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prepare to add fft
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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",
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"import pandas as pd\n",
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"import numpy as np\n",
"import copy\n",
"\n",
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"import xrft\n",
"\n",
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"from uncertainties import ufloat\n",
"from uncertainties import unumpy as unp\n",
"from uncertainties import umath\n",
"\n",
"import matplotlib.pyplot as plt\n",
"plt.rcParams['font.size'] = 18\n",
"\n",
"from DataContainer.ReadData import read_hdf5_file, read_hdf5_global, read_hdf5_run_time\n",
"from Analyser.ImagingAnalyser import ImageAnalyser\n",
"from Analyser.FitAnalyser import FitAnalyser\n",
"from ToolFunction.ToolFunction import *\n",
"\n",
"from ToolFunction.HomeMadeXarrayFunction import errorbar, dataarray_plot_errorbar\n",
"xr.plot.dataarray_plot.errorbar = errorbar\n",
"xr.plot.accessor.DataArrayPlotAccessor.errorbar = dataarray_plot_errorbar\n",
"\n",
"imageAnalyser = ImageAnalyser()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Start a client for parallel computing"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
test fft
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{
"name": "stderr",
"output_type": "stream",
"text": [
"D:\\Program Files\\Python\\Python38\\Lib\\site-packages\\distributed\\node.py:182: UserWarning: Port 8787 is already in use.\n",
"Perhaps you already have a cluster running?\n",
"Hosting the HTTP server on port 65030 instead\n",
" warnings.warn(\n"
]
},
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{
"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",
test fft
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" <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-06ca1baa-f5d4-11ed-9390-9c7bef43b4fb</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",
test fft
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" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:65030/status\" target=\"_blank\">http://127.0.0.1:65030/status</a>\n",
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" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" \n",
"\n",
" </table>\n",
"\n",
" \n",
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" <button style=\"margin-bottom: 12px;\" data-commandlinker-command=\"dask:populate-and-launch-layout\" data-commandlinker-args='{\"url\": \"http://127.0.0.1:65030/status\" }'>\n",
" Launch dashboard in JupyterLab\n",
" </button>\n",
" \n",
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"\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",
test fft
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" <p style=\"color: #9D9D9D; margin-bottom: 0px;\">e8410166</p>\n",
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" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
test fft
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" <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:65030/status\" target=\"_blank\">http://127.0.0.1:65030/status</a>\n",
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" </td>\n",
" <td style=\"text-align: left;\">\n",
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" <strong>Workers:</strong> 6\n",
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" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
test fft
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" <strong>Total threads:</strong> 24\n",
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" </td>\n",
" <td style=\"text-align: left;\">\n",
test fft
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" <strong>Total memory:</strong> 55.88 GiB\n",
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" </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",
test fft
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" <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-c2d7c34b-da7d-4218-ac9a-8a33f289f15b</p>\n",
prepare to add fft
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" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
test fft
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" <strong>Comm:</strong> tcp://127.0.0.1:65031\n",
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" </td>\n",
" <td style=\"text-align: left;\">\n",
test fft
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" <strong>Workers:</strong> 6\n",
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" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
test fft
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" <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:65030/status\" target=\"_blank\">http://127.0.0.1:65030/status</a>\n",
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" </td>\n",
" <td style=\"text-align: left;\">\n",
test fft
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" <strong>Total threads:</strong> 24\n",
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" </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",
test fft
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" <strong>Total memory:</strong> 55.88 GiB\n",
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" </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",
test fft
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" <strong>Comm: </strong> tcp://127.0.0.1:65062\n",
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" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads: </strong> 4\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
test fft
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" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:65067/status\" target=\"_blank\">http://127.0.0.1:65067/status</a>\n",
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" </td>\n",
" <td style=\"text-align: left;\">\n",
test fft
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" <strong>Memory: </strong> 9.31 GiB\n",
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" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
test fft
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" <strong>Nanny: </strong> tcp://127.0.0.1:65034\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",
test fft
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" <strong>Local directory: </strong> C:\\Users\\Jianshun Gao\\AppData\\Local\\Temp\\dask-worker-space\\worker-v4ohhhqe\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",
test fft
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" <strong>Comm: </strong> tcp://127.0.0.1:65066\n",
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" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads: </strong> 4\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
test fft
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" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:65077/status\" target=\"_blank\">http://127.0.0.1:65077/status</a>\n",
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" </td>\n",
" <td style=\"text-align: left;\">\n",
test fft
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" <strong>Memory: </strong> 9.31 GiB\n",
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" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
test fft
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" <strong>Nanny: </strong> tcp://127.0.0.1:65035\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",
test fft
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" <strong>Local directory: </strong> C:\\Users\\Jianshun Gao\\AppData\\Local\\Temp\\dask-worker-space\\worker-b3vo1gqh\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",
test fft
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" <strong>Comm: </strong> tcp://127.0.0.1:65064\n",
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" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads: </strong> 4\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
test fft
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" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:65075/status\" target=\"_blank\">http://127.0.0.1:65075/status</a>\n",
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" </td>\n",
" <td style=\"text-align: left;\">\n",
test fft
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" <strong>Memory: </strong> 9.31 GiB\n",
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" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
test fft
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" <strong>Nanny: </strong> tcp://127.0.0.1:65036\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",
test fft
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" <strong>Local directory: </strong> C:\\Users\\Jianshun Gao\\AppData\\Local\\Temp\\dask-worker-space\\worker-65l26hd_\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",
test fft
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" <strong>Comm: </strong> tcp://127.0.0.1:65065\n",
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" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads: </strong> 4\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
test fft
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" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:65071/status\" target=\"_blank\">http://127.0.0.1:65071/status</a>\n",
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" </td>\n",
" <td style=\"text-align: left;\">\n",
test fft
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" <strong>Memory: </strong> 9.31 GiB\n",
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" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
test fft
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" <strong>Nanny: </strong> tcp://127.0.0.1:65037\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",
test fft
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" <strong>Local directory: </strong> C:\\Users\\Jianshun Gao\\AppData\\Local\\Temp\\dask-worker-space\\worker-6q6tun74\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",
test fft
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" <strong>Comm: </strong> tcp://127.0.0.1:65063\n",
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" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads: </strong> 4\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
test fft
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" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:65072/status\" target=\"_blank\">http://127.0.0.1:65072/status</a>\n",
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" </td>\n",
" <td style=\"text-align: left;\">\n",
test fft
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" <strong>Memory: </strong> 9.31 GiB\n",
prepare to add fft
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" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
test fft
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" <strong>Nanny: </strong> tcp://127.0.0.1:65038\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",
test fft
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" <strong>Local directory: </strong> C:\\Users\\Jianshun Gao\\AppData\\Local\\Temp\\dask-worker-space\\worker-efysz1ue\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",
test fft
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" <strong>Comm: </strong> tcp://127.0.0.1:65061\n",
prepare to add fft
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" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads: </strong> 4\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
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" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:65068/status\" target=\"_blank\">http://127.0.0.1:65068/status</a>\n",
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" </td>\n",
" <td style=\"text-align: left;\">\n",
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" <strong>Memory: </strong> 9.31 GiB\n",
