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analyseScript/backupScript/20230630_Data_Analysis_2.ipynb

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2023-07-01 09:21:45 +02:00
{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Import supporting package"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import xarray as xr\n",
"import numpy as np\n",
"import copy\n",
"\n",
"from uncertainties import ufloat\n",
"from uncertainties import unumpy as unp\n",
"from uncertainties import umath\n",
"import random\n",
"import matplotlib.pyplot as plt\n",
"plt.rcParams['font.size'] = 12\n",
"\n",
"from DataContainer.ReadData import read_hdf5_file\n",
"from Analyser.ImagingAnalyser import ImageAnalyser\n",
"from Analyser.FitAnalyser import FitAnalyser\n",
"from Analyser.FitAnalyser import NewFitModel, DensityProfileBEC2dModel\n",
"from ToolFunction.ToolFunction import *\n",
"\n",
"from scipy.optimize import curve_fit\n",
"\n",
"from ToolFunction.HomeMadeXarrayFunction import errorbar, dataarray_plot_errorbar\n",
"xr.plot.dataarray_plot.errorbar = errorbar\n",
"xr.plot.accessor.DataArrayPlotAccessor.errorbar = dataarray_plot_errorbar\n",
"\n",
"imageAnalyser = ImageAnalyser()\n",
"\n",
"# %matplotlib notebook"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Start a client for parallel computing"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
" <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
" <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-8ba1675f-1745-11ee-81b0-80e82ce2fa8e</p>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
"\n",
" <tr>\n",
" \n",
" <td style=\"text-align: left;\"><strong>Connection method:</strong> Cluster object</td>\n",
" <td style=\"text-align: left;\"><strong>Cluster type:</strong> distributed.LocalCluster</td>\n",
" \n",
" </tr>\n",
"\n",
" \n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" \n",
"\n",
" </table>\n",
"\n",
" \n",
"\n",
" \n",
" <details>\n",
" <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Cluster Info</h3></summary>\n",
" <div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
" <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\">\n",
" </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <h3 style=\"margin-bottom: 0px; margin-top: 0px;\">LocalCluster</h3>\n",
" <p style=\"color: #9D9D9D; margin-bottom: 0px;\">5686383f</p>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Workers:</strong> 8\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads:</strong> 128\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total memory:</strong> 149.01 GiB\n",
" </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <td style=\"text-align: left;\"><strong>Status:</strong> running</td>\n",
" <td style=\"text-align: left;\"><strong>Using processes:</strong> True</td>\n",
"</tr>\n",
"\n",
" \n",
" </table>\n",
"\n",
" <details>\n",
" <summary style=\"margin-bottom: 20px;\">\n",
" <h3 style=\"display: inline;\">Scheduler Info</h3>\n",
" </summary>\n",
"\n",
" <div style=\"\">\n",
" <div>\n",
" <div style=\"width: 24px; height: 24px; background-color: #FFF7E5; border: 3px solid #FF6132; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <h3 style=\"margin-bottom: 0px;\">Scheduler</h3>\n",
" <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-579b49b9-56d3-437e-8497-ccb704499216</p>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Comm:</strong> tcp://127.0.0.1:58438\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Workers:</strong> 8\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads:</strong> 128\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Started:</strong> Just now\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total memory:</strong> 149.01 GiB\n",
" </td>\n",
" </tr>\n",
" </table>\n",
" </div>\n",
" </div>\n",
"\n",
" <details style=\"margin-left: 48px;\">\n",
" <summary style=\"margin-bottom: 20px;\">\n",
" <h3 style=\"display: inline;\">Workers</h3>\n",
" </summary>\n",
"\n",
" \n",
" <div style=\"margin-bottom: 20px;\">\n",
" <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <details>\n",
" <summary>\n",
" <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 0</h4>\n",
" </summary>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Comm: </strong> tcp://127.0.0.1:58495\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads: </strong> 16\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:58496/status\" target=\"_blank\">http://127.0.0.1:58496/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Memory: </strong> 18.63 GiB\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Nanny: </strong> tcp://127.0.0.1:58441\n",
" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
" <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-fjkqs65s\n",
" </td>\n",
" </tr>\n",
"\n",
" \n",
"\n",
" \n",
"\n",
" </table>\n",
" </details>\n",
" </div>\n",
" </div>\n",
" \n",
" <div style=\"margin-bottom: 20px;\">\n",
" <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <details>\n",
" <summary>\n",
" <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 1</h4>\n",
" </summary>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Comm: </strong> tcp://127.0.0.1:58478\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads: </strong> 16\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:58481/status\" target=\"_blank\">http://127.0.0.1:58481/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Memory: </strong> 18.63 GiB\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Nanny: </strong> tcp://127.0.0.1:58442\n",
" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
" <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-2zpxzh7e\n",
" </td>\n",
" </tr>\n",
"\n",
" \n",
"\n",
" \n",
"\n",
" </table>\n",
" </details>\n",
" </div>\n",
" </div>\n",
" \n",
" <div style=\"margin-bottom: 20px;\">\n",
" <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <details>\n",
" <summary>\n",
" <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 2</h4>\n",
" </summary>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Comm: </strong> tcp://127.0.0.1:58485\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads: </strong> 16\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:58489/status\" target=\"_blank\">http://127.0.0.1:58489/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Memory: </strong> 18.63 GiB\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Nanny: </strong> tcp://127.0.0.1:58443\n",
" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
" <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-xj22aq7d\n",
" </td>\n",
" </tr>\n",
"\n",
" \n",
"\n",
" \n",
"\n",
" </table>\n",
" </details>\n",
" </div>\n",
" </div>\n",
" \n",
" <div style=\"margin-bottom: 20px;\">\n",
" <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <details>\n",
" <summary>\n",
" <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 3</h4>\n",
" </summary>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Comm: </strong> tcp://127.0.0.1:58483\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads: </strong> 16\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:58486/status\" target=\"_blank\">http://127.0.0.1:58486/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Memory: </strong> 18.63 GiB\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Nanny: </strong> tcp://127.0.0.1:58444\n",
" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
" <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-ywxt37o0\n",
" </td>\n",
" </tr>\n",
"\n",
" \n",
"\n",
" \n",
"\n",
" </table>\n",
" </details>\n",
" </div>\n",
" </div>\n",
" \n",
" <div style=\"margin-bottom: 20px;\">\n",
" <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <details>\n",
" <summary>\n",
" <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 4</h4>\n",
" </summary>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Comm: </strong> tcp://127.0.0.1:58460\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads: </strong> 16\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:58473/status\" target=\"_blank\">http://127.0.0.1:58473/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Memory: </strong> 18.63 GiB\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Nanny: </strong> tcp://127.0.0.1:58445\n",
" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
" <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-r165h314\n",
" </td>\n",
" </tr>\n",
"\n",
" \n",
"\n",
" \n",
"\n",
" </table>\n",
" </details>\n",
" </div>\n",
" </div>\n",
" \n",
" <div style=\"margin-bottom: 20px;\">\n",
" <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <details>\n",
" <summary>\n",
" <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 5</h4>\n",
" </summary>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Comm: </strong> tcp://127.0.0.1:58492\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads: </strong> 16\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:58493/status\" target=\"_blank\">http://127.0.0.1:58493/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Memory: </strong> 18.63 GiB\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Nanny: </strong> tcp://127.0.0.1:58446\n",
" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
" <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-y9scwwnw\n",
" </td>\n",
" </tr>\n",
"\n",
" \n",
"\n",
" \n",
"\n",
" </table>\n",
" </details>\n",
" </div>\n",
" </div>\n",
" \n",
" <div style=\"margin-bottom: 20px;\">\n",
" <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <details>\n",
" <summary>\n",
" <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 6</h4>\n",
" </summary>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Comm: </strong> tcp://127.0.0.1:58477\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads: </strong> 16\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:58479/status\" target=\"_blank\">http://127.0.0.1:58479/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Memory: </strong> 18.63 GiB\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Nanny: </strong> tcp://127.0.0.1:58447\n",
" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
" <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-6gncvkv7\n",
" </td>\n",
" </tr>\n",
"\n",
" \n",
"\n",
" \n",
"\n",
" </table>\n",
" </details>\n",
" </div>\n",
" </div>\n",
" \n",
" <div style=\"margin-bottom: 20px;\">\n",
" <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <details>\n",
" <summary>\n",
" <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 7</h4>\n",
" </summary>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Comm: </strong> tcp://127.0.0.1:58484\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads: </strong> 16\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:58488/status\" target=\"_blank\">http://127.0.0.1:58488/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Memory: </strong> 18.63 GiB\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Nanny: </strong> tcp://127.0.0.1:58448\n",
" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
" <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-4vducatr\n",
" </td>\n",
" </tr>\n",
"\n",
" \n",
"\n",
" \n",
"\n",
" </table>\n",
" </details>\n",
" </div>\n",
" </div>\n",
" \n",
"\n",
" </details>\n",
"</div>\n",
"\n",
" </details>\n",
" </div>\n",
"</div>\n",
" </details>\n",
" \n",
"\n",
" </div>\n",
"</div>"
],
"text/plain": [
"<Client: 'tcp://127.0.0.1:58438' processes=8 threads=128, memory=149.01 GiB>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from dask.distributed import Client\n",
"client = Client(n_workers=8, threads_per_worker=16, processes=True, memory_limit='20GB')\n",
"client"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Start a client for Mongo DB"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import pymongo\n",
"import xarray_mongodb\n",
"\n",
"from DataContainer.MongoDB import MongoDB\n",
"\n",
"mongoClient = pymongo.MongoClient('mongodb://control:DyLab2021@127.0.0.1:27017/?authMechanism=DEFAULT')"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Set global path for experiment"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"groupList = [\n",
" \"images/MOT_3D_Camera/in_situ_absorption\",\n",
" \"images/ODT_1_Axis_Camera/in_situ_absorption\",\n",
" \"images/ODT_2_Axis_Camera/in_situ_absorption\",\n",
"]\n",
"\n",
"dskey = {\n",
" \"images/MOT_3D_Camera/in_situ_absorption\": \"camera_0\",\n",
" \"images/ODT_1_Axis_Camera/in_situ_absorption\": \"camera_1\",\n",
" \"images/ODT_2_Axis_Camera/in_situ_absorption\": \"camera_2\",\n",
"}\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"img_dir = 'C:/Users/control/DyLab/Experiments/DyBEC/'\n",
"SequenceName = \"Repetition_scan\"\n",
"folderPath = img_dir + SequenceName + \"/\" + get_date()\n",
"\n",
"mongoDB = mongoClient[SequenceName]\n",
"\n",
"DB = MongoDB(mongoClient, mongoDB, date=get_date())"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Repetition Scans"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## scan MOT freq"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The detected scaning axes and values are: \n",
"\n",
"{'initial_freq': array([101. , 101.25, 101.5 , 101.75, 102. , 102.25, 102.5 , 102.75,\n",
" 103. , 103.25]), 'runs': array([0., 1., 2.])}\n"
]
},
{
"data": {
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib notebook\n",
"shotNum = \"0000\"\n",
"filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n",
"\n",
"dataSetDict = {\n",
" dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n",
" for i in range(len(groupList))\n",
"}\n",
"\n",
"dataSet = dataSetDict[\"camera_1\"]\n",
"\n",
"print_scanAxis(dataSet)\n",
"\n",
"scanAxis = get_scanAxis(dataSet)\n",
"\n",
"dataSet = auto_rechunk(dataSet)\n",
"\n",
"dataSet = imageAnalyser.get_absorption_images(dataSet)\n",
"\n",
"imageAnalyser.center = (310, 825)\n",
"imageAnalyser.span = (550, 1250)\n",
"imageAnalyser.fraction = (0.1, 0.1)\n",
"\n",
"dataSet_cropOD = imageAnalyser.crop_image(dataSet.OD)\n",
"dataSet_cropOD = imageAnalyser.substract_offset(dataSet_cropOD).load()\n",
"\n",
"Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n",
"Ncount_mean = calculate_mean(Ncount)\n",
"Ncount_std = calculate_std(Ncount)\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n",
"plt.xlabel('MOT AOM Freq (MHz)')\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()\n",
"\n",
"# DB.create_global(shotNum, dataSet)\n",
"# DB.add_data(shotNum, dataSet_cropOD, engine='xarray')"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## scan Push freq"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The detected scaning axes and values are: \n",
"\n",
"{'push_freq': array([101.1, 101.6, 102.1, 102.6, 103.1, 103.6, 104.1, 104.6, 105.1,\n",
" 105.6, 106.1]), 'runs': array([0., 1., 2.])}\n"
]
},
{
"data": {
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],
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"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib notebook\n",
"shotNum = \"0001\"\n",
"filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n",
"\n",
"dataSetDict = {\n",
" dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n",
" for i in range(len(groupList))\n",
"}\n",
"\n",
"dataSet = dataSetDict[\"camera_1\"]\n",
"\n",
"print_scanAxis(dataSet)\n",
"\n",
"scanAxis = get_scanAxis(dataSet)\n",
"\n",
"dataSet = auto_rechunk(dataSet)\n",
"\n",
"dataSet = imageAnalyser.get_absorption_images(dataSet)\n",
"\n",
"imageAnalyser.center = (310, 825)\n",
"imageAnalyser.span = (550, 1250)\n",
"imageAnalyser.fraction = (0.1, 0.1)\n",
"\n",
"dataSet_cropOD = imageAnalyser.crop_image(dataSet.OD)\n",
"dataSet_cropOD = imageAnalyser.substract_offset(dataSet_cropOD).load()\n",
"\n",
"Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n",
"Ncount_mean = calculate_mean(Ncount)\n",
"Ncount_std = calculate_std(Ncount)\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n",
"plt.xlabel('Push AOM Freq (MHz)')\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()\n",
"\n",
"# DB.create_global(shotNum, dataSet)\n",
