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analyseScript/20230630_Data_Analysis.ipynb

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{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Import supporting package"
]
},
{
"cell_type": "code",
"execution_count": 63,
"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": 64,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\data\\AppData\\Roaming\\Python\\Python39\\site-packages\\distributed\\node.py:182: UserWarning: Port 8787 is already in use.\n",
"Perhaps you already have a cluster running?\n",
"Hosting the HTTP server on port 52367 instead\n",
" warnings.warn(\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
" <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
" <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-b21bc166-4363-11ee-942c-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://127.0.0.1:52367/status\" target=\"_blank\">http://127.0.0.1:52367/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;\">f17f13c5</p>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:52367/status\" target=\"_blank\">http://127.0.0.1:52367/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-cfebc6e8-3543-4422-83e0-ad5571cb200d</p>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Comm:</strong> tcp://127.0.0.1:52370\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://127.0.0.1:52367/status\" target=\"_blank\">http://127.0.0.1:52367/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://127.0.0.1:52406\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://127.0.0.1:52410/status\" target=\"_blank\">http://127.0.0.1:52410/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://127.0.0.1:52373\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-1_z2lt_0\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://127.0.0.1:52417\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://127.0.0.1:52418/status\" target=\"_blank\">http://127.0.0.1:52418/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://127.0.0.1:52374\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-iq7wc4yz\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://127.0.0.1:52412\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://127.0.0.1:52415/status\" target=\"_blank\">http://127.0.0.1:52415/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://127.0.0.1:52375\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-4s052vw8\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://127.0.0.1:52420\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://127.0.0.1:52421/status\" target=\"_blank\">http://127.0.0.1:52421/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://127.0.0.1:52376\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-u1n0zi10\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://127.0.0.1:52423\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://127.0.0.1:52424/status\" target=\"_blank\">http://127.0.0.1:52424/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://127.0.0.1:52377\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-3vuc5miu\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://127.0.0.1:52405\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://127.0.0.1:52407/status\" target=\"_blank\">http://127.0.0.1:52407/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://127.0.0.1:52378\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-suw918q8\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://127.0.0.1:52409\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://127.0.0.1:52413/status\" target=\"_blank\">http://127.0.0.1:52413/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://127.0.0.1:52379\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-7dc78218\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://127.0.0.1:52435\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://127.0.0.1:52436/status\" target=\"_blank\">http://127.0.0.1:52436/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://127.0.0.1:52380\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-kdlokiw6\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://127.0.0.1:52370' processes=8 threads=128, memory=149.01 GiB>"
]
},
"execution_count": 64,
"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": 65,
"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": 66,
"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"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Evaporative Cooling"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [],
"source": [
"# img_dir = 'C:/Users/control/DyLab/Experiments/DyBEC/'\n",
"img_dir = '//DyLabNAS/Data/'\n",
"SequenceName = \"Evaporative_Cooling\"\n",
"folderPath = img_dir + SequenceName + \"/\" + get_date()\n",
"# folderPath = img_dir + SequenceName + \"/\" + '2023/06/30'# get_date()\n",
"\n",
"mongoDB = mongoClient[SequenceName]\n",
"\n",
"DB = MongoDB(mongoClient, mongoDB, date=get_date())"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "Could not find any dimension coordinates to use to order the datasets for concatenation",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32mf:\\Jianshun\\analyseScript\\20230630_Data_Analysis.ipynb Cell 11\u001b[0m in \u001b[0;36m1\n\u001b[1;32m----> <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/20230630_Data_Analysis.ipynb#Y504sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m xr\u001b[39m.\u001b[39;49mopen_mfdataset(filePath)\n",
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\backends\\api.py:1010\u001b[0m, in \u001b[0;36mopen_mfdataset\u001b[1;34m(paths, chunks, concat_dim, compat, preprocess, engine, data_vars, coords, combine, parallel, join, attrs_file, combine_attrs, **kwargs)\u001b[0m\n\u001b[0;32m 997\u001b[0m combined \u001b[39m=\u001b[39m _nested_combine(\n\u001b[0;32m 998\u001b[0m datasets,\n\u001b[0;32m 999\u001b[0m concat_dims\u001b[39m=\u001b[39mconcat_dim,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1005\u001b[0m combine_attrs\u001b[39m=\u001b[39mcombine_attrs,\n\u001b[0;32m 1006\u001b[0m )\n\u001b[0;32m 1007\u001b[0m \u001b[39melif\u001b[39;00m combine \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mby_coords\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[0;32m 1008\u001b[0m \u001b[39m# Redo ordering from coordinates, ignoring how they were ordered\u001b[39;00m\n\u001b[0;32m 1009\u001b[0m \u001b[39m# previously\u001b[39;00m\n\u001b[1;32m-> 1010\u001b[0m combined \u001b[39m=\u001b[39m combine_by_coords(\n\u001b[0;32m 1011\u001b[0m datasets,\n\u001b[0;32m 1012\u001b[0m compat\u001b[39m=\u001b[39;49mcompat,\n\u001b[0;32m 1013\u001b[0m data_vars\u001b[39m=\u001b[39;49mdata_vars,\n\u001b[0;32m 1014\u001b[0m coords\u001b[39m=\u001b[39;49mcoords,\n\u001b[0;32m 1015\u001b[0m join\u001b[39m=\u001b[39;49mjoin,\n\u001b[0;32m 1016\u001b[0m combine_attrs\u001b[39m=\u001b[39;49mcombine_attrs,\n\u001b[0;32m 1017\u001b[0m )\n\u001b[0;32m 1018\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 1019\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m 1020\u001b[0m \u001b[39m\"\u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m is an invalid option for the keyword argument\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 1021\u001b[0m \u001b[39m\"\u001b[39m\u001b[39m ``combine``\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(combine)\n\u001b[0;32m 1022\u001b[0m )\n",
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\core\\combine.py:975\u001b[0m, in \u001b[0;36mcombine_by_coords\u001b[1;34m(data_objects, compat, data_vars, coords, fill_value, join, combine_attrs, datasets)\u001b[0m\n\u001b[0;32m 973\u001b[0m concatenated_grouped_by_data_vars \u001b[39m=\u001b[39m []\n\u001b[0;32m 974\u001b[0m \u001b[39mfor\u001b[39;00m \u001b[39mvars\u001b[39m, datasets_with_same_vars \u001b[39min\u001b[39;00m grouped_by_vars:\n\u001b[1;32m--> 975\u001b[0m concatenated \u001b[39m=\u001b[39m _combine_single_variable_hypercube(\n\u001b[0;32m 976\u001b[0m \u001b[39mlist\u001b[39;49m(datasets_with_same_vars),\n\u001b[0;32m 977\u001b[0m fill_value\u001b[39m=\u001b[39;49mfill_value,\n\u001b[0;32m 978\u001b[0m data_vars\u001b[39m=\u001b[39;49mdata_vars,\n\u001b[0;32m 979\u001b[0m coords\u001b[39m=\u001b[39;49mcoords,\n\u001b[0;32m 980\u001b[0m compat\u001b[39m=\u001b[39;49mcompat,\n\u001b[0;32m 981\u001b[0m join\u001b[39m=\u001b[39;49mjoin,\n\u001b[0;32m 982\u001b[0m combine_attrs\u001b[39m=\u001b[39;49mcombine_attrs,\n\u001b[0;32m 983\u001b[0m )\n\u001b[0;32m 984\u001b[0m concatenated_grouped_by_data_vars\u001b[39m.\u001b[39mappend(concatenated)\n\u001b[0;32m 986\u001b[0m \u001b[39mreturn\u001b[39;00m merge(\n\u001b[0;32m 987\u001b[0m concatenated_grouped_by_data_vars,\n\u001b[0;32m 988\u001b[0m compat\u001b[39m=\u001b[39mcompat,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 991\u001b[0m combine_attrs\u001b[39m=\u001b[39mcombine_attrs,\n\u001b[0;32m 992\u001b[0m )\n",
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\core\\combine.py:622\u001b[0m, in \u001b[0;36m_combine_single_variable_hypercube\u001b[1;34m(datasets, fill_value, data_vars, coords, compat, join, combine_attrs)\u001b[0m\n\u001b[0;32m 616\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(datasets) \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[0;32m 617\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m 618\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mAt least one Dataset is required to resolve variable names \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 619\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mfor combined hypercube.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 620\u001b[0m )\n\u001b[1;32m--> 622\u001b[0m combined_ids, concat_dims \u001b[39m=\u001b[39m _infer_concat_order_from_coords(\u001b[39mlist\u001b[39;49m(datasets))\n\u001b[0;32m 624\u001b[0m \u001b[39mif\u001b[39;00m fill_value \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 625\u001b[0m \u001b[39m# check that datasets form complete hypercube\u001b[39;00m\n\u001b[0;32m 626\u001b[0m _check_shape_tile_ids(combined_ids)\n",
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\core\\combine.py:145\u001b[0m, in \u001b[0;36m_infer_concat_order_from_coords\u001b[1;34m(datasets)\u001b[0m\n\u001b[0;32m 140\u001b[0m tile_ids \u001b[39m=\u001b[39m [\n\u001b[0;32m 141\u001b[0m tile_id \u001b[39m+\u001b[39m (position,) \u001b[39mfor\u001b[39;00m tile_id, position \u001b[39min\u001b[39;00m \u001b[39mzip\u001b[39m(tile_ids, order)\n\u001b[0;32m 142\u001b[0m ]\n\u001b[0;32m 144\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(datasets) \u001b[39m>\u001b[39m \u001b[39m1\u001b[39m \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m concat_dims:\n\u001b[1;32m--> 145\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m 146\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mCould not find any dimension coordinates to use to \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 147\u001b[0m \u001b[39m\"\u001b[39m\u001b[39morder the datasets for concatenation\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 148\u001b[0m )\n\u001b[0;32m 150\u001b[0m combined_ids \u001b[39m=\u001b[39m \u001b[39mdict\u001b[39m(\u001b[39mzip\u001b[39m(tile_ids, datasets))\n\u001b[0;32m 152\u001b[0m \u001b[39mreturn\u001b[39;00m combined_ids, concat_dims\n",
"\u001b[1;31mValueError\u001b[0m: Could not find any dimension coordinates to use to order the datasets for concatenation"
]
}
],
"source": [
"xr.open_mfdataset(filePath)"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "cannot reindex or align along dimension 'phony_dim_2' because of conflicting dimension sizes: {1088, 0}",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32mf:\\Jianshun\\analyseScript\\20230630_Data_Analysis.ipynb Cell 11\u001b[0m in \u001b[0;36m4\n\u001b[0;32m <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/20230630_Data_Analysis.ipynb#Y503sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m shotNum \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m0071\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/20230630_Data_Analysis.ipynb#Y503sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m filePath \u001b[39m=\u001b[39m folderPath \u001b[39m+\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m/\u001b[39m\u001b[39m\"\u001b[39m \u001b[39m+\u001b[39m shotNum \u001b[39m+\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m/*.h5\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/20230630_Data_Analysis.ipynb#Y503sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m dataSetDict \u001b[39m=\u001b[39m {\n\u001b[0;32m <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/20230630_Data_Analysis.ipynb#Y503sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n\u001b[0;32m <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/20230630_Data_Analysis.ipynb#Y503sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m [\u001b[39m1\u001b[39m]\n\u001b[0;32m <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/20230630_Data_Analysis.ipynb#Y503sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m }\n\u001b[0;32m <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/20230630_Data_Analysis.ipynb#Y503sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m dataSet \u001b[39m=\u001b[39m dataSetDict[\u001b[39m\"\u001b[39m\u001b[39mcamera_0\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[0;32m <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/20230630_Data_Analysis.ipynb#Y503sZmlsZQ%3D%3D?line=10'>11</a>\u001b[0m print_scanAxis(dataSet)\n",
"\u001b[1;32mf:\\Jianshun\\analyseScript\\20230630_Data_Analysis.ipynb Cell 11\u001b[0m in \u001b[0;36m5\n\u001b[0;32m <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/20230630_Data_Analysis.ipynb#Y503sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m shotNum \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m0071\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/20230630_Data_Analysis.ipynb#Y503sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m filePath \u001b[39m=\u001b[39m folderPath \u001b[39m+\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m/\u001b[39m\u001b[39m\"\u001b[39m \u001b[39m+\u001b[39m shotNum \u001b[39m+\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m/*.h5\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/20230630_Data_Analysis.ipynb#Y503sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m dataSetDict \u001b[39m=\u001b[39m {\n\u001b[1;32m----> <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/20230630_Data_Analysis.ipynb#Y503sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n\u001b[0;32m <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/20230630_Data_Analysis.ipynb#Y503sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m [\u001b[39m1\u001b[39m]\n\u001b[0;32m <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/20230630_Data_Analysis.ipynb#Y503sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m }\n\u001b[0;32m <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/20230630_Data_Analysis.ipynb#Y503sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m dataSet \u001b[39m=\u001b[39m dataSetDict[\u001b[39m\"\u001b[39m\u001b[39mcamera_0\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[0;32m <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/20230630_Data_Analysis.ipynb#Y503sZmlsZQ%3D%3D?line=10'>11</a>\u001b[0m print_scanAxis(dataSet)\n",
"File \u001b[1;32mf:\\Jianshun\\analyseScript\\DataContainer\\ReadData.py:226\u001b[0m, in \u001b[0;36mread_hdf5_file\u001b[1;34m(filePath, group, datesetOfGlobal, preprocess, join, parallel, engine, phony_dims, excludeAxis, maxFileNum, **kwargs)\u001b[0m\n\u001b[0;32m 223\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 224\u001b[0m kwargs\u001b[39m.\u001b[39mupdate({\u001b[39m'\u001b[39m\u001b[39mpreprocess\u001b[39m\u001b[39m'\u001b[39m:preprocess})\n\u001b[1;32m--> 226\u001b[0m ds \u001b[39m=\u001b[39m xr\u001b[39m.\u001b[39mopen_mfdataset(fullFilePath, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m 228\u001b[0m newDimKey \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mappend([\u001b[39m'\u001b[39m\u001b[39mx\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39my\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mz\u001b[39m\u001b[39m'\u001b[39m], [ \u001b[39mchr\u001b[39m(i) \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39m97\u001b[39m, \u001b[39m97\u001b[39m\u001b[39m+\u001b[39m\u001b[39m23\u001b[39m)])\n\u001b[0;32m 230\u001b[0m oldDimKey \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39msort(\n\u001b[0;32m 231\u001b[0m [\n\u001b[0;32m 232\u001b[0m key \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 235\u001b[0m ]\n\u001b[0;32m 236\u001b[0m )\n",
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\backends\\api.py:1010\u001b[0m, in \u001b[0;36mopen_mfdataset\u001b[1;34m(paths, chunks, concat_dim, compat, preprocess, engine, data_vars, coords, combine, parallel, join, attrs_file, combine_attrs, **kwargs)\u001b[0m\n\u001b[0;32m 997\u001b[0m combined \u001b[39m=\u001b[39m _nested_combine(\n\u001b[0;32m 998\u001b[0m datasets,\n\u001b[0;32m 999\u001b[0m concat_dims\u001b[39m=\u001b[39mconcat_dim,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1005\u001b[0m combine_attrs\u001b[39m=\u001b[39mcombine_attrs,\n\u001b[0;32m 1006\u001b[0m )\n\u001b[0;32m 1007\u001b[0m \u001b[39melif\u001b[39;00m combine \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mby_coords\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[0;32m 1008\u001b[0m \u001b[39m# Redo ordering from coordinates, ignoring how they were ordered\u001b[39;00m\n\u001b[0;32m 1009\u001b[0m \u001b[39m# previously\u001b[39;00m\n\u001b[1;32m-> 1010\u001b[0m combined \u001b[39m=\u001b[39m combine_by_coords(\n\u001b[0;32m 1011\u001b[0m datasets,\n\u001b[0;32m 1012\u001b[0m compat\u001b[39m=\u001b[39;49mcompat,\n\u001b[0;32m 1013\u001b[0m data_vars\u001b[39m=\u001b[39;49mdata_vars,\n\u001b[0;32m 1014\u001b[0m coords\u001b[39m=\u001b[39;49mcoords,\n\u001b[0;32m 1015\u001b[0m join\u001b[39m=\u001b[39;49mjoin,\n\u001b[0;32m 1016\u001b[0m combine_attrs\u001b[39m=\u001b[39;49mcombine_attrs,\n\u001b[0;32m 1017\u001b[0m )\n\u001b[0;32m 1018\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 1019\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m 1020\u001b[0m \u001b[39m\"\u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m is an invalid option for the keyword argument\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 1021\u001b[0m \u001b[39m\"\u001b[39m\u001b[39m ``combine``\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(combine)\n\u001b[0;32m 1022\u001b[0m )\n",
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\core\\combine.py:975\u001b[0m, in \u001b[0;36mcombine_by_coords\u001b[1;34m(data_objects, compat, data_vars, coords, fill_value, join, combine_attrs, datasets)\u001b[0m\n\u001b[0;32m 973\u001b[0m concatenated_grouped_by_data_vars \u001b[39m=\u001b[39m []\n\u001b[0;32m 974\u001b[0m \u001b[39mfor\u001b[39;00m \u001b[39mvars\u001b[39m, datasets_with_same_vars \u001b[39min\u001b[39;00m grouped_by_vars:\n\u001b[1;32m--> 975\u001b[0m concatenated \u001b[39m=\u001b[39m _combine_single_variable_hypercube(\n\u001b[0;32m 976\u001b[0m \u001b[39mlist\u001b[39;49m(datasets_with_same_vars),\n\u001b[0;32m 977\u001b[0m fill_value\u001b[39m=\u001b[39;49mfill_value,\n\u001b[0;32m 978\u001b[0m data_vars\u001b[39m=\u001b[39;49mdata_vars,\n\u001b[0;32m 979\u001b[0m coords\u001b[39m=\u001b[39;49mcoords,\n\u001b[0;32m 980\u001b[0m compat\u001b[39m=\u001b[39;49mcompat,\n\u001b[0;32m 981\u001b[0m join\u001b[39m=\u001b[39;49mjoin,\n\u001b[0;32m 982\u001b[0m combine_attrs\u001b[39m=\u001b[39;49mcombine_attrs,\n\u001b[0;32m 983\u001b[0m )\n\u001b[0;32m 984\u001b[0m concatenated_grouped_by_data_vars\u001b[39m.\u001b[39mappend(concatenated)\n\u001b[0;32m 986\u001b[0m \u001b[39mreturn\u001b[39;00m merge(\n\u001b[0;32m 987\u001b[0m concatenated_grouped_by_data_vars,\n\u001b[0;32m 988\u001b[0m compat\u001b[39m=\u001b[39mcompat,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 991\u001b[0m combine_attrs\u001b[39m=\u001b[39mcombine_attrs,\n\u001b[0;32m 992\u001b[0m )\n",
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\core\\combine.py:633\u001b[0m, in \u001b[0;36m_combine_single_variable_hypercube\u001b[1;34m(datasets, fill_value, data_vars, coords, compat, join, combine_attrs)\u001b[0m\n\u001b[0;32m 630\u001b[0m _check_dimension_depth_tile_ids(combined_ids)\n\u001b[0;32m 632\u001b[0m \u001b[39m# Concatenate along all of concat_dims one by one to create single ds\u001b[39;00m\n\u001b[1;32m--> 633\u001b[0m concatenated \u001b[39m=\u001b[39m _combine_nd(\n\u001b[0;32m 634\u001b[0m combined_ids,\n\u001b[0;32m 635\u001b[0m concat_dims\u001b[39m=\u001b[39;49mconcat_dims,\n\u001b[0;32m 636\u001b[0m data_vars\u001b[39m=\u001b[39;49mdata_vars,\n\u001b[0;32m 637\u001b[0m coords\u001b[39m=\u001b[39;49mcoords,\n\u001b[0;32m 638\u001b[0m compat\u001b[39m=\u001b[39;49mcompat,\n\u001b[0;32m 639\u001b[0m fill_value\u001b[39m=\u001b[39;49mfill_value,\n\u001b[0;32m 640\u001b[0m join\u001b[39m=\u001b[39;49mjoin,\n\u001b[0;32m 641\u001b[0m combine_attrs\u001b[39m=\u001b[39;49mcombine_attrs,\n\u001b[0;32m 642\u001b[0m )\n\u001b[0;32m 644\u001b[0m \u001b[39m# Check the overall coordinates are monotonically increasing\u001b[39;00m\n\u001b[0;32m 645\u001b[0m \u001b[39mfor\u001b[39;00m dim \u001b[39min\u001b[39;00m concat_dims:\n",
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\core\\combine.py:235\u001b[0m, in \u001b[0;36m_combine_nd\u001b[1;34m(combined_ids, concat_dims, data_vars, coords, compat, fill_value, join, combine_attrs)\u001b[0m\n\u001b[0;32m 231\u001b[0m \u001b[39m# Each iteration of this loop reduces the length of the tile_ids tuples\u001b[39;00m\n\u001b[0;32m 232\u001b[0m \u001b[39m# by one. It always combines along the first dimension, removing the first\u001b[39;00m\n\u001b[0;32m 233\u001b[0m \u001b[39m# element of the tuple\u001b[39;00m\n\u001b[0;32m 234\u001b[0m \u001b[39mfor\u001b[39;00m concat_dim \u001b[39min\u001b[39;00m concat_dims:\n\u001b[1;32m--> 235\u001b[0m combined_ids \u001b[39m=\u001b[39m _combine_all_along_first_dim(\n\u001b[0;32m 236\u001b[0m combined_ids,\n\u001b[0;32m 237\u001b[0m dim\u001b[39m=\u001b[39;49mconcat_dim,\n\u001b[0;32m 238\u001b[0m data_vars\u001b[39m=\u001b[39;49mdata_vars,\n\u001b[0;32m 239\u001b[0m coords\u001b[39m=\u001b[39;49mcoords,\n\u001b[0;32m 240\u001b[0m compat\u001b[39m=\u001b[39;49mcompat,\n\u001b[0;32m 241\u001b[0m fill_value\u001b[39m=\u001b[39;49mfill_value,\n\u001b[0;32m 242\u001b[0m join\u001b[39m=\u001b[39;49mjoin,\n\u001b[0;32m 243\u001b[0m combine_attrs\u001b[39m=\u001b[39;49mcombine_attrs,\n\u001b[0;32m 244\u001b[0m )\n\u001b[0;32m 245\u001b[0m (combined_ds,) \u001b[39m=\u001b[39m combined_ids\u001b[39m.\u001b[39mvalues()\n\u001b[0;32m 246\u001b[0m \u001b[39mreturn\u001b[39;00m combined_ds\n",
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\core\\combine.py:270\u001b[0m, in \u001b[0;36m_combine_all_along_first_dim\u001b[1;34m(combined_ids, dim, data_vars, coords, compat, fill_value, join, combine_attrs)\u001b[0m\n\u001b[0;32m 268\u001b[0m combined_ids \u001b[39m=\u001b[39m \u001b[39mdict\u001b[39m(\u001b[39msorted\u001b[39m(group))\n\u001b[0;32m 269\u001b[0m datasets \u001b[39m=\u001b[39m combined_ids\u001b[39m.\u001b[39mvalues()\n\u001b[1;32m--> 270\u001b[0m new_combined_ids[new_id] \u001b[39m=\u001b[39m _combine_1d(\n\u001b[0;32m 271\u001b[0m datasets, dim, compat, data_vars, coords, fill_value, join, combine_attrs\n\u001b[0;32m 272\u001b[0m )\n\u001b[0;32m 273\u001b[0m \u001b[39mreturn\u001b[39;00m new_combined_ids\n",
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\core\\combine.py:293\u001b[0m, in \u001b[0;36m_combine_1d\u001b[1;34m(datasets, concat_dim, compat, data_vars, coords, fill_value, join, combine_attrs)\u001b[0m\n\u001b[0;32m 291\u001b[0m \u001b[39mif\u001b[39;00m concat_dim \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 292\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m--> 293\u001b[0m combined \u001b[39m=\u001b[39m concat(\n\u001b[0;32m 294\u001b[0m datasets,\n\u001b[0;32m 295\u001b[0m dim\u001b[39m=\u001b[39;49mconcat_dim,\n\u001b[0;32m 296\u001b[0m data_vars\u001b[39m=\u001b[39;49mdata_vars,\n\u001b[0;32m 297\u001b[0m coords\u001b[39m=\u001b[39;49mcoords,\n\u001b[0;32m 298\u001b[0m compat\u001b[39m=\u001b[39;49mcompat,\n\u001b[0;32m 299\u001b[0m fill_value\u001b[39m=\u001b[39;49mfill_value,\n\u001b[0;32m 300\u001b[0m join\u001b[39m=\u001b[39;49mjoin,\n\u001b[0;32m 301\u001b[0m combine_attrs\u001b[39m=\u001b[39;49mcombine_attrs,\n\u001b[0;32m 302\u001b[0m )\n\u001b[0;32m 303\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mValueError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n\u001b[0;32m 304\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mencountered unexpected variable\u001b[39m\u001b[39m\"\u001b[39m \u001b[39min\u001b[39;00m \u001b[39mstr\u001b[39m(err):\n",
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\core\\concat.py:251\u001b[0m, in \u001b[0;36mconcat\u001b[1;34m(objs, dim, data_vars, coords, compat, positions, fill_value, join, combine_attrs)\u001b[0m\n\u001b[0;32m 239\u001b[0m \u001b[39mreturn\u001b[39;00m _dataarray_concat(\n\u001b[0;32m 240\u001b[0m objs,\n\u001b[0;32m 241\u001b[0m dim\u001b[39m=\u001b[39mdim,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 248\u001b[0m combine_attrs\u001b[39m=\u001b[39mcombine_attrs,\n\u001b[0;32m 249\u001b[0m )\n\u001b[0;32m 250\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(first_obj, Dataset):\n\u001b[1;32m--> 251\u001b[0m \u001b[39mreturn\u001b[39;00m _dataset_concat(\n\u001b[0;32m 252\u001b[0m objs,\n\u001b[0;32m 253\u001b[0m dim\u001b[39m=\u001b[39;49mdim,\n\u001b[0;32m 254\u001b[0m data_vars\u001b[39m=\u001b[39;49mdata_vars,\n\u001b[0;32m 255\u001b[0m coords\u001b[39m=\u001b[39;49mcoords,\n\u001b[0;32m 256\u001b[0m compat\u001b[39m=\u001b[39;49mcompat,\n\u001b[0;32m 257\u001b[0m positions\u001b[39m=\u001b[39;49mpositions,\n\u001b[0;32m 258\u001b[0m fill_value\u001b[39m=\u001b[39;49mfill_value,\n\u001b[0;32m 259\u001b[0m join\u001b[39m=\u001b[39;49mjoin,\n\u001b[0;32m 260\u001b[0m combine_attrs\u001b[39m=\u001b[39;49mcombine_attrs,\n\u001b[0;32m 261\u001b[0m )\n\u001b[0;32m 262\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 263\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mTypeError\u001b[39;00m(\n\u001b[0;32m 264\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mcan only concatenate xarray Dataset and DataArray \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 265\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mobjects, got \u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mtype\u001b[39m(first_obj)\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m\n\u001b[0;32m 266\u001b[0m )\n",
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\core\\concat.py:483\u001b[0m, in \u001b[0;36m_dataset_concat\u001b[1;34m(datasets, dim, data_vars, coords, compat, positions, fill_value, join, combine_attrs)\u001b[0m\n\u001b[0;32m 480\u001b[0m \u001b[39m# Make sure we're working on a copy (we'll be loading variables)\u001b[39;00m\n\u001b[0;32m 481\u001b[0m datasets \u001b[39m=\u001b[39m [ds\u001b[39m.\u001b[39mcopy() \u001b[39mfor\u001b[39;00m ds \u001b[39min\u001b[39;00m datasets]\n\u001b[0;32m 482\u001b[0m datasets \u001b[39m=\u001b[39m \u001b[39mlist\u001b[39m(\n\u001b[1;32m--> 483\u001b[0m align(\u001b[39m*\u001b[39;49mdatasets, join\u001b[39m=\u001b[39;49mjoin, copy\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m, exclude\u001b[39m=\u001b[39;49m[dim], fill_value\u001b[39m=\u001b[39;49mfill_value)\n\u001b[0;32m 484\u001b[0m )\n\u001b[0;32m 486\u001b[0m dim_coords, dims_sizes, coord_names, data_names, vars_order \u001b[39m=\u001b[39m _parse_datasets(\n\u001b[0;32m 487\u001b[0m datasets\n\u001b[0;32m 488\u001b[0m )\n\u001b[0;32m 489\u001b[0m dim_names \u001b[39m=\u001b[39m \u001b[39mset\u001b[39m(dim_coords)\n",
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\core\\alignment.py:787\u001b[0m, in \u001b[0;36malign\u001b[1;34m(join, copy, indexes, exclude, fill_value, *objects)\u001b[0m\n\u001b[0;32m 591\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m 592\u001b[0m \u001b[39mGiven any number of Dataset and/or DataArray objects, returns new\u001b[39;00m\n\u001b[0;32m 593\u001b[0m \u001b[39mobjects with aligned indexes and dimension sizes.\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 777\u001b[0m \n\u001b[0;32m 778\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m 779\u001b[0m aligner \u001b[39m=\u001b[39m Aligner(\n\u001b[0;32m 780\u001b[0m objects,\n\u001b[0;32m 781\u001b[0m join\u001b[39m=\u001b[39mjoin,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 785\u001b[0m fill_value\u001b[39m=\u001b[39mfill_value,\n\u001b[0;32m 786\u001b[0m )\n\u001b[1;32m--> 787\u001b[0m aligner\u001b[39m.\u001b[39;49malign()\n\u001b[0;32m 788\u001b[0m \u001b[39mreturn\u001b[39;00m aligner\u001b[39m.\u001b[39mresults\n",
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\core\\alignment.py:573\u001b[0m, in \u001b[0;36mAligner.align\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 571\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39massert_no_index_conflict()\n\u001b[0;32m 572\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39malign_indexes()\n\u001b[1;32m--> 573\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49massert_unindexed_dim_sizes_equal()\n\u001b[0;32m 575\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mjoin \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39moverride\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[0;32m 576\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39moverride_indexes()\n",
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\core\\alignment.py:472\u001b[0m, in \u001b[0;36mAligner.assert_unindexed_dim_sizes_equal\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 470\u001b[0m add_err_msg \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 471\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(sizes) \u001b[39m>\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m--> 472\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m 473\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mcannot reindex or align along dimension \u001b[39m\u001b[39m{\u001b[39;00mdim\u001b[39m!r}\u001b[39;00m\u001b[39m \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 474\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mbecause of conflicting dimension sizes: \u001b[39m\u001b[39m{\u001b[39;00msizes\u001b[39m!r}\u001b[39;00m\u001b[39m\"\u001b[39m \u001b[39m+\u001b[39m add_err_msg\n\u001b[0;32m 475\u001b[0m )\n",