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" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
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" <strong>Nanny: </strong> tcp://127.0.0.1:65039\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\\Jianshun Gao\\AppData\\Local\\Temp\\dask-worker-space\\worker-i87d0xvj\n",
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" </td>\n",
" </tr>\n",
"\n",
" \n",
"\n",
" \n",
"\n",
" </table>\n",
" </details>\n",
" </div>\n",
" </div>\n",
" \n",
"\n",
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" </details>\n",
"</div>\n",
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"\n",
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" </details>\n",
" </div>\n",
"</div>\n",
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" </details>\n",
" \n",
"\n",
" </div>\n",
"</div>"
],
"text/plain": [
test fft
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"<Client: 'tcp://127.0.0.1:65031' processes=6 threads=24, memory=55.88 GiB>"
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]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from dask.distributed import Client\n",
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"client = Client(n_workers=6, threads_per_worker=4, processes=True, memory_limit='10GB')\n",
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"client"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Set global path for experiment"
]
},
{
"cell_type": "code",
"execution_count": 3,
"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": 4,
"metadata": {},
"outputs": [],
"source": [
test fft
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"img_dir = 'F:/'\n",
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"SequenceName = \"Evaporative_Cooling\" + \"/\"\n",
"folderPath = img_dir + SequenceName + \"2023/05/09\" # get_date()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Check the stability of our BEC"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The detected scaning axes and values are: \n",
"\n",
"{'runs': array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10.,\n",
" 11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21.,\n",
" 22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32.,\n",
" 33., 34., 35., 36., 37., 38., 39., 40., 41., 42., 43.,\n",
" 44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54.,\n",
" 55., 56., 57., 58., 59., 60., 61., 62., 63., 64., 65.,\n",
" 66., 67., 68., 69., 70., 71., 72., 73., 74., 75., 76.,\n",
" 77., 78., 79., 80., 81., 82., 83., 84., 85., 86., 87.,\n",
" 88., 89., 90., 91., 92., 93., 94., 95., 96., 97., 98.,\n",
" 99., 100., 101., 102., 103., 104., 105., 106., 107., 108., 109.,\n",
" 110., 111., 112., 113., 114., 115., 116., 117., 118., 119., 120.,\n",
" 121., 122., 123., 124., 125., 126., 127., 128., 129., 130., 131.,\n",
" 132., 133., 134., 135., 136., 137., 138., 139., 140., 141., 142.,\n",
" 143., 144., 145., 146., 147., 148., 149., 150., 151., 152., 153.,\n",
" 154., 155., 156., 157., 158., 159., 160., 161., 162., 163., 164.,\n",
" 165., 166., 167., 168., 169., 170., 171., 172., 173., 174., 175.,\n",
" 176., 177., 178., 179., 180., 181., 182., 183., 184., 185., 186.,\n",
" 187., 188., 189., 190., 191., 192., 193., 194., 195., 196., 197.,\n",
" 198., 199., 200., 201., 202., 203., 204., 205., 206., 207., 208.,\n",
" 209., 210., 211., 212., 213., 214., 215., 216., 217., 218., 219.,\n",
" 220., 221., 222., 223., 224., 225., 226., 227., 228., 229., 230.,\n",
" 231., 232., 233., 234., 235., 236., 237., 238., 239., 240., 241.,\n",
" 242., 243., 244., 245., 246., 247., 248., 249., 250., 251., 252.,\n",
" 253., 254., 255., 256., 257., 258., 259., 260., 261., 262., 263.,\n",
" 264., 265., 266., 267., 268., 269., 270., 271., 272., 273., 274.,\n",
" 275., 276., 277., 278., 279., 280., 281., 282., 283., 284., 285.,\n",
" 286., 287., 288., 289., 290., 291., 292., 293., 294., 295., 296.,\n",
" 297., 298., 299., 300., 301., 302., 303., 304., 305., 306., 307.,\n",
" 308., 309., 310., 311., 312., 313., 314., 315., 316., 317., 318.,\n",
" 319., 320., 321., 322., 323., 324., 325., 326., 327., 328., 329.,\n",
" 330., 331., 332., 333., 334., 335., 336., 337., 338., 339., 340.,\n",
" 341., 342., 343., 344., 345., 346., 347., 348., 349., 350., 351.,\n",
" 352., 353., 354., 355., 356., 357., 358., 359., 360., 361., 362.,\n",
" 363., 364., 365., 366., 367., 368., 369., 370., 371., 372., 373.,\n",
" 374., 375., 376., 377., 378., 379., 380., 381., 382., 383., 384.,\n",
" 385., 386., 387., 388., 389., 390., 391., 392., 393., 394., 395.,\n",
" 396., 397., 398., 399., 400., 401., 402., 403., 404., 405., 406.,\n",
" 407., 408., 409., 410., 411., 412., 413., 414., 415., 416., 417.,\n",
" 418., 419., 420., 421., 422., 423., 424., 425., 426., 427., 428.,\n",
" 429., 430., 431., 432., 433., 434., 435., 436., 437., 438., 439.,\n",
" 440., 441., 442., 443., 444., 445., 446., 447., 448., 449., 450.,\n",
" 451., 452., 453., 454., 455., 456., 457., 458., 459., 460., 461.,\n",
" 462., 463., 464., 465., 466., 467., 468., 469., 470., 471., 472.,\n",
" 473., 474., 475., 476., 477., 478., 479., 480., 481., 482., 483.,\n",
" 484., 485., 486., 487., 488., 489., 490., 491., 492., 493., 494.,\n",
" 495., 496., 497., 498., 499., 500., 501., 502., 503., 504., 505.,\n",
" 506., 507., 508., 509., 510., 511., 512., 513., 514., 515., 516.,\n",
" 517., 518., 519., 520., 521., 522., 523., 524., 525., 526., 527.,\n",
" 528., 529., 530., 531., 532., 533., 534., 535., 536., 537., 538.,\n",
" 539., 540., 541., 542., 543., 544., 545., 546., 547., 548., 549.])}\n"
]
},
{
"data": {
test fft
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"image/png": "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"text/plain": [
test fft
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"<Figure size 432x288 with 1 Axes>"
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]
},
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"metadata": {
"needs_background": "light"
},
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"output_type": "display_data"
}
],
"source": [
"shotNum = \"0007\"\n",
"filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n",
"\n",
"dataSetOfGlobalDict = {\n",
" dskey[groupList[i]]: read_hdf5_global(filePath, groupList[i])\n",
" for i in [0]\n",
"}\n",
"\n",
"dataSetDict = {\n",
" dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i], datesetOfGlobal=dataSetOfGlobalDict[dskey[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",
"dataSet = swap_xy(dataSet)\n",
"\n",
"dataSet = imageAnalyser.get_absorption_images(dataSet)\n",
"\n",
"imageAnalyser.center = (959, 876)\n",
"imageAnalyser.span = (100, 100)\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).load()\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"Ncount.plot.errorbar(ax=ax, fmt='ob')\n",
"\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
test fft
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"execution_count": 6,
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"metadata": {},
"outputs": [],
"source": [
"dataSet_cropOD = auto_rechunk(dataSet_cropOD)\n",
"\n",
"fitAnalyser = FitAnalyser(\"Two Gaussian-2D\", fitDim=2)\n",
"params = fitAnalyser.guess(dataSet_cropOD, dask=\"parallelized\")\n",
"fitResult = fitAnalyser.fit(dataSet_cropOD, params, dask=\"parallelized\").load()\n",
"\n",
"fitValue = fitAnalyser.get_fit_value(fitResult)\n",
"fitStd = fitAnalyser.get_fit_std(fitResult)"
]
},
{
"cell_type": "code",
test fft
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"execution_count": 7,
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"metadata": {},
"outputs": [],
"source": [
"BEC_Ncount_val = fitValue['A_amplitude']\n",
"BEC_Ncount_std = fitStd['A_amplitude']\n",
"\n",
"thermal_Ncount_val = fitValue['B_amplitude']\n",
"thermal_Ncount_std = fitStd['B_amplitude']\n",
"\n",
"BEC_width_x_val = fitValue['A_sigmax']\n",
"BEC_width_x_std = fitStd['A_sigmax']\n",
"BEC_width_y_val = fitValue['A_sigmay']\n",
"BEC_width_y_std = fitStd['A_sigmay']\n",
"\n",
"thermal_width_x_val = fitValue['B_sigmax']\n",
"thermal_width_x_std = fitStd['B_sigmax']\n",
"thermal_width_y_val = fitValue['B_sigmay']\n",
"thermal_width_y_std = fitStd['B_sigmay']\n",
"\n",
"BEC_center_x_val = fitValue['A_centerx']\n",
"BEC_center_x_std = fitStd['A_centerx']\n",
"BEC_center_y_val = fitValue['A_centery']\n",
"BEC_center_y_std = fitStd['A_centery']\n",
"\n",
"thermal_center_x_val = fitValue['B_centerx']\n",
"thermal_center_x_std = fitStd['B_centerx']\n",
"thermal_center_y_val = fitValue['B_centery']\n",
"thermal_center_y_std = fitStd['B_centery']"
]
},
{
"cell_type": "code",
test fft
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"execution_count": 8,
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"metadata": {},
test fft
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"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"<xarray.DataArray ()>\n",
"array(853.42940839)\n"
]
}
],
prepare to add fft
2023-05-18 16:09:20 +02:00
"source": [
"total_Ncount_val = BEC_Ncount_val + thermal_Ncount_val\n",
"total_Ncount_std = BEC_Ncount_std + thermal_Ncount_std\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"total_Ncount_val.plot.errorbar(ax=ax, yerr=total_Ncount_std, fmt='ob')\n",
"# plt.ylim([0, 1100])\n",
"plt.ylabel('Ncount from fit')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()\n",
"\n",
"print(total_Ncount_val.mean())"
]
},
{
"cell_type": "code",
test fft
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"execution_count": 9,
prepare to add fft
2023-05-18 16:09:20 +02:00
"metadata": {},
test fft
2023-05-19 09:34:58 +02:00
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAEHCAYAAABiAAtOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAA2iElEQVR4nO2de7weRXn4v08OyYEkFSSQWKIkAoK3FpGLINVys1aotdoW1HgBhEi41OtP24abCGgVEAtCCVhETypQpYD9WZFgQvFnKUbBnyJFhCaxqNwEJIQESKZ/zC7Zs2d3dmZ39n3ffc/z/Xzmc867Ozs7u3N5ZuZ55lkxxqAoiqIoMZnS7wwoiqIow4cKF0VRFCU6KlwURVGU6KhwURRFUaKjwkVRFEWJjgoXRVEUJTpb9DsDg8p2221n5s+fX+vaJ598khkzZsTNkNI6Wm7dQ8usv/zgBz942BizfdE5FS4lzJ8/n5UrV9a6dsWKFRxwwAFxM6S0jpZb99Ay6y8isrrsnC6LKYqiKNFR4aIoiqJER4WLoiiKEh0VLoqiKEp0VLgoiqIo0VHhoiiKokRHhYuiKIoSHRUuiqIoSnRUuCiKoijRUeGiKIqiREeFi6IoihIdFS6K0mGWLoX582HKFPt36dJ+50hRLOq4UlE6ytKlsHAhrFtnf69ebX8DLFjQv3wpCujMRVE6y+LFmwVLyrp19rgSD50d1sNbuIjIe0RkvuP8fBF5T5RcKYpSyZo1YceVcNLZ4erVYMzm2aEKmGpCZi6XA691nH9NEkdRlB6w445hx5VwdHZYnxDhIhXnpwKbGuRFUZQAzjoLpk8ff2z6dHtciYPODusTqnMxRQdFZBvgMOBXTTOkKIofCxbAkiUwbx6I2L9LlqgyPyY6O6yPU7iIyGkislFENmIFy1j6OxuAR4DDgSt7kGdFURIWLIBVq2DTJvtXBUtcdHZYnypT5DuAL2OXxN4D3ALcl4tjgLXArcBXI+dPURSlb6TCevFiuxS2445WsKgQr8YpXIwx1wHXAYjIPOBMY8xNvciYoijKILBggQqTOnjpXERkJrAK2LbV3CiKogwo2f0u221ng+59Kcdrh74xZq2IHAF8t+X8KIqiDBx5bwiPPLL5nHpGKCbEWuynwPyW8qFk0B3BijJYFO13yaJ7XyYSIlw+AywSkV3byowSf0ewCipFaY7Pvhbd+zKeEOHyUuAXwI9F5Osi8mkROTUXTmkpn5OGmDuC1XVFN9EBweDhs69F976MJ0S4nA7sjt2J/1bgY8mxfFAaEHNHsLqu6B4xBwQqpOqTf3eHHjpxv0sW3fsykRDh8mKPsFPsDE42Yu4IVtcV3SPWgEBnrfUpendXXAHvfe9mbwizZtmgnhHK8RYuxpjVPqHNzE4GYu4IVtcV3SPWgEBnrfUpe3ff/OZmbwgPP2xDm54RymaeXZmR6vdcekBIZYjpL6oXriu6UtG7QqwBgc5aw8jW49UlQ+Q1a6rre932kL/u+OOLZ55lx33u0/N9OsYY74DdF/MXwGeBy4B/zIUvhqQ3yGHPPfc0dVm+fPlz/4+NGTN9ujG2Ktgwfbo9nmdszJh584wRsX+L4oQyNmbMrFmb7z1rVpx007R9n61tYry7bLn1+t7ZtGK803nzxqeRhnnz6udtEKlbZlmK3nlRmDWruGwWLdr8vkXCy67o/vl00jAyUq9cq56xbrsFVpoyeVF2YkJEuzv/R8BGrGv99G/2/42+6Q16iCVcfBt5Wx11mwJgUDqwWM9Yp6Nq4/02EVbptXU7ul4QUxjXLbPs/bODL1fnWxavTBD4toeydhQSRJrfo067jSVcLgI2AEdhlfebgDcAuwFjwH8C2/imN+ghlnApq3j5ytBWR92mAPB9traJ9YwhHVW2Ey8bYfa6I3eNgGPNhJvmr6iDnjp18/F0ZO6b31Dh4jtLyb6/NC9VQqRuxx+Sbt2Zi8896rTbWMJlNXBp8v+sRLgclDm/ArjYN71c2tsC5wA/B9YDDwHLgdfl4r0GWAY8AfwW+BbwqpI0d8B6dH4IeApYCfylb556PXNpq6NuUwAMyswl1jP6dlS+HVSvZwqDUh5FhHbqvu8vVLiEzBLy763uDKPuzKVo5rlokRXG2eNTp1a/J5/ZWeyZS4hC/wXA95P/n03+bpk5fy3wpwHpAc95W/4B8F7ga8DxwNlYR5lzM/H2BW7GzppOBU4DXgLcIiK/l0tzW6wftLcBFwMfwH4W4GoROSo0j00oU6ofeuh4Bd62JS5Bm1p2+SqI6ygiYxkM+N67TCE5paQWN313ZfmqcgWS0kvrrKVL3YroXtH0nWVp4/35vou0HmefZ+1amDZtfDyp+D6vT3soa0fHHTfRsGf//Sfe85ln4AMfaKaUF2lhn06Z1MkH7FcmP5T8PwW7RPb+zPkTgHW+6WWuuwW78/93K+Ldhp2tzM0cm5sc+3Yu7mcAA7w5c2wkSeMRYGZVvmLNXIyZuMa7aNHEUdzUqcZMmxZ/5OujE2iiN2i6fu5779CRb1Ody6JFE9OcNi18eSTGDLHqHVe9m17NXMry4TNqrvv+QmcuZXmZMWPiOy56nnQJz9WWQ5ciq4xusuVftiyWr/P5OlP1jhctCnqNz0GkZbGbSZbFkt+3Av8BjALTsTqXn/iml6Tx+kQInJT8ngpML4i3SxJvgjUa8EXsEt0LMsf+B/h5Qdx3J+kcXpW3mMIlT1lhz5q1uULMmGHMlCn2+MhI/cI3prpz6udyiu+9fZYkRkbiWIuNjZXfIy0j386x6Tv0Eb6u/GStmWJaIRYRQzEd+v5iCZe0Q/dR9KfvMI07a9Z4gZMKJp937hoYzJpVLLyq2kBWwPnEb1IfYgmXxcCjwGjy+/CkU38Su+S0EXifb3pJGp9OOvu3AN/ALrcZ4GfAuzLx3pEcP6YgjWOTc4clv383+T1WEPclybnPVuWtzZmLawRhTPGoGcIFjG8F76divureVYrzmPlNy63qfmUj2jZmnq6BSIqrIynqnGJYs42OmnEdbVU+6oaqWUCocHHlMaQTd5X1okX+lnptCGTfEKN+uoSL1/dcEs4GzjHGbAAwxlwtIs8C70oEy9eMMVcFpAfW0gzgUuAerN5lGvAR4CsiMtUYczlWOQ9wf0Ea6bFUPxMSdxwishBYCDBnzhxWrFjh9xQ51q5dy4oVK1i2bDYXXLALv/3tVOyXotN1cfPc7yyzZ69nxYpbueSS11O0v/WSSzZx+OH/7pWHZctmc845u7Fhw8hz933f+zZy1113c8ghD+buuy8PPLDlhDTS/LSJ694nn3zfuGeoTqtZftNyW7PmDykqH4th7tyb+dCHZnPZZTvx4IOjzJ69gWOOsV//zh+bO/dBTj55Ytx8GZRRlpdHHjGcfPJdAIi8FGMm1pc5c9ZzzTWwbt3497tuHXzkI+uZOzf8Xbnq1ezZOxWWZRjj24YxTLhP9t2lZeZLWX2bMmUT69b5q6Cffnr87/Sd3nXXfVx88cvIl1n2fLYuPPDA6IS4bTJlyiaMkXH1s2Y3V02Z1OlFwFp+GeBeYFrm+POxs6RfYXvZU5J4BxWkcVBy7oPJ79clv88oiDslOXdtVd6azlya6Ah84pdthqwa6afT+Kq15V5ZOrnuHTKqK9PThCwHpeWWLkeWvXff5yrbb5Jd4667ZJnmpayOpe+jajYRuqnWtYxZxyIsNOSXyWKYIsfMs0snUtYe23xfRfeLCTGWxSZcCFsBW9W9PknjG0lnf2bBuSuScy/DzmQM8KaCeIcm5xYmv/dMfv9dQdzpybl/qspbU+ESui6fbeC+FTRVMKc0MfdswzuAL2X39m14aQdZto8i/6xleXje8zZ436vqearKIRUwLqFe9TyukAovX11VfsBRhs8y5owZ9fJcp3Nssokyffbs336EXgoY38GRL9GECzAbu5nyl9ilsI3J7OIiYE5IWkl6Fyed/YkF51J9zGvpoM7Ft8IUjSTKdC5FITuSa8sOv23KrGWqnifbEY6NTbT/933WmJZoY2PNO6oYs4D0OWPvL/ExwPAVaHm9hU+byZff4sV31hoU9WK
