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93917 lines
5.4 MiB
93917 lines
5.4 MiB
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pymongo\n",
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"import xarray_mongodb\n",
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"import bson\n",
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"import datetime\n",
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"\n",
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"# datetime.datetime.utcnow()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 74,
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"metadata": {},
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"outputs": [],
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"source": [
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"mongoClient = pymongo.MongoClient()\n",
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"mongoDB = mongoClient.testDB\n",
|
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"mongoCollection = mongoDB.testCollection"
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]
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},
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{
|
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Task executing\n",
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"\n",
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"Task2 executing \n",
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"\n",
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"Task2 done\n",
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"\n",
|
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"Task done\n",
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"\n"
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]
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}
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],
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"source": [
|
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"from time import sleep\n",
|
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"from multiprocessing.pool import ThreadPool\n",
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" \n",
|
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"# task executed in a worker thread\n",
|
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"def task():\n",
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" # report a message\n",
|
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" print(f'Task executing\\n')\n",
|
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" # block for a moment\n",
|
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" sleep(1)\n",
|
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" # report a message\n",
|
|
" print(f'Task done\\n')\n",
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" \n",
|
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"def task2():\n",
|
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" # report a message\n",
|
|
" print(f'Task2 executing \\n')\n",
|
|
" # block for a moment\n",
|
|
" sleep(1)\n",
|
|
" # report a message\n",
|
|
" print(f'Task2 done\\n')\n",
|
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" \n",
|
|
"# protect the entry point\n",
|
|
"if __name__ == '__main__':\n",
|
|
" # create and configure the thread pool\n",
|
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" pool = ThreadPool()\n",
|
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" # issue tasks to the thread pool\n",
|
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" pool.apply_async(task)\n",
|
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" pool.apply_async(task2)\n",
|
|
" # close the thread pool\n",
|
|
" pool.close()\n",
|
|
" # wait for all tasks to finish\n",
|
|
" pool.join()"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {},
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Import supporting package"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 4,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"import xarray as xr\n",
|
|
"import pandas as pd\n",
|
|
"import numpy as np\n",
|
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"import copy\n",
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|
"\n",
|
|
"import glob\n",
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"\n",
|
|
"import xrft\n",
|
|
"import finufft\n",
|
|
"\n",
|
|
"from uncertainties import ufloat\n",
|
|
"from uncertainties import unumpy as unp\n",
|
|
"from uncertainties import umath\n",
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"\n",
|
|
"from datetime import datetime\n",
|
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"\n",
|
|
"import matplotlib.pyplot as plt\n",
|
|
"plt.rcParams['font.size'] = 18\n",
|
|
"\n",
|
|
"from DataContainer.ReadData import read_hdf5_file, read_hdf5_global, read_hdf5_run_time, read_csv_file\n",
|
|
"from Analyser.ImagingAnalyser import ImageAnalyser\n",
|
|
"from Analyser.FitAnalyser import FitAnalyser\n",
|
|
"from Analyser.FitAnalyser import ThomasFermi2dModel, DensityProfileBEC2dModel, Polylog22dModel\n",
|
|
"from Analyser.FFTAnalyser import fft, ifft, fft_nutou\n",
|
|
"from ToolFunction.ToolFunction import *\n",
|
|
"\n",
|
|
"from ToolFunction.HomeMadeXarrayFunction import errorbar, dataarray_plot_errorbar\n",
|
|
"xr.plot.dataarray_plot.errorbar = errorbar\n",
|
|
"xr.plot.accessor.DataArrayPlotAccessor.errorbar = dataarray_plot_errorbar\n",
|
|
"\n",
|
|
"imageAnalyser = ImageAnalyser()"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {},
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Import supporting package"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 5,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"import xarray as xr\n",
|
|
"import numpy as np\n",
|
|
"\n",
|
|
"from uncertainties import ufloat\n",
|
|
"from uncertainties import unumpy as unp\n",
|
|
"from uncertainties import umath\n",
|
|
"\n",
|
|
"import matplotlib.pyplot as plt\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 ThomasFermi2dModel, DensityProfileBEC2dModel, Polylog22dModel\n",
|
|
"from Analyser.FitAnalyser import NewFitModel\n",
|
|
"from ToolFunction.ToolFunction import *\n",
|
|
"\n",
|
|
"from ToolFunction.HomeMadeXarrayFunction import errorbar, dataarray_plot_errorbar\n",
|
|
"xr.plot.dataarray_plot.errorbar = errorbar\n",
|
|
"xr.plot.accessor.DataArrayPlotAccessor.errorbar = dataarray_plot_errorbar\n",
|
|
"\n",
|
|
"imageAnalyser = ImageAnalyser()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 71,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"1738.8478693742436\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"sigma = 8.4743e-14 * 0.5\n",
|
|
"M = 0.6827\n",
|
|
"print(1/sigma*5.86e-6**2 / M**2)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 72,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"8.583565255602754e-06"
|
|
]
|
|
},
|
|
"execution_count": 72,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"5.86e-6/M"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {},
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Start a client for parallel computing"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"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",
|
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" <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
|
|
" <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-04ea26e8-06a5-11ee-b304-80e82ce2fa8e</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",
|
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" \n",
|
|
" </tr>\n",
|
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"\n",
|
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" \n",
|
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" <tr>\n",
|
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" <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",
|
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" \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;\">a00dee60</p>\n",
|
|
" <table style=\"width: 100%; text-align: left;\">\n",
|
|
" <tr>\n",
|
|
" <td style=\"text-align: left;\">\n",
|
|
" <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
|
|
" </td>\n",
|
|
" <td style=\"text-align: left;\">\n",
|
|
" <strong>Workers:</strong> 6\n",
|
|
" </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td style=\"text-align: left;\">\n",
|
|
" <strong>Total threads:</strong> 60\n",
|
|
" </td>\n",
|
|
" <td style=\"text-align: left;\">\n",
|
|
" <strong>Total memory:</strong> 55.88 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-75925e85-0e64-4d7a-90f1-89d8e84d8c64</p>\n",
|
|
" <table style=\"width: 100%; text-align: left;\">\n",
|
|
" <tr>\n",
|
|
" <td style=\"text-align: left;\">\n",
|
|
" <strong>Comm:</strong> tcp://127.0.0.1:61431\n",
|
|
" </td>\n",
|
|
" <td style=\"text-align: left;\">\n",
|
|
" <strong>Workers:</strong> 6\n",
|
|
" </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td style=\"text-align: left;\">\n",
|
|
" <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
|
|
" </td>\n",
|
|
" <td style=\"text-align: left;\">\n",
|
|
" <strong>Total threads:</strong> 60\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> 55.88 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://127.0.0.1:61467\n",
|
|
" </td>\n",
|
|
" <td style=\"text-align: left;\">\n",
|
|
" <strong>Total threads: </strong> 10\n",
|
|
" </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td style=\"text-align: left;\">\n",
|
|
" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:61469/status\" target=\"_blank\">http://127.0.0.1:61469/status</a>\n",
|
|
" </td>\n",
|
|
" <td style=\"text-align: left;\">\n",
|
|
" <strong>Memory: </strong> 9.31 GiB\n",
|
|
" </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td style=\"text-align: left;\">\n",
|
|
" <strong>Nanny: </strong> tcp://127.0.0.1:61434\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-69okx1iv\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://127.0.0.1:61462\n",
|
|
" </td>\n",
|
|
" <td style=\"text-align: left;\">\n",
|
|
" <strong>Total threads: </strong> 10\n",
|
|
" </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td style=\"text-align: left;\">\n",
|
|
" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:61465/status\" target=\"_blank\">http://127.0.0.1:61465/status</a>\n",
|
|
" </td>\n",
|
|
" <td style=\"text-align: left;\">\n",
|
|
" <strong>Memory: </strong> 9.31 GiB\n",
|
|
" </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td style=\"text-align: left;\">\n",
|
|
" <strong>Nanny: </strong> tcp://127.0.0.1:61435\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-yhx5z4uy\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://127.0.0.1:61452\n",
|
|
" </td>\n",
|
|
" <td style=\"text-align: left;\">\n",
|
|
" <strong>Total threads: </strong> 10\n",
|
|
" </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td style=\"text-align: left;\">\n",
|
|
" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:61459/status\" target=\"_blank\">http://127.0.0.1:61459/status</a>\n",
|
|
" </td>\n",
|
|
" <td style=\"text-align: left;\">\n",
|
|
" <strong>Memory: </strong> 9.31 GiB\n",
|
|
" </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td style=\"text-align: left;\">\n",
|
|
" <strong>Nanny: </strong> tcp://127.0.0.1:61436\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-ftw5n8mm\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://127.0.0.1:61468\n",
|
|
" </td>\n",
|
|
" <td style=\"text-align: left;\">\n",
|
|
" <strong>Total threads: </strong> 10\n",
|
|
" </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td style=\"text-align: left;\">\n",
|
|
" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:61471/status\" target=\"_blank\">http://127.0.0.1:61471/status</a>\n",
|
|
" </td>\n",
|
|
" <td style=\"text-align: left;\">\n",
|
|
" <strong>Memory: </strong> 9.31 GiB\n",
|
|
" </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td style=\"text-align: left;\">\n",
|
|
" <strong>Nanny: </strong> tcp://127.0.0.1:61437\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-v8x817a_\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://127.0.0.1:61473\n",
|
|
" </td>\n",
|
|
" <td style=\"text-align: left;\">\n",
|
|
" <strong>Total threads: </strong> 10\n",
|
|
" </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td style=\"text-align: left;\">\n",
|
|
" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:61474/status\" target=\"_blank\">http://127.0.0.1:61474/status</a>\n",
|
|
" </td>\n",
|
|
" <td style=\"text-align: left;\">\n",
|
|
" <strong>Memory: </strong> 9.31 GiB\n",
|
|
" </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td style=\"text-align: left;\">\n",
|
|
" <strong>Nanny: </strong> tcp://127.0.0.1:61438\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-g6p_tw9j\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://127.0.0.1:61461\n",
|
|
" </td>\n",
|
|
" <td style=\"text-align: left;\">\n",
|
|
" <strong>Total threads: </strong> 10\n",
|
|
" </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td style=\"text-align: left;\">\n",
|
|
" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:61463/status\" target=\"_blank\">http://127.0.0.1:61463/status</a>\n",
|
|
" </td>\n",
|
|
" <td style=\"text-align: left;\">\n",
|
|
" <strong>Memory: </strong> 9.31 GiB\n",
|
|
" </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <td style=\"text-align: left;\">\n",
|
|
" <strong>Nanny: </strong> tcp://127.0.0.1:61439\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-vhsgp3au\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://127.0.0.1:61431' processes=6 threads=60, memory=55.88 GiB>"
|
|
]
|
|
},
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"from dask.distributed import Client\n",
|
|
"client = Client(n_workers=6, threads_per_worker=10, processes=True, memory_limit='10GB')\n",
|
|
"client"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {},
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Set global path for experiment"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# filepath = \"//DyLabNAS/Data/Evaporative_Cooling/2023/05/03/0043/*.h5\"\n",
|
|
"# filepath = \"//DyLabNAS/Data/Evaporative_Cooling/2023/04/18/0003/2023-04-18_0003_Evaporative_Cooling_000.h5\"\n",
|
|
"\n",
|
|
"# filepath = \"//DyLabNAS/Data/Repetition_scan/2023/04/21/0002/*.h5\"\n",
|
|
"\n",
|
|
"# filepath = r\"./testData/0002/*.h5\"\n",
|
|
"\n",
|
|
"# filepath = r\"./testData/0002/2023-04-21_0002_Evaporative_Cooling_0.h5\"\n",
|
|
"\n",
|
|
"# filepath = r'd:/Jianshun Gao/Simulations/analyseScripts/testData/0002/2023-04-21_0002_Evaporative_Cooling_0.h5'\n",
|
|
"\n",
|
|
"# filepath = \"//DyLabNAS/Data/Evaporative_Cooling/2023/04/18/0003/*.h5\"\n",
|
|
"\n",
|
|
"# filepath = \"//DyLabNAS/Data/Evaporative_Cooling/2023/05/04/0000/*.h5\"\n",
|
|
"\n",
|
|
"filepath = './result_from_experiment/2023-04-24/0013/2023-04-24_0013_Evaporative_Cooling_08.h5'"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"groupList = [\n",
|
|
" \"images/MOT_3D_Camera/in_situ_absorption\",\n",
|
|
" \"images/ODT_1_Axis_Camera/in_situ_absorption\",\n",
|
|
" \"images/ODT_2_Axis_Camera/in_situ_absorption\",\n",
|
|
"]\n",
|
|
"\n",
|
|
"dskey = {\n",
|
|
" \"images/MOT_3D_Camera/in_situ_absorption\": \"camera_1\",\n",
|
|
" \"images/ODT_1_Axis_Camera/in_situ_absorption\": \"camera_2\",\n",
|
|
" \"images/ODT_2_Axis_Camera/in_situ_absorption\": \"camera_3\",\n",
|
|
"}\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"img_dir = '//DyLabNAS/Data/'\n",
|
|
"SequenceName = \"Evaporative_Cooling\" + \"/\"\n",
|
|
"folderPath = img_dir + SequenceName + '2023/05/23'# get_date()"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {},
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# An example for one experimental run"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {},
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Load the data"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"f:\\Jianshun\\analyseScript\\DataContainer\\ReadData.py:178: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
|
|
" if not key in datesetOfGlobal.scanAxis\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
"<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
|
|
"<defs>\n",
|
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"<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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"<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
|
|
"<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
|
|
"<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
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"</symbol>\n",
|
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"<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
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"<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
|
|
"<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
|
|
"<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
|
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"<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
