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analyseScript/backupScript/20230630_Data_Analysis.ipynb

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regular backup
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{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Import supporting package"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import xarray as xr\n",
"import numpy as np\n",
"import copy\n",
"\n",
"from uncertainties import ufloat\n",
"from uncertainties import unumpy as unp\n",
"from uncertainties import umath\n",
"import random\n",
"import matplotlib.pyplot as plt\n",
"plt.rcParams['font.size'] = 12\n",
"\n",
"from DataContainer.ReadData import read_hdf5_file\n",
"from Analyser.ImagingAnalyser import ImageAnalyser\n",
"from Analyser.FitAnalyser import FitAnalyser\n",
"from Analyser.FitAnalyser import NewFitModel, DensityProfileBEC2dModel\n",
"from ToolFunction.ToolFunction import *\n",
"\n",
"from scipy.optimize import curve_fit\n",
"\n",
"from ToolFunction.HomeMadeXarrayFunction import errorbar, dataarray_plot_errorbar\n",
"xr.plot.dataarray_plot.errorbar = errorbar\n",
"xr.plot.accessor.DataArrayPlotAccessor.errorbar = dataarray_plot_errorbar\n",
"\n",
"imageAnalyser = ImageAnalyser()\n",
"\n",
"# %matplotlib notebook"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Start a client for parallel computing"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
" <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
imporve the BEC fitting
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" <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-b66089ef-4000-11ee-9814-80e82ce2fa8e</p>\n",
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" <table style=\"width: 100%; text-align: left;\">\n",
"\n",
" <tr>\n",
" \n",
" <td style=\"text-align: left;\"><strong>Connection method:</strong> Cluster object</td>\n",
" <td style=\"text-align: left;\"><strong>Cluster type:</strong> distributed.LocalCluster</td>\n",
" \n",
" </tr>\n",
"\n",
" \n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" \n",
"\n",
" </table>\n",
"\n",
" \n",
"\n",
" \n",
" <details>\n",
" <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Cluster Info</h3></summary>\n",
" <div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
" <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\">\n",
" </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <h3 style=\"margin-bottom: 0px; margin-top: 0px;\">LocalCluster</h3>\n",
imporve the BEC fitting
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" <p style=\"color: #9D9D9D; margin-bottom: 0px;\">2c2de3a6</p>\n",
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" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Workers:</strong> 8\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads:</strong> 128\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total memory:</strong> 149.01 GiB\n",
" </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <td style=\"text-align: left;\"><strong>Status:</strong> running</td>\n",
" <td style=\"text-align: left;\"><strong>Using processes:</strong> True</td>\n",
"</tr>\n",
"\n",
" \n",
" </table>\n",
"\n",
" <details>\n",
" <summary style=\"margin-bottom: 20px;\">\n",
" <h3 style=\"display: inline;\">Scheduler Info</h3>\n",
" </summary>\n",
"\n",
" <div style=\"\">\n",
" <div>\n",
" <div style=\"width: 24px; height: 24px; background-color: #FFF7E5; border: 3px solid #FF6132; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <h3 style=\"margin-bottom: 0px;\">Scheduler</h3>\n",
imporve the BEC fitting
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" <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-426d0ae2-dc44-435c-a257-5550c48c51d6</p>\n",
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" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
imporve the BEC fitting
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" <strong>Comm:</strong> tcp://127.0.0.1:62265\n",
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" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Workers:</strong> 8\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads:</strong> 128\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Started:</strong> Just now\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total memory:</strong> 149.01 GiB\n",
" </td>\n",
" </tr>\n",
" </table>\n",
" </div>\n",
" </div>\n",
"\n",
" <details style=\"margin-left: 48px;\">\n",
" <summary style=\"margin-bottom: 20px;\">\n",
" <h3 style=\"display: inline;\">Workers</h3>\n",
" </summary>\n",
"\n",
" \n",
" <div style=\"margin-bottom: 20px;\">\n",
" <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <details>\n",
" <summary>\n",
" <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 0</h4>\n",
" </summary>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
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" <strong>Comm: </strong> tcp://127.0.0.1:62318\n",
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" </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",
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" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:62319/status\" target=\"_blank\">http://127.0.0.1:62319/status</a>\n",
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" </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",
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" <strong>Nanny: </strong> tcp://127.0.0.1:62268\n",
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" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
imporve the BEC fitting
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" <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-po12jmk2\n",
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" </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",
imporve the BEC fitting
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" <strong>Comm: </strong> tcp://127.0.0.1:62313\n",
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" </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",
imporve the BEC fitting
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" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:62315/status\" target=\"_blank\">http://127.0.0.1:62315/status</a>\n",
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" </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",
imporve the BEC fitting
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" <strong>Nanny: </strong> tcp://127.0.0.1:62269\n",
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" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
imporve the BEC fitting
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" <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-gw2s7vyv\n",
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" </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",
imporve the BEC fitting
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" <strong>Comm: </strong> tcp://127.0.0.1:62300\n",
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" </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",
imporve the BEC fitting
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" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:62302/status\" target=\"_blank\">http://127.0.0.1:62302/status</a>\n",
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" </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",
imporve the BEC fitting
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" <strong>Nanny: </strong> tcp://127.0.0.1:62270\n",
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" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
imporve the BEC fitting
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" <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-cfh0nxcz\n",
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" </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",
imporve the BEC fitting
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" <strong>Comm: </strong> tcp://127.0.0.1:62312\n",
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" </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",
imporve the BEC fitting
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" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:62314/status\" target=\"_blank\">http://127.0.0.1:62314/status</a>\n",
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" </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",
imporve the BEC fitting
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" <strong>Nanny: </strong> tcp://127.0.0.1:62271\n",
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" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
imporve the BEC fitting
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" <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-9zm5rjei\n",
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" </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",
imporve the BEC fitting
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" <strong>Comm: </strong> tcp://127.0.0.1:62301\n",
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" </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",
imporve the BEC fitting
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" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:62303/status\" target=\"_blank\">http://127.0.0.1:62303/status</a>\n",
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" </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",
imporve the BEC fitting
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" <strong>Nanny: </strong> tcp://127.0.0.1:62272\n",
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" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
imporve the BEC fitting
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" <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-k9dnmkx4\n",
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" </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",
imporve the BEC fitting
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" <strong>Comm: </strong> tcp://127.0.0.1:62321\n",
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" </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",
imporve the BEC fitting
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" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:62322/status\" target=\"_blank\">http://127.0.0.1:62322/status</a>\n",
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" </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",
imporve the BEC fitting
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" <strong>Nanny: </strong> tcp://127.0.0.1:62273\n",
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" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
imporve the BEC fitting
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" <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-s6ag_a96\n",
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" </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",
imporve the BEC fitting
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" <strong>Comm: </strong> tcp://127.0.0.1:62307\n",
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" </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",
imporve the BEC fitting
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" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:62310/status\" target=\"_blank\">http://127.0.0.1:62310/status</a>\n",
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" </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",
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" <strong>Nanny: </strong> tcp://127.0.0.1:62274\n",
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" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
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" <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-ggzqb3hl\n",
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" </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",
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" <strong>Comm: </strong> tcp://127.0.0.1:62306\n",
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" </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",
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" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:62308/status\" target=\"_blank\">http://127.0.0.1:62308/status</a>\n",
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" </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",
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" <strong>Nanny: </strong> tcp://127.0.0.1:62275\n",
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" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
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" <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-dcr0glu5\n",
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" </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": [
imporve the BEC fitting
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"<Client: 'tcp://127.0.0.1:62265' processes=8 threads=128, memory=149.01 GiB>"
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]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from dask.distributed import Client\n",
"client = Client(n_workers=8, threads_per_worker=16, processes=True, memory_limit='20GB')\n",
"client"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Start a client for Mongo DB"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import pymongo\n",
"import xarray_mongodb\n",
"\n",
"from DataContainer.MongoDB import MongoDB\n",
"\n",
"mongoClient = pymongo.MongoClient('mongodb://control:DyLab2021@127.0.0.1:27017/?authMechanism=DEFAULT')"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Set global path for experiment"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"groupList = [\n",