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
prepare to add fft
2023-05-18 16:09:20 +02:00
"source": [
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"BEC_Ncount_val.plot.errorbar(ax=ax, yerr=BEC_Ncount_std, fmt='ob')\n",
"plt.ylim([0, 750])\n",
"plt.ylabel('Ncount of BEC part')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
test fft
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"execution_count": 10,
prepare to add fft
2023-05-18 16:09:20 +02:00
"metadata": {},
test fft
2023-05-19 09:34:58 +02:00
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAEHCAYAAABiAAtOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAA64klEQVR4nO29e7geRZXo/VvZJDuEHEQ3JkqUHQEBL0dAAeFzMIFEUfDyOTPKKOoBwTjh4ngbUcNtkMAcB1EHZTwBxwvJKDoq4mVUbsnBD6PkCI4fIKBI4gTloiCEXIRknT+qO+l0+lLVXb3f9917/Z6nnnfv7urq6q7qWlVrraoSVcUwDMMwYjKp1xkwDMMwxh8mXAzDMIzomHAxDMMwomPCxTAMw4iOCRfDMAwjOiZcDMMwjOiYcDEMwzCi03PhIiJaEtYVxN1PRK4SkYdF5HERuVFEjipJ9ykicomIrBWRjSJym4gsFBHp/qkMwzAmNjv1OgMJNwJLcseeyP4jInsDNwFPAh8D/gS8E/iBiLxaVa/NxJ0CXAMcBFwC3AG8GrgUmAmc28lTGIZhGACI7wx9EbkeWKyq15WcPxI4S1ULRxIV6SrwRVU9oSbeV4G/Al6iqrcmx6YDtwEbgf01eRgROQX4DPBuVb0kk8bXgdcCz1XV1SH5NAzDMPwJUYvNxfX6y5gBzGmaERGZkgiLonO7AK8DlqeCBUBV1wGXA/sCh2QueQuwHrgsl9QngcnAcU3zaRiGYdQT0+ayG7Cp4bV/jRMGj4nIA4mt5CmZ8y8ChoEfF1y7Mvk9BEBEJgEvBm5R1Y25uD8FlO0FkWEYhhGZSpuLiLwIODBz6AgRKbrmacApwO0N8vBT4GvAr4BdgWOA04A5IvL/JKOTPZK4awuuT4/NSn6fCuxcFFdVN4nIQ5m4hmEYRgfUGfTfAJyT/K3Au5JQxGPAu0MzoKovzR36koj8J7AY+Lvkd1pyrmhklI5OpuV+y0ZRGzNxtkNEFgALAHbeeeeXPPvZz67NfxFbtmxh0qSeO+IZgVi5DR5WZr3lrrvuekhVn150rk64fAFYDghwPXABzgsriwLrgNsL1FBN+SecUDsWJ1zWJ8eHC+JOTX7X536L4qbx1xedUNUlJF5rBx98sK5atSos1wnLly9n7ty5ja41eoeV2+BhZdZbRKTUMapSuCQeVauTRE4EVqjqvVFzV3zfJ0TkPmD35NB9yW+ROis9lqrBHgY2FMUVkeEkzRXxcmsYhmHk8RpPJl5c/wq8rdvsbL3fVOBZwP3JoV/g1FyHF0Q/LPldBaCqW4CfAQclwiTLobhRWLMhiWEYhuGFl3BJjOqPAA/EvLmIjJSc+ihuVPXtzP2/DcwVkQMy108HTgbuxjkGpHwZZ1dZkEv3PbhJmFdGyL5hGIZRQsgM/Rtw81j+V8T7nykihyVprwGm47zFjgR+gptdn/JhYB7wQxH5BPAobob+LOBY3X426GXAicDFIjIbN0P/GJyDwvljodozDMOYyIQIl78HVojIPwAfV9VHI9x/OfB84H8AI8Bm3ChkEXBx1kFAVX8lIi8D/hH4EDAFp/56VXbplyTun0VkPnA+8OYk7V8Dp+Nm7huGYRgdEiJcrsN5Wp2JG3E8yI5eV6qqe/smqKrfAr4VEP8O4PWecR/BzZc5zTd9wzAMIw4hwmUNzu3YMAzDMCrxFi6qOrfDfBiGYRjjCJvaahiGYUTHhIthGIYRnSDhIiIvE5HviMiDIvKkiGzOhSe7yqhhGIYxOHgLFxF5OW4+yktxc1AmJf/fjJv1/v8DV3SQR8MwDGPACBm5LAJ+h5uXckJy7AJVPQx4FfAc3MZdhmEYxgQnRLgcClyuqg8CW7LXq+oPcaOWj8bNnmEYhjGIhAiXYbatPJzulfLfMudvBV4SIU+GYRjGgBMiXH6HW6kYVX0ct5DlCzPnn4VbFNIwDMOY4ITM0L8ZeFnm/x8C7002i5mEW2blJxHzZhiGYQwoISOXzwEPicjOyf8fwW3K9QXcXi+bgA9GzZ1hGIYxkIQs/3INmS2OVfUeEdkXtwz+ZuBHqvqn+Fk0DMMwBo1WM/RV9XFVvVpVv2uCJSLLlsHUqSACs2e7/w3DMAaIEJsLsHUf+rnAXsmhe4AV2b1XjBYsWwYLFsCmxCFv9Wr3P8Dxx/cuX4ZhGAGELv/ydpw78vdwm259Jvl7rYicED13E5FFi2B9bpuc9evdccMwjAHBe+QiIsfhjPdrgIuA25NTLwD+FviciGxQVdufvg1r1oQdNwzD6ENC1GIfAX4JHJbb4vhqEbkU54b8EcCESxv23NOpwoqOG4ZhDAgharH9gM/nBAsAiTH/88C+sTI2YVm8GKZN2/7YtGnuuGEYxoAQIlx+X3Negftb5MUAZ7RfsgRGR5232Oio+9+M+YZhDBAhwuULwIkiMj1/QkR2BU7EjV6Mthx/PNx7L2zZ4n57JVjMJdowjIaE2FxuBF4D/CKxsfwyOf48YCHwEHBjsu/LVlT1f8fIqDHGmEu0YRgtCBEu12T+/p84NRi4jcIARnNxJIkz1Dh3Ru+oconOC5e5c93v8uXud9kyF2/NGueIsHixCSTDmGCECJcTO8uF0Z5ly+Ckk9xIY3S0fYPu6xK9bBmsXOnuO3s2HHMMfPGL2wSTjXgMiF8/jf5HVS0UhJe85CXalBtuuKHxtY1YulR12jRV2BamTXPHmzI6un16aRgdrb6vSP11fcqYl9tEoYv6mTCwZbZ0qerw8LZvI8K76AXAKi1pQ1utLRYbEZkmIveIiIrIpwvO7yciV4nIwyLyuIjcKCJHlaT1FBG5RETWishGEblNRBaKiBTFH2i6mNXv4xJddF9VCrFJoBMXW3Vie8rsmePMYaavhAtwHvD0ohMisjdwE3A48DHg74HpwA9EZH4u7hSc/edvcZM6TwfuBC4Fzukq815kPbDS0NYTq4tZ/T4u0SHp2yTQiYutOrE9E0TY9o1wEZEXA++hvPG/ENgNOFpVL1TVS4EjgPuAz+RGJCcDhwDvU9X3qeplqvqXwDeAj4jIaEePUU2+x5LStudS1nC3bdDrXKJ907dJoL2l1y7lXdXPQWWCCNu+EC4iMgRcBnwfJwDy53cBXgcsV9Vb0+Oqug64HLcywCGZS94CrE/SzPJJYDJwXLzcB1DUY0nx7bkUNRRjMavf975F2CRQRy8a+X5QwdiqE9szUYRtmTFmLAPwAeBxYHYSFPh05vzhybHzC659RXLu1OT/STjB8qOCuMPAFuBrdXnqxKBfZuzOGsOrqDKMLl3qDIMi8Q2EPvcte6YmhvweGTs7NQ53aNSuxMcxYyzoqH4OpEG/V3WhA6gw6PeDYHlOIljOSP4vEi5/lRxbWHD985NzFyT/jyT/X1lyvweAm+ry1YlwqWqEfT74XjUUTT3HmnwwXXx4nsKq04aqV2VX1qGp68gMCH0vXObMcSHPBPAWC94srAM+i9tw7OKKOOmYelPBuY25OFVx0/iFuhwRWQAsAJg5cybL00mBgaxbt67w2hlvfSv7XXQRQ3mbC7B5eJg73/pWHqi455w1ayhyddM1a1jRMK8+eN131ixmvPe97HX55Qw/8ACbZszgnpNP5oFZs7ZNrvTgsPe/n6kFxs6N738/K2fN8k5nxrXXst/HPsakJ54Ats30ZfVqNp90EnfecQcPzN/OD6S03GLQq7I7bMYMpt6/45J/G2fMYGWk+2bf9aaZM125595tV5R+az3M03Z5uOkml4dnPGP7PMyaBd///vYXdFgPekKZ1AHObhDOKkuv5B5vxamp/iJzbDbjdeSiqrpwYXHvtajnku3djIw0H/G0pW2vO6SXFqOnXTT68cj7uBy5dK2C6bGKp7DM+kHtFJKHAR7F0EQtljT6oWFzWXoF6Q8nDf13gH0yYU4iHK5I/t+N8WJzWbpUddIk/wpX10D6CKgYtPlYQ6+N0QjXqR9LhNW4tLmk9+7KHtdjm05hmfWDnck3D/0gCFvQVLiMNgll6RWkv1siFOrCB3DzWTYC1xWkc1YS76WZYz/C2XGGc3GPSOKeUZe/ToRL2eijqNL
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
prepare to add fft
2023-05-18 16:09:20 +02:00
"source": [
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"thermal_Ncount_val.plot.errorbar(ax=ax, yerr=thermal_Ncount_std, fmt='or')\n",
"plt.ylim([0, 500])\n",
"plt.ylabel('Ncount of thermal part')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
test fft
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"execution_count": 11,
prepare to add fft
2023-05-18 16:09:20 +02:00
"metadata": {},
test fft
2023-05-19 09:34:58 +02:00
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
prepare to add fft
2023-05-18 16:09:20 +02:00
"source": [
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"BEC_width_x_val.plot.errorbar(ax=ax, yerr=BEC_width_x_std, fmt='ob')\n",
"\n",
"plt.ylabel('X-axis Width of BEC part')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
test fft
2023-05-19 09:34:58 +02:00
"execution_count": 12,
prepare to add fft
2023-05-18 16:09:20 +02:00
"metadata": {},
test fft
2023-05-19 09:34:58 +02:00
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
prepare to add fft
2023-05-18 16:09:20 +02:00
"source": [
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"BEC_width_y_val.plot.errorbar(ax=ax, yerr=BEC_width_y_std, fmt='ob')\n",
"\n",
"plt.ylabel('Y-axis Width of BEC part')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
test fft
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"execution_count": 13,
prepare to add fft
2023-05-18 16:09:20 +02:00
"metadata": {},
test fft
2023-05-19 09:34:58 +02:00
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
prepare to add fft
2023-05-18 16:09:20 +02:00
"source": [
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"thermal_width_x_val.plot.errorbar(ax=ax, yerr=thermal_width_x_std, fmt='or')\n",
"\n",
"plt.ylabel('X-axis width of thermal part')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
test fft
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"execution_count": 14,
prepare to add fft
2023-05-18 16:09:20 +02:00
"metadata": {},
test fft
2023-05-19 09:34:58 +02:00
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAEeCAYAAABG2VgdAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAABmw0lEQVR4nO29e3xcR3n//3kkW7JkByW28cZJa9kkJDa9RJAQAglY0NAfv7T9trS/0l9RbpDEEAIkXHqhNhAgTkubtqSUAHYD5CJaaOmVhktTIodA3RBAKRQ7DrEtEzCC2NhBUSxZ0vP9Y3a8s7Mzc2bOnpV2pef9eu1L2rPnMuc2z8xzJWaGIAiCIBRJ21w3QBAEQZh/iHARBEEQCkeEiyAIglA4IlwEQRCEwhHhIgiCIBSOCBdBEAShcES4CIIgCIUTLVyI6GNE9ILA7+cT0ceKaZYgCILQyqTMXK4EcEbg93UArqirNYIgCMK8oEi12FIAxwvcnyAIgtCiLAr9SERrAKw1Fq0nopc4Vl0O4FoA3y2uaYIgCEKrQqHcYkT0bgDvBpCVgIwAzAB4DTPfVVzzBEEQhFYkS7icA6APSnh8DMA2AP9lrcYAxgB8jZm/15hmCoIgCK1EUC3GzA8DeBgAiKgXwGeY+duz0TBBEAShdQkKFw0RLQNwOYCjAES4CIIgCEGivMWYeQzACij1lyAIgiAESXFF/m8A5zWqIYIgCML8IUW4/AGAVxHRa4iIGtUgQRAEofUJeotVrUj0JQC9UHEvhwE8BmDcWo2Z+ZeKbKAgCILQeqQIl/3IjncBM6+rs02CIAhCixMtXARBEAQhFkm5LwiCIBSOCBdBEAShcKKCKDVEdAaAtwB4AYBTUCucmJlDafkFQRCEBUBKsbBfAPANAFcD6ADwLABPAVgC5UE2DeBA8U0UBEEQWo0Utdh7AUwCOAeAdje+nplPA/A6ACcDuK7Q1gmCIAgtSYpwuQjANmZ+BBWXZAIAZt4O4HMA/qTY5gmCIAitSIpwOQkqcBJQMxhAVZ/UfAVKAAmCIAgLnBThMgrgVABg5p9C2VvOMn4/BUB7cU0TBEEQWpUUb7FhVCeu3AHgeiJ6EEpIvRHl2i/NysqVK3nt2rW5t3/qqaewdOnS7BWFpkXuYesj97B5+PrXv/4EMz/T9VuKcPkkgOuIqIuZnwbwTigBc1/596cB/FFdLW0wa9euxUMPPZR7+6GhIfT39xfXIGHWkXvY+sg9bB6IaMT3W7RwYeZPAfiU8f2bRPRzAF4J5Yb8OWbeW09DBUEQhPlBUhClDTN/D8BfFdQWQRAEYZ6QS7gQ0dlQQZQAsLfsniwIgiAIANLTv7wMwAcBrLeW7wbwZmb+zwLbJgiCILQo0cKlLFg+D2ACwHYA3yn/9HMAfhfA54joFcz8pcJbKQiCILQUKTOXm6FiXS5g5u+bPxDR+wDsBLAVwAuLa54gCILQiqQEUf4igI/aggUAmPlxAB+FyjsmCIIgLHBShMtRAD8N/P4kgCN1tUYQ5gv9/eojCAuUFOHy9wB+l4hqVGlEtBjK7vL3RTVMEARBaF1SbC4fAfAiAPcT0V8C2F1evgGqgFg7gI8Q0RpzI2aWGi+CIAgLjBTh8m2oVPsE4O+s38hYx0aSWQqCICwwUoTLe1Gp41IIRPQOAM8DcC6AdQBGmHmtZ91PALjCs6vfZuZ/KLJtgiAIQn5Scovd2IDj3wzgMFT55JMjt7nMsezBohokCIIg1E9ducUK4Ayd7JKIvg1gWdYGzHx3w1slCIIg1EWKt1jh5MmiTIpnENGctl0wELdbQRAsWrGDPlr+PE1E/0FEL5jrBgmCIAjVzLVaLIUfAvhLAF+HKrF8DoAbAHyZiC5h5ntdGxHRJgCbAKBUKmFoaCh3A8bGxurafr7Sd+QIAGC4Ba7NbN3DVromrYa8h60BMRfqAJYbbXPxeYt5tnk2VPnlHzDzs7PWP++881gqUTYAfU1a4IWflXs4OAhcdRUwMQH09gJbtwIDA4095gJC3sPmgYi+zsznuX5rRbXYCZj5UQCfBnAmEZ011+0RBAwOAps2KcECACMj6vvg4Ny2SxBmmZYWLmX2l/+unMtGCAIAYPNmYHy8etn4uFouCAsIr82FiC7Ps0NmvjN/c3Kh1WGjs3xcQajlgCfbkW+5IMxTQgb9T6CS7iUWBlC4cCGipQCmmfmYtfy5AH4bwC5mfqzo4wpCMmvWKFWYa7kgLCBCwuWljT44EV0GoLf89ZkAOohoS/n7CDPfVf7/2VCVLv8ZwKOoeIu9FsA0yt5ggjDnbN2qbCymaqy7Wy0XhAWEV7gw845ZOP5VADZay95X/rsDgBYuPwRwL5TAGwDQBeAggE8B+GNm3g1BaAa0V5h4iwkLnDmNc2Hm/sj1fgh3TjFBaD4GBoDt29X/LeCeLQiNIFm4EFEJwHkAToHD22wODPrCXDI4COzcqUbpa9fKKF0QBAAJwqWcy+tDAK5G2IVZhMtCwRXTcfnlwE03Abt2zW3bBEGYU1LiXN4O4HUA/haqrgoB+EMA10EZ2R8C8PKiGyg0Ma6YjpkZYN++uWmPIAhNQ4pwuQLA55n5cgCfKy/7OjN/BKrY18ryX2Gh4Ivd0DMZQRAWLCnC5VkAPl/+f6b8dzEAMPNTAD4OpTITFgq+2I3OztlthyAITUeKcHkawPHy/2NQAZOrjN9/COBnC2qX0Aps3apiOEza2oB16+amPYKQgtQhaigp3mIjAM4AAGY+TkTfBfAKVGJRLoakYFkY2FmQdUxHZ6cSLKXSXLVMEIQmIWXm8iUArzS+3wXgd4noPiIagkrD8ukC2ya0AgMDwAUXAD096q8IFsXQkMS4CAualJnLLQC+SESdzDwB4I+h1GKXQqVg2Qbg3cU3sUVooZomgiAIjSZauDDzQaiUK/r7NIA3lz+CRoSMIAjCvKjn0lrkMSLGbiMGSkEQmoQ86V+eDZWleAUc6fgl/UuA/n5geBjo64tbF2jsDEhmWUKjkWdswZKS/mU1gDsA/JJe5FitIfVcBAt5YQUX8lwITUTKzGUbVMr7DwD4MoCfNKJBQgsyOgo8+SSww6jSIEksBWFBkyJcXgbgVmZ+e6MaIyQwPKxGqnM9Sh0cBPbsAZirl4+MqKSWgAgYm0bPMOaTSnW+z8bm8fmlGPTHAHy3UQ0RZomijf6bN6tklS7Gx9XvglAv4qzScqQIl89CReELNtpQX8/2sS9Ovccqog0mvuSVsb8Lwlyg6xDt2KFUuIODc92ieUeKcHkbgHVE9JdE9Cwichn0hYWGL3ll7O+CMNu46hBt2rQwBUwDZ4TRwoWZj0B5i70Zqn7LFBFNW5+phrSyVRgdDY+GTMN37GjJHmGNRqRvm00Vgit5paa7W/0uCM2Eqw6RqHALJ8UV+fehUr6MAngQ4i1WYXQUOHpUfTS2Qds2fJu/h/Zrj7Da2lSCyI6O+tpslyfu6orLC+Yqa7xtWyV5paa3V7zFFjLNbB/xqWpFhVsoKd5ibwIwBOAVzHw8Y92FgxYaLvRoaGDAbfjWv69d695+377awlszM8CxY/UJF5/Q8qE7imuucasTtm1TSStN5qH3izBPWLNGPbuu5UJhpNhclgP4tAgWi5C3FFAZDbkeZvN3E3N24MJ2+zW32bFD/Q2pz/bty1eeWNQJwnzApcoVFW7hpAiXhwGIaLfJmkqvWaM6fp//Q0dHtfeXbWz0cfSoEiKDg7XbTEwAu3cDGza4t/XtO+uYok4olvnuXptlg5wrBgbUbFtXTO3tVd9bQYXbQs9MilpsM4BPE9E/MfNDjWpQy+GbYgOV0dDmze7ZBpEqrnXwYGWZa3bgY2ICuPxy4JRT3Nv4ZiKdnW5BklWeWNQJ84c8wXsp24yOKnWxntU3W1DtwACwfbv6X1S4DSFl5nIZgO8D2ElEXyaiO4joY9bn9ga1s3nZutVtr1ixojIa8o3smWuN6KFZQHt77bKZGeDQIff6vpnIunVuDy9tpPeNMJtRndBCIzkAaerLPMzGbCEmRmTfPr+NMc9xGnGt5pp5HmuTIlyuBPCL5W0uhBI2Vzo
