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analyseScript/testMongoDB.ipynb

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debug
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{
"cells": [
{
"cell_type": "code",
debug
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"execution_count": 1,
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"metadata": {},
"outputs": [],
"source": [
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"import pymongo\n",
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"import xarray_mongodb\n",
"import bson\n",
"import datetime\n",
"\n",
"# datetime.datetime.utcnow()"
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]
},
{
"cell_type": "code",
debug
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"execution_count": 2,
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"metadata": {},
"outputs": [],
"source": [
first working version for DB
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"mongoClient = pymongo.MongoClient('mongodb://control:DyLab2021@127.0.0.1:27017/?authMechanism=DEFAULT')\n",
debug
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"mongoDB = mongoClient.testDB\n",
"mongoCollection = mongoDB.testCollection"
]
},
{
"cell_type": "code",
debug
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
"# mongoClient.admin.command(\n",
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"# 'createUser', 'control', \n",
"# pwd='DyLab2021',\n",
"# roles=[{'role': 'readWriteAnyDatabase', 'db': 'admin'}]\n",
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"# )"
]
},
{
"cell_type": "code",
debug
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"execution_count": 4,
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"metadata": {},
debug
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"outputs": [],
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"source": [
"# mongoClient.admin.command(\n",
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"# 'dropUser', 'contorl'\n",
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"# )"
]
},
{
"cell_type": "code",
debug
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"execution_count": 5,
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"metadata": {},
debug
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"outputs": [],
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"source": [
"# mongoClient.admin.command('usersInfo')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
debug
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Task executing\n",
"Task2 executing \n",
"\n",
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"\n",
debug
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"Task2 done\n",
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"\n",
debug
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"Task done\n",
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"\n"
]
}
],
"source": [
"from time import sleep\n",
"from multiprocessing.pool import ThreadPool\n",
" \n",
"# task executed in a worker thread\n",
"def task():\n",
" # report a message\n",
" print(f'Task executing\\n')\n",
" # block for a moment\n",
" sleep(1)\n",
" # report a message\n",
" print(f'Task done\\n')\n",
" \n",
"def task2():\n",
" # 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",
" \n",
"# protect the entry point\n",
"if __name__ == '__main__':\n",
" # create and configure the thread pool\n",
" pool = ThreadPool()\n",
" # issue tasks to the thread pool\n",
" pool.apply_async(task)\n",
" pool.apply_async(task2)\n",
" # close the thread pool\n",
" pool.close()\n",
" # wait for all tasks to finish\n",
" pool.join()"
]
},
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{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Import supporting package"
]
},
{
"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
"outputs": [],
"source": [
"import xarray as xr\n",
"import pandas as pd\n",
"import numpy as np\n",
"import copy\n",
"\n",
"import glob\n",
"\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",
"\n",
"from datetime import datetime\n",
"\n",
"import matplotlib.pyplot as plt\n",
"plt.rcParams['font.size'] = 18\n",
"\n",
"from DataContainer.ReadData import read_hdf5_file, read_hdf5_global, read_hdf5_run_time, 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",
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"execution_count": 8,
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"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()"
]
},
not finished
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{
"cell_type": "code",
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"execution_count": 9,
not finished
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"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",
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"execution_count": 10,
not finished
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"8.583565255602754e-06"
]
},
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"execution_count": 10,
not finished
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"5.86e-6/M"
]
},
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{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Start a client for parallel computing"
]
},
{
"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
" <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
debug
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" <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-e928dd09-0a9f-11ee-b50c-80e82ce2fa8e</p>\n",
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" <table style=\"width: 100%; text-align: left;\">\n",
"\n",
" <tr>\n",
" \n",
" <td style=\"text-align: left;\"><strong>Connection method:</strong> Cluster object</td>\n",
" <td style=\"text-align: left;\"><strong>Cluster type:</strong> distributed.LocalCluster</td>\n",
" \n",
" </tr>\n",
"\n",
" \n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
debug
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" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
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" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" \n",
"\n",
" </table>\n",
"\n",
" \n",
"\n",
" \n",
" <details>\n",
" <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Cluster Info</h3></summary>\n",
" <div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
" <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\">\n",
" </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <h3 style=\"margin-bottom: 0px; margin-top: 0px;\">LocalCluster</h3>\n",
debug
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" <p style=\"color: #9D9D9D; margin-bottom: 0px;\">f62d3f1c</p>\n",
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" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
debug
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" <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
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" </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",
debug
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" <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-2b02de43-b3ad-400f-ae4c-0914b85d2b71</p>\n",
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" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
debug
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" <strong>Comm:</strong> tcp://127.0.0.1:62759\n",
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" </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",
debug
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" <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
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" </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",
debug
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" <strong>Comm: </strong> tcp://127.0.0.1:62781\n",
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" </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",
debug
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" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:62787/status\" target=\"_blank\">http://127.0.0.1:62787/status</a>\n",
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" </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",
debug
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" <strong>Nanny: </strong> tcp://127.0.0.1:62762\n",
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" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
debug
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" <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-3gcis6xr\n",
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" </td>\n",
" </tr>\n",
"\n",
" \n",
"\n",
" \n",
"\n",
" </table>\n",
" </details>\n",
" </div>\n",
" </div>\n",
" \n",
" <div style=\"margin-bottom: 20px;\">\n",
" <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <details>\n",
" <summary>\n",
" <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 1</h4>\n",
" </summary>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
debug
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" <strong>Comm: </strong> tcp://127.0.0.1:62795\n",
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" </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",
debug
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" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:62799/status\" target=\"_blank\">http://127.0.0.1:62799/status</a>\n",