" \"images/MOT_3D_Camera/in_situ_absorption\",\n",
" \"images/ODT_1_Axis_Camera/in_situ_absorption\",\n",
" \"images/ODT_2_Axis_Camera/in_situ_absorption\",\n",
"]\n",
"\n",
"dskey = {\n",
" \"images/MOT_3D_Camera/in_situ_absorption\": \"camera_0\",\n",
" \"images/ODT_1_Axis_Camera/in_situ_absorption\": \"camera_1\",\n",
" \"images/ODT_2_Axis_Camera/in_situ_absorption\": \"camera_2\",\n",
"}\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"img_dir = 'C:/Users/control/DyLab/Experiments/DyBEC/'\n",
"SequenceName = \"Repetition_scan\"\n",
"folderPath = img_dir + SequenceName + \"/\" + get_date()\n",
"\n",
"mongoDB = mongoClient[SequenceName]\n",
"\n",
"DB = MongoDB(mongoClient, mongoDB, date=get_date())"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Repetition Scans"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## scan MOT freq"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"%matplotlib notebook\n",
"shotNum = \"0000\"\n",
"filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n",
"\n",
"dataSetDict = {\n",
" dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n",
" for i in range(len(groupList))\n",
"}\n",
"\n",
"dataSet = dataSetDict[\"camera_1\"]\n",
"\n",
"print_scanAxis(dataSet)\n",
"\n",
"scanAxis = get_scanAxis(dataSet)\n",
"\n",
"dataSet = auto_rechunk(dataSet)\n",
"\n",
"dataSet = imageAnalyser.get_absorption_images(dataSet)\n",
"\n",
"imageAnalyser.center = (310, 825)\n",
"imageAnalyser.span = (550, 1250)\n",
"imageAnalyser.fraction = (0.1, 0.1)\n",
"\n",
"dataSet_cropOD = imageAnalyser.crop_image(dataSet.OD)\n",
"dataSet_cropOD = imageAnalyser.substract_offset(dataSet_cropOD).load()\n",
"\n",
"Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n",
"Ncount_mean = calculate_mean(Ncount)\n",
"Ncount_std = calculate_std(Ncount)\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n",
"plt.xlabel('MOT AOM Freq (MHz)')\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()\n",
"\n",
"# DB.create_global(shotNum, dataSet)\n",
"# DB.add_data(shotNum, dataSet_cropOD, engine='xarray')"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## scan Push freq"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"%matplotlib notebook\n",
"shotNum = \"0001\"\n",
"filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n",
"\n",
"dataSetDict = {\n",
" dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n",
" for i in range(len(groupList))\n",
"}\n",
"\n",
"dataSet = dataSetDict[\"camera_1\"]\n",
"\n",
"print_scanAxis(dataSet)\n",
"\n",
"scanAxis = get_scanAxis(dataSet)\n",
"\n",
"dataSet = auto_rechunk(dataSet)\n",
"\n",
"dataSet = imageAnalyser.get_absorption_images(dataSet)\n",
"\n",
"imageAnalyser.center = (310, 825)\n",
"imageAnalyser.span = (550, 1250)\n",
"imageAnalyser.fraction = (0.1, 0.1)\n",
"\n",
"dataSet_cropOD = imageAnalyser.crop_image(dataSet.OD)\n",
"dataSet_cropOD = imageAnalyser.substract_offset(dataSet_cropOD).load()\n",
"\n",
"Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n",
"Ncount_mean = calculate_mean(Ncount)\n",
"Ncount_std = calculate_std(Ncount)\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n",
"plt.xlabel('Push AOM Freq (MHz)')\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()\n",
"\n",
"# DB.create_global(shotNum, dataSet)\n",
"# DB.add_data(shotNum, dataSet_cropOD, engine='xarray')"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## scan Z comp current"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"%matplotlib notebook\n",
"shotNum = \"0005\"\n",
"filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n",
"\n",
"dataSetDict = {\n",
" dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n",
" for i in [0, 1]\n",
"}\n",
"\n",
"dataSet = dataSetDict[\"camera_1\"]\n",
"\n",
"print_scanAxis(dataSet)\n",
"\n",
"scanAxis = get_scanAxis(dataSet)\n",
"\n",
"dataSet = auto_rechunk(dataSet)\n",
"\n",
"dataSet = imageAnalyser.get_absorption_images(dataSet)\n",
"\n",
"imageAnalyser.center = (305, 870)\n",
"imageAnalyser.span = (400, 400)\n",
"imageAnalyser.fraction = (0.1, 0.1)\n",
"\n",
"dataSet_cropOD = imageAnalyser.crop_image(dataSet.OD)\n",
"dataSet_cropOD = imageAnalyser.substract_offset(dataSet_cropOD).load()\n",
"\n",
"Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n",
"Ncount_mean = calculate_mean(Ncount)\n",
"Ncount_std = calculate_std(Ncount)\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n",
"plt.xlabel('comp Z current (A)')\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()\n",
"\n",
"# DB.create_global(shotNum, dataSet)\n",
"# DB.add_data(shotNum, dataSet_cropOD, engine='xarray')"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Evaporative Cooling"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"img_dir = '//DyLabNAS/Data/'\n",
"SequenceName = \"Evaporative_Cooling\" + \"/\"\n",
"folderPath = img_dir + SequenceName + '2023/06/30'# get_date()\n",
"\n",
"# mongoDB = mongoClient[SequenceName]\n",
"\n",
"# DB = MongoDB(mongoClient, mongoDB, date=get_date())"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Check BEC"
]
},
{
"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The detected scaning axes and values are: \n",
"\n",
"{'compZ_current_sg': array([0.15 , 0.155, 0.16 , 0.165, 0.17 , 0.175, 0.18 , 0.185, 0.19 ,\n",
" 0.195, 0.2 , 0.205, 0.21 , 0.215, 0.22 , 0.225, 0.23 , 0.235,\n",
" 0.24 , 0.245]), 'runs': array([0., 1., 2.])}\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"shotNum = \"0000\"\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 = (880, 990)\n",
"imageAnalyser.span = (150, 150)\n",
"imageAnalyser.fraction = (0.1, 0.1)\n",
"\n",
"dataSet_cropOD = imageAnalyser.crop_image(dataSet.OD)\n",
"dataSet_cropOD = imageAnalyser.substract_offset(dataSet_cropOD).load()\n",
"\n",
"Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n",
"Ncount_mean = calculate_mean(Ncount)\n",
"Ncount_std = calculate_std(Ncount)\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"Ncount_mean.plot.errorbar(ax=ax, yerr = None, fmt='-ob')\n",
"plt.xlabel('comp Z current (A)')\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()\n",
"\n",
"# DB.create_global(shotNum, dataSet)\n",
"# DB.add_data(shotNum, dataSet_cropOD, engine='xarray')"
]
},
{
"cell_type": "code",
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"execution_count": 25,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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"<xarray.plot.facetgrid.FacetGrid at 0x19caa8b8a60>"
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]
},
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"metadata": {},
"output_type": "execute_result"
},
{
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"text/plain": [
"<Figure size 6100x900 with 61 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dataSet_cropOD.plot.pcolormesh(cmap='jet', col=scanAxis[0], row=scanAxis[1], vmin=0, vmax=3)"
]
},
{
"cell_type": "code",
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"execution_count": 22,
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"metadata": {},
"outputs": [],
"source": [
"data = dataSet_cropOD.chunk((1,1,150,150))#.sel(runs = 0)\n",
"\n",
"fitModel = DensityProfileBEC2dModel()\n",
"fitAnalyser_1 = FitAnalyser(fitModel, fitDim=2)\n",
"\n",
"params = fitAnalyser_1.guess(data, dask=\"parallelized\", guess_kwargs=dict(pureBECThreshold=1.2))\n",
"\n",
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"fitResult_1 = fitAnalyser_1.fit(data, params).load()\n",
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"\n",
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"fitCurve = fitAnalyser_1.eval(fitResult_1, x=np.arange(150), y=np.arange(150), dask=\"parallelized\").load()"
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]
},
{
"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
"outputs": [
{
"data": {
"text/html": [
imporve the BEC fitting
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"<table><tr><th> name </th><th> value </th><th> initial value </th><th> min </th><th> max </th><th> vary </th></tr><tr><td> amp_bec </td><td> 1.44550963 </td><td> None </td><td> 0.00000000 </td><td> 3.70514761 </td><td> True </td></tr><tr><td> amp_th </td><td> 0.81463531 </td><td> None </td><td> 0.00000000 </td><td> 3.70514761 </td><td> True </td></tr><tr><td> x0_bec </td><td> 71.2451253 </td><td> None </td><td> 61.2451253 </td><td> 81.2451253 </td><td> True </td></tr><tr><td> y0_bec </td><td> 71.9052925 </td><td> None </td><td> 61.9052925 </td><td> 81.9052925 </td><td> True </td></tr><tr><td> x0_th </td><td> 71.2451253 </td><td> None </td><td> 61.2451253 </td><td> 81.2451253 </td><td> True </td></tr><tr><td> y0_th </td><td> 71.9052925 </td><td> None </td><td> 61.9052925 </td><td> 81.9052925 </td><td> True </td></tr><tr><td> sigmax_bec </td><td> 24.3865938 </td><td> None </td><td> 0.00000000 </td><td> 28.0000000 </td><td> True </td></tr><tr><td> sigmay_bec </td><td> 25.4098361 </td><td> None </td><td> 0.00000000 </td><td> 62.0000000 </td><td> True </td></tr><tr><td> sigma_th </td><td> 16.2393832 </td><td> None </td><td> 0.00000000 </td><td> 150.000000 </td><td> True </td></tr></table>"
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],
"text/plain": [
imporve the BEC fitting
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"Parameters([('amp_bec', <Parameter 'amp_bec', value=1.4455096264728464, bounds=[0:3.7051476090827027]>), ('amp_th', <Parameter 'amp_th', value=0.814635307459713, bounds=[0:3.7051476090827027]>), ('x0_bec', <Parameter 'x0_bec', value=71.24512534818942, bounds=[61.24512534818942:81.24512534818942]>), ('y0_bec', <Parameter 'y0_bec', value=71.90529247910864, bounds=[61.90529247910864:81.90529247910864]>), ('x0_th', <Parameter 'x0_th', value=71.24512534818942, bounds=[61.24512534818942:81.24512534818942]>), ('y0_th', <Parameter 'y0_th', value=71.90529247910864, bounds=[61.90529247910864:81.90529247910864]>), ('sigmax_bec', <Parameter 'sigmax_bec', value=24.38659382883903, bounds=[0:28]>), ('sigmay_bec', <Parameter 'sigmay_bec', value=25.40983606557377, bounds=[0:62]>), ('sigma_th', <Parameter 'sigma_th', value=16.239383183237685, bounds=[0:150]>)])"
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]
},
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"execution_count": 14,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"params.sel(runs=0, compZ_current_sg=0.2).item()"
]
},
{
"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
"outputs": [],
"source": [
"fitResult_1 = fitAnalyser_1.fit(data.sel(runs=0, compZ_current_sg=0.2), params.sel(runs=0, compZ_current_sg=0.2)).load()"
]
},
{
"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
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" display: inline-block;\n",
"}\n",
"\n",
".xr-dim-list {\n",
" display: inline-block !important;\n",
" list-style: none;\n",
" padding: 0 !important;\n",
" margin: 0;\n",
"}\n",
"\n",
".xr-dim-list li {\n",
" display: inline-block;\n",
" padding: 0;\n",
" margin: 0;\n",
"}\n",
"\n",
".xr-dim-list:before {\n",
" content: '(';\n",
"}\n",
"\n",
".xr-dim-list:after {\n",
" content: ')';\n",
"}\n",
"\n",
".xr-dim-list li:not(:last-child):after {\n",
" content: ',';\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-has-index {\n",
" font-weight: bold;\n",
"}\n",
"\n",
".xr-var-list,\n",
".xr-var-item {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-var-item > div,\n",
".xr-var-item label,\n",
".xr-var-item > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-even);\n",
" margin-bottom: 0;\n",
"}\n",
"\n",
".xr-var-item > .xr-var-name:hover span {\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-var-list > li:nth-child(odd) > div,\n",
".xr-var-list > li:nth-child(odd) > label,\n",
".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-odd);\n",
"}\n",
"\n",
".xr-var-name {\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-var-dims {\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-var-dtype {\n",
" grid-column: 3;\n",
" text-align: right;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-var-preview {\n",
" grid-column: 4;\n",
"}\n",
"\n",
".xr-index-preview {\n",
" grid-column: 2 / 5;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-var-name,\n",
".xr-var-dims,\n",
".xr-var-dtype,\n",
".xr-preview,\n",
".xr-attrs dt {\n",
" white-space: nowrap;\n",
" overflow: hidden;\n",
" text-overflow: ellipsis;\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-var-name:hover,\n",
".xr-var-dims:hover,\n",
".xr-var-dtype:hover,\n",
".xr-attrs dt:hover {\n",
" overflow: visible;\n",
" width: auto;\n",
" z-index: 1;\n",
"}\n",
"\n",
".xr-var-attrs,\n",
".xr-var-data,\n",
".xr-index-data {\n",
" display: none;\n",
" background-color: var(--xr-background-color) !important;\n",
" padding-bottom: 5px !important;\n",
"}\n",
"\n",
".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
".xr-var-data-in:checked ~ .xr-var-data,\n",
".xr-index-data-in:checked ~ .xr-index-data {\n",
" display: block;\n",
"}\n",
"\n",
".xr-var-data > table {\n",
" float: right;\n",
"}\n",
"\n",
".xr-var-name span,\n",
".xr-var-data,\n",
".xr-index-name div,\n",
".xr-index-data,\n",
".xr-attrs {\n",
" padding-left: 25px !important;\n",
"}\n",
"\n",
".xr-attrs,\n",
".xr-var-attrs,\n",
".xr-var-data,\n",
".xr-index-data {\n",
" grid-column: 1 / -1;\n",
"}\n",
"\n",
"dl.xr-attrs {\n",
" padding: 0;\n",
" margin: 0;\n",
" display: grid;\n",
" grid-template-columns: 125px auto;\n",
"}\n",
"\n",
".xr-attrs dt,\n",
".xr-attrs dd {\n",
" padding: 0;\n",
" margin: 0;\n",
" float: left;\n",
" padding-right: 10px;\n",
" width: auto;\n",
"}\n",
"\n",
".xr-attrs dt {\n",
" font-weight: normal;\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-attrs dt:hover span {\n",
" display: inline-block;\n",
" background: var(--xr-background-color);\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-attrs dd {\n",