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
prepare to add fft
2023-05-18 16:09:20 +02:00
"source": [
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"thermal_width_y_val.plot.errorbar(ax=ax, yerr=thermal_width_y_std, fmt='or')\n",
"\n",
"plt.ylabel('Y-axis width of thermal part')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
test fft
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"execution_count": 15,
prepare to add fft
2023-05-18 16:09:20 +02:00
"metadata": {},
test fft
2023-05-19 09:34:58 +02:00
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
prepare to add fft
2023-05-18 16:09:20 +02:00
"source": [
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"BEC_center_x_val.plot.errorbar(ax=ax, yerr=BEC_center_x_std, fmt='ob')\n",
"\n",
"plt.ylabel('X-axis center of BEC part')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
test fft
2023-05-19 09:34:58 +02:00
"execution_count": 16,
prepare to add fft
2023-05-18 16:09:20 +02:00
"metadata": {},
test fft
2023-05-19 09:34:58 +02:00
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
prepare to add fft
2023-05-18 16:09:20 +02:00
"source": [
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"BEC_center_y_val.plot.errorbar(ax=ax, yerr=BEC_center_y_std, fmt='ob')\n",
"\n",
"plt.ylabel('Y-axis center of BEC part')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
test fft
2023-05-19 09:34:58 +02:00
"execution_count": 17,
prepare to add fft
2023-05-18 16:09:20 +02:00
"metadata": {},
test fft
2023-05-19 09:34:58 +02:00
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
prepare to add fft
2023-05-18 16:09:20 +02:00
"source": [
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"thermal_center_x_val.plot.errorbar(ax=ax, yerr=thermal_center_x_std, fmt='or')\n",
"\n",
"plt.ylabel('X-axis center of thermal part')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
test fft
2023-05-19 09:34:58 +02:00
"execution_count": 18,
prepare to add fft
2023-05-18 16:09:20 +02:00
"metadata": {},
test fft
2023-05-19 09:34:58 +02:00
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
prepare to add fft
2023-05-18 16:09:20 +02:00
"source": [
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"thermal_center_y_val.plot.errorbar(ax=ax, yerr=thermal_center_y_std, fmt='or')\n",
"\n",
"plt.ylabel('Y-axis center of thermal part')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
test fft
2023-05-19 09:34:58 +02:00
"execution_count": 19,
prepare to add fft
2023-05-18 16:09:20 +02:00
"metadata": {},
test fft
2023-05-19 09:34:58 +02:00
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
prepare to add fft
2023-05-18 16:09:20 +02:00
"source": [
"fitFullResult = fitAnalyser.get_fit_full_result(fitResult)\n",
"condensateFraction = fitFullResult['A_amplitude'] / (fitFullResult['A_amplitude'] + fitFullResult['B_amplitude'])\n",
"condensateFraction_value, condensateFraction_std = seperate_uncertainty(condensateFraction)\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"condensateFraction_value.plot.errorbar(ax=ax, yerr=condensateFraction_std, fmt='ob')\n",
"\n",
"plt.ylabel('Condensate Fraction')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
test fft
2023-05-19 09:34:58 +02:00
"execution_count": 20,
prepare to add fft
2023-05-18 16:09:20 +02:00
"metadata": {},
test fft
2023-05-19 09:34:58 +02:00
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The total Ncount is: 849.84 ± 73.69\n",
"The total Ncount from fit is: 853.43 ± 66.18\n",
"The Ncount of the BEC part is: 528.79 ± 65.37\n",
"The Ncount of the thermal part is: 324.64 ± 35.62\n",
"The x-axis width of the BEC part is: 4.06 ± 0.28\n",
"The y-axis width of the BEC part is: 11.03 ± 0.36\n",
"The x-axis width of the thermal part is: 15.30 ± 0.91\n",
"The y-axis width of the thermal part is: 12.99 ± 0.61\n",
"The x-axis center of the BEC part is: 47.44 ± 1.82\n",
"The y-axis center of the BEC part is: 51.13 ± 1.83\n",
"The x-axis center of the thermal part is: 49.62 ± 1.54\n",
"The y-axis center of the thermal part is: 51.17 ± 1.37\n",
"The condensate fraction is: 0.6180 ± 0.0464\n"
]
}
],
prepare to add fft
2023-05-18 16:09:20 +02:00
"source": [
"val = Ncount.mean().item()\n",
"std = Ncount.std().item()\n",
"print(f'The total Ncount is: {val: .2f} \\u00B1 {std: .2f}')\n",
"\n",
"val = total_Ncount_val.mean().item()\n",
"std = total_Ncount_val.std().item()\n",
"print(f'The total Ncount from fit is: {val: .2f} \\u00B1 {std: .2f}')\n",
"\n",
"val = BEC_Ncount_val.mean().item()\n",
"std = BEC_Ncount_val.std().item()\n",
"print(f'The Ncount of the BEC part is: {val: .2f} \\u00B1 {std: .2f}')\n",
"\n",
"val = thermal_Ncount_val.mean().item()\n",
"std = thermal_Ncount_val.std().item()\n",
"print(f'The Ncount of the thermal part is: {val: .2f} \\u00B1 {std: .2f}')\n",
"\n",
"val = BEC_width_x_val.mean().item()\n",
"std = BEC_width_x_val.std().item()\n",
"print(f'The x-axis width of the BEC part is: {val: .2f} \\u00B1 {std: .2f}')\n",
"\n",
"val = BEC_width_y_val.mean().item()\n",
"std = BEC_width_y_val.std().item()\n",
"print(f'The y-axis width of the BEC part is: {val: .2f} \\u00B1 {std: .2f}')\n",
"\n",
"val = thermal_width_x_val.mean().item()\n",
"std = thermal_width_x_val.std().item()\n",
"print(f'The x-axis width of the thermal part is: {val: .2f} \\u00B1 {std: .2f}')\n",
"\n",
"val = thermal_width_y_val.mean().item()\n",
"std = thermal_width_y_val.std().item()\n",
"print(f'The y-axis width of the thermal part is: {val: .2f} \\u00B1 {std: .2f}')\n",
"\n",
"val = BEC_center_x_val.mean().item()\n",
"std = BEC_center_x_val.std().item()\n",
"print(f'The x-axis center of the BEC part is: {val: .2f} \\u00B1 {std: .2f}')\n",
"\n",
"val = BEC_center_y_val.mean().item()\n",
"std = BEC_center_y_val.std().item()\n",
"print(f'The y-axis center of the BEC part is: {val: .2f} \\u00B1 {std: .2f}')\n",
"\n",
"val = thermal_center_x_val.mean().item()\n",
"std = thermal_center_x_val.std().item()\n",
"print(f'The x-axis center of the thermal part is: {val: .2f} \\u00B1 {std: .2f}')\n",
"\n",
"val = thermal_center_y_val.mean().item()\n",
"std = thermal_center_y_val.std().item()\n",
"print(f'The y-axis center of the thermal part is: {val: .2f} \\u00B1 {std: .2f}')\n",
"\n",
"val = condensateFraction_value.mean().item()\n",
"std = condensateFraction_value.std().item()\n",
"print(f'The condensate fraction is: {val: .4f} \\u00B1 {std: .4f}')"
]
},
{
"cell_type": "code",
test fft
2023-05-19 09:34:58 +02:00
"execution_count": 21,
prepare to add fft
2023-05-18 16:09:20 +02:00
"metadata": {},
"outputs": [
{
"data": {
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (runs: 550)\n",
"Coordinates:\n",
" * runs (runs) float64 0.0 1.0 2.0 3.0 4.0 ... 546.0 547.0 548.0 549.0\n",
"Data variables:\n",
" runTine (runs) datetime64[ns] 2023-05-09T14:30:03 ... 2023-05-09T15:56:53\n",
"Attributes: (12/101)\n",
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" ... ...\n",
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" z_offset_img: 0.189\n",
" runs: [ 0. 1. 2. 3. 4. 5. 6. ...\n",
" scanAxis: [&#x27;runs&#x27;]\n",
test fft
2023-05-19 09:34:58 +02:00
" scanAxisLength: [550.]</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-cd06b240-0b7e-418f-a977-82ca527b4c0a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cd06b240-0b7e-418f-a977-82ca527b4c0a' 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'>runs</span>: 550</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-724ea8c0-0592-496a-964d-b89b9b308e96' class='xr-section-summary-in' type='checkbox' checked><label for='section-724ea8c0-0592-496a-964d-b89b9b308e96' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>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 ... 547.0 548.0 549.0</div><input id='attrs-32419c0b-933c-4dea-b1c1-27cae21d968d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-32419c0b-933c-4dea-b1c1-27cae21d968d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7089c4b7-9f1b-469c-a99a-23822b3b1795' class='xr-var-data-in' type='checkbox'><label for='data-7089c4b7-9f1b-469c-a99a-23822b3b1795' 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., ..., 547., 548., 549.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ec0f52a0-d8a2-4577-a9d1-dd42c40d9b93' class='xr-section-summary-in' type='checkbox' checked><label for='section-ec0f52a0-d8a2-4577-a9d1-dd42c40d9b93' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>runTine</span></div><div class='xr-var-dims'>(runs)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2023-05-09T14:30:03 ... 2023-05-...</div><input id='attrs-3d78a83c-f2ae-4c0f-8466-fdc80820018d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3d78a83c-f2ae-4c0f-8466-fdc80820018d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-175d5729-7c48-4351-9fd8-0e4a6e4858d8' class='xr-var-data-in' type='checkbox'><label for='data-175d5729-7c48-4351-9fd8-0e4a6e4858d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2023-05-09T14:30:03.000000000&#x27;, &#x27;2023-05-09T14:30:11.000000000&#x27;,\n",
prepare to add fft
2023-05-18 16:09:20 +02:00
" &#x27;2023-05-09T14:30:19.000000000&#x27;, &#x27;2023-05-09T14:30:27.000000000&#x27;,\n",
" &#x27;2023-05-09T14:30:35.000000000&#x27;, &#x27;2023-05-09T14:30:43.000000000&#x27;,\n",
" &#x27;2023-05-09T14:30:52.000000000&#x27;, &#x27;2023-05-09T14:31:00.000000000&#x27;,\n",
" &#x27;2023-05-09T14:31:08.000000000&#x27;, &#x27;2023-05-09T14:31:16.000000000&#x27;,\n",
" &#x27;2023-05-09T14:31:24.000000000&#x27;, &#x27;2023-05-09T14:31:33.000000000&#x27;,\n",
" &#x27;2023-05-09T14:31:41.000000000&#x27;, &#x27;2023-05-09T14:31:49.000000000&#x27;,\n",
" &#x27;2023-05-09T14:31:57.000000000&#x27;, &#x27;2023-05-09T14:32:05.000000000&#x27;,\n",
" &#x27;2023-05-09T14:32:13.000000000&#x27;, &#x27;2023-05-09T14:32:22.000000000&#x27;,\n",
" &#x27;2023-05-09T14:32:30.000000000&#x27;, &#x27;2023-05-09T14:32:38.000000000&#x27;,\n",
" &#x27;2023-05-09T14:32:46.000000000&#x27;, &#x27;2023-05-09T14:32:54.000000000&#x27;,\n",
" &#x27;2023-05-09T14:33:03.000000000&#x27;, &#x27;2023-05-09T14:33:11.000000000&#x27;,\n",
" &#x27;2023-05-09T14:33:19.000000000&#x27;, &#x27;2023-05-09T14:33:27.000000000&#x27;,\n",
" &#x27;2023-05-09T14:33:35.000000000&#x27;, &#x27;2023-05-09T14:33:44.000000000&#x27;,\n",
" &#x27;2023-05-09T14:33:52.000000000&#x27;, &#x27;2023-05-09T14:34:00.000000000&#x27;,\n",
" &#x27;2023-05-09T14:34:08.000000000&#x27;, &#x27;2023-05-09T14:34:16.000000000&#x27;,\n",
" &#x27;2023-05-09T14:34:24.000000000&#x27;, &#x27;2023-05-09T14:34:32.000000000&#x27;,\n",
" &#x27;2023-05-09T14:34:41.000000000&#x27;, &#x27;2023-05-09T14:34:49.000000000&#x27;,\n",
" &#x27;2023-05-09T14:34:57.000000000&#x27;, &#x27;2023-05-09T14:35:05.000000000&#x27;,\n",
" &#x27;2023-05-09T14:35:13.000000000&#x27;, &#x27;2023-05-09T14:35:21.000000000&#x27;,\n",
"...\n",
" &#x27;2023-05-09T15:51:57.000000000&#x27;, &#x27;2023-05-09T15:52:05.000000000&#x27;,\n",
" &#x27;2023-05-09T15:52:13.000000000&#x27;, &#x27;2023-05-09T15:52:21.000000000&#x27;,\n",
" &#x27;2023-05-09T15:52:29.000000000&#x27;, &#x27;2023-05-09T15:52:37.000000000&#x27;,\n",
" &#x27;2023-05-09T15:52:45.000000000&#x27;, &#x27;2023-05-09T15:52:53.000000000&#x27;,\n",
" &#x27;2023-05-09T15:53:01.000000000&#x27;, &#x27;2023-05-09T15:53:09.000000000&#x27;,\n",
" &#x27;2023-05-09T15:53:17.000000000&#x27;, &#x27;2023-05-09T15:53:25.000000000&#x27;,\n",
" &#x27;2023-05-09T15:53:33.000000000&#x27;, &#x27;2023-05-09T15:53:41.000000000&#x27;,\n",
" &#x27;2023-05-09T15:53:49.000000000&#x27;, &#x27;2023-05-09T15:53:57.000000000&#x27;,\n",
" &#x27;2023-05-09T15:54:05.000000000&#x27;, &#x27;2023-05-09T15:54:13.000000000&#x27;,\n",
" &#x27;2023-05-09T15:54:21.000000000&#x27;, &#x27;2023-05-09T15:54:29.000000000&#x27;,\n",
" &#x27;2023-05-09T15:54:37.000000000&#x27;, &#x27;2023-05-09T15:54:45.000000000&#x27;,\n",