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" </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",
debug
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" <strong>Nanny: </strong> tcp://127.0.0.1:62763\n",
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" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
debug
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" <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-z8bj7z95\n",
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" </td>\n",
" </tr>\n",
"\n",
" \n",
"\n",
" \n",
"\n",
" </table>\n",
" </details>\n",
" </div>\n",
" </div>\n",
" \n",
" <div style=\"margin-bottom: 20px;\">\n",
" <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <details>\n",
" <summary>\n",
" <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 2</h4>\n",
" </summary>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
debug
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" <strong>Comm: </strong> tcp://127.0.0.1:62789\n",
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" </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",
debug
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" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:62791/status\" target=\"_blank\">http://127.0.0.1:62791/status</a>\n",
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" </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",
debug
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" <strong>Nanny: </strong> tcp://127.0.0.1:62764\n",
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" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
debug
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" <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-67f2wwr0\n",
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" </td>\n",
" </tr>\n",
"\n",
" \n",
"\n",
" \n",
"\n",
" </table>\n",
" </details>\n",
" </div>\n",
" </div>\n",
" \n",
" <div style=\"margin-bottom: 20px;\">\n",
" <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <details>\n",
" <summary>\n",
" <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 3</h4>\n",
" </summary>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
debug
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" <strong>Comm: </strong> tcp://127.0.0.1:62797\n",
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" </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",
debug
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" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:62802/status\" target=\"_blank\">http://127.0.0.1:62802/status</a>\n",
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" </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",
debug
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" <strong>Nanny: </strong> tcp://127.0.0.1:62765\n",
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" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
debug
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" <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-g84sd6u2\n",
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" </td>\n",
" </tr>\n",
"\n",
" \n",
"\n",
" \n",
"\n",
" </table>\n",
" </details>\n",
" </div>\n",
" </div>\n",
" \n",
" <div style=\"margin-bottom: 20px;\">\n",
" <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <details>\n",
" <summary>\n",
" <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 4</h4>\n",
" </summary>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
debug
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" <strong>Comm: </strong> tcp://127.0.0.1:62796\n",
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" </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",
debug
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" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:62798/status\" target=\"_blank\">http://127.0.0.1:62798/status</a>\n",
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" </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",
debug
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" <strong>Nanny: </strong> tcp://127.0.0.1:62766\n",
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" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
debug
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" <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-55nj59xw\n",
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" </td>\n",
" </tr>\n",
"\n",
" \n",
"\n",
" \n",
"\n",
" </table>\n",
" </details>\n",
" </div>\n",
" </div>\n",
" \n",
" <div style=\"margin-bottom: 20px;\">\n",
" <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <details>\n",
" <summary>\n",
" <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 5</h4>\n",
" </summary>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
debug
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" <strong>Comm: </strong> tcp://127.0.0.1:62790\n",
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" </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",
debug
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" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:62793/status\" target=\"_blank\">http://127.0.0.1:62793/status</a>\n",
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" </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",
debug
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" <strong>Nanny: </strong> tcp://127.0.0.1:62767\n",
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" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
debug
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" <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-s47mqrsf\n",
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" </td>\n",
" </tr>\n",
"\n",
" \n",
"\n",
" \n",
"\n",
" </table>\n",
" </details>\n",
" </div>\n",
" </div>\n",
" \n",
"\n",
" </details>\n",
"</div>\n",
"\n",
" </details>\n",
" </div>\n",
"</div>\n",
" </details>\n",
" \n",
"\n",
" </div>\n",
"</div>"
],
"text/plain": [
debug
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"<Client: 'tcp://127.0.0.1:62759' processes=6 threads=60, memory=55.88 GiB>"
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]
},
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"execution_count": 11,
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"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",
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"execution_count": 12,
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"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",
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"execution_count": 13,
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"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",
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"execution_count": 14,
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"metadata": {},
"outputs": [],
"source": [
"img_dir = '//DyLabNAS/Data/'\n",
"SequenceName = \"Evaporative_Cooling\" + \"/\"\n",
"folderPath = img_dir + SequenceName + '2023/05/23'# get_date()"
]
},
debug
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{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# An example for one experimental run"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load the data"
]
},
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{
"cell_type": "code",
debug
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"execution_count": 15,
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"metadata": {},
"outputs": [
{
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".xr-attrs dt:hover span {\n",
" display: inline-block;\n",
" background: var(--xr-background-color);\n",
" padding-right: 10px;\n",
"}\n",
"\n",
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" grid-column: 2;\n",
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" display: inline-block;\n",
" vertical-align: middle;\n",
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" fill: currentColor;\n",
"}\n",
"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
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"Dimensions: (compX_final_current: 11, runs: 3, y: 1200, x: 1920)\n",
"Coordinates:\n",
" * compX_final_current (compX_final_current) float64 0.0 0.002 ... 0.018 0.02\n",
" * runs (runs) float64 0.0 1.0 2.0\n",
debug
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"Dimensions without coordinates: y, x\n",
regular backup
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"Data variables:\n",
regular backup
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" atoms (compX_final_current, runs, y, x) uint16 dask.array&lt;chunksize=(9, 3, 1200, 1920), meta=np.ndarray&gt;\n",
" background (compX_final_current, runs, y, x) uint16 dask.array&lt;chunksize=(9, 3, 1200, 1920), meta=np.ndarray&gt;\n",
" dark (compX_final_current, runs, y, x) uint16 dask.array&lt;chunksize=(9, 3, 1200, 1920), meta=np.ndarray&gt;\n",
" shotNum (compX_final_current, runs) &lt;U2 dask.array&lt;chunksize=(11, 3), meta=np.ndarray&gt;\n",
" OD (compX_final_current, runs, y, x) float64 dask.array&lt;chunksize=(9, 3, 1200, 1920), meta=np.ndarray&gt;\n",
"Attributes: (12/120)\n",
" TOF_free: 0.022\n",
debug
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" abs_img_freq: 110.858\n",
" absorption_imaging_flag: True\n",
" backup_data: True\n",
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" blink_off_time: 0.001\n",
" blink_on_time: 0.001\n",
debug
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" ... ...\n",
" z_offset: 0.189\n",