" grid-column: 2;\n",
" white-space: pre-wrap;\n",
" word-break: break-all;\n",
"}\n",
"\n",
".xr-icon-database,\n",
".xr-icon-file-text2,\n",
".xr-no-icon {\n",
" display: inline-block;\n",
" vertical-align: middle;\n",
" width: 1em;\n",
" height: 1.5em !important;\n",
" stroke-width: 0;\n",
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
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"Dimensions: ()\n",
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"Coordinates:\n",
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" compZ_current_sg float64 0.2\n",
" runs float64 0.0\n",
"Data variables:\n",
" amp_bec object 1.727+/-0.010\n",
" amp_th object 0.504+/-0.010\n",
" x0_bec object 71.62+/-0.06\n",
" y0_bec object 71.041+/-0.017\n",
" x0_th object 71.84+/-0.17\n",
" y0_th object 71.82+/-0.14\n",
" sigmax_bec object 25.61+/-0.09\n",
" sigmay_bec object 8.746+/-0.035\n",
" sigma_th object 14.84+/-0.15\n",
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"Attributes:\n",
" IMAGE_SUBCLASS: IMAGE_GRAYSCALE\n",
" IMAGE_VERSION: 1.2\n",
" IMAGE_WHITE_IS_ZERO: 0\n",
" x_start: 805\n",
" x_end: 955\n",
" y_end: 1065\n",
" y_start: 915\n",
" x_center: 880\n",
" y_center: 990\n",
" x_span: 150\n",
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" y_span: 150</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-58b2c231-ed46-46d3-86f5-e5c61270a97b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-58b2c231-ed46-46d3-86f5-e5c61270a97b' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-0ec45f1e-8206-48de-b2c8-7b776117bce6' class='xr-section-summary-in' type='checkbox' checked><label for='section-0ec45f1e-8206-48de-b2c8-7b776117bce6' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>compZ_current_sg</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.2</div><input id='attrs-df90e88d-60c4-4ef5-b9c5-c58d13d3f592' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-df90e88d-60c4-4ef5-b9c5-c58d13d3f592' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bacf6ba9-e3e8-436e-8903-89f9f79a0994' class='xr-var-data-in' type='checkbox'><label for='data-bacf6ba9-e3e8-436e-8903-89f9f79a0994' 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(0.2)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>runs</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-7f3d9b5b-2789-468a-bfa9-9b112a6763a7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7f3d9b5b-2789-468a-bfa9-9b112a6763a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c94aff09-b1f0-4b82-be69-0c36a7b59b34' class='xr-var-data-in' type='checkbox'><label for='data-c94aff09-b1f0-4b82-be69-0c36a7b59b34' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-63722144-e714-4b63-9d6e-d7fd1fc74010' class='xr-section-summary-in' type='checkbox' checked><label for='section-63722144-e714-4b63-9d6e-d7fd1fc74010' class='xr-section-summary' >Data variables: <span>(9)</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>amp_bec</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1.727+/-0.010</div><input id='attrs-4d2da1e9-f211-4ac0-a076-3945985ce79e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4d2da1e9-f211-4ac0-a076-3945985ce79e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-739bbefa-6360-4b8a-873b-3d518c752ec7' class='xr-var-data-in' type='checkbox'><label for='data-739bbefa-6360-4b8a-873b-3d518c752ec7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(1.7272227602934593+/-0.010398514342153583, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amp_th</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-prev
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],
"text/plain": [
"<xarray.Dataset>\n",
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"Dimensions: ()\n",
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"Coordinates:\n",
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" compZ_current_sg float64 0.2\n",
" runs float64 0.0\n",
"Data variables:\n",
" amp_bec object 1.727+/-0.010\n",
" amp_th object 0.504+/-0.010\n",
" x0_bec object 71.62+/-0.06\n",
" y0_bec object 71.041+/-0.017\n",
" x0_th object 71.84+/-0.17\n",
" y0_th object 71.82+/-0.14\n",
" sigmax_bec object 25.61+/-0.09\n",
" sigmay_bec object 8.746+/-0.035\n",
" sigma_th object 14.84+/-0.15\n",
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"Attributes:\n",
" IMAGE_SUBCLASS: IMAGE_GRAYSCALE\n",
" IMAGE_VERSION: 1.2\n",
" IMAGE_WHITE_IS_ZERO: 0\n",
" x_start: 805\n",
" x_end: 955\n",
" y_end: 1065\n",
" y_start: 915\n",
" x_center: 880\n",
" y_center: 990\n",
" x_span: 150\n",
" y_span: 150"
]
},
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"execution_count": 16,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fitAnalyser_1.get_fit_full_result(fitResult_1)"
]
},
{
"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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"outputs": [],
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"source": [
"fitCurve = fitAnalyser_1.eval(fitResult_1, x=np.arange(150), y=np.arange(150), dask=\"parallelized\").load()"
]
},
{
"cell_type": "code",
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"execution_count": 23,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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"<xarray.plot.facetgrid.FacetGrid at 0x19ca1f31910>"
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]
},
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"execution_count": 23,
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"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"image/png": "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"text/plain": [
"<Figure size 6100x900 with 61 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fitCurve.plot.pcolormesh(cmap='jet', col=scanAxis[0], row=scanAxis[1])"
]
},
{
"cell_type": "code",
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"execution_count": 24,
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"metadata": {},
"outputs": [
{
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"ename": "TypeError",
"evalue": "int() argument must be a string, a bytes-like object or a number, not 'NoneType'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32mf:\\Jianshun\\analyseScript\\20230630_Data_Analysis.ipynb Cell 28\u001b[0m in \u001b[0;36m2\n\u001b[0;32m <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/20230630_Data_Analysis.ipynb#X36sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m val \u001b[39m=\u001b[39m fitAnalyser_1\u001b[39m.\u001b[39mget_fit_value(fitResult_1)\n\u001b[1;32m----> <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/20230630_Data_Analysis.ipynb#X36sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m std \u001b[39m=\u001b[39m fitAnalyser_1\u001b[39m.\u001b[39;49mget_fit_std(fitResult_1)\n\u001b[0;32m <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/20230630_Data_Analysis.ipynb#X36sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m data \u001b[39m=\u001b[39m val[\u001b[39m'\u001b[39m\u001b[39mcondensate_fraction\u001b[39m\u001b[39m'\u001b[39m]\n\u001b[0;32m <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/20230630_Data_Analysis.ipynb#X36sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m data_std \u001b[39m=\u001b[39m std[\u001b[39m'\u001b[39m\u001b[39mcondensate_fraction\u001b[39m\u001b[39m'\u001b[39m]\n",
"File \u001b[1;32mf:\\Jianshun\\analyseScript\\Analyser\\FitAnalyser.py:1373\u001b[0m, in \u001b[0;36mFitAnalyser.get_fit_std\u001b[1;34m(self, fitResult, dask, **kwargs)\u001b[0m\n\u001b[0;32m 1364\u001b[0m output_core_dims\u001b[39m=\u001b[39m[ [] \u001b[39mfor\u001b[39;00m _ \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39mlen\u001b[39m(params))]\n\u001b[0;32m 1366\u001b[0m kwargs\u001b[39m.\u001b[39mupdate(\n\u001b[0;32m 1367\u001b[0m {\n\u001b[0;32m 1368\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mdask\u001b[39m\u001b[39m\"\u001b[39m: dask,\n\u001b[0;32m 1369\u001b[0m \u001b[39m\"\u001b[39m\u001b[39moutput_core_dims\u001b[39m\u001b[39m\"\u001b[39m: output_core_dims,\n\u001b[0;32m 1370\u001b[0m }\n\u001b[0;32m 1371\u001b[0m )\n\u001b[1;32m-> 1373\u001b[0m value \u001b[39m=\u001b[39m xr\u001b[39m.\u001b[39mapply_ufunc(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_get_fit_std, fitResult, kwargs\u001b[39m=\u001b[39m\u001b[39mdict\u001b[39m(params\u001b[39m=\u001b[39mparams), \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m 1375\u001b[0m value \u001b[39m=\u001b[39m xr\u001b[39m.\u001b[39mDataset(\n\u001b[0;32m 1376\u001b[0m data_vars\u001b[39m=\u001b[39m{\n\u001b[0;32m 1377\u001b[0m params[i]: value[i]\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1380\u001b[0m attrs\u001b[39m=\u001b[39mfitResult\u001b[39m.\u001b[39mattrs\n\u001b[0;32m 1381\u001b[0m )\n\u001b[0;32m 1383\u001b[0m \u001b[39mreturn\u001b[39;00m value\n",
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\core\\computation.py:1196\u001b[0m, in \u001b[0;36mapply_ufunc\u001b[1;34m(func, input_core_dims, output_core_dims, exclude_dims, vectorize, join, dataset_join, dataset_fill_value, keep_attrs, kwargs, dask, output_dtypes, output_sizes, meta, dask_gufunc_kwargs, *args)\u001b[0m\n\u001b[0;32m 1194\u001b[0m \u001b[39m# feed DataArray apply_variable_ufunc through apply_dataarray_vfunc\u001b[39;00m\n\u001b[0;32m 1195\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39many\u001b[39m(\u001b[39misinstance\u001b[39m(a, DataArray) \u001b[39mfor\u001b[39;00m a \u001b[39min\u001b[39;00m args):\n\u001b[1;32m-> 1196\u001b[0m \u001b[39mreturn\u001b[39;00m apply_dataarray_vfunc(\n\u001b[0;32m 1197\u001b[0m variables_vfunc,\n\u001b[0;32m 1198\u001b[0m \u001b[39m*\u001b[39;49margs,\n\u001b[0;32m 1199\u001b[0m signature\u001b[39m=\u001b[39;49msignature,\n\u001b[0;32m 1200\u001b[0m join\u001b[39m=\u001b[39;49mjoin,\n\u001b[0;32m 1201\u001b[0m exclude_dims\u001b[39m=\u001b[39;49mexclude_dims,\n\u001b[0;32m 1202\u001b[0m keep_attrs\u001b[39m=\u001b[39;49mkeep_attrs,\n\u001b[0;32m 1203\u001b[0m )\n\u001b[0;32m 1204\u001b[0m \u001b[39m# feed Variables directly through apply_variable_ufunc\u001b[39;00m\n\u001b[0;32m 1205\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39many\u001b[39m(\u001b[39misinstance\u001b[39m(a, Variable) \u001b[39mfor\u001b[39;00m a \u001b[39min\u001b[39;00m args):\n",
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\core\\computation.py:303\u001b[0m, in \u001b[0;36mapply_dataarray_vfunc\u001b[1;34m(func, signature, join, exclude_dims, keep_attrs, *args)\u001b[0m\n\u001b[0;32m 298\u001b[0m result_coords, result_indexes \u001b[39m=\u001b[39m build_output_coords_and_indexes(\n\u001b[0;32m 299\u001b[0m args, signature, exclude_dims, combine_attrs\u001b[39m=\u001b[39mkeep_attrs\n\u001b[0;32m 300\u001b[0m )\n\u001b[0;32m 302\u001b[0m data_vars \u001b[39m=\u001b[39m [\u001b[39mgetattr\u001b[39m(a, \u001b[39m\"\u001b[39m\u001b[39mvariable\u001b[39m\u001b[39m\"\u001b[39m, a) \u001b[39mfor\u001b[39;00m a \u001b[39min\u001b[39;00m args]\n\u001b[1;32m--> 303\u001b[0m result_var \u001b[39m=\u001b[39m func(\u001b[39m*\u001b[39;49mdata_vars)\n\u001b[0;32m 305\u001b[0m out: \u001b[39mtuple\u001b[39m[DataArray, \u001b[39m.\u001b[39m\u001b[39m.\u001b[39m\u001b[39m.\u001b[39m] \u001b[39m|\u001b[39m DataArray\n\u001b[0;32m 306\u001b[0m \u001b[39mif\u001b[39;00m signature\u001b[39m.\u001b[39mnum_outputs \u001b[39m>\u001b[39m \u001b[39m1\u001b[39m:\n",
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\core\\computation.py:760\u001b[0m, in \u001b[0;36mapply_variable_ufunc\u001b[1;34m(func, signature, exclude_dims, dask, output_dtypes, vectorize, keep_attrs, dask_gufunc_kwargs, *args)\u001b[0m\n\u001b[0;32m 755\u001b[0m \u001b[39mif\u001b[39;00m vectorize:\n\u001b[0;32m 756\u001b[0m func \u001b[39m=\u001b[39m _vectorize(\n\u001b[0;32m 757\u001b[0m func, signature, output_dtypes\u001b[39m=\u001b[39moutput_dtypes, exclude_dims\u001b[39m=\u001b[39mexclude_dims\n\u001b[0;32m 758\u001b[0m )\n\u001b[1;32m--> 760\u001b[0m result_data \u001b[39m=\u001b[39m func(\u001b[39m*\u001b[39;49minput_data)\n\u001b[0;32m 762\u001b[0m \u001b[39mif\u001b[39;00m signature\u001b[39m.\u001b[39mnum_outputs \u001b[39m==\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[0;32m 763\u001b[0m result_data \u001b[39m=\u001b[39m (result_data,)\n",
"File \u001b[1;32mf:\\Jianshun\\analyseScript\\Analyser\\FitAnalyser.py:1339\u001b[0m, in \u001b[0;36mFitAnalyser._get_fit_std\u001b[1;34m(self, fitResult, params)\u001b[0m\n\u001b[0;32m 1329\u001b[0m \u001b[39m\"\"\"get standard deviation of parameters from fit result\u001b[39;00m\n\u001b[0;32m 1330\u001b[0m \n\u001b[0;32m 1331\u001b[0m \u001b[39m:param fitResult: The result of the fit \u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1336\u001b[0m \u001b[39m:rtype: 1D numpy array\u001b[39;00m\n\u001b[0;32m 1337\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m 1338\u001b[0m func \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mvectorize(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_get_fit_std_single)\n\u001b[1;32m-> 1339\u001b[0m res \u001b[39m=\u001b[39m \u001b[39mtuple\u001b[39;49m(\n\u001b[0;32m 1340\u001b[0m func(fitResult, key)\n\u001b[0;32m 1341\u001b[0m \u001b[39mfor\u001b[39;49;00m key \u001b[39min\u001b[39;49;00m params\n\u001b[0;32m 1342\u001b[0m )\n\u001b[0;32m 1344\u001b[0m \u001b[39mreturn\u001b[39;00m res\n",
"File \u001b[1;32mf:\\Jianshun\\analyseScript\\Analyser\\FitAnalyser.py:1340\u001b[0m, in \u001b[0;36m<genexpr>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m 1329\u001b[0m \u001b[39m\"\"\"get standard deviation of parameters from fit result\u001b[39;00m\n\u001b[0;32m 1330\u001b[0m \n\u001b[0;32m 1331\u001b[0m \u001b[39m:param fitResult: The result of the fit \u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1336\u001b[0m \u001b[39m:rtype: 1D numpy array\u001b[39;00m\n\u001b[0;32m 1337\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m 1338\u001b[0m func \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39mvectorize(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_get_fit_std_single)\n\u001b[0;32m 1339\u001b[0m res \u001b[39m=\u001b[39m \u001b[39mtuple\u001b[39m(\n\u001b[1;32m-> 1340\u001b[0m func(fitResult, key)\n\u001b[0;32m 1341\u001b[0m \u001b[39mfor\u001b[39;00m key \u001b[39min\u001b[39;00m params\n\u001b[0;32m 1342\u001b[0m )\n\u001b[0;32m 1344\u001b[0m \u001b[39mreturn\u001b[39;00m res\n",