"# DB.add_data(shotNum, dataSet_cropOD, engine='xarray')"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## scan Z comp current"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The detected scaning axes and values are: \n",
"\n",
"{'compZ_final_current': array([0.242 , 0.2427, 0.2434, 0.2441, 0.2448, 0.2455, 0.2462, 0.2469,\n",
" 0.2476, 0.2483, 0.249 , 0.2497, 0.2504, 0.2511, 0.2518]), 'runs': array([0., 1., 2.])}\n"
]
},
{
"data": {
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"data": {
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],
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib notebook\n",
"shotNum = \"0005\"\n",
"filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n",
"\n",
"dataSetDict = {\n",
" dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n",
" for i in [0, 1]\n",
"}\n",
"\n",
"dataSet = dataSetDict[\"camera_1\"]\n",
"\n",
"print_scanAxis(dataSet)\n",
"\n",
"scanAxis = get_scanAxis(dataSet)\n",
"\n",
"dataSet = auto_rechunk(dataSet)\n",
"\n",
"dataSet = imageAnalyser.get_absorption_images(dataSet)\n",
"\n",
"imageAnalyser.center = (305, 870)\n",
"imageAnalyser.span = (400, 400)\n",
"imageAnalyser.fraction = (0.1, 0.1)\n",
"\n",
"dataSet_cropOD = imageAnalyser.crop_image(dataSet.OD)\n",
"dataSet_cropOD = imageAnalyser.substract_offset(dataSet_cropOD).load()\n",
"\n",
"Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n",
"Ncount_mean = calculate_mean(Ncount)\n",
"Ncount_std = calculate_std(Ncount)\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n",
"plt.xlabel('comp Z current (A)')\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()\n",
"\n",
"# DB.create_global(shotNum, dataSet)\n",
"# DB.add_data(shotNum, dataSet_cropOD, engine='xarray')"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Evaporative Cooling"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"img_dir = '//DyLabNAS/Data/'\n",
"SequenceName = \"Evaporative_Cooling\" + \"/\"\n",
"folderPath = img_dir + SequenceName + '2023/06/30'# get_date()\n",
"\n",
"# mongoDB = mongoClient[SequenceName]\n",
"\n",
"# DB = MongoDB(mongoClient, mongoDB, date=get_date())"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Check BEC"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"ename": "OSError",
"evalue": "no files to open",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mOSError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32mf:\\Jianshun\\analyseScript\\20230630_Data_Analysis_2.ipynb Cell 20\u001b[0m in \u001b[0;36m4\n\u001b[0;32m <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/20230630_Data_Analysis_2.ipynb#X25sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m shotNum \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m0000\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/20230630_Data_Analysis_2.ipynb#X25sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m filePath \u001b[39m=\u001b[39m folderPath \u001b[39m+\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m/\u001b[39m\u001b[39m\"\u001b[39m \u001b[39m+\u001b[39m shotNum \u001b[39m+\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m/*.h5\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/20230630_Data_Analysis_2.ipynb#X25sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m dataSetDict \u001b[39m=\u001b[39m {\n\u001b[0;32m <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/20230630_Data_Analysis_2.ipynb#X25sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n\u001b[0;32m <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/20230630_Data_Analysis_2.ipynb#X25sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m [\u001b[39m0\u001b[39m]\n\u001b[0;32m <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/20230630_Data_Analysis_2.ipynb#X25sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m }\n\u001b[0;32m <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/20230630_Data_Analysis_2.ipynb#X25sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m dataSet \u001b[39m=\u001b[39m dataSetDict[\u001b[39m\"\u001b[39m\u001b[39mcamera_0\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[0;32m <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/20230630_Data_Analysis_2.ipynb#X25sZmlsZQ%3D%3D?line=10'>11</a>\u001b[0m print_scanAxis(dataSet)\n",
"\u001b[1;32mf:\\Jianshun\\analyseScript\\20230630_Data_Analysis_2.ipynb Cell 20\u001b[0m in \u001b[0;36m5\n\u001b[0;32m <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/20230630_Data_Analysis_2.ipynb#X25sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m shotNum \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m0000\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/20230630_Data_Analysis_2.ipynb#X25sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m filePath \u001b[39m=\u001b[39m folderPath \u001b[39m+\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m/\u001b[39m\u001b[39m\"\u001b[39m \u001b[39m+\u001b[39m shotNum \u001b[39m+\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m/*.h5\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/20230630_Data_Analysis_2.ipynb#X25sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m dataSetDict \u001b[39m=\u001b[39m {\n\u001b[1;32m----> <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/20230630_Data_Analysis_2.ipynb#X25sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n\u001b[0;32m <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/20230630_Data_Analysis_2.ipynb#X25sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m [\u001b[39m0\u001b[39m]\n\u001b[0;32m <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/20230630_Data_Analysis_2.ipynb#X25sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m }\n\u001b[0;32m <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/20230630_Data_Analysis_2.ipynb#X25sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m dataSet \u001b[39m=\u001b[39m dataSetDict[\u001b[39m\"\u001b[39m\u001b[39mcamera_0\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[0;32m <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/20230630_Data_Analysis_2.ipynb#X25sZmlsZQ%3D%3D?line=10'>11</a>\u001b[0m print_scanAxis(dataSet)\n",
"File \u001b[1;32mf:\\Jianshun\\analyseScript\\DataContainer\\ReadData.py:150\u001b[0m, in \u001b[0;36mread_hdf5_file\u001b[1;34m(filePath, group, datesetOfGlobal, preprocess, join, parallel, engine, phony_dims, excludeAxis, maxFileNum, **kwargs)\u001b[0m\n\u001b[0;32m 139\u001b[0m kwargs\u001b[39m.\u001b[39mupdate(\n\u001b[0;32m 140\u001b[0m {\n\u001b[0;32m 141\u001b[0m \u001b[39m'\u001b[39m\u001b[39mjoin\u001b[39m\u001b[39m'\u001b[39m: join, \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 146\u001b[0m }\n\u001b[0;32m 147\u001b[0m )\n\u001b[0;32m 149\u001b[0m \u001b[39mif\u001b[39;00m datesetOfGlobal \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m--> 150\u001b[0m datesetOfGlobal \u001b[39m=\u001b[39m xr\u001b[39m.\u001b[39;49mopen_mfdataset(\n\u001b[0;32m 151\u001b[0m fullFilePath, \n\u001b[0;32m 152\u001b[0m group\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mglobals\u001b[39;49m\u001b[39m\"\u001b[39;49m, \n\u001b[0;32m 153\u001b[0m concat_dim\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mfileNum\u001b[39;49m\u001b[39m\"\u001b[39;49m, \n\u001b[0;32m 154\u001b[0m combine\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mnested\u001b[39;49m\u001b[39m\"\u001b[39;49m, \n\u001b[0;32m 155\u001b[0m preprocess\u001b[39m=\u001b[39;49m_read_shot_number_from_hdf5, \n\u001b[0;32m 156\u001b[0m engine\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mh5netcdf\u001b[39;49m\u001b[39m\"\u001b[39;49m, \n\u001b[0;32m 157\u001b[0m phony_dims\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39maccess\u001b[39;49m\u001b[39m\"\u001b[39;49m, \n\u001b[0;32m 158\u001b[0m combine_attrs\u001b[39m=\u001b[39;49m_read_globals_attrs,\n\u001b[0;32m 159\u001b[0m parallel\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m, )\n\u001b[0;32m 161\u001b[0m datesetOfGlobal\u001b[39m.\u001b[39mattrs[\u001b[39m'\u001b[39m\u001b[39mscanAxis\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39msetdiff1d(datesetOfGlobal\u001b[39m.\u001b[39mattrs[\u001b[39m'\u001b[39m\u001b[39mscanAxis\u001b[39m\u001b[39m'\u001b[39m], excludeAxis)\n\u001b[0;32m 163\u001b[0m _assgin_scan_axis \u001b[39m=\u001b[39m partial(_assign_scan_axis_partial, datesetOfGlobal\u001b[39m=\u001b[39mdatesetOfGlobal, fullFilePath\u001b[39m=\u001b[39mfullFilePath)\n",