" z_offset_img: 0.189\n",
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" compX_final_current: [0. 0.002 0.004 0.006 0.008 0.01 0...\n",
" runs: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1...\n",
" scanAxis: [&#x27;compX_final_current&#x27; &#x27;runs&#x27;]\n",
debug
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" scanAxisLength: [33. 33.]</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-b9ae155a-cf09-4eb8-842a-5e3d68fb091f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b9ae155a-cf09-4eb8-842a-5e3d68fb091f' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>compX_final_current</span>: 11</li><li><span class='xr-has-index'>runs</span>: 3</li><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-17d8eed4-ed1b-4e5a-bcdc-3ad6bea77ff4' class='xr-section-summary-in' type='checkbox' checked><label for='section-17d8eed4-ed1b-4e5a-bcdc-3ad6bea77ff4' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>compX_final_current</span></div><div class='xr-var-dims'>(compX_final_current)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.002 0.004 ... 0.018 0.02</div><input id='attrs-863961cf-d72e-46e9-a866-67470bc1388c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-863961cf-d72e-46e9-a866-67470bc1388c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9344fd09-ba24-41d8-90ab-485ade4d6a91' class='xr-var-data-in' type='checkbox'><label for='data-9344fd09-ba24-41d8-90ab-485ade4d6a91' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0. , 0.002, 0.004, 0.006, 0.008, 0.01 , 0.012, 0.014, 0.016, 0.018,\n",
" 0.02 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>runs</span></div><div class='xr-var-dims'>(runs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0</div><input id='attrs-907ea110-b888-4cd7-a485-3ca7076a8dc3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-907ea110-b888-4cd7-a485-3ca7076a8dc3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6b9aa47-6f8c-484f-af8b-a8cafbff10be' class='xr-var-data-in' type='checkbox'><label for='data-b6b9aa47-6f8c-484f-af8b-a8cafbff10be' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0., 1., 2.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6906b7c8-0730-4704-8b36-2414b062c545' class='xr-section-summary-in' type='checkbox' checked><label for='section-6906b7c8-0730-4704-8b36-2414b062c545' 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'>(compX_final_current, runs, y, x)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(9, 3, 1200, 1920), meta=np.ndarray&gt;</div><input id='attrs-58fe1a57-5da5-4654-9b36-4d2bb4330969' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-58fe1a57-5da5-4654-9b36-4d2bb4330969' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-add8fc41-8deb-43d6-8742-8f88232b9f88' class='xr-var-data-in' type='checkbox'><label for='data-add8fc41-8deb-43d6-8742-8f88232b9f88' 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",
debug
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" <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",
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" <td> 145.02 MiB </td>\n",
" <td> 118.65 MiB </td>\n",
debug
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" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
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" <td> (11, 3, 1200, 1920) </td>\n",
" <td> (9, 3, 1200, 1920) </td>\n",
debug
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" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
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" <td colspan=\"2\"> 2 chunks in 104 graph layers </td>\n",
debug
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" </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",
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debug
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debug
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debug
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"\n",
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debug
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debug
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>background</span></div><div class='xr-var-dims'>(compX_final_current, runs, y, x)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(9, 3, 1200, 1920), meta=np.ndarray&gt;</div><input id='attrs-4f5a1e6d-f0bf-4445-86fa-05f123bf471c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f5a1e6d-f0bf-4445-86fa-05f123bf471c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97971573-5ecd-4996-b065-4771111ac722' class='xr-var-data-in' type='checkbox'><label for='data-97971573-5ecd-4996-b065-4771111ac722' 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",
debug
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" <tr>\n",
" <td>\n",
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" <tbody>\n",
" \n",
" <tr>\n",
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regular backup
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" <td> 145.02 MiB </td>\n",
" <td> 118.65 MiB </td>\n",
debug
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" </tr>\n",
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regular backup
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" <td> (11, 3, 1200, 1920) </td>\n",
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debug
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" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
regular backup
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" <td colspan=\"2\"> 2 chunks in 104 graph layers </td>\n",
debug
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regular backup
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debug
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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'>(compX_final_current, runs, y, x)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(9, 3, 1200, 1920), meta=np.ndarray&gt;</div><input id='attrs-03aad486-7c52-4d72-88c1-26a5ce472582' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03aad486-7c52-4d72-88c1-26a5ce472582' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dfe5474a-18e1-4e4a-8e5f-30cd418675c1' class='xr-var-data-in' type='checkbox'><label for='data-dfe5474a-18e1-4e4a-8e5f-30cd418675c1' 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",
debug
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" <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",
regular backup
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" <td> 145.02 MiB </td>\n",
" <td> 118.65 MiB </td>\n",
debug
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" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
regular backup
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" <td> (11, 3, 1200, 1920) </td>\n",
" <td> (9, 3, 1200, 1920) </td>\n",
debug
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" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
regular backup
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" <td colspan=\"2\"> 2 chunks in 104 graph layers </td>\n",
debug
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" </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",
regular backup
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debug
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"\n",
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debug
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"\n",
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regular backup
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debug
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"\n",
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regular backup
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debug
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"\n",
" <!-- Text -->\n",
regular backup
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" <text x=\"92.474299\" y=\"102.474299\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,92.474299,102.474299)\">3</text>\n",
debug
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"</svg>\n",
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" </tr>\n",
debug
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>shotNum</span></div><div class='xr-var-dims'>(compX_final_current, runs)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(11, 3), meta=np.ndarray&gt;</div><input id='attrs-0fe8bb8f-ab0e-401f-a5aa-a4b4ada06ef2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0fe8bb8f-ab0e-401f-a5aa-a4b4ada06ef2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5dcb4741-d547-41e8-8edd-ccc10624b80a' class='xr-var-data-in' type='checkbox'><label for='data-5dcb4741-d547-41e8-8edd-ccc10624b80a' 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'><table>\n",
debug
2023-06-02 18:42:18 +02:00
" <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",
regular backup
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" <td> 264 B </td>\n",
" <td> 264 B </td>\n",
debug
2023-06-02 18:42:18 +02:00
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
regular backup
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" <td> (11, 3) </td>\n",
" <td> (11, 3) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> <U2 numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
" </td>\n",
" <td>\n",
" <svg width=\"93\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
"\n",
" <!-- Horizontal lines -->\n",
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"\n",
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"</svg>\n",
" </td>\n",