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\numpy\\lib\\function_base.py:2163\u001b[0m, in \u001b[0;36mvectorize.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 2160\u001b[0m vargs \u001b[39m=\u001b[39m [args[_i] \u001b[39mfor\u001b[39;00m _i \u001b[39min\u001b[39;00m inds]\n\u001b[0;32m 2161\u001b[0m vargs\u001b[39m.\u001b[39mextend([kwargs[_n] \u001b[39mfor\u001b[39;00m _n \u001b[39min\u001b[39;00m names])\n\u001b[1;32m-> 2163\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_vectorize_call(func\u001b[39m=\u001b[39;49mfunc, args\u001b[39m=\u001b[39;49mvargs)\n",
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\numpy\\lib\\function_base.py:2249\u001b[0m, in \u001b[0;36mvectorize._vectorize_call\u001b[1;34m(self, func, args)\u001b[0m\n\u001b[0;32m 2246\u001b[0m outputs \u001b[39m=\u001b[39m ufunc(\u001b[39m*\u001b[39minputs)\n\u001b[0;32m 2248\u001b[0m \u001b[39mif\u001b[39;00m ufunc\u001b[39m.\u001b[39mnout \u001b[39m==\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m-> 2249\u001b[0m res \u001b[39m=\u001b[39m asanyarray(outputs, dtype\u001b[39m=\u001b[39;49motypes[\u001b[39m0\u001b[39;49m])\n\u001b[0;32m 2250\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 2251\u001b[0m res \u001b[39m=\u001b[39m \u001b[39mtuple\u001b[39m([asanyarray(x, dtype\u001b[39m=\u001b[39mt)\n\u001b[0;32m 2252\u001b[0m \u001b[39mfor\u001b[39;00m x, t \u001b[39min\u001b[39;00m \u001b[39mzip\u001b[39m(outputs, otypes)])\n",
"\u001b[1;31mTypeError\u001b[0m: int() argument must be a string, a bytes-like object or a number, not 'NoneType'"
]
regular backup
2023-07-01 09:21:45 +02:00
}
],
"source": [
"val = fitAnalyser_1.get_fit_value(fitResult_1)\n",
"std = fitAnalyser_1.get_fit_std(fitResult_1)\n",
"\n",
"data = val['condensate_fraction']\n",
"data_std = std['condensate_fraction']\n",
"\n",
"data.plot.errorbar(x=scanAxis[0], hue=scanAxis[1], fmt='o')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<ErrorbarContainer object of 3 artists>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data = data.sel(runs=[0, 1])\n",
"data_mean = calculate_mean(data)\n",
"data_std = calculate_std(data)\n",
"\n",
"data_mean.plot.errorbar(x=scanAxis[0], yerr=data_std, fmt='o')"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"val = fitAnalyser_1.get_fit_value(fitResult_1)\n",
"std = fitAnalyser_1.get_fit_std(fitResult_1)\n",
"\n",
"data = val['BEC_amplitude']\n",
"data_std = std['BEC_amplitude']\n",
"\n",
"data.plot.errorbar(x=scanAxis[0], hue=scanAxis[1], fmt='o')\n",
"plt.show()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Check BEC"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The detected scaning axes and values are: \n",
"\n",
"{'compZ_current_sg': array([0.195, 0.196, 0.197, 0.198, 0.199, 0.2 , 0.201, 0.202, 0.203,\n",
" 0.204]), 'runs': array([0., 1., 2.])}\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"shotNum = \"0001\"\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 = (880, 990)\n",
"imageAnalyser.span = (150, 150)\n",
"imageAnalyser.fraction = (0.1, 0.1)\n",
"\n",
"dataSet_cropOD = imageAnalyser.crop_image(dataSet.OD)\n",
"dataSet_cropOD = imageAnalyser.substract_offset(dataSet_cropOD).load()\n",
"\n",
"Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n",
"Ncount_mean = calculate_mean(Ncount)\n",
"Ncount_std = calculate_std(Ncount)\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='-ob')\n",
"plt.xlabel('comp Z current (A)')\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()\n",
"\n",
"# DB.create_global(shotNum, dataSet)\n",
"# DB.add_data(shotNum, dataSet_cropOD, engine='xarray')"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.plot.facetgrid.FacetGrid at 0x2456516b6a0>"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 3100x900 with 31 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dataSet_cropOD.plot.pcolormesh(cmap='jet', col=scanAxis[0], row=scanAxis[1], vmin=0, vmax=3)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"dataSet_cropOD.chunk((1,1,150,150))#.sel(runs = 0)\n",
"\n",
"fitModel = DensityProfileBEC2dModel()\n",
"fitAnalyser_1 = FitAnalyser(fitModel, fitDim=2)\n",
"\n",
"params = fitAnalyser_1.guess(data, dask=\"parallelized\", guess_kwargs=dict(pureBECThreshold=1.2))\n",
"\n",
"# fitResult_1 = fitAnalyser_1.fit(data, params).load()\n",
"\n",
"# fitCurve = fitAnalyser.eval(fitResult, x=np.range(150), y=np.range(150), dask=\"parallelized\").load()"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table><tr><th> name </th><th> value </th><th> initial value </th><th> min </th><th> max </th><th> vary </th><th> expression </th></tr><tr><td> BEC_amplitude </td><td> 637.041125 </td><td> None </td><td> 0.00000000 </td><td> inf </td><td> True </td><td> </td></tr><tr><td> thermal_amplitude </td><td> 397.705792 </td><td> None </td><td> 0.00000000 </td><td> inf </td><td> True </td><td> </td></tr><tr><td> BEC_centerx </td><td> 72.8975065 </td><td> None </td><td> 24.2944367 </td><td> 121.500576 </td><td> True </td><td> </td></tr><tr><td> BEC_centery </td><td> 71.7963537 </td><td> None </td><td> 55.6292698 </td><td> 87.9634377 </td><td> True </td><td> </td></tr><tr><td> thermal_centerx </td><td> 74.3826292 </td><td> None </td><td> 15.7165440 </td><td> 133.048714 </td><td> True </td><td> </td></tr><tr><td> thermal_centery </td><td> 74.0107099 </td><td> None </td><td> 18.5607659 </td><td> 129.460654 </td><td> True </td><td> </td></tr><tr><td> BEC_sigmax </td><td> 4.86030698 </td><td> None </td><td> -inf </td><td> inf </td><td> False </td><td> 3 * thermal_sigmax - deltax </td></tr><tr><td> BEC_sigmay </td><td> 1.61670840 </td><td> None </td><td> 0.00000000 </td><td> 8.08354198 </td><td> True </td><td> </td></tr><tr><td> thermal_sigmax </td><td> 19.5553617 </td><td> None </td><td> 0.00000000 </td><td> 97.7768087 </td><td> True </td><td> </td></tr><tr><td> thermal_sigmay </td><td> 18.4833146 </td><td> None </td><td> -inf </td><td> inf </td><td> False </td><td> thermalAspectRatio * thermal_sigmax </td></tr><tr><td> deltax </td><td> 53.8057783 </td><td> None </td><td> 0.00000000 </td><td> inf </td><td> True </td><td> </td></tr><tr><td> thermalAspectRatio </td><td> 0.94517887 </td><td> None </td><td> 0.80000000 </td><td> 1.20000000 </td><td> True </td><td> </td></tr><tr><td> condensate_fraction </td><td> 0.61564921 </td><td> None </td><td> -inf </td><td> inf </td><td> False </td><td> BEC_amplitude / (BEC_amplitude + thermal_amplitude) </td></tr></table>"
],
"text/plain": [
"Parameters([('BEC_amplitude', <Parameter 'BEC_amplitude', value=637.0411254483137, bounds=[0:inf]>), ('thermal_amplitude', <Parameter 'thermal_amplitude', value=397.7057922330245, bounds=[0:inf]>), ('BEC_centerx', <Parameter 'BEC_centerx', value=72.8975065177683, bounds=[24.294436732015214:121.5005763035214]>), ('BEC_centery', <Parameter 'BEC_centery', value=71.79635371752663, bounds=[55.629269760909324:87.96343767414393]>), ('thermal_centerx', <Parameter 'thermal_centerx', value=74.38262924274176, bounds=[15.716544002995732:133.04871448248778]>), ('thermal_centery', <Parameter 'thermal_centery', value=74.01070987487839, bounds=[18.560765933754475:129.4606538160023]>), ('BEC_sigmax', <Parameter 'BEC_sigmax', value=4.860306978575309, bounds=[-inf:inf], expr='3 * thermal_sigmax - deltax'>), ('BEC_sigmay', <Parameter 'BEC_sigmay', value=1.6167083956617305, bounds=[0:8.083541978308652]>), ('thermal_sigmax', <Parameter 'thermal_sigmax', value=19.555361746582008, bounds=[0:97.77680873291004]>), ('thermal_sigmay', <Parameter 'thermal_sigmay', value=18.483314647041304, bounds=[-inf:inf], expr='thermalAspectRatio * thermal_sigmax'>), ('deltax', <Parameter 'deltax', value=53.805778261170715, bounds=[0:inf]>), ('thermalAspectRatio', <Parameter 'thermalAspectRatio', value=0.945178866367528, bounds=[0.8:1.2]>), ('condensate_fraction', <Parameter 'condensate_fraction', value=0.6156492129261868, bounds=[-inf:inf], expr='BEC_amplitude / (BEC_amplitude + thermal_amplitude)'>)])"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"params.sel(runs=0, compZ_current_sg=0.195).item()"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"fitResult_1 = fitAnalyser_1.fit(data.sel(runs=0, compZ_current_sg=0.195), params.sel(runs=0, compZ_current_sg=0.195)).load()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\data\\AppData\\Roaming\\Python\\Python39\\site-packages\\numpy\\lib\\function_base.py:2246: RuntimeWarning: invalid value encountered in _get_fit_full_result_single (vectorized)\n",
" outputs = ufunc(*inputs)\n"
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (compZ_current_sg: 10, runs: 3)\n",
"Coordinates:\n",
" * compZ_current_sg (compZ_current_sg) float64 0.195 0.196 ... 0.203 0.204\n",
" * runs (runs) float64 0.0 1.0 2.0\n",
"Data variables: (12/13)\n",
" BEC_amplitude (compZ_current_sg, runs) object 689.6738589244688+/-...\n",
" thermal_amplitude (compZ_current_sg, runs) object 0.0+/-nan ... 0.0+/-nan\n",
" BEC_centerx (compZ_current_sg, runs) object 72.72766759442194+/-...\n",
" BEC_centery (compZ_current_sg, runs) object 71.93797641698205+/-...\n",
" thermal_centerx (compZ_current_sg, runs) object 73.54413421644256+/-...\n",
" thermal_centery (compZ_current_sg, runs) object 74.08047311882555+/-...\n",
" ... ...\n",
" BEC_sigmay (compZ_current_sg, runs) object 8.315587888174065+/-...\n",
" thermal_sigmax (compZ_current_sg, runs) object 86.82572665210536+/-...\n",
" thermal_sigmay (compZ_current_sg, runs) object 81.25225556328859+/-...\n",
" deltax (compZ_current_sg, runs) object 233.58068450863982+/...\n",
" thermalAspectRatio (compZ_current_sg, runs) object 0.9358085292951404+/...\n",
" condensate_fraction (compZ_current_sg, runs) object 1.0+/-nan ... 1.0+/-nan\n",
"Attributes:\n",
" IMAGE_SUBCLASS: IMAGE_GRAYSCALE\n",
" IMAGE_VERSION: 1.2\n",
" IMAGE_WHITE_IS_ZERO: 0\n",
" x_start: 805\n",
" x_end: 955\n",
" y_end: 1065\n",
" y_start: 915\n",
" x_center: 880\n",
" y_center: 990\n",
" x_span: 150\n",
" y_span: 150</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-85b78723-b4e6-419a-a82e-b67f9b95e290' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-85b78723-b4e6-419a-a82e-b67f9b95e290' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>compZ_current_sg</span>: 10</li><li><span class='xr-has-index'>runs</span>: 3</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-540d96e0-a316-411f-a919-b81185a1f7fc' class='xr-section-summary-in' type='checkbox' checked><label for='section-540d96e0-a316-411f-a919-b81185a1f7fc' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>compZ_current_sg</span></div><div class='xr-var-dims'>(compZ_current_sg)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.195 0.196 0.197 ... 0.203 0.204</div><input id='attrs-8c24d1d9-4b77-44c7-ae84-a1051fc96235' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8c24d1d9-4b77-44c7-ae84-a1051fc96235' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02078451-79f1-43e6-93d0-0b16d2b3cddb' class='xr-var-data-in' type='checkbox'><label for='data-02078451-79f1-43e6-93d0-0b16d2b3cddb' 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([0.195, 0.196, 0.197, 0.198, 0.199, 0.2 , 0.201, 0.202, 0.203, 0.204])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>runs</span></div><div class='xr-var-dims'>(runs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0</div><input id='attrs-9244fdb5-8138-4439-8b7b-0c346bc261a9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9244fdb5-8138-4439-8b7b-0c346bc261a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ab36985-c706-478d-9816-a7932135ffba' class='xr-var-data-in' type='checkbox'><label for='data-5ab36985-c706-478d-9816-a7932135ffba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0., 1., 2.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1c56e838-5fa8-4a9d-b01b-8bf89a7464c2' class='xr-section-summary-in' type='checkbox' checked><label for='section-1c56e838-5fa8-4a9d-b01b-8bf89a7464c2' class='xr-section-summary' >Data variables: <span>(13)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>BEC_amplitude</span></div><div class='xr-var-dims'>(compZ_current_sg, runs)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>689.6738589244688+/-nan ... 505....</div><input id='attrs-d470ea5c-1048-44af-99d4-278d4c605dce' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d470ea5c-1048-44af-99d4-278d4c605dce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33ccac21-2e18-4a22-910d-f6b2e3d25062' class='xr-var-data-in' type='checkbox'><label for='data-33ccac21-2e18-4a22-910d-f6b2e3d25062' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#
" 678.39907423435+/-nan],\n",
" [692.5450583097982+/-nan, 709.7153109568274+/-nan,\n",
" 693.070902295569+/-nan],\n",
" [671.9030647731805+/-nan, 763.5527107892661+/-nan,\n",
" 701.6228860278444+/-nan],\n",
" [703.0376002067666+/-nan, 691.0256217303906+/-nan,\n",
" 698.0049200957969+/-nan],\n",
" [713.8173887255239+/-nan, 680.8242923026909+/-nan,\n",
" 628.8406808124307+/-nan],\n",
" [671.7766869904086+/-nan, 668.5923608192708+/-nan,\n",
" 698.6631413542333+/-nan],\n",
" [659.0140752932108+/-nan, 596.3510167435243+/-nan,\n",
" 632.2665864507762+/-nan],\n",
" [619.2762490805766+/-nan, 617.946024341381+/-nan,\n",
" 640.2602092235951+/-nan],\n",
" [636.4920366481963+/-nan, 586.3450868568624+/-nan,\n",
" 572.5404723960091+/-nan],\n",
" [562.1581960978315+/-nan, 601.8578305946143+/-nan,\n",