"\u001b[1;31mValueError\u001b[0m: cannot reindex or align along dimension 'phony_dim_2' because of conflicting dimension sizes: {1088, 0}"
]
}
],
"source": [
"shotNum = \"0071\"\n",
"filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n",
"\n",
"dataSetDict = {\n",
" dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n",
" for i in [1]\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 = (535, 995)\n",
"imageAnalyser.span = (350, 350)\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.ylim([0, 3500])\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# ALS"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# 0 - 0.025 A"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The detected scaning axes and values are: \n",
"\n",
"{'als_final_current': array([0. , 0.0009, 0.0018, 0.0027, 0.0036, 0.0045, 0.0054, 0.0063,\n",
" 0.0072, 0.0081, 0.009 , 0.0099, 0.0108, 0.0117, 0.0126, 0.0135,\n",
" 0.0144, 0.0153, 0.0162, 0.0171, 0.018 , 0.0189, 0.0198, 0.0207,\n",
" 0.0216, 0.0225, 0.0234, 0.0243]), 'runs': array([0., 1.])}\n"
]
},
{
"data": {
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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"shotNum = \"0017\"\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 = (535, 995)\n",
"imageAnalyser.span = (350, 350)\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.ylim([0, 3500])\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"Ncount_mean_total = copy.copy(Ncount_mean)\n",
"Ncount_std_total = copy.copy(Ncount_std)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# 0.025 - 0.050 A"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The detected scaning axes and values are: \n",
"\n",
"{'als_final_current': array([0.025 , 0.0259, 0.0268, 0.0277, 0.0286, 0.0295, 0.0304, 0.0313,\n",
" 0.0322, 0.0331, 0.034 , 0.0349, 0.0358, 0.0367, 0.0376, 0.0385,\n",
" 0.0394, 0.0403, 0.0412, 0.0421, 0.043 , 0.0439, 0.0448, 0.0457,\n",
" 0.0466, 0.0475, 0.0484, 0.0493]), 'runs': array([0., 1.])}\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"shotNum = \"0018\"\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 = (535, 995)\n",
"imageAnalyser.span = (350, 350)\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.ylim([0, 3500])\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"Ncount_mean_total = xr.concat((Ncount_mean_total, Ncount_mean), dim='als_final_current')\n",
"Ncount_std_total = xr.concat((Ncount_std_total, Ncount_std), dim='als_final_current')"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# 0.050 - 0.075 A"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The detected scaning axes and values are: \n",
"\n",
"{'als_final_current': array([0.05 , 0.0509, 0.0518, 0.0527, 0.0536, 0.0545, 0.0554, 0.0563,\n",
" 0.0572, 0.0581, 0.059 , 0.0599, 0.0608, 0.0617, 0.0626, 0.0635,\n",
" 0.0644, 0.0653, 0.0662, 0.0671, 0.068 , 0.0689, 0.0698, 0.0707,\n",
" 0.0716, 0.0725, 0.0734, 0.0743, 0.0752, 0.0761]), 'runs': array([0., 1.])}\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"shotNum = \"0019\"\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 = (535, 995)\n",
"imageAnalyser.span = (350, 350)\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.ylim([0, 3500])\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"Ncount_mean_total = xr.concat((Ncount_mean_total, Ncount_mean), dim='als_final_current')\n",
"Ncount_std_total = xr.concat((Ncount_std_total, Ncount_std), dim='als_final_current')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"Ncount_mean_total.plot.errorbar(ax=ax, yerr = Ncount_std_total, fmt='ob')\n",
"plt.ylim([0, 3500])\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# 0.075 - 0.100 A"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The detected scaning axes and values are: \n",
"\n",
"{'als_final_current': array([0.075 , 0.0759, 0.0768, 0.0777, 0.0786, 0.0795, 0.0804, 0.0813,\n",
" 0.0822, 0.0831, 0.084 , 0.0849, 0.0858, 0.0867, 0.0876, 0.0885,\n",
" 0.0894, 0.0903, 0.0912, 0.0921, 0.093 , 0.0939, 0.0948, 0.0957,\n",
" 0.0966, 0.0975, 0.0984, 0.0993, 0.1002, 0.1011]), 'runs': array([0., 1.])}\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"shotNum = \"0020\"\n",
"filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n",
"\n",
"dataSetDict = {\n",
" dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n",
" for i in [0]\n",
"}\n",
"\n",
"dataSet = dataSetDict[\"camera_0\"]\n",
"\n",
"print_scanAxis(dataSet)\n",
"\n",
"scanAxis = get_scanAxis(dataSet)\n",
"\n",
"dataSet = auto_rechunk(dataSet)\n",
"\n",
"dataSet = imageAnalyser.get_absorption_images(dataSet)\n",
"\n",
"imageAnalyser.center = (535, 995)\n",
"imageAnalyser.span = (350, 350)\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.ylim([0, 4500])\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"Ncount_mean_total = xr.concat((Ncount_mean_total, Ncount_mean), dim='als_final_current')\n",
"Ncount_std_total = xr.concat((Ncount_std_total, Ncount_std), dim='als_final_current')"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# 0.100 - 0.125 A"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The detected scaning axes and values are: \n",
"\n",
"{'als_final_current': array([0.1 , 0.1009, 0.1018, 0.1027, 0.1036, 0.1045, 0.1054, 0.1063,\n",
" 0.1072, 0.1081, 0.109 , 0.1099, 0.1108, 0.1117, 0.1126, 0.1135,\n",
" 0.1144, 0.1153, 0.1162, 0.1171, 0.118 , 0.1189, 0.1198, 0.1207,\n",
" 0.1216, 0.1225, 0.1234, 0.1243, 0.1252, 0.1261]), 'runs': array([0., 1.])}\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"shotNum = \"0021\"\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 = (535, 995)\n",
"imageAnalyser.span = (350, 350)\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.ylim([0, 5000])\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"Ncount_mean_total = xr.concat((Ncount_mean_total, Ncount_mean), dim='als_final_current')\n",
"Ncount_std_total = xr.concat((Ncount_std_total, Ncount_std), dim='als_final_current')"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# 0.125 - 0.150 A"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The detected scaning axes and values are: \n",
"\n",
"{'als_final_current': array([0.125 , 0.1259, 0.1268, 0.1277, 0.1286, 0.1295, 0.1304, 0.1313,\n",
" 0.1322, 0.1331, 0.134 , 0.1349, 0.1358, 0.1367, 0.1376, 0.1385,\n",
" 0.1394, 0.1403, 0.1412, 0.1421, 0.143 , 0.1439, 0.1448, 0.1457,\n",
" 0.1466, 0.1475, 0.1484, 0.1493, 0.1502, 0.1511]), 'runs': array([0., 1.])}\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"shotNum = \"0022\"\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 = (535, 995)\n",
"imageAnalyser.span = (350, 350)\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.ylim([0, 5000])\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"Ncount_mean_total = xr.concat((Ncount_mean_total, Ncount_mean), dim='als_final_current')\n",
"Ncount_std_total = xr.concat((Ncount_std_total, Ncount_std), dim='als_final_current')"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# 0.150 - 0.175 A"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The detected scaning axes and values are: \n",
"\n",
"{'als_final_current': array([0.15 , 0.1509, 0.1518, 0.1527, 0.1536, 0.1545, 0.1554, 0.1563,\n",
" 0.1572, 0.1581, 0.159 , 0.1599, 0.1608, 0.1617, 0.1626, 0.1635,\n",
" 0.1644, 0.1653, 0.1662, 0.1671, 0.168 , 0.1689, 0.1698, 0.1707,\n",
" 0.1716, 0.1725, 0.1734, 0.1743, 0.1752, 0.1761]), 'runs': array([0., 1.])}\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"shotNum = \"0023\"\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 = (535, 995)\n",
"imageAnalyser.span = (350, 350)\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.ylim([0, 4500])\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"Ncount_mean_total = xr.concat((Ncount_mean_total, Ncount_mean), dim='als_final_current')\n",
"Ncount_std_total = xr.concat((Ncount_std_total, Ncount_std), dim='als_final_current')"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# 0.175 - 0.200 A"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The detected scaning axes and values are: \n",
"\n",
"{'als_final_current': array([0.175 , 0.1759, 0.1768, 0.1777, 0.1786, 0.1795, 0.1804, 0.1813,\n",
" 0.1822, 0.1831, 0.184 , 0.1849, 0.1858, 0.1867, 0.1876, 0.1885,\n",
" 0.1894, 0.1903, 0.1912, 0.1921, 0.193 , 0.1939, 0.1948, 0.1957,\n",
" 0.1966, 0.1975, 0.1984, 0.1993, 0.2002, 0.2011, 0.202 ]), 'runs': array([0., 1.])}\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"shotNum = \"0024\"\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 = (535, 995)\n",
"imageAnalyser.span = (350, 350)\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.ylim([0, 4500])\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"Ncount_mean_total = xr.concat((Ncount_mean_total, Ncount_mean), dim='als_final_current')\n",
"Ncount_std_total = xr.concat((Ncount_std_total, Ncount_std), dim='als_final_current')"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# 0.200 - 0.225 A"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The detected scaning axes and values are: \n",
"\n",
"{'als_final_current': array([0.2 , 0.2009, 0.2018, 0.2027, 0.2036, 0.2045, 0.2054, 0.2063,\n",
" 0.2072, 0.2081, 0.209 , 0.2099, 0.2108, 0.2117, 0.2126, 0.2135,\n",
" 0.2144, 0.2153, 0.2162, 0.2171, 0.218 , 0.2189, 0.2198, 0.2207,\n",
" 0.2216, 0.2225, 0.2234, 0.2243, 0.2252, 0.2261]), 'runs': array([0., 1.])}\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"shotNum = \"0025\"\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 = (535, 995)\n",
"imageAnalyser.span = (350, 350)\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.ylim([0, 4500])\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"Ncount_mean_total = xr.concat((Ncount_mean_total, Ncount_mean), dim='als_final_current')\n",
"Ncount_std_total = xr.concat((Ncount_std_total, Ncount_std), dim='als_final_current')"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# 0.220 - 0.250 A"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The detected scaning axes and values are: \n",
"\n",
"{'als_final_current': array([0.22 , 0.2209, 0.2218, 0.2227, 0.2236, 0.2245, 0.2254, 0.2263,\n",
" 0.2272, 0.2281, 0.229 , 0.2299, 0.2308, 0.2317, 0.2326, 0.2335,\n",
" 0.2344, 0.2353, 0.2362, 0.2371, 0.238 , 0.2389, 0.2398, 0.2407,\n",
" 0.2416, 0.2425, 0.2434, 0.2443, 0.2452, 0.2461, 0.247 , 0.2479,\n",
" 0.2488, 0.2497, 0.2506, 0.2515]), 'runs': array([0., 1.])}\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"shotNum = \"0026\"\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 = (535, 995)\n",
"imageAnalyser.span = (350, 350)\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.ylim([0, 3500])\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"Ncount_mean_total = xr.concat((Ncount_mean_total, Ncount_mean), dim='als_final_current')\n",
"Ncount_std_total = xr.concat((Ncount_std_total, Ncount_std), dim='als_final_current')"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# 0.250 - 0.275 A"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The detected scaning axes and values are: \n",
"\n",
"{'als_final_current': array([0.25 , 0.2509, 0.2518, 0.2527, 0.2536, 0.2545, 0.2554, 0.2563,\n",
" 0.2572, 0.2581, 0.259 , 0.2599, 0.2608, 0.2617, 0.2626, 0.2635,\n",
" 0.2644, 0.2653, 0.2662, 0.2671, 0.268 , 0.2689, 0.2698, 0.2707,\n",
" 0.2716, 0.2725, 0.2734, 0.2743, 0.2752, 0.2761, 0.277 ]), 'runs': array([0., 1.])}\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"shotNum = \"0027\"\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 = (535, 995)\n",
"imageAnalyser.span = (350, 350)\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.ylim([0, 3500])\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"Ncount_mean_total = xr.concat((Ncount_mean_total, Ncount_mean), dim='als_final_current')\n",
"Ncount_std_total = xr.concat((Ncount_std_total, Ncount_std), dim='als_final_current')"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# 0.270 - 0.300 A"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The detected scaning axes and values are: \n",
"\n",
"{'als_final_current': array([0.27 , 0.2709, 0.2718, 0.2727, 0.2736, 0.2745, 0.2754, 0.2763,\n",
" 0.2772, 0.2781, 0.279 , 0.2799, 0.2808, 0.2817, 0.2826, 0.2835,\n",
" 0.2844, 0.2853, 0.2862, 0.2871, 0.288 , 0.2889, 0.2898, 0.2907,\n",
" 0.2916, 0.2925, 0.2934, 0.2943, 0.2952, 0.2961, 0.297 , 0.2979,\n",
" 0.2988, 0.2997, 0.3006]), 'runs': array([0., 1.])}\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"shotNum = \"0028\"\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 = (535, 995)\n",
"imageAnalyser.span = (350, 350)\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.ylim([0, 3000])\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"Ncount_mean_total = xr.concat((Ncount_mean_total, Ncount_mean), dim='als_final_current')\n",
"Ncount_std_total = xr.concat((Ncount_std_total, Ncount_std), dim='als_final_current')"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# 0.300 - 0.325 A"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The detected scaning axes and values are: \n",
"\n",
"{'als_final_current': array([0.3 , 0.3009, 0.3018, 0.3027, 0.3036, 0.3045, 0.3054, 0.3063,\n",
" 0.3072, 0.3081, 0.309 , 0.3099, 0.3108, 0.3117, 0.3126, 0.3135,\n",
" 0.3144, 0.3153, 0.3162, 0.3171, 0.318 , 0.3189, 0.3198, 0.3207,\n",
" 0.3216, 0.3225, 0.3234, 0.3243, 0.3252, 0.3261, 0.327 ]), 'runs': array([0., 1.])}\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"shotNum = \"0029\"\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 = (535, 995)\n",
"imageAnalyser.span = (350, 350)\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.ylim([0, 3500])\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"Ncount_mean_total = xr.concat((Ncount_mean_total, Ncount_mean), dim='als_final_current')\n",
"Ncount_std_total = xr.concat((Ncount_std_total, Ncount_std), dim='als_final_current')"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# 0.325 - 0.350 A"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The detected scaning axes and values are: \n",
"\n",
"{'als_final_current': array([0.325 , 0.3259, 0.3268, 0.3277, 0.3286, 0.3295, 0.3304, 0.3313,\n",
" 0.3322, 0.3331, 0.334 , 0.3349, 0.3358, 0.3367, 0.3376, 0.3385,\n",
" 0.3394, 0.3403, 0.3412, 0.3421, 0.343 , 0.3439, 0.3448, 0.3457,\n",
" 0.3466, 0.3475, 0.3484, 0.3493, 0.3502, 0.3511]), 'runs': array([0., 1.])}\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"shotNum = \"0030\"\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 = (535, 995)\n",
"imageAnalyser.span = (350, 350)\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.ylim([0, 3500])\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"Ncount_mean_total = xr.concat((Ncount_mean_total, Ncount_mean), dim='als_final_current')\n",
"Ncount_std_total = xr.concat((Ncount_std_total, Ncount_std), dim='als_final_current')"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# 0.350 - 0.375 A"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The detected scaning axes and values are: \n",
"\n",
"{'als_final_current': array([0.35 , 0.3509, 0.3518, 0.3527, 0.3536, 0.3545, 0.3554, 0.3563,\n",
" 0.3572, 0.3581, 0.359 , 0.3599, 0.3608, 0.3617, 0.3626, 0.3635,\n",
" 0.3644, 0.3653, 0.3662, 0.3671, 0.368 , 0.3689, 0.3698, 0.3707,\n",
" 0.3716, 0.3725, 0.3734, 0.3743, 0.3752, 0.3761, 0.377 ]), 'runs': array([0., 1.])}\n"
]
},
{
"data": {
"image/png": 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8LCSk/AiDYZT/P9BUuHPKHO5Y/pHPJ+kFAAD+7cKF8lOb//lPedAaPLh2p/8rpsw5dMj1dW8WS/n6pjJljrtw5A2Ax/EXM2BemzZJXbtKQ4dKkyaV/7e203w0pilzKt9U8de/+vYmC8Ib4AUXLki7d0uvvFL+36Z2Z1Xl7f373y1NbvsBs3LHNB+NYcocf5unjvAGeFhD/mptDC790LvllgDdc0+8Nm+u5qsHAPgFd07zkZgoHTwo5eZKGzaU/9csU+b44zx1hDfAg/zxTe9N1W1/YWGQJk5s3ui3H41HUzz17+5pPpo3l+LipDvuKP+vWU6V+uM8dYQ3wEP89U3vLZfbfqn8qJtZtr8p/uKGM3+63slbmObDf+epI7w1AU39eitf8dc3vbfUvP0Wr20/7wHz8+XPsLrrnRr7qX93TvPhDg0N0KdO/fhH2PbttXu+vwZYwlsjt2lT+YdMU73eSvLdURN/fdN7i79sf1O/5rAx8OXn2OUufZg4sbk++MA7ycUXR/4qpvm49C7RChaL1Lmzd6b52LzZ0qAbBup7w4G/BdgKhLdGrOJD59KjH03leitf86c3vS8CrD9sf1O/5rAx8OXnWG0ufVizpo/Hg5Sv7nT0l2k+PvggQhMnNq/3+7ghnwP+FGArI7w1Uo3peiuzXm/kr296b6l5+41ab3999oHG9B5wBzOeOvb1z7A2p/6PHw/W3//uudOnvv4DpGKaj06dnJd7a5qPCxek55/vW+99oKH7kL8E2EsR3hqppn69lbvV5xefv77pveVy2y+Vf2p6cvt5D/zIHy6fqM9pP3f+DOvzB4CvT/37OrxWSEyUDhzwzTQff/+7RYWFLVVxk9OlatoH3LEP+eM8dYS3RsrXHzqNSUN+8fn6r1Zfq+5DLzz8nF599YJHt5/3QDl3nXZsyJG7+p728/XP0Nen/v3pDxBfTfPR0H3AXftQYqL02Wc/Pt62zbfz1BHeGilff+g0Fu74xefLv1r9waUfem+/XaY//Slb48a5nEPEbXgPuO/ITUNu+mjIaT9f/wxrc+o/PLxYN97omX3Z1+HVHzR0H3DnPlQ5sN50k2/PmhDeGqmmfr1VZfW9S8udpyzMODmlO7VuXV4zw5BGjjS8sv28B8pPOTX0yE1DwldD30O+/hnW5tKHX/+6wGP7s6/Dqz+48UZDYWHnZLG4Dsg17QO+3oc8hfBmAmfPSi1aBGrs2DG1vlajqV9vVaEhd2n50ykL1J073wNmnaC1oUduGhq+Gvoe8oef4eWud3r11QuKjfXcYa/GGjzqonlzKTk5X1L99oHG+ruQ8NaINZbrrer7odvQu7Q4ZeE/GvqLtyHvAX/7Quq6aOiRm4aGL3e8h/zhZ1jd9U51OfVfnwli3Rk8zHrXviTFxv5Hr756od43DPjjDQcNZsAtTp48aUgyTp486fa+z5ypOOFkGCdOnK/z88vKDCM31zA2bCj/b1mZ24foMX/+s2F06vTj9kuGcdVV5csvp6ysvF3l51X+Z7EYRufOzrU4f/68sWXLFuP8+fIa5+ZW//zK/3JzPbb5blN5HzpzxrdjubTONanvPlBZfd8Df/5z+b7iav+xWOo2Bm+qqPG5c+eNq65yvQ3VvQ8q27Chdu+BDRtcP9+d7yF//BnWdl9u6D7s6vmdO9dt7CdP/vjcbdvM83ugco0bug0Nfb43PkdrmyUIb27iz+HNrBryoVufXxqXfhBXBMD6/uLzJ2YNb74MT/X5A8BfVK5xRQ0vraOn3keV+fo95OmfYW32ZXftww0JHu74A8hXKtfY159j/hTeOG0Kj/PFBKvuOF3TWK+VMAtfz3HVWK55bMhpx4Zec+Xr95Cvf4buvumpQl3udPT1JL/wDMIb/FJDP3TddZdWY7lu0Ix8/Yu3MV3zWN/patwRvnx5vZGvf4a+3od9/QcQPIfwBr/U0A9dd96l5Q/ztNXnYmez8/Uv3sY2TUN9p6txxx8wvprg1Nc/Q1/vw74Oj41NSMiPJ55DQnw7lgDfvjzM4MKF8jf3f/5T/iE3eLDnTxU29EO34ojB+PHlgafyX571OV1T8YvPFzZtklJSfnw8alT5L86VKxv3kT9f/+Kt+APg0CHXRy4slvL1jXmahgqJidKYMQ37HPDFBKe+/hn6eh/2dXiE53DkDZflq+9EdMeRs8Zwe3hTvl7F13Nc+fp6LX9jxommff0z9PU+7OvwCM8hvKFa7vpOxPpw14euv30fXV009etVfP2LV2ocfwA0db78Gfp6H/Z1eITnEN5MoPIv57//3eKVX9buDA6emNm8Lh+6/vR9dHXB9Sr+EZ7M/AcAyvnyZ9iUw6O7+dM1Z75GePNzl84MfsstAV45bemu4OCpmc2bwi9Od16vYtavd5L8Yx8w6x8A+JEvf4aJidLBg7656ckf/gCC+xHe/Jgvr3dyR3Bw1/ib6i9Od12vYuavd6rQVPcBNB6+vGbQH/4AgnsR3vyUr693amhw8PX4GwN3XK/SlG94ACpr6qfcWrf+cft/8Qv+ADI7wpuf8vX1Tg0NDr4ef2PQ0OtVCNAA0DgR3vyUr+fnaWhw8PX4KzPzX9wNuV6FAA0AjRPhzU/5w/w8DZlZ3Z3jN3P4cof6Xq/iTwEaAOA+fMOCn/L1zOAV6juzur+Mv7GouF6lLvzhDwAAgPtx5M1P+dP8PPW5S8qfxt9UNaYJOpv60VcAqIzw5sfMPj+P2cdvdgToxuPCBWn3bumVV8r/y00mQNNGePNzl17v9PbbZaaan4f5hXyLAO0evjzy56vvFwbgv3we3t59911NnTpVkZGRCgkJUadOnTRmzBh9/PHHTu2mTJkii8VS5V9kZKTLfletWqXIyEhZrVZ1795d6enpstvtVdodPXpUU6ZMUXh4uIKDgxUbG6ucnByPbGt9VT4ycuONhumOlDDBqm8RoM3Ll98vDMB/+fyGhWeeeUaFhYWaOXOmevfurWPHjumJJ57QoEGDtHPnTg0bNszRtmXLlnr33Xednt+yZcsqfS5dulQLFy5Uamqq4uPjlZeXp7S0NB06dEirV692tCstLdXw4cP1ww8/aOXKlWrXrp0yMzOVkJCgXbt2aciQIZ7bcMCLCNDmU9M8fRZL+Tx9Y8aY4+dZcfQSQMP5PLxlZmaqXbt2TssSEhLUo0cPZWRkOIW3Zs2aadCgQZftr7CwUEuWLNG0adOUkZEhSYqLi5PdbldaWppmzZql3v/3XUFr1qxRQUGB3n//fcXGxkqShg4dqujoaM2dO1d79+5156YCQK3VZZ6+uDivDQuAH/D5adNLg5skhYaGqnfv3vr222/r3N+OHTtUUlKipKQkp+VJSUkyDENbtmxxLNu8ebN69erlCG6SFBAQoMmTJ+ujjz7SoUu/UwgAvIR5+gBUx+dH3lw5efKkPvnkE6ejbpJ07tw5dejQQceOHVNERITGjh2rRx55RDabzdGmoKBAktS3b1+n50ZERCg8PNyxvqLtYBfzJERFRUmSPv30U3W6dIba/1NaWqrS0lLH41OnTkmS7Ha7y2vrGqK8u8BK/bu1e48zy/grfm7u/vn5A3/6GTTmOrtT27YW1eYjum3bMtntzucjqbF3eLPO/vQe9qamti/Xdjv9MrzNmDFDZ8+e1YIFCxzLoqOjFR0drT59+kiS9uzZoyeffFI5OTnKy8tTaGiopPLTplarVSEubgmz2WwqLCx0PC4sLHQKfpXbVayvzrJly5Senl5l+TvvvKPg4OBabmntlJQ0lzRaUvkNHkFB5psnoOKA5549Ph1GrWRnZ/t6CG5XeR/auXOnX+xDjbHO7nThghQWFq/CwiBJribrMxQefk6nTmVr2zbXfVBj7/BGnf3xPexNTWVfLi4urlU7vwtvCxcu1Msvv6xVq1YpJibGsfzBBx90ajdixAhdf/31Gj9+vJ577jmn9ZbqZiV1sa4ubSubN2+eZs+e7Xh86tQpde7cWfHx8WrdunW1z6uPs2d//P9hw4apTZtAt/aPcna7XdnZ2RoxYoQCAxtXjSvvQyNHjvTpRLeNuc7u9vTTFk2cKEmGDOPHzyOLpfxIW2ZmC91yy6gqz6PG3uHtOp8/X3FUZqTHX8tfNLV9ueIsXk38Krylp6dryZIlWrp0qe6///4a248bN04hISH68MMPHcvCwsJUUlKi4uLiKkfAioqKnAJhWFiYy6NrRUVFkuTyqFwFq9Uqq9VaZXlgYKDbd7DK3XmifzhrjDWuug/5bizO4/CDgfixX/5SCggov+u08s0LV11l0VNPSYmJl/8Ip8beQZ09r6nUuLbb6PMbFiqkp6dr8eLFWrx4sebPn1/r5xmGoWbNftyMimvd8vPzndodOXJEx48fd5x2rWh7abvKz63cFmjKTp0qn5rCYpG2b2eGf29KTJQOHJByc6UNG8r/yzx9QNPmF+Ht0Ucf1eLFi5WWlqaHH3641s974403VFxc7DR9SEJCgoKCgpSVleXUNisrSxaLRWPHjnUsGzdunPbt2+c0JUhZWZnWr1+vgQMHquOl09L7SEhI+eHyLVve5Hsd4XWbNkn/N7uOJGnUKGb497b6fL8wgMbL56dNn3jiCS1atEgJCQm6+eabnU6BStKgQYN08OBBTZo0SRMnTlSPHj1ksVi0Z88ePfXUU7ruuuuUnJzsaG+z2ZSWlqaFCxfKZrM5JuldvHixkpOTHXO8SdLUqVOVmZmpCRMmaPny5WrXrp2efvppff7559q1a5fXagD4q4oZ/i+dXLVihn++YgsAvM/n4e3tt9+WVD4/244dO6qsNwxDrVu3Vvv27fX73/9e33//vS5cuKCuXbsqJSVF8+fPr3Jn6YIFC9SqVStlZmZqxYoV6tChg1JTU53uXpXKr1vLycnR3Llz9cADD6i4uFj9+vXT9u3b+XYFNHmNbYZ/AGgsfB7edu/eXWObK6+8UpvqeI4mJSVFKSkpNbZr37691q1bV6e+gaaAGf4BwD/5xTVvAPwPM/wDgH8ivAFwKSLCve0AAO5BeAPg0uDB0lVXlV/b5orFInXuXN4OAOA9hDegCQgJKb9GzTBU6+lmmjeXVq4s//9LA1zF46ee4mYFAPA2whuAaiUmlk8HcumUh1ddxTQhAOArPr/bFIB/S0wsnw7kb38rvzkhIqL8VClH3ADANwhvAGpUMcM/AMD3OG0KAABgIoQ3AAAAEyG8AQAAmAjhDQAAwEQIbwAAACZCeAMAADARwhsAAICJEN4AAABMhPAGAABgIoQ3AAAAEyG8AQAAmAjhDQAAwEQIbwAAACZCeAMAADARwhsAAICJEN4AAABMhPAGAABgIoQ3AAAAEyG8AQAAmAjhDQAAwEQIbwAAACZCeAMAADARwhsAAICJEN4AAABMhPAGAABgIoQ3AAAAEyG8AQAAmAjhDQAAwEQIbwAAACZCeAMAADARwhsAAICJEN4AAABMhPAGAABgIoQ3AAAAEyG8AQAAmAjhDQAAwER8Ht7effddTZ06VZGRkQoJCVGnTp00ZswYffzxx1XafvLJJ/r5z3+u0NBQtWnTRomJifr6669d9rtq1SpFRkbKarWqe/fuSk9Pl91ur9Lu6NGjmjJlisLDwxUcHKzY2Fjl5OS4fTsBAADcwefh7ZlnntGBAwc0c+ZMbdu2TStXrtTRo0c1aNAgvfvuu452+/btU1xcnM6fP6/XX39dL7zwgr744gsNHjxYx44dc+pz6dKlmjlzphITE7Vz505Nnz5dGRkZmjFjhlO70tJSDR8+XDk5OVq5cqXefPNNtW/fXgkJCdqzZ49Xth8AAKAuAnw9gMzMTLVr185pWUJCgnr06KGMjAwNGzZMkrRo0SJZrVZt3bpVrVu3liTFxMSoZ8+eWrFihR577DFJUmFhoZYsWaJp06YpIyNDkhQXFye73a60tDTNmjVLvXv3liStWbNGBQUFev/99xUbGytJGjp0qKKjozV37lzt3bvXKzUAAACoLZ8febs0uElSaGioevfurW+//VaSVFZWpq1bt+q2225zBDdJ6tq1q4YOHarNmzc7lu3YsUMlJSVKSkpy6jMpKUmGYWjLli2OZZs3b1avXr0cwU2SAgICNHnyZH300Uc6dOiQuzYTAADALXx+5M2VkydP6pNPPnEcdfvqq6907tw5RUVFVWkbFRWl7OxslZSUKCgoSAUFBZKkvn37OrWLiIhQeHi4Y70kFRQUaPDgwS77lKRPP/1UnTp1cjnG0tJSlZaWOh6fOnVKkmS3211eW9dQFX16om+Uo8beQZ09jxp7B3X2vKZW49pup1+GtxkzZujs2bNasGCBpPJToZJks9mqtLXZbDIMQydOnFBERIQKCwtltVoVEhLism1FXxX9Vtdn5dd1ZdmyZUpPT6+y/J133lFwcHANW1h/2dnZHusb5aixd1Bnz6PG3kGdPa+p1Li4uLhW7eod3h555BElJyerY8eOVdb95z//0XPPPadFixbVud+FCxfq5Zdf1qpVqxQTE+O0zmKxVPu8yutq266ubSubN2+eZs+e7Xh86tQpde7cWfHx8U6ndt3FbrcrOztbI0aMUGBgoNv7BzX2FursedTYO6iz5zW1GlecxatJvcNbenq6EhISXIa3w4cPKz09vc7hLT09XUuWLNHSpUt1//33O5aHhYVJcn0krKioSBaLRW3atHG0LSkpUXFxcZUjYEVFRU6BMCwsrNo+JddH+ipYrVZZrdYqywMDAz26g3m6f1Bjb6HOnkeNvYM6e15TqXFtt7HeNywYhlHtujNnztS5yOnp6Vq8eLEWL16s+fPnO6275ppr1LJlS+Xn51d5Xn5+vnr06KGgoCBJP17rdmnbI0eO6Pjx4+rTp49jWd++favtU5JTWwAAAH9QpyNv//znP/WPf/zD8Xjbtm3at2+fU5tz587p5Zdf1jXXXFPrfh999FEtXrxYaWlpevjhh6sOMiBAt9xyizZt2qTf/e53atWqlSTpm2++UW5urh588EFH24SEBAUFBSkrK0sDBw50LM/KypLFYtHYsWMdy8aNG6fp06dr7969jrZlZWVav369Bg4c6PKoIgAAgC/VKbxt3rzZcZG+xWLRI4884rJdy5YttXbt2lr1+cQTT2jRokVKSEjQzTffrA8//NBp/aBBgySVH5nr37+/Ro8erdTUVJWUlGjRokUKDw/XnDlzHO1tNpvS0tK0cOFC2Ww2xcfHKy8vT4sXL1ZycrJjjjdJmjp1qjIzMzVhwgQtX75c7dq109NPP63PP/9cu3btqktpAAAAvKJO4e2ee+7R6NGjZRiGBgwYoLVr11Y5tWi1Wh2nOWvj7bffllQ+P9uOHTuqrK84PRsZGandu3froYce0vjx4xUQEKBhw4ZpxYoVatu2rdNzFixYoFatWikzM1MrVqxQhw4dlJqa6rh7tfJYc3JyNHfuXD3wwAMqLi5Wv379tH37dg0ZMqTWdQEAAPCWOoW3iIgIRURESJJyc3MVExOj0NDQBg1g9+7dtW4bExNT6yNiKSkpSklJqbFd+/bttW7dulqPAQAAwJfqfbcpR6YAAAC8r0GT9K5fv14bNmzQwYMHde7cOad1FotFX331VYMGBwAAAGf1Dm+PPfaY5s2bp969eys6OtrlnGcAAABwr3qHt9WrV2vGjBlatWqVO8cDAACAy6j3JL1HjhzRuHHj3DkWAAAA1KDe4S0mJoZr2gAAALys3uHt97//vZ544gl9/PHH7hwPAAAALqPe17wlJSWpsLBQAwYMUIcOHRxfHl/BYrHof//3fxs8QAAAAPyo3uEtLCxM4eHh7hwLAAAAalDv8FaXb0YAAACAe9T7mjcAAAB4X72PvP31r3+tsc1NN91U3+4BAADgQr3DW1xcnCwWy2XbXLhwob7dAwAAwIV6h7fc3Nwqy44fP64333xT7733njIzMxs0MAAAAFRV7/A2ZMgQl8tvu+02/fd//7d27NihhISEeg8MAAAAVXnkhoVx48bp1Vdf9UTXAAAATZpHwtuJEydUWlrqia4BAACatHqfNv3mm2+qLCstLdU///lPzZs3T4MGDWrQwAAAAFBVvcNbt27dXN5tahiGevXqpT/+8Y8NGhgAAACqqnd4e+GFF6qEt6CgIHXr1k39+/dXs2bM/wsAAOBu9Q5vU6ZMceMwAAAAUBv1Dm8VTp8+rQ8++ECFhYUKDw/XoEGD1KpVK3eMDQAAAJdoUHhbsWKF0tPTVVxcLMMwJEkhISFKT0/X7Nmz3TJAAAAA/Kje4e3FF1/U3Llz9Ytf/EJTpkxRx44ddfjwYa1bt06//e1v1bZtW915553uHCsAAECTV+/w9uSTT2rSpElav3690/IJEyZo8uTJevLJJwlvAAAAblbvW0L37dunyZMnu1w3efJk/etf/6r3oAAAAOBavcNby5YtVVRU5HJdUVGRWrZsWe9BAQAAwLV6h7fBgwdr8eLFOnz4sNPyI0eO6JFHHtFNN93U4MEBAADAWb2vecvIyNBPf/pT9ejRQ8OHD1dERIT+85//6N1331VgYKA2bdrkznECAABADTjydt111ykvL09jxoxRXl6e1q5dq7y8PI0dO1YfffSRevfu7c5xAgAAQA2c5+3aa6/VK6+84q6xAAAAoAZ1PvKWn5+v7777rtr13333nfLz8xs0KAAAALhWp/D217/+VTExMfr++++rbfP9998rJiZGO3fubPDgAAAA4KxO4S0zM1Pjx49XTExMtW1iYmJ0++236/nnn2/w4AAAAOCsTuHtvffe09ixY2tsd+utt+rDDz+s75gAAABQjTqFt2PHjqlTp041touIiNDRo0frPSgAAAC4VqfwFhISUu23KlR24sQJBQcH13tQAAAAcK1O4e26667Tjh07amy3fft2XXfddfUeFAAAAFyrU3i7/fbbtWbNGu3Zs6faNrm5uVq7dq3uuOOOBg8OAAAAzuo0Se8999yjrKwsxcfHKzk5WWPGjFH37t0lSfv379eWLVu0Zs0aRUdHa9q0aR4ZMAAAQFNWp/DWokUL7dy5U3feeaeeeeYZPfvss07rDcPQL37xC7344otq0aKFWwcKAACAenw9VlhYmLZt26aPP/5Y77zzjr799ltJUpcuXTRy5Ehdf/31bh8kAAAAytX7u01jYmIuO1kvAAAA3K9ONyxERUXV+l90dHSt+jx9+rTmzp2r+Ph4tW3bVhaLRYsXL67SbsqUKbJYLFX+RUZGuux31apVioyMlNVqVffu3ZWeni673V6l3dGjRzVlyhSFh4crODhYsbGxysnJqUtZAAAAvKZOR95sNpssFstl25w5c0Yff/xxje0qFBYWavXq1YqOjtbYsWMv+7VaLVu21Lvvvltl2aWWLl2qhQsXKjU1VfHx8crLy1NaWpoOHTqk1atXO9qVlpZq+PDh+uGHH7Ry5Uq1a9dOmZmZSkhI0K5duzRkyJBabQMAAIC31Cm87d69u9p1ZWVlWr16tR555BFZLBZNmjSpVn127dpVJ06ckMVi0fHjxy8b3po1a6ZBgwZdtr/CwkItWbJE06ZNU0ZGhiQpLi5OdrtdaWlpmjVrlnr37i1JWrNmjQoKCvT+++8rNjZWkjR06FBFR0dr7ty52rt3b622AQAAwFvqdNq0Ohs3blTv3r31wAMPKDo6Wh9//LFeeumlWj234vSnu+zYsUMlJSVKSkpyWp6UlCTDMLRlyxbHss2bN6tXr16O4CZJAQEBmjx5sj766CMdOnTIbeMCAABwh3rfsCCVH4l76KGHlJeXpxtuuEHvvPOOhg8f7q6xVXHu3Dl16NBBx44dU0REhMaOHatHHnlENpvN0aagoECS1LdvX6fnRkREKDw83LG+ou3gwYOrvE5UVJQk6dNPP632u1xLS0tVWlrqeHzq1ClJkt1ud3ltXUNV9OmJvlGOGnsHdfY8auwd1NnzmlqNa7ud9Qpv+fn5euihh7Rz5051795dGzZs0MSJE+vTVa1FR0crOjpaffr0kSTt2bNHTz75pHJycpSXl6fQ0FBJ5adNrVarQkJCqvRhs9lUWFjoeFxYWOgU/Cq3q1hfnWXLlik9Pb3K8nfeecej3+uanZ3tsb5Rjhp7B3X2PGrsHdTZ85pKjYuLi2vVrk7h7dtvv1VaWpo2bNggm82mp556Sv/93/+twMDAeg2yLh588EGnxyNGjND111+v8ePH67nnnnNaf7nTsJeuq0vbyubNm6fZs2c7Hp86dUqdO3dWfHy8WrduXe3z6stutys7O1sjRozwSr2bImrsHdTZ86ixd1Bnz2tqNa44i1eTOoW3a6+9VufPn1dCQoLmzp2rVq1aKT8/v9r2N9xwQ126r7Nx48YpJCREH374oWNZWFiYSkpKVFxcXOUIWFFRkdPcdGFhYS6PrhUVFUmSy6NyFaxWq6xWa5XlgYGBHt3BPN0/qLG3UGfPo8beQZ09r6nUuLbbWKfwVnGN1/bt27Vjx45q2xmGIYvFogsXLtSl+3oxDEPNmv1430XFtW75+fkaOHCgY/mRI0d0/Phxx2nXirauwmfFssptAQAA/EGdwtvatWs9NY56eeONN1RcXOw0fUhCQoKCgoKUlZXlFN6ysrJksVg0duxYx7Jx48Zp+vTp2rt3r6NtWVmZ1q9fr4EDB6pjx45e2xYAAIDaqFN4u/vuuz0yiO3bt+vs2bM6ffq0JOmzzz7TG2+8IUkaNWqUjh07pkmTJmnixInq0aOHLBaL9uzZo6eeekrXXXedkpOTHX3ZbDalpaVp4cKFstlsjkl6Fy9erOTkZMccb5I0depUZWZmasKECVq+fLnatWunp59+Wp9//rl27drlkW0FAABoiAZNFeIu9913nw4ePOh4vHHjRm3cuFGStH//fl1xxRVq3769fv/73+v777/XhQsX1LVrV6WkpGj+/PlV7ixdsGCBWrVqpczMTK1YsUIdOnRQamqqFixY4NTOarUqJydHc+fO1QMPPKDi4mL169dP27dv59sVAACAX/KL8HbgwIEa22zatKlOfaakpCglJaXGdu3bt9e6devq1DcAAICvuOUbFgAAAOAdhDcAAAATIbwBAACYCOENAADARAhvAAAAJkJ4AwAAMBHCGwAAgIkQ3gAAAEyE8AYAAGAihDcAAAATIbwBAACYCOENAADARAhvAAAAJkJ4AwAAMBHCGwAAgIkQ3gAAAEyE8AYAAGAihDcAAAATIbwBAACYCOENAADARAhvAAAAJkJ4AwAAMBHCGwAAgIkQ3gAAAEyE8AYAAGAihDcAAAATIbwBAACYCOENAADARAhvAAAAJkJ4AwAAMBHCGwAAgIkQ3gAAAEyE8AYAAGAihDcAAAATIbwBAACYCOENAADARAhvAAAAJkJ4AwAAMBHCGwAAgIkQ3gAAAEyE8AYAAGAihDcAAAATIbwBAACYiM/D2+nTpzV37lzFx8erbdu2slgsWrx4scu2n3zyiX7+858rNDRUbdq0UWJior7++muXbVetWqXIyEhZrVZ1795d6enpstvtVdodPXpUU6ZMUXh4uIKDgxUbG6ucnBx3biIAAIDb+Dy8FRYWavXq1SotLdXYsWOrbbdv3z7FxcXp/Pnzev311/XCCy/oiy++0ODBg3Xs2DGntkuXLtXMmTOVmJionTt3avr06crIyNCMGTOc2pWWlmr48OHKycnRypUr9eabb6p9+/ZKSEjQnj17PLG5AAAADRLg6wF07dpVJ06ckMVi0fHjx/X888+7bLdo0SJZrVZt3bpVrVu3liTFxMSoZ8+eWrFihR577DFJ5WFwyZIlmjZtmjIyMiRJcXFxstvtSktL06xZs9S7d29J0po1a1RQUKD3339fsbGxkqShQ4cqOjpac+fO1d69ez29+QAAAHXi8yNvFotFFovlsm3Kysq0detW3XbbbY7gJpUHv6FDh2rz5s2OZTt27FBJSYmSkpKc+khKSpJhGNqyZYtj2ebNm9WrVy9HcJOkgIAATZ48WR999JEOHTrUwK0DAABwL58feauNr776SufOnVNUVFSVdVFRUcrOzlZJSYmCgoJUUFAgSerbt69Tu4iICIWHhzvWS1JBQYEGDx7ssk9J+vTTT9WpUyeXYyotLVVpaanj8alTpyRJdrvd5bV1DVXRpyf6Rjlq7B3U2fOosXdQZ89rajWu7XaaIrwVFhZKkmw2W5V1NptNhmHoxIkTioiIUGFhoaxWq0JCQly2reirot/q+qz8uq4sW7ZM6enpVZa/8847Cg4Ornmj6ik7O9tjfaMcNfYO6ux51Ng7qLPnNZUaFxcX16qdKcJbhcudXq28rrbt6tq2snnz5mn27NmOx6dOnVLnzp0VHx/vdGrXXex2u7KzszVixAgFBga6vX9QY2+hzp5Hjb2DOnteU6txxVm8mpgivIWFhUlyfSSsqKhIFotFbdq0cbQtKSlRcXFxlSNgRUVFiomJceq3uj4l10f6KlitVlmt1irLAwMDPbqDebp/UGNvoc6eR429gzp7XlOpcW230ec3LNTGNddco5YtWyo/P7/Kuvz8fPXo0UNBQUGSfrzW7dK2R44c0fHjx9WnTx/Hsr59+1bbpySntgAAAP7AFOEtICBAt9xyizZt2qTTp087ln/zzTfKzc1VYmKiY1lCQoKCgoKUlZXl1EdWVpYsFovTXHLjxo3Tvn37nKYEKSsr0/r16zVw4EB17NjRY9sEAABQH35x2nT79u06e/asI5h99tlneuONNyRJo0aNUnBwsNLT09W/f3+NHj1aqampKikp0aJFixQeHq45c+Y4+rLZbEpLS9PChQtls9kUHx+vvLw8LV68WMnJyY453iRp6tSpyszM1IQJE7R8+XK1a9dOTz/9tD7//HPt2rXLu0UAAACoBb8Ib/fdd58OHjzoeLxx40Zt3LhRkrR//35169ZNkZGR2r17tx566CGNHz9eAQEBGjZsmFasWKG2bds69bdgwQK1atVKmZmZWrFihTp06KDU1FQtWLDAqZ3ValVOTo7mzp2rBx54QMXFxerXr5+2b9+uIUOGeH7DAQAA6sgvwtuBAwdq1S4mJqbWR8RSUlKUkpJSY7v27dtr3bp1teoTAADA10xxzRsAAADKEd4AAABMhPAGAABgIoQ3AAAAEyG8AQAAmAjhDQAAwEQIbwAAACZCeAMAADARwhsAAICJEN4AAABMhPAGAABgIoQ3AAAAEyG8AQAAmAjhDQAAwEQIbwAAACZCeAMAADARwhsAAICJEN4AAABMhPAGAABgIoQ3AAAAEyG8AQAAmAjhDQAAwEQIbwAAACZCeAMAADARwhsAAICJEN4AAABMhPAGAABgIoQ3AAAAEyG8AQAAmAjhDQAAwEQIbwAAACZCeAMAADARwhsAAICJEN4AAABMhPAGAABgIoQ3AAAAEyG8AQAAmAjhDQAAwEQIbwAAACZCeAMAADARwhsAAICJEN4AAABMhPAGAABgIqYJb7t375bFYnH578MPP3Rq+8knn+jnP/+5QkND1aZNGyUmJurrr7922e+qVasUGRkpq9Wq7t27Kz09XXa73RubBAAAUGcBvh5AXWVkZGjo0KFOy/r06eP4/3379ikuLk79+vXT66+/rpKSEi1atEiDBw/WP/7xD7Vt29bRdunSpVq4cKFSU1MVHx+vvLw8paWl6dChQ1q9erXXtgkAAKC2TBfeevbsqUGDBlW7ftGiRbJardq6datat24tSYqJiVHPnj21YsUKPfbYY5KkwsJCLVmyRNOmTVNGRoYkKS4uTna7XWlpaZo1a5Z69+7t+Q0CAACoA9OcNq2NsrIybd26VbfddpsjuElS165dNXToUG3evNmxbMeOHSopKVFSUpJTH0lJSTIMQ1u2bPHWsAEAAGrNdEfeZsyYoYkTJyo4OFixsbFauHChbrzxRknSV199pXPnzikqKqrK86KiopSdna2SkhIFBQWpoKBAktS3b1+ndhEREQoPD3esr05paalKS0sdj0+dOiVJstvtHrlmrqJPrsfzHGrsHdTZ86ixd1Bnz2tqNa7tdpomvF1xxRWaOXOm4uLiFBYWpn//+996/PHHFRcXp7/85S8aOXKkCgsLJUk2m63K8202mwzD0IkTJxQREaHCwkJZrVaFhIS4bFvRV3WWLVum9PT0KsvfeecdBQcH13Mra5adne2xvlGOGnsHdfY8auwd1NnzmkqNi4uLa9XONOHt+uuv1/XXX+94PHjwYI0bN059+/bV3LlzNXLkSMc6i8VSbT+V19W2nSvz5s3T7NmzHY9PnTqlzp07Kz4+3umUrbvY7XZlZ2drxIgRCgwMdHv/oMbeQp09jxp7B3X2vKZW44qzeDUxTXhzpU2bNho9erSeffZZnTt3TmFhYZLk8qhZUVGRLBaL2rRpI0kKCwtTSUmJiouLqxwpKyoqUkxMzGVf22q1ymq1VlkeGBjo0R3M0/2DGnsLdfY8auwd1NnzmkqNa7uNpr9hwTAMSeVHyq655hq1bNlS+fn5Vdrl5+erR48eCgoKkvTjtW6Xtj1y5IiOHz/uNP0IAACAvzB1eDtx4oS2bt2qfv36KSgoSAEBAbrlllu0adMmnT592tHum2++UW5urhITEx3LEhISFBQUpKysLKc+s7KyZLFYNHbsWC9tBQAAQO2Z5rTppEmT1KVLF/3kJz9ReHi4vvzySz3xxBP6/vvvnQJYenq6+vfvr9GjRys1NdUxSW94eLjmzJnjaGez2ZSWlqaFCxfKZrM5JuldvHixkpOTmeMNAAD4JdOEt6ioKL322mt69tlndebMGdlsNt1444166aWX1L9/f0e7yMhI7d69Ww899JDGjx+vgIAADRs2TCtWrHD6dgVJWrBggVq1aqXMzEytWLFCHTp0UGpqqhYsWODtzQMAAKgV04S31NRUpaam1qptTEyMdu3aVau2KSkpSklJacjQAAAAvMbU17wBAAA0NYQ3AAAAEyG8AQAAmAjhDQAAwEQIbwAAACZCeAMAADARwhsAAICJEN4AAABMhPAGAABgIoQ3AAAAEyG8AQAAmAjhDQAAwEQIbwAAACZCeAMAADARwhsAAICJEN4AAABMhPAGAABgIoQ3AAAAEyG8AQAAmAjhDQAAwEQIbwAAACZCeAMAADARwhsAAICJEN4AAABMhPAGAABgIoQ3AAAAEyG8AQAAmAjhDQAAwEQIbwAAACZCeAMAADARwhsAAICJEN4AAABMhPAGAABgIoQ3AAAAEyG8AQAAmAjhDQAAwEQIbwAAACZCeAMAADARwhsAAICJEN4AAABMhPAGAABgIoQ3AAAAEyG8AQAAmAjhTdKZM2c0a9YsdezYUUFBQerXr59effVVXw8LAACgigBfD8AfJCYmKi8vT8uXL9e1116rDRs26I477tDFixc1adIkXw8PAADAocmHt23btik7O9sR2CRp6NChOnjwoH7729