|
|
"</symbol>\n",
|
|
"</defs>\n",
|
|
"</svg>\n",
|
|
"<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
|
|
" *\n",
|
|
" */\n",
|
|
"\n",
|
|
":root {\n",
|
|
" --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
|
|
" --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
|
|
" --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
|
|
" --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
|
|
" --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
|
|
" --xr-background-color: var(--jp-layout-color0, white);\n",
|
|
" --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
|
|
" --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
|
|
"}\n",
|
|
"\n",
|
|
"html[theme=dark],\n",
|
|
"body[data-theme=dark],\n",
|
|
"body.vscode-dark {\n",
|
|
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
|
|
" --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
|
|
" --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
|
|
" --xr-border-color: #1F1F1F;\n",
|
|
" --xr-disabled-color: #515151;\n",
|
|
" --xr-background-color: #111111;\n",
|
|
" --xr-background-color-row-even: #111111;\n",
|
|
" --xr-background-color-row-odd: #313131;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-wrap {\n",
|
|
" display: block !important;\n",
|
|
" min-width: 300px;\n",
|
|
" max-width: 700px;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-text-repr-fallback {\n",
|
|
" /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
|
|
" display: none;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-header {\n",
|
|
" padding-top: 6px;\n",
|
|
" padding-bottom: 6px;\n",
|
|
" margin-bottom: 4px;\n",
|
|
" border-bottom: solid 1px var(--xr-border-color);\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-header > div,\n",
|
|
".xr-header > ul {\n",
|
|
" display: inline;\n",
|
|
" margin-top: 0;\n",
|
|
" margin-bottom: 0;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-obj-type,\n",
|
|
".xr-array-name {\n",
|
|
" margin-left: 2px;\n",
|
|
" margin-right: 10px;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-obj-type {\n",
|
|
" color: var(--xr-font-color2);\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-sections {\n",
|
|
" padding-left: 0 !important;\n",
|
|
" display: grid;\n",
|
|
" grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-section-item {\n",
|
|
" display: contents;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-section-item input {\n",
|
|
" display: none;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-section-item input + label {\n",
|
|
" color: var(--xr-disabled-color);\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-section-item input:enabled + label {\n",
|
|
" cursor: pointer;\n",
|
|
" color: var(--xr-font-color2);\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-section-item input:enabled + label:hover {\n",
|
|
" color: var(--xr-font-color0);\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-section-summary {\n",
|
|
" grid-column: 1;\n",
|
|
" color: var(--xr-font-color2);\n",
|
|
" font-weight: 500;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-section-summary > span {\n",
|
|
" display: inline-block;\n",
|
|
" padding-left: 0.5em;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-section-summary-in:disabled + label {\n",
|
|
" color: var(--xr-font-color2);\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-section-summary-in + label:before {\n",
|
|
" display: inline-block;\n",
|
|
" content: 'â–º';\n",
|
|
" font-size: 11px;\n",
|
|
" width: 15px;\n",
|
|
" text-align: center;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-section-summary-in:disabled + label:before {\n",
|
|
" color: var(--xr-disabled-color);\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-section-summary-in:checked + label:before {\n",
|
|
" content: 'â–¼';\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-section-summary-in:checked + label > span {\n",
|
|
" display: none;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-section-summary,\n",
|
|
".xr-section-inline-details {\n",
|
|
" padding-top: 4px;\n",
|
|
" padding-bottom: 4px;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-section-inline-details {\n",
|
|
" grid-column: 2 / -1;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-section-details {\n",
|
|
" display: none;\n",
|
|
" grid-column: 1 / -1;\n",
|
|
" margin-bottom: 5px;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-section-summary-in:checked ~ .xr-section-details {\n",
|
|
" display: contents;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-array-wrap {\n",
|
|
" grid-column: 1 / -1;\n",
|
|
" display: grid;\n",
|
|
" grid-template-columns: 20px auto;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-array-wrap > label {\n",
|
|
" grid-column: 1;\n",
|
|
" vertical-align: top;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-preview {\n",
|
|
" color: var(--xr-font-color3);\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-array-preview,\n",
|
|
".xr-array-data {\n",
|
|
" padding: 0 5px !important;\n",
|
|
" grid-column: 2;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-array-data,\n",
|
|
".xr-array-in:checked ~ .xr-array-preview {\n",
|
|
" display: none;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-array-in:checked ~ .xr-array-data,\n",
|
|
".xr-array-preview {\n",
|
|
" display: inline-block;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-dim-list {\n",
|
|
" display: inline-block !important;\n",
|
|
" list-style: none;\n",
|
|
" padding: 0 !important;\n",
|
|
" margin: 0;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-dim-list li {\n",
|
|
" display: inline-block;\n",
|
|
" padding: 0;\n",
|
|
" margin: 0;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-dim-list:before {\n",
|
|
" content: '(';\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-dim-list:after {\n",
|
|
" content: ')';\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-dim-list li:not(:last-child):after {\n",
|
|
" content: ',';\n",
|
|
" padding-right: 5px;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-has-index {\n",
|
|
" font-weight: bold;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-var-list,\n",
|
|
".xr-var-item {\n",
|
|
" display: contents;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-var-item > div,\n",
|
|
".xr-var-item label,\n",
|
|
".xr-var-item > .xr-var-name span {\n",
|
|
" background-color: var(--xr-background-color-row-even);\n",
|
|
" margin-bottom: 0;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-var-item > .xr-var-name:hover span {\n",
|
|
" padding-right: 5px;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-var-list > li:nth-child(odd) > div,\n",
|
|
".xr-var-list > li:nth-child(odd) > label,\n",
|
|
".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
|
|
" background-color: var(--xr-background-color-row-odd);\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-var-name {\n",
|
|
" grid-column: 1;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-var-dims {\n",
|
|
" grid-column: 2;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-var-dtype {\n",
|
|
" grid-column: 3;\n",
|
|
" text-align: right;\n",
|
|
" color: var(--xr-font-color2);\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-var-preview {\n",
|
|
" grid-column: 4;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-index-preview {\n",
|
|
" grid-column: 2 / 5;\n",
|
|
" color: var(--xr-font-color2);\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-var-name,\n",
|
|
".xr-var-dims,\n",
|
|
".xr-var-dtype,\n",
|
|
".xr-preview,\n",
|
|
".xr-attrs dt {\n",
|
|
" white-space: nowrap;\n",
|
|
" overflow: hidden;\n",
|
|
" text-overflow: ellipsis;\n",
|
|
" padding-right: 10px;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-var-name:hover,\n",
|
|
".xr-var-dims:hover,\n",
|
|
".xr-var-dtype:hover,\n",
|
|
".xr-attrs dt:hover {\n",
|
|
" overflow: visible;\n",
|
|
" width: auto;\n",
|
|
" z-index: 1;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-var-attrs,\n",
|
|
".xr-var-data,\n",
|
|
".xr-index-data {\n",
|
|
" display: none;\n",
|
|
" background-color: var(--xr-background-color) !important;\n",
|
|
" padding-bottom: 5px !important;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
|
|
".xr-var-data-in:checked ~ .xr-var-data,\n",
|
|
".xr-index-data-in:checked ~ .xr-index-data {\n",
|
|
" display: block;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-var-data > table {\n",
|
|
" float: right;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-var-name span,\n",
|
|
".xr-var-data,\n",
|
|
".xr-index-name div,\n",
|
|
".xr-index-data,\n",
|
|
".xr-attrs {\n",
|
|
" padding-left: 25px !important;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-attrs,\n",
|
|
".xr-var-attrs,\n",
|
|
".xr-var-data,\n",
|
|
".xr-index-data {\n",
|
|
" grid-column: 1 / -1;\n",
|
|
"}\n",
|
|
"\n",
|
|
"dl.xr-attrs {\n",
|
|
" padding: 0;\n",
|
|
" margin: 0;\n",
|
|
" display: grid;\n",
|
|
" grid-template-columns: 125px auto;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-attrs dt,\n",
|
|
".xr-attrs dd {\n",
|
|
" padding: 0;\n",
|
|
" margin: 0;\n",
|
|
" float: left;\n",
|
|
" padding-right: 10px;\n",
|
|
" width: auto;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-attrs dt {\n",
|
|
" font-weight: normal;\n",
|
|
" grid-column: 1;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-attrs dt:hover span {\n",
|
|
" display: inline-block;\n",
|
|
" background: var(--xr-background-color);\n",
|
|
" padding-right: 10px;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-attrs dd {\n",
|
|
" grid-column: 2;\n",
|
|
" white-space: pre-wrap;\n",
|
|
" word-break: break-all;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-icon-database,\n",
|
|
".xr-icon-file-text2,\n",
|
|
".xr-no-icon {\n",
|
|
" display: inline-block;\n",
|
|
" vertical-align: middle;\n",
|
|
" width: 1em;\n",
|
|
" height: 1.5em !important;\n",
|
|
" stroke-width: 0;\n",
|
|
" stroke: currentColor;\n",
|
|
" fill: currentColor;\n",
|
|
"}\n",
|
|
"</style><pre class='xr-text-repr-fallback'><xarray.Dataset>\n",
|
|
"Dimensions: (y: 1200, x: 1920)\n",
|
|
"Dimensions without coordinates: y, x\n",
|
|
"Data variables:\n",
|
|
" atoms (y, x) uint16 dask.array<chunksize=(1200, 1920), meta=np.ndarray>\n",
|
|
" background (y, x) uint16 dask.array<chunksize=(1200, 1920), meta=np.ndarray>\n",
|
|
" dark (y, x) uint16 dask.array<chunksize=(1200, 1920), meta=np.ndarray>\n",
|
|
" shotNum <U2 '08'\n",
|
|
" OD (y, x) float64 dask.array<chunksize=(1200, 1920), meta=np.ndarray>\n",
|
|
"Attributes: (12/96)\n",
|
|
" TOF_free: 0.02\n",
|
|
" abs_img_freq: 110.858\n",
|
|
" absorption_imaging_flag: True\n",
|
|
" backup_data: True\n",
|
|
" blink_off_time: nan\n",
|
|
" blink_on_time: nan\n",
|
|
" ... ...\n",
|
|
" y_offset: 0\n",
|
|
" y_offset_img: 0\n",
|
|
" z_offset: 0.189\n",
|
|
" z_offset_img: 0.189\n",
|
|
" scanAxis: []\n",
|
|
" scanAxisLength: []</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-07d83317-71ec-4d58-9a49-e6a57dae7947' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-07d83317-71ec-4d58-9a49-e6a57dae7947' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>y</span>: 1200</li><li><span>x</span>: 1920</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5d02fae2-1258-4b9f-8f95-ee326e3a1014' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5d02fae2-1258-4b9f-8f95-ee326e3a1014' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-3c73367d-e76f-4ce1-8e1b-fe5259bfdd6c' class='xr-section-summary-in' type='checkbox' checked><label for='section-3c73367d-e76f-4ce1-8e1b-fe5259bfdd6c' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>atoms</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1200, 1920), meta=np.ndarray></div><input id='attrs-8ac4e188-6f1b-4dcf-966c-0026c9d2d03f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8ac4e188-6f1b-4dcf-966c-0026c9d2d03f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a939a0d-9e74-4b6b-8bb2-35ba1c8f2d60' class='xr-var-data-in' type='checkbox'><label for='data-6a939a0d-9e74-4b6b-8bb2-35ba1c8f2d60' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>IMAGE_SUBCLASS :</span></dt><dd>IMAGE_GRAYSCALE</dd><dt><span>IMAGE_VERSION :</span></dt><dd>1.2</dd><dt><span>IMAGE_WHITE_IS_ZERO :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
|
|
" <tr>\n",
|
|
" <td>\n",
|
|
" <table style=\"border-collapse: collapse;\">\n",
|
|
" <thead>\n",
|
|
" <tr>\n",
|
|
" <td> </td>\n",
|
|
" <th> Array </th>\n",
|
|
" <th> Chunk </th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <th> Bytes </th>\n",
|
|
" <td> 4.39 MiB </td>\n",
|
|
" <td> 4.39 MiB </td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <th> Shape </th>\n",
|
|
" <td> (1200, 1920) </td>\n",
|
|
" <td> (1200, 1920) </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th> Dask graph </th>\n",
|
|
" <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th> Data type </th>\n",
|
|
" <td colspan=\"2\"> uint16 numpy.ndarray </td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
" </table>\n",
|
|
" </td>\n",
|
|
" <td>\n",
|
|
" <svg width=\"170\" height=\"125\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
|
|
"\n",
|
|
" <!-- Horizontal lines -->\n",
|
|
" <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
|
|
" <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" style=\"stroke-width:2\" />\n",
|
|
"\n",
|
|
" <!-- Vertical lines -->\n",
|
|
" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"75\" style=\"stroke-width:2\" />\n",
|
|
" <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"75\" style=\"stroke-width:2\" />\n",
|
|
"\n",
|
|
" <!-- Colored Rectangle -->\n",
|
|
" <polygon points=\"0.0,0.0 120.0,0.0 120.0,75.0 0.0,75.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
|
|
"\n",
|
|
" <!-- Text -->\n",
|
|
" <text x=\"60.000000\" y=\"95.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1920</text>\n",
|
|
" <text x=\"140.000000\" y=\"37.500000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,37.500000)\">1200</text>\n",
|
|
"</svg>\n",
|
|
" </td>\n",
|
|
" </tr>\n",
|
|
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>background</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1200, 1920), meta=np.ndarray></div><input id='attrs-988d1a71-6c5c-4b0d-b888-ccccd6a326ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-988d1a71-6c5c-4b0d-b888-ccccd6a326ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e40348be-ca96-4f6a-a7e5-3b8ee3416e8d' class='xr-var-data-in' type='checkbox'><label for='data-e40348be-ca96-4f6a-a7e5-3b8ee3416e8d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>IMAGE_SUBCLASS :</span></dt><dd>IMAGE_GRAYSCALE</dd><dt><span>IMAGE_VERSION :</span></dt><dd>1.2</dd><dt><span>IMAGE_WHITE_IS_ZERO :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
|
|
" <tr>\n",
|
|
" <td>\n",
|
|
" <table style=\"border-collapse: collapse;\">\n",
|
|
" <thead>\n",
|
|
" <tr>\n",
|
|
" <td> </td>\n",
|
|
" <th> Array </th>\n",
|
|
" <th> Chunk </th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <th> Bytes </th>\n",
|
|
" <td> 4.39 MiB </td>\n",
|
|
" <td> 4.39 MiB </td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <th> Shape </th>\n",
|
|
" <td> (1200, 1920) </td>\n",
|
|
" <td> (1200, 1920) </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th> Dask graph </th>\n",
|
|
" <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th> Data type </th>\n",
|
|
" <td colspan=\"2\"> uint16 numpy.ndarray </td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
" </table>\n",
|
|
" </td>\n",
|
|
" <td>\n",
|
|
" <svg width=\"170\" height=\"125\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
|
|
"\n",
|
|
" <!-- Horizontal lines -->\n",
|
|
" <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
|
|
" <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" style=\"stroke-width:2\" />\n",
|
|
"\n",
|
|
" <!-- Vertical lines -->\n",
|
|
" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"75\" style=\"stroke-width:2\" />\n",
|
|
" <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"75\" style=\"stroke-width:2\" />\n",
|
|
"\n",
|
|
" <!-- Colored Rectangle -->\n",
|
|
" <polygon points=\"0.0,0.0 120.0,0.0 120.0,75.0 0.0,75.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
|
|
"\n",
|
|
" <!-- Text -->\n",
|
|
" <text x=\"60.000000\" y=\"95.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1920</text>\n",
|
|
" <text x=\"140.000000\" y=\"37.500000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,37.500000)\">1200</text>\n",
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"</svg>\n",
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" </td>\n",
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" </tr>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dark</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1200, 1920), meta=np.ndarray></div><input id='attrs-f7385613-c015-45f4-aa3a-4734dceec6ce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f7385613-c015-45f4-aa3a-4734dceec6ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-82e6a8c5-9d0a-4e99-a616-9b276a1d671e' class='xr-var-data-in' type='checkbox'><label for='data-82e6a8c5-9d0a-4e99-a616-9b276a1d671e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>IMAGE_SUBCLASS :</span></dt><dd>IMAGE_GRAYSCALE</dd><dt><span>IMAGE_VERSION :</span></dt><dd>1.2</dd><dt><span>IMAGE_WHITE_IS_ZERO :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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|