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" &#x27;2023-05-09T15:55:09.000000000&#x27;, &#x27;2023-05-09T15:55:17.000000000&#x27;,\n",
" &#x27;2023-05-09T15:55:25.000000000&#x27;, &#x27;2023-05-09T15:55:33.000000000&#x27;,\n",
" &#x27;2023-05-09T15:55:41.000000000&#x27;, &#x27;2023-05-09T15:55:49.000000000&#x27;,\n",
" &#x27;2023-05-09T15:55:57.000000000&#x27;, &#x27;2023-05-09T15:56:05.000000000&#x27;,\n",
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test fft
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" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-17f5e238-5610-487b-a37e-5dba450723f9' class='xr-section-summary-in' type='checkbox' ><label for='section-17f5e238-5610-487b-a37e-5dba450723f9' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>runs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-37314a82-908e-4d92-9dcc-3bf449a409a2' class='xr-index-data-in' type='checkbox'/><label for='index-37314a82-908e-4d92-9dcc-3bf449a409a2' 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, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0,\n",
prepare to add fft
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" 9.0,\n",
" ...\n",
" 540.0, 541.0, 542.0, 543.0, 544.0, 545.0, 546.0, 547.0, 548.0,\n",
" 549.0],\n",
test fft
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" dtype=&#x27;float64&#x27;, name=&#x27;runs&#x27;, length=550))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-925fbf77-16f9-48bc-b0e4-7231ed587a6e' class='xr-section-summary-in' type='checkbox' ><label for='section-925fbf77-16f9-48bc-b0e4-7231ed587a6e' class='xr-section-summary' >Attributes: <span>(101)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>TOF_free :</span></dt><dd>0.02</dd><dt><span>abs_img_freq :</span></dt><dd>110.858</dd><dt><span>absorption_imaging_flag :</span></dt><dd>True</dd><dt><span>backup_data :</span></dt><dd>True</dd><dt><span>blink_off_time :</span></dt><dd>nan</dd><dt><span>blink_on_time :</span></dt><dd>nan</dd><dt><span>c_duration :</span></dt><dd>0.2</dd><dt><span>cmot_final_current :</span></dt><dd>0.65</dd><dt><span>cmot_hold :</span></dt><dd>0.06</dd><dt><span>cmot_initial_current :</span></dt><dd>0.18</dd><dt><span>compX_current :</span></dt><dd>0.005</dd><dt><span>compX_current_sg :</span></dt><dd>0</dd><dt><span>compX_final_current :</span></dt><dd>0.005</dd><dt><span>compX_initial_current :</span></dt><dd>0.005</dd><dt><span>compY_current :</span></dt><dd>0</dd><dt><span>compY_current_sg :</span></dt><dd>0</dd><dt><span>compY_final_current :</span></dt><dd>0.0</dd><dt><span>compY_initial_current :</span></dt><dd>0</dd><dt><span>compZ_current :</span></dt><dd>0</dd><dt><span>compZ_current_sg :</span></dt><dd>0.189</dd><dt><span>compZ_final_current :</span></dt><dd>0.2729</dd><dt><span>compZ_initial_current :</span></dt><dd>0</dd><dt><span>default_camera :</span></dt><dd>0</dd><dt><span>evap_1_arm_1_final_pow :</span></dt><dd>0.35</dd><dt><span>evap_1_arm_1_mod_depth_final :</span></dt><dd>0</dd><dt><span>evap_1_arm_1_mod_depth_initial :</span></dt><dd>1.0</dd><dt><span>evap_1_arm_1_mod_ramp_duration :</span></dt><dd>1.15</dd><dt><span>evap_1_arm_1_pow_ramp_duration :</span></dt><dd>1.65</dd><dt><span>evap_1_arm_1_start_pow :</span></dt><dd>7</dd><dt><span>evap_1_arm_2_final_pow :</span></dt><dd>5</dd><dt><span>evap_1_arm_2_ramp_duration :</span></dt><dd>0.5</dd><dt><span>evap_1_arm_2_start_pow :</span></dt><dd>0</dd><dt><span>evap_1_mod_ramp_trunc_value :</span></dt><dd>1</dd><dt><span>evap_1_pow_ramp_trunc_value :</span></dt><dd>1.0</dd><dt><span>evap_1_rate_constant_1 :</span></dt><dd>0.525</dd><dt><span>evap_1_rate_constant_2 :</span></dt><dd>0.51</dd><dt><span>evap_2_arm_1_final_pow :</span></dt><dd>0.037</dd><dt><span>evap_2_arm_1_start_pow :</span></dt><dd>0.35</dd><dt><span>evap_2_arm_2_final_pow :</span></dt><dd>0.09</dd><dt><span>evap_2_arm_2_start_pow :</span></dt><dd>5</dd><dt><span>evap_2_ramp_duration :</span></dt><dd>1.0</dd><dt><span>evap_2_ramp_trunc_value :</span></dt><dd>1.0</dd><dt><span>evap_2_rate_constant_1 :</span></dt><dd>0.37</dd><dt><span>evap_2_rate_constant_2 :</span></dt><dd>0.71</dd><dt><span>evap_3_arm_1_final_pow :</span></dt><dd>0.1038</dd><dt><span>evap_3_arm_1_mod_depth_final :</span></dt><dd>0.43</dd><dt><span>evap_3_arm_1_mod_depth_initial :</span></dt><dd>0</dd><dt><span>evap_3_arm_1_start_pow :</span></dt><dd>0.037</dd><dt><span>evap_3_ramp_duration :</span></dt><dd>0.1</dd><dt><span>evap_3_ramp_trunc_value :</span></dt><dd>1.0</dd><dt><span>evap_3_rate_constant_1 :</span></dt><dd>-0.879</dd><dt><span>evap_3_rate_constant_2 :</span></dt><dd>-0.297</dd><dt><span>final_amp :</span></dt><dd>0.0001</dd><dt><span>final_freq :</span></dt><dd>104.0</dd><dt><span>gradCoil_current :</span></dt><dd>0.18</dd><dt><span>gradCoil_current_sg :</span></dt><dd>0</dd><dt><span>imaging_method :</span></dt><dd>in_situ_absorption</dd><dt><span>imaging_pulse_duration :</span></dt><dd>2.5e-05</dd><dt><span>imaging_wavelength :</span></dt><dd>4.21291e-07</dd><dt><span>initial_amp :</span></dt><dd>0.62</dd><dt><span>initial_freq :</span></dt><dd>102.13</dd><dt><span>mod_depth_initial :</span></dt><dd>1.0</dd><dt><span>mot_3d_amp :</span></dt><dd>0.62</dd><dt><span>mot_3d_camera_exposure_time :</span></dt><dd>0.002</dd><dt><span>mot_3d_camera_tri
prepare to add fft
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" 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27.\n",
" 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41.\n",
" 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54. 55.\n",
" 56. 57. 58. 59. 60. 61. 62. 63. 64. 65. 66. 67. 68. 69.\n",
" 70. 71. 72. 73. 74. 75. 76. 77. 78. 79. 80. 81. 82. 83.\n",
" 84. 85. 86. 87. 88. 89. 90. 91. 92. 93. 94. 95. 96. 97.\n",
" 98. 99. 100. 101. 102. 103. 104. 105. 106. 107. 108. 109. 110. 111.\n",
" 112. 113. 114. 115. 116. 117. 118. 119. 120. 121. 122. 123. 124. 125.\n",
" 126. 127. 128. 129. 130. 131. 132. 133. 134. 135. 136. 137. 138. 139.\n",
" 140. 141. 142. 143. 144. 145. 146. 147. 148. 149. 150. 151. 152. 153.\n",
" 154. 155. 156. 157. 158. 159. 160. 161. 162. 163. 164. 165. 166. 167.\n",
" 168. 169. 170. 171. 172. 173. 174. 175. 176. 177. 178. 179. 180. 181.\n",
" 182. 183. 184. 185. 186. 187. 188. 189. 190. 191. 192. 193. 194. 195.\n",
" 196. 197. 198. 199. 200. 201. 202. 203. 204. 205. 206. 207. 208. 209.\n",
" 210. 211. 212. 213. 214. 215. 216. 217. 218. 219. 220. 221. 222. 223.\n",
" 224. 225. 226. 227. 228. 229. 230. 231. 232. 233. 234. 235. 236. 237.\n",
" 238. 239. 240. 241. 242. 243. 244. 245. 246. 247. 248. 249. 250. 251.\n",
" 252. 253. 254. 255. 256. 257. 258. 259. 260. 261. 262. 263. 264. 265.\n",
" 266. 267. 268. 269. 270. 271. 272. 273. 274. 275. 276. 277. 278. 279.\n",
" 280. 281. 282. 283. 284. 285. 286. 287. 288. 289. 290. 291. 292. 293.\n",
" 294. 295. 296. 297. 298. 299. 300. 301. 302. 303. 304. 305. 306. 307.\n",
" 308. 309. 310. 311. 312. 313. 314. 315. 316. 317. 318. 319. 320. 321.\n",
" 322. 323. 324. 325. 326. 327. 328. 329. 330. 331. 332. 333. 334. 335.\n",
" 336. 337. 338. 339. 340. 341. 342. 343. 344. 345. 346. 347. 348. 349.\n",
" 350. 351. 352. 353. 354. 355. 356. 357. 358. 359. 360. 361. 362. 363.\n",
" 364. 365. 366. 367. 368. 369. 370. 371. 372. 373. 374. 375. 376. 377.\n",
" 378. 379. 380. 381. 382. 383. 384. 385. 386. 387. 388. 389. 390. 391.\n",
" 392. 393. 394. 395. 396. 397. 398. 399. 400. 401. 402. 403. 404. 405.\n",
" 406. 407. 408. 409. 410. 411. 412. 413. 414. 415. 416. 417. 418. 419.\n",
" 420. 421. 422. 423. 424. 425. 426. 427. 428. 429. 430. 431. 432. 433.\n",
" 434. 435. 436. 437. 438. 439. 440. 441. 442. 443. 444. 445. 446. 447.\n",
" 448. 449. 450. 451. 452. 453. 454. 455. 456. 457. 458. 459. 460. 461.\n",
" 462. 463. 464. 465. 466. 467. 468. 469. 470. 471. 472. 473. 474. 475.\n",
" 476. 477. 478. 479. 480. 481. 482. 483. 484. 485. 486. 487. 488. 489.\n",
" 490. 491. 492. 493. 494. 495. 496. 497. 498. 499. 500. 501. 502. 503.\n",
" 504. 505. 506. 507. 508. 509. 510. 511. 512. 513. 514. 515. 516. 517.\n",
" 518. 519. 520. 521. 522. 523. 524. 525. 526. 527. 528. 529. 530. 531.\n",
" 532. 533. 534. 535. 536. 537. 538. 539. 540. 541. 542. 543. 544. 545.\n",
" 546. 547. 548. 549.]</dd><dt><span>scanAxis :</span></dt><dd>[&#x27;runs&#x27;]</dd><dt><span>scanAxisLength :</span></dt><dd>[550.]</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
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"Coordinates:\n",
" * runs (runs) float64 0.0 1.0 2.0 3.0 4.0 ... 546.0 547.0 548.0 549.0\n",
"Data variables:\n",
" runTine (runs) datetime64[ns] 2023-05-09T14:30:03 ... 2023-05-09T15:56:53\n",
"Attributes: (12/101)\n",
" TOF_free: 0.02\n",
" abs_img_freq: 110.858\n",
" absorption_imaging_flag: True\n",
" backup_data: True\n",
" blink_off_time: nan\n",
" blink_on_time: nan\n",
" ... ...\n",
" y_offset_img: 0\n",
" z_offset: 0.189\n",
" z_offset_img: 0.189\n",
" runs: [ 0. 1. 2. 3. 4. 5. 6. ...\n",
" scanAxis: ['runs']\n",
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]
},
test fft
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"execution_count": 21,
prepare to add fft
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
test fft
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"i=0\n",
"runTime = read_hdf5_run_time(filePath, datesetOfGlobal=dataSetOfGlobalDict[dskey[groupList[i]]])\n",
"runTime"
prepare to add fft
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]
},
{
"cell_type": "code",
test fft
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"execution_count": 22,
prepare to add fft
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"metadata": {},
test fft
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"outputs": [
{
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".xr-var-list,\n",
".xr-var-item {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-var-item > div,\n",
".xr-var-item label,\n",
".xr-var-item > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-even);\n",
" margin-bottom: 0;\n",
"}\n",
"\n",
".xr-var-item > .xr-var-name:hover span {\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-var-list > li:nth-child(odd) > div,\n",
".xr-var-list > li:nth-child(odd) > label,\n",
".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-odd);\n",
"}\n",
"\n",
".xr-var-name {\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-var-dims {\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-var-dtype {\n",
" grid-column: 3;\n",
" text-align: right;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-var-preview {\n",
" grid-column: 4;\n",
"}\n",
"\n",
".xr-index-preview {\n",
" grid-column: 2 / 5;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-var-name,\n",
".xr-var-dims,\n",
".xr-var-dtype,\n",
".xr-preview,\n",
".xr-attrs dt {\n",
" white-space: nowrap;\n",
" overflow: hidden;\n",
" text-overflow: ellipsis;\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-var-name:hover,\n",
".xr-var-dims:hover,\n",
".xr-var-dtype:hover,\n",
".xr-attrs dt:hover {\n",
" overflow: visible;\n",
" width: auto;\n",
" z-index: 1;\n",
"}\n",
"\n",
".xr-var-attrs,\n",
".xr-var-data,\n",
".xr-index-data {\n",
" display: none;\n",
" background-color: var(--xr-background-color) !important;\n",
" padding-bottom: 5px !important;\n",
"}\n",
"\n",
".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
".xr-var-data-in:checked ~ .xr-var-data,\n",
".xr-index-data-in:checked ~ .xr-index-data {\n",
" display: block;\n",
"}\n",
"\n",
".xr-var-data > table {\n",
" float: right;\n",
"}\n",
"\n",
".xr-var-name span,\n",
".xr-var-data,\n",
".xr-index-name div,\n",
".xr-index-data,\n",
".xr-attrs {\n",
" padding-left: 25px !important;\n",
"}\n",
"\n",
".xr-attrs,\n",
".xr-var-attrs,\n",
".xr-var-data,\n",
".xr-index-data {\n",
" grid-column: 1 / -1;\n",
"}\n",
"\n",
"dl.xr-attrs {\n",
" padding: 0;\n",
" margin: 0;\n",
" display: grid;\n",
" grid-template-columns: 125px auto;\n",
"}\n",
"\n",
".xr-attrs dt,\n",
".xr-attrs dd {\n",
" padding: 0;\n",
" margin: 0;\n",
" float: left;\n",
" padding-right: 10px;\n",
" width: auto;\n",
"}\n",
"\n",