" </tr>\n",
debug
2023-06-14 14:54:57 +02:00
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>OD</span></div><div class='xr-var-dims'>(compX_final_current, runs, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(9, 3, 1200, 1920), meta=np.ndarray&gt;</div><input id='attrs-9b7351c0-40ea-46cb-87aa-81e82e2a9bd9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b7351c0-40ea-46cb-87aa-81e82e2a9bd9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43315b10-c41f-435d-afd2-182f7bb50ddb' class='xr-var-data-in' type='checkbox'><label for='data-43315b10-c41f-435d-afd2-182f7bb50ddb' 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",
regular backup
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" <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> 580.08 MiB </td>\n",
" <td> 474.61 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (11, 3, 1200, 1920) </td>\n",
" <td> (9, 3, 1200, 1920) </td>\n",
debug
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" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
regular backup
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" <td colspan=\"2\"> 2 chunks in 322 graph layers </td>\n",
debug
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" </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",
regular backup
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" <svg width=\"374\" height=\"139\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
debug
2023-06-02 18:42:18 +02:00
"\n",
" <!-- Horizontal lines -->\n",
regular backup
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" <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
" <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n",
debug
2023-06-02 18:42:18 +02:00
"\n",
" <!-- Vertical lines -->\n",
regular backup
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" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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debug
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"\n",
" <!-- Colored Rectangle -->\n",
regular backup
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" <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
debug
2023-06-02 18:42:18 +02:00
"\n",
" <!-- Text -->\n",
regular backup
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" <text x=\"12.706308\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >11</text>\n",
" <text x=\"45.412617\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,45.412617,12.706308)\">1</text>\n",
"\n",
"\n",
" <!-- Horizontal lines -->\n",
" <line x1=\"95\" y1=\"0\" x2=\"109\" y2=\"14\" style=\"stroke-width:2\" />\n",
" <line x1=\"95\" y1=\"75\" x2=\"109\" y2=\"89\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Vertical lines -->\n",
" <line x1=\"95\" y1=\"0\" x2=\"95\" y2=\"75\" style=\"stroke-width:2\" />\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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" <text x=\"92.474299\" y=\"102.474299\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,92.474299,102.474299)\">3</text>\n",
debug
2023-06-02 18:42:18 +02:00
"</svg>\n",
" </td>\n",
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debug
2023-06-14 14:54:57 +02:00
"</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-a63ebf49-9886-4681-ba1a-6e4718d49545' class='xr-section-summary-in' type='checkbox' ><label for='section-a63ebf49-9886-4681-ba1a-6e4718d49545' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>compX_final_current</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-67fac2f8-b2f6-4fb9-8d02-3c04ac6d24f9' class='xr-index-data-in' type='checkbox'/><label for='index-67fac2f8-b2f6-4fb9-8d02-3c04ac6d24f9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([0.0, 0.002, 0.004, 0.006, 0.008, 0.01, 0.012, 0.014, 0.016,\n",
regular backup
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" 0.018, 0.02],\n",
debug
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" dtype=&#x27;float64&#x27;, name=&#x27;compX_final_current&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>runs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fea26cbe-902d-436a-9a27-54039881eb60' class='xr-index-data-in' type='checkbox'/><label for='index-fea26cbe-902d-436a-9a27-54039881eb60' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([0.0, 1.0, 2.0], dtype=&#x27;float64&#x27;, name=&#x27;runs&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5d03d618-2e62-4ba2-9cc6-da9ff33c423f' class='xr-section-summary-in' type='checkbox' ><label for='section-5d03d618-2e62-4ba2-9cc6-da9ff33c423f' class='xr-section-summary' >Attributes: <span>(120)</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>0.022</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>0.001</dd><dt><span>blink_on_time :</span></dt><dd>0.001</dd><dt><span>c_duration :</span></dt><dd>0.2</dd><dt><span>carrier_amp :</span></dt><dd>3</dd><dt><span>carrier_freq :</span></dt><dd>0.85</dd><dt><span>carrier_offset :</span></dt><dd>0</dd><dt><span>carrier_phase :</span></dt><dd>0</dd><dt><span>channel_in_use :</span></dt><dd>1</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</dd><dt><span>compX_current_sg :</span></dt><dd>0.0</dd><dt><span>compX_initial_current :</span></dt><dd>0</dd><dt><span>compY_current :</span></dt><dd>0</dd><dt><span>compY_current_sg :</span></dt><dd>0.0</dd><dt><span>compY_final_current :</span></dt><dd>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>0.264</dd><dt><span>compZ_initial_current :</span></dt><dd>0</dd><dt><span>default_camera :</span></dt><dd>0</dd><dt><span>deltaf :</span></dt><dd>0.161</dd><dt><span>evap_1_arm_1_final_pow :</span></dt><dd>0.35</dd><dt><span>evap_1_arm_1_mod_depth_final :</span></dt><dd>0</dd><dt><span>evap_1_arm_1_mod_depth_initial :</span></dt><dd>1.0</dd><dt><span>evap_1_arm_1_mod_ramp_duration :</span></dt><dd>1.15</dd><dt><span>evap_1_arm_1_pow_ramp_duration :</span></dt><dd>1.65</dd><dt><span>evap_1_arm_1_start_pow :</span></dt><dd>7</dd><dt><span>evap_1_arm_2_final_pow :</span></dt><dd>5</dd><dt><span>evap_1_arm_2_ramp_duration :</span></dt><dd>0.5</dd><dt><span>evap_1_arm_2_start_pow :</span></dt><dd>0</dd><dt><span>evap_1_mod_ramp_trunc_value :</span></dt><dd>1</dd><dt><span>evap_1_pow_ramp_trunc_value :</span></dt><dd>1.0</dd><dt><span>evap_1_rate_constant_1 :</span></dt><dd>0.525</dd><dt><span>evap_1_rate_constant_2 :</span></dt><dd>0.51</dd><dt><span>evap_2_arm_1_final_pow :</span></dt><dd>0.037</dd><dt><span>evap_2_arm_1_start_pow :</span></dt><dd>0.35</dd><dt><span>evap_2_arm_2_final_pow :</span></dt><dd>0.09</dd><dt><span>evap_2_arm_2_start_pow :</span></dt><dd>5</dd><dt><span>evap_2_ramp_duration :</span></dt><dd>1.0</dd><dt><span>evap_2_ramp_trunc_value :</span></dt><dd>1.0</dd><dt><span>evap_2_rate_constant_1 :</span></dt><dd>0.37</dd><dt><span>evap_2_rate_constant_2 :</span></dt><dd>0.71</dd><dt><span>evap_3_arm_1_final_pow :</span></dt><dd>0.1038</dd><dt><span>evap_3_arm_1_mod_depth_final :</span></dt><dd>0.43</dd><dt><span>evap_3_arm_1_mod_depth_initial :</span></dt><dd>0</dd><dt><span>evap_3_arm_1_start_pow :</span></dt><dd>0.037</dd><dt><span>evap_3_ramp_duration :</span></dt><dd>0.1</dd><dt><span>evap_3_ramp_trunc_value :</span></dt><dd>1.0</dd><dt><span>evap_3_rate_constant_1 :</sp
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" 0.002 0.004 0.006 0.008 0.01 0.012 0.014 0.016 0.018 0.02 0. 0.002\n",
" 0.004 0.006 0.008 0.01 0.012 0.014 0.016 0.018 0.02 ]</dd><dt><span>runs :</span></dt><dd>[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 2. 2.\n",
" 2. 2. 2. 2. 2. 2. 2. 2. 2.]</dd><dt><span>scanAxis :</span></dt><dd>[&#x27;compX_final_current&#x27; &#x27;runs&#x27;]</dd><dt><span>scanAxisLength :</span></dt><dd>[33. 33.]</dd></dl></div></li></ul></div></div>"
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],
"text/plain": [
"<xarray.Dataset>\n",
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"Dimensions: (compX_final_current: 11, runs: 3, y: 1200, x: 1920)\n",
"Coordinates:\n",
" * compX_final_current (compX_final_current) float64 0.0 0.002 ... 0.018 0.02\n",
" * runs (runs) float64 0.0 1.0 2.0\n",
debug
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"Dimensions without coordinates: y, x\n",
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"Data variables:\n",
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" atoms (compX_final_current, runs, y, x) uint16 dask.array<chunksize=(9, 3, 1200, 1920), meta=np.ndarray>\n",
" background (compX_final_current, runs, y, x) uint16 dask.array<chunksize=(9, 3, 1200, 1920), meta=np.ndarray>\n",
" dark (compX_final_current, runs, y, x) uint16 dask.array<chunksize=(9, 3, 1200, 1920), meta=np.ndarray>\n",
" shotNum (compX_final_current, runs) <U2 dask.array<chunksize=(11, 3), meta=np.ndarray>\n",
" OD (compX_final_current, runs, y, x) float64 dask.array<chunksize=(9, 3, 1200, 1920), meta=np.ndarray>\n",
"Attributes: (12/120)\n",
" TOF_free: 0.022\n",
debug
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" abs_img_freq: 110.858\n",
" absorption_imaging_flag: True\n",
" backup_data: True\n",
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" blink_off_time: 0.001\n",
" blink_on_time: 0.001\n",
debug
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" ... ...\n",
" z_offset: 0.189\n",
" z_offset_img: 0.189\n",
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" compX_final_current: [0. 0.002 0.004 0.006 0.008 0.01 0...\n",
" runs: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1...\n",
" scanAxis: ['compX_final_current' 'runs']\n",
" scanAxisLength: [33. 33.]"