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\backends\\api.py:947\u001b[0m, in \u001b[0;36mopen_mfdataset\u001b[1;34m(paths, chunks, concat_dim, compat, preprocess, engine, data_vars, coords, combine, parallel, join, attrs_file, combine_attrs, **kwargs)\u001b[0m\n\u001b[0;32m 944\u001b[0m paths \u001b[39m=\u001b[39m [os\u001b[39m.\u001b[39mfspath(p) \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(p, os\u001b[39m.\u001b[39mPathLike) \u001b[39melse\u001b[39;00m p \u001b[39mfor\u001b[39;00m p \u001b[39min\u001b[39;00m paths]\n\u001b[0;32m 946\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m paths:\n\u001b[1;32m--> 947\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mOSError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mno files to open\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m 949\u001b[0m \u001b[39mif\u001b[39;00m combine \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mnested\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[0;32m 950\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(concat_dim, (\u001b[39mstr\u001b[39m, DataArray)) \u001b[39mor\u001b[39;00m concat_dim \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
"\u001b[1;31mOSError\u001b[0m: no files to open"
]
}
],
"source": [
"shotNum = \"0000\"\n",
"filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n",
"\n",
"dataSetDict = {\n",
" dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n",
" for i in [0]\n",
"}\n",
"\n",
"dataSet = dataSetDict[\"camera_0\"]\n",
"\n",
"print_scanAxis(dataSet)\n",
"\n",
"scanAxis = get_scanAxis(dataSet)\n",
"\n",
"dataSet = auto_rechunk(dataSet)\n",
"\n",
"dataSet = imageAnalyser.get_absorption_images(dataSet)\n",
"\n",
"imageAnalyser.center = (880, 990)\n",
"imageAnalyser.span = (150, 150)\n",
"imageAnalyser.fraction = (0.1, 0.1)\n",
"\n",
"dataSet_cropOD = imageAnalyser.crop_image(dataSet.OD)\n",
"dataSet_cropOD = imageAnalyser.substract_offset(dataSet_cropOD).load()\n",
"\n",
"Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n",
"Ncount_mean = calculate_mean(Ncount)\n",
"Ncount_std = calculate_std(Ncount)\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='-ob')\n",
"plt.xlabel('comp Z current (A)')\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()\n",
"\n",
"# DB.create_global(shotNum, dataSet)\n",
"# DB.add_data(shotNum, dataSet_cropOD, engine='xarray')"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The detected scaning axes and values are: \n",
"\n",
"{'compZ_current_sg': array([0.195, 0.196, 0.197, 0.198, 0.199, 0.2 , 0.201, 0.202, 0.203,\n",
" 0.204]), 'runs': array([0., 1., 2.])}\n"
]
},
{
"data": {
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"text/plain": [
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},
"metadata": {},
"output_type": "display_data"
},
{
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],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"shotNum = \"0001\"\n",
"filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n",
"\n",
"dataSetDict = {\n",
" dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n",
" for i in [0]\n",
"}\n",
"\n",
"dataSet = dataSetDict[\"camera_0\"]\n",
"\n",
"print_scanAxis(dataSet)\n",
"\n",
"scanAxis = get_scanAxis(dataSet)\n",
"\n",
"dataSet = auto_rechunk(dataSet)\n",
"\n",
"dataSet = imageAnalyser.get_absorption_images(dataSet)\n",
"\n",
"imageAnalyser.center = (880, 990)\n",
"imageAnalyser.span = (150, 150)\n",
"imageAnalyser.fraction = (0.1, 0.1)\n",
"\n",
"dataSet_cropOD = imageAnalyser.crop_image(dataSet.OD)\n",
"dataSet_cropOD = imageAnalyser.substract_offset(dataSet_cropOD).load()\n",
"\n",
"Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n",
"Ncount_mean = calculate_mean(Ncount)\n",
"Ncount_std = calculate_std(Ncount)\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='-ob')\n",
"plt.xlabel('comp Z current (A)')\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()\n",
"\n",
"# DB.create_global(shotNum, dataSet)\n",
"# DB.add_data(shotNum, dataSet_cropOD, engine='xarray')"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Measure magnification of side-imaging again"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The detected scaning axes and values are: \n",
"\n",
"{'TOF_free': array([0.005, 0.006, 0.007, 0.008, 0.009, 0.01 , 0.011, 0.012, 0.013,\n",
" 0.014, 0.015, 0.016, 0.017, 0.018, 0.019, 0.02 , 0.021]), 'runs': array([0., 1., 2.])}\n"
]
},
{
"data": {
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"metadata": {},
"output_type": "display_data"
},
{
"data": {
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},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"shotNum = \"0005\"\n",
"filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n",
"\n",
"dataSetDict = {\n",
" dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n",
" for i in [0]\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, 990)\n",
"imageAnalyser.span = (1000, 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)\n",
"Ncount_mean = calculate_mean(Ncount)\n",
"Ncount_std = calculate_std(Ncount)\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n",
"plt.xlabel('TOF (s)')\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {},
"outputs": [
{
"data": {
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{
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],
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"metadata": {},
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}
],
"source": [
"remove_bad_shots(dataSet_cropOD, TOF_free=0.005)\n",
"Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n",
"Ncount_mean = calculate_mean(Ncount)\n",
"Ncount_std = calculate_std(Ncount)\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n",
"plt.xlabel('TOF (s)')\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"dataSet_cropOD = dataSet_cropOD[1:-1, :, :, :]\n",
"dataSet_cropOD_chunk = dataSet_cropOD.chunk((1, 1, 1000, 200))\n",
"fitAnalyser = FitAnalyser(\"Gaussian-2D\", fitDim=2)\n",
"params = fitAnalyser.guess(dataSet_cropOD_chunk, dask=\"parallelized\")\n",
"fitResult = fitAnalyser.fit(dataSet_cropOD_chunk, params, dask=\"parallelized\").load()"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key
"text/plain": [
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],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"val = fitAnalyser.get_fit_value(fitResult)\n",
"std = fitAnalyser.get_fit_std(fitResult)\n",
"\n",
"fitCurve = fitAnalyser.eval(fitResult, x=np.arange(990), y=np.arange(350), dask=\"parallelized\").load()\n",
"\n",
"# dataKey = 'sigmax'\n",
"dataKey = 'centerx'\n",
"# dataKey = 'sigmay'\n",
"# dataKey = 'centery'\n",
"\n",
"val_mean = val[dataKey].mean(dim='runs')\n",
"std_mean = val[dataKey].std(dim='runs')\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"val_mean.plot.errorbar(yerr=std_mean, fmt='--ob')\n",
"\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"M = 2.3513 ± 0.0036 \n"
]
}
],
"source": [
"def free_fall(x, A, B):\n",
" return A*(x**2) + B\n",
"\n",
"fitModel_freefall = NewFitModel(free_fall)\n",
"fitAnalyser_freefall = FitAnalyser(fitModel_freefall, fitDim=1)\n",
"\n",
"params = fitAnalyser_freefall.fitModel.make_params()\n",
"params.add(name=\"A\", value= 1e6, max = np.inf, min =-np.inf, vary = True)\n",
"params.add(name=\"B\", value= 1000, max = np.inf, min =-np.inf, vary = True)\n",
"\n",
"fitResult_freefall = fitAnalyser_freefall.fit(val_mean, params, x='TOF_free', dask=\"parallelized\")\n",
"\n",
"val = fitAnalyser_freefall.get_fit_value(fitResult_freefall)\n",
"std = fitAnalyser_freefall.get_fit_std(fitResult_freefall)\n",
"\n",
"fit_vals = tuple([val['A'].item(), val['B'].item()])\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"val_mean.plot.errorbar(ax=ax, yerr=std_mean, fmt='or')\n",