".xr-attrs dt {\n",
" font-weight: normal;\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-attrs dt:hover span {\n",
" display: inline-block;\n",
" background: var(--xr-background-color);\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-attrs dd {\n",
" grid-column: 2;\n",
" white-space: pre-wrap;\n",
" word-break: break-all;\n",
"}\n",
"\n",
".xr-icon-database,\n",
".xr-icon-file-text2,\n",
".xr-no-icon {\n",
" display: inline-block;\n",
" vertical-align: middle;\n",
" width: 1em;\n",
" height: 1.5em !important;\n",
" stroke-width: 0;\n",
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;OD&#x27; (time: 550)&gt;\n",
"array([ 750.47641876, 738.34281204, 784.41476569, 796.02169322,\n",
" 952.51855344, 882.92079597, 863.59651678, 866.57709198,\n",
" 941.99125428, 783.16551019, 946.27689189, 918.33176133,\n",
" 941.81141492, 947.74774665, 892.61913887, 977.17520626,\n",
" 945.34126351, 956.52682689, 804.78165476, 939.49484698,\n",
" 953.56682753, 879.61475127, 846.05592616, 830.90774024,\n",
" 910.80224254, 839.43361196, 863.23083974, 873.50170576,\n",
" 850.29285459, 949.59349556, 707.93266373, 946.74069024,\n",
" 941.71185143, 946.57095286, 914.32343568, 947.09283187,\n",
" 954.03294364, 784.23261906, 786.97273688, 832.62952621,\n",
" 903.46885276, 794.84132388, 987.33131008, 920.97693631,\n",
" 982.49210229, 790.82171889, 796.04783468, 672.41580595,\n",
" 726.07270248, 709.64654892, 820.34697312, 839.24755133,\n",
" 830.20821813, 905.60581009, 832.01909227, 614.3819873 ,\n",
" 723.89815083, 930.88065587, 825.30243762, 842.16853182,\n",
" 960.03822443, 970.87588969, 867.93951095, 796.77918204,\n",
" 715.07236109, 867.86554561, 949.15778283, 938.56330193,\n",
" 857.52360377, 880.71776388, 856.94886599, 923.54732893,\n",
" 840.56332593, 934.82056594, 938.21743126, 841.27262899,\n",
" 935.776538 , 810.94173848, 926.17365109, 746.68729357,\n",
"...\n",
" 865.51482127, 833.61692314, 821.20906768, 933.87516973,\n",
" 810.80092789, 824.63722508, 859.85285532, 913.23783203,\n",
" 789.32182143, 814.52479359, 843.87902457, 857.31154799,\n",
" 896.47897516, 872.95758519, 761.01860691, 806.85333498,\n",
" 947.18607913, 882.95786654, 660.90304299, 779.06534297,\n",
" 824.68260644, 960.00725562, 931.83023265, 925.32091745,\n",
" 876.67147414, 808.28701944, 865.12927984, 907.22865863,\n",
" 849.53390823, 827.70871779, 726.90703872, 878.79705242,\n",
" 960.28888691, 750.46295033, 903.46216093, 862.60511899,\n",
" 956.07697944, 881.35524969, 837.32695128, 791.87607618,\n",
" 811.78036383, 902.4373154 , 942.28581666, 874.3906838 ,\n",
" 896.64409276, 787.28302139, 963.13514734, 877.87315412,\n",
" 833.86614596, 826.5946265 , 735.16788438, 922.53477054,\n",
" 880.6268579 , 867.12639832, 852.01398293, 828.11720597,\n",
" 891.6310036 , 807.47838578, 895.25022758, 822.18630467,\n",
" 943.8055441 , 845.66585589, 729.57792525, 884.88667118,\n",
" 796.64506694, 855.18595889, 803.11938466, 832.46778894,\n",
" 858.2150589 , 937.40605043, 853.13728532, 910.90015676,\n",
" 780.99561864, 883.83375992, 804.26394636, 978.32360651,\n",
" 901.75651529, 884.02352999])\n",
"Coordinates:\n",
" * time (time) datetime64[ns] 2023-05-09T14:30:03 ... 2023-05-09T15:56:53</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'OD'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 550</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-4c8959c9-9de2-4cde-b4aa-7cd7c662f9ad' class='xr-array-in' type='checkbox' checked><label for='section-4c8959c9-9de2-4cde-b4aa-7cd7c662f9ad' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>750.5 738.3 784.4 796.0 952.5 882.9 ... 883.8 804.3 978.3 901.8 884.0</span></div><div class='xr-array-data'><pre>array([ 750.47641876, 738.34281204, 784.41476569, 796.02169322,\n",
" 952.51855344, 882.92079597, 863.59651678, 866.57709198,\n",
" 941.99125428, 783.16551019, 946.27689189, 918.33176133,\n",
" 941.81141492, 947.74774665, 892.61913887, 977.17520626,\n",
" 945.34126351, 956.52682689, 804.78165476, 939.49484698,\n",
" 953.56682753, 879.61475127, 846.05592616, 830.90774024,\n",
" 910.80224254, 839.43361196, 863.23083974, 873.50170576,\n",
" 850.29285459, 949.59349556, 707.93266373, 946.74069024,\n",
" 941.71185143, 946.57095286, 914.32343568, 947.09283187,\n",
" 954.03294364, 784.23261906, 786.97273688, 832.62952621,\n",
" 903.46885276, 794.84132388, 987.33131008, 920.97693631,\n",
" 982.49210229, 790.82171889, 796.04783468, 672.41580595,\n",
" 726.07270248, 709.64654892, 820.34697312, 839.24755133,\n",
" 830.20821813, 905.60581009, 832.01909227, 614.3819873 ,\n",
" 723.89815083, 930.88065587, 825.30243762, 842.16853182,\n",
" 960.03822443, 970.87588969, 867.93951095, 796.77918204,\n",
" 715.07236109, 867.86554561, 949.15778283, 938.56330193,\n",
" 857.52360377, 880.71776388, 856.94886599, 923.54732893,\n",
" 840.56332593, 934.82056594, 938.21743126, 841.27262899,\n",
" 935.776538 , 810.94173848, 926.17365109, 746.68729357,\n",
"...\n",
" 865.51482127, 833.61692314, 821.20906768, 933.87516973,\n",
" 810.80092789, 824.63722508, 859.85285532, 913.23783203,\n",
" 789.32182143, 814.52479359, 843.87902457, 857.31154799,\n",
" 896.47897516, 872.95758519, 761.01860691, 806.85333498,\n",
" 947.18607913, 882.95786654, 660.90304299, 779.06534297,\n",
" 824.68260644, 960.00725562, 931.83023265, 925.32091745,\n",
" 876.67147414, 808.28701944, 865.12927984, 907.22865863,\n",
" 849.53390823, 827.70871779, 726.90703872, 878.79705242,\n",
" 960.28888691, 750.46295033, 903.46216093, 862.60511899,\n",
" 956.07697944, 881.35524969, 837.32695128, 791.87607618,\n",
" 811.78036383, 902.4373154 , 942.28581666, 874.3906838 ,\n",
" 896.64409276, 787.28302139, 963.13514734, 877.87315412,\n",
" 833.86614596, 826.5946265 , 735.16788438, 922.53477054,\n",
" 880.6268579 , 867.12639832, 852.01398293, 828.11720597,\n",
" 891.6310036 , 807.47838578, 895.25022758, 822.18630467,\n",
" 943.8055441 , 845.66585589, 729.57792525, 884.88667118,\n",
" 796.64506694, 855.18595889, 803.11938466, 832.46778894,\n",
" 858.2150589 , 937.40605043, 853.13728532, 910.90015676,\n",
" 780.99561864, 883.83375992, 804.26394636, 978.32360651,\n",
" 901.75651529, 884.02352999])</pre></div></div></li><li class='xr-section-item'><input id='section-f804cc38-6645-4485-b406-6754104ee681' class='xr-section-summary-in' type='checkbox' checked><label for='section-f804cc38-6645-4485-b406-6754104ee681' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2023-05-09T14:30:03 ... 2023-05-...</div><input id='attrs-9213bcb7-5ee6-48cf-b09a-0c5cd151b8af' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9213bcb7-5ee6-48cf-b09a-0c5cd151b8af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42a72a86-fa48-4c92-9382-71700e4cf714' class='xr-var-data-in' type='checkbox'><label for='data-42a72a86-fa48-4c92-9382-71700e4cf714' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2023-05-09T14:30:03.000000000&#x27;, &#x27;2023-05-09T14:30:11.000000000&#x27;,\n",
" &#x27;2023-05-09T14:30:19.000000000&#x27;, ..., &#x27;2023-05-09T15:56:37.000000000&#x27;,\n",
" &#x27;2023-05-09T15:56:45.000000000&#x27;, &#x27;2023-05-09T15:56:53.000000000&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-94c26d6c-b870-4a89-9cbc-a73faa341448' class='xr-section-summary-in' type='checkbox' ><label for='section-94c26d6c-b870-4a89-9cbc-a73faa341448' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ff35afc6-833c-4fd3-a313-3442ff8b7a76' class='xr-index-data-in' type='checkbox'/><label for='index-ff35afc6-833c-4fd3-a313-3442ff8b7a76' 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(DatetimeIndex([&#x27;2023-05-09 14:30:03&#x27;, &#x27;2023-05-09 14:30:11&#x27;,\n",
" &#x27;2023-05-09 14:30:19&#x27;, &#x27;2023-05-09 14:30:27&#x27;,\n",
" &#x27;2023-05-09 14:30:35&#x27;, &#x27;2023-05-09 14:30:43&#x27;,\n",
" &#x27;2023-05-09 14:30:52&#x27;, &#x27;2023-05-09 14:31:00&#x27;,\n",
" &#x27;2023-05-09 14:31:08&#x27;, &#x27;2023-05-09 14:31:16&#x27;,\n",
" ...\n",
" &#x27;2023-05-09 15:55:41&#x27;, &#x27;2023-05-09 15:55:49&#x27;,\n",
" &#x27;2023-05-09 15:55:57&#x27;, &#x27;2023-05-09 15:56:05&#x27;,\n",
" &#x27;2023-05-09 15:56:13&#x27;, &#x27;2023-05-09 15:56:21&#x27;,\n",
" &#x27;2023-05-09 15:56:29&#x27;, &#x27;2023-05-09 15:56:37&#x27;,\n",
" &#x27;2023-05-09 15:56:45&#x27;, &#x27;2023-05-09 15:56:53&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=550, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-07416fc0-8e7f-46a6-b75f-5eaaa11e8225' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-07416fc0-8e7f-46a6-b75f-5eaaa11e8225' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray 'OD' (time: 550)>\n",
"array([ 750.47641876, 738.34281204, 784.41476569, 796.02169322,\n",
" 952.51855344, 882.92079597, 863.59651678, 866.57709198,\n",
" 941.99125428, 783.16551019, 946.27689189, 918.33176133,\n",
" 941.81141492, 947.74774665, 892.61913887, 977.17520626,\n",
" 945.34126351, 956.52682689, 804.78165476, 939.49484698,\n",
" 953.56682753, 879.61475127, 846.05592616, 830.90774024,\n",
" 910.80224254, 839.43361196, 863.23083974, 873.50170576,\n",
" 850.29285459, 949.59349556, 707.93266373, 946.74069024,\n",
" 941.71185143, 946.57095286, 914.32343568, 947.09283187,\n",
" 954.03294364, 784.23261906, 786.97273688, 832.62952621,\n",
" 903.46885276, 794.84132388, 987.33131008, 920.97693631,\n",
" 982.49210229, 790.82171889, 796.04783468, 672.41580595,\n",
" 726.07270248, 709.64654892, 820.34697312, 839.24755133,\n",
" 830.20821813, 905.60581009, 832.01909227, 614.3819873 ,\n",
" 723.89815083, 930.88065587, 825.30243762, 842.16853182,\n",
" 960.03822443, 970.87588969, 867.93951095, 796.77918204,\n",
" 715.07236109, 867.86554561, 949.15778283, 938.56330193,\n",
" 857.52360377, 880.71776388, 856.94886599, 923.54732893,\n",
" 840.56332593, 934.82056594, 938.21743126, 841.27262899,\n",
" 935.776538 , 810.94173848, 926.17365109, 746.68729357,\n",
"...\n",
" 865.51482127, 833.61692314, 821.20906768, 933.87516973,\n",
" 810.80092789, 824.63722508, 859.85285532, 913.23783203,\n",
" 789.32182143, 814.52479359, 843.87902457, 857.31154799,\n",
" 896.47897516, 872.95758519, 761.01860691, 806.85333498,\n",
" 947.18607913, 882.95786654, 660.90304299, 779.06534297,\n",
" 824.68260644, 960.00725562, 931.83023265, 925.32091745,\n",
" 876.67147414, 808.28701944, 865.12927984, 907.22865863,\n",
" 849.53390823, 827.70871779, 726.90703872, 878.79705242,\n",
" 960.28888691, 750.46295033, 903.46216093, 862.60511899,\n",
" 956.07697944, 881.35524969, 837.32695128, 791.87607618,\n",
" 811.78036383, 902.4373154 , 942.28581666, 874.3906838 ,\n",
" 896.64409276, 787.28302139, 963.13514734, 877.87315412,\n",
" 833.86614596, 826.5946265 , 735.16788438, 922.53477054,\n",
" 880.6268579 , 867.12639832, 852.01398293, 828.11720597,\n",
" 891.6310036 , 807.47838578, 895.25022758, 822.18630467,\n",
" 943.8055441 , 845.66585589, 729.57792525, 884.88667118,\n",
" 796.64506694, 855.18595889, 803.11938466, 832.46778894,\n",
" 858.2150589 , 937.40605043, 853.13728532, 910.90015676,\n",
" 780.99561864, 883.83375992, 804.26394636, 978.32360651,\n",
" 901.75651529, 884.02352999])\n",
"Coordinates:\n",
" * time (time) datetime64[ns] 2023-05-09T14:30:03 ... 2023-05-09T15:56:53"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Ncount_time = xr.DataArray(\n",
" data=Ncount,\n",
" dims=[\"time\"],\n",
" coords={\n",
" \"time\": runTime.runTine.to_numpy(),\n",
" }\n",
")\n",
"Ncount_time"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x20143f96310>]"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"Ncount_time.plot()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0.0, 70000.0)"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"Ncount_time_interp = Ncount_time.interp(time=pd.date_range(\"2023-05-09T14:30:03.000000000\", \"2023-05-09T15:56:53.000000000\", periods=500))\n",
"da_fft = xrft.fft(Ncount_time_interp)\n",
"da_fft_amp = np.abs(da_fft)\n",
"# da_fft_amp.isel(freq_time=range(251,370)).plot()\n",
"da_fft_amp.plot()\n",
"# plt.xlim([-0.05, 0.05])\n",
"plt.ylim([0, 7e4])"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0.0, 70000.0)"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# da_test2.isel(time=range(300)).plot()\n",