/q9ttvV/PmzX08SgAAgHJN/rTp5s2bFRoaqgkTJjgtT0pK0uHDh7V3714fjQwAAKCqJn/kraCgQP/1X/+lgADnUkRFRTnW//SnP63yvNLSUpWWljoenzx5UpJUVFQku93u9nHa7XYVFxersLBQgYGBbu8f1NhbqLPnUWPvoM6e19RqfPr0aUmSYRiXbdfkw1thYaGuvvrqKsttNptjvSvLli1Tenp6leXdu3d37wABAECTcvr0aV1xxRXVrm/y4U2SLBZLndfNmzdPs2fPdjy+ePGiioqKFBYWdtn+6uvUqVPq3Lmzvv32W7Vu3drt/YMaewt19jxq7B3U2fOaWo0Nw9Dp06fVsWPHy7Zr8uEtLCzM5dG1oqIiST8egbuU1WqV1Wp1WtamTRu3j+9SrVu3bhI7sC9RY++gzp5Hjb2DOnteU6rx5Y64VWjyNyz07dtX//rXv1RWVua0PD8/X5LUp08fXwwLAADApSYf3saNG6czZ87oz3/+s9PydevWqWPHjho4cKCPRgYAAFBVkz9t+otf/EIjRozQfffdp1OnTqlHjx565ZVXtGPHDq1fv95v5nizWq16+OGHq5yqhftQY++gzp5Hjb2DOnseNXbNYtR0P2oTcObMGS1YsECvv/66ioqKFBkZqXnz5mnixIm+HhoAAIATwhsAAICJNPlr3gAAAMyE8AYAAGAihDcPOHPmjGbNmqWOHTsqKChI/fr106uvvlrj83bt2qURI0aoY8eOslqtateunYYNG6Zt27ZVaRsXFyeLxVLlX0JCQpW2drtd6enp6tatm6xWqyIjI7Vq1Sq3bKsv+VOdDxw44LKdxWKp1Zj8lTdqLElnz57VokWLdO2118pqtSosLExDhw7Vl19+6dSuMe7L/lTjxrofS56v8+Vq5+ozg335R56ocWPelyXuNvWIxMRE5eXlafny5br22mu1YcMG3XHHHbp48aImTZpU7fMKCwt13XXXKTk5WR06dFBRUZGeffZZ3XzzzXrppZc0efJkp/ZXX321Xn75ZadlriYKnj59ul566SU9+uij6t+/v3bu3KmZM2fq9OnTmj9/vlu22Rf8rc6S9MADD1R57Z49e9ZvA/2AN2p85swZDR06VIcPH1ZqaqqioqJ08uRJvf/++youLnbqtzHuy/5WY6nx7ceS5+scERGhDz74oMrzt2zZoscee0zjxo1zWs6+/CNP1VhqnPuyJMmAW/3lL38xJBkbNmxwWj5ixAijY8eORllZWZ36O3/+vNGpUydj8ODBTsuHDBliXHfddTU+v6CgwLBYLEZGRobT8mnTphktW7Y0CgsL6zQef+Fvdd6/f78hyXj88cfr9Lr+zFs1njlzphESEmJ89dVXl31+Y9yX/a3GjXE/Ngzv1dmVuLg4Izg42Dh58qRjGftyzRpa48a6L1fgtKmbbd68WaGhoZowYYLT8qSkJB0+fFh79+6tU3+BgYFq06aNAgLqd5B0y5YtMgxDSUlJVcZz7tw57dixo179+pq/1bkx8kaNi4uL9fzzz2vChAm6+uqrL/v8xrgv+1uNGytffV589dVX2rNnj375y186fbUT+3LNGlrjxo7w5mYFBQX6r//6ryo7XFRUlGN9TS5evKiysjIdPnxYDz/8sL744gvNmTOnSruvvvpKNptNAQEBuuaaa7RgwQKdO3euynjatm2rDh061Hs8/sjf6lxh+fLlatGihYKDg3XjjTfqrbfeqsfW+Qdv1Pjjjz/W2bNn1bNnT91333268sor1aJFC/3kJz/RX/7ylyrjaWz7sr/VuEJj2o8l735eVPbCCy/IMAwlJydXGQ/7clXurHGFxrYvV+Awg5sVFha6/Ou24gvuCwsLa+xj1KhR2rlzp6TyL+N97bXXdPPNNzu1ufHGG3X77bcrMjJS586d0/bt2/W73/1Of//735Wbm6tmzZo5Xq/itSsLCQlRixYtajUef+RvdbZarZo2bZpGjBihiIgIffPNN1q1apXGjBmj5557rtoPFn/mjRofOnRIkvTYY4+pb9++evHFF9WsWTM98cQTuuWWW7R9+3aNHDnS8XqNbV/2txo3xv1Y8t7nRWUXLlzQunXrFBkZqZ/97GdVxsO+XJU7a9xY92UHn560bYR69uxpJCQkVFl++PBhQ5KxbNmyGvv44osvjI8++sh48803jQkTJhiBgYFVriNwZcWKFYYkY9OmTY5lI0aMMCIjI122b9GihXHvvffW2K8/8rc6u3L+/Hnj+uuvN8LCwgy73V5jv/7GGzV++eWXDUlGeHi4cerUKcfys2fPGh07djR+9rOfOZY1xn3Z32rsitn3Y8PwzefF1q1bq73min3ZNXfW2JXGsC9X4LSpm4WFhbn8C6OoqEiSXP61damePXuqf//+uvXWW/X6669r+PDhmjFjhi5evHjZ51XckfPhhx/WOJ6zZ8/q/PnztRqPP/K3OrsSGBio22+/XYWFhVWmvDADb9Q4LCxMkvTTn/5UrVq1cjwvODhYQ4YM0SeffFLjeMy8L/tbjV0x+34s+ebzYs2aNQoMDNRdd91V6/GwL7uvxq40hn25AuHNzfr27at//etfKisrc1qen58vSerTp0+d+xwwYIBOnDihY8eO1ap9xam8ivEcO3ZMR44ccdt4/IG/1bk6xv99+1xt2vobb9S44noYVwzDaPT7sr/V+HLtJHPux5L3Py+OHj2qrVu36tZbb1W7du1cjod9uWYNqXF1zL4vO/j0uF8jtG3bNkOS8eqrrzotT0hIqNft0hcvXjSGDBlitGnTpsbDvI899pghydiyZYtjWcUt6cuXL3dqe++995r2lnTD8L86u3L+/HmjX79+Rnh4eJ3H4w+8VePY2FgjLCzM6Tb/s2fPGhEREcbw4cMdyxrjvuxvNXbF7PuxYXj/8+Lxxx83JBnbtm1z+Xz25Zo1tMauNIZ9uQLhzQNGjBhhXHnllcbq1auNd99915g2bZohyVi/fr2jzdSpU43mzZsbBw4ccCy79dZbjYULFxp//vOfjd27dxsbNmww4uPjDUlGZmamo91f//pXY+TIkcazzz5rvPPOO8Zbb71l3HfffUbz5s2NYcOGGRcuXHAaT3JysmG1Wo3HH3/c2L17tzF//nzDYrEYS5cu9XwxPMif6vzggw8a999/v/HKK68Yubm5xosvvmj079/fkGSsXbvWK/XwBE/X2DAM47333jNatGhhDBo0yNi8ebOxZcsWY/DgwUZgYKDx/vvvO7VtjPuyP9W4se7HhuGdOleIjIw0OnfuXOWzuDL2Zc/WuDHvy4ZBePOI06dPGykpKUaHDh2MFi1aGFFRUcYrr7zi1Obuu+82JBn79+93LHvssceM/v37G1deeaXRvHlzIywszBg5cqSxdetWp+d++eWXxqhRo4xOnToZVqvVCAoKMvr27WssXbrUKCkpqTKe8+fPGw8//LDRpUsXo0WLFsa1115r/OEPf/DItnuTP9V5zZo1xoABAwybzWYEBAQYV155pTFy5Ehj586dHtt+b/B0jSv87W9/M4YMGWIEBwcbwcHBxrBhw4z33nuvSrvGuC/7U40b635sGN6r83vvvWdIMhYtWnTZ8bAv73cs80SNG/O+bBiGYTGM/zsBDAAAAL9n8iv2AAAAmhbCGwAAgIkQ3gAAAEyE8AYAAGAihDcAAAATIbwBAACYCOENAADARAhvAAAAJkJ4A+AXDhw4IIvFoqysLLf3nZOTo5/85CcKCQmRxWLRli1blJWVJYvFogMHDrj99Srr1q2bpkyZ4tHXMIPPPvtMixcv9ni9gaYgwNcDAABPMgxDv/zlL3XttdfqrbfeUkhIiHr16qWysjJ98MEHioiI8PUQm4TPPvtM6enpiouLU7du3Xw9HMDUCG8AGrXDhw+rqKhI48aN0/Dhw53WtW3b1kej8i273S6LxaKAgKq/AoqLixUcHOyDUQGoLU6bAvCof//730pKSlLPnj0VHBysTp066ZZbblF+fn6Nzz127Jjuuecede7cWVarVW3bttXPfvYz7dq1q1avvXjxYl111VWSpIceekgWi8Vx1MfVadO4uDj16dNHeXl5Gjx4sIKDg3X11Vdr+fLlunjxoqNdSUmJ5syZo379+umKK66QzWZTbGys3nzzzdoXpgYbNmxQbGysQkNDFRoaqn79+mnNmjWO9dWdjo2Li1NcXJzj8e7du2WxWPTSSy9pzpw56tSpk6xWq/79739rypQpCg0NVX5+vuLj49WqVStHwD1//ryWLFmiyMhIR+2TkpJ07Ngxp9fr1q2bRo8erR07duiGG25Qy5YtFRkZqRdeeMHRJisrSxMmTJAkDR06VBaLxWOnyIGmgCNvADzq8OHDCgsL0/Lly9W2bVsVFRVp3bp1GjhwoP7f//t/6tWrV7XPvfPOO/XJJ59o6dKluvbaa/XDDz/ok08+UWFhYa1eOzk5WdHR0UpMTNQDDzygSZMmyWq1XvY5R44c0a9+9SvNmTNHDz/8sDZv3qx58+apY8eOuuuuuyRJpaWlKioq0m9+8xt16tRJ58+f165du5SYmKi1a9c62tXXokWL9OijjyoxMVFz5szRFVdcoYKCAh08eLDefc6bN0+xsbF69tln1axZM7Vr105SeUi79dZbde+99yo1NVVlZWW6ePGixowZo7/97W+aO3eufvrTn+rgwYN6+OGHFRcXp//5n/9Ry5YtHX3/7//+r+bMmaPU1FS1b99ezz//vH7961+rR48euummm3TzzTcrIyND8+fPV2Zmpm644QZJ0jXXXNOgOgFNlgEAXlRWVmacP3/e6Nmzp/Hggw86lu/fv9+QZKxdu9axLDQ01Jg1a1aDXq+i38cff9xp+dq1aw1Jxv79+x3LhgwZYkgy9u7d69S2d+/exsiRIy+7TXa73fj1r39tXH/99U7runbtatx99921Hu/XX39tNG/e3PjVr3512XbV9TtkyBBjyJAhjse5ubmGJOOmm26q0vbuu+82JBkvvPCC0/JXXnnFkGT8+c9/dlqel5dnSDKefvppp3EEBQUZBw8edCw7d+6cYbPZjHvvvdexbOPGjYYkIzc397LbBaBmnDYF4FFlZWXKyMhQ79691aJFCwUEBKhFixb68ssv9a9//euyzx0wYICysrK0ZMkSffjhh7Lb7R4fb4cOHTRgwACnZVFRUVWOem3cuFE/+9nPFBoaqoCAAAUGBmrNmjU1blNNsrOzdeHCBc2YMaNB/Vzqtttuq/W6rVu3qk2bNrrllltUVlbm+NevXz916NBBu3fvdmrfr18/denSxfE4KChI1157bYOOFAKoHuENgEfNnj1bCxcu1NixY/X2229r7969ysvLU3R0tM6dO3fZ57722mu6++679fzzzys2NlY2m0133XWXjhw54rHxhoWFVVlmtVqdxrpp0yb98pe/VKdOnbR+/Xp98MEHysvL09SpU1VSUtKg16+4pqziWj13qe6u2uDgYLVu3dpp2ffff68ffvhBLVq0UGBgoNO/I0eO6Pjx407ta1MzAO7DNW8APGr9+vW66667lJGR4bT8+PHjatOmzWWfGx4erqeeekpPPfWUvvnmG7311ltKTU3V0aNHtWPHDg+O+vLWr1+v7t2767XXXpPFYnEsLy0tbXDfFXfAfvfdd+rcuXO17YKCgly+3vHjxxUeHl5leeVx1rQ8PDxcYWFh1da4VatW1Y4LgOcR3gB4lMViqXKTwF/+8hcdOnRIPXr0qHU/Xbp00f3336+cnBy999577h5mnVgsFrVo0cIp+Bw5csQtd5vGx8erefPmeuaZZxQbG1ttu27duumf//yn07IvvvhCn3/+ucvwVhejR4/Wq6++qgsXLmjgwIEN6qtCxT7A0Tig4QhvADxq9OjRysrKUmRkpKKiovTxxx/r8ccfr/G04MmTJzV06FBNmjRJkZGRatWqlfLy8rRjxw4lJiZ6afSujR49Wps2bdL06dM1fvx4ffvtt3r00UcVERGhL7/8skF9d+vWTfPnz9ejjz6qc+fO6Y477tAVV1yhzz77TMePH1d6erqk8jtxJ0+erOnTp+u2227TwYMH9bvf/c4tc9dNnDhRL7/8skaNGqWZM2dqwIABCgwM1Hfffafc3FyNGTNG48aNq1Offfr0kSStXr1arVq1UlBQkLp37+7ylCuAyyO8AfColStXKjAwUMuWLdOZM2d0ww03aNOmTUpLS7vs84KCgjRw4EC99NJLOnDggOx2u7p06aKHHnpIc+fO9dLoXUtKStLRo0f17LPP6oUXXtDVV1+t1NRUfffdd45w1RCPPPKIevbsqVWrVulXv/qVAgIC1LNnT6WkpDjaTJo0SYcPH9azzz6rtWvXqk+fPnrmmWfc8vrNmzfXW2+9pZUrV+qll17SsmXLFBAQoKuuukpDhgxR375969xn9+7d9dRTT2nlypWKi4vThQsXtHbtWr46DKgHi2EYhq8HAQAAgNrhblMAAAAT4bQpAFMyDEMXLly4bJvmzZtXe5elL1y4cEGXO9lhsVjUvHlzL44IgBlx5A2AKa1bt67KHGSX/tuzZ4+vh+nkmmuuuex4K75XFAAuh2veAJhSYWGh9u/ff9k2vXr18qs5yfLz8y87F1yrVq0u+12vACAR3gAAAEyF06YAAAAmQngDAAAwEcIbAACAiRDeAAAATITwBgAAYCKENwAAABMhvAEAAJjI/weNxftWCNYiQwAAAABJRU5ErkJggg==",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"shotNum = \"0031\"\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 = (535, 995)\n",
"imageAnalyser.span = (350, 350)\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.ylim([0, 3500])\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"Ncount_mean_total = xr.concat((Ncount_mean_total, Ncount_mean), dim='als_final_current')\n",
"Ncount_std_total = xr.concat((Ncount_std_total, Ncount_std), dim='als_final_current')"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# 0.375 - 0.400 A"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The detected scaning axes and values are: \n",
"\n",
"{'als_final_current': array([0.375 , 0.3759, 0.3768, 0.3777, 0.3786, 0.3795, 0.3804, 0.3813,\n",
" 0.3822, 0.3831, 0.384 , 0.3849, 0.3858, 0.3867, 0.3876, 0.3885,\n",
" 0.3894, 0.3903, 0.3912, 0.3921, 0.393 , 0.3939, 0.3948, 0.3957,\n",
" 0.3966, 0.3975, 0.3984, 0.3993, 0.4002, 0.4011, 0.402 ]), 'runs': array([0., 1.])}\n"
]
},
{
"data": {
"image/png": 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gxAnv5hcXJ73+utSxo2P7VVdVtpvhPm8ctkdjCx6e1hjfSwlvPsoXwos3+UJwcuUJ73xq9p64OOngQSknR1q7tvK/+/ebJ7hx2L5pa4zBw5Ma63sp4c1H+UJ48SZfuFLQVSe8u+JTM+GvYZo3l6KjpbvvrvyvWQ6VNsY3naaqPoe+2QYarrG+lxLefJQvhBdvcuWVgvW9SssVJ7y74lMzh0yaJle/6XC1ovfU99B3Yw0entRY30sJbz6qqd/mwBeuFGzoCe+u+NTMIZOmq7G+6TQ1DTn0zTbQcI31vZTw5qN8Ibx4k69cKVh1wnunTo7ttTnhvaGfmn3pkAl7bTyvsb7peMvJkz9uw1u3eub3pqG/w2wDDddY30sJbz7KV8KLN/nKlYJxcdKBA3U/4b2hn5o5ZNK0NdY3HW/w1qkHDf0dZhtouMb6Xkp482G+El68KS5O2rv3x8dbttT9SkFXnOxfnxPeG/qp2ZcOmTS0hhfu9di2zXw3mvbGnkdXv+k01YtevHnqQUN/hxtr8Kiv+t7v0FXvpb70O0R483EXh5e33iqvV3gx8w0+L/zDdPPNdftD5c2T/Rv6qdkXLtqQGl5DZzeavv/+GG3cWENhYNeQw/YXaqoXvXj71ANX/A7zIb7Sxo2WBt3vsKE7Anzud8iAS/zwww+GJOOHH35w+dinTxtG5Z8awzh+/FydnvvGG4Zx1VU/Pl+qfPzGGy6fpttcuP6nT9f+eW+8YRgWi+O6S5VtFkv1Gpw7d87YtGmTce5c3WpcmzlcPI+a5nCh8vLKfytn61A1RufOlf0ux1M1rO3zpfOGxXK+1tthfefvKt5+/fJyw8jJMYy1ayv/e7l/8wu35Yb+G5pZTo7z352Lf3Jy6jf+5f5muPJ3+Icffnzeli21e87Fc6nLNuQrzp07Z8yatcewWM43eBv21t/BuqhtliC8uYgvhrfG8ke7Pr9wVX80a/pj7eyPpjvCm2FU1rlTJ8fX79y5dvVvSPi7kKdqWLfnn3d7+HSVhr5xelrVtnz27LkG/Rua3dq1tQtva9fWb/za/M3w5u/whXMw64f4s2fPGSEhxYZUPbx54kNsQ/8O1lVtswSHTRspVx4uMONhV1862b8hu+u9ecikoTW8/PMtprjgwucOl9TBP/9p8ZnfA2/whas1XXXoOyjox8gQFFT71zf7t3T8858WFRa2lOT8NAt3b8O+9F5yIcJbI+WqDc6s36voSyf7Sw07b88VF23UR0Nr6Gv/BvVh9vvsNYZ/g4bwlas163vFekN5+5w/V/D2Nuzt168J4a2RcsUGZ+ZPbL7widuVGhL+6quhNXTlv4E3rvJqDG98je33oK586WpNb3xFm6/uNaoLb2/D3n79mhDeGqmGbnC+9MZVn8MFvvKJ21fUJ/w0tIaXf75Rq38Ds96jyxfcdJPR5H8PXHXY0ox8da9RXdx0k6GQkLOyWJy8Galu23Bjei8hvJlAUJB07lyZNm1602MbnNnfuHzpE7e31Tf8NLSGl3q+ZFz2+VVzN+s9unwBvweVvHXY0tt8da9RXTRvLiUk5Enyzjbsq79DhLdGqqEbXGN44+L+SA0PPw2tYU3PDw09q3XrKi75fG/v/W0Mb3wSvwdVvHHY0tt8da9RXUVFfad16yq8tvfUF3+HCG+NWEMOF/jKDWIbylsn+/sCV4WfhtbQ2Y2m//znLI0Z4/wwSBVv7/1tLG98UuP4PeD7devO1/YaNeTOBWPGGF7de+prv0OEt0auvocLGtMblzdO9vcFrgw/Da1h69Y/nmty661GrZ7v7b2/vvbG11BN9fegqfOVc/5ccecCb+899aXfIT/vvTQ8pWqDr+tzli6tPLRmsTjuvTHjG5e3VZ0o60muDD/emL8vHLaseuNLTHQ89HzVVZXbv5n2XKHpiouTRo2q/KD23XeVvzODB3vu73fV6RsX/w2pOn2jKR2+dxX2vKFGvvKJDfW7SsoXwk9D+MreX187XFJf9b3JKxoHb+218va5q40V4Q2X1Biu0mqqb1q+En7qy5cOW/rS4RLATLx97mpjRXjDZTX0E5s3brAK3wo/9eWLV3mhaeKCifrx9rmrjRXhDW5l5u+FbAwaQ/jxhcOWTXXvrS/hQ6A5mf30DV9FeIPbmP17IRsLXwg/DcVhy6aND4HmZfbTN3wV4Q1uwUmqvoXwA7PylQ+B7Pmrn8Zw+oYvIrzBLThJFfgR50vVj698CKxpz9/GjTXsToID7lzgeoQ3uAUnqQJoKF/4EHipPX933dVc773HyVq10RjuXOBLuEkv3IKTVAE0lLc/BF5uz5/FIq1c2VspKZK/v3vm0JjU54bxcI49b3ALTlIF0FDe/hB4+T1/FhUUBOqf/+TwKTyL8Aa34CRV38KtLmBG3v4Q6O09f0BNCG9wm8ZwjzEA3uPtD4He3vMH1ITwBrdqDPcYAxqK20zUnzc/BF5+z5+h0NBi3XSTk5PiADcivMHtuMcYmjJuMNtw3voQWJs9f7/5TT5/0+BxXg9vO3fu1KRJkxQeHq6goCB16tRJo0aN0ocffujQb+LEibJYLNV+wsPDnY67bNkyhYeHy2q1qnv37kpNTVVZWVm1fkePHtXEiRMVGhqqwMBARUVFKTs72y3rCqBp8ZUbzDYG3voQeKk9f+vWVSgqihPe4Hlev1XIc889p8LCQk2dOlW9evXSsWPH9NRTT2nQoEHatm2bbrnlFnvfli1baufOnQ7Pb9myZbUxFy5cqHnz5ikpKUkxMTHKzc1VcnKyDh06pBUrVtj7lZaWatiwYTpx4oSWLl2qdu3aKT09XbGxsdqxY4eGDBnivhUH0KjV5jYT06ZJo0axN9rXxcVJP/+5dMUVlY+3bJFiYqTz5w1t2eLduaFp8np4S09PV7t27RzaYmNj1aNHD6WlpTmEt2bNmmnQoEGXHK+wsFALFizQ5MmTlZaWJkmKjo5WWVmZkpOTNW3aNPX67zGMlStXKj8/X++++66ioqIkSUOHDlVERIRmzpypPXv2uHJVATQhdbnBLPe+8n3O9vydP++9+aBp8/ph04uDmyQFBwerV69e+uabb+o8XmZmpkpKShQfH+/QHh8fL8MwtGnTJnvbxo0b1bNnT3twkyQ/Pz9NmDBBH3zwgQ5dfKwD9cJtKtAUcZsJAO7i9T1vzvzwww/66KOPHPa6SdLZs2fVoUMHHTt2TGFhYRo9erQee+wx2Ww2e5/8/HxJUp8+fRyeGxYWptDQUPvyqr6DndwgqG/fvpKkTz75RJ0u/jK2/yotLVVpaan98cmTJyVJZWVlTs+ta6iqMd0xNipRY8+oT50ru/rbn2eGf6K2bS2qzZ/Ytm3LVVbm2qsVG+O27O1toEUL6dw5x/k0xjr7Gl+qsbNtwNVqu54+Gd4efPBBnTlzRnPnzrW3RUREKCIiQr1795Yk7d69W88884yys7OVm5ur4OBgSZWHTa1Wq4Kc7OKx2WwqLCy0Py4sLHQIfhf2q1pek0WLFik1NbVa+/bt2xUYGFjLNa27rKwst42NStTYM+pa56qd5rt3u34u7lBRIYWExKiwMECSs3tNGAoNPauTJ7Pcdt5UY9qWS0qaSxopSdq2bZsCAnznfiuNqc6+qqnUuLi4uFb9fC68zZs3Ty+//LKWLVumyMhIe/sjjzzi0G/48OG64YYbNHbsWL3wwgsOyy013ZTHybK69L3Q7NmzNX36dPvjkydPqnPnzoqJiVHr1q1rfF59lZWVKSsrS8OHD5c/X6LnFtTYM5pSnZcvt+iuuyTJkGH8+PfEYqnc05ae3kJ33DHC5a/bWGt87lzVXolbvTqPKo21zr6kqdW46ije5fhUeEtNTdWCBQu0cOFCPfTQQ5ftP2bMGAUFBen999+3t4WEhKikpETFxcXV9oAVFRU5BMKQkBCne9eKiookyeleuSpWq1VWq7Vau7+/v1s3MHePD2rsKU2hzr/8peTnJyUmOt4u5KqrLHr2WSkuzr1/gptCjX0BdXa/plLj2q6j1y9YqJKamqqUlBSlpKRozpw5tX6eYRhq1uzH1ag61y0vL8+h35EjR1RQUGA/7FrV9+J+Fz73wr4AUB98ywgAV/OJ8Pb4448rJSVFycnJevTRR2v9vNdff13FxcUOtw+JjY1VQECAMjIyHPpmZGTIYrFo9OjR9rYxY8Zo3759DrcEKS8v15o1azRw4EB1vPiujABQD3zLCABX8vph06eeekrz589XbGysbr/9dodDoJI0aNAgHTx4UOPHj9ddd92lHj16yGKxaPfu3Xr22Wd1/fXXKyEhwd7fZrMpOTlZ8+bNk81ms9+kNyUlRQkJCfZ7vEnSpEmTlJ6ernHjxmnx4sVq166dli9frs8++0w7duzwWA0AAABqy+vh7a233pJUeX+2zMzMassNw1Dr1q3Vvn17Pf300/r+++9VUVGhrl27KjExUXPmzKl2ZencuXPVqlUrpaena8mSJerQoYOSkpIcrl6VKs9by87O1syZM/Xwww+ruLhY/fr109atW/l2BQAA4JO8Ht527dp12T5XXnmlNtTxSwATExOVmJh42X7t27fX6tWr6zQ2AACAt/jEOW8AAACoHa/veQOAxq7qK+IAwBXY8wYAAGAihDcAAAATIbwBAACYCOENAADARAhvAAAAJkJ4AwAAMBHCGwAAgIkQ3gAAAEyE8AYAAGAihDcAAAATIbwBAACYCOENAABc0pkzksVS+XPmjLdnA8IbAACAiRDeAAAATITwBgAAYCKENwAAcEkVFT/+/9tvOz6G5xHeAABAjTZskHr1+vHxiBFSt26V7fAOwhsAAHBqwwZp7Fjp0CHH9kOHKtsJcN5BeAMAANVUVEhTp0qGUX1ZVdu0aRxC9QbCGwAAqOYf/5C+/bbm5YYhffNNZT94FuENAABU8913ru0H1/Hz9gQAAI1fRUXlHprvvpPCwqTBg6Xmzb09K1xKWJhr+8F12PMGAHCrDRsqr04cOlQaP77yv1yt6PsGD5auuqryK7GcsVikzp0r+8GzCG8AALepulrx4nOnuFrR9zVvLi1dWvn/Fwe4qsfPPsseVG8gvAEA3IKrFc0vLk56/XWpY0fH9quuqmyPi/POvJo6whsAwC24WrFxiIuT9u798fGWLdL+/QQ3byK8AQDcgqsVG48LD43efDOHSr2N8AYAcAuuVgTcg/AGAHALrlZsPIKCKg9zG0bl/8O7CG8AgMs6c6YybFkslf9fG1ytCLgH4Q0A4DZVVyt26uTYztWKQP3xDQsAgMu68HYeb78txcTUfo9ZXJw0ahTfsAC4CnveAACXtGGD1KvXj49HjKj7NyQ0by5FR0t33135X4IbUH+ENwBAjaq+IeHQIcd2viEB8B7CGwDAKb4hAfBNhDcAgFN8QwLgmwhvAACn+IYEwDcR3gAATvENCYBvIrwBAJziGxIA30R4AwA4xTckAL6J8AYAqFHVNyR07OjYzjckAN7j9fC2c+dOTZo0SeHh4QoKClKnTp00atQoffjhh9X6fvTRR/r5z3+u4OBgtWnTRnFxcfrqq6+cjrts2TKFh4fLarWqe/fuSk1NVVlZWbV+R48e1cSJExUaGqrAwEBFRUUpOzvb5esJAGYVFyft3fvj4y1bpP37CW6At3g9vD333HM6cOCApk6dqi1btmjp0qU6evSoBg0apJ07d9r77du3T9HR0Tp37pxee+01vfjii/r88881ePBgHTt2zGHMhQsXaurUqYqLi9O2bds0ZcoUpaWl6cEHH3ToV1paqmHDhik7O1tLly7Vm2++qfbt2ys2Nla7d+/2yPoDgBlceGj05ps5VAp4k9e/2zQ9PV3t2rVzaIuNjVWPHj2UlpamW265RZI0f/58Wa1Wbd68Wa1bt5YkRUZG6tprr9WSJUv0xBNPSJIKCwu1YMECTZ48WWlpaZKk6OholZWVKTk5WdOmTVOv/37Py8qVK5Wfn693331XUVFRkqShQ4cqIiJCM2fO1J49ezxSAwAAgNry+p63i4ObJAUHB6tXr1765ptvJEnl5eXavHmz7rzzTntwk6SuXbtq6NCh2rhxo70tMzNTJSUlio+PdxgzPj5ehmFo06ZN9raNGzeqZ8+e9uAmSX5+fpowYYI++OADHbr4+2AAoIkKCqq8Ka9hVP4/AO/x+p43Z3744Qd99NFH9r1uX375pc6ePau+fftW69u3b19lZWWppKREAQEBys/PlyT16dPHoV9YWJhCQ0PtyyUpPz9fg51c4171Op988ok6derkdI6lpaUqLS21Pz558qQkqayszOm5dQ1VNaY7xkYlauwZ1Nn9qLFnUGf3a2o1ru16+mR4e/DBB3XmzBnNnTtXUuWhUEmy2WzV+tpsNhmGoePHjyssLEyFhYWyWq0KcvLR0Gaz2ceqGremMS98XWcWLVqk1NTUau3bt29XYGDgZdaw/rKystw2NipRY8+gzu5HjT2DOrtfU6lxcXFxrfrVO7w99thjSkhIUMeLrx+X9N133+mFF17Q/Pnz6zzuvHnz9PLLL2vZsmWKjIx0WGap6U6RFy2rbb+69r3Q7NmzNX36dPvjkydPqnPnzoqJiXE4tOsqZWVlysrK0vDhw+Xv7+/y8UGNPYU6ux819gzq7H5NrcZVR/Eup97hLTU1VbGxsU7D2+HDh5Wamlrn8JaamqoFCxZo4cKFeuihh+ztISEhkpzvCSsqKpLFYlGbNm3sfUtKSlRcXFxtD1hRUZFDIAwJCalxTMn5nr4qVqtVVqu1Wru/v79bNzB3jw9q7CnU2f2osWdQZ/drKjWu7TrW+4IFwzBqXHb69Ok6Fzk1NVUpKSlKSUnRnDlzHJZdc801atmypfLy8qo9Ly8vTz169FBAQICkH891u7jvkSNHVFBQoN69e9vb+vTpU+OYkhz6AgAA+II67Xn7+OOP9a9//cv+eMuWLdq3b59Dn7Nnz+rll1/WNddcU+txH3/8caWkpCg5OVmPPvpo9Un6+emOO+7Qhg0b9Ic//EGtWrWSJH399dfKycnRI488Yu8bGxurgIAAZWRkaODAgfb2jIwMWSwWjR492t42ZswYTZkyRXv27LH3LS8v15o1azRw4ECnexUBAAC8qU7hbePGjfaT9C0Wix577DGn/Vq2bKlVq1bVasynnnpK8+fPV2xsrG6//Xa9//77DssHDRokqXLPXP/+/TVy5EglJSWppKRE8+fPV2hoqGbMmGHvb7PZlJycrHnz5slmsykmJka5ublKSUlRQkKC/R5vkjRp0iSlp6dr3LhxWrx4sdq1a6fly5frs88+044dO+pSGgAAAI+oU3i7//77NXLkSBmGoQEDBmjVqlXVDi1arVb7Yc7aeOuttyRV3p8tMzOz2vKqw7Ph4eHatWuXZs2apbFjx8rPz0+33HKLlixZorZt2zo8Z+7cuWrVqpXS09O1ZMkSdejQQUlJSfarVy+ca3Z2tmbOnKmHH35YxcXF6tevn7Zu3aohQ4bUui4AAACeUqfwFhYWprCwMElSTk6OIiMjFRwc3KAJ7Nq1q9Z9IyMja71HLDExUYmJiZft1759e61evbrWcwAAAPCmel9typ4pAAAAz2vQTXrXrFmjtWvX6uDBgzp79qzDMovFoi+//LJBkwMAAICjeoe3J554QrNnz1avXr0UERHh9J5nAAAAcK16h7cVK1bowQcf1LJly1w5HwAAAFxCvW/Se+TIEY0ZM8aVcwEAAMBl1Du8RUZGck4bAACAh9U7vD399NN66qmn9OGHH7pyPgAAALiEep/zFh8fr8LCQg0YMEAdOnSwf3l8FYvFon//+98NniAAAAB+VO/wFhISotDQUFfOBQAAAJdR7/BWl29GAAAAgGvU+5w3AAAAeF6997y9/fbbl+1z880313d4AAAAOFHv8BYdHS2LxXLJPhUVFfUdHgAAAE7UO7zl5ORUaysoKNCbb76pd955R+np6Q2aGAAAAKqrd3gbMmSI0/Y777xT//u//6vMzEzFxsbWe2IAAACozi0XLIwZM0br1q1zx9AAAABNmlvC2/Hjx1VaWuqOoQEAAJq0eh82/frrr6u1lZaW6uOPP9bs2bM1aNCgBk0MAAAA1dU7vHXr1s3p1aaGYahnz57605/+1KCJAQAAoLp6h7cXX3yxWngLCAhQt27d1L9/fzVrxv1/AQAAXK3e4W3ixIkunAYAAABqo97hrcqpU6f03nvvqbCwUKGhoRo0aJBatWrlirkBAADgIg0Kb0uWLFFqaqqKi4tlGIYkKSgoSKmpqZo+fbpLJggAAIAf1Tu8/fWvf9XMmTN12223aeLEierYsaMOHz6s1atX6/e//73atm2re+65x5VzBQAAaPLqHd6eeeYZjR8/XmvWrHFoHzdunCZMmKBnnnmG8AYAAOBi9b4kdN++fZowYYLTZRMmTNCnn35a70kBAADAuXqHt5YtW6qoqMjpsqKiIrVs2bLekwIAAIBz9Q5vgwcPVkpKig4fPuzQfuTIET322GO6+eabGzw5AAAAOKr3OW9paWn66U9/qh49emjYsGEKCwvTd999p507d8rf318bNmxw5TwBAACgBux5u/7665Wbm6tRo0YpNzdXq1atUm5urkaPHq0PPvhAvXr1cuU8AQAAoAbe5+26667TK6+84qq5AAAA4DLqvOctLy9P3377bY3Lv/32W+Xl5TVoUgAAAHCuTuHt7bffVmRkpL7//vsa+3z//feKjIzUtm3bGjw5AAAAOKpTeEtPT9fYsWMVGRlZY5/IyEj96le/0l/+8pcGTw4AAACO6hTe3nnnHY0ePfqy/X7xi1/o/fffr++cAAAAUIM6hbdjx46pU6dOl+0XFhamo0eP1ntSAAAAcK5O4S0oKKjGb1W40PHjxxUYGFjvSQEAAMC5OoW366+/XpmZmZftt3XrVl1//fX1nhQAAACcq1N4+9WvfqWVK1dq9+7dNfbJycnRqlWrdPfddzd4cgAAAHBUp5v03n///crIyFBMTIwSEhI0atQode/eXZK0f/9+bdq0SStXrlRERIQmT57slgkDAAA0ZXUKby1atNC2bdt0zz336LnnntPzzz/vsNwwDN12223661//qhYtWrh0ogAAAKjH12OFhIRoy5Yt+vDDD7V9+3Z98803kqQuXbro1ltv1Q033ODySQIAAKBSvb/bNDIy8pI36wUAAIDr1emChb59+9b6JyIiolZjnjp1SjNnzlRMTIzatm0ri8WilJSUav0mTpwoi8VS7Sc8PNzpuMuWLVN4eLisVqu6d++u1NRUlZWVVet39OhRTZw4UaGhoQoMDFRUVJSys7PrUhYAAACPqdOeN5vNJovFcsk+p0+f1ocffnjZflUKCwu1YsUKRUREaPTo0Zf8Wq2WLVtq586d1doutnDhQs2bN09JSUmKiYlRbm6ukpOTdejQIa1YscLer7S0VMOGDdOJEye0dOlStWvXTunp6YqNjdWOHTs0ZMiQWq0DAACAp9QpvO3atavGZeXl5VqxYoUee+wxWSwWjR8/vlZjdu3aVcePH5fFYlFBQcElw1uzZs00aNCgS45XWFioBQsWaPLkyUpLS5MkRUdHq6ysTMnJyZo2bZp69eolSVq5cqXy8/P17rvvKioqSpI0dOhQRUREaObMmdqzZ0+t1gEAAMBT6nTYtCbr169Xr1699PDDDysiIkIffvihXnrppVo9t+rwp6tkZmaqpKRE8fHxDu3x8fEyDEObNm2yt23cuFE9e/a0BzdJ8vPz04QJE/TBBx/o0KFDLpsXAACAK9T7ggWpck/crFmzlJubqxtvvFHbt2/XsGHDXDW3as6ePasOHTro2LFjCgsL0+jRo/XYY4/JZrPZ++Tn50uS+vTp4/DcsLAwhYaG2pdX9R08eHC11+nbt68k6ZNPPqnxu1xLS0tVWlpqf3zy5ElJUllZmdNz6xqqakx3jI1K1NgzqLP7UWPPoM7u19RqXNv1rFd4y8vL06xZs7Rt2zZ1795da9eu1V133VWfoWotIiJCERER6t27tyRp9+7deuaZZ5Sdna3c3FwFBwdLqjxsarVaFRQUVG0Mm82mwsJC++PCwkKH4Hdhv6rlNVm0aJFSU1OrtW/fvt2t3+ualZXltrFRiRp7BnV2P2rsGdTZ/ZpKjYuLi2vVr07h7ZtvvlFycrLWrl0rm82mZ599Vv/7v/8rf3//ek2yLh555BGHx8OHD9cNN9ygsWPH6oUXXnBYfqnDsBcvq0vfC82ePVvTp0+3Pz558qQ6d+6smJgYtW7dusbn1VdZWZmysrI0fPhwj9S7KaLGnkGd3Y8aewZ1dr+mVuOqo3iXU6fwdt111+ncuXOKjY3VzJkz1apVK+Xl5dXY/8Ybb6zL8HU2ZswYBQUF6f3337e3hYSEqKSkRMXFxdX2gBUVFTncmy4kJMTp3rWioiJJcrpXrorVapXVaq3W7u/v79YNzN3jgxp7CnV2P2rsGdTZ/ZpKjWu7jnUKb1XneG3dulWZmZk19jMMQxaLRRUVFXUZvl4Mw1CzZj9ed1F1rlteXp4GDhxobz9y5IgKCgrsh12r+joLn1VtF/YFAADwBXUKb6tWrXLXPOrl9ddfV3FxscPtQ2JjYxUQEKCMjAyH8JaRkSGLxaLRo0fb28aMGaMpU6Zoz5499r7l5eVas2aNBg4cqI4dO3psXQAAAGqjTuHtvvvuc8sktm7dqjNnzujUqVOSpL179+r111+XJI0YMULHjh3T+PHjddddd6lHjx6yWCzavXu3nn32WV1//fVKSEiwj2Wz2ZScnKx58+bJZrPZb9KbkpKihIQE+z3eJGnSpElKT0/XuHHjtHjxYrVr107Lly/XZ599ph07drhlXQEAABqiQbcKcZXf/va3OnjwoP3x+vXrtX79eknS/v37dcUVV6h9+/Z6+umn9f3336uiokJdu3ZVYmKi5syZU+3K0rlz56pVq1ZKT0/XkiVL1KFDByUlJWnu3LkO/axWq7KzszVz5kw9/PDDKi4uVr9+/bR161a+XQEAAPgknwhvBw4cuGyfDRs21GnMxMREJSYmXrZf+/bttXr16jqNDQAA4C0u+YYFAAAAeAbhDQAAwEQIbwAAACZCeAMAADARwhsAAICJEN4AAABMhPAGAABgIoQ3AAAAEyG8AQAAmAjhDQAAwEQIbwAAACZCeAMAADARwhsAAICJEN4AAABMhPAGAABgIoQ3AAAAEyG8AQAAmAjhDQAAwEQIbwAAACZCeAMAADARwhsAAICJEN4AAABMhPAGAABgIoQ3AAAAEyG8AQAAmAjhDQAAwEQIbwAAACZCeAMAADARwhsAAICJEN4AAABMhPAGAABgIoQ3AAAAEyG8AQAAmAjhDQAAwEQIbwAAACZCeAMAADARwhsAAICJEN4AAABMhPAGAABgIoQ3AAAAEyG8AQAAmAjhDQAAwEQIbwAAACbi9fB26tQpzZw5UzExMWrbtq0sFotSUlKc9v3oo4/085//XMHBwWrTpo3i4uL01VdfOe27bNkyhYeHy2q1qnv37kpNTVVZWVm1fkePHtXEiRMVGhqqwMBARUVFKTs725WrCAAA4DJeD2+FhYVasWKFSktLNXr06Br77du3T9HR0Tp37pxee+01vfjii/r88881ePBgHTt2zKHvwoULNXXqVMXFxWnbtm2aMmWK0tLS9OCDDzr0Ky0t1bBhw5Sdna2lS5fqzTffVPv27RUbG6vdu3e7Y3UBAAAaxM/bE+jatauOHz8ui8WigoIC/eUvf3Hab/78+bJardq8ebNat24tSYqMjNS1116rJUuW6IknnpBUGQYXLFigyZMnKy0tTZIUHR2tsrIyJScna9q0aerVq5ckaeXKlcrPz9e7776rqKgoSdLQoUMVERGhmTNnas+ePe5efQAAgDrx+p43i8Uii8VyyT7l5eXavHmz7rzzTntwkyqD39ChQ7Vx40Z7W2ZmpkpKShQfH+8wRnx8vAzD0KZNm+xtGzduVM+ePe3BTZL8/Pw0YcIEffDBBzp06FAD1w4AAMC1vL7nrTa+/PJLnT17Vn379q22rG/fvsrKylJJSYkCAgKUn58vSerTp49Dv7CwMIWGhtqXS1J+fr4GDx7sdExJ+uSTT9SpUyencyotLVVpaan98cmTJyVJZWVlTs+ta6iqMd0xNipRY8+gzu5HjT2DOrtfU6txbdfTFOGtsLBQkmSz2aots9lsMgxDx48fV1hYmAoLC2W1WhUUFOS0b9VYVePWNOaFr+vMokWLlJqaWq19+/btCgwMvPxK1VNWVpbbxkYlauwZ1Nn9qLFnUGf3ayo1Li4urlU/U4S3Kpc6vHrhstr2q2vfC82ePVvTp0+3Pz558qQ6d+6smJgYh0O7rlJWVqasrCwNHz5c/v7+Lh8f1NhTqLP7UWPPoM7u19RqXHUU73JMEd5CQkIkOd8TVlRUJIvFojZt2tj7lpSUqLi4uNoesKKiIkVGRjqMW9OYkvM9fVWsVqusVmu1dn9/f7duYO4eH9TYU6iz+1Fjz6DO7tdUalzbdfT6BQu1cc0116hly5bKy8urtiwvL089evRQQECApB/Pdbu475EjR1RQUKDevXvb2/r06VPjmJIc+gIAAPgCU4Q3Pz8/3XHHHdqwYYNOnTplb//666+Vk5OjuLg4e1tsbKwCAgKUkZHhMEZGRoYsFovDveTGjBmjffv2OdwSpLy8XGvWrNHAgQPVsWNHt60TAABAffjEYdOtW7fqzJkz9mC2d+9evf7665KkESNGKDAwUKmpqerfv79GjhyppKQklZSUaP78+QoNDdWMGTPsY9lsNiUnJ2vevHmy2WyKiYlRbm6uUlJSlJCQYL/HmyRNmjRJ6enpGjdunBYvXqx27dpp+fLl+uyzz7Rjxw7PFgEAAKAWfCK8/fa3v9XBgwftj9evX6/169dLkvbv369u3bopPDxcu3bt0qxZszR27Fj5+fnplltu0ZIlS9S2bVuH8ebOnatWrVopPT1dS5YsUYcOHZSUlKS5c+c69LNarcrOztbMmTP18MMPq7i4WP369dPWrVs1ZMgQ9684AABAHflEeDtw4ECt+kVGRtZ6j1hiYqISExMv2699+/ZavXp1rcYEAADwNlOc8wYAAIBKhDcAAAATIbwBAACYCOENAADARAhvAAAAJkJ4AwAAMBHCGwAAgIkQ3gAAAEyE8AYAAGAihDcAAAATIbwBAACYCOENAADARAhvAAAAJkJ4AwAAMBHCGwAAgIkQ3gAAAEyE8AYAAGAihDcAAAATIbwBAACYCOENAADARAhvAAAAJkJ4AwAAMBHCGwAAgIkQ3gAAAEyE8AYAAGAihDcAAAATIbwBAACYCOENAADARAhvAAAAJkJ4AwAAMBHCGwAAgIkQ3gAAAEyE8AYAAGAihDcAAAATIbwBAACYCOENAADARAhvAAAAJkJ4AwAAMBHCGwAAgIkQ3gAAAEyE8AYAAGAihDcAAAATIbwBAACYiGnC265du2SxWJz+vP/++w59P/roI/385z9XcHCw2rRpo7i4OH311VdOx122bJnCw8NltVrVvXt3paamqqyszBOrBAAAUGd+3p5AXaWlpWno0KEObb1797b//759+xQdHa1+/frptddeU0lJiebPn6/BgwfrX//6l9q2bWvvu3DhQs2bN09JSUmKiYlRbm6ukpOTdejQIa1YscJj6wQAAFBbpgtv1157rQYNGlTj8vnz58tqtWrz5s1q3bq1JCkyMlLXXnutlixZoieeeEKSVFhYqAULFmjy5MlKS0uTJEVHR6usrEzJycmaNm2aevXq5f4VAgAAqAPTHDatjfLycm3evFl33nmnPbhJUteuXTV06FBt3LjR3paZmamSkhLFx8c7jBEfHy/DMLRp0yZPTRsAAKDWTLfn7cEHH9Rdd92lwMBARUVFad68ebrpppskSV9++aXOnj2rvn37Vnte3759lZWVpZKSEgUEBCg/P1+S1KdPH4d+YWFhCg0NtS+vSWlpqUpLS+2PT548KUkqKytzyzlzVWNyPp77UGPPoM7uR409gzq7X1OrcW3X0zTh7YorrtDUqVMVHR2tkJAQ/ec//9GTTz6p6Oho/f3vf9ett96qwsJCSZLNZqv2fJvNJsMwdPz4cYWFhamwsFBWq1VBQUFO+1aNVZNFixYpNTW1Wvv27dsVGBhYz7W8vKysLLeNjUrU2DOos/tRY8+gzu7XVGpcXFxcq36mCW833HCDbrjhBvvjwYMHa8yYMerTp49mzpypW2+91b7MYrHUOM6Fy2rbz5nZs2dr+vTp9scnT55U586dFRMT43DI1lXKysqUlZWl4cOHy9/f3+Xjgxp7CnV2P2rsGdTZ/ZpajauO4l2OacKbM23atNHIkSP1/PPP6+zZswoJCZEkp3vNioqKZLFY1KZNG0lSSEiISkpKVFxcXG1PWVFRkSIjIy/52larVVartVq7v7+/Wzcwd48Pauwp1Nn9qLFnUGf3ayo1ru06mv6CBcMwJFXuKbvmmmvUsmVL5eXlVeuXl5enHj16KCAgQNKP57pd3PfIkSMqKChwuP0IAACArzB1eDt+/Lg2b96sfv36KSAgQH5+frrjjju0YcMGnTp1yt7v66+/Vk5OjuLi4uxtsbGxCggIUEZGhsOYGRkZslgsGj16tIfWAgAAoPZMc9h0/Pjx6tKli/7nf/5HoaGh+uKLL/TUU0/p+++/dwhgqamp6t+/v0aOHKmkpCT7TXpDQ0M1Y8YMez+bzabk5GTNmzdPNpvNfpPelJQUJSQkcI83AADgk0wT3vr27atXX31Vzz//vE6fPi2bzaabbrpJL730kvr372/vFx4erl27dmnWrFkaO3as/Pz8dMstt2jJkiUO364gSXPnzlWrVq2Unp6uJUuWqEOHDkpKStLcuXM9vXoAAAC1YprwlpSUpKSkpFr1jYyM1I4dO2rVNzExUYmJiQ2ZGgAAgMeY+pw3AACApobwBgAAYCKENwAAABMhvAEAAJgI4Q0AAMBECG8AAAAmQngDAAAwEcIbAACAiRDeAAAATITwBgAAYCKENwAAABMhvAEAAJgI4Q0AAMBECG8AAAAmQngDAAAwEcIbAACAiRDeAAAATITwBgAAYCKENwAAABMhvAEAAJgI4Q0AAMBECG8AAAAmQngDAAAwEcIbAACAiRDeAAAATITwBgAAYCKENwAAABMhvAEAAJgI4Q0AAMBECG8AAAAmQngDAAAwEcIbAACAiRDeAAAATITwBgAAYCKENwAAABMhvAEAAJgI4Q0AAMBECG8AAAAmQngDAAAwEcIbAACAiRDeAAAATITwBgAAYCKENwAAABMhvEk6ffq0pk2bpo4dOyogIED9+vXTunXrvD0tAACAavy8PQFfEBcXp9zcXC1evFjXXXed1q5dq7vvvlvnz5/X+PHjvT09AAAAuyYf3rZs2aKsrCx7YJOkoUOH6uDBg/r973+vX/3qV2revLmXZwkAAFCpyR823bhxo4KDgzVu3DiH9vj4eB0+fFh79uzx0swAAACqa/J73vLz8/WTn/xEfn6Opejbt699+U9/+tNqzystLVVpaan98Q8//CBJKioqUllZmcvnWVZWpuLiYhUWFsrf39/l44Maewp1dj9q7BnU2f2aWo1PnTolSTIM45L9mnx4Kyws1NVXX12t3Waz2Zc7s2jRIqWmplZr7969u2snCAAAmpRTp07piiuuqHF5kw9vkmSxWOq8bPbs2Zo+fbr98fnz51VUVKSQkJBLjldfJ0+eVOfOnfXNN9+odevWLh8f1NhTqLP7UWPPoM7u19RqbBiGTp06pY4dO16yX5MPbyEhIU73rhUVFUn6cQ/cxaxWq6xWq0NbmzZtXD6/i7Vu3bpJbMDeRI09gzq7HzX2DOrsfk2pxpfa41alyV+w0KdPH3366acqLy93aM/Ly5Mk9e7d2xvTAgAAcKrJh7cxY8bo9OnTeuONNxzaV69erY4dO2rgwIFemhkAAEB1Tf6w6W233abhw4frt7/9rU6ePKkePXrolVdeUWZmptasWeMz93izWq169NFHqx2qhetQY8+gzu5HjT2DOrsfNXbOYlzuetQm4PTp05o7d65ee+01FRUVKTw8XLNnz9Zdd93l7akBAAA4ILwBAACYSJM/5w0AAMBMCG8AAAAmQnhzg9OnT2vatGnq2LGjAgIC1K9fP61bt+6yz9uxY4eGDx+ujh07ymq1ql27drrlllu0ZcsWh34HDhyQxWKp8Sc2NrZWfWszJ1/m7jpLlV+D9uSTT6p3794KCgpS+/btddttt+ndd9+t1resrEypqanq1q2brFarwsPDtWzZMpesq7f4Uo0b67bsiRqfO3dO8+fPV/fu3dWiRQt17dpVs2fP1tmzZ6v1bYzbseRbdWZbvrTk5GRZLJYab9W1Y8cORUVFKTAwUKGhoZo4caKOHj1arV9j3ZYlSQZcbvjw4UabNm2M559/3ti5c6eRkJBgSDJefvnlSz5v3bp1xtSpU41169YZu3btMjZs2GDExMQYkoyXXnrJ3q+kpMR47733qv3MmjXLkGQ8//zz9r779+83JBkPP/xwtf4FBQVuq4EnuLvOhmEY99xzj9GsWTNj7ty5RnZ2trF+/XojMjLS8PPzM/bs2ePQNyEhwbBarcYf/vAHIycnx0hKSjIsFouxcOFCl6+7p/hSjRvrtuyJGsfFxRkBAQFGWlqakZWVZTz22GNGixYtjDvuuKPauI1xOzYM36oz23LN/u///s+wWq1G+/btjeuvv77a8l27dhl+fn7GqFGjjO3btxtr1qwxOnXqZPTu3dsoKSlx6NtYt2XDMAzCm4v9/e9/NyQZa9eudWgfPny40bFjR6O8vLxO4507d87o1KmTMXjw4Mv2jY6ONgIDA40ffvjB3lb1R+LJJ5+s0+v6Ok/UuaSkxGjevLkxYcIEh76HDx82JBmJiYn2tvz8fMNisRhpaWkOfSdPnmy0bNnSKCwsrNN8fIGv1bgxbsueqPF7771nSDKeeuoph75paWmGJGP79u32tsa4HRuG79WZbdm5srIyo1+/fkZiYqIxZMgQp+Gtf//+Rq9evYyysjJ72zvvvGNIMpYvX25va6zbchUOm7rYxo0bFRwcrHHjxjm0x8fH6/Dhw9qzZ0+dxvP391ebNm3k53fpW/J9+eWX2r17t375y182ia8Q8USdmzVrpmbNmlX7qpLWrVurWbNmCggIsLdt2rRJhmEoPj6+2nzOnj2rzMzMOs3HF/hajRsjT9T4nXfekSSNGDHCoe/IkSMlyeEG5Y1xO5Z8r86NkStqvHjxYhUVFWnhwoVOlx86dEi5ubm65557HGr/05/+VNddd502btxob2us23IVwpuL5efn6yc/+Um1sNW3b1/78ss5f/68ysvLdfjwYT366KP6/PPPNWPGjEs+58UXX5RhGEpISHC6fPHixWrRooUCAwN100036W9/+1st18g3eaLO/v7+mjJlilavXq1Nmzbp5MmTOnDggCZPnqwrrrhCkydPdphP27Zt1aFDh3rPx9f4Wo2rNKZt2RM1PnfunCRVu8lp1eOPP/7YYT6NbTuWfK/OVdiWf7R3714tWLBAzz33nIKDg2t8jQvHvPh1LnyNxrotV2ny37DgaoWFhbr66qurtVd9wX1hYeFlxxgxYoS2bdsmqXIPxKuvvqrbb7+9xv4VFRVavXq1wsPD9bOf/cxhmdVq1eTJkzV8+HCFhYXp66+/1rJlyzRq1Ci98MILNYY9X+epOj/zzDO64oordOedd+r8+fOSpC5dumjnzp3q0aOHw3yqXvtCQUFBatGiRa3m42t8rcaNcVv2RI179eolqXLPUPfu3e3t//znP6u9RmPcjiXfqzPbsqPz589r0qRJiouLq7bn8uLXuHDMi1+nKWzLVQhvbmCxWOq1rMqyZct04sQJfffdd1qzZo1+9atfafXq1br77rud9s/MzNShQ4f05JNPVlsWFhamFStWOLSNGzdOAwcOVFJSkiZOnHjZQ7K+yhN1XrhwoZYsWaKUlBQNHjxYJ0+e1J/+9CcNHz5c27dv1w033OCy+fgiX6pxY92W3V3j2267TT169NCsWbPUvn179e/fX++//77mzJmj5s2bq1kzxwMwjXE7lnyrzmzLjp5++ml98cUXtd7zWNNYF7c31m1ZElebutqgQYOM/v37V2vPz883JBl//vOf6zxmbGysceWVVxoVFRVOl48ZM8bw9/c3vv/++1qPuXjxYkOSsXfv3jrPxxd4os579+41LBZLtZOKz507Z/To0cOIjo62t911111G27Ztq415+vRpQ5Ixe/bsOs/H23ytxjUx87bsqb8XX3zxhTFo0CBDkiHJCAoKMpYuXWqEhoYaw4YNs/drjNuxYfhenWvSFLflgwcPGi1btjSWLl1qHD9+3P7zs5/9zPjJT35iHD9+3CguLjYMwzAyMzMNScbf//73auOMHTvWCAsLsz9urNtyFc55c7E+ffro008/VXl5uUN7Xl6eJNV435pLGTBggI4fP65jx45VW3b06FFt3rxZv/jFL9SuXbtaj2n891vRLv7UbRaeqPO///1vGYah/v37O/Tz9/dXRESEwzkTffr00bFjx3TkyBGXzcfbfK3GNTHztuypvxc9evTQe++9p2+//VYff/yxjh49qnHjxqmgoEA333yzw3wa23Ys+V6da9IUt+WvvvpKZ8+e1dSpU3XllVfaf9555x19+umnuvLKKzV79myHMarGvPh1LnyNxrot23k1OjZCW7ZsMSQZ69atc2iPjY2t1yXp58+fN4YMGWK0adPG4dLoKk8++aQhydiyZUutxzx37pzRr18/IzQ0tM7z8RWeqPPu3bsNScbixYsd+paUlBjdu3c3+vXrZ2+ruiz94r4PPPCAaS9L97UaO2P2bdnTfy8u9MgjjxhBQUHGt99+a29rjNuxYfhenZ1pqtvy8ePHjZycnGo/ERERRrdu3YycnBzjiy++sPcfMGCA0bt3b4fxqm7T8txzz9nbGuu2XIXw5gbDhw83rrzySmPFihXGzp07jcmTJxuSjDVr1tj7TJo0yWjevLlx4MABe9svfvELY968ecYbb7xh7Nq1y1i7dq39ZpDp6elOXys8PNzo3LlzjYdUH3nkEeOhhx4yXnnlFSMnJ8f461//avTv39+QZKxatcql6+1p7q5zRUWF0b9/fyMgIMCYP3++sWPHDuONN94woqOjnd6gs+qGkE8++aSxa9cuY86cOaa/IaQv1bixbsue+HvxxBNPGKtXrzZycnKMdevWGXFxcUazZs2c3jy1MW7HhuFbdWZbdqyxMzXd5y0nJ8fw8/MzxowZY2RlZRkvv/yy0blz50vepLexbcuGQXhzi1OnThmJiYlGhw4djBYtWhh9+/Y1XnnlFYc+9913nyHJ2L9/v73tiSeeMPr3729ceeWVRvPmzY2QkBDj1ltvNTZv3uz0dapuTDh//vwa57Jy5UpjwIABhs1mM/z8/Iwrr7zSuPXWW41t27a5ZF29yRN1PnHihDF37lzjJz/5iREYGGi0a9fOiI6Odrqn89y5c8ajjz5qdOnSxWjRooVx3XXXGX/84x9dvt6e5Es1bqzbsidqnJqaalxzzTWG1Wo12rRpY8TGxhpvv/220/k0xu3YMHyrzmzLjjV2pqbwZhiGsX37dmPQoEFGQECAYbPZjHvvvdfpOd+NdVs2DMOwGMZ/D7IDAADA55nvrEgAAIAmjPAGAABgIoQ3AAAAEyG8AQAAmAjhDQAAwEQIbwAAACZCeAMAADARwhsAAICJEN4A+IQDBw7IYrEoIyPD5WNnZ2frf/7nfxQUFCSLxaJNmzYpIyNDFotFBw4ccPnrXahbt26aOHGiW1/DDPbu3auUlBS31xtoCvy8PQEAcCfDMPTLX/5S1113nf72t78pKChIPXv2VHl5ud577z2FhYV5e4pNwt69e5Wamqro6Gh169bN29MBTI3wBqBRO3z4sIqKijRmzBgNGzbMYVnbtm29NCvvKisrk8VikZ9f9beA4uJiBQYGemFWAGqLw6YA3Oo///mP4uPjde211yowMFCdOnXSHXfcoby8vMs+99ixY7r//vvVuXNnWa1WtW3bVj/72c+0Y8eOWr12SkqKrrrqKknSrFmzZLFY7Ht9nB02jY6OVu/evZWbm6vBgwcrMDBQV199tRYvXqzz58/b+5WUlGjGjBnq16+frrjiCtlsNkVFRenNN9+sfWEuY+3atYqKilJwcLCCg4PVr18/rVy50r68psOx0dHRio6Otj/etWuXLBaLXnrpJc2YMUOdOnWS1WrVf/7zH02cOFHBwcHKy8tTTEyMWrVqZQ+4586d04IFCxQeHm6vfXx8vI4dO+bwet26ddPIkSOVmZmpG2+8US1btlR4eLhefPFFe5+MjAyNGzdOkjR06FBZLBa3HSIHmgL2vAFwq8OHDyskJESLFy9W27ZtVVRUpNWrV2vgwIH6v//7P/Xs2bPG595zzz366KOPtHDhQl133XU6ceKEPvroIxUWFtbqtRMSEhQREaG4uDg9/PDDGj9+vKxW6yWfc+TIEf3617/WjBkz9Oijj2rjxo2aPXu2OnbsqHvvvVeSVFpaqqKiIv3ud79Tp06ddO7cOe3YsUNxcXFatWqVvV99zZ8/X48//rji4uI0Y8YMXXHFFcrPz9fBgwfrPebs2bMVFRWl559/Xs2aNVO7du0kVYa0X/ziF3rggQeUlJSk8vJynT9/XqNGjdI//vEPzZw5Uz/96U918OBBPfroo4qOjtb/+3//Ty1btrSP/e9//1szZsxQUlKS2rdvr7/85S/6zW9+ox49eujmm2/W7bffrrS0NM2ZM0fp6em68cYbJUnXXHNNg+oENFkGAHhQeXm5ce7cOePaa681HnnkEXv7/v37DUnGqlWr7G3BwcHGtGnTGvR6VeM++eSTDu2rVq0yJBn79++3tw0ZMsSQZOzZs8ehb69evYxbb731kutUVlZm/OY3vzFuuOEGh2Vdu3Y17rvvvlrP96uvvjKaN29u/PrXv75kv5rGHTJkiDFkyBD745ycHEOScfPNN1fre9999xmSjBdffNGh/ZVXXjEkGW+88YZDe25uriHJWL58ucM8AgICjIMHD9rbzp49a9hsNuOBBx6wt61fv96QZOTk5FxyvQBcHodNAbhVeXm50tLS1KtXL7Vo0UJ+fn5q0aKFvvjiC3366aeXfO6AAQOUkZGhBQsW6P3331dZWZnb59uhQwcNGDDAoa1v377V9nqtX79eP/vZzxQcHCw/Pz/5+/tr5cqVl12ny8nKylJFRYUefPDBBo1zsTvvvLPWyzZv3qw2bdrojjvuUHl5uf2nX79+6tChg3bt2uXQv1+/furSpYv9cUBAgK677roG7SkEUDPCGwC3mj59uubNm6fRo0frrbfe0p49e5Sbm6uIiAidPXv2ks999dVXdd999+kvf/mLoqKiZLPZdO+99+rIkSNum29ISEi1NqvV6jDXDRs26Je//KU6deqkNWvW6L333lNubq4mTZqkkpKSBr1+1TllVefquUpNV9UGBgaqdevWDm3ff/+9Tpw4oRYtWsjf39/h58iRIyooKHDoX5uaAXAdznkD4FZr1qzRvffeq7S0NIf2goICtWnT5pLPDQ0N1bPPPqtnn31WX3/9tf72t78pKSlJR48eVWZmphtnfWlr1qxR9+7d9eqrr8pisdjbS0tLGzx21RWw3377rTp37lxjv4CAAKevV1BQoNDQ0GrtF87zcu2hoaEKCQmpscatWrWqcV4A3I/wBsCtLBZLtYsE/v73v+vQoUPq0aNHrcfp0qWLHnroIWVnZ+udd95x9TTrxGKxqEWLFg7B58iRIy652jQmJkbNmzfXc889p6ioqBr7devWTR9//LFD2+eff67PPvvMaXiri5EjR2rdunWqqKjQwIEDGzRWlaptgL1xQMMR3gC41ciRI5WRkaHw8HD17dtXH374oZ588snLHhb84YcfNHToUI0fP17h4eFq1aqVcnNzlZmZqbi4OA/N3rmRI0dqw4YNmjJlisaOHatvvvlGjz/+uMLCwvTFF180aOxu3bppzpw5evzxx3X27FndfffduuKKK7R3714VFBQoNTVVUuWVuBMmTNCUKVN055136uDBg/rDH/7gknvX3XXXXXr55Zc1YsQITZ06VQMGDJC/v7++/fZb5eTkaNSoURozZkydxuzdu7ckacWKFWrVqpUCAgLUvXt3p4dcAVwa4Q2AWy1dulT+/v5atGiRTp8+rRtvvFEbNmxQcnLyJZ8XEBCggQMH6qWXXtKBAwdUVlamLl26aNasWZo5c6aHZu9cfHy8jh49queff14vvviirr76aiUlJenbb7+1h6uGeOyxx3Tttddq2bJl+vWvfy0/Pz9de+21SkxMtPcZP368Dh8+rOeff16rVq1S79699dxzz7nk9Zs3b66//e1vWrp0qV566SUtWrRIfn5+uuqqqzRkyBD16dOnzmN2795dzz77rJYuXaro6GhVVFRo1apVfHUYUA8WwzAMb08CAAAAtcPVpgAAACbCYVMApmQYhioqKi7Zp3nz5jVeZekNFRUVutTBDovFoubNm3twRgDMiD1vAExp9erV1e5BdvHP7t27vT1NB9dcc80l51v1vaIAcCmc8wbAlAoLC7V///5L9unZs6dP3ZMsLy/vkveCa9Wq1SW/6xUAJMIbAACAqXDYFAAAwEQIbwAAACZCeAMAADARwhsAAICJEN4AAABMhPAGAABgIoQ3AAAAE/n/rUVLALo8RUcAAAAASUVORK5CYII=",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"shotNum = \"0032\"\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 = (535, 995)\n",