"tof = np.linspace(5,21,50)*1e-3\n",
"plt.plot(tof, free_fall(tof, *fit_vals), 'b--',label='fit: A=%5.3f, B=%5.3f' % fit_vals)\n",
"plt.grid(visible =1)\n",
"plt.xlabel('TOF (s)')\n",
"plt.ylabel('Cloud Center (pixels)')\n",
"plt.legend()\n",
"plt.show()\n",
"\n",
"#pixel= 3.45e-6\n",
"pixel = 5.86e-6\n",
"g = 9.80998\n",
"M = val['A'].item() * 2 * pixel/g\n",
"dM = std['A'].item() * 2 * pixel/g\n",
"\n",
"print('M = %.4f \\u00B1 %.4f '% tuple([M,dM]))"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Measure temperature at the end of evaporative cooling 2, truncation 0.7"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The detected scaning axes and values are: \n",
"\n",
"{'TOF_free': array([0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01 ,\n",
" 0.011, 0.012, 0.013, 0.014, 0.015]), 'runs': array([0., 1., 2.])}\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"shotNum = \"0007\"\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 = (350, 990)\n",
"imageAnalyser.span = (650, 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)\n",
"Ncount_mean = calculate_mean(Ncount)\n",
"Ncount_std = calculate_std(Ncount)\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n",
"plt.xlabel('TOF (s)')\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"dataSet_cropOD_chunk = dataSet_cropOD.chunk((1, 1, 650, 200))\n",
"fitAnalyser = FitAnalyser(\"Gaussian-2D\", fitDim=2)\n",
"params = fitAnalyser.guess(dataSet_cropOD_chunk, dask=\"parallelized\")\n",
"fitResult = fitAnalyser.fit(dataSet_cropOD_chunk, params, dask=\"parallelized\").load()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3.769727404282448\n",
"0.07242840439128356\n"
]
}
],
"source": [
"data = fitAnalyser.get_fit_value(fitResult)\n",
"\n",
"print(data.amplitude.sel(TOF_free=0.01).mean('runs').item() * 147 / 1e5)\n",
"print(data.amplitude.sel(TOF_free=0.01).std('runs').item() * 147 / 1e5)"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"val = fitAnalyser.get_fit_value(fitResult)\n",
"std = fitAnalyser.get_fit_std(fitResult)\n",
"\n",
"fitCurve = fitAnalyser.eval(fitResult, x=np.arange(990), y=np.arange(350), dask=\"parallelized\").load()\n",
"\n",
"dataKey = 'sigmax'\n",
"# dataKey = 'centerx'\n",
"# dataKey = 'sigmay'\n",
"# dataKey = 'centery'\n",
"\n",
"val_mean = val[dataKey].mean(dim='runs')\n",
"std_mean = val[dataKey].std(dim='runs')\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"val_mean.plot.errorbar(yerr=std_mean, fmt='--ob')\n",
"\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key
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},
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},
{
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],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"val = fitAnalyser.get_fit_value(fitResult)\n",
"std = fitAnalyser.get_fit_std(fitResult)\n",
"\n",
"fitCurve = fitAnalyser.eval(fitResult, x=np.arange(990), y=np.arange(950), dask=\"parallelized\").load()\n",
"\n",
"# dataKey = 'sigmax'\n",
"# dataKey = 'centerx'\n",
"dataKey = 'sigmay'\n",
"# dataKey = 'centery'\n",
"\n",
"val_mean = val[dataKey].mean(dim='runs')\n",
"std_mean = val[dataKey].std(dim='runs')\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"val_mean.plot.errorbar(yerr=std_mean, fmt='--ob')\n",
"\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key
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},
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{
"data": {
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"<img src=\"data:image/png;base64,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
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"T = 0.526 ± 0.006 uK\n"
]
}
],
"source": [
"def expansion(x, A, B):\n",
" return np.sqrt(A*x*x+B)\n",
"\n",
"val_truncated = val_mean[0:]\n",
"std_mean_truncated = std_mean[0:]\n",
"\n",
"fitModel_temperature = NewFitModel(expansion)\n",
"fitAnalyser_temperature = FitAnalyser(fitModel_temperature, fitDim=1)\n",
"\n",
"params = fitAnalyser_temperature.fitModel.make_params()\n",
"params.add(name=\"A\", value= 1e6, max = np.inf, min = 1, vary = True)\n",
"params.add(name=\"B\", value= 1000, max = np.inf, min = 0, vary = True)\n",
"\n",
"fitResult_temperature = fitAnalyser_temperature.fit(val_truncated, params, x='TOF_free', dask=\"parallelized\")\n",
"\n",
"val = fitAnalyser_temperature.get_fit_value(fitResult_temperature)\n",
"std = fitAnalyser_temperature.get_fit_std(fitResult_temperature)\n",
"\n",
"fit_vals = tuple([val['A'].item(), val['B'].item()])\n",
"fit_vals_std = tuple([std['A'].item(), std['B'].item()])\n",
"\n",
"tof = (np.arange(2,16,0.1)*1e-3)\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"val_mean.plot.errorbar(ax=ax, yerr=std_mean, fmt='or')\n",
"plt.plot(tof, expansion(tof, *fit_vals), 'b--',\n",
" label='fit: A=%.3f, B=%.3f' % tuple(fit_vals))\n",
"plt.xlabel('TOF (s)')\n",
"plt.ylabel('Cloud Width - Vertical (pixels)')\n",
"#plt.ylabel('Cloud Width - Horizontal (pixels)')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.legend()\n",
"\n",
"\n",
"m = 164*1.673e-27\n",
"kb = 1.38e-23\n",
"pixel = 5.86e-6\n",
"M = 2.3513\n",
"\n",
"T = fit_vals[0] * (m/kb) * ((pixel*pixel)/(M*M)) / (1e-6)\n",
"dT = fit_vals_std[0] * (m/kb) * ((pixel*pixel)/(M*M)) / (1e-6)\n",
"\n",
"print('T = %.3f \\u00B1 %.3f uK'% tuple([T,dT]))"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Measure temperature at the end of evaporative cooling 2, truncation 0.65"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The detected scaning axes and values are: \n",
"\n",
"{'TOF_free': array([0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01 ,\n",
" 0.011, 0.012, 0.013, 0.014, 0.015]), 'runs': array([0., 1., 2.])}\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"shotNum = \"0008\"\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 = (350, 990)\n",
"imageAnalyser.span = (650, 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)\n",
"Ncount_mean = calculate_mean(Ncount)\n",
"Ncount_std = calculate_std(Ncount)\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n",
"plt.xlabel('TOF (s)')\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"dataSet_cropOD_chunk = dataSet_cropOD.chunk((1, 1, 650, 200))\n",
"fitAnalyser = FitAnalyser(\"Gaussian-2D\", fitDim=2)\n",
"params = fitAnalyser.guess(dataSet_cropOD_chunk, dask=\"parallelized\")\n",
"fitResult = fitAnalyser.fit(dataSet_cropOD_chunk, params, dask=\"parallelized\").load()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4.158908069274802\n",
"0.17858152215917347\n"
]
}
],
"source": [
"data = fitAnalyser.get_fit_value(fitResult)\n",
"\n",
"print(data.amplitude.sel(TOF_free=0.01).mean('runs').item() * 147 / 1e5)\n",
"print(data.amplitude.sel(TOF_free=0.01).std('runs').item() * 147 / 1e5)"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key
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],
"source": [
"val = fitAnalyser.get_fit_value(fitResult)\n",
"std = fitAnalyser.get_fit_std(fitResult)\n",
"\n",
"fitCurve = fitAnalyser.eval(fitResult, x=np.arange(990), y=np.arange(350), dask=\"parallelized\").load()\n",
"\n",
"dataKey = 'sigmax'\n",
"# dataKey = 'centerx'\n",
"# dataKey = 'sigmay'\n",
"# dataKey = 'centery'\n",
"\n",
"val_mean = val[dataKey].mean(dim='runs')\n",
"std_mean = val[dataKey].std(dim='runs')\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"val_mean.plot.errorbar(yerr=std_mean, fmt='--ob')\n",
"\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key
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"data": {