debug
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]
},
debug
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"execution_count": 15,
debug
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
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"shotNum = \"0069\"\n",
"filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n",
"# filePath = \"//DyLabNAS/Data/Evaporative_Cooling/2023/05/12/0065/*.h5\"\n",
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"# filePath = './result_from_experiment/2023-04-24/0013/2023-04-24_0013_Evaporative_Cooling_08.h5'\n",
debug
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"\n",
"dataSetDict = {\n",
" dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n",
" for i in [0] # range(len(groupList))\n",
"}\n",
"\n",
"dataSet = dataSetDict[\"camera_1\"]\n",
"dataSet = swap_xy(dataSet)\n",
"\n",
"scanAxis = get_scanAxis(dataSet)\n",
"\n",
"dataSet = auto_rechunk(dataSet)\n",
"\n",
"dataSet = imageAnalyser.get_absorption_images(dataSet)\n",
"\n",
"dataSet"
debug
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]
},
{
"cell_type": "code",
debug
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"execution_count": 16,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(11, 3)"
]
},
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"execution_count": 16,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataSet.shotNum.shape"
]
},
{
"cell_type": "code",
debug
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"execution_count": 17,
debug
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
debug
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"['atoms', 'background', 'dark', 'shotNum', 'OD']"
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]
},
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"execution_count": 17,
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"metadata": {},
"output_type": "execute_result"
}
],
debug
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"source": [
debug
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"list(dataSet.data_vars)"
debug
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]
},
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{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Calculate an plot OD images"
]
},
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{
"cell_type": "code",
debug
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"execution_count": 18,
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"metadata": {},
debug
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"outputs": [
{
"data": {
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"image/png": "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
"text/plain": [
"<Figure size 3400x900 with 34 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",
debug
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"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"from DataContainer.MongoDB import MongoDB\n",
"\n",
"DB = MongoDB(mongoClient, mongoDB, date='2023/05/23')\n",
"\n",
"DB.create_global('0069', dataSet)\n",
"\n",
"DB.read_global_all('0069')\n",
"\n",
"DB.add_data('0069', dataSet_cropOD, engine='xarray')"
]
},
{
"cell_type": "code",
"execution_count": 20,
first working version for DB
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'TOF_free': 0.022,\n",
" 'abs_img_freq': 110.858,\n",
" 'absorption_imaging_flag': True,\n",
" 'backup_data': True,\n",
" 'blink_off_time': 0.001,\n",
" 'blink_on_time': 0.001,\n",
" 'c_duration': 0.2,\n",
" 'carrier_amp': 3,\n",
" 'carrier_freq': 0.85,\n",
" 'carrier_offset': 0,\n",
" 'carrier_phase': 0,\n",
" 'channel_in_use': 1,\n",
" 'cmot_final_current': 0.65,\n",
" 'cmot_hold': 0.06,\n",
" 'cmot_initial_current': 0.18,\n",
" 'compX_current': 0,\n",
" 'compX_current_sg': 0.0,\n",
" 'compX_initial_current': 0,\n",
" 'compY_current': 0,\n",
" 'compY_current_sg': 0.0,\n",
" 'compY_final_current': 0,\n",
" 'compY_initial_current': 0,\n",
" 'compZ_current': 0,\n",
" 'compZ_current_sg': 0.189,\n",
" 'compZ_final_current': 0.264,\n",
" 'compZ_initial_current': 0,\n",
" 'default_camera': 0,\n",
" 'deltaf': 0.161,\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': 1.0,\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': 1.0,\n",
" 'evap_3_rate_constant_1': -0.879,\n",
" 'evap_3_rate_constant_2': -0.297,\n",
" 'final_amp': 0.00012,\n",
" 'final_freq': 104.0,\n",
" 'final_pow_1': 0.1038,\n",
" 'final_pow_2': 0.09,\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_fin': 0.43,\n",
" 'mod_depth_ini': 0,\n",
" 'mod_depth_initial': 1.0,\n",
" 'mot_3d_amp': 0.62,\n",
" 'mot_3d_camera_trigger_duration': 0.00025,\n",
" 'mot_3d_freq': 102.13,\n",
" 'mot_load_duration': 2,\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': 1.0,\n",
" 'odt_hold_time_5': 0.01,\n",
" 'operation_mode': 'SWEEP',\n",
" 'pow_arm_1': 7,\n",
" 'pow_arm_2': 0,\n",
" 'pulse_delay': 8e-05,\n",
" 'pulse_width': 0.01,\n",
" 'push_amp': 0.16,\n",
" 'push_freq': 102.66,\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",
" 'save_results': False,\n",
" 'sin_mod_amplitude': 0.00519,\n",
" 'sin_mod_dc_offset': 0.1038,\n",
" 'sin_mod_duration': nan,\n",
" 'sin_mod_freq': nan,\n",
" 'sin_mod_phase': 0.0,\n",
" 'start_pow_1': 0.037,\n",
" 'start_pow_2': 0.09,\n",
" 'stern_gerlach_duration': 0.001,\n",
" 'sweep_duration': 0.4,\n",
" 'sweep_start_freq': 0.7695,\n",
" 'sweep_stop_freq': 0.9305,\n",
" 'tf_meas_ramp_duration': 0.1,\n",
" 'wait_after_2dmot_off': 0,\n",
" 'wait_time_between_images': 0.22,\n",
" 'wavetype': 'SINE',\n",
" 'x_offset': 0.0,\n",
" 'x_offset_img': 0,\n",
" 'y_offset': 0.0,\n",
" 'y_offset_img': 0,\n",
" 'z_offset': 0.189,\n",
" 'z_offset_img': 0.189,\n",
debug
2023-06-14 14:54:57 +02:00
" 'compX_final_current': array([0. , 0.002, 0.004, 0.006, 0.008, 0.01 , 0.012, 0.014, 0.016,\n",
" 0.018, 0.02 , 0. , 0.002, 0.004, 0.006, 0.008, 0.01 , 0.012,\n",
" 0.014, 0.016, 0.018, 0.02 , 0. , 0.002, 0.004, 0.006, 0.008,\n",
" 0.01 , 0.012, 0.014, 0.016, 0.018, 0.02 ]),\n",
" 'runs': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1.,\n",
" 1., 1., 1., 1., 1., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.]),\n",
" 'scanAxis': ['compX_final_current', 'runs'],\n",
" 'scanAxisLength': array([33., 33.])}"
first working version for DB
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]
},
debug
2023-06-14 14:54:57 +02:00
"execution_count": 20,
first working version for DB
2023-06-14 09:57:06 +02:00
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"DB.read_global_all('0069')"
]
},
{
"cell_type": "code",
debug
2023-06-14 14:54:57 +02:00
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'OD': <xarray.DataArray 'OD' (y: 300, x: 300)>\n",
" array([[-0.16156055, -0.05236126, -0.06646893, ..., -0.00193041,\n",
" -0.04918329, -0.00193041],\n",
" [-0.05072057, 0.01406993, -0.00193041, ..., 0.04252135,\n",
" -0.03091795, 0.10034844],\n",
" [-0.069753 , -0.06862178, -0.12908558, ..., 0.04187221,\n",
" -0.00193041, -0.07390391],\n",
" ...,\n",
" [ 0.12868977, -0.02390931, -0.00193041, ..., 0.01487671,\n",
" 0.07562783, 0.10343011],\n",
" [ 0.09570806, 0.11247994, -0.04739278, ..., 0.01288468,\n",
" -0.04845042, 0.02981829],\n",
" [ 0.20392165, 0.07039025, -0.04955846, ..., 0.02792256,\n",
" -0.12976378, -0.03367911]])\n",
" Coordinates:\n",
" compX_final_current float64 0.002\n",
" runs float64 0.0\n",
" Dimensions without coordinates: y, x\n",
" Attributes:\n",
" IMAGE_SUBCLASS: IMAGE_GRAYSCALE\n",
" IMAGE_VERSION: 1.2\n",
" IMAGE_WHITE_IS_ZERO: 0\n",
" x_start: 810\n",
" x_end: 1110\n",
" y_end: 1025\n",
" y_start: 725\n",
" x_center: 960\n",
" y_center: 875\n",
" x_span: 300\n",
" y_span: 300}"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"res = DB.load_data_single('0069', 1, field={'OD':None})\n",
"res"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
regular backup
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"metadata": {},