" <tr>\n",
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" <td>\n",
|
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" <table style=\"border-collapse: collapse;\">\n",
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|
" <thead>\n",
|
|
" <tr>\n",
|
|
" <td> </td>\n",
|
|
" <th> Array </th>\n",
|
|
" <th> Chunk </th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <th> Bytes </th>\n",
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|
" <td> 4.39 MiB </td>\n",
|
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" <td> 4.39 MiB </td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <th> Shape </th>\n",
|
|
" <td> (1200, 1920) </td>\n",
|
|
" <td> (1200, 1920) </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th> Dask graph </th>\n",
|
|
" <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th> Data type </th>\n",
|
|
" <td colspan=\"2\"> uint16 numpy.ndarray </td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
" </table>\n",
|
|
" </td>\n",
|
|
" <td>\n",
|
|
" <svg width=\"170\" height=\"125\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
|
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"\n",
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" <!-- Horizontal lines -->\n",
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" <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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" <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" style=\"stroke-width:2\" />\n",
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"\n",
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" <!-- Vertical lines -->\n",
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" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"75\" style=\"stroke-width:2\" />\n",
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" <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"75\" style=\"stroke-width:2\" />\n",
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"\n",
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" <!-- Colored Rectangle -->\n",
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" <polygon points=\"0.0,0.0 120.0,0.0 120.0,75.0 0.0,75.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
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"\n",
|
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" <!-- Text -->\n",
|
|
" <text x=\"60.000000\" y=\"95.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1920</text>\n",
|
|
" <text x=\"140.000000\" y=\"37.500000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,37.500000)\">1200</text>\n",
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|
"</svg>\n",
|
|
" </td>\n",
|
|
" </tr>\n",
|
|
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>shotNum</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'><U2</div><div class='xr-var-preview xr-preview'>'08'</div><input id='attrs-affcb095-4c70-4748-ac52-42a9043fad20' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-affcb095-4c70-4748-ac52-42a9043fad20' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6cd2934-ff48-4526-a727-493e6e8590a2' class='xr-var-data-in' type='checkbox'><label for='data-b6cd2934-ff48-4526-a727-493e6e8590a2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array('08', dtype='<U2')</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>OD</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1200, 1920), meta=np.ndarray></div><input id='attrs-2bc0ca13-ce66-4d3d-a6df-c293dbb46896' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2bc0ca13-ce66-4d3d-a6df-c293dbb46896' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c4c6170-7c9a-4697-9d88-c2dd4a4022c5' class='xr-var-data-in' type='checkbox'><label for='data-5c4c6170-7c9a-4697-9d88-c2dd4a4022c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>IMAGE_SUBCLASS :</span></dt><dd>IMAGE_GRAYSCALE</dd><dt><span>IMAGE_VERSION :</span></dt><dd>1.2</dd><dt><span>IMAGE_WHITE_IS_ZERO :</span></dt><dd>0</dd><dt><span>TOF_free :</span></dt><dd>0.02</dd><dt><span>abs_img_freq :</span></dt><dd>110.858</dd><dt><span>absorption_imaging_flag :</span></dt><dd>True</dd><dt><span>backup_data :</span></dt><dd>True</dd><dt><span>blink_off_time :</span></dt><dd>nan</dd><dt><span>blink_on_time :</span></dt><dd>nan</dd><dt><span>c_duration :</span></dt><dd>0.2</dd><dt><span>cmot_final_current :</span></dt><dd>0.65</dd><dt><span>cmot_hold :</span></dt><dd>0.06</dd><dt><span>cmot_initial_current :</span></dt><dd>0.18</dd><dt><span>compX_current :</span></dt><dd>0.005</dd><dt><span>compX_current_sg :</span></dt><dd>0</dd><dt><span>compX_final_current :</span></dt><dd>0.005</dd><dt><span>compX_initial_current :</span></dt><dd>0.005</dd><dt><span>compY_current :</span></dt><dd>0</dd><dt><span>compY_current_sg :</span></dt><dd>0</dd><dt><span>compY_final_current :</span></dt><dd>0.0</dd><dt><span>compY_initial_current :</span></dt><dd>0</dd><dt><span>compZ_current :</span></dt><dd>0</dd><dt><span>compZ_current_sg :</span></dt><dd>0.189</dd><dt><span>compZ_final_current :</span></dt><dd>0.2812</dd><dt><span>compZ_initial_current :</span></dt><dd>0</dd><dt><span>default_camera :</span></dt><dd>0</dd><dt><span>evap_1_arm_1_final_pow :</span></dt><dd>0.35</dd><dt><span>evap_1_arm_1_mod_depth_final :</span></dt><dd>0</dd><dt><span>evap_1_arm_1_mod_depth_initial :</span></dt><dd>1.0</dd><dt><span>evap_1_arm_1_mod_ramp_duration :</span></dt><dd>1.15</dd><dt><span>evap_1_arm_1_pow_ramp_duration :</span></dt><dd>1.65</dd><dt><span>evap_1_arm_1_start_pow :</span></dt><dd>7</dd><dt><span>evap_1_arm_2_final_pow :</span></dt><dd>5</dd><dt><span>evap_1_arm_2_ramp_duration :</span></dt><dd>0.5</dd><dt><span>evap_1_arm_2_start_pow :</span></dt><dd>0</dd><dt><span>evap_1_mod_ramp_trunc_value :</span></dt><dd>1</dd><dt><span>evap_1_pow_ramp_trunc_value :</span></dt><dd>1.0</dd><dt><span>evap_1_rate_constant_1 :</span></dt><dd>0.525</dd><dt><span>evap_1_rate_constant_2 :</span></dt><dd>0.51</dd><dt><span>evap_2_arm_1_final_pow :</span></dt><dd>0.037</dd><dt><span>evap_2_arm_1_start_pow :</span></dt><dd>0.35</dd><dt><span>evap_2_arm_2_final_pow :</span></dt><dd>0.09</dd><dt><span>evap_2_arm_2_start_pow :</span></dt><dd>5</dd><dt><span>evap_2_ramp_duration :</span></dt><dd>1.0</dd><dt><span>evap_2_ramp_trunc_value :</span></dt><dd>0.7</dd><dt><span>evap_2_rate_constant_1 :</span></dt><dd>0.37</dd><dt><span>evap_2_rate_constant_2 :</span></dt><dd>0.71</dd><dt><span>evap_3_arm_1_final_pow :</span></dt><dd>0.1038</dd><dt><span>evap_3_arm_1_mod_depth_final :</span></dt><dd>0.43</dd><dt><span>evap_3_arm_1_mod_depth_initial :</span></dt><dd>0</dd><dt><span>evap_3_arm_1_start_pow :</span></dt><dd>0.037</dd><dt><span>evap_3_ramp_duration :</span></dt><dd>0.1</dd><dt><span>evap_3_ramp_trunc_value :</span></dt><dd>1</dd><dt><span>evap_3_rate_constant_1 :</span></dt><dd>-0.879</dd><dt><span>evap_3_rate_constant_2 :</span></dt><dd>-0.297</dd><dt><span>final_amp :</span></dt><dd>8e-05</dd><dt><span>final_freq :</span></dt><dd>104.0</dd><dt><span>gradCoil_current :</span></dt><dd>0.18</dd><dt><span>gradCoil_current_sg :</span></dt><dd>0</dd><dt><span>imaging_method :</span></dt><dd>in_situ_absorption</dd><dt><span>imaging_pulse_duration :</span></dt><dd>2.5e-05</dd><dt><span>imaging_wavelength :</span></dt><dd>4.21291e-07</dd><dt><span>initial_amp :</span></dt><dd>0.62</dd><dt><span>initial_freq :</span></dt><dd>102.13</dd><dt><span>mod_depth_initial :</span></dt><dd>1.0</dd><dt><span>mot_3d_amp :</span></dt><dd>0.62</dd><dt><span>mot_3d_camera_exposure_time :</span></dt><dd>0.002</dd><dt><span>mot_3d_camera_trigger_duration :</span></dt><dd>0.00025</dd><dt><span>mot_3d_freq :</span></dt><dd>102.13</dd><dt><span>mot_load_duration :</span></dt><dd>4</dd><dt><span>odt_axis_camera_trigger_duration :</span></dt><dd>0.002</dd><dt><span>odt_hold_time_1 :</span></dt><dd>0.01</dd><dt><span>odt_hold_time_2 :</span></dt><dd>0.1</dd><dt><span>odt_hold_time_3 :</span></dt><dd>0.1</dd><dt><span>odt_hold_time_4 :</span></dt><dd>1</dd><dt><span>pow_arm_1 :</span></dt><dd>7</dd><dt><span>pow_arm_2 :</span></dt><dd>0</dd><dt><span>pulse_delay :</span></dt><dd>8e-05</dd><dt><span>push_amp :</span></dt><dd>0.16</dd><dt><span>push_freq :</span></dt><dd>102.25</dd><dt><span>ramp_duration :</span></dt><dd>1</dd><dt><span>recomp_ramp_duration :</span></dt><dd>0.5</dd><dt><span>recomp_ramp_pow_fin_arm_1 :</span></dt><dd>0.1038</dd><dt><span>recomp_ramp_pow_fin_arm_2 :</span></dt><dd>0.09</dd><dt><span>recomp_ramp_pow_ini_arm_1 :</span></dt><dd>0.1038</dd><dt><span>recomp_ramp_pow_ini_arm_2 :</span></dt><dd>0.09</dd><dt><span>runs :</span></dt><dd>1</dd><dt><span>save_results :</span></dt><dd>False</dd><dt><span>stern_gerlach_duration :</span></dt><dd>0.001</dd><dt><span>wait_after_2dmot_off :</span></dt><dd>0</dd><dt><span>wait_time_between_images :</span></dt><dd>0.22</dd><dt><span>x_offset :</span></dt><dd>0</dd><dt><span>x_offset_img :</span></dt><dd>0</dd><dt><span>y_offset :</span></dt><dd>0</dd><dt><span>y_offset_img :</span></dt><dd>0</dd><dt><span>z_offset :</span></dt><dd>0.189</dd><dt><span>z_offset_img :</span></dt><dd>0.189</dd><dt><span>scanAxis :</span></dt><dd>[]</dd><dt><span>scanAxisLength :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><table>\n",
|
|
" <tr>\n",
|
|
" <td>\n",
|
|
" <table style=\"border-collapse: collapse;\">\n",
|
|
" <thead>\n",
|
|
" <tr>\n",
|
|
" <td> </td>\n",
|
|
" <th> Array </th>\n",
|
|
" <th> Chunk </th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <th> Bytes </th>\n",
|
|
" <td> 17.58 MiB </td>\n",
|
|
" <td> 17.58 MiB </td>\n",
|
|
" </tr>\n",
|
|
" \n",
|
|
" <tr>\n",
|
|
" <th> Shape </th>\n",
|
|
" <td> (1200, 1920) </td>\n",
|
|
" <td> (1200, 1920) </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th> Dask graph </th>\n",
|
|
" <td colspan=\"2\"> 1 chunks in 16 graph layers </td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th> Data type </th>\n",
|
|
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
" </table>\n",
|
|
" </td>\n",
|
|
" <td>\n",
|
|
" <svg width=\"170\" height=\"125\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
|
|
"\n",
|
|
" <!-- Horizontal lines -->\n",
|
|
" <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
|
|
" <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" style=\"stroke-width:2\" />\n",
|
|
"\n",
|
|
" <!-- Vertical lines -->\n",
|
|
" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"75\" style=\"stroke-width:2\" />\n",
|
|
" <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"75\" style=\"stroke-width:2\" />\n",
|
|
"\n",
|
|
" <!-- Colored Rectangle -->\n",
|
|
" <polygon points=\"0.0,0.0 120.0,0.0 120.0,75.0 0.0,75.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
|
|
"\n",
|
|
" <!-- Text -->\n",
|
|
" <text x=\"60.000000\" y=\"95.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1920</text>\n",
|
|
" <text x=\"140.000000\" y=\"37.500000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,37.500000)\">1200</text>\n",
|
|
"</svg>\n",
|
|
" </td>\n",
|
|
" </tr>\n",
|
|
"</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-75a1db00-8766-4ae2-b045-4f28442932cd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-75a1db00-8766-4ae2-b045-4f28442932cd' class='xr-section-summary' title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-3c1c80f9-2453-4739-bbe9-764f36eaf3e4' class='xr-section-summary-in' type='checkbox' ><label for='section-3c1c80f9-2453-4739-bbe9-764f36eaf3e4' class='xr-section-summary' >Attributes: <span>(96)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>TOF_free :</span></dt><dd>0.02</dd><dt><span>abs_img_freq :</span></dt><dd>110.858</dd><dt><span>absorption_imaging_flag :</span></dt><dd>True</dd><dt><span>backup_data :</span></dt><dd>True</dd><dt><span>blink_off_time :</span></dt><dd>nan</dd><dt><span>blink_on_time :</span></dt><dd>nan</dd><dt><span>c_duration :</span></dt><dd>0.2</dd><dt><span>cmot_final_current :</span></dt><dd>0.65</dd><dt><span>cmot_hold :</span></dt><dd>0.06</dd><dt><span>cmot_initial_current :</span></dt><dd>0.18</dd><dt><span>compX_current :</span></dt><dd>0.005</dd><dt><span>compX_current_sg :</span></dt><dd>0</dd><dt><span>compX_final_current :</span></dt><dd>0.005</dd><dt><span>compX_initial_current :</span></dt><dd>0.005</dd><dt><span>compY_current :</span></dt><dd>0</dd><dt><span>compY_current_sg :</span></dt><dd>0</dd><dt><span>compY_final_current :</span></dt><dd>0.0</dd><dt><span>compY_initial_current :</span></dt><dd>0</dd><dt><span>compZ_current :</span></dt><dd>0</dd><dt><span>compZ_current_sg :</span></dt><dd>0.189</dd><dt><span>compZ_final_current :</span></dt><dd>0.2812</dd><dt><span>compZ_initial_current :</span></dt><dd>0</dd><dt><span>default_camera :</span></dt><dd>0</dd><dt><span>evap_1_arm_1_final_pow :</span></dt><dd>0.35</dd><dt><span>evap_1_arm_1_mod_depth_final :</span></dt><dd>0</dd><dt><span>evap_1_arm_1_mod_depth_initial :</span></dt><dd>1.0</dd><dt><span>evap_1_arm_1_mod_ramp_duration :</span></dt><dd>1.15</dd><dt><span>evap_1_arm_1_pow_ramp_duration :</span></dt><dd>1.65</dd><dt><span>evap_1_arm_1_start_pow :</span></dt><dd>7</dd><dt><span>evap_1_arm_2_final_pow :</span></dt><dd>5</dd><dt><span>evap_1_arm_2_ramp_duration :</span></dt><dd>0.5</dd><dt><span>evap_1_arm_2_start_pow :</span></dt><dd>0</dd><dt><span>evap_1_mod_ramp_trunc_value :</span></dt><dd>1</dd><dt><span>evap_1_pow_ramp_trunc_value :</span></dt><dd>1.0</dd><dt><span>evap_1_rate_constant_1 :</span></dt><dd>0.525</dd><dt><span>evap_1_rate_constant_2 :</span></dt><dd>0.51</dd><dt><span>evap_2_arm_1_final_pow :</span></dt><dd>0.037</dd><dt><span>evap_2_arm_1_start_pow :</span></dt><dd>0.35</dd><dt><span>evap_2_arm_2_final_pow :</span></dt><dd>0.09</dd><dt><span>evap_2_arm_2_start_pow :</span></dt><dd>5</dd><dt><span>evap_2_ramp_duration :</span></dt><dd>1.0</dd><dt><span>evap_2_ramp_trunc_value :</span></dt><dd>0.7</dd><dt><span>evap_2_rate_constant_1 :</span></dt><dd>0.37</dd><dt><span>evap_2_rate_constant_2 :</span></dt><dd>0.71</dd><dt><span>evap_3_arm_1_final_pow :</span></dt><dd>0.1038</dd><dt><span>evap_3_arm_1_mod_depth_final :</span></dt><dd>0.43</dd><dt><span>evap_3_arm_1_mod_depth_initial :</span></dt><dd>0</dd><dt><span>evap_3_arm_1_start_pow :</span></dt><dd>0.037</dd><dt><span>evap_3_ramp_duration :</span></dt><dd>0.1</dd><dt><span>evap_3_ramp_trunc_value :</span></dt><dd>1</dd><dt><span>evap_3_rate_constant_1 :</span></dt><dd>-0.879</dd><dt><span>evap_3_rate_constant_2 :</span></dt><dd>-0.297</dd><dt><span>final_amp :</span></dt><dd>8e-05</dd><dt><span>final_freq :</span></dt><dd>104.0</dd><dt><span>gradCoil_current :</span></dt><dd>0.18</dd><dt><span>gradCoil_current_sg :</span></dt><dd>0</dd><dt><span>imaging_method :</span></dt><dd>in_situ_absorption</dd><dt><span>imaging_pulse_duration :</span></dt><dd>2.5e-05</dd><dt><span>imaging_wavelength :</span></dt><dd>4.21291e-07</dd><dt><span>initial_amp :</span></dt><dd>0.62</dd><dt><span>initial_freq :</span></dt><dd>102.13</dd><dt><span>mod_depth_initial :</span></dt><dd>1.0</dd><dt><span>mot_3d_amp :</span></dt><dd>0.62</dd><dt><span>mot_3d_camera_exposure_time :</span></dt><dd>0.002</dd><dt><span>mot_3d_camera_trigger_duration :</span></dt><dd>0.00025</dd><dt><span>mot_3d_freq :</span></dt><dd>102.13</dd><dt><span>mot_load_duration :</span></dt><dd>4</dd><dt><span>odt_axis_camera_trigger_duration :</span></dt><dd>0.002</dd><dt><span>odt_hold_time_1 :</span></dt><dd>0.01</dd><dt><span>odt_hold_time_2 :</span></dt><dd>0.1</dd><dt><span>odt_hold_time_3 :</span></dt><dd>0.1</dd><dt><span>odt_hold_time_4 :</span></dt><dd>1</dd><dt><span>pow_arm_1 :</span></dt><dd>7</dd><dt><span>pow_arm_2 :</span></dt><dd>0</dd><dt><span>pulse_delay :</span></dt><dd>8e-05</dd><dt><span>push_amp :</span></dt><dd>0.16</dd><dt><span>push_freq :</span></dt><dd>102.25</dd><dt><span>ramp_duration :</span></dt><dd>1</dd><dt><span>recomp_ramp_duration :</span></dt><dd>0.5</dd><dt><span>recomp_ramp_pow_fin_arm_1 :</span></dt><dd>0.1038</dd><dt><span>recomp_ramp_pow_fin_arm_2 :</span></dt><dd>0.09</dd><dt><span>recomp_ramp_pow_ini_arm_1 :</span></dt><dd>0.1038</dd><dt><span>recomp_ramp_pow_ini_arm_2 :</span></dt><dd>0.09</dd><dt><span>runs :</span></dt><dd>1</dd><dt><span>save_results :</span></dt><dd>False</dd><dt><span>stern_gerlach_duration :</span></dt><dd>0.001</dd><dt><span>wait_after_2dmot_off :</span></dt><dd>0</dd><dt><span>wait_time_between_images :</span></dt><dd>0.22</dd><dt><span>x_offset :</span></dt><dd>0</dd><dt><span>x_offset_img :</span></dt><dd>0</dd><dt><span>y_offset :</span></dt><dd>0</dd><dt><span>y_offset_img :</span></dt><dd>0</dd><dt><span>z_offset :</span></dt><dd>0.189</dd><dt><span>z_offset_img :</span></dt><dd>0.189</dd><dt><span>scanAxis :</span></dt><dd>[]</dd><dt><span>scanAxisLength :</span></dt><dd>[]</dd></dl></div></li></ul></div></div>"
|
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],
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"text/plain": [