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"<img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAeAAAAFoCAYAAACPNyggAAAAAXNSR0IArs4c6QAAIABJREFUeF7tnQu4VVXV/t9zUNACUUzTUkRLQ0UDgYpQg7xUKqRmpXlDENBCMZRQEvPjS7zgBU3BCyoIavVHRdAvb8Ah/DQFxEsEigrqpyJ5AURAQPg/Y68OnHM4++y51hhr7jnhXc/jk8mcY777N8ccL3Pttdes2LBhwwbwIgESIAESIIFACVRUVFQEKk0lq4IGrOLHziRAAiRAAjkToAHnDJjhSYAESIAESKA+AjRg5gUJkAAJkAAJlIEADbgM0DkkCZAACZAACdCAmQMkQAIkQAIkUAYCNOAyQOeQJEACJEACJEADZg6QAAmQAAmQQBkI0IDLAJ1DkgAJkAAJkAANmDlAAiRAAiRAAmUgQAMuA3QOSQIkQAIkQAI0YOYACZAACZAACZSBAA24DNA5JAmQAAmQAAnQgJkDJEACJEACJFAGAjTgMkDnkCRAAiRAAiRAA2YOkAAJkAAJkEAZCNCAywCdQ5IACZAACZAADZg5QAIkQAIkQAJlIEADLgN0DkkCJEACJEACNGDmAAmQAAmQAAmUgQANuAzQOSQJkAAJkAAJ0ICZAyRAAiRAAiRQBgI04DJA55AkQAIkQAIkQANmDpAACZAACZBAGQjQgMsAnUOSAAmQAAmQAA2YOUACJEACJEACZSBAAy4DdA5JAiRAAiRAAjRg5gAJkAAJkAAJlIEADbgM0DkkCZAACZAACdCAmQMkQAIkQAIkUAYCNOAyQOeQJEACJEACJEADZg6QAAmQAAmQQBkI0IDLAJ1DkgAJkAAJkAANmDlAAiRAAiRAAmUgQAMuA3QOSQIkQAIkQAI0YOYACZAACZAACZSBAA24DNA5JAmQAAmQAAnQgJkDJEACJEACJFAGAjTgMkDPOmSrVq2wzz77ZOr+6aefolmzZpn6lrtTrNqp22/mxMpbKMWqPVTdb775JhYtWuQ3ATOMRgPOAK1cXX74wx9i6tSpmYb/3//9X3Tu3DlT33J3ilU7dfvNnFh5C6VYtYeqW1MrfWYtDdgnbeVYmqQKdaG4IIlVO3W7zK5dm1h504DtcqA6kqZW2qspHpEG7JO2cixNUrE4KeFn6B4rc+rOMNnKLmSuBFinu6ZW2ippOBoN2Cdt5ViapIp1gXN3oEyaDN1jzZVYdTPHMyRpiS6aWmmvhjtgn0xzG0uTVCxOuU1L0cCxMqdu5oorgVBzRVMrXT+7RTvugC0oeoqhSapQF4oLuli1U7fL7Nq1iZU3d8B2OVAdSVMr7dVwB+yTaW5jaZKKxSm3aeEO2D/aekdkjvufiFCZa2qlT4rcAfukrRxLk1ShLhQXJLFqp26X2bVrEytv7oDtcoA7YHuWWSJWbNiwYUOWjiH3oQGHPDuba4vVEKjbf56RuS1zTa20VdJwNO6AfdJWjqVJqlgXOHcHyqTJ0D3WXIlVN3M8Q5KW6KKplfZqikekAXuivXr1ahxxxBHYdtttsWLFClx44YXo1KkT2rZtW/hHrgEDBqB79+5FFWmSisXJ00TXGCZW5tTNXHElEGquaGql62e3aEcDtqDoEEPuiK9bt65gwMuWLUObNm0wY8YM9OjRA1VVVQ4RAE1ShbpQXD54rNqp22V27drEyps74M1z4P33gdtuA/r2BXbfPX2OaGpl+tGy96ABZ2eXuee7776LPn364JZbbkHHjh3RunVrtGzZEiNGjMAuu+zCHXAdArEWVurOvEQydYyVNw249nS//bbcDQQeeAD42c+A668HWrZMlxI04HS8rFsH+RCW7Hy7deuGuXPn4uqrr8bpp5+ONWvWFE4puuOOOwo74nvuuacWi7Fjx0L+kWvhwoUYP358JlYydvPmzTP1LXenWLVTt9/MiZW3UIpVu7XuxYsbo2fPtlixohHWr69EZeV6NG36Be6660Xsttsa54QaMmRI5oNrnAcxaMgdsAHEtCGWLFlS2Pm+/PLLG01x5cqVhf8m5lzs0vytjruDtLOkbx8rc+rWz33aCGSeEDvnHGD0aOCLLzYRbNQI6N0bGDXKnaqmVrqPom9JA9YzdIogO135/reiogJitvLg1cyZMzca8OTJkzFmzBg8IPddilyapIp1gQuKWLVTt9PSMGsUK2/m+KYU6NABmD1785Ro3x6YNcs9VTS10n0UfUsasJ6hU4RXXnkF/fr1Q2VlJeSJ6P79+2O77bbD0KFDC7egGzduXLgN3apVKxpwHQKxFlbqdloaZo1i5U0D3pQCXboA06fXTgnZAffpA4wc6Z4qNGB3Vnm0DPI7YO0H1SQVi5OWfvr+sTKn7vRzre2xtTNftw646CLgxhuBJk0A+f9yG1rMVx5dmTMn3YNYmlqpncs0/bkDTkOrzG01SRXrAufuwH/SxZorsere2nN8+fLkaedp04CbbgKOPTYx4wkTgJNOAq67Lp35Ck9NrfS54mjAPmkrx9IkFYuTEn6G7rEyp+4Mk63ssjUzX7sWOPXU5AGsH/4wAfnCC4B87yvfBx9ySHq4mlqZfrTsPWjA2dl576lJqlgX+Na+O/CeZHzorRzIt8oHDR99FGjRAujUaXPkfBFHWdLQbFB+B1wHJQ3YLLecA8XKnLqdp9is4dbEXI7JGT4cuPhi4OyzgdtvN8O4MZBms2KvpnhE7oB90laOpUmqWBc4d8DKpMnQPdZciVX31pTjq1Ylv+m9917g0kuB//ovoLIyQ5KW6KKplfZqaMA+meY2liapWJxym5aigWNlTt3MFVcCaXPlhBOAxx8H7r4b+OUvXUdJ305TK9OPlr0Hd8DZ2XnvqUmqtAvF+4drYMBYtVO33yyKlffWsAOW284VFcnLNGTHm+XBqjTZpKmVacbRtqUBawl67K9JKhYnjxP1n6FiZU7dzBVXAi65Iq+3l1fYP/II0Lixa2RdO02t1I2crjcNOB2vsrbWJJXLQinrh+MOOBj8seZKrLq31B2wvEhDHrS69lqgZ8/kTVbykg0fl6ZW+tBXPQYN2Cdt5ViapGJxUsLP0D1W5tSdYbKVXbY05kuXAqecAjzxRHKc4PnnJ7egfV2aWulLo4xDA/ZJWzmWJqliXeBb6u5AmQq5do81V2LVvSXmuNx27t8f+MtfgKOPzjVd6w2uqZU+1dKAfdJWjqVJKhYnJfwM3WNlTt0ZJlvZZUthvmABsO++gDx0tWQJ8NWvKsFk7K6plRmHzNSNBpwJW3k6aZIq1gW+Je4OypM97qPGmiux6t4SclwM94YbgIEDk58ZHXmke77l0VJTK/PQUywmDdgnbeVYmqRicVLCz9A9VubUnWGylV1iY/7228CwYUBV1QocdlhTfPIJIEeZDxoEXHFFcopROS9NrfSpmwbsk7ZyLE1SxbbAa6KKVTt1KxM+ZfdYece2AxbzbdcOWLYsOTKw+hoxIvneN4RLUyt96qcB+6StHEuTVCxOSvgZusfKnLozTLayS0zM5dSi0aNrm6/seOUVk6NGKUEYddfUSiMJTmFowE6YwmikSaqYFnhd2rFqp26/6yZW3rHtgDt0SI4JrHvJ8YHypqsQLk2t9KmfBuyTtnIsTVKxOCnhZ+geK3PqzjDZyi6xMF+3Ljmn9+WXa39g2QH36ZO8bCOES1MrfeqnAfukrRxLk1SxLPD6EMWqnbqVCZ+ye6y8Y9kBf/RRcoDCtGnAdtsBn3+e3IYW823eHJgzB2jZMuWk5dRcUytzklRvWBqwT9rKsTRJxeKkhJ+he6zMqTvDZCu7hM5cfmbUsSPw1lvA//t/wD77AEOGAI8++jmOPbYJ/vu/wzFfmQpNrVROZaruNOBUuMrbWJNUoS/whsjGqp26/a6XWHmHvgOu3uXOmJGY7F57bZrXUJlraqXPrKUB+6StHEuTVKEuFBcksWqnbpfZtWsTK+9QDViM9/e/B+bNAx56KDlGsO4VKnNNrbTLyNKRaMClGQXTQpNUoS4UF7ixaqdul9m1axMr7xANWF6sIYcpPPkkMHw48Nvf1n+YQqjMNbXSLiNLR6IBl2YUTA
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"val = fitAnalyser.get_fit_value(fitResult)\n",
"std = fitAnalyser.get_fit_std(fitResult)\n",
"\n",
"fitCurve = fitAnalyser.eval(fitResult, x=np.arange(990), y=np.arange(950), dask=\"parallelized\").load()\n",
"\n",
"# dataKey = 'sigmax'\n",
"# dataKey = 'centerx'\n",
"dataKey = 'sigmay'\n",
"# dataKey = 'centery'\n",
"\n",
"val_mean = val[dataKey].mean(dim='runs')\n",
"std_mean = val[dataKey].std(dim='runs')\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"val_mean.plot.errorbar(yerr=std_mean, fmt='--ob')\n",
"\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"T = 0.649 ± 0.005 uK\n"
]
}
],
"source": [
"def expansion(x, A, B):\n",
" return np.sqrt(A*x*x+B)\n",
"\n",
"val_truncated = val_mean[2:]\n",
"std_mean_truncated = std_mean[2:]\n",
"\n",
"fitModel_temperature = NewFitModel(expansion)\n",
"fitAnalyser_temperature = FitAnalyser(fitModel_temperature, fitDim=1)\n",
"\n",
"params = fitAnalyser_temperature.fitModel.make_params()\n",
"params.add(name=\"A\", value= 1e6, max = np.inf, min = 1, vary = True)\n",
"params.add(name=\"B\", value= 1000, max = np.inf, min = 0, vary = True)\n",
"\n",
"fitResult_temperature = fitAnalyser_temperature.fit(val_truncated, params, x='TOF_free', dask=\"parallelized\")\n",
"\n",
"val = fitAnalyser_temperature.get_fit_value(fitResult_temperature)\n",
"std = fitAnalyser_temperature.get_fit_std(fitResult_temperature)\n",
"\n",
"fit_vals = tuple([val['A'].item(), val['B'].item()])\n",
"fit_vals_std = tuple([std['A'].item(), std['B'].item()])\n",
"\n",
"tof = (np.arange(2,16,0.1)*1e-3)\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"val_mean.plot.errorbar(ax=ax, yerr=std_mean, fmt='or')\n",
"plt.plot(tof, expansion(tof, *fit_vals), 'b--',\n",
" label='fit: A=%.3f, B=%.3f' % tuple(fit_vals))\n",
"plt.xlabel('TOF (s)')\n",
"#plt.ylabel('Cloud Width - Vertical (pixels)')\n",
"plt.ylabel('Cloud Width - Horizontal (pixels)')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.legend()\n",
"\n",
"\n",
"m = 164*1.673e-27\n",
"kb = 1.38e-23\n",
"pixel = 5.86e-6\n",
"M = 2.3513\n",
"\n",
"T = fit_vals[0] * (m/kb) * ((pixel*pixel)/(M*M)) / (1e-6)\n",
"dT = fit_vals_std[0] * (m/kb) * ((pixel*pixel)/(M*M)) / (1e-6)\n",
"\n",
"print('T = %.3f \\u00B1 %.3f uK'% tuple([T,dT]))"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Measure temperature at the end of evaporative cooling 2, truncation 0.6"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The detected scaning axes and values are: \n",
"\n",
"{'TOF_free': array([0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01 ,\n",
" 0.011, 0.012, 0.013, 0.014, 0.015]), 'runs': array([0., 1., 2.])}\n"
]
},
{
"data": {
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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"shotNum = \"0009\"\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 = (350, 990)\n",
"imageAnalyser.span = (650, 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)\n",
"Ncount_mean = calculate_mean(Ncount)\n",
"Ncount_std = calculate_std(Ncount)\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n",
"plt.xlabel('TOF (s)')\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"dataSet_cropOD_chunk = dataSet_cropOD.chunk((1, 1, 650, 200))\n",
"fitAnalyser = FitAnalyser(\"Gaussian-2D\", fitDim=2)\n",
"params = fitAnalyser.guess(dataSet_cropOD_chunk, dask=\"parallelized\")\n",
"fitResult = fitAnalyser.fit(dataSet_cropOD_chunk, params, dask=\"parallelized\").load()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4.809698089774189\n",
"0.160561481247611\n"
]
}
],
"source": [
"data = fitAnalyser.get_fit_value(fitResult)\n",
"\n",
"print(data.amplitude.sel(TOF_free=0.01).mean('runs').item() * 147 / 1e5)\n",
"print(data.amplitude.sel(TOF_free=0.01).std('runs').item() * 147 / 1e5)"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"val = fitAnalyser.get_fit_value(fitResult)\n",
"std = fitAnalyser.get_fit_std(fitResult)\n",
"\n",
"fitCurve = fitAnalyser.eval(fitResult, x=np.arange(990), y=np.arange(350), dask=\"parallelized\").load()\n",
"\n",
"dataKey = 'sigmax'\n",
"# dataKey = 'centerx'\n",
"# dataKey = 'sigmay'\n",
"# dataKey = 'centery'\n",
"\n",
"val_mean = val[dataKey].mean(dim='runs')\n",
"std_mean = val[dataKey].std(dim='runs')\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"val_mean.plot.errorbar(yerr=std_mean, fmt='--ob')\n",