"da_fft = xrft.fft(\n",
" Ncount_time.isel(time=range(300)).interp(\n",
" time=pd.date_range(\n",
" Ncount_time.time[0].item(), Ncount_time.time[299].item(), periods=300\n",
" # \"2023-05-09T14:30:03.000000000\", \"2023-05-09T15:10:06.000000000\", periods=300\n",
" )\n",
" )\n",
")\n",
"# np.abs(da_fft).isel(freq_time=range(151,300)).plot()\n",
"np.abs(da_fft).plot()\n",
"# plt.xlim([0, 0.003])\n",
"plt.ylim([0, 7e4])\n",
"# plt.yscale(\"log\")"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;runTine&#x27; ()&gt;\n",
"array(&#x27;2023-05-09T14:30:03.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)\n",
"Coordinates:\n",
" runs float64 0.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'runTine'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e40fa033-8c32-47ea-98d9-24143f39f680' class='xr-array-in' type='checkbox' checked><label for='section-e40fa033-8c32-47ea-98d9-24143f39f680' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>2023-05-09T14:30:03</span></div><div class='xr-array-data'><pre>array(&#x27;2023-05-09T14:30:03.000000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></div></li><li class='xr-section-item'><input id='section-d17bf513-b085-4af2-bd47-8045c34e24c7' class='xr-section-summary-in' type='checkbox' checked><label for='section-d17bf513-b085-4af2-bd47-8045c34e24c7' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>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-019e4ba7-2972-457b-98cc-6ce8928efedc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-019e4ba7-2972-457b-98cc-6ce8928efedc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9bd41e9-b097-40f7-8510-4328447f3ed4' class='xr-var-data-in' type='checkbox'><label for='data-b9bd41e9-b097-40f7-8510-4328447f3ed4' 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-1f893e4b-97d8-4ccd-9b9a-a87f71e07633' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1f893e4b-97d8-4ccd-9b9a-a87f71e07633' class='xr-section-summary' title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-2e049df9-56e1-46f5-967e-fe771c1acf85' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2e049df9-56e1-46f5-967e-fe771c1acf85' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray 'runTine' ()>\n",
"array('2023-05-09T14:30:03.000000000', dtype='datetime64[ns]')\n",
"Coordinates:\n",
" runs float64 0.0"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"runTime.runTine[0]"
]
},
{
prepare to add fft
2023-05-18 16:09:20 +02:00
"cell_type": "code",
test fft
2023-05-19 09:34:58 +02:00
"execution_count": 27,
prepare to add fft
2023-05-18 16:09:20 +02:00
"metadata": {},
test fft
2023-05-19 09:34:58 +02:00
"outputs": [
{
"data": {
"text/plain": [
"(0.0, 100000.0)"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import finufft\n",
"\n",
"Ncount_time_array = Ncount_time.to_numpy()\n",
"time = runTime.runTine.to_numpy()\n",
"time0 = int(time[0])\n",
"time = np.array(\n",
" [\n",
" float(value) - time0\n",
" for value in time\n",
" ]\n",
")\n",
"time = time / time.max() * 2 * np.pi\n",
"\n",
"# desired number of Fourier modes (uniform outputs)\n",
"N = 1001\n",
"\n",
"# calculate the transform\n",
"Ncount_time_array = Ncount_time.to_numpy()\n",
"Ncount_time_array = np.array(Ncount_time_array, dtype=complex)\n",
"f = xr.DataArray(\n",
" data=finufft.nufft1d1(time, Ncount_time_array, N),\n",
" dims=['time_freq'],\n",
" coords={\n",
" \"time_freq\":np.linspace(-0.125/2,0.125/2,N)\n",
" }\n",
")\n",
"\n",
"np.abs(f).plot()\n",
"plt.xlim([0, 0.002])\n",
"plt.ylim([0, 1e5])"
]
prepare to add fft
2023-05-18 16:09:20 +02:00
},
{
"cell_type": "code",
test fft
2023-05-19 09:34:58 +02:00
"execution_count": 28,
prepare to add fft
2023-05-18 16:09:20 +02:00
"metadata": {},
test fft
2023-05-19 09:34:58 +02:00
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x20154dc4ac0>]"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAacAAAEaCAYAAAC1u5gzAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAsbElEQVR4nO3deZxUxbn/8c8zwMywLwKyKCIo4oKgIi6JEZebKEaTmOtVo7nXxOUaV4w3192oMTG/RNFIzILmijFqFLeIMTFuEIyioIAboKIssu/bzDBb/f6o6qGn5/RMd892mvm+X69+9XSd51RXdff006dOnXPMOYeIiEicFLR2A0RERFIpOYmISOwoOYmISOwoOYmISOwoOYmISOwoOYmISOwoOYmISOxklJzMbD8ze8TM5pvZZjMrMbMFZjbBzPqniX/WzDaa2XYzm2Fmx6epu7uZTTSz5WZWZmYfmtkPzMwiYgvM7Krw3GVmtszM7jKzzmnqHmdmb4Q2bDCzKWa2dz19zKjNIiLSvCyTg3DN7ATgBmAm8AVQCYwAvgdsAUY559aE2KHA2yHmHmAzcCFwEHCyc+7lpHoLgdeBQ4CJwHzgZOBbwK3OuVtS2vEr4ArgGeBvwP7A5cAM4ETnXHVS7OnAk8A84H6gOzAeqAJGO+dWJMVm3GYREWl+GSWntCubnQE8AVzjnPtFKHsC+DZwmHNubijrAnwIlAHDXXhSM7sEuA+4wjk3Manep4BTgX2dc0tC2YHA+8AzzrlvJ8VeDtwLnOOcezSUdQAW45PNgc65baF8FPAO8Afn3EVJdWTc5nR69+7tBg8enPmLJyIivPPOO+ucc31Sy9s3st4l4b4nQBheOw2YlviSB3DObTOzB4DbgMPxWykA3wFK8Fs2ye4BTgfOBH4Rys4GLCxLdj/wc+Bc4NFQdiwwALg5kZhCO+aa2TTgTDO71DlXkUObIw0ePJjZs2fXFyIiIinMbElUeVYTIsys2Mx6m9keZvZV4Pdh0Qvh/mCgCHgzYvWZ4f7wUFcBcCgwxzlXlhL7NuASsUnrVZOSJMK6cyNiqacd3YBh2bZZRERaRraz9S4A1gLLgBeBHsC5zrkZYfmAcL88Yt1E2cBw3xPoGBXrnNsBrEuKTdS9LiyLqrt32IeVbTuyiRURkRaQ7bDes8ACoAt+EsNpQO+k5Z3CfVQCKUuJqS82Ed8p6XGnBmITMeVN2I7U2FrM7CLgIoBBgwalaZqIiGQrq+TknPsCP1sP4NkwcWGWmXVyzt2B338EfpgsVXG4L0m5j4pNxJckPS4B+tYTm2ndjYmtxTk3CZgEMHr0aF17RESkiTTqIFzn3HvAHOCSUJSYnh01DJYoSwyVbQRKo2LNrAi/RZY81LYCP3QXlUQG4of8ynNoRzaxIiLSApriDBEdgV7h7/fxw2NHRcQdGe5nA4Rjkt4FDolIOGPwM/OSp7/NCu0dkxxoZsXAqIhY6mnHFuDjbNssIiItI9MzRPRLU34c/kDVmeCnXwNTgbFmNjIprgt+MsUn1J5t9xh+f85F1DYef4zS40llj+Nn8I1Pib0w1PFIUtl0YCVwQXjuRDtGAmOBKc65ihzbLCIizSzTfU6/DacpehV/bFMxcBhwFrAVuDop9jrgBOAfZnY3fivlQvwQ2SkpB7Pejz/LxAQzG4w/Q8Q4/BkibnfOLU4EOufeN7P7gMvM7Gn89PX98WeMmM7OY5wIxy9diU9oM8zsfvz08avwsw1/nNK/bNoskjc+XbON30z7lF98+2Dat9OpNCV/ZJqcHgP+E/gu0Ae/BbMEf5zTL51zSxOBzrlPzexL+ANjrwUK8cN3J6WeBsg5V25mJwK34w+y3Q1YhD8l0X0R7RiPP/PDRcAp+OnmE/EH21YnBzrnpphZKXAjcCd+6O4V/NkslqfEZtxmkXwy/vE5fLB8C987em9G7NG9tZsjkrGMkpNz7gn8aYoy4pybD3wjw9hNwGXh1lBsFXBXuGVS9/PA8xnGZtxmERFpXtrOFxGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2FFyEhGR2MkoOZnZMDO7zcxmmtlaM9tqZnPN7AYz6xwRv5+ZPWtmG81su5nNMLPj09Td3cwmmtlyMyszsw/N7AdmZhGxBWZ2lZktCLHLzOyuqDaE+HFm9kZowwYzm2Jme6eJzbjNIiLSvDLdcvo+cBWwCLgN+BGwELgdeMPMOiYCzWwo8AZwFPCLENsFeNHMTkyu1MwKgZeAi4HHgctDvb8BfhzRjruBCcBHIXYKcAUw1cxq9cXMTgeeBzqGNvwS+ArwLzMbkBKbcZtFRKT5tc8w7kngDufc5qSy35nZJ8ANwPnAr0P5HUAP4DDn3FwAM/sj8CFwn5kNd865EHsBcDhwhXNuYii738yeAq43swedc0tCHQfiE9LTzrlvJxphZp8D9wJnAY+Gsg7ARGAZcIxzblso/xvwDnALcFFSX7Jps0jecejjK/kloy0n59zslMSU8Hi4PwggDK+dBkxLfMmH9bcBDwDD8Mko4TtACXB/Sr33AB2AM5PKzgYsLEt2f6jj3KSyY4EBwAOJxBTaMReYBpwZElgubRYRkWbW2AkRe4T71eH+YKAIeDMidma4Pxz8/iPgUGCOc64sJfZtwFE7KRwOVIdlNcK6cyNiqacd3fBJJ6s2i4hIy8g5OZlZO+AmoJIwnIbfWgFYHrFKomxguO+J3x9UJ9Y5twNYlxSbqHtdWBZVd++wDyvbdmQTK5KXNCgt+aYxW0734CcQ3OycWxjKOoX7qARSlhJTX2wivlPS404NxGZad2NiazGzi8xstpnNXrt2bZqmiYhItnJKTmb2E+AyYJJz7o6kRSXhvihiteKUmPpiE/ElSY9LGojNtO7GxNbinJvknBvtnBvdp0+fNE0TaX3acJJ8k3VyMrNbgBuBB/FTwJOtCPdRw2CJssRQ2UagNCrWzIqA3tQealuBH7qLSiID8UN+5Tm0I5tYERFpAVklp5CYfgw8BFwQMb36ffzw2FERqx8Z7mcDOOeqgXeBQyISzhj8zLzZSWWzQnvHpLSpGBgVEUs97dgCfJxtm0XylY6EkHyTcXIys5vxielh4PshudQSpl9PBcaa2cikdbvgj2n6hNqz7R7D789JPuYIYDx+osXjSWWP40cnxqfEXhjqeCSpbDqwErggPHeiHSOBscAU51xFjm0WEZFmltFBuGZ2KXArsBR4GfhOytmFVjvnXgp/XwecAPzDzO7Gb6VciB8iOyVla+t+4HvABDMbDMwHxgHfAm53zi1OBDrn3jez+4DLzOxp4AVgf/wZIqazc8YgzrkKM7sSn9BmmNn9+OnjVwFrqXv2iWzaLJJ39AGWfJPpGSISx/kMwg/ppZqOPw0RzrlPzexLwM+Ba4FC/PDdSc65l5NXcs6Vh9MD3Y4/yHY3/CmSLgfui3ie8cBi/JbWKfjp5hPxMwZrbck556aYWSl+/9id+KG7V4BrnHPLU2IzbrOIiDS/jJKTc+484LxMK3XOzQe+kWHsJvzMv8syiK0C7gq3TOp+Hn9+vUxiM26ziIg0L10yQ6QN0MC05BslJxERiR0lJ5E2QZtOkl+UnEREJHaUnETaAO1zknyj5CQiIrGj5CTSBmjDSfKNkpOIiMSOkpOIiMSOkpNIG6AJEZJvlJxERCR2lJxE2gCdWF/
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"BEC_Ncount_time = xr.DataArray(\n",
" data=BEC_Ncount_val,\n",
" dims=[\"time\"],\n",
" coords={\n",
" \"time\": runTime.runTine.to_numpy(),\n",
" }\n",
")\n",
"\n",
"BEC_Ncount_time_array = BEC_Ncount_time.to_numpy()\n",
"\n",
"# desired number of Fourier modes (uniform outputs)\n",
"N = 1001\n",
"\n",
"# calculate the transform\n",
"BEC_Ncount_time_array = BEC_Ncount_time.to_numpy()\n",
"BEC_Ncount_time_array = np.array(BEC_Ncount_time_array, dtype=complex)\n",
"f = xr.DataArray(\n",
" data=finufft.nufft1d1(time, BEC_Ncount_time_array, N),\n",
" dims=['time_freq'],\n",
" coords={\n",
" \"time_freq\":np.linspace(-0.125/2,0.125/2,N)\n",
" }\n",
")\n",
"\n",
"np.abs(f).plot()\n",
"# plt.xlim([0, 0.002])\n",
"# plt.ylim([0, 1e5])"
]
prepare to add fft
2023-05-18 16:09:20 +02:00
},
{
"cell_type": "code",
test fft
2023-05-19 09:34:58 +02:00
"execution_count": 29,
prepare to add fft
2023-05-18 16:09:20 +02:00
"metadata": {},
test fft
2023-05-19 09:34:58 +02:00
"outputs": [
{
"data": {
"text/plain": [
"(0.0, 1600.0)"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"BEC_width_y_time = xr.DataArray(\n",
" data=BEC_width_y_val,\n",
" dims=[\"time\"],\n",
" coords={\n",
" \"time\": runTime.runTine.to_numpy(),\n",
" }\n",
")\n",
"\n",
"BEC_width_y_time_array = BEC_width_y_time.to_numpy()\n",
"\n",
"# desired number of Fourier modes (uniform outputs)\n",
"N = 1001\n",
"\n",
"# calculate the transform\n",
"BEC_width_y_time_array = BEC_width_y_time.to_numpy()\n",
"BEC_width_y_time_array = np.array(BEC_width_y_time_array, dtype=complex)\n",
"f = xr.DataArray(\n",
" data=finufft.nufft1d1(time, BEC_width_y_time_array, N),\n",
" dims=['time_freq'],\n",
" coords={\n",
" \"time_freq\":np.linspace(-0.125/2,0.125/2,N)\n",
" }\n",
")\n",
"\n",
"np.abs(f).plot()\n",
"plt.xlim([0, 0.01])\n",
"plt.ylim([0, 1600])"
]
prepare to add fft
2023-05-18 16:09:20 +02:00
},
{
"cell_type": "code",
test fft
2023-05-19 09:34:58 +02:00
"execution_count": 30,
prepare to add fft
2023-05-18 16:09:20 +02:00
"metadata": {},
test fft
2023-05-19 09:34:58 +02:00
"outputs": [
{
"data": {
"text/plain": [
"(0.0, 2000.0)"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"BEC_center_y_time = xr.DataArray(\n",
" data=BEC_center_y_val,\n",
" dims=[\"time\"],\n",
" coords={\n",
" \"time\": runTime.runTine.to_numpy(),\n",
" }\n",
")\n",
"\n",
"# desired number of Fourier modes (uniform outputs)\n",
"N = 1001\n",
"\n",
"# calculate the transform\n",
"BEC_center_y_time_array = BEC_center_y_time.to_numpy()\n",
"BEC_center_y_time_array = np.array(BEC_center_y_time_array, dtype=complex)\n",
"f = xr.DataArray(\n",
" data=finufft.nufft1d1(time, BEC_center_y_time_array, N),\n",
" dims=['time_freq'],\n",
" coords={\n",
" \"time_freq\":np.linspace(-0.125/2,0.125/2,N)\n",
" }\n",
")\n",
"\n",
"np.abs(f).plot()\n",
"plt.xlim([0, 0.01])\n",
"plt.ylim([0, 2000])"
]
prepare to add fft
2023-05-18 16:09:20 +02:00
},
{
"cell_type": "code",
test fft
2023-05-19 09:34:58 +02:00
"execution_count": 53,
prepare to add fft
2023-05-18 16:09:20 +02:00
"metadata": {},
test fft
2023-05-19 09:34:58 +02:00
"outputs": [
{
"ename": "TypeError",
"evalue": "only size-1 arrays can be converted to Python scalars",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32md:\\Jianshun Gao\\Simulations\\analyseScripts\\20230509_Data_Analysis.ipynb Cell 35\u001b[0m in \u001b[0;36m<cell line: 11>\u001b[1;34m()\u001b[0m\n\u001b[0;32m <a href='vscode-notebook-cell://127.0.0.1:8080/d%3A/Jianshun%20Gao/Simulations/analyseScripts/20230509_Data_Analysis.ipynb#X46sdnNjb2RlLXJlbW90ZQ%3D%3D?line=2'>3</a>\u001b[0m a \u001b[39m=\u001b[39m datetime\u001b[39m.\u001b[39mstrptime(\u001b[39m\"\u001b[39m\u001b[39m2023-05-09T14:35:00\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m%\u001b[39m\u001b[39mY-\u001b[39m\u001b[39m%\u001b[39m\u001b[39mm-\u001b[39m\u001b[39m%d\u001b[39;00m\u001b[39mT\u001b[39m\u001b[39m%\u001b[39m\u001b[39mH:\u001b[39m\u001b[39m%\u001b[39m\u001b[39mM:\u001b[39m\u001b[39m%\u001b[39m\u001b[39mS\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m <a href='vscode-notebook-cell://127.0.0.1:8080/d%3A/Jianshun%20Gao/Simulations/analyseScripts/20230509_Data_Analysis.ipynb#X46sdnNjb2RlLXJlbW90ZQ%3D%3D?line=4'>5</a>\u001b[0m a \u001b[39m=\u001b[39m xr\u001b[39m.\u001b[39mDataArray(\n\u001b[0;32m <a href='vscode-notebook-cell://127.0.0.1:8080/d%3A/Jianshun%20Gao/Simulations/analyseScripts/20230509_Data_Analysis.ipynb#X46sdnNjb2RlLXJlbW90ZQ%3D%3D?line=5'>6</a>\u001b[0m data \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mdate_range(\n\u001b[0;32m <a href='vscode-notebook-cell://127.0.0.1:8080/d%3A/Jianshun%20Gao/Simulations/analyseScripts/20230509_Data_Analysis.ipynb#X46sdnNjb2RlLXJlbW90ZQ%3D%3D?line=6'>7</a>\u001b[0m a, Ncount_time\u001b[39m.\u001b[39mtime[\u001b[39m0\u001b[39m]\u001b[39m.\u001b[39mitem(), periods\u001b[39m=\u001b[39m\u001b[39m2\u001b[39m\n\u001b[0;32m <a href='vscode-notebook-cell://127.0.0.1:8080/d%3A/Jianshun%20Gao/Simulations/analyseScripts/20230509_Data_Analysis.ipynb#X46sdnNjb2RlLXJlbW90ZQ%3D%3D?line=7'>8</a>\u001b[0m )\n\u001b[0;32m <a href='vscode-notebook-cell://127.0.0.1:8080/d%3A/Jianshun%20Gao/Simulations/analyseScripts/20230509_Data_Analysis.ipynb#X46sdnNjb2RlLXJlbW90ZQ%3D%3D?line=8'>9</a>\u001b[0m )\n\u001b[1;32m---> <a href='vscode-notebook-cell://127.0.0.1:8080/d%3A/Jianshun%20Gao/Simulations/analyseScripts/20230509_Data_Analysis.ipynb#X46sdnNjb2RlLXJlbW90ZQ%3D%3D?line=10'>11</a>\u001b[0m (\u001b[39mint\u001b[39;49m(a) \u001b[39m-\u001b[39m \u001b[39m1683642540000000000\u001b[39m) \u001b[39m%\u001b[39m \u001b[39m5.4e11\u001b[39m \u001b[39m<\u001b[39m \u001b[39m3.6e11\u001b[39m\n",