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"<xarray.Dataset>\n",
|
|
"Dimensions: (y: 1200, x: 1920)\n",
|
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"Dimensions without coordinates: y, x\n",
|
|
"Data variables:\n",
|
|
" atoms (y, x) uint16 dask.array<chunksize=(1200, 1920), meta=np.ndarray>\n",
|
|
" background (y, x) uint16 dask.array<chunksize=(1200, 1920), meta=np.ndarray>\n",
|
|
" dark (y, x) uint16 dask.array<chunksize=(1200, 1920), meta=np.ndarray>\n",
|
|
" shotNum <U2 '08'\n",
|
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" OD (y, x) float64 dask.array<chunksize=(1200, 1920), meta=np.ndarray>\n",
|
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"Attributes: (12/96)\n",
|
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" TOF_free: 0.02\n",
|
|
" abs_img_freq: 110.858\n",
|
|
" absorption_imaging_flag: True\n",
|
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" backup_data: True\n",
|
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" blink_off_time: nan\n",
|
|
" blink_on_time: nan\n",
|
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" ... ...\n",
|
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" y_offset: 0\n",
|
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" y_offset_img: 0\n",
|
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" z_offset: 0.189\n",
|
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" z_offset_img: 0.189\n",
|
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" scanAxis: []\n",
|
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" scanAxisLength: []"
|
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]
|
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},
|
|
"execution_count": 10,
|
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"metadata": {},
|
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"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"shotNum = \"0069\"\n",
|
|
"filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n",
|
|
"# filePath = \"//DyLabNAS/Data/Evaporative_Cooling/2023/05/12/0065/*.h5\"\n",
|
|
"filePath = './result_from_experiment/2023-04-24/0013/2023-04-24_0013_Evaporative_Cooling_08.h5'\n",
|
|
"\n",
|
|
"dataSetDict = {\n",
|
|
" dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n",
|
|
" for i in [0] # range(len(groupList))\n",
|
|
"}\n",
|
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"\n",
|
|
"dataSet = dataSetDict[\"camera_1\"]\n",
|
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"dataSet = swap_xy(dataSet)\n",
|
|
"\n",
|
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"scanAxis = get_scanAxis(dataSet)\n",
|
|
"\n",
|
|
"dataSet = auto_rechunk(dataSet)\n",
|
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"\n",
|
|
"dataSet = imageAnalyser.get_absorption_images(dataSet)\n",
|
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"\n",
|
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"dataSet"
|
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]
|
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},
|
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{
|
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"cell_type": "code",
|
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['atoms', 'background', 'dark', 'shotNum', 'OD']"
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"metadata": {},
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"output_type": "execute_result"
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}
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"source": [
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"list(dataSet.data_vars)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"</style><pre class='xr-text-repr-fallback'><xarray.DataArray 'OD' (y: 300, x: 300)>\n",
|
|
"array([[-0.06868947, -0.04667641, -0.03211941, ..., 0.01331776,\n",
|
|
" -0.01568196, -0.01466019],\n",
|
|
" [-0.04738689, -0.10095033, -0.04738689, ..., -0.00086687,\n",
|
|
" -0.02864644, 0.04425356],\n",
|
|
" [ 0.01488149, 0.02990479, 0.02943848, ..., 0.08614451,\n",
|
|
" -0.04168887, -0.17522026],\n",
|
|
" ...,\n",
|
|
" [-0.04435198, -0.08981436, -0.12101118, ..., -0.03211941,\n",
|
|
" -0.10466366, -0.00086687],\n",
|
|
" [-0.10622739, -0.16213502, -0.04253957, ..., -0.03117222,\n",
|
|
" 0.01331776, -0.03117222],\n",
|
|
" [-0.02148616, -0.06029029, 0.17348652, ..., 0.04109733,\n",
|
|
" -0.07082546, -0.01590475]])\n",
|
|
"Dimensions without coordinates: y, x\n",
|
|
"Attributes: (12/107)\n",
|
|
" IMAGE_SUBCLASS: IMAGE_GRAYSCALE\n",
|
|
" IMAGE_VERSION: 1.2\n",
|
|
" IMAGE_WHITE_IS_ZERO: 0\n",
|
|
" TOF_free: 0.02\n",
|
|
" abs_img_freq: 110.858\n",
|
|
" absorption_imaging_flag: True\n",
|
|
" ... ...\n",
|
|
" y_end: 1025\n",
|
|
" y_start: 725\n",
|
|
" x_center: 960\n",
|
|
" y_center: 875\n",
|
|
" x_span: 300\n",
|
|
" y_span: 300</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'OD'</div><ul class='xr-dim-list'><li><span>y</span>: 300</li><li><span>x</span>: 300</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-89e2107d-c2e0-47e3-8299-9b54f752e314' class='xr-array-in' type='checkbox' checked><label for='section-89e2107d-c2e0-47e3-8299-9b54f752e314' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-0.06869 -0.04668 -0.03212 0.05976 ... 0.0411 -0.07083 -0.0159</span></div><div class='xr-array-data'><pre>array([[-0.06868947, -0.04667641, -0.03211941, ..., 0.01331776,\n",
|
|
" -0.01568196, -0.01466019],\n",
|
|
" [-0.04738689, -0.10095033, -0.04738689, ..., -0.00086687,\n",
|
|
" -0.02864644, 0.04425356],\n",
|
|
" [ 0.01488149, 0.02990479, 0.02943848, ..., 0.08614451,\n",
|
|
" -0.04168887, -0.17522026],\n",
|
|
" ...,\n",
|
|
" [-0.04435198, -0.08981436, -0.12101118, ..., -0.03211941,\n",
|
|
" -0.10466366, -0.00086687],\n",
|
|
" [-0.10622739, -0.16213502, -0.04253957, ..., -0.03117222,\n",
|
|
" 0.01331776, -0.03117222],\n",
|
|
" [-0.02148616, -0.06029029, 0.17348652, ..., 0.04109733,\n",
|
|
" -0.07082546, -0.01590475]])</pre></div></div></li><li class='xr-section-item'><input id='section-40c6dd16-72af-4172-9192-4317d6778a5d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-40c6dd16-72af-4172-9192-4317d6778a5d' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-062d1af4-3459-470c-95ec-6b0664377f6d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-062d1af4-3459-470c-95ec-6b0664377f6d' class='xr-section-summary' title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-fc27c4ae-e4c0-4689-ac5f-911fb0edefaa' class='xr-section-summary-in' type='checkbox' ><label for='section-fc27c4ae-e4c0-4689-ac5f-911fb0edefaa' class='xr-section-summary' >Attributes: <span>(107)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>IMAGE_SUBCLASS :</span></dt><dd>IMAGE_GRAYSCALE</dd><dt><span>IMAGE_VERSION :</span></dt><dd>1.2</dd><dt><span>IMAGE_WHITE_IS_ZERO :</span></dt><dd>0</dd><dt><span>TOF_free :</span></dt><dd>0.02</dd><dt><span>abs_img_freq :</span></dt><dd>110.858</dd><dt><span>absorption_imaging_flag :</span></dt><dd>True</dd><dt><span>backup_data :</span></dt><dd>True</dd><dt><span>blink_off_time :</span></dt><dd>nan</dd><dt><span>blink_on_time :</span></dt><dd>nan</dd><dt><span>c_duration :</span></dt><dd>0.2</dd><dt><span>cmot_final_current :</span></dt><dd>0.65</dd><dt><span>cmot_hold :</span></dt><dd>0.06</dd><dt><span>cmot_initial_current :</span></dt><dd>0.18</dd><dt><span>compX_current :</span></dt><dd>0.005</dd><dt><span>compX_current_sg :</span></dt><dd>0</dd><dt><span>compX_final_current :</span></dt><dd>0.005</dd><dt><span>compX_initial_current :</span></dt><dd>0.005</dd><dt><span>compY_current :</span></dt><dd>0</dd><dt><span>compY_current_sg :</span></dt><dd>0</dd><dt><span>compY_final_current :</span></dt><dd>0.0</dd><dt><span>compY_initial_current :</span></dt><dd>0</dd><dt><span>compZ_current :</span></dt><dd>0</dd><dt><span>compZ_current_sg :</span></dt><dd>0.189</dd><dt><span>compZ_final_current :</span></dt><dd>0.2812</dd><dt><span>compZ_initial_current :</span></dt><dd>0</dd><dt><span>default_camera :</span></dt><dd>0</dd><dt><span>evap_1_arm_1_final_pow :</span></dt><dd>0.35</dd><dt><span>evap_1_arm_1_mod_depth_final :</span></dt><dd>0</dd><dt><span>evap_1_arm_1_mod_depth_initial :</span></dt><dd>1.0</dd><dt><span>evap_1_arm_1_mod_ramp_duration :</span></dt><dd>1.15</dd><dt><span>evap_1_arm_1_pow_ramp_duration :</span></dt><dd>1.65</dd><dt><span>evap_1_arm_1_start_pow :</span></dt><dd>7</dd><dt><span>evap_1_arm_2_final_pow :</span></dt><dd>5</dd><dt><span>evap_1_arm_2_ramp_duration :</span></dt><dd>0.5</dd><dt><span>evap_1_arm_2_start_pow :</span></dt><dd>0</dd><dt><span>evap_1_mod_ramp_trunc_value :</span></dt><dd>1</dd><dt><span>evap_1_pow_ramp_trunc_value :</span></dt><dd>1.0</dd><dt><span>evap_1_rate_constant_1 :</span></dt><dd>0.525</dd><dt><span>evap_1_rate_constant_2 :</span></dt><dd>0.51</dd><dt><span>evap_2_arm_1_final_pow :</span></dt><dd>0.037</dd><dt><span>evap_2_arm_1_start_pow :</span></dt><dd>0.35</dd><dt><span>evap_2_arm_2_final_pow :</span></dt><dd>0.09</dd><dt><span>evap_2_arm_2_start_pow :</span></dt><dd>5</dd><dt><span>evap_2_ramp_duration :</span></dt><dd>1.0</dd><dt><span>evap_2_ramp_trunc_value :</span></dt><dd>0.7</dd><dt><span>evap_2_rate_constant_1 :</span></dt><dd>0.37</dd><dt><span>evap_2_rate_constant_2 :</span></dt><dd>0.71</dd><dt><span>evap_3_arm_1_final_pow :</span></dt><dd>0.1038</dd><dt><span>evap_3_arm_1_mod_depth_final :</span></dt><dd>0.43</dd><dt><span>evap_3_arm_1_mod_depth_initial :</span></dt><dd>0</dd><dt><span>evap_3_arm_1_start_pow :</span></dt><dd>0.037</dd><dt><span>evap_3_ramp_duration :</span></dt><dd>0.1</dd><dt><span>evap_3_ramp_trunc_value :</span></dt><dd>1</dd><dt><span>evap_3_rate_constant_1 :</span></dt><dd>-0.879</dd><dt><span>evap_3_rate_constant_2 :</span></dt><dd>-0.297</dd><dt><span>final_amp :</span></dt><dd>8e-05</dd><dt><span>final_freq :</span></dt><dd>104.0</dd><dt><span>gradCoil_current :</span></dt><dd>0.18</dd><dt><span>gradCoil_current_sg :</span></dt><dd>0</dd><dt><span>imaging_method :</span></dt><dd>in_situ_absorption</dd><dt><span>imaging_pulse_duration :</span></dt><dd>2.5e-05</dd><dt><span>imaging_wavelength :</span></dt><dd>4.21291e-07</dd><dt><span>initial_amp :</span></dt><dd>0.62</dd><dt><span>initial_freq :</span></dt><dd>102.13</dd><dt><span>mod_depth_initial :</span></dt><dd>1.0</dd><dt><span>mot_3d_amp :</span></dt><dd>0.62</dd><dt><span>mot_3d_camera_exposure_time :</span></dt><dd>0.002</dd><dt><span>mot_3d_camera_trigger_duration :</span></dt><dd>0.00025</dd><dt><span>mot_3d_freq :</span></dt><dd>102.13</dd><dt><span>mot_load_duration :</span></dt><dd>4</dd><dt><span>odt_axis_camera_trigger_duration :</span></dt><dd>0.002</dd><dt><span>odt_hold_time_1 :</span></dt><dd>0.01</dd><dt><span>odt_hold_time_2 :</span></dt><dd>0.1</dd><dt><span>odt_hold_time_3 :</span></dt><dd>0.1</dd><dt><span>odt_hold_time_4 :</span></dt><dd>1</dd><dt><span>pow_arm_1 :</span></dt><dd>7</dd><dt><span>pow_arm_2 :</span></dt><dd>0</dd><dt><span>pulse_delay :</span></dt><dd>8e-05</dd><dt><span>push_amp :</span></dt><dd>0.16</dd><dt><span>push_freq :</span></dt><dd>102.25</dd><dt><span>ramp_duration :</span></dt><dd>1</dd><dt><span>recomp_ramp_duration :</span></dt><dd>0.5</dd><dt><span>recomp_ramp_pow_fin_arm_1 :</span></dt><dd>0.1038</dd><dt><span>recomp_ramp_pow_fin_arm_2 :</span></dt><dd>0.09</dd><dt><span>recomp_ramp_pow_ini_arm_1 :</span></dt><dd>0.1038</dd><dt><span>recomp_ramp_pow_ini_arm_2 :</span></dt><dd>0.09</dd><dt><span>runs :</span></dt><dd>1</dd><dt><span>save_results :</span></dt><dd>False</dd><dt><span>stern_gerlach_duration :</span></dt><dd>0.001</dd><dt><span>wait_after_2dmot_off :</span></dt><dd>0</dd><dt><span>wait_time_between_images :</span></dt><dd>0.22</dd><dt><span>x_offset :</span></dt><dd>0</dd><dt><span>x_offset_img :</span></dt><dd>0</dd><dt><span>y_offset :</span></dt><dd>0</dd><dt><span>y_offset_img :</span></dt><dd>0</dd><dt><span>z_offset :</span></dt><dd>0.189</dd><dt><span>z_offset_img :</span></dt><dd>0.189</dd><dt><span>scanAxis :</span></dt><dd>[]</dd><dt><span>scanAxisLength :</span></dt><dd>[]</dd><dt><span>x_start :</span></dt><dd>810</dd><dt><span>x_end :</span></dt><dd>1110</dd><dt><span>y_end :</span></dt><dd>1025</dd><dt><span>y_start :</span></dt><dd>725</dd><dt><span>x_center :</span></dt><dd>960</dd><dt><span>y_center :</span></dt><dd>875</dd><dt><span>x_span :</span></dt><dd>300</dd><dt><span>y_span :</span></dt><dd>300</dd></dl></div></li></ul></div></div>"
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"text/plain": [
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"<xarray.DataArray 'OD' (y: 300, x: 300)>\n",
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"array([[-0.06868947, -0.04667641, -0.03211941, ..., 0.01331776,\n",
|
|
" -0.01568196, -0.01466019],\n",
|
|
" [-0.04738689, -0.10095033, -0.04738689, ..., -0.00086687,\n",
|
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" -0.02864644, 0.04425356],\n",
|
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" [ 0.01488149, 0.02990479, 0.02943848, ..., 0.08614451,\n",
|
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" -0.04168887, -0.17522026],\n",
|
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" ...,\n",
|
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" [-0.04435198, -0.08981436, -0.12101118, ..., -0.03211941,\n",
|
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" -0.10466366, -0.00086687],\n",
|
|
" [-0.10622739, -0.16213502, -0.04253957, ..., -0.03117222,\n",
|
|
" 0.01331776, -0.03117222],\n",
|
|
" [-0.02148616, -0.06029029, 0.17348652, ..., 0.04109733,\n",
|
|
" -0.07082546, -0.01590475]])\n",
|
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"Dimensions without coordinates: y, x\n",
|
|
"Attributes: (12/107)\n",
|
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" IMAGE_SUBCLASS: IMAGE_GRAYSCALE\n",
|
|
" IMAGE_VERSION: 1.2\n",
|
|
" IMAGE_WHITE_IS_ZERO: 0\n",
|
|
" TOF_free: 0.02\n",
|
|
" abs_img_freq: 110.858\n",
|
|
" absorption_imaging_flag: True\n",
|
|
" ... ...\n",
|
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" y_end: 1025\n",
|
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" y_start: 725\n",
|
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" x_center: 960\n",
|
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" y_center: 875\n",
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" x_span: 300\n",
|
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" y_span: 300"
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]
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},
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
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}
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],
|
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"source": [
|
|
"# dataSet_cropOD = imageAnalyser.crop_image(dataSet.OD)\n",
|
|
"dataSet_cropOD"
|
|
]
|
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},
|
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
|
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"source": [
|
|
"## Calculate an plot OD images"
|
|
]
|
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},
|
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{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
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"metadata": {},
|
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"outputs": [
|
|
{
|
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"data": {
|
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 640x480 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# imageAnalyser.center = (960, 1040)\n",
|
|
"# imageAnalyser.span = (100, 100)\n",
|
|
"# imageAnalyser.fraction = (0.1, 0.1)\n",
|
|
"\n",
|
|
"imageAnalyser.center = (960, 875)\n",
|
|
"imageAnalyser.span = (300, 300)\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",
|
|
"dataSet_cropOD.plot.pcolormesh(cmap='jet', vmin=0, col=scanAxis[0], row=scanAxis[1])\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 91,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"numpy.ndarray"
|
|
]
|
|
},
|
|
"execution_count": 91,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"type(np.array([0]))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 111,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"ename": "NameError",
|
|
"evalue": "name 'name' is not defined",
|
|
"output_type": "error",
|
|
"traceback": [
|
|
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
|
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
|
|
"\u001b[1;32mf:\\Jianshun\\analyseScript\\testMongoDB.ipynb Cell 24\u001b[0m in \u001b[0;36m1\n\u001b[1;32m----> <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/testMongoDB.ipynb#Y133sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m name \u001b[39min\u001b[39;00m dataSet\u001b[39m.\u001b[39mOD\u001b[39m.\u001b[39mattrs\n",
|
|
"\u001b[1;31mNameError\u001b[0m: name 'name' is not defined"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"name in dataSet.OD.attrs"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 131,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"['atoms', 'background', 'dark', 'shotNum', 'OD']"
|
|
]
|
|
},