"\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"val = fitAnalyser.get_fit_value(fitResult)\n",
"std = fitAnalyser.get_fit_std(fitResult)\n",
"\n",
"fitCurve = fitAnalyser.eval(fitResult, x=np.arange(990), y=np.arange(950), dask=\"parallelized\").load()\n",
"\n",
"# dataKey = 'sigmax'\n",
"# dataKey = 'centerx'\n",
"dataKey = 'sigmay'\n",
"# dataKey = 'centery'\n",
"\n",
"val_mean = val[dataKey].mean(dim='runs')\n",
"std_mean = val[dataKey].std(dim='runs')\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"val_mean.plot.errorbar(yerr=std_mean, fmt='--ob')\n",
"\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 136,
"metadata": {},
"outputs": [
{
"data": {
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],
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"<IPython.core.display.HTML object>"
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{
"name": "stdout",
"output_type": "stream",
"text": [
"T = 0.858 ± 0.004 uK\n"
]
}
],
"source": [
"def expansion(x, A, B):\n",
" return np.sqrt(A*x*x+B)\n",
"\n",
"val_truncated = val_mean[2:]\n",
"std_mean_truncated = std_mean[2:]\n",
"\n",
"fitModel_temperature = NewFitModel(expansion)\n",
"fitAnalyser_temperature = FitAnalyser(fitModel_temperature, fitDim=1)\n",
"\n",
"params = fitAnalyser_temperature.fitModel.make_params()\n",
"params.add(name=\"A\", value= 1e6, max = np.inf, min = 1, vary = True)\n",
"params.add(name=\"B\", value= 1000, max = np.inf, min = 0, vary = True)\n",
"\n",
"fitResult_temperature = fitAnalyser_temperature.fit(val_truncated, params, x='TOF_free', dask=\"parallelized\")\n",
"\n",
"val = fitAnalyser_temperature.get_fit_value(fitResult_temperature)\n",
"std = fitAnalyser_temperature.get_fit_std(fitResult_temperature)\n",
"\n",
"fit_vals = tuple([val['A'].item(), val['B'].item()])\n",
"fit_vals_std = tuple([std['A'].item(), std['B'].item()])\n",
"\n",
"tof = (np.arange(2,16,0.1)*1e-3)\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"val_mean.plot.errorbar(ax=ax, yerr=std_mean, fmt='or')\n",
"plt.plot(tof, expansion(tof, *fit_vals), 'b--',\n",
" label='fit: A=%.3f, B=%.3f' % tuple(fit_vals))\n",
"plt.xlabel('TOF (s)')\n",
"plt.ylabel('Cloud Width - Vertical (pixels)')\n",
"# plt.ylabel('Cloud Width - Horizontal (pixels)')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.legend()\n",
"\n",
"\n",
"m = 164*1.673e-27\n",
"kb = 1.38e-23\n",
"pixel = 5.86e-6\n",
"M = 2.3513\n",
"\n",
"T = fit_vals[0] * (m/kb) * ((pixel*pixel)/(M*M)) / (1e-6)\n",
"dT = fit_vals_std[0] * (m/kb) * ((pixel*pixel)/(M*M)) / (1e-6)\n",
"\n",
"print('T = %.3f \\u00B1 %.3f uK'% tuple([T,dT]))"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Measure temperature at the end of evaporative cooling 2, truncation 0.675"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The detected scaning axes and values are: \n",
"\n",
"{'TOF_free': array([0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01 ,\n",
" 0.011, 0.012, 0.013, 0.014, 0.015]), 'runs': array([0., 1., 2.])}\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"shotNum = \"0010\"\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 = (350, 990)\n",
"imageAnalyser.span = (650, 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)\n",
"Ncount_mean = calculate_mean(Ncount)\n",
"Ncount_std = calculate_std(Ncount)\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n",
"plt.xlabel('TOF (s)')\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"dataSet_cropOD_chunk = dataSet_cropOD.chunk((1, 1, 650, 200))\n",
"fitAnalyser = FitAnalyser(\"Gaussian-2D\", fitDim=2)\n",
"params = fitAnalyser.guess(dataSet_cropOD_chunk, dask=\"parallelized\")\n",
"fitResult = fitAnalyser.fit(dataSet_cropOD_chunk, params, dask=\"parallelized\").load()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4.027352128668683\n",
"0.03427747912979168\n"
]
}
],
"source": [
"data = fitAnalyser.get_fit_value(fitResult)\n",
"\n",
"print(data.amplitude.sel(TOF_free=0.01).mean('runs').item() * 147 / 1e5)\n",
"print(data.amplitude.sel(TOF_free=0.01).std('runs').item() * 147 / 1e5)"
]
},
{
"cell_type": "code",
"execution_count": 147,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"val = fitAnalyser.get_fit_value(fitResult)\n",
"std = fitAnalyser.get_fit_std(fitResult)\n",
"\n",
"fitCurve = fitAnalyser.eval(fitResult, x=np.arange(990), y=np.arange(350), dask=\"parallelized\").load()\n",
"\n",
"dataKey = 'sigmax'\n",
"# dataKey = 'centerx'\n",
"# dataKey = 'sigmay'\n",
"# dataKey = 'centery'\n",
"\n",
"val_mean = val[dataKey].mean(dim='runs')\n",
"std_mean = val[dataKey].std(dim='runs')\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"val_mean.plot.errorbar(yerr=std_mean, fmt='--ob')\n",
"\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 150,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key
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"metadata": {},
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],
"source": [
"val = fitAnalyser.get_fit_value(fitResult)\n",
"std = fitAnalyser.get_fit_std(fitResult)\n",
"\n",
"fitCurve = fitAnalyser.eval(fitResult, x=np.arange(990), y=np.arange(950), dask=\"parallelized\").load()\n",
"\n",
"# dataKey = 'sigmax'\n",
"# dataKey = 'centerx'\n",
"dataKey = 'sigmay'\n",
"# dataKey = 'centery'\n",
"\n",
"val_mean = val[dataKey].mean(dim='runs')\n",
"std_mean = val[dataKey].std(dim='runs')\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"val_mean.plot.errorbar(yerr=std_mean, fmt='--ob')\n",
"\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 152,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key
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"data": {
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"<img src=\"data:image/png;base64,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
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"T = 0.553 ± 0.004 uK\n"
]
}
],
"source": [
"def expansion(x, A, B):\n",
" return np.sqrt(A*x*x+B)\n",
"\n",
"val_truncated = val_mean[2:]\n",
"std_mean_truncated = std_mean[2:]\n",
"\n",
"fitModel_temperature = NewFitModel(expansion)\n",
"fitAnalyser_temperature = FitAnalyser(fitModel_temperature, fitDim=1)\n",
"\n",
"params = fitAnalyser_temperature.fitModel.make_params()\n",
"params.add(name=\"A\", value= 1e6, max = np.inf, min = 1, vary = True)\n",
"params.add(name=\"B\", value= 1000, max = np.inf, min = 0, vary = True)\n",
"\n",
"fitResult_temperature = fitAnalyser_temperature.fit(val_truncated, params, x='TOF_free', dask=\"parallelized\")\n",
"\n",
"val = fitAnalyser_temperature.get_fit_value(fitResult_temperature)\n",
"std = fitAnalyser_temperature.get_fit_std(fitResult_temperature)\n",
"\n",
"fit_vals = tuple([val['A'].item(), val['B'].item()])\n",
"fit_vals_std = tuple([std['A'].item(), std['B'].item()])\n",
"\n",
"tof = (np.arange(2,16,0.1)*1e-3)\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"val_mean.plot.errorbar(ax=ax, yerr=std_mean, fmt='or')\n",
"plt.plot(tof, expansion(tof, *fit_vals), 'b--',\n",
" label='fit: A=%.3f, B=%.3f' % tuple(fit_vals))\n",
"plt.xlabel('TOF (s)')\n",
"# plt.ylabel('Cloud Width - Vertical (pixels)')\n",
"plt.ylabel('Cloud Width - Horizontal (pixels)')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.legend()\n",
"\n",
"\n",
"m = 164*1.673e-27\n",
"kb = 1.38e-23\n",
"pixel = 5.86e-6\n",
"M = 2.3513\n",
"\n",
"T = fit_vals[0] * (m/kb) * ((pixel*pixel)/(M*M)) / (1e-6)\n",
"dT = fit_vals_std[0] * (m/kb) * ((pixel*pixel)/(M*M)) / (1e-6)\n",
"\n",
"print('T = %.3f \\u00B1 %.3f uK'% tuple([T,dT]))"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Measure temperature at the end of evaporative cooling 2, truncation 0.725"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The detected scaning axes and values are: \n",
"\n",
"{'TOF_free': array([0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01 ,\n",
" 0.011, 0.012, 0.013, 0.014, 0.015]), 'runs': array([0., 1., 2.])}\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"shotNum = \"0011\"\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 = (350, 990)\n",
