXY8WKFf1OeIBeJD719fXYsmULRo4cialTp6Zkp9Rc3xbzPmLZRnJiQH6fX3CB5VobxPVG/uayO0UFSa1Ri8Zb+AV+3CywWGm5PrePLVokFhw/mNveEP1eMXEO40smXDdSLEtHCWqNGm2NnQCE3UoGswHBYBCFxT2uH6FF1O2MbqQq5F5sq28RvZ1Y6yarUTBLzmSJ3X0iGAzBxus+bhco5hWKU7rsbtFYS5FEX0QuPo8vatYV+Q4piY2S8yBZbHzkWj2r/zFG1v2I8AwbNgyjRo2Sd5I8Dh8+jCVLluCyyy7Do48+2i+FB8gy8RH6EEKhEPbu3Yu9e/di5syZKC0tjevDktvZQAlkEZHj5wfCi4jf6xcs3pTrkissDru+dAZd1CRRIOze62wR3/0T4RH6gpMvcm6RLaERyGIIPWcyW977PF7BeJpQHVY8yHEL8vvSxZsYMXZW5GLYUzwpfq2R91Jn0CJvQL5ozMwhUV/jPeFW5jf15G6Q+OLr6OgRLaF4EdeS6zlXPyy5vOfIcovH6XSisrISQ4cOxejRo2MfIEBdXR3OPfdcLFu2DKtWreo3I7OFyOpX7vf7UVlZidbWVixYsABFRakpDhNDTkGh1OJpECm6k/uYsboGEEgbFEA6ZsO3ePgtWfgZfVzIDtshUawYCjHhccq8BYifGBGrpQ3XDcO1FBtrmyPqUoAe1193uyNiMVNq9ZD3PNFsskQJ+AOCBbfczxgIWwmxGrAKJbCwn6OIAIllrQmlMRNxGzxiEKwS7jUxLwV/o8D9XW/UgwkxcWWVpgKn04mKigqUlJTEbfHU19fjnHPOwVlnnYVnnnmmXwsPkIXiQ6wah8OB8vJyaDQaLFiwIKOVv0IjFsSsHbVKLRnr4AqSV2SnJ5b1VpDgXHkgvPiQBV1KSKQQcs8oIZY1YOW0zBFyc7Y1hjcFOoOOXRRJxliym5MS+IkTJEVZTo82u8DcJ/IahESSn4bMTTrQGXRR4sQ9B27Bqy3fijyJomUxXHYXOps7Y95PLGuTWD/cVHnu90XIZafWqCWTU4Q2eelKsyYWz5AhQ+Ju2dXY2IhzzjkHp5xyCv7xj3/0e+EBslB8AKC5uRkbN25EcXExZs6cGdEYNB5e/b2wPzhV8L8oQq6IyEmRJtbdwcdg7hnIJmUx5EsIE+nxZrQao6wfvgCRL3Qi6dVCEOvHKBAv6WrtYhMwiEtSaPcv1f1BCK7rsI0To+K6d0IiO2upQLkQ3IWTPwYgVsKKy+6WtGKEkjm4A9rIIh9LeMUSBgjcQtVE5wBJQbosJCPBJBWZbFxcLhcqKysxePBgjBkzJi7haW5uxrnnnot58+bhxRdfTGlmX28iq8SHYRgcPHgQ1dXVmDJlCsaOHdsrgnFSM+8JsWonAHkzWmK5rOROjQTCGU95A3IFZ6ZIpSxLwV/MXd2RhYvE6okVJ4vVnj+e0eVciLVAFtmcwhxBl5vepIeBI2JicR9+nY3P44u6L7/Al4uc99sgkFzCFa1Ykz6VWgqJplYTy0hn0MXdFiiTEOEZNGhQ3GtRa2srzjvvPEydOhWvvfYaFR4OWSU+QHgA3Pz58zF48OCUPo9YI0s+QnEfOX5oOYF0bnzGbLPA5/bBaDZG7GCFnitvYKQFoLRanLuoxDtULn9QgWA6bzgDS3jRilUUK9QixmQJL6jx1DFxA+FFQ6LjhR6XlxVHkqlHeqARuKLAPT+DSS+5mAuJFHlfSI0Pcb0lk06R2iQuQokqQOyECn5ncSHriLVgA0HoDDrJ7hfJItnjtQHA7XajsrISAwcOxLhx4+ISno6ODixduhSjR4/GG2+8kbAHp6+RVeKjUqkwefJk5OQo91MrRc68HSVoNBpFuxqhZASyO+Q+DtsJlyMwYtYPt/O0JdcSs7GlHLiZSkJfcrLYmaxGeJyeKLGUGoUcy+1CxM3v9YsumFyEBL+jUTx9npwrcedpddooQeFaWFz3Fj/WE0//OAJZ2IgVEwwE2WaoYpsYMvlUalEkNWRyMu6krJxkZiQmEynxT6TbgdvtRkVFBQYMGBC38HR1dWHp0qUYMmQI3nnnnYz0nMx2skp8soUG3oTHRJGbip0ouUV5AMLTU0mcx2DSxxQhJRMo+bUZANDR1ImmWuWp7EKuJm46tk6vRzAYYmM0XOvHYNJHFLkS5Eyr9Lo8yC3KEd0x5/DqXwhErIjFyg+4y3Gb6gw6OLrFz9GW12ORFwzKjfgfALQ8a4O4c6USHwL+YNy91IL+ILvx4Vp+XDeyzxeI6nOXypgRAHz4z3EpeVyPx4PKykoUFRVh/PjxcQmP3W7HT3/6UxQUFGD16tXsCG5KJFknPumO8Uj1D0tGQDRRuHGIWHEjn9sXlVAgFFAn7i+uy01OzEGsxiTgT/x9koqHEJHILcqJ2KFzrSohIYpFqhfIWOiNetaiM9tMKBoSuVv3+wKwcbLX1Fq17FlNpMi4+ZiyeVV8evrOhb+XxPUWq5ec0RJfjEesWJRLKoWnoqICBQUFmDBhQlxrkdPpxEUXXQSj0YgPP/wwbV1YeiNZJz6pgmS8xcp8k9PlIBZCWUfxNJzkw114xcQi2f5vjUajaNccb1cBNlbAe58cHU4UDs5jfxea6xNrEJtYLMPv9SsqphX7fOS8ZiWthqwS01nFiDV2QKrwWAkqtUpW8gwhVi2SGMTa5b/nySoY5kMsnoKCAkycODEu4XG73bj44ovBMAw++eSTfjUYLh76jfgAPcLz/jORRWJvPRk5v+PdP5fiL78x4+2npWdzxCIeX7nSrgltDeGpo9wFkF88KkQ3p1bH2eWCs8sVJWj5A3KhN+ojXGH5gwoQ8PvZxYyfdEBcT7GE1GyLXGCFBFtn0LExJ0uOiXVz8dvCiMEdCyEHr8uLjhPD0/itguLtGi0GsQzkxIr44u91+6BWqyIWSKniYiGkYmhyi5sJQX8g4ppL5riLdOD1elFZWYm8vLy4hcfj8eCyyy6D2+3Gp59+GjXQkhJN1olPutxuUgLEMAxCoZCiWA2/hYicYkex9FOp51Wy6xRCLOMst0j5lyV/UKSbyJoX/dhC5ys3hZcfUyGLMMmA6zmPXMF4izXPCu2JyZDd7XbRWioCX3y5v0uJhN6oR8AfgKvbJWhlSSWiyBUNhmHQ3e5A1wn3Yiii4acWBrOBtX741oacOBgXsesvFAxF1QpxfyddDvjfYZLx5uxyoqu1M+bzS7XZseVFi6bU5NJYeL1eVFRUIDc3F5MmTYpr/fF6vbjyyivR3t6Ozz//HLm50U1bKdFknfhkA4FAIKbwJCtfX6jGJuJ5RFrqOzqcrNVD/ufCt364SQdkFAMhp8CaNW1MgJ7RBAYJl1hHU5dknIgPv3q++Yh0UonBbIjIdONvJrjvF1n0SWEqv7aJP+qbLHBy5je1N3XiyD7hMe/cDEjudSJUlMqFiBOJ+RGXoMFsgMFsgMlqinTx5sir+fIotL7E4Md9UlVISiye3NxcTJ48OS7h8fv9uOaaa1BfX48vv/wS+fmxx45QwlDxEaCzsxMA4m6BIdRIkeCVaHUv100nd+6NUoRqfvg7XZ/HF9EDzlYQvRMlixn3fWg+2hP4ltPWp6OlSzALj2/1CCG10yeTWq354pZeR1MnHCLTS4WI9bl1tXYLdp+WC78BJ9d6lduJIX9QATyuxMRBrJ0OIZboxQMRnjceGxJ+jiRljvp8PlRWVsJms8UtPIFAADfccAMOHTqEr776CoWF8jp6U8LQqqcTkN1r0B9EdXU1NBoNBg4cCL8vdtCQxDdCTEixvzyZyPli8q2eZGDNsyEUDMHZ5Yxwf4kNJCOuOKnRDlqdFs3HWiXTl615logR1GRIGbFKuKnZJEXckmuV3BwkC3OOKSIRwuPyykrFVorP7YtKmuC+B+Q5TRYD3M7ojY/X5Y24fzzEijEmc7RFMiDCY7Va4xaeYDCIW265BTt37sS3336LgQNT2+anL5J1lk8m2umQ+M5rjw7Cv1cNxcknn4xJkyYhxAk6Fw3Jl71okV0an1iuOu44ayCxDstk9y+1qAgVq+qNenhcvohdslanEcxaIjt+ItyxYipAj9VDjiVuR/I79zHIwkm6TNjywrt8ua115JyPVGwunLou/VxC6fgqtQoBf5AVBakMRLdTWTwmFpZcC2wcq06qGS35TIVeo96kFxz9IUWySxPkpF0rhQiPxWLBlClT4vJuBINB3Hbbbdi8eTPWrFmT8m4sfZWsE59MEAwGwTAM1Go1VCoV1Go1ioqKMGnSJADAQ1dK++ZvOGs/+zNpmsj9nbTySVexaargCxDDMDFTfAP+gOCOG+hxQcY7egLoKXzMGyCevUXcVtwGnzqDTnGvM/7452QstgW8pA2x90oMJYkixGWpUqvYolByvJgIxlM/pQQ2htmZ2ucBwvGZrVu3wmw2xy08oVAId9xxB77//nusWbMGJSUlKTjT/kG/FR/+oqlSqQStrreeGoa8vDz297/fF+2G4/p63/9b9JCp7vb0uh3kZDfFkzDBHwbHn7ciFkeRm6HHtVTkBOOB2P3iCHqOwJHXrmT8gpwsLaGO3Xy8MoLyfAHi9puz5VnYjDfNCYs0WRNk+ZYO351nMOllpfFLQbIPhVBq6ShJRCCzwUwmE6ZOnRq38Nx999348ssvsWbNGowYMULxY1B6yDrxSYfbjWGYCCuEWDyxeOOxIbDZbHjtD5FfEo/Hg2tO3w0AqK2thcvlwut/7DHF3+OldYeYUJQLj/vFI4ujVIxAKnEh4rliWFuxumTLhUxpBVI7Z8XJiaN0NHWxzTnJ3/luRoNAOrvS+Aa/zobrRjNZYmeCOTrCad5drd2wi2xEuFalx+WByx7pMuTGx7gtePguPe7nrSTGZM0LbxxImrNQCrjb4WFvDwZCSfmu3ndlp+DfxYTozSeUWxrE4jEajQkJz4MPPoiPPvoIa9asiXugHKWHrBOfVEOEJxQKKSoiffq28Jc8GAxi+/btEbfNnTsXixcvBgC0t7djw4YNKC8vBwD8a9WQmK4pOcjtXECsHovMrt1K4Na9SE09FcPRGemS5BeaStF6vAOtxztk9aETywCLlQzidnhgtpmi6pCErB6p+B9/0R8wdADyBuYL9sXj0tYg3oVASNBNVqOokOYU5rDxNDELjxvrIVZPulPuvd7wJuqXPz2CXbt2oampCYFA8hqZEuHR6/WYNm1aXMLDMAweeeQRvPXWW1izZg3GjUtNe5/+Rr8SH67wyLV2uJCCNJ8vehExGAx447EhmDVrFk4++WSUlpYCADZv3owffvhB8PG41hEQ3u2RJIN4dnh8lNTBCMFNOuC73MSQsn6EkhaIS05OcFvMFefktdfh19nwUZrtJiS0JotJltVjUtDjrLA4skaECTFRLjeCkmQAMdck163m9/mh1oaXA/L+ca0foZR7IGwBJcKUKVPw0opCTJ8+HTqdDocOHcJ3332HrVu34ujRo3C7ez5bsUQeMQKBAKqqqqDX6zF9+vS4hefxxx/HSy+9hK+//hoTJ05U/BgUYbIu1ToVbjcSGCeuNq7wvPMneX5bu92OqqoqFBQUhBMRPhAvUtTpdCguLsa/VwHBYDHa2toA9HyJ7ruqE394PS8im04K7k7c7/MnPOQrXaRrzDHBnGOOKTxK4Y93JiLs7HJGWJdcq0qtUiMYCMbVuNTV7WJFQC4qtRpMKAQVZ3GV+h5Zc8xwOz0IBkMRM3r49Urc3zubOyVro5TCdYeq1Wrk5+cjPz8f48aNg9PpRGtrK5qamrBv3z5YLBYMGDAAAwYMQE5Ojqw1IhAIYOvWrdBqtQlZPH/+85/xt7/9DWvXrsXUqVMVPwZFnD5v+fBb5YglFkjR0tKCLVu2YNiwYZg8ebKiC5nUC3FRq9W46ZyD+N///sf+jZzfu38pjbwvp7BPqfDwO1qTuhe5yQbc3W6srtf84HSsFGUA8DjFU6Ft+TbZ0y+FUtL5adbcBV3o3DqbO6PEkuvqNNssgjEUZ5czQniMVqPsLDgydVZv0rMxHaEecrEbzmrZfmoqgWvTK5KazreIpIqXbSIjzKVa4cSLxWLBiBEjMGfOHNaL4HK5sHXrVnz//ffYvXs3mpubRbNHicWj0Wgwffr0uJJrGIbB3//+d6xatQpffPEFZs6cmejLovDIOssHCAtEMuIkxNohj6V098MwDI4cOYJDhw5h8uTJGDQoOXUHEyZMwPjx49Hd3Y2hQ5vxyKs5+O6771BYWIiBAwfipYcHsPcNBUPQaDRwdbuidtLv/GkELrm9Lubz8XfpyYK4wUiMRW/SCwaqhToAsEJ44jV1NrfDEGcbfiBsMTAMA5VKFSE8YlaEvcMeUQ8DhEW5s7mTTc0W61wgdW0yIUaxZapSqdjz9J5YzF3dLlndHLgE/AFoNOoIAfK4fILjt6Ug50I+z5ajLYLiwxUepU10dXqdLJEmXoTi4mKEQiF0dnaipaUF+/fvh9frRUFBAQYMGICioiIYjUYEg0FUVVVBrVZjxowZcQvPCy+8gEcffRSff/455s2bp/gxKLHps5YPN75DaneUEAqFsGfPHtTW1mL27NkJCw/fvadSqZCbm4uxY8cCAObPn4+cnBwcOXIE//vf/1BZWRnh7uFbRIXFkfUhUl/+WCnAYhlvckcpMCEmwt1FdupyrB+C0ap8jIAc3JzzMueYZPcp46LWqKNay+hNetHFM56xFnxrPG9guDmlVqdBd7s96rOQcs3xH0toBpDclGm/zydq9chFLH6pNIajVqtRUFCA8ePH46STTsL8+fORn5+PhoYGrF+/Hhs3bsSGDRsQDAYTsnhee+01PPTQQ/jPf/6DsrIyxY9BkUdWWj6Jkmhigd/vx/bt2+Hz+TBv3jyYTMoXLCWQtFKr1YpRo0bB7XajpaUl4j41NTXI1o9LTk2O3qiP6aLxOj2KrR+vywOTxYwQQsgtymGLIi25Vji7HNCecJVJZbpxrTWh1GxCMBAUzBwL+AOKaobiwevyyrZgDEadqKtNCr7lmorZOW89NQwX/eowXuFY9/GgUqlgtVphtVpRWloKt9uNqqoqBINB+P1+bNiwgbWICgoKZAkRwzB48803cffdd+OTTz7Bj370o4TOkSJNVlo+8SYdcOM78QqPy+XCli1boFarMXfu3KQLj5wEB5PJhOHDe9LA/3hrkG12Snjs57G7C5Av+EsripSf6AmUjl9WMpxNCJVKHRHDMZqN0Bl0ov79wmLxZo5SQgIAHY093cC595VqAipmYcqxqPIG5kNn0IvGurh99/hNXtUaNYLBEPtPTOykxmkD4e8WV5hINl5UvVCc/e/8Pj9eeXiApLjE+lyUEgwGsXv3buh0OixatAinnHIK27Nt7969+O6777Bt2zbU19ezqd18GIbBe++9hzvuuAPvv/8+Tj311KSeIyWa7NxKxwERHpJBFo/wdHZ2Ytu2bSguLsa4ceMkjydp0pffVR//Scfgrad6ZgyVlJQAb/S01q+oqIBOp8PDPx+Ih55N7ahel90ta8y2HIQ6HyezJ1hOoQ1dJ0Q5pzAc03F1u6MWU41GAwh4x8iQNammp65uFyuyfp8POn1igsvF7/ULJjb4fX7o/DpodRp0NHUiX6RnGxERkpxitkVeG7FStNVaNYL+IAxmA/jLtKMjbFXGm/XGvZ6TRTAYbgQcCoUwc+ZMaLXhJa2wsBCFhYUYP348HA4HWltbUV9fjz179iAnJ4e1iHJycqBWq/HRRx/hl7/8Jd555x2ceeaZST9PSjRZafkohetmA+ITnoaGBlRWVmL06NEYP3687OOTUY8TD6eccgomTJgQYREI7coTsXq4xDsOOV7sHfH1+iKdpENMKKKbslUk4UIoQaKhpkEybkNu0+l1bLsYORNJjQJWYWdzZ8TvRHiIFcS1hrgxNK6FI/bcclPdpZqPJgppgZOK70koFEJ1dTUCgUCE8HBRqVSw2WwYOXIk5s2bh8WLF6OkpAR2ux3PPPMMRo8ejQsuuADXX389XnnlFSxZsiTp50kRJivFR4lwJJpYwDAMDh48iL1792L69OkYNiz5u7NUoFarMWDAAEyaNEm0U0NjYyNbLc4dLxAviQpQKua98BGyouS2IuLCFXKui0usT12s/nWk/1rh4MhCUpNANwYDp6BXLP1ZqrtB+4lR4LHg1vj0Nojw+P1+UeERwmAwoKSkBNOnT8fy5ctx44034sCBA7BYLLj++utx4YUX4q233krx2VOALBUfuSSaWBAMBrFjxw40NDRg7ty5KCpKjpWQblQqFRtL4i6ahw8fxnfffYeqqipZj/O33/bUfbzwYHomMgoJUjzFqcTFJoScDQm/JU9HU088yO3sEV0hi4idBcUTCrER2ZZcCxv3UTq2gGvxuEViR+QcHR3KEwaI2MlNNpDq1J6qCaREeHw+H2bNmgWdRLNSKTZt2oQ///nPWLFiBVpaWrBu3TpMnz4dmzZtSvIZU4TotTEfEt9JpFVOdXU1gHCasz6JfvtU8dZTw3DVvY2y719WVgan0xmVOZdMgv5AxIhuuXgc4V03N8Wan25NanESmWskNMgs6A9Ao9GE2w/FOc7B6/IKthzqbOlA3oCwcBMrzO30RCQQkNeTaIdoObidXrZeiN+sVAil9XUkXfrah1J3jXEJhULYvn07vF4vZs+eHbfwfP/997jsssvwzDPP4PLLL4dKpcKMGTMwY8aM5J4wRZSstHykhIRYO/wZPEpwOBzYvHkzTCYTZs+e3SuEhwu/J5wUFouF7TPH5xdLawEA/7jfylbIx4NUmrHQbUR4yM9OmbNcCosLYOeN4HZ1K3cFkvEKOoO8z11JyrE1zxYVe4t37Dk33hPvY9jb7ewIBj5y4lRSCHVjSCWhUAg7duyAx+NJSHg2bNiAiy++GE8++SSuvfbajAywpGSp+IiRjMSC1tZWbNmyBUOGDMGUKVPiKkTLduQEd196uJBN5yZuhgMHDsh6/HgmTEoWwcosMOULDxe5IuQQiX3JqVXip0jHKqIl8R2Suh1IQ8wrUZQUyD57by77s5K6HaXFpUBYeHbu3AmXy5WQq23Lli248MIL8eijj+Lmm2+mwpNBeo34cFOp40ksAIAjR46guroaEyZMwOjRo3vlhafE6pFCp9NhyJDwInDKKaeg+UgzvF4vfrZoGwBgz549SXkeAnehTlY3A6naGrHsNqBnaqfH1SM4YpsQbgKDVGdvW0Hs9GO9jCLRWK15hGJE1tzY72fz0VbBvx/ZeyTmsZmGCI/T6UzIU1FVVYVly5bhwQcfxK233torv/99iawUH/5FwXW1xWPthEIh7N27F4cPH8bs2bNRXFyczNPNSp6/P0/2fTUaDVb/YwymTJmCk08+WfR+Lz+sPCEjHiuJS7wp10KxHi5iM3+625M3zlnM0tFoNLDmiU9glXKH8cdmk/vG6q1mMBsUTdSVmjgKJP65yoVhGOzatSth4dmxYwd+8pOf4K677sIdd9xBhScLyErx4ZJox4JAIIBt27ahvb0d8+fPjxiJ3R+J5ZIjFiWZW/LkbT3v93fffYc7Lm5EQ0ND1HHJbC+TioFmscRIDrHcbNx0a3WMjD2pVkNiFpbQFFSl2XKpHukej0tNDCI8drs9IeHZvXs3lixZgttuuw333HMPFZ4sIWvFh9sqJ97EArfbjc2bNwNASlrlKEXu7KBUPH68RX55eXl47dFBePWRgZg3bx6sVivq6sKdtO++qg1/vteAVx6N9vcnUlSYk2AjSzm0NQi7ocTcb9yFX6ymh8RLlAqx0P3ldMa25VkiLCFi/cTqoec4ETvLH5Areb9MQoSnu7s7IeHZt28flixZghtvvBG/+93vqPBkEVmZas0VHSC+GTydnZ2orq7GoEGDMG7cuLhiRL2RVAgQ0FMpbrPZMHr0aEyf7kZzcxEaGxuxb98+ABNkPU6sdjRyhMfe6RBsFKo0882WF/u5QoEQcgrCizTpbJ3IuA8Hb9gd1/1mMOrhsrth5DRXFepeLSRMTIhRVB/V3tCG4pGx44dkvIS9rRur/zEGF/zioOzniBeGYbB79250dXVhzpw5MBjiS4c/dOgQlixZgssvvxyPPPIIFZ4sIytX5FtvvRUXXngh3nzzzaiGmnJobGzE1q1bMXLkSEyYMKHfCE+ykFNgajKZMGLECMydOxeLFy8GEDm++tChQwCUL9QvPlQQd5xHiu62TsXHEGtHyt1GhIIUdEZ0QzhRZKq0OSsXIaHhWjutx9ujbidY88XjSoD4kLl4SYbLjWEY7NmzB52dnZg9e3bcwlNbW4slS5bg/PPPx5NPPknXgCwkKz+R22+/HWVlZXjuuecwatQoLFu2DC+//DJaWlqkB3kxDA4dOoQ9e/Zg6tSpEZ2hKcIkI3tOaIFwOsMpzT/88EPCj89FajRCLI7uU5bZFTHqWSP+vEaLEX6ftKsrXgEK+oOSNT5iqdE+tw/2E8kT/BiSz62svodYPQDY/1MBEZ729nbMnj0bRmN8dUTHjh3Dueeei7POOgvPPPMMFZ4sJSs/lfHjx+PBBx9EZWUldu/ejdNOOw3/+te/MGbMGJx77rl47rnn0NDQECFEXq8XO3fuxPHjxzFnzhwMGJDYvBBKYkybNg1A+LO87+rOpD52IgKUDIwKZg7xM9f4o6uVYLYlfxpttsAwDPbu3Yv29nbMmTMnbuFpaGjAueeei1NPPRX/+Mc/qPBkMVn9yahUKowZMwb33HMPNm7ciAMHDuC8887D6tWrMWHCBJxxxhn429/+hsrKSixevJgdeWuzxdfynRI//141VPDvAwYMwOTJkxN+/DsvbU74MVKB0myzWEgJGz/NWghSlxTL5ZZNMAyDffv2obW1NSGLp6mpCeeeey7mz5+PF154oU8WkPclslp8uKhUKpSWlmL58uVYt24dampqcMkll+C9997Dj3/8Y6jVaqjVahw/fjyhgDAlNbz7l1K8+5dSfPjPcQCAfz0tnPzgE3FfCf29q7U7rrHYcnHZpTuBxyM8bQ0dorepNRrRTDoyVjtepApkjx2UnkmValfb/v370dLSgjlz5sSdkdra2orzzjsP06ZNw6uvvkqFpxfQa8SHi0qlwtChQzFx4kQcOHAAt9xyC37+859j3bp1mDlzJhYtWoQnnngC+/fvp0KUZl79/UBZ3Yxzc8OL6ScvTWL/tvxnTfj+++8F7z979mxZzy8nRTkVkGaifLj1Pl2t0okU3O7ZStEZdPC6PIoEsak2ul5LDskqMCXC09zcnJDwtLe347zzzsPYsWPxxhtvyB6vQMksvVJ8gPBO+Ne//jX++te/4k9/+hN+8Ytf4KuvvkJDQwNuvfVWbN68GfPnz8eCBQvw6KOPYvfu3VSITiDVBj8R3v1LaULHz58/H4sWLcKTt0enxHo88jpbJ6v9EJ9Y83pS8Vx+rz9iFlGs5q8kLZqLnLY/Qrz11DC898wo2NtSU5TKMAwOHDiApqYmzJ49O27h6ezsxNKlSzF06FC8/fbbva5JcH+m14qPXq/Htm3bcOWVV7J/U6lUKCwsxPXXX4///ve/aGpqwm9/+1vs3LkTixcvxuzZs7FixQp27G5/RW69T6IzfcSeh4gU1+ohGI1GFBQUsL8/fWd4F7tx40YAwEPXRzYX5Ta3lOK/r04FAHQ0tsm6PwCYLOI907ixGTFrI1aXAwBw2V1w2eUPdQsGQ1Ei1FjXJPv4bIAMcGxsbMScOXNgNsfX66+7uxs//elPUVhYiNWrV8edlk3JDL1WfABI7nJUKhXy8vJw1VVX4eOPP0ZTUxN+97vf4fDhw/jxj3+M6dOn4/7770dFRUW/FqJso6urCxUVFezvJSVhATv55JPx1O0auN09rqnf3ehEe7t4nUs2w40nMSEGXpdXcQzJ3ukEE2LYERG9AVIOcfz4ccyePTtu4XE6nbjoootgNpvx0UcfxZ2kQMkcvVp8lJCTk4PLLrsM77//PpqamvD444+jqakJ5513HiZPnoy7774bGzZsSJlLihKbtrY2VFZWYtSoUVG3abVaDBo0CFOnTmX/Rua7XHfmXgDAU3dosv7z4xahSlk8ctx8yeyB19ksngiRTA4dOoT6+nrMmTMHFkt8qeMulwsXX3wxVCoVPvnkE0EBc7lc+Pzzz/HII4/gpz/9KUaMGMF2SlmxYoWs52lqasLy5csxfvx4mEwmFBQUYPHixXjxxRdlufAPHTqEm2++GSNHjoTRaMTAgQNx5plnYvXq1bKef+vWrbjiiiswdOhQGAwGFBcX4/zzz8c333wj6/hsp19G5iwWCy688EJceOGFcLvd+Prrr7F69WpcfPHFMBqN+MlPfoJly5ahrKyMBi/TyLZt2zBp0iQUFxfjnT8Bl9xeJ3n/SZMmgWGYcBeML73Yt2/fiay4sYL315sMigss+bzy8IC0TO3UGXTQQZfQFNdsgwjP7Nmz4xYej8eDyy67DB6PB19++SWsVuGU8s2bN+Occ86J+1wrKytx5plnoq0t7Ka1Wq2w2+1Yv3491q9fj/feew+ffPKJqKvvs88+w0UXXQSXK7zByMnJQVtbG7766it89dVXuPbaa/HSSy+Jtvx58cUXccsttyAQCPfry83NRVNTEz766CN89NFH+N3vfidbRLOVfmP5iGEymfCTn/wEr732GhobG/HSSy8hEAjgyiuvxJgxY3Drrbdi7dq18PuT24qEEonH4cK0adMUj7tQqVTIzw/Hpk466STMnTtX8H5eb7To8JMTutu7ou6TCojLTU4DUqM5tjtJZ4id4dfVGn5tAQWduZPJ4cOHcfToUcyePVtUMGLh9XpxxRVXoKOjA5999hlycqR78+Xn5+O0007Db3/7W7z11lsYPFheMkpXVxeWLFmCtrY2TJgwAVu2bIHdbofT6cTf/vY36HQ6fPXVV7j99tsFj6+pqcHFF18Ml8uFk046Cfv27UNXVxe6urrw0EMPAQBeeeUVPPnkk4LHl5eX4+c//zkCgQCWLVuGo0ePorOzEy0tLbj55psBACtXrsS7774r6/VkK/1efLjo9XqcffbZePHFF9HQ0IC33noLer0eN910E0aNGoWf//zn+OKLLwQXMopyGIZBTU0NAOC1VcVRXSlIk1S5PcNI81Mh1q1bByBs/QBg642EII1ElZCMRVtsFxyPK9Eex1wir8uLt54aFvG3ZNT41NTU4MiRIwkJj9/vxzXXXIPjx4/jyy+/ZDccYixevBjt7e1Ys2YNnnjiCVx66aWyExJWrVqFxsZGmEwmfPbZZ5gzZw6A8Prwy1/+EitXrgQAPP/889i/f3/U8Q899BCcTicGDx6MTz/9FOPGha81q9WKlStX4qabbgIAPProo+joiHZ33nXXXQgGg5g6dSreffddDB0aLuAuLCzEs88+izPPPDPifr0VKj4iaLVanHbaaXj22Wdx7NgxfPDBB8jNzcWvf/1rjBw5Etdffz3+85//RATAKfIhNR5HjhzBS48UxlxM+ChNqV68eDHrcjNZzNi4cSPb/DTbkWP9xItaG12MyRegRKitrUVdXR1mz54dd+eRQCCA66+/HocOHcLXX3+NwsLCmMckUmT6+uuvAwAuvfRSjBw5Mur22267DVarFcFgEG+++WbEbU6nk43p3HLLLYLzw+69914A4Wy9jz76KOK2w4cPY/369QCAO++8U3BcODm+rq5OtC6uN0DFRwYajQYnn3wy/vrXv6Kurg6fffYZiouLce+996K0tBRXXXUVPvjgAzgcjtgPRkEoFMKuXbvQ0tKCuXPnJr0dEnfxJN2s+bveESNGRHxewWAQrz4uv3iSn5mm0WlEp4lqdYlV21tkjMkm8BugdrV2sS63WCRTdICw8NTW1iYkPMFgED//+c+xa9curFmzJuU9G/ft24cjR8INaM8++2zB+1itVraT+1dffRVx2/r169kNqdjxpaWl7LBG/vFff/01+/NZZ50lePyiRYvY95N/fG+Cio9C1Go1ysrK8PTTT+PgwYP45ptvMGbMGDz88MMoLS3FZZddhrfffhvd3amdGNlbCQaD2L59O+x2O+bOnRt3qq1c/vvqVMFFtbi4GNOnT2d/v/eqDlRVVSl+fLdTukbnpRVF8HulR1ynAqku3ADg90p34U6Uuro61NTUYNasWQkJz2233YYtW7ZgzZo1smM2ibBz50725ylTpojej9y2e/du0eOlehqS43ft2iV4/MCBAzFwoHCnEI1GgwkTJgge35ug4pMAarUac+fOxWOPPYa9e/eivLwc06ZNw1NPPYXS0lJcdNFF+Ne//oWOjg7aXQFhv31VVRV8Pl9CQ8JSwZQpU3DyyScrPu69ZyLTwl94MF92ca7L7oTPk/74YSp7tQHAkSNHcPjwYcyePTtmUoAYoVAIt99+O77//nusWbOGrfdKNcePH2d/lnpOclt3d3eEBU2Oz8/Pl9xYkeO5z8f9PdbrFTu+N0HFJ0mo1WpMnz4dv//977Fz505s3boV8+fPx7PPPouRI0di2bJleOWVV2LOJOqr+Hw+VFZWQq1WY/bs2YK+bLmkqoWOVPv968/eByD8Zfc440t/TqQnWluD8mLaWKMfhDpMJMrRo0dx6NAhzJo1KyHhueuuu/DVV19hzZo1GDEitePnudjtPYkaUuLBvY17DPk5lkVPbucem4zjexNUfFKASqXCpEmT8NBDD2Hr1q3YtWsX/u///g+vvfYaO5Po+eefR2NjY78QIrfbjS1btsBisWDGjBlp6TgsN34hV8jmz58PAGw84NcXhjtBk55zrzwsPxYhdy4Pf4JqR1On7OcAhM+JWD1c4fnoufGKHleMo0eP4uDBg5g1axbbOFYpoVAIDzzwAD7++GOsXbtWsOCY0jeg4pNiVCoVxo4di3vvvRebNm3C/v37sWTJErz33nsYP348zjzzTPz973/HsWPH+qQQORwObNmyBYWFhZgyZUrKhnsJiQhfgEh/N6WPA4BNEV6wYAEAoKioCLf99CjWr1+PW88/gtra2oj7v7SiSPQ5xBIT+Khk9IZTAkldFyJRATp27BgOHjyImTNnxi08DMPg97//Pd5++22sXbsWY8cKFwunEm58ihSICsG9jXsM+VnqWO7t/HhYosf3Jqj4pBGVSoWRI0fizjvvxPr169litE8//RSTJ0/G//3f/+Evf/kLamtr+4QQdXZ2oqKiAiUlJRg/frxoHUs6MVnMUYPv+ILD/Z3bg41rSYwYMQJz587Fj370IwwZMoSt19iwYQMOHjyI7u5uvPi72CnBfMSG8hGErB+hNjv8+T1SwpMo9fX12L9/P2bMmCGYWiwHhmHw2GOP4eWXX8aaNWvYgHq6GTKkp6asvl58zhG5LScnJ6J2iRzf0dEhKSDkeO7zcX+Xem6p43sTVHwyBJlJ9Ktf/Qrfffcdjhw5gmuuuQZr167F9OnTsXjxYjz55JM4cOBArxSitrY2bN26FaNHj8bo0aOzQnjihS8IXItKr9dj6NChmDlzJgBg1KhRcLlcqKiowPr167Fv3z785a6exIpkzcJJFolaPPX19di3bx9mzpypuFaLwDAMnn76afzjH//A119/LZlllmq4z83NXONDbps0KTJuxj1eKhONHM/PiCPHNzc3o6VFuI1TMBjE3r17BY/vTVDxyQJUKhWKi4txyy234Ouvv0ZDQwN++ctfYuPGjZg3bx4WLlyIP/zhD71mJlFjYyO2bduGiRMnYtiw5NaOZDuDBw/GtGnTcPLJJ2PChAkIBAKorq4GEE7LbWlpwb8eK47Kkvv3qqGCVk/AH4Czq2cHnexanEQ4fvw49u3bhxkzZiQkPH/961/x9NNP44svvsCMGTOSe5IKGT9+PIYPHw4A+OKLLwTv43Q62Y4ZZ5xxRsRtixYtYmcTiR1fV1eHPXv2CB7/4x//mP1Z7PgffviBTTTgH9+boOKTZahUKhQVFeH666/HZ599hsbGRixfvhzbt29nZxKtXLkS27dvz8pREEePHsXu3bsxffp0xX3a0kEst1YicN1yGo0GAwYMwOTJk/GjH/2I/dvevXvx3XffYfv27ex9/bwR4dxebXJnL0lZlqlwuTU0NGDv3r2YMWNGxPwlJTAMg+effx6PPfYYPvvsM9G+fOnmqquuAgC8/fbbUbE8APj73/8Oh8MBjUaDyy+/POI2i8WCCy64AADwz3/+E11d0QW+jz/+OIBwvGbZsmURt40aNQqLFi0CADz11FOCPSUfe+wxAGHXL7m2eiNUfLIY0jTz6quvxieffIKmpiY89NBDOHjwIE477TRMnz4dDzzwACorKzMuRAzD4PDhw2y2U1GReMC9v0GSLMaPH49FixZFDVC779ou1NfXn+jInf00NDRgz549mD59ekLC8+qrr+J3v/sdPvnkEyxcuDDJZxmOu7S2trL/yHfE5XJF/J3fmeTOO+/E4MGD4XK5cO6556KyshJAuFzgn//8Jx588EEAwE033cT2bePy8MMPw2KxoKGhAeeddx4OHDgAIGwxPfzww3j22WcBAA888ICgxfjEE09Ao9Gguroal156KRvfaW9vxy9+8Qt8/vnnEffrraiY3uDHoUThcDjw+eef44MPPsB///tfFBQU4LzzzsOyZcswb968tF6UpE9bY2NjQhXt2cTP7jwGIJxwkIxU5KvvbxKM91z0q8N44nYVmpubYbfbkZeXh79+GHatBfwBvPlECTtaQq1W462nhuEXT9gjXHFAuKMBwzBserbeqMcrDw/AJbfXJdXyaWxsZC1bOT3WhGAYBm+88QbuvPNOfPLJJzj11FOTdn5cSktLUVcnPZYDAK6++mq8+uqrEX/jj1Sw2WzweDysJXLGGWcoGqmQm5sLh8PBNgK95ppr8PLLL8seqZCXl4euri7W7U5HKlAyhtVqxUUXXYS33noLTU1N+Mtf/oKuri5cdNFFmDBhApYvX47vv/+evXhTRar7tGUSbqZbqnjvmVEYOXIk5s+fj0WLFkW1VCFdv7OBpqYm7Nq1C9OmTUtIeN59910sX74c77//fsqEJ1Fmz56NXbt24fbbb8fYsWPh9/thsViwaNEivPDCC/j8888lO3Scc8452L59O2688UaUlpbC7XYjLy8PP/7xj/H+++/jlVdekXSV3nDDDdi0aRN+9rOfoaSkBC6XCwMHDsSyZcuwdu3aXi88QD8dJtfXMJlMWLp0KZYuXQqv14u1a9di9erVuOKKK6DRaLBkyRKcf/75WLx4cUKdBfiQPm0ejwdz587NqnY5vRGj0Xgi2N3E/i0cM8jn/R7eM6rUKjAhBq89OihqwJ2Solc5NDU1YefOnZg2bVpCLtUPP/wQt956K9555x12NECqEIrXKGHQoEF4+umn8fTTT8d1/OjRo/H888/H/fyzZs2K6prdl0ia5fPqq6+yY2ql/q1Zs0b0MTI9trYvYDAYcM455+Cll15CQ0MD3nzzTeh0Otxwww0YNWoUbrnlFnz55ZcJzyTy+/3YunUrAoFA1vVpSwb/XjU0aZX/SiHuuTefKInK/tq6dSsAwOcRjw/JTVKQS3NzMys8iXSV/vTTT3HzzTfjjTfewJIlS5J4hpTeSNLdbmq1GoMGDRL9J7ZIVVZWYvLkyXj66aexf/9+aLVadmztjTfeiLPOOktywfzss88wbdo0PP/886itrYXBYGDH1l544YW47rrrekWacjLR6XQ4/fTT8eyzz6K+vh6rV6+GzWbDbbfdhpEjR+KGG27Ap59+qngmkdfrRUVFBbRaLWbNmpVUa4oShtsFgRuzIc1PH7y+p6cXt54k2cLT0tKCHTt2YOrUqQkJzxdffIFrr70Wr7zyCs4///wkniGlt5J08Rk2bBgaGxtF/5E5GFwyPba2P6DRaHDKKafgb3/7G+rq6vDf//4XgwcPxt13343S0lJcffXV+PDDD+F0Ssc5SJ82m82G6dOn9+psm94IyZybNGkSnF3hLC2dTofrz9iHq07dierqajQ0NCRl7HtLSwu2b9+OqVOnirb3l8PatWtx1VVX4fnnn8dFF12U8HlR+gZZkXCQ6bG1/Q2NRoOTTjoJTz/9NA4dOsQ2cFy5ciVKS0vxs5/9DO+8807UTKK2tjZs3ryZrV9JVZ+2bCJTrjcljBs3DieddBLmzp0Lq9WK2tpa/O9//2NddPG4WFtbW7Fjxw5MmTIlIeH5/vvv8bOf/Qx//etf8bOf/axXd7qgJJesWD0yOba2v6NWqzFv3jw8/vjj2Lt3L3744QdMmTIFTz75JEpLS3HxxRfjjTfewMcff4xZs2ZBr9dj3LhxdBHJAt57ZhSbkadSqWCz2TB69GgsXLgQZWVlbA3OunXrsGXLFtTW1sZsWAmEhWf79u2YNGkSBg2Kvx3QDz/8gIsvvhirVq3CNddcQ68ZSgQZF59Mj62l9KBWqzFjxgw88sgj2LVrFyorK9lheVdddRWmTp2Kffv2oa2trd/FzzINN+7zj7tip7ObzWaUlpbinT+NwOLFi1FcXIz29nZs2LAB5eXlOHToEOx2e9Tn2NbWxgpPIpNDN2/ejAsvvBCPPvoobrrpJio8lCiSLj4tLS2YPXs2rFYrTCYTRo0ahSuuuALfffed4P0zPbaWIoxKpcLkyZMxbtw4NDU14YknnsDpp5+OV155BaNHj8aSJUvwwgsv9JuZRL0Zg8GAoUOHYtasWTj55JNRWloKh8OBzZs344cffsD+/fvR2dmJtrY2VFdXY+LEiQkJz9atW3H++efjoYcewq233kqFhyJI0ut8XC4Xtm7divz8fDidTtTU1KCmpgZvvvkmrr32Wjz//PPQanueNt6xtaSNeaJjayniNDY24le/+hVWr16Ns846C0DYhVlTU4PVq1fjnXfewfLly7Fw4UIsXboUP/nJT1BSUkIXmzTCb1AaC51Oh+LiYhQXFyMYDKKtrQ3Nzc3YunUrgsEg8vPzodfrEQqF4orpbd++HT/5yU9w11134Y477qDXAkWUpFk+Q4YMwe9+9ztUV1fD4/Ggvb0dLpcLP/zwA04//XQA4YwzfsZapsfWUsQZPHgwDh8+zAoPELaIRo0ahd/+9rf44YcfUFNTgwsvvBD/+c9/MGnSJJx22ml9aiZRssi2UQpAOPFk4MCBKCkpAcMwGDFiBKxWK3bt2oX//e9/2LFjB5qamtiWMLHYvXs3zjvvPPz617/GPffcQ4WHIknSxOeMM87AihUrMG3aNLaWR6PRoKysDF9++SWWLl0KAPjHP/7BNtqjZD9S7XJUKhWGDRuGX//61/juu+9w9OhRXHXVVVizZg2mT5+OH/3oR1i1alWvnUmUSYTqdVIxTqGjowNVVVWYMGECxo0bhwkTJmDx4sWYNWsWjEYjDh48iO+++w7btm3D8ePHRVO49+3bhyVLluDGG2/EQw89RIWHEpO0JByo1WqsWrUKQLgX2H/+8x/2tkyPraUkBzKT6Be/+AXWrFmD48eP4+c//zk2bNjAziT64x//iD179lAhyhI6OztRVVWFcePGRbi8VSoVcnNzMXbsWJSVlWH+/PnIycnBkSNH8L///Q+VlZU4evQo68I+ePAglixZgiuuuAKPPPIIFR6KLNLW223MmDEoKipCa2srDh8+zP6dP7Y2JydH8Hi5Y2vF3G99Yexsb0GlUmHAgAG48cYbccMNN6CjowOffPIJPvjgA6xatQqlpaVYunQpzj///H5TL5RtEOEZO3Yshg4Vn3GkUqlgtVphtVoxatQouN1utLS0oKamBkuXLsXIkSPhdrtx6qmn4oknnqCfJUU2Gb9SMj22lpJaVCoVCgoKcM011+CTTz5BY2MjHnjgARw4cAD/93//hxkzZuDBBx/MiplE/YWuri5UVVVhzJgxiifNmkwmDB8+HD/60Y+wZs0a2Gw2qNVqvPfee5g2bRoefPBBHDt2LEVnTulLpE18Dh06hNbWVgCIKCTN9NhaSnrJzc3F5ZdfjtWrV6OpqQl/+MMfUF9fj3PPPRdTpkzBPffcg40bN8oOclOU0dXVha1bt2L06NEJjThvaGjAtddei6lTp+LgwYNoaWnBgw8+iAMHDghO76RQ+CRlmBzDMJJ+XoZhcMEFF+DDDz+EWq3G7t27MX58T9uSBx98EI888gjMZjN27dqF0tLSiOOfeOIJ3H333dBoNNi9e3fU9MArr7wSb7zxBoqLi7Fnzx7k5uZG3P6LX/wC//znP2Gz2VBXVxf3vHlK6nC5XPjqq6+wevVqfPrpp7BYLOxwvIULF0ak51Pio7u7G5WVlRg1ahRGjIh/wFxTUxPOPvtszJ07F6+++irt70eJi6RYPnV1dZg3bx6ee+45HD58mA0oh0IhbNy4EWeffTY+/PBDAMDNN98cITxA5sfWUjKP2WzGsmXL8K9//QsNDQ147rnn4PV6cfnll2PcuHH41a9+hW+//TYpDTP7I0R4Ro4cmZDwtLS04LzzzsP06dPxyiuvUOGhxE1SLJ/a2toIV5rBYIDNZoPdbo9oaihUZErI9NhaSnbi9/vx3Xff4f3338fHH3+MQCCAJUuWYNmyZTjllFOg1+szfYpZj91uR2VlJUpLS6O8Ckpob2/HOeecg9GjR+Pdd9+lozQoCZEU8XG73XjppZdQXl6Obdu2oaWlBR0dHTAajRg6dCjKyspw3XXX4aSTTpJ8nKamJjz++OP49NNPcfToURiNRkyZMgVXX301rrvuupiZNIcOHcLjjz+Or7/+GsePH4fNZsOsWbNw880344ILLkj0ZVIyTCAQwPr16/H++++z4x/OPfdcLF26FKeffjqMRmOmTzHrIMIzYsQIwaa9cuns7MR5552H4uJirF69us8ND6Skn6SID4WSboLBIMrLy7F69Wp8+OGHaG9vx1lnnYWlS5fijDPOgMViyfQpZhyHw4GKigoMHz4co0Ypa8PDpbu7G0uXLkVeXh4+/vhjKvKUpEDFh9LrCYVC2LJlCytEx48fxxlnnIGlS5fi7LPP7peFxQ6HA5WVlRg6dChGjx6d0OP89Kc/hV6vx6effhqzjRWFIhcqPpQ+RSgUQnV1Nd5//3188MEHqK2txWmnnYalS5fi3HPPRW5ubp+P+zmdTlRUVCQsPC6XCxdccAEYhsFnn30WUdxNoSQKFR9Kn4VhGOzatYuNEe3duxennHIKli1bhiVLlqCgoKDPCRERnpKSEowePTru1+fxeHDJJZfA6XTiiy++EO08QqHECxUfSr+AYRjs378fq1evxgcffIDq6mosXrwYy5Ytw3nnnYeBAwf2eiFyOp2orKxEcXExxowZE/frISnuLS0t+PrrrwWnA1MoiULFh9LvYBiGnUn0wQcfYMuWLSgrK2NnEg0ZMqTXCZHL5UJFRQUGDx6MsWPHxn3+Pp8PV111FY4ePYo1a9agsLAwyWdKoYSh4kPp1zAMg6NHj+KDDz7ABx98gA0bNmDu3Ln4yU9+gmXLlmH48OFZL0QulwuVlZUYOHAgxo0bF/f5+v1+XH/99di3bx+++eYbDBgwIMlnSqH0QMWHQjkBwzBoaGjAhx9+iNWrV2PdunWYPn06li5diqVLlyYUQ0kVbrcbFRUVCQtPIBDAzTffjOrqanzzzTcJjdGmUORAxYdCEYBhGLS2trJC9O2332LixIlYunQpli1bhvHjx2dciIjwDBgwIKHzCQaDuPXWW1FeXo7vvvuOjh2hpAUqPhRKDBiGQUdHBz7++GN88MEH+PrrrzFq1Ch2JtGkSZPSPsfG7XajsrIShYWFmDBhQtzCEwqF8Jvf/AbffPMNvvvuO7bDPIWSaqj4UCgK6erqwn/+8x988MEH+OKLL1BSUsJaRDNmzEi5EHk8HlRUVKCgoAATJ05MSHjuuusu/Pe//8W3336bUBcECkUpVHwolASw2+347LPP8MEHH+Czzz5DUVERm6wwd+7cpAtRMoXn/vvvZ12KY8eOTep5UiixyPgkU0p8dHd34/HHH0dZWRkGDBgAg8GAoUOH4tRTT8WKFSvQ2dkpeFxTUxOWL1+O8ePHw2QyoaCgAIsXL8aLL74IOfuQQ4cO4eabb8bIkSNhNBoxcOBAnHnmmVi9enWSX2HvwGaz4ZJLLsE777yDpqYmPP3002hra8P555+PiRMn4s4778T69euTMhzP6/WisrIS+fn5CQkPwzB4+OGH8e6772LNmjVUeCgZgVo+vZBvv/0Wl112GZqamgAAWq0WVqs1QnCqqqowY8aMiOP4YyusVis8Hg8CgQAA5WMrcnJy4HA42PHX1157LV566aWMB+KzAY/HgzVr1mD16tX45JNPoNfrsWTJEpx//vk46aSTFI8j8Hq9qKioQG5uLiZPnpyQ8Pzxj3/E888/j2+++SZiDD2FklYYSq9i/fr1jMlkYgAwp59+OrN+/XomGAwyDMMwLpeLqaioYO6//37m8OHDEcd1dnYygwcPZgAwEyZMYLZs2cIwDMN4vV7mb3/7G6PT6RgAzC233CL4vIcPH2YsFgsDgDnppJOYffv2MQzDMHa7nXnooYcYAAwA5vHHH0/hq++d+Hw+5ssvv2RuvPFGZuDAgUxRURFz9dVXMx999BHT0dHBOJ1OyX/t7e3M119/zWzatIlxOBwx7y/2z+FwMCtXrmQKCgqYqqqqpLy29evXM5dccgkzdOhQRq/XM0VFRcypp57KvPrqq0woFJI8trGxkbnjjjuYcePGMUajkcnPz2cWLVrEvPDCCzGPZRiGOXjwIHPTTTcxpaWljMFgYAYMGMCcccYZzPvvv5+U10ZJLVR8ehFOp5MZNWoUA4C54IILWNGRwwMPPMAAYEwmU5QwMQzD/OEPf2AAMBqNhhUWLldccQUDgBk8eDDT0dERdftNN93EAGBycnKY9vZ2Ra+rP+H3+5lvvvmG+cUvfsEMGTKEycvLYy6//HLm3XffZdra2qIEo6Ojg/n666+ZjRs3Jiw8f/jDH5j8/Hxm8+bNSXkt999/P7vpAMDk5eUxer2e/f2ss85iPB6P4LEVFRVMYWEhe1+r1cpotVr29zPOOEP0WIZhmP/+97+M2Wxm75+Tk8Oo1Wr292uvvVaWgFEyBxWfXsSzzz7LCkhLS4uiY4cPH85+KYWw2+2M1WplADAPPfRQxG0Oh4O1tlauXCl4fE1NDfvFf/nllxWdW38lEAgw33//PfPrX/+aGT58OJOTk8NcfPHFzJtvvsm0tLQwBw4cYM455xxm7dq1jN1uT0h4Vq1axeTm5jIbNmxIyrm/8MIL7Od96aWXMkePHmUYJmxJv/HGG4zNZmMAMDfeeGPUsdQKpzAMFZ9eRVlZGftlV8LevXvZL+S7774rer+zzz6bAcAsWLAg4u9ffPEFe7zUrnnixIlxnR+FYYLBIFNeXs7ceeedzOjRoxmz2cwMGTKEWbBgAXP06NGEhOeZZ55hbDYb8/333yflXAOBACses2bNErQwXnnlFQYAo1arme3bt0fcRq1wCsMwDM126yWQgDMAnHzyyTh8+DCuv/56DB06FAaDAYMHD8bSpUvx+eefRx27c+dO9mepADO5bffu3aLHT548Oebxu3btkvGKKFzUajUWLFiAJ598Ehs3bsSwYcOQk5ODzs5OjB07Fpdccgn+/e9/o7OzU1ZWIhBOLvjXv/6F++67Dx9//DEWL16clHOtqKhAY2MjAGD58uWCyQ9XX301Bg0ahFAohNdeey3ittdffx0AcOmllwqO9r7ttttgtVoRDAbx5ptvRtzmdDrZzMpbbrlFsOP2vffeCyCcEfrRRx8pfn2U9EDFp5dQW1sLn88HADh27BimTZuGl19+GS0tLTCbzWhqasInn3yCc845B7fcckvEscePH2d/LikpEX0Oclt3dzccDkfU8fn5+ZKTLMnx3OejKKO7uxtnnnkmJk6ciO3bt2P37t3YvHkzZs2ahWeeeQYjR47EBRdcgNdffx1tbW2iQsQwDN555x3ceeedWL16NU499dSknWNdXR3786RJkwTvo1KpMH78eADAF198wf593759OHLkCADg7LPPFjzWarWyQvnVV19F3LZ+/Xq43W7J40tLSzFx4kTB4ynZAxWfXkJHRwf78x//+EfodDq89dZbcDgc6OjowJEjR3DppZcCAJ599ln85S9/Ye9vt9vZn6XEg3sb9xjyc6wRyuR27rEUZVgsFlxzzTV45513oNPpoFKpMHXqVKxcuRLV1dXsHKIXXngBo0ePxk9+8hO89NJLaG5ujhCiDz/8ELfddhveeecdnHHGGSk7X6n6JXLbvn372I0TtcIpBCo+vQRSS0N+fvbZZ3HppZey9SLDhg3Dm2++iZkzZwIAHnnkEbZ+h9J70Gg0uO2226DX66NuU6lUmDBhAu6//35UVFRgz549OPPMM/Hmm29i7NixOPvss/Hss8/i9ddfx80334w333wT5557btLPsbS0lP2ZKwZcAoEA9u3bx/7c0tICgFrhlB6o+PQSbDYb+/OwYcNwySWXRN1HrVZj+fLlAIDW1lZUVlZGHUsKRIXg3sY9hvwsdSz3du6xlNSgUqkwevRo3HXXXSgvL8fBgwdx/vnn491338Utt9yCv/zlL1i2bFlKnnv27NnsyIXHH39ccJPz3HPPobW1lf29u7sbALXCKT1Q8eklcHeJEyZMEL0f8XUDPb55bov8+vp60WPJbTk5ObBarezfyfEdHR2SAkSOpy3504tKpcKIESNw++23o7y8HLt378b111+fsufTaDRYsWIFAGDPnj0499xzUVlZCZ/Ph6amJvzpT3/C8uXLI7o4pLvrNyX7oVdEL6GgoIAVIKnWKly/P7kf17cu5ibh3sYPInOPl/Khk+OlfPGU1KJSqRLq+yaXm2++Gffccw+AcFB/zpw5bNblHXfcgby8PNx3333s/fPz8wFQK5zSAxWfXgQJHO/Zs0c0y2nPnj3szySNdfz48eycFm7mERen04l169ZFPA9h0aJFMJlMksfX1dWxz53KADcle/jjH/+IjRs34vrrr8fUqVMxbNgwzJ49G/fddx927tzJ9gjMz89nR3JTK5zCksEaI4pCvv/+e7bY86233oq6PRgMMjNnzmQAMCUlJRHtd0hhn9lsZmpqaqKOffzxx2UV9hUXFzOdnZ1Rt99yyy0MAMZms9HCPgrDMAxz1llnMQCYc845h/0bLXimEKj49DIuvPBCto/W22+/zfh8PoZhGObIkSPMpZdeyn4xX3311YjjuC1NJk2axFRUVDAME25p8o9//IPtySWnpcnixYuZ/fv3MwzDsM0qVSoVbWlCYamrq2M0Gg0DgHnnnXcibiOtnq677jrBYx0Oh6xWTw8//LDg8bW1tbTVUy+Aik8vw+FwMD/60Y/YL5fBYGDy/7+9ewltYo/CAP4FtSQ2k0cFKS1IKtbaGhDsSkwUF6XoQgpBKAoai7UouCjiQgTFjRAR3PhY9CGIgkgFC4KiXRR8gPhAxPiiEVMxWChWmykJDem5izDDNK8m5d5pw/1+IAz850yahf06r3Pc7nkNHrP/w2qymzkqiqL30cIimjk6nU79FwwACQaDbOZIMjs7K+3t7QJAvF6v/geShmfhJMLwqUjpdFr6+vpkx44dUlNTI6tWrZL6+nrp7OyU58+fF6399euX9Pb2SmNjo1itVnG5XHob+1K6ZI+NjUl3d7d4PB6pqqqSNWvWSFtbG9vY/89EIhE5c+aMvHnzRhKJhIhker6Njo6Kz+fTO1W/ffs2p5Zn4SQiwmFyRFS2d+/e6S80WywWuFwuxONx/Z2furo6DA0NYdu2bXnrswcbKoqCZDKJVCoFoPzBhk6nE6qq6l0VgsEgBgcHOdhwGWP4EFHZ/vz5g8uXL2N0dBRjY2OYnJyEoijYuHEjOjo6cPz48XlPqeUzMTGBUCiEBw8e4MePH7BarfB6vTh06BC6uroWfDcoEokgFArhyZMniMViUBQFW7duRU9PDwKBwL/5dek/wPAhIiLT8T0fIiIyHcOHiIhMx/AhIiLTMXyIiMh0DB8iIjIdw4eIiEzH8CEiItMxfIiIyHQMHyIiMh3Dh4iITMfwISrAYrGU/G/Xrl0FjzMxMYGTJ0+iqakJNpsNNTU18Pv96O/vLziR1igSiaCnpwcNDQ2wWq1Yu3Yt2tvbce/evX/z6xKZir3diAqora0tup5KpfD7928AwKlTp3Dx4sWcfbK7N9vtdiSTSb37c7ndmx0OB1RVxdzcHADg8OHDGBgYYPdmqjxLNsyBqMJdunRJH6T3+fPnnHXj3JpNmzbJq1evRCQzt+bKlSv6IL9S5tZs375dH6wWj8fl7Nmz+mdzbg1VIoYP0SI1NzcLAPH5fHnXtYmdNptNvn37lrN+4cKFkiZ21tbWytTUVM760aNHBYA4HA5O7KSKw3s+RIvw4sULfPr0CQBw5MiRvPvcvHkTANDZ2YmGhoac9RMnTsButyOdTuP27dvz1mZmZvR7OseOHYPL5cqpP336NABgenoa9+/fX+xXIVoSDB+iRRgYGACQuQezb9++nPUvX75gfHwcALB79+68x7Db7fD7/QCAx48fz1t79uwZEolE0XqPx4Pm5ua89UTLHcOHqEyqquLu3bsAgP3792P16tU5+3z48EHf9nq9BY+lrX38+LFg/ebNmxesD4fDJfzkRMsHw4eoTHfu3IGqqgAKX3KLxWL6dn19fcFjaWvT09P6MY31brc7b7hl1xs/j6gSMHyIytTf3w8A2LJlC1pbW/PuE4/H9e1i4WFcM9Zo28VqjevGWqJKwPAhKkM4HMbLly8BFD7rIaKFMXyIyqCd9VitVhw4cKDgfoqi6NvaC6L5GNeMNdp2sVrjurGWqBIwfIhKNDs7i1u3bgEAAoEA3G53wX3r6ur07Z8/fxbcT1tzOByw2+059VNTU0UDSKs3fh5RJWD4EJVoeHgYk5OTABa+5GZ8ws345Fo2ba2lpaVgfbEn2bT6Yk/EES1HDB+iEmmX3DZs2ICdO3cW3bepqQnr1q0DADx69CjvPjMzM3j69CmATI83I5/PB5vNVrQ+Go3qL7pm1xMtdwwfohKMj49jZGQEANDV1VVSI8+DBw8CyDya/f3795z1q1evQlVVrFixIuf+UXV1NQKBAADg+vXr+Pv3b059KBQCkLnf09HRUc7XIVp6S93fh6gSnDt3TgDIypUrJRaLlVRjbCza0tIir1+/FpFMY9Fr165JVVVVyY1F/X6/fP36VUREVFWV8+fPi8ViYWNRqlgcqUC0gLm5Oaxfvx7RaBR79+7F8PBwybXZIxUURUEymUQqlQJQ/kgFp9MJVVWRTqcBAMFgEIODgxypQBWHl92IFjAyMoJoNAqg/Hd7WltbEQ6H0dvbi8bGRqRSKVRXV8Pn86Gvrw8PHz4sGDwAsGfPHrx//x7d3d3weDxIJBJwuVxoa2vD0NAQbty4weChisQzHyIiMh3PfIiIyHQMHyIiMh3Dh4iITMfwISIi0zF8iIjIdAwfIiIyHcOHiIhMx/AhIiLTMXyIiMh0DB8iIjIdw4eIiEzH8CEiItMxfIiIyHT/ALpPXnnC+YByAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib import cm\n",
+    "\n",
+    "X, Y = np.meshgrid(x, y)\n",
+    "\n",
+    "fig, ax = plt.subplots(subplot_kw={\"projection\": \"3d\"})\n",
+    "\n",
+    "ax.plot_surface(X, Y, sim_fT_flatfield_np[i], cmap=cm.coolwarm,\n",
+    "                       linewidth=0, antialiased=False)\n",
+    "# plt.plot(x, fit_fT_flatfield[100], label = \"BEC fit\")\n",
+    "# plt.xlabel(\"y axis [px]\")\n",
+    "# plt.ylabel(\"OD\")\n",
+    "# plt.title(\"OD distribution cross-section \\n f={}, flatfield lightsource\".format(str(f[i])))\n",
+    "# plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table><tr><th> name </th><th> value </th><th> initial value </th><th> min </th><th> max </th><th> vary </th><th> expression </th></tr><tr><td> BEC_amplitude </td><td>  449.958235 </td><td> None </td><td>  0.00000000 </td><td>         inf </td><td> True </td><td>  </td></tr><tr><td> thermal_amplitude </td><td>  234.346332 </td><td> None </td><td>  0.00000000 </td><td>         inf </td><td> True </td><td>  </td></tr><tr><td> BEC_centerx </td><td>  600.490128 </td><td> None </td><td>  560.542078 </td><td>  640.438179 </td><td> True </td><td>  </td></tr><tr><td> BEC_centery </td><td>  960.653900 </td><td> None </td><td>  915.342306 </td><td>  1005.96549 </td><td> True </td><td>  </td></tr><tr><td> thermal_centerx </td><td>  600.301836 </td><td> None </td><td>  499.705803 </td><td>  700.897869 </td><td> True </td><td>  </td></tr><tr><td> thermal_centery </td><td>  960.557510 </td><td> None </td><td>  858.713576 </td><td>  1062.40144 </td><td> True </td><td>  </td></tr><tr><td> BEC_sigmax </td><td>  3.99480505 </td><td> None </td><td>        -inf </td><td>         inf </td><td> False </td><td> 3 * thermal_sigmax - deltax </td></tr><tr><td> BEC_sigmay </td><td>  4.53115930 </td><td> None </td><td>  0.00000000 </td><td>  22.6557965 </td><td> True </td><td>  </td></tr><tr><td> thermal_sigmax </td><td>  33.5320110 </td><td> None </td><td>  0.00000000 </td><td>  167.660055 </td><td> True </td><td>  </td></tr><tr><td> thermal_sigmay </td><td>  33.9479780 </td><td> None </td><td>        -inf </td><td>         inf </td><td> False </td><td> thermalAspectRatio * thermal_sigmax </td></tr><tr><td> deltax </td><td>  96.6012280 </td><td> None </td><td>  0.00000000 </td><td>         inf </td><td> True </td><td>  </td></tr><tr><td> thermalAspectRatio </td><td>  1.01240507 </td><td> None </td><td>  0.80000000 </td><td>  1.20000000 </td><td> True </td><td>  </td></tr><tr><td> condensate_fraction </td><td>  0.65754089 </td><td> None </td><td>        -inf </td><td>         inf </td><td> False </td><td> BEC_amplitude / (BEC_amplitude + thermal_amplitude) </td></tr></table>"
+      ],
+      "text/plain": [
+       "Parameters([('BEC_amplitude', <Parameter 'BEC_amplitude', value=449.95823450592684, bounds=[0:inf]>), ('thermal_amplitude', <Parameter 'thermal_amplitude', value=234.346332071024, bounds=[0:inf]>), ('BEC_centerx', <Parameter 'BEC_centerx', value=600.4901284357314, bounds=[560.5420779016479:640.438178969815]>), ('BEC_centery', <Parameter 'BEC_centery', value=960.6538995040197, bounds=[915.3423064697963:1005.9654925382431]>), ('thermal_centerx', <Parameter 'thermal_centerx', value=600.3018356023492, bounds=[499.70580257202505:700.8978686326734]>), ('thermal_centery', <Parameter 'thermal_centery', value=960.5575103290612, bounds=[858.7135761857239:1062.4014444723985]>), ('BEC_sigmax', <Parameter 'BEC_sigmax', value=3.9948050534083563, bounds=[-inf:inf], expr='3 * thermal_sigmax - deltax'>), ('BEC_sigmay', <Parameter 'BEC_sigmay', value=4.531159303422342, bounds=[0:22.65579651711171]>), ('thermal_sigmax', <Parameter 'thermal_sigmax', value=33.53201101010806, bounds=[0:167.66005505054028]>), ('thermal_sigmay', <Parameter 'thermal_sigmay', value=33.94797804777908, bounds=[-inf:inf], expr='thermalAspectRatio * thermal_sigmax'>), ('deltax', <Parameter 'deltax', value=96.60122797691582, bounds=[0:inf]>), ('thermalAspectRatio', <Parameter 'thermalAspectRatio', value=1.012405072798814, bounds=[0.8:1.2]>), ('condensate_fraction', <Parameter 'condensate_fraction', value=0.6575408911220945, bounds=[-inf:inf], expr='BEC_amplitude / (BEC_amplitude + thermal_amplitude)'>)])"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
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+   "source": [
+    "params.item()"
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+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\data\\AppData\\Roaming\\Python\\Python39\\site-packages\\numpy\\lib\\function_base.py:2246: RuntimeWarning: invalid value encountered in _get_fit_full_result_single (vectorized)\n",
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+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:              ()\n",
+       "Data variables: (12/13)\n",
+       "    BEC_amplitude        object 0.0005808768046449142+/-nan\n",
+       "    thermal_amplitude    object 588.5963406894194+/-nan\n",
+       "    BEC_centerx          object 600.511601780056+/-nan\n",
+       "    BEC_centery          object 960.6113364263412+/-nan\n",
+       "    thermal_centerx      object 600.4841611552301+/-nan\n",
+       "    thermal_centery      object 960.6425220578551+/-nan\n",
+       "    ...                   ...\n",
+       "    BEC_sigmay           object 5.279015683706861+/-nan\n",
+       "    thermal_sigmax       object 11.152763795479629+/-nan\n",
+       "    thermal_sigmay       object 13.231941010349708+/-nan\n",
+       "    deltax               object 19.549896361379112+/-nan\n",
+       "    thermalAspectRatio   object 1.1864270823804945+/-nan\n",
+       "    condensate_fraction  object (9.868838645965612+/-nan)e-07</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-63557ffa-e3b5-4773-810c-266fa087ce33' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-63557ffa-e3b5-4773-810c-266fa087ce33' 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-4dfa7f99-04f5-436a-84f5-b9397742ba7f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4dfa7f99-04f5-436a-84f5-b9397742ba7f' 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-af0244dd-0669-4ce9-b80d-6f812388606d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-af0244dd-0669-4ce9-b80d-6f812388606d' class='xr-section-summary' >Data variables: <span>(13)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>BEC_amplitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0.0005808768046449142+/-nan</div><input id='attrs-34bd94b7-52ac-4f36-a49d-b6f672e2b23e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-34bd94b7-52ac-4f36-a49d-b6f672e2b23e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bed0e479-26cd-4fb5-b2cf-a23b86dba17e' class='xr-var-data-in' type='checkbox'><label for='data-bed0e479-26cd-4fb5-b2cf-a23b86dba17e' 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(0.0005808768046449142+/-nan, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thermal_amplitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>588.5963406894194+/-nan</div><input id='attrs-1b6fbad9-a3cf-4588-9f9b-4937e8762f61' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1b6fbad9-a3cf-4588-9f9b-4937e8762f61' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5271c939-2859-479e-94a7-a9d037ab6713' class='xr-var-data-in' type='checkbox'><label for='data-5271c939-2859-479e-94a7-a9d037ab6713' 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(588.5963406894194+/-nan, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>BEC_centerx</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>600.511601780056+/-nan</div><input id='attrs-4f90806c-441e-46d7-b982-0424724b696a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4f90806c-441e-46d7-b982-0424724b696a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e965908-0549-42f2-80c0-6b208be35523' class='xr-var-data-in' type='checkbox'><label for='data-5e965908-0549-42f2-80c0-6b208be35523' 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(600.511601780056+/-nan, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>BEC_centery</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>960.6113364263412+/-nan</div><input id='attrs-3e0c98a2-b443-4ddc-b479-09c35fca6ecc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3e0c98a2-b443-4ddc-b479-09c35fca6ecc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b1f02b8-a762-4c1b-9e90-ea31999aa076' class='xr-var-data-in' type='checkbox'><label for='data-4b1f02b8-a762-4c1b-9e90-ea31999aa076' 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(960.6113364263412+/-nan, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thermal_centerx</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>600.4841611552301+/-nan</div><input id='attrs-12fb82a3-6fac-49be-acb8-f914a20e30b0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-12fb82a3-6fac-49be-acb8-f914a20e30b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-755807ed-4375-4c2c-9b0a-3d66f0d59a6f' class='xr-var-data-in' type='checkbox'><label for='data-755807ed-4375-4c2c-9b0a-3d66f0d59a6f' 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(600.4841611552301+/-nan, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thermal_centery</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>960.6425220578551+/-nan</div><input id='attrs-82de31de-c679-451c-b0ea-ebe24c7d8d58' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-82de31de-c679-451c-b0ea-ebe24c7d8d58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-87cc188e-3660-4dc5-9b5a-41ac73793ed8' class='xr-var-data-in' type='checkbox'><label for='data-87cc188e-3660-4dc5-9b5a-41ac73793ed8' 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(960.6425220578551+/-nan, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>BEC_sigmax</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>13.908395025059775+/-nan</div><input id='attrs-4f6d3b6b-ec92-498c-adc9-8207a8a64447' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4f6d3b6b-ec92-498c-adc9-8207a8a64447' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3fa875f-84fa-426a-8f43-c21f739bc1cb' class='xr-var-data-in' type='checkbox'><label for='data-d3fa875f-84fa-426a-8f43-c21f739bc1cb' 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(13.908395025059775+/-nan, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>BEC_sigmay</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>5.279015683706861+/-nan</div><input id='attrs-6919cdb4-e31d-4be0-a5ab-75658f5c3e00' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6919cdb4-e31d-4be0-a5ab-75658f5c3e00' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4986f84c-2037-462b-b946-559d8c278c02' class='xr-var-data-in' type='checkbox'><label for='data-4986f84c-2037-462b-b946-559d8c278c02' 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(5.279015683706861+/-nan, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thermal_sigmax</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>11.152763795479629+/-nan</div><input id='attrs-8017aabe-03cc-4043-ab00-c3d800613e1c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8017aabe-03cc-4043-ab00-c3d800613e1c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c9267a9-7784-42b8-827d-55472af5c625' class='xr-var-data-in' type='checkbox'><label for='data-7c9267a9-7784-42b8-827d-55472af5c625' 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(11.152763795479629+/-nan, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thermal_sigmay</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>13.231941010349708+/-nan</div><input id='attrs-d5b614cf-0a52-4239-bd74-f9271f465643' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d5b614cf-0a52-4239-bd74-f9271f465643' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f03aa7b6-90f5-428d-a8b2-0e599efbd807' class='xr-var-data-in' type='checkbox'><label for='data-f03aa7b6-90f5-428d-a8b2-0e599efbd807' 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(13.231941010349708+/-nan, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>deltax</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>19.549896361379112+/-nan</div><input id='attrs-bb7198b2-b885-437b-9ac8-d298e6f4e563' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bb7198b2-b885-437b-9ac8-d298e6f4e563' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5073361e-d0e5-4f64-828e-c431f9269642' class='xr-var-data-in' type='checkbox'><label for='data-5073361e-d0e5-4f64-828e-c431f9269642' 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(19.549896361379112+/-nan, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thermalAspectRatio</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1.1864270823804945+/-nan</div><input id='attrs-ce0e2899-85e0-47b1-a407-37ac5f46e9de' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ce0e2899-85e0-47b1-a407-37ac5f46e9de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf60a434-746d-44ba-bce7-41cf629c6478' class='xr-var-data-in' type='checkbox'><label for='data-bf60a434-746d-44ba-bce7-41cf629c6478' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(1.1864270823804945+/-nan, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>condensate_fraction</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>(9.868838645965612+/-nan)e-07</div><input id='attrs-b3995a3d-e637-4673-91e0-00aaaee65b32' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b3995a3d-e637-4673-91e0-00aaaee65b32' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8c1531b-d9ac-4275-ac0d-1bf2e555ecb6' class='xr-var-data-in' type='checkbox'><label for='data-d8c1531b-d9ac-4275-ac0d-1bf2e555ecb6' 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(9.868838645965612e-07+/-nan, dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-28230dcd-6b0e-40cf-a653-948f9a433bd7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-28230dcd-6b0e-40cf-a653-948f9a433bd7' 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-ab6a74ee-09b6-4bd9-8b1f-c3db0ccb75f2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ab6a74ee-09b6-4bd9-8b1f-c3db0ccb75f2' 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: (12/13)\n",