" 505.5308972550412+/-nan]], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thermal_amplitude</span></div><div class='xr-var-dims'>(compZ_current_sg, runs)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0.0+/-nan 0.0+/-nan ... 0.0+/-nan</div><input id='attrs-755e727f-0bf0-4f3e-843a-2dab00cba93a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-755e727f-0bf0-4f3e-843a-2dab00cba93a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6b5f3af-f10c-4615-8dfe-c03b44401263' class='xr-var-data-in' type='checkbox'><label for='data-a6b5f3af-f10c-4615-8dfe-c03b44401263' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.0+/-nan, 0.0+/-nan, 0.0+/-nan],\n",
" [0.0+/-nan, 0.0+/-nan, 0.0+/-nan],\n",
" [0.0+/-nan, 0.0+/-nan, 0.0+/-nan],\n",
" [0.0+/-nan, 0.0+/-nan, 0.0+/-nan],\n",
" [0.0+/-nan, 0.0+/-nan, 0.0+/-nan],\n",
" [0.0+/-nan, 0.0+/-nan, 0.0+/-nan],\n",
" [0.0+/-nan, 0.0+/-nan, 0.0+/-nan],\n",
" [0.0+/-nan, 0.0+/-nan, 0.0+/-nan],\n",
" [0.0+/-nan, 0.0+/-nan, 0.0+/-nan],\n",
" [0.0+/-nan, 0.0+/-nan, 0.0+/-nan]], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>BEC_centerx</span></div><div class='xr-var-dims'>(compZ_current_sg, runs)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>72.72766759442194+/-nan ... 70.0...</div><input id='attrs-35f63dc2-bbf3-4c9c-821f-9c1fa0dbfaaf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-35f63dc2-bbf3-4c9c-821f-9c1fa0dbfaaf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e10eb00-1ddb-4634-8bd8-bdbf44e73678' class='xr-var-data-in' type='checkbox'><label for='data-7e10eb00-1ddb-4634-8bd8-bdbf44e73678' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[72.72766759442194+/-nan, 72.71934488859581+/-nan,\n",
" 71.40355589999807+/-nan],\n",
" [70.83342680729962+/-nan, 68.66496511093739+/-nan,\n",
" 70.29374802397487+/-nan],\n",
" [70.76765532584542+/-nan, 71.03271256497091+/-nan,\n",
" 72.67427312885016+/-nan],\n",
" [72.20891853944283+/-nan, 70.38077535505248+/-nan,\n",
" 70.7339464630603+/-nan],\n",
" [70.3178117834581+/-nan, 72.32035180100357+/-nan,\n",
" 71.63296400536021+/-nan],\n",
" [70.87533638684343+/-nan, 70.88128055305225+/-nan,\n",
" 72.52001102186628+/-nan],\n",
" [73.06561017377334+/-nan, 70.0741947920574+/-nan,\n",
" 71.6924852554308+/-nan],\n",
" [72.1222142035208+/-nan, 71.82506798407782+/-nan,\n",
" 69.85696478117038+/-nan],\n",
" [71.15702324146866+/-nan, 70.15080743468083+/-nan,\n",
" 71.35706452253848+/-nan],\n",
" [71.41542581324093+/-nan, 70.62063274120308+/-nan,\n",
" 70.08904848938136+/-nan]], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>BEC_centery</span></div><div class='xr-var-dims'>(compZ_current_sg, runs)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>71.93797641698205+/-nan ... 70.8...</div><input id='attrs-6ea8e18b-3805-4152-bdd7-86bae1e6e7ee' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6ea8e18b-3805-4152-bdd7-86bae1e6e7ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c894ede5-ddb7-4a01-a88b-3721bc0f82ea' class='xr-var-data-in' type='checkbox'><label for='data-c894ede5-ddb7-4a01-a88b-3721bc0f82ea' 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([[71.93797641698205+/-nan, 72.6381937001966+/-nan,\n",
" 72.02512192557639+/-nan],\n",
" [71.55174018891496+/-nan, 70.87577074483377+/-nan,\n",
" 72.4977446833421+/-nan],\n",
" [71.34716548816778+/-nan, 70.44251861831816+/-nan,\n",
" 70.78633409098687+/-nan],\n",
" [70.37015516272982+/-nan, 72.97486141381597+/-nan,\n",
" 71.85640793410928+/-nan],\n",
" [71.3331531748868+/-nan, 69.31373705739671+/-nan,\n",
" 71.93481571209645+/-nan],\n",
" [69.33385592249886+/-nan, 71.17080956684265+/-nan,\n",
" 73.27962703564992+/-nan],\n",
" [72.41744755655539+/-nan, 71.47251010551959+/-nan,\n",
" 71.42576907220035+/-nan],\n",
" [69.73001451172685+/-nan, 70.44457455629144+/-nan,\n",
" 72.22164046797901+/-nan],\n",
" [72.21512332594007+/-nan, 70.69934127336529+/-nan,\n",
" 71.13947812812009+/-nan],\n",
" [69.56836922034678+/-nan, 70.64440607038694+/-nan,\n",
" 70.83100648311071+/-nan]], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thermal_centerx</span></div><div class='xr-var-dims'>(compZ_current_sg, runs)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>73.54413421644256+/-nan ... 70.9...</div><input id='attrs-41a5756f-acda-42a0-a469-e9069b9497c1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-41a5756f-acda-42a0-a469-e9069b9497c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c15db94d-392d-48f0-acb4-1cd4ff293b33' class='xr-var-data-in' type='checkbox'><label for='data-c15db94d-392d-48f0-acb4-1cd4ff293b33' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[73.54413421644256+/-nan, 73.30274113050602+/-nan,\n",
" 71.92304853372369+/-nan],\n",
" [71.00483901753078+/-nan, 68.9161916636921+/-nan,\n",
" 69.86466122796406+/-nan],\n",
" [70.79675629053958+/-nan, 71.49749669681296+/-nan,\n",
" 72.82082446441498+/-nan],\n",
" [73.2807451001814+/-nan, 71.68639061568933+/-nan,\n",
" 71.53256545890429+/-nan],\n",
" [70.90654770148933+/-nan, 72.19906890315161+/-nan,\n",
" 71.67310443723218+/-nan],\n",
" [70.73737318190581+/-nan, 71.46506714732851+/-nan,\n",
" 74.05600639847734+/-nan],\n",
" [72.77539585636279+/-nan, 70.47520515991539+/-nan,\n",
" 72.51736039865762+/-nan],\n",
" [72.32662798141047+/-nan, 72.24230286579716+/-nan,\n",
" 70.73841779714252+/-nan],\n",
" [71.16743021062787+/-nan, 70.37056367958343+/-nan,\n",
" 71.15981995834142+/-nan],\n",
" [71.90161813663846+/-nan, 71.46282211339187+/-nan,\n",
" 70.91060958388397+/-nan]], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thermal_centery</span></div><div class='xr-var-dims'>(compZ_current_sg, runs)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>74.08047311882555+/-nan ... 72.3...</div><input id='attrs-e6b8a1b9-c35e-4ba8-a41f-f1bc5a5eab6a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e6b8a1b9-c35e-4ba8-a41f-f1bc5a5eab6a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f282012-b605-4fc0-846b-557b91b11120' class='xr-var-data-in' type='checkbox'><label for='data-2f282012-b605-4fc0-846b-557b91b11120' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[74.08047311882555+/-nan, 73.62629217434753+/-nan,\n",
" 73.62520544473455+/-nan],\n",
" [72.87625912011066+/-nan, 72.6999927167971+/-nan,\n",
" 73.76435921973764+/-nan],\n",
" [72.6033358300646+/-nan, 72.56878492161754+/-nan,\n",
" 72.58676659949938+/-nan],\n",
" [72.74308214550841+/-nan, 74.18604052561388+/-nan,\n",
" 73.12900436786026+/-nan],\n",
" [72.620379447618+/-nan, 71.40143590558816+/-nan,\n",
" 73.40067743800589+/-nan],\n",
" [70.92167921588822+/-nan, 73.42198207438085+/-nan,\n",
" 75.22592665604574+/-nan],\n",
" [74.13075036384711+/-nan, 72.88427291602504+/-nan,\n",
" 73.12795529075576+/-nan],\n",
" [72.51777171279258+/-nan, 72.93096162147843+/-nan,\n",
" 74.04389516161456+/-nan],\n",
" [73.75867853397996+/-nan, 72.5554168542469+/-nan,\n",
" 72.86426197278085+/-nan],\n",
" [72.12097010480343+/-nan, 72.12419149184741+/-nan,\n",
" 72.33848082985858+/-nan]], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>BEC_sigmax</span></div><div class='xr-var-dims'>(compZ_current_sg, runs)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>26.896495447676273+/-nan ... 25....</div><input id='attrs-a11274a0-8dd4-4972-a53c-e8b7172975be' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a11274a0-8dd4-4972-a53c-e8b7172975be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b92aba32-6f9f-41b6-af1c-c3d072d23a36' class='xr-var-data-in' type='checkbox'><label for='data-b92aba32-6f9f-41b6-af1c-c3d072d23a36' 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([[26.896495447676273+/-nan, 27.0325742082552+/-nan,\n",
" 26.479676881987302+/-nan],\n",
" [26.315693756433717+/-nan, 27.244908144332072+/-nan,\n",
" 27.02759984086839+/-nan],\n",
" [26.713107942138123+/-nan, 26.9711867062004+/-nan,\n",
" 26.987293340194242+/-nan],\n",
" [27.641154691028845+/-nan, 26.470827885941674+/-nan,\n",
" 26.38327779768096+/-nan],\n",
" [26.759769526175404+/-nan, 26.660578970567798+/-nan,\n",
" 26.3428112841589+/-nan],\n",
" [26.78446860717432+/-nan, 27.111706894893814+/-nan,\n",
" 26.6854888514423+/-nan],\n",
" [26.516327819324886+/-nan, 26.050621310128548+/-nan,\n",
" 27.45170198499784+/-nan],\n",
" [26.34825532680844+/-nan, 26.719568712533402+/-nan,\n",
" 26.426454164620168+/-nan],\n",
" [25.85371437669528+/-nan, 26.865199333328206+/-nan,\n",
" 25.83529222770207+/-nan],\n",
" [26.253152412705276+/-nan, 26.347280678246875+/-nan,\n",
" 25.591605982303747+/-nan]], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>BEC_sigmay</span></div><div class='xr-var-dims'>(compZ_current_sg, runs)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>8.315587888174065+/-nan ... 7.70...</div><input id='attrs-9c71929f-6fb4-4a80-b512-f44ec38124d2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9c71929f-6fb4-4a80-b512-f44ec38124d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-471e3f5f-26dc-4346-9fa4-c59720755910' class='xr-var-data-in' type='checkbox'><label for='data-471e3f5f-26dc-4346-9fa4-c59720755910' 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([[8.315587888174065+/-nan, 8.782235758255771+/-nan,\n",
" 8.437766586779095+/-nan],\n",
" [8.529205764813772+/-nan, 8.511414989849603+/-nan,\n",
" 8.429409693985567+/-nan],\n",
" [8.967636353540735+/-nan, 9.164394391604722+/-nan,\n",
" 8.606991456397717+/-nan],\n",
" [9.233793777545207+/-nan, 8.582932310143866+/-nan,\n",
" 8.265777917899998+/-nan],\n",
" [8.397604136244158+/-nan, 8.59086220029662+/-nan,\n",
" 8.157109611196294+/-nan],\n",
" [8.627906789844802+/-nan, 8.256198791171032+/-nan,\n",
" 8.640826875042585+/-nan],\n",
" [8.14338113151272+/-nan, 8.220716306663133+/-nan,\n",
" 8.48807755362483+/-nan],\n",
" [8.246264238222604+/-nan, 8.634373369783843+/-nan,\n",
" 8.247200673198572+/-nan],\n",
" [10.334307171965747+/-nan, 8.022444912030716+/-nan,\n",
" 8.115392321803794+/-nan],\n",
" [8.495199941648103+/-nan, 8.485154844745717+/-nan,\n",
" 7.709214756106329+/-nan]], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thermal_sigmax</span></div><div class='xr-var-dims'>(compZ_current_sg, runs)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>86.82572665210536+/-nan ... 82.5...</div><input id='attrs-be0cccab-618b-4a16-bbea-12184db7aec1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-be0cccab-618b-4a16-bbea-12184db7aec1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25d3f76e-aef5-4131-a06c-f722f065d250' class='xr-var-data-in' type='checkbox'><label for='data-25d3f76e-aef5-4131-a06c-f722f065d250' 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([[86.82572665210536+/-nan, 71.59852761893276+/-nan,\n",
" 83.87370192103053+/-nan],\n",
" [81.43219928653899+/-nan, 92.66676021573817+/-nan,\n",
" 76.37885478459056+/-nan],\n",
" [78.24219536265402+/-nan, 76.26902196708801+/-nan,\n",
" 73.17435018641264+/-nan],\n",
" [70.8440059901167+/-nan, 84.83185559838373+/-nan,\n",
" 89.59878886970975+/-nan],\n",
" [92.25586777309792+/-nan, 75.93305747810473+/-nan,\n",
" 75.04395198742893+/-nan],\n",
" [86.55825348791429+/-nan, 87.95181807386554+/-nan,\n",
" 92.1624263432616+/-nan],\n",
" [84.09836215498714+/-nan, 80.74406538220822+/-nan,\n",
" 83.62396899899504+/-nan],\n",
" [72.55523806865114+/-nan, 84.46730135582732+/-nan,\n",
" 73.87356268023888+/-nan],\n",
" [90.73267205327764+/-nan, 74.50432786545196+/-nan,\n",
" 87.60209625408987+/-nan],\n",
" [77.83520249094289+/-nan, 83.7594063318159+/-nan,\n",
" 82.57851746784988+/-nan]], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thermal_sigmay</span></div><div class='xr-var-dims'>(compZ_current_sg, runs)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>81.25225556328859+/-nan ... 79.4...</div><input id='attrs-e6e872db-477a-424b-a1cd-2fd2b6688e06' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e6e872db-477a-424b-a1cd-2fd2b6688e06' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a3e3279-2918-4ff7-bf45-bcbd3573be14' class='xr-var-data-in' type='checkbox'><label for='data-4a3e3279-2918-4ff7-bf45-bcbd3573be14' 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([[81.25225556328859+/-nan, 61.95483669688191+/-nan,\n",
" 78.75323107717729+/-nan],\n",
" [74.70732651156752+/-nan, 86.87150425190022+/-nan,\n",
" 68.50907599986216+/-nan],\n",
" [68.76379284998367+/-nan, 68.36583766085947+/-nan,\n",
" 70.0952760020868+/-nan],\n",
" [63.712746170899784+/-nan, 76.46633870305563+/-nan,\n",
" 81.5164971076558+/-nan],\n",
" [85.57776753084637+/-nan, 70.11409857464966+/-nan,\n",
" 65.9021485593791+/-nan],\n",
" [79.78542663388451+/-nan, 83.10109517361872+/-nan,\n",
" 82.35948784709004+/-nan],\n",
" [76.94153076049392+/-nan, 73.31782172661772+/-nan,\n",
" 76.97840260007256+/-nan],\n",
" [70.23041620669188+/-nan, 80.23679101803242+/-nan,\n",
" 66.09756880496701+/-nan],\n",
" [108.87920646393316+/-nan, 64.6550573761848+/-nan,\n",
" 80.2343621274528+/-nan],\n",
" [72.15165082016149+/-nan, 77.71732820925727+/-nan,\n",
" 79.45951777679701+/-nan]], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>deltax</span></div><div class='xr-var-dims'>(compZ_current_sg, runs)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>233.58068450863982+/-nan ... 222...</div><input id='attrs-21757c6e-666f-47c7-90f8-f9ba64d42fe5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-21757c6e-666f-47c7-90f8-f9ba64d42fe5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f85f186-6be0-4384-be12-27ac158005ba' class='xr-var-data-in' type='checkbox'><label for='data-3f85f186-6be0-4384-be12-27ac158005ba' 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([[233.58068450863982+/-nan, 187.7630086485431+/-nan,\n",
" 225.14142888110428+/-nan],\n",
" [217.98090410318324+/-nan, 250.7553725028824+/-nan,\n",
" 202.1089645129033+/-nan],\n",
" [208.01347814582394+/-nan, 201.83587919506363+/-nan,\n",
" 192.53575721904366+/-nan],\n",
" [184.89086327932125+/-nan, 228.0247389092095+/-nan,\n",
" 242.41308881144826+/-nan],\n",
" [250.00783379311835+/-nan, 201.1385934637464+/-nan,\n",