"outputs": [],
"source": [
regular backup
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"from collections import OrderedDict\n",
"\n",
debug
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"import numpy as np\n",
regular backup
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"import pymongo\n",
"import xarray_mongodb\n",
"import bson\n",
"import builtins\n",
"\n",
"import xarray as xr\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 _break_dataSet(self, dataSet, scanAxis=None):\n",
" \n",
" if scanAxis is None:\n",
" scanAxis = dataSet.attrs['scanAxis'] \n",
" \n",
" dataArray = dataSet.shotNum\n",
" \n",
" stackedDataArray = dataArray.stack(_scanAxis=tuple(scanAxis)) \n",
"\n",
" return stackedDataArray\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",
debug
2023-06-14 14:54:57 +02:00
" }\n",
"\n",
" self.mongoDB['global'].delete_many(data)\n",
" \n",
" data = {\n",
" 'year': self.year,\n",
" 'month': self.month,\n",
" 'day': self.day,\n",
" 'shotNum': shotNum,\n",
regular backup
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" 'runNum': 0,\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",
" if 'scanAxis' in dataSet.attrs:\n",
" \n",
" del data['global_parameters']['scanAxis']\n",
" del data['global_parameters']['scanAxisLength']\n",
" \n",
" scanAxis = dataSet.attrs['scanAxis']\n",
" data['global_parameters'].update(\n",
" {\n",
" key:0\n",
" for key in scanAxis\n",
" }\n",
" )\n",
" \n",
" stackedDataArray = self._break_dataSet(dataSet)\n",
debug
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" try:\n",
" stackedDataArray.load()\n",
" except:\n",
" pass\n",
" \n",
regular backup
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" stackedDataArray = stackedDataArray.groupby('_scanAxis')\n",
" \n",
" for i in stackedDataArray:\n",
" \n",
" stackedDataArray_single = i[1]\n",
" \n",
" data.update(\n",
" {\n",
" 'runNum': int(stackedDataArray_single.item())\n",
" }\n",
" )\n",
" \n",
" data['global_parameters'].update(\n",
" {\n",
" key: stackedDataArray_single[key].item()\n",
" for key in scanAxis\n",
" }\n",
" )\n",
" \n",
" if '_id' in data:\n",
" del data['_id']\n",
" \n",
" self.mongoDB['global'].insert_one(data)\n",
" \n",
" else:\n",
" self.mongoDB['global'].insert_one(data)\n",
" \n",
" def _add_data_normal(self, shotNum, runNum, data):\n",
" \n",
" if runNum is None:\n",
" runNum = 0\n",
" \n",
" filter = { \n",
" 'year': self.year,\n",
" 'month': self.month,\n",
" 'day': self.day,\n",
" 'shotNum': shotNum,\n",
" 'runNum': runNum,\n",
" }\n",
" \n",
" self.mongoDB['global'].update_one(filter, {\"$set\": data}, upsert=False)\n",
" \n",
debug
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" def _add_data_xarray_dataArray(self, shotNum, dataArray, name=None, scanAxis=None):\n",
regular backup
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" \n",
" if scanAxis is None:\n",
" scanAxis = list(dataArray.coords)\n",
" \n",
" dataArray.attrs = self._convert_numpy_type(dataArray.attrs)\n",
" \n",
" stackedDataArray = dataArray.stack(_scanAxis=tuple(scanAxis))\n",
" stackedDataArray = stackedDataArray.groupby('_scanAxis')\n",
" \n",
" filter = { \n",
" 'year': self.year,\n",
" 'month': self.month,\n",
" 'day': self.day,\n",
" 'shotNum': shotNum,\n",
" }\n",
" \n",
" for i in stackedDataArray:\n",
" \n",
" stackedDataArray_single = i[1].drop('_scanAxis')\n",
" \n",
" global_parameters = {\n",
" 'global_parameters.' + key: stackedDataArray_single[key].item()\n",
" for key in scanAxis\n",
" }\n",
"\n",
" filter.update(global_parameters)\n",
" \n",
" mongoID, _ = self.xdb.put(stackedDataArray_single)\n",
" \n",
" data_label = {\n",
" dataArray.name: \n",
" {\n",
" 'name': dataArray.name,\n",
" 'mongoID': mongoID,\n",
" 'engine': 'xarray',\n",
" 'dtype': 'dataArray',\n",
" }\n",
" }\n",
" \n",
" self.mongoDB['global'].update_one(filter, {\"$set\": data_label}, upsert=False)\n",
" \n",
" def _add_data_xarray_dataSet(self, shotNum, dataSet, name, scanAxis=None):\n",
" \n",
" if scanAxis is None:\n",
" scanAxis = list(dataSet.coords)\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",
" \n",
" stackedDataSet = dataSet.stack(_scanAxis=tuple(scanAxis))\n",
" stackedDataSet = stackedDataSet.groupby('_scanAxis')\n",
" \n",
" filter = { \n",
" 'year': self.year,\n",
" 'month': self.month,\n",
" 'day': self.day,\n",
" 'shotNum': shotNum,\n",
" }\n",
" \n",
" for i in stackedDataSet:\n",
" \n",
" stackedDataSet_single = i[1].drop('_scanAxis')\n",
" \n",
" global_parameters = {\n",
" 'global_parameters.' + key: stackedDataSet_single[key].item()\n",
" for key in scanAxis\n",
" }\n",
"\n",
" filter.update(global_parameters)\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, {\"$set\": data_label}, upsert=False)\n",
" \n",
" def _add_data_additional(self, shotNum, runNum, data, name):\n",
" \n",
" if runNum is None:\n",
" runNum = 0\n",
" \n",
" filter = { \n",
" 'year': self.year,\n",
" 'month': self.month,\n",
" 'day': self.day,\n",
" 'shotNum': shotNum,\n",
" 'runNum': runNum,\n",
" }\n",
" \n",
" mongoID = self.mongoDB.additional.insert_one(data).inserted_id\n",
" \n",
" data_label = {\n",
" name: \n",
" {\n",
" 'name': name,\n",
" 'mongoID': mongoID,\n",
" 'engine': 'additional',\n",
" 'dtype': 'dict',\n",
" }\n",
" }\n",
" \n",
" self.mongoDB['global'].update_one(filter, {\"$set\": data_label}, upsert=False)\n",
" \n",
" def add_data(self, shotNum, data, runNum=None, 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, runNum=runNum, data=data)\n",
" elif engine == 'xarray':\n",
" if isinstance(data, type(xr.Dataset())):\n",
debug
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" self._add_data_xarray_dataSet(shotNum=shotNum, dataSet=data, name=name)\n",
regular backup
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" else:\n",
debug
2023-06-14 14:54:57 +02:00
" self._add_data_xarray_dataArray(shotNum=shotNum, dataArray=data, name=name)\n",
regular backup
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" elif engine == 'additional':\n",
" self._add_data_additional(shotNum=shotNum, runNum=runNum, data=data, name=name)\n",
" \n",
" def read_global_single(self, shotNum, runNum, 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",
" 'runNum': runNum,\n",
" }\n",
" \n",
" return self.mongoDB['global'].find_one(filter)\n",
" \n",
regular backup
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" def read_global_all(self, shotNum, date=None):\n",
" \n",
" from xarray.core.utils import equivalent\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",
" result = {}\n",
" dropped_attrs = OrderedDict()\n",
" \n",
debug
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" docs = self.mongoDB['global'].find(filter).sort('runNum')\n",
" docs = [doc['global_parameters'] for doc in docs]\n",
first working version for DB
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" \n",
" for doc in docs:\n",
" \n",
debug
2023-06-14 14:54:57 +02:00
" global_parameters = doc\n",
regular backup
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" \n",
" result.update(\n",
" {\n",
" key: value\n",
first working version for DB
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" for key, value in global_parameters.items()\n",
regular backup
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" if key not in result and key not in dropped_attrs.keys()\n",
" }\n",
" )\n",
first working version for DB
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" \n",
regular backup
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" result = {\n",
" key: value\n",
" for key, value in result.items()\n",
first working version for DB
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" if key not in global_parameters or equivalent(global_parameters[key], value)\n",
regular backup
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" }\n",
first working version for DB
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" \n",