+       "    BEC_amplitude        object 0.0005808768046449142+/-nan\n",
+       "    thermal_amplitude    object 588.5963406894194+/-nan\n",
+       "    BEC_centerx          object 600.511601780056+/-nan\n",
+       "    BEC_centery          object 960.6113364263412+/-nan\n",
+       "    thermal_centerx      object 600.4841611552301+/-nan\n",
+       "    thermal_centery      object 960.6425220578551+/-nan\n",
+       "    ...                   ...\n",
+       "    BEC_sigmay           object 5.279015683706861+/-nan\n",
+       "    thermal_sigmax       object 11.152763795479629+/-nan\n",
+       "    thermal_sigmay       object 13.231941010349708+/-nan\n",
+       "    deltax               object 19.549896361379112+/-nan\n",
+       "    thermalAspectRatio   object 1.1864270823804945+/-nan\n",
+       "    condensate_fraction  object (9.868838645965612+/-nan)e-07"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fitAnalyser.get_fit_full_result(fitResult)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Calculate the parameter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nfit_fT_flatfield_x1000_result = fit_fT_flatfield_x1000_result.T\\nfit_fT_flatfield_x1000_std = fit_fT_flatfield_x1000_std.T\\n'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "'''\n",
+    "fit_fT_flatfield_x1000_result = fit_fT_flatfield_x1000_result.T\n",
+    "fit_fT_flatfield_x1000_std = fit_fT_flatfield_x1000_std.T\n",
+    "'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\n## calculate the peak densities and the radii\\nlam = 421e-9\\nsig0 = 3*lam**2/(2*np.pi)\\neff_px_size = 2.493e-6\\n\\n\\nRxc_fit = fit_fT_flatfield_x1000_result[6]*eff_px_size\\nRxc_err = Rxc_fit*fit_fT_flatfield_x1000_std[6]/fit_fT_flatfield_x1000_std[6]\\nRyc_fit = fit_fT_flatfield_x1000_result[7]*eff_px_size\\nRyc_err = Ryc_fit*fit_fT_flatfield_x1000_std[7]/fit_fT_flatfield_x1000_result[7]\\nRxth_fit = fit_fT_flatfield_x1000_result[8]*np.sqrt(2)*eff_px_size\\nRxth_err = Rxth_fit*fit_fT_flatfield_x1000_std[8]/Rxth_fit*fit_fT_flatfield_x1000_result[8]\\nRyth_fit = fit_fT_flatfield_x1000_result[9]*np.sqrt(2)*eff_px_size\\nRyth_err = Ryth_fit*fit_fT_flatfield_x1000_std[9]/fit_fT_flatfield_x1000_result[9]\\nNc_fit = fit_fT_flatfield_x1000_result[0]/sig0*eff_px_size*eff_px_size\\nNc_err = Nc_fit*fit_fT_flatfield_x1000_std[0]/fit_fT_flatfield_x1000_result[0]\\nNth_fit = fit_fT_flatfield_x1000_result[1]/sig0*eff_px_size*eff_px_size\\nNth_err = Nth_fit*fit_fT_flatfield_x1000_std[1]/fit_fT_flatfield_x1000_result[1]\\nN_fit = Nc_fit + Nth_fit\\nN_err = np.sqrt(Nth_err**2 + Nc_err**2)\\nn2D0c_fit = Nc_fit*5/(2*np.pi*Rxc_fit*Ryc_fit)\\nn2D0th_fit =Nth_fit/(2*np.pi*1.20206*Rxth_fit/np.sqrt(2)*Ryth_fit/np.sqrt(2))\\nf_fit = fit_fT_flatfield_x1000_result[11]\\nf_err = fit_fT_flatfield_x1000_std[11]\\n\\n## Assumed to be given\\ntof = 20e-3\\nomgx = 2*np.pi*200\\nomgy = 2*np.pi*100\\nomgz = 2*np.pi*200\\nomg = (omgx*omgy*omgz)**(1/3)\\nm = 164*const.u \\na0 = 4*np.pi*const.epsilon_0*const.hbar**2/(const.e**2*const.electron_mass)\\n\\n## Calculate T\\nT_x_fit = Rxth_fit**2*m/(const.k)*(1/omg**2+tof**2)**(-1)\\nT_x_err = T_x_fit * 2*Rxth_err/Rxth_fit\\nT_y_fit = Ryth_fit**2*m/(const.k)*(1/omg**2+tof**2)**(-1)\\nT_y_err = T_y_fit * 2*Ryth_err/Ryth_fit\\nT_fit = (T_x_fit + T_y_fit)/2\\nT_err = 1/2*np.sqrt(T_x_err**2+T_y_err**2)\\n\\n## Calculate scattering lenght a\\naho = np.sqrt(const.hbar/(m*omg))\\nahox = np.sqrt(const.hbar/(m*omgx))\\nahoy = np.sqrt(const.hbar/(m*omgy))\\nahoz = np.sqrt(const.hbar/(m*omgz))\\na_x = Ryc_fit**5*ahox/(15*Nc_fit)*(const.hbar*omgx/m*(1+omgx**2*tof**2)/omgx**2)**(-5/2)\\na_x_err = a_x*5*Ryc_err/Ryc_fit\\na_y = Rxc_fit**5*ahoy/(15*Nc_fit)*(const.hbar*omgy/m*(1+omgy**2*tof**2)/omgy**2)**(-5/2)\\na_y_err = a_y*5*Rxc_err/Rxc_fit\\na_fac_fit = (a_x+a_y)/2/a0\\na_fac_err =1/2/a0*np.sqrt(a_x_err**2+a_y_err**2)\\n'"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "'''\n",
+    "## calculate the peak densities and the radii\n",
+    "lam = 421e-9\n",
+    "sig0 = 3*lam**2/(2*np.pi)\n",
+    "eff_px_size = 2.493e-6\n",
+    "\n",
+    "\n",
+    "Rxc_fit = fit_fT_flatfield_x1000_result[6]*eff_px_size\n",
+    "Rxc_err = Rxc_fit*fit_fT_flatfield_x1000_std[6]/fit_fT_flatfield_x1000_std[6]\n",
+    "Ryc_fit = fit_fT_flatfield_x1000_result[7]*eff_px_size\n",
+    "Ryc_err = Ryc_fit*fit_fT_flatfield_x1000_std[7]/fit_fT_flatfield_x1000_result[7]\n",
+    "Rxth_fit = fit_fT_flatfield_x1000_result[8]*np.sqrt(2)*eff_px_size\n",
+    "Rxth_err = Rxth_fit*fit_fT_flatfield_x1000_std[8]/Rxth_fit*fit_fT_flatfield_x1000_result[8]\n",
+    "Ryth_fit = fit_fT_flatfield_x1000_result[9]*np.sqrt(2)*eff_px_size\n",
+    "Ryth_err = Ryth_fit*fit_fT_flatfield_x1000_std[9]/fit_fT_flatfield_x1000_result[9]\n",
+    "Nc_fit = fit_fT_flatfield_x1000_result[0]/sig0*eff_px_size*eff_px_size\n",
+    "Nc_err = Nc_fit*fit_fT_flatfield_x1000_std[0]/fit_fT_flatfield_x1000_result[0]\n",
+    "Nth_fit = fit_fT_flatfield_x1000_result[1]/sig0*eff_px_size*eff_px_size\n",
+    "Nth_err = Nth_fit*fit_fT_flatfield_x1000_std[1]/fit_fT_flatfield_x1000_result[1]\n",
+    "N_fit = Nc_fit + Nth_fit\n",
+    "N_err = np.sqrt(Nth_err**2 + Nc_err**2)\n",
+    "n2D0c_fit = Nc_fit*5/(2*np.pi*Rxc_fit*Ryc_fit)\n",
+    "n2D0th_fit =Nth_fit/(2*np.pi*1.20206*Rxth_fit/np.sqrt(2)*Ryth_fit/np.sqrt(2))\n",
+    "f_fit = fit_fT_flatfield_x1000_result[11]\n",
+    "f_err = fit_fT_flatfield_x1000_std[11]\n",
+    "\n",
+    "## Assumed to be given\n",
+    "tof = 20e-3\n",
+    "omgx = 2*np.pi*200\n",
+    "omgy = 2*np.pi*100\n",
+    "omgz = 2*np.pi*200\n",
+    "omg = (omgx*omgy*omgz)**(1/3)\n",
+    "m = 164*const.u \n",
+    "a0 = 4*np.pi*const.epsilon_0*const.hbar**2/(const.e**2*const.electron_mass)\n",
+    "\n",
+    "## Calculate T\n",
+    "T_x_fit = Rxth_fit**2*m/(const.k)*(1/omg**2+tof**2)**(-1)\n",
+    "T_x_err = T_x_fit * 2*Rxth_err/Rxth_fit\n",
+    "T_y_fit = Ryth_fit**2*m/(const.k)*(1/omg**2+tof**2)**(-1)\n",
+    "T_y_err = T_y_fit * 2*Ryth_err/Ryth_fit\n",
+    "T_fit = (T_x_fit + T_y_fit)/2\n",
+    "T_err = 1/2*np.sqrt(T_x_err**2+T_y_err**2)\n",
+    "\n",
+    "## Calculate scattering lenght a\n",
+    "aho = np.sqrt(const.hbar/(m*omg))\n",
+    "ahox = np.sqrt(const.hbar/(m*omgx))\n",
+    "ahoy = np.sqrt(const.hbar/(m*omgy))\n",
+    "ahoz = np.sqrt(const.hbar/(m*omgz))\n",
+    "a_x = Ryc_fit**5*ahox/(15*Nc_fit)*(const.hbar*omgx/m*(1+omgx**2*tof**2)/omgx**2)**(-5/2)\n",
+    "a_x_err = a_x*5*Ryc_err/Ryc_fit\n",
+    "a_y = Rxc_fit**5*ahoy/(15*Nc_fit)*(const.hbar*omgy/m*(1+omgy**2*tof**2)/omgy**2)**(-5/2)\n",
+    "a_y_err = a_y*5*Rxc_err/Rxc_fit\n",
+    "a_fac_fit = (a_x+a_y)/2/a0\n",
+    "a_fac_err =1/2/a0*np.sqrt(a_x_err**2+a_y_err**2)\n",
+    "'''\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "IndexError",
+     "evalue": "index 12 is out of bounds for axis 0 with size 11",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32mf:\\Jianshun\\analyseScript\\test_fit.ipynb Cell 21\u001b[0m in \u001b[0;36m2\n\u001b[0;32m     <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/test_fit.ipynb#X36sZmlsZQ%3D%3D?line=23'>24</a>\u001b[0m n2D0c_fit \u001b[39m=\u001b[39m Nc_fit\u001b[39m*\u001b[39m\u001b[39m5\u001b[39m\u001b[39m/\u001b[39m(\u001b[39m2\u001b[39m\u001b[39m*\u001b[39mnp\u001b[39m.\u001b[39mpi\u001b[39m*\u001b[39mRxc_fit\u001b[39m*\u001b[39mRyc_fit)\n\u001b[0;32m     <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/test_fit.ipynb#X36sZmlsZQ%3D%3D?line=24'>25</a>\u001b[0m n2D0th_fit \u001b[39m=\u001b[39mNth_fit\u001b[39m/\u001b[39m(\u001b[39m2\u001b[39m\u001b[39m*\u001b[39mnp\u001b[39m.\u001b[39mpi\u001b[39m*\u001b[39m\u001b[39m1.20206\u001b[39m\u001b[39m*\u001b[39mRxth_fit\u001b[39m/\u001b[39mnp\u001b[39m.\u001b[39msqrt(\u001b[39m2\u001b[39m)\u001b[39m*\u001b[39mRyth_fit\u001b[39m/\u001b[39mnp\u001b[39m.\u001b[39msqrt(\u001b[39m2\u001b[39m))\n\u001b[1;32m---> <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/test_fit.ipynb#X36sZmlsZQ%3D%3D?line=25'>26</a>\u001b[0m f_fit \u001b[39m=\u001b[39m fit_fT_flatfield_result[\u001b[39m12\u001b[39;49m]\n\u001b[0;32m     <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/test_fit.ipynb#X36sZmlsZQ%3D%3D?line=26'>27</a>\u001b[0m f_err \u001b[39m=\u001b[39m fit_fT_flatfield_std[\u001b[39m12\u001b[39m]\n\u001b[0;32m     <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/test_fit.ipynb#X36sZmlsZQ%3D%3D?line=28'>29</a>\u001b[0m \u001b[39m## Assumed to be given\u001b[39;00m\n",
+      "\u001b[1;31mIndexError\u001b[0m: index 12 is out of bounds for axis 0 with size 11"
+     ]
+    }
+   ],
+   "source": [
+    "fit_fT_flatfield_result = fit_fT_flatfield_result.T\n",
+    "fit_fT_flatfield_std = fit_fT_flatfield_std.T\n",
+    "\n",
+    "## calculate the peak densities and the radii\n",
+    "lam = 421e-9\n",
+    "sig0 = 3*lam**2/(2*np.pi)\n",
+    "eff_px_size = 2.493e-6\n",
+    "\n",
+    "\n",
+    "Rxc_fit = fit_fT_flatfield_result[6]*eff_px_size\n",
+    "Rxc_err = Rxc_fit*fit_fT_flatfield_std[6]/fit_fT_flatfield_std[6]\n",
+    "Ryc_fit = fit_fT_flatfield_result[7]*eff_px_size\n",
+    "Ryc_err = Ryc_fit*fit_fT_flatfield_std[7]/fit_fT_flatfield_result[7]\n",
+    "Rxth_fit = fit_fT_flatfield_result[8]*np.sqrt(2)*eff_px_size\n",
+    "Rxth_err = Rxth_fit*fit_fT_flatfield_std[8]/Rxth_fit*fit_fT_flatfield_result[8]\n",
+    "Ryth_fit = fit_fT_flatfield_result[9]*np.sqrt(2)*eff_px_size\n",
+    "Ryth_err = Ryth_fit*fit_fT_flatfield_std[9]/fit_fT_flatfield_result[9]\n",
+    "Nc_fit = fit_fT_flatfield_result[0]/sig0*eff_px_size*eff_px_size\n",
+    "Nc_err = Nc_fit*fit_fT_flatfield_std[0]/fit_fT_flatfield_result[0]\n",
+    "Nth_fit = fit_fT_flatfield_result[1]/sig0*eff_px_size*eff_px_size\n",
+    "Nth_err = Nth_fit*fit_fT_flatfield_std[1]/fit_fT_flatfield_result[1]\n",
+    "N_fit = Nc_fit + Nth_fit\n",
+    "N_err = np.sqrt(Nth_err**2 + Nc_err**2)\n",
+    "n2D0c_fit = Nc_fit*5/(2*np.pi*Rxc_fit*Ryc_fit)\n",
+    "n2D0th_fit =Nth_fit/(2*np.pi*1.20206*Rxth_fit/np.sqrt(2)*Ryth_fit/np.sqrt(2))\n",
+    "f_fit = fit_fT_flatfield_result[12]\n",
+    "f_err = fit_fT_flatfield_std[12]\n",
+    "\n",
+    "## Assumed to be given\n",
+    "tof = 20e-3\n",
+    "omgx = 2*np.pi*200\n",
+    "omgy = 2*np.pi*100\n",
+    "omgz = 2*np.pi*200\n",
+    "omg = (omgx*omgy*omgz)**(1/3)\n",
+    "m = 164*const.u \n",
+    "a0 = 4*np.pi*const.epsilon_0*const.hbar**2/(const.e**2*const.electron_mass)\n",
+    "\n",
+    "## Calculate T\n",
+    "T_x_fit = Rxth_fit**2*m/(const.k)*(1/omg**2+tof**2)**(-1)\n",
+    "T_x_err = T_x_fit * 2*Rxth_err/Rxth_fit\n",
+    "T_y_fit = Ryth_fit**2*m/(const.k)*(1/omg**2+tof**2)**(-1)\n",
+    "T_y_err = T_y_fit * 2*Ryth_err/Ryth_fit\n",
+    "T_fit = (T_x_fit + T_y_fit)/2\n",
+    "T_err = 1/2*np.sqrt(T_x_err**2+T_y_err**2)\n",
+    "\n",
+    "## Calculate scattering lenght a\n",
+    "aho = np.sqrt(const.hbar/(m*omg))\n",
+    "ahox = np.sqrt(const.hbar/(m*omgx))\n",
+    "ahoy = np.sqrt(const.hbar/(m*omgy))\n",
+    "ahoz = np.sqrt(const.hbar/(m*omgz))\n",
+    "a_x = Ryc_fit**5*ahox/(15*Nc_fit)*(const.hbar*omgx/m*(1+omgx**2*tof**2)/omgx**2)**(-5/2)\n",
+    "a_x_err = a_x*5*Ryc_err/Ryc_fit\n",
+    "a_y = Rxc_fit**5*ahoy/(15*Nc_fit)*(const.hbar*omgy/m*(1+omgy**2*tof**2)/omgy**2)**(-5/2)\n",
+    "a_y_err = a_y*5*Rxc_err/Rxc_fit\n",
+    "a_fac_fit = (a_x+a_y)/2/a0\n",
+    "a_fac_err =1/2/a0*np.sqrt(a_x_err**2+a_y_err**2)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Plot the results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Peak density Thermal cloud n2D0th')"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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9EoxQf+wtVr6gDPdQyM3NFXv16iXF6OjoKK5fv15UKBQat4mKihLHjRsnmpiYVMjeQGX5/1XWvQw9xZTq168vvvPOO1p7GH/zzTfS62FmZibeunVLbbn8I1MK64GWnZ0t/vrrr2KVKlWkbTw8PMQHDx5o3Obbb79VGX0SHx+v8nh6err4+uuvS2VmzJihNQaiiu7p06eiIAiijY1NaYdC5QDbGiWHbQ31ytO5qz7nPPnxf1/6ODKl5JS1kSkLFiwQW7VqJe7Zs0fMyclRW+bq1auim5ub9L+ZO3euxvryv59DQ0O17vvixYti586dVb5rtX0vxcTEiNbW1tKIlj/++KNAmfzfcZ6enhpH+nNkStlgtAXogbzM26RJk5CWlobNmzdLWcOZM2fi119/RUJCAgBg2rRpWLx4sTFDISp36tevj88//1y6vWXLllKMhqjkfPvtt9i1axcAwMnJCSdPnsTgwYMhk2n+yqpatSqWL1+OQ4cOFWnuUqKK4Pjx4/j+++9Ru3ZtjWWmTJkirbeQnZ2N3377rdj7NTU1xaBBg3DhwgVp39HR0RrnSM/JycFnn30GIG9e/5CQkALrHVlaWiIkJAQ2NjYAgGXLluHp06fFjpWovFKOItP2/ibSB9saRERUXr3zzjs4ceIEunfvDhMTE7Vl6tWrpzLSzFDrHTVs2BD79+/HlClTpPvGjRuHiIgIteW/+uoraWTc1KlT0aFDhwJlhg8fjr59+wIAoqKisHr1aoPESsZh1GSKiYkJvv32W1y+fBmTJ09G06ZN4ebmBhcXFzRo0AD/+9//EBYWhsWLF2tdJI/oZdW9e3fpekREBFJTUwuUSU1NxdKlS9GpUye4u7vDwsIClSpVQmBgID755BM8efKk0P2EhYVhwYIF6Ny5M7y8vGBlZQUrKytUrVoVvXr1wvr166FQKAzynM6dOwdnZ2cIggALCwts3ry5yHVlZWXh66+/RrNmzSCXy2Fvbw8/Pz/Mnj1bp+f9ovv372PWrFlo1qwZnJ2dYW5ujipVqqBjx4746aefkJWVpXV7QRAgCIK0wHVqaioWL16Mpk2bwsHBATY2NvDz88OsWbOQmJhYaDzHjh3DsGHDULt2bdja2sLc3Bzu7u5o1KgR+vfvjzVr1uDRo0dqt1PG8uJ0V8r7lf7880/pvvyXY8eOQaFQoGrVqhAEAc7OzoU+fyDvWFLWMWDAgELLvyg1NVX6kRUAVqxYgbp16+q8fVBQECZMmKDx8aioKMycORMBAQGoVKkSLCws4OHhgR49emDt2rWFHuevvfZagddw48aN6NixI6pUqQJLS0t4e3tj5MiRuHnzpk4xP3r0CDNmzECdOnVgbW0NZ2dntGrVCj/++CNycnJ0e+L5/P3335gwYQLq1asHBwcHWFpawsvLC/3798e+ffu0bqvu2Ll//z6mT5+O2rVrw9raGg4ODmjVqhWWL19e6OulUCjwyy+/oEePHvD09ISlpSWsra3h4+ODwMBATJgwATt27NA47cKNGzewaNEi9OzZE9WqVYO1tTUsLCzg5uaGzp0748cff0RGRobW55J/wcF58+apPd41Ke5ngi7mzp2r8r4D8v6HgwYNgre3NywsLODq6ooePXrg0KFDWut6MSGhifJEHQAuX75c5NhfVKVKFWzdulVaMP7w4cM4depUgXJHjx6VPqPbt2+P+vXrq63PxcVF+hzJzMyUkqya5OTkICQkBH379oWPjw9sbGxgYWEBT09PdOvWDd9++y1iY2OL8xSJDCokJETrZeXKlZg9ezb8/PywevVqaTF6IkNhW0M7tjUqXlvD2IrbLgAMfy5/9epVvPPOO6hduzbs7OwgCAJ27twJIO/HZWV55Q/N//zzD4YNGwZfX19YWVnB29sbAwcORHh4uMq+FAoFfvvtNwQFBcHNzQ2WlpaoVasWPvroI6SkpGiNMz09HTt27MA777yD5s2bo3LlyjAzM4NcLkf9+vUxfvz4UpvW8ujRoxg7dizq1q0LBwcHmJmZwdnZGa+++io+/vhjXLt2rVj1x8fHY8GCBWjRooX0OeDm5oYOHTpg2bJlGts2Sj4+PhAEAT4+PoXuS9eyycnJmDdvHho0aABbW1s4OjqiSZMm+PLLL9V+LxRVZGSkdLwpO13FxcVh7ty58Pf3l6Y0Ve47PT1dY126tnu6dOkidc6KjIws9NjUlSAIWLx4MVq1agUg7/ti4cKFBcqJoih1VhAEARMnTtRY56RJk6TrmzZtUnlM+TvEn3/+qRLDi5fCpjsv7vcGPVfaQ2OIXkbQYei9KIpiVlaWStno6GiVxw8ePFjo4tx2dnbirl27NO5j7ty5Oi2w1aRJkwL7z0+XofeHDh2SFgmztbVVO7xRV9HR0WL9+vU1xuvh4SGGhYXpPAzyyy+/FC0sLLS+BjVr1hRv3rypsQ5lubZt24p37twR69atq7EuHx8fMTIyUm09CoVCHD16tE7/l8mTJxfYXtswY13qRL6hrZ988ol036ZNm7S+hqIoiuPHj5fKHz58uNDyL/rpp5+k7Rs0aKD39tosX75ctLKy0vq8/f39tQ4Rz388paWliT179tRYl6WlpXjw4EGtMR07dkx0dHTUWEerVq3E+Ph4nYbnpqamigMHDiz0f9utWzcxOTlZbR0vHjv79u0THRwcNNbVuXNnMTMzU21dT548EQMDA3U63nbs2FFg+3Xr1um0bfXq1dUu/Jf/uRR2UccQnwm6mDNnjsr7bsGCBaJMJtO4z3nz5hVrf6Ioivv27VP5H6pT1CkvRFEU+/fvL207atSoAo9Pnz5denzx4sVa69qyZYtUtm/fvhrLnTt3TqxevXqh/2su3k1liSAIokwmK/QiCIIoCII4bNgwMTc3t7TDpnKgsO84JbY1NGNbI+9S0doa6hhimi9DtAuMcS6/Zs0atced8tz7xffV0qVLRVNTU7X7tbCwEH///XdRFEUxOTlZ7NKli8YY69Wrp3XBbx8fH52Okw8//FDra2bIab5iY2PFDh066BTXi3Sd5mvnzp1a21YARC8vLzEsLExjHfpMt6VL2fDwcLFq1aoa46lbt64YGRlpkGm+8r/Xhg0bJp49e1b08PDQuG91UwEXhbOzs1Tno0eP1JbRZ5qv/A4cOCBtZ2VlVaBtfOXKFelxPz8/rXUpFArR3t5eBCDKZDKV93n++LRdNH0mF/d7g1QZbQF6Iiq+F3s85V98d+fOnejbty9ycnJgYmKC7t27o3379qhSpQpSUlIQGhqKTZs2ISUlBW+++SYOHTqE9u3bF9hHeno6TE1N0aJFC7Rq1Qo1atSAvb094uPjERERgfXr1yM6Ohrnz59Hr169cOrUKZiZmen9XDZu3IihQ4ciOzsbzs7O2L9/P5o2bar/iwIgIyMDnTp1wtWrVwHkLTg2evRo1KtXD0lJSdi5cycOHjyI3r17w8HBodD6ZsyYgSVLlgDIW+B1wIABUg+0R48eYefOnTh69Chu376NNm3a4OLFi6hSpYrG+pKTk9GtWzfcvHkTPXv2RJcuXVCpUiX8+++/+PHHHxEVFYXIyEgMHz4coaGhBbZftmwZfv75ZwB5//N+/fqhcePGcHZ2RkZGBiIjI3HmzBm12xZGuQjpm2++CSBviodPP/20QDnlgmtjxozBZ599BoVCgVWrVqFfv34a605LS5OmDKpWrRqCgoL0ji9/z/shQ4bovb0mK1aswPjx46XbPXr0QLdu3eDg4IBbt24hODgYERERuHLlClq3bo0LFy7A2dlZa50jR47E7t270aRJEwwYMABeXl6Ii4vDL7/8gr///hsZGRkYPHgwbt68qXZB1xs3bqB79+7SFJiNGzfG22+/DQ8PD0RFRWHdunU4efIkRo0aVejzy8rKQseOHaVRAF5eXhg4cCDq168PCwsL3LlzByEhIbh58yb27duHN954A3/88YfWqdMuXLiARYsWQRRFjBs3Di1atICFhQXOnTuH5cuXIy0tDQcPHsTChQsxZ86cAtuPGTMG586dAwB4e3tjwIABqFGjBhwcHJCSkoKbN2/ir7/+wpkzZ9TuPy0tDYIgoEmTJmjTpg1q164NR0dHJCcn4969e9i0aRNu3bqFu3fvokuXLrh48aLK+93Pzw87duxAeHg4Zs+eDQDo37+/Tr0YDf2ZoKuVK1diw4YN8PDwwPDhw1G/fn1kZWXhwIED2Lx5M0RRxJw5c9CmTRupV2pR5O9Z6O3tXey4XzRw4ECpN1X+3lPq9t+kSROtdeX/nnixR6TSiRMn0KlTJ6n3WvXq1dGvXz/UrVsXFhYWiImJwZkzZ7Bv375yt2AuVWxeXl5aR8eZmprC0dERDRs2xMCBA4v0vUqkDdsa6rGtUXHbGsZS3HaBMc7lT548ic8++wympqYYO3asdC4fHh6uNoa9e/di+/btcHFxwahRo+Dn54f09HRs374d+/btQ2ZmJvr164fIyEgMGzYMBw4cQOvWrdG3b19UqVIF9+7dw/fff4/79+/j2rVrmDp1KkJCQtTGlp6ejkqVKqFjx45o3LgxPDw8YGZmhujoaISFhWHz5s3Izs7GwoUL4eLiojKdkjE8efIEzZs3l6ZpksvlGDhwIAIDA2Fvb4+nT5/i4sWL2Lt3Lx48eFCkfezfvx99+vSRRuG1bdsWffr0gYuLC+7du4f169fjypUruH//Ptq2bYuzZ8+iTp06BnuO6jx58gQdOnSQRqDVqFEDI0eORLVq1RAbG4sNGzbg9OnT6Nu3L7Kzsw2676ioKHTv3h3x8fEYPHgw2rVrB1tbW1y7dg0//PADnj59irCwMEybNg3BwcFF3s/jx4+l7zorK6tC2/j6ev311+Ho6IiEhASkp6fj3Llz0mgVQL92j0wmQ+PGjfHnn38iNzcX169fR7NmzQAAn376KeLi4vDxxx9L303Kz9v8NB0zxf3eoBcYK0sjCILo7u6uc3kfHx/RxMTEWOFQEaWmpor79+8XFyxYIL755puil5eXThl3Qzh//rz4448/iqNHjxYbN24smpub65wJj46OFpcsWSIOGjRI9Pf3F11dXUUzMzPRzs5O9Pf3FydOnChev37dqPFrg3zZX21WrFihtpdMVFSUKJfLRQCiq6urePbsWbXbnz17VipXtWpVtT3Iz549Kz58+FBjDJmZmeKUKVOkONauXau2nLbeYt99950oCIL0PAzZk7tJkyZqeyv88MMPhfYgEUVR3L17t/R4y5YtNb4W+f8XmnpH59+Xubm5uGfPngJlnjx5otITR93/TtkLTi6Xa32tkpKSxAsXLhS4X5eeMcrHdVmIrEePHiIAURAE8d9//9VYbs2aNVK9n3/+eaH1quPi4iLVceLEiSLV8aKIiAhpRIqJiYnaXm+pqalit27dpH2/9dZbaut6sUfIrFmzCvQSVigUKj3Tvvrqq0LrGjNmTIGF87KyssTBgwer7E/T/2vatGlSmfHjx6t9r2dlZYlDhw6Vyv3www8Fyrw4msPLy0vtAuWnT5+Weq1VqlRJzMjIUHn88ePH0uiKJk2aiM+ePVMbtyiKYmRkpNoeMOHh4VqPN4VCIS5evFiKVdOCgvouCGnIzwRd5P88AyB27NhR7eu1aNEiqUzXrl2LvL/s7GyxVq1aUl2aehMXZ2TKo0ePVJ5TQkKCyuO+vr7SY4X1IszOzhZNTExEIG/Bxhffb4mJiSoLS77//vtidna22rpSU1ML7RVKRBVPabVncnJyxMOHD4vTp08XW7RoIVaqVEk0NTUVHR0dxRYtWoifffaZ1h63uvZCVXeey7YG2xpsa+jOECNTitsuMNa5vLu7u3jjxg2NzyH/+wqA+MorrxQ4bxNFURwzZoxURjny/IsvvihQ7vHjx9J5mYmJicbj/cCBAxrP10Qxr32g7EFvZ2encSSOoUam5B9l0759e/Hp06dqy+Xm5orbt28vcH9h74nk5GSVdu6SJUsKlMnOzlZ5nZs2bao2BkOOTMn/+nXr1q3Aoue5ubniBx98oHKMGGpkCgDRwcFB/PvvvwuUu3PnjvTdYmJiIsbExBR5n/PmzZP29+abb2osV9SRKaIoqixG/+2332rcvy7nHPnf47/88ovWOHVhqO8NUmXUZIqbm5vO5X18fESZTGascKiItE2TYuxkivKD/8WLLh/e+acFUf4A4+joKJ1kAxDNzMzUnoCUBF0aOFevXhWdnJykctOnT5cemzRpknT/X3/9pXVfq1evlsquX7++SPHm5OSI1apVk04u1NHUwJk1a5Z0f/369cUHDx4UKQalzMxM6XUxNzcX79y5o7Fs/qlmNL3WjRo1EgGITk5OGk+alIYMGSICeUMu7927V+Dx/PuaP3++xnpWrlwplVuwYEGBx5VDsXv06KE1Hk0M3cDJPyXQrFmzNJZr2bKl9H7T1mjW5MWpJjQNwdXX1KlTpTrff/99jeWSk5Olk39BENQ2LvOfvGh6L4iiKN66dUtrubCwMOnxOnXqiFlZWWrrSU9PVzmxUff/evjwoXTMaItJFPNeY+V7uUaNGgUef/EzX9vny6BBg6Ryx48fV3ns9OnTWhsLhqT8n1SvXl3t4/omUwz5maCL/D/YODk5afxhTaFQiJ6eniKQN82CtgaoNp999pm0P39/f1GhUKgtV5xkiiiKKlNK3L59W+Wx/FPbpaSkFFqXtvL5n8/AgQP1jpOIKr7Sas+8OI2STCYrML2Lq6urePr0abXbv/nmm6Krq6vWi7KewMBAlW3Z1ig6tjUKV17bGpoYIplSnHaBMc/l9+7dq7W+/O8rCwsL8f79+2rLPXjwQOW3FG0de/Kfm4WEhGjdvzb5n4u6H5VF0TDJlFOnTkl11KhRQ2snMF1iVfeeWLp0qfR4v379NNaTk5MjNmzYUCqrbqpCQyVTHj16JJqZmUmfT+qSaKKYl1Bp0aJFsdoFSi8mU7QdHx999FGh///C3L59W+pUKQiC1iRBcZIp48aN0/gZlv+3iGXLlhVaV2Hli5NMKc73Bqky6gL0+sjKytI6RJFKj6OjI9q3b4/33nsPGzZsMMhUJrowNzdHo0aNMHLkSHz//fd6Tfnj6emJjz/+GAcPHsTjx4+RlZWF+Ph4ZGRk4I8//kCTJk2QnZ2Nd999F6dPnzbisyjczp07VS5r167F2LFj0bRpU8TFxQEA3N3d8cEHHwAARFHE+vXrAQDNmjXDq6++qrX+/v37S4sBF7Z4sSYmJibS8MKzZ8/qNE2KQqHA2LFjpcXEW7ZsiePHj8PDw6NIMSidOHFCel169uyJ6tWrayw7ffp0rXVdunRJWthu5MiRqFSpktbyb7/9NgAgNzcXR44c0VjOxMQE7777rsbH80+BoByimZ+1tTUA4M6dOwYfTlsUnTt3lqYCCg4OVrtA6LVr16Rh6T169CjS50R8fLzKbV2mTdDF9u3bAeRNl6LtmLCzs8P//vc/AHnvM+XCjJpMnjxZ42M1a9aEp6cnAPX/4/xDct99912N01lYWlpKMWmyadMmZGZmAij8mDczM0P//v0B5B1fyqHs6gQEBGj9fNF2HCuPYQDFXqSxMC1atAAA3L17V/psKCpjfSboaujQoRoXU5TJZNLUXpmZmbh7967e9YeGhkpTspmammLlypVGO/fK/zyePn2q8phyajsg7xgvjJWVlXT9xUUjf/31VwB5r4+6aUSIiIDSac9kZ2fDxcUFM2bMwKlTp5CRkYGEhASkpKTg559/RuXKlfH48WN069ZN7WLm27dvx6NHjzRe8i9ErW1KULY19MO2RskrqbaGMRWnXWCsc3lfX19069at0NiVevToIcX5Ig8PD5VFzLW1T1q3bi1dL047QHmOD0DjtMCGoPy8A4CPP/5YWqzckJTtUQDSZ606JiYmmDFjhtrtDG3fvn3S+3/48OEa296CIGDq1KkG37+zszMGDRqk8fHCPscKk5qait69e0vTAL/zzjsIDAzUP1AdlFS7pziK+71BqsrEmimJiYmIjY012A9nZDivvvpqgR84Z86cWSL7vn79OkxMTKTb6hoZmjRv3hzNmzcvcL+5uTk6dOiAP/74A1WrVkVaWhrWrFmj8kVd0pTzyWpSt25dbNmyRZrb8erVq9L/pFKlSoX+4AsAtra2SExMxI0bN9Q+npubi507d2Lbtm24cOECYmJikJKSgtzc3AJlU1JSkJycDLlcrnF/GRkZ6Nu3r/SDcdeuXbFlyxaVH1iLSrkGA4BC58lt2rQp7O3tkZycrPbx48ePS9eVr4E20dHR0nVNryUA1KpVS+MPogBUGnkJCQkFHu/YsSM2b96M69evo2PHjpgxYwY6dOig05evMchkMowZMwYff/wxYmJisG/fPvTs2VOlzKpVq6TrY8aMKekQNYqNjcW9e/cAAA0bNoSLi4vW8p06dZLW1yjspF3dZ0x+yrVP1P2P9TmOC3s8/3EcGxtb6HGcP54bN27A19dXbTldnp+6OoG8+bHd3d0RExOD1atXA8g7LgIDA/X+8f7w4cPYuHEjzp07h/v37yMlJUVtIxvIe486OTnpVX9+xvpM0FVxXvPC3LhxQ5r7HgC++OILvPLKK/oHqaP83x/a1oQojvj4eKmR7ufnh2rVqhllP0QlISsrC3/88Qf++ecfxMbGQhAEODs7IzAwEB06dIC5uXlph1hulVZ7ZsKECfjpp59UfhgB8s7LR40ahXr16qFly5aIj4/HihUr8PHHH+tVv3LNC2trawwcOFBjObY19MO2Rskrz20NpeK0C4x1Lp9/3QZdKBOamri6ukrJG21lXV1dpevazldjY2MREhKCQ4cO4dq1a0hISEBaWpraskVdp0QXJ06cAJB3vtqjRw+D1y+KovS54uTkhICAAK3lX3/9dem6MZNI+nzWqVsPq7iaNm2q8nvfi4rT7lEoFBg0aBCuXLkCIG+tksWLFxctUB3o2u4xVptIF8X93iBVBkumXL58Wep1oZSenq5xwSkg70MlMTERW7duRW5ubqEfKlTytH246eru3bv49ttvcfjwYURFRSE3Nxfe3t54/fXXMW3aNHh5eRlt35o4Ojqidu3auHDhglG/mIvCxsYGLi4uaNy4Md5880307dsXFhYW0uORkZHS9YMHD+LgwYM61/1iQxLIOzF54403cP78eZ3rKayBM3PmTCQlJQHI62EVHBws9VgrrpiYGOl6jRo1tJYVBAHVqlUr8NmklP+1XLx4sV5fsOpeS6XCfszN///MyMgo8PiXX36JEydOICYmBn/++Sf+/PNPWFhYoEmTJmjVqhXat2+PoKCgIi3OWVSjRo3CvHnzkJ2djZ9//lmlgZOZmYlffvkFQN6osPwngPp4sbdeYmKiysl4UTx8+FC6XqtWrULL5y+Tf1t1dP0/K3ua5Zf/ONbW4xEo/DjPfxwPHz5ca9kXGes4NjExwYoVK9CnTx9kZWVh9erVWL16NRwcHPDKK6+gdevWeP3117UuDJuUlIR+/frp1ctV048ZujLWZ4KuivvZoUlERAQ6duwo9ZSaMmVKoT0fi0v5HQAUfG/b2tpKJ+kZGRmwtbXVWpeyRxmQN4JMKf+PTnXr1i1WvESlaeXKlZg9e7bG0XVOTk749NNPy+QPiOVBabVnCvtxtUWLFqhXrx6uXbum8qOWLtLT07FhwwYAQL9+/VQWji8M2xrasa1RcdsaxlScdoGxzuXd3d31qkvdovT55T+utJXV5Xx106ZNGDdunMr5ojbFPcfXRvl7kIuLS6Gjx4oiOTlZShLVrFmz0PLOzs6Qy+VISkoqtD1aHPp81lWqVAkODg5ITEw02P6N1e7Jzc3F8OHDsXv3bgB57fv9+/er1Gdo+V8Xde0epfxtGk00tXuKy1iv98vKYMmUHTt2YP78+Sr3JScnY8SIEYVuK4oiBEHAtGnTDBUOlRGrVq3CO++8Iw0ftLCwgEwmw40bN3Djxg0EBwdj69at6NixY4nG9eTJE9y8eRNA4T9kGpsuw9jz0/WEQ50Xh3FnZ2ejc+fO0jA+Jycn9OzZE35+fnB1dYWlpaXUi/y7775DaGgoAGjsFa6k7PkM5A2v1Pc5apOamipd16X3mbZhuoZ8LfMr7rQ5Pj4+uHDhAhYsWID169cjMTERmZmZOHXqFE6dOoVFixbB2dkZH330ESZPnlwiPRyqVKmCnj17Ytu2bdi/fz+io6Ol3gs7duyQfqQdNWpUkZ+/mZkZXFxcEBsbCyDvh4viJlPyD43VZch2/pOdwobVFuf/rDyOTU1NC+1tXFjcZfU47t69O86ePYu5c+dKw8gTExOlH2Y+/vhj+Pn5YdGiRejcuXOB7fv27Ys//vgDQN6JZI8ePdCoUSO4ubnB2tpaim/jxo3YtGkTgMI/mwpjrNdSV8aYcisqKgpBQUFSQ3Hs2LH4+uuvDb6f/B4+fKjyY4Gyt7OSg4ODlEx5+vSp1mRKTk6O1IA2NTVVeT/kb1gXlpAhKqs++OADLF68WDpX8vDwQNWqVQHk/cATHR2NJ0+eYPz48bh79y6++OKL0gz3pWTM9oxyJIC+319bt26VvrNGjx6ttSzbGvphW6PitjWMqTgxGes4eXFUXGH0eQ7Feb7Hjx/H4MGDpfd5QEAAOnTogOrVq0Mul6v8qKscWVfcc3xtlOeTxjqX1Lc9qowlKSnJoNM8vagon3WGTKYY430siiLGjRsnTd3m6+uLI0eOFDo7RXHlT4iqa/covTgFmDr5yxhy9qay+LlZnhksmeLg4KDSI+fevXuQyWRSY0AdmUwGe3t7+Pn5YezYsYXOx0rly86dOzF27FiYmZlh5syZGD9+vHSM3Lp1C7Nnz8aWLVvw1ltv4cqVKxpHqBiKQqFAbGwsTp8+jblz5yItLQ1mZmZa5w0si/J/yc+dO1ea/74oNmzYIDVuOnbsiB07dmj8glfOS6+LhQsXYuPGjTh16hR27NiB/v37Y9OmTQbp3ZQ/Pk3DgPPLf5Lwovyv5bFjx9C2bdviBWdALi4uWLZsGb7++mv8888/OHXqFE6cOIGjR48iOTkZT548wdSpU3Ht2jWsXLmyRGIaP348tm3bBoVCgeDgYGk6CuWwe5lMhpEjRxZrH61bt5bmhj116hRatmxZrPry9+bQdiyoK2PIniAvUh7HOTk5yMrK0ppQKSxu5XFsamqK9PR0g/XMNISGDRtix44dSElJwcmTJ3Hq1Cn89ddfOHXqFLKzsxEeHo6uXbvil19+weDBg6Xt/vrrLymR0rBhQ/zxxx8FTkyVTp48abB4y/JnQlHExMQgKChIOsEfOnQofvrpJ6P/KPL3339L12vVqlWgd3GtWrWkaSIiIyOledLVefDggdSArlmzpkrs+Xti55+PmKi8+PPPP7Fo0SIAQJ8+fbBgwQLUqVNHpczNmzcxe/ZsbN26FYsWLUK3bt3YZipBxmzPxMXFITw8HADg7++vV1zKKb7q1Kmj91Q+hWFbg22NitzWKIvK8rm8McyZM0c6t1u5cqXGUZe6tN0Mwd7eHvHx8UY7l9S3PZq/XHHbo+qmUlQy5GddWfHuu+9K34+enp44evSo1t+kDUEURZw9e1a6/eIUePlnvsifdNFEOUX5i9tS2WKw1NTkyZMREREhXYC8jFz++1683L17FxcuXMAvv/zCRkEFk5WVJSUpli9fjoULF8Lb2xuCIEAQBNSuXRubN29Gz549kZycbNResp07d4YgCDA1NYW7uzv69OmDK1euwNPTE7///nuBRmtZl38uQ2UDrKgOHz4sXf/mm2+09pTI/6FeGDs7Oxw8eFD6IXzHjh3o16+fQXpu5x+ufOfOHa1lRVHEv//+q/FxQ76WxmJmZoYWLVpg+vTp2LFjB548eYI1a9ZIPXZWrVolzQVqbO3bt5eGAK9Zs0Z6fZW9CLt06aJx0UJdderUSbqefzHAonJzc5Ou3759u9Dyt27dkq7rOzReH/nrLmwh8cKOc+VxnJOTI424K2vs7OzQuXNnzJ8/H8eOHcPDhw+lhQxFUcS0adNUepzl/2z67LPPNCZSAP0+mwpTHj4TdPXo0SMEBQVJx8/AgQMRHBxcIr2SfvvtN+m6uh+O/Pz8pOv//POP1rryP55/OyDv/6VMrhRngVOi0vLDDz8AyOtpvWXLFrXnpMpz5lGjRkEURXz//fclHeZLy9jtmdmzZyMrKwumpqZ6Te1z584d/PXXXwAKH5VSFGxrsK1RkdsaZVF5OJc3lKysLGmNmKZNm2qdvtKQ5/jaKH9sf/z4sU4jB/Rlb28vjfzQpT365MkTaQSIuvao8r2ZlZWltR5RFHWeBq6wz7r4+HiDjkoxhilTpuDHH38EkPfcjh49Ch8fH6Pvd//+/dJrY21tXWAaa33aPbm5ubhw4QKAvOQxpzEuu4zWop4zZ47R5+OmsuvAgQOIjo6Gq6ur1qnehg4dCgD4/fffjRZLpUqV4OrqqrLYkre3N7777rsy1TtIV40aNZJ64/7xxx/F6kHx+PFj6bq26c5iY2M1zgWsibKRo+wtt3PnToM0cgIDA6XryhNrTc6fP691ftU2bdpI15WjIco6c3NzjBgxAhMnTpTuK0rPfOUPkPpMiyAIAsaOHQsgbw2Gw4cP4+eff5bqMMRc7m+//bY0B++lS5ekhUWLysXFRer1fvHiRTx58kRreeVoCKDwRRiLQ5/juLDHy+NxXLlyZXz99dfSyWZsbKxK40LXz6asrCwcO3ZM677yJw8KO97L42upzpMnT9C+fXupQf7WW2/hl19+KZFEypUrV1Tet6NGjSpQJv9c54V9/+efq//F6eAqVaqEevXqAcj7kUrbD1pEZdGpU6cgk8nw2WefFVr2008/hSAIBh2NR9oZsz2zefNmLF++HADw3nvvoXbt2jpvq+x1a2ZmJu3bkNjWYFujIrc1yqLyeJwU1dOnT6Vp+gqbbt2YvxHll79j9549ewxevyAI0udKXFxcoZ91hbVHlb9rPXnyRGtCJTw8XOtoEn0+644ePar18dL2/vvvY+nSpQDypgw8evRooevAGIJCoVA5hxs6dGiBEZL169eXEnZXr17VumbzqVOnpO+UVq1aqR2ZpE/bloyHyRQyihMnTgAAEhIS4ObmhipVqqi9KE+IjNnr4LfffsOjR48QHx+P5ORkbNu2DZaWlnjzzTfRo0ePcjc1iKmpKQYOHAggb35VXRrgmuSfG1Nb7/iFCxcWqWGibOS0bt0agGEaOa1bt5YWz9q9e7c0Ek6dwnoINm3aVMr2Hz16tMRO2Awhfy+L/PNG60o5nFzf4bojRoyQesP89NNPWLt2LYC8ESDdunXTO44X2djYYNasWdLtsWPH4saNGzpvHxoaip9++knlvj59+gDIe52+/fZbjds+e/ZM6s0iCII0R68xvPHGG9L177//XuP/MDMzU4pJkwEDBkgnbd9++63KYoJlnabjWNfPpp9++qnQBFn+KTYKO97L82eCUnx8PDp06CCN1HjjjTewYcMGgyzAXJhHjx7hrbfekkYZvf7662oXYG7Xrp002ujw4cPSFDAvio2NxcaNGwHkrSvQq1evAmXefvttAHmNCeV0IETlRVxcHORyuU5zebu6usLBwUHjIvVkeMZqzxw/flwaiRIUFFRg3VFtcnJyEBISAgDo2bOn1pGbRcW2BtsaQMVta5RF5flcXl+6fiakpKRobbcZkvJcEsgbEW+M6ayU7VEA0vSe6igUCixevFjtdkrKjkQ5OTnS95Q6y5Yt0xpTt27dpONu7dq1Wtfu+eabb7TWVZo+/vhj6TV1cXHBkSNH9OqgUFSiKGLGjBk4ffo0gLxE9MyZMwuUEwQBffv2lbbR9n/57rvvpOv9+/dXW0afti0ZD1egIaNQngBkZWXh8ePHGi/KBWjT09NLJC47Ozv07t0bf//9Nzw9PbF//37MnTu3RPZtSLNmzZJ6jH355Zf48ssvtc6HGRsbiwULFuDy5csq9+fv6TB79my1daxcuVLlQ11ftra2OHDggEojp2/fvkVu5Jibm+N///sfgLwfmgcMGKB2yOmKFSuwYcMGrXXJZDIsXLhQuj1gwAAcOHBA6zZXr17FhAkT9A9cRw8fPsSMGTO0NtzS0tKwbt066XbDhg313o+vry8A4MaNG3q9/5ycnKSTuh07duDhw4cA8ho+hprfd8qUKdKPpnFxcWjVqhV+++03rT0vHjx4gAkTJqBjx44qvSABYOLEidICjF999ZXaHl/p6ekYPHgwoqOjAeSduNasWdMgz0edgIAAqRfa9evXMXHixALvv5ycHIwZM0brsQAAXl5eeOeddwDk/ZD++uuvax1Cnpubi8OHD+PTTz8t5rPQ7Pfff8e3336rdTj4nTt3pJ5Xtra2Kr3T8n82zZ8/X2Uxc6W9e/eqPWF9kfJYB4CwsDCtZcviZ4I+kpKS0KlTJ+mzvmfPnti8ebPR597OycnBxo0b0bhxY2mqvKpVqyI4OFhteVNTUylpKooihg4dKp0PKGVkZGDYsGFSI+Hdd9+VRq3lN2HCBGmagg0bNuCDDz7Q+KNPeno6Dh06VLQnSWQEdnZ2SElJQUZGRqFl09PTkZKSYrQFcqkgY7RnTp8+jW7duiE9PR2tWrXCrl279PqM3rdvn3TuZYwpvpTY1mBboyK3NcqasngubyxyuVxaB+Kff/5ROwvBs2fP0K9fP9y/f79EYnrllVfQtWtXAHntkzfeeEPj9FiiKGL37t1672P48OFSx4nffvtN7WeeQqHAu+++K03zFBgYiPbt2xco16VLF+n67Nmz1baTVq9eLY1i1MTV1VVKnD958gRDhgxRW9esWbNw6tQprXWVlk8//VRK+Ds5OeHIkSNSssmYLl++jG7duqkk/H7++WeN60C+9957UiLx66+/xpEjRwqUWbt2LbZs2QIgb70XdaP7Af3atmQ8JfJtpFzM7MGDB0hNTdX4g5ggCFi9enVJhERGpuyV2rlz50JPGEuDg4MDRowYgfnz52PVqlUq2f/ywNPTExs2bMAbb7yB7OxszJw5Ez///DP69OmDevXqwdraGsnJybh9+zZOnz6NEydOQKFQoF27dir1jBw5Ep9//jmePXuGHTt2ICAgAEOGDEHVqlXx+PFjbN++HX/++SdcXV3RoEEDlSGn+lA2crp27Yrjx49j165d6Nu3L7Zs2VKkhSI//PBDbNu2DVevXsXZs2dRv359jBkzBnXr1kVSUhJ27dqF/fv3w8fHBw4ODlqH0vbq1QsfffQRPv/8cyQmJqJr165o3bo1unbtCm9vb5iamiI+Ph5Xr17FsWPHEB4eDhMTkwKjHwwlMzMTS5YswZIlSxAYGIhXX30VdevWhYODA5KSknDz5k389ttv0o/+r776qtR41Ef79u1x+fJlpKamokePHhg2bBicnJykIfnNmjVDpUqV1G47btw4lTURBEEwaINeEAT89ttvGDJkCLZv3474+HgMHjwYn3zyCbp06YL69eujcuXKePbsGR48eICjR4/i5MmTGhvNPj4++OabbzB+/Hjk5OSgT58+6NWrF7p16wZ7e3vcvn0bwcHB0hRBHh4e0jz2xrRixQo0bdoUqampWL58Oc6ePYshQ4bA3d0dUVFRWLduHa5cuYI33ngDO3fu1FrXV199hYsXL0rHaL169dCrVy+0bdsWVapUQXZ2Nh4/foxLly7h0KFDePjwIdq3b2+0nvzKNVE++OADtGvXDs2bN0e1atVgbW2NuLg4nDt3Dps3b5Z+KJ8yZYqU8AKAN998E56enoiKisLZs2dRr149jBo1CtWqVUNiYiL279+PPXv2wNraGr1799Y6JYKjoyMaN26MCxcuIDQ0FOPHj0eHDh1UfpDMP31UWftM0EeXLl1w/vx5AHlzBQ8ePBj79u3Tuo21tbXKWkXqpKWlqRyDubm5SE5ORlxcHM6fP48///xT+rEDyOsxt2XLFpU1i140YcIEbNu2DcePH0dYWBgaNmyIcePGoUaNGnjw4AFWr16N69evS/VpOlblcjk2bdqEjh07IiMjA1999RW2bduG/v37o27dujA3N8ejR49w7tw57NmzB40aNSr0+RKVlAYNGuDYsWNYs2aN9OOtJmvWrEFOTk6RftSkojF0e+b06dPo3LkzUlJS0KJFCxw4cEDv5Jiyrezl5WXUzzK2NdjWqMhtjbKorJ3LG9OkSZOk9ajeeustDB48GK1bt4adnR2uXr2K4OBgxMTEYMiQIfjll19KJKZ169ahWbNm0vRy1atXx4ABAxAYGAh7e3skJCTg8uXL2LNnD+7du6f39Ep2dnYIDg5Gz549oVAoMHnyZOzcuRN9+vSBs7Mz7t+/j19++UVKSNvZ2UmjEF/Uq1cv1KpVC7du3cKpU6cQGBiIUaNGwd3dHY8ePcLOnTtx9OhRvPrqq7h7967WkU6LFy/GoUOH8OjRI+zZswf+/v4YOXIkqlWrJo0QP3nyJAIDAxEdHV2mRk2tWrUKs2fPlm5PnDgRd+7cKXT9l/yjDzU5ceKEShI9LS0NiYmJuHHjBk6dOiW1t4C80fNff/01hgwZorE+Nzc3LFmyBBMmTEBOTg66dOmCoUOHom3btsjJycGBAwewdetWAHmdzlauXAlLS0u1dbVv315Kxo0aNQpTp06Ft7e3NAtBjRo1SmSKs5eeaES3bt0SmzZtKspkMpWLIAga76Oyz9vbWwQgzpkzR2OZyZMniwBEDw8Pg+13zpw5IgDR29vbIPWtWLFCBCACEB8/fmyQOnWl3G9x34InT54UfX19VerTdLG1tRUvX75coI49e/aIVlZWGrdzd3cXz5w5Iw4bNky6LyIiokA9wcHB0uPBwcFq43327JnYpk0bqVzPnj3FrKysIj336OhosX79+hrj9vDwEMPCwsS2bdvq9FovX75ctLOz0+m11HQMKh9v27ZtofFrKhsZGalTDADEdu3aiXFxcQXqDg0Nlcpoep8+ePBAdHZ21lh3aGio1vjr1q0rle3YsWOhz7cocnNzxaVLl4qurq46vR5WVlbitGnTxPj4eLX1/fTTT6KlpaXWOvz8/NQe30q6Hk+6lg0NDRUdHR01xtOqVSsxPj5ep2MrMzNT/N///ifKZDKdXq+hQ4eqjaewY0eXsuvWrdMpBkEQxMmTJ4sKhaJA/WfPnhUrVaqkcVu5XC7u3btX+m7Qdtzu379fNDEx0ViXOob4TNCFLvHrWlbXzw5dYo+IiNC7ripVqoiffPKJmJ6ertNzj4+PF4OCgrTWGRAQIN67d6/Quv7++2/p/KSwz02ismL16tWiIAiipaWl+PPPP2sst2rVKtHCwkKUyWQaz7FIPyXdnjl58qT0ndKiRQsxOTlZ7zpiYmJEU1NTnb6jC/uO0xXbGmxrVOS2hlL+cx59zukM3S4o6XN5UdTtfaXPcxBF1ddz2LBhBR7Pzc0Vhw4dqvW59ezZU0xLSyv0mC/sc0Mfjx49UnmOmi6CIBTYVtfXfefOnaJcLtdav5eXl3j+/HmtsYaFhWltJzVv3lyMjY2Vvuu0Hdfh4eFi1apVNdZVt25dMTIyUqe6ClPYsaFP2fz/e30umj57dPnf57+YmZmJvXv3Fq9fv67z81+0aJFoZmamsU47Oztxw4YNWuvIyckRW7durbGOF4+/wt5DRS37sjPaNF9Pnz5FUFAQzp8/DxcXF/Tt2xeiKMLS0hJvv/022rdvD1tbW4iiiMqVK2PYsGFGWUCPSodyIcDo6Git8ziWpvwL1ZbXKRNatmyJW7du4ZdffkG/fv3g6+sLW1tbmJqaolKlSmjatCnGjBmDTZs24dGjR/D39y9QR/fu3REWFoYRI0bA09MTZmZmqFy5MgICAjB//nxcvnzZYAtx29jYYP/+/dL0Rrt378Zbb72ldeE0Tdzd3XH+/HksWbIETZs2hZ2dHWxtbVG/fn3MmjULFy5cQOPGjXWub9y4cbh37x4WLVqETp06wd3dHRYWFrCwsECVKlXQpk0bvPfeezhy5IhRFzn29vbG/fv3ERwcjKFDh8Lf3x9yuRwmJiawtrZGzZo1MXDgQOzZswdHjx5VO+2NLjw8PBAWFobJkyfDz88Ptra2Uk8xXXTo0EG6bqzFIAVBwKRJk/Dvv/8iJCQEQ4YMQd26dVG5cmWYmppKw8QHDBiAVatW4eHDh1iyZIm0KN+Lxo8fj1u3buGDDz5Ao0aN4ODgAHNzc7i5uaFr164IDg7GxYsXVeaINrbXXnsN165dw7Rp01CzZk1YWlqicuXKaNGiBX744QeEhoZqfD4vMjc3xw8//IAbN25g5syZaN68OZydnWFqagpra2v4+vqia9eu+Pzzz3H58mWV6RsMbciQIbh06RK+/vpr9OrVCzVq1ICNjQ1MTEwgl8vRqFEjvPvuuzh//jy+/fZbtQujBwYG4tKlS5g4cSKqV68Oc3NzyOVy+Pn54YMPPpCGV+uiS5cuOHnyJAYNGgRfX1+VUTCalJXPhLLI3Nwczs7OqFGjBl5//XV8+OGH2Lt3L6KiojBv3jyNPale5OjoiMOHD2Pjxo3o1q0b3N3dYW5uDldXVwQFBWHlypU4c+YMvLy8Cq2refPmuHXrFlauXKlSl4WFBby9vdGjRw98//33Uq8vorJg+PDhaNu2LTIzMzF27Fh4e3tj+PDhmDVrFj7++GMMGzYMXl5eGDduHLKystC2bVsMGzastMN+aRiqPXPq1CmVESkHDx5Uu6hsYdauXYucnBzIZDKMGDGiyPHog20NtjUqclujrClL5/LGJAgC1q1bh40bNyIoKEhqk1WtWhXdunXDpk2bsGvXLp3O1w3J1dUVx44dw/79+zFkyBBUq1YNNjY2MDMzg4uLC9q2bYu5c+fi5s2bRd5Hr169cPfuXcyfPx/NmzdH5cqVpfqDgoKwdOlS3LhxAwEBAVrrady4Ma5cuYJJkyahRo0asLS0hKOjo9SGPH78uM5ratWvXx9Xr17FnDlz4OfnB2tra8jlcgQEBOCLL77AuXPnNE5f9TJQtl89PT3RrFkzjB07FitXrkRUVBS2bduGOnXq6FzXjBkzEBYWhnfeeQe1atWCjY0N7OzsUL9+fbz//vu4cuUKBgwYUGg8f/zxB7744gu0aNECjo6OJbI2JqkSRFHP8Wk6mj17Nj777DM0b94cR44cgbW1NWQyGapUqSINDUtNTcX8+fOxaNEijB8/vtBFdqls8PHxwb179zBnzhyN641kZGSgWrVqePjwIQICAnD8+HGVxcZeFB8fr3GYr9LcuXMxb948eHt7IzIyUmvZnJwcrXOqPnr0CA0bNkRsbCyaNWuGM2fOaK2PiP6Tm5sLHx8fREVFwdnZGQ8ePIC5uXlph0VERFTuJCcnY+TIkdJ0hS/+2KhsqvXp0werV6+W1rGg4imp9kz+RErLli2LnEgBgJo1a+LOnTvo1KlTuVrInEhfbGsQEVFZZrSRKfv27YMgCPj88881nnTa2Njgyy+/xOTJk7FixQppsR0qWxISEhAXFyddlAsHpqWlqdz/7NkzaRtLS0v8+OOPEAQBYWFhaNWqFX7//XeVXkERERFYsWIFmjVrpjaR9mL9aWlpAPJOrvLfHxcXV2Db1q1bY/78+bh06ZLKOgqJiYn49ddf0aJFC8TGxkIQBMyfP99grxXRy2Dfvn2IiooCkLcYJBs3RERERWNvb4+tW7fizJkzmDp1Klq3bo1atWqhVq1aaN26NaZOnYozZ85gy5YtTKQUQ2m0Z/7++28pkdKqVatiJVKOHTsmzQNf0deOIGJbg4iIyjKjjUyRy+VITU1FRkaGNEJAJpOhUqVKBX78fvz4Mdzd3REUFFTkRefIeJQ9twozbNgwrF27VuW+X3/9FWPHjpUSIcqpeZ49e4bMzEyp3KeffopZs2apbKsciaKLFw/j/DErh+UpFAokJSVJZWxtbfHjjz9qXSiKiFQpFAo0b94c58+fh6mpKW7fvl2i02IRERER6as02jNBQUEIDQ0FkDetobYfhD09PXHu3DmNjw8ZMgTr16+Hk5MToqOj+eMyVVhsaxARUVmneR6kYsrOzoZcLleZasna2hopKSkFyrq6ukIul+Py5cvGCodKyeDBgxEUFIQff/wRBw8exJ07d5CYmAhbW1vUrVsXrVu3xhtvvIG2bdsadL/r1q3DoUOHcOLECdy7dw9PnjxBTk4OXFxcULduXXTq1AkjRoyAm5ubQfdLVBFduXIF0dHRiI+Px5o1a3D+/HkAeXO9s3FDREREFVlR2zPK0S9A3sgYbbStNZWUlIRt27YBAIYOHcpEClU4bGsQEVF5YrSRKTVq1EBkZKTKyJTq1asjMjISt2/fRrVq1aSy2dnZsLKygqmpKTIyMowRDhERFdHw4cMLLHDo4+OD8+fPF7rWERERERERkSZsaxARUXlitJEpvr6+iIiIQFRUFHx9fQEAgYGBiIyMxPr16/HJJ59IZdeuXYvc3Fx4eHgYKxwiIiomExMTeHl5oXPnzpgzZw4bN0RERDoKCQkxWF1Dhw41WF1ERGUF2xpERFQeGG1kyqJFizBz5kx8//33mDBhAoC8hcR69OgBU1NTjBkzBo0aNcKlS5ewatUq5OTkYMqUKViyZIkxwiEiIiIiIioVMpkMgiAUux5BEJCTk2OAiIiIiIiISF9GS6bcunULnTp1QsuWLfHbb79J9w8aNAgbN25UaUyIooi6devi1KlTkMvlxgiHdJSbm4uYmBjY2dkZpMFHRERERBWfKIpISUmBu7s7ZDJZaYdT5vj4+Bjs3DoiIsIg9VRkbNMQERERkT50bc8YLZmiiSiK+Pnnn7Fp0yZERUVBLpejc+fOmD59OhMpZcCDBw/g6elZ2mEQERERUTkUFRWFqlWrlnYY9JJjm4aIiIiIiqKw9kyJJ1OobEtKSoKDgwOioqJgb29f2uEQERERUTmQnJwMT09PJCYmsoMUlTq2aYiIiIhIH7q2Z4y2AD2VT8ph8Pb29mx4EBEREZFeOKVS2TVy5Eij1CuXy/HNN98Ype6iYpuGiIiIiIqisPZMqSVTnjx5ghMnTsDExARt2rSBg4NDaYVCRERERERUorKysnDjxg2Ym5ujTp06WsveuHEDWVlZqFu3LszMzIq0v7Vr10IQBBhyYgJBEODq6lrmkilERERERMZgtGTKP//8gx9//BH169fH9OnTVR7buHEjRo0ahYyMDACAjY0N1q1bhzfffNNY4RAREREREZUZmzZtwvDhwzFmzBgsX75ca9nPPvsMv/32G9avX4+BAwcWeZ+Wlpbo169fkbd/0bp16wxWFxERERFRWWe0ZMpvv/2GdevWYfHixSr3x8TEYNSoUUhPT5fue/bsGQYNGoTw8HBUr17dWCERERERERGVCdu2bQMADBkypNCyo0aNwq+//oqtW7cWK5kil8sRHBxc5O1fxGQKEREREb1MZMaq+K+//gIA9OzZU+X+lStXIj09HQ0aNMDt27cRFRWFtm3bIisrC999952xwiEiIiIiIiozwsPDAQANGzYstGyTJk0AAFeuXDFqTEREREREpJnRRqY8fPgQgiDA29tb5f59+/ZBEAR8+umn0iiUpUuXolGjRjh69KixwiEiIiKil9DDpHRExKXC18kGbnKr0g6HSBITEwO5XA5bW9tCy9rZ2cHBwQEPHz4s8v4mTZoEuVxe5O1Lqk4iIiIienmV9fabIBpyBcJ8LC0tYWtri7i4OOm+9PR02Nvbw8zMDImJiTA3N1cpb2ZmhpSUlCLtb+TIkcWOWR25XP5SLaiYnJwMuVyOpKQk2Nvbl3Y4REREREW26dx9fLj9CnJFQCYAC3v7o3+gV2mHVSHxHFJ/Dg4OyMzMVJn+WBNRFGFtbQ0zMzMkJyeXQHTlG49HIiIiovKnNNtvup4/Gm1kiqmpaYET/XPnzkGhUKBFixYqiRQAsLW1RWpqapH3t3btWgiCAEPmhgRBgKur60uVTCEiIqKKraz39DGUh0np0ok4AOSKwEfbw9GmlnOFft5Ufvj6+uLy5cs4ffo0WrRoobXsqVOnkJmZiRo1apRQdEREREREJae8tN+Mlkzx8fHB9evXce7cOQQGBgIAdu/eDUEQ0KpVK5WyCoUCSUlJcHd3L9Y+LS0t0a9fv2LVkR8XVCQiIqKK5GUaqRERlyqdiCspRBGRcWll6mScXl4dO3bEpUuXMHPmTBw5cgSmpuqbZjk5Ofjwww8hCAI6depUwlESERERERlfeWm/GS2Z0rFjR1y7dg3vvPMOli1bhocPH2LlypUAgB49eqiUvXLlChQKBapWrVqsfcrlcgQHBxerjvyYTCEiIqKKorz09DEUXycbyASonJCbCAJ8nKxLLyiifCZNmoTvv/8eJ06cQIcOHfDNN9+gcePGKmXCwsIwdepUnDhxApaWlpg8ebLe+0lKSsLNmzfh7OwMX19ftWUiIiJw/PhxDB06tEjPhYiIiIioOMpL+01mrIpnzJgBBwcHnD9/Hi1btkSfPn3w7NkztGvXDi1btlQpq1yUvrDh7URERERUNNp6+lREbnIrLOztDxNBAJB3Iv55b78KmTii8qlq1apYsWIFAOD48eNo2rQpPDw80LJlS7Rq1Qru7u4IDAzE8ePHIQgCVq5cCS8v/UaSLViwAK6urmjRogVq1KiB1q1b4+rVqwXKnTp1CiNGjDDI8yIiIiIi0ld5ab8ZbWSKh4cHQkNDMX36dJw+fRoODg7o3r07vvrqK5VyoigiODgYoiiiXbt2Rd7fpEmTIJfLixu20eskIiIiKg3lpaePIfUP9EKbWs6IjEuDj5N1mTsRJxoyZAgqV66Md999F5GRkXj48CEePnyoUqZatWr4/vvv0blzZ73q/u233zBnzhzUqlUL3bt3R3R0NHbs2IFmzZph06ZN6N69uyGfChERERFRsZSH9psgGnLF9iJQKBR48OABgLwEjKa5gqlkJCcnQy6XIykpCfb29qUdDhERERnQpnP38dH2cChEUerpU1HXTKGSxXPI4lEoFAgNDcWpU6fw6NEjAICbmxtatmyJdu3aQSbTf0KBFi1aIC0tDWfPnoWFhQUA4Nq1a+jbty/u3LmD9evXo2/fvgCAX3/9FUOHDoVCoTDckypFPB6JiIiISB+6nj+WeubCxMQE3t7epR0GERERUYVXHnr6EL2MTExM0KFDB3To0MFgdYaHh2PBggVSIgUA6tWrh7///hvdu3fHoEGDkJ6eznVSiIiIiIh0VOrJFCIiIiIqOW5yKyZRiF4CMpkMtra2Be63s7PD77//jjfeeAMjR45ERkYGbGxsSiFCIiIiIqLypUSSKTExMbhy5Qri4+ORnZ2ttay+PaOSkpJw8+ZNODs7w9fXV22ZiIgIHD9+nL2uiIiIiIjopeDr64uwsDC1j1laWmLPnj3o27cvJkyYgDZt2pRwdERERERE5Y9R10y5cuUKJk6ciOPHj+sWjCAgJydH5/oXLFiAzz77TErQtGjRAitWrED9+vVVylW0OYCNifMLExEREZG+eA5Z9kybNg2//voroqKiYG5urraMQqHA4MGDsXnzZgiCUGHaSzweiYiIiEgfpb5mys2bN/Hqq68iJSUFoijC3Nwczs7OBltg/rfffsOcOXNQq1YtdO/eHdHR0dixYweaNWuGTZs2oXv37gbZDxERERERUXkzZMgQPHz4EGFhYXjllVfUljExMcGGDRvg7u6OCxculHCERERERETli9GSKXPnzkVycjLc3d2xfPlydOnSBSYmJgarf9myZfD398fZs2elRRWvXbuGvn37ok+fPli/fj369u1rsP0RERFRxfUwKR0RcanwdbLheiJEVCE0btwYGzZsKLScIAj4+uuvSyAiIiIiIqLyzWjJlNDQUAiCgJCQEAQFBRm8/vDwcCxYsEBKpABAvXr18Pfff6N79+4YNGgQ0tPTuU4KERERabXp3H18uP0KckVAJgALe/ujf6BXaYdFRERERERERGWI0ZIpSUlJsLCwwGuvvWaU+mUyGWxtbQvcb2dnh99//x1vvPEGRo4ciYyMDNjY2BglBiIiIirfHialS4kUAMgVgY+2h6NNLWeOUCEiIiIiIiIiicxYFbu5ucHExAQymXF24evri7CwMLWPWVpaYs+ePejevTsmTJiAn3/+2SgxEBERUfkWEZcqJVKUFKKIyLi00gmIiMjIrl27hgYNGuD8+fOlHQoRERERUblitGRKjx49kJaWZrSFDIOCgrBt2zZkZWWpfdzMzAzbtm1D37598eeffxolBiIiIirffJ1sIBNU7zMRBPg4WZdOQERERpaeno7w8HCkpqaWdihEREREROWK0ZIps2bNgpOTE6ZMmYLMzEyD1z9kyBAEBQVpHJ0CACYmJtiwYQOmTJmCNm3aGDwGIiIiKt/c5FZY2NsfJkJeRsVEEPB5bz9O8UVEREREREREKoy2ZkpGRgaCg4MxZMgQBAQEYMaMGWjWrBns7Oy0buflpduCr40bN8aGDRsKLScIAr7++mud6iQiIqKXT/9AL7Sp5YzIuDT4OFkzkUJEBjdy5EiD1CMIAlavXm2QuoiIiIiISD9GS6b4+vpK1xMTEzF69OhCtxEEATk5OcYKiYiIiEgtN7kVkyhEZDRr166FIAgQRbHwwmoot2UyhYiIiIio9BgtmVKUhkJRGxdERERERAQ8TEpHRFwqfJ1smCAsQ4YOHQpBEAovSEREREREZZbRkikRERHGqlqra9euYcCAAQgODkaTJk1KJQYiIiIiopK26dx9fLj9CnJFQCYAC3v7cvBnLgAAqVJJREFUo3+gblPoknGtXbu2tEMgIiIiIqJiMloyxdvb21hVa5Weno7w8HCkpqaWyv6JiIiIiEraw6R0KZECALki8NH2cLSp5cwRKkRERERERAYgK+0AiIiIiIioeCLiUqVEipJCFBEZl1Y6AREREREREVUwRhuZ8qInT57g3r17SEtLQ5s2bUpqt0REREREFZ6vkw1kAlQSKiaCAB8n69ILisosrt9CRERERKQ/oydTdu/ejblz5+LSpUsA8k7cc3JypMcTEhIwcOBAAMC2bdtgY2Nj7JCIiIiIiCoUN7kVFvb2x0fbw6EQRZgIAj7v7ccpvsqBzMxMbNu2DSdOnMCDBw+QmpoKURTVlhUEAUeOHCn2PjXVT0REREREmhk1mfLFF19g1qxZWk/WHR0dYW1tjV27dmH//v3o27evMUMiIiIiIqqQ+gd6oU0tZ0TGpcHHyZqJlHLg1KlT6N+/P2JiYiCKojRiRNl+yj+CJP/jxdGgQQNERESgSpUqxa6LiIiIiOhlYrQ1U86cOYNZs2bB1NQU33zzDeLi4uDq6qq27Ntvvw1RFLF7925jhVMq0tLScODAAXz66afo3bs3vL29IQgCBEHA3LlzDbKPx48fY/r06ahduzasrKxQqVIlvPrqq/j555/Z44yIiIjoJeMmt0KL6pWZSCkHoqKi0K1bN0RHR8Pf3x/vv/8+RFGEjY0NPv74Y4wePRq+vr4QRRGVK1fGxx9/jE8++aTY+zUzM4O3tzcsLCwM8CyIiIiIiF4eRhuZsnTpUgDAhx9+iMmTJ2st27ZtWwDAuXPnjBVOqTh79iy6du1qtPrPnz+P119/HU+fPgUA2NraIiUlBSdOnMCJEyewZcsW7N69mw0lIiIiIqIy5uuvv0ZSUhK6dOmCvXv3QhAEfPXVV7C1tcX8+fOlcj/99BMmTZqES5cuYdeuXaUYMRERERHRy81oI1NOnDgBAHj33XcLLVu5cmXY2toiOjraWOGUGkdHR7Rv3x7vvfceNmzYYLDh9ElJSejevTuePn2KOnXq4Ny5c0hJSUFqaiq+//57mJmZ4dChQ5g6dapB9kdERERERIZz6NAhCIKAefPmaZ2+a8KECZg3bx727t2LVatWlWCERERERESUn9GSKbGxsbCzs4OTk5NO5c3MzJCVlWWQfRtiLmFDePXVVxEfH4/Dhw/jq6++woABAww2SmTx4sV49OgRrKyssH//fjRt2hQAYG5ujnfeeQfz5s0DAKxcuRK3bt0yyD6JiIiIiMgw7t+/D5lMhoCAAJX71bWJ3nnnHQiCgLVr15ZQdERERERE9CKjJVOsra2RlpaG3NzcQssmJycjMTERjo6OBtl3WVkrxMTExGh1h4SEAAAGDBgAX1/fAo9PnDgRtra2UCgU+PXXX40WBxERVUwPk9Jx6m4cHiall3YoREQVkiiKsLOzg0z2X5PMxsYGycnJBdozcrkccrkcN27cMHgcKSkp+OGHHzBx4kSMHz8en3/+Ofbu3YuYmBiD74uIiIiIqDwz2poptWrVwrlz53D58mU0atRIa9lt27ZBFEU0bNiw2Ptt0KABIiIiDDadVll08+ZN3L9/HwDQpUsXtWVsbW3x6quv4sCBAzh06JA0UoWIiKgwm87dx4fbryBXBGQCsLC3P/oHepV2WEREFYqHhwfu3LmDzMxMafR61apVcevWLVy/fh316tWTyqalpSExMRHm5uYGjeHKlSvo1KkTYmNj1T7u7OyMgIAAlYuPj49BYyAiIiIiKi+MNjKlR48eEEURX3zxhdZyd+7cwcyZMyEIAt54441i79fMzAze3t4VetH18PBw6bqfn5/GcsrHrl27ZvSYiIioYniYlC4lUgAgVwQ+2h7OESpERAZWs2ZNAEBkZKR0X/PmzQEAy5cvVyn79ddfQxRFgycy3nvvPTx+/BiiKOLVV1/FgAED0Lx5c1hbW0MURcTGxuLgwYNYuHAh+vbti+rVq+s8jTMRERERUUVjtJEpEydOxLJly7BlyxZYWVnhgw8+UHn833//xcaNG7Fo0SIkJSXB19cXI0eONFY4FUr+IfceHh4ayykfS05OxrNnz2Bra1ugTGZmJjIzM6XbycnJBoyUiIjKm4i4VCmRoqQQRUTGpcFNblU6QRERVUBdunTB/v37sWfPHtSuXRsAMHr0aISEhOCHH37AnTt30LhxY1y6dAkHDhyAIAgYOHCgQWM4efIkBEFAcHAwhg4dKt0viiJu376NCxcu4MKFC7h48SIuXLiAJ0+eICEhwaAxEBERERGVF0ZLptjb22PXrl3o3LkzQkJCpDU+gLwpqNLT83q4iqKIypUrY/v27RV6NIkhpaSkSNetra01lsv/WEpKitpkysKFCzkFGBERSXydbCAToJJQMREE+Dhp/r4hIiL99ezZE1u3bpWm7wWA1q1b47333sOiRYtw8OBB/P7779L6KW3atMHMmTMNGoOpqSksLS1VEikAIAgCatWqhVq1aqF///7S/TExMbhw4YJBYyAiIiIiKi+MlkwB8oapX7x4ETNmzMCOHTukxejT0tIAQJraa8mSJWoXUTeElJQUhISE4MaNG8jOzoaXlxcaNGiAgIAAuLu7G2Wf5cmHH36IadOmSbeTk5Ph6elZihEREVFpcpNbYWFvf3y0PRwKUYSJIODz3n4clUJEZGCenp4IDQ0tcP+XX36Jjh07YuPGjYiKioJcLkfnzp0xdOhQmJoatvlWt25dXL58Wefy7u7ubEMRERER0UvLqMkUAPD29saWLVuQkJCA06dPIyYmBgqFAlWqVEHLli3h7OxstH1X1AUV7ezspOtpaWmwt7dXW06ZtHpxm/wsLCw4IoiIiFT0D/RCm1rOiIxLg4+TNRMpREQlrEOHDujQoYPR9zNs2DD873//w/Hjx/Hqq68afX9EREREROWZ0ZMpSo6OjujatWtJ7Q7AfwsqAnnD4j08PBAREYErV64gNTVVWlDx999/V4kzLi6uROPUV/