" 198.78904467812788+/-nan],\n",
" [232.89029185656855+/-nan, 236.74374732670282+/-nan,\n",
" 249.80179017834251+/-nan],\n",
" [225.77875864563654+/-nan, 216.1815748364961+/-nan,\n",
" 223.42020501198726+/-nan],\n",
" [191.31745887914497+/-nan, 226.68233535494855+/-nan,\n",
" 195.19423387609646+/-nan],\n",
" [246.34430178313767+/-nan, 196.64778426302766+/-nan,\n",
" 236.97099653456755+/-nan],\n",
" [207.2524550601234+/-nan, 224.9309383172008+/-nan,\n",
" 222.1439464212459+/-nan]], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thermalAspectRatio</span></div><div class='xr-var-dims'>(compZ_current_sg, runs)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0.9358085292951404+/-nan ... 0.9...</div><input id='attrs-0b0eba8b-9176-4ab8-8eb9-e813489b2880' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0b0eba8b-9176-4ab8-8eb9-e813489b2880' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f8a61623-4da8-4c3b-ad5c-1c19d6a230e4' class='xr-var-data-in' type='checkbox'><label for='data-f8a61623-4da8-4c3b-ad5c-1c19d6a230e4' 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([[0.9358085292951404+/-nan, 0.8653088095138317+/-nan,\n",
" 0.9389502224585925+/-nan],\n",
" [0.917417522381922+/-nan, 0.937461329711474+/-nan,\n",
" 0.8969639070011807+/-nan],\n",
" [0.8788581727706161+/-nan, 0.8963775317633028+/-nan,\n",
" 0.9579214003748328+/-nan],\n",
" [0.8993385577290508+/-nan, 0.9013870811109844+/-nan,\n",
" 0.9097946315568302+/-nan],\n",
" [0.9276132737846415+/-nan, 0.9233672514091381+/-nan,\n",
" 0.8781806769773902+/-nan],\n",
" [0.9217541183987102+/-nan, 0.9448479519073387+/-nan,\n",
" 0.8936341100693223+/-nan],\n",
" [0.9148992773330864+/-nan, 0.9080273748858471+/-nan,\n",
" 0.9205303637405402+/-nan],\n",
" [0.96795790457252+/-nan, 0.9499154078573739+/-nan,\n",
" 0.8947391516917874+/-nan],\n",
" [1.2+/-nan, 0.8678027066151909+/-nan, 0.9158954586512751+/-nan],\n",
" [0.9269796764331312+/-nan, 0.9278638855363573+/-nan,\n",
" 0.9622298899678458+/-nan]], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>condensate_fraction</span></div><div class='xr-var-dims'>(compZ_current_sg, runs)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1.0+/-nan 1.0+/-nan ... 1.0+/-nan</div><input id='attrs-ab430e3e-e622-44e7-af94-1a3483b6e081' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ab430e3e-e622-44e7-af94-1a3483b6e081' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9aa9f244-9242-4a77-b9fa-67dba69fe136' class='xr-var-data-in' type='checkbox'><label for='data-9aa9f244-9242-4a77-b9fa-67dba69fe136' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.0+/-nan, 1.0+/-nan, 1.0+/-nan],\n",
" [1.0+/-nan, 1.0+/-nan, 1.0+/-nan],\n",
" [1.0+/-nan, 1.0+/-nan, 1.0+/-nan],\n",
" [1.0+/-nan, 1.0+/-nan, 1.0+/-nan],\n",
" [1.0+/-nan, 1.0+/-nan, 1.0+/-nan],\n",
" [1.0+/-nan, 1.0+/-nan, 1.0+/-nan],\n",
" [1.0+/-nan, 1.0+/-nan, 1.0+/-nan],\n",
" [1.0+/-nan, 1.0+/-nan, 1.0+/-nan],\n",
" [1.0+/-nan, 1.0+/-nan, 1.0+/-nan],\n",
" [1.0+/-nan, 1.0+/-nan, 1.0+/-nan]], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e3cf7b9f-e5e2-47ae-a96e-78c485f96b00' class='xr-section-summary-in' type='checkbox' ><label for='section-e3cf7b9f-e5e2-47ae-a96e-78c485f96b00' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>compZ_current_sg</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2e427862-3c88-47f3-8ccf-18f2738047b4' class='xr-index-data-in' type='checkbox'/><label for='index-2e427862-3c88-47f3-8ccf-18f2738047b4' 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([0.195, 0.196, 0.197, 0.198, 0.199, 0.2, 0.201, 0.202, 0.203,\n",
" 0.204],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;compZ_current_sg&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>runs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-264b65b5-3e19-4204-ae7b-e3b355793b39' class='xr-index-data-in' type='checkbox'/><label for='index-264b65b5-3e19-4204-ae7b-e3b355793b39' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([0.0, 1.0, 2.0], dtype=&#x27;float64&#x27;, name=&#x27;runs&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-070dda3f-03d2-443a-9f43-b2edc6940404' class='xr-section-summary-in' type='checkbox' ><label for='section-070dda3f-03d2-443a-9f43-b2edc6940404' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>IMAGE_SUBCLASS :</span></dt><dd>IMAGE_GRAYSCALE</dd><dt><span>IMAGE_VERSION :</span></dt><dd>1.2</dd><dt><span>IMAGE_WHITE_IS_ZERO :</span></dt><dd>0</dd><dt><span>x_start :</span></dt><dd>805</dd><dt><span>x_end :</span></dt><dd>955</dd><dt><span>y_end :</span></dt><dd>1065</dd><dt><span>y_start :</span></dt><dd>915</dd><dt><span>x_center :</span></dt><dd>880</dd><dt><span>y_center :</span></dt><dd>990</dd><dt><span>x_span :</span></dt><dd>150</dd><dt><span>y_span :</span></dt><dd>150</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (compZ_current_sg: 10, runs: 3)\n",
"Coordinates:\n",
" * compZ_current_sg (compZ_current_sg) float64 0.195 0.196 ... 0.203 0.204\n",
" * runs (runs) float64 0.0 1.0 2.0\n",
"Data variables: (12/13)\n",
" BEC_amplitude (compZ_current_sg, runs) object 689.6738589244688+/-...\n",
" thermal_amplitude (compZ_current_sg, runs) object 0.0+/-nan ... 0.0+/-nan\n",
" BEC_centerx (compZ_current_sg, runs) object 72.72766759442194+/-...\n",
" BEC_centery (compZ_current_sg, runs) object 71.93797641698205+/-...\n",
" thermal_centerx (compZ_current_sg, runs) object 73.54413421644256+/-...\n",
" thermal_centery (compZ_current_sg, runs) object 74.08047311882555+/-...\n",
" ... ...\n",
" BEC_sigmay (compZ_current_sg, runs) object 8.315587888174065+/-...\n",
" thermal_sigmax (compZ_current_sg, runs) object 86.82572665210536+/-...\n",
" thermal_sigmay (compZ_current_sg, runs) object 81.25225556328859+/-...\n",
" deltax (compZ_current_sg, runs) object 233.58068450863982+/...\n",
" thermalAspectRatio (compZ_current_sg, runs) object 0.9358085292951404+/...\n",
" condensate_fraction (compZ_current_sg, runs) object 1.0+/-nan ... 1.0+/-nan\n",
"Attributes:\n",
" IMAGE_SUBCLASS: IMAGE_GRAYSCALE\n",
" IMAGE_VERSION: 1.2\n",
" IMAGE_WHITE_IS_ZERO: 0\n",
" x_start: 805\n",
" x_end: 955\n",
" y_end: 1065\n",
" y_start: 915\n",
" x_center: 880\n",
" y_center: 990\n",
" x_span: 150\n",
" y_span: 150"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fitAnalyser_1.get_fit_full_result(fitResult_1)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"fitCurve = fitAnalyser_1.eval(fitResult_1, x=np.arange(150), y=np.arange(150), dask=\"parallelized\").load()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.plot.facetgrid.FacetGrid at 0x2457e7e1d00>"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<Figure size 3100x900 with 31 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fitCurve.plot.pcolormesh(cmap='jet', col=scanAxis[0], row=scanAxis[1])"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"val = fitAnalyser_1.get_fit_value(fitResult_1)\n",
"std = fitAnalyser_1.get_fit_std(fitResult_1)\n",
"\n",
"data = val['condensate_fraction']\n",
"data_std = std['condensate_fraction']\n",
"\n",
"data.plot.errorbar(x=scanAxis[0], hue=scanAxis[1], fmt='o')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<ErrorbarContainer object of 3 artists>"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data = data.sel(runs=[0, 1])\n",
"data_mean = calculate_mean(data)\n",
"data_std = calculate_std(data)\n",
"\n",
"data_mean.plot.errorbar(x=scanAxis[0], yerr=data_std, fmt='o')"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\data\\AppData\\Roaming\\Python\\Python39\\site-packages\\numpy\\lib\\function_base.py:2246: RuntimeWarning: invalid value encountered in _get_fit_full_result_single (vectorized)\n",
" outputs = ufunc(*inputs)\n"
]
},
{
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" grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-section-item input {\n",
" display: none;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
" color: var(--xr-disabled-color);\n",
"}\n",
"\n",
".xr-section-item input:enabled + label {\n",
" cursor: pointer;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
"\n",
".xr-section-summary {\n",
" grid-column: 1;\n",
" color: var(--xr-font-color2);\n",
" font-weight: 500;\n",
"}\n",
"\n",
".xr-section-summary > span {\n",
" display: inline-block;\n",
" padding-left: 0.5em;\n",
"}\n",
"\n",
".xr-section-summary-in:disabled + label {\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-section-summary-in + label:before {\n",
" display: inline-block;\n",
" content: 'â–º';\n",
" font-size: 11px;\n",
" width: 15px;\n",
" text-align: center;\n",
"}\n",
"\n",
".xr-section-summary-in:disabled + label:before {\n",
" color: var(--xr-disabled-color);\n",
"}\n",
"\n",
".xr-section-summary-in:checked + label:before {\n",
" content: 'â–¼';\n",
"}\n",
"\n",
".xr-section-summary-in:checked + label > span {\n",
" display: none;\n",
"}\n",
"\n",
".xr-section-summary,\n",
".xr-section-inline-details {\n",
" padding-top: 4px;\n",
" padding-bottom: 4px;\n",
"}\n",
"\n",
".xr-section-inline-details {\n",
" grid-column: 2 / -1;\n",
"}\n",
"\n",
".xr-section-details {\n",
" display: none;\n",
" grid-column: 1 / -1;\n",
" margin-bottom: 5px;\n",
"}\n",
"\n",
".xr-section-summary-in:checked ~ .xr-section-details {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-array-wrap {\n",
" grid-column: 1 / -1;\n",
" display: grid;\n",
" grid-template-columns: 20px auto;\n",
"}\n",
"\n",
".xr-array-wrap > label {\n",
" grid-column: 1;\n",
" vertical-align: top;\n",
"}\n",
"\n",
".xr-preview {\n",
" color: var(--xr-font-color3);\n",
"}\n",
"\n",
".xr-array-preview,\n",
".xr-array-data {\n",
" padding: 0 5px !important;\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-array-data,\n",
".xr-array-in:checked ~ .xr-array-preview {\n",
" display: none;\n",
"}\n",
"\n",
".xr-array-in:checked ~ .xr-array-data,\n",
".xr-array-preview {\n",
" display: inline-block;\n",
"}\n",
"\n",
".xr-dim-list {\n",
" display: inline-block !important;\n",
" list-style: none;\n",
" padding: 0 !important;\n",
" margin: 0;\n",
"}\n",
"\n",
".xr-dim-list li {\n",
" display: inline-block;\n",
" padding: 0;\n",
" margin: 0;\n",
"}\n",
"\n",
".xr-dim-list:before {\n",
" content: '(';\n",
"}\n",
"\n",
".xr-dim-list:after {\n",
" content: ')';\n",
"}\n",
"\n",
".xr-dim-list li:not(:last-child):after {\n",
" content: ',';\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-has-index {\n",
" font-weight: bold;\n",
"}\n",
"\n",
".xr-var-list,\n",
".xr-var-item {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-var-item > div,\n",
".xr-var-item label,\n",
".xr-var-item > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-even);\n",
" margin-bottom: 0;\n",
"}\n",
"\n",
".xr-var-item > .xr-var-name:hover span {\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-var-list > li:nth-child(odd) > div,\n",
".xr-var-list > li:nth-child(odd) > label,\n",
".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-odd);\n",
"}\n",
"\n",
".xr-var-name {\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-var-dims {\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-var-dtype {\n",
" grid-column: 3;\n",
" text-align: right;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-var-preview {\n",
" grid-column: 4;\n",
"}\n",
"\n",
".xr-index-preview {\n",
" grid-column: 2 / 5;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-var-name,\n",
".xr-var-dims,\n",
".xr-var-dtype,\n",
".xr-preview,\n",
".xr-attrs dt {\n",
" white-space: nowrap;\n",
" overflow: hidden;\n",
" text-overflow: ellipsis;\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-var-name:hover,\n",
".xr-var-dims:hover,\n",
".xr-var-dtype:hover,\n",
".xr-attrs dt:hover {\n",
" overflow: visible;\n",
" width: auto;\n",
" z-index: 1;\n",
"}\n",
"\n",
".xr-var-attrs,\n",
".xr-var-data,\n",
".xr-index-data {\n",
" display: none;\n",
" background-color: var(--xr-background-color) !important;\n",
" padding-bottom: 5px !important;\n",
"}\n",
"\n",
".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
".xr-var-data-in:checked ~ .xr-var-data,\n",
".xr-index-data-in:checked ~ .xr-index-data {\n",
" display: block;\n",
"}\n",
"\n",
".xr-var-data > table {\n",
" float: right;\n",
"}\n",
"\n",
".xr-var-name span,\n",
".xr-var-data,\n",
".xr-index-name div,\n",
".xr-index-data,\n",
".xr-attrs {\n",
" padding-left: 25px !important;\n",
"}\n",
"\n",
".xr-attrs,\n",
".xr-var-attrs,\n",
".xr-var-data,\n",
".xr-index-data {\n",
" grid-column: 1 / -1;\n",
"}\n",
"\n",
"dl.xr-attrs {\n",
" padding: 0;\n",
" margin: 0;\n",
" display: grid;\n",
" grid-template-columns: 125px auto;\n",
"}\n",
"\n",
".xr-attrs dt,\n",
".xr-attrs dd {\n",
" padding: 0;\n",
" margin: 0;\n",
" float: left;\n",
" padding-right: 10px;\n",
" width: auto;\n",
"}\n",
"\n",
".xr-attrs dt {\n",
" font-weight: normal;\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-attrs dt:hover span {\n",
" display: inline-block;\n",
" background: var(--xr-background-color);\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-attrs dd {\n",
" grid-column: 2;\n",
" white-space: pre-wrap;\n",
" word-break: break-all;\n",
"}\n",
"\n",
".xr-icon-database,\n",
".xr-icon-file-text2,\n",
".xr-no-icon {\n",
" display: inline-block;\n",
" vertical-align: middle;\n",
" width: 1em;\n",
" height: 1.5em !important;\n",
" stroke-width: 0;\n",
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (compZ_current_sg: 10, runs: 3)\n",
"Coordinates:\n",
" * compZ_current_sg (compZ_current_sg) float64 0.195 0.196 ... 0.203 0.204\n",
" * runs (runs) float64 0.0 1.0 2.0\n",
"Data variables: (12/13)\n",
" BEC_amplitude (compZ_current_sg, runs) object 689.6738589244688+/-...\n",
" thermal_amplitude (compZ_current_sg, runs) object 0.0+/-nan ... 0.0+/-nan\n",
" BEC_centerx (compZ_current_sg, runs) object 72.72766759442194+/-...\n",
" BEC_centery (compZ_current_sg, runs) object 71.93797641698205+/-...\n",
" thermal_centerx (compZ_current_sg, runs) object 73.54413421644256+/-...\n",