"imageAnalyser.span = (650, 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)\n",
"Ncount_mean = calculate_mean(Ncount)\n",
"Ncount_std = calculate_std(Ncount)\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n",
"plt.xlabel('TOF (s)')\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"dataSet_cropOD_chunk = dataSet_cropOD.chunk((1, 1, 650, 200))\n",
"fitAnalyser = FitAnalyser(\"Gaussian-2D\", fitDim=2)\n",
"params = fitAnalyser.guess(dataSet_cropOD_chunk, dask=\"parallelized\")\n",
"fitResult = fitAnalyser.fit(dataSet_cropOD_chunk, params, dask=\"parallelized\").load()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3.338057111672083\n",
"0.07935760046682089\n"
]
}
],
"source": [
"data = fitAnalyser.get_fit_value(fitResult)\n",
"\n",
"print(data.amplitude.sel(TOF_free=0.01).mean('runs').item() * 147 / 1e5)\n",
"print(data.amplitude.sel(TOF_free=0.01).std('runs').item() * 147 / 1e5)"
]
},
{
"cell_type": "code",
"execution_count": 160,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key
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"source": [
"val = fitAnalyser.get_fit_value(fitResult)\n",
"std = fitAnalyser.get_fit_std(fitResult)\n",
"\n",
"fitCurve = fitAnalyser.eval(fitResult, x=np.arange(990), y=np.arange(350), dask=\"parallelized\").load()\n",
"\n",
"dataKey = 'sigmax'\n",
"# dataKey = 'centerx'\n",
"# dataKey = 'sigmay'\n",
"# dataKey = 'centery'\n",
"\n",
"val_mean = val[dataKey].mean(dim='runs')\n",
"std_mean = val[dataKey].std(dim='runs')\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"val_mean.plot.errorbar(yerr=std_mean, fmt='--ob')\n",
"\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 162,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key
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],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"val = fitAnalyser.get_fit_value(fitResult)\n",
"std = fitAnalyser.get_fit_std(fitResult)\n",
"\n",
"fitCurve = fitAnalyser.eval(fitResult, x=np.arange(990), y=np.arange(950), dask=\"parallelized\").load()\n",
"\n",
"# dataKey = 'sigmax'\n",
"# dataKey = 'centerx'\n",
"dataKey = 'sigmay'\n",
"# dataKey = 'centery'\n",
"\n",
"val_mean = val[dataKey].mean(dim='runs')\n",
"std_mean = val[dataKey].std(dim='runs')\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"val_mean.plot.errorbar(yerr=std_mean, fmt='--ob')\n",
"\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 164,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"T = 0.432 ± 0.004 uK\n"
]
}
],
"source": [
"def expansion(x, A, B):\n",
" return np.sqrt(A*x*x+B)\n",
"\n",
"val_truncated = val_mean[3:]\n",
"std_mean_truncated = std_mean[3:]\n",
"\n",
"fitModel_temperature = NewFitModel(expansion)\n",
"fitAnalyser_temperature = FitAnalyser(fitModel_temperature, fitDim=1)\n",
"\n",
"params = fitAnalyser_temperature.fitModel.make_params()\n",
"params.add(name=\"A\", value= 1e6, max = np.inf, min = 1, vary = True)\n",
"params.add(name=\"B\", value= 1000, max = np.inf, min = 0, vary = True)\n",
"\n",
"fitResult_temperature = fitAnalyser_temperature.fit(val_truncated, params, x='TOF_free', dask=\"parallelized\")\n",
"\n",
"val = fitAnalyser_temperature.get_fit_value(fitResult_temperature)\n",
"std = fitAnalyser_temperature.get_fit_std(fitResult_temperature)\n",
"\n",
"fit_vals = tuple([val['A'].item(), val['B'].item()])\n",
"fit_vals_std = tuple([std['A'].item(), std['B'].item()])\n",
"\n",
"tof = (np.arange(2,16,0.1)*1e-3)\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"val_mean.plot.errorbar(ax=ax, yerr=std_mean, fmt='or')\n",
"plt.plot(tof, expansion(tof, *fit_vals), 'b--',\n",
" label='fit: A=%.3f, B=%.3f' % tuple(fit_vals))\n",
"plt.xlabel('TOF (s)')\n",
"# plt.ylabel('Cloud Width - Vertical (pixels)')\n",
"plt.ylabel('Cloud Width - Horizontal (pixels)')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.legend()\n",
"\n",
"\n",
"m = 164*1.673e-27\n",
"kb = 1.38e-23\n",
"pixel = 5.86e-6\n",
"M = 2.3513\n",
"\n",
"T = fit_vals[0] * (m/kb) * ((pixel*pixel)/(M*M)) / (1e-6)\n",
"dT = fit_vals_std[0] * (m/kb) * ((pixel*pixel)/(M*M)) / (1e-6)\n",
"\n",
"print('T = %.3f \\u00B1 %.3f uK'% tuple([T,dT]))"
]
},
{
"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": []
},
{
"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": []
},
{
"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": 78,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The detected scaning axes and values are: \n",
"\n",
"{'carrier_freq': array([8.325, 8.33 , 8.335, 8.34 , 8.345, 8.35 , 8.355, 8.36 , 8.365,\n",
" 8.37 , 8.375, 8.38 , 8.385, 8.39 , 8.395, 8.4 , 8.405, 8.41 ,\n",
" 8.415, 8.42 , 8.425, 8.43 , 8.435, 8.44 , 8.445])}\n"
]
},
{
"data": {
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],
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"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib notebook\n",
"shotNum = \"0024\"\n",
"filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n",
"\n",
"dataSetDict = {\n",
" dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i], excludeAxis = ['sweep_start_freq', 'sweep_stop_freq'])\n",
" for i in [0]\n",
"}\n",
"\n",
"dataSet = dataSetDict[\"camera_0\"]\n",
"\n",
"print_scanAxis(dataSet)\n",
"\n",
"scanAxis = get_scanAxis(dataSet)\n",
"\n",
"dataSet = auto_rechunk(dataSet)\n",
"\n",
"dataSet = imageAnalyser.get_absorption_images(dataSet)\n",
"\n",
"imageAnalyser.center = (135, 990)\n",
"imageAnalyser.span = (250, 250)\n",
"imageAnalyser.fraction = (0.1, 0.1)\n",
"\n",
"dataSet_cropOD = imageAnalyser.crop_image(dataSet.OD)\n",
"dataSet_cropOD = imageAnalyser.substract_offset(dataSet_cropOD).load()\n",
"\n",
"Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n",
"Ncount_mean = calculate_mean(Ncount)\n",
"Ncount_std = calculate_std(Ncount)\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n",
"\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"#plt.ylim([0, 3500])\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.011, 0.012, 0.013, 0.014, 0.015]\n",
"14\n"
]
},
{
"data": {
"text/plain": [
"0.008499999999999999"
]
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l = list(np.arange(2e-3, 16e-3, 1e-3))\n",
"# l = np.logspace(np.log10(250e-6), np.log10(500e-3), num=15)\n",
"\n",
"l = [round(item, 7) for item in l]\n",
"#random.shuffle(l)\n",
"\n",
"print(l)\n",
"print(len(l))\n",
"np.mean(l)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"10.6 / 1e3"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pixel = 5.86e-6\n",
"M = 0.6827\n",
"F = (1/(0.3725*8.4743e-14)) * (pixel / M)**2\n",
"NCount = 85000\n",
"AtomNumber = NCount * F / 1e8\n",
"print(AtomNumber)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"muB = 9.274e-24\n",
"hbar = 6.626e-34 / (2 * np.pi)\n",
"gJ = 1.24\n",
"Delta = 2 * np.pi * 100 * 1e3\n",
"\n",
"Bz = (Delta*hbar) / (muB*gJ)\n",
"print(Bz * 1e4)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## ODT 1 Calibration"
]
},
{
"cell_type": "code",
"execution_count": 165,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"14.912\n",
"1.171\n",
"299.302\n"
]
}
],
"source": [
"v_high = 2.7\n",
"\"\"\"High Power\"\"\"\n",
"P_arm1_high = 5.776 * v_high - 0.683\n",
"\n",
"v_mid = 0.2076\n",
"\"\"\"Intermediate Power\"\"\"\n",
"P_arm1_mid = 5.815 * v_mid - 0.03651\n",
"\n",
"v_low = 0.062\n",
"\"\"\"Low Power\"\"\"\n",
"P_arm1_low = 5271 * v_low - 27.5\n",
"\n",
"print(round(P_arm1_high, 3))\n",
"print(round(P_arm1_mid, 3))\n",
"print(round(P_arm1_low, 3))"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## ODT 2 Power Calibration"
]
},
{
"cell_type": "code",
"execution_count": 166,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.874\n"
]
}
],
"source": [
"v = 0.842\n",
"P_arm2 = 2.302 * v - 0.06452\n",
"print(round(P_arm2, 3))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.12"
},
"vscode": {
"interpreter": {
"hash": "c05913ad4f24fdc6b2418069394dc5835b1981849b107c9ba6df693aafd66650"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Reference in New Issue Copy Permalink