"File \u001b[1;32mD:\\Program Files\\Python\\Python38\\Lib\\site-packages\\xarray\\core\\common.py:159\u001b[0m, in \u001b[0;36mAbstractArray.__int__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 158\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__int__\u001b[39m(\u001b[39mself\u001b[39m: Any) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mint\u001b[39m:\n\u001b[1;32m--> 159\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mint\u001b[39;49m(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mvalues)\n",
"\u001b[1;31mTypeError\u001b[0m: only size-1 arrays can be converted to Python scalars"
]
}
],
"source": [
"from datetime import datetime\n",
"\n",
"a = datetime.strptime(\"2023-05-09T14:35:00\", \"%Y-%m-%dT%H:%M:%S\")\n",
"\n",
"a = xr.DataArray(\n",
" data = pd.date_range(\n",
" a, Ncount_time.time[0].item(), periods=2\n",
" )\n",
")\n",
"\n",
"(int(a[0]) - 1683642540000000000) % 5.4e11 < 3.6e11"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"540000000000"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1683643080000000000 - 1683642540000000000"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"datetime.datetime(2023, 5, 9, 14, 30)"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from datetime import datetime\n",
"a = datetime.strptime(\"2023-05-09T14:30:00\", \"%Y-%m-%dT%H:%M:%S\")\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"mask = xr.DataArray(\n",
" data = np.full(runTime.runTine.shape,fill_value=False, dtype=bool),\n",
" dims = [\"time\"],\n",
" coords = {\n",
" \"time\":runTime.runTine.to_numpy()\n",
" }\n",
")\n",
"\n",
"for i in range(len(mask)):\n",
" if (int(mask.time[i]) - 1683642540000000000) % 5.4e11 > 3.6e11:\n",
" mask[i] = True\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"xr.where(mask, np.nan, Ncount_time).plot.errorbar(fmt='ob')\n",
"Ncount_time.where(mask).plot.errorbar(fmt='or')\n",
"\n",
"plt.show()\n"
]
prepare to add fft
2023-05-18 16:09:20 +02:00
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Close to the BEC transition point, in evaporative cooling 2 with truncation value = 0.77"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"shotNum = \"0015\"\n",
"filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n",
"\n",
"dataSetDict = {\n",
" dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n",
" for i in [0]\n",
"}\n",
"\n",
"dataSet = dataSetDict[\"camera_0\"]\n",
"\n",
"print_scanAxis(dataSet)\n",
"\n",
"scanAxis = get_scanAxis(dataSet)\n",
"\n",
"dataSet = auto_rechunk(dataSet)\n",
"\n",
"dataSet = imageAnalyser.get_absorption_images(dataSet)\n",
"\n",
"imageAnalyser.center = (879, 956)\n",
"imageAnalyser.span = (200, 200)\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).load()\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"Ncount.plot.errorbar(ax=ax, fmt='ob')\n",
"\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"Ncount.plot.errorbar(ax=ax, fmt='ob')\n",
"plt.ylim([0, 3000])\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dataSet_cropOD = auto_rechunk(dataSet_cropOD)\n",
"\n",
"fitAnalyser = FitAnalyser(\"Gaussian-2D\", fitDim=2)\n",
"params = fitAnalyser.guess(dataSet_cropOD, dask=\"parallelized\")\n",
"fitResult = fitAnalyser.fit(dataSet_cropOD, params, dask=\"parallelized\").load()\n",
"\n",
"fitValue = fitAnalyser.get_fit_value(fitResult)\n",
"fitStd = fitAnalyser.get_fit_std(fitResult)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"thermal_Ncount_val = fitValue['amplitude']\n",
"thermal_Ncount_std = fitStd['amplitude']\n",
"\n",
"thermal_width_x_val = fitValue['sigmax']\n",
"thermal_width_x_std = fitStd['sigmax']\n",
"thermal_width_y_val = fitValue['sigmay']\n",
"thermal_width_y_std = fitStd['sigmay']\n",
"\n",
"thermal_center_x_val = fitValue['centerx']\n",
"thermal_center_x_std = fitStd['centerx']\n",
"thermal_center_y_val = fitValue['centery']\n",
"thermal_center_y_std = fitStd['centery']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"total_Ncount_val = thermal_Ncount_val\n",
"total_Ncount_std = thermal_Ncount_std\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"total_Ncount_val.plot.errorbar(ax=ax, yerr=total_Ncount_std, fmt='ob')\n",
"plt.ylim([0, 3000])\n",
"plt.ylabel('Ncount from fit')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"thermal_width_x_val.plot.errorbar(ax=ax, yerr=thermal_width_x_std, fmt='or')\n",
"\n",
"plt.ylabel('X-axis width of thermal part')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"thermal_width_y_val.plot.errorbar(ax=ax, yerr=thermal_width_y_std, fmt='or')\n",
"\n",
"plt.ylabel('Y-axis width of thermal part')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"thermal_center_x_val.plot.errorbar(ax=ax, yerr=thermal_center_x_std, fmt='or')\n",
"\n",
"plt.ylabel('X-axis center of thermal part')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"thermal_center_y_val.plot.errorbar(ax=ax, yerr=thermal_center_y_std, fmt='or')\n",
"\n",
"plt.ylabel('Y-axis center of thermal part')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"val = Ncount.mean().item()\n",
"std = Ncount.std().item()\n",
"print(f'The total Ncount is: {val: .2f} \\u00B1 {std: .2f}')\n",
"\n",
"val = total_Ncount_val.mean().item()\n",
"std = total_Ncount_val.std().item()\n",
"print(f'The total Ncount from fit is: {val: .2f} \\u00B1 {std: .2f}')\n",
"\n",
"val = thermal_width_x_val.mean().item()\n",
"std = thermal_width_x_val.std().item()\n",
"print(f'The x-axis width of the thermal part is: {val: .2f} \\u00B1 {std: .2f}')\n",
"\n",
"val = thermal_width_y_val.mean().item()\n",
"std = thermal_width_y_val.std().item()\n",
"print(f'The y-axis width of the thermal part is: {val: .2f} \\u00B1 {std: .2f}')\n",
"\n",
"val = thermal_center_x_val.mean().item()\n",
"std = thermal_center_x_val.std().item()\n",
"print(f'The x-axis center of the thermal part is: {val: .2f} \\u00B1 {std: .2f}')\n",
"\n",
"val = thermal_center_y_val.mean().item()\n",
"std = thermal_center_y_val.std().item()\n",
"print(f'The y-axis center of the thermal part is: {val: .2f} \\u00B1 {std: .2f}')"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## At the end of ODT loading"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"shotNum = \"0020\"\n",
"filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n",
"\n",
"dataSetDict = {\n",
" dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n",
" for i in [0]\n",
"}\n",
"\n",
"dataSet = dataSetDict[\"camera_0\"]\n",
"\n",
"print_scanAxis(dataSet)\n",
"\n",
"scanAxis = get_scanAxis(dataSet)\n",
"\n",
"dataSet = auto_rechunk(dataSet)\n",
"\n",
"dataSet = imageAnalyser.get_absorption_images(dataSet)\n",
"\n",
"imageAnalyser.center = (550, 800)\n",
"imageAnalyser.span = (900, 1600)\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).load()\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"Ncount.plot.errorbar(ax=ax, fmt='ob')\n",
"\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dataSet"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"Ncount.plot.errorbar(ax=ax, fmt='ob')\n",
"plt.ylim([0, 150000])\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dataSet_cropOD = dataSet_cropOD.chunk((1, 900, 1600))\n",
"dataSet_cropOD"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"# dataSet_cropOD = auto_rechunk(dataSet_cropOD)\n",
"\n",
"fitAnalyser = FitAnalyser(\"Gaussian-2D\", fitDim=2)\n",
"params = fitAnalyser.guess(dataSet_cropOD, dask=\"parallelized\")\n",
"fitResult = fitAnalyser.fit(dataSet_cropOD, params, dask=\"parallelized\").load()\n",
"\n",
"fitValue = fitAnalyser.get_fit_value(fitResult)\n",
"fitStd = fitAnalyser.get_fit_std(fitResult)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"thermal_Ncount_val = fitValue['amplitude']\n",
"thermal_Ncount_std = fitStd['amplitude']\n",
"\n",
"thermal_width_x_val = fitValue['sigmax']\n",
"thermal_width_x_std = fitStd['sigmax']\n",
"thermal_width_y_val = fitValue['sigmay']\n",
"thermal_width_y_std = fitStd['sigmay']\n",
"\n",
"thermal_center_x_val = fitValue['centerx']\n",
"thermal_center_x_std = fitStd['centerx']\n",
"thermal_center_y_val = fitValue['centery']\n",
"thermal_center_y_std = fitStd['centery']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"total_Ncount_val = thermal_Ncount_val\n",
"total_Ncount_std = thermal_Ncount_std\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"total_Ncount_val.plot.errorbar(ax=ax, yerr=total_Ncount_std, fmt='ob')\n",
"plt.ylim([0, 160000])\n",
"plt.ylabel('Ncount from fit')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"thermal_width_x_val.plot.errorbar(ax=ax, yerr=thermal_width_x_std, fmt='or')\n",
"\n",
"plt.ylabel('Y-axis width of thermal part')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"thermal_width_y_val.plot.errorbar(ax=ax, yerr=thermal_width_y_std, fmt='or')\n",
"\n",
"plt.ylabel('X-axis width of thermal part')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"thermal_center_x_val.plot.errorbar(ax=ax, yerr=thermal_center_x_std, fmt='or')\n",
"\n",
"plt.ylabel('Y-axis center of thermal part')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"thermal_center_y_val.plot.errorbar(ax=ax, yerr=thermal_center_y_std, fmt='or')\n",
"\n",
"plt.ylabel('X-axis center of thermal part')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"val = Ncount.mean().item()\n",
"std = Ncount.std().item()\n",
"print(f'The total Ncount is: {val: .2f} \\u00B1 {std: .2f}')\n",
"\n",
"val = total_Ncount_val.mean().item()\n",
"std = total_Ncount_val.std().item()\n",
"print(f'The total Ncount from fit is: {val: .2f} \\u00B1 {std: .2f}')\n",
"\n",
"val = thermal_width_x_val.mean().item()\n",
"std = thermal_width_x_val.std().item()\n",
"print(f'The y-axis width of the thermal part is: {val: .2f} \\u00B1 {std: .2f}')\n",
"\n",
"val = thermal_width_y_val.mean().item()\n",
"std = thermal_width_y_val.std().item()\n",
"print(f'The x-axis width of the thermal part is: {val: .2f} \\u00B1 {std: .2f}')\n",
"\n",
"val = thermal_center_x_val.mean().item()\n",
"std = thermal_center_x_val.std().item()\n",
"print(f'The y-axis center of the thermal part is: {val: .2f} \\u00B1 {std: .2f}')\n",
"\n",
"val = thermal_center_y_val.mean().item()\n",
"std = thermal_center_y_val.std().item()\n",
"print(f'The x-axis center of the thermal part is: {val: .2f} \\u00B1 {std: .2f}')"
]
},
{
"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": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"l = list(np.arange(0.001, 0.025, 0.0005))\n",
"# l = np.logspace(np.log10(100e-3), np.log10(20), num=20)\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)"
]
},
{
"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",
test fft
2023-05-19 09:34:58 +02:00
"version": "3.8.10"
prepare to add fft
2023-05-18 16:09:20 +02:00
},
"vscode": {
"interpreter": {
"hash": "c05913ad4f24fdc6b2418069394dc5835b1981849b107c9ba6df693aafd66650"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Reference in New Issue Copy Permalink