|
|
"execution_count": 131,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"list(dataSet.data_vars)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 166,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"import pymongo\n",
|
|
"import xarray_mongodb\n",
|
|
"import bson\n",
|
|
"import builtins\n",
|
|
"\n",
|
|
"from ToolFunction.ToolFunction import get_date\n",
|
|
"\n",
|
|
"\n",
|
|
"npTypeDict = {v: getattr(builtins, k) for k, v in np.sctypeDict.items() if k in vars(builtins)}\n",
|
|
"npArrayType = type(np.array([0]))\n",
|
|
"\n",
|
|
"\n",
|
|
"class MongoDB:\n",
|
|
" \n",
|
|
" def __init__(self, mongoClient, mongoDB, date=None) -> None:\n",
|
|
" self.mongoClient = mongoClient\n",
|
|
" self.mongoDB = mongoDB\n",
|
|
" self.xdb = xarray_mongodb.XarrayMongoDB(mongoDB)\n",
|
|
" \n",
|
|
" if date is None:\n",
|
|
" date= get_date()\n",
|
|
" self.set_date(date)\n",
|
|
" \n",
|
|
" def _convert_numpy_type(self, data):\n",
|
|
" for key in data:\n",
|
|
" typeKey = type(data[key])\n",
|
|
" if typeKey in npTypeDict:\n",
|
|
" data[key] = data[key].item()\n",
|
|
" elif typeKey == npArrayType:\n",
|
|
" data[key] = data[key].tolist()\n",
|
|
" else:\n",
|
|
" try:\n",
|
|
" data[key] = data[key].item()\n",
|
|
" except:\n",
|
|
" pass\n",
|
|
" return data\n",
|
|
" \n",
|
|
" def set_date(self, date):\n",
|
|
" date = date.split(\"/\")\n",
|
|
" self.year = int(date[0])\n",
|
|
" self.month = int(date[1])\n",
|
|
" self.day = int(date[2])\n",
|
|
" \n",
|
|
" def create_global(self, shotNum, dataSet=None, date=None):\n",
|
|
" if not date is None:\n",
|
|
" self.set_date(date)\n",
|
|
" \n",
|
|
" data = {\n",
|
|
" 'year': self.year,\n",
|
|
" 'month': self.month,\n",
|
|
" 'day': self.day,\n",
|
|
" 'shotNum': shotNum,\n",
|
|
" 'global_parameters' : {},\n",
|
|
" }\n",
|
|
" \n",
|
|
" global_parameters = self._convert_numpy_type(dataSet.attrs)\n",
|
|
" \n",
|
|
" if not dataSet is None:\n",
|
|
" data['global_parameters'].update(global_parameters)\n",
|
|
" \n",
|
|
" data = self._convert_numpy_type(data)\n",
|
|
" \n",
|
|
" self.mongoDB['global'].insert_one(data)\n",
|
|
" \n",
|
|
" def _add_data_normal(self, shotNum, data):\n",
|
|
" filter = { \n",
|
|
" 'year': self.year,\n",
|
|
" 'month': self.month,\n",
|
|
" 'day': self.day,\n",
|
|
" 'shotNum': shotNum,\n",
|
|
" }\n",
|
|
" \n",
|
|
" self.mongoDB['global'].update_one(filter, {\"$set\": data}, upsert=False)\n",
|
|
" \n",
|
|
" def _add_data_xarray_dataArray(self, shotNum, dataArray):\n",
|
|
" filter = { \n",
|
|
" 'year': self.year,\n",
|
|
" 'month': self.month,\n",
|
|
" 'day': self.day,\n",
|
|
" 'shotNum': shotNum,\n",
|
|
" }\n",
|
|
" \n",
|
|
" dataArray.attrs = self._convert_numpy_type(dataArray.attrs)\n",
|
|
" \n",
|
|
" mongoID, _ = self.xdb.put(dataArray)\n",
|
|
" \n",
|
|
" data_label = {\n",
|
|
" dataArray.name: \n",
|
|
" {\n",
|
|
" 'name': dataArray.name,\n",
|
|
" 'mongoID': mongoID,\n",
|
|
" 'engine': 'xarray',\n",
|
|
" 'dtype': 'dataArray',\n",
|
|
" }\n",
|
|
" }\n",
|
|
" \n",
|
|
" self.mongoDB['global'].update_one(filter, {\"$set\": data_label}, upsert=False)\n",
|
|
" \n",
|
|
" def _add_data_xarray_dataSet(self, shotNum, dataSet, name):\n",
|
|
" filter = { \n",
|
|
" 'year': self.year,\n",
|
|
" 'month': self.month,\n",
|
|
" 'day': self.day,\n",
|
|
" 'shotNum': shotNum,\n",
|
|
" }\n",
|
|
" \n",
|
|
" dataSet.attrs = self._convert_numpy_type(dataSet.attrs)\n",
|
|
" \n",
|
|
" for key in list(dataSet.data_vars):\n",
|
|
" dataSet[key].attrs = self._convert_numpy_type(dataSet[key].attrs)\n",
|
|
" print(key)\n",
|
|
" \n",
|
|
" mongoID, _ = self.xdb.put(dataSet)\n",
|
|
" \n",
|
|
" data_label = {\n",
|
|
" name: \n",
|
|
" {\n",
|
|
" 'name': name,\n",
|
|
" 'mongoID': mongoID,\n",
|
|
" 'engine': 'xarray',\n",
|
|
" 'dtype': 'dataSet',\n",
|
|
" }\n",
|
|
" }\n",
|
|
" \n",
|
|
" self.mongoDB['global'].update_one(filter, {\"$set\": data_label}, upsert=False)\n",
|
|
" \n",
|
|
" def _add_data_additional(self, shotNum, data, name):\n",
|
|
" filter = { \n",
|
|
" 'year': self.year,\n",
|
|
" 'month': self.month,\n",
|
|
" 'day': self.day,\n",
|
|
" 'shotNum': shotNum,\n",
|
|
" }\n",
|
|
" \n",
|
|
" mongoID = self.mongoDB.additional.insert_one(data).inserted_id\n",
|
|
" \n",
|
|
" data_label = {\n",
|
|
" name: \n",
|
|
" {\n",
|
|
" 'name': name,\n",
|
|
" 'mongoID': mongoID,\n",
|
|
" 'engine': 'additional',\n",
|
|
" 'dtype': 'dict',\n",
|
|
" }\n",
|
|
" }\n",
|
|
" \n",
|
|
" self.mongoDB['global'].update_one(filter, {\"$set\": data_label}, upsert=False)\n",
|
|
" \n",
|
|
" def add_data(self, shotNum, data, date=None, name=None, engine='normal'):\n",
|
|
" if not date is None:\n",
|
|
" self.set_date(date)\n",
|
|
" \n",
|
|
" if engine == 'normal':\n",
|
|
" self._add_data_normal(shotNum=shotNum, data=data)\n",
|
|
" elif engine == 'xarray':\n",
|
|
" if isinstance(data, type(xr.Dataset())):\n",
|
|
" self._add_data_xarray_dataSet(shotNum=shotNum, data=data, name=name)\n",
|
|
" else:\n",
|
|
" self._add_data_xarray_dataArray(shotNum=shotNum, data=data, name=name)\n",
|
|
" elif engine == 'additional':\n",
|
|
" self._add_data_additional(shotNum=shotNum, data=data, name=name)\n",
|
|
" \n",
|
|
" def read_global(self, shotNum, date=None):\n",
|
|
" \n",
|
|
" if not date is None:\n",
|
|
" self.set_date(date)\n",
|
|
" \n",
|
|
" filter = { \n",
|
|
" 'year': self.year,\n",
|
|
" 'month': self.month,\n",
|
|
" 'day': self.day,\n",
|
|
" 'shotNum': shotNum,\n",
|
|
" }\n",
|
|
" \n",
|
|
" return self.mongoDB['global'].find_one(filter)\n",
|
|
" \n",
|
|
" def _load_data_single(self, mongoID, engine):\n",
|
|
" if engine == 'xarray':\n",
|
|
" return self.xdb.get(mongoID)\n",
|
|
" if engine == 'additional':\n",
|
|
" return self.mongoDB.additional.find_one({'_id': mongoID})\n",
|
|
" \n",
|
|
" def load_data_single(self, shotNum=None, globalDict=None, date=None, field=None):\n",
|
|
" \n",
|
|
" if not date is None:\n",
|
|
" self.set_date(date)\n",
|
|
" \n",
|
|
" if globalDict is None:\n",
|
|
" globalDict = self.read_global(shotNum)\n",
|
|
" \n",
|
|
" if field is None:\n",
|
|
" field = globalDict\n",
|
|
" \n",
|
|
" res = {}\n",
|
|
" \n",
|
|
" for key in field:\n",
|
|
" if isinstance(globalDict[key], dict) and ('mongoID' in globalDict[key]):\n",
|
|
" mongoID = globalDict[key]['mongoID']\n",
|
|
" engine = globalDict[key]['engine']\n",
|
|
" \n",
|
|
" res.update(\n",
|
|
" {\n",
|
|
" key: self._load_data_single(mongoID=mongoID, engine=engine)\n",
|
|
" }\n",
|
|
" )\n",
|
|
" \n",
|
|
" return res\n",
|
|
" \n",
|
|
" def load_data(self, shotNum=None, globalDict=None, date=None, field=None):\n",
|
|
" pass\n",
|
|
" \n",
|
|
"DB = MongoDB(mongoClient, mongoDB, date='2023/05/23')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 169,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"{'test': {'_id': ObjectId('6486e8ed129ec5a1a24a737a'), 'test': 10}}"
|
|
]
|
|
},
|
|
"execution_count": 169,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"res = DB.load_data_single(shotNum='0069', field=['test'])\n",
|
|
"res"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 170,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"data = {\n",
|
|
" 'test':100\n",
|
|
"}\n",
|
|
"DB._add_data_additional(shotNum, data, name='test')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 126,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"DB._add_data_xarray_dataArray(shotNum, dataSet_cropOD)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 148,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"int"
|
|
]
|
|
},
|
|
"execution_count": 148,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"test = DB._convert_numpy_type(dataSet.OD.attrs)\n",
|
|
"\n",
|
|
"type(test['IMAGE_WHITE_IS_ZERO'])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 147,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"numpy.uint8"
|
|
]
|
|
},
|
|
"execution_count": 147,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"type(dataSet.OD.attrs['IMAGE_WHITE_IS_ZERO'])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 149,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"atoms\n",
|
|
"background\n",
|
|
"dark\n",
|
|
"shotNum\n",
|
|
"OD\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"DB._add_data_xarray_dataSet(shotNum, dataSet, 'aaa')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 122,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"(ObjectId('64835527129ec5a1a24a7369'), None)"
|
|
]
|
|
},
|
|
"execution_count": 122,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"xdb.put(dataSet_cropOD)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 125,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"DB.create_global(shotNum, dataSet)"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {},
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Do a 2D two-peak gaussian fit to the OD images"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {},
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Do the fit"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"from Analyser.FitAnalyser import ThomasFermi2dModel, DensityProfileBEC2dModel, polylog2_2d\n",
|
|
"\n",
|
|
"fitModel = DensityProfileBEC2dModel()\n",
|
|
"# fitModel = ThomasFermi2dModel()\n",
|
|
"\n",
|
|
"fitAnalyser = FitAnalyser(fitModel, fitDim=2)\n",
|
|
"\n",
|
|
"# fitAnalyser = FitAnalyser(\"Gaussian-2D\", fitDim=2)\n",
|
|
"\n",
|
|
"# dataSet_cropOD = dataSet_cropOD.chunk((1,1,100,100))\n",
|
|
"\n",
|
|
"params = fitAnalyser.guess(dataSet_cropOD, guess_kwargs=dict(pureBECThreshold=0.3), dask=\"parallelized\")\n",
|
|
"fitResult = fitAnalyser.fit(dataSet_cropOD, params).load()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"params.compute().item()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"fitCurve = fitAnalyser.eval(fitResult, x=np.arange(300), y=np.arange(300), dask=\"parallelized\").load()\n",
|
|
"\n",
|
|
"fitCurve.plot.pcolormesh(cmap='jet', vmin=0, col=scanAxis[0], row=scanAxis[1])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"fitModel2 = Polylog22dModel(prefix='thermal_')\n",
|
|
"fitAnalyser2 = FitAnalyser(fitModel2, fitDim=2)\n",
|
|
"fitCurve2 = fitAnalyser2.eval(fitResult, x=np.arange(300), y=np.arange(300), dask=\"parallelized\").load()\n",
|
|
"\n",
|
|
"fitModel3 = ThomasFermi2dModel(prefix='BEC_')\n",
|
|
"fitAnalyser3 = FitAnalyser(fitModel3, fitDim=2)\n",
|
|
"fitCurve3 = fitAnalyser3.eval(fitResult, x=np.arange(300), y=np.arange(300), dask=\"parallelized\").load()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"fig = plt.figure()\n",
|
|
"ax = fig.gca()\n",
|
|
"\n",
|
|
"dataSet_cropOD.sum(dim='x').plot(ax=ax, col=scanAxis[0], row=scanAxis[1])\n",
|
|
"fitCurve.sum(dim='x').plot(ax=ax, col=scanAxis[0], row=scanAxis[1])\n",
|
|
"fitCurve2.sum(dim='x').plot(ax=ax, col=scanAxis[0], row=scanAxis[1])\n",
|
|
"fitCurve3.sum(dim='x').plot(ax=ax, col=scanAxis[0], row=scanAxis[1])\n",
|
|
"\n",
|
|
"plt.show()\n",
|
|
"\n",
|
|
"fig = plt.figure()\n",
|
|
"ax = fig.gca()\n",
|
|
"\n",
|
|
"dataSet_cropOD.sum(dim='y').plot(ax=ax, col=scanAxis[0], row=scanAxis[1])\n",
|
|
"fitCurve.sum(dim='y').plot(ax=ax, col=scanAxis[0], row=scanAxis[1])\n",
|
|
"fitCurve2.sum(dim='y').plot(ax=ax, col=scanAxis[0], row=scanAxis[1])\n",
|
|
"fitCurve3.sum(dim='y').plot(ax=ax, col=scanAxis[0], row=scanAxis[1])\n",
|
|
"\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"value = fitAnalyser.get_fit_value(fitResult)\n",
|
|
"std = fitAnalyser.get_fit_std(fitResult)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"fitResult.item()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"value"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {},
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# upload data to MongoDB"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 121,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"xdb = xarray_mongodb.XarrayMongoDB(mongoDB)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"value = fitAnalyser.get_fit_value(fitResult)\n",
|
|
"value"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
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"metadata": {},
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"outputs": [],
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"source": [
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"dataSet_cropOD"
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|
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{
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"</style><pre class='xr-text-repr-fallback'><xarray.DataArray 'OD' (y: 300, x: 300)>\n",
|
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"array([[-0.06868947, -0.04667641, -0.03211941, ..., 0.01331776,\n",
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|
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|
|
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|
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|
|
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|
|
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|
|
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|
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|
|
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|
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"Attributes: (12/107)\n",
|
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|
|
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|
|
" y_end: 1025\n",
|
|
" y_start: 725\n",
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" x_center: 960\n",
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" y_center: 875\n",
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" x_span: 300\n",
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" y_span: 300</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'OD'</div><ul class='xr-dim-list'><li><span>y</span>: 300</li><li><span>x</span>: 300</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-aa5f2943-63ae-43bc-9f95-88e68afcf350' class='xr-array-in' type='checkbox' checked><label for='section-aa5f2943-63ae-43bc-9f95-88e68afcf350' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-0.06869 -0.04668 -0.03212 0.05976 ... 0.0411 -0.07083 -0.0159</span></div><div class='xr-array-data'><pre>array([[-0.06868947, -0.04667641, -0.03211941, ..., 0.01331776,\n",
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" -0.01568196, -0.01466019],\n",
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" [-0.04738689, -0.10095033, -0.04738689, ..., -0.00086687,\n",
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" -0.02864644, 0.04425356],\n",
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" [ 0.01488149, 0.02990479, 0.02943848, ..., 0.08614451,\n",
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" -0.04168887, -0.17522026],\n",
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" ...,\n",
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" [-0.04435198, -0.08981436, -0.12101118, ..., -0.03211941,\n",
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" -0.10466366, -0.00086687],\n",
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" [-0.10622739, -0.16213502, -0.04253957, ..., -0.03117222,\n",
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" 0.01331776, -0.03117222],\n",
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" [-0.02148616, -0.06029029, 0.17348652, ..., 0.04109733,\n",
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" -0.07082546, -0.01590475]])</pre></div></div></li><li class='xr-section-item'><input id='section-ff5884e7-0b98-4827-84dc-8fbd5fbbb3d2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ff5884e7-0b98-4827-84dc-8fbd5fbbb3d2' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-0cfbae91-9e93-40fc-91d8-4bf465583ed7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0cfbae91-9e93-40fc-91d8-4bf465583ed7' class='xr-section-summary' title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-07d0cd36-7ad6-4f92-ac5c-27a7e45eaed0' class='xr-section-summary-in' type='checkbox' ><label for='section-07d0cd36-7ad6-4f92-ac5c-27a7e45eaed0' class='xr-section-summary' >Attributes: <span>(107)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>IMAGE_SUBCLASS :</span></dt><dd>IMAGE_GRAYSCALE</dd><dt><span>IMAGE_VERSION :</span></dt><dd>1.2</dd><dt><span>IMAGE_WHITE_IS_ZERO :</span></dt><dd>0</dd><dt><span>TOF_free :</span></dt><dd>0.02</dd><dt><span>abs_img_freq :</span></dt><dd>110.858</dd><dt><span>absorption_imaging_flag :</span></dt><dd>True</dd><dt><span>backup_data :</span></dt><dd>True</dd><dt><span>blink_off_time :</span></dt><dd>nan</dd><dt><span>blink_on_time :</span></dt><dd>nan</dd><dt><span>c_duration :</span></dt><dd>0.2</dd><dt><span>cmot_final_current :</span></dt><dd>0.65</dd><dt><span>cmot_hold :</span></dt><dd>0.06</dd><dt><span>cmot_initial_current :</span></dt><dd>0.18</dd><dt><span>compX_current :</span></dt><dd>0.005</dd><dt><span>compX_current_sg :</span></dt><dd>0</dd><dt><span>compX_final_current :</span></dt><dd>0.005</dd><dt><span>compX_initial_current :</span></dt><dd>0.005</dd><dt><span>compY_current :</span></dt><dd>0</dd><dt><span>compY_current_sg :</span></dt><dd>0</dd><dt><span>compY_final_current :</span></dt><dd>0.0</dd><dt><span>compY_initial_current :</span></dt><dd>0</dd><dt><span>compZ_current :</span></dt><dd>0</dd><dt><span>compZ_current_sg :</span></dt><dd>0.189</dd><dt><span>compZ_final_current :</span></dt><dd>0.2812</dd><dt><span>compZ_initial_current :</span></dt><dd>0</dd><dt><span>default_camera :</span></dt><dd>0</dd><dt><span>evap_1_arm_1_final_pow :</span></dt><dd>0.35</dd><dt><span>evap_1_arm_1_mod_depth_final :</span></dt><dd>0</dd><dt><span>evap_1_arm_1_mod_depth_initial :</span></dt><dd>1.0</dd><dt><span>evap_1_arm_1_mod_ramp_duration :</span></dt><dd>1.15</dd><dt><span>evap_1_arm_1_pow_ramp_duration :</span></dt><dd>1.65</dd><dt><span>evap_1_arm_1_start_pow :</span></dt><dd>7</dd><dt><span>evap_1_arm_2_final_pow :</span></dt><dd>5</dd><dt><span>evap_1_arm_2_ramp_duration :</span></dt><dd>0.5</dd><dt><span>evap_1_arm_2_start_pow :</span></dt><dd>0</dd><dt><span>evap_1_mod_ramp_trunc_value :</span></dt><dd>1</dd><dt><span>evap_1_pow_ramp_trunc_value :</span></dt><dd>1.0</dd><dt><span>evap_1_rate_constant_1 :</span></dt><dd>0.525</dd><dt><span>evap_1_rate_constant_2 :</span></dt><dd>0.51</dd><dt><span>evap_2_arm_1_final_pow :</span></dt><dd>0.037</dd><dt><span>evap_2_arm_1_start_pow :</span></dt><dd>0.35</dd><dt><span>evap_2_arm_2_final_pow :</span></dt><dd>0.09</dd><dt><span>evap_2_arm_2_start_pow :</span></dt><dd>5</dd><dt><span>evap_2_ramp_duration :</span></dt><dd>1.0</dd><dt><span>evap_2_ramp_trunc_value :</span></dt><dd>0.7</dd><dt><span>evap_2_rate_constant_1 :</span></dt><dd>0.37</dd><dt><span>evap_2_rate_constant_2 :</span></dt><dd>0.71</dd><dt><span>evap_3_arm_1_final_pow :</span></dt><dd>0.1038</dd><dt><span>evap_3_arm_1_mod_depth_final :</span></dt><dd>0.43</dd><dt><span>evap_3_arm_1_mod_depth_initial :</span></dt><dd>0</dd><dt><span>evap_3_arm_1_start_pow :</span></dt><dd>0.037</dd><dt><span>evap_3_ramp_duration :</span></dt><dd>0.1</dd><dt><span>evap_3_ramp_trunc_value :</span></dt><dd>1</dd><dt><span>evap_3_rate_constant_1 :</span></dt><dd>-0.879</dd><dt><span>evap_3_rate_constant_2 :</span></dt><dd>-0.297</dd><dt><span>final_amp :</span></dt><dd>8e-05</dd><dt><span>final_freq :</span></dt><dd>104.0</dd><dt><span>gradCoil_current :</span></dt><dd>0.18</dd><dt><span>gradCoil_current_sg :</span></dt><dd>0</dd><dt><span>imaging_method :</span></dt><dd>in_situ_absorption</dd><dt><span>imaging_pulse_duration :</span></dt><dd>2.5e-05</dd><dt><span>imaging_wavelength :</span></dt><dd>4.21291e-07</dd><dt><span>initial_amp :</span></dt><dd>0.62</dd><dt><span>initial_freq :</span></dt><dd>102.13</dd><dt><span>mod_depth_initial :</span></dt><dd>1.0</dd><dt><span>mot_3d_amp :</span></dt><dd>0.62</dd><dt><span>mot_3d_camera_exposure_time :</span></dt><dd>0.002</dd><dt><span>mot_3d_camera_trigger_duration :</span></dt><dd>0.00025</dd><dt><span>mot_3d_freq :</span></dt><dd>102.13</dd><dt><span>mot_load_duration :</span></dt><dd>4</dd><dt><span>odt_axis_camera_trigger_duration :</span></dt><dd>0.002</dd><dt><span>odt_hold_time_1 :</span></dt><dd>0.01</dd><dt><span>odt_hold_time_2 :</span></dt><dd>0.1</dd><dt><span>odt_hold_time_3 :</span></dt><dd>0.1</dd><dt><span>odt_hold_time_4 :</span></dt><dd>1</dd><dt><span>pow_arm_1 :</span></dt><dd>7</dd><dt><span>pow_arm_2 :</span></dt><dd>0</dd><dt><span>pulse_delay :</span></dt><dd>8e-05</dd><dt><span>push_amp :</span></dt><dd>0.16</dd><dt><span>push_freq :</span></dt><dd>102.25</dd><dt><span>ramp_duration :</span></dt><dd>1</dd><dt><span>recomp_ramp_duration :</span></dt><dd>0.5</dd><dt><span>recomp_ramp_pow_fin_arm_1 :</span></dt><dd>0.1038</dd><dt><span>recomp_ramp_pow_fin_arm_2 :</span></dt><dd>0.09</dd><dt><span>recomp_ramp_pow_ini_arm_1 :</span></dt><dd>0.1038</dd><dt><span>recomp_ramp_pow_ini_arm_2 :</span></dt><dd>0.09</dd><dt><span>runs :</span></dt><dd>1</dd><dt><span>save_results :</span></dt><dd>False</dd><dt><span>stern_gerlach_duration :</span></dt><dd>0.001</dd><dt><span>wait_after_2dmot_off :</span></dt><dd>0</dd><dt><span>wait_time_between_images :</span></dt><dd>0.22</dd><dt><span>x_offset :</span></dt><dd>0</dd><dt><span>x_offset_img :</span></dt><dd>0</dd><dt><span>y_offset :</span></dt><dd>0</dd><dt><span>y_offset_img :</span></dt><dd>0</dd><dt><span>z_offset :</span></dt><dd>0.189</dd><dt><span>z_offset_img :</span></dt><dd>0.189</dd><dt><span>scanAxis :</span></dt><dd>[]</dd><dt><span>scanAxisLength :</span></dt><dd>[]</dd><dt><span>x_start :</span></dt><dd>810</dd><dt><span>x_end :</span></dt><dd>1110</dd><dt><span>y_end :</span></dt><dd>1025</dd><dt><span>y_start :</span></dt><dd>725</dd><dt><span>x_center :</span></dt><dd>960</dd><dt><span>y_center :</span></dt><dd>875</dd><dt><span>x_span :</span></dt><dd>300</dd><dt><span>y_span :</span></dt><dd>300</dd></dl></div></li></ul></div></div>"
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"<xarray.DataArray 'OD' (y: 300, x: 300)>\n",
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"array([[-0.06868947, -0.04667641, -0.03211941, ..., 0.01331776,\n",
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" -0.01568196, -0.01466019],\n",
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" [-0.04738689, -0.10095033, -0.04738689, ..., -0.00086687,\n",
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" -0.02864644, 0.04425356],\n",
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" [ 0.01488149, 0.02990479, 0.02943848, ..., 0.08614451,\n",
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" -0.04168887, -0.17522026],\n",
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" ...,\n",
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" [-0.04435198, -0.08981436, -0.12101118, ..., -0.03211941,\n",
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" -0.10466366, -0.00086687],\n",
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" [-0.10622739, -0.16213502, -0.04253957, ..., -0.03117222,\n",
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" 0.01331776, -0.03117222],\n",
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" [-0.02148616, -0.06029029, 0.17348652, ..., 0.04109733,\n",
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" -0.07082546, -0.01590475]])\n",
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"Dimensions without coordinates: y, x\n",
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"Attributes: (12/107)\n",
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" IMAGE_SUBCLASS: IMAGE_GRAYSCALE\n",
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" IMAGE_VERSION: 1.2\n",
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" IMAGE_WHITE_IS_ZERO: 0\n",
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" TOF_free: 0.02\n",
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" abs_img_freq: 110.858\n",
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" absorption_imaging_flag: True\n",
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" ... ...\n",
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" y_end: 1025\n",
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" y_start: 725\n",
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" x_center: 960\n",
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" y_center: 875\n",
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" x_span: 300\n",
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" y_span: 300"
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]
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"execution_count": 41,
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"metadata": {},
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"output_type": "execute_result"
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}
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"source": [
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"dataSet_cropOD"
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"bool"
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"metadata": {},
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"output_type": "execute_result"
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"source": [
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"type(dataSet_cropOD.attrs['absorption_imaging_flag'].item())"
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"metadata": {},
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"outputs": [],
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"source": [
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"del dataSet_cropOD.attrs['IMAGE_WHITE_IS_ZERO']"
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"cell_type": "code",
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"text": [
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"C:\\Users\\data\\AppData\\Local\\Temp\\ipykernel_13060\\1656438333.py:2: DeprecationWarning: `np.typeDict` is a deprecated alias for `np.sctypeDict`.\n",
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" npTypeDict = {v: getattr(builtins, k) for k, v in np.typeDict.items() if k in vars(builtins)}\n"
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"source": [
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"import builtins\n",
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"npTypeDict = {v: getattr(builtins, k) for k, v in np.typeDict.items() if k in vars(builtins)}\n",
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"\n",
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"for key in dataSet_cropOD.attrs:\n",
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" typeKey = type(dataSet_cropOD.attrs[key])\n",
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" dataSet_cropOD.attrs[key] = dataSet_cropOD.attrs[key].item()"
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"C:\\Users\\data\\AppData\\Local\\Temp\\ipykernel_13060\\3602545102.py:2: DeprecationWarning: `np.typeDict` is a deprecated alias for `np.sctypeDict`.\n",
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"import builtins\n",
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"a = {v: getattr(builtins, k) for k, v in np.typeDict.items() if k in vars(builtins)}\n",
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"_id, _ = xdb.put(dataSet_cropOD)"
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"}\n",
|
|
"\n",
|
|
"html[theme=dark],\n",
|
|
"body[data-theme=dark],\n",
|
|
"body.vscode-dark {\n",
|
|
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
|
|
" --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
|
|
" --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
|
|
" --xr-border-color: #1F1F1F;\n",
|
|
" --xr-disabled-color: #515151;\n",
|
|
" --xr-background-color: #111111;\n",
|
|
" --xr-background-color-row-even: #111111;\n",
|
|
" --xr-background-color-row-odd: #313131;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-wrap {\n",
|
|
" display: block !important;\n",
|
|
" min-width: 300px;\n",
|
|
" max-width: 700px;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-text-repr-fallback {\n",
|
|
" /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
|
|
" display: none;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-header {\n",
|
|
" padding-top: 6px;\n",
|
|
" padding-bottom: 6px;\n",
|
|
" margin-bottom: 4px;\n",
|
|
" border-bottom: solid 1px var(--xr-border-color);\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-header > div,\n",
|
|
".xr-header > ul {\n",
|
|
" display: inline;\n",
|
|
" margin-top: 0;\n",
|
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" margin-bottom: 0;\n",
|
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|
|
"\n",
|
|
".xr-obj-type,\n",
|
|
".xr-array-name {\n",
|
|
" margin-left: 2px;\n",
|
|
" margin-right: 10px;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-obj-type {\n",
|
|
" color: var(--xr-font-color2);\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-sections {\n",
|
|
" padding-left: 0 !important;\n",
|
|
" display: grid;\n",
|
|
" grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-section-item {\n",
|
|
" display: contents;\n",
|
|
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|
|
"\n",
|
|
".xr-section-item input {\n",
|
|
" display: none;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-section-item input + label {\n",
|
|
" color: var(--xr-disabled-color);\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-section-item input:enabled + label {\n",
|
|
" cursor: pointer;\n",
|
|
" color: var(--xr-font-color2);\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-section-item input:enabled + label:hover {\n",
|
|
" color: var(--xr-font-color0);\n",
|
|
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|
|
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|
|
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|
|
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|
|
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|
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" font-weight: 500;\n",
|
|
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|
|
"\n",
|
|
".xr-section-summary > span {\n",
|
|
" display: inline-block;\n",
|
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|
|
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|
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"\n",
|
|
".xr-section-summary-in:disabled + label {\n",
|
|
" color: var(--xr-font-color2);\n",
|
|
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|
|
"\n",
|
|
".xr-section-summary-in + label:before {\n",
|
|
" display: inline-block;\n",
|
|
" content: 'â–º';\n",
|
|
" font-size: 11px;\n",
|
|
" width: 15px;\n",
|
|
" text-align: center;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-section-summary-in:disabled + label:before {\n",
|
|
" color: var(--xr-disabled-color);\n",
|
|
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|
|
"\n",
|
|
".xr-section-summary-in:checked + label:before {\n",
|
|
" content: 'â–¼';\n",
|
|
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|
|
"\n",
|
|
".xr-section-summary-in:checked + label > span {\n",
|
|
" display: none;\n",
|
|
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|
|
"\n",
|
|
".xr-section-summary,\n",
|
|
".xr-section-inline-details {\n",
|
|
" padding-top: 4px;\n",
|
|
" padding-bottom: 4px;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-section-inline-details {\n",
|
|
" grid-column: 2 / -1;\n",
|
|
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|
|
"\n",
|
|
".xr-section-details {\n",
|
|
" display: none;\n",
|
|
" grid-column: 1 / -1;\n",
|
|
" margin-bottom: 5px;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-section-summary-in:checked ~ .xr-section-details {\n",
|
|
" display: contents;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-array-wrap {\n",
|
|
" grid-column: 1 / -1;\n",
|
|
" display: grid;\n",
|
|
" grid-template-columns: 20px auto;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-array-wrap > label {\n",
|
|
" grid-column: 1;\n",
|
|
" vertical-align: top;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-preview {\n",
|
|
" color: var(--xr-font-color3);\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-array-preview,\n",
|
|
".xr-array-data {\n",
|
|
" padding: 0 5px !important;\n",
|
|
" grid-column: 2;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-array-data,\n",
|
|
".xr-array-in:checked ~ .xr-array-preview {\n",
|
|
" display: none;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-array-in:checked ~ .xr-array-data,\n",
|
|
".xr-array-preview {\n",
|
|
" display: inline-block;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-dim-list {\n",
|
|
" display: inline-block !important;\n",
|
|
" list-style: none;\n",
|
|
" padding: 0 !important;\n",
|
|
" margin: 0;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-dim-list li {\n",
|
|
" display: inline-block;\n",
|
|
" padding: 0;\n",
|
|
" margin: 0;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-dim-list:before {\n",
|
|
" content: '(';\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-dim-list:after {\n",
|
|
" content: ')';\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-dim-list li:not(:last-child):after {\n",
|
|
" content: ',';\n",
|
|
" padding-right: 5px;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-has-index {\n",
|
|
" font-weight: bold;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-var-list,\n",
|
|
".xr-var-item {\n",
|
|
" display: contents;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-var-item > div,\n",
|
|
".xr-var-item label,\n",
|
|
".xr-var-item > .xr-var-name span {\n",
|
|
" background-color: var(--xr-background-color-row-even);\n",
|
|
" margin-bottom: 0;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-var-item > .xr-var-name:hover span {\n",
|
|
" padding-right: 5px;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-var-list > li:nth-child(odd) > div,\n",
|
|
".xr-var-list > li:nth-child(odd) > label,\n",
|
|
".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
|
|
" background-color: var(--xr-background-color-row-odd);\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-var-name {\n",
|
|
" grid-column: 1;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-var-dims {\n",
|
|
" grid-column: 2;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-var-dtype {\n",
|