7eYNHR0RqTKdHR0QDyplxTN8UXERGRJm5yKyZRiIgquFGjRmH16tWYPHkyTp48CSsrfu4TEREREWlSYsmU0lBRF1T08/OTroeHh6Nu3bpqy4WHhwMA6tWrVyJxERERERFR+WFqaoodO3agc+fOaN68OTZt2qSxbUFERERE9LKr0MmUirqgYu3ateHl5YX79+/j4MGD6Nu3b4EyqampOH78OACgU6dOJR0iERERERHpICUlBXv27MHly5eRkJCA7OxsjWUFQcDq1asNuv/Hjx+jdu3a2L59O/z9/dGyZUv06tULTZs2RUBAgMbpgomIiIiIXjYGSaZUq1bNENVAEATcvXvXIHUBFXtBxaFDh+LTTz/Fxo0bMXv27AJrvfzwww949uwZTExMMHjw4NIJkoiIiIiINFq5ciVmzJiB1NRU6T5RFAuUEwQBoigaPJly7NgxdOnSBVlZWQCA3NxcnDhxAidPnpT2W716dQQEBKBJkyZo0qQJAgICIJfLDRYDEREREVF5YZBkSmRkpNbHlSf/hT0mCIIhwpGUhQUVExISoFAopNu5ubkA8haHz782i6Wlpcq6JnPnzsW8efMAABEREQWSJTNmzMDPP/+MR48eoVu3bggJCUGTJk2QlZWF1atXY/bs2QCAsWPHolatWsZ6ekREREREVASbN2/G+PHjAQA2NjZo0aIFXF1dYWpacpMHzJkzB5mZmbC0tMTbb7+NqlWrIjIyEhcvXsTVq1eRnZ2N27dv4/bt29i8eTOAvDZbTk5OicVIRERERFRWCKKmLIce1q1bp/b+hIQEzJ8/H4mJiWjRogWCgoJQtWpVAHmLox89ehSnTp2Co6MjPvnkEzg4OGDYsGHFDUeSk5ODli1bIicnp9QWVPTx8cG9e/cKLTds2DCsXbtWul1YMgUAzp8/j9dffx1Pnz4FANjZ2SEjI0OaGqBTp07YvXs3LCwsdI43OTkZcrkcSUlJGhe2JyIiIiLKj+eQ+mvWrBn++ecfdOnSBRs3biyV6bTkcjmePXuGgwcPomPHjiqPZWdn48qVKwgLC8OFCxcQFhaGy5cvIz09XeogVlbxeCQiIiIifeh6/miQbk/qEiCpqakIDAyEIAg4ePCg2nU75s+fj8OHD6N///5YtWoVzpw5Y4hwJBV9QcUmTZrg6tWr+PLLL7F3715ERUXBxsYGfn5+GDZsGEaOHAmZTFbaYRIRERER0QuuXr0KQRCwZs2aUluXxMzMDDY2NgUSKcrHAgICEBAQIN2Xm5uLGzdulGSIRERERERlhkFGpqjz8ccfY+HChdiwYQP69euntezmzZsxYMAAzJo1CwsWLDBoHGFhYfj888+xfft2yGQyLqhYCPbiIiIiIiJ98RxSfy4uLsjJyUF8fHypxdC+fXucOnVKWmuxouDxSERERET60PX80WjJlDp16uDevXs6nZgrFArY2trCx8cH169fN1gM+RdUzP80lWuzcEHFgtjwICIiIiJ98RxSfx07dsSxY8cQHx9fah28fvvtN7z99tvYtWsXevToUSoxGAOPRyIiIiLSR6knU6ytrWFpaalzTytHR0dkZWUhNTXVYDG0bdsWx48f17qgolL+BMvLvKAiGx5EREREpC+eQ+pv37596NGjBz7//HPMnDmz1OLo0aMHwsPD8ffff8PV1bXU4jAkHo9EREREpI8SXTNFHRsbG8THx+P27duoWbOm1rK3bt1CUlISKleubNAYLl68CEEQsGvXLr0WVCQiIiIiIjKmbt26Yfbs2Zg9ezYAYPLkybCysirRGHr06IGaNWvi5MmTaNSoEX7++Wd069atRGMgIiIiIiovjDYy5Y033sDu3bsRFBSEffv2wcLCQm25rKwsdO3aFaGhoejRowd27txpsBicnJyQlZWF5ORkncorF1SsV6+ewWIob9iLi4iIiIj0xXPIolu0aBE++OADWFlZoV69elqn/BIEAUeOHDHYvmUymTRCX8nNzQ3du3dHYGAgAgIC4O/vD1NTo/XBMwoej0RERESkj1Kf5uvvv/9G69atIYoiatWqhffffx/t2rWDh4cHACA6OhqhoaFYvHgxrl+/DplMhpMnT6J58+YGi6GiLqhoTGx4EBEREZG+eA5ZNLNmzcJXX32F3Nxc6NIsEwQBCoXCYPufPn06Ll26hIsXL6pMz5w/wWJmZob69esjICBAuhiyzWYMPB6JiIiISB+lnkwBgDVr1mD8+PHIyckp0ONJSRRFmJiY4Mcff8SYMWMMuv+KuqCiMbHhQURERET64jmk/tasWYPRo0cDAHx9fdG+fXu4uroWOgpkzpw5Ronn/v37uHDhgsrlwYMH0uPlaY1JHo9EREREpI8ykUwBgLCwMHz88cc4dOgQcnNzVR6TyWTo1KkTFixYgCZNmhhl/xVxQUVjYsODiEi9h0npiIhLha+TDdzkJTunPRFRWcdzSP0FBATg0qVLGDZsGH7++WfIZLLSDqmA+Ph4leRKWFgYbt++zWQKEREREVUoZSaZopSUlISwsDDExsYCAFxcXBAQEAC5XG60fSoXVFy7di0sLCy4oKIO2PAgIipo07n7+HD7FeSKgEwAFvb2R/9Ar9IOi4iozOA5pP5sbW2Rnp6Op0+fwsHBobTD0Vl6ejqsrMp2pwIej0RERESkjzKXTCkNFXVBRWNiw4OISNXDpHS0+uIocvN9W5oIAk7MbMcRKkREz/EcUn+urq7Izs5WWauEDIPHIxERERHpQ9fzxwqdRZg6dWqBBRVjYmKwatUqrFq1CkD5XFCRiIhKTkRcqkoiBQAUoojIuDQmU4iIqMiaNWuGAwcO4OnTp6hcubJR9qFQKPDHH38gISEBbdq0gYeHBwDg6NGjCA4ORkJCAl555RVMmjSJSQciIiIiokKU2MiUjIwMJCQkIDs7W2s5Ly/jTJtSkRZUNCb24iIiUsWRKUREheM5pP6OHz+Odu3aYdKkSfj6668NXv+zZ88QFBSE8+fPQxRFWFlZYdeuXUhKSkLfvn1haWmJnJwc5OTkoG7dujhz5gxsbW0NHkdp4PFIRERERPooEyNT0tLS8NVXX2HDhg24c+dOoeWNmcjw8vKCl5cXevXqJd2naUFFIiIiJTe5FRb29sdH28OhEEWYCAI+7+3HRAoRlU2KHCAzGbCuVNqRUCFeffVVrFixAu+++y7S0tLwwQcfwNfX12D1f/PNN7h06RK+/PJLeHt746OPPsKUKVNgYWGBzZs3o3fv3sjJycHy5csxdepULFmyBHPmzDHY/omIiIiIKhqjjUxJTExEmzZtcPXqVeizi9zcXGOEo7PysKCiMbEXFxGReg+T0hEZlwYfJ2smUoio9GWlAU9vA09uAXG3gLibedfj7wI1OgADN5RoODyH1F+1atUAALGxsUhPTwcAVKpUCXZ2dhq3EQQBd+/e1al+f39/tGzZEitWrAAA7NmzB7169cL06dOxaNEilbJvvvkmIiMjceHChaI8lTKHxyMRERER6aPUR6YsWLAA4eHhMDMzw8SJE9GrVy+4u7uX+cXeX+ZEChERaeYmt2IShYhKXupT1WRJ3M2824lRADR0WEq4V6IhUtFERkYWuO/p06d4+vSpxm2UUxPrWv+kSZOk240bNwYAtGzZskDZNm3a4PDhwzrXTURERET0MjJaZmPnzp0QBAHffvstJkyYYKzdEBERERGVb7m5QPKDgqNM4m4CaZp/WIdVJcC5NuBU6/nf2oBzLcC+asnFTkUWHBxs1PplMhkUCoV0W9lpTC6XFyhra2v7Uq8bSURERESkC6MlU6KjoyGTyTBixAhj7QIKhQJ//PEHEhIS0KZNG3h4eAAAjh49iuDgYCQkJOCVV17BpEmTOLybiIiIiEpXThYQ/2/BUSZxt4HsNM3byb0Ap5oFEyc2lUsudjK4YcOGGbV+d3d3REVFSbdtbW2xbNky1KlTp0DZe/fuwdnZ2ajxEBERERGVd0ZLplSqVAkZGRmwtLQ0Sv3Pnj1DUFAQzp8/D1EUYWVlhV27diEpKQl9+/aFpaUlcnJysH//fmzYsAFnzpyBra2tUWIhIiIiIpJI65ncBJ7ceP73Zl4iRVSo30ZmBlSunpcskRImtfKSKOY2JRs/lYigoCAIgoCVK1eievXqBq+/SZMmOH36tHTbwsIC77zzjtqyf/zxhzQNGBERERERqWe0ZErr1q2xbds2REdHSyNGDOmbb77BpUuX8OWXX8Lb2xsfffQRpkyZAgsLC2zevBm9e/dGTk4Oli9fjqlTp2LJkiWYM2eOweMgIiIiopdURnLeyBKVpMkNIPE+NK5nYm6nOspEmThx9AFMzEoyeiplJ06cgJmZmVESKQCwcOFCPH78uNBysbGxqFevHvr06WOUOIiIiIiIKgpBFEUNLb3iOX/+PFq2bIkRI0Zg+fLlBq/f398fLVu2xIoVKwAAe/bsQa9evTB9+nQsWrRIpeybb76JyMhIXLhwweBxVDTJycmQy+VISkri1GhEREREAJAW/1+iJO7Wf4mT5GjN21hVApzr5CVKnOvkrWXiVBuwdwf0WES8vOA5pP48PT3x7NkzJCQklHYoFQ6PRyIiIiLSh67nj0YbmdKkSROsXbsWI0eORHZ2NmbNmoVq1aoZrP7IyEhMmjRJuq0clt6yZcsCZdu0aYPDhw8bbN9EREREVMGIIpD6RHWEiXJ6rtRYzdvZVslLlKgkTuoANk4lFzuVS23atMHGjRtx+/Zt1KxZs7TDISIiIiKiQhgtmaJMnJiYmGDt2rVYu3YtKlWqBDs7O43bCIKAu3fv6lS/TCaDQvHfnNNWVlYAALlcXqCsra0tcnJy9AmfiIiIiCoiUcwbUaJMlORPnmQkat5O7vlfssSp1n+jTawcSyx0qlhmzJiBrVu3Yvr06di1axeECjhiiYiIiIioIjFaMiUyMrLAfU+fPsXTp081bqNPA8Ld3R1RUVHSbVtbWyxbtgx16tQpUPbevXtwdnbWuW4iIiIiKudyc4HEe3mJkrj8iZNbQFaKho2EvLVLVEaZPF/bxMK2JKOnl0Djxo2xYcMGDB8+HK1atcJ7772Hli1bwsXFpcQTK8ePH0doaCg++eSTEt0vEREREVF5YrRkSnBwsLGqBpA3jdjp06el2xYWFnjnnXfUlv3jjz+kacCIiIiIqAJR5AAJEc8TJfmm5oq7DeSkq99GZgpUqp5veq7nSZPKNQAzq5KNn15aJiYm0vUzZ87grbfeKnQbQRCMMuL+r7/+wrx585hMISIiIiLSwmjJlGHDhhmragDAwoUL8fjx40LLxcbGol69eujTp49R4yEiIiIiI1LkAPH/Ak+uA7E38v4qkya52eq3MbEAnGoWnJ6rUjXA1Lxk4yd6gSiKpR0CERERERHpwWjJFGPz9PSEp6dnoeVcXFyMPkqGiIiIiAwkVwEkRAKx1/MlTm4AcbcARZb6bcys861jkm96LkcfQGaifhuiUhYaGmrU+keOHKlz2UuXLhkxEiIiIiKiiqHcJlOIiIiIqByT1jS58Txx8vxv3C0gJ0P9NmbWz5MldQGXOv/9ta8KyGQlGz9RMbVt29ao9a9duxaCIOg8Aqak12khIiIiIipvjJ5MEUURO3bswIYNG/DPP/8gNjYWgiDA2dkZgYGBGDRoEHr16mXUk3cuqEhERERUSkQRSIr6b2qu2BtA7LW8pEl2mvptTC3zRpq41M0bZeJSN+8i92LShEhHrq6uCAwMxJYtWwotu3DhQixYsKAEoiIiIiIiKr+Mmkx5/Pgx3nrrLZw6dQqA6rzA9+7dw/3797Ft2za0atUKmzdvRpUqVYwSBxdUJCIiIjIyUQSSY1TXNIl9vq5J1jP125iY/zc9l0sdwKVe3nVOz0UvIVEU8fTpU6SlpcHLy6vY9TVt2hRhYWGwsLAotKyZmVmx90dEREREVNEZLZmSlZWF119/HVeuXIEoimjWrBk6duyIqlWrAgAePHiAw4cP48yZMzh58iS6dOmCM2fOwNyci4ESERERlVmiCDx7nG9qrmvPkyc3gcwk9dvITIHKNfNNzfX84ugLmHDWWXq5hYWF4dNPP8Xhw4eRmpoKQRCQk5MjPZ6QkICZM2dCEAR89913OreXmjRpgn379uHhw4dwc3PTWlYulxskgUNEREREVJEJoq6T6Opp6dKlmDp1Kuzt7bF+/Xp0795dbbn9+/dj0KBBSElJwTfffINJkybpVL++CypevHgRCoVC521eVsnJyZDL5UhKSoK9vX1ph0NERESl6dmT/0aY5F/XJCNRfXnBBKhcXXVqLue6efeZsOd7RcZzyKL55ZdfMHr0aGRnZ0v3CYJQoN3SqVMnHDlyBLt370a3bt10qjs1NRVxcXFwd3d/6Uae8HgkIiIiIn3oev5otK6AmzdvhiAI+OGHHzQmUgCga9eu+OGHHzBkyBBs2rRJ52QKF1QkIiIiMpDMlOdrmVwFHl97PtrkOpAWp768IMsbVSIlTJ4nTyrXAEwLn1KIiIDr169jzJgxyM7OxqRJkzB06FB07twZT58+LVB26NChOHz4MHbt2qVzMsXGxgY2NjaGDpuIiIiI6KVltGTK9evXYWZmhv79+xdatn///hg1ahSuX7+uc/1cUJGIiIhITzlZwNPbeYmSx1efJ02uAYn3NWwgAI7e/61lokycONUCzCxLNHSiiubrr79GVlYW3nnnHXz77bcAABMT9WsFBQUFAQBOnz5dUuEREREREdELjJZMSU9Ph7W1NUxNC9+FqakprK2tkZ6ernP9XFCRiIiISIPcXCDp/vNRJlefJ0+u5SVScnPUb2NbBXCtl5c4can3X+LE3LpkYyd6SRw9ehSCIOCDDz4otKy7uzusra1x/76mxGfxJCYmIiYmBikpKQAAOzs7uLu7w8HBwSj7IyIiIiIqj4yWTHF1dUVUVBTu379f6GKGkZGRSExM1GvRQy6oSERERIS8dU2UI0weX/1vbZOsZ+rLW9g/n57redJEmUCxrlSycRO95GJiYmBjY4OqVavqVN7KygpJSUkG239oaChCQkJw6NAhPHr0SG2ZKlWqoFOnThg6dCjatWtnsH0TEREREZVHRluAfujQoVi/fj3efPNNbN26VeOaJaIook+fPti1axfefvttrFu3Tqf6X+YFFY2JizUSERGVUZnPni8Af011xEnqE/XlTcwBp9p5iRPXeoBL/bzr8qoA15IjA+M5pP4cHByQkZGB9PR0qa3k5uaG2NjYAgvQp6enw97eHg4ODnjyRMN7XkdJSUkYPHgwDhw4AACFrkGpjK1r165Yv3495HJ5sfZfEng8EhEREZE+Sn0B+mnTpuHXX3/Fzp070a5dO8yePRtt2rSREh/Z2dn4888/8emnn+Kvv/6CTCbD1KlTda6fCyoSERFRhaTIBp7e+W+UiXLESeI9DRsIgKMP4Fr/v+m5XOsDlaoBJuxwQlRW+fj44MqVK7h9+zZq1aqltez+/fuhUChQr169Yu0zOzsbQUFBuHjxIkRRhK+vLzp37gw/Pz94eHjA2jpvWr+0tDRER0cjPDwcv//+O/7991/s378fHTp0wOnTp3WaypmIiIiIqKIx2llwo0aNsGTJEkybNg3Hjx9Hp06dYGpqCicnJwiCgCdPniAnJ0fqCbV48WI0atTIWOEQERERlS2iCCRFFVzXJO4WkJutfhtb1xem51Kua8IOJkTlTefOnXH58mUsXboUP/zwg8ZyT58+xfvvvw9BENCtW7di7XPZsmW4cOEC7O3tsXz5cgwYMECn7TZv3oyxY8ciLCwM33//PaZMmVKsOIiIiIiIyiOjTfOltHfvXnzwwQe4fv262sfr1auHL7/8stgNAzIMDoknIiIygsxnz5Ml4XmjTJSXTA3rH5jb5ZueK9/FpnLJxk2kI55D6u/x48eoWbMmUlNT8cknn2DatGmoVauWNM1Xeno6duzYgVmzZuHevXtwcnLCnTt3ivX6NmvWDOfPn8fGjRvRt29fvbbdsmUL+vfvj6ZNm+Ls2bNFjqEk8HgkIiIiIn3oev5o9GSK0pUrV/DPP/8gNjYWAODi4oKmTZvC39/f6PtOTExETEwMUlJSAAB2dnZwd3eHg4OD0fdd3rDhQUREVAy5uUBCRL6EyfPkSUKE+vIyM8CplmrSxLUeIPfkuiZUrvAcsmj27t2Lt956C9nZ2TAzM0Nubi4UCgXq1KmDf//9F1lZWRBFERYWFti7dy/at29frP05ODggKysLaWlpem8riiJsbGxgbm6OxMTEYsVhbDweiYiIiEgfpb5myov8/f1LJHGiFBoaipCQEBw6dAiPHj1SW6ZKlSro1KkThg4dinbt2pVYbERERFQBpCfkTcuVP2kSex3ITlVf3s4tby0T1/qAq1/e38o1AVPzko2biMqM7t2746+//sK7776Lf/75R7o//6j+xo0bY/ny5QgMDCz2/hQKBUxMTIq0rSAIkMlkyM3NLXYcRERERETlUYVbOTApKQmDBw/GgQMHAADaBt48fPgQISEhCAkJQdeuXbF+/XrI5fKSCpWIiIjKA0UOEH/3v4TJo+d/kx+oL29qmbeOiTJhorzYOJVs3ERULjRr1gxnz57F5cuXceLECcTExEChUKBKlSpo1aoVmjZtarB9+fr64urVqwgNDdW7M9nRo0eRlpZWoh3kiIiIiIjKEqMlUyIjI/Hdd9/B29sbkydP1lp2yZIliI6OxtSpU+Hp6VnkfWZnZyMoKAgXL16EKIrw9fVF586d4efnBw8PD1hbWwMA0tLSEB0djfDwcPz+++/4999/sX//fnTo0AGnT5+GqWmFyzERERGRLlLjXljXJByIvQEoMtWXl3upJkxc/YBK1QATnksQkX4aNGiABg0aGHUfvXv3Rnh4OIYMGYLt27ejWbNmOm137tw5DBs2DIIgoHfv3kaNkYiIiIiorDJaS/+XX37B0qVLsWTJkkLLpqWlYenSpXB2dsaHH35Y5H0uW7YMFy5cgL29PZYvX44BAwbotN3mzZsxduxYhIWF4fvvv8eUKVOKHAMRERGVAzmZQNwt1Sm6Hl8Fnj1WX97MJm8tE2mKLr+8BeKtHEo0bCKi4pgxYwbWrVuHe/fuoUWLFmjXrh26dOmitfPZwYMHERoaitzcXPj6+mLGjBml/CyIiIiIiEqH0Ragb9myJc6cOYObN2+iRo0aWsvevn0btWvXRsuWLXHixIki77NZs2Y4f/48Nm7ciL59++q17ZYtW9C/f380bdoUZ8+eLXIM5R0XayQiogrn2RPg8RXg0ZXnU3SF5yVScnPUFBaASr6q65q41gccfACZrKQjJyo3eA5Zfty7dw89evRAeHg4BEEotLyyuejv74/du3fD29vb2CEWG49HIiIiItKHruePRkumuLu7Iy4uDunp6YUucpiTkwMrKytUqVIFUVFRRd6ng4MDsrKykJaWpve2oijCxsYG5ubmSExMLHIM5R0bHkREVG7lKoD4f58nTfJdnj1SX95CDlTxU52iy7kOYGFbsnETVQA8hyy6Y8eOYcOGDbh8+TLi4+ORnZ2tsawgCLh7926x95mbm4vg4GCEhITg5MmTGheVl8lkaNWqFYYNG4Zhw4YVefH6ksbjkYiIiIj0oev5o9Gm+YqPj4ednZ1OJ9ympqawt7fHkydPirVPhUJR5BN8QRAgk8k0NiSIiIioDMlKBR5fAx5dzkuYKKfqytbQoaJS9bzESRX//6bpklcFdOiVTURkDKIoYuTIkQgJCZFuF0aXkSS6kMlkGDVqFEaNGoX09HRcv34dMTExSElJAQDY2dnB3d0ddevWhZWVlUH2SURERERU3hktmeLg4IC4uDikpKTAzs5Oa9mUlBQkJSWhUqVKxdqnr68vrl69itDQULRr106vbY8ePYq0tDT4+/sXKwYiIiIyIFEEUh49T5jkG23y9C4ANT88mlrlrW1Sxf954sQ/77aF9nMRIqKStmzZMqxbtw4A0KRJE/Ts2RPu7u4wNTVaE00tKysrBAQEICAgoET3S0RERERU3hjtTL1x48Y4dOgQtmzZgpEjR2otu2nTJuTm5hY7kdG7d2+Eh4djyJAh2L59O5o1a6bTdufOncOwYcMgCAJ69+5drBiIiIioiBQ5zxeFD/9vxMmjcCAtTn15W9d8SRM/oEoDoHJ1QFY+pqEhopdbcHAwBEHA6NGjsWLFitIOp1SsXbsWI0aMKLTcH3/8gQ4dOpRAREREREREmhktmdKnTx/8/vvveP/999G0aVM0aNBAbblLly7hgw8+gCAI6NevX7H2OWPGDKxbtw737t1DixYt0K5dO3Tp0gV+fn7w8PCAtbU1ACAtLQ3R0dEIDw/HwYMHERoaitzcXPj6+mLGjBnFioGIiIh0kJGUNy1X/rVNYq8DisyCZQUZ4FQrX9LkeQLF1qXk4yYiMpBbt24BAL744otSjqT0yWQyODs7a3zcwsKiBKMhIiIiIlLPaMmUYcOGYenSpbh69SpeeeUVjBkzBt27d4e3tzcEQUBkZCT27NmDn3/+GRkZGahfvz5GjRpVrH3a2tri2LFj6NGjB8LDwxEaGorQ0FCt2yjnJvb398fu3bthY2NTrBiIiIgoH1EEkqOBh8qRJs//Jt5TX97cLm8xeGXCpIof4FIPMOOc/URUsVhaWsLS0hKOjo6lHYpOUlNTMXHiRAiCgNWrVxu0bk9PT0RGRhq0TiIiIiIiQzNaMsXMzAy7d+/G66+/jjt37uD777/H999/X6CcKIqoWbMm9uzZY5D5gb29vXHx4kUEBwcjJCQEJ0+e1LiovEwmQ+vWrTFs2DAMGzasyIvXExEREYDcXCD+X+DhxbykycNLeUmU9Hj15e2rqiZNqvgDDj6ATFaSURMRlQp/f3+cPHkSz549g62tbWmHU6iMjAysXbvWKMkUIiIiIqLywKirG/r6+uL8+fP46quvEBwcjJiYGJXHq1atipEjR2LGjBkGbUDIZDKMGjUKo0aNQnp6Oq5fv46YmBikpKQAAOzs7ODu7o66devCyoo9XYmIiPSmyAae3PgvYaIccZL1rGBZwQRwrgO4NVBd48S6UsnHTURURrz77rv466+/sGbNGkyaNKm0wyEiIiIiokIYNZkC5CUuFixYgAULFuD+/ft49OgRAMDNzQ2enp7G3j2srKwQEBCAgIAAo++LiIioQspKy1vfJP+Ik9jrgCKrYFlTy7xEiVsDwK1h3qLwLvUAM8sSD5uIqCx766238M477+CDDz6Ao6MjhgwZUtohERERERGRFkZPpuTn5eUFLy+vktwlERER6SM9IW+EiXLEycNLwNPbgKhmykwL+fPRJs8TJ24NgMo1AZMSPb0gIirzRo4cqfExa2trDB8+HLNnz0ZgYCDs7Ow0li3uFFvz588v8rZpaWlF3rYwT548QZMmTXDz5k0oFAq4ubmhZcuWGD16NF577TWj7ZeIiIiISB+CqFyBnQhAcnIy5HI5kpKSYG9vX9rhEBGRMaU8yjdN16W864n31Ze1cfkvYaIcceLoAwhCiYZMRGUTzyG1k8lkEAQBLza91N2njrKcIAhQKBTFjqOoDBFDfmvXrsWIESOk246OjkhNTUVW1n8jH0eMGIGVK1dqXV8zMzMTmZmZ0u3k5GR4enryeCQiIiIinejanmHX0edSU1MxceJELqhIREQVjygCCZGqi8I/vASkxqov7+D1PGGSL3liV6VEQyYiqkiGDh1arCSGobm6usLCwkKvbXJzcxEVFWXQONzd3TFnzhz07t0btWvXhoWFBRQKBc6cOYM5c+bg8OHDCA4Oho2NDZYtW6axnoULF2LevHkGjY2IiIiI6EUcmfLc06dP4ezsbNCeVuURexUSEZVzysRJzIW8NU5iLuYlTjISC5YVZHnTcilHnFR5vkA8F4YnIj3xHLJ88PHxQVRUFDZs2IB+/frptW1cXBxcXFxKrL2Um5uL3r17Y9euXZDJZLhx4wZq1qyptixHphARERFRcXBkChERUUWnT+JEZga41su3vklDwLU+YG5TsjETEVGpadKkCaKiohAWFqZ3MqWkR9bIZDIsXrwYu3btQm5uLvbs2YNp06apLWthYaH3SBsiIiIiIn0xmUJERFQeKBMnDy/mJU+0JU5MzAGXeoB7I8C9MeDWKO+2qXlJRkxERFrMnz8ftra2GhMEL/ruu++QmJiITz75pMj7DAgIwI4dOxAWFlbkOkpSjRo14OTkhLi4OPz777+lHQ4RERERveQqVDJl/vz5Rd42LS3NgJEQEREVg0ri5OLzkSfaRpzUZ+KEiKicmTt3LqpUqaJzMuWbb77B/fv3i5VMadKkCQAUKZliYmICLy8vyGSyIu+fiIiIiKg8q1DJlLlz55aphR2JiIgKVdTEiVujvOQJEydERKSjtm3bIjQ0FAAgiqJebScHBwdERkYaKTL17t69i7i4OACAr69vie6biIiIiOhFFSqZouTq6qr3nLm5ubmIiooyUkREREQomDhR/mXihIiIChEfHw9LS8ti1WFlZYW2bdsaKKLiKSyZI4oi3nvvPQB566d07969pEIjIiIiIlLLaMmU+fPnY9SoUfDw8DDWLgrw8vJCVFQUvv32W70XVIyLi4OLi4uRIiMiopdSyiMgOgyICfvvb3pCwXIFEieNnidOuJguEREBW7ZsQUpKCmrXrl3aoRjMvXv30K9fP4waNQodO3aEr68vBEFAbm4uzp49i7lz5+L3338HAIwbN65CPXciIiIiKp+MlkyZO3cuPv30U3Tt2hVjx45Fly5djD4FV5MmTRAVFYWwsDC9kymcHoyIiIolPTFviq7o88//hgEpMQXLMXFCRPRSWrp0KZYuXapy35MnT1CtWjWN24iiiMTERCQnJ0MQBHTr1s3YYZaoc+fO4dy5cwAACwsL2NnZISUlBZmZmVKZESNG4LvvviutEImIiIiIJEZLpnh5eeH+/fvYvXs39uzZg6pVq2L06NEYOXKk0UarBAQEYMeOHUVaUJGIiEhnWWnAo8uqo07i76opKADOdQCPgLxpujwCAFc/Jk6IiF5CiYmJBdYcUSgUOq9D0r59+2ItPr97925YWlqiU6dORa7DkHW6urpi2bJlOH36NC5evIgnT54gISEBlpaW8PX1RcuWLTFy5Ei0atXKYPESERERERWHIIqiaIyKRVHEoUOHsHLlSuzduxfZ2dkQBAEmJibo2rUrxo0bh86dOxt0RMjBgwfRtWtXVKpUSVqoUFeJiYlo1KgRZDIZ/v33X4PFVN4kJydDLpcjKSkJ9vb2pR0OEVHpU2QDsdfyJU4u5N0WFQXLOnjnJUw8mgDuAYBbQ8DCtuRjJiIqYTyHLNylS5dw8eJFAHltpZEjR0Iul+Pbb7/VuI1MJoO9vT38/PxQvXr1Yu1fJpPBzc0N0dHRxarH2HUaAo9HIiIiItKHruePRkum5BcbG4s1a9ZgzZo1uHPnTt6OBQFVq1bFmDFjMHLkSLi7uxd7P+np6Th79iwAoE2bNpy6qwjY8CCil1pubt4IEylxch54dAXIyShY1sYlL2niEZCXOHFvDNhULvmYiYjKAJ5D6k8mk6FKlSqIiVEzJWQ52V9JPwdd8XgkIiIiIn2UqWRKfkePHsXKlSuxc+dOZGVlGX20CumHDQ8iemmIIpAcnZc4iT6flzyJuQRkJhUsayHPW9tEmTjxCADsPQB+XxERAeA5ZHkgk8kgl8sLrNtSHMOHD2cyhYiIiIjKvTKbTFF6+vQpQkJCsHLlSty8eVNKoFStWhXjxo3DuHHjULkye/iWNDY8iKjCynz2fGH4f4AHzy/PHhUsZ2oJVGmgOuqkUjVAJiv5mImIygmeQ5Z9MpnM4J3WRFFkMoWIiIiIyj1dzx+NtgB9YZKSkhAbG4uEhAQIggBlTicqKgqzZ8/GF198gfnz52PKlCmlFSIREZVXuQrgyU3VxMmT64CYq1pOMAFc6j1f5+R54sSlLmBiVjpxExERGYmXl5dRZgBwcXExeJ1ERERERGVRiSZTcnJysH37dqxcuRLHjh2DKIoQRRFubm4YNWoU+vbti0OHDmH58uW4e/cupk+fDktLS4wfP77Qunfv3g1LS0t06tTJYPEao04iIjKClMf/JU6i/8lbJD4rpWA5ew+galPAo2neX7dGgLl1iYdLRERU0iIjI0s7BCIiIiKicq1Epvm6desWVq1ahZCQEMTFxUEURQiCgKCgIIwfPx5vvPEGTExMpPK5ubn45JNP8Pnnn6Nu3bq4evVqofuQyWRwc3NDdHS0weI2Rp1lHYfEE1GZl50OPLz0X+LkwXkg6X7BcmY2eYvCV236XwLF3q3k4yUiegnwHJLKEh6PRERERKSPUp/mKysrC1u3bsWqVavw119/AcibU7dSpUoYPnw4xo8fjxo1aqjdViaTYcGCBVi2bBnu3r2r8z6NkRcqbp0pKSlYsmQJtm3bhoiICJiYmKBWrVoYMGAAJk6cCHNzc73rnDt3LubNm1doudu3b2t8jYmIyoXcXCD+br7EyT/A43AgN+eFggLgXEc1ceJcBzAptdksiYiIiIiIiIioAjHar0weHh6Ij4+XkhEtWrTA+PHj0a9fP1hYWBS6vSAIcHR0RFRUlM77TE9PR0hISJFjNrR79+7htddek4bUW1tbIzMzE//88w/++ecf/Prrrzhy5AgcHR2LVL+ZmRkqVaqk8XFTU/6ISETlTHri8zVOzj5PoJwHMhILlrNxUU2cuDcGLNnzlIiIiIiIiIiIjMNov7Y/ffoUdnZ2GDx4MCZMmAB/f3+961iyZAmePXumc/nk5GSMGDFC7/0Yg0KhQI8ePRAZGQk3NzeEhISgQ4cOyM3NxZYtWzBmzBhcuHABgwcPxv79+4u0j5YtW+LYsWOGDZyIqKTk5gJPbwNRZ4GoM8CDc8CTGwXLmVoCbg3/W+ekalNA7gkYYRFdIiIiIiIiIiIidYyWTPnpp5/w9ttvw8bGpsh19OnTR+eyXl5eEIzww5qLi0uRtlu7di2uXLkCANi2bRtatGgBIG8Ks/79+yM3NxeDBg3CgQMHcOTIEbRv395gMRMRlUmZKXkjTaLO5l0enFM/6qRSNaBqs/8SJ65+gIlZiYdLRERERERERESkZLRkyrhx44xVtVrKqbTKinXr1gEA2rVrJyVS8hswYABmzZqFiIgIhISEMJlCRBWLKALx/+YlTKLOAFHngNirgJirWs7UCvAIAKoGAp7N8/7aOpdOzERERCVo/vz5GDVqFDw8PEo7FCIiIiIi0oHRkikymQxubm6Ijo7Wqbyvry+ioqKQk/PiosLlT1paGk6ePAkA6NKli9oygiCgc+fO+Omnn3Do0KGSDI+IyPCy0oCYC/9N1xV1FkiLK1hO7gV45kucVPHnqBMiInopzZ07F59++im6du2KsWPHokuXLkYZaU9ERERERIZh1BXKlYvPG6t8WXX9+nXk5ub1vvbz89NYTvnYo0ePEB8fr3UxeXWuXr0KPz8/3L17FyYmJvDw8ECbNm3wv//9D40bN9apjszMTGRmZkq3k5OT9YqBiFQ9TEpHRFwqfJ1s4Ca3Ku1wjEMUgaSofNN1nQUeXQFyX0iGm5gDbo0Az2Z5l6rNAHu3UgmZiIiorPHy8sL9+/exe/du7NmzB1WrVsXo0aMxcuRIjlYhIiIiIiqDjJpM0UdWVhZkMllph2EQMTEx0nVtDaH8j8XExOidTImLi0N8fDwcHByQnJyMW7du4datW1i9ejU++ugjfPrpp4XWsXDhQsybN0+v/RKRepvO3ceH268gVwRkArCwtz/6B3qVdljFp8gBHocD9/8G7p/OG32S8rBgOTu3/6br8myWt2i8qUXJx0tERFQORERE4NChQ1i5ciX27t2LqKgozJ07FwsWLEDXrl0xbtw4dO7cmaNViIiIiIjKiDKRTElMTERsbCwcHBxKOxSDSElJka5bW1trLJf/sfzbFKZmzZr46quv0KtXL/j6+sLMzAxZWVk4duwYPvroI5w/fx6fffYZHB0dMX36dK11ffjhh5g2bZp0Ozk5GZ6enjrHQkR5HialS4kUAMgVgY+2h6NNLefyN0Il8xkQ/c9/yZMH/wBZz1TLyEzzpuhSTtfl2QyQewL8wYeIiEgngiDg9ddfx+uvv47Y2FisWbMGa9aswZ07d1RGq4wZMwYjR46Eu7t7ice4b98+tG7dGnK5vMT3TURERERU1hgsmXL58mVcvHhR5b709HSEhIRo3EYURSQmJmLr1q3Izc1FQECAocKp0AYPHlzgPnNzc3Tq1Alt2rRBmzZtcO7cOcydOxejR4/W2vixsLCAhQV7jhMVV0RcqpRIUVKIIiLj0sp+MiXlMRD193/Jk4eXAVGhWsZCnpcw8Xol7+IeAJhrThYTERGR7lxcXDBz5kzMnDkTR48excqVK7Fz505ERUVhzpw5mD9/fqmMVunZsyd++eUXDBo0qET2R0RERERUlhksmbJjxw7Mnz9f5b7k5GSMGDGi0G1FUYQgCCojJMozOzs76XpaWprGcvkfy79NcVhaWuLzzz9Hx44d8ezZMxw5cgS9e/c2SN1EpJmvkw1kAlQSKiaCAB+nMpZwEEUg7nZe0kSZPEmIKFhO7vlf4sTzFcClLiAzKfl4iYiIXjJBQUEICgrC06dPERISgpUrV+LmzZvYs2ePNFpl3LhxGDduHCpXrmzUWApb03Lv3r1YsWIF9uzZY9Q4iIiIiIjKAoMlUxwcHODl9d/aAPfu3YNMJkPVqlU1biOTyWBvbw8/Pz+MHTsWr776qqHCKVX5h+BHR0ejQYMGastFR0er3aa4WrRoIV3/999/DVYvEWnmJrfCwt7++Gh7OBSiCBNBwOe9/Up/VEpOFvDw0n/Jk6i/gbSnLxQSAFe/fMmT5oADp/sjIiIqTUlJSYiNjUVCQgIEQZASG1FRUZg9eza++OILzJ8/H1OmTDHoftevX4/z58+jWbNmAKB1FExSUhL2799v0P0TEREREZVVBkumTJ48GZMnT5Zuy2QyODs7IyJCTY/nCq5u3bqQyWTIzc1FeHg4unTporZceHg4AKBKlSp6Lz5PRGVP/0AvtKnljMi4NPg4WZdOIiUzJW+B+Hun8pIn0eeBnAzVMqaWgEfT58mTFoBnIGDJudCJiIhKW05ODrZv346VK1fi2LFjEEURoijCzc0No0aNQt++fXHo0CEsX74cd+/exfTp02FpaYnx48cbLIbExET8+OOPWLp0KQRBwIgRI7B48WI0btxYujRs2BA2Nja4cuUKHB0dDbZvIiIiIqKyzGgL0M+ZMwe2trbGqr5Ms7a2RqtWrXD8+HEcPHgQ7733XoEyoiji999/BwB06tTJoPv/+++/peu+vr4GrZuItHOTW5VsEiU9Abh3Grh3Mi+B8vBSwfVOrCvnJU2UyZMqDQBT85KLkYiIiLS6desWVq1ahZCQEMTFxUnTIAcFBWH8+PF44403YGKSN92mv78/pk6dik8++QSff/45li1bZtBkyrvvvosxY8YgLCwMrVq1QsuWLZGTk4MtW7ZgzZo1EAQBgiDAwcEBCQkJeOuttwy2byIiIiKiskwQC5sItwLbt28fWrdurXWB9qJavXo1Ro8eDUEQcPr0aTRv3lzl8c2bN6N///4AgMOHD6N9+/Y61atsWGmSmZmJtm3b4syZM7CxscGDBw/g4OCgc9zJycmQy+VISkqCvb29ztsRUQl5FpuXNFEmTx5fBfDCx7ijD+DVEvBumZdAqVwDKKGFaomI6OXEc0j9ZWVlYevWrVi1ahX++usvAHnn+pUqVcLw4cMxfvx41KhRQ+P2oijCwcEBmZmZyMjI0FiuOLp06YJ3330X3bp1AwDcvXsXFy5cwKVLlxAZGQkfHx/MmDHDKO2p4uDxSERERET60PX88aVOppiYmOCXX37BoEGDDF53Tk4OAgICcOXKFXh4eGDdunVo3749cnNzsW3bNowePRrJycno0qVLgXmG586di3nz5gEAIiIi4OPjIz32559/YsGCBRg+fDhee+01aU2a7Oxs/PXXX/jwww9x7tw5AMCXX36J999/X6+42fAgKmOSop8nT07k/Y27VbCMUy3Au9XzS0tA7lHycRIR0UuN55D6c3Z2Rnx8vLQWSosWLTB+/Hj069cPFhYWOtXh4+ODqKgoKBSKwgu/RHg8EhEREZE+dD1/NMg0X/PnzwcAODk54X//+5/Kffr65JNPDBGSTgrLI+3duxcrVqzAnj179K7b1NQUu3fvRrt27RAZGYkOHTrA2toaubm5Us+xxo0b49dff9U75iNHjuDIkSMAACsrK9jY2CApKQnZ2dkA8tarmTlzpt6JFCIqZaIIJEQ8T56cAiJPAIn3Xij0fLF47+cjT7xbAbbOpRIuERERFd3Tp09hZ2eHwYMHY8KECfD399e7jiVLluDZs2cGi+mtt95CQEAAPvzwQ62j4YmIiIiIXkYGSabMnTsXgiCgdu3aUjJFeZ++jJ1MWb9+Pc6fP49mzZoBgNYYk5KSCowa0YePjw8uX76MxYsXY/v27YiIiICZmRnq16+PgQMHYuLEiTA312/dAn9/fyxevBinT5/GlStXEBcXh8TERFhbW6NevXp49dVXMXbs2CI1xoiohIkiEHc7b9RJ5PNpu1JiVMsIJoBbw7zEiU9rwLM5YF2pdOIlIiIig/npp5/w9ttvw8bGpsh19OnTx4ARAadPn8aOHTswY8YMmJubw9bWFg0bNkSjRo2ki7+/PywtLQ26XyIiIiKi8sAg03y99tprEAQBXl5eWLduncp9+goNDS1uOFp9//33mD59OrKzsyEIgpTcaNy4sXRp2LAhbGxsMHPmTPz888+Ii4szakxlCYfEExmRKALx/wKRx4GI43l/nz1WLSMzAzya4P/t3XlcVFX/B/DPHfZ1ABdAQUTcxQXcM9xw1xZNM8u0tFwyn1wftdx6fuWSZqm5lZmalku5Ze6Cpqm5AJL7hoq7gAz7NpzfH8TEyDYMc5lh+Lxfr3kF95577vcuQ+frufcc1PxnyC7v1oCNk3HiJSIi0hHbkOYhJCQEderUgbe3NwBg+PDhOH/+PC5evIj09HRIkgSFQoG6detqdbB07drVyJFr4/1IRERERCXBOVOKkJ6ejrCwMLRr1w4dO3ZEVlYWzp8/j8TEREiSBEmS4OLigmfPnqF///7YvHmzsUMuM0w8iAws/i4Q9ce/nScJ97XXW9oCXi1z3jrxeSHnZys748RKRESkJ7YhS06hUMDT0xP3798vvjAAX19fREdHIysrS7aYLCwssGHDBgwaNAgAcO/ePXh5eUGtVuPy5cuIiIjQfM6fP4/Y2FhIkmRyc7bwfiQiIiKikijTOVPKGxsbG7Rt2xbdu3fHhx9+iN69ewMAbt68ifDwcJw/fx63b99GzZo1MWnSJCNHS1S+PVSlIiomGb6VHeCprACdBAkP/uk4+acD5fk5TxRWOR0mvu0B3yCgegvAikNlEBERVUQlfa5N7ufgHBwckJ6ervndx8cHGzduxBtvvAF/f3/4+/tj8ODBmvX37t3D+fPnZY2JiIiIiMhUVMjOlFx79+7V+t3Pzw9+fn7o37+/kSIiMi+bz9zFtG1/I1sACgmY268xBrasYeywDCvpyT/Ddv3TeRJ3U3u9ZJEzbJdvEFAzKGfYLmt748RKRERE5VZGRgYUCoWs+6hTpw527tyJwYMHw9LSEkIIZGdnF1rey8sLXl5essZERERERGQqjNaZsnv3bhw8eBAWFhbo1asXunTpIvs++/fvj8DAQEybNk2v+VyISHcPVamajhQAyBbAx9suoH3dKuX7DZWUOO05T55e0V4vKXImjK8ZlPP2SY02nPOEiIiISiU+Ph5PnjyBi4uLrPuZMmUKBg0ahCpVquCFF16AJEk4ffo0mjZtigYNGsjemUNEREREZMpk60zZtm0bJk2ahG7dumHlypVa6yZMmIDFixdrfl+8eDEmTJiABQsWyBUOAODkyZPYvn07Jk2aBGtrazg6OqJp06Zakyc2btwYtrYccoeotKJikjUdKbnUQuB2TEr56kzJTAXungJuHcn5PDwP4LkDc2/877BdNdoCdi5lHycRERGZtMjISERERGgtS01Nxfr16wvdRgiB+Ph4/PLLL8jOzkZgYKCsMb7++uvw9PTEd999h5MnTwIAlixZgqVLl8LGxgb+/v5auVPTpk3h4OAga0xERERERKZCts6UXbt24c6dOwgKCtJaHhYWhq+//hoAUKNGDVhbW+PGjRtYtGgRevfujY4dO8oVEn788UfUqVMH1tbWAICBAwfi/PnzWLNmDdLT0yFJEhQKBerWrauVJHTt2lW2mIjMlW9lBygkaHWoWEgSalY28SGustU5HSa5nSd3TwHqdO0yVerndJ7UDMqZON7ezRiREhERUTmyfft2/O9//9NalpCQgHfffbfYbYUQkCQJEyZMkCs8jaCgIE0Op1Ao8Mknn8Db2xsREREIDw/HTz/9hNWrV0OSJEiSBD8/P1y9elX2uIiIiIiIjE22zpQzZ84AAIKDg7WWr1mzBgDQt29fbN26FQqFAmPHjsWyZcvw3XffydqZ0rVrV2zYsAGDBg0CAHz66afw8vKCWq3G5cuXERERofkcOHAAP//8MyRJglqtli0mInPlqbTD3H6N8fG2C1ALAQtJwpx+/qb3VooQwLOofztPov4AUp9pl3HyBGp1Amp1BGp1AJw8jBAoERERlWcuLi6oUePfuePu3LkDhUJR5JwjCoUCzs7O8Pf3x4gRI/I9qCa3CRMmoGfPnnjhhRc0y4QQuHbtmqZzhRPQExEREVFFIQkhRPHFSq5q1apQqVRIT9d+ortu3bq4efMmTp48iVatWgEAHj58iOrVq6NmzZq4deuWHOEAAJydnbFkyRK88847AAALCwts3LgRb7zxRoHl7927h/Pnz6N3796yxWRqEhISoFQqoVKp4OzsbOxwyAw8VKXidkwKala2N52OlOQYIOrovx0o8Xe111s75QzZlduBUrkOwHmWiIiICsU2ZMkpFAp4eHjgwYMHxg7F7PB+JCIiIqKS0LX9KNubKfHx8XBy0p50OTY2Fjdu3ICrq6umIwUAPD094eDggIcPH8oVDgCgTp062LlzJwYPHgxLS0sIIZCdnV1oeS8vryKfFCOi4nkq7YzfiZKZCtw58U/nSSjw6G/t9QorwLvVP2+edASqBQIWsv15JCIiIsKsWbPg6Oho7DCIiIiIiEhHsv1roaOjI1QqFTIzM2FlZQUAOH78OACgbdu2+crnlpHTlClTMGjQIFSpUgUvvPACJEnC6dOn0bRpUzRo0AAKhUL2GIioDAgBxFwDbhwCbhwG7vwJZKVpl6na6N/OE58XABv+YwYRERGVnVmzZhk7BC0PHjyAhYUF3N3djR0KEREREZFJkq0zpX79+vjrr7+wZ88evPLKKwCAzZs3Q5KkfGP9pqSkQKVSoVatWnKFAwB4/fXX4enpie+++w4nT54EACxZsgRLly6FjY0N/P39tSaeb9q0KRwcHGSNiYgMJDU+Z+iuG4dzPgn3tNc7VQP8OuUM3eXbHnDiPxQQERFRxSaEwNy5czF//nwkJSUBABwcHNCkSRMEBAQgMDAQAQEB8Pf3h6Ul39olIiIioopNthZxv379cOrUKbz33nu4cuUKHj58iM2bN0OhUGDAgAFaZc+cOQMhBHx9feUKRyMoKEjTmaNQKPDJJ5/A29tbM4HiTz/9hNWrV0OSJEiSBD8/P1y9elX2uIiohLLVwIMI4OY/nSf3zgBC/e96C5ucN05qBwO1uwBV6nPeEyIiIjKK//3vfwCAypUr44MPPtBaVlIzZ840WFwrVqzA9OnTtZYlJSXhxIkTmofPgJxRBBo1aoTAwEAEBgZi9OjRBouBiIiIiKi8kG0C+rS0NLRp0waRkZGQJAm5uxk3bhwWLVqkVfajjz7CN998g08//TRfY15OkyZNQr9+/fDCCy9olgkhcO3aNU3nyvnz57F3794yi8nYOFkjmbTER8DNkJzhu26GAqlx2usr1cnpOKkdDPi0A6ztjRMnERFRBcM2ZNEUCgUkSUK9evVw6dIlrWUlpVariy+ko6ZNm+Lvv/9GUFAQvvzyS1SqVAnXr19HREQEwsLCEBYWhps3byJvyihJkkFjkAPvRyIiIiIqCV3bj7J1pgA5TzV9/fXXOHnyJFxcXNCnTx8MGjRIq0xGRgZatWoFlUqFzZs3a01MT2WPiQeZFHUmEP0XcP0AcCMEePzcxPE2zjlDduV2oLjUME6cREREFRzbkEXr2LEjJElCjRo1sG7dOq1lJRUaGmqwuBwdHZGamoro6GhUq1atwDKJiYmazpVz584hIiICkZGRBotBDrwfiYiIiKgkTKIzxdhiYmIQGRmJFi1asBGtIyYeZHTJMcD1g8D1/TkdKOmqPCsloFozwO+fobu8WgAWVsaKlIiIiP7BNmT5VL16daSlpSE2NtbYoRgU70ciIiIiKgld249mPYvg+vXrMXnyZAQFBeHIkSPGDoeICiIE8PB8ztsn1/YD988ByNPHa+cG1OkK1O6aM4G8Q2WjhUpERERkTlq3bo3du3cjPT0dNjY2xg6HiIiIiMikmXVnym+//QYA+O9//1ts2VOnTqFhw4Z8comoLKQn5sx5cn0/cP0QkPRIe71HY6BOd6Bud6B6c0BhYZw4iYiIiMzYqFGjsGPHDuzYsQMDBw40djhERERERCZN9s6UxMRE7N69G5GRkYiLi0NmZmahZSVJwvfff2+wfd+4cQMKhQKdO3cutux3332HtWvXYvv27Xj55ZcNFgMR/SPmRk7nybX9wJ0TQHaevwVWDjlvndTplvMWinPBY3YTERERVRS7d+/GwYMHYWFhgV69eqFLly6lqq9Hjx4IDAzUfGrVqoVu3brhzTffxMSJE9GuXTt4eXkZKHoiIiIiIvMj65wpa9euxUcffYSkpCTNsoJ2J0kShBCQJAlqtdpg+7e1tYWTkxOePn1abNkDBw6gR48eGDp0KH744QeDxVDecHxhMphsdc7k8Vd+B67uBeJuaq93q/XP2yfdAJ92gCWHliAiIiqv2IYsuW3btmHSpEno1q0bVq5cqbVuwoQJWLx4cb5lCxYs0Ht/CoVCa8J7Z2dnBAYGwt/fHz/99BMkScLq1avN4sEy3o9EREREVBJGnzNl//79GD58OIQQsLW1Rdu2bVGtWjVYWpbdyGK5JyC3o6Yo7dq1gyRJOHPmTBlFR2SGMpKBmyE5nSfX9gEpeSYzVVgBPi/kDN1VpztQubbx4iQiIiIysl27duHOnTsICgrSWh4WFoavv/4aAFCjRg1YW1vjxo0bWLRoEXr37o2OHTvqtb9p06YhPDwc4eHhePz4MVQqFUJDQ7Xmluzbty88PT3x0ksvoWXLlprOlrLM4YiIiIiITJVsreIvvvgCQgi0bdsWO3fuROXKZT9pdKNGjXD06FGcPn0arVu3LrKsg4MDXFxc8PDhwzKKjshMJD3J6Ty5uge4dQTISvt3na1LTudJvZ6AXzBgyycDiYiIiABoHuIKDg7WWr5mzRoAOR0bW7duhUKhwNixY7Fs2TJ89913enemfP7555qfHzx4gLCwMK3PvXv3NOu+/fZbfPvttwAAa2trNGrUSGuIsFatWukVAxERERFReSbbMF8uLi5ITEzElStXUKdOHTl2UawFCxZgypQpePXVV7Ft27Yiy6rVatjZ2UGhUCAtLa3IsuaMr8RTsYQAYq79M3zXHuDeWQB5/oy41ADq9Qbq9wJqtAUsrIwWKhEREZUNtiFLrmrVqlCpVEhPT9daXrduXdy8eRMnT57UdFo8fPgQ1atXR82aNXHr1i1Z4omNjc3XwXLr1i3NMM25b/pLkoSsrCxZYjAU3o9EREREVBK6th9l60xxdHSEhYUFVCqVHNXrRKVSwdfXFyqVClOnTtV6Gut5YWFhaNGiBapXr47o6OgyjNK0MPGgAmVnA/fOAFd+A67syT//SbWAfztQqjYEihlWj4iIiMwL25AlZ21tDScnJ8TG/jssamxsLKpUqQJXV1et5QDg5OSErKwspKamllmMCQkJCA8P13SunDt3DtevX0dmZmaZxaAP3o9EREREVBJGnzPFz88PV69ehVqthoWFhVy7KZJSqcTq1asxYMAAzJs3D+Hh4Vi4cCEaNmyoVS4pKQkTJkyAJElo06aNUWIlMjnqLODuCeDSLuDKbiAxzxB4CivAt31O50m9XoBzNePFSURERFQOOTo6QqVSITMzE1ZWOW/yHj9+HADQtm3bfOVzyxjSnDlz0KJFC3Tr1q3A9c7OzujQoQM6dOigWVaWnTlERERERKZEts6UwYMHY+rUqdi7dy/69Okj126K1a9fP2zcuBHvvvsu9u/fj/3796NZs2Zo27YtKlWqhHv37mH//v2auVI++OADo8VKZHRZGUDUH8DlnTnDeOWdQN7aKWf+kwZ9OP8JERERUSnVr18ff/31F/bs2YNXXnkFALB582ZIkpRvUvqUlBSoVCrUqlXLoDFMnz4dnp6euH//vs7b2NnZGTQGIiIiIqLyQrbOlHHjxuHXX3/FBx98gHr16hlt3hQAeOONN1CvXj385z//wZ9//onw8HBERERo1ueOdDZjxgx06tTJSFESGUlmKnAzJOcNlGt7gbQ8Q/PZueYM39XwZaBWR8DSxmhhEhEREZmTfv364dSpU3jvvfdw5coVPHz4EJs3b4ZCocCAAQO0yp45cwZCCPj6+ho8jpKM+nzgwAE0bNgQXl5eBo+DiIiIiMjUydaZ8vPPP+Ptt9/GzJkz0bRpU/Tv3x+tW7eGk5NTkdsNGTJElngCAgJw7NgxnDp1Cjt37sT58+fx+PFjKBQKNGrUCMOGDUP79u1l2TeRyclIBq4fyOlAuX4AyEj6d51D1Zy3Txq8DNR8kRPIExEREcngww8/xIYNGxAZGYmPP/5Y06kxduzYfG+gbNu2DZIkGT1fGTp0KJ4+fWryE9ATEREREclBtgnoFQoFpH8moRZCaH4uMhhJYsPcyDhZoxnLTANuHAIu/Apc2wdkpvy7ztkLaPBSzhso3q0BhXHmOSIiIqLyiW1I/SQlJeHrr7/GyZMn4eLigj59+mDQoEFaZTIyMtCqVSuoVCps3rwZrVq10nt/a9aswalTp9CmTRu0bNkSTZs2hYeHBx48eKDT9p6ennjy5AnUarXeMZQF3o9EREREVBJGn4C+Ro0aOnWglIXcxr6FBf+BmCoYdSZwMxS4uC1nDpT0hH/XudYEGr4CNHgFqB4ImMj3lYiIiKiicHR0xPTp04ssY21trTVEcWlER0dj9erV+P777zXLnj17hnfeeQeBgYEICAhAs2bNChxNQAgBlUoFS0vZUkgiIiIiIpMmW0v49u3bclWtk5CQEKxYsQJ//PEHYmJiIEkS3NzcULt2bfTo0QN9+/ZF48aNjRojVVwPVamIikmGb2UHeCoNPIlnthq4fSznDZTLvwGpz/5d51wdaNQX8H8NqBbADhQiIiKiCiQ4OBg3b97EX3/9hevXr0OSJKSnp2P9+vX48ccfAeSMFlCrVi0EBAQgMDAQTZs2RfXq1fHLL78gLS1NlnlbiIiIiIjKA9mG+TIWIQTGjBmDVatWaX5/Xu4bM71798Znn32GJk2alGmMpoyvxMtv85m7mLbtb2QLQCEBc/s1xsCWNUpXaXY2EP1XTgfKpZ1A8pN/1zlUBRq9mtOB4tUKUChKty8iIiKi57ANWf48e/YMlSpVgqOjIwYMGIDw8HBcvHgRmZmZmjIFjTQwfvx4LFy4sCxDLTHej0RERERUErq2H82uM+Wzzz7DzJkzAQD29vbo0KEDfH19IYRAXFwcbty4gYiICKjVakiSBGtra3zzzTcYPny4kSM3DUw85PVQlYp280KQnedbZyFJOD61k35vqDy9BkRuAiK3Aqq7/y63c80ZwqtRv5xJ5DkHChEREcmIbUj9JSYmYvfu3YiMjERcXJxWZ8bzJEnSGqKrtBQKhdacKZmZmbhw4QLCw8MRFhaG8PBwREZGIjk5GXZ2dnj99dexfPly2NkZ+M1qA+P9SEREREQlYfQ5U/KKiYlBaGgo7ty5g5SUFE1nh6E9efIEn3/+OSRJwquvvopvv/0WlSpVylcuOTkZ69evx8KFCxEVFYURI0YgKysLI0eOlCUuolxRMclaHSkAoBYCt2NSdO9MSXoKXPgFOL8JeBjx73Jrp5xJ5P37AbU6AhZWhgqbiIiIiGSwdu1afPTRR0hKStIsK+zNeiGEwTtTrl27huvXr2t+t7KyQkBAAAICAjBs2DDN8mfPnsHFxcVk5sQkIiIiIjIGWTtTsrKyMGXKFCxfvhwZGRma5Xk7U549ewY/Pz+kpKQgKioKnp6eeu9vw4YNSE9PR7NmzbB161YoChnOyMHBAaNHj8aQIUMwfPhwbNmyBR999BFatGiB5s2b671/ouL4VnaAQkK+N1NqVrYvesOMFODqHiByM3DjMCDUOcsVlkDtLkCTgUC9noCVaT8lSEREREQ59u/fj+HDh0MIAVtbW7Rt2xbVqlUr0wnea9eujdq1axdbztXVtQyiISIiIiIybbK21AcMGIBdu3YBABo1aoSrV68iKytLq4yrqyvefPNNLF++HDt37sSoUaP03t/Ro0chSRImTZpUaEdKXg4ODvj555+RnJyM33//HTNmzMCePXv03j9RcTyVdpjbrzE+3nYBaiFgIUmY08+/4LdSsrNzJpKP3Axc2gVkJP67rnpzoMkbOW+hOFQuuwMgIiIiIoP44osvIIRA27ZtsXPnTlSubLw2nVqd86COhQWHhiUiIiIiKoxsc6Zs3rwZgwYNgru7O/bs2YOAgAB4enriyZMnmsZ6rqNHj6JTp07o1asXdu/erfc+/fz8cPv2bdy/fx8eHh46bxcXFwdfX18kJSXh4sWLqF+/vt4xlHccX7hsPFSl4nZMCmpWts/fkRJ/F4j4CQjfqD0PikuNnDdQmgwEKtcp24CJiIiIisA2ZMm5uLggMTERV65cQZ06Zd+2CwkJwYoVK/DHH38gJiYGkiTBzc0NtWvXRo8ePdC3b180bty4zOMyBN6PRERERFQSRp8z5YcffoAkSViwYAECAgKKLNuqVStIkoS///67VPuMjY2Fra1tiTpSAMDNzQ0jRozAokWLsG/fvgrdmUJlw1Npp92JkpkGXNkNhG8Abh0B8E8fp40SaPQq0PQNwLsNoMMbV0RERERk+rKysuDo6FjmHSlCCIwZMwarVq3S/J7735iYGMTExOCvv/7Cp59+it69e+Ozzz5DkyZNyjRGIiIiIiJTJFtnSlhYGADgtddeK7asnZ0dlEolnj59Wqp9JiYmokqVKnpt26dPH3z55Zc4ceIExo0bV6o4iHT28HxOB0rkFiAt/t/lvu2BgLdzJpTnPChEREREZsfPzw9Xr16FWq0u0+G1Pv/8c6xcuRIAYG9vjw4dOsDX1xdCCMTFxeHGjRuIiIiAWq3G77//joMHD+Kbb77B8OHDyyxGIiIiIiJTJFtnikqlglKphJ2dbv8QnJ2dXep9CiF0miulIC1btgQAXLp0qdRxEBUpTZXTeRK2HngU+e9yZy+g2ZtAwFuAa02jhUdERERE8hs8eDCmTp2KvXv3ok+fPmWyzydPnuDzzz+HJEl49dVX8e2336JSpUr5yiUnJ2P9+vVYuHAhoqKiMGLECGRlZWHkyJFlEicRERERkSmSbcwgV1dXqFQqpKWlFVv23r17SEhIQNWqVeUKp1j29vawt7dHXFyc0WIgM/cgAtg1FviyPrBnUk5HioU10KgvMHgbMC4S6PwJO1KIiIiIKoBx48ahZcuW+OCDD3D9+vUy2eeGDRuQnp6Opk2bYuvWrQV2pACAg4MDRo8ejcjISLz++usQQuCjjz7CuXPnyiROIiIiIiJTJNubKU2bNsWhQ4dw9OhRdO/evciyueP1tm7dutT7zcjIwMWLF9GgQYMSv6Xi5OSE+Pj4UsdApJGRAlzcBpz5HngQ9u/yKg2A5u8ATV4H7N2MFh4RERERGcfPP/+Mt99+GzNnzkTTpk3Rv39/tG7dGk5OTkVuN2TIEL33efToUUiShEmTJumUKzk4OODnn39GcnIyfv/9d8yYMQN79uzRe/9EREREROWZJHJnHDSwtWvXYtiwYWjRogWOHDkCe3t7eHp64smTJ1Cr1ZpyW7ZsweDBg6FWq7Fz585SveKuUCggSRIAwMbGBo0bN0ZgYCACAwMREBCAJk2awNrautDtC4qvoklISIBSqYRKpYKzs7Oxwym/nl4Dzv0ARGzMGdYLABRWQMNXgJbDgRptgX/uVSIiIqLyjm3IksubuwghND8XRZIkZGVl6b1PPz8/3L59G/fv34eHh4fO28XFxcHX1xdJSUm4ePEi6tevr3cMZYH3IxERERGVhK7tR9neTBkyZAhWrlyJM2fOoG3bthg9ejQyMzMBAGfPnkVkZCQ2bdqEw4cPQwiB4ODgUo8V7OXlhXv37gEA0tLScObMGZw9e1az3tLSEg0aNEBAQICmg6VZs2ZwdHQs1X6JAADZ2cD1/cCp5UDUH/8ud/EBWrwLNBsMOFYxXnxEREREZDJq1KihUweKIcXGxsLW1rZEHSkA4ObmhhEjRmDRokXYt2+fyXemEBERERHJQbY3U4CcCQ779OmDs2fPFpooCCHQunVr/P7773BzK/1wR3FxcTh37hzOnTuHsLAwnDt3DlFRUVpl8sYiSRJq166NgIAA/Pbbb0hNTeWbKXyKq2TSE4HwjcBfK4Fn/9xrkgKo0z3nLRS/YKCEQ84RERERlSdsQ5YPFhYWqFKlCh49elTibY8ePYpOnTqhf//+2LJliwzRGQ7vRyIiIiIqCV3bj7J2pgBAZmYmvv32W6xevRp///03srOzNesaNmyIESNGYNSoUUUOv1Va8fHxCAsL03SuhIWF4caNG8h76JIkaV6vZ2cKEw+dPLsN/PUtEP4jkJ6Qs8xWmTMXSsv3AJcaxoyOiIiIqMywDVk+KBQKeHh44MGDByXeNiUlBY6OjmjYsCEuXLggQ3SGw/uRiIiIiErCZDpT8kpKSsKjR4+gVqvh7u4OFxeXstp1PomJiQgPD9fqYLl69SqEEOxMYeJROCGAuyeBk8uAq3sA8U/nYKU6QJtRQNNBgLWDcWMkIiIiKmNsQ5YPpelMAQBHR0c4OzvrvX1Z4f1IRERERCVh9DlTCuLo6IjatWuX5S4L5eTkhPbt26N9+/aaZSkpKYiIiDBeUGS6srOBa/uA418B907/u9wvGGgzmkN5EREREZHeYmJiEBoaijt37iAlJQUzZ86UbV8ZGRm4ePEiGjRoAEUJ269OTk6Ij4+XJzAiIiIiIhNXpp0pps7e3h4vvPCCscMgU6LOBC78Chz/Gnh6OWeZhQ3Q9A2gzQdAVU6+SURERET6ycrKwpQpU7B8+XJkZGRoluftTHn27Bn8/PyQkpKCqKgoeHp6lmqfz549Q5MmTWBjY4PGjRsjMDAQgYGBCAgIQJMmTYodfjk9Pb1U+yciIiIiKq8M0ply9+5dQ1QDAKhRg/NMkAnISMmZC+XEUkAVnbPMxjlnQvnWowEnd+PGR0RERETl3oABA7Br1y4AQKNGjXD16lVkZWVplXF1dcWbb76J5cuXY+fOnRg1apTe+/Py8sK9e/cAAGlpaThz5gzOnj2rWW9paYkGDRogICBA08HSrFkzODo66r1PIiIiIiJzYZA5UywsLAwRCyRJypc86Ov+/fs4e/Ys1Go1GjdujDp16hS7zaJFi5CUlCTra/WmrsKPL5yRDJxZDfy5BEiJyVnmUDVnKK+Ww3MmmCciIiIiLRW+DamHzZs3Y9CgQXB3d8eePXsQEBAAT09PPHnyJN8cjkePHkWnTp3Qq1cv7N69u1T7jYuLw7lz5zTzRp47dw5RUVFaZSRJ0vq5du3aCAgIwG+//YbU1FSTn2OS9yMRERERlUSZTkBf0rF2i5KdnV2q7dPS0jBy5Ehs2LBBa3n79u2xdOlS+Pv7F7ptYclLRVJhE4+MFODsGuDPr4HkpznLXGsCL/wHaPYmYGVnzOiIiIiITFqFbUOWQo8ePXDw4EGsW7cOgwcPBlB4PpKamgpHR0d4eXnhzp07Bo8lPj4eYWFhms6VsLAw3LhxA3lTRUmSIISAJEkmny/xfiQiIiKikijTCeiff5Ip1+nTpzFy5EhIkoRRo0ahc+fO8PLyApDz5khISAhWrlwJIQRWrVqFli1bljqW1157Dfv27cPzfURHjx5Fq1atsGLFCgwdOrTU+yEzkZkKnFubM7F80uOcZa41gfb/BZoMBCw4rRARERERGV5YWBiAnPylOHZ2dlAqlXj69Kkssbi4uKBz587o3LmzZlliYiLCw8O1OliuXr2aL88iIiIiIqooDPJmSkFu3ryJFi1awNvbGwcPHoS7e8FzTDx58gRdunRBdHQ0wsLC4Ovrq/c+d+zYgX79+kGSJIwYMQLDhw+Hra0tQkNDMW/ePDx8+BCSJOHrr7/G2LFj823PN1Mq0FNc6iwgYgNwZB6Q+DBnmUuNnE6Upm8AFlbGjY+IiIioHKkwbUgDsrGxgYODA+Li4jTLispHXFxckJGRgZSUlLIMU0tKSgoiIiLwwgsvGC0GXfB+JCIiIqKS0LX9aLjxuZ7z+eefIyEhAatXry60IwUAqlatitWrV0OlUuGzzz4r1T7XrVsHSZIwfPhwrFixAi1atIC/vz/Gjh2LS5cuoXfv3hBCYNy4cfjqq69KtS8qp4QAruwBVrwA/PZRTkeK0ht4aTHw4Tkg8G12pBARERGR7FxdXaFSqZCWllZs2Xv37iEhIQFVq1Ytg8gKZ29vb/IdKUREREREcpGtM+XgwYNwdHREq1atii3bqlUrODo64uDBg6Xa59mzZwEA06dPz7dOqVRi165dGDNmDIQQmDRpEhYtWlSq/VE5c+8s8EMvYNMgIOYqYOcG9JgHjD0HNH8HsLQ2doREREREVEE0bdoUQM5wxMVZtWoVAKB169ayxkRERERERIWTbUKIp0+fwtJS9+qzs7NLPQbw06dP4eDggBo1ahS4XpIkLF26FDY2Nli0aBEmT54MAJgwYUKp9ksmTnUfODgDuPBrzu+WtkCbD4AXxwG2SqOGRkREREQV06BBg3Dw4EHMmDEDQUFBsLe3L7Dcli1bMH/+fEiShLfffttg+79//z7Onj0LtVqNxo0bo06dOsVus2jRIiQlJWHmzJkGi4OIiIiIqLyQrTOlatWqmknm805kWJCQkBCkpKRoJqfXl6WlpU7znSxcuBAKhQILFy7E5MmToVAoMG7cuFLtm0xQVjpwajlwdAGQmQxAApq9BXT6GFBWN3Z0RERERFSBDRkyBCtXrsSZM2fQtm1bjB49GpmZmQBy3riPjIzEpk2bcPjwYQghEBwcjD59+pR6v2lpaRg5ciQ2bNigtbx9+/ZYunQp/P39C912wYIFePLkCTtTiIiIiKhCkq0zpWfPnvjuu+8wfPhw7N+/H3Xr1i2w3PXr1/Hee+9BkiT07NmzVPv08fHBlStXcPv2bdSsWbPIsl988QWEEPjyyy8xceJESJJUqn2TiblxCNg7BYi9kfO7dxug1wLAs4lx4yIiIiIiAqBQKLBr1y706dMHZ8+exZgxYzTr8g7nJYRA69atsXnzZoPs97XXXsO+ffsghNBafvToUbRq1QorVqzA0KFDDbIvIiIiIiJzItucKbNmzYKrqyvu3r2Lpk2bYsiQIVi7di0OHjyIQ4cOYe3atRg6dCiaNm2K27dvw8XFpdRPOAUGBgIADhw4oFP5BQsWYMKECRBCYMKECaUeZoxMQNITYOs7wIbXcjpSHKoCfVcBw/axI4WIiIiITErVqlXx559/YunSpWjSpAkkSYIQQvNp0KABvv76axw9ehRubm6l3t+OHTuwd+9eAMDIkSNx+vRpREZGYvHixfD09ERaWhqGDRuGpUuXlnpfRERERETmRhLPP5JkQGFhYejbty+io6MLffNDCAEvLy9s374dzZs3L9X+1q5di2HDhqFNmzY4ceKEztvlnYxekiSdhgozVwkJCVAqlVCpVHB2djZ2OLoTAojcAuybAqQ+AyQLoPUooONUwLYcHQcRERFROVRu25AmJikpCY8ePYJarYa7uztcXFwMWn/fvn2xa9cuDB8+HN9++63WOpVKhcGDB+P333+HJElYuHAhxo8fr1XG09MTT548Mfl8ifcjEREREZWEru1H2d5MAXLeFLl48SLmzZuHZs2awcLCQvOUlUKhQLNmzTBv3jxcvHix1B0pAPDyyy/DwsICf/31F44eParzdrlzp1A5pboP/DQQ2D4ipyPFvTHwfgjQYw47UoiIiIio3HB0dETt2rVRr149g3ekADlzsQDA9OnT861TKpXYtWsXxowZAyGE1gNnREREREQk85spz8vMzERcXBwAwM3NDVZWVmW1a51ER0cjOzsbPj4+xg7FaMrdU1yXdgG7xgJp8YCFNdDhv0C7cYCFad1bREREROas3LUhKyhbW1tYW1sjISGhyHK5HSmSJGmGRgb4ZgoRERERmSdd24+yTUBfECsrK7i7u5flLkvE29vb2CGQrjJSgP3TgHNrc36vFgC8uhKoWt+oYRERERERPe/u3bsGq6tGjRp6b2tpaalTR8jChQuhUCg0b/ArFAqMGzdO7/0SEREREZmDMu1MITKIp9eAzYOBmKsAJKDdR0CnTwBLa2NHRkRERESUj6+vr0HqkSQJWVlZem/v4+ODK1eu4Pbt26hZs2aRZb/44gsIIfDll19i4sSJhc6BSURERERUUcg6Z4opefDgAR4/fmzsMKi0ruwBvuuc05Hi6AEM2QF0/ZQdKURERERksnLnjSztJzs7u1RxBAYGAgAOHDigU/ncIb6EEJgwYQKePn1aqv0TEREREZVnZv1mihACc+fOxfz585GUlAQAcHBwQJMmTRAQEIDAwEAEBATA398flpZmfSrKPyGAo18AR+bk/O7TDhiwDnCsYty4iIiIiIiKERUVVeDy06dPY+TIkZAkCaNGjULnzp3h5eUFALh//z5CQkKwcuVKCCGwatUqtGzZslRxBAcHY+PGjVi7di1GjBih0zYLFy4EACxatAhCCL6hQkREREQVVplOQF/Wli9fjg8//LDAdXmTACsrKzRq1AiBgYEIDAzE6NGjyypEk2OSkzWqs4DdHwHhG3J+bzUC6D6Hk8wTERERmQiTbEOauJs3b6JFixbw9vbGwYMHC51b8smTJ+jSpQuio6MRFhZWqiHD4uLi4O7ujuzsbISEhKBDhw46bztlyhQsWLAAkiRxAnoiIiIiMiu6th/NujOladOm+PvvvxEUFIQvv/wSlSpVwvXr1xEREYGwsDCEhYXh5s2byHsKykNyICeTSzwyUoBfhgHX9gKSAui9CGjxrrGjIiIiIqI8TK4NWQ4MGzYM69atw8mTJ9GqVasiy54+fRpt2rTBu+++i++//76MIswvOjoa2dnZ8PHxMVoMuuD9SEREREQlwc4UAI6OjkhNTUV0dDSqVatWYJnExERN58q5c+cQERGByMjIMo7UdJhU4pGZBvw8ELh1BLCwAfqvARr0MW5MRERERJSPSbUhywlvb28kJCRApVLpVN7Z2RkuLi64e/euzJGVf7wfiYiIiKgkdG0/mvVEIUqlEjY2NoV2pACAk5MTgoKCEBQUVIaRUbGyMoCtQ3M6UqwcgLe2AjXbGTsqIiIiIiKDePr0aYnmbczOzuYE8ERERERERqQwdgB56fpUlq5atWqFxMREpKenG7ReMryHqlScuBmDh6rUnMnmd48Dru0DLG2BNzezI4WIiIiIzErVqlWRmpqKkJCQYsuGhIQgJSUFVapUkS2eBw8e4PHjx7LVT0RERERU3snWmTJ9+vQSlVepVOjatatBYxgxYgSysrKwY8cOg9ZbEomJiZg9ezYaN24MR0dHKJVKtGzZEl9++SUyMjJKVffjx48xceJE1KtXD3Z2dnBzc0NQUBBWr16N8jR62+Yzd9FuXgje/O4vtJsXgs2bfwQiNubMkfL6j4Av3xoiIiIiIvPSs2dPCCEwfPhwXLt2rdBy169fx3vvvQdJktCzZ0+DxiCEwJw5c6BUKuHt7Y1q1arB2dkZL774IsaOHYsffvgBERERyMrKMuh+nydnzkREREREZCiyzZmiUCiwbNkyjB49utiySUlJ6NKlC86cOWPwyd8HDRqEY8eO4dSpU/Dy8jJo3cW5c+cOOnbsiNu3bwMA7O3toVarNW/KBAQE4PDhw3B1dS1x3efOnUP37t0RGxsLIGd+mLS0NE2i061bN+zatQs2NjYlqresxxd+qEpFu3khyM5zF1pAjeM2H8Gzx2Sg7Qeyx0BEREREpcM5KkruwYMHaNy4MeLj42FtbY0BAwagc+fOqF69OiRJwr179xAaGoqtW7ciLS0Nrq6uiIyMRPXq1Q0Ww/Lly/Hhhx8WuE6SJM3PVlZWaNSoEQIDAxEYGKhTjqcrOXIm3o9EREREVBK6th9lezPFwsIC//nPf7Bt27YiyyUlJaF79+44ffo0PD09DRrDa6+9Bj8/P2RkZCAgIADbt28vszc21Go1XnrpJdy+fRuenp44ePAgkpOTkZKSgk2bNsHJyQnh4eF46623Sly3SqVCnz59EBsbi/r16+PMmTNITExEcnIyvvnmG1hZWeHAgQMYP368DEdmWFExyVodKQCghgVu+wwA2hguSSMiIiIiMiXVqlXDwYMH4eXlhfT0dGzcuBHDhw9Hjx490L17dwwfPhwbNmxAWloavLy8cODAAYN2pADAqlWrAABBQUE4ffo0bt68iX379mHevHkYMGAA/Pz8AAAZGRkIDw/H999/X2jniz7kzJmIiIiIiAxNtjdT1q1bh3fffRe2trbYv39/gRO8Jycno2fPnjh+/Dg8PDwQGhqKevXqGSwGhUKh9UQVkDM2cZ8+fdCqVSsEBgaiSZMmsLKyMtg+c33//fd47733AAAnTpxA27Zttdb//PPPePPNNwEAhw4dQnBwsM51z5gxA5999hns7Oxw8eJF+Pr6aq2fO3cuPv74Y1hYWODSpUuoW7euznWbxpsp2Tg+rgU8PQzbuUZERERE8uCbAPpLSkrC8uXLsXnzZvz999+aN80tLCzQpEkTDBw4EKNHj4aTk5PB9+3o6IjU1FRER0ejWrVqBZZJTExEREQEwsLCcO7cOURERCAyMtIg+5crZ+L9SEREREQloWv7UbbOFACYP38+pk2bBldXVxw9ehT+/v6adSkpKejVqxf++OMPuLu7IyQkBA0aNDDo/j/66COcP38e58+f15rc/vlX1hs2bIjmzZtrXltv3bp1qffdvn17HDt2DJ06dSpwUkkhBPz8/BAVFYUhQ4Zg3bp1Otft4+ODu3fv4t1338WaNWvyrU9KSoKnpyeSkpIwc+ZMfPrppzrXbYzEY/OZu/h4299Qi5whvua0TMfA1waUyb6JiIiIqPT4j9eGkZmZibi4OACAm5ubLA995VW9enWkpaVphg4ua3LlTLwfiUhXD1WpiIpJhm9lB3gq7YwdDhERGYnRh/kCgClTpmDs2LF49uwZevbsiejoaABAamoqXnrpJfzxxx+oUqUKDh06ZPCOFABYvHgxjhw5gmfPnuHmzZv45ZdfMH36dPTq1Quenp4QQiAjIwMRERFYs2YNPvzwQ7Rr167U+01JScGff/4JAIVOEilJEnr06AEAOHDggM51X716FXfv3i2ybkdHR82bQCWp21gGtqyB433i8bPV/+G4+5cY2LefsUMiIh09VKXixM0YPFSlGjsUIiKics/Kygru7u5wd3eXvSMFAFq1aoXExETN/CRlSc6cyRgqWpuIx2u+KtKxbj5zF+3mheDN7/5Cu3kh2HzmrrFDkl1Fur4V6VgBHq85q0jHWh5Yyr2DxYsX4/Hjx9iyZQu6d++OgwcP4p133kFoaCgqVaqEgwcPolGjRnKHAV9fX/j6+qJfv3//oT4mJgbh4eGIiIhAeHg4wsPDcf369VLv6/Lly8jOzgYArbdxnpe77tGjR4iLi4Obm1uxdV+4cCHf9oXVvXfvXly6dEnXsI3K885v8LS4DDSfDigsjB0OEelg85m7mLbtb2QLQCEBc/s1xsCWNYwdFhEREeloxIgR2LlzJ3bs2IGBAweW6b7lzJnKWkVrE/F4zfd4K9KxPlSlao4VALIF8PG2C2hft4rZvqFSka5vRTpWgMdrzsdbkY61vJD1zZRcP/74Izp27IgrV66gbt26OHz4MFxdXXHw4EE0adKkLEIoUOXKldG1a1dMnjwZP/30Ey5fvozExMRS1/vgwQPNz0VNEpl3Xd5tDFl3QkICkpKSCi2Xnp6OhIQErY9RPL6Y81+f0r8ZRETyKyz54JMSRERE5UfPnj0xcOBATJw4Effu3SvTfRsyZzJmTlPR2kQ8XvM93op0rAAQFZOsNXcrAKiFwO2YFOMEJLOKdH0r0rECPF5zPt6KdKzlSZl0plhZWWHnzp1o1qwZUlNT4eLigoMHD6JZs2ZlsfsSsbMr/RMIeTtk7O3tCy2Xd52unTiGrnvu3LlQKpWaj7e3t05xGFRmKpD0KOfnKvXLfv9EVGIVLfkgIiIyR6+99hr8/PyQkZGBgIAAbN++HTJOqanFkHmNMXOaitYm4vGa7/FWpGMFAN/KDlBI2sssJAk1Kxf+96g8q0jXtyIdK8DjBcz3eCvSsZYnBhnm63//+59O5Vq1aoWIiAgEBQVh9+7d2L17d74yM2fONERIpKNp06ZhwoQJmt8TEhLKvkPFyg74+AEQFwXYm95r+0SUX27ykfd/7OacfBAREZmj7du3Y8eOHZrf+/fvj6pVq6JPnz5o1aoVAgMD0aRJkzKZv6U0jJnTVLQ2EY/XfI+3Ih0rAHgq7TC3X2N8vO0C1ELAQpIwp5+/2Q7xVZGub0U6VoDHC5jv8VakYy1PDNKZMnv2bEiSVHzBfxTWkQKYR2eKk5OT5ueUlMJ7C/Ouy7tNSep2dnYuVd02NjawsbHRad+ysrACqtQ1dhREpKOKlnwQERGZo7Fjx+L8+fM4f/48VCoVAODx48dYs2YN1qxZAyBnlIGGDRuiefPmCAwMRGBgIFq3bl3qfRsyZzJmTlPR2kQ8XvM93op0rLkGtqyB9nWr4HZMCmpWtjfrY61I17ciHSvA4zXn461Ix1qeGKQzpX379iXqTDF31apV0/x8//79QueFuX//foHblKTuwjpTcut2dnaGo6OjTnUTEZVERUo+iIiIzNHixYs1P0dFRSE8PBwREREIDw9HeHg4Hjx4gIyMDEREROD8+fNYs2YNJElCVlZWqfctZ85U1ipam4jHa77HW5GONZen0q5CHCdQsa5vRTpWgMdrzsdbkY61vDBIZ8qRI0cMUY3ZaNCgARQKBbKzs3HhwgX07NmzwHIXLlwAAHh4eMDNTbfhrfz9/bW2b9CgQZF1N2zYsCShExGVSEVKPoiIiMyZr68vfH190a9fP82ymJiYfB0s169fN8j+5MyZjKGitYl4vOarIh1rRVSRrm9FOlaAx2vOKtKxlgdlMgF9RWNvb4927doBAPbt21dgGSEE9u/fDwDo1q2bznXXq1cPNWrUKLLu5ORkHDt2rMR1ExERERGR6codiqusVK5cGV27dsXkyZPx008/4fLly4VOAl9ScuZMRERERERyYGeKTIYOHQoACA0NxV9//ZVv/datW3Hr1i0AwJAhQ0pUd275TZs24fbt2/nWL1u2DElJSbCwsMBbb71VwsiJiIiIiEhu06dPL1F5lUqFrl27yhSN7uzsDPdkpJw5ExERERGRobEzRSZDhw5F48aNIYTAa6+9hsOHDwMAsrOzsXXrVrz//vsAgJ49eyI4OFhr29mzZ0OSJEiSVGBnyaRJk+Dh4YGUlBT07t0b586dAwBkZGRgxYoVmDFjBgBgxIgRqFuXk7oTEREREZmaOXPmYMWKFTqVTUpKQvfu3TXtfnNRmpyJiIiIiKisGWTOlKKkp6fj119/xfHjx3Hv3j0kJydDCFFgWUmSNA3o8s7S0hK7du1Cp06dcPv2bXTp0gX29vbIzs5GWloaACAgIAAbN24scd1KpRK7d+9G9+7dcenSJbRo0QJOTk5IS0tDZmYmgJzX4L/66iuDHhMRERERERmGhYUF/vOf/8Dd3V1rnpLn5XaknD592mQnYNeXnDkTEREREZGhydqZcuLECQwcOBAPHjyAEAKSJAGApjMl9/fcZXl/Nwc1a9ZEZGQkFi5ciG3btiEqKgpWVlZo1KgRBg0ahLFjx8La2lqvups3b46LFy9i/vz52L17N6Kjo+Hg4AB/f38MHToUw4YNg0LBF4+IiIiIiEzR6tWr8e6772Lw4MHYv38/goKC8pVJTk5Gr169cPLkSXh4eJjNg2d5yZkzEREREREZkiQKe02klKKjo9GkSROoVCo0adIEPXr0wBdffAFHR0eMGzcOjx49QkhICG7duoXKlStj1KhRsLCwwKxZs+QIh3SkUqng4uKC6OhoODs7GzscIiIiIioHEhIS4O3tjfj4eCiVSmOHU27Mnz8f06ZNg6urK44ePQp/f3/NupSUFPTq1Qt//PEH3N3dERISggYNGhgx2vKDOQ0RERERlYSu+YxsnSnjx4/H4sWL0bNnT+zevRuSJEGhUMDDwwMPHjzQlFuxYgX+85//oFevXti5c6ccoVAJ3Lt3D97e3sYOg4iIiIjKoejoaHh5eRk7jHLlo48+wtKlS1G9enWcOHEC3t7eSE1NRZ8+fRAaGooqVaogJCQEjRo1Mnao5QZzGiIiIiLSR3H5jGydKY0aNcKVK1fw119/oUWLFgBQYGcKkDP54owZM7By5UrNJINkHNnZ2Xjw4AGcnJzKdNi13N4/Pj1mfnhtzRuvr/nitTVfvLbmzVjXVwiBxMREVKtWjUPN6uGNN97Ali1bUL9+fRw8eBDvvPMODh8+jEqVKuHw4cNo0qSJsUMsV4yR0/Bvq3nj9TVfvLbmi9fWvPH6mi9Tz2dk60zJnRA9PT1dE4BCoYCbmxtiYmK0yqpUKlSqVAmtW7fGn3/+KUc4ZOISEhKgVCqhUqn4R9DM8NqaN15f88Vra754bc0br2/5lJmZie7du+PIkSOws7NDamoqXF1dcfjwYTRr1szY4ZEO+N0zb7y+5ovX1nzx2po3Xl/zZerXVrbHxoQQcHJy0urJcXBwQEJCAp7vv1EqlVAqlbhy5Ypc4RAREREREZkkKysr7Ny5E82aNUNqaipcXFxw8OBBdqQQEREREZkQS7kqrl69Om7cuIH09HTY2NgAALy8vHDt2jVcvnwZDRs21JRNSUlBfHw8rK2t5QqHiIiIiIjIKP73v//pVK5Vq1aIiIhAUFAQdu/ejd27d+crM3PmTEOHR0REREREOpCtM6VOnTq4ceMGbt++jXr16gEAWrdujWvXrmHlypVYsmSJpuyiRYsghEDNmjXlCodMnI2NDWbNmqXpeCPzwWtr3nh9zRevrfnitTVvvL6mafbs2SWau6OwjhSAnSmmit8988bra754bc0Xr6154/U1X6Z+bWWbM2XZsmUYO3YsvvjiC0yaNAkAcPz4cbRv3x6SJKF79+4ICAjA+fPnsXfvXgDArFmzmBwQEREREZFZ6dixo8EmQg8NDTVIPUREREREVDKydaZER0djyJAhaNy4sdZbKFOmTMGCBQtydi5JmvlT2rdvjwMHDnCoLyIiIiIiIiIiIiIiMimydaYU5dChQ9i0aROio6OhVCrRo0cPDBkyBJaWso06RkREREREREREREREpBejdKYQERERERERERERERGVFwpjB0BERERERERERERERGTKyqQzJTs7G2fOnMEvv/yC9evXl8UuyUgSExMxe/ZsNG7cGI6OjlAqlWjZsiW+/PJLZGRklKrux48fY+LEiahXrx7s7Ozg5uaGoKAgrF69GnzBSn5yXNv79+9j+fLlGDBgAGrXrg07OzvY2dnB19cXgwYNQkhIiIGPggoi5/f2eaNGjYIkSZAkCTVr1jRo3VQwua/vo0ePMGPGDDRv3hxubm6ws7ODj48PevTogXnz5iEzM9MAR0EFkfPa/vLLL3jppZdQrVo1WFtbw8HBAfXq1cP777+PiIgIwxwAFSglJQV79+7FZ599hn79+sHHx0fzd3P27NkG2QfbVKYlPT0dP/30Ez744AO8/PLLCA4ORufOnQv8BAcHGztcs8d8xrwxpzFfzGnMF/MZ88acxvyYfT4jZLZkyRJRtWpVoVAoNJ+84uLiRKNGjUS9evVEbGys3OGQjG7fvi1q1qwpAAgAwt7eXtjY2Gh+DwgIEHFxcXrVffbsWVGpUiVNXY6OjsLS0lLze7du3URaWpqBj4hyyXFt7969