" thermal_centery (compZ_current_sg, runs) object 74.08047311882555+/-...\n",
" ... ...\n",
" BEC_sigmay (compZ_current_sg, runs) object 8.315587888174065+/-...\n",
" thermal_sigmax (compZ_current_sg, runs) object 86.82572665210536+/-...\n",
" thermal_sigmay (compZ_current_sg, runs) object 81.25225556328859+/-...\n",
" deltax (compZ_current_sg, runs) object 233.58068450863982+/...\n",
" thermalAspectRatio (compZ_current_sg, runs) object 0.9358085292951404+/...\n",
" condensate_fraction (compZ_current_sg, runs) object 1.0+/-nan ... 1.0+/-nan\n",
"Attributes:\n",
" IMAGE_SUBCLASS: IMAGE_GRAYSCALE\n",
" IMAGE_VERSION: 1.2\n",
" IMAGE_WHITE_IS_ZERO: 0\n",
" x_start: 805\n",
" x_end: 955\n",
" y_end: 1065\n",
" y_start: 915\n",
" x_center: 880\n",
" y_center: 990\n",
" x_span: 150\n",
" y_span: 150</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-33a03347-635f-4e2f-a5d3-df79adba0cad' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-33a03347-635f-4e2f-a5d3-df79adba0cad' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>compZ_current_sg</span>: 10</li><li><span class='xr-has-index'>runs</span>: 3</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7856859d-8f08-444d-a62b-68fc1f9eb730' class='xr-section-summary-in' type='checkbox' checked><label for='section-7856859d-8f08-444d-a62b-68fc1f9eb730' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>compZ_current_sg</span></div><div class='xr-var-dims'>(compZ_current_sg)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.195 0.196 0.197 ... 0.203 0.204</div><input id='attrs-0cc9cd0e-1960-4550-8338-7ce2383f3b48' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0cc9cd0e-1960-4550-8338-7ce2383f3b48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8cc942a8-8947-421b-9910-5b08d06cec29' class='xr-var-data-in' type='checkbox'><label for='data-8cc942a8-8947-421b-9910-5b08d06cec29' 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([0.195, 0.196, 0.197, 0.198, 0.199, 0.2 , 0.201, 0.202, 0.203, 0.204])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>runs</span></div><div class='xr-var-dims'>(runs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0</div><input id='attrs-683036f9-22c7-4e6c-a2a2-13581c608c8d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-683036f9-22c7-4e6c-a2a2-13581c608c8d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c995726-80e3-4a7a-967b-ba6fcb5cce18' class='xr-var-data-in' type='checkbox'><label for='data-1c995726-80e3-4a7a-967b-ba6fcb5cce18' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0., 1., 2.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ec3f9762-a2a2-44f7-9831-d18466a23b54' class='xr-section-summary-in' type='checkbox' checked><label for='section-ec3f9762-a2a2-44f7-9831-d18466a23b54' class='xr-section-summary' >Data variables: <span>(13)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>BEC_amplitude</span></div><div class='xr-var-dims'>(compZ_current_sg, runs)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>689.6738589244688+/-nan ... 505....</div><input id='attrs-9d483981-f747-4a5a-8ffe-38aba660f3d8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9d483981-f747-4a5a-8ffe-38aba660f3d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43600e21-a38c-4a20-81b6-1b71a7e31b5c' class='xr-var-data-in' type='checkbox'><label for='data-43600e21-a38c-4a20-81b6-1b71a7e31b5c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#
" 678.39907423435+/-nan],\n",
" [692.5450583097982+/-nan, 709.7153109568274+/-nan,\n",
" 693.070902295569+/-nan],\n",
" [671.9030647731805+/-nan, 763.5527107892661+/-nan,\n",
" 701.6228860278444+/-nan],\n",
" [703.0376002067666+/-nan, 691.0256217303906+/-nan,\n",
" 698.0049200957969+/-nan],\n",
" [713.8173887255239+/-nan, 680.8242923026909+/-nan,\n",
" 628.8406808124307+/-nan],\n",
" [671.7766869904086+/-nan, 668.5923608192708+/-nan,\n",
" 698.6631413542333+/-nan],\n",
" [659.0140752932108+/-nan, 596.3510167435243+/-nan,\n",
" 632.2665864507762+/-nan],\n",
" [619.2762490805766+/-nan, 617.946024341381+/-nan,\n",
" 640.2602092235951+/-nan],\n",
" [636.4920366481963+/-nan, 586.3450868568624+/-nan,\n",
" 572.5404723960091+/-nan],\n",
" [562.1581960978315+/-nan, 601.8578305946143+/-nan,\n",
" 505.5308972550412+/-nan]], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thermal_amplitude</span></div><div class='xr-var-dims'>(compZ_current_sg, runs)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0.0+/-nan 0.0+/-nan ... 0.0+/-nan</div><input id='attrs-4f4279dd-9275-4e09-8946-5047ebfb0b5a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4f4279dd-9275-4e09-8946-5047ebfb0b5a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-33f02b00-e9f5-4798-85fe-61139c2c18bc' class='xr-var-data-in' type='checkbox'><label for='data-33f02b00-e9f5-4798-85fe-61139c2c18bc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.0+/-nan, 0.0+/-nan, 0.0+/-nan],\n",
" [0.0+/-nan, 0.0+/-nan, 0.0+/-nan],\n",
" [0.0+/-nan, 0.0+/-nan, 0.0+/-nan],\n",
" [0.0+/-nan, 0.0+/-nan, 0.0+/-nan],\n",
" [0.0+/-nan, 0.0+/-nan, 0.0+/-nan],\n",
" [0.0+/-nan, 0.0+/-nan, 0.0+/-nan],\n",
" [0.0+/-nan, 0.0+/-nan, 0.0+/-nan],\n",
" [0.0+/-nan, 0.0+/-nan, 0.0+/-nan],\n",
" [0.0+/-nan, 0.0+/-nan, 0.0+/-nan],\n",
" [0.0+/-nan, 0.0+/-nan, 0.0+/-nan]], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>BEC_centerx</span></div><div class='xr-var-dims'>(compZ_current_sg, runs)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>72.72766759442194+/-nan ... 70.0...</div><input id='attrs-06456cfe-f594-4225-824d-4d29dcc4c68a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-06456cfe-f594-4225-824d-4d29dcc4c68a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44dd8410-d7ed-4fed-ae1b-b8beee69327c' class='xr-var-data-in' type='checkbox'><label for='data-44dd8410-d7ed-4fed-ae1b-b8beee69327c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[72.72766759442194+/-nan, 72.71934488859581+/-nan,\n",
" 71.40355589999807+/-nan],\n",
" [70.83342680729962+/-nan, 68.66496511093739+/-nan,\n",
" 70.29374802397487+/-nan],\n",
" [70.76765532584542+/-nan, 71.03271256497091+/-nan,\n",
" 72.67427312885016+/-nan],\n",
" [72.20891853944283+/-nan, 70.38077535505248+/-nan,\n",
" 70.7339464630603+/-nan],\n",
" [70.3178117834581+/-nan, 72.32035180100357+/-nan,\n",
" 71.63296400536021+/-nan],\n",
" [70.87533638684343+/-nan, 70.88128055305225+/-nan,\n",
" 72.52001102186628+/-nan],\n",
" [73.06561017377334+/-nan, 70.0741947920574+/-nan,\n",
" 71.6924852554308+/-nan],\n",
" [72.1222142035208+/-nan, 71.82506798407782+/-nan,\n",
" 69.85696478117038+/-nan],\n",
" [71.15702324146866+/-nan, 70.15080743468083+/-nan,\n",
" 71.35706452253848+/-nan],\n",
" [71.41542581324093+/-nan, 70.62063274120308+/-nan,\n",
" 70.08904848938136+/-nan]], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>BEC_centery</span></div><div class='xr-var-dims'>(compZ_current_sg, runs)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>71.93797641698205+/-nan ... 70.8...</div><input id='attrs-13278abc-ce92-4663-8f0d-ac92a42c1067' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-13278abc-ce92-4663-8f0d-ac92a42c1067' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f535313e-90d0-4e18-b4fa-ecbec787f2ab' class='xr-var-data-in' type='checkbox'><label for='data-f535313e-90d0-4e18-b4fa-ecbec787f2ab' 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([[71.93797641698205+/-nan, 72.6381937001966+/-nan,\n",
" 72.02512192557639+/-nan],\n",
" [71.55174018891496+/-nan, 70.87577074483377+/-nan,\n",
" 72.4977446833421+/-nan],\n",
" [71.34716548816778+/-nan, 70.44251861831816+/-nan,\n",
" 70.78633409098687+/-nan],\n",
" [70.37015516272982+/-nan, 72.97486141381597+/-nan,\n",
" 71.85640793410928+/-nan],\n",
" [71.3331531748868+/-nan, 69.31373705739671+/-nan,\n",
" 71.93481571209645+/-nan],\n",
" [69.33385592249886+/-nan, 71.17080956684265+/-nan,\n",
" 73.27962703564992+/-nan],\n",
" [72.41744755655539+/-nan, 71.47251010551959+/-nan,\n",
" 71.42576907220035+/-nan],\n",
" [69.73001451172685+/-nan, 70.44457455629144+/-nan,\n",
" 72.22164046797901+/-nan],\n",
" [72.21512332594007+/-nan, 70.69934127336529+/-nan,\n",
" 71.13947812812009+/-nan],\n",
" [69.56836922034678+/-nan, 70.64440607038694+/-nan,\n",
" 70.83100648311071+/-nan]], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thermal_centerx</span></div><div class='xr-var-dims'>(compZ_current_sg, runs)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>73.54413421644256+/-nan ... 70.9...</div><input id='attrs-f0de6890-3cb9-4797-b0b9-08950b2969eb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f0de6890-3cb9-4797-b0b9-08950b2969eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d68f0e5-2165-4773-8c04-a3ae45920229' class='xr-var-data-in' type='checkbox'><label for='data-2d68f0e5-2165-4773-8c04-a3ae45920229' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[73.54413421644256+/-nan, 73.30274113050602+/-nan,\n",
" 71.92304853372369+/-nan],\n",
" [71.00483901753078+/-nan, 68.9161916636921+/-nan,\n",
" 69.86466122796406+/-nan],\n",
" [70.79675629053958+/-nan, 71.49749669681296+/-nan,\n",
" 72.82082446441498+/-nan],\n",
" [73.2807451001814+/-nan, 71.68639061568933+/-nan,\n",
" 71.53256545890429+/-nan],\n",
" [70.90654770148933+/-nan, 72.19906890315161+/-nan,\n",
" 71.67310443723218+/-nan],\n",
" [70.73737318190581+/-nan, 71.46506714732851+/-nan,\n",
" 74.05600639847734+/-nan],\n",
" [72.77539585636279+/-nan, 70.47520515991539+/-nan,\n",
" 72.51736039865762+/-nan],\n",
" [72.32662798141047+/-nan, 72.24230286579716+/-nan,\n",
" 70.73841779714252+/-nan],\n",
" [71.16743021062787+/-nan, 70.37056367958343+/-nan,\n",
" 71.15981995834142+/-nan],\n",
" [71.90161813663846+/-nan, 71.46282211339187+/-nan,\n",
" 70.91060958388397+/-nan]], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thermal_centery</span></div><div class='xr-var-dims'>(compZ_current_sg, runs)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>74.08047311882555+/-nan ... 72.3...</div><input id='attrs-6f945b1a-67bc-46e6-8b30-34acf1e5a021' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6f945b1a-67bc-46e6-8b30-34acf1e5a021' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0494c586-4554-44cc-9e28-6135184ff2d3' class='xr-var-data-in' type='checkbox'><label for='data-0494c586-4554-44cc-9e28-6135184ff2d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[74.08047311882555+/-nan, 73.62629217434753+/-nan,\n",
" 73.62520544473455+/-nan],\n",
" [72.87625912011066+/-nan, 72.6999927167971+/-nan,\n",
" 73.76435921973764+/-nan],\n",
" [72.6033358300646+/-nan, 72.56878492161754+/-nan,\n",
" 72.58676659949938+/-nan],\n",
" [72.74308214550841+/-nan, 74.18604052561388+/-nan,\n",
" 73.12900436786026+/-nan],\n",
" [72.620379447618+/-nan, 71.40143590558816+/-nan,\n",
" 73.40067743800589+/-nan],\n",
" [70.92167921588822+/-nan, 73.42198207438085+/-nan,\n",
" 75.22592665604574+/-nan],\n",
" [74.13075036384711+/-nan, 72.88427291602504+/-nan,\n",
" 73.12795529075576+/-nan],\n",
" [72.51777171279258+/-nan, 72.93096162147843+/-nan,\n",
" 74.04389516161456+/-nan],\n",
" [73.75867853397996+/-nan, 72.5554168542469+/-nan,\n",
" 72.86426197278085+/-nan],\n",
" [72.12097010480343+/-nan, 72.12419149184741+/-nan,\n",
" 72.33848082985858+/-nan]], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>BEC_sigmax</span></div><div class='xr-var-dims'>(compZ_current_sg, runs)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>26.896495447676273+/-nan ... 25....</div><input id='attrs-e6bdad93-ede4-4e28-977d-f3425ddb48fb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e6bdad93-ede4-4e28-977d-f3425ddb48fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-991df3de-abb6-482f-ad91-de995ba1dcca' class='xr-var-data-in' type='checkbox'><label for='data-991df3de-abb6-482f-ad91-de995ba1dcca' 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([[26.896495447676273+/-nan, 27.0325742082552+/-nan,\n",
" 26.479676881987302+/-nan],\n",
" [26.315693756433717+/-nan, 27.244908144332072+/-nan,\n",
" 27.02759984086839+/-nan],\n",
" [26.713107942138123+/-nan, 26.9711867062004+/-nan,\n",
" 26.987293340194242+/-nan],\n",
" [27.641154691028845+/-nan, 26.470827885941674+/-nan,\n",
" 26.38327779768096+/-nan],\n",
" [26.759769526175404+/-nan, 26.660578970567798+/-nan,\n",
" 26.3428112841589+/-nan],\n",
" [26.78446860717432+/-nan, 27.111706894893814+/-nan,\n",
" 26.6854888514423+/-nan],\n",
" [26.516327819324886+/-nan, 26.050621310128548+/-nan,\n",
" 27.45170198499784+/-nan],\n",
" [26.34825532680844+/-nan, 26.719568712533402+/-nan,\n",
" 26.426454164620168+/-nan],\n",
" [25.85371437669528+/-nan, 26.865199333328206+/-nan,\n",
" 25.83529222770207+/-nan],\n",
" [26.253152412705276+/-nan, 26.347280678246875+/-nan,\n",
" 25.591605982303747+/-nan]], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>BEC_sigmay</span></div><div class='xr-var-dims'>(compZ_current_sg, runs)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>8.315587888174065+/-nan ... 7.70...</div><input id='attrs-ce54c9ce-a5fa-49ba-aaa8-a4fea8d049f7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ce54c9ce-a5fa-49ba-aaa8-a4fea8d049f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a1ca6d21-f996-4091-994b-5d3ed367bcb8' class='xr-var-data-in' type='checkbox'><label for='data-a1ca6d21-f996-4091-994b-5d3ed367bcb8' 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([[8.315587888174065+/-nan, 8.782235758255771+/-nan,\n",
" 8.437766586779095+/-nan],\n",
" [8.529205764813772+/-nan, 8.511414989849603+/-nan,\n",
" 8.429409693985567+/-nan],\n",
" [8.967636353540735+/-nan, 9.164394391604722+/-nan,\n",
" 8.606991456397717+/-nan],\n",
" [9.233793777545207+/-nan, 8.582932310143866+/-nan,\n",
" 8.265777917899998+/-nan],\n",
" [8.397604136244158+/-nan, 8.59086220029662+/-nan,\n",
" 8.157109611196294+/-nan],\n",
" [8.627906789844802+/-nan, 8.256198791171032+/-nan,\n",
" 8.640826875042585+/-nan],\n",
" [8.14338113151272+/-nan, 8.220716306663133+/-nan,\n",