|
" grid-column: 3;\n",
|
|
" text-align: right;\n",
|
|
" color: var(--xr-font-color2);\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-var-preview {\n",
|
|
" grid-column: 4;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-index-preview {\n",
|
|
" grid-column: 2 / 5;\n",
|
|
" color: var(--xr-font-color2);\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-var-name,\n",
|
|
".xr-var-dims,\n",
|
|
".xr-var-dtype,\n",
|
|
".xr-preview,\n",
|
|
".xr-attrs dt {\n",
|
|
" white-space: nowrap;\n",
|
|
" overflow: hidden;\n",
|
|
" text-overflow: ellipsis;\n",
|
|
" padding-right: 10px;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-var-name:hover,\n",
|
|
".xr-var-dims:hover,\n",
|
|
".xr-var-dtype:hover,\n",
|
|
".xr-attrs dt:hover {\n",
|
|
" overflow: visible;\n",
|
|
" width: auto;\n",
|
|
" z-index: 1;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-var-attrs,\n",
|
|
".xr-var-data,\n",
|
|
".xr-index-data {\n",
|
|
" display: none;\n",
|
|
" background-color: var(--xr-background-color) !important;\n",
|
|
" padding-bottom: 5px !important;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
|
|
".xr-var-data-in:checked ~ .xr-var-data,\n",
|
|
".xr-index-data-in:checked ~ .xr-index-data {\n",
|
|
" display: block;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-var-data > table {\n",
|
|
" float: right;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-var-name span,\n",
|
|
".xr-var-data,\n",
|
|
".xr-index-name div,\n",
|
|
".xr-index-data,\n",
|
|
".xr-attrs {\n",
|
|
" padding-left: 25px !important;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-attrs,\n",
|
|
".xr-var-attrs,\n",
|
|
".xr-var-data,\n",
|
|
".xr-index-data {\n",
|
|
" grid-column: 1 / -1;\n",
|
|
"}\n",
|
|
"\n",
|
|
"dl.xr-attrs {\n",
|
|
" padding: 0;\n",
|
|
" margin: 0;\n",
|
|
" display: grid;\n",
|
|
" grid-template-columns: 125px auto;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-attrs dt,\n",
|
|
".xr-attrs dd {\n",
|
|
" padding: 0;\n",
|
|
" margin: 0;\n",
|
|
" float: left;\n",
|
|
" padding-right: 10px;\n",
|
|
" width: auto;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-attrs dt {\n",
|
|
" font-weight: normal;\n",
|
|
" grid-column: 1;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-attrs dt:hover span {\n",
|
|
" display: inline-block;\n",
|
|
" background: var(--xr-background-color);\n",
|
|
" padding-right: 10px;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-attrs dd {\n",
|
|
" grid-column: 2;\n",
|
|
" white-space: pre-wrap;\n",
|
|
" word-break: break-all;\n",
|
|
"}\n",
|
|
"\n",
|
|
".xr-icon-database,\n",
|
|
".xr-icon-file-text2,\n",
|
|
".xr-no-icon {\n",
|
|
" display: inline-block;\n",
|
|
" vertical-align: middle;\n",
|
|
" width: 1em;\n",
|
|
" height: 1.5em !important;\n",
|
|
" stroke-width: 0;\n",
|
|
" stroke: currentColor;\n",
|
|
" fill: currentColor;\n",
|
|
"}\n",
|
|
"</style><pre class='xr-text-repr-fallback'><xarray.DataArray 'OD' (y: 300, x: 300)>\n",
|
|
"array([[-0.06868947, -0.04667641, -0.03211941, ..., 0.01331776,\n",
|
|
" -0.01568196, -0.01466019],\n",
|
|
" [-0.04738689, -0.10095033, -0.04738689, ..., -0.00086687,\n",
|
|
" -0.02864644, 0.04425356],\n",
|
|
" [ 0.01488149, 0.02990479, 0.02943848, ..., 0.08614451,\n",
|
|
" -0.04168887, -0.17522026],\n",
|
|
" ...,\n",
|
|
" [-0.04435198, -0.08981436, -0.12101118, ..., -0.03211941,\n",
|
|
" -0.10466366, -0.00086687],\n",
|
|
" [-0.10622739, -0.16213502, -0.04253957, ..., -0.03117222,\n",
|
|
" 0.01331776, -0.03117222],\n",
|
|
" [-0.02148616, -0.06029029, 0.17348652, ..., 0.04109733,\n",
|
|
" -0.07082546, -0.01590475]])\n",
|
|
"Dimensions without coordinates: y, x\n",
|
|
"Attributes: (12/105)\n",
|
|
" IMAGE_SUBCLASS: IMAGE_GRAYSCALE\n",
|
|
" IMAGE_VERSION: 1.2\n",
|
|
" TOF_free: 0.02\n",
|
|
" abs_img_freq: 110.858\n",
|
|
" absorption_imaging_flag: True\n",
|
|
" backup_data: True\n",
|
|
" ... ...\n",
|
|
" y_end: 1025\n",
|
|
" y_start: 725\n",
|
|
" x_center: 960\n",
|
|
" y_center: 875\n",
|
|
" x_span: 300\n",
|
|
" y_span: 300</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'OD'</div><ul class='xr-dim-list'><li><span>y</span>: 300</li><li><span>x</span>: 300</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-f95708f7-be64-47fe-91d2-b18344519223' class='xr-array-in' type='checkbox' checked><label for='section-f95708f7-be64-47fe-91d2-b18344519223' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-0.06869 -0.04668 -0.03212 0.05976 ... 0.0411 -0.07083 -0.0159</span></div><div class='xr-array-data'><pre>array([[-0.06868947, -0.04667641, -0.03211941, ..., 0.01331776,\n",
|
|
" -0.01568196, -0.01466019],\n",
|
|
" [-0.04738689, -0.10095033, -0.04738689, ..., -0.00086687,\n",
|
|
" -0.02864644, 0.04425356],\n",
|
|
" [ 0.01488149, 0.02990479, 0.02943848, ..., 0.08614451,\n",
|
|
" -0.04168887, -0.17522026],\n",
|
|
" ...,\n",
|
|
" [-0.04435198, -0.08981436, -0.12101118, ..., -0.03211941,\n",
|
|
" -0.10466366, -0.00086687],\n",
|
|
" [-0.10622739, -0.16213502, -0.04253957, ..., -0.03117222,\n",
|
|
" 0.01331776, -0.03117222],\n",
|
|
" [-0.02148616, -0.06029029, 0.17348652, ..., 0.04109733,\n",
|
|
" -0.07082546, -0.01590475]])</pre></div></div></li><li class='xr-section-item'><input id='section-ec8518af-0290-4b43-8c4d-ff2a52107f93' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ec8518af-0290-4b43-8c4d-ff2a52107f93' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-38c2e4d0-d87a-4176-b362-ac32eb91d6b6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-38c2e4d0-d87a-4176-b362-ac32eb91d6b6' class='xr-section-summary' title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-a83ae685-b870-46e3-81b5-181114ff5eab' class='xr-section-summary-in' type='checkbox' ><label for='section-a83ae685-b870-46e3-81b5-181114ff5eab' class='xr-section-summary' >Attributes: <span>(105)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>IMAGE_SUBCLASS :</span></dt><dd>IMAGE_GRAYSCALE</dd><dt><span>IMAGE_VERSION :</span></dt><dd>1.2</dd><dt><span>TOF_free :</span></dt><dd>0.02</dd><dt><span>abs_img_freq :</span></dt><dd>110.858</dd><dt><span>absorption_imaging_flag :</span></dt><dd>True</dd><dt><span>backup_data :</span></dt><dd>True</dd><dt><span>blink_off_time :</span></dt><dd>nan</dd><dt><span>blink_on_time :</span></dt><dd>nan</dd><dt><span>c_duration :</span></dt><dd>0.2</dd><dt><span>cmot_final_current :</span></dt><dd>0.65</dd><dt><span>cmot_hold :</span></dt><dd>0.06</dd><dt><span>cmot_initial_current :</span></dt><dd>0.18</dd><dt><span>compX_current :</span></dt><dd>0.005</dd><dt><span>compX_current_sg :</span></dt><dd>0</dd><dt><span>compX_final_current :</span></dt><dd>0.005</dd><dt><span>compX_initial_current :</span></dt><dd>0.005</dd><dt><span>compY_current :</span></dt><dd>0</dd><dt><span>compY_current_sg :</span></dt><dd>0</dd><dt><span>compY_final_current :</span></dt><dd>0.0</dd><dt><span>compY_initial_current :</span></dt><dd>0</dd><dt><span>compZ_current :</span></dt><dd>0</dd><dt><span>compZ_current_sg :</span></dt><dd>0.189</dd><dt><span>compZ_final_current :</span></dt><dd>0.2812</dd><dt><span>compZ_initial_current :</span></dt><dd>0</dd><dt><span>default_camera :</span></dt><dd>0</dd><dt><span>evap_1_arm_1_final_pow :</span></dt><dd>0.35</dd><dt><span>evap_1_arm_1_mod_depth_final :</span></dt><dd>0</dd><dt><span>evap_1_arm_1_mod_depth_initial :</span></dt><dd>1.0</dd><dt><span>evap_1_arm_1_mod_ramp_duration :</span></dt><dd>1.15</dd><dt><span>evap_1_arm_1_pow_ramp_duration :</span></dt><dd>1.65</dd><dt><span>evap_1_arm_1_start_pow :</span></dt><dd>7</dd><dt><span>evap_1_arm_2_final_pow :</span></dt><dd>5</dd><dt><span>evap_1_arm_2_ramp_duration :</span></dt><dd>0.5</dd><dt><span>evap_1_arm_2_start_pow :</span></dt><dd>0</dd><dt><span>evap_1_mod_ramp_trunc_value :</span></dt><dd>1</dd><dt><span>evap_1_pow_ramp_trunc_value :</span></dt><dd>1.0</dd><dt><span>evap_1_rate_constant_1 :</span></dt><dd>0.525</dd><dt><span>evap_1_rate_constant_2 :</span></dt><dd>0.51</dd><dt><span>evap_2_arm_1_final_pow :</span></dt><dd>0.037</dd><dt><span>evap_2_arm_1_start_pow :</span></dt><dd>0.35</dd><dt><span>evap_2_arm_2_final_pow :</span></dt><dd>0.09</dd><dt><span>evap_2_arm_2_start_pow :</span></dt><dd>5</dd><dt><span>evap_2_ramp_duration :</span></dt><dd>1.0</dd><dt><span>evap_2_ramp_trunc_value :</span></dt><dd>0.7</dd><dt><span>evap_2_rate_constant_1 :</span></dt><dd>0.37</dd><dt><span>evap_2_rate_constant_2 :</span></dt><dd>0.71</dd><dt><span>evap_3_arm_1_final_pow :</span></dt><dd>0.1038</dd><dt><span>evap_3_arm_1_mod_depth_final :</span></dt><dd>0.43</dd><dt><span>evap_3_arm_1_mod_depth_initial :</span></dt><dd>0</dd><dt><span>evap_3_arm_1_start_pow :</span></dt><dd>0.037</dd><dt><span>evap_3_ramp_duration :</span></dt><dd>0.1</dd><dt><span>evap_3_ramp_trunc_value :</span></dt><dd>1</dd><dt><span>evap_3_rate_constant_1 :</span></dt><dd>-0.879</dd><dt><span>evap_3_rate_constant_2 :</span></dt><dd>-0.297</dd><dt><span>final_amp :</span></dt><dd>8e-05</dd><dt><span>final_freq :</span></dt><dd>104.0</dd><dt><span>gradCoil_current :</span></dt><dd>0.18</dd><dt><span>gradCoil_current_sg :</span></dt><dd>0</dd><dt><span>imaging_method :</span></dt><dd>in_situ_absorption</dd><dt><span>imaging_pulse_duration :</span></dt><dd>2.5e-05</dd><dt><span>imaging_wavelength :</span></dt><dd>4.21291e-07</dd><dt><span>initial_amp :</span></dt><dd>0.62</dd><dt><span>initial_freq :</span></dt><dd>102.13</dd><dt><span>mod_depth_initial :</span></dt><dd>1.0</dd><dt><span>mot_3d_amp :</span></dt><dd>0.62</dd><dt><span>mot_3d_camera_exposure_time :</span></dt><dd>0.002</dd><dt><span>mot_3d_camera_trigger_duration :</span></dt><dd>0.00025</dd><dt><span>mot_3d_freq :</span></dt><dd>102.13</dd><dt><span>mot_load_duration :</span></dt><dd>4</dd><dt><span>odt_axis_camera_trigger_duration :</span></dt><dd>0.002</dd><dt><span>odt_hold_time_1 :</span></dt><dd>0.01</dd><dt><span>odt_hold_time_2 :</span></dt><dd>0.1</dd><dt><span>odt_hold_time_3 :</span></dt><dd>0.1</dd><dt><span>odt_hold_time_4 :</span></dt><dd>1</dd><dt><span>pow_arm_1 :</span></dt><dd>7</dd><dt><span>pow_arm_2 :</span></dt><dd>0</dd><dt><span>pulse_delay :</span></dt><dd>8e-05</dd><dt><span>push_amp :</span></dt><dd>0.16</dd><dt><span>push_freq :</span></dt><dd>102.25</dd><dt><span>ramp_duration :</span></dt><dd>1</dd><dt><span>recomp_ramp_duration :</span></dt><dd>0.5</dd><dt><span>recomp_ramp_pow_fin_arm_1 :</span></dt><dd>0.1038</dd><dt><span>recomp_ramp_pow_fin_arm_2 :</span></dt><dd>0.09</dd><dt><span>recomp_ramp_pow_ini_arm_1 :</span></dt><dd>0.1038</dd><dt><span>recomp_ramp_pow_ini_arm_2 :</span></dt><dd>0.09</dd><dt><span>runs :</span></dt><dd>1</dd><dt><span>save_results :</span></dt><dd>False</dd><dt><span>stern_gerlach_duration :</span></dt><dd>0.001</dd><dt><span>wait_after_2dmot_off :</span></dt><dd>0</dd><dt><span>wait_time_between_images :</span></dt><dd>0.22</dd><dt><span>x_offset :</span></dt><dd>0</dd><dt><span>x_offset_img :</span></dt><dd>0</dd><dt><span>y_offset :</span></dt><dd>0</dd><dt><span>y_offset_img :</span></dt><dd>0</dd><dt><span>z_offset :</span></dt><dd>0.189</dd><dt><span>z_offset_img :</span></dt><dd>0.189</dd><dt><span>scanAxisLength :</span></dt><dd>[]</dd><dt><span>x_start :</span></dt><dd>810</dd><dt><span>x_end :</span></dt><dd>1110</dd><dt><span>y_end :</span></dt><dd>1025</dd><dt><span>y_start :</span></dt><dd>725</dd><dt><span>x_center :</span></dt><dd>960</dd><dt><span>y_center :</span></dt><dd>875</dd><dt><span>x_span :</span></dt><dd>300</dd><dt><span>y_span :</span></dt><dd>300</dd></dl></div></li></ul></div></div>"
|
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],
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"text/plain": [
|
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"<xarray.DataArray 'OD' (y: 300, x: 300)>\n",
|
|
"array([[-0.06868947, -0.04667641, -0.03211941, ..., 0.01331776,\n",
|
|
" -0.01568196, -0.01466019],\n",
|
|
" [-0.04738689, -0.10095033, -0.04738689, ..., -0.00086687,\n",
|
|
" -0.02864644, 0.04425356],\n",
|
|
" [ 0.01488149, 0.02990479, 0.02943848, ..., 0.08614451,\n",
|
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" -0.04168887, -0.17522026],\n",
|
|
" ...,\n",
|
|
" [-0.04435198, -0.08981436, -0.12101118, ..., -0.03211941,\n",
|
|
" -0.10466366, -0.00086687],\n",
|
|
" [-0.10622739, -0.16213502, -0.04253957, ..., -0.03117222,\n",
|
|
" 0.01331776, -0.03117222],\n",
|
|
" [-0.02148616, -0.06029029, 0.17348652, ..., 0.04109733,\n",
|
|
" -0.07082546, -0.01590475]])\n",
|
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"Dimensions without coordinates: y, x\n",
|
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"Attributes: (12/105)\n",
|
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" IMAGE_SUBCLASS: IMAGE_GRAYSCALE\n",
|
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" IMAGE_VERSION: 1.2\n",
|
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" TOF_free: 0.02\n",
|
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" abs_img_freq: 110.858\n",
|
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" absorption_imaging_flag: True\n",
|
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" backup_data: True\n",
|
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" ... ...\n",
|
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" y_end: 1025\n",
|
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" y_start: 725\n",
|
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" x_center: 960\n",
|
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" y_center: 875\n",
|
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" x_span: 300\n",
|
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" y_span: 300"
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]
|
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},
|
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"execution_count": 62,
|
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"metadata": {},
|
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"output_type": "execute_result"
|
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}
|
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],
|
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"source": [
|
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"xdb.get(_id)"
|
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]
|
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},
|
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{
|
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"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
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"c = bson.objectid.ObjectId('646e4919802812f029b385d7')\n",
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"c"
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]
|
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},
|
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{
|
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"cell_type": "code",
|
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"execution_count": null,
|
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"metadata": {},
|
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"outputs": [],
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"source": [
|
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"xdb.get(c)"
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]
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},
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{
|
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"cell_type": "code",
|
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"execution_count": 63,
|
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"metadata": {},
|
|
"outputs": [],
|
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"source": [
|
|
"import datetime\n",
|
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"post = {\"author\": \"Mike\",\n",
|
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" \"data_id\": _id,\n",
|
|
" \"tags\": [\"mongodb\", \"python\", \"pymongo\"],\n",
|
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" \"date\": datetime.datetime.utcnow(),\n",
|
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" }\n"
|
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]
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},
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{
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"cell_type": "code",
|
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"execution_count": 64,
|
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"metadata": {},
|
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"outputs": [
|
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{
|
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"data": {
|
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"text/plain": [
|
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"ObjectId('648318ae129ec5a1a24a7353')"
|
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]
|
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},
|
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"execution_count": 64,
|