KyRJ0tSRW6+dnZ3WsmHDhomsrCyZjozk/N4+LzQ0VOua+/j4GKReKpzc13fTpk3C2dlZU5+1tbVQKpVa3+Fnz54Z7oBIQ65rm5aWJl566SWta+jo6Cisra01vysUCrFo0SIZjoqEyPlbmff85/3MmjWr1PWzTWVa/vzzT+Hl5SUUCoWQJEmTL0mSpPV73vUkH+Yz5o05jfliTmO+mM+YN+Y05snc8xlZO1PGjBmjafgrlUphYWFRYAIwZMgQoVAoxLp16+QMh2SUlZUlGjduLAAIT09PcfDgQSGEEGq1WmzatEk4OTkJAKJnz54lrjs+Pl54eHgIAKJ+/frizJkzQggh0tPTxTfffCOsrKwEADF69GiDHhPlkOvaRkVFCQAiODhYrFu3Tty/f19T78WLF8Urr7yi+UM4ffp0gx8Xyfu9fV5ycrLw8/MTVlZWokWLFkw8yoDc13fLli1CoVAIAGLgwIEiPDxcsy4xMVEcO3ZMjB8/XiQlJRnicCgPOa/tzJkzNX97P/jgA3Hv3j1N3WfPnhUvvviiACAkSdL8/5gMKzQ0VLi6uorg4GAxefJk8fPPP2vaQaVNPtimMi13794VLi4uQpIk0bRpUzFlyhQhSZJwcnISM2bMEO+//77w8/MTkiSJKlWqiBkzZojZs2cbO2yzxXzGvDGnMV/MacwX8xnzxpzGfJl7PiNbZ8q+ffuEJEnC2dlZ7NixQwghhIeHR4GdKbllX3vtNbnCIZmtXr1a84fqxIkT+db/9NNPmvWHDh0qUd3Tp08XAISdnZ24detWvvVz5swRAISFhYW4evWq3sdABZPr2sbHx4tz584Vuj47O1v06NFD08ucmpqqV/xUODm/t88bN26cACA++eQTMXToUCYeZUDO6/vgwQPh6uoqAIjx48cbKmTSkZzXNvfJsA4dOhS4Pj4+Xjg6OgoAYurUqfqET8Uo6MllHx8fgyQfbFOZlnHjxglJkkSvXr1Edna2EEIISZKEp6enVrnly5cLS0tL8fLLLxsjzAqD+Yx5Y05jvpjTmC/mM+aNOY35Mvd8RrbOlFdffVUoFArx9ddfa5YV1pmiUqmEJEnCz89PrnBIZkFBQQKA6NSpU4Hrs7Ozha+vrwAghgwZUqK6a9SoIQCId999t8D1iYmJmj+CM2fOLHHsVDQ5r21xtmzZovmfZ1hYmEHrprK7tidPnhQKhULUrVtXpKamMvEoI3Je36lTpwoAwsvLS6SnpxsiXCoBOa9t7mv1EydOLLRMYGCgACA+/PDDEtVN+jNU8sE2lWlp2LChUCgUWk9EFtSZIoQQn3/+uVAoFOLbb78tyxArFOYz5o05jfliTmO+mM+YN+Y0FYs55TOyTUB/6tQpAMCwYcOKLevs7AxnZ2c8fPhQrnBIRikpKfjzzz8BAD179iywjCRJ6NGjBwDgwIEDOtd99epV3L17t8i6HR0dERQUVOK6qXhyXltd2Nraan5Wq9UGrbuiK6trm56ejmHDhkEIgVWrVmldU5KP3Nd3/fr1AIDBgwfD2tq6FJFSScl9bWvVqgUAOHfuXIHrVSoVrl27BgBo0aJFieom42KbyvTcvXsXCoUCgYGBWssLmmx1zJgxkCQJa9euLaPoKhbmM+aNOY35Yk5jvpjPmDfmNKQPU2lTydaZEhcXB6VSCScnJ90CUSjYsCinLl++jOzsbACAv79/oeVy1z169AhxcXE61X3hwoV82xdV96VLl3Sql3Qj57XVxZEjRwAA1tbWqFu3rsHqpbK7tv/73/9w+fJlDB8+HB07dtQrVio5Oa9vVFQUHjx4AADo0KEDwsPDMXDgQHh4eMDGxgbe3t544403cPLkyVIeBRVE7u/u6NGjAeT8/R0zZgzu378PABBCICwsDH369EFSUhLatGmDt956S9/DICNgm8r0CCHg5OQEheLflMzBwQEJCQkQQmiVVSqVUCqVuHLlSlmHWSEwnzFvzGnMF3Ma88V8xrwxpyF9mEqbSrbOFGdnZyQkJCAzM7PYsjExMYiPj0flypXlCodklPs/IQCoXr16oeXyrsu7jSHrTkhIQFJSkk51U/HkvLbFiYqKwsqVKwEAAwcOhLOzs0HqpRxlcW3Dw8PxxRdfwN3dHV988UXJgyS9yXl9c5/gAYDTp0+jdevW2LJlC1QqFezs7HDv3j1s3rwZ7dq1w9y5c/WInooi93d3zJgx+O9//wuFQoHly5fDy8sLTk5OsLW1RfPmzXHjxg1MnToVISEhsLS01O8gyCjYpjI91atXh0qlQnp6umaZl5cX1Go1Ll++rFU2JSUF8fHxSElJKeswKwTmM+aNOY35Yk5jvpjPmDfmNKQPU2lTydaZ0qhRIwghcObMmWLL/vjjjwCA5s2byxUOySgxMVHzs729faHl8q7Lu42x6qbiGev8p6amYsCAAUhJSUGlSpXYgJGB3Nc2KysLw4YNQ1ZWFpYsWQJXV1f9AiW9yHl9nz17pvn5008/hbu7O/bt24fk5GTEx8fj8uXLCA4OhhACH3/8MXbs2FHyA6BCyf3dVSgUmDt3LtasWQNHR0cAQFJSkmbYobS0NKhUKiQnJ5c0dDIytqlMT506dQAAt2/f1ixr3bo1AGj+8TXXokWLIIRAzZo1yyq8CoX5jHljTmO+mNOYL+Yz5o05DenDVNpUsnWm9OvXD0IIzJ49W/PqVkFOnDiBmTNnQpIkvP7663KFQ0TlRFZWFt58802cO3cOVlZW+Omnn4rscSbTNG/ePERERKBPnz78225m8v4/PTs7G1u3bkX37t01w9TUr18fO3fuRLVq1QAAs2fPNkaYpKeYmBgEBwfjnXfeQdu2bXH8+HHEx8fj4cOH2LZtG6pUqYIVK1agdevWmtfliUg/PXv2hBACv/32m2bZe++9ByEEli1bhl69euGTTz5Bnz59MGvWLEiShEGDBhkxYiLSFXMa88CcxjwxnzF/zGlITrJ1powcORK1a9fG4cOH0aNHD+zbt0/zBysmJgYhISEYMWIEOnXqhOTkZDRt2pTJQTmVd16cooYeyLtO17l05KybilfW51+tVmPw4MHYsWMHLC0t8dNPP6Fbt25610eFk/PaXrp0Cf/3f/8HR0dHLF++XP8gSW9l9Xf5xRdfRJs2bfKVcXBwwAcffAAAOH/+PB4/fqxT3VQ8uf8uDx06FEeOHEGHDh2wf/9+tGvXDkqlEh4eHujbty+OHz+OypUr49atW5g6dap+B0FGwTaV6Xn55ZfRoUMHzUSaQM7f1cmTJ0MIgX379mHevHnYs2cPhBAICgri904mzGfMG3Ma88WcxnwxnzFvzGlIH6bSppKtM8XGxga///47atSogUOHDqF3796IiYkBALi7u6Nr1674/vvvkZmZCT8/P2zfvl1r8kUqP3J76wEU2aObd13ebQxZt7Ozs+YVPio9Oa/t83KTjs2bN8PCwgIbNmxA//799aqLiifntR0zZgwyMjLwySefwNXVFUlJSVqfrKwsADmTv+Uu02V+LdKdnNc371OVDRo0KLRc3nV37tzRqW4qnpzX9vLly9izZw8AYOLEiZAkKV+ZqlWrYsiQIQCAbdu25Zskm0wX21Smx9vbG6GhoViyZInW8vnz5+PAgQMYNmwYunTpgv79+2P16tU4dOgQrK2tjRSteWM+Y96Y05gv5jTmi/mMeWNOQ/owlTaVrL0XderUQUREBCZOnAhXV1cIIbQ+Tk5OGDduHE6fPg0fHx85QyEZNWjQQNMRduHChULL5a7z8PCAm5ubTnX7+/vn276ouhs2bKhTvaQbOa9tXmq1Gm+99RY2bdqkSToGDhyoX9CkEzmvbVRUFABg2rRpcHJyyvfZuHEjAODu3buaZcuWLSvN4dBz5Ly+DRs2hIWFBQAU2DDNlbdBWlQ5Khk5r+2lS5c0P/v5+RVaLneeh5SUFDx58kSnusn42KYqX7p06YLVq1dj//792LJlC4YNG8YJUmXEfMa8MacxX8xpzBfzGfPGnIb0YSptKtlfBVEqlViwYAFiYmJw4cIFHDhwAHv37kV4eDhiY2OxaNEiTuJVztnb26Ndu3YAgH379hVYRgiB/fv3A0CJXnGuV68eatSoUWTdycnJOHbsWInrpuLJeW1z5SYdeZ/eeuONN/QPmnRSFteWjEfO62tra4v27dsD0G6oPu/y5csAchIPTphsOHJe27xvCBf19F3eYQ749HT5wTYVUeGYz5g35jTmizmN+WI+Y96Y05A+TKZNJWSgVqvFxYsXxcmTJ8XVq1fl2AWZmNWrVwsAQpIkcerUqXzrN2/eLAAIAOLQoUMlqnv69OkCgLC3txdRUVH51s+fP18AEBYWFrzfZCDntc3KyhKvv/66ACAsLS3Fpk2bDBU26UDOa1uUoUOHCgDCx8fHYHVSfnJe3/Xr12u2PXHiRL71ycnJolq1agKAaNOmjd7HQAWT69revn1bs91LL71UYJmkpCRRq1YtAUA0adJE72OgkvHx8REAxKxZs0pVD9tUpkutVovTp0+LrVu3inXr1hk7nAqJ+Yx5Y05jvpjTmC/mM+aNOU3FYk75jEE7UzIyMsR///tfoVQqhUKh0HwqV64sPvvsM5GdnW3I3ZEJyczMFI0bNxYARPXq1TV/6NRqtdiyZYtwdnYWAETPnj3zbTtr1izNH7qCvgjx8fHCw8NDABANGzYUZ8+eFUIIkZ6eLpYvXy6sra0FADF69GhZj7GikuvaZmVliUGDBmmSji1btpTF4VAecn5vi8LEo2zIeX3VarVo1aqVACC8vb3Fvn37hFqtFkIIcfnyZREcHCwACIVCIQ4fPizrcVZEcl7bl156SbN+8ODB4saNGyI7O1tkZGSIP//8U7Ro0UKznv/gK5+4uDjx9OlTzcfb21sAEJMnT9ZanpiYqLUd21Tl05IlS0TVqlW18qe84uLiRKNGjUS9evVEbGyskaI0f8xnzBtzGvPFnMZ8MZ8xb8xpzJs55zMG7Uzp1auXUCgUQpKkfB+FQiGGDh1qyN2RiYmKihI1a9bU3PT29vbC1tZW83tAQICIi4vLt50uDZizZ8+KSpUqaco5OTkJKysrze/dunUTaWlpMh9hxSXHtT169KhmnZWVlXB3dy/ywye85CHn97YwTDzKjpzX9+HDh6Jhw4aacnZ2dkKpVGp9r7/99luZj7DikuvaPn36VDRv3lxTJrduS0tLrWWTJk0qg6OsuHKf3Cru83zbmm2q8mfMmDGa/EmpVAoLC4t8nSlCCDFkyBChUCiY8MuM+Yx5Y05jvpjTmC/mM+aNOY35Mud8xmBzpmzduhV79+6FEAK1a9fGtGnTsGzZMkyePBnVqlWDEAI//vgjjh49aqhdkompWbMmIiMjMXPmTPj7+0OSJFhZWaF58+ZYuHAhTp06pff8OM2bN8fFixcxfvx41KlTB5mZmXBwcMCLL76I7777Dnv37oWNjY2Bj4hyyXFts7OzNT9nZmbi8ePHRX5SU1MNfVgEeb+3ZHxyXl8PDw+EhYVh4cKFaNmyJaytrZGamoqaNWti2LBhCAsLw/vvv2/gI6Jccl3bypUr49SpU1i9ejW6d+8Od3d3ZGZmwtLSErVq1cLgwYNx7NgxLFiwQIajorLANpXp2L9/P5YvXw5HR0ds374d8fHxqFKlSoFl33zzTQghsGvXrjKOsmJhPmPemNOYL+Y05ov5jHljTkP6MHabShJCCENU9Morr+C3335Dt27dsHPnTq2gExIS0LlzZ4SHh2P48OH49ttvDbFLIiIiIiKicqlv377YtWsXFi1ahI8++ggA4OnpiSdPnkCtVmuVTUhIgIuLC2rVqoUbN24YI1wiIiIiogrPYJ0p3t7eePDgAS5cuIAGDRrkW3/48GF07doVzZs3x5kzZwyxSyIiIiIionIpt+MkPj4eTk5OWsue70wBABcXF2RmZiI5ObmsQyUiIiIiIgAGG+YrJiYGtra2BXakAECLFi005YiIiIiIiCqyuLg4KJVKTUdKcRQKRYGdLEREREREVDYM1pmSnp4OpVJZ6Prcdenp6YbaJRERERERUbnk7OyMhIQEZGZmFls2JiYG8fHxqFy5chlERkREREREBTFYZwoRERERERHpplGjRhBC6DQE8o8//gggZ8JNIiIiIiIyDnamEBERERERlbF+/fpBCIHZs2cjOzu70HInTpzAzJkzIUkSXn/99TKMkIiIiIiI8jLYBPQKhQKSJJUuGElCVlaWIcIhIiIiIiIyWenp6WjcuDFu3ryJ4OBgTJgwAUOHDkVMTAweP36MyMhIbNq0CevWrUNmZiaaNWuGs2fPQqHg83BERERERMZg0M6U0pIkiZMqEhERERFRhXD9+nV069YNd+7cKfTBNCEE/Pz8cOjQIfj4+JRxhERERERElMvSUBXNmjXLUFURERERERGZvTp16iAiIgKfffYZfvjhB8TFxWmtd3Z2xrBhwzBjxgy4uroaKUoiIiIiIgIM+GYKERERERER6e/SpUt48OAB1Go1PDw84O/vDwsLC2OHRUREREREYGcKERERERFRmcrOzsaVK1eQkJAANzc31K1b19ghERERERFRMTh7IRERERERURnIzMzElClT4ObmhsaNG6Ndu3Zo0KABqlSpgs8//xx8zo2IiIiIyHTxzRQiIiIiIqIy0Lt3b+zbt6/AThNJkvD2229j7dq1ZR8YEREREREVi2+mEJEs3nnnHUiShHfeecfYoVAZ4nU3LWq1GosWLUJAQAAcHBwgSRIkScKOHTuMHZrBmMs9t3//frz00kvw9PSElZUVJElCnTp1jB0WERnQ1q1bsXfvXgghULt2bUybNg3Lli3D5MmTUa1aNQgh8OOPP+Lo0aPGDpWI/mEu7QwqGV5308KcpvxgTkMVgaWxAyAiKk/Wrl2L27dvo2PHjujYsaOxwyETZSr3ybhx4/DNN98AAKytreHu7g4AsLW1NVpMujKVc1gW1q9fj6FDhwIAFAoFKlWqBIVCgfbt2xs5MiIypA0bNgAAunXrhp07d8LGxkaz7pNPPkHnzp0RHh6OjRs3okOHDsYKk4gqgIrUziL9mcp9wpymfGBOQxUFO1OISBaenp6oV68ePD09jR2KQa1du1bzxKi5N4ZIf6ZwnyQmJmLVqlUAgC+++AKTJk2CJElGiUUfup7D8v63JjU1FRMmTAAAjBo1CgsXLoSDg4ORoyIiOYSFhUGSJHz11VdaHSkA4OzsjPnz56Nr164IDw83UoRE9Lzy3s4ojCm0Vcn0mcJ9wpymfGBOQxUJO1OISBZz587F3LlzjR0GUYV15coVZGZmAgBGjx5drpKOkijvf2sOHDiA2NhYODg44Msvv4S9vb2xQyIimcTExMDW1hYNGjQocH2LFi005YjINJT3dgZRececpnxgTkMVCedMISIiMkMpKSmanx0dHY0YCRXl2LFjAICgoCAmHURmLj09HUqlstD1uevS09PLKiQiIiKTxpymfGBOQxUJO1OISBaFTaDWsWNHSJKE2bNnQwiB7777Dq1bt4azszOcnJzQtm1bzZjiBcm7fUZGBubNm4cmTZrAwcEBrq6u6Nq1K/bu3Vvo9jVr1oQkSVi7dm2JYl+7di0kSdK8ovvpp59qJr7L/dy+fVuXU6MlOjoa//3vf9GsWTMolUrY2dnBz88Pr7zyCtavX4+0tLR82xw5cgQDBgxA9erVYWNjg8qVKyM4OBg//PAD1Gp1ofsq7bnPtXHjRrRr1w5OTk5QKpVo3bo1vv32WwghdDrmR48eYerUqWjatCmUSiVsbW1Rq1YtvPfee7h06ZJssW/ZsgU9e/aEu7s7rKys4OLigjp16uDll1/GsmXL8p1rlUqFTZs24a233kLjxo3h5uYGW1tb+Pj44M0338SpU6fy7UPf+0Sfc1KY3Bjyvkaed/95l+c9r5mZmfjyyy/RokULuLi4QJIkHDlyRO9zURBd7veSnkNdJmvU5ztjqO9LYby9vSFJEr788ksAwL59+7SO8cyZM3rXTURERIbBnEY3zGmY0+h7TgrDnCY/5jREJkIQEclg6NChAoAYOnSo1vIOHToIAGL69OnilVdeEQCEpaWlcHZ2FgA0n5kzZxZYb+7206ZNE0FBQZrtXVxctLafNWtWgdv7+PgIAOKHH34oUeybNm0S7u7uwsrKSgAQDg4Owt3dXetz9+7dEp2j9evXC1tbW03M1tbWQqlUah1HeHi41jbjx4/XrJMkSbi4uAgLCwvNss6dO4uEhIQiz52+5z47O1u8++67Wvt3dXUVCoVCABBvvPFGodc912+//SYcHR01dVhZWQkHBwetc7Bu3TqDxz5s2DCtco6OjsLe3l5rWVRUlNY2s2bNyreNjY2N1vEvXrxYaxt97hN9z0lhcmNwdXXV1JF3/3379s13XqdMmSJeeOEFzXnN3TY0NFTvc/E8Xe/3kp7D4u45fb8zpb3nipKUlCQ8PDyEu7u7ph5XV1fN8Xl4eIikpKQS10tEpk2SJOHp6VnqMkRUdpjTFI85DXOa0pyTwjCn0cachsh0sDOFiGRRXOLh6uoqlEqlWLt2rUhJSRFCCBEdHS1eeuklAUAoFApx7dq1fPXmbq9UKoWNjY1YuXKlSE1NFUIIcffuXdG/f3/N/8h37tyZb3t9E4/n919YYqOr33//XUiSJACIdu3aiWPHjgm1Wi2EEEKlUok//vhDvP/+++LixYuabZYuXao5thEjRoiHDx8KIXIaMV999ZWwtLQUAMTAgQML3Gdpz/3ixYs1+//www/F06dPhRBCxMfHi9mzZ2sadYWdu7/++ktYW1sLAGLkyJHi8uXLIisrSwghxJ07d8QHH3ygadydOXPGYLEfO3ZMs27+/PkiNjZWsy4mJkbs379fDB06VNy/f19ruxUrVojx48eLU6dOiWfPngkhcpKvW7duiY8++khIkiQsLCxEWFhYoee6uPukNOekOKGhoZrrVZjcOB0dHYWjo6P44YcfNOc1JiZGc65Kcy6E0O9+1/UcFvV9Lc13prTfF108efJEE1/eYyci8yRJklAoFKX6WFhYGPswiCoU5jRFY07DnKa056Q4zGmY0xCZGnamEJEsiks8AIiQkJB826WlpYlq1aoJAOKzzz7Ltz7v9t9//32+9Wq1WrRv314AEA0bNsy33hQSj8zMTOHr6ysAiBdffFGkp6cXu01KSopwc3MTAMSgQYMKLLNkyRLNuSmokVqac5+amqrZ/9tvv13g/qdOnaqpv6Bz17JlSwFAzJgxo9Dj/M9//iMAiFdeecVgsc+fP18AEN26dSt0v/oYM2aMACCGDx+eb52u90lpzklxSpJ4ABC7du0qUf15FXUu9Lnf88amb+JR2u9Maf9W6WLfvn0CgLC3t9cknERkviRJKvVHoVAY+zCIKhTmNIVjTsOcJhdzmsIxpyEyP5wzhYiMol27dujUqVO+5TY2NujevTsAIDIystDtvb298e677+ZbrlAoMH36dADApUuX8PfffxsoYsMJDQ1FVFQUAOCrr76CtbV1sdscPHgQcXFxAIDZs2cXWOaDDz6Ap6cnAODnn38utC59zv2BAwc0+585c2aB9U6dOhW2trYFrjt//jzOnDkDKysrTJw4sdDYhgwZAgA4dOhQgeO+6hO7i4sLAODp06dFjr9cUr179wYAHD9+XK/tDXVODKFRo0Z46aWX9N6+qHOhz/1uCIb6zpT2b1VRwsPDAQBNmjSBhYWFXnUAQPXq1dGvXz+9tyeisjFr1qxSfwr7fzARGQdzGuY0BWFOkx9zGv0wpyEyPZbGDoCIKqbWrVsXuq5atWoAoGk0FCR3IrWCtG/fHpaWlsjKysLZs2fRuHHj0gVrYCdOnAAAeHh4oEWLFjptc/bsWQA5CVfdunULLGNhYYHOnTtj48aNmvIF0efc591/7dq1C9xWqVSiefPm+PPPP/Oty22QZmdno169eoXuP7dhnZycjNjYWFStWrXUsXfp0gW2trYIDw9HUFAQhg8fjs6dO8PX17fQunLdunULy5cvR2hoKG7evInExERkZ2drlbl3716x9RTEUOfEENq1a1dsGX3PhT73uyEY6jtT2r9VRclNPAICAvTaHgCePHmCBw8elKoOIiobs2bNMnYIRGRgzGmY0xSEOU1+zGn0w5yGyPSwM4WIjMLJyanQdZaWOX+aMjMzCy1TvXr1QtfZ2NigUqVKePz4MZ48eaJ/kDJ59OgRAMDHx0fnbXKPo6jjBgAvLy+t8gXR59yXdP/Pe/DgAYCcRvTjx4+LrCNXSkpKvmX6xF6rVi2sXr0ao0aNwsmTJ3Hy5EkAQJUqVdCpUye8+eabePnll/Mlstu3b8egQYOQnp6uWebs7AxbW1tIkoSMjAw8e/YMycnJOh3P8wx1TgyhuGSmNOdCn/vdEAz1nSnt36qihIWFAShd4lGlShWkpqbCyspK7zqIiIhIP8xpmNMUhzlNfsxpdMechsj0cJgvIiqXCnuCqzzR5xh03Uau86NvvblPItWvXx8iZ76uYj81a9Y0WNxvvfUW7ty5g5UrV2LgwIHw9vbG06dPsWXLFrz66qvo0KEDEhISNOVjY2PxzjvvID09HZ07d8aRI0eQkpIClUqFx48f49GjR9i6dWupYjL2OcmrqNexDXUujPWdNfZ3pjCJiYm4efMmACAwMFDveiRJgq2tbaleqSciIiLjYE5j+LrlrNfY7XfmNEVjTsOchqgssDOFiMqlol5DTk9PR2xsLID8T6fkPnWRlpZW6PYqlcoAERYudzzT3DFXdZF7HNHR0UWWyz0vVapU0TO6ovdf3Ovf9+/fL3C5h4cHgJzXqvV96qm03NzcMHLkSGzatAl3797FjRs3MHXqVEiShGPHjmmNQbtnzx4kJCTA1dUVv/32Gzp06AA7Ozut+nKfTtKXKZwTXZT2XOhzvxuCsb8zxYmIiIAQApaWlvD39y+wTGxsLKZNm4aGDRvCwcEBbm5uaNmyJVatWqUpM3z4cDg4OGgNTzB8+HDY29sjOjoaQ4cORaVKleDq6opRo0YhKysLSUlJmDhxIry9veHk5IRXX31V8zeTiIiIyg5zmoIxpykccxr9MKeRB3MaqojYmUJE5dLRo0chhChw3bFjx5CVlQUA+cYzdXV1BVB4YyQ7O7vIsXkVipw/m4XtWxcvvPACAODx48dF7iuv3OO4d+8erl27VmAZtVqN0NBQAEDLli31jq+o/UdHR2uePHleQkICzp07V+C63PFrMzIysH37doPGpi8/Pz/MnTsXb775JoCcyf1y5d4f9erVg729fYHbHzp0qNC6dblPTPGcFKS050Kf+x0o/XfN2N+Z4uSOLdywYUPY2NjkW5+UlIQ2bdrghx9+wKuvvoolS5Zg2rRpqFGjhlYSFxERgSZNmmjOV+4ypVKJ9u3bw9HREXPmzEHz5s2xatUqzJ8/H+3atUN0dDQ+/vhj9O/fHzt37uRcDkREREbAnCY/5jQlw5xGN8xp5MGchioidqYQUbl09+5drFu3Lt/y7OxszJkzBwDQoEGDfBM1Nm3aFEDOeKkFNWjWrVtX5JNKzs7OAID4+Hh9Q0enTp1Qq1YtAMD48eORkZFR7DZdu3ZFpUqVAEDraaO8Vq1apRmzdtCgQXrHV9j+c5O2//u//yuwzBdffIHU1NQC17Vo0UIzhuonn3yCp0+fFrk/fSe/K0jeMXELkvtEUt5XipVKJQDg2rVrBT7xFxERgZ9++qnQOnW5T4x5TkqitOdCn/sdKP13zdjfmeIUN7bwli1bcOPGDezbtw9z5szB8OHDMXnyZPz666+YN28eACArKwsXL15Es2bNNNvlLouJicHatWuxbNkyjBw5Etu2bYNCocD06dMxYsQIbNmyBaNHj8YPP/yAunXr4tSpU7IfMxEREWljTpMfc5qCMacpHeY08mBOQxURO1OIqFxSKpUYPXo0vvvuO01jKDo6GoMGDdI8lfH555/n2y63cXH58mWMGDFC8xpoQkICvvrqK4waNQpubm6F7jf31dU9e/YU+vp3cSwsLPDNN99AkiQcP34cwcHBOH78uOaV1oSEBBw5cgSDBw/GpUuXAOQ0jnMbTz///DNGjRqlmeAvJSUFS5cuxbhx4wAAAwcORPPmzfWKrTB2dnaYMWMGgJzkbNy4cVrn7v/+7/8wZ84cuLi4FLi9JElYuXIlbGxscPfuXbRu3Rq//PKL1uSD9+/fx4YNG9C1a1dMmTLFYLF/+OGHeP311/Hrr79qTciXlJSElStXYv369QCAXr16adZ169YNCoUCcXFxeOuttzTXOiMjA1u2bEG3bt2KnMRPl/vEmOekJEp7LvS534HSf9eM/Z0pTu5TXIWNLfzs2TMAwJkzZwqt4/Lly0hPT9dKPHKXvf/+++jQoYNmuZOTE6ysrNCmTRuMGTNGqx5nZ2eOT0xERGQEzGmY0+iKOU3pMKeRB3MaqpAEEZEMhg4dKgCIoUOHai3v0KGDACBmzZpV6LazZs0SAESHDh3yrcvdftq0aeLFF18UAISVlZVwdXUVADSf6dOnF1r/kCFDtMq6uLgIhUIhAIgPP/yw0NiFEOLatWvC1tZWABAKhUK4u7sLHx8f4ePjI6Kjo3U8OznWrVsnbGxsNHHY2NgIFxcXrdjCw8O1thk/frxmnSRJwtXVVVhaWmqWderUSSQkJBS4v9Kee7VaLd5++23NvhQKhXB1dRUWFhYCgHjjjTeKPHdCCHHgwAFRqVIlTR0WFhaiUqVKwt7eXuu433vvPYPFnhtT7sfR0THfeX7xxRdFUlKS1nZTpkzRKqNUKoWVlZUAIHx9fcXGjRs1655XkvtE33NSnNDQ0ELjy6XLeS3tuchV0vtd13NY3D2n73emtN+XoqSlpWnO3x9//FFgmfv374vatWtrzvG4cePEiRMntMqsX79eABCnTp3KtywkJESr7NWrVwUAsWDBAq3l2dnZwtnZWQwZMqREx0BERFQRMKcpHnMa5jSlOSfFYU6TgzlNDuY0ZAr4ZgoRlUvW1tY4fPgw5syZg3r16iE9PR1KpRLBwcH4/fffC31tGwDWrFmDxYsXo1mzZrCzs0N2djbatWuHzZs3Y+nSpUXut06dOggNDcXLL7+MKlWqIDY2Fnfu3MGdO3c0YxrrasiQIbhy5QrGjRuHhg0bwtLSEhkZGfDz88Orr76KH3/8EQ0aNNDaZtGiRQgJCcFrr70Gd3d3JCUlwcnJCZ06dcKaNWtw8ODBIp+oKQ2FQoH169dj/fr1aNOmDezs7JCVlYXAwECsXLmyyNeic3Xt2hU3btzA3Llz8eKLL0KpVCI+Ph4KhQINGzbE8OHDsWvXrmKvQ0nMmDEDS5YsQd++fVG/fn1YWloiKSkJVatWRdeuXbFmzRocOXIEDg4OWtvNmzcP69evR6tWrWBnZ4fMzEzUrl0bH3/8McLDw1GtWrVC91mS+8QY56SkSnMucpX0fjfUd82Y35nCXLhwAZmZmZAkSTNMx/OqVauGixcvYvv27ejcuTN+/PFHvPDCC/jwww81ZSIiIqBQKLSG/oiIiIAkSfnGVs99aqxVq1Zay2/duoWEhIRCX80nIiIi+TCnYU6jK+Y0pcecxrCY01BFJQlRihnHiIjKWMeOHXH06FHMmjWr0DFDiYjMTXJyMjp16oTIyEjNMCDBwcF48OABLl++rCkXHByM6OjofBNUTps2DfPnz0d8fLxm7GYA+PXXX9G/f3+EhoaiY8eOZXIsREREFR1zGiKqiJjTkDngmylEREREJiQmJibfMltbWwBA9erVNcvOnz+vNbZw7rKCnsiKiIiAn5+fVtKRuxxAvnqIiIiIiIj0xZyGzJWlsQMgIiIion+NHz8ep0+fxiuvvAI/Pz+kpqZiy5YtOHfuHDZt2gQgZ3La2NhYrYQhd1lBiUd4eDjat29f4HIfH59CJ1olIiIiIiIqKeY0ZK7YmUJERERkQrp06YKnT59iw4YNiI2NRdWqVdG8eXOcPHlSMz7w+fPnAWg/fVXQMgB4/PgxHj9+jMDAwHz7ioiIyDcWMRERERERUWkwpyFzxTlTiKhc4fjCRERERERUnjGnISIiKp/YmUJERERERERERERERFQETkBPRERERERERERERERUBHamEBERERERERERERERFYGdKUREREREREREREREREVgZwoREREREREREREREVER2JlCRERERERERERERERUBHamEBERERERERERERERFYGdKUREREREREREREREREVgZwoREREREREREREREVER2JlCRERERERERERERERUBHamEBERERERERERERERFeH/Ad+IMflpf3IAAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1600x2000 with 10 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(nrows = 5, ncols = 2, figsize = (16,20))\n",
+    "fig.suptitle(\"Fit Results for Simulation with Flatfield light source\", fontsize = 28)\n",
+    "fig.tight_layout(h_pad=4)\n",
+    "plt.subplots_adjust(wspace = 0.25, hspace = 0.6, top=0.9)\n",
+    "\n",
+    "# Plot the results for f\n",
+    "x_axis = np.linspace(0, 1, 10000)\n",
+    "\n",
+    "axs[0,0].errorbar(f, f_fit, fmt = '.', yerr = f_err)\n",
+    "axs[0,0].set_xlabel(\"input condensate fraction $f_{sim}$\")\n",
+    "axs[0,0].set_ylabel(\"fitted condensate fraction $f_{fit}$\")\n",
+    "axs[0,0].set_ylim(0, 1)\n",
+    "axs[0,0].plot(x_axis, x_axis)\n",
+    "axs[0,0].set_title(\"Condensate Fraction f\")\n",
+    "\n",
+    "# Plot the results for T\n",
+    "Tc = 400e-9                             \n",
+    "T_sim = (np.ones(len(x_axis))-x_axis**(1/3)) * Tc     \n",
+    "\n",
+    "axs[0,1].errorbar(f, T_fit/1e-9, fmt = '.', yerr = T_err)\n",
+    "axs[0,1].set_xlabel(\"input condensate fraction $f_{sim}$\")\n",
+    "axs[0,1].set_ylabel(\"fitted Temperature $T_{fit}$ [nK]\")\n",
+    "axs[0,1].set_ylim(0, 600)\n",
+    "axs[0,1].plot(x_axis,  T_sim/1e-9)\n",
+    "axs[0,1].set_title(\"Temperature T\")\n",
+    "\n",
+    "# Plot the results for a\n",
+    "\n",
+    "axs[1,0].errorbar(f, a_fac_fit, fmt = '.', yerr = a_fac_err)\n",
+    "axs[1,0].set_xlabel(\"input condensate fraction $f_{sim}$\")\n",
+    "axs[1,0].set_ylim(-50, 500)\n",
+    "axs[1,0].set_ylabel(\"fitted scattering length $a_{fit}$ [a0]\")\n",
+    "axs[1,0].plot(x_axis, np.ones(len(x_axis))*300)\n",
+    "axs[1,0].set_title(\"Scattering Length a\")\n",
+    "\n",
+    "# Plot the results for N\n",
+    "\n",
+    "axs[1,1].errorbar(f, N_fit/1000, fmt = '.', yerr = N_err/1000)\n",
+    "axs[1,1].set_xlabel(\"input condensate fraction $f_{sim}$\")\n",
+    "axs[1,1].set_ylabel(\"fitted total atom number \\n $N_{fit}$ [$10^3$]\")\n",
+    "axs[1,1].set_ylim(0.5e5/1000, 2e5/1000)\n",
+    "axs[1,1].plot(x_axis, np.ones(len(x_axis))*1e5/1000)\n",
+    "axs[1,1].set_title(\"Total Atom Number N\")\n",
+    "\n",
+    "# plot the results for N condensate\n",
+    "\n",
+    "axs[2,0].errorbar(f, Nc_fit/1000, fmt = '.', yerr = Nc_err/1000)\n",
+    "axs[2,0].set_xlabel(\"input condensate fraction $f_{sim}$\")\n",
+    "axs[2,0].set_ylabel(\"fitted atom number \\n in the condensate $Nc_{fit}$ [$10^3$]\")\n",
+    "axs[2,0].set_ylim(0, 1.2e5/1000)\n",
+    "axs[2,0].plot(x_axis, x_axis*1e5/1000)\n",
+    "axs[2,0].set_title(\"Atom Number in the Condensate Nc\")\n",
+    "\n",
+    "# Plot the results for N thermal\n",
+    "\n",
+    "axs[2,1].errorbar(f, Nth_fit/1000, fmt = '.', yerr = Nth_err/1000)\n",
+    "axs[2,1].set_xlabel(\"input condensate fraction $f_{sim}$\")\n",
+    "axs[2,1].set_ylabel(\"fitted atom number \\n in the thermal gas $Nth_{fit}$ [$10^3$]\")\n",
+    "axs[2,1].set_ylim(0, 1.2e5/1000)\n",
+    "axs[2,1].plot(x_axis, 1e5*(np.ones(len(x_axis))-x_axis)/1000)\n",
+    "axs[2,1].set_title(\"Atom Number in the Thermal Part Nth\")\n",
+    "\n",
+    "# plot the results for R condensate\n",
+    "\n",
+    "a = 300 * a0\n",
+    "N = 1e5\n",
+    "Rxc_sim = np.sqrt(const.hbar*omgy/m)*np.sqrt((1+omgy**2*tof**2)/omgy**2)*(15*N*x_axis*a/ahoy)**(1/5)\n",
+    "Ryc_sim = np.sqrt(const.hbar*omgx/m)*np.sqrt((1+omgx**2*tof**2)/omgx**2)*(15*N*x_axis*a/ahox)**(1/5)\n",
+    "axs[3,0].errorbar(f, Rxc_fit/1e-6, fmt = '.', yerr = Rxc_err/1e-6)\n",
+    "axs[3,0].errorbar(f, Ryc_fit/1e-6, fmt = '.', yerr = Ryc_err/1e-6)\n",
+    "axs[3,0].set_xlabel(\"input condensate fraction $f_{sim}$\")\n",
+    "axs[3,0].set_ylabel(\"Radius condensate \\n $Rc_{fit}$ [$\\mu$m]\")\n",
+    "axs[3,0].plot(x_axis, Rxc_sim/1e-6)\n",
+    "axs[3,0].plot(x_axis, Ryc_sim/1e-6)\n",
+    "axs[3,0].set_title(\"Radius of the Condensate Rc\")\n",
+    "\n",
+    "# plot the results for R thermal\n",
+    "\n",
+    "Rth_sim = np.sqrt(const.k*T_sim/m*(1/omg**2+tof**2))\n",
+    "axs[3,1].errorbar(f, Rxth_fit/1e-6, fmt = '.', yerr = Rxc_err/1e-6)\n",
+    "axs[3,1].errorbar(f, Ryth_fit/1e-6, fmt = '.', yerr = Ryc_err/1e-6)\n",
+    "axs[3,1].set_xlabel(\"input condensate fraction $f_{sim}$\")\n",
+    "axs[3,1].set_ylabel(\"Radius thermal cloud \\n $Rth_{fit}$ [$\\mu$m]\")\n",
+    "axs[3,1].plot(x_axis, Rth_sim/1e-6)\n",
+    "axs[3,1].set_title(\"Radius of the Thermal Cloud Rth\")\n",
+    "\n",
+    "# plot the results for n2D condensate\n",
+    "\n",
+    "mu = const.hbar*omg/2*(15*N*x_axis*a/aho)**(2/5)\n",
+    "g = 4*const.pi*const.hbar**2*a/m\n",
+    "Rzc = np.sqrt(const.hbar*omgz/m)*np.sqrt((1+omgz**2*tof**2)/omgz**2)*(15*N*x_axis*a/ahoz)**(1/5)\n",
+    "n2D0c_sim = 4/3*Rzc*mu/g*1/(1+omg**2*tof**2)**(3/2)\n",
+    "axs[4,0].errorbar(f, n2D0c_fit, fmt = '.', yerr = Rxc_err/1e-13)\n",
+    "axs[4,0].set_xlabel(\"input condensate fraction $f_{sim}$\")\n",
+    "axs[4,0].set_ylabel(\"Peak density condensate\\n $n2Dc_{fit}$ [$10^{13}m^{-2}$]\")\n",
+    "axs[4,0].plot(x_axis, n2D0c_sim)\n",
+    "axs[4,0].set_title(\"Peak density Condensate n2D0c\")\n",
+    "\n",
+    "# plot the results for n2D thermal\n",
+    "\n",
+    "# n2D0th_sim = (1-x_axis)*N*integrate.nquad(lambda x,y: np.real(Li2_vec(np.exp(-(x/Rth_sim)**2-(y/Rth_sim)**2))), \n",
+    "#                                             [[-6/np.sqrt(2)*Rth_sim, 6/np.sqrt(2)*Rth_sim], [-6/np.sqrt(2)*Rth_sim, 6/np.sqrt(2)*Rth_sim]])[0]**(-1) \n",
+    "axs[4,1].errorbar(f, n2D0th_fit/1e-13, fmt = '.', yerr = Rxc_err/1e-13)\n",
+    "axs[4,1].set_xlabel(\"input condensate fraction $f_{sim}$\")\n",
+    "axs[4,1].set_ylabel(\"Peak density thermal cloud\\n $n2Dth_{fit}$ [$10^{13} m^{-2}$]\")\n",
+    "# axs[4,1].plot(x_axis, Rth_sim/1e-13)\n",