" 8.48807755362483+/-nan],\n",
" [8.246264238222604+/-nan, 8.634373369783843+/-nan,\n",
" 8.247200673198572+/-nan],\n",
" [10.334307171965747+/-nan, 8.022444912030716+/-nan,\n",
" 8.115392321803794+/-nan],\n",
" [8.495199941648103+/-nan, 8.485154844745717+/-nan,\n",
" 7.709214756106329+/-nan]], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thermal_sigmax</span></div><div class='xr-var-dims'>(compZ_current_sg, runs)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>86.82572665210536+/-nan ... 82.5...</div><input id='attrs-66a6aff3-3dae-4b3c-95b4-910117493779' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-66a6aff3-3dae-4b3c-95b4-910117493779' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35a3739b-0403-42a8-90d3-62fa8f79fcd2' class='xr-var-data-in' type='checkbox'><label for='data-35a3739b-0403-42a8-90d3-62fa8f79fcd2' 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([[86.82572665210536+/-nan, 71.59852761893276+/-nan,\n",
" 83.87370192103053+/-nan],\n",
" [81.43219928653899+/-nan, 92.66676021573817+/-nan,\n",
" 76.37885478459056+/-nan],\n",
" [78.24219536265402+/-nan, 76.26902196708801+/-nan,\n",
" 73.17435018641264+/-nan],\n",
" [70.8440059901167+/-nan, 84.83185559838373+/-nan,\n",
" 89.59878886970975+/-nan],\n",
" [92.25586777309792+/-nan, 75.93305747810473+/-nan,\n",
" 75.04395198742893+/-nan],\n",
" [86.55825348791429+/-nan, 87.95181807386554+/-nan,\n",
" 92.1624263432616+/-nan],\n",
" [84.09836215498714+/-nan, 80.74406538220822+/-nan,\n",
" 83.62396899899504+/-nan],\n",
" [72.55523806865114+/-nan, 84.46730135582732+/-nan,\n",
" 73.87356268023888+/-nan],\n",
" [90.73267205327764+/-nan, 74.50432786545196+/-nan,\n",
" 87.60209625408987+/-nan],\n",
" [77.83520249094289+/-nan, 83.7594063318159+/-nan,\n",
" 82.57851746784988+/-nan]], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thermal_sigmay</span></div><div class='xr-var-dims'>(compZ_current_sg, runs)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>81.25225556328859+/-nan ... 79.4...</div><input id='attrs-37305f5f-db5d-4263-be4b-fd9bce83e1dc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-37305f5f-db5d-4263-be4b-fd9bce83e1dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd182249-4b22-43ed-8735-7c696353b5d0' class='xr-var-data-in' type='checkbox'><label for='data-cd182249-4b22-43ed-8735-7c696353b5d0' 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([[81.25225556328859+/-nan, 61.95483669688191+/-nan,\n",
" 78.75323107717729+/-nan],\n",
" [74.70732651156752+/-nan, 86.87150425190022+/-nan,\n",
" 68.50907599986216+/-nan],\n",
" [68.76379284998367+/-nan, 68.36583766085947+/-nan,\n",
" 70.0952760020868+/-nan],\n",
" [63.712746170899784+/-nan, 76.46633870305563+/-nan,\n",
" 81.5164971076558+/-nan],\n",
" [85.57776753084637+/-nan, 70.11409857464966+/-nan,\n",
" 65.9021485593791+/-nan],\n",
" [79.78542663388451+/-nan, 83.10109517361872+/-nan,\n",
" 82.35948784709004+/-nan],\n",
" [76.94153076049392+/-nan, 73.31782172661772+/-nan,\n",
" 76.97840260007256+/-nan],\n",
" [70.23041620669188+/-nan, 80.23679101803242+/-nan,\n",
" 66.09756880496701+/-nan],\n",
" [108.87920646393316+/-nan, 64.6550573761848+/-nan,\n",
" 80.2343621274528+/-nan],\n",
" [72.15165082016149+/-nan, 77.71732820925727+/-nan,\n",
" 79.45951777679701+/-nan]], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>deltax</span></div><div class='xr-var-dims'>(compZ_current_sg, runs)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>233.58068450863982+/-nan ... 222...</div><input id='attrs-8573a8f9-3a54-409b-ae51-eb58aeec04e0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8573a8f9-3a54-409b-ae51-eb58aeec04e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-056dbe47-b131-4e2f-b0c3-e518da49da31' class='xr-var-data-in' type='checkbox'><label for='data-056dbe47-b131-4e2f-b0c3-e518da49da31' 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([[233.58068450863982+/-nan, 187.7630086485431+/-nan,\n",
" 225.14142888110428+/-nan],\n",
" [217.98090410318324+/-nan, 250.7553725028824+/-nan,\n",
" 202.1089645129033+/-nan],\n",
" [208.01347814582394+/-nan, 201.83587919506363+/-nan,\n",
" 192.53575721904366+/-nan],\n",
" [184.89086327932125+/-nan, 228.0247389092095+/-nan,\n",
" 242.41308881144826+/-nan],\n",
" [250.00783379311835+/-nan, 201.1385934637464+/-nan,\n",
" 198.78904467812788+/-nan],\n",
" [232.89029185656855+/-nan, 236.74374732670282+/-nan,\n",
" 249.80179017834251+/-nan],\n",
" [225.77875864563654+/-nan, 216.1815748364961+/-nan,\n",
" 223.42020501198726+/-nan],\n",
" [191.31745887914497+/-nan, 226.68233535494855+/-nan,\n",
" 195.19423387609646+/-nan],\n",
" [246.34430178313767+/-nan, 196.64778426302766+/-nan,\n",
" 236.97099653456755+/-nan],\n",
" [207.2524550601234+/-nan, 224.9309383172008+/-nan,\n",
" 222.1439464212459+/-nan]], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thermalAspectRatio</span></div><div class='xr-var-dims'>(compZ_current_sg, runs)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0.9358085292951404+/-nan ... 0.9...</div><input id='attrs-5c4420c5-2bc4-4752-adfa-bb2357961c08' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5c4420c5-2bc4-4752-adfa-bb2357961c08' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb0f7160-ae96-4cdd-9060-753cd759364b' class='xr-var-data-in' type='checkbox'><label for='data-fb0f7160-ae96-4cdd-9060-753cd759364b' 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([[0.9358085292951404+/-nan, 0.8653088095138317+/-nan,\n",
" 0.9389502224585925+/-nan],\n",
" [0.917417522381922+/-nan, 0.937461329711474+/-nan,\n",
" 0.8969639070011807+/-nan],\n",
" [0.8788581727706161+/-nan, 0.8963775317633028+/-nan,\n",
" 0.9579214003748328+/-nan],\n",
" [0.8993385577290508+/-nan, 0.9013870811109844+/-nan,\n",
" 0.9097946315568302+/-nan],\n",
" [0.9276132737846415+/-nan, 0.9233672514091381+/-nan,\n",
" 0.8781806769773902+/-nan],\n",
" [0.9217541183987102+/-nan, 0.9448479519073387+/-nan,\n",
" 0.8936341100693223+/-nan],\n",
" [0.9148992773330864+/-nan, 0.9080273748858471+/-nan,\n",
" 0.9205303637405402+/-nan],\n",
" [0.96795790457252+/-nan, 0.9499154078573739+/-nan,\n",
" 0.8947391516917874+/-nan],\n",
" [1.2+/-nan, 0.8678027066151909+/-nan, 0.9158954586512751+/-nan],\n",
" [0.9269796764331312+/-nan, 0.9278638855363573+/-nan,\n",
" 0.9622298899678458+/-nan]], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>condensate_fraction</span></div><div class='xr-var-dims'>(compZ_current_sg, runs)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1.0+/-nan 1.0+/-nan ... 1.0+/-nan</div><input id='attrs-983c1ac2-ac0d-468e-be38-93c03bfcb92c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-983c1ac2-ac0d-468e-be38-93c03bfcb92c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f19f4d6f-f0b4-48f8-bcf9-9481a97aa34e' class='xr-var-data-in' type='checkbox'><label for='data-f19f4d6f-f0b4-48f8-bcf9-9481a97aa34e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[1.0+/-nan, 1.0+/-nan, 1.0+/-nan],\n",
" [1.0+/-nan, 1.0+/-nan, 1.0+/-nan],\n",
" [1.0+/-nan, 1.0+/-nan, 1.0+/-nan],\n",
" [1.0+/-nan, 1.0+/-nan, 1.0+/-nan],\n",
" [1.0+/-nan, 1.0+/-nan, 1.0+/-nan],\n",
" [1.0+/-nan, 1.0+/-nan, 1.0+/-nan],\n",
" [1.0+/-nan, 1.0+/-nan, 1.0+/-nan],\n",
" [1.0+/-nan, 1.0+/-nan, 1.0+/-nan],\n",
" [1.0+/-nan, 1.0+/-nan, 1.0+/-nan],\n",
" [1.0+/-nan, 1.0+/-nan, 1.0+/-nan]], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d882c17d-efd5-4d74-8630-ec83e37d1354' class='xr-section-summary-in' type='checkbox' ><label for='section-d882c17d-efd5-4d74-8630-ec83e37d1354' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>compZ_current_sg</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d45f36c6-4b94-4da0-8ab8-a816b5e557db' class='xr-index-data-in' type='checkbox'/><label for='index-d45f36c6-4b94-4da0-8ab8-a816b5e557db' 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([0.195, 0.196, 0.197, 0.198, 0.199, 0.2, 0.201, 0.202, 0.203,\n",
" 0.204],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;compZ_current_sg&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>runs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-51ece157-12c2-4892-9adc-25d34eea1662' class='xr-index-data-in' type='checkbox'/><label for='index-51ece157-12c2-4892-9adc-25d34eea1662' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([0.0, 1.0, 2.0], dtype=&#x27;float64&#x27;, name=&#x27;runs&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-930c4c69-9751-4826-ba77-94cd803648ef' class='xr-section-summary-in' type='checkbox' ><label for='section-930c4c69-9751-4826-ba77-94cd803648ef' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>IMAGE_SUBCLASS :</span></dt><dd>IMAGE_GRAYSCALE</dd><dt><span>IMAGE_VERSION :</span></dt><dd>1.2</dd><dt><span>IMAGE_WHITE_IS_ZERO :</span></dt><dd>0</dd><dt><span>x_start :</span></dt><dd>805</dd><dt><span>x_end :</span></dt><dd>955</dd><dt><span>y_end :</span></dt><dd>1065</dd><dt><span>y_start :</span></dt><dd>915</dd><dt><span>x_center :</span></dt><dd>880</dd><dt><span>y_center :</span></dt><dd>990</dd><dt><span>x_span :</span></dt><dd>150</dd><dt><span>y_span :</span></dt><dd>150</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (compZ_current_sg: 10, runs: 3)\n",
"Coordinates:\n",
" * compZ_current_sg (compZ_current_sg) float64 0.195 0.196 ... 0.203 0.204\n",
" * runs (runs) float64 0.0 1.0 2.0\n",
"Data variables: (12/13)\n",
" BEC_amplitude (compZ_current_sg, runs) object 689.6738589244688+/-...\n",
" thermal_amplitude (compZ_current_sg, runs) object 0.0+/-nan ... 0.0+/-nan\n",
" BEC_centerx (compZ_current_sg, runs) object 72.72766759442194+/-...\n",
" BEC_centery (compZ_current_sg, runs) object 71.93797641698205+/-...\n",
" thermal_centerx (compZ_current_sg, runs) object 73.54413421644256+/-...\n",
" thermal_centery (compZ_current_sg, runs) object 74.08047311882555+/-...\n",
" ... ...\n",
" BEC_sigmay (compZ_current_sg, runs) object 8.315587888174065+/-...\n",
" thermal_sigmax (compZ_current_sg, runs) object 86.82572665210536+/-...\n",
" thermal_sigmay (compZ_current_sg, runs) object 81.25225556328859+/-...\n",
" deltax (compZ_current_sg, runs) object 233.58068450863982+/...\n",
" thermalAspectRatio (compZ_current_sg, runs) object 0.9358085292951404+/...\n",
" condensate_fraction (compZ_current_sg, runs) object 1.0+/-nan ... 1.0+/-nan\n",
"Attributes:\n",
" IMAGE_SUBCLASS: IMAGE_GRAYSCALE\n",
" IMAGE_VERSION: 1.2\n",
" IMAGE_WHITE_IS_ZERO: 0\n",
" x_start: 805\n",
" x_end: 955\n",
" y_end: 1065\n",
" y_start: 915\n",
" x_center: 880\n",
" y_center: 990\n",
" x_span: 150\n",
" y_span: 150"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fitAnalyser_1.get_fit_full_result(fitResult_1)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"val = fitAnalyser_1.get_fit_value(fitResult_1)\n",
"std = fitAnalyser_1.get_fit_std(fitResult_1)\n",
"\n",
"data = val['BEC_amplitude']\n",
"data_std = std['BEC_amplitude']\n",
"\n",
"data.plot.errorbar(x=scanAxis[0], hue=scanAxis[1], fmt='o')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"val = fitAnalyser_1.get_fit_value(fitResult_1)\n",
"std = fitAnalyser_1.get_fit_std(fitResult_1)\n",
"\n",
"data = val['BEC_amplitude'].mean('runs')* 146.59032426564943 / 1e5\n",
"data_std = val['BEC_amplitude'].std('runs')* 146.59032426564943 / 1e5\n",
"\n",
"data.plot.errorbar(yerr=data_std, fmt='o')\n",
"\n",
"plt.ylabel('Atom number in BEC (1e5)')\n",
"plt.xlabel('comp Z current (A)')\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"146.59032426564943"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1 / 8.4743e-14 /0.5 / 2.3513**2 * 5.86e-6**2 "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
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},
{
"cell_type": "code",
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{
"cell_type": "code",
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{
"cell_type": "code",
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{
"cell_type": "code",
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{
"cell_type": "code",
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{
"cell_type": "code",
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},
{
"cell_type": "code",
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{
"cell_type": "code",
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},
{
"cell_type": "code",
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},
{
"cell_type": "code",
"execution_count": null,
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"source": []
},
{
"cell_type": "code",
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},
{
"cell_type": "code",
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},
{
"cell_type": "code",
"execution_count": null,
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"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
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"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%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(0.15, 0.25, 0.005))\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": [
"[10.25, 10.255, 10.26, 10.265, 10.27, 10.275, 10.28, 10.285, 10.29, 10.295, 10.3, 10.305, 10.31, 10.315, 10.32, 10.325, 10.33, 10.335, 10.34, 10.345, 10.35, 10.355]"
]
},
{
"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 = 0.0587\n",
"\"\"\"Low Power\"\"\"\n",
"P_arm1_low = 5271 * v_low - 27.5\n",
"\n",
"print(round(P_arm1_high, 3))\n",
"print(round(P_arm1_mid, 3))\n",
"print(round(P_arm1_low, 3))"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## ODT 2 Power Calibration"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"v = 0.7607\n",
"P_arm2 = 2.302 * v - 0.06452\n",
"print(round(P_arm2, 3))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.12"
},
"vscode": {
"interpreter": {
"hash": "c05913ad4f24fdc6b2418069394dc5835b1981849b107c9ba6df693aafd66650"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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