regular backup
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" dropped_attrs.update(\n",
" {\n",
" key: []\n",
first working version for DB
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" for key in global_parameters if key not in result \n",
regular backup
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" }\n",
" )\n",
" \n",
first working version for DB
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" for doc in docs:\n",
" \n",
debug
2023-06-14 14:54:57 +02:00
" global_parameters = doc\n",
first working version for DB
2023-06-14 09:57:06 +02:00
" \n",
" dropped_attrs.update(\n",
" {\n",
" key: np.append(dropped_attrs[key], global_parameters[key])\n",
" for key in dropped_attrs.keys()\n",
" }\n",
" ) \n",
" \n",
" scan_attrs = OrderedDict()\n",
" scan_length = []\n",
debug
2023-06-14 14:54:57 +02:00
" \n",
first working version for DB
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" for attrs_key in dropped_attrs.keys():\n",
" flag = True\n",
" for key in scan_attrs.keys():\n",
" if equivalent(scan_attrs[key], dropped_attrs[attrs_key]):\n",
" flag = False\n",
" result.update({attrs_key: key})\n",
" break\n",
debug
2023-06-14 14:54:57 +02:00
" if flag:\n",
" scan_attrs.update({\n",
" attrs_key: dropped_attrs[attrs_key]\n",
" })\n",
" scan_length = np.append(scan_length, len(dropped_attrs[attrs_key]))\n",
first working version for DB
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"\n",
" result.update(\n",
" {\n",
" key: value\n",
" for key, value in scan_attrs.items()\n",
" }\n",
" )\n",
"\n",
" result.update(\n",
" {\n",
" \"scanAxis\": list(scan_attrs.keys()),\n",
" \"scanAxisLength\": scan_length,\n",
" }\n",
" )\n",
"\n",
" return result\n",
regular backup
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" \n",
regular backup
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" 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, runNum=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_single(shotNum=shotNum, runNum=runNum)\n",
" \n",
" if field is None:\n",
" field = globalDict\n",
" \n",
debug
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" res = {}\n",
regular backup
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" \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",
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" \n",
regular backup
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"DB = MongoDB(mongoClient, mongoDB, date='2023/05/23')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"res = DB.load_data_single(shotNum='0069', field=['test'])\n",
"res"
]
},
debug
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{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"DB.add_data('0069', dataSet_cropOD, engine='xarray')"
]
},
regular backup
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{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data = {\n",
" 'test':100\n",
"}\n",
"DB._add_data_additional(shotNum, data, name='test')"
]
},
{
"cell_type": "code",
debug
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"execution_count": null,
regular backup
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"metadata": {},
debug
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"outputs": [],
regular backup
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"source": [
"DB._add_data_xarray_dataArray(shotNum, dataSet_cropOD)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"test = DB._convert_numpy_type(dataSet.OD.attrs)\n",
"\n",
"type(test['IMAGE_WHITE_IS_ZERO'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"type(dataSet.OD.attrs['IMAGE_WHITE_IS_ZERO'])"
]
},
{
"cell_type": "code",
debug
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"execution_count": null,
regular backup
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"metadata": {},
debug
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"outputs": [],
regular backup
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"source": [
"DB._add_data_xarray_dataSet(shotNum, dataSet, 'aaa')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"xdb.put(dataSet_cropOD)"
]
},
{
"cell_type": "code",
debug
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"execution_count": null,
regular backup
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"metadata": {},
"outputs": [],
"source": [
"DB.create_global(shotNum, dataSet)"
]
},
first working version for DB
2023-06-14 09:57:06 +02:00
{
"cell_type": "code",
debug
2023-06-14 14:54:57 +02:00
"execution_count": 28,
first working version for DB
2023-06-14 09:57:06 +02:00
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'TOF_free': 0.022,\n",
" 'abs_img_freq': 110.858,\n",
" 'absorption_imaging_flag': True,\n",
" 'backup_data': True,\n",
" 'blink_off_time': 0.001,\n",
" 'blink_on_time': 0.001,\n",
" 'c_duration': 0.2,\n",
" 'carrier_amp': 3,\n",
" 'carrier_freq': 0.85,\n",
" 'carrier_offset': 0,\n",
" 'carrier_phase': 0,\n",
" 'channel_in_use': 1,\n",
" 'cmot_final_current': 0.65,\n",
" 'cmot_hold': 0.06,\n",
" 'cmot_initial_current': 0.18,\n",
" 'compX_current': 0,\n",
" 'compX_current_sg': 0.0,\n",
" 'compX_initial_current': 0,\n",
" 'compY_current': 0,\n",
" 'compY_current_sg': 0.0,\n",
" 'compY_final_current': 0,\n",
" 'compY_initial_current': 0,\n",
" 'compZ_current': 0,\n",
" 'compZ_current_sg': 0.189,\n",
" 'compZ_final_current': 0.264,\n",
" 'compZ_initial_current': 0,\n",
" 'default_camera': 0,\n",
" 'deltaf': 0.161,\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': 1.0,\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': 1.0,\n",
" 'evap_3_rate_constant_1': -0.879,\n",
" 'evap_3_rate_constant_2': -0.297,\n",
" 'final_amp': 0.00012,\n",
" 'final_freq': 104.0,\n",
" 'final_pow_1': 0.1038,\n",
" 'final_pow_2': 0.09,\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_fin': 0.43,\n",
" 'mod_depth_ini': 0,\n",
" 'mod_depth_initial': 1.0,\n",
" 'mot_3d_amp': 0.62,\n",
" 'mot_3d_camera_trigger_duration': 0.00025,\n",
" 'mot_3d_freq': 102.13,\n",
" 'mot_load_duration': 2,\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': 1.0,\n",
" 'odt_hold_time_5': 0.01,\n",
" 'operation_mode': 'SWEEP',\n",
" 'pow_arm_1': 7,\n",
" 'pow_arm_2': 0,\n",
" 'pulse_delay': 8e-05,\n",
" 'pulse_width': 0.01,\n",
" 'push_amp': 0.16,\n",
" 'push_freq': 102.66,\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",
" 'save_results': False,\n",
" 'sin_mod_amplitude': 0.00519,\n",
" 'sin_mod_dc_offset': 0.1038,\n",
" 'sin_mod_duration': nan,\n",
" 'sin_mod_freq': nan,\n",
" 'sin_mod_phase': 0.0,\n",
" 'start_pow_1': 0.037,\n",
" 'start_pow_2': 0.09,\n",
" 'stern_gerlach_duration': 0.001,\n",
" 'sweep_duration': 0.4,\n",
" 'sweep_start_freq': 0.7695,\n",
" 'sweep_stop_freq': 0.9305,\n",
" 'tf_meas_ramp_duration': 0.1,\n",
" 'wait_after_2dmot_off': 0,\n",
" 'wait_time_between_images': 0.22,\n",
" 'wavetype': 'SINE',\n",
" 'x_offset': 0.0,\n",
" 'x_offset_img': 0,\n",
" 'y_offset': 0.0,\n",
" 'y_offset_img': 0,\n",
" 'z_offset': 0.189,\n",
" 'z_offset_img': 0.189,\n",
debug
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" 'compX_final_current': array([0. , 0.002, 0.004, 0.006, 0.008, 0.01 , 0.012, 0.014, 0.016,\n",
" 0.018, 0.02 , 0. , 0.002, 0.004, 0.006, 0.008, 0.01 , 0.012,\n",
" 0.014, 0.016, 0.018, 0.02 , 0. , 0.002, 0.004, 0.006, 0.008,\n",
" 0.01 , 0.012, 0.014, 0.016, 0.018, 0.02 ]),\n",
" 'runs': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1.,\n",
" 1., 1., 1., 1., 1., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.]),\n",
" 'scanAxis': ['compX_final_current', 'runs'],\n",
" 'scanAxisLength': array([33., 33.])}"
first working version for DB
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]
},
debug
2023-06-14 14:54:57 +02:00
"execution_count": 28,
first working version for DB
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"DB.read_global_all('0069')"
]
},
regular backup
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{