"imageAnalyser.span = (350, 350)\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.ylim([0, 3500])\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"Ncount_mean_total = xr.concat((Ncount_mean_total, Ncount_mean), dim='als_final_current')\n",
"Ncount_std_total = xr.concat((Ncount_std_total, Ncount_std), dim='als_final_current')"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# 0.400 - 0.425 A"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The detected scaning axes and values are: \n",
"\n",
"{'als_final_current': array([0.4 , 0.4009, 0.4018, 0.4027, 0.4036, 0.4045, 0.4054, 0.4063,\n",
" 0.4072, 0.4081, 0.409 , 0.4099, 0.4108, 0.4117, 0.4126, 0.4135,\n",
" 0.4144, 0.4153, 0.4162, 0.4171, 0.418 , 0.4189, 0.4198, 0.4207,\n",
" 0.4216, 0.4225, 0.4234, 0.4243, 0.4252, 0.4261]), 'runs': array([0., 1.])}\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"shotNum = \"0033\"\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 = (535, 995)\n",
"imageAnalyser.span = (350, 350)\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.ylim([0, 3000])\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"Ncount_mean_total = xr.concat((Ncount_mean_total, Ncount_mean), dim='als_final_current')\n",
"Ncount_std_total = xr.concat((Ncount_std_total, Ncount_std), dim='als_final_current')"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# 0.425 - 0.450 A"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The detected scaning axes and values are: \n",
"\n",
"{'als_final_current': array([0.425 , 0.4259, 0.4268, 0.4277, 0.4286, 0.4295, 0.4304, 0.4313,\n",
" 0.4322, 0.4331, 0.434 , 0.4349, 0.4358, 0.4367, 0.4376, 0.4385,\n",
" 0.4394, 0.4403, 0.4412, 0.4421, 0.443 , 0.4439, 0.4448, 0.4457,\n",
" 0.4466, 0.4475, 0.4484, 0.4493, 0.4502, 0.4511, 0.452 ]), 'runs': array([0., 1.])}\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"shotNum = \"0034\"\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 = (535, 995)\n",
"imageAnalyser.span = (350, 350)\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.ylim([0, 2500])\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"Ncount_mean_total = xr.concat((Ncount_mean_total, Ncount_mean), dim='als_final_current')\n",
"Ncount_std_total = xr.concat((Ncount_std_total, Ncount_std), dim='als_final_current')"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# 0.450 - 0.475 A"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The detected scaning axes and values are: \n",
"\n",
"{'als_final_current': array([0.45 , 0.4509, 0.4518, 0.4527, 0.4536, 0.4545, 0.4554, 0.4563,\n",
" 0.4572, 0.4581, 0.459 , 0.4599, 0.4608, 0.4617, 0.4626, 0.4635,\n",
" 0.4644, 0.4653, 0.4662, 0.4671, 0.468 , 0.4689, 0.4698, 0.4707,\n",
" 0.4716, 0.4725, 0.4734, 0.4743, 0.4752, 0.4761]), 'runs': array([0., 1.])}\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"shotNum = \"0035\"\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 = (535, 995)\n",
"imageAnalyser.span = (350, 350)\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.ylim([0, 3000])\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"Ncount_mean_total = xr.concat((Ncount_mean_total, Ncount_mean), dim='als_final_current')\n",
"Ncount_std_total = xr.concat((Ncount_std_total, Ncount_std), dim='als_final_current')"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# 0.475 - 0.500 A"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The detected scaning axes and values are: \n",
"\n",
"{'als_final_current': array([0.475 , 0.4759, 0.4768, 0.4777, 0.4786, 0.4795, 0.4804, 0.4813,\n",
" 0.4822, 0.4831, 0.484 , 0.4849, 0.4858, 0.4867, 0.4876, 0.4885,\n",
" 0.4894, 0.4903, 0.4912, 0.4921, 0.493 , 0.4939, 0.4948, 0.4957,\n",
" 0.4966, 0.4975, 0.4984, 0.4993, 0.5002, 0.5011, 0.502 ]), 'runs': array([0., 1.])}\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"shotNum = \"0036\"\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 = (535, 995)\n",
"imageAnalyser.span = (350, 350)\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.ylim([0, 3000])\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
"Ncount_mean_total = xr.concat((Ncount_mean_total, Ncount_mean), dim='als_final_current')\n",
"Ncount_std_total = xr.concat((Ncount_std_total, Ncount_std), dim='als_final_current')"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# 0.500 - 0.525 A"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The detected scaning axes and values are: \n",
"\n",
"{'als_final_current': array([0.5 , 0.5009, 0.5018, 0.5027, 0.5036, 0.5045, 0.5054, 0.5063,\n",
" 0.5072, 0.5081, 0.509 , 0.5099, 0.5108, 0.5117, 0.5126, 0.5135,\n",
" 0.5144, 0.5153, 0.5162, 0.5171, 0.518 , 0.5189, 0.5198, 0.5207,\n",
" 0.5216, 0.5225, 0.5234, 0.5243, 0.5252, 0.5261, 0.527 ]), 'runs': array([0., 1.])}\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"shotNum = \"0037\"\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 = (535, 995)\n",
"imageAnalyser.span = (350, 350)\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.ylim([0, 3000])\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"Ncount_mean_total = xr.concat((Ncount_mean_total, Ncount_mean), dim='als_final_current')\n",
"Ncount_std_total = xr.concat((Ncount_std_total, Ncount_std), dim='als_final_current')"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# 0.525 - 0.550 A"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The detected scaning axes and values are: \n",
"\n",
"{'als_final_current': array([0.525 , 0.5259, 0.5268, 0.5277, 0.5286, 0.5295, 0.5304, 0.5313,\n",
" 0.5322, 0.5331, 0.534 , 0.5349, 0.5358, 0.5367, 0.5376, 0.5385,\n",
" 0.5394, 0.5403, 0.5412, 0.5421, 0.543 , 0.5439, 0.5448, 0.5457,\n",
" 0.5466, 0.5475, 0.5484, 0.5493, 0.5502, 0.5511, 0.552 ]), 'runs': array([0., 1.])}\n"
]
},
{
"data": {
"image/png": 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aVFDQQpLzQWiqV40T3hopX0+3V/L1OXe+/nz4B8IvGsoXsza+Xvr3dXiU/Odnmb8hvDVSvp5ul3x/zp2vPx8AGsrM98m7miunP/jDzzJ/RHhrpHz9G5Ovz7nz9ecDgLv4cunf1+eN3nKLofDwYlkszpeom9pV45V8Ht527dqlKVOmKCoqSiEhIerQoYPGjBmjDz/80KHe5MmTZbFYqvyJiopy2u7KlSsVFRUlq9Wqrl27KjU1VWVlZVXqnTx5UpMnT1ZERISCg4MVExOj7Oxsj+yrN/l6ut3X59z5+vMBwJ18ufTvy/DYrJk0dWqeJK4av5LPw9vzzz+vQ4cOaebMmdq6datWrFihkydPasiQIdq1a5dD3RYtWmjv3r0Of1577bUqbS5evFgzZ85UQkKCtm/froceekhpaWmaPn26Q73S0lKNGDFC2dnZWrFihd588021bdtWcXFx2rNnj0f32xt8+RuTr89T8PXnA0Bj4svwGBPztdavv8xV41fw+RMW0tPT1aZNG4eyuLg49ejRQ2lpabrtttvs5ddcc42GDBlSY3sFBQVatGiRpk2bprS0NElSbGysysrKlJycrFmzZqnPf85gX7VqlfLz8/Xee+8pJiZGkjR8+HANGDBAc+bM0b59+9y5qz6RkCDdfrt07bUVr7durf8TFi5elEJDK/5+4ULdzlXw9XkKvv58AID7xMcbiotr2M+yxsTnM29XBzdJCg0NVZ8+fXT06NF6t5eZmamSkhIlJiY6lCcmJsowDG3evNletmnTJvXu3dse3CQpICBAkyZN0gcffKBjV5/pblK++I3J1+fc+frz3c0XN7kFAH/CVePf8/nMmzPffvutPvroI4dZN0kqLi5Wu3btdOrUKUVGRmrs2LF68sknZbPZ7HXy8/MlSf369XN4b2RkpCIiIuzbK+sOdfLTu3///pKkjz/+WB2uvjPjf5SWlqq0tNT++ty5c5KksrIyp+fWNVRlm660XfGWQPv769uEq+9/9lmLJkxo9p/nyX2foipPPF227LK++87Qd9/Vrz91Vd/Pb8gYo+58Mc4N/TdgNo3xWL58ueKeX5XP5rzlFsPnP7zrM84lJVLlMZiTU67bb69f/319DDdvLl26dHV/PO/KMW4KY1DXf7N+Gd6mT5+uixcvav78+fayAQMGaMCAAerbt68kac+ePfrDH/6g7Oxs5ebmKvQ/63oFBQWyWq0KcTI9YbPZVFBQYH9dUFDgEPyurFe5vTpLlixRampqlfIdO3YoODi4jntaf1lZWfV+T0lJM0mjJUnbt29XUFD9LrF09f1WqzRnTqT++Md+KixsYS8PDy/W/ffny2r9Wlu31qsr9eLq57syxqg/b45zQ/8NmFVjOZb37o3USy/1+8/NWiuEhxdr6tQ8jz7hoK5qG+e9eyu+hyqDx113BdS7/031GK5UOcaVi2eN4LR0p4qKiupUz2IYzm6m4DsLFizQokWLtHLlSj388MM11v3LX/6icePG6fe//70effRRSdIDDzygtWvXqri4uEr93r17q2vXrsrMzJQkNW/eXPfff7+ef/55h3p79+7Vj370I7366quaMGGC0892NvPWsWNHnT59Wi1btqzXPtdFWVmZsrKyNHLkSAUGBtbrvRcvStddV/GeM2fK6r3s1tD3nzsnRURUvP/tt+v/G2dD1fXzGzLGqDtfjHNDj2GzaUzH8qZNFTPoFT+pqs6gr19/2eNPOqhOXcbZXf1vasdwpcZ0LNfFuXPnFBERoW+//bbGLOFXM2+pqalatGiRFi9eXGtwk6T4+HiFhITo/ffft5eFh4erpKRERUVFVWbACgsLFR0d7VDX2exaYWGhJDmdlatktVpltVqrlAcGBnr0AHOl/SurV7y/vp/ZsPcHBX3/9+HDAxxee0N9P9/T/w9RwZvj3NBj2KzMfixfvizNnl3d/Rotslik3/wmQHff7dvzn6obZ3f2v6kew5XMfizXVV330ecXLFRKTU1VSkqKUlJSNG/evDq/zzAMXXPN97tRea5bXl6eQ70TJ07o9OnT9mXXyrpX17vyvVfWbcoaw7NB0bRxwYc5mf1+je7sP8cwruQX4e2pp55SSkqKkpOT9cQTT9T5fW+88YaKioocbh8SFxenoKAgZWRkONTNyMiQxWLR2LFj7WXx8fHav3+/wy1BysvLtW7dOg0ePFjtr76pTBPEs0EB+IrZ79do9v7Df/l82fTZZ5/VwoULFRcXp5/85CcOS6CSNGTIEB0+fFgTJ07UhAkT1KNHD1ksFu3Zs0fLly/XDTfcoKlTp9rr22w2JScna8GCBbLZbBo1apRyc3OVkpKiqVOn2u/xJklTpkxRenq6xo8fr6VLl6pNmzZ67rnn9Nlnn2nnzp1eGwN/Vfls0Kun/CufDdpUb44IwDvMfr9Gs/cf/svn4e3tt9+WVHF/tsoLCa5kGIZatmyptm3b6ve//72++eYbXb58WZ07d9aMGTM0b968KleWzp8/X2FhYUpPT9eyZcvUrl07JSUlOVy9KlWct5adna05c+bokUceUVFRkQYOHKht27Zp2LBhnttpL6ucbq+P2p4NarFUPBt0zJimfa8dAJ5Teb/GY8ecfxdZLBXb/fV+jWbvP/yXz8Pb7t27a61z3XXXaWM91+lmzJihGTNm1Fqvbdu2WrNmTb3abgrqc65GbKzXugWgCal8RvO4cfrP/Rq/32aG51qavf/wX35xzhv8D+dqAPAHlc9ovvp+6WZ5rqUvnzGNxsvnM2/wT5yrAcBfJCRUnKLxzjuyP2Fh6FDzzFi54xnTwJUIb3CKczUA+JNmzcx9igbP5YQ7sWwKpyrP1ZCqPtydczUAAPAdwhuqxbkaAAD4H5ZNUSPO1QAAwL8Q3lArs5+r4cp97gAA8FcsmwIAAJgI4Q0AAMBECG8AAAAmwjlv8DjOOQMAwH0Ib6gV4QsAAP/BsikAAICJEN4AAABMhPAGAABgIoQ3AAAAE+GCBQAAPIwLv+BOzLwBAACYCOENAADARAhvAAAAJkJ4AwAAMBHCGwAAgIkQ3gAAAEyE8AYAAGAihDcAAAATIbwBAACYCOENAADARAhvAAAAJkJ4AwAAMBHCGwAAgIkQ3gAAAEyE8AYAAGAihDcAAAATIbwBAACYCOENAADARAhvAAAAJkJ4AwAAMBHCGwAAgIkQ3gAAAEyE8AYAAGAihDcAAAATIbwBAACYCOENAADARAhvAAAAJkJ4AwAAMBHCGwAAgIkQ3gAAAEyE8AYAAGAihDcAAAATIbwBAACYCOENAADARAhvAAAAJkJ4AwAAMBGfh7ddu3ZpypQpioqKUkhIiDp06KAxY8boww8/rFL3o48+0u23367Q0FC1atVKCQkJOnDggNN2V65cqaioKFmtVnXt2lWpqakqKyurUu/kyZOaPHmyIiIiFBwcrJiYGGVnZ7t9PwEAANzB5+Ht+eef16FDhzRz5kxt3bpVK1as0MmTJzVkyBDt2rXLXm///v2KjY3VpUuX9Prrr+vll1/W559/rqFDh+rUqVMObS5evFgzZ85UQkKCtm/froceekhpaWmaPn26Q73S0lKNGDFC2dnZWrFihd588021bdtWcXFx2rNnj1f2HwAAoD4CfN2B9PR0tWnTxqEsLi5OPXr0UFpamm677TZJ0sKFC2W1WrVlyxa1bNlSkhQdHa2ePXtq2bJlevrppyVJBQUFWrRokaZNm6a0tDRJUmxsrMrKypScnKxZs2apT58+kqRVq1YpPz9f7733nmJiYiRJw4cP14ABAzRnzhzt27fPK2MAAABQVz6febs6uElSaGio+vTpo6NHj0qSysvLtWXLFt1999324CZJnTt31vDhw7Vp0yZ7WWZmpkpKSpSYmOjQZmJiogzD0ObNm+1lmzZtUu/eve3BTZICAgI0adIkffDBBzp27Ji7dhMAAMAtfD7z5sy3336rjz76yD7r9uWXX6q4uFj9+/evUrd///7KyspSSUmJgoKClJ+fL0nq16+fQ73IyEhFRETYt0tSfn6+hg4d6rRNSfr444/VoUMHp30sLS1VaWmp/fW5c+ckSWVlZU7PrWuoyjY90TYqMMbewTh7HmPsHYyz5zW1Ma7rfvpleJs+fbouXryo+fPnS6pYCpUkm81Wpa7NZpNhGDpz5owiIyNVUFAgq9WqkJAQp3Ur26pst7o2r/xcZ5YsWaLU1NQq5Tt27FBwcHAte+i6rKwsj7WNCoyxdzDOnscYewfj7HlNZYyLiorqVM/vwtuCBQv05z//WStXrlR0dLTDNovFUu37rtxW13r1rXuluXPn6rHHHrO/PnfunDp27KhRo0Y5LO26S1lZmbKysjRy5EgFBga6vX0wxt7COHseY+wdjLPnNbUxrlzFq41fhbfU1FQtWrRIixcv1sMPP2wvDw8Pl+R8JqywsFAWi0WtWrWy1y0pKVFRUVGVGbDCwkKHQBgeHl5tm5Lzmb5KVqtVVqu1SnlgYKBHDzBPtw/G2FsYZ89jjL2Dcfa8pjLGdd1Hn1+wUCk1NVUpKSlKSUnRvHnzHLZ1795dLVq0UF5eXpX35eXlqUePHgoKCpL0/bluV9c9ceKETp8+rb59+9rL+vXrV22bkhzqAgAA+AO/CG9PPfWUUlJSlJycrCeeeKLK9oCAAN11113auHGjzp8/by8/cuSIcnJylJCQYC+Li4tTUFCQMjIyHNrIyMiQxWLR2LFj7WXx8fHav3+/wy1BysvLtW7dOg0ePFjt27d3304CAAC4gc+XTZ999lktXLhQcXFx+slPfqL333/fYfuQIUMkVczM3XzzzRo9erSSkpJUUlKihQsXKiIiQrNnz7bXt9lsSk5O1oIFC2Sz2TRq1Cjl5uYqJSVFU6dOtd/jTZKmTJmi9PR0jR8/XkuXLlWbNm303HPP6bPPPtPOnTu9MwAAAAD14PPw9vbbb0uquD9bZmZmle2GYUiSoqKitHv3bj3++OMaN26cAgICdNttt2nZsmVq3bq1w3vmz5+vsLAwpaena9myZWrXrp2SkpLsV69Wslqtys7O1pw5c/TII4+oqKhIAwcO1LZt2zRs2DAP7TEAAIDrfB7edu/eXee60dHRdZ4RmzFjhmbMmFFrvbZt22rNmjV17gMAAIAv+cU5bwAAAKgbwhsAAICJEN4AAABMhPAGAABgIoQ3AAAAEyG8AQAAmAjhDQAAwEQIbwAAACZCeAMAADARwhsAAICJEN4AAABMhPAGAABgIoQ3AAAAEyG8AQAAmAjhDQAAwEQIbwAAACZCeAMAADARwhsAAICJEN4AAABMhPAGAABgIoQ3AAAAEyG8AQAAmAjhDQAAwEQIbwAAACZCeAMAADARwhsAAICJEN4AAABMhPAGAABgIoQ3AAAAEyG8AQAAmIjL4e3JJ5/U8ePHnW77+uuv9eSTT7rcKQAAADjncnhLTU3VV1995XTb8ePHlZqa6nKnAAAA4JzL4c0wjGq3XbhwQYGBga42DQAAgGoE1Kfyv/71L/3jH/+wv966dav279/vUKe4uFh//vOf1b17d7d0EAAAAN+rV3jbtGmTfTnUYrFUe15bixYttHr16ob3DgAAAA7qFd4eeOABjR49WoZhaNCgQVq9erX69u3rUMdqtap79+5q0aKFWzsKAACAeoa3yMhIRUZGSpJycnIUHR2t0NBQj3QMAAAAVdUrvF1p2LBh7uwHAAAA6sDl8CZJ69at0yuvvKLDhw+ruLjYYZvFYtGXX37ZoM4BAADAkcvh7emnn9bcuXPVp08fDRgwQFar1Z39AgAAgBMuh7cXX3xR06dP18qVK93ZHwAAANTA5Zv0njhxQvHx8e7sCwAAAGrhcniLjo7mnDYAAAAvczm8/f73v9ezzz6rDz/80J39AQAAQA1cPuctMTFRBQUFGjRokNq1a6fw8HCH7RaLRf/85z8b3EEAAAB8z+XwFh4eroiICHf2BQAAALVwObzt3r3bjd0AAABAXbh8zhsAAAC8z+WZt7/97W+11rn11ltdbR4AAABOuBzeYmNjZbFYaqxz+fJlV5sHAACAEy6Ht5ycnCplp0+f1ptvvqm///3vSk9Pb1DHAAAAUJXL4W3YsGFOy++++2796le/UmZmpuLi4lzuGAAAAKryyAUL8fHxWr9+fZ3qnj9/XnPmzNGoUaPUunVrWSwWpaSkVKk3efJkWSyWKn+ioqKctrty5UpFRUXJarWqa9euSk1NVVlZWZV6J0+e1OTJkxUREaHg4GDFxMQoOzu7XvsLAADgLS7PvNXkzJkzKi0trVPdgoICvfjiixowYIDGjh2rl156qdq6LVq00K5du6qUXW3x4sVasGCBkpKSNGrUKOXm5io5OVnHjh3Tiy++aK9XWlqqESNG6OzZs1qxYoXatGmj9PR0xcXFaefOndXOLgIAAPiKy+HtyJEjVcpKS0v1r3/9S3PnztWQIUPq1E7nzp115swZWSwWnT59usbwds0119TabkFBgRYtWqRp06YpLS1NUsXFFWVlZUpOTtasWbPUp08fSdKqVauUn5+v9957TzExMZKk4cOHa8CAAZozZ4727dtXp30AAADwFpeXTbt06aKuXbs6/ImKitL48eMVFBSk//mf/6lTO5XLn+6SmZmpkpISJSYmOpQnJibKMAxt3rzZXrZp0yb17t3bHtwkKSAgQJMmTdIHH3ygY8eOua1fAAAA7uDyzNvLL79cJXQFBQWpS5cuuvnmm3XNNe4/na64uFjt2rXTqVOnFBkZqbFjx+rJJ5+UzWaz18nPz5ck9evXz+G9kZGRioiIsG+vrDt06NAqn9O/f39J0scff6wOHTo47UtpaanD0vC5c+ckSWVlZU7PrWuoyjY90TYqMMbewTh7HmPsHYyz5zW1Ma7rfroc3iZPnuzqW10yYMAADRgwQH379pUk7dmzR3/4wx+UnZ2t3NxchYaGSqpYNrVarQoJCanShs1mU0FBgf11QUGBQ/C7sl7l9uosWbJEqampVcp37Nih4ODg+u1cPWRlZXmsbVRgjL2DcfY8xtg7GGfPaypjXFRUVKd6Db5g4fz589q7d68KCgoUERGhIUOGKCwsrKHNVvHoo486vB45cqRuvPFGjRs3Tn/84x8dtte0DHv1tvrUvdLcuXP12GOP2V+fO3dOHTt21KhRo9SyZctq3+eqsrIyZWVlaeTIkQoMDHR7+2CMvYVx9jzG2DsYZ89ramNcuYpXmwaFt2XLlik1NVVFRUUyDEOSFBISotTUVIdg4ynx8fEKCQnR+++/by8LDw9XSUmJioqKqsyAFRYWKjo62qGus9m1wsJCSXI6K1fJarXKarVWKQ8MDPToAebp9sEYewvj7HmMsXcwzp7XVMa4rvvo8olpf/rTnzRnzhzdeuutWr9+vd555x299tprGjZsmH77299q7dq1rjZdL4ZhOJxfV3muW15enkO9EydO6PTp0/Zl18q6V9e78r1X1gUAAPAHLoe3P/zhD5o4caL++te/avz48frxj3+s8ePHa8uWLbr33nv1hz/8wZ39dOqNN95QUVGRw+1D4uLiFBQUpIyMDIe6GRkZslgsGjt2rL0sPj5e+/fvd7glSHl5udatW6fBgwerffv2nt4FAACAenF52XT//v1asmSJ022TJk1SfHx8ndvatm2bLl68qPPnz0uSPvnkE73xxhuSpDvvvFOnTp3SxIkTNWHCBPXo0UMWi0V79uzR8uXLdcMNN2jq1Kn2tmw2m5KTk7VgwQLZbDb7TXpTUlI0depU+z3eJGnKlClKT0/X+PHjtXTpUrVp00bPPfecPvvsM+3cudOVYQEAAPAol8NbixYt7OeGXa2wsNDpkw+q8+tf/1qHDx+2v96wYYM2bNggSTp48KCuvfZatW3bVr///e/1zTff6PLly+rcubNmzJihefPmVbmydP78+QoLC1N6erqWLVumdu3aKSkpSfPnz3eoZ7ValZ2drTlz5uiRRx5RUVGRBg4cqG3btvF0BQAA4JdcDm9Dhw5VSkqKYmNjHZYXT5w4oSeffFK33nprnds6dOhQrXU2btxYr/7NmDFDM2bMqLVe27ZttWbNmnq1DQAA4Csuh7e0tDT96Ec/Uo8ePTRixAhFRkbq66+/1q5duxQYGFjvsAUAAIDauXzBwg033KDc3FyNGTNGubm5Wr16tXJzczV27Fh98MEHDueWAQAAwD0adJ+3Xr166dVXX3VXXwAAAFCLes+85eXl6auvvqp2+1dffeX03mkAAABouHqFt7/97W+Kjo7WN998U22db775RtHR0dq+fXuDOwcAAABH9Qpv6enpGjdunMMjpq4WHR2te+65Ry+99FKDOwcAAABH9Qpvf//73x2eUFCdn/70pw7PGwUAAIB71Cu8nTp1Sh06dKi1XmRkpE6ePOlypwAAAOBcvcJbSEhItU9VuNKZM2cUHBzscqcAAADgXL3C2w033KDMzMxa623btk033HCDy50CAACAc/UKb/fcc49WrVqlPXv2VFsnJydHq1ev1r333tvgzgEAAMBRvW7S+8ADDygjI0OjRo3S1KlTNWbMGHXt2lVSxQPkN2/erFWrVmnAgAGaNm2aRzoMAADQlNUrvDVv3lzbt2/XL37xCz3//PN64YUXHLYbhqH/+q//0p/+9Cc1b97crR0FAACAC4/HCg8P19atW/Xhhx9qx44dOnr0qCSpU6dOuuOOO3TjjTe6vZMAAACo4PKzTaOjo2u8WS8AAADcr17hrX///nWua7FY9M9//rPeHQIAAED16hXebDabLBZLjXUuXLigDz/8sNZ6AAAAqL96hbfdu3dXu628vFwvvviinnzySVksFk2cOLGhfQMAAMBV6nWft+ps2LBBffr00SOPPKIBAwboww8/1Nq1a93RNAAAAK7QoPC2e/duDR48WPfcc49atmypHTt2aPv27Ro4cKCbugcAAIAruRTe8vLydOedd2rEiBEqKCjQK6+8ov/93//ViBEj3N0/AAAAXKFe4e3o0aO67777dNNNN+nDDz/U8uXL9emnn2rChAme6h8AAACuUK8LFnr16qVLly4pLi5Oc+bMUVhYmPLy8qqtf9NNNzW4gwAAAPhevcJbaWmpJGnbtm3KzMystp5hGLJYLLp8+XLDegcAAAAH9Qpvq1ev9lQ/AAAAUAf1Cm/33Xefp/oBAACAOnDLfd4AAADgHYQ3AAAAEyG8AQAAmAjhDQAAwEQIbwAAACZCeAMAADARwhsAAICJEN4AAABMhPAGAABgIoQ3AAAAEyG8AQAAmAjhDQAAwEQIbwAAACZCeAMAADARwhsAAICJEN4AAABMhPAGAABgIoQ3AAAAEyG8AQAAmAjhDQAAwEQIbwAAACZCeAMAADARwhsAAICJEN4AAABMhPAGAABgIoQ3AAAAE/F5eDt//rzmzJmjUaNGqXXr1rJYLEpJSXFa96OPPtLtt9+u0NBQtWrVSgkJCTpw4IDTuitXrlRUVJSsVqu6du2q1NRUlZWVVal38uRJTZ48WREREQoODlZMTIyys7PduYsAAABu4/PwVlBQoBdffFGlpaUaO3ZstfX279+v2NhYXbp0Sa+//rpefvllff755xo6dKhOnTrlUHfx4sWaOXOmEhIStH37dj300ENKS0vT9OnTHeqVlpZqxIgRys7O1ooVK/Tmm2+qbdu2iouL0549ezyxuwAAAA0S4OsOdO7cWWfOnJHFYtHp06f10ksvOa23cOFCWa1WbdmyRS1btpQkRUdHq2fPnlq2bJmefvppSRVhcNGiRZo2bZrS0tIkSbGxsSorK1NycrJmzZqlPn36SJJWrVql/Px8vffee4qJiZEkDR8+XAMGDNCcOXO0b98+T+8+AABAvfh85s1ischisdRYp7y8XFu2bNHdd99tD25SRfAbPny4Nm3aZC/LzMxUSUmJEhMTHdpITEyUYRjavHmzvWzTpk3q3bu3PbhJUkBAgCZNmqQPPvhAx44da+DeAQAAuJfPZ97q4ssvv1RxcbH69+9fZVv//v2VlZWlkpISBQUFKT8/X5LUr18/h3qRkZGKiIiwb5ek/Px8DR061GmbkvTxxx+rQ4cOTvtUWlqq0tJS++tz585JksrKypyeW9dQlW16om1UYIy9g3H2PMbYOxhnz2tqY1zX/TRFeCsoKJAk2Wy2KttsNpsMw9CZM2cUGRmpgoICWa1WhYSEOK1b2VZlu9W1eeXnOrNkyRKlpqZWKd+xY4eCg4Nr3ykXZWVleaxtVGCMvYNx9jzG2DsYZ89rKmNcVFRUp3qmCG+ValpevXJbXevVt+6V5s6dq8cee8z++ty5c+rYsaNGjRrlsLTrLmVlZcrKytLIkSMVGBjo9vbBGHsL4+x5jLF3MM6e19TGuHIVrzamCG/h4eGSnM+EFRYWymKxqFWrVva6JSUlKioqqjIDVlhYqOjoaId2q2tTcj7TV8lqtcpqtVYpDwwM9OgB5un2wRh7C+PseYyxdzDOntdUxriu++jzCxbqonv37mrRooXy8vKqbMvLy1OPHj0UFBQk6ftz3a6ue+LECZ0+fVp9+/a1l/Xr16/aNiU51AUAAPAHpghvAQEBuuuuu7Rx40adP3/eXn7kyBHl5OQoISHBXhYXF6egoCBlZGQ4tJGRkSGLxeJwL7n4+Hjt37/f4ZYg5eXlWrdunQYPHqz27dt7bJ8AAABc4RfLptu2bdPFixftweyTTz7RG2+8IUm68847FRwcrNTUVN18880aPXq0kpKSVFJSooULFyoiIkKzZ8+2t2Wz2ZScnKwFCxbIZrNp1KhRys3NVUpKiqZOnWq/x5skTZkyRenp6Ro/fryWLl2qNm3a6LnnntNnn32mnTt3encQAAAA6sAvwtuvf/1rHT582P56w4YN2rBhgyTp4MGD6tKli6KiorR79249/vjjGjdunAICAnTbbbdp2bJlat26tUN78+fPV1hYmNLT07Vs2TK1a9dOSUlJmj9/vkM9q9Wq7OxszZkzR4888oiKioo0cOBAbdu2TcOGDfP8jgMAANSTX4S3Q4cO1aledHR0nWfEZsyYoRkzZtRar23btlqzZk2d2gQAAPA1U5zzBgAAgAqENwAAABMhvAEAAJgI4Q0AAMBECG8AAAAmQngDAAAwEcIbAACAiRDeAAAATITwBgAAYCKENwAAABMhvAEAAJgI4Q0AAMBECG8AAAAmQngDAAAwEcIbAACAiRDeAAAATITwBgAAYCKENwAAABMhvAEAAJgI4Q0AAMBECG8AAAAmQngDAAAwEcIbAACAiRDeAAAATITwBgAAYCKENwAAABMhvAEAAJgI4Q0AAMBECG8AAAAmQngDAAAwEcIbAACAiRDeAAAATITwBgAAYCKENwAAABMhvAEAAJgI4Q0AAMBECG8AAAAmQngDAAAwEcIbAACAiRDeAAAATITwBgAAYCKENwAAABMhvAEAAJgI4Q0AAMBECG8AAAAmQngDAAAwEcIbAACAiRDeAAAATITwBgAAYCKENwAAABMhvAEAAJgI4Q0AAMBETBPedu/eLYvF4vTP+++/71D3o48+0u23367Q0FC1atVKCQkJOnDggNN2V65cqaioKFmtVnXt2lWpqakqKyvzxi4BAADUW4CvO1BfaWlpGj58uENZ37597X/fv3+/YmNjNXDgQL3++usqKSnRwoULNXToUP3jH/9Q69at7XUXL16sBQsWKCkpSaNGjVJubq6Sk5N17Ngxvfjii17bJwAAgLoyXXjr2bOnhgwZUu32hQsXymq1asuWLWrZsqUkKTo6Wj179tSyZcv09NNPS5IKCgq0aNEiTZs2TWlpaZKk2NhYlZWVKTk5WbNmzVKfPn08v0MAAAD1YJpl07ooLy/Xli1bdPfdd9uDmyR17txZw4cP16ZNm+xlmZmZKikpUWJiokMbiYmJMgxDmzdv9la3AQAA6sx0M2/Tp0/XhAkTFBwcrJiYGC1YsEC33HKLJOnLL79UcXGx+vfvX+V9/fv3V1ZWlkpKShQUFKT8/HxJUr9+/RzqRUZGKiIiwr69OqWlpSotLbW/PnfunCSprKzMI+fMVbbJ+Xiewxh7B+PseYyxdzDOntfUxriu+2ma8Hbttddq5syZio2NVXh4uP7973/rmWeeUWxsrP7617/qjjvuUEFBgSTJZrNVeb/NZpNhGDpz5owiIyNVUFAgq9WqkJAQp3Ur26rOkiVLlJqaWqV8x44dCg4OdnEva5eVleWxtlGBMfYOxtnzGGPvYJw9r6mMcVFRUZ3qmSa83Xjjjbrxxhvtr4cOHar4+Hj169dPc+bM0R133GHfZrFYqm3nym11refM3Llz9dhjj9lfnzt3Th07dtSoUaMclmzdpaysTFlZWRo5cqQCAwPd3j4YY29hnD2PMfYOxtnzmtoYV67i1cY04c2ZVq1aafTo0XrhhRdUXFys8PBwSXI6a1ZYWCiLxaJWrVpJksLDw1VSUqKioqIqM2WFhYWKjo6u8bOtVqusVmuV8sDAQI8eYJ5uH4yxtzDOnscYewfj7HlNZYzruo+mv2DBMAxJFTNl3bt3V4sWLZSXl1elXl5ennr06KGgoCBJ35/rdnXdEydO6PTp0w63HwEAAPAXpg5vZ86c0ZYtWzRw4EAFBQUpICBAd911lzZu3Kjz58/b6x05ckQ5OTlKSEiwl8XFxSkoKEgZGRkObWZkZMhisWjs2LFe2gsAAIC6M82y6cSJE9WpUyf98Ic/VEREhL744gs9++yz+uabbxwCWGpqqm6++WaNHj1aSUlJ9pv0RkREaPbs2fZ6NptNycnJWrBggWw2m/0mvSkpKZo6dSr3eAMAAH7JNOGtf//+eu211/TCCy/owoULstlsuuWWW7R27VrdfPPN9npRUVHavXu3Hn/8cY0bN04BAQG67bbbtGzZMoenK0jS/PnzFRYWpvT0dC1btkzt2rVTUlKS5s+f7+3dAwAAqBPThLekpCQlJSXVqW50dLR27txZp7ozZszQjBkzGtI1AAAArzH1OW8AAABNDeENAADARAhvAAAAJkJ4AwAAMBHCGwAAgIkQ3gAAAEyE8AYAAGAihDcAAAATIbwBAACYCOENAADARAhvAAAAJkJ4AwAAMBHCGwAAgIkQ3gAAAEyE8AYAAGAihDcAAAATIbwBAACYCOENAADARAhvAAAAJkJ4AwAAMBHCGwAAgIkQ3gAAAEyE8AYAAGAihDcAAAATIbwBAACYCOENAADARAhvAAAAJkJ4AwAAMBHCGwAAgIkQ3gAAAEyE8AYAAGAihDcAAAATIbwBAACYCOENAADARAhvAAAAJkJ4AwAAMBHCGwAAgIkQ3gAAAEyE8AYAAGAihDcAAAATIbwBAACYCOENAADARAhvAAAAJkJ4AwAAMBHCGwAAgIkQ3gAAAEyE8AYAAGAihDcAAAATIbwBAACYCOENAADARAhvAAAAJkJ4AwAAMBHCGwAAgIkQ3iRduHBBs2bNUvv27RUUFKSBAwdq/fr1vu4WAABAFQG+7oA/SEhIUG5urpYuXapevXrplVde0b333qvvvvtOEydO9HX3AAAA7Jp8eNu6dauysrLsgU2Shg8frsOHD+u3v/2t7rnnHjVr1szHvQQAAKjQ5JdNN23apNDQUI0fP96hPDExUcePH9e+fft81DMAAICqmvzMW35+vn7wgx8oIMBxKPr372/f/qMf/ajK+0pLS1VaWmp//e2330qSCgsLVVZW5vZ+lpWVqaioSAUFBQoMDHR7+2CMvYVx9jzG2DsYZ89ramN8/vx5SZJhGDXWa/LhraCgQN26datSbrPZ7NudWbJkiVJTU6uUd+3a1b0dBAAATcr58+d17bXXVru9yYc3SbJYLPXeNnfuXD322GP21999950KCwsVHh5eY3uuOnfunDp27KijR4+qZcuWbm8fjLG3MM6exxh7B+PseU1tjA3D0Pnz59W+ffsa6zX58BYeHu50dq2wsFDS9zNwV7NarbJarQ5lrVq1cnv/rtayZcsmcQD7EmPsHYyz5zHG3sE4e15TGuOaZtwqNfkLFvr166dPP/1U5eXlDuV5eXmSpL59+/qiWwAAAE41+fAWHx+vCxcu6C9/+YtD+Zo1a9S+fXsNHjzYRz0DAACoqskvm/7Xf/2XRo4cqV//+tc6d+6cevTooVdffVWZmZlat26d39zjzWq16oknnqiyVAv3YYy9g3H2PMbYOxhnz2OMnbMYtV2P2gRcuHBB8+fP1+uvv67CwkJFRUVp7ty5mjBhgq+7BgAA4IDwBgAAYCJN/pw3AAAAMyG8AQAAmAjhzQMuXLigWbNmqX379goKCtLAgQO1fv36Wt+XkZEhi8Xi9M+JEyfs9c6dO6fFixcrNjZW7dq1U2hoqPr166enn35aJSUlDm0eOnSo2jbr0id/5ulxlqT58+frxhtvlM1mU1BQkLp166YHHnhAhw8frtJuWVmZUlNT1aVLF1mtVkVFRWnlypVu219f8KcxbqzHsjfG+ErFxcXq1auXLBaLli1bVmV7YzyOJf8aZ45lR/UZ49jYWKf14uLiqrTbWI9liatNPSIhIUG5ublaunSpevXqpVdeeUX33nuvvvvuO02cOLHW969evVpRUVEOZeHh4fa/HzlyRMuXL9cvfvELPfbYYwoNDdU777yjlJQUZWVlKSsrq8pTHh555JEqn92zZ88G7KXveXqcJens2bO699579YMf/EBhYWH65JNPtGjRIr311lv6+OOPHeo/9NBDWrt2rZ566indfPPN2r59u2bOnKnz589r3rx57tlpL/O3MZYa37HsjTG+0oIFC3Tx4sVqtzfG41jyv3GWOJavVtcx7tatm/785z87lDm7SX5jPZYlSQbc6q9//ashyXjllVccykeOHGm0b9/eKC8vr/a9q1evNiQZubm5NX7GhQsXjAsXLlQpf+aZZwxJxjvvvGMvO3jwoCHJeOaZZ+q5J/7NG+Ncna1btxqSjFWrVtnL8vPzDYvFYqSlpTnUnTZtmtGiRQujoKDApc/yJX8b48Z4LHt7jPft22c0b97c2LBhg9OxbIzHsWH43zhzLDuqzxgPGzbMuOGGG2qt11iP5Uosm7rZpk2bFBoaqvHjxzuUJyYm6vjx49q3b1+DPyMkJEQhISFVygcNGiRJOnr0aIM/w995Y5yr07p1a0lSQMD3E9ebN2+WYRhKTEys0p/i4mJlZmZ6rD+e4m9j3Bh5c4wvXbqkKVOmaPr06frhD3/otE5jPI4l/xvnxsiX3xfONNZjuRLhzc3y8/P1gx/8oMoPnf79+9u312b06NFq1qyZbDabEhIS6vQeSdq1a5ck6YYbbqiybenSpWrevLmCg4N1yy236K233qpTm/7K2+NcXl6u4uJi/d///Z9mzZqlXr16KSEhwaE/rVu3Vrt27Vzuj7/xtzGu1JiOZW+O8ZNPPqmLFy/qqaeeqrE/je04lvxvnCtxLDuq6xh/+eWXstlsCggIUPfu3TV//nwVFxdX6U9jPJYrNe5fa32goKBA3bp1q1Je+YD7goKCat/brl07zZ8/X0OGDFHLli2Vl5enpUuXasiQIfr73/+uAQMGVPvef/3rX/rd736n+Ph4+8EpVdydetq0aRo5cqQiIyN15MgRrVy5UmPGjNEf//hHTZ06tQF76zveHOcTJ04oMjLS/nrw4MHKyclRaGioQ38qP/tKISEhat68eY398Vf+NsaN8Vj21hj/4x//0O9+9zu9/fbbCgkJ0alTp6rtT2M7jiX/G2eOZUf1GeNbbrlF99xzj6KiolRcXKxt27bpd7/7nd59913l5OTommuusX9eYzyW7Xy8bNvo9OzZ04iLi6tSfvz4cUOSsWTJknq1d/DgQSM0NNT46U9/WmOdjh07Gr169arTOv6lS5eMG2+80QgPDzfKysrq1R9/4c1xLisrM3Jzc413333X+OMf/2j07NnT6NWrl3H8+HF7nZEjRxpRUVFO227evLnx4IMP1qs//sDfxtgZsx/L3hjjsrIy48YbbzQmTZrkUE9OzrlqjMexYfjfODvDseyoLj/7Ki1btsyQZGzcuNFe1liP5Uosm7pZeHi400RfWFgoSU5/E6hJly5ddMstt+j99993uv3w4cMaPny4AgIClJ2dXaf2AwMDdc8996igoEBffPFFvfrjL7w5zgEBAfrhD3+oH//4x5o6dap27dqlAwcOaOnSpbX25+LFi7p06VK9++MP/G2MnTH7seyNMV6+fLkOHDigJ554QmfPntXZs2d17tw5SVJJSYnOnj2ry5cv19gfMx/Hkv+NszMcy45q+9l3pUmTJkmSQ93GeixXIry5Wb9+/fTpp5+qvLzcoTwvL0+S1Ldv33q3aRiGfSr4SocPH1ZsbKwMw1BOTo6uv/76erUpyWm7ZuDNcb7a9ddfr/bt2+vzzz936M+pU6eq3JOoIf3xNX8b45ralMx5LHtjjPPz8/Xtt9+qZ8+euu6663TdddfZl6EWLFig6667zv55jfE4lvxvnGtqU+JYrlTX74tKV9ZtrMeyna+m/BqrylscrF+/3qE8Li6u1sulnTlw4IARGhpqjB071qH88OHDRpcuXYyOHTsaX375Zb3avHTpkjFw4EAjIiKi3v3xF94aZ2e++OIL45prrjEefvhhe1nlZelLly51qPvggw+a9rJ0fxtjZ8x+LHtjjD/99FMjJyfH4c+rr75qSDJ+9atfGTk5Ocb58+cNw2icx7Fh+N84O8Ox7Kg+3xdPP/20IcnYvHmzvayxHsuVCG8eMHLkSOO6664zXnzxRWPXrl3GtGnTDEnGunXr7HWmTJliNGvWzDh06JC9bMSIEUZqaqqxadMmIzs721i+fLnRvn17IywszMjLy7PX++abb4xu3boZVqvVWLdunbF3716HP0ePHrXXffTRR42HH37YePXVV42cnBzjT3/6k3HzzTcbkozVq1d7ZTw8xdPj/M9//tO47bbbjOeee87IzMw0duzYYTz77LPG9ddfb7Ru3dqhTcMwjKlTpxpWq9V45plnjN27dxvz5s0zLBaLsXjxYs8Phof40xg31mPZ02PsTE3nYjXG49gw/GucOZZdG+O//e1vxh133GG88MILxo4dO4y33nrL+PWvf200a9bMuO2224zLly879KexHsuGQXjziPPnzxszZsww2rVrZzRv3tzo37+/8eqrrzrUue+++wxJxsGDB+1ls2bNMvr06WOEhYUZAQEBRvv27Y1JkyYZn332mcN7c3JyDEnV/nniiSfsdVetWmUMGjTIsNlsRkBAgHHdddcZd9xxh7F9+3ZPDoFXeHqcT5w4YUyaNMno3r27ERwcbDRv3tzo1q2b8atf/co4cuRIlf5cunTJeOKJJ4xOnToZzZs3N3r16mX893//t0f23Vv8aYwb67Hs6TF2pqbw1hiPY8Pwr3HmWHZtjL/44gvjzjvvNDp06GBYrVYjKCjI6Nevn7F48WKjpKSkSn8a67FsGIZhMYz/LLIDAADA75nvrEgAAIAmjPAGAABgIoQ3AAAAEyG8AQAAmAjhDQAAwEQIbwAAACZCeAMAADARwhsAAICJEN4A+IVDhw7JYrEoIyPD7W1nZ2frhz/8oUJCQmSxWLR582ZlZGTIYrHo0KFDbv+8K3Xp0kWTJ0/26GeYwSeffKKUlBSPjzfQFAT4ugMA4EmGYehnP/uZevXqpbfeekshISHq3bu3ysvLtXfvXkVGRvq6i03CJ598otTUVMXGxqpLly6+7g5gaoQ3AI3a8ePHVVhYqPj4eI0YMcJhW+vWrX3UK98qKyuTxWJRQEDVHwFFRUUKDg72Qa8A1BXLpgA86t///rcSExPVs2dPBQcHq0OHDrrrrruUl5dX63tPnTqlBx54QB07dpTValXr1q314x//WDt37qzTZ6ekpOj666+XJD3++OOyWCz2WR9ny6axsbHq27evcnNzNXToUAUHB6tbt25aunSpvvvuO3u9kpISzZ49WwMHDtS1114rm82mmJgYvfnmm3UfmFq88soriomJUWhoqEJDQzVw4ECtWrXKvr265djY2FjFxsbaX+/evVsWi0Vr167V7Nmz1aFDB1mtVv373//W5MmTFRoaqry8PI0aNUphYWH2gHvp0iUtWrRIUVFR9rFPTEzUqVOnHD6vS5cuGj16tDIzM3XTTTepRYsWioqK0ssvv2yvk5GRofHjx0uShg8fLovF4rElcqApYOYNgEcdP35c4eHhWrp0qVq3bq3CwkKtWbNGgwcP1v/93/+pd+/e1b73F7/4hT766CMtXrxYvXr10tmzZ/XRRx+poKCgTp89depUDRgwQAkJCXrkkUc0ceJEWa3WGt9z4sQJ/fznP9fs2bP1xBNPaNOmTZo7d67at2+vX/7yl5Kk0tJSFRYW6je/+Y06dOigS5cuaefOnUpISNDq1avt9Vy1cOFCPfXUU0pISNDs2bN17bXXKj8/X4cPH3a5zblz5yomJkYvvPCCrrnmGrVp00ZSRUj76U9/qgcffFBJSUkqLy/Xd999pzFjxuidd97RnDlz9KMf/UiHDx/WE088odjYWP3v//6vWrRoYW/7n//8p2bPnq2kpCS1bdtWL730ku6//3716NFDt956q37yk58oLS1N8+bNU3p6um666SZJUvfu3Rs0TkCTZQCAF5WXlxuXLl0yevbsaTz66KP28oMHDxqSjNWrV9vLQkNDjVmzZjXo8yrbfeaZZxzKV69ebUgyDh48aC8bNmyYIcnYt2+fQ90+ffoYd9xxR437VFZWZtx///3GjTfe6LCtc+fOxn333Vfn/h44cMBo1qyZ8fOf/7zGetW1O2zYMGPYsGH21zk5OYYk49Zbb61S97777jMkGS+//LJD+auvvmpIMv7yl784lOfm5hqSjOeee86hH0FBQcbhw4ftZcXFxYbNZjMefPBBe9mGDRsMSUZOTk6N+wWgdiybAvCo8vJypaWlqU+fPmrevLkCAgLUvHlzffHFF/r0009rfO+gQYOUkZGhRYsW6f3331dZWZnH+9uuXTsNGjTIoax///5VZr02bNigH//4xwoNDVVAQIACAwO1atWqWvepNllZWbp8+bKmT5/eoHaudvfdd9d525YtW9SqVSvdddddKi8vt/8ZOHCg2rVrp927dzvUHzhwoDp16mR/HRQUpF69ejVophBA9QhvADzqscce04IFCzR27Fi9/fbb2rdvn3JzczVgwAAVFxfX+N7XXntN9913n1566SXFxMTIZrPpl7/8pU6cOOGx/oaHh1cps1qtDn3duHGjfvazn6lDhw5at26d9u7dq9zcXE2ZMkUlJSUN+vzKc8oqz9Vzl+quqg0ODlbLli0dyr755hudPXtWzZs3V2BgoMOfEydO6PTp0w716zJmANyHc94AeNS6dev0y1/+UmlpaQ7lp0+fVqtWrWp8b0REhJYvX67ly5fryJEjeuutt5SUlKSTJ08qMzPTg72u2bp169S1a1e99tprslgs9vLS0tIGt115BexXX32ljh07VlsvKCjI6eedPn1aERERVcqv7Gdt5REREQoPD692jMPCwqrtFwDPI7wB8CiLxVLlIoG//vWvOnbsmHr06FHndjp16qSHH35Y2dnZ+vvf/+7ubtaLxWJR8+bNHYLPiRMn3HK16ahRo9SsWTM9//zziomJqbZely5d9K9//cuh7PPPP9dnn33mNLzVx+jRo7V+/XpdvnxZgwcPblBblSqPAWbjgIYjvAHwqNGjRysjI0NRUVHq37+/PvzwQz3zzDO1Lgt+++23Gj58uCZOnKioqCiFhYUpNzdXmZmZSkhI8FLvnRs9erQ2btyohx56SOPGjdPRo0f11FNPKTIyUl988UWD2u7SpYvmzZunp556SsXFxbr33nt17bXX6pNPPtHp06eVmpoqqeJK3EmTJumhhx7S3XffrcOHD+t3v/udW+5dN2HCBP35z3/WnXfeqZkzZ2rQoEEKDAzUV199pZycHI0ZM0bx8fH1arNv376SpBdffFFhYWEKCgpS165dnS65AqgZ4Q2AR61YsUKBgYFasmSJLly4oJtuukkbN25UcnJyje8LCgrS4MGDtXbtWh06dEhlZWXq1KmTHn/8cc2ZM8dLvXcuMTFRJ0+e1AsvvKCXX35Z3bp1U1JSkr766it7uGqIJ598Uj179tTKlSv185//XAEBAerZs6dmzJhhrzNx4kQdP35cL7zwglavXq2+ffvq+eefd8vnN2vWTG+99ZZWrFihtWvXasmSJQoICND111+vYcOGqV+/fvVus2vXrlq+fLlWrFih2NhYXb58WatXr+bRYYALLIZhGL7uBAAAAOqGq00BAABMhGVTAKZkGIYuX75cY51mzZpVe5WlL1y+fFk1LXZYLBY1a9bMiz0CYEbMvAEwpTVr1lS5B9nVf/bs2ePrbjro3r17jf2tfK4oANSEc94AmFJBQYEOHjxYY53evXv71T3J8vLyarwXXFhYWI3PegUAifAGAABgKiybAgAAmAjhDQAAwEQIbwAAACZCeAMAADARwhsAAICJEN4AAABMhPAGAABgIv8fhPXQobvSY88AAAAASUVORK5CYII=",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"shotNum = \"0038\"\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 = (535, 995)\n",
"imageAnalyser.span = (350, 350)\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.ylim([0, 3000])\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"Ncount_mean_total = xr.concat((Ncount_mean_total, Ncount_mean), dim='als_final_current')\n",
"Ncount_std_total = xr.concat((Ncount_std_total, Ncount_std), dim='als_final_current')"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Summary"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 2400x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(24,6))\n",
"ax = fig.gca()\n",
"Ncount_mean_total.plot.errorbar(ax=ax, yerr = Ncount_std_total, fmt='ob')\n",
"plt.ylim([0, 4500])\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"f = 0.44224 ± 0.00038 A\n",
"fwhm = 0.02101 ± 0.00111 A\n"
]
}
],
"source": [
"Brange=(0.4, 0.48)\n",
"\n",
"data = Ncount_mean_total.where(Brange[0]<Ncount_mean_total.als_final_current).where(Ncount_mean_total.als_final_current<Brange[1])\n",
"data = data.dropna(dim='als_final_current')\n",
"data_std = Ncount_std_total.where(Brange[0]<Ncount_mean_total.als_final_current).where(Ncount_mean_total.als_final_current<Brange[1])\n",
"data_std = data_std.dropna(dim='als_final_current')\n",
"\n",
"fitAnalyser = FitAnalyser(\"Gaussian With Offset\", fitDim=1)\n",
"params = fitAnalyser.guess(data, x='als_final_current', guess_kwargs=dict(negative=True), dask=\"parallelized\")\n",
"fitResult = fitAnalyser.fit(data, params, x='als_final_current', dask=\"parallelized\").load()\n",
"fitCurve = fitAnalyser.eval(fitResult, x=np.linspace(Brange[0], Brange[1], 500), dask=\"parallelized\").load()\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"data.plot.errorbar(ax=ax, yerr = data_std, fmt='ob')\n",
"fitCurve.plot.errorbar(ax=ax)\n",
"\n",
"plt.ylim([0, 4500])\n",
"plt.ylabel('NCount')\n",
"plt.xlabel('Z_offset current (A)')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()\n",
"\n",
"f_1 = fitAnalyser.get_fit_value(fitResult).center\n",
"df_1 = fitAnalyser.get_fit_std(fitResult).center\n",
"\n",
"print('f = %.5f \\u00B1 %.5f A'% tuple([np.abs(f_1),df_1]))\n",
"\n",
"s_1 = fitAnalyser.get_fit_value(fitResult).sigma\n",
"ds_1 = fitAnalyser.get_fit_std(fitResult).sigma\n",
"\n",
"fwhm_1 = 2.3548200*s_1\n",
"dfwhm_1 = 2.3548200*ds_1\n",
"\n",
"print('fwhm = %.5f \\u00B1 %.5f A'% tuple([np.abs(fwhm_1),dfwhm_1]))"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"f = 0.28128 ± 0.00016 A\n",
"fwhm = 0.00680 ± 0.00039 A\n"
]
}
],
"source": [
"Brange=(0.26, 0.3)\n",
"\n",
"data = Ncount_mean_total.where(Brange[0]<Ncount_mean_total.als_final_current).where(Ncount_mean_total.als_final_current<Brange[1])\n",
"data = data.dropna(dim='als_final_current')\n",
"data_std = Ncount_std_total.where(Brange[0]<Ncount_mean_total.als_final_current).where(Ncount_mean_total.als_final_current<Brange[1])\n",
"data_std = data_std.dropna(dim='als_final_current')\n",
"\n",
"fitAnalyser = FitAnalyser(\"Gaussian With Offset\", fitDim=1)\n",
"params = fitAnalyser.guess(data, x='als_final_current', guess_kwargs=dict(negative=True), dask=\"parallelized\")\n",
"fitResult = fitAnalyser.fit(data, params, x='als_final_current', dask=\"parallelized\").load()\n",
"fitCurve = fitAnalyser.eval(fitResult, x=np.linspace(Brange[0], Brange[1], 500), dask=\"parallelized\").load()\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"data.plot.errorbar(ax=ax, yerr = data_std, fmt='ob')\n",
"fitCurve.plot.errorbar(ax=ax)\n",
"\n",
"plt.ylim([0, 4500])\n",
"plt.ylabel('NCount')\n",
"plt.xlabel('Z_offset current (A)')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()\n",
"\n",
"f_1 = fitAnalyser.get_fit_value(fitResult).center\n",
"df_1 = fitAnalyser.get_fit_std(fitResult).center\n",
"\n",
"print('f = %.5f \\u00B1 %.5f A'% tuple([np.abs(f_1),df_1]))\n",
"\n",
"s_1 = fitAnalyser.get_fit_value(fitResult).sigma\n",
"ds_1 = fitAnalyser.get_fit_std(fitResult).sigma\n",
"\n",
"fwhm_1 = 2.3548200*s_1\n",
"dfwhm_1 = 2.3548200*ds_1\n",
"\n",
"print('fwhm = %.5f \\u00B1 %.5f A'% tuple([np.abs(fwhm_1),dfwhm_1]))"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"params.add(name=\"A_amplitude\", value= 1, max=np.inf, min=-np.inf, vary=True)\n",