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"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"posts = mongoCollection\n",
|
|
"post_id = posts.insert_one(post).inserted_id\n",
|
|
"post_id"
|
|
]
|
|
},
|
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{
|
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"cell_type": "code",
|
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"execution_count": 94,
|
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"metadata": {},
|
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"outputs": [
|
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{
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"data": {
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"text/plain": [
|
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"{'_id': ObjectId('648318ae129ec5a1a24a7353'),\n",
|
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" 'author': 'Mike',\n",
|
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" 'data_id': ObjectId('6482fb08129ec5a1a24a734f'),\n",
|
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" 'tags': ['mongodb', 'python', 'pymongo'],\n",
|
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" 'date': datetime.datetime(2023, 6, 9, 12, 18, 52, 771000),\n",
|
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" 'test': DBRef('xarray.meta', ObjectId('6482f4a3129ec5a1a24a7342'), 'testDB')}"
|
|
]
|
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},
|
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"execution_count": 94,
|
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"metadata": {},
|
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"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"posts.find_one({'_id': bson.objectid.ObjectId('648318ae129ec5a1a24a7353')})"
|
|
]
|
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},
|
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{
|
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"cell_type": "code",
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"execution_count": 67,
|
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"metadata": {},
|
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"outputs": [
|
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{
|
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"data": {
|
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"text/plain": [
|
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"DBRef('xarray.meta', ObjectId('6482f4a3129ec5a1a24a7342'), 'testDB')"
|
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]
|
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},
|
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"execution_count": 67,
|
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"metadata": {},
|
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"output_type": "execute_result"
|
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}
|
|
],
|
|
"source": [
|
|
"for i in posts.find_one({'_id': bson.objectid.ObjectId('648318ae129ec5a1a24a7353')}):\n",
|
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" bb = i\n",
|
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" \n",
|
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"bb['test']"
|
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]
|
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},
|
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{
|
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"cell_type": "code",
|
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"execution_count": 15,
|
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"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"import gridfs\n",
|
|
"\n",
|
|
"fs = gridfs.GridFS(mongoDB, 'xarray')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
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"execution_count": 19,
|
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"metadata": {},
|
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"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"{'dims': ('y', 'x'),\n",
|
|
" 'attrs': {'IMAGE_SUBCLASS': 'IMAGE_GRAYSCALE',\n",
|
|
" 'IMAGE_VERSION': '1.2',\n",
|
|
" 'IMAGE_WHITE_IS_ZERO': 0,\n",
|
|
" 'TOF_free': 0.02,\n",
|
|
" 'abs_img_freq': 110.858,\n",
|
|
" 'absorption_imaging_flag': True,\n",
|
|
" 'backup_data': True,\n",
|
|
" 'blink_off_time': nan,\n",
|
|
" 'blink_on_time': nan,\n",
|
|
" 'c_duration': 0.2,\n",
|
|
" 'cmot_final_current': 0.65,\n",
|
|
" 'cmot_hold': 0.06,\n",
|
|
" 'cmot_initial_current': 0.18,\n",
|
|
" 'compX_current': 0.005,\n",
|
|
" 'compX_current_sg': 0,\n",
|
|
" 'compX_final_current': 0.005,\n",
|
|
" 'compX_initial_current': 0.005,\n",
|
|
" 'compY_current': 0,\n",
|
|
" 'compY_current_sg': 0,\n",
|
|
" 'compY_final_current': 0.0,\n",
|
|
" 'compY_initial_current': 0,\n",
|
|
" 'compZ_current': 0,\n",
|
|
" 'compZ_current_sg': 0.189,\n",
|
|
" 'compZ_final_current': 0.2812,\n",
|
|
" 'compZ_initial_current': 0,\n",
|
|
" 'default_camera': 0,\n",
|
|
" 'evap_1_arm_1_final_pow': 0.35,\n",
|
|
" 'evap_1_arm_1_mod_depth_final': 0,\n",
|
|
" 'evap_1_arm_1_mod_depth_initial': 1.0,\n",
|
|
" 'evap_1_arm_1_mod_ramp_duration': 1.15,\n",
|
|
" 'evap_1_arm_1_pow_ramp_duration': 1.65,\n",
|
|
" 'evap_1_arm_1_start_pow': 7,\n",
|
|
" 'evap_1_arm_2_final_pow': 5,\n",
|
|
" 'evap_1_arm_2_ramp_duration': 0.5,\n",
|
|
" 'evap_1_arm_2_start_pow': 0,\n",
|
|
" 'evap_1_mod_ramp_trunc_value': 1,\n",
|
|
" 'evap_1_pow_ramp_trunc_value': 1.0,\n",
|
|
" 'evap_1_rate_constant_1': 0.525,\n",
|
|
" 'evap_1_rate_constant_2': 0.51,\n",
|
|
" 'evap_2_arm_1_final_pow': 0.037,\n",
|
|
" 'evap_2_arm_1_start_pow': 0.35,\n",
|
|
" 'evap_2_arm_2_final_pow': 0.09,\n",
|
|
" 'evap_2_arm_2_start_pow': 5,\n",
|
|
" 'evap_2_ramp_duration': 1.0,\n",
|
|
" 'evap_2_ramp_trunc_value': 0.7,\n",
|
|
" 'evap_2_rate_constant_1': 0.37,\n",
|
|
" 'evap_2_rate_constant_2': 0.71,\n",
|
|
" 'evap_3_arm_1_final_pow': 0.1038,\n",
|
|
" 'evap_3_arm_1_mod_depth_final': 0.43,\n",
|
|
" 'evap_3_arm_1_mod_depth_initial': 0,\n",
|
|
" 'evap_3_arm_1_start_pow': 0.037,\n",
|
|
" 'evap_3_ramp_duration': 0.1,\n",
|
|
" 'evap_3_ramp_trunc_value': 1,\n",
|
|
" 'evap_3_rate_constant_1': -0.879,\n",
|
|
" 'evap_3_rate_constant_2': -0.297,\n",
|
|
" 'final_amp': 8e-05,\n",
|
|
" 'final_freq': 104.0,\n",
|
|
" 'gradCoil_current': 0.18,\n",
|
|
" 'gradCoil_current_sg': 0,\n",
|
|
" 'imaging_method': 'in_situ_absorption',\n",
|
|
" 'imaging_pulse_duration': 2.5e-05,\n",
|
|
" 'imaging_wavelength': 4.21291e-07,\n",
|
|
" 'initial_amp': 0.62,\n",
|
|
" 'initial_freq': 102.13,\n",
|
|
" 'mod_depth_initial': 1.0,\n",
|
|
" 'mot_3d_amp': 0.62,\n",
|
|
" 'mot_3d_camera_exposure_time': 0.002,\n",
|
|
" 'mot_3d_camera_trigger_duration': 0.00025,\n",
|
|
" 'mot_3d_freq': 102.13,\n",
|
|
" 'mot_load_duration': 4,\n",
|
|
" 'odt_axis_camera_trigger_duration': 0.002,\n",
|
|
" 'odt_hold_time_1': 0.01,\n",
|
|
" 'odt_hold_time_2': 0.1,\n",
|
|
" 'odt_hold_time_3': 0.1,\n",
|
|
" 'odt_hold_time_4': 1,\n",
|
|
" 'pow_arm_1': 7,\n",
|
|
" 'pow_arm_2': 0,\n",
|
|
" 'pulse_delay': 8e-05,\n",
|
|
" 'push_amp': 0.16,\n",
|
|
" 'push_freq': 102.25,\n",
|
|
" 'ramp_duration': 1,\n",
|
|
" 'recomp_ramp_duration': 0.5,\n",
|
|
" 'recomp_ramp_pow_fin_arm_1': 0.1038,\n",
|
|
" 'recomp_ramp_pow_fin_arm_2': 0.09,\n",
|
|
" 'recomp_ramp_pow_ini_arm_1': 0.1038,\n",
|
|
" 'recomp_ramp_pow_ini_arm_2': 0.09,\n",
|
|
" 'runs': 1,\n",
|
|
" 'save_results': False,\n",
|
|
" 'stern_gerlach_duration': 0.001,\n",
|
|
" 'wait_after_2dmot_off': 0,\n",
|
|
" 'wait_time_between_images': 0.22,\n",
|
|
" 'x_offset': 0,\n",
|
|
" 'x_offset_img': 0,\n",
|
|
" 'y_offset': 0,\n",
|
|
" 'y_offset_img': 0,\n",
|
|
" 'z_offset': 0.189,\n",
|
|
" 'z_offset_img': 0.189,\n",
|
|
" 'scanAxis': [],\n",
|
|
" 'scanAxisLength': [],\n",
|
|
" 'x_start': 810,\n",
|
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" 'x_end': 1110,\n",
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|
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" 'coords': {},\n",
|
|
" 'name': 'OD'}"
|
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]
|
|
},
|
|
"execution_count": 19,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"f = dataSet_cropOD.to_dict()\n",
|
|
"f"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 26,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"{\"dims\": [\"y\", \"x\"], \"attrs\": {\"IMAGE_SUBCLASS\": \"IMAGE_GRAYSCALE\", \"IMAGE_VERSION\": \"1.2\", \"IMAGE_WHITE_IS_ZERO\": 0, \"TOF_free\": 0.02, \"abs_img_freq\": 110.858, \"absorption_imaging_flag\": true, \"backup_data\": true, \"blink_off_time\": NaN, \"blink_on_time\": NaN, \"c_duration\": 0.2, \"cmot_final_current\": 0.65, \"cmot_hold\": 0.06, \"cmot_initial_current\": 0.18, \"compX_current\": 0.005, \"compX_current_sg\": 0, \"compX_final_current\": 0.005, \"compX_initial_current\": 0.005, \"compY_current\": 0, \"compY_current_sg\": 0, \"compY_final_current\": 0.0, \"compY_initial_current\": 0, \"compZ_current\": 0, \"compZ_current_sg\": 0.189, \"compZ_final_current\": 0.2812, \"compZ_initial_current\": 0, \"default_camera\": 0, \"evap_1_arm_1_final_pow\": 0.35, \"evap_1_arm_1_mod_depth_final\": 0, \"evap_1_arm_1_mod_depth_initial\": 1.0, \"evap_1_arm_1_mod_ramp_duration\": 1.15, \"evap_1_arm_1_pow_ramp_duration\": 1.65, \"evap_1_arm_1_start_pow\": 7, \"evap_1_arm_2_final_pow\": 5, \"evap_1_arm_2_ramp_duration\": 0.5, \"evap_1_arm_2_start_pow\": 0, \"evap_1_mod_ramp_trunc_value\": 1, \"evap_1_pow_ramp_trunc_value\": 1.0, \"evap_1_rate_constant_1\": 0.525, \"evap_1_rate_constant_2\": 0.51, \"evap_2_arm_1_final_pow\": 0.037, \"evap_2_arm_1_start_pow\": 0.35, \"evap_2_arm_2_final_pow\": 0.09, \"evap_2_arm_2_start_pow\": 5, \"evap_2_ramp_duration\": 1.0, \"evap_2_ramp_trunc_value\": 0.7, \"evap_2_rate_constant_1\": 0.37, \"evap_2_rate_constant_2\": 0.71, \"evap_3_arm_1_final_pow\": 0.1038, \"evap_3_arm_1_mod_depth_final\": 0.43, \"evap_3_arm_1_mod_depth_initial\": 0, \"evap_3_arm_1_start_pow\": 0.037, \"evap_3_ramp_duration\": 0.1, \"evap_3_ramp_trunc_value\": 1, \"evap_3_rate_constant_1\": -0.879, \"evap_3_rate_constant_2\": -0.297, \"final_amp\": 8e-05, \"final_freq\": 104.0, \"gradCoil_current\": 0.18, \"gradCoil_current_sg\": 0, \"imaging_method\": \"in_situ_absorption\", \"imaging_pulse_duration\": 2.5e-05, \"imaging_wavelength\": 4.21291e-07, \"initial_amp\": 0.62, \"initial_freq\": 102.13, \"mod_depth_initial\": 1.0, \"mot_3d_amp\": 0.62, \"mot_3d_camera_exposure_time\": 0.002, \"mot_3d_camera_trigger_duration\": 0.00025, \"mot_3d_freq\": 102.13, \"mot_load_duration\": 4, \"odt_axis_camera_trigger_duration\": 0.002, \"odt_hold_time_1\": 0.01, \"odt_hold_time_2\": 0.1, \"odt_hold_time_3\": 0.1, \"odt_hold_time_4\": 1, \"pow_arm_1\": 7, \"pow_arm_2\": 0, \"pulse_delay\": 8e-05, \"push_amp\": 0.16, \"push_freq\": 102.25, \"ramp_duration\": 1, \"recomp_ramp_duration\": 0.5, \"recomp_ramp_pow_fin_arm_1\": 0.1038, \"recomp_ramp_pow_fin_arm_2\": 0.09, \"recomp_ramp_pow_ini_arm_1\": 0.1038, \"recomp_ramp_pow_ini_arm_2\": 0.09, \"runs\": 1, \"save_results\": false, \"stern_gerlach_duration\": 0.001, \"wait_after_2dmot_off\": 0, \"wait_time_between_images\": 0.22, \"x_offset\": 0, \"x_offset_img\": 0, \"y_offset\": 0, \"y_offset_img\": 0, \"z_offset\": 0.189, \"z_offset_img\": 0.189, \"scanAxis\": [], 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0.0846553024904636, 0.04638601390284709, 0.080478768506254, -0.09183864915342518, -0.01856644804709926, 0.04956398267919356, 0.1149649445774233, -0.016615227915837517, 0.041692743471097485, -0.015465670368851034, -0.03312773316591988, -0.052160165335248886, -0.01856644804709926, 0.05320035032257742, -0.07107112962094696, 0.1149649445774233, -0.06242476394713177, -0.031172220443027247, 0.014400601183090018, 0.028986092201982755, 0.11125042717300772, -0.01739617289890891, 0.0471423482386622, -0.13007860242770466, -0.03163852961445209, -0.05046381208707059, -0.03028075615399181, 0.04109732815133379, -0.07082545955460887, -0.0008668709476984035, -0.03163852961445209, 0.052242954366250004, -0.06274227466578586, -0.08090957862123475, 0.017825262064454117, -0.0008668709476984035, -0.042539567348266485, 0.14995601878688525, -0.09995777359192937, 0.01893575634848135, 0.11927744089436482, 0.191024136862407, -0.016134343078486785, 0.033034680727982935, 0.054192906235329026, 0.10637865940589916, 0.05131888222287179, -0.046676406978992606, 0.03246954931989343, -0.016134343078486785, -0.0008668709476984035, 0.048730070191673656, -0.032615569262278675, -0.049657035117130446, -0.06540539208526956, -0.12716059627199047, -0.05913577907167423, 0.11035876416252598, 0.08768652639374666, -0.0008668709476984035, -0.052160165335248886, 0.050426423439852174, -0.033656693770689375, 0.06069102205173491, -0.048876090134059066, 0.1149649445774233, -0.10095032950468102, -0.10440754988853876, 0.05797362907523506, -0.06242476394713177, -0.045987306228168046, -0.03312773316591988, 0.1894868575435205, 0.015940247368682884, 0.030881827366881894, -0.01637105748366366, -0.016134343078486785, -0.14819158570455693, 0.10449364471012788, 0.04792329322173365, 0.016524871764170817, 0.06695572539106266, 0.03139399127052303, -0.0008668709476984035, -0.05129772457459031, -0.017961304306998445, -0.032615569262278675, -0.10622738660552475, 0.015393649924081888, 0.09150644918331677, -0.05129772457459031, -0.16638130942527174, -0.09101796794199588, -0.13220287300878536, -0.0008668709476984035, 0.07324110120602345, 0.04792329322173365, 0.03617440073265069, -0.0549340922179742, -0.10095032950468102, 0.13728346753311876, 0.014400601183090018, 0.06367165018987278, 0.05975775086873645, -0.03535304701886781, 0.050426423439852174, -0.017961304306998445, -0.01739617289890891, -0.03476842262337982, -0.0008668709476984035, -0.11864990660408185, 0.04494266508359582, 0.03139399127052303, 0.015393649924081888, -0.10095032950468102, 0.03485121165438083, 0.19203679517679315, -0.0008668709476984035, -0.033656693770689375, 0.06264653477462746, 0.015393649924081888, 0.015393649924081888, 0.03246954931989343, 0.05975775086873645, -0.09101796794199588, -0.05397669626164674, 0.14371435786340914, -0.019915065918392816, -0.019915065918392816, -0.0008668709476984035, 0.05320035032257742, 0.13266452167682422, 0.03759940988009765, -0.019915065918392816, 0.01893575634848135, -0.05802528478764703, 0.06231203067383315, -0.0008668709476984035, 0.042618240992040375, -0.07148443816165181, -0.04849491993695307, -0.1454480997588059, 0.054192906235329026, 0.05629154289225023, -0.09439292895852194, 0.042618240992040375, 0.07917583672583797, -0.0822125104016508, -0.049657035117130446, -0.08494998815823984, -0.017961304306998445, -0.044669493606091244, -0.0453186335185322, -0.048119755798243916, -0.016615227915837517, -0.032119414451802934, -0.0597073709706318, -0.0597073709706318, 0.0388734577018157, -0.05643672210250919, -0.031172220443027247, 0.026161801440220855, -0.015904748312238905, 0.06538251459350221, 0.12696650056218647, -0.018258613659567643, -0.03790814262804748, 0.07609417018842993, -0.09101796794199588, -0.0008668709476984035, 0.017825262064454117, -0.09439292895852194, 0.09265918706312508, -0.0763744234558435, -0.07497484310142023, -0.0008668709476984035, 0.05320035032257742, 0.12775050687439518, 0.03995512357255676, -0.040087584100979734, -0.08942026828914343, -0.03790814262804748, -0.0008668709476984035, -0.0763744234558435, -0.0008668709476984035, 0.04425356433277114, 0.03550077322317643, 0.14484494023369524, -0.05305262411826865, 0.015662431003512178, 0.09295188426995646, 0.15328380887955995, -0.027184179265071762, 0.04852588438187807, 0.08251473799135262, 0.012205210619654372, 0.039407028190241555, 0.025108615455562273, 0.05394136554729662, 0.025108615455562273, -0.0008668709476984035, -0.015255608399798073, 0.04109732815133379, -0.07082545955460887, -0.015904748312238905]], \"coords\": {}, \"name\": \"OD\"}\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(ff)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 27,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"ObjectId('6482f4a3129ec5a1a24a7342')"
|
|
]
|
|
},
|
|
"execution_count": 27,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"import json\n",
|
|
"ff = json.dumps(f) \n",
|
|
"fs.put(ff, encoding='utf-8')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"fs.put(b\"hello world\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 29,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"<gridfs.grid_file.GridOut at 0x29a4137bfa0>"
|
|
]
|
|
},
|
|
"execution_count": 29,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"c = bson.objectid.ObjectId('6482f4a3129ec5a1a24a7342')\n",
|
|
"\n",
|
|
"fs.get(c)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 30,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"<gridfs.grid_file.GridOut object at 0x0000029A41A738B0>\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(fs.get(c))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"np.sqrt(np.sum([0.061**2, 0.334**2, 0.447**2]))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "py39",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.9.12"
|
|
},
|
|
"orig_nbformat": 4
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|