"cell_type": "code",
debug
2023-06-14 14:54:57 +02:00
"execution_count": null,
regular backup
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"metadata": {},
debug
2023-06-14 14:54:57 +02:00
"outputs": [],
regular backup
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"source": [
"DB.load_data_single(shotNum='0069', runNum=0).keys()"
]
},
{
"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": null,
"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",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dataSet_cropOD"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dataSet_cropOD.attrs['name'] = 'name'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dataSet_cropOD.attrs['IMAGE_WHITE_IS_ZERO']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dataSet_cropOD"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"type(dataSet_cropOD.attrs['absorption_imaging_flag'].item())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"del dataSet_cropOD.attrs['IMAGE_WHITE_IS_ZERO']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import builtins\n",
"npTypeDict = {v: getattr(builtins, k) for k, v in np.typeDict.items() if k in vars(builtins)}\n",
"\n",
"for key in dataSet_cropOD.attrs:\n",
" typeKey = type(dataSet_cropOD.attrs[key])\n",
" if typeKey in npTypeDict:\n",
" dataSet_cropOD.attrs[key] = dataSet_cropOD.attrs[key].item()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import builtins\n",
"a = {v: getattr(builtins, k) for k, v in np.typeDict.items() if k in vars(builtins)}\n",
"a\n",
"a[type(dataSet_cropOD.attrs['absorption_imaging_flag'])]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"_id, _ = xdb.put(dataSet_cropOD)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"_id"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# _id = '646e3cbbdb91e17db4b4cbd2'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
debug
2023-06-02 18:42:18 +02:00
"source": [
"xdb.get(_id)"
]
},
{
"cell_type": "code",
not finished
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"execution_count": null,
debug
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"metadata": {},
not finished
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"outputs": [],
debug
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"source": [
"c = bson.objectid.ObjectId('646e4919802812f029b385d7')\n",
"c"
]
},
{
"cell_type": "code",
not finished
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"execution_count": null,
debug
2023-06-02 18:42:18 +02:00
"metadata": {},
not finished
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"outputs": [],
regular backup
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"source": [
"xdb.get(c)"
]
},
debug
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{
"cell_type": "code",
regular backup
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"execution_count": null,
debug
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"metadata": {},
"outputs": [],
"source": [
"import datetime\n",
"post = {\"author\": \"Mike\",\n",
" \"data_id\": _id,\n",
" \"tags\": [\"mongodb\", \"python\", \"pymongo\"],\n",
not finished
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" \"date\": datetime.datetime.utcnow(),\n",
" }\n"
debug
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]
},
{
"cell_type": "code",
regular backup
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"execution_count": null,
debug
2023-05-25 19:11:19 +02:00
"metadata": {},
regular backup
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"outputs": [],
debug
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"source": [
"posts = mongoCollection\n",
"post_id = posts.insert_one(post).inserted_id\n",
"post_id"
]
},
{
"cell_type": "code",
regular backup
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"execution_count": null,
debug
2023-05-25 19:11:19 +02:00
"metadata": {},
regular backup
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"outputs": [],
debug
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"source": [
not finished
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"posts.find_one({'_id': bson.objectid.ObjectId('648318ae129ec5a1a24a7353')})"
debug
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]
},
{
"cell_type": "code",
regular backup
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"execution_count": null,
not finished
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"metadata": {},
regular backup
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"outputs": [],
not finished
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"source": [
"for i in posts.find_one({'_id': bson.objectid.ObjectId('648318ae129ec5a1a24a7353')}):\n",
" bb = i\n",
" \n",
"bb['test']"
]
},
{
"cell_type": "code",
regular backup
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"execution_count": null,
debug
2023-05-25 19:11:19 +02:00
"metadata": {},
"outputs": [],
"source": [
"import gridfs\n",
"\n",
"fs = gridfs.GridFS(mongoDB, 'xarray')"
]
},
not finished
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{
"cell_type": "code",
regular backup
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"execution_count": null,
not finished
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"metadata": {},
regular backup
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"outputs": [],
not finished
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"source": [
"f = dataSet_cropOD.to_dict()\n",
"f"
]
},
{
"cell_type": "code",
regular backup
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"execution_count": null,
not finished
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"metadata": {},
regular backup
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"outputs": [],
not finished
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"source": [
"print(ff)"
]
},
{
"cell_type": "code",
regular backup
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"execution_count": null,
not finished
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"metadata": {},
regular backup
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"outputs": [],
not finished
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"source": [
"import json\n",
"ff = json.dumps(f) \n",
"fs.put(ff, encoding='utf-8')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
debug
2023-05-25 19:11:19 +02:00
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fs.put(b\"hello world\")"
]
},
{
"cell_type": "code",
regular backup
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"execution_count": null,
debug
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"metadata": {},
regular backup
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"outputs": [],
debug
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"source": [
not finished
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"c = bson.objectid.ObjectId('6482f4a3129ec5a1a24a7342')\n",
debug
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"\n",
not finished
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"fs.get(c)"
]
},
{
"cell_type": "code",
regular backup
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"execution_count": null,
not finished
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"metadata": {},
regular backup
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"outputs": [],
not finished
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"source": [
"print(fs.get(c))"
debug
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]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"np.sqrt(np.sum([0.061**2, 0.334**2, 0.447**2]))"
]
},
debug
2023-05-24 16:54:29 +02:00
{
"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",
not finished
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"version": "3.9.12"
debug
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},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
Reference in New Issue Copy Permalink