"params.add(name=\"A_center\", value= 0, max=np.inf, min=-np.inf, vary=True)\n",
"params.add(name=\"A_sigma\", value= 1, max=np.inf, min= 0, vary=True)\n",
"params.add(name=\"B_amplitude\", value= 1, max=np.inf, min=-np.inf, vary=True)\n",
"params.add(name=\"B_center\", value= 0, max=np.inf, min=-np.inf, vary=True)\n",
"params.add(name=\"B_sigma\", value= 1, max=np.inf, min= 0, vary=True)\n",
"params.add(name=\"B_offset\", value= 0, max=np.inf, min=-np.inf, vary=True)\n",
"params.add(name=\"A_fwhm\", expr=\"2.3548200*A_sigma\")\n",
"params.add(name=\"A_height\", expr=\"0.3989423*A_amplitude/max(1e-15, A_sigma)\")\n",
"params.add(name=\"B_fwhm\", expr=\"2.3548200*B_sigma\")\n",
"params.add(name=\"B_height\", expr=\"0.3989423*B_amplitude/max(1e-15, B_sigma)\")\n"
]
}
],
"source": [
"from Analyser.FitAnalyser import GaussianModel, GaussianWithOffsetModel\n",
"\n",
"fitModel = GaussianModel(prefix='A_') + GaussianWithOffsetModel(prefix='B_')\n",
"fitAnalyser = FitAnalyser(fitModel, fitDim=1)\n",
"fitAnalyser.print_params_set_template()"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"f = 0.22367 ± 0.07911 A\n",
"fwhm = 0.00031 ± 0.07203 A\n",
"f = 0.22962 ± 0.00013 A\n",
"fwhm = 0.00583 ± 0.00038 A\n"
]
}
],
"source": [
"Brange=(0.22, 0.240)\n",
"\n",
"from Analyser.FitAnalyser import GaussianModel, GaussianWithOffsetModel\n",
"\n",
"data = Ncount_mean_total.where(Brange[0]<Ncount_mean_total.als_final_current).where(Ncount_mean_total.als_final_current<Brange[1])\n",
"data = data.dropna(dim='als_final_current')\n",
"data_std = Ncount_std_total.where(Brange[0]<Ncount_mean_total.als_final_current).where(Ncount_mean_total.als_final_current<Brange[1])\n",
"data_std = data_std.dropna(dim='als_final_current')\n",
"\n",
"fitModel = GaussianModel(prefix='A_') + GaussianWithOffsetModel(prefix='B_')\n",
"fitAnalyser = FitAnalyser(fitModel, fitDim=1)\n",
"# params = fitAnalyser.guess(data, x='als_final_current', guess_kwargs=dict(negative=True), dask=\"parallelized\")\n",
"params = fitAnalyser.fitModel.make_params()\n",
"params.add(name=\"A_amplitude\", value= -0.65, max=np.inf, min=-0.7, vary=True)\n",
"params.add(name=\"A_center\", value= 0.22321, max=np.inf, min=-np.inf, vary=True)\n",
"params.add(name=\"A_sigma\", value= 0.00005, max=np.inf, min= 0, vary=True)\n",
"params.add(name=\"B_amplitude\", value= -11, max=np.inf, min=-np.inf, vary=True)\n",
"params.add(name=\"B_center\", value= 0.2280, max=np.inf, min=-np.inf, vary=True)\n",
"params.add(name=\"B_sigma\", value= 0.0025, max=np.inf, min= 0, vary=True)\n",
"params.add(name=\"B_offset\", value= 2500, max=np.inf, min=-np.inf, vary=True)\n",
"\n",
"fitResult = fitAnalyser.fit(data, params, x='als_final_current', dask=\"parallelized\").load()\n",
"fitCurve = fitAnalyser.eval(fitResult, x=np.linspace(Brange[0], Brange[1], 500), dask=\"parallelized\").load()\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"data.plot.errorbar(ax=ax, yerr = data_std, fmt='ob')\n",
"fitCurve.plot.errorbar(ax=ax)\n",
"\n",
"plt.ylim([0, 4500])\n",
"plt.ylabel('NCount')\n",
"plt.xlabel('Z_offset current (A)')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()\n",
"\n",
"f_1 = fitAnalyser.get_fit_value(fitResult).A_center\n",
"df_1 = fitAnalyser.get_fit_std(fitResult).A_center\n",
"\n",
"print('f = %.5f \\u00B1 %.5f A'% tuple([np.abs(f_1),df_1]))\n",
"\n",
"s_1 = fitAnalyser.get_fit_value(fitResult).A_sigma\n",
"ds_1 = fitAnalyser.get_fit_std(fitResult).A_sigma\n",
"\n",
"fwhm_1 = 2.3548200*s_1\n",
"dfwhm_1 = 2.3548200*ds_1\n",
"\n",
"print('fwhm = %.5f \\u00B1 %.5f A'% tuple([np.abs(fwhm_1),dfwhm_1]))\n",
"\n",
"f_1 = fitAnalyser.get_fit_value(fitResult).B_center\n",
"df_1 = fitAnalyser.get_fit_std(fitResult).B_center\n",
"\n",
"print('f = %.5f \\u00B1 %.5f A'% tuple([np.abs(f_1),df_1]))\n",
"\n",
"s_1 = fitAnalyser.get_fit_value(fitResult).B_sigma\n",
"ds_1 = fitAnalyser.get_fit_std(fitResult).B_sigma\n",
"\n",
"fwhm_1 = 2.3548200*s_1\n",
"dfwhm_1 = 2.3548200*ds_1\n",
"\n",
"print('fwhm = %.5f \\u00B1 %.5f A'% tuple([np.abs(fwhm_1),dfwhm_1]))"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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q3BQKBbp3737f8dLT0/Hee+9h3LhxmDdvnuEzdDodoqKiMGXKFLRv3x4A8PXXXyMhIQH79u1DSEgIAKB3794ICgrCtGnTcPDgwVrMtG4VFQGTJ5ct2oCSNkkCpkwBBg3iaVMiIiJzJfup0oq4urrC2rrqdeXWrVtRUFCAiIgIo/aIiAgIIbB+/XpD27p169C2bVtD0QYA1tbWGDlyJA4dOoSkpKRqx1/f9u4Frl2reLsQwNWrJf2IiIjIPMk+41aquLgYxcXFyMzMxOrVq7Ft2zZ8+umnRn1u374NDw8PpKamwtPTE4MHD8a7774LjUZj6JOQkAAACAgIMNrX09MTrq6uhu2lfXv06FEmlsDAQADAiRMn0LRp03Lj1Wq10Gq1hvfZ2dkAAJ1OV+61dDVVOmZFY1+9KqEyh/PqVT10unKm5UzIg3JtaCwpX0vKFWC+DZkl5Qow37oYu7pMpnAbP348Pv/8cwCAjY0NPvnkE7zyyiuG7UFBQQgKCkLHjh0BAHv27MFHH32EuLg4HD58GA4ODgBKTpWqVCrY29uX+QyNRoP09HTD+/T0dKOi7+5+pdsrMn/+fERHR5dp3759O+zs7CqTcrXExsaW2375sguAxx64/+XLB7B5c8V5mZKKcm2oLClfS8oVYL4NmSXlCjDf2pCfn1+j/U2mcJs5cyYiIyORkpKCjRs34rXXXkNeXh7++c9/AgBef/11o/79+vVD586dMXToUHz55ZdG2yVJqvBz7t1Wlb53mzFjBqZOnWp4n52dDW9vb4SGhsLJyanC/apLp9MhNjYW/fr1g1KpLLP9ySeBzz4TuH4dEKJs3JIk0LQp8M9/djP5a9welGtDY0n5WlKuAPNtyCwpV4D51qbSM3TVZTKFm4+PD3x8fAAA/fv3B1BSHI0ePRpNmjQpd58hQ4bA3t4eBw4cMLS5uLigoKAA+fn5ZWa+MjIyEBwcbNS3vFm1jIwMACh3Nq6USqWCSqUq065UKuv0H3VF4yuVwCeflNw9KknGNymU1J8SFi0C1Grz+Q9X119LU2NJ+VpSrgDzbcgsKVeA+dbWmDVhsjcndO3aFXq9HomJifftJ4SAQvF3GqXXtsXHxxv1S05ORlpamuFUa2nfe/vdve/dfc1BeHjJkh/3XpbXrBmXAiEiImoITLZw27VrFxQKBVq2bFlhnzVr1iA/P99oiZCwsDCo1WrExMQY9Y2JiYEkSRg8eLChbciQITh9+rTRsh96vR4rVqxAt27d4OXlVWv51JfwcODSJWDXLuD770v+vHiRRRsREVFDIPup0pdffhlOTk7o2rUr3N3dkZaWhtWrV+PHH3/Em2++iSZNmuDy5csYMWIEhg8fDj8/P0iShD179uDjjz9Ghw4dEBkZaRhPo9EgKioKs2fPhkajMSzAO2fOHERGRhrWcAOAsWPHYsmSJRg2bBgWLFgANzc3LF26FGfOnMGOHTvk+HLUCisr4J6l8YiIiKgBkL1wCwkJwbJly7B8+XJkZWXBwcEBQUFB+O677zBy5EgAgJOTE9zd3fHhhx/i5s2bKCoqgq+vLyZNmoSZM2eWuYN01qxZcHR0xJIlS7Bw4UJ4eHhg+vTpmDVrllE/lUqFuLg4TJs2DRMnTkR+fj46deqELVu2mN1TE4iIiKjhk71wi4iIKLNY7r0aN26MtVV82OakSZMwadKkB/Zzd3fH8uXLqzQ2ERERkRxM9ho3IiIiIjLGwo2IiIjITLBwIyIiIjITLNyIiIiIzAQLNyIiIiIzwcKNiIiIyEywcCMiIiIyEyzciIiIiMyE7Avw0oMdupiBjX8l4cplBf7acgZKaytIkgSFBFgpJEiSBKs77xUKCYp7tpX5u3Snj6LkvdJKAaWVAjbWEmysrKC0kqC0VsDGSgEba8Wd7RJs7rQprRRQK61gpZDk/tIQEZEFEUJAXyyg1RdDqysq+VNfjEJ9MbT6IsPf9cUCRcXF0BcJFBULFImSP0vf60vbikr7lr4v+VOvL4I+U0J/uRMuBws3M3DqRja+O3AFgAJ7blyWOxwDlbUC9ipr2CqtYK+ygp2NNexVVrBVWv/93sYKznZKNLKzgbOtEo3tbOBsp7zzsoG9TUkRSkREDVuhvhhZ+YXIyC9ERl4hsvJ1yMgrxK3bOuRq9cgt0CNPq0euVo+8Qj1ytUXILdAhT1uEAn0RtLqS4qxY1E+8vTxN82cTCzcz0LFpI7z6eAucO38BzVu2hCRJKBZAUbGAEKLk7+LO34tL/l4sBMSdPvf+vVjgzp9//wZSWFQMXVHJbyqlf9fphVFbYVExxF3/YUp+0ymsUW421gq4Oarg7qSGu5MKbo5quNorcSNVgsvFDLRwc4KHk5qze0REJkoIgdQcLa5l3cbNWwW4casAydkFSL5155VdgMy8QuRo9bX+2TZWCqisFVApFVBZW0F15yyRtZUEa0XJmSVrhQQrhQRrhQJWd/5uZdRu3E8hSYAQsMm6VOvx1gYWbmYg2LcxAr0csFl3Dv2fbAOlUilbLPo7BZxWV4y8Qj3yC4tKXlo98gqLkH+nLU/795+3buuQmV/y21Xp3zPzdSUFob4Y1zJv41rm7Xs+yQorz/8BoOQ/ZrPGtvDW2MHXxQ6t3RzQxt0Rbdwd0djepv6/CEREFkhbBPx5NQuX0gtwMT0Pl9LycCk9H5fT85BfWFSpMRQS4Gxng8Z2SmjsbeB852yMg9oaDqqSl/09fzqorGFr83dhprK2gkpZcumOoo5+qdfpdNi8+WKdjF1TLNyoSqytFLC2UsDOBjUumm4XFiEtV4uUHC1SsgtwM7sAKTla3Lh1GycSr0Fr5YCkrNsoLCpGYloeEtPyyozh5qhCWw9HtHV3RKC3Mzp7O6NZY1uefiUiqoHUHC3+upqFkzeycepGNk5ez8aVDCuIQ4fK7a+QAA8nNTwaqeHZyBYejdSG9x6N1HCxt4HG3gZOamWdFVuWgoUbycbWxgreGjt4a+yM2kt+07mC/v0fg8LKGjdu3caVjHxcSc/HpfR8nLuZgzM3c3At83ZJ0Zejxd5zaYb9XextEOTtjGDfxghp5YLApo1gbcUbqImIyiOEwNmbufjjcgaOXMrEkSuZuJyeX05PyfDLcgtXe/i62KOFqx18Xezh3dgONtb8PlsfWLiRSbNSSGjW2A7NGtvhkVbG23K1epy7mYOzN3Nw4nq24bfD9LxC7Dydgp2nUwAADiprdGuhwSN+rujZxhWtmjhwRo6ILFparhb/O5eG386lYu+5NKTmaI22SxLQ2s0BHb0aoZ2nE1q72SEp4SD+b1CorJfrEAs3MmMOKmt09mmMzj6NDW0FuiKcvJGNY1eycOhiBvYnpuPWbR3iTqcg7nQK/gWgpas9+nVwx5MdPNCpmTOn7YnIIlxOz8Ov8TewNSEZx6/dMtpmq7RCsG9jPOTbGMG+jdHZxxlO6r8LNJ1Oh81n6jtiKg8LN2pQ1EorPOTTGA/5NMbYx1qgqFjg1I1s/H4+Df87n4aDiRlITMvD53sS8fmeRDRxVOHpAE8MDW6GDl5OnIkjogblSno+Nh6/js3xN3DierbRtvaeTni8TRM83sYVwb6NobK2kilKqgoWbtSgWSkkdGzaCB2bNsIrPVshp0CHPWdTsf3ETew6nYLUHC1i9l1CzL5LaOPugPCHmmFocDO4OqjkDp2IqFoKdEXYdiIZqw5dxf7EdEO7lULCI61c8FRHT/Rt7wY3R7WMUVJ1sXAji+KoVmJAoBcGBHqhUF+M38+n4eej17D95E2cvZmLBVtO48PtZ/F0oCdeDPFFJ29nzsIRkVm4mpGP5fsu4ac/riK7oGTNNEkCHm3ligGBngjt4AENl1AyeyzcyGLZWCvQ298Nvf3dcOu2Dlvib+CHw1fx19UsrPszCev+TEJgs0Z4tWcrPNnBg9fCEZFJOnolE1/vvYgtCTcMTxVo6myLYQ83w7CHvdHU2VbeAKlWsXAjAtDIVonhXX0wvKsP/rqahW/3X8bG49dx/NotvLryKPzcHDChdysMDPTi0iJEZBIOX8rAh9vPGp0O7dHaFWMfa4GerZuYzS+beXmAg0PJ33NzAXt7eeMxdSzciO4R5O2MD7ydMbO/P5bvu4Rl+y7hfEouXv/xL3y84xzefLItng7w5ClUIpLF0SuZ+Cj2rGH9SqWVhEGdmiKyRwv4ezjJHB3VNRZuRBVwcVBhamhbRD7eEt/tv4yv/3cRl9Pz8dr3f+Ir74uY2b8durbQyB0mEVmIqxn5mL/lFDbHJwMArBUShj3sjdee8OPpUAvCwo3oAZzUSkzo7YcxjzTHl3sT8cVviTh2NQv/9/l+hHXwwNsD28OL3zSJqI7kF+rx390X8PlviSjUF0MhAUODm2HiE63LPHmGGj4WbkSVZK+yxpS+bTCimw8+3nEOPx6+iq0nkvHbuVRM7dcGYx5pzuvfiKhW7Th5E1HrE5CcXQAACGnpgneeac9TohaMP2WIqsjNUY15QwKweVIPPOzbGPmFRXjv11N45tPfcfxaltzhEVEDkJ6rxaQf/kTkt38gObsA3hpbfDYyGN+P69bgiraior///ttvxu+pLBZuRNXU1sMRP70SgvnhAWhkq8TJG9kIX7oPi+POQV9ULHd4RGSmNv51Hf0++g2//HUdCgl4pWdLxL7eE2EdPRrcTVFr1wLt2//9vn9/oHnzknYqHws3ohpQKCQ839UHcW/0xNMBntAXC3wQexb/9/l+XE7Pkzs8IjIjeVo93vjpL0z84U9k5BXC38MR6yc8ihlPtYNa2fAeR7V2LTB0KJCUZNyelFTSzuKtfCzciGqBq4MKn47ojI+eC4KjyhpHr2Sh/6K9WP9n0oN3JiKLd+L6LQxc/D/8fPQaFBIwqU9r/PLaYwhs5ix3aHWiqAiYPBkQouy20rYpU3jatDws3IhqiSRJGNK5GbZM6YGuLTTIKyzClB+PYc4vJ1Co56lTIipLCIHv9l/CkCX7kJiWBw8nNX4Y1x1T+7WBjXXD/RG9dy9w7VrF24UArl4t6UfGGu6/CiKZNGtshx/GdcekJ/wAADH7LuH5Lw/g5p27woiIAKBQX4yZ6+Ixe8MJFBYVo287N2yZ3APdWrrIHVqdu3GjdvtZEtkLt2PHjuHpp5+Gj48PbG1todFoEBISghUrVpTpe/ToUfTt2xcODg5wdnZGeHg4EhMTyx138eLF8Pf3h0qlQosWLRAdHQ2dTlemX0pKCsaMGQNXV1fY2dkhJCQEcXFxtZ4nWRYrhYSpoW3x1YsPw1FtjSOXMzFg8f/w55VMuUMjIhOQlqvFC18dwA+HrkKSgBlP+ePLFx9GYwt5CLynZ+32sySyF25ZWVnw9vbGvHnzsHnzZnz77bdo3rw5Ro0ahffee8/Q7/Tp0+jVqxcKCwvx008/4ZtvvsHZs2fRo0cPpKamGo05d+5cTJ48GeHh4di2bRvGjx+PefPmYcKECUb9tFot+vTpg7i4OCxatAgbNmyAu7s7wsLCsGfPnnrJnxq2vu3d8ctrj6GtuyNSc7R4/ssD2HYiWe6wiEhGp25kY9Cnv+PwpUw4qqzxzegueKVnqwZ3x+j99OgBNGsGVJSyJAHe3iX9yJjsC/D26tULvXr1MmobMGAALl68iC+++AJRUVEAgLfffhsqlQqbNm2Ck1PJGjbBwcFo3bo1Fi5ciPfffx8AkJ6ejvfeew/jxo3DvHnzDJ+h0+kQFRWFKVOmoP2de4+//vprJCQkYN++fQgJCQEA9O7dG0FBQZg2bRoOHjxYH18CauBauNpj7fhH8Nr3R7HrTCr+seII3hnQHmMebYGiImDPHgm//dYU9vYSevcGrBrezWNEdMeBxHSMW/4HcrR6tHC1x5cvPgw/Nwe5w6p3VlbAokUld49KkvFNCqXF3Mcf8/theWSfcauIq6srrK1L6kq9Xo9Nmzbh2WefNRRtAODr64vevXtj3bp1hratW7eioKAAERERRuNFRERACIH169cb2tatW4e2bdsaijYAsLa2xsiRI3Ho0CEk3XuPMlE12aus8eWLD+P5rj4QApiz8STGfHwSzZsL9OtnjQ8/fBj9+llz/SKiBmzbiWS8+M0h5Gj16NpCg/XjH7XIoq1UeDiwZg3g5WXc3qxZSXt4uDxxmTrZZ9xKFRcXo7i4GJmZmVi9ejW2bduGTz/9FABw4cIF3L59G4GBgWX2CwwMRGxsLAoKCqBWq5GQkAAACAgIMOrn6ekJV1dXw3YASEhIQI9y5mFLP+fEiRNo2rRpufFqtVpotVrD++zsbACATqcr91q6miodsy7GNjUNOdfoAW3RtJEKC2PPYXfyReR31AFJgYAo+RUzKUlg6FBg1aoiDBlSzn3yZq4hH9vyMN+Gq6q5rj5yDVEbTqJYAP3aueHDYQFQK83na1VXx3bgQKBnT8DVVQkA2LhRj759BaysADm/NHX5b7mmY5pM4TZ+/Hh8/vnnAAAbGxt88skneOWVVwCUnP4EAI1GU2Y/jUYDIQQyMzPh6emJ9PR0qFQq2Nvbl9u3dKzScSsa8+7PLc/8+fMRHR1dpn379u2ws6u7h/7GxsbW2dimpqHm6lUEFOwOgOrxU3AMugbJSiB9cyAgFBBCAiAwYUIhrK1jG+xpgoZ6bCvCfBuuyuS667qE9ZdL/jN3dyvGU42uY2fs9boOrU7UxbEtKLACMAAAkJe3Fdu2mc7ibXWRb35+fo32N5nCbebMmYiMjERKSgo2btyI1157DXl5efjnP/9p6HO/Czfv3lbZflXte7cZM2Zg6tSphvfZ2dnw9vZGaGio0enc2qLT6RAbG4t+/fpBqVTW+vimpKHnumePhJsHrWF3Sw3Xgcfg0DEJklUx0jZ1AooVACSkpdnByelp9OzZsGbdGvqxvRfzbbgqm2vM/stYv/8MAODlHs3xz36tzfImhLo8tnl3PWTmySefRDnzLvWuLvMtPUNXXSZTuPn4+MDHxwcA0L9/fwAlxdHo0aPh4lKypk15M2AZGRmQJAnOzs4AABcXFxQUFCA/P7/MzFdGRgaCg4MN711cXCocEyh/hq+USqWCSqUq065UKuv0G1Zdj29KGmqupTdB55/2QmqRAk0GHYV9uxuQFMVI/eWhO8UbkJpqjQaYPoCGe2wrwnwbrvvlunzfJczdXFK0TXzCD1P7tTHLou1udXFs7x6uZPxaHb5G6ibfmo1nsjcndO3aFXq9HomJiWjVqhVsbW0RHx9fpl98fDz8/PygVqsB/H1t2719k5OTkZaWho4dOxraAgICKhwTgFFfotpy97pEt895IHVtMIReAbu2N+Hy1HEAokw/IjIv3x24jHd+OQEAeLVXqwZRtJFpMNnCbdeuXVAoFGjZsiWsra0xcOBArF27Fjk5OYY+V65cwa5duxB+160nYWFhUKvViImJMRovJiYGkiRh8ODBhrYhQ4bg9OnTRst+6PV6rFixAt26dYPXvbe6ENWCe9cvup3ojtT1D0EUS3DomARN35Pw9hZcv4jITP185Bpmry+5Ee6Vx1ti2pNtWbRRrZH9VOnLL78MJycndO3aFe7u7khLS8Pq1avx448/4s0330STJk0AANHR0ejSpQsGDBiA6dOno6CgAG+//TZcXV3xxhtvGMbTaDSIiorC7NmzodFoEBoaisOHD2POnDmIjIw0rOEGAGPHjsWSJUswbNgwLFiwAG5ubli6dCnOnDmDHTt21PvXgixDeesX3b7gjvRfg+A68Bgcgy8hdKASVlZt5A6ViKpo95kUvPXzcQDA2EdbYPpT/izaHsDevvyHzVP5ZJ9xCwkJwaFDhzBhwgT07dsXkZGRSE5OxnfffYd///vfhn7+/v7YvXs3lEolhg4dijFjxsDPzw+//fabobgrNWvWLHz88cdYs2YNQkNDsXjxYkyfPh1Lliwx6qdSqRAXF4fevXtj4sSJGDhwIG7cuIEtW7agZ8+e9ZI/WabS9YvuXm0m72RTFP/RAQCw/fo5LPv9okzREVF1/HU1C+NXHoW+WGBwJy9EPd2ORRvVOtln3CIiIsoslluR4ODgSs+ETZo0CZMmTXpgP3d3dyxfvrxSYxLVpvBwYNAgYNcuPbZsOYannuqE3r2bY8luHT6MPYt3N51EU2dbhHbwkDtUInqAS2l5GBtzGPmFRXjMzxX/HhoEhYJFG9U+2WfciCyZlRXQs6fA448noWfPkkUnJz7hhxe6lTxhYfKqY0hIuiV3mER0Hxl5hRi97BDS8wrRwcsJ/x35EGys+eOV6gb/ZRGZGEmSMOeZDujR2hW3dUWIXP4HbmYXyB0WEZVDV1SM8SuP4HJ6Ppo1tsWyiC5wVJvQehbU4LBwIzJBSisFPh3xEPzcHJCcXYDI5X8gv1Avd1hEdI95W87gQGIG7G2s8PXoLnBzVMsdEjVwLNyITFQjWyW+Gd0FGnsbxCfdwpurj0Pw1isik7HvpoQVB68CAD56rhPaejjKHBFZAhZuRCbMx8UOn48KhtJKwq/xN/D1/3inKZEp+ONyJtZcLPkR+ka/NryJiOoNCzciE9eluQZRT5esP7hgy2kcvpQhc0REli0luwATV/2FIiHhqQ7ueO0JP7lDIgvCwo3IDLwY4otngrygLxaYsPIoUnO0codEZJGKigUmrzqGtNxCeNoJLAjvwLXaqF6xcCMyA5IkYX54AFq7OSAlR4uJPxyFvqhY7rCILM4nceewPzEddjZWiGhTBDsb2ZdDJQvDwo3ITNirrPHZqGDY21jhQGIGPt5xTu6QiCzKvvNp+GRnyf+7d59pD3dbmQMii8TCjciMtGrigH8PDQIALNl9HgcT02WOiMgypOQUYNKqYxACGN7FG4OCPOUOiSwUCzciM/N0oCf+7+FmEAJ4/cdjuHVbJ3dIRA1acbHAGz/9hbRcLdq6O+KdgR3kDoksGAs3IjP0zsAOaO5ih+u3CjBrXTzXdyOqQ98duIy959KgViqw5IXOsLWxkjsksmAs3IjMkL3KGh8P7wwrhYRNx29g3Z9JcodE1CBdSM3F/C2nAAAz+7eDnxsX2SV5sXAjMlOdvJ3xet/WAIC3N5zA1Yx8mSMialj0RcWY+tNfKNAVo0drV4zs5it3SEQs3IjM2au9/NC1uQa5Wj2mreEjsYhq09LdF/DX1Sw4qa3x76GBUCi4XhvJj4UbkRmzUkj4z7BAqJUK7E9Mxw+HrsodElGDEH/tFj6JK1n641+DO8KzEdf+INPAwo3IzPm62OPNJ/0BAPM2n0JS1m2ZIyIyb7qiYry55i/oiwWeDvDEM0FecodEZMDCjagBGPNIczzk44xcrR4z1/IuU6Ka+OK3RJxOzoHG3gb/GtyRj7Qik8LCjagBsFJI+PfQINhYK7DnbCrWHLkmd0hEZikxNReL7pwinT2gHTT2NjJHRGSMhRtRA+Hn5oDX+7YBAPxr00mk5BTIHBGReSkuFpixNh6F+mI83qYJBndqKndIRGWwcCNqQMb1aIGApo2QXaDHvF9PyR0OkVn56Y+rOHgxA7ZKK8zlKVIyUSzciBoQaysF5g7pCEkC1h+7jn3n0+QOicgspOQUYN7mkl923ghtA2+NncwREZWPhRtRAxPYzNmwUGjUhgQU6otljojI9M379RSyC/QIbNYIEY+2kDscogqxcCNqgP4Z2hauDjZITM3Dl3sT5Q6HyKQdTEzH+mPXIUnA3MEBsOJCu2TCWLgRNUCN7JSY2b8dAGDxznN8HBZRBfRFxXjnlxMAgOe7+iCgWSOZIyK6PxZuRA3UkM5N0a2FBgW6YkRvPCF3OEQmacWByzidnANnOyXeDG0rdzhED8TCjaiBkiQJ7w3uCGuFhB2nUrD7TIrcIRGZlLRcLT6IPQug5PKCxlyzjcwACzeiBqy1uyNGP9IcAPDer6egL+KNCkSl/r31NHIK9Ojg5YTnu/rIHQ5RpbBwI2rgJj3RGo3tlDifkovvD12ROxwik/DX1Sz89EfJE0beHdSBNySQ2WDhRtTANbJTYmq/kicqfBR7FrfydTJHRCQvIQTm3lmgOrxzUwT7amSOiKjyql24vfvuu7h+/Xq5227cuIF33323UuPs3LkTY8eOhb+/P+zt7dG0aVMMGjQIR44cMeo3ZswYSJJU5uXv71/uuIsXL4a/vz9UKhVatGiB6Oho6HRlf2ClpKRgzJgxcHV1hZ2dHUJCQhAXF1ep2InMxfNdfdDazQGZ+Tp8svOc3OEQyWrbiZs4dCkDKmsF3gzjDQlkXqpduEVHR+PatfIfZH39+nVER0dXapz//ve/uHTpEiZPnozNmzdj0aJFSElJQffu3bFz506jvra2tti/f7/R68cffywz5ty5czF58mSEh4dj27ZtGD9+PObNm4cJEyYY9dNqtejTpw/i4uKwaNEibNiwAe7u7ggLC8OePXsq+ZUgMn3WVgrMHtAeALB83yUkpubKHBGRPAr1xViwpWS2bVyPlvBsZCtzRERVY13dHYUQFW7Lzc2FUqms1DhLliyBm5ubUVtYWBj8/Pwwb948PPHEE4Z2hUKB7t2733e89PR0vPfeexg3bhzmzZsHAOjVqxd0Oh2ioqIwZcoUtG9f8gPs66+/RkJCAvbt24eQkBAAQO/evREUFIRp06bh4MGDlcqByBw83qYJnvB3w87TKZi3+RS+Gt1F7pCI6t33By/jUno+XB1s8I9ereQOh6jKqlS4HT9+HMeOHTO837x5M06fPm3U5/bt21i5ciVatarcf4h7izYAcHBwQPv27XH16tWqhAcA2Lp1KwoKChAREWHUHhERgVmzZmH9+vWGwm3dunVo27atoWgDAGtra4wcORIzZ85EUlISmjZtWuUYiEzVzP7tsOdsKnacSsHhSxno0pzX9pDluHVbh0VxJZcKTOnbBg6qas9dEMmmSv9q161bZzgFKklShdex2draYtmyZdUO6tatWzh69KjRbBtQUhR6eHggNTUVnp6eGDx4MN59911oNH//8ElISAAABAQEGO3r6ekJV1dXw/bSvj169Cjz+YGBgQCAEydOVFi4abVaaLVaw/vs7GwAgE6nK/daupoqHbMuxjY1lpQrUL/5+jZWYehDTfHjH9cwf/MprIrsAkmqv7vpeGwbNlPPd3HcWWTm69CqiT2e7eRRozhNPdfaxnxrf+zqqlLh9vLLL2PAgAEQQqBr165YtmwZOnbsaNRHpVKhVatWsLWt/nUDEyZMQF5eHmbNmmVoCwoKQlBQkOHz9uzZg48++ghxcXE4fPgwHBwcAJScKlWpVLC3ty8zrkajQXp6uuF9enq6UdF3d7/S7RWZP39+udfxbd++HXZ2dpXMtOpiY2PrbGxTY0m5AvWXbwcBKBVWOHolC/9euRUBmoove6grPLYNmynmm6EFlv1pBUBCH5dsbN+2tVbGNcVc6xLzrbn8/Jo9grBKhZunpyc8PT0BALt27UJwcLChYKots2fPxsqVK7F48WIEBwcb2l9//XWjfv369UPnzp0xdOhQfPnll0bb7zeDcO+2qvS924wZMzB16lTD++zsbHh7eyM0NBROTk4V7lddOp0OsbGx6NevX6WvHzRXlpQrIE++SXbn8Pnei9iT6YR/jnik3taw4rFt2Ew535nrT6BIJKFbi8b454iHazzTbMq51gXmW3tKz9BVV7VP8Pfs2bNGH1ye6OhovPfee5g7dy5ee+21B/YfMmQI7O3tceDAAUObi4sLCgoKkJ+fX2bmKyMjw6gYdHFxKXdWLSMjAwDKnY0rpVKpoFKpyrQrlco6/Udd1+ObEkvKFajffMc/0Rqr/riGcyl52Bh/E8Me9q6Xzy3FY9uwmVq+F1JzsfbPkuWrpoW1g41N7T3aytRyrWvMt3bGrIkaLcC7YsUK9O/fHx06dEDLli2NXpW9OaFUdHQ05syZgzlz5mDmzJmV3k8IAYXi7zRKr22Lj4836pecnIy0tDSjU7sBAQFl+t29772ngYkaika2SkzoXfJ/9KPYsyjQFckcEVHd+Sj2LIqKBfq2c0Owb2O5wyGqkWrPuL3//vuYMWMG2rdvj6CgoHJnnyrrX//6F+bMmYOoqCi88847ld5vzZo1yM/PN1oiJCwsDGq1GjExMejWrZuhPSYmBpIkYfDgwYa2IUOGYPz48Th48KChr16vx4oVK9CtWzd4eXlVOyciU/diSHMs+/0Srt8qwHf7L2Pc4y3lDomoVhQVAXv3AjduAIX2t7Dp+A0AwBuhXGyXzF+1C7cvvvgCEyZMwOLFi2sUwAcffIC3334bYWFhePrpp41OewJA9+7dcfnyZYwYMQLDhw+Hn58fJEnCnj178PHHH6NDhw6IjIw09NdoNIiKisLs2bOh0WgQGhqKw4cPY86cOYiMjDQsBQIAY8eOxZIlSzBs2DAsWLAAbm5uWLp0Kc6cOYMdO3bUKC8iU6dWWuH1vm0w7efj+GzPBYzo5gN7Lo9AZm7tWmDSJCApqeR9k2fPws4P6KzxQjvP2r/+mKi+Vfu7dHJyMoYMGVLjADZu3AigZP21rVvL3uUjhICTkxPc3d3x4Ycf4ubNmygqKoKvry8mTZqEmTNnlrmDdNasWXB0dMSSJUuwcOFCeHh4YPr06UZ3qQIl16nFxcVh2rRpmDhxIvLz89GpUyds2bKlTq7hIzI14Q81xdLd53EpPR/f7r+MV7kgKZmxtWuBoUOB0vXhVU0zYOeXAlEs4df322CtHxAeLm+MRDVV7cItODgYFy5cKLPWWlXt3r37gX0aN26MtWvXVmncSZMmYdKkSQ/s5+7ujuXLl1dpbKKGwtpKgUl9WmPqT3/h898uYFSILxclJbNUVARMnvx30QYAzo+fAQDkHm8GfaY9pkwBBg0CrKzkiZGoNlT75oQPP/wQH3zwQZmHwROReXkmyAstXe2Rla/D8n2X5A6HqFr27gXufny2yjsdap8MCL0Ct/a1hhDA1asl/YjMWbV/tY6IiEB6ejq6du0KDw8PuLi4GG2XJAl//fVXjQMkorplbaXA5L6tMXnVMXzxWyJGhfjCSW05t/tTw3DjhvH7Ro+UPNoq97g3inJsK+xHZG6qXbi5uLjA1dW1NmMhIpkMCPTC4p3ncT4lFzG/X8KkPq3lDomoSu6sDQ+g5No22+bpEEUSbh1oVWE/InNU7cKtMtemEZF5sFJImNynNSb+8Ce+3JuI0Y80RyNbzrqR+ejRA2jWrORu0kaP3plti/97tk2SSraX83hqIrNSowV4iajheDrAE23cHZBToMey3y/KHQ5RlVhZAYsWATaembBtkVYy27a/ZLat9OlWH3/MGxPI/FV7xu233357YJ/HH3+8usMTUT1TKCRMfKJk1i1m3yWM69GS67qRWQkPB3qfOYdTt4C8E01RlF3y2MNmzUqKNi4FQg1Btb8r9+rV64EP6S0q4mN0iMxJ/wBPfBh7FhfT8vD9wSt8mgKZlePXsnDqViqsFBKWvuaHolEl17T16MGZNmo4ql247dq1q0xbWloaNmzYgN9//x1LliypUWBEVP+sFBJe7dkK034+ji/3ltxhqlbyJx6Zh0/izgMABnXywv/1t39AbyLzVO3CraInCzz77LP4xz/+ga1btyIsLKzagRGRPAZ3boqPd5zF9VsFWHPkGkZ295U7JKIHSki6hR2nbkIhARN6+8kdDlGdqZObE4YMGYJVq1bVxdBEVMdsrBV4+c4p0s/2XIC+qFjmiIgebMmuktm2gUFeaNXEQeZoiOpOnRRumZmZ0Gq1dTE0EdWD57r4wMXeBtcyb2Pj8etyh0N0X4mpudh6IhkAZ9uo4av2qdIrV66UadNqtTh+/DhmzJiB7t271ygwIpKPrY0Vxj7WAv/ZdgZLd13AoKCmUCjufzMSkVy+3JsIIYC+7dzQxt1R7nCI6lS1C7fmzZuXe1epEAJt27bFp59+WqPAiEheo0J88dmeCziXkovtJ28irKOH3CERlZGSXYCfjyQBAF7p2eoBvYnMX7ULt2+++aZM4aZWq9G8eXN06dIFCgXX9iUyZ05qJUaHNMenu85jya7zeLKD+wOXACKqb9/8fgmFRcUI9m2MLs01codDVOeqXbiNGTOmFsMgIlMU8WhzfPW/RMQn3cL/zqehR+smcodEZJBdoMPKA5cBAP/gbBtZiBpPi+Xk5GD79u344YcfEBsbi5ycnNqIi4hMgIuDCsO7+AAAvtzLx2CRafn+4BXkaPVo7eaAPv5ucodDVC9qVLgtXLgQXl5eeOqpp/DCCy/gySefhJeXFz788MPaio+IZDb20RZQSMBvZ1Nx6ka23OEQAQC0+iJ887+SXyZefrwlb54hi1Htwu3bb7/FtGnT8Pjjj2PVqlXYu3cvfvzxR/Ts2RNvvvkmvvvuu9qMk4hk4uNih6c6egIAvuKsG5mI9X8mISVHCw8nNQZ1aip3OET1ptqF20cffYQRI0bg119/xbBhw/Doo49i2LBh2LRpE55//nl89NFHtRknEckoskcLAMAvfyUh+VaBzNGQpSsuFvj8t0QAwEuPtYCNNW+GI8tR7X/tp0+fxsiRI8vdNnLkSJw6daraQRGRaens0xhdm2ugKxKI2XdJ7nDIwsWeuonE1Dw4qa3xfDcfucMhqlfVLtxsbW2RkZFR7raMjAzY2tpWOygiMj3j7jwG6/uDl5Gr1cscDVmyr/aWzLaN7O4LB1W1F0cgMkvVLtx69OiBOXPm4Pp148fhJCcn491338Xjjz9e4+CIyHT08XdDS1d7ZBfo8dPhq3KHQxbq+LUsHL6UCaWVhNGPNJc7HKJ6V+3Cbd68ebh58yb8/PwwcOBAvPzyyxg4cCBatWqF5ORkzJs3rzbjJCKZKRQSInuUzLp9/b+LfPg8yeLrO3eSDgj0gruTWuZoiOpftQu3Dh064PDhwxg0aBAOHz6MZcuW4fDhwxg8eDAOHTqE9u3b12acRGQCwh9qChd7GyRl3caWhGS5wyELk3yrAL8evwGg5KYEIktUo4sD2rRpgx9++KG2YiEiE6dWWmFUiC8+3nEOX+5NxIBATz4Gi+rN8v2XoC8W6NZCg45NG8kdDpEsqjzjFh8fj2vXrlW4/dq1a4iPj69RUERkukZ194XKWoHj127hyOVMucMhC5FfqMf3B68A4GwbWbYqFW6//fYbgoODcfPmzQr73Lx5E8HBwdi2bVuNgyMi0+PioMKQziULni77/ZK8wZDF+PnINdy6rYOvix36tHOXOxwi2VSpcFuyZAmGDh2K4ODgCvsEBwfjueeew1dffVXj4IjINJXezbf1RDKuZ92WNxhq8IqLBb6580tCxCPNYcXHW5EFq1Lh9vvvv2Pw4MEP7PfMM8/gwIED1Y2JiExcO08ndG+pQVGxwHcHLssdDjVwu86k4GJaHhzV1hj2sLfc4RDJqkqFW2pqKpo2ffAz4Tw9PZGSklLtoIjI9EU8WnKd0Q+HrqBAVyRzNNSQlS4BMqKrD+y54C5ZuCoVbvb29hU+LeFumZmZsLOzq3ZQRGT6+rZzR7PGtsjK12HDsSS5w6EG6tSNbOy7kA4rBRfcJQKqWLh16NABW7dufWC/LVu2oEOHDpUac+fOnRg7diz8/f1hb2+Ppk2bYtCgQThy5EiZvkePHkXfvn3h4OAAZ2dnhIeHIzExsdxxFy9eDH9/f6hUKrRo0QLR0dHQ6XRl+qWkpGDMmDFwdXWFnZ0dQkJCEBcXV6nYiSyZlULC6JDmAEpuUhBCyBsQNUjf7r8EAAjr6AEvZz5KkahKhdtzzz2Hr7/+Gnv27Kmwz65du7Bs2TI8//zzlRrzv//9Ly5duoTJkydj8+bNWLRoEVJSUtC9e3fs3LnT0O/06dPo1asXCgsL8dNPP+Gbb77B2bNn0aNHD6SmphqNOXfuXEyePBnh4eHYtm0bxo8fj3nz5mHChAlG/bRaLfr06YO4uDgsWrQIGzZsgLu7O8LCwu6bIxGV+L+HvWGrtMLp5BwcSHzwbDxRVWTlF2LdnyWzuWM420YEoIoL8L788suIiYlBaGgoIiMjMWjQILRoUXKdy8WLF7F+/Xp8/fXXCAoKwrhx4yo15pIlS+Dm5mbUFhYWBj8/P8ybNw9PPPEEAODtt9+GSqXCpk2b4OTkBKDkDtbWrVtj4cKFeP/99wEA6enpeO+99zBu3DjDY7d69eoFnU6HqKgoTJkyxfBUh6+//hoJCQnYt28fQkJCAAC9e/dGUFAQpk2bhoMHD1bly0NkcRrZKfFscFOsOHAFy36/iJBWLnKHRA3I6j+uoUBXjHaeTnjYt7Hc4RCZhCrNuNnY2GDbtm3o06cP/vvf/+Kpp56Cv78//P398dRTT+Gzzz5D3759sWXLFtjY2FRqzHuLNgBwcHBA+/btcfVqyYOs9Xo9Nm3ahGeffdZQtAGAr68vevfujXXr1hnatm7dioKCAkRERBiNGRERASEE1q9fb2hbt24d2rZtayjaAMDa2hojR47EoUOHkJTE63aIHqR0JmTHqZu4mpEvbzDUYNx9x/LoEF8+oYPojirfnuPi4oLNmzfjyJEj2L59u6G48vHxwZNPPonOnTvXOKhbt27h6NGjhtm2Cxcu4Pbt2wgMDCzTNzAwELGxsSgoKIBarUZCQgIAICAgwKifp6cnXF1dDdsBICEhAT169Ch3TAA4ceJEhXfRarVaaLVaw/vs7GwAgE6nK/daupoqHbMuxjY1lpQrYP75+jZW4zE/F/zvfDpifk/E9LC2FfY191yrivlW384zqbiSkY9Gttbo38HN5L6GPLYNW13mW9Mxq31fdXBw8H0X4q2JCRMmIC8vD7NmzQJQcvoTADQaTZm+Go0GQghkZmbC09MT6enpUKlUsLe3L7dv6Vil41Y05t2fW5758+cjOjq6TPv27dvr9I7a2NjYOhvb1FhSroB559veWsL/YIXvD1xCW90FqKzu39+cc60O5lt1/z2pAKBAsHMhdu0w3Sfx8Ng2bHWRb35+zc5MVKlwK2/GqyKSJOGvv/6qckCzZ8/GypUrsXjx4jKF4f2myu/eVtl+Ve17txkzZmDq1KmG99nZ2fD29kZoaKjR6dzaotPpEBsbi379+kGpVNb6+KbEknIFGka+YcUC2xb9jssZ+bjtHoAhXctfJLUh5FoVzLd6ElPzcHr/75AkYNbwx+GjMb3lpXhsG7a6zLf0DF11Valw02g0D7zOIDc3F0eOHKnW9QjR0dF47733MHfuXLz22muGdheXkguey5sBy8jIgCRJcHZ2NvQtKChAfn5+mZmvjIwMo2LQxcWlwjGB8mf4SqlUKqhUqjLtSqWyTv9R1/X4psSScgXMP98xjzZH9MaTWHHwKl58pMV9vweYe65VxXyr5oc/Sq4v7uPvhlbujWorrDrBY9uw1UW+NR2vSoXb7t27K9ym1+vxxRdf4N1334UkSRgxYkSVAomOjsacOXMwZ84czJw502hbq1atYGtri/j4+DL7xcfHw8/PD2q1GsDf17bFx8ejW7duhn7JyclIS0tDx44dDW0BAQEVjgnAqC8R3d+zwc3wn21ncC4lFwcvZqB7S95hSlWXq9Xj5yPXAAAv3lknkIj+VqW7SiuyevVqtG/fHhMnTkRQUBCOHDmC7777rtL7/+tf/8KcOXMQFRWFd955p8x2a2trDBw4EGvXrkVOTo6h/cqVK9i1axfCw8MNbWFhYVCr1YiJiTEaIyYmBpIkGT1rdciQITh9+rTRsh96vR4rVqxAt27d4OXlVekciCydk1qJwZ1Lbubh80uputYdvYYcrR4tXe3xmJ+r3OEQmZwaPfRt9+7deOutt3D48GE89NBD2L59O/r06VOlMT744AO8/fbbCAsLw9NPP13m4fTdu3cHUDIj16VLFwwYMADTp09HQUEB3n77bbi6uuKNN94w9NdoNIiKisLs2bOh0WgQGhqKw4cPY86cOYiMjDSs4QYAY8eOxZIlSzBs2DAsWLAAbm5uWLp0Kc6cOYMdO3bU4CtDZJlGdvPF9wevYFtCMlKyC+DmpJY7JDIjQggs319S9L8Y4guFgkuAEN2rWoVbfHw83nrrLWzbtg0tWrTA999/j+HDh1crgI0bNwIoWX+tvMdplT5Gx9/f31AoDh06FNbW1njiiSewcOFCNGnSxGifWbNmwdHREUuWLMHChQvh4eGB6dOnG+5SLaVSqRAXF4dp06Zh4sSJyM/PR6dOnbBlyxb07NmzWvkQWbL2XiULpf5xOROrDl/FpD6t5Q6JzMi+C+k4n5ILexsrPBvcTO5wiExSlQq3q1evIioqCt9//z00Gg0+/vhj/OMf/6jRhXb3u27uXsHBwZWeCZs0aRImTZr0wH7u7u5Yvnx5pWMgovsbFeKLPy5n4vuDVzC+VytYW9XKFRlkAZbvuwQACH+oGRzVlnMBPFFVVKlwa9OmDQoLCxEWFoZp06bB0dGx3Iv7Sz300EM1DpCIzEtYRw+42NsgObsAO06lIKyjh9whkRm4nnUbO07dBFBympSIylelwq30SQFbtmwp97RmKSEEJElCUVFRzaIjIrOjsrbCc128sXT3Baw4cJmFG1XKqsNXUSyAkJYuaO3uKHc4RCarSoXbsmXL6ioOImpARnTzwX/3XMD/zqfhQmouWjVxkDskMmG6omKsOnQFAPBCdx+ZoyEybVUq3EaPHl1XcRBRA9KssR36+Lthx6kUrDxwBW8PbP/gnchixZ1KQUqOFq4ONghtzxlaovvhVcNEVCdGdi+5Tmn1kavIL9TLHA2ZspUHS5YA+b+HvWFjzR9LRPfD/yFEVCceb90EPho75BTosfGv63KHQybqcnoe9p5LgyQBz3flaVKiB2HhRkR1QqGQMPLO9Urf7r9sWJOR6G7f37m2rWebJvA2wYfJE5kaFm5EVGeGBZec+jpxPRvHrmbJHQ6ZGK2+CKv/KHku6QvduAQIUWWwcCOiOtPY3gYDAj0B8PmlVNbWhGRk5BXCs5Eavds2efAORMTCjYjq1qg7NylsOn4DGXmFMkdDpmTlwZLTpMO7+PAJG0SVxP8pRFSnOnk7o2NTJxTqi7HmaJLc4ZCJOHczB4cuZsBKIeG5Lt5yh0NkNli4EVGdkiQJI+9cv/TjH9dQzHsUCH/PtvVt5waPRmqZoyEyHyzciKjOPdPJC45qa1zJuI0ztyS5wyGZ3S4sws9HeVMCUXWwcCOiOmdnY41nH2oGAPg9mYWbpdt4/DpyCvTw0djhMT9XucMhMiss3IioXozoVrKmW0KmhBu3CmSOhuRUepp0RDcfKBQs5ImqgoUbEdWLNu6O6NK8MQQk/HRn7S6yPAlJt/DX1SworSQMC24mdzhEZoeFGxHVmxFdSn5Q/3QkCbqiYpmjITmUzrY91dETLg4qmaMhMj8s3Iio3vRr7w4Ha4GUHC3iTt2UOxyqZzkFOmw4VrIkzAvd+FxSoupg4UZE9UZlrUB3t5L1QEpnXshyrD92HfmFRfBzc0DXFhq5wyEySyzciKhePeJeDEkC9p5Lw8W0PLnDoXoihMDKO489e6GbDySJNyUQVQcLNyKqVy5q4PE7S0B8f5DPL7UUR69k4XRyDtRKBcI786YEoupi4UZE9e75riU/uFcfuYYCXZHM0VB9WHmnSB8Y6IVGdkqZoyEyXyzciKje9WrTBF6N1MjK12Fz/A25w6E6lpVfiE3HS47zC935pASimmDhRkT1zkoh4fmuJXcVrjjA06UN3Zoj11CoL0YHLycENWskdzhEZo2FGxHJ4rku3rBWSDh6JQsnr2fLHQ7VESEEvr9zB/EL3Xx5UwJRDbFwIyJZuDmpEdrBHcDf1z9Rw7M/MR2JaXlwUFnjmU5ecodDZPZYuBGRbEZ2K7neaf2fScjV6mWOhupC6Xp9gzt7wUFlLXM0ROaPhRsRySaklQtautojr7AI6/9MkjscqmWpOVpsS0gGAIzoypsSiGoDCzciko0kSRjR7e+bFIQQMkdEtemnP65CXyzwkI8z2ns5yR0OUYPAwo2IZDU0uBlU1gqcTs7B0StZcodDtaSoWOCHQ3/flEBEtUP2wi0nJwfTpk1DaGgomjRpAkmSMGfOnDL9xowZA0mSyrz8/f3LHXfx4sXw9/eHSqVCixYtEB0dDZ1OV6ZfSkoKxowZA1dXV9jZ2SEkJARxcXG1nSYRVcDZzgYDg0ouWl/JpUEajN/OpeJa5m00slXi6UBPucMhajBkL9zS09PxxRdfQKvVYvDgwffta2tri/379xu9fvzxxzL95s6di8mTJyM8PBzbtm3D+PHjMW/ePEyYMMGon1arRZ8+fRAXF4dFixZhw4YNcHd3R1hYGPbs2VObaRLRfbxw53TppvgbyMwrlDkaqg0rD5TMtg0Nbga10krmaIgaDtlv8fH19UVmZiYkSUJaWhq++uqrCvsqFAp07979vuOlp6fjvffew7hx4zBv3jwAQK9evaDT6RAVFYUpU6agffv2AICvv/4aCQkJ2LdvH0JCQgAAvXv3RlBQEKZNm4aDBw/WUpZEdD+dvJ3RwcsJJ65nY82Raxj3eEu5Q6IauHGrADtP3wQAwzWMRFQ7ZJ9xKz3lWVu2bt2KgoICREREGLVHRERACIH169cb2tatW4e2bdsaijYAsLa2xsiRI3Ho0CEkJfEuN6L6IEmS4TqolQcvo7iYNymYsx//uIZiAXRvqUGrJg5yh0PUoMg+41YVt2/fhoeHB1JTU+Hp6YnBgwfj3XffhUajMfRJSEgAAAQEBBjt6+npCVdXV8P20r49evQo8zmBgYEAgBMnTqBp06blxqLVaqHVag3vs7NLVn7X6XTlXktXU6Vj1sXYpsaScgUsK9/75dq/QxPM3WyFS+n5+O3sTTzayqW+w6t1lnRsgZI8i4qBn/64BgB4/uFmDTZ3Szy2d//Z0NVlvjUd02wKt6CgIAQFBaFjx44AgD179uCjjz5CXFwcDh8+DAeHkt/q0tPToVKpYG9vX2YMjUaD9PR0w/v09HSjou/ufqXbKzJ//nxER0eXad++fTvs7OyqllwVxMbG1tnYpsaScgUsK9+Kcn3IWYG9NxX48JfDuNW2uJ6jqjuWdGzjMyWk5hbCUSmgv3wUm6/KHVHdsqRjCzDf2pCfn1+j/c2mcHv99deN3vfr1w+dO3fG0KFD8eWXXxptv9+p13u3VaXv3WbMmIGpU6ca3mdnZ8Pb2xuhoaFwcqr99Yp0Oh1iY2PRr18/KJXKWh/flFhSroBl5fugXP1u5mDvp/txIssKDz3WCx5OahmirD2WdGyBknyXLCq5K39kSEs806+1zBHVHUs8tsy3dpSeoasusyncyjNkyBDY29vjwIEDhjYXFxcUFBQgPz+/zMxXRkYGgoODjfqWN6uWkZEBAOXOxpVSqVRQqVRl2pVKZZ3+o67r8U2JJeUKWFa+FeXaoZkGXZo3xuFLmVj7ZzIm920YP/gt5dheSs/D2VsKSBIwontzi8jZUo5tKeZbO2PWhOw3J9SUEAIKxd9plF7bFh8fb9QvOTkZaWlphlOtpX3v7Xf3vnf3JaL6MbJ7yU0KPxy6An1RwzldaglWHS65tq1na1d4a+rukhEiS2bWhduaNWuQn59vtERIWFgY1Go1YmJijPrGxMRAkiSjteKGDBmC06dPGy37odfrsWLFCnTr1g1eXl51nQIR3SOsowc09jZIzi5A3OkUucOhSirQFeHno9cBAM939ZY5GqKGyyROlW7ZsgV5eXnIyckBAJw8eRJr1qwBAPTv3x+pqakYMWIEhg8fDj8/P0iShD179uDjjz9Ghw4dEBkZaRhLo9EgKioKs2fPhkajQWhoKA4fPow5c+YgMjLSsIYbAIwdOxZLlizBsGHDsGDBAri5uWHp0qU4c+YMduzYUb9fBCICAKisrTDs4Wb4fE8iVh68gic7eMgdElXCloQbyLqtQ2MbgZ6tXeUOh6jBMonC7dVXX8Xly38/6mb16tVYvXo1AODixYto1KgR3N3d8eGHH+LmzZsoKiqCr68vJk2ahJkzZ5a5g3TWrFlwdHTEkiVLsHDhQnh4eGD69OmYNWuWUT+VSoW4uDhMmzYNEydORH5+Pjp16oQtW7agZ8+edZ84EZXrha6++HxPIn47m4rL6XnwdSl7lziZlhV3npTwiHsxrBS1tzYnERkzicLt0qVLD+yzdu3aKo05adIkTJo06YH93N3dsXz58iqNTUR1y8fFDo+3aYLfzqbi+0NXMOOpdnKHRPdx6kY2jlzOhLVCQnc3Lp5MVJfM+ho3Imq4Rt55VNLqP65Bqy+SORq6n+8Plsy29WvnBicbmYMhauBYuBGRSXrC3w2ejdTIyCvElvhkucOhCuRp9Vj3Z8njAZ/v2kzmaIgaPhZuRGSSrK0UGN6lZNZt5cHLD+hNctlw7DpytXq0dLVH9xYVr31JRLWDhRsRmazhXb1hpZBw+FImTifXbLVxqn1CCKw4UFJUj+jmc9+nzRBR7WDhRkQmy91JjX7t3AEAK+/ctUim49jVLJy8kQ2VtQJDg3malKg+sHAjIpNW+iSFdX8mIU+rlzkautvKOzclDAj0grMd70ogqg8s3IjIpD3SygUtXO2Rq9Vjw7HrcodDd2TlF2LjXyXH44XuPjJHQ2Q5WLgRkUlTKCSM6FpSGKw4cBlCcJ0wU/Dz0SRo9cVo5+mEzt7OcodDZDFYuBGRyRsa3Aw21gqcvJGNY1ez5A7H4gkhDHf6juzOmxKI6hMLNyIyeY3tbTAg0BPA349WIvn8fj4dial5sLexwqBOTeUOh8iisHAjIrPwQreSmxQ2Hb+OrPxCmaOxbDH7LgEomQl1UJnEkxOJLAYLNyIyCw/5OKOdpxO0+mKsOXJN7nAs1tWMfMSdvgkAGBXSXN5giCwQCzciMguSJGHknbsXvz94hTcpyKTkBhGgR2tX+Lk5yB0OkcVh4UZEZmNQp6awt7FCYloe9l9Ilzsci3O7sAirDl8FAIzmbBuRLFi4EZHZcFBZY8hDJRfDr+DzS+vdL38l4dZtHZo1tkVvfze5wyGySCzciMislN6ksP3ETaRkF8gcjeUQQmD5vpJi+cUQX1gpuAQIkRxYuBGRWWnn6YRg38bQFwv8eOe0HdW9Py5n4uSNbKiVCvzfw95yh0NksVi4EZHZKb1J4YdDV1BUzJsU6sPyO0uADO7UlM8lJZIRCzciMjtPdfREYzslrt8qwM7TKXKH0+DdzC7A1oRkAMCLvCmBSFYs3IjI7KiVVobTdTH7LsocTcO38uAV6IsFujbXoL2Xk9zhEFk0Fm5EZJZGhfhCIZU8ful0crbc4TRYhfpifH+w5DFjLz7iK3M0RMTCjYjMUrPGdgjr6AEA+OZ/nHWrK1sSbiAtVwt3JxWe7OAhdzhEFo+FGxGZrZceawEAWH/sOtJytTJH0/AIIbDs90sASpZhUVrxRwaR3Pi/kIjM1kM+jRHUrBEK9cVYeeCK3OE0OEcuZ+LY1SzYWCvwfFcfucMhIrBwIyIzJkkSxt6ZdfvuwGVo9UUyR9SwfLW35BR0eOemaOKokjkaIgJYuBGRmesf4AkPJzXScrXY+NcNucNpMC6n52HbyZIlQEpPSROR/Fi4EZFZU1opDHc7fvO/ixCCC/LWhmW/X4IQQK+2TdDa3VHucIjoDhZuRGT2RnT1gVqpwMkb2TiQmCF3OGbvVr4OP/1R8jixyMdayhwNEd2NhRsRmT1nOxs8+1AzAMA3v3NpkJr6/tAV5BcWwd/DEY/6ucgdDhHdhYUbETUIEY+WXIe149RNXErLkzka81WoLzY8jSKyR0tIkiRzRER0NxZuRNQg+Lk5oHfbJhAC+HJvotzhmK1f46/jZrYWbo4qPBPkJXc4RHQP2Qu3nJwcTJs2DaGhoWjSpAkkScKcOXPK7Xv06FH07dsXDg4OcHZ2Rnh4OBITy/8GvXjxYvj7+0OlUqFFixaIjo6GTqcr0y8lJQVjxoyBq6sr7OzsEBISgri4uNpMkYjqySs9WwEAVh+5htQcLshbVUIIfPlbyWzb6Eeaw8Za9h8RRHQP2f9Xpqen44svvoBWq8XgwYMr7Hf69Gn06tULhYWF+Omnn/DNN9/g7Nmz6NGjB1JTU436zp07F5MnT0Z4eDi2bduG8ePHY968eZgwYYJRP61Wiz59+iAuLg6LFi3Chg0b4O7ujrCwMOzZs6cu0iWiOtSthQadvJ2NTvdR5e1PTMfJG9mwVVrhhW5ccJfIFFnLHYCvry8yMzMhSRLS0tLw1Vdfldvv7bffhkqlwqZNm+Dk5AQACA4ORuvWrbFw4UK8//77AEoKwffeew/jxo3DvHnzAAC9evWCTqdDVFQUpkyZgvbt2wMAvv76ayQkJGDfvn0ICQkBAPTu3RtBQUGYNm0aDh48WNfpE1EtkiQJ/+jZEv9YcRTf7b+MV3v5wUEl+7c5s/HZnpIzGEODm8HZzkbmaIioPLLPuEmS9MCLX/V6PTZt2oRnn33WULQBJUVf7969sW7dOkPb1q1bUVBQgIiICKMxIiIiIITA+vXrDW3r1q1D27ZtDUUbAFhbW2PkyJE4dOgQkpKSapgdEdW3fu090NLVHtkFeqw6xMdgVVb8tVv47WwqrBQSxvXgEiBEpsosfhW9cOECbt++jcDAwDLbAgMDERsbi4KCAqjVaiQkJAAAAgICjPp5enrC1dXVsB0AEhIS0KNHj3LHBIATJ06gadOm5cak1Wqh1f59DU12djYAQKfTlXstXU2VjlkXY5saS8oVsKx86yvXlx71xawNJ/Hl3kQ8/3BT2a7VMqdju2TXOQDA0x094OmkrFbM5pRvTVlSrgDzrYuxq8ssCrf09HQAgEajKbNNo9FACIHMzEx4enoiPT0dKpUK9vb25fYtHat03IrGvPtzyzN//nxER0eXad++fTvs7OwenFQ1xcbG1tnYpsaScgUsK9+6zlVdDDgprXAzW4u5K7ahm5u8T1Mw9WN78zaw7YQVAAntpKvYvPlqjcYz9XxrkyXlCjDf2pCfn1+j/c2icCt1v1Oqd2+rbL+q9r3bjBkzMHXqVMP77OxseHt7IzQ01Oh0bm3R6XSIjY1Fv379oFQqa318U2JJuQKWlW995prc6CL+s/0cDmY74Z0XH4FCUf/rkZnLsX1rbQIErqOvfxNEDu1c7XHMJd/aYEm5Asy3NpWeoasusyjcXFxKVu4ubwYsIyMDkiTB2dnZ0LegoAD5+fllZr4yMjIQHBxsNG5FYwLlz/CVUqlUUKlUZdqVSmWd/qOu6/FNiSXlClhWvvWR66hHWuCzPRdxITUPO8+m46kAzzr9vPsx5WOblHUbv/x1AwAw4YnWtRKnKedb2ywpV4D51taYNSH7zQmV0apVK9ja2iI+Pr7Mtvj4ePj5+UGtVgP4+9q2e/smJycjLS0NHTt2NLQFBARUOCYAo75EZF6c1EpEPNocALAo7hyKi/nw+fJ8+Vsi9MUCj7RyQWefxnKHQ0QPYBaFm7W1NQYOHIi1a9ciJyfH0H7lyhXs2rUL4eHhhrawsDCo1WrExMQYjRETEwNJkozWihsyZAhOnz5ttOyHXq/HihUr0K1bN3h5cdVwInM29rEWcFBZ43RyDmJP3ZQ7HJOTlqvFD3fuvB3fy0/maIioMkziVOmWLVuQl5dnKMpOnjyJNWvWAAD69+8POzs7REdHo0uXLhgwYACmT5+OgoICvP3223B1dcUbb7xhGEuj0SAqKgqzZ8+GRqNBaGgoDh8+jDlz5iAyMtKwhhsAjB07FkuWLMGwYcOwYMECuLm5YenSpThz5gx27NhRv18EIqp1znY2GP2IL5bsuoBP4s4htL07n715ly9+S4RWX4ygZo34MHkiM2EShdurr76Ky5cvG96vXr0aq1evBgBcvHgRzZs3h7+/P3bv3o233noLQ4cOhbW1NZ544gksXLgQTZo0MRpv1qxZcHR0xJIlS7Bw4UJ4eHhg+vTpmDVrllE/lUqFuLg4TJs2DRMnTkR+fj46deqELVu2oGfPnnWfOBHVucjHWmLZ75dw4no24k6loG97d7lDMgkpOQX4dv8lAMDkvq1Z0BKZCZMo3C5dulSpfsHBwZWeCZs0aRImTZr0wH7u7u5Yvnx5pcYkIvPT2N4GL4Y0x2d7LmBR3Dn0aefGIgXAf3dfQIGuGJ28ndG7rZvc4RBRJZnFNW5ERDUxrkcL2CqtEJ90C7vOpMgdjuySbxVg5cGSa9veCG3DQpbIjLBwI6IGz8VBhRdDfAEAi3acgxCWfYfpkl3nUagvRpfmjfGYn6vc4RBRFbBwIyKLMO7xlrCzscJf125h24lkucORTVLWbaw6XDLbNrVfW862EZkZFm5EZBFcHVSIfKwFAODf285AX1Qsc0Ty+HTneeiKBEJauiCkFe8kJTI3LNyIyGKMe7wlNPY2SEzNw+oj1+QOp95dTs/D6j9KnkP6RmgbmaMhoupg4UZEFsNRrcSE3iULzX684yxuFxbJHFH9+vfWM9AXC/Rs0wQPN6/4kX5EZLpYuBGRRRnZ3QdNnW1xM1uLmH2X5A6n3vx5JRO/xt+AJAHTn/KXOxwiqiYWbkRkUVTWVobThEt3n0dWfqHMEdU9IQTmbT4FABj6UDO083SSOSIiqi4WbkRkcQZ1agp/D0fkFOixdPcFucOpc7Enb+LwpUyolQpM5bVtRGaNhRsRWRwrhYS3wkpOFy77/SIupuXJHFHd0RUVY8HW0wCAlx5rAc9GtjJHREQ1wcKNiCxSr7ZN0LNNE+iKBN7bdFLucOpMzO+XkJiaBxd7G/yjZyu5wyGiGmLhRkQWSZIkzB7QHtYKCXGnUxrko7BSsguwKO4cAOCtp/zhqFbKHBER1RQLNyKyWH5uDoh4tDkA4F8bT6JQ37AW5V2w5TRytXp08nbG0IeayR0OEdUCFm5EZNEm9mkNVwcbJKblYdnvF+UOp9b8cSkDa/9MgiQB0c90gELBR1sRNQQs3IjIojmplYYbFT7ecQ5XM/JljqjmdEXFmL3hBABgeBdvBHk7yxsQEdUaFm5EZPGGBjdD95Ya3NYVYea6eAgh5A6pRr74LRGnbmTD2U6Jf4a2lTscIqpFLNyIyOJJkoT54YGwsVZg77k0rD+WJHdI1XY+JddwQ8LbA9rDxUElc0REVJtYuBERAWjhao/JfVoDAN7deBIZeeb3RIXiYoHpPx9Hob4YPds0wZDOTeUOiYhqGQs3IqI7Xn68Jfw9HJGZr0PUeuNTpnl5gCSVvPJMdL3e7w5cxh+XM2FvY4W5QzpCknhDAlFDw8KNiOgOpZUC/xkaBGuFhM3xyVhz5JrcIVXa+ZQczN9S8jzSaWH+aNbYTuaIiKgusHAjIrpLQLNGeL1fyfM85/xyAlfSTf8u0wJdESb+cAwFumL0aO2KUd195Q6JiOoICzcionv8o2crdG2uQV5hEab8+Cf0Raa9MO/7W0/j1I1saOxt8MGwIK7ZRtSAsXAjIrqHlULCh88FwVFljaNXsvCf7WfkDqlCO0/fxLLfLwEAFg4LhJuTWt6AiKhOsXAjIipHs8Z2WPBsIADg8z2J2Bx/3bDtt9+AoiK5IvvbxbQ8TFl1DAAw5pHmeMLfXd6AiKjOsXAjIqrA04GeeKVnSwDAP9cch9I1GwDQvz/QvDmwdq18sWUX6BC5/DCyC/QI9m2MGf395QuGiOoNCzciovtone+P2xddIVkXoUn4EShstQCApCRg6FB5ireiYoHJP/yJC6l58Gykxn9HPgSVtVX9B0JE9Y6FGxFRBYqKgNenSEj7pTN0WbZQNs6H27DDkGz0KF3ibcqU+j1tKoTAvzadxK4zqVBZK/DFqIfh5sjr2ogsBQs3IqIK7N0LXLsGFBfYIGV1VxTl20DleQtNhvwBWBVBCODq1ZJ+9WXxzvOI2XcJAPCfYUEIaNao/j6ciGTHwo2IqAI3bvz9d32GA1JWd0Gx1gq2zdPRZOCfgKK4TL+69OVvifgw9iwAYM7A9ngmyKt+PpiITAYLNyKiCnh6Gr8vTHZG6rqHIfQK2LW9iSZDjkCyLirTr7YJIfBJ3DnM3VzyZIQpfVtjzKMt6vZDicgkmU3htnv3bkiSVO7rwIEDRn2PHj2Kvn37wsHBAc7OzggPD0diYmK54y5evBj+/v5QqVRo0aIFoqOjodPp6iMlIjJxPXoAzZqVPJ+0VMFlV6SsC0axTgE7vxR4jzqI9g9p6ywGXVExZm9IMMy0/TO0Dab0bVNnn0dEps1sCrdS8+bNw/79+41eHTt2NGw/ffo0evXqhcLCQvz000/45ptvcPbsWfTo0QOpqalGY82dOxeTJ09GeHg4tm3bhvHjx2PevHmYMGFCfadFRCbIygpYtKjk70bFW6IbUld3RbHWGpJbJoYs/R0nr2fX+uen52oxNuYwVhy4AkkCZg9oj9eeaF3rn0NE5sNa7gCqqnXr1ujevXuF299++22oVCps2rQJTk5OAIDg4GC0bt0aCxcuxPvvvw8ASE9Px3vvvYdx48Zh3rx5AIBevXpBp9MhKioKU6ZMQfv27es+ISIyaeHhwJo1wKRJJUuAlGoCF0x/+BH8eP0PXE7Px+Clv2Pak20x9tEWtfLIqf+dS8PUn44hJUcLW6UVFg3vhNAOHjUel4jMm9nNuN2PXq/Hpk2b8OyzzxqKNgDw9fVF7969sW7dOkPb1q1bUVBQgIiICKMxIiIiIITA+vXr6ytsIjJx4eHAyZN/v9+8Gbh4ERj/giM2THgUvds2QaG+GO/9egpDP9uHY1ezqv1ZN7MLMGXVnxj59UGk5Gjh5+aAteMfYdFGRADMcMZtwoQJGD58OOzs7BASEoLZs2fjscceAwBcuHABt2/fRmBgYJn9AgMDERsbi4KCAqjVaiQkJAAAAgICjPp5enrC1dXVsL0iWq0WWu3f17VkZ5ecJtHpdHVyjVzpmJZw/Z0l5QpYVr7mnGtxMQAoAQAhIToUF5e02SslfP5CJ6z64xoWbD2Lo1eyMHjJ7+jXzg2jujaFEJXL93J6Ppbvv4zVR5NQoCuGJAEvdPXGtNA2sLWxMouvmTkf36qypFwB5lsXY1eX2RRujRo1wuTJk9GrVy+4uLjg/Pnz+M9//oNevXrh119/xZNPPon09HQAgEajKbO/RqOBEAKZmZnw9PREeno6VCoV7O3ty+1bOlZF5s+fj+jo6DLt27dvh52dXTWzfLDY2Ng6G9vUWFKugGXla465FhRYARgAANi2bRvUauNVdxsBeKsj8OtVBQ6lKhB7KgWxp1KgUVlh3aU4NHcU8LAVsLMGrBRAng5I10q4nCvhZKaEq3l/n15t7iDwbIsi+CguYteOi/WYZe0wx+NbXZaUK8B8a0N+fn6N9jebwq1z587o3Lmz4X2PHj0wZMgQBAQEYNq0aXjyyScN2ySp4utL7t5W2X7lmTFjBqZOnWp4n52dDW9vb4SGhhqdpq0tOp0OsbGx6NevH5RKZa2Pb0osKVfAsvI151zz8v7++5NPPolyfucDAIwAcC4lF8v3X8GGY9eRoS3GnmQJe5LvP75CAh7zc0HkY83RvYXmgd+DTJE5H9+qsqRcAeZbm0rP0FWX2RRu5XF2dsaAAQPw2Wef4fbt23BxcQGAcmfLMjIyIEkSnJ2dAQAuLi4oKChAfn5+mRmyjIwMBAcH3/ezVSoVVCpVmXalUlmn/6jrenxTYkm5ApaVrznmene4JfFX3Ld908Z4f2hjzHyqDT75KRb5Tr44czMXiWl5yC3QQ18s0MhWCTdHFQKaNcLDvhqEdnCHq0PZ7ynmyByPb3VZUq4A862tMWvCrAs3oGRhSqBkhqxVq1awtbVFfHx8mX7x8fHw8/ODWl3yTL/Sa9vi4+PRrVs3Q7/k5GSkpaUZLTFCRGRvD8PzSSvLzsYagRqB/v3bG75ZCyFQLACrWrjzlIgsj1nfVZqZmYlNmzahU6dOUKvVsLa2xsCBA7F27Vrk5OQY+l25cgW7du1CeHi4oS0sLAxqtRoxMTFGY8bExECSJAwePLiesiAiSyJJEos2Iqo2s5lxGzFiBHx8fPDwww/D1dUV586dwwcffICbN28aFV/R0dHo0qULBgwYgOnTp6OgoABvv/02XF1d8cYbbxj6aTQaREVFYfbs2dBoNAgNDcXhw4cxZ84cREZGcg03IiIiMjlmU7gFBgbixx9/xGeffYbc3FxoNBo89thj+O6779ClSxdDP39/f+zevRtvvfUWhg4dCmtrazzxxBNYuHAhmjRpYjTmrFmz4OjoiCVLlmDhwoXw8PDA9OnTMWvWrPpOj4iIiOiBzKZwmz59OqZPn16pvsHBwdixY0el+k6aNAmTJk2qSWhERERE9cKsr3EjIiIisiQs3IiIiIjMBAs3IiIiIjPBwo2IiIjITLBwIyIiIjITLNyIiIiIzAQLNyIiIiIzwcKNiIiIyEywcCMiIiIyEyzciIiIiMwECzciIiIiM8HCjYiIiMhMsHAjIiIiMhMs3IiIiIjMBAs3IiIiIjPBwo2IiIjITLBwIyIiIjITLNyIiIiIzAQLNyIiIiIzwcKNiIiIyEywcCMiIiIyEyzciIiIiMwECzciIiIiM8HCjYiIiMhMsHAjIiIiMhMs3IiIiIjMBAs3IiIiIjPBwo2IiIjITLBwIyIiIjITLNyIiIiIzAQLNyIiIiIzwcINQG5uLqZMmQIvLy+o1Wp06tQJq1atkjssIiIiIiPWcgdgCsLDw3H48GEsWLAAbdq0wffff4/nn38excXFGDFihNzhEREREQFg4YbNmzcjNjbWUKwBQO/evXH58mW8+eabeO6552BlZSVzlEREREQ8VYp169bBwcEBw4YNM2qPiIjA9evXcfDgQZkiIyIiIjJm8TNuCQkJaNeuHaytjb8UgYGBhu2PPPJImf20Wi20Wq3h/a1btwAAGRkZ0Ol0tR6nTqdDfn4+0tPToVQqa318U2JJuQKWla8l5Qow34bMknIFmG9tysnJAQAIIaq1v8UXbunp6WjZsmWZdo1GY9henvnz5yM6OrpMe4sWLWo3QCIiImpwcnJy0KhRoyrvZ/GFGwBIklTlbTNmzMDUqVMN74uLi5GRkQEXF5f7jldd2dnZ8Pb2xtWrV+Hk5FTr45sSS8oVsKx8LSlXgPk2ZJaUK8B8a5MQAjk5OfDy8qrW/hZfuLm4uJQ7q5aRkQHg75m3e6lUKqhUKqM2Z2fnWo/vXk5OThbxnwawrFwBy8rXknIFmG9DZkm5Asy3tlRnpq2Uxd+cEBAQgFOnTkGv1xu1x8fHAwA6duwoR1hEREREZVh84TZkyBDk5ubi559/Nmpfvnw5vLy80K1bN5kiIyIiIjJm8adKn3rqKfTr1w+vvvoqsrOz4efnhx9++AFbt27FihUrTGYNN5VKhXfeeafM6dmGyJJyBSwrX0vKFWC+DZkl5QowX1Miierej9qA5ObmYtasWfjpp5+QkZEBf39/zJgxA8OHD5c7NCIiIiIDFm5EREREZsLir3EjIiIiMhcs3IiIiIjMBAu3GsrNzcWUKVPg5eUFtVqNTp06YdWqVQ/cb+3atXj++efh5+cHW1tbNG/eHC+88ALOnTtXbv8dO3YgJCQEdnZ2cHV1xZgxY5CSklKm3/nz5zFq1Cj4+PjA1tYWrVq1wtSpU8tdqy4xMRHh4eFwdnaGg4MD+vXrh6NHjza4XOfMmQNJksq81Gr1feM2tXzPnj2LZ599Fo0bN4adnR26deuGX375pdwxzf3YVjZXUz62mzZtwosvvoiAgAAolcr7Lsyt0+kQHR2N5s2bQ6VSwd/fH4sXLy63b1WPrTnnW53ja2q5RkVFYcCAAWjatCkkScKYMWMq7NsQjm1l8zX3Y3vkyBFMmDABAQEBcHR0hLu7O/r27YudO3eW2786x7ZCgmqkX79+wtnZWXz22Wdi586dIjIyUgAQK1euvO9+Xbt2Fc8884z45ptvxO7du8V3330n2rVrJxwcHERCQoJR3927dwtra2sxaNAgsX37drFixQrRtGlT0bFjR1FQUGDol5KSIlxcXESLFi1ETEyM2Llzp/jggw+Eg4OD6NSpkygqKjLq6+XlJTp06CB+/vln8euvv4rHHntMODo6itOnTzeoXN955x0BQGzdulXs37/f8Dp48OB94zalfC9evCg0Go3o0KGDWLVqldi0aZN4+umnhSRJYs2aNUZjmvuxrUqupnxsx44dK1q3bi3+7//+TwQHB4v7fbuNjIwUKpVK/Pvf/xa7du0S06dPF5Ikiblz5xr1q86xNed8q3N8TS1XOzs70b17d/GPf/xD2NjYiNGjR5fbr6Ec28rma+7H9o033hAPP/yw+PDDD0VcXJz45ZdfRP/+/QUAsXz5cqO+1T22FWHhVgO//vqrACC+//57o/Z+/foJLy8vodfrK9z35s2bZdqSkpKEUqkUL730klF7ly5dRPv27YVOpzO0/f777wKAWLp0qaHtyy+/FADEjh07jPafN2+eACCOHj1qaHvzzTeFUqkUly5dMrTdunVLuLq6iv/7v/9rULmWfoNITU2tMMZ7mVq+r7zyilCr1eLatWuGNr1eL9q1aye8vb2NClVzP7ZVydWUj+3dcU6YMKHCHwAJCQlCkiQxb948o/Zx48YJW1tbkZ6ebmir6rEVwrzzrerxNbVc7+1rb29fYSHTEI7tvX3vl6+5H9vyxtTr9SIwMFC0atXKqL06x/Z+eKq0BtatWwcHBwcMGzbMqD0iIgLXr1/HwYMHK9zXzc2tTJuXlxeaNWuGq1evGtqSkpJw+PBhjBo1CtbWfy+798gjj6BNmzZYt26doU2pVAIo+yiN0kdx3T0FvW7dOjzxxBPw9fU1tDk5OSE8PBwbN24s8yQJc861Okwt399//x1BQUFo2rSpoc3KygpPPfUUrl69ikOHDhnFbs7Htiq5Vkd95AsACkXlvr2uX78eQghERESUief27dvYunWrUexVObal+5hrvlVlarlWpW9DOLZV7VsVppZreWNaWVkhODi4zJjVObb3w8KtBhISEtCuXTujH0QAEBgYaNheFYmJibh8+TI6dOhg9Bl3j3nv59z9GYMHD4aPjw/eeOMNnDhxArm5ufjtt9+wYMECDBw4EO3atQMA3L59GxcuXKhwzNu3byMxMbFB5Hq3gIAAWFlZwd3dHS+++CKuXLlSYXymlm9hYWG5C0GWth0/fhxAwzi2lc31bqZ2bKsiISEBTZo0gYeHx33jqc6xLd3fHPO9W2WPr6nlWlkN5dhWR0M6tnq9Hnv37jUas7rH9n5YuNVAenp6uQ+hL20r74aAiuj1erz00ktwcHDA66+/bvQZd4957+fc/RmNGjXCgQMHoNPp0LFjRzg6OqJnz57o1q0bVq9ebeiXmZkJIUSVYjfXXAGgVatWmDt3Lr755hvs2LEDU6dOxa+//oquXbsiKSmp3BhNLd/27dvj+PHjyM3NNer3v//9z2ishnBsK5srYLrHtioqisfe3h42NjY1Orb3G9/U8wWqfnxNLdfKaijHtioa4rGdM2cOzp8/j3feecfQVt1jez8W/8irmrrf3TX323Y3IQReeukl7N27Fz///DO8vb0rPdbd7ZmZmRg0aBDy8/OxcuVKeHt7IyEhAf/617/wzDPP4NdffzX6baWqsZtrrqNGjTIap3fv3ujduzdCQkLw73//G4sWLapyTvWd72uvvYYNGzbgxRdfxMKFC2Fvb49PP/0U+/btA1B2et+cj21VcjX1Y1tZVYmnOrGba77VOb6mlmtVNIRjW1kN7dh+9dVXmDt3Lt544w0MGjSoSvFVNvZSLNxqwMXFpdxKOSMjA0D5swv3EkIgMjISK1aswPLly8sccBcXFwDlV+QZGRlGn/H+++/j2LFjuHz5Mjw9PQEAPXr0gL+/P5544gmsXLkSo0ePRuPGjSFJUpViN9dcK9K1a1e0adMGBw4cKHe7qeXbp08fLFu2DG+88QZatWoFoGRm6l//+hdmzpxpuB6sIRzbyuZaEVM4tlXh4uKCY8eOlWnPy8tDYWGhIZ7qHNvS8c0x34rc7/iaWq6V1VCObU2Z67FdtmwZXnnlFbz88sv4z3/+Y7Stusf2fniqtAYCAgJw6tSpMhcWxsfHAwA6dux43/1L/xEtW7YMX331FUaOHFmmT+kYpWPe+zl3f8axY8fQtGlTQyFTqkuXLgD+vgbA1tYWfn5+FY5pa2uLli1bNohcHxRTRReimlq+ADB69GgkJyfj5MmTOHfuHE6cOAGg5Le1Hj16AGgYx7ayuT4oJjmPbVUEBAQgNTUVycnJ942nOse2dHxzzPdBMZV3fE0t18pqKMe2NpjbsV22bBkiIyMxevRofPbZZ2Vmz6p7bO+ryvehksHmzZsFALFq1Sqj9rCwsAfenlxcXCxeeuklIUmS+OKLL+77OV27dhUdO3Y0Gm///v0CgPjvf/9raIuIiBDW1tZGyygIIcT27dsFAPHxxx8b2qZNmyZsbGzElStXDG3Z2dmiSZMm4rnnnmtQuZZn//79QqFQiClTppS73dTyLU9WVpZo3ry5GDx4sFG7uR/bquRaHlM5tnerzPIYCxYsMGp/5ZVXyiyPUdVjK4R551ue+x1fU8v1XvdbHqMhHNt73S/f8pjbsV22bJlQKBTixRdfNFpG5F7VObb3w8Kthvr16ycaN24svvjiC7Fz504xbtw4AUCsWLHC0Gfs2LHCysrKaA2X1157TQAQY8eONVp8cP/+/UZrkAkhxK5du4S1tbUYMmSIiI2NFStXrhTe3t5lFi79448/hI2NjWjXrp1Yvny52Llzp/jkk0+Em5ubcHd3N1ovJyUlRXh6eoqAgACxbt06sXnzZvH4448LR0dHcerUqQaVa2BgoPj3v/8tNm7cKGJjY8XcuXOFs7Oz8PLyEtevXzeLY3vz5k0xbdo0sWHDBrFz506xdOlS0bx5c9GyZUuRlJRkNKa5H9uq5GrKx/bSpUti9erVYvXq1SIsLEwAMLw/fPiwUd/SBWn/85//iN27d4uZM2dWuABvVY+tOedbneNrarnu3r3bsE2tVotevXoZ3qekpBj6NZRjW9l8zf3Y/vTTT0KhUIiHHnpI/P7772XGvXfB+Ooc24qwcKuhnJwcMWnSJOHh4SFsbGxEYGCg+OGHH4z6jB49WgAQFy9eNLT5+voKAOW+fH19y3zO9u3bRffu3YVarRYajUa8+OKL5S4AePToUTFkyBDRrFkzoVKpRMuWLUVkZKRRpV/q/PnzYvDgwcLJyUnY2dmJPn36iCNHjjS4XIcPHy78/PyEvb29UCqVwtfXV/zjH/+47w92U8s3PT1dhIaGiiZNmgilUil8fHzExIkTK1y80pyPbVVyNeVju2zZsgr73jsLUVhYKN555x3h4+MjbGxsRJs2bcQnn3xSbuxVPbbmnG91jq+p5dqzZ88K++7atcuob0M4tpXN19yPbennVPS6+/OFqN6xrYgkhBAgIiIiIpPHmxOIiIiIzAQLNyIiIiIzwcKNiIiIyEywcCMiIiIyEyzciIiIiMwECzciIiIiM8HCjYiIiMhMsHAjIiIiMhMs3IjoviRJeuBrzpw5dfLZixcvhp+fH2xsbCBJErKysgAAUVFR8PHxgbW1NZydnWv9c5cuXYqYmJhaH9ec7Nu3D3PmzDF8zStr7NixCAsLK3dbfHw8JEmCUqnEjRs3yu3z+OOPY8qUKVWMlshy8MkJRHRfBw4cKLddr9fjxRdfRFJSEvbu3YuuXbvW6uceO3YMnTt3RmRkJEaPHg1ra2t06dIFmzZtwuDBgzFr1iw89dRTUKlUePjhh2v1szt27AhXV1fs3r27Vsc1JwsXLsSbb76Jixcvonnz5pXa588//8TDDz+MgwcPlntMJk+ejE8++QQAsGDBArz11ltl+uzZswf9+vVDfHw82rZtW6MciBoia7kDICLT1r1793LbJ02ahIsXL+Lzzz+v9aINAE6cOAEAGDdunNH4CQkJhs93c3Or9c81Rbdv34ZarYYkSWW25efnw87OToaoylqwYAG6du1abtGm1WqxcuVKBAUFIS0tDd988025hVvPnj3Rtm1bfPDBB/jiiy/qI2wi81KtJ5wSkUX79ttvBQDx0ksvVWv/r7/+WgQGBgqVSiUaN24sBg8eLE6ePGnYXt6DqkePHl3uw6LfeecdIYQQcXFxomfPnkKj0Qi1Wi28vb1FeHi4yMvLM4yr1WrFv/71L9G2bVthY2MjXF1dxZgxY0RKSoqhT3mfce+DqO9VVFQkPvnkExEUFCTUarVo1KiR6Natm9iwYYOhz92x3s3X19fo4dWlD7netm2biIiIEK6urgKAuH37tujZs6fo0KGD2LNnjwgJCRG2trbiueeeE0IIcevWLfHGG2+I5s2bC6VSKby8vMTkyZNFbm6u0ecBEBMmTBDffvut8Pf3F7a2tiIwMFBs3LjR0Oedd96p1EPR75acnCyUSqVYsmRJudtXrVolAIjFixeLmTNnCgBi79695fZ9//33hb29vcjOzq7w84gsFQs3IqqSo0ePCltbW9GlSxdRUFBQ5f3nzZsnAIjnn39e/Prrr+Lbb78VLVu2FI0aNRJnz54VQghx4sQJERUVJQCIZcuWif3794vz58+Lo0ePipdeekkAEFu3bhX79+8XV69eFRcvXhRqtVr069dPrF+/XuzevVusXLlSjBo1SmRmZgohSoqrsLAwYW9vL6Kjo0VsbKz46quvRNOmTUX79u1Ffn6+Ib+WLVuKzp07i/3794v9+/eLo0eP3jenUaNGCUmSRGRkpNiwYYPYsmWLmDt3rli0aJGhT1ULt6ZNm4qXX35ZbNmyRaxZs0bo9XpDYert7S0WL14sdu3aJfbs2SPy8vJEp06dhKurq/jwww/Fjh07xKJFi0SjRo3EE088IYqLi43iaN68uejatav46aefxObNm0WvXr2EtbW1uHDhghBCiKtXr4qJEycKAGLt2rWGr8OtW7cq/BqUFvN3F+B369evn1CpVCIjI0OcP39eSJIkxowZU27fgwcPCgDil19+ud+XncgisXAjokpLTU0Vvr6+okmTJuLKlStV3j8zM1PY2tqK/v37G7VfuXJFqFQqMWLECENbaQFz+PBho76ls0GpqamGtjVr1ggA4tixYxV+9g8//CAAiJ9//tmo/fDhwwKAWLp0qaGtQ4cOomfPnpXK6bfffhMAxKxZs+7br6qF24svvlimb+lMZFxcnFH7/PnzhUKhKPO1Kv26bN682SgOd3d3o9ms5ORkoVAoxPz58w1t//nPfwQAcfHixfvmVerVV18Vtra2RkViqUuXLgmFQiGGDx9ulEtFs2qFhYVCkiTx1ltvVeqziSwJ7yolokopKirC8OHDce3aNfz444/w9vau8hj79+/H7du3MWbMGKN2b29vPPHEE4iLi6tWbJ06dYKNjQ1efvllLF++HImJiWX6bNq0Cc7Ozhg4cCD0er3h1alTJ3h4eFT7RoQtW7YAACZMmFCt/Svy7LPPltveuHFjPPHEE0ZtmzZtQseOHdGpUyej3J588klIklQmt969e8PR0dHw3t3dHW5ubrh8+XK1471+/TqaNGlS7nV4y5YtQ3FxMcaOHWtoGzt2LPLy8vDjjz+W6a9UKuHs7IykpKRqx0PUULFwI6JKmTZtGuLi4vD++++jd+/e1RojPT0dAODp6Vlmm5eXl2F7VbVq1Qo7duyAm5sbJkyYgFatWqFVq1ZYtGiRoc/NmzeRlZUFGxsbKJVKo1dycjLS0tKq9dmpqamwsrKCh4dHtfavSHlfo4rab968iePHj5fJy9HREUKIMrm5uLiUGUOlUuH27dvVjrf0Bop7FRcXIyYmBl5eXggODkZWVhaysrLQt29f2Nvb4+uvvy53PLVaXaN4iBoq3lVKRA/0ww8/4MMPP8Rzzz2HN954o9rjlBYM5a3hdf36dbi6ulZ77B49eqBHjx4oKirCH3/8gcWLF2PKlClwd3fH8OHD4erqChcXF2zdurXc/e+egaqKJk2aoKioCMnJyRUWW0BJYaTVasu0V1SsljdzVVG7q6srbG1t8c0335S7T02+rpXl6uqKo0ePlmnfsWOHYSavvILxwIEDOHnyJNq3b2/UnpmZWS9xE5kbzrgR0X0dP34ckZGR6NixY4WzI5UVEhICW1tbrFixwqj92rVr2LlzJ/r06VOj8QHAysoK3bp1w5IlSwDAUEwMGDAA6enpKCoqwsMPP1zmdfeaYVWZfXrqqacAAP/973/v26958+Y4fvy4UdvOnTuRm5tb6dwqMmDAAFy4cAEuLi7l5lbZddjuplKpAKDSXwd/f3+kp6fj1q1bRu1ff/01FAoF1q9fj127dhm9vvvuOwAoU3Bev34dBQUFZYo5IuKMGxHdR2ZmJgYPHgytVou33noL8fHx5fZr0qQJWrVq9cDxnJ2dMXv2bMycORMvvvginn/+eaSnpyM6OhpqtRrvvPNOteL87LPPsHPnTjz99NPw8fFBQUGBoRjo27cvAGD48OFYuXIl+vfvj8mTJ6Nr165QKpW4du0adu3ahUGDBmHIkCEAgICAAKxatQo//vgjWrZsCbVajYCAgHI/u0ePHhg1ahTee+893Lx5EwMGDIBKpcKff/4JOzs7TJw4EQAwatQozJ49G2+//TZ69uyJkydP4tNPP0WjRo2qlfPdpkyZgp9//hmPP/44Xn/9dQQGBqK4uBhXrlzB9u3b8cYbb6Bbt25VGrM030WLFmH06NFQKpVo27ZthTOTvXr1ghACBw8eRGhoKICS2cQNGzbgySefxKBBg8rd76OPPsK3336L+fPnQ6lUAvh70efqnpInatDkvjuCiEzXrl27yl3P697X3XdFVsZXX30lAgMDhY2NjWjUqJEYNGiQOHHihFGfqtxVun//fjFkyBDh6+srVCqVcHFxET179iyznIROpxMLFy40rLfm4OAg/P39xSuvvCLOnTtn6Hfp0iURGhoqHB0dK72O20cffSQ6duxoyCkkJMRobTStViumTZsmvL29ha2trejZs6c4duxYhXeV3pu3EMKwjlt5cnNzRVRUlGGNukaNGomAgADx+uuvi+TkZEM/3FnH7V73xiGEEDNmzBBeXl5CoVA8cB23oqIi0bx5czF+/HhD28cffywAiPXr11e432effVbmbt9Ro0aJgICACvchsmR85BUREdWKDz74AHPnzkVSUhJsbW2rNUZ2dja8vLzw0UcfYdy4cbUcIZH54zVuRERUKyZMmIBGjRoZri+sjo8++gg+Pj6IiIioxciIGg4WbkRUK4qKiozWELv3VVRUJHeIVMfUajW+++47w40N1eHk5ISYmBhYW/MSbKLy8FQpEdWKXr16Yc+ePRVu9/X1xaVLl+ovICKiBoiFGxHVijNnziAnJ6fC7SqVqsI7M4mIqHJYuBERERGZCV7jRkRERGQmWLgRERERmQkWbkRERERmgoUbERERkZlg4UZERERkJli4EREREZkJFm5EREREZuL/AUHYOQ6tI+7vAAAAAElFTkSuQmCC",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"f = 0.21004 ± 0.00012 A\n",
"fwhm = 0.00118 ± 0.00025 A\n"
]
}
],
"source": [
"Brange=(0.208, 0.212)\n",
"\n",
"data = Ncount_mean_total.where(Brange[0]<Ncount_mean_total.als_final_current).where(Ncount_mean_total.als_final_current<Brange[1])\n",
"data = data.dropna(dim='als_final_current')\n",
"data_std = Ncount_std_total.where(Brange[0]<Ncount_mean_total.als_final_current).where(Ncount_mean_total.als_final_current<Brange[1])\n",
"data_std = data_std.dropna(dim='als_final_current')\n",
"\n",
"fitAnalyser = FitAnalyser(\"Gaussian With Offset\", fitDim=1)\n",
"params = fitAnalyser.guess(data, x='als_final_current', guess_kwargs=dict(negative=True), dask=\"parallelized\")\n",
"fitResult = fitAnalyser.fit(data, params, x='als_final_current', dask=\"parallelized\").load()\n",
"fitCurve = fitAnalyser.eval(fitResult, x=np.linspace(Brange[0], Brange[1], 500), dask=\"parallelized\").load()\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"data.plot.errorbar(ax=ax, yerr = data_std, fmt='ob')\n",
"fitCurve.plot.errorbar(ax=ax)\n",
"\n",
"plt.ylim([0, 4500])\n",
"plt.ylabel('NCount')\n",
"plt.xlabel('Z_offset current (A)')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()\n",
"\n",
"f_1 = fitAnalyser.get_fit_value(fitResult).center\n",
"df_1 = fitAnalyser.get_fit_std(fitResult).center\n",
"\n",
"print('f = %.5f \\u00B1 %.5f A'% tuple([np.abs(f_1),df_1]))\n",
"\n",
"s_1 = fitAnalyser.get_fit_value(fitResult).sigma\n",
"ds_1 = fitAnalyser.get_fit_std(fitResult).sigma\n",
"\n",
"fwhm_1 = 2.3548200*s_1\n",
"dfwhm_1 = 2.3548200*ds_1\n",
"\n",
"print('fwhm = %.5f \\u00B1 %.5f A'% tuple([np.abs(fwhm_1),dfwhm_1]))"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"f = 0.18586 ± 0.00021 A\n",
"fwhm = 0.00085 ± 0.00025 A\n"
]
}
],
"source": [
"Brange=(0.183, 0.189)\n",
"\n",
"data = Ncount_mean_total.where(Brange[0]<Ncount_mean_total.als_final_current).where(Ncount_mean_total.als_final_current<Brange[1])\n",
"data = data.dropna(dim='als_final_current')\n",
"data_std = Ncount_std_total.where(Brange[0]<Ncount_mean_total.als_final_current).where(Ncount_mean_total.als_final_current<Brange[1])\n",
"data_std = data_std.dropna(dim='als_final_current')\n",
"\n",
"fitAnalyser = FitAnalyser(\"Gaussian With Offset\", fitDim=1)\n",
"# params = fitAnalyser.guess(data, x='als_final_current', guess_kwargs=dict(negative=True), dask=\"parallelized\")\n",
"params = fitAnalyser.fitModel.make_params()\n",
"params.add(name=\"amplitude\", value= -1500, max=np.inf, min=-np.inf, vary=True)\n",
"params.add(name=\"center\", value= 0.1858, max=np.inf, min=-np.inf, vary=True)\n",
"params.add(name=\"sigma\", value= 0.001, max=np.inf, min= 0, vary=True)\n",
"params.add(name=\"offset\", value= 3700, max=np.inf, min=-np.inf, vary=True)\n",
"fitResult = fitAnalyser.fit(data, params, x='als_final_current', dask=\"parallelized\").load()\n",
"fitCurve = fitAnalyser.eval(fitResult, x=np.linspace(Brange[0], Brange[1], 500), dask=\"parallelized\").load()\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"data.plot.errorbar(ax=ax, yerr = data_std, fmt='ob')\n",
"fitCurve.plot.errorbar(ax=ax)\n",
"\n",
"plt.ylim([0, 4500])\n",
"plt.ylabel('NCount')\n",
"plt.xlabel('Z_offset current (A)')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()\n",
"\n",
"f_1 = fitAnalyser.get_fit_value(fitResult).center\n",
"df_1 = fitAnalyser.get_fit_std(fitResult).center\n",
"\n",
"print('f = %.5f \\u00B1 %.5f A'% tuple([np.abs(f_1),df_1]))\n",
"\n",
"s_1 = fitAnalyser.get_fit_value(fitResult).sigma\n",
"ds_1 = fitAnalyser.get_fit_std(fitResult).sigma\n",
"\n",
"fwhm_1 = 2.3548200*s_1\n",
"dfwhm_1 = 2.3548200*ds_1\n",
"\n",
"print('fwhm = %.5f \\u00B1 %.5f A'% tuple([np.abs(fwhm_1),dfwhm_1]))"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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crbderX1Wa5/W4hpErY6vtU/quP+XrdF5myiits4e//jlawh2+vs09dhV79O/18FOgFRiB6mDPaQOdre+7CGV2MFdJoGnswQeThJ4Ojuii7MEAR5OCOriBA8nSSufRmSc2oZGXCmtw80qBf6Xq0CFbwM8uith79QAQdIEweHWl6QZgkMTlA5NiF7RDJlMRLMookkU0dwMNIu3Xjer/u5SbRdbvJa5pUucxTve0NhojwVHs1psr3pPS59hxgupRSB4tuqPkStwl39oLGtQFzs8ZuExMLjZgGZRRL2yGYAAZXOzpYdjwwSgUWnpQdgwAaWKeksPwihuMgd0kzujr78b+gW4Y0CQBwYGe0Im4dXQZDhlUzNOF1bhp6vlyLtagbNF1bhUWoubVQqtdvKRd+/rWjWAavOMU5cARYN1TvHZ3fqdqr7RsuO4myYruBGQwc0G9PJxRfbsaGRnZyMuLg4ODob9b2vtTtPWfntq/X2tfV4rfbb6vlZ2mmCc1dXAsGGqP6e/04TfR9vDvoWLBNpdFzPUujUd/f9WqVTi0KFD+N3vfqfz/WeJ/7fKpmYoGpuhUDZD0dik+nNjM+obmlBVr0R5nRLltUpU1ClRUqPAtfJ6lNY0oKq+ESeuVeLEtUpsQwEAQOpgh8Hd5XjoPm88ERGIAA+n1gZNnVRJtQJZvxQh+3QR/vdrMaoU+hOGh5ME/u4yCEoJjh6SorlWgqZ6R4gNDhAb7SAq7SE22qO50Q5ioz3e+H92iHzADnYCYGcnwF4QYCcIsLMD7AQB9naCap8gQBBam9dufWZb2diInIMHERsbC4mD7sxzOyfnTaKuDujXT/XnkycBJyv9EWxsbMShr7MtPQwGN1vg6GCHrl2c4CUDunZxgkTC0z2G2rYNSEkBlMWq189P4B1d7aFUKnHNDRgY7Gmz33+1DY0oKKvD+eIanLpehZPXK3DscrnqtNbZYvzvbDFW7D6FYT28MC6yK56ICISjgyrh334d0tdfc52pzqK5WcShcyX4JPcy9p0ohPK26RZ3mQMigj0RFuSB0AB3dPdyRojcBR7Oqp+Ppiag+9q7P0/6+TEd872kVCrhLQO6yZ2t7me4pgZoqlT9OchTtcSVNVIqlXC1gtIxuNE9S31H151/aarv6OLK3J2Ls6MD+vi5oY+fGx7p7w9ANUt8tqga/ztbjN35hThyoRTfnS/Bd+dLsHrfabzwUE84Xw/B7Fm/TdE+9hjD/72uuVnEvpOFeDPzV5y+UaXZ3j/QHSP7+SGury/Cgjxg18L1nsBvj3QaN04V0m7/e4iPdCJjMLjRPelud3QJguqOrtGj+RdnZyYIgibMJf2+B66W1WL7sQJ88P0lXK+ox5KvTkJZegml0n4AfDXvY/i/d50qrMTC7fk4eqkMAOAqdcDYQUGYMCQY/QM92tSX+pFO+tZxe+stfu9Q+zC40T3pm2901066nSgCV66o2sXGdtiwyMp17eKMGcP74PmYnvjsyFWkfnoGEnkN/MbnovrnrijN6gexQcLwfw9qahbxdtaveCf7LJqaRTg72iP5Dz3w5z/01Jz+bI/ERNX3CJ+cQKbC4Eb3pOvXTduOOhepgz2CG0Jw5d1AeP7+V7gNvgDX8KuQBpfi5ucPQlnixvB/DymqqkfKJz/i+/OlAICE/v545cl+JrtRxd6e3yOt4WMb24bBje5JAQGmbUedz/XrgNggQVl2P9Se8Yf3E8ch6VIL/2cP4eaXg1B/3lfTjmzX2aIqTHo/FwXldXBxtMeysWEYMyjI0sMiahGfnED3pOho1XUkLS6QKwDBwap2nV1NjaoegqD6M6ncHuoVBXJc3/QH1F+Sw07aCN/EH+Dc97pOO7Itx6+U46l/foeC8jp093LGlzP+wNBGVo/Bje5J6ju6AN3wxju6yBB3hv/mOkfc+Gwoak4EQrAX4f3kMQT//hrDv406ea0Sz204jIo6JQYGe+LzF3+HXj6ulh4W0V0xuNE9S31HV2Cg9vauXXk3IN2d3vDfbIfi/w5EdV5XCHaAJPo4Dl8sttgYqX0uFNfg2Q2HUVnfiMiQLvgoeSi8XKWWHhaRQRjc6J6WmAhcugRkZjZi9uwfkJnZiAsXGNrIMHrDvyjAOS8cEfIANIkiXvjwKH69ba0vsm5V9Uokb8pFSU0DBgS54/3Jg+Ei5eXeZDsY3OieZ28PxMSIeOihAsTEiDw9Sm2SmKh6DI/arl3AhQsCPn0pAg+GdEFVfSNe2HwUNS08AomsR3OziFn/OY5zN2vg7y7D+5MHw8PJCpbCJ2oDBjeiTu7Oxzk1WeczqC3q9rD/0EOq1zKJPf71bCT83WU4f7MGqTvyW31eL1ne+m/OI+tUEaQOdlj/XCR83WSWHhJRmzG4EXVi27b99nBnQPU4p+7dVdvp7rxcpVj7zCDY2wnY/mMBth5tZdVnsqgT1yrw+r7TAIC0J/sjvKunZQdE1E4MbkSdlPpZrgUF2tvVj3NieDPM4O5yzB55HwDg1Z0nUVRZb+ER0Z3qlU146dPjUDaJiO/nhz8ODrb0kIjajcGNqBO627NcAdXjnHja1DAvPNQTYUEeqKpvxOIvTlh6OHSHf+Wcx5kb1fB2lWJFYhiElhZ4JLIBDG5EnVBbnuVKd+dgb4dVT4XD3k7AnhOF2Hui0NJDolsul9Ri3cGzAIAlT/bjsh9k8xjciDohPsvV9PoFuuP5h3oCAJbv+gUNjc0WHhEBQNpXJ6BobMYfenvj8TA+5oJsH4MbUSfEZ7max/S43vB2leJSSS0++O6ipYfT6X195iayThVBYi9gyZP9eYqU7gkMbkSdEJ/lah6uUgf8LV51o8LaA2dRXttg4RF1XqIo4h97VXeRPjusO3r78nFWdG+weHA7fvw4Hn/8cXTr1g1OTk6Qy+WIiorC5s2btdpNnjwZgiDofIWGhurtd+3atQgNDYVUKkWPHj2QlpYGpVKp066oqAiTJ0+Gt7c3nJ2dERUVhaysLLMcK5G14LNczWf8g8EI9XdDRZ0S//r6vKWH02ntyS9EXkEFXBztMT2ul6WHQ2QyFn/OR3l5OYKDg/H0008jKCgINTU1+Oijj/Dss8/i4sWLSE1N1bR1cnLCgQMHtN7v5OSk0+eyZcuwaNEizJs3D/Hx8cjNzUVqaioKCgqwfv16TTuFQoHhw4ejvLwca9asga+vL9LT05GQkID9+/cjJibGfAdOZGHqxzmlpGgvCdK1qyq08bFg7WNvJ2BOfF9M/eAHfHDoIl54qCc8nR0tPaxOpalZxOpba7b9Obonb0ige4rFg1tsbCxiY2O1to0aNQoXLlzA+vXrtYKbnZ0dhg0b1mp/JSUlWLp0KaZOnYrly5drPkOpVCI1NRWzZs1Cv1srjm7YsAH5+fk4dOgQoqKiAABxcXGIiIjA3LlzcfjwYRMeKZH1SUwERowAPDxUr3ftAuLjOdNmrBH3++L+AHf8cr0S7//vAmbH97X0kDqVPfmFOHezBp7OEkyN7mHp4RCZlMVPlbbE29sbDg5tz5V79uxBfX09kpKStLYnJSVBFEXs2LFDs2379u3o27evJrQBgIODAyZOnIgjR46g4M6VSYnuQfoe50TGEQQBKQ/3BgBs/PYiKup0L9Mg8xBFEe/mnAMATIrqDjcZn0VK9xaLz7ipNTc3o7m5GWVlZdiyZQv27t2Ld955R6tNXV0d/P39cfPmTQQEBGDMmDF49dVXIZfLNW3y8/MBAGFhYVrvDQgIgLe3t2a/um20nquvw8PDAQAnTpxAUFCQ3vEqFAooFArN68rKSgCAUqnUey2dsdR9mqPvzoD1a5mqJJJbf1ZCX4k6e/0MqdGdHr7PC318XfBrUQ0+PHQBU6K6at5PbWfo9+ChcyXIK6iATGKHZwYHsd63dPafYVMwdw0N7ddqgtu0adPwr3/9CwDg6OiIt99+Gy+88IJmf0REBCIiIjBgwAAAQE5ODt58801kZWUhNzcXrq6qO4ZKSkoglUrh4uKi8xlyuRwlJSWa1yUlJVqh7/Z26v0tWbFiBdLS0nS279u3D87OzoYccrtkZmaare/OgPXTVV9vD2AUAGDv3r2QyVp+XEJnrV9banS7B90E/Fpkj/dyziCw6hfYC523hqZyt/r986QdADsM9mrE9zn7O2ZQNoTff8YzVw1ra2sNamc1wW3BggVITk5GUVERvvrqK/z1r39FTU0N/va3vwEAXnrpJa32I0eOxKBBgzBu3Dj8+9//1trf2lo9d+5rS9vbzZ8/H7Nnz9a8rqysRHBwMOLj4+Hu7t7i+9pLqVQiMzMTI0eOhETCqf+2Yv1aVlPz258feeQR6Pmdp9PXz5Aa6TNc2YS9r3+N0hol0DUCKPip09bQWIZ8D/5aVI1T3x2CnQCkPfMQgruY75doW9PZf4ZNwdw1VJ+5uxurCW7dunVDt27dAACPPfYYAFU4mjRpEnx8fPS+Z+zYsXBxccH333+v2ebl5YX6+nrU1tbqzHyVlpYiMjJSq62+WbXS0lIA0DsbpyaVSiGV6t6pJJFIzPpDYe7+73Wsny5Pz9ufWdp6bTpr/W4/ZFUNDH2fBBOHhuDtA2ex+UgBJgV13hqaSmv1+/QH1XXJI/v5oaevR0cOy2bw+8945qqhoX1a7c0JQ4YMQWNjI86fb30dJFEUYWf322Gor23Ly8vTaldYWIji4mLNqVZ12zvb3f7e29sSEbXHxGEhkNgLOHa5HFeqLT2ae1eNohHbjqmC28RhIRYeDZH5WG1wy87Ohp2dHXr27Nlim61bt6K2tlZriZCEhATIZDJkZGRotc3IyIAgCBgzZoxm29ixY3Hq1CmtZT8aGxuxefNmDB06FIGBgSY7HiLqnHzdZXh0gOrZYd8VWe1fuTbvy5+uoUrRiO5ezvh9L29LD4fIbCx+qvT555+Hu7s7hgwZAj8/PxQXF2PLli349NNP8fe//x0+Pj64dOkSnnnmGUyYMAG9e/eGIAjIycnBW2+9hf79+yM5OVnTn1wuR2pqKhYtWgS5XK5ZgHfJkiVITk7WrOEGAFOmTEF6ejrGjx+PlStXwtfXF+vWrcPp06exfz8vaiUi0/jj4GB8+dM1HC0WUNfQxFNVZrD5+0sAgD8NDYGdHZ9JSvcuiwe3qKgobNy4EZs2bUJ5eTlcXV0RERGBDz/8EBMnTgQAuLu7w8/PD2+88QZu3LiBpqYmhISEICUlBQsWLNC5g3ThwoVwc3NDeno6Vq9eDX9/f8ybNw8LFy7UaieVSpGVlYW5c+dixowZqK2txcCBA7F7924+NYGITCaqpxe6dnHC1bI67Dt5A+MG81SeKZ28VokT1yrhaG+HcZFdLT0cIrOyeHBLSkrSWSz3Tl26dMG2bdva1G9KSgpSUlLu2s7Pzw+bNm1qU99E1Lm4uNx+A0fb2dkJeGpQINYcOIctxwoY3Exs27GrAIAR/XzRxYWPF6N7Gy+4ICLqAE89EAQBIg5fKMPlEsPWa6K7a2xqxo7j1wAAiYM420b3PgY3IqIOEOAhw30eqmm7L3/i4/RM5Ztfi1FcrYDcxRExffUvHUV0L2FwIyLqIA94q4LbF8evQTTm3CtpfH7rNOmTEYGQ2POfNLr38buciKiDhMtFSOwF1Qr/hVWWHo7Nq1E0IvPkDQBA4gP6nytNdK9hcCMi6iDODkDsfarTeV/+dM3Co7F9WaeKoGhsRoiXM8KC+KQE6hwY3IiIOtCoMH8AwFc/8XSpsXbnXQcAPBYW0OqzpYnuJQxuREQdKK6vD5wd7XG1rA4/Xa2w9HBsVm1DI7JPFwEAHg8LsPBoiDoOgxsRUQdycrRHXF9fAMC+E4UWHo3tOnCqCPXKZnSTO6N/oLulh0PUYRjciIg6WHx/PwDAXga3dtvF06TUSTG4ERF1sLhQX0jsBZy7WYOzRdWWHo7NqVc2IfvUTQDAY7euGSTqLBjciIg6mLtMgqhe3gCAfSc569ZW350vQZ2yCX7uUt5NSp0OgxsRkQU8ojldesPCI7E9Wb+oajb8fj+eJqVOh8GNiMgCRt7vB0EAfrpSjhuV9ZYejs0QRRFZv6juJh15v5+FR0PU8RjciIgswNddhvBbp/lyTt+08Ghsx8nrVbheUQ8niT2ienlZejhEHY7BjYjIQmJvLQuiXo+M7u7ArZD7hz7ekEnsLTwaoo7H4EZEZCFxoarg9r9fi6FsarbwaGzDwVvBbcT9vhYeCZFlMLgREVlIeJAHvFwcUaVoxNFLZZYejtWrUQJ51yoB/DZbSdTZMLgREVmInZ2AmFsPnefp0rs7UyFAFIG+fm7wc5dZejhEFsHgRkRkQbG3Tpdmn2Jwu5vTFaqlP/7Qx9vCIyGyHAY3IiILeqiPN+wE4MyNahRWcFmQloiiiFPlquAWzeBGnRiDGxGRBXk6O2LArWVBDp0rtvBorNeF4lqUNQiQ2AsY2oPLgFDnxeBGRGRhv7v1+Ktvz5ZYeCTW63/nVLUZHNIFTo5cBoQ6LwY3IiIL+31v1QzSoXPFEEXRwqOxTv87q5qNVNeKqLNicCMisrAHQ+RwtLfD9Yp6XCiusfRwrE5DYzOOXFAtl/J7Pi2BOjkGNyIiC3NytMcDIZ4AgG/P8XTpnX68XIaahia4Ooi439/N0sMhsigGNyIiK/CH3qrr3A6d5Q0Kd/rmV1VN+nqKsLMTLDwaIsticCMisgK/Uwe3cyVoauZ1brdT3217nwfrQsTgRkRkBcKDPOAmdUBFnRInbz3WiYDahkb8fLUCANDHncGNiMGNiMgKONjbYWhPOQDgW67npnHsUjkam0UEeMggl1p6NESWx+BGRGQlflvPjcFN7fAF1c0aQ7p3gcDL24gY3IiIrMXvb13n9sPFMiibmi08Gutw+HwpAFVwIyIrCG7Hjx/H448/jm7dusHJyQlyuRxRUVHYvHmzTttjx45hxIgRcHV1haenJxITE3H+/Hm9/a5duxahoaGQSqXo0aMH0tLSoFQqddoVFRVh8uTJ8Pb2hrOzM6KiopCVlWXy4yQiups+vq7wcJKgTtmEE7zODfXKJhy/Ug4AGNKDwY0IsILgVl5ejuDgYCxfvhy7du3CBx98gO7du+PZZ5/F0qVLNe1OnTqF2NhYNDQ04LPPPsP777+PM2fOIDo6Gjdv3tTqc9myZZg5cyYSExOxd+9eTJs2DcuXL8f06dO12ikUCgwfPhxZWVlYs2YNvvjiC/j5+SEhIQE5OTkdcvxERGp2dgIG35pZyr1QauHRWN6Pl8vR0NQMXzcpQuTOlh4OkVVwsPQAYmNjERsbq7Vt1KhRuHDhAtavX4/U1FQAwOLFiyGVSrFz5064u7sDACIjI9GnTx+sXr0aq1atAgCUlJRg6dKlmDp1KpYvX675DKVSidTUVMyaNQv9+vUDAGzYsAH5+fk4dOgQoqKiAABxcXGIiIjA3Llzcfjw4Y4oARGRxuDucuz/pQhHLpZi6kM9LT0ci1Jf3za0pxcEXuBGBMAKZtxa4u3tDQcHVa5sbGzEzp078dRTT2lCGwCEhIQgLi4O27dv12zbs2cP6uvrkZSUpNVfUlISRFHEjh07NNu2b9+Ovn37akIbADg4OGDixIk4cuQICgoKzHR0RET6De6hurP0h4ulaO7k67mpr28beqsmRGQFM25qzc3NaG5uRllZGbZs2YK9e/finXfeAQCcO3cOdXV1CA8P13lfeHg4MjMzUV9fD5lMhvz8fABAWFiYVruAgAB4e3tr9gNAfn4+oqOj9fYJACdOnEBQUJDe8SoUCigUCs3rykrV9ShKpVLvtXTGUvdpjr47A9bPOKyf8QytYV8fZ8gkdiirVeLU9XL08XXtiOFZHUVjM45dVj2fNDLYnd+DRmL9jGfuGhrar9UEt2nTpuFf//oXAMDR0RFvv/02XnjhBQCq058AIJfr/tYll8shiiLKysoQEBCAkpISSKVSuLi46G2r7kvdb0t93v65+qxYsQJpaWk62/ft2wdnZ/Ndi5GZmWm2vjsD1s84rJ/xDKlhsJMdflXaIeO/3+D3fp1z1u1cJaBodICrg4jTuV/jzK0zpfweNA7rZzxz1bC2ttagdlYT3BYsWIDk5GQUFRXhq6++wl//+lfU1NTgb3/7m6ZNa9c43L7P0HZtbXu7+fPnY/bs2ZrXlZWVCA4ORnx8vNbpXFNRKpXIzMzEyJEjIZFITN7/vY71Mw7rZ7y21PBX6Vn8evA86l274rHHwlpte6/6Z8554MRZ/P4+Pzz++EB+DxqJ9TOeuWuoPnN3N1YT3Lp164Zu3boBAB577DEAqnA0adIkeHl5AdA/A1ZaWgpBEODp6QkA8PLyQn19PWpra3VmvkpLSxEZGal57eXl1WKfgP4ZPjWpVAqpVHcZb4lEYtYfCnP3f69j/YzD+hnPkBoO6+WDdw6ex9HL5Z223j9eUT3mamhPb60a8HvQOKyf8cxVQ0P7tNqbE4YMGYLGxkacP38evXr1gpOTE/Ly8nTa5eXloXfv3pDJZAB+u7btzraFhYUoLi7GgAEDNNvCwsJa7BOAVlsioo4yqJsn7O0EFJTXoaC8ztLD6XDNzSKOXS4HADzIhXeJtFhtcMvOzoadnR169uwJBwcHPPHEE9i2bRuqqqo0bS5fvozs7GwkJiZqtiUkJEAmkyEjI0Orv4yMDAiCgDFjxmi2jR07FqdOndJa9qOxsRGbN2/G0KFDERgYaLbjIyJqiYvUAf0DVZdcdMb13M4XV6OiTgmZxA73B5j+0hMiW2bxU6XPP/883N3dMWTIEPj5+aG4uBhbtmzBp59+ir///e/w8fEBAKSlpWHw4MEYNWoU5s2bh/r6eixevBje3t6YM2eOpj+5XI7U1FQsWrQIcrkc8fHxyM3NxZIlS5CcnKxZww0ApkyZgvT0dIwfPx4rV66Er68v1q1bh9OnT2P//v0dXgsiIrXB3eX4+WoFjlwsxZhB+u9uv1cdvaS6mzSiqyck9lY7v0BkERYPblFRUdi4cSM2bdqE8vJyuLq6IiIiAh9++CEmTpyoaRcaGoqDBw/i5Zdfxrhx4+Dg4ICHH34Yq1ev1oQ7tYULF8LNzQ3p6elYvXo1/P39MW/ePCxcuFCrnVQqRVZWFubOnYsZM2agtrYWAwcOxO7duxETE9Mhx09EpM/g7nJs+N8F/HCx8824qYNbZAhPkxLdyeLBLSkpSWex3JZERkYaPBOWkpKClJSUu7bz8/PDpk2bDOqTiKijqEPLr0XVqKxXwl3WeS4oZ3AjahnnoImIrJCPmxTBcieIIvDTrQetdwZlNQ04d7MGADCoG4Mb0Z0Y3IiIrNSgYFVw+fHWHZadwY9XVLNtPX1cIHdxtPBoiKwPgxsRkZV6oJsnAGge/dQZaE6TcraNSC8GNyIiK6U+Vfjj5XKIYud49BWvbyNqHYMbEZGVuj/AHVIHO1TUKXG+uMbSwzE7ZVMzfrr1xAQGNyL9GNyIiKyUo4MdwoI8AADHLt37p0tPXa9CnbIJ7jIH9PJxtfRwiKwSgxsRkRV74NbM04+d4M7So5dUa9Y9ENIFdnaChUdDZJ0Y3IiIrNigYE8AnWPG7aerqtOkA28dMxHpYnAjIrJi6hm3MzeqUK1otPBozOunq+UAgAgGN6IWMbgREVkxP3cZgjyd0CwCP9/Dp0sr6pQ4f2vh3YiunpYdDJEVY3AjIrJyAzvBem75BarTpMFyJy68S9QKBjciIiv3QLd7/wkKx2/NJnK2jah1DG5ERFZuYLBqSZCfrlbcswvx/sTgRmQQBjciIivXP9AD9nYCiqsVuFZRb+nhmMXPt+4o5Y0JRK1jcCMisnIyiT36+rkBuDdvULhRWY/CynrYCcCAIHdLD4fIqjG4ERHZAPVM1PFbS2bcS9SnSe/zc4Ozo4NlB0Nk5RjciIhsQERX1XVuP996lue9RLN+G69vI7orBjciIhugnnHLK6hAc/O9dYOC+sHy4bduwiCiljG4ERHZgD6+rpBJ7FCtaMT54mpLD8dkmptF/MwZNyKDMbgREdkAB3s7hAWpZqSO30OnSy+W1KCyvhFSBzv09Xez9HCIrB6DGxGRjVDPSP18D92goF4GpH+gOyT2/CeJ6G74U0JEZCPCb13n9tM9tCSI5okJXL+NyCAMbkRENmLgrRm3X65XQdHYZNnBmAjvKCVqGwY3IiIbESx3QhdnCRqamnHqepWlh2M0ZVMzTlyrBMAZNyJDMbgREdkIQRAQfg9d53a6sAoNjc1wlzmgu5ezpYdDZBMY3IiIbIh6Id574c5SzWnSYE8IgmDZwRDZCAY3IiIboj6leC/MuKlvsuD1bUSGY3AjIrIh6lOlZ29Wo1rRaNnBGEnzxISufGICkaEY3IiIbIiPmxRBnk4QRSDvqu2eLq1RNOLXItUNFgN5YwKRwRjciIhsjHqG6icbPl2aX1CBZhHwd5fB111m6eEQ2Yx2B7dXX30V165d07vv+vXrePXVVw3q58CBA5gyZQpCQ0Ph4uKCoKAgjB49GkePHtVqN3nyZAiCoPMVGhqqt9+1a9ciNDQUUqkUPXr0QFpaGpRKpU67oqIiTJ48Gd7e3nB2dkZUVBSysrIMGjsRkSXcC9e5qZ+YEMEHyxO1SbuDW1paGq5evap337Vr15CWlmZQP//85z9x8eJFzJw5E7t27cKaNWtQVFSEYcOG4cCBA1ptnZyc8N1332l9ffrppzp9Llu2DDNnzkRiYiL27t2LadOmYfny5Zg+fbpWO4VCgeHDhyMrKwtr1qzBF198AT8/PyQkJCAnJ8fAShARdSzNjJsN31l6/LY7SonIcA7tfaMoii3uq66uhkQiMaif9PR0+Pr6am1LSEhA7969sXz5cjz88MOa7XZ2dhg2bFir/ZWUlGDp0qWYOnUqli9fDgCIjY2FUqlEamoqZs2ahX79+gEANmzYgPz8fBw6dAhRUVEAgLi4OERERGDu3Lk4fPiwQcdARNSRwoI8IAhAQXkdblYp4OMmtfSQ2ox3lBK1T5uC288//4zjx49rXu/atQunTp3SalNXV4ePPvoIvXr1MqjPO0MbALi6uqJfv364cuVKW4YHANizZw/q6+uRlJSktT0pKQkLFy7Ejh07NMFt+/bt6Nu3rya0AYCDgwMmTpyIBQsWoKCgAEFBQW0eAxGRObnJJOjl44qzRdX4+Wo5ht/vZ+khtUlJtQJXy+oAAGG8o5SoTdoU3LZv3645BSoIQovXsTk5OWHjxo3tHlRFRQWOHTumNdsGqEKhv78/bt68iYCAAIwZMwavvvoq5HK5pk1+fj4AICwsTOu9AQEB8Pb21uxXt42Ojtb5/PDwcADAiRMnWgxuCoUCCoVC87qyUvXYFqVSqfdaOmOp+zRH350B62cc1s94pq5hWJA7zhZV49ilUjzUW373N1iRY5dKAAA9vZ3hZG9YTfg9aBzWz3jmrqGh/bYpuD3//PMYNWoURFHEkCFDsHHjRgwYMECrjVQqRa9eveDk5NSWrrVMnz4dNTU1WLhwoWZbREQEIiIiNJ+Xk5ODN998E1lZWcjNzYWrqysA1alSqVQKFxcXnX7lcjlKSko0r0tKSrRC3+3t1PtbsmLFCr3X8e3btw/OzuZ7dEtmZqbZ+u4MWD/jsH7GM1UN7csFAPY4cPws7lOcMUmfHWX3FdXYvVCNXbt2tem9/B40DutnPHPVsLa21qB2bQpuAQEBCAgIAABkZ2cjMjJSE5hMZdGiRfjoo4+wdu1aREZGara/9NJLWu1GjhyJQYMGYdy4cfj3v/+ttb+1R6fcua8tbW83f/58zJ49W/O6srISwcHBiI+Ph7u7e4vvay+lUonMzEyMHDnS4OsH6Tesn3FYP+OZuoZdr1Zg678O47pCikcfjbWpR0Zt+/AYgGI8OrQfHhvWzaD38HvQOKyf8cxdQ/WZu7tp980JMTEx7X1ri9LS0rB06VIsW7YMf/3rX+/afuzYsXBxccH333+v2ebl5YX6+nrU1tbqzHyVlpZqhUEvLy+9s2qlpaUAoHc2Tk0qlUIq1b0gWCKRmPWHwtz93+tYP+OwfsYzVQ0HBHeBo70dyuuUKKxqRDcbeUi7KIrIK1D9AzUoRN7mWvB70Disn/HMVUND+2x3cAOAzZs34+OPP8alS5dQV1entU8QBJw7d87gvtLS0rBkyRIsWbIECxYsMPh9oijCzu63VU3U17bl5eVh6NChmu2FhYUoLi7WOrUbFhaGvLw8nT7V2+48DUxEZC2kDva4P9AdP10px/Gr5TYT3K6W1aG0pgESewH3B5j+7ATRva7dwW3VqlWYP38++vXrh4iICL2zT4Z67bXXsGTJEqSmpuKVV14x+H1bt25FbW2t1hIhCQkJkMlkyMjI0ApuGRkZEAQBY8aM0WwbO3Yspk2bhsOHD2vaNjY2YvPmzRg6dCgCAwPbfUxEROYW0dUDP10px09XyvFkhG38faV+2kOovztkEnvLDobIBrU7uK1fvx7Tp0/H2rVrjRrA66+/jsWLFyMhIQGPP/641mlPABg2bBguXbqEZ555BhMmTEDv3r0hCAJycnLw1ltvoX///khOTta0l8vlSE1NxaJFiyCXyxEfH4/c3FwsWbIEycnJmqVAAGDKlClIT0/H+PHjsXLlSvj6+mLdunU4ffo09u/fb9RxERGZm2oNtEs29QQFPjGByDjtDm6FhYUYO3as0QP46quvAKjWX9uzZ4/OflEU4e7uDj8/P7zxxhu4ceMGmpqaEBISgpSUFCxYsEDnDtKFCxfCzc0N6enpWL16Nfz9/TFv3jytu1QB1XVqWVlZmDt3LmbMmIHa2loMHDgQu3fvNss1fEREpqR+6kBeQQUam5rhYG/9j58+zoV3iYzS7uAWGRmJc+fO6ay11lYHDx68a5suXbpg27Ztbeo3JSUFKSkpd23n5+eHTZs2talvIiJr0NPbBW5SB1QpGnHmRjX6BVr3NWNNzSLyC9Qzbp6WHQyRjWr3r2dvvPEGXn/9dZ2HwRMRUcewsxM0Tx6whdOlZ4uqUdvQBBdHe/TyMe1SUkSdRbtn3JKSklBSUoIhQ4bA398fXl5eWvsFQcBPP/1k9ACJiKhlEcGeOHSuBD9dLceEIYatiWYp6ueThnX1gL2d7aw7R2RN2h3cvLy84O3tbcqxEBFRG6mvFTt+pcKyAzHA8VuzgjxNStR+7Q5uhlybRkRE5qW+O/PMjSrUNTTBydF6l9hQz7gN5I0JRO1m/bcgERFRi/zdZfB1k6KpWcSJa9Y761bX0IRThVUAOONGZIx2z7h9/fXXd23z0EMPtbd7IiIygCAIiAj2RObJGzh+pRwPdm/5UX2WdOJaBZqaRfi4SRHgIbP0cIhsVruDW2zs3R9q3NTU1N7uiYjIQBFdPZB58gZ+umq9M263r992t387iKhl7Q5u2dnZOtuKi4vxxRdf4Ntvv0V6erpRAyMiIsOoTz1a85Ig6lA5qJunZQdCZOPaHdxaerLAU089hb/85S/Ys2cPEhIS2j0wIiIyTHiQJwDgUkktymoa0MXF0bID0uMnPjGByCTMcnPC2LFj8Z///MccXRMR0R08nCXo6a169N9PVjjrVlrTgMultQCgWTCYiNrHLMGtrKwMCoXCHF0TEZEe4ZonKFjfdW7qMNnTxwUeThLLDobIxrX7VOnly5d1tikUCvz888+YP38+hg0bZtTAiIjIcBHBnthx/JrmlKQ14fptRKbT7uDWvXt3vXcGiaKIvn374p133jFqYEREZDj1DQo/XS2HKIpWdeem5o5Srt9GZLR2B7f3339f5y8GmUyG7t27Y/DgwbCz49q+REQdpV+AOxzsBBRXN6CgvA5duzh32GfX1ACut54ZX10NuLj8tk8Uxd9uTGBwIzJau4Pb5MmTTTgMIiIyhkxij9AAN+QXVOLnqxUdGtxac6W0DmW1Sjja2+H+ADdLD4fI5hk9LVZVVYV9+/bhk08+QWZmJqqqqkwxLiIiaiP1UhvWdJ2b+sHy9we6Q+pgvc9RJbIVRgW31atXIzAwEI8++ij+9Kc/4ZFHHkFgYCDeeOMNU42PiIgMpA5ux60ouP12YwKXASEyhXafKv3ggw8wd+5cPProo5g8eTICAwNx7do1bNq0CX//+9/h4+ODZ5991pRjJSKiVqivIcsvUD0X1N7O8jco8Po2ItNqd3B788038cwzz2Dz5s1a28ePH4+JEyfizTffZHAjIupAvX1d4exoj5qGJpy7WY37/Cx7TZmyqRn511TryjG4EZlGu0+Vnjp1ChMnTtS7b+LEifjll1/aPSgiImo7ezsBYUGqU5I/Xi6z8GiAX65Xol7ZDHeZA3p4udz9DUR0V+0Obk5OTigtLdW7r7S0FE5OTu0eFBERtU9kSBcAwNFLlg9u6jFEhnSBnRWctiW6F7Q7uEVHR2PJkiW4du2a1vbCwkK8+uqreOihh4weHBERtY06uP1gZcGNiEyj3de4LV++HL/73e/Qu3dvDB8+HAEBAbh+/ToOHDgAiUSCbdu2mXKcRERkgAe6qULS+Zs1KK1pgNzF0WJjUQe3BxjciEym3TNu/fv3R25uLkaPHo3c3Fxs3LgRubm5GDNmDI4cOYJ+/fqZcpxERGSALi6O6OWjup7Mkte5XSuvw/WKetjbCRjIGxOITKbdM24AcN999+GTTz4x1ViIiMgEHgyR49zNGvxwqQzD7/ezyBjUs239Atzh7GjUPzVEdJs2z7jl5eXh6tWrLe6/evUq8vLyjBoUERG1nzXcoMDr24jMo03B7euvv0ZkZCRu3LjRYpsbN24gMjISe/fuNXpwRETUdpHdVWHppyvlaGhstsgYeH0bkXm0Kbilp6dj3LhxiIyMbLFNZGQk/vjHP+K9994zenBERNR2Pb1d4OksgaKxGSevV3b459c2NGo+lzNuRKbVpuD27bffYsyYMXdt9+STT+L7779v75iIiMgIgiAgspvlTpf+dEX1yK0ADxmCPLmmJ5EptSm43bx5E0FBQXdtFxAQgKKionYPioiIjKM+XXr0kv6F0s3p2GWeJiUylzYFNxcXlxaflnC7srIyODs7t3tQRERknNtn3ERR7NDP/uFiqdYYiMh02hTc+vfvjz179ty13e7du9G/f3+D+jxw4ACmTJmC0NBQuLi4ICgoCKNHj8bRo0d12h47dgwjRoyAq6srPD09kZiYiPPnz+vtd+3atQgNDYVUKkWPHj2QlpYGpVKp066oqAiTJ0+Gt7c3nJ2dERUVhaysLIPGTkRkrSKCPeFgJ+BGpQJXy+o67HObRRHHLpcD4PVtRObQpuD2xz/+ERs2bEBOTk6LbbKzs7Fx40Y8/fTTBvX5z3/+ExcvXsTMmTOxa9curFmzBkVFRRg2bBgOHDigaXfq1CnExsaioaEBn332Gd5//32cOXMG0dHRuHnzplafy5Ytw8yZM5GYmIi9e/di2rRpWL58OaZPn67VTqFQYPjw4cjKysKaNWvwxRdfwM/PDwkJCa0eIxGRtZNJ7BHWVfXA+SMXOu506ZkbVaioU8LF0R79At077HOJOos2rYr4/PPPIyMjA/Hx8UhOTsbo0aPRo0cPAMCFCxewY8cObNiwAREREZg6dapBfaanp8PX11drW0JCAnr37o3ly5fj4YcfBgAsXrwYUqkUO3fuhLu76i+DyMhI9OnTB6tXr8aqVasAACUlJVi6dCmmTp2K5cuXAwBiY2OhVCqRmpqKWbNmaZ7qsGHDBuTn5+PQoUOIiooCAMTFxSEiIgJz587F4cOH21IeIiKrMqynF368XI7vz5fgqciuHfKZuZdKAAAPdpdDYt/uh/MQUQva9FPl6OiIvXv3Yvjw4fjnP/+JRx99FKGhoQgNDcWjjz6Kd999FyNGjMDu3bvh6GjY8/HuDG0A4Orqin79+uHKlSsAgMbGRuzcuRNPPfWUJrQBQEhICOLi4rB9+3bNtj179qC+vh5JSUlafSYlJUEURezYsUOzbfv27ejbt68mtAGAg4MDJk6ciCNHjqCgoMCgYyAiskbDenoBAL6/UNJhn6kOburPJiLTavNzSLy8vLBr1y4cPXoU+/bt04Srbt264ZFHHsGgQYOMHlRFRQWOHTummW07d+4c6urqEB4ertM2PDwcmZmZqK+vh0wmQ35+PgAgLCxMq11AQAC8vb01+wEgPz8f0dHRevsEgBMnTrR4F61CoYBCodC8rqxUrVmkVCr1XktnLHWf5ui7M2D9jMP6Gc8SNYwIdIW9nYArpXW4eLPSbEtzqA5JAkDED5fUNya4m/RY+T1oHNbPeOauoaH9tvsBcpGRka0uxGuM6dOno6amBgsXLgSgOv0JAHK5XKetXC6HKIooKytDQEAASkpKIJVK4eLioretui91vy31efvn6rNixQqkpaXpbN+3b59Z76jNzMw0W9+dAetnHNbPeB1dw2Bne1ysFvDvLw5iiI957i6tr7cHMAoSnypU1CvhaCfi6k+HcN0MTz/k96BxWD/jmauGtbW1BrVrU3DTN+PVEkEQ8NNPP7WlewDAokWL8NFHH2Ht2rU6wVAQhFY/r63t2tr2dvPnz8fs2bM1rysrKxEcHIz4+Hit07mmolQqkZmZiZEjR0IikZi8/3sd62cc1s94lqrhCYczWP/NRdS7B+OxxwaY5TNqalT/lXVT/bI7tKc3nhhl2l/s+T1oHNbPeOauofrM3d20KbjJ5fJWwwwAVFdX4+jRo3dtp09aWhqWLl2KZcuW4a9//atmu5eX6loJfTNgpaWlEAQBnp6emrb19fWora3VmfkqLS3VCoNeXl4t9gnon+FTk0qlkEqlOtslEolZfyjM3f+9jvUzDutnvI6u4e96+2D9Nxdx5GKZ2T5X3a0sWPX3aVRvbzN+Fr8HjcH6Gc9cNTS0zzYFt4MHD7a4r7GxEevXr8err74KQRDwzDPPtKVrpKWlYcmSJViyZAkWLFigta9Xr15wcnJCXp7uvHteXh569+4NmUwG4Ldr2/Ly8jB06FBNu8LCQhQXF2PAgN9+4wwLC2uxTwBabYmIbNGD3eWa69yulNYiWG76SzmamgAIIqTdVL/0Dg7hjQlE5mKSe7W3bNmCfv36YcaMGYiIiMDRo0fx4YcfGvz+1157DUuWLEFqaipeeeUVnf0ODg544oknsG3bNlRVVWm2X758GdnZ2UhMTNRsS0hIgEwmQ0ZGhlYfGRkZEARB61mrY8eOxalTp7SW/WhsbMTmzZsxdOhQBAYGGnwMRETWyFXqgIHBngCAb34tNnn/27YB/foBjv7lsHdSorneAYkxHti2zeQfRUQw4uYEQDUD9/LLLyM3NxcPPPAA9u3bh+HDh7epj9dffx2LFy9GQkICHn/8cZ2H0w8bNgyAakZu8ODBGDVqFObNm4f6+nosXrwY3t7emDNnjqa9XC5HamoqFi1aBLlcjvj4eOTm5mLJkiVITk7WrOEGAFOmTEF6ejrGjx+PlStXwtfXF+vWrcPp06exf/9+IypDRGQ9Yu7zwdFLZfj6zE08M7Sbyfrdtg0YNw4QRcDjd6pQWHfJGyVX7TBuHLB1K3Db79VEZALtCm55eXl4+eWXsXfvXvTo0QMff/wxJkyY0K4BfPXVVwBU66/pe5yW+hl7oaGhmqA4btw4ODg44OGHH8bq1avh4+Oj9Z6FCxfCzc0N6enpWL16Nfz9/TFv3jzNXapqUqkUWVlZmDt3LmbMmIHa2loMHDgQu3fvRkxMTLuOh4jI2jx0nw/eyDyDb88Vo7GpGQ4mWBi3qQmYOVMV2gBA1kP1BJv6Cz4QRUAQgFmzgNGjAXt7oz+OiG5pU3C7cuUKUlNT8fHHH0Mul+Ott97CX/7yF6Mu0mvturk7RUZGGjwTlpKSgpSUlLu28/Pzw6ZNmwweAxGRrQkL8oCnswTltUocv1KOB7u3fOOVob75Brh6VfVnwVEJaWA5AKDugjcAVaC7ckXVLjbW6I8jolvaFNzuu+8+NDQ0ICEhAXPnzoWbm5vei/vVHnjgAaMHSERExrG3E/CH3t7Y+fN1fH3mpkmC2/Xrv/1ZFlICwU6EssQFTZXOLbYjIuO1KbipnxSwe/duvac11URRhCAIaGpqMm50RERkEg/d54OdP19Hzq/FmB3f1+j+AgJ++7PTrdOkdRd8Wm1HRMZrU3DbuHGjucZBRERm9FAfVaj6+Wo5SqoV8HLVXYeyLaKjga5dgYICEU69igAA9bdOkwKqa9y6dlW1IyLTaVNwmzRpkrnGQUREZuTvIUP/QHecuFaJrF+K8H+Dg43qz94eWLMGeOavFXBwr0dzgz3qLqmCm3r99bfe4o0JRKZmknXciIjI+iX09wcA7DlRaJL+EhOBiS+r+qo77wM0qVJa165cCoTIXBjciIg6iUcGqILb/34tRrWi0SR9Xmy6AQCoPaPqe9cu4MIFhjYic2FwIyLqJPr4uqKHtwsampqRfarI6P7O3azG2aJqONgJqDvvCwB46CGeHiUyJwY3IqJOQhAEPHLrdOleE5wu3XdCNds2pLsXRAUfXE7UERjciIg6kUf6+wEAsk8Voa7BuCWb/pt3DQAwItTf6HERkWEY3IiIOpGIrp4IljuhpqEJe060f3XcX65XIr+gEhJ7AfH3c7E2oo7C4EZE1InY2QkY94BqKZAtP1xtdz9bj6reOzzUD12cHU0yNiK6OwY3IqJO5qnIIAgCcOhcCa6U1rb5/cqmZuz4sQAAMP7BrqYeHhG1gsGNiKiT6drFGb/vpVosVz1z1hbZp4pQUtMAb1cpYu7TfcwVEZkPgxsRUSekninbevQqGpua2/TeT45cBgAkPhAEB3v+M0LUkfgTR0TUCT3S3x9yF0cUlNe16UkKZ4uqkX36JgQBeHpINzOOkIj0YXAjIuqEZBJ7PDssBADwr5zzEEXRoPet//ocAGDE/X7o4e1itvERkX4MbkREndRzUSFwdrRHXkEF9p28cdf2F4pr8Pkx1U0Jf4npZe7hEZEeDG5ERJ2Ul6sUU37fAwDw//acQkNj69e6rdp9Ck3NIh4O9UVkSJeOGCIR3YHBjYioE5v6UE94uzri3M0avJtzrsV2Wb/cwJ4ThbC3EzA3oW8HjpCIbsfgRkTUiXk4SbBoVD8AwJqsX3HkQqlOm2vldfj71p8BAFN+3x2h/u4dOkYi+g2DGxFRJ/dkRCBGDwxEU7OI5E25+OHib+HtSmktJr53GKU1Degf6I458ZxtI7IkB0sPgIiILEsQBKxIDMPVsjocvVSG//vXdxhxvx9cpA7Ye6IQtQ1NCPJ0wrsTIyGT2Ft6uESdGmfciIgIzo4O+GDKEIwZGIhmEdh38ga2/1iA2oYmDO7eBZ++MAzBcmdLD5Oo0+OMGxERAQBcpA54a8IgJEf3RM6Zm1A2NWNwdzmienrBzk6w9PCICAxuRER0hwFBHhgQ5GHpYRCRHjxVSkRERGQjGNyIiIiIbASDGxEREZGNYHAjIiIishEMbkREREQ2wuLBraqqCnPnzkV8fDx8fHwgCAKWLFmi027y5MkQBEHnKzQ0VG+/a9euRWhoKKRSKXr06IG0tDQolUqddkVFRZg8eTK8vb3h7OyMqKgoZGVlmfowiYiIiIxm8eVASkpKsH79ekRERGDMmDF47733Wmzr5OSEAwcO6Gy707Jly7Bo0SLMmzcP8fHxyM3NRWpqKgoKCrB+/XpNO4VCgeHDh6O8vBxr1qyBr68v0tPTkZCQgP379yMmJsZ0B0pEdI9ycQFE0dKjIOocLB7cQkJCUFZWBkEQUFxc3Gpws7Ozw7Bhw1rtr6SkBEuXLsXUqVOxfPlyAEBsbCyUSiVSU1Mxa9Ys9OuneqDyhg0bkJ+fj0OHDiEqKgoAEBcXh4iICMydOxeHDx820VESERERGc/ip0rVpzxNZc+ePaivr0dSUpLW9qSkJIiiiB07dmi2bd++HX379tWENgBwcHDAxIkTceTIERQUFJhsXERERETGsviMW1vU1dXB398fN2/eREBAAMaMGYNXX30Vcrlc0yY/Px8AEBYWpvXegIAAeHt7a/ar20ZHR+t8Tnh4OADgxIkTCAoK0jsWhUIBhUKheV1ZWQkAUCqVeq+lM5a6T3P03RmwfsZh/YzHGhqH9TMO62c8c9fQ0H5tJrhFREQgIiICAwYMAADk5OTgzTffRFZWFnJzc+Hq6gpAdapUKpXCxcVFpw+5XI6SkhLN65KSEq3Qd3s79f6WrFixAmlpaTrb9+3bB2dn8z2IOTMz02x9dwasn3FYP+OxhsZh/YzD+hnPXDWsra01qJ3NBLeXXnpJ6/XIkSMxaNAgjBs3Dv/+97+19rd26vXOfW1pe7v58+dj9uzZmteVlZUIDg5GfHw83N3dW3xfeymVSmRmZmLkyJGQSCQm7/9ex/oZh/UzHmtoHNbPOKyf8cxdQ/WZu7uxmeCmz9ixY+Hi4oLvv/9es83Lywv19fWora3VmfkqLS1FZGSkVlt9s2qlpaUAoHc2Tk0qlUIqlepsl0gkZv2hMHf/9zrWzzisn/FYQ+OwfsZh/Yxnrhoa2qfFb04wliiKsLP77TDU17bl5eVptSssLERxcbHmVKu67Z3tbn/v7W2JiIiILM2mg9vWrVtRW1urtURIQkICZDIZMjIytNpmZGRAEASMGTNGs23s2LE4deqU1rIfjY2N2Lx5M4YOHYrAwEBzHwIRERGRwaziVOnu3btRU1ODqqoqAMDJkyexdetWAMBjjz2Gmzdv4plnnsGECRPQu3dvCIKAnJwcvPXWW+jfvz+Sk5M1fcnlcqSmpmLRokWQy+WaBXiXLFmC5ORkzRpuADBlyhSkp6dj/PjxWLlyJXx9fbFu3TqcPn0a+/fv79giEBEREd2FVQS3F198EZcuXdK83rJlC7Zs2QIAuHDhAjw8PODn54c33ngDN27cQFNTE0JCQpCSkoIFCxbo3EG6cOFCuLm5IT09HatXr4a/vz/mzZuHhQsXarWTSqXIysrC3LlzMWPGDNTW1mLgwIHYvXs3n5pAREREVscqgtvFixfv2mbbtm1t6jMlJQUpKSl3befn54dNmza1qW8iIiIiS7Dpa9yIiIiIOhMGNyIiIiIbweBGREREZCMY3IiIiIhsBIMbERERkY1gcCMiIiKyEQxuRERERDaCwY2IiIjIRjC4EREREdkIBjciIiIiG8HgRkRERGQjGNyIiIiIbASDGxEREZGNYHAjIiIishEMbkREREQ2gsGNiIiIyEYwuBERERHZCAY3IiIiIhvB4EZERERkIxjciIiIiGwEgxsRERGRjWBwIyIiIrIRDG5ERERENoLBjYiIiMhGMLgRERER2QgGNyIiIiIbweBGREREZCMY3IiIiIhsBIMbERERkY1gcCMiIiKyEQxuRERERDbC4sGtqqoKc+fORXx8PHx8fCAIApYsWaK37bFjxzBixAi4urrC09MTiYmJOH/+vN62a9euRWhoKKRSKXr06IG0tDQolUqddkVFRZg8eTK8vb3h7OyMqKgoZGVlmfIQiYiIiEzC4sGtpKQE69evh0KhwJgxY1psd+rUKcTGxqKhoQGfffYZ3n//fZw5cwbR0dG4efOmVttly5Zh5syZSExMxN69ezFt2jQsX74c06dP12qnUCgwfPhwZGVlYc2aNfjiiy/g5+eHhIQE5OTkmONwiYiIiNrNwdIDCAkJQVlZGQRBQHFxMd577z297RYvXgypVIqdO3fC3d0dABAZGYk+ffpg9erVWLVqFQBVEFy6dCmmTp2K5cuXAwBiY2OhVCqRmpqKWbNmoV+/fgCADRs2ID8/H4cOHUJUVBQAIC4uDhEREZg7dy4OHz5s7sMnIiIiMpjFZ9wEQYAgCK22aWxsxM6dO/HUU09pQhugCn1xcXHYvn27ZtuePXtQX1+PpKQkrT6SkpIgiiJ27Nih2bZ9+3b07dtXE9oAwMHBARMnTsSRI0dQUFBg5NERERERmY7FZ9wMce7cOdTV1SE8PFxnX3h4ODIzM1FfXw+ZTIb8/HwAQFhYmFa7gIAAeHt7a/YDQH5+PqKjo/X2CQAnTpxAUFCQ3jEpFAooFArN68rKSgCAUqnUey2dsdR9mqPvzoD1Mw7rZzzW0Disn3FYP+OZu4aG9msTwa2kpAQAIJfLdfbJ5XKIooiysjIEBASgpKQEUqkULi4uetuq+1L321Kft3+uPitWrEBaWprO9n379sHZ2fnuB9VOmZmZZuu7M2D9jMP6GY81NA7rZxzWz3jmqmFtba1B7WwiuKm1dkr19n2Gtmtr29vNnz8fs2fP1ryurKxEcHAw4uPjtU7nmopSqURmZiZGjhwJiURi8v7vdayfcVg/47GGxmH9jMP6Gc/cNVSfubsbmwhuXl5eAPTPgJWWlkIQBHh6emra1tfXo7a2Vmfmq7S0FJGRkVr9ttQnoH+GT00qlUIqlepsl0gkZv2hMHf/9zrWzzisn/FYQ+OwfsZh/Yxnrhoa2qfFb04wRK9eveDk5IS8vDydfXl5eejduzdkMhmA365tu7NtYWEhiouLMWDAAM22sLCwFvsEoNWWiIiIyNJsIrg5ODjgiSeewLZt21BVVaXZfvnyZWRnZyMxMVGzLSEhATKZDBkZGVp9ZGRkQBAErbXixo4di1OnTmkt+9HY2IjNmzdj6NChCAwMNNsxEREREbWVVZwq3b17N2pqajSh7OTJk9i6dSsA4LHHHoOzszPS0tIwePBgjBo1CvPmzUN9fT0WL14Mb29vzJkzR9OXXC5HamoqFi1aBLlcjvj4eOTm5mLJkiVITk7WrOEGAFOmTEF6ejrGjx+PlStXwtfXF+vWrcPp06exf//+ji0CERER0V1YRXB78cUXcenSJc3rLVu2YMuWLQCACxcuoHv37ggNDcXBgwfx8ssvY9y4cXBwcMDDDz+M1atXw8fHR6u/hQsXws3NDenp6Vi9ejX8/f0xb948LFy4UKudVCpFVlYW5s6dixkzZqC2thYDBw7E7t27ERMTY/4DJyIiImoDqwhuFy9eNKhdZGSkwTNhKSkpSElJuWs7Pz8/bNq0yaA+iYiIiCzJJq5xIyIiIiIGNyIiIiKbweBGREREZCMY3IiIiIhsBIMbERERkY1gcCMiIiKyEQxuRERERDaCwY2IiIjIRjC4EREREdkIBjciIiIiG8HgRkRERGQjGNyIiIiIbASDGxEREZGNYHAjIiIishEMbkREREQ2gsGNiIiIyEYwuBERERHZCAY3IiIiIhvB4EZERERkIxjciIiIiGwEgxsRERGRjWBwIyIiIrIRDG5ERERENoLBjYiIiMhGMLgRERER2QgGNyIiIiIbweBGREREZCMY3IiIiIhsBIMbERERkY1gcCMiIiKyETYT3A4ePAhBEPR+ff/991ptjx07hhEjRsDV1RWenp5ITEzE+fPn9fa7du1ahIaGQiqVokePHkhLS4NSqeyIQyIiIiJqEwdLD6Ctli9fjri4OK1tAwYM0Pz51KlTiI2NxcCBA/HZZ5+hvr4eixcvRnR0NI4fPw4fHx9N22XLlmHRokWYN28e4uPjkZubi9TUVBQUFGD9+vUddkxEREREhrC54NanTx8MGzasxf2LFy+GVCrFzp074e7uDgCIjIxEnz59sHr1aqxatQoAUFJSgqVLl2Lq1KlYvnw5ACA2NhZKpRKpqamYNWsW+vXrZ/4DIiIiIjKQzZwqNURjYyN27tyJp556ShPaACAkJARxcXHYvn27ZtuePXtQX1+PpKQkrT6SkpIgiiJ27NjRUcMmIiIiMojNzbhNnz4dEyZMgLOzM6KiorBo0SL84Q9/AACcO3cOdXV1CA8P13lfeHg4MjMzUV9fD5lMhvz8fABAWFiYVruAgAB4e3tr9rdEoVBAoVBoXldWVgIAlEqlWa6RU/fJ6+/ah/UzDutnPNbQOKyfcVg/45m7hob2azPBzcPDAzNnzkRsbCy8vLxw9uxZ/OMf/0BsbCz++9//4pFHHkFJSQkAQC6X67xfLpdDFEWUlZUhICAAJSUlkEqlcHFx0dtW3VdLVqxYgbS0NJ3t+/btg7OzczuP8u4yMzPN1ndnwPoZh/UzHmtoHNbPOKyf8cxVw9raWoPa2UxwGzRoEAYNGqR5HR0djbFjxyIsLAxz587FI488otknCEKL/dy+z9B2+syfPx+zZ8/WvK6srERwcDDi4+O1TtOailKpRGZmJkaOHAmJRGLy/u91rJ9xWD/jsYbGYf2Mw/oZz9w1VJ+5uxubCW76eHp6YtSoUXj33XdRV1cHLy8vANA7W1ZaWgpBEODp6QkA8PLyQn19PWpra3VmyEpLSxEZGdnqZ0ulUkilUp3tEonErD8U5u7/Xsf6GYf1Mx5raBzWzzisn/HMVUND+7T5mxNEUQSgmiHr1asXnJyckJeXp9MuLy8PvXv3hkwmA/DbtW13ti0sLERxcbHWEiNERERE1sCmg1tZWRl27tyJgQMHQiaTwcHBAU888QS2bduGqqoqTbvLly8jOzsbiYmJmm0JCQmQyWTIyMjQ6jMjIwOCIGDMmDEddBREREREhrGZU6XPPPMMunXrhgcffBDe3t749ddf8frrr+PGjRta4SstLQ2DBw/GqFGjMG/ePM0CvN7e3pgzZ46mnVwuR2pqKhYtWgS5XK5ZgHfJkiVITk7mGm5ERERkdWwmuIWHh+PTTz/Fu+++i+rqasjlcvzhD3/Ahx9+iMGDB2vahYaG4uDBg3j55Zcxbtw4ODg44OGHH8bq1au1npoAAAsXLoSbmxvS09OxevVq+Pv7Y968eVi4cGFHHx4RERHRXdlMcJs3bx7mzZtnUNvIyEjs37/foLYpKSlISUkxZmhEREREHcKmr3EjIiIi6kwY3IiIiIhsBIMbERERkY1gcCMiIiKyEQxuRERERDaCwY2IiIjIRjC4EREREdkIBjciIiIiG8HgRkRERGQjGNyIiIiIbASDGxEREZGNYHAjIiIishEMbkREREQ2gsGNiIiIyEYwuBERERHZCAY3IiIiIhvB4EZERERkIxjciIiIiGwEgxsRERGRjWBwIyIiIrIRDG5ERERENoLBjYiIiMhGMLgRERER2QgGNyIiIiIbweBGREREZCMY3IiIiIhsBIMbERERkY1gcCMiIiKyEQxuRERERDaCwY2IiIjIRjC4EREREdkIBjcA1dXVmDVrFgIDAyGTyTBw4ED85z//sfSwiIiIiLQ4WHoA1iAxMRG5ublYuXIl7rvvPnz88cd4+umn0dzcjGeeecbSwyMiIiICwOCGXbt2ITMzUxPWACAuLg6XLl3C3//+d/zxj3+Evb29hUdJRERExFOl2L59O1xdXTF+/Hit7UlJSbh27RoOHz5soZERERERaev0M275+fm4//774eCgXYrw8HDN/t/97nc671MoFFAoFJrXFRUVAIDS0lIolUqTj1OpVKK2thYlJSWQSCQm7/9ex/oZh/UzHmtoHNbPOKyf8cxdw6qqKgCAKIqttuv0wa2kpAQ9e/bU2S6XyzX79VmxYgXS0tJ0tvfo0cO0AyQiIqJOo6qqCh4eHi3u7/TBDQAEQWjzvvnz52P27Nma183NzSgtLYWXl1er/bVXZWUlgoODceXKFbi7u5u8/3sd62cc1s94rKFxWD/jsH7GM3cNRVFEVVUVAgMDW23X6YObl5eX3lm10tJSAL/NvN1JKpVCKpVqbfP09DT5+O7k7u7OHzojsH7GYf2Mxxoah/UzDutnPHPWsLWZNrVOf3NCWFgYfvnlFzQ2Nmptz8vLAwAMGDDAEsMiIiIi0tHpg9vYsWNRXV2Nzz//XGv7pk2bEBgYiKFDh1poZERERETaOv2p0kcffRQjR47Eiy++iMrKSvTu3RuffPIJ9uzZg82bN1vNGm5SqRSvvPKKzulZMgzrZxzWz3isoXFYP+OwfsazlhoK4t3uO+0EqqursXDhQnz22WcoLS1FaGgo5s+fjwkTJlh6aEREREQaDG5ERERENqLTX+NGREREZCsY3IiIiIhsBIObGVRXV2PWrFkIDAyETCbDwIED8Z///Oeu77t69SpmzZqFmJgYeHp6QhAEZGRktNi+pqYGixcvxn333QepVAovLy/ExcXh119/1bRZsmQJBEFo8cuQcXU0a6ofAJw9exbPPvssunXrBicnJ/Tq1QuzZ89u8aka1sDaanjmzBk89dRT6NKlC5ydnTF06FB8+eWXxh6m2Zi7fhcvXmz15zIhIUGrvVKpRFpaGrp37w6pVIrQ0FCsXbvWVIdrctZWv9TUVIwaNQpBQUEQBAGTJ0820ZGajzXV8OjRo5g+fTrCwsLg5uYGPz8/jBgxAgcOHDDlIZuUNdXvypUrGDt2LHr27AkXFxd4eHhg0KBBeOedd3SWIjNEp7+r1BwSExORm5uLlStX4r777sPHH3+Mp59+Gs3NzXjmmWdafN/Zs2fx0UcfYeDAgXjsscfwySeftNi2uroacXFxuHbtGubNm4fw8HBUVFTg0KFDqK2t1bRLTk7W+UsMAKZOnYpz587p3Wdp1lS/mzdvYtiwYXB3d8drr72Gbt264ccff8Qrr7yC7OxsHD16FHZ21vf7jzXV8OLFi4iKikJAQADeffdduLq64p///CfGjBmDLVu24KmnnjLpsZuCuesXEBCA7777Tmf7jh07sGrVKowdO1Zr+7Rp0/Dhhx/itddew+DBg7F3717MnDkTVVVVWLBggXEHawbWVr8333wT4eHhePLJJ/H+++8bd3AdxJpq+Mknn+DIkSOYMmUKIiIiUFNTg3fffRfDhw/Hpk2b8Nxzzxl/wCZmTfWrqamBu7s7Fi1ahG7duqGhoQG7du3CjBkzcPz4cbz33nttOziRTOq///2vCED8+OOPtbaPHDlSDAwMFBsbG1t8b1NTk+bPubm5IgBx48aNetvOnDlTdHFxEc+dO9fmMV64cEEUBEGcOHFim99rbtZWv3//+98iAHH//v1a25cvXy4CEI8dO3aXI+p41lbDF154QZTJZOLVq1c12xobG8X7779fDA4O1vpMa9BR9dMnNjZWdHZ2FisqKjTb8vPzRUEQxOXLl2u1nTp1qujk5CSWlJQY3H9HsLb63dmvi4uLOGnSJIP7tARrq+GNGzd02jU2Norh4eFir169DO67o1hb/Vryf//3f6KDg4NYX19vcP+iKIrWN1Vg47Zv3w5XV1eMHz9ea3tSUhKuXbuGw4cPt/heQ2duamtr8d5772H8+PHo2bNnm8f4/vvvQxRFJCcnt/m95mZt9ZNIJAB0H0OifryZTCYz6DM7krXV8Ntvv0VERASCgoI02+zt7fHoo4/iypUrOHLkiEGf2VE6on76nDt3Djk5Ofi///s/rcfp7NixA6IoIikpSWc8dXV12LNnT7s/0xysrX7G9msJ1lZDX19fnbb29vaIjIzElStX2v155mJt9WuJj48P7Ozs2rxerG19N9uA/Px83H///XBw0D4LHR4ertlvrKNHj6KmpgZ9+vTBiy++iC5dusDR0REPPvgg/vvf/7b63ubmZmRkZKB3796IiYkxeiymZm31GzNmDLp164Y5c+bgxIkTqK6uxtdff42VK1fiiSeewP3332/0eEzN2mrY0NCgd8FK9baff/7Z6PGYUkfUT5+WfqHKz8+Hj48P/P39O3Q87WVt9bNFtlDDxsZGfPPNN+jfv79ZxmIMa62fKIpobGxEWVkZPv30U2RkZGDOnDk647wbBjcTKykp0ftgevU2U1zQXlBQAABYtWoV8vLy8MEHH2D79u1wd3fHE088gb1797b43n379uHKlSv485//bPQ4zMHa6ufh4YHvv/8eSqUSAwYMgJubG2JiYjB06FBs2bLF6LGYg7XVsF+/fvj5559RXV2t1cf//vc/k43HlDqifndqamrCpk2bEBoait///vcGjcfFxQWOjo6sH1qvny2yhRouWbIEZ8+exSuvvGLysRjLWuu3atUqSCQSyOVyPP3005g1axaWL1/e5s/izQlmIAhCu/YZqrm5GQDg6OiI3bt3w83NDQAQFxeHPn364LXXXsMjjzyi970bNmyAg4ODVd9VZU31Kysrw+jRo1FbW4uPPvoIwcHByM/Px2uvvYYnn3wS//3vf9v821JHsKYa/vWvf8UXX3yB5557DqtXr4aLiwveeecdHDp0CIB1nsYyd/3utGfPHhQUFOAf//iHVYzHWNZWP1tkzTV87733sGzZMsyZMwejR482+VhMwRrrN3nyZIwYMQKlpaU4cOAA/vGPf6CioqLNd4hb3784Ns7Ly0tvmi8tLQUAvb8FtOczAOB3v/ud5h9MAHB2dkZMTAx27Nih933FxcX48ssv8fjjj+ucdrEW1la/VatW4fjx47h06RICAgIAANHR0QgNDcXDDz+Mjz76CJMmTTJ6TKZkbTUcPnw4Nm7ciDlz5qBXr14AVLNwr732GhYsWKB17Zs16Ij63WnDhg2QSCR6787z8vLC8ePHdbbX1NSgoaHBLOMxhrXVzxZZcw03btyIF154Ac8//7zVBmVrrZ+/v7/m3974+Hh06dIF8+bNw5QpUzBo0CCDP8v6ftW1cWFhYfjll1901mbJy8sDAAwYMMDoz1Cfp9dHFMUWZzA+/PBDNDQ0WPU1INZWv+PHjyMoKEgT2tQGDx4MwPquLwKsr4YAMGnSJBQWFuLkyZP49ddfceLECQCq33yjo6ONHo8pdUT9bldUVISdO3fiySef1HsReFhYGG7evInCwsIOGY+xrK1+tshaa7hx40YkJydj0qRJePfdd61ythew3vrdaciQIQBU61y2BYObiY0dOxbV1dX4/PPPtbZv2rQJgYGBGDp0qNGfERAQgKioKHz77beorKzUbK+trUVOTg6GDRum930bNmxAYGAgHn30UaPHYC7WVr/AwEBcvXpVc02Xmnr9nq5duxo9HlOzthqqOTg44P7770fv3r1RUVGB9evXY/To0QgJCTF6PKbUEfW73QcffAClUtnidaejR4+GIAjYtGmT1vaMjAw4OTlZ3VqM1lY/W2SNNczIyEBycjImTpyI9957z2pDG2Cd9dMnOzsbANC7d++2fWCbFg8hg4wcOVLs0qWLuH79evHAgQPi1KlTRQDi5s2bNW2mTJki2tvbixcvXtR675YtW8QtW7aIq1atEgGI06dP12y73bfffis6OjqKw4YNE7dv3y7u2LFDjI6OFiUSiXjo0CGdMX3//fciAHHBggXmOWgTsqb6/fDDD6Kjo6N4//33i5s2bRIPHDggvv3226Kvr6/o5+cn3rx507zFaCdrquGNGzfEuXPnil988YV44MABcd26dWL37t3Fnj17igUFBeYtRDt1RP3UQkND77qeXXJysiiVSsV//OMf4sGDB8UFCxaIgiCIy5YtM80Bm5i11e/gwYOaPmQymRgbG6t5XVRUZJqDNjFrquFnn30m2tnZiQ888ID47bffit99953WV1vXIesI1lS/xYsXiy+88IL40UcfiQcPHhR37Ngh/uUvfxHt7e3F8ePHt/nYGNzMoKqqSkxJSRH9/f1FR0dHMTw8XPzkk0+02kyaNEkEIF64cEFrO4AWv+70zTffiDExMaKzs7Po7OwsPvzww+K3336rd0xTp04VBUFo14K9Hc3a6nfs2DFx7NixYteuXUWpVCr27NlTTE5OFi9fvmzS4zYla6phSUmJGB8fL/r4+IgSiUTs1q2bOGPGDKsNvaLYcfX79ttvRQDi4sWLWx1PQ0OD+Morr4jdunUTHR0dxfvuu098++23jT5Oc7G2+sXExLTYZ3Z2trGHaxbWVEP157T0defnWwNrqt+XX34pjhgxQvTz8xMdHBxEV1dXcciQIeLbb78tKpXKNh+bcGuQRERERGTleI0bERERkY1gcCMiIiKyEQxuRERERDaCwY2IiIjIRjC4EREREdkIBjciIiIiG8HgRkRERGQjGNyIiIiIbASDGxEZTBCEu34tWbLELJ+9du1a9O7dG46OjhAEAeXl5QCA1NRUdOvWDQ4ODvD09DT5565btw4ZGRkm79eWHDp0CEuWLNHU3FBTpkxp8VmqeXl5EAQBEokE169f19vmoYcewqxZs9o4WqJ7G5+cQEQG+/777/Vub2xsxHPPPYeCggJ88803GDJkiEk/9/jx4xg0aBCSk5MxadIkODg4YPDgwdi5cyfGjBmDhQsX4tFHH4VUKsWDDz5o0s8eMGAAvL29cfDgQZP2a0tWr16Nv//977hw4QK6d+9u0Ht+/PFHPPjggzh8+LDe/yczZ87E22+/DQBYuXIlXn75ZZ02OTk5GDlyJPLy8tC3b1+jjoHoXuFg6QEQke0YNmyY3u0pKSm4cOEC/vWvf5k8tAHAiRMnAABTp07V6j8/P1/z+b6+vib/XGtUV1cHmUwGQRB09tXW1sLZ2dkCo9K1cuVKDBkyRG9oUygU+OijjxAREYHi4mK8//77eoNbTEwM+vbti9dffx3r16/viGETWb82P92UiOg2H3zwgQhA/POf/9yu92/YsEEMDw8XpVKp2KVLF3HMmDHiyZMnNfv1PSB80qRJYkhIiM72V155RRRFUczKyhJjYmJEuVwuymQyMTg4WExMTBRramo0/SoUCvG1114T+/btKzo6Oore3t7i5MmTxaKiIk0bfZ8REhLS6vE0NTWJb7/9thgRESHKZDLRw8NDHDp0qPjFF19o2tw+1tuFhISIkyZN0rzeuHGjCEDcu3evmJSUJHp7e4sAxLq6OjEmJkbs37+/mJOTI0ZFRYlOTk7iH//4R1EURbGiokKcM2eO2L17d1EikYiBgYHizJkzxerqaq3PAyBOnz5d/OCDD8TQ0FDRyclJDA8PF7/66itNm1deeaXND2cvLCwUJRKJmJ6ernf/f/7zHxGAuHbtWnHBggUiAPGbb77R23bVqlWii4uLWFlZ2eLnEXUmDG5E1G7Hjh0TnZycxMGDB4v19fVtfv/y5ctFAOLTTz8t/ve//xU/+OADsWfPnqKHh4d45swZURRF8cSJE2JqaqoIQNy4caP43XffiWfPnhWPHTsm/vnPfxYBiHv27BG/++478cqVK+KFCxdEmUwmjhw5UtyxY4d48OBB8aOPPhKfffZZsaysTBRFVbhKSEgQXVxcxLS0NDEzM1N87733xKCgILFfv35ibW2t5vh69uwpDho0SPzuu+/E7777Tjx27Firx/Tss8+KgiCIycnJ4hdffCHu3r1bXLZsmbhmzRpNm7YGt6CgIPH5558Xd+/eLW7dulVsbGzUBNPg4GBx7dq1YnZ2tpiTkyPW1NSIAwcOFL29vcU33nhD3L9/v7hmzRrRw8NDfPjhh8Xm5matcXTv3l0cMmSI+Nlnn4m7du0SY2NjRQcHB/HcuXOiKIrilStXxBkzZogAxG3btmnqUFFR0WIN1GH+9gB+u5EjR4pSqVQsLS0Vz549KwqCIE6ePFlv28OHD4sAxC+//LK1shN1GgxuRNQuN2/eFENCQkQfHx/x8uXLbX5/WVmZ6OTkJD722GNa2y9fvixKpVLxmWee0WxTB5jc3FytturZoJs3b2q2bd26VQQgHj9+vMXP/uSTT0QA4ueff661PTc3VwQgrlu3TrOtf//+YkxMjEHH9PXXX4sAxIULF7barq3B7bnnntNpq56JzMrK0tq+YsUK0c7OTqdW6rrs2rVLaxx+fn5as1mFhYWinZ2duGLFCs22f/zjHyIA8cKFC60el9qLL74oOjk5aYVEtYsXL4p2dnbihAkTtI6lpVm1hoYGURAE8eWXXzbos4nudbyrlIjarKmpCRMmTMDVq1fx6aefIjg4uM19fPfdd6irq8PkyZO1tgcHB+Phhx9GVlZWu8Y2cOBAODo64vnnn8emTZtw/vx5nTY7d+6Ep6cnnnjiCTQ2Nmq+Bg4cCH9//3bfiLB7924AwPTp09v1/pY89dRTerd36dIFDz/8sNa2nTt3YsCAARg4cKDWsT3yyCMQBEHn2OLi4uDm5qZ57efnB19fX1y6dKnd47127Rp8fHz0Xoe3ceNGNDc3Y8qUKZptU6ZMQU1NDT799FOd9hKJBJ6enigoKGj3eIjuJQxuRNRmc+fORVZWFlatWoW4uLh29VFSUgIACAgI0NkXGBio2d9WvXr1wv79++Hr64vp06ejV69e6NWrF9asWaNpc+PGDZSXl8PR0RESiUTrq7CwEMXFxe367Js3b8Le3h7+/v7ten9L9NWope03btzAzz//rHNcbm5uEEVR59i8vLx0+pBKpairq2v3eNU3UNypubkZGRkZCAwMRGRkJMrLy1FeXo4RI0bAxcUFGzZs0NufTCYzajxE9xLeVUpEbfLJJ5/gjTfewB//+EfMmTOn3f2oA4O+NbyuXbsGb2/vdvcdHR2N6OhoNDU14YcffsDatWsxa9Ys+Pn5YcKECfD29oaXlxf27Nmj9/23z0C1hY+PD5qamlBYWNhi2AJUwUihUOhsbyms6pu5amm7t7c3nJyc8P777+t9jzF1NZS3tzeOHTums33//v2amTx9gfH777/HyZMn0a9fP63tZWVlHTJuIlvAGTciMtjPP/+M5ORkDBgwoMXZEUNFRUXByckJmzdv1tp+9epVHDhwAMOHDzeqfwCwt7fH0KFDkZ6eDgCaMDFq1CiUlJSgqakJDz74oM7X7WuGtWX26dFHHwUA/POf/2y1Xffu3fHzzz9rbTtw4ACqq6sNPraWjBo1CufOnYOXl5feYzN0HbbbSaVSADC4DqGhoSgpKUFFRYXW9g0bNsDOzg47duxAdna21teHH34IADqB89q1a6ivr9cJc0SdFWfciMggZWVlGDNmDBQKBV5++WXk5eXpbefj44NevXrdtT9PT08sWrQICxYswHPPPYenn34aJSUlSEtLg0wmwyuvvNKucb777rs4cOAAHn/8cXTr1g319fWaMDBixAgAwIQJE/DRRx/hsccew8yZMzFkyBBIJBJcvXoV2dnZGD16NMaOHQsACAsLw3/+8x98+umn6NmzJ2QyGcLCwvR+dnR0NJ599lksXboUN27cwKhRoyCVSvHjjz/C2dkZM2bMAAA8++yzWLRoERYvXoyYmBicPHkS77zzDjw8PNp1zLebNWsWPv/8czz00EN46aWXEB4ejubmZly+fBn79u3DnDlzMHTo0Db1qT7eNWvWYNKkSZBIJOjbt2+LM5OxsbEQRRGHDx9GfHw8ANVs4hdffIFHHnkEo0eP1vu+N998Ex988AFWrFgBiUQC4LdFn9t7Sp7onmPpuyOIyDZkZ2frXc/rzq/b74o0xHvvvSeGh4eLjo6OooeHhzh69GjxxIkTWm3aclfpd999J44dO1YMCQkRpVKp6OXlJcbExOgsJ6FUKsXVq1dr1ltzdXUVQ0NDxRdeeEH89ddfNe0uXrwoxsfHi25ubgav4/bmm2+KAwYM0BxTVFSU1tpoCoVCnDt3rhgcHCw6OTmJMTEx4vHjx1u8q/TO4xZFUbOOmz7V1dViamqqZo06Dw8PMSwsTHzppZfEwsJCTTvcWsftTneOQxRFcf78+WJgYKBoZ2d313XcmpqaxO7du4vTpk3TbHvrrbdEAOKOHTtafN+7776rc7fvs88+K4aFhbX4HqLOho+8IiIik3v99dexbNkyFBQUwMnJqV19VFZWIjAwEG+++SamTp1q4hES2SZe40ZERCY3ffp0eHh4aK4vbI8333wT3bp1Q1JSkglHRmTbGNyIyOSampq01hC786upqcnSQyQzk8lk+PDDDzU3NrSHu7s7MjIy4ODAy7GJ1HiqlIhMLjY2Fjk5OS3uDwkJwcWLFztuQERE9wgGNyIyudOnT6OqqqrF/VKptMU7M4mIqGUMbkREREQ2gte4EREREdkIBjciIiIiG8HgRkRERGQjGNyIiIiIbASDGxEREZGNYHAjIiIishEMbkREREQ24v8DKx1TLK75KsAAAAAASUVORK5CYII=",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"f = 0.17055 ± 0.00175 A\n",
"fwhm = 0.00069 ± 0.00203 A\n"
]
}
],
"source": [
"Brange=(0.167, 0.173)\n",
"\n",
"data = Ncount_mean_total.where(Brange[0]<Ncount_mean_total.als_final_current).where(Ncount_mean_total.als_final_current<Brange[1])\n",
"data = data.dropna(dim='als_final_current')\n",
"data_std = Ncount_std_total.where(Brange[0]<Ncount_mean_total.als_final_current).where(Ncount_mean_total.als_final_current<Brange[1])\n",
"data_std = data_std.dropna(dim='als_final_current')\n",
"\n",
"fitAnalyser = FitAnalyser(\"Gaussian With Offset\", fitDim=1)\n",
"# params = fitAnalyser.guess(data, x='als_final_current', guess_kwargs=dict(negative=True), dask=\"parallelized\")\n",
"params = fitAnalyser.fitModel.make_params()\n",
"params.add(name=\"amplitude\", value= -10, max=np.inf, min=-np.inf, vary=True)\n",
"params.add(name=\"center\", value= 0.1708, max=np.inf, min=-np.inf, vary=True)\n",
"params.add(name=\"sigma\", value= 0.001, max=np.inf, min= 0, vary=True)\n",
"params.add(name=\"offset\", value= 3700, max=np.inf, min=-np.inf, vary=True)\n",
"fitResult = fitAnalyser.fit(data, params, x='als_final_current', dask=\"parallelized\").load()\n",
"fitCurve = fitAnalyser.eval(fitResult, x=np.linspace(Brange[0], Brange[1], 500), dask=\"parallelized\").load()\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"data.plot.errorbar(ax=ax, yerr = data_std, fmt='ob')\n",
"fitCurve.plot.errorbar(ax=ax)\n",
"\n",
"plt.ylim([0, 4500])\n",
"plt.ylabel('NCount')\n",
"plt.xlabel('Z_offset current (A)')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()\n",
"\n",
"f_1 = fitAnalyser.get_fit_value(fitResult).center\n",
"df_1 = fitAnalyser.get_fit_std(fitResult).center\n",
"\n",
"print('f = %.5f \\u00B1 %.5f A'% tuple([np.abs(f_1),df_1]))\n",
"\n",
"s_1 = fitAnalyser.get_fit_value(fitResult).sigma\n",
"ds_1 = fitAnalyser.get_fit_std(fitResult).sigma\n",
"\n",
"fwhm_1 = 2.3548200*s_1\n",
"dfwhm_1 = 2.3548200*ds_1\n",
"\n",
"print('fwhm = %.5f \\u00B1 %.5f A'% tuple([np.abs(fwhm_1),dfwhm_1]))"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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TpQ3x9PSEs3Pz88r9+/ejqqoKMTExRuUxMTEQQmD37t2Gsl27dmHw4MGGpA0AnJ2dMXv2bHz99dcoKiqyuv9EREREtubwETe9u3fv4u7du7h27Rp27NiBAwcO4K233jKqc/v2bfTq1QtXr16Ft7c3pk2bhldeeQUajcZQp7CwEADg7+9vdKy3tzc8PT0N+/V1Q0JCTPoSEBAAADh16hR69+5ttr/V1dWorq42bJeXlwMAtFqt2Xvp9PT7GqtD9sFYyAdjIS+Mh3wwFo6h+3G7/Pq9Flqt7WNhbTuySdzmzZuHv/71rwCAjh074s0338Sf/vQnw/7AwEAEBgZi2LBhAIC8vDysXbsWOTk5yM/PR6dOnQDopkpVKhXc3d1NPkOj0aC0tNSwXVpaapT01a2n39+Q1atXIyUlxaT84MGDcHNza/J8s7Ozm6xD9sFYyAdjIS+Mh3wwFvZVWekEYDIA4I03/hfDh1+Bk5Nun61iUVlZadVxskncli1bhtjYWFy5cgWff/45nnvuOVRUVODFF18EADz//PNG9cPCwjBixAhMnz4d77zzjtF+SZIa/Jz6+5pTt66lS5di8eLFhu3y8nL4+voiPDwcXbp0afA4rVaL7OxshIWFwcXFpcF61PoYC/lgLOSF8ZAPxqJ5KiqA7t11P6dr17QwM4bTpF27JLz4opNh+89/Dkbv3gKvvXYHbm77bRYL/Uxdc8kmcevbty/69u0LAJg0aRIAXXL09NNPo0ePHmaPiYyMhLu7O44fP24o8/DwQFVVFSorK01GvsrKyhAUFGRU19yoWllZGQCYHY3TU6lUUKlUJuUuLi4WBdTSetT6GAv5YCzkhfGQD8bCMnV/RLqfWfOOz8wEZs4EhDAuv3RJwh/+0BHx8d6YNMk2sbC2Ddk+nDBq1CjU1NTgp59+arSeEAIdOtw7Df29bQUFBUb1iouLUVJSYphq1detX6/usXXrEhERUdtVWwssXGiatAH3yjZvHobaWvv2qz7ZJm65ubno0KED7rvvvgbr7Ny5E5WVlUZLhERERECtViMjI8OobkZGBiRJwrRp0wxlkZGROH36tNGyHzU1Ndi2bRtGjx4NHx8fm50PERERta66SdUXX6BZSdaXXwIXLza8XwgJJSVu+Oqrhm+jsgeHT5U+88wz6NKlC0aNGgUvLy+UlJRgx44d+OSTT/DSSy+hR48e+PnnnzFr1izMnDkTgwYNgiRJyMvLw7p16zB06FDExsYa2tNoNEhKSsLy5cuh0WgMC/AmJycjNjbWsIYbAMyZMwfp6emYMWMG1qxZg549e2Ljxo344YcfcOjQIUf8OIiIiMgKmZlAXNy97UmTgD59gPXrgaiopo//5RfLPsfSeq3F4YlbcHAwtmzZgq1bt+L69evo1KkTAgMD8cEHH2D27NkAgC5dusDLywtvvPEGLl++jNraWvTr1w9xcXFYtmyZyROkiYmJ6Ny5M9LT05GWloZevXohISEBiYmJRvVUKhVycnIQHx+PBQsWoLKyEsOHD0dWVhbfmkBERKQQmZnA9Omm05xFRbrynTubTt68vS37LEvrtRaHJ24xMTEmi+XW1717d2RmZjar3bi4OMTVTb0b4OXlha1btzarbSIiIpKHpu5NkyRg0SJg6lQYlvQwJyREN0JXVGS+LUkS8PC4jQcfdOxDIrK9x42IiIioKU3fmwZcuKCr1xgnJ920KqBL9urSb//xj4WNJn/2wMSNiIiIFMuW96ZFRemmVes/m9inD/Dxx7UIDnbwDW5g4kZEREQKZut706KigO+/v7e9bx9w9iwQGWlm/tQBmLgRERGRYunvTWvoZUeSBPj66upZqu506EMPNX5vnL0xcSMiIiLFsuTetHXr5JV8tQQTNyIiIlK0xu5Ns2QpECVx+HIgRERERC0VFQVMmAB07arb3rcPCA9vOyNtehxxIyIiojZBzvem2QpH3IiIiIjqcHc3vwivHHDEjYiIiEghOOJGREREbYKcR8pshSNuRERERArBxI2IiIgcqqJCt+aaJOm+p4YxcSMiIiJSCCZuRERERArBxI2IiIhIIZi4ERERESkEEzciIiIihWDiRkRERKQQTNyIiIiIFIKJGxEREZFCMHEjIiIiUggmbkRERORQtbX3vv/iC+NtMsbEjYiIiBwmMxMYMuTe9qRJQP/+unIyxcSNiIiIHCIzE5g+HSgqMi4vKtKVM3kzxcSNiIiI7K62Fli4EBDCdJ++bNEiTpvWx8SNiIiI7O7LL4GLFxveLwRw4YKuHt3DxI2IiIjs7pdfbFuvvWDiRkRERHbn7W3beu0FEzciIiKyu5AQoE8fQJLM75ckwNdXV4/uYeJGREREdufkBKxfr/u+fvKm3163TleP7nF44vbtt9/i0UcfRd++feHq6gqNRoPg4GBs27bNpO7JkycxYcIEdOrUCd26dUNUVBR++ukns+1u2LABfn5+UKlUGDBgAFJSUqDVak3qXblyBdHR0fD09ISbmxuCg4ORk5Nj8/MkIiIiY1FRwM6dgI+PcXmfPrryqCjH9EvOHJ64Xb9+Hb6+vkhNTcW+ffvw/vvvo3///njyySexcuVKQ73Tp08jNDQUd+7cwaeffor33nsPP/74I0JCQnD16lWjNletWoWFCxciKioKBw4cwLx585Camor58+cb1auursb48eORk5OD9evXY8+ePfDy8kJERATy8vLscv5ERETtWVQU8P3397b37QPOnmXS1hBnR3cgNDQUoaGhRmWTJ0/G2bNnsWnTJiQlJQEAVqxYAZVKhb1796JLly4AgKCgINx///1IS0vDq6++CgAoLS3FypUrMXfuXKSmpho+Q6vVIikpCYsWLcKQX5do3rx5MwoLC3H06FEEBwcDAMaNG4fAwEDEx8fjxIkT9vgREBERtWt1p0MfeojTo41x+IhbQzw9PeHsrMsra2pqsHfvXjz22GOGpA0A+vXrh3HjxmHXrl2Gsv3796OqqgoxMTFG7cXExEAIgd27dxvKdu3ahcGDBxuSNgBwdnbG7Nmz8fXXX6Oo/lLORERERA7k8BE3vbt37+Lu3bu4du0aduzYgQMHDuCtt94CAJw5cwa3b99GQECAyXEBAQHIzs5GVVUV1Go1CgsLAQD+/v5G9by9veHp6WnYDwCFhYUIMfO4iv5zTp06hd69e5vtb3V1Naqrqw3b5eXlAACtVmv2Xjo9/b7G6pB9MBbywVjIC+MhH+0lFrrTc/n1ey3keLq2joW17cgmcZs3bx7++te/AgA6duyIN998E3/6058A6KY/AUCj0Zgcp9FoIITAtWvX4O3tjdLSUqhUKri7u5utq29L325Dbdb9XHNWr16NlJQUk/KDBw/Czc2tsVMFAGRnZzdZh+yDsZAPxkJeGA/5aOuxqKpyAjAZAHDgwAGo1fJ9z5WtYlFZWWnVcbJJ3JYtW4bY2FhcuXIFn3/+OZ577jlUVFTgxRdfNNSRGlrspd4+S+s1t25dS5cuxeLFiw3b5eXl8PX1RXh4uNF0bn1arRbZ2dkICwuDi4tLg/Wo9TEW8sFYyAvjIR/tJRYVFfe+nzhxIsyMvTicrWOhn6lrLtkkbn379kXfvn0BAJMmTQKgS46efvppeHh4ADA/AlZWVgZJktCtWzcAgIeHB6qqqlBZWWky8lVWVoagoCDDtoeHR4NtAuZH+PRUKhVUKpVJuYuLi0UBtbQetT7GQj4YC3lhPOSjrcei7qnpztVxfWmKrWJhbRuyfThh1KhRqKmpwU8//YSBAwfC1dUVBQUFJvUKCgowaNAgqNVqAPfubatft7i4GCUlJRg2bJihzN/fv8E2ARjVJSIiInI02SZuubm56NChA+677z44OztjypQpyMzMxM2bNw11zp8/j9zcXETVWewlIiICarUaGRkZRu1lZGRAkiRMmzbNUBYZGYnTp08bLftRU1ODbdu2YfTo0fCpvyIgERERkQM5fKr0mWeeQZcuXTBq1Ch4eXmhpKQEO3bswCeffIKXXnoJPXr0AACkpKRg5MiRmDx5MhISElBVVYUVK1bA09MTL7zwgqE9jUaDpKQkLF++HBqNBuHh4cjPz0dycjJiY2MNa7gBwJw5c5Ceno4ZM2ZgzZo16NmzJzZu3IgffvgBhw4dsvvPgoiIqD1ydweEcHQvlMHhiVtwcDC2bNmCrVu34vr16+jUqRMCAwPxwQcfYPbs2YZ6fn5+OHLkCJYsWYLp06fD2dkZDz/8MNLS0gzJnV5iYiI6d+6M9PR0pKWloVevXkhISEBiYqJRPZVKhZycHMTHx2PBggWorKzE8OHDkZWVhbFjx9rl/ImIiIgs5fDELSYmxmSx3IYEBQVZPBIWFxeHuLi4Jut5eXlh69atFrVJRERE5EiyvceNiIiIiIwxcSMiIiJSCCZuRERERArBxI2IiIhIIZi4ERERESkEEzciIiIihWDiRkRERKQQTNyIiIiIFIKJGxEREZFCMHEjIiIiUggmbkREREQKwcSNiIiISCGYuBEREREpBBM3IiIiIoVg4kZERESkEEzciIiIiBSCiRsRERGRQjBxIyIiIlIIJm5ERERECsHEjYiIiEghmLgRERERKQQTNyIiIiKFYOJGBKCiApAk3VdFhaN7Q0REZB4TNyIiIiKFYOJGREREpBBM3IiIiIgUgokbERERWaW8/N79wVlZQG2to3vU9jFxIyIiombLzASGDLm3PWkS0L+/rpxaDxM3IiIiapbMTGD6dKCoyLi8qEhXzuSt9TBxIyIiIovV1gILFwJCmO7Tly1axGnT1mJ14vbKK6/g0qVLZvf98ssveOWVVyxq5/Dhw5gzZw78/Pzg7u6O3r17Y+rUqfjmm2+M6kVHR0OSJJMvPz8/s+1u2LABfn5+UKlUGDBgAFJSUqDVak3qXblyBdHR0fD09ISbmxuCg4ORk5NjUd+JiIjamy+/BC5ebHi/EMCFC7p6ZHtWJ24pKSm42EDkLl26hJSUFIva+ctf/oJz585h4cKF2LdvH9avX48rV65gzJgxOHz4sFFdV1dXHDt2zOjrk08+MWlz1apVWLhwIaKionDgwAHMmzcPqampmD9/vlG96upqjB8/Hjk5OVi/fj327NkDLy8vREREIC8vz8KfBBEpARdZJrKNX36xbT1qHmdrDxTmxkh/devWLbi4uFjUTnp6Onr27GlUFhERgUGDBiE1NRUPP/ywobxDhw4YM2ZMo+2VlpZi5cqVmDt3LlJTUwEAoaGh0Gq1SEpKwqJFizDk17spN2/ejMLCQhw9ehTBwcEAgHHjxiEwMBDx8fE4ceKERedARETUXnh727YeNU+zErfvvvsO3377rWF73759OH36tFGd27dvY/v27Rg4cKBFbdZP2gCgU6dOGDJkCC5cuNCc7gEA9u/fj6qqKsTExBiVx8TEIDExEbt37zYkbrt27cLgwYMNSRsAODs7Y/bs2Vi2bBmKiorQu3fvZveBiIiorQoJAfr00T2IYG4MR5J0+0NC7N+39qBZiduuXbsMU6CSJDV4H5urqyu2bNlidadu3LiBkydPGo22AbqksFevXrh69Sq8vb0xbdo0vPLKK9BoNIY6hYWFAAB/f3+jY729veHp6WnYr68bYuZvVkBAAADg1KlTDSZu1dXVqK6uNmyXl5cDALRardl76fT0+xqrQ/ZRNxa6b13qbStPRQXQvbvuPK5d08Ld3cEdspA9rou2EmN74L9T8iHXWLz+uoSZM50gSYAQkqFcknSZXFpaLe7eFbh711E9tD1bx8LadpqVuD3zzDOYPHkyhBAYNWoUtmzZgmHDhhnVUalUGDhwIFxdXa3qEADMnz8fFRUVSExMNJQFBgYiMDDQ8Hl5eXlYu3YtcnJykJ+fj06dOgHQTZWqVCq4m/mNpdFoUFpaatguLS01Svrq1tPvb8jq1avN3sd38OBBuLm5NXmO2dnZTdYh+8jOzkZVlROAyQCAAwcOQK1W5uNQSj+P1rwulP6zcQT+OyUfcouFSgXEx3vjnXf8UVZ27/e9h8dt/PGPhVCpfsG+fQ7sYCuyVSwqKyutOq5ZiZu3tze8f520zs3NRVBQkCFhspXly5dj+/bt2LBhA4KCggzlzz//vFG9sLAwjBgxAtOnT8c777xjtF+SJDSk/r7m1K1r6dKlWLx4sWG7vLwcvr6+CA8PR5cuXRo8TqvVIjs7G2FhYRbfB0ito24s7ty5F4uJEycqZqSqvro33SvpPOxxXSj1Z+MI/HdKPuQci0mTgORk4KuvavDLL7p72h580AVOTiMAjHB092zO1rHQz9Q1l9UPJ4wdO9baQxuUkpKClStXYtWqVXjuueearB8ZGQl3d3ccP37cUObh4YGqqipUVlaajHyVlZUZJYMeHh5mR9XKysoAwOxonJ5KpYJKpTIpd3FxsSigltaj1ufi4gIhXIy2lRqauv1W4nm05nWh9J+NI/DfKfmQayxcXIAJExzdC/uyVSysbcPqxA0Atm3bhg8//BA///wzbt++bbRPkiScOXPG4rZSUlKQnJyM5ORkLFu2zOLjhBDo0OHeqib6e9sKCgowevRoQ3lxcTFKSkqMpnb9/f1RUFBg0qa+rP40MBEREZEjWZ24vfrqq1i6dCmGDBmCwMBAs6NPlvrzn/+M5ORkJCUl4eWXX7b4uJ07d6KystJoiZCIiAio1WpkZGQYJW4ZGRmQJAnTpk0zlEVGRmLevHk4ceKEoW5NTQ22bduG0aNHw8fHx+pzIiIiIrI1qxO3TZs2Yf78+diwYUOLOvD6669jxYoViIiIwKOPPmo07QkAY8aMwc8//4xZs2Zh5syZGDRoECRJQl5eHtatW4ehQ4ciNjbWUF+j0SApKQnLly+HRqNBeHg48vPzkZycjNjYWMNSIAAwZ84cpKenY8aMGVizZg169uyJjRs34ocffsChQ4dadF5EREREtmZ14lZcXIzIyMgWd+Dzzz8HoFt/bf/+/Sb7hRDo0qULvLy88MYbb+Dy5cuora1Fv379EBcXh2XLlpk8QZqYmIjOnTsjPT0daWlp6NWrFxISEoyeUgV096nl5OQgPj4eCxYsQGVlJYYPH46srKxWuYePiByn7nsTv/gCCA8HnJwc1x8iImtYnbgFBQXhzJkzJmutNdeRI0earNO9e3dkZmY2q924uDjExcU1Wc/Lywtbt25tVtvU9vCXetuWmQnU/edg0iTdAqHr1wNRUY7rFxFRc1n9rtI33ngDr7/+usnL4ImUZtcuCXVm0DFpEtC/v+6XvdLUT0BruVQZMjOB6dN1q7zXVVSkK1dinImo/bI6cYuJiUFpaSlGjRqF3r17IyAgwOgrMDDQlv0kahXHjnlj5kynNvFLPTMTbSYBtZXaWmDhQvOv5dGXLVrEBJeIlMPqqVIPDw94enrasi9EdlVbC7z7rn+Dv9QlSfdLfepU+U+b6keV6p+LPgHdubN9Tgl++SVw8WLD+4UALlzQ1QsNtVu3iIisZnXiZsm9aWQbtbW6Xyz6lalDQuSfSCjBV19JKC1t+NVsSvml3tSoUnMT0IoKQP9ClFu3oOg3DPzyi23rERE5mtVTpWQfmZm66a5x44BZs3R/Wjv9VVGh+yUuScav/7F3G3LRVn6pN2dUqb359Q19NqtHRORoVo+4ffHFF03Weeihh6xtnmD76S8+OWmsrfxSbysJaGsICdE9PVpUZH5EUpJ0+0NC7N83IiJrWJ24hYaGNvoSdgCo5R2/VrP19BeXQzD14IMCHh63UVamhhCmf5eV8ku9rSSgrcHJSfd3fPp0XTzrXk/6f77WrWvf/4EhImWxOnHLzc01KSspKcGePXvw97//Henp6S3qWHtny5uq2/KN69be/6e7j8sFgAskSSj6lzpHlRoXFaX7Ox4XZ7wkSJ8+uvgq9e8+EbVPVt/jNnbsWJOvxx57DO+//z7CwsLMvgWBLGer6a+2vByCre7/27q1FvVfS9unj3ISWv2oEnAv4dSzJgFti2vBRUUB339/b3vfPuDsWWXEl4iorlZ5OCEyMhIff/xxazTdbthq+qut3riuH0Wsf27WrL82ZYpQ/C91/ahSSxPQtrwWXN3E9aGH5D+SSkRkTqskbteuXUN1dXVrNN1u6Ke/GrqNUJIAX9+mp7/a4o3rrTGKaO0v9fLye0/ZZmVZPzpli6d1WzqqxDcMEBHJn9WJ2/nz502+/vWvf+Gzzz7D0qVLMWbMGFv2s92x1fSXrW9cl8M0mlxGEeU4OmVtAtqWp9SJiNoSqxO3/v37Y8CAAUZffn5+mDFjBtRqNd566y1b9rNd0k9/9e5tXN6c6S9bjdwB8klU5DCK2NZGp+SSDBMRUeOsfqr0vffeM1kORK1Wo3///hg5ciQ6dODavrYQFaVb8sPaNyfYajkEOT2ZaotRxLojR199JaE5Sw7aeqmW+v1xxBp7ckiGiYioaVYnbtHR0TbsBjXGyallr1xq6XIIrZGotERLl7+ov6bdlCnOJqOajbH1+y/lsMYe14IjIlIGqxM3vZs3b+LYsWMoLS2Fp6cnxowZg86dO9uib2RDLRm5k9uLulsyitjQyOGlS5Z/vi1Hp+Qyksm14IiIlKFF85lpaWnw8fHBI488gj/84Q+YOHEifHx88MYbb9iqf2RD+pG7J57Q/Wnp6Jgcp9Gsuf/Pkhvw9fUaY6vRKTk9EGDrteCIiKh1WJ24vf/++4iPj8dDDz2Ejz/+GF9++SU++eQTjB07Fi+99BI++OADW/aTHEiu02hRUcC5c0BuLvDhh7o/G1v+oqmRQ72//73x/bZ64ENuDwTYai04IiJqPVZPla5duxazZs3Ctm3bjMpnzJiB2bNnY+3atXjyySdb3EFyPDlPozXn/j9LRwSLi5v+zMamavUJV1UV4O7e8v7YeyRzwgSga1fd9r599n9Qoj7d68l039+61fjPtDHu7ub//hIRKYnVI26nT5/G7Nmzze6bPXs2/vnPf1rdKZKX1phGq60FjhwBPvpI96c9pgMtHRHs1avpOg2NTjXnIYfWGMnUJydCWJ/g8A0DRETyZXXi5urqirKyMrP7ysrK4OrqanWnSH5sOY1mq3eMNldTU5x6//EflrVn7k0F3313b7upBYptucYeERG1D1YnbiEhIUhOTsaleo/jFRcX45VXXsFDzVkYixTBFi/qtuU7RpvLkpFDfT1Ldelyb4Tr9m3A3//evqYWKG7rDwQ4YlSViKitszpxS01NxeXLlzFo0CBMmTIFzzzzDKZMmYKBAweiuLgYqamptuwnyURLptHk8BSlLaY4zbH2TQpt9YEAR42qEhG1dVYnbkOHDkV+fj6mTp2K/Px8bNmyBfn5+Zg2bRq+/vprDKn7biQiyOcpyvojh59/XoNTp6xvr6UJqS1GMm2ppffJ2XpUVQ7vxyUikosWreP2wAMP4KOPPkJxcTG0Wi2Ki4uxfft2PPDAA7bqH7UhcnqKsu5I4YMPihZNR9oiIW0rDwTYelRVLu/HJSKSi2YnbgUFBbjYyG+pixcvoqCgoEWdorZJruvBtZQtElJbPA0qB7YcVbV2+pmIqC1rVuL2xRdfICgoCJcvX26wzuXLlxEUFIQDBw60uHPUtrTVpyjbakJqDVuNqsrhfkgiIjlqVuKWnp6O6dOnIygoqME6QUFBePzxx/Huu++2uHMkPy0ZGWqrT1G21YTUGrZKYuVyPyQRkdw0K3H7+9//jmnTpjVZ7/e//z2OHz9ubZ+oDbPmHaOtwd0duHNHi92797R4arKtJqTWsFUSK6f7IYmI5KRZidvVq1fR24J1E7y9vXHlyhWrO0VtW3PfMWoPLb3HrK0u69FctkpiOf1MRGResxI3d3f3Bt+WUNe1a9fg5uZmdaeo7dO/Y/SJJ3R/toXRKLkt6+EothhV5fQzEZF5zUrchg4div379zdZLysrC0OHDrWozcOHD2POnDnw8/ODu7s7evfujalTp+Kbb74xqXvy5ElMmDABnTp1Qrdu3RAVFYWffvrJbLsbNmyAn58fVCoVBgwYgJSUFGi1WpN6V65cQXR0NDw9PeHm5obg4GDk5ORY1Hei+trKsh4t1dJRVU4/ExGZ16zE7fHHH8fmzZuRl5fXYJ3c3Fxs2bIFTzzxhEVt/uUvf8G5c+ewcOFC7Nu3D+vXr8eVK1cwZswYHD582FDv9OnTCA0NxZ07d/Dpp5/ivffew48//oiQkBBcvXrVqM1Vq1Zh4cKFiIqKwoEDBzBv3jykpqZi/vz5RvWqq6sxfvx45OTkYP369dizZw+8vLwQERHR6DkSNaStLOthCy0dVeX0MxGRGaIZqqurRVBQkOjYsaOYN2+eOHDggPjxxx/Fjz/+KA4cOCCeffZZ0bFjRzFy5EhRXV1tUZuXL182Kbt586bw8vIS48ePN5TNmDFDeHp6ihs3bhjKzp07J1xcXER8fLyhrKSkRKjVavHMM88Ytblq1SohSZI4deqUoSw9PV0AEEePHjWUabVaMWTIEDFq1CiL+q9348YNAcCof+bcuXNH7N69W9y5c6dZ7ZPtMRby0VgsbtzQp8JC7NsnRE2NAzrYzvDakA/GQj5sHQtL84b6mjXi1rFjRxw4cADjx4/HX/7yFzzyyCPw8/ODn58fHnnkEbz99tuYMGECsrKy0LFjR4va7Nmzp0lZp06dMGTIEFy4cAEAUFNTg7179+Kxxx5Dly5dDPX69euHcePGYdeuXYay/fv3o6qqCjExMUZtxsTEQAiB3bt3G8p27dqFwYMHIzg42FDm7OyM2bNn4+uvv0ZR/ZU/icjuOP1MRHSPc3MP8PDwwL59+/DNN9/g4MGDhuSqb9++mDhxIkaMGNHiTt24cQMnT57Eww8/DAA4c+YMbt++jYCAAJO6AQEByM7ORlVVFdRqNQoLCwEA/v7+RvW8vb3h6elp2A8AhYWFCDFzd7P+c06dOtXgU7TV1dWorq42bJeXlwMAtFqt2Xvp9PT7GqtD9sFYyEdjsdAVuRj2M1ytj9eGfDAW8mHrWFjbTrMTN72goKBGF+Jtifnz56OiogKJiYkAgNLSUgCARqMxqavRaCCEwLVr1+Dt7Y3S0lKoVCq4m7nBSKPRGNrSt9tQm3U/15zVq1cjJSXFpPzgwYMWPVGbnZ3dZB2yD8ZCPszFoqrKCcBkAMCBAwegVjv2dQm1tcD333vg2jU1unevwpAhpW12FJDXhnwwFvJhq1hUVlZadVyzEjdzI14NkSQJ//d//9fsDi1fvhzbt2/Hhg0bTBJDqaG1Aerts7Rec+vWtXTpUixevNiwXV5eDl9fX4SHhxtN59an1WqRnZ2NsLAwuLi4NFiPWh9jIR9NxeLOHf3/TCfat2P17NolYfFiJxQV3fu3oXdvgTfeqEVkpJn3cykUrw35YCzkw9ax0M/UNVezEjeNRtNoMgMAt27dwjfffNNkPXNSUlKwcuVKrFq1Cs8995yh3MPDA4D5EbCysjJIkoRu3boZ6lZVVaGystJk5KusrMwoGfTw8GiwTcD8CJ+eSqWCSqUyKXdxcbEooJbWo9bHWMiHnGORmQnMnGn6/tRLlyTMnOncJp90lXM82hvGQj5sFQtr22hW4nbkyJEG99XU1GDTpk145ZVXIEkSZs2a1ayOpKSkIDk5GcnJyVi2bJnRvoEDB8LV1RUFBQUmxxUUFGDQoEFQq9UA7t3bVlBQgNGjRxvqFRcXo6SkBMOGDTOU+fv7N9gmAKO6RNR+NfXSe0nSvfR+6lQ+PEFEratZT5U2ZMeOHRgyZAgWLFiAwMBAfPPNN/jggw8sPv7Pf/4zkpOTkZSUhJdfftlkv7OzM6ZMmYLMzEzcvHnTUH7+/Hnk5uYiqs5/cyMiIqBWq5GRkWHURkZGBiRJMnrXamRkJE6fPo0TJ04YympqarBt2zaMHj0aPvUXkCKidokvvSciubD64QRANwK3ZMkS5Ofn47e//S0OHjyI8ePHN6uN119/HStWrEBERAQeffRRk5fTjxkzBoBuRG7kyJGYPHkyEhISUFVVhRUrVsDT0xMvvPCCob5Go0FSUhKWL18OjUaD8PBw5OfnIzk5GbGxsRgyZIih7pw5c5Ceno4ZM2ZgzZo16NmzJzZu3IgffvgBhw4dasFPhojaEr70nojkwqrEraCgAEuWLMGBAwcwYMAAfPjhh5g5c6ZVHfj8888B6NZfM/c6LfHr3ISfn58hUZw+fTqcnZ3x8MMPIy0tDT169DA6JjExEZ07d0Z6ejrS0tLQq1cvJCQkGJ5S1VOpVMjJyUF8fDwWLFiAyspKDB8+HFlZWRg7dqxV50NEbQ9fek9EctGsxO3ChQtISkrChx9+CI1Gg3Xr1uG//uu/WnSTXmP3zdUXFBRk8UhYXFwc4uLimqzn5eWFrVu3WtwHImp/9C+9Lyoyf5+bJOn286X3RNTampW4PfDAA7hz5w4iIiIQHx+Pzp07m725X++3v/1tiztIRORo+pfeT5+uS9LqJm986T0R2VOzEjf9mwKysrLMTmvqCSEgSRJqax27UCYRka3oX3q/cKHxgwp9+uiStra2FAgRyVOzErctW7a0Vj+IiGQvKkq35MeXX+oeRPD21k2PcqSNiOylWYnb008/3Vr9ICJSBCcnIDTU0b0govbKJuu4EREREVHra9E6bkREZJ3aWk65ElHzMXEjIrKzzEwgLk63vIhenz66J1f5kAMRNYZTpUREdpSZqVtWpG7SBui2p0/X7SciaggTNyIiO2nqZfWA7mX1XEmJiBrCxI2IyE5a42X1FRW6RYAlSfc9kaVqa4EjR4CPPtL9yf8wKAMTNyIiO2mNl9XX/WX7xRf85UuWycwE+vcHxo0DZs3S/dm/P6fqlYCJGxGRndj6ZfWZmcCQIfe2J03iL19qmv4+y/qjv7zPUhmYuBER2Yn+ZfX695vWJ0mAr69lL6vnQw5kDd5nqXxM3IiI7ET/snrANHlrzsvq+cuXrNUa91mSfTFxIyKyI/3L6nv3Ni7v00dXbsk6bvzlS9Zqjfssyb64AC8RkZ219GX1/OVL1rL1fZZkf0zciIgcoCUvq+cvX7KW/j7LoiLzU+2SpNtvyX2W5BicKiUiUhhbPuRA7Yut7rMkx2HiRkSkMPzlSy1hi/ssyXGYuBERKZD+l6+Pj3E5f/mSJaKigHPngNxc4MMPdX+ePcu/N0rAe9yIiBQqKgqYMAHo2lW3vW8fEB7OkTayTEvusyTHYeJGRKRgXbqYv8m8uSoqgE6ddN9fu9by9oiodXCqlIiIbKK8/N4L77OyuAAwUWtg4kZERC3G96YS2QcTNyIiahG+N5XIfpi4ERGR1fjeVCL7YuJGRERW43tTieyLiRsRERmNiH31lWTxCBnfm0pkX0zciIjaufoPFkyZ4oxnngnHrl0NvFOrDr43lci+uI4bEVE7pn+woP49aqWlasycCTg7N76avpxfWl5bq5ui/eUXXeIYEsLFiUn5HD7idvPmTcTHxyM8PBw9evSAJElITk42qRcdHQ1Jkky+/Pz8zLa7YcMG+Pn5QaVSYcCAAUhJSYFWqzWpd+XKFURHR8PT0xNubm4IDg5GTk6OrU+TiEh2GnuwANCNtjX1YIFc35uamalbjmTcOGDWLN2fXJ6E2gKHJ26lpaXYtGkTqqurMW3atEbrurq64tixY0Zfn3zyiUm9VatWYeHChYiKisKBAwcwb948pKamYv78+Ub1qqurMX78eOTk5GD9+vXYs2cPvLy8EBERgby8PFueJhGR7DT9YIFk0YMFtn5vakXFvYV8KyqadyxwbxSx/rlxeRJqCxw+VdqvXz9cu3YNkiShpKQE7777boN1O3TogDFjxjTaXmlpKVauXIm5c+ciNTUVABAaGgqtVoukpCQsWrQIQ369mWPz5s0oLCzE0aNHERwcDAAYN24cAgMDER8fjxMnTtjoLImI5MeWDxZERQFTpzp+arKp5UkkSTeKOHUqp01JmRw+4qaf8rSV/fv3o6qqCjExMUblMTExEEJg9+7dhrJdu3Zh8ODBhqQNAJydnTF79mx8/fXXKKq/miQRURti6wcL9C8tf+IJ3Z+OSIy4PAm1dQ4fcWuO27dvo1evXrh69Sq8vb0xbdo0vPLKK9BoNIY6hYWFAAB/f3+jY729veHp6WnYr68bYuaO2YCAAADAqVOn0Lt3b7N9qa6uRnV1tWG7vLwcAKDVas3eS6en39dYHbIPxkI+GAvHGDMG6N3bGZcu6aZF65Mkgd69gTFjamDP0Og+y+XX77XN+uwLFyRY8qvtwoUaaLVmb+6TFV4b8mHrWFjbjmISt8DAQAQGBmLYsGEAgLy8PKxduxY5OTnIz89Hp06dAOimSlUqFdzd3U3a0Gg0KC0tNWyXlpYaJX116+n3N2T16tVISUkxKT948CDc3NyaPJ/s7Owm65B9MBbywVjY3+zZ3nj11ZEABPQPJOgICAH84Q/5OHDAvouwVVU5AZgMADhw4ADUastfu/Dzzx4AHrSg3nHs29fwv/Fyw2tDPmwVi8rKSquOU0zi9vzzzxtth4WFYcSIEZg+fTreeecdo/2NTb3W39ecunUtXboUixcvNmyXl5fD19cX4eHh6NKlS4PHabVaZGdnIywsDC4uLg3Wo9bHWMgHY+E4kyYBv/1tLZ5/3gmXLt0r9/S8jTffdML06SMAjLBrn+o+kDBx4kSY+X94gyZOBN5+WzQ5ivjii6MVcY8brw35sHUs9DN1zaWYxM2cyMhIuLu74/jx44YyDw8PVFVVobKy0mTkq6ysDEFBQUZ1zY2qlZWVAYDZ0Tg9lUoFlUplUu7i4mJRQC2tR62PsZAPxsIx/vM/gYgIoGtX3fbnn9fgzp1sTJkyySHx6FDn7utjx1wQHm75/XIuLsCbb+qeHpUk44cUdP8Xl7B+PaBWK+vvGa8N+bBVLKxtw+EPJ7SUEAId6lzl+nvbCgoKjOoVFxejpKTEMNWqr1u/Xt1j69YlImrL6iZGDz4oHDYaVf8tDpMmNX/9Nf3yJPVvUbZ2eRIiOVF04rZz505UVlYaLRESEREBtVqNjIwMo7oZGRmQJMlorbjIyEicPn3aaNmPmpoabNu2DaNHj4ZP/UWJiIio1ejXX6v/QL81669FRQHnzgG5ucCHH+r+PHuWSRspnyymSrOyslBRUYGbN28CAL7//nvs3LkTADBp0iRcvXoVs2bNwsyZMzFo0CBIkoS8vDysW7cOQ4cORWxsrKEtjUaDpKQkLF++HBqNBuHh4cjPz0dycjJiY2MNa7gBwJw5c5Ceno4ZM2ZgzZo16NmzJzZu3IgffvgBhw4dsu8PgYioHWuN9df0y5PYom+OXp+OSE8Widuzzz6Ln3/+2bC9Y8cO7NixAwBw9uxZdO3aFV5eXnjjjTdw+fJl1NbWol+/foiLi8OyZctMniBNTExE586dkZ6ejrS0NPTq1QsJCQlITEw0qqdSqZCTk4P4+HgsWLAAlZWVGD58OLKysjB27NjWP3EiIgLQvPXXbJGMWSozU5dQ1u1bnz6613xx9I4cQRaJ27lz55qsk9nMd5TExcUhLi6uyXpeXl7YunVrs9omIiLbsuVbHGxFP3VbfxRQP3XL++XIERR9jxsREdmGu7suQRECzVp+w1Zs/RaHlmpq6hbQTd3WWr7EHJFNMHEjIiKHCwnRTUE2tHymJAG+vrp69sBXZ5FcMXEjIiKHc3LS3TcGmCZv+u116+z3UIAcp26JACZuREQkE/r11+qvxOSI9dfkNnVLpMfEjYiIZCMqCvj++3vb+/Y5Zv01uU3dEukxcSMiIlmpOx360EOOWTNNblO3RHpM3IiIiMzgq7NIjmSxjhsREZEcRUXp3tbANyeQXDBxIyIiaoStXp1FZAucKiUiIiJSCI64ERGRrOjf4kBEpjjiRkRERKQQTNyIiIiIFIKJGxEREZFCMHEjIiIiUggmbkREREQKwcSNiIiISCGYuBEREREpBBM3IiIiO6io0L2gXpJ03xNZg4kbERERkUIwcSMiIiJSCCZuREREdlBbe+/7L74w3iayFBM3IiKiVpaZCQwZcm970iSgf39dOVFzMHEjIiJqRZmZwPTpQFGRcXlRka6cyRs1BxM3IiKiVlJbCyxcCAhhuk9ftmgRp03JckzciIiIWsmXXwIXLza8XwjgwgVdPSJLMHEjIiJqJb/8Ytt6REzciIiIWom3t23rETFxIyIiaiUhIUCfPrq3JZgjSYCvr64ekSWYuBEREbUSJydg/Xrd9/WTN/32unW6ekSWYOJGRETUiqKigJ07AR8f4/I+fXTlUVGO6Rcpk8MTt5s3byI+Ph7h4eHo0aMHJElCcnKy2bonT57EhAkT0KlTJ3Tr1g1RUVH46aefzNbdsGED/Pz8oFKpMGDAAKSkpECr1ZrUu3LlCqKjo+Hp6Qk3NzcEBwcjJyfHlqdIRETtXFQU8P3397b37QPOnmXSRs3n8MSttLQUmzZtQnV1NaZNm9ZgvdOnTyM0NBR37tzBp59+ivfeew8//vgjQkJCcPXqVaO6q1atwsKFCxEVFYUDBw5g3rx5SE1Nxfz5843qVVdXY/z48cjJycH69euxZ88eeHl5ISIiAnl5ea1xukRE1E7VnQ596CFOj5J1nB3dgX79+uHatWuQJAklJSV49913zdZbsWIFVCoV9u7diy5dugAAgoKCcP/99yMtLQ2vvvoqAF0iuHLlSsydOxepqakAgNDQUGi1WiQlJWHRokUY8ut7RzZv3ozCwkIcPXoUwcHBAIBx48YhMDAQ8fHxOHHiRGufPhEREZHFHD7iJkkSpIYet/lVTU0N9u7di8cee8yQtAG6pG/cuHHYtWuXoWz//v2oqqpCTEyMURsxMTEQQmD37t2Gsl27dmHw4MGGpA0AnJ2dMXv2bHz99dcoqv9+EiIiIiu5u+sW3BVC9z2RNRw+4maJM2fO4Pbt2wgICDDZFxAQgOzsbFRVVUGtVqOwsBAA4O/vb1TP29sbnp6ehv0AUFhYiBAzz2DrP+fUqVPo3bu32T5VV1ejurrasF1eXg4A0Gq1Zu+l09Pva6wO2QdjIR+MhbwwHvLBWMiHrWNhbTuKSNxKS0sBABqNxmSfRqOBEALXrl2Dt7c3SktLoVKp4G7mvzMajcbQlr7dhtqs+7nmrF69GikpKSblBw8ehJubW5PnlJ2d3WQdsg/GQj4YC3lhPOSDsZAPW8WisrLSquMUkbjpNTalWnefpfWaW7eupUuXYvHixYbt8vJy+Pr6Ijw83Gg6tz6tVovs7GyEhYXBxcWlwXrU+hgL+WAs5IXxkA/GQj5sHQv9TF1zKSJx8/DwAGB+BKysrAySJKFbt26GulVVVaisrDQZ+SorK0NQUJBRuw21CZgf4dNTqVRQqVQm5S4uLhYF1NJ61PoYC/lgLOSF8ZAPxkI+bBULa9tw+MMJlhg4cCBcXV1RUFBgsq+goACDBg2CWq0GcO/etvp1i4uLUVJSgmHDhhnK/P39G2wTgFFdIiIiIkdTROLm7OyMKVOmIDMzEzdv3jSUnz9/Hrm5uYiqs4JhREQE1Go1MjIyjNrIyMiAJElGa8VFRkbi9OnTRst+1NTUYNu2bRg9ejR86i9zTURERORAspgqzcrKQkVFhSEp+/7777Fz504AwKRJk+Dm5oaUlBSMHDkSkydPRkJCAqqqqrBixQp4enrihRdeMLSl0WiQlJSE5cuXQ6PRIDw8HPn5+UhOTkZsbKxhDTcAmDNnDtLT0zFjxgysWbMGPXv2xMaNG/HDDz/g0KFD9v0hEBERETVBFonbs88+i59//tmwvWPHDuzYsQMAcPbsWfTv3x9+fn44cuQIlixZgunTp8PZ2RkPP/ww0tLS0KNHD6P2EhMT0blzZ6SnpyMtLQ29evVCQkICEhMTjeqpVCrk5OQgPj4eCxYsQGVlJYYPH46srCyMHTu29U+ciIiIqBlkkbidO3fOonpBQUEWj4TFxcUhLi6uyXpeXl7YunWrRW0SEREROZIi7nEjIiIiIiZuRERERIrBxI2IiIhIIZi4ERERESkEEzciIiIihWDiRkRERKQQTNyIiIiIFIKJGxEREZFCMHEjIiIiUggmbkREREQKwcSNiIiISCGYuBEREREpBBM3IiIiIoVg4kZERESkEEzciIiIiBSCiRsRERGRQjBxIyIiIlIIJm5ERERECsHEjYiIiEghmLgRERERKQQTNyIiIiKFYOJGREREpBBM3IiIiIgUgokbERERkUIwcSMiIiJSCCZuRERERArBxI2IiIhIIZi4ERERESkEEzciIiIihWDiRkRERKQQikncjhw5AkmSzH4dP37cqO7JkycxYcIEdOrUCd26dUNUVBR++ukns+1u2LABfn5+UKlUGDBgAFJSUqDVau1xSkRERETN4uzoDjRXamoqxo0bZ1Q2bNgww/enT59GaGgohg8fjk8//RRVVVVYsWIFQkJC8O2336JHjx6GuqtWrcLy5cuRkJCA8PBw5OfnIykpCUVFRdi0aZPdzomIiIjIEopL3O6//36MGTOmwf0rVqyASqXC3r170aVLFwBAUFAQ7r//fqSlpeHVV18FAJSWlmLlypWYO3cuUlNTAQChoaHQarVISkrCokWLMGTIkNY/ISIiIiILKWaq1BI1NTXYu3cvHnvsMUPSBgD9+vXDuHHjsGvXLkPZ/v37UVVVhZiYGKM2YmJiIITA7t277dVtIiIiIosobsRt/vz5mDlzJtzc3BAcHIzly5fjwQcfBACcOXMGt2/fRkBAgMlxAQEByM7ORlVVFdRqNQoLCwEA/v7+RvW8vb3h6elp2N+Q6upqVFdXG7bLy8sBAFqtttF75PT7eB+d4zEW8sFYyAvjIR+MhXzYOhbWtqOYxK1r165YuHAhQkND4eHhgX//+9947bXXEBoair/97W+YOHEiSktLAQAajcbkeI1GAyEErl27Bm9vb5SWlkKlUsHd3d1sXX1bDVm9ejVSUlJMyg8ePAg3N7cmzyc7O7vJOmQfjIV8MBbywnjIB2MhH7aKRWVlpVXHKSZxGzFiBEaMGGHYDgkJQWRkJPz9/REfH4+JEyca9kmS1GA7dfdZWs+cpUuXYvHixYbt8vJy+Pr6Ijw83Giatj6tVovs7GyEhYXBxcWl0c+g1sVYyAdjIS+Mh3wwFvJh61joZ+qaSzGJmzndunXD5MmT8fbbb+P27dvw8PAAALOjZWVlZZAkCd26dQMAeHh4oKqqCpWVlSYjZGVlZQgKCmr0s1UqFVQqlUm5i4uLRQG1tB61PsZCPhgLeWE85IOxkA9bxcLaNhT/cIIQAoBuhGzgwIFwdXVFQUGBSb2CggIMGjQIarUawL172+rXLS4uRklJidESI0RERERyoOjE7dq1a9i7dy+GDx8OtVoNZ2dnTJkyBZmZmbh586ah3vnz55Gbm4uoqChDWUREBNRqNTIyMozazMjIgCRJmDZtmp3OgoiIiMgyipkqnTVrFvr27Yvf/e538PT0xL/+9S+8/vrruHz5slHylZKSgpEjR2Ly5MlISEgwLMDr6emJF154wVBPo9EgKSkJy5cvh0ajMSzAm5ycjNjYWK7hRkRERLKjmMQtICAAn3zyCd5++23cunULGo0GDz74ID744AOMHDnSUM/Pzw9HjhzBkiVLMH36dDg7O+Phhx9GWlqa0VsTACAxMRGdO3dGeno60tLS0KtXLyQkJCAxMdHep0dERETUJMUkbgkJCUhISLCoblBQEA4dOmRR3bi4OMTFxbWka0RERER2oeh73IiIiIjaEyZuREREClJRAUiS7quiwtG9IXtj4kZERKQgtbX3vv/iC+NtavuYuBERESlEZiZQd9GDSZOA/v115dQ+MHEjIiJSgMxMYPp0oKjIuLyoSFfO5K19YOJGREQkc7W1wMKFwK8vCzKiL1u0iNOm7QETNyIiIpn76isJFy82vF8I4MIF4Msv7dcncgwmbkRERDL3yy+2rUfKxcSNiIhI5ry9bVuPlIuJGxERkcw9+KBAnz66tdvMkSTA1xcICbFvv8j+mLgRERHJnJMTsH697vv6yZt+e906XT1q25i4ERERKUBUFLBzJ+DjY1zep4+uPCrKMf0i+1LMS+aJiIjau6goYOpU3dOjv/yiu6ctJIQjbe0JEzciIiIFcXICQkMd3QtyFE6VEhERESkEEzciIiIihWDiRkRERKQQTNyIiIiIFIKJGxEREZFCMHEjIiIiUggmbkREREQKwcSNiIiISCGYuBEREREpBBM3IiIiIoVg4kZERESkEEzciIiIiBSCiRsRERGRQjBxIyIiIlIIJm5ERERECsHEjYiIiEghmLgRERERKQQTNwC3bt3CokWL4OPjA7VajeHDh+Pjjz92dLeIiIiIjDg7ugNyEBUVhfz8fKxZswYPPPAAPvzwQzzxxBO4e/cuZs2a5ejuEREREQFg4oZ9+/YhOzvbkKwBwLhx4/Dzzz/jpZdewuOPPw4nJycH95KIiIiIU6XYtWsXOnXqhBkzZhiVx8TE4NKlSzhx4oSDekZERERkrN2PuBUWFuI3v/kNnJ2NfxQBAQGG/f/v//0/k+Oqq6tRXV1t2L5x4wYAoKysDFqttsHP02q1qKysRGlpKVxcXGxxCmQlxkI+GAt5YTzkg7GQD1vH4ubNmwAAIUSzjmv3iVtpaSnuu+8+k3KNRmPYb87q1auRkpJiUj5gwADbdpCIiIjarJs3b6Jr164W12/3iRsASJLU7H1Lly7F4sWLDdt3795FWVkZPDw8Gm2vvLwcvr6+uHDhArp06WJ9p6nFGAv5YCzkhfGQD8ZCPmwdCyEEbt68CR8fn2Yd1+4TNw8PD7OjamVlZQDujbzVp1KpoFKpjMq6detm8ed26dKFF6FMMBbywVjIC+MhH4yFfNgyFs0ZadNr9w8n+Pv745///CdqamqMygsKCgAAw4YNc0S3iIiIiEy0+8QtMjISt27dwmeffWZUvnXrVvj4+GD06NEO6hkRERGRsXY/VfrII48gLCwMzz77LMrLyzFo0CB89NFH2L9/P7Zt22bzNdxUKhVefvllk2lWsj/GQj4YC3lhPOSDsZAPucRCEs19DrUNunXrFhITE/Hpp5+irKwMfn5+WLp0KWbOnOnorhEREREZMHEjIiIiUoh2f48bERERkVIwcSMiIiJSCCZujbh16xYWLVoEHx8fqNVqDB8+HB9//LFFx165cgXR0dHw9PSEm5sbgoODkZOTY1Tn3LlzkCSpwa+IiAij+lqtFikpKejfvz9UKhX8/PywYcMGm52vnMkpFo3VtbRPStfa8QB0r5V77bXXMGzYMLi7u8PLywuPPPIIjh49alKX14Y8YtHerw17xOLOnTtYsWIFBgwYgI4dO6Jfv35YunQpbt++bVKX14U8YmHz60JQg8LCwkS3bt3E22+/LQ4fPixiY2MFALF9+/ZGj6uqqhLDhg0Tffr0Edu2bRMHDx4UU6dOFc7OzuLIkSNG9Y4dO2bytWTJEgFAvP3220btxsbGCpVKJf77v/9b5ObmioSEBCFJkli1alWrnL+cyCkWZ8+eFQDEggULTOqXlJS02s9ATlo7HkII8eSTT4oOHTqIxMREkZOTI3bs2CGCgoKEs7OzOHHihFFdXhvyiEV7vzbsEYuoqCihVqtFamqqyM7OFq+88oro2LGjmDJlikm7vC7kEQtbXxdM3Brwt7/9TQAQH374oVF5WFiY8PHxETU1NQ0em56eLgCIo0ePGsq0Wq0YMmSIGDVqVJOfHRoaKtzc3MSNGzcMZYWFhUKSJJGammpUd+7cucLV1VWUlpZaemqKI7dY6C/C1157zYqzUT57xKOqqko4OTmJ2bNnGx1/6dIlAUDExcUZynhtyCcW7fnasEcsjh07JgCI119/3ej41NRUAUAcPHjQUMbrQj6xsPV1wanSBuzatQudOnXCjBkzjMpjYmJw6dIlnDhxotFjBw8ejODgYEOZs7MzZs+eja+//hpFRUUNHnvmzBnk5eXhP//zP41eqbF7924IIRATE2PSn9u3b2P//v3NPUXFkFss2jt7xKNDhw7o0KGDyetgunTpgg4dOkCtVhvKeG3IJxbtmT1i8fe//x0AMGnSJKPjJ0+eDABGC8nzupBPLGyNiVsDCgsL8Zvf/AbOzsZrFAcEBBj2N3asvp65Y0+dOtXgse+99x6EEIiNjTVps0ePHujVq1ez+6N0couF3po1a9CxY0e4ubnhwQcfxP/8z/80eS5tgT3i4eLignnz5mHr1q3YvXs3ysvLce7cOcydOxddu3bF3LlzjdrktSGPWOi1x2vDHrG4c+cOAJgsAKvf/u6774za5HUhj1jo2eq6aPdvTmhIaWkp7rvvPpNy/Uvnzb2Yvu6x5l5O39SxtbW12Lp1K/z8/PAf//EfFrXp7u6Ojh07NtofpZNbLFQqFebOnYuwsDB4e3vj/Pnz2LBhA6ZOnYp33nmnwUSvrbBXPNauXYuuXbvisccew927dwEAffv2xeHDhzFo0KAm2+S1Yf9YtOdrwx6xGDJkCADdaM+AAQMM9b766iuTz+B1IZ9Y2Pq6YOLWCEmSrNpn7bH79+9HUVERXnvtNZv3R+nkFAtvb29s2rTJqGzGjBkYPXo0EhISEB0dbfI/vbbGHvFYtWoV0tLSkJycjJCQEJSXl+Ott95CWFgYDh48iBEjRtikP0onp1i092ujtWPxyCOPYNCgQViyZAm8vLwwcuRIHD9+HMuWLYOTkxM6dOhg9jhr+qN0coqFra8LTpU2wMPDw2xWXlZWBgBmM/KWHrt582a4uLjgqaeesrjNiooK3Llzp9H+KJ3cYmGOi4sLHn/8cZSWluJf//qXRccolT3i8c9//hMrVqxASkoKli9fjtDQUPz+97/H3/72N3Tr1g2LFy9usk1eG/aPhTnt5dqwRyw6duyIrKws9O3bF+Hh4ejevTumT5+OZcuWoXv37ujdu3eTbfK6sH8szGnJdcHErQH+/v745z//iZqaGqPygoICAMCwYcMaPVZfz9Jjr1y5gr179+L3v/89evbsabbNq1evori4uNn9UTq5xaIh4te3x9X/X29bY494/N///R+EEBg5cqRRPRcXFwQGBhrdo8JrQz6xaEh7uDbs9e/UoEGDcOzYMVy8eBHfffcdrly5ghkzZqCkpAQPPfSQUZu8LuQRi4ZYfV3Y5NnUNmjfvn0CgPj444+NyiMiIpp8nHjjxo0CgDh+/LihTKvViqFDh4rRo0ebPea1114TAMS+ffvM7tc/2r1mzRqj8j/96U9t/tFuucXCnDt37ojhw4cLT0/PRvvTFtgjHnl5eQKAyd/3qqoqMWDAADF8+HBDGa8N+cTCnPZybdj736m6nn/+eeHu7i4uXrxoKON1IZ9YmNOS64KJWyPCwsJE9+7dxaZNm8Thw4fF3LlzBQCxbds2Q505c+YIJycnce7cOUNZVVWVGDp0qPD19RXbt28X2dnZIjIy0uwCfnp+fn7C19dX1NbWNtgf/WKKr732mjhy5IhYtmxZu1pMUS6xeP7558Vzzz0nPvroI5Gbmyvef/99MXLkSAFAbNmyxabnLVetHY/a2loxcuRIoVarxYoVK8ShQ4fEZ599JkJDQwUA8cEHHxj1h9eGPGLR3q8Ne/w79eqrr4qtW7eK3Nxc8fHHH4uoqCjRoUMHswvL8rqQRyxsfV0wcWvEzZs3RVxcnOjVq5fo2LGjCAgIEB999JFRnaeffloAEGfPnjUqLy4uFk899ZTQaDRCrVaLMWPGiOzsbLOf8/e//10AECtWrGi0P3fu3BEvv/yy6Nu3r+jYsaN44IEHxJtvvtmic1QKOcVi8+bNYtSoUUKj0QhnZ2fRvXt3MXHiRHHgwIEWn6dS2CMe169fF4mJieI3v/mNcHNzEz179hShoaFmR0J5bcgjFu392rBHLFJSUsTAgQOFSqUS3bp1ExEREeKLL74w2x9eF/KIha2vC0mIXydZiYiIiEjW2u6dokRERERtDBM3IiIiIoVg4kZERESkEEzciIiIiBSCiRsRERGRQjBxIyIiIlIIJm5ERERECsHEjYiIiEghmLgRUZMkSWryKzk5uVU+e8OGDRg0aBA6duwISZJw/fp1AEBSUhL69u0LZ2dndOvWzeafu3HjRmRkZNi8XSU5evQokpOTDT9zS82ZMwcRERFm9xUUFECSJLi4uOCXX34xW+ehhx7CokWLmtlbovaBb04goiYdP37cbHlNTQ2eeuopFBUV4csvv8SoUaNs+rnffvstRowYgdjYWDz99NNwdnbGyJEjsXfvXkybNg2JiYl45JFHoFKp8Lvf/c6mnz1s2DB4enriyJEjNm1XSdLS0vDSSy/h7Nmz6N+/v0XH/OMf/8Dvfvc7nDhxwmxMFi5ciDfffBMAsGbNGixZssSkTl5eHsLCwlBQUIDBgwe36ByI2hpnR3eAiORvzJgxZsvj4uJw9uxZ/PWvf7V50gYAp06dAgDMnTvXqP3CwkLD5/fs2dPmnytHt2/fhlqthiRJJvsqKyvh5ubmgF6ZWrNmDUaNGmU2aauursb27dsRGBiIkpISvPfee2YTt7Fjx2Lw4MF4/fXXsWnTJnt0m0g5rHrDKRG1e++//74AIP74xz9adfzmzZtFQECAUKlUonv37mLatGni+++/N+wfO3asAGD09fTTT4t+/fqZlL/88stCCCFycnLE2LFjDS+H9vX1FVFRUaKiosLQbnV1tfjzn/8sBg8eLDp27Cg8PT1FdHS0uHLliqGOuc/o169fo+dTW1sr3nzzTREYGCjUarXo2rWrGD16tNizZ4+hTt2+1tWvXz/x9NNPG7a3bNkiAIgDBw6ImJgY4enpKQCI27dvi7Fjx4qhQ4eKvLw8ERwcLFxdXcXjjz8uhBDixo0b4oUXXhD9+/cXLi4uwsfHRyxcuFDcunXL6PMAiPnz54v3339f+Pn5CVdXVxEQECA+//xzQ52XX37Z5GcAQOTm5jb4MyguLhYuLi4iPT3d7P6PP/5YABAbNmwQy5YtEwDEl19+abb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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"Brange=(0.07, 0.095)\n",
"\n",
"from Analyser.FitAnalyser import GaussianModel, GaussianWithOffsetModel\n",
"\n",
"data = Ncount_mean_total.where(Brange[0]<Ncount_mean_total.als_final_current).where(Ncount_mean_total.als_final_current<Brange[1])\n",
"data = data.dropna(dim='als_final_current')\n",
"data_std = Ncount_std_total.where(Brange[0]<Ncount_mean_total.als_final_current).where(Ncount_mean_total.als_final_current<Brange[1])\n",
"data_std = data_std.dropna(dim='als_final_current')\n",
"\n",
"# fitModel = GaussianModel(prefix='A_') + GaussianWithOffsetModel(prefix='B_')\n",
"# fitAnalyser = FitAnalyser(fitModel, fitDim=1)\n",
"# # fitAnalyser = FitAnalyser(\"Gaussian With Offset\", fitDim=1)\n",
"# # params = fitAnalyser.guess(data, x='als_final_current', guess_kwargs=dict(negative=True), dask=\"parallelized\")\n",
"# params = fitAnalyser.fitModel.make_params()\n",
"# params.add(name=\"A_amplitude\", value= -1, max=np.inf, min=-0.7, vary=True)\n",
"# params.add(name=\"A_center\", value= 0.09, max=np.inf, min=-np.inf, vary=True)\n",
"# params.add(name=\"A_sigma\", value= 0.0005, max=np.inf, min= 0, vary=True)\n",
"# params.add(name=\"B_amplitude\", value= -11, max=np.inf, min=-np.inf, vary=True)\n",
"# params.add(name=\"B_center\", value= 0.085, max=np.inf, min=-np.inf, vary=True)\n",
"# params.add(name=\"B_sigma\", value= 0.01, max=np.inf, min= 0, vary=True)\n",
"# params.add(name=\"B_offset\", value= 3500, max=np.inf, min=-np.inf, vary=True)\n",
"\n",
"# fitResult = fitAnalyser.fit(data, params, x='als_final_current', dask=\"parallelized\").load()\n",
"# fitCurve = fitAnalyser.eval(fitResult, x=np.linspace(Brange[0], Brange[1], 500), dask=\"parallelized\").load()\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"data.plot.errorbar(ax=ax, yerr = data_std, fmt='ob')\n",
"# fitCurve.plot.errorbar(ax=ax)\n",
"\n",
"plt.ylim([0, 4500])\n",
"plt.ylabel('NCount')\n",
"plt.xlabel('Z_offset current (A)')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()\n",
"\n",
"# f_1 = fitAnalyser.get_fit_value(fitResult).A_center\n",
"# df_1 = fitAnalyser.get_fit_std(fitResult).A_center\n",
"\n",
"# print('f = %.5f \\u00B1 %.5f A'% tuple([np.abs(f_1),df_1]))\n",
"\n",
"# s_1 = fitAnalyser.get_fit_value(fitResult).A_sigma\n",
"# ds_1 = fitAnalyser.get_fit_std(fitResult).A_sigma\n",
"\n",
"# fwhm_1 = 2.3548200*s_1\n",
"# dfwhm_1 = 2.3548200*ds_1\n",
"\n",
"# print('fwhm = %.5f \\u00B1 %.5f A'% tuple([np.abs(fwhm_1),dfwhm_1]))\n",
"\n",
"# f_1 = fitAnalyser.get_fit_value(fitResult).B_center\n",
"# df_1 = fitAnalyser.get_fit_std(fitResult).B_center\n",
"\n",
"# print('f = %.5f \\u00B1 %.5f A'% tuple([np.abs(f_1),df_1]))\n",
"\n",
"# s_1 = fitAnalyser.get_fit_value(fitResult).B_sigma\n",
"# ds_1 = fitAnalyser.get_fit_std(fitResult).B_sigma\n",
"\n",
"# fwhm_1 = 2.3548200*s_1\n",
"# dfwhm_1 = 2.3548200*ds_1\n",
"\n",
"# print('fwhm = %.5f \\u00B1 %.5f A'% tuple([np.abs(fwhm_1),dfwhm_1]))"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;OD&#x27; (als_final_current: 1)&gt;\n",
"array([332.40889692])\n",
"Coordinates:\n",
" * als_final_current (als_final_current) float64 0.0903</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'OD'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>als_final_current</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-003831ed-ef8c-4353-a89a-07d86978ba42' class='xr-array-in' type='checkbox' checked><label for='section-003831ed-ef8c-4353-a89a-07d86978ba42' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>332.4</span></div><div class='xr-array-data'><pre>array([332.40889692])</pre></div></div></li><li class='xr-section-item'><input id='section-4cc78a1f-a9a6-41c2-925f-7a400b6a7c67' class='xr-section-summary-in' type='checkbox' checked><label for='section-4cc78a1f-a9a6-41c2-925f-7a400b6a7c67' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>als_final_current</span></div><div class='xr-var-dims'>(als_final_current)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0903</div><input id='attrs-77640650-a651-4b7f-9a87-bfb5fff7b00a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-77640650-a651-4b7f-9a87-bfb5fff7b00a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-159832ff-7ba7-40e1-bdef-5e2129e28470' class='xr-var-data-in' type='checkbox'><label for='data-159832ff-7ba7-40e1-bdef-5e2129e28470' 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([0.0903])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ab676435-7c40-4818-b13d-724fa314ba5f' class='xr-section-summary-in' type='checkbox' ><label for='section-ab676435-7c40-4818-b13d-724fa314ba5f' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>als_final_current</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-84aad126-830b-4d0d-b08c-642b0b846989' class='xr-index-data-in' type='checkbox'/><label for='index-84aad126-830b-4d0d-b08c-642b0b846989' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([0.0903], dtype=&#x27;float64&#x27;, name=&#x27;als_final_current&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-733a1cca-031d-4106-9c7c-717e605f3e7c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-733a1cca-031d-4106-9c7c-717e605f3e7c' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray 'OD' (als_final_current: 1)>\n",
"array([332.40889692])\n",
"Coordinates:\n",
" * als_final_current (als_final_current) float64 0.0903"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.where(data==data.min(), drop='True')"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [],
"source": [
"Ncount_mean_total_origin = copy.deepcopy(Ncount_mean_total)\n",
"Ncount_mean_std_origin = copy.deepcopy(Ncount_std_total)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.backends.zarr.ZarrStore at 0x29546678dd0>"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"res = Ncount_mean_total_origin.to_dataset()\n",
"res = res.assign(std = Ncount_mean_std_origin)\n",
"res.to_zarr('./CompZ.zarr')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Ncount_mean_total = copy.deepcopy(Ncount_mean_total_origin)\n",
"Ncount_mean_std = copy.deepcopy(Ncount_mean_std_origin)\n",
"\n",
"B = np.sqrt((Ncount_mean_total['als_final_current'] * 10.6021 + 0.3254)**2 + (0.4333**2 - 0.3254**2))\n",
"Ncount_mean_total['als_final_current'] = B\n",
"Ncount_std_total['als_final_current'] = B"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.patches as patches\n",
"\n",
"B0 = [1.2950, 1.3060, 2.1740, 2.3360, 2.5910, 2.7400, 2.8030, 2.7800, 3.3570, 4.9490, 5.0830]# , 7.1720, 7.2040, 7.1340]\n",
"B0_width = [0.0090, 0.0100, 0.0005, 0.0005, 0.0010, 0.0005, 0.0210, 0.0150, 0.0430, 0.0005, 0.1300]# , 0.0240, 0.0005, 0.0360]\n",
"\n",
"fig = plt.figure(figsize=(24,6))\n",
"ax = fig.gca()\n",
"Ncount_mean_total.plot.errorbar(ax=ax, yerr = Ncount_std_total, fmt='ob')\n",
"\n",
"for i in range(len(B0)):\n",
" rect = patches.Rectangle((B0[i] - B0_width[i], 000), B0_width[i]*2, 4000, linewidth=1, edgecolor='r', facecolor='r', alpha=0.4)\n",
" ax.add_patch(rect)\n",
"\n",
"plt.ylim([0, 4500])\n",
"plt.ylabel('NCount')\n",
"plt.xlabel('Magnetic Field (G)')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"f = [0.44224, 0.28128, 0.22367, 0.22962, 0.21004, 0.18586, 0.17055, 0.0903] \n",
"df = [0.00038, 0.00016, 0.01, 0.00013, 0.00012, 0.00021, 0.00175, 0]\n",
"B = [(1.2950 + 1.3060)/2, 2.1740, 2.3360, 2.5910, 2.7400, (2.8030 + 2.7800)/2, 3.3570, 5.0830]# , 7.1720, 7.2040, 7.1340]\n",
"\n",
"# f = [0.44224, 0.28128, 0.21004, 0.18586, 0.17055, 0.0903] \n",
"# df = [0.00038, 0.00016, 0.00012, 0.00021, 0.00175, 0]\n",
"# B = [(1.2950 + 1.3060)/2, 2.1740, 2.3360, 2.5910, 3.3570, 5.0830]# , 7.1720, 7.2040, 7.1340]\n",
"B = np.flip(B)\n",
"\n",
"data = xr.DataArray(\n",
" data=B,\n",
" dims='x',\n",
" coords=dict(x=f)\n",
")\n",
"\n",
"data_std = xr.DataArray(\n",
" data=df,\n",
" dims='x',\n",
" coords=dict(x=f)\n",
")\n",
"\n",
"fitAnalyser = FitAnalyser(\"Linear\", fitDim=1)\n",
"params = fitAnalyser.guess(data, dask=\"parallelized\")\n",
"# params = fitAnalyser.fitModel.make_params()\n",
"fitResult = fitAnalyser.fit(data, params, dask=\"parallelized\").load()\n",
"fitCurve = fitAnalyser.eval(fitResult, x=np.linspace(f[-1], f[0], 500), dask=\"parallelized\").load()\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"data.plot.errorbar(ax=ax, xerr = df, fmt='ob')\n",
"fitCurve.plot.errorbar(ax=ax)\n",
"\n",
"# plt.ylim([0, 4500])\n",
"plt.ylabel('Magnetic Field (G)')\n",
"plt.xlabel('Z_offset current (A)')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()\n",
"\n",
"slope = fitAnalyser.get_fit_value(fitResult).slope\n",
"dslope = fitAnalyser.get_fit_std(fitResult).slope\n",
"\n",
"print('slope = %.5f \\u00B1 %.5f A'% tuple([np.abs(slope),dslope]))\n",
"\n",
"intercept = fitAnalyser.get_fit_value(fitResult).intercept\n",
"dintercept = fitAnalyser.get_fit_std(fitResult).intercept\n",
"\n",
"print('intercept = %.5f \\u00B1 %.5f A'% tuple([np.abs(intercept),dintercept]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def magnetic_field_func(x, b0=0, by0=0, alpha=1):\n",
" return 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= 0.4333, max=np.inf, min=-np.inf, vary=True)\n",
"params_quadratic.add(name=\"by0\", value= 0.33732, max=np.inf, min=-np.inf, vary=True)\n",
"params_quadratic.add(name=\"alpha\", value= 10.6021, 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": null,
"metadata": {},
"outputs": [],
"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.ylabel('Magnetic Field (G)')\n",
"plt.xlabel('Z_offset current (A)')\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": null,
"metadata": {},
"outputs": [],
"source": [
"alpha = fitAnalyser.get_fit_value(fitResult_quadratic).alpha\n",
"dalpha = fitAnalyser.get_fit_std(fitResult_quadratic).alpha\n",
"\n",
"print('alpha = %.5f \\u00B1 %.5f G/A'% tuple([np.abs(alpha),dalpha]))\n",
"\n",
"beta = fitAnalyser.get_fit_value(fitResult_quadratic).by0\n",
"dbeta = fitAnalyser.get_fit_std(fitResult_quadratic).by0\n",
"\n",
"print('beta = %.5f \\u00B1 %.5f A'% tuple([beta,dbeta]))\n",
"\n",
"b0 = fitAnalyser.get_fit_value(fitResult_quadratic).b0\n",
"db0 = fitAnalyser.get_fit_std(fitResult_quadratic).b0\n",
"\n",
"print('b0 = %.5f \\u00B1 %.5f A'% tuple([b0,db0]))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"ax.errorbar([0], [9.46], fmt='ob', label='Theory')\n",
"ax.errorbar([1], [9.52], yerr=[0.05], fmt='or', label='RF')\n",
"ax.errorbar([2], [9.523], yerr=[0.019], fmt='og', label='ALS Linear')\n",
"ax.errorbar([3], [9.576], yerr=[0.032], fmt='ok', label='ALS Quadratic')\n",
"\n",
"plt.ylabel('Alpha X (G/A)')\n",
"\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"ax.errorbar([1], [-0.164], yerr=[0.017], fmt='or', label='RF')\n",
"ax.errorbar([2], [0.105], yerr=[0.006], fmt='og', label='ALS Linear')\n",
"ax.errorbar([3], [ -0.138], yerr=[0.018], fmt='ok', label='ALS Quadratic')\n",
"\n",
"plt.xlim([-0.1, 3.1])\n",
"plt.ylabel('Beta X (G)')\n",
"\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"ax.errorbar([0], [10.75], fmt='ob', label='Theory')\n",
"ax.errorbar([1], [10.6021], yerr=[0.0022], fmt='or', label='RF')\n",
"ax.errorbar([2], [10.73], yerr=[0.12], fmt='og', label='ALS Linear')\n",
"ax.errorbar([3], [10.683], yerr=[0.028], fmt='ok', label='ALS Quadratic')\n",
"\n",
"plt.ylabel('Alpha Z (G/A)')\n",
"\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"ax.errorbar([1], [0.3254], yerr=[0.0008], fmt='or', label='RF')\n",
"ax.errorbar([2], [0.337], yerr=[0.031], fmt='og', label='ALS Linear')\n",
"ax.errorbar([3], [0.365], yerr=[0.014], fmt='ok', label='ALS Quadratic')\n",
"\n",
"plt.xlim([-0.1, 3.1])\n",
"plt.ylabel('Beta Z (G)')\n",
"\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"ax.errorbar([0], [10.18], fmt='ob', label='Theory')\n",
"ax.errorbar([1], [10.30], yerr=[0.05], fmt='or', label='RF')\n",
"ax.errorbar([2], [10.447], yerr=[0.023], fmt='og', label='ALS Linear')\n",
"ax.errorbar([3], [10.516], yerr=[0.038], fmt='ok', label='ALS Quadratic')\n",
"\n",
"plt.ylabel('Alpha Y (G/A)')\n",
"\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"ax.errorbar([1], [0.202], yerr=[0.015], fmt='or', label='RF')\n",
"ax.errorbar([2], [0.0762 ], yerr=[0.0064], fmt='og', label='ALS Linear')\n",
"ax.errorbar([3], [0.039 ], yerr=[0.019], fmt='ok', label='ALS Quadratic')\n",
"\n",
"plt.xlim([-0.1, 3.1])\n",
"plt.ylabel('Beta Y (G)')\n",
"\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"ax.errorbar([1], [-0.164], yerr=[0.017], fmt='or', label='RF')\n",
"ax.errorbar([3], [-0.138], yerr=[0.018], fmt='ok', label='ALS Quadratic')\n",
"ax.errorbar([4], [0.417], yerr=[0.01], fmt='ob', label='RF average')\n",
"\n",
"plt.xlim([-0.1, 4.1])\n",
"plt.ylabel('B0 X (G)')\n",
"\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"ax.errorbar([1], [0.440], yerr=[0.009], fmt='or', label='RF')\n",
"ax.errorbar([3], [0.313], yerr=[0.071], fmt='ok', label='ALS Quadratic')\n",
"ax.errorbar([4], [0.417], yerr=[0.01], fmt='ob', label='RF average')\n",
"\n",
"plt.xlim([-0.1, 4.1])\n",
"plt.ylabel('B0 Y (G)')\n",
"\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"ax.errorbar([1], [0.4333], yerr=[0.0007], fmt='or', label='RF')\n",
"ax.errorbar([3], [0.243 ], yerr=[0.054], fmt='ok', label='ALS Quadratic')\n",
"ax.errorbar([4], [0.417], yerr=[0.01], fmt='ob', label='RF average')\n",
"\n",
"plt.xlim([-0.1, 4.1])\n",
"plt.ylabel('B0 Z (G)')\n",
"\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Test"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib notebook\n",
"shotNum = \"0024\"\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 = (135, 990)\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": {
"scrolled": false
},
"outputs": [],
"source": [
"l = list(np.arange(525e-3, 552e-3, 0.9e-3))\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 * 100 * 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 = 0.062\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 = 0.842\n",
"P_arm2 = 2.302 * v - 0.06452\n",
"print(round(P_arm2, 3))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
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"language_info": {
"codemirror_mode": {
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"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.12"
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"vscode": {
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