+    "axs[4,1].set_title(\"Peak density Thermal cloud n2D0th\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Manual Light Source"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: 'C:/Users/QFBri/Code/analyseScript-Master III/Data/Simulations/OD_fT_manual.npy'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[1;32mf:\\Jianshun\\analyseScript\\test_fit.ipynb Cell 25\u001b[0m in \u001b[0;36m2\n\u001b[0;32m      <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/test_fit.ipynb#X43sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39m# load the data\u001b[39;00m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/test_fit.ipynb#X43sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m sim_fT_manual_np \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39;49mload(\u001b[39m\"\u001b[39;49m\u001b[39mC:/Users/QFBri/Code/analyseScript-Master III/Data/Simulations/OD_fT_manual.npy\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n\u001b[0;32m      <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/test_fit.ipynb#X43sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m sim_fT_manual_np \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mnan_to_num(sim_fT_manual_np)\n\u001b[0;32m      <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/test_fit.ipynb#X43sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m \u001b[39m# define axis\u001b[39;00m\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\numpy\\lib\\npyio.py:417\u001b[0m, in \u001b[0;36mload\u001b[1;34m(file, mmap_mode, allow_pickle, fix_imports, encoding)\u001b[0m\n\u001b[0;32m    415\u001b[0m     own_fid \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n\u001b[0;32m    416\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> 417\u001b[0m     fid \u001b[39m=\u001b[39m stack\u001b[39m.\u001b[39menter_context(\u001b[39mopen\u001b[39;49m(os_fspath(file), \u001b[39m\"\u001b[39;49m\u001b[39mrb\u001b[39;49m\u001b[39m\"\u001b[39;49m))\n\u001b[0;32m    418\u001b[0m     own_fid \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[0;32m    420\u001b[0m \u001b[39m# Code to distinguish from NumPy binary files and pickles.\u001b[39;00m\n",
+      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'C:/Users/QFBri/Code/analyseScript-Master III/Data/Simulations/OD_fT_manual.npy'"
+     ]
+    }
+   ],
+   "source": [
+    "# load the data\n",
+    "sim_fT_manual_np = np.load(\"C:/Users/QFBri/Code/analyseScript-Master III/Data/Simulations/OD_fT_manual.npy\")\n",
+    "sim_fT_manual_np = np.nan_to_num(sim_fT_manual_np)\n",
+    "\n",
+    "# define axis\n",
+    "x = np.linspace(0, 1201, 1200)\n",
+    "y = np.linspace(0, 1921, 1920)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# crop the data around the center to get manly the area of the cloud\n",
+    "sim_fT_manual_np = sim_fT_manual_np[0:12, 470:-470, 830:-830]\n",
+    "\n",
+    "f = np.array([0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0])\n",
+    "x = x[470:-470]\n",
+    "y = y[830:-830]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## perform the BEC fit\n",
+    "\n",
+    "# set the fit Model\n",
+    "fitModel = DensityProfileBEC2dModel()\n",
+    "fitAnalyser = FitAnalyser(fitModel, fitDim=2)\n",
+    "\n",
+    "fit_fT_manual_result = np.empty((11,12))\n",
+    "fit_fT_manual_std = np.empty((11,12))\n",
+    "\n",
+    "# params = fitAnalyser.guess(data_sim, dask=\"parallelized\")\n",
+    "params = fitAnalyser.fitModel.make_params()\n",
+    "params.add(name=\"A_amplitude\", value=  1000, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"A_centerx\", value=  0, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"A_centery\", value=  0, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"A_sigmax\", value= 23, max=  0, min=-np.inf, vary=True)\n",
+    "params.add(name=\"A_sigmay\", value=  30, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"B_amplitude\", value=  3300, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"B_centerx\", value=  0, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"B_centery\", value=  0, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"B_sigmax\", value=  30, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"B_sigmay\", value=  30, max=np.inf, min=-np.inf, vary=True)\n",
+    "\n",
+    "for i in range (11):\n",
+    "    print(i)\n",
+    "    sim_fT_manual = xr.DataArray(\n",
+    "    data = sim_fT_manual_np[i], \n",
+    "    dims = [\"x\", \"y\"],\n",
+    "    coords = dict(\n",
+    "        x = (\"x\", x),\n",
+    "        y = (\"y\", y),\n",
+    "        )\n",
+    "    )\n",
+    "    # perform the fit for one simulation with flatfield\n",
+    "    params = fitAnalyser.guess(sim_fT_manual, dask=\"parallelized\")\n",
+    "    fitResult = fitAnalyser.fit(sim_fT_manual, params, dask=\"parallelized\").load()\n",
+    "    fitCurve = fitAnalyser.eval(fitResult, x=x, y=y).load()\n",
+    "    fitValue = fitAnalyser.get_fit_value(fitResult)\n",
+    "    fitStd = fitAnalyser.get_fit_std(fitResult)\n",
+    "    # store the results as numpy array\n",
+    "    fit_fT_manual = fitCurve.to_numpy()\n",
+    "    fitValue_array = fitValue.to_array()\n",
+    "    fitStd_array = fitStd.to_array()\n",
+    "    fit_fT_manual_result[i] = fitValue_array.to_numpy()\n",
+    "    fit_fT_manual_std[i] = fitStd_array.to_numpy()\n",
+    "    # plot fit\n",
+    "    plt.figure(figsize=(12,8))\n",
+    "    plt.errorbar(x, sim_fT_manual_np[i,130], fmt = '.', label = \"simulated data\")\n",
+    "    plt.plot(x, fit_fT_manual[130], label = \"BEC fit\")\n",
+    "    plt.xlabel(\"y axis [px]\")\n",
+    "    plt.ylabel(\"OD\")\n",
+    "    plt.title(\"OD distribution cross-section \\n f={}, mnanual light source\".format(str(f[i])))\n",
+    "    plt.legend()\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fit_fT_manual_result = fit_fT_manual_result.T\n",
+    "fit_fT_manual_std = fit_fT_manual_std.T\n",
+    "\n",
+    "## calculate the peak densities and the radii\n",
+    "lam = 421e-9\n",
+    "sig0 = 3*lam**2/(2*np.pi)\n",
+    "eff_px_size = 2.493e-6\n",
+    "\n",
+    "\n",
+    "Rxc_fit = fit_fT_manual_result[6]*eff_px_size\n",
+    "Rxc_err = Rxc_fit*fit_fT_manual_std[6]/fit_fT_manual_std[6]\n",
+    "Ryc_fit = fit_fT_manual_result[7]*eff_px_size\n",
+    "Ryc_err = Ryc_fit*fit_fT_manual_std[7]/fit_fT_manual_result[7]\n",
+    "Rxth_fit = fit_fT_manual_result[8]*np.sqrt(2)*eff_px_size\n",
+    "Rxth_err = Rxth_fit*fit_fT_manual_std[8]/Rxth_fit*fit_fT_manual_result[8]\n",
+    "Ryth_fit = fit_fT_manual_result[9]*np.sqrt(2)*eff_px_size\n",
+    "Ryth_err = Ryth_fit*fit_fT_manual_std[9]/fit_fT_manual_result[9]\n",
+    "Nc_fit = fit_fT_manual_result[0]/sig0*eff_px_size*eff_px_size\n",
+    "Nc_err = Nc_fit*fit_fT_manual_std[0]/fit_fT_manual_result[0]\n",
+    "Nth_fit = fit_fT_manual_result[1]/sig0*eff_px_size*eff_px_size\n",
+    "Nth_err = Nth_fit*fit_fT_manual_std[1]/fit_fT_manual_result[1]\n",
+    "N_fit = Nc_fit + Nth_fit\n",
+    "N_err = np.sqrt(Nth_err**2 + Nc_err**2)\n",
+    "n2D0c_fit = Nc_fit*5/(2*np.pi*Rxc_fit*Ryc_fit)\n",
+    "n2D0th_fit =Nth_fit/(2*np.pi*1.20206*Rxth_fit/np.sqrt(2)*Ryth_fit/np.sqrt(2))\n",
+    "f_fit = fit_fT_manual_result[11]\n",
+    "f_err = fit_fT_manual_std[11]\n",
+    "\n",
+    "## Assumed to be given\n",
+    "tof = 20e-3\n",
+    "omgx = 2*np.pi*200\n",
+    "omgy = 2*np.pi*100\n",
+    "omgz = 2*np.pi*200\n",
+    "omg = (omgx*omgy*omgz)**(1/3)\n",
+    "m = 164*const.u \n",
+    "a0 = 4*np.pi*const.epsilon_0*const.hbar**2/(const.e**2*const.electron_mass)\n",
+    "\n",
+    "## Calculate T\n",
+    "T_x_fit = Rxth_fit**2*m/(const.k)*(1/omg**2+tof**2)**(-1)\n",
+    "T_x_err = T_x_fit * 2*Rxth_err/Rxth_fit\n",
+    "T_y_fit = Ryth_fit**2*m/(const.k)*(1/omg**2+tof**2)**(-1)\n",
+    "T_y_err = T_y_fit * 2*Ryth_err/Ryth_fit\n",
+    "T_fit = (T_x_fit + T_y_fit)/2\n",
+    "T_err = 1/2*np.sqrt(T_x_err**2+T_y_err**2)\n",
+    "\n",
+    "## Calculate scattering lenght a\n",
+    "aho = np.sqrt(const.hbar/(m*omg))\n",
+    "ahox = np.sqrt(const.hbar/(m*omgx))\n",
+    "ahoy = np.sqrt(const.hbar/(m*omgy))\n",
+    "ahoz = np.sqrt(const.hbar/(m*omgz))\n",
+    "a_x = Ryc_fit**5*ahox/(15*Nc_fit)*(const.hbar*omgx/m*(1+omgx**2*tof**2)/omgx**2)**(-5/2)\n",
+    "a_x_err = a_x*5*Ryc_err/Ryc_fit\n",
+    "a_y = Rxc_fit**5*ahoy/(15*Nc_fit)*(const.hbar*omgy/m*(1+omgy**2*tof**2)/omgy**2)**(-5/2)\n",
+    "a_y_err = a_y*5*Rxc_err/Rxc_fit\n",
+    "a_fac_fit = (a_x+a_y)/2/a0\n",
+    "a_fac_err =1/2/a0*np.sqrt(a_x_err**2+a_y_err**2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, axs = plt.subplots(nrows = 5, ncols = 2, figsize = (16,20))\n",
+    "fig.suptitle(\"Fit Results for Simulation with manual light source\", fontsize = 28)\n",
+    "fig.tight_layout(h_pad=4)\n",
+    "plt.subplots_adjust(wspace = 0.25, hspace = 0.6, top=0.9)\n",
+    "\n",
+    "# Plot the results for f\n",
+    "x_axis = np.linspace(0, 1, 10000)\n",
+    "\n",
+    "axs[0,0].errorbar(f, f_fit, fmt = '.', yerr = f_err)\n",
+    "axs[0,0].set_xlabel(\"input condensate fraction $f_{sim}$\")\n",
+    "axs[0,0].set_ylabel(\"fitted condensate fraction $f_{fit}$\")\n",
+    "axs[0,0].set_ylim(0, 1)\n",
+    "axs[0,0].plot(x_axis, x_axis)\n",
+    "axs[0,0].set_title(\"Condensate Fraction f\")\n",
+    "\n",
+    "# Plot the results for T\n",
+    "Tc = 400e-9                             \n",
+    "T_sim = (np.ones(len(x_axis))-x_axis**(1/3)) * Tc     \n",
+    "\n",
+    "axs[0,1].errorbar(f, T_fit/1e-9, fmt = '.', yerr = T_err)\n",
+    "axs[0,1].set_xlabel(\"input condensate fraction $f_{sim}$\")\n",
+    "axs[0,1].set_ylabel(\"fitted Temperature $T_{fit}$ [nK]\")\n",
+    "axs[0,1].set_ylim(0, 600)\n",
+    "axs[0,1].plot(x_axis,  T_sim/1e-9)\n",
+    "axs[0,1].set_title(\"Temperature T\")\n",
+    "\n",
+    "# Plot the results for a\n",
+    "\n",
+    "axs[1,0].errorbar(f, a_fac_fit, fmt = '.', yerr = a_fac_err)\n",
+    "axs[1,0].set_xlabel(\"input condensate fraction $f_{sim}$\")\n",
+    "axs[1,0].set_ylim(-50, 500)\n",
+    "axs[1,0].set_ylabel(\"fitted scattering length $a_{fit}$ [a0]\")\n",
+    "axs[1,0].plot(x_axis, np.ones(len(x_axis))*300)\n",
+    "axs[1,0].set_title(\"Scattering Length a\")\n",
+    "\n",
+    "# Plot the results for N\n",
+    "\n",
+    "axs[1,1].errorbar(f, N_fit/1000, fmt = '.', yerr = N_err/1000)\n",
+    "axs[1,1].set_xlabel(\"input condensate fraction $f_{sim}$\")\n",
+    "axs[1,1].set_ylabel(\"fitted total atom number \\n $N_{fit}$ [$10^3$]\")\n",
+    "axs[1,1].set_ylim(0.5e5/1000, 2e5/1000)\n",
+    "axs[1,1].plot(x_axis, np.ones(len(x_axis))*1e5/1000)\n",
+    "axs[1,1].set_title(\"Total Atom Number N\")\n",
+    "\n",
+    "# plot the results for N condensate\n",
+    "\n",
+    "axs[2,0].errorbar(f, Nc_fit/1000, fmt = '.', yerr = Nc_err/1000)\n",
+    "axs[2,0].set_xlabel(\"input condensate fraction $f_{sim}$\")\n",
+    "axs[2,0].set_ylabel(\"fitted atom number \\n in the condensate $Nc_{fit}$ [$10^3$]\")\n",
+    "axs[2,0].set_ylim(0, 1.2e5/1000)\n",
+    "axs[2,0].plot(x_axis, x_axis*1e5/1000)\n",
+    "axs[2,0].set_title(\"Atom Number in the Condensate Nc\")\n",
+    "\n",
+    "# Plot the results for N thermal\n",
+    "\n",
+    "axs[2,1].errorbar(f, Nth_fit/1000, fmt = '.', yerr = Nth_err/1000)\n",
+    "axs[2,1].set_xlabel(\"input condensate fraction $f_{sim}$\")\n",
+    "axs[2,1].set_ylabel(\"fitted atom number \\n in the thermal gas $Nth_{fit}$ [$10^3$]\")\n",
+    "axs[2,1].set_ylim(0, 1.2e5/1000)\n",
+    "axs[2,1].plot(x_axis, 1e5*(np.ones(len(x_axis))-x_axis)/1000)\n",
+    "axs[2,1].set_title(\"Atom Number in the Thermal Part Nth\")\n",
+    "\n",
+    "# plot the results for R condensate\n",
+    "\n",
+    "a = 300 * a0\n",
+    "N = 1e5\n",
+    "Rxc_sim = np.sqrt(const.hbar*omgy/m)*np.sqrt((1+omgy**2*tof**2)/omgy**2)*(15*N*x_axis*a/ahoy)**(1/5)\n",
+    "Ryc_sim = np.sqrt(const.hbar*omgx/m)*np.sqrt((1+omgx**2*tof**2)/omgx**2)*(15*N*x_axis*a/ahox)**(1/5)\n",
+    "axs[3,0].errorbar(f, Rxc_fit/1e-6, fmt = '.', yerr = Rxc_err/1e-6)\n",
+    "axs[3,0].errorbar(f, Ryc_fit/1e-6, fmt = '.', yerr = Ryc_err/1e-6)\n",
+    "axs[3,0].set_xlabel(\"input condensate fraction $f_{sim}$\")\n",
+    "axs[3,0].set_ylabel(\"Radius condensate \\n $Rc_{fit}$ [$\\mu$m]\")\n",
+    "axs[3,0].plot(x_axis, Rxc_sim/1e-6)\n",
+    "axs[3,0].plot(x_axis, Ryc_sim/1e-6)\n",
+    "axs[3,0].set_title(\"Radius of the Condensate Rc\")\n",
+    "\n",
+    "# plot the results for R thermal\n",
+    "\n",
+    "Rth_sim = np.sqrt(const.k*T_sim/m*(1/omg**2+tof**2))\n",
+    "axs[3,1].errorbar(f, Rxth_fit/1e-6, fmt = '.', yerr = Rxc_err/1e-6)\n",
+    "axs[3,1].errorbar(f, Ryth_fit/1e-6, fmt = '.', yerr = Ryc_err/1e-6)\n",
+    "axs[3,1].set_xlabel(\"input condensate fraction $f_{sim}$\")\n",
+    "axs[3,1].set_ylabel(\"Radius thermal cloud \\n $Rth_{fit}$ [$\\mu$m]\")\n",
+    "axs[3,1].plot(x_axis, Rth_sim/1e-6)\n",
+    "axs[3,1].set_title(\"Radius of the Thermal Cloud Rth\")\n",
+    "\n",
+    "# plot the results for n2D condensate\n",
+    "\n",
+    "mu = const.hbar*omg/2*(15*N*x_axis*a/aho)**(2/5)\n",
+    "g = 4*const.pi*const.hbar**2*a/m\n",
+    "Rzc = np.sqrt(const.hbar*omgz/m)*np.sqrt((1+omgz**2*tof**2)/omgz**2)*(15*N*x_axis*a/ahoz)**(1/5)\n",
+    "n2D0c_sim = 4/3*Rzc*mu/g*1/(1+omg**2*tof**2)**(3/2)\n",
+    "axs[4,0].errorbar(f, n2D0c_fit, fmt = '.', yerr = Rxc_err/1e-13)\n",
+    "axs[4,0].set_xlabel(\"input condensate fraction $f_{sim}$\")\n",
+    "axs[4,0].set_ylabel(\"Peak density condensate\\n $n2Dc_{fit}$ [$10^{13}m^{-2}$]\")\n",
+    "axs[4,0].plot(x_axis, n2D0c_sim)\n",
+    "axs[4,0].set_title(\"Peak density Condensate n2D0c\")\n",
+    "\n",
+    "# plot the results for n2D thermal\n",
+    "\n",
+    "# n2D0th_sim = (1-x_axis)*N*integrate.nquad(lambda x,y: np.real(Li2_vec(np.exp(-(x/Rth_sim)**2-(y/Rth_sim)**2))), \n",
+    "#                                             [[-6/np.sqrt(2)*Rth_sim, 6/np.sqrt(2)*Rth_sim], [-6/np.sqrt(2)*Rth_sim, 6/np.sqrt(2)*Rth_sim]])[0]**(-1) \n",
+    "axs[4,1].errorbar(f, n2D0th_fit/1e-13, fmt = '.', yerr = Rxc_err/1e-13)\n",
+    "axs[4,1].set_xlabel(\"input condensate fraction $f_{sim}$\")\n",
+    "axs[4,1].set_ylabel(\"Peak density thermal cloud\\n $n2Dth_{fit}$ [$10^{13} m^{-2}$]\")\n",
+    "# axs[4,1].plot(x_axis, Rth_sim/1e-13)\n",
+    "axs[4,1].set_title(\"Peak density Thermal cloud n2D0th\")\n",
+    "\n",
+    "# plt.savefig(\"C:/Users/QFBri/Code/analyseScript-Master III/Data/Fit_Results/FitResults_fT_flatfield_PureBECThreshold{}.jpg\".format(str(PureBECThreshold*10)), dpi = 300, bbox_inches='tight')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Real Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "groupList = [\n",
+    "    \"images/MOT_3D_Camera/in_situ_absorption\",\n",
+    "    \"images/ODT_1_Axis_Camera/in_situ_absorption\",\n",
+    "    \"images/ODT_2_Axis_Camera/in_situ_absorption\",\n",
+    "]\n",
+    "\n",
+    "dskey = {\n",
+    "    \"images/MOT_3D_Camera/in_situ_absorption\": \"camera_1\",\n",
+    "    \"images/ODT_1_Axis_Camera/in_situ_absorption\": \"camera_2\",\n",
+    "    \"images/ODT_2_Axis_Camera/in_situ_absorption\": \"camera_3\",\n",
+    "}\n",
+    "\n",
+    "img_dir = '//DyLabNAS/Data/'\n",
+    "SequenceName = \"Evaporative_Cooling\" + \"/\"\n",
+    "folderPath = img_dir + SequenceName + '2023/05/12'# get_date()\n",
+    "\n",
+    "shotNum = \"0065\"\n",
+    "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n",
+    "\n",
+    "dataSetDict = {\n",
+    "    dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n",
+    "    for i in [0] # range(len(groupList))\n",
+    "}\n",
+    "\n",
+    "dataSet = dataSetDict[\"camera_1\"]\n",
+    "dataSet = swap_xy(dataSet)\n",
+    "\n",
+    "scanAxis = get_scanAxis(dataSet)\n",
+    "\n",
+    "dataSet = auto_rechunk(dataSet)\n",
+    "\n",
+    "dataSet = imageAnalyser.get_absorption_images(dataSet)\n",
+    "\n",
+    "dataSet\n",
+    "\n",
+    "OD = dataSet[\"OD\"]\n",
+    "\n",
+    "OD_np = OD.to_numpy()\n",
+    "\n",
+    "dataSet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# crop image\n",
+    "OD_np = OD_np[6, 151:451, 800:1100]\n",
+    "y = np.linspace(800, 1100, 300)\n",
+    "x = np.linspace(151, 451, 300)\n",
+    "plt.imshow(OD_np)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## perform the BEC fit\n",
+    "\n",
+    "# set the fit Model\n",
+    "fitModel = DensityProfileBEC2dModel()\n",
+    "fitAnalyser = FitAnalyser(fitModel, fitDim=2)\n",
+    "\n",
+    "OD = xr.DataArray(\n",
+    "    data = OD_np, \n",
+    "    dims = [\"x\", \"y\"],\n",
+    "    coords = dict(\n",
+    "        x = (\"x\", x),\n",
+    "        y = (\"y\", y),\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "# perform the fit for one simulation with flatfield\n",
+    "params = fitAnalyser.guess(OD, dask=\"parallelized\", guess_kwargs=dict(pureBECThreshold=0.6))\n",
+    "'''\n",
+    "params = fitAnalyser.fitModel.make_params()\n",
+    "params.add(name=\"A_amplitude\", value=  1000, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"A_centerx\", value=  0, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"A_centery\", value=  0, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"A_sigmax\", value= 23, max=  0, min=-np.inf, vary=True)\n",
+    "params.add(name=\"A_sigmay\", value=  30, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"B_amplitude\", value=  3300, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"B_centerx\", value=  0, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"B_centery\", value=  0, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"B_sigmax\", value=  30, max=np.inf, min=-np.inf, vary=True)\n",
+    "params.add(name=\"B_sigmay\", value=  30, max=np.inf, min=-np.inf, vary=True)\n",
+    "'''\n",
+    "fitResult = fitAnalyser.fit(OD, params, dask=\"parallelized\").load()\n",
+    "fitCurve = fitAnalyser.eval(fitResult, x=x, y=y).load()\n",
+    "fitValue = fitAnalyser.get_fit_value(fitResult)\n",
+    "fitStd = fitAnalyser.get_fit_std(fitResult)\n",
+    "\n",
+    "# store the results as numpy array\n",
+    "fit = fitCurve.to_numpy()\n",
+    "fitValue_array = fitValue.to_array()\n",
+    "fitStd_array = fitStd.to_array()\n",
+    "fit_result = fitValue_array.to_numpy()\n",
+    "fit_std = fitStd_array.to_numpy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# print(np.shape(OD_np))\n",
+    "plt.figure(figsize = (12, 8))\n",
+    "plt.plot(x, OD_np[135], marker = \"o\", linewidth = 0)\n",
+    "plt.plot(x, fit[135], label = \"BEC fit\")\n",
+    "plt.xlabel(\"pixel\")\n",
+    "plt.ylabel(\"OD\")\n",
+    "plt.title(\"Simulated Data\")\n",
+    "plt.xlabel(\"y axis [px]\")\n",
+    "plt.ylabel(\"OD\")\n",
+    "plt.title(\"OD distribution cross-section \\n real Data\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from Analyser.FitAnalyser import ThomasFermi2dModel, Polylog22dModel\n",
+    "\n",
+    "# fitModel_draw = ThomasFermi2dModel(prefix='BEC_')\n",
+    "fitModel_draw = Polylog22dModel(prefix='thermal_')\n",
+    "\n",
+    "fitAnalyser_draw = FitAnalyser(fitModel_draw, fitDim=2)\n",
+    "\n",
+    "fitCurve = fitAnalyser_draw.eval(fitResult, x=x, y=y).load()\n",
+    "\n",
+    "fit = fitCurve.to_numpy()\n",
+    "\n",
+    "# print(np.shape(OD_np))\n",
+    "plt.figure(figsize = (12, 8))\n",
+    "plt.plot(x, OD_np[135], marker = \"o\", linewidth = 0)\n",
+    "plt.plot(x, fit[135], label = \"BEC fit\")\n",
+    "plt.xlabel(\"pixel\")\n",
+    "plt.ylabel(\"OD\")\n",
+    "plt.title(\"Simulated Data\")\n",
+    "plt.xlabel(\"y axis [px]\")\n",
+    "plt.ylabel(\"OD\")\n",
+    "plt.title(\"OD distribution cross-section \\n real Data\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fitAnalyser.get_fit_full_result(fitResult)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fitValue"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"sigxc:\", fit_result[6])\n",
+    "print(\"sigyc:\", fit_result[7])\n",
+    "print(\"sigxth:\", fit_result[8])\n",
+    "print(\"sigyth:\", fit_result[9])\n",
+    "print(\"BEC amplitude\", fit_result[0])\n",
+    "print(\"thermal amplitude:\", fit_result[1])\n",
+    "print(\"condensate fraction:\", fit_result[11])"
+   ]
+  }
+ ],
+ "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
+}