analyseScript/testMongoDB.ipynb

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{
"cells": [
{
"cell_type": "code",
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"execution_count": 24,
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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",
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"execution_count": 25,
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"metadata": {},
"outputs": [],
"source": [
"mongoClient = pymongo.MongoClient()\n",
"mongoDB = mongoClient.testDB\n",
"mongoCollection = mongoDB.testCollection"
]
},
{
"cell_type": "code",
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"execution_count": 26,
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"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Task executing\n",
"\n",
"Task2 executing \n",
"\n",
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"Task done\n",
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"Task2 done\n",
"\n",
"\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": 27,
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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": 28,
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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()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Start a client for parallel computing"
]
},
{
"cell_type": "code",
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"execution_count": 29,
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"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\data\\AppData\\Roaming\\Python\\Python39\\site-packages\\distributed\\node.py:182: UserWarning: Port 8787 is already in use.\n",
"Perhaps you already have a cluster running?\n",
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"Hosting the HTTP server on port 53497 instead\n",
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" warnings.warn(\n"
]
},
{
"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",
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" <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-f81d966f-fb10-11ed-96c4-80e82ce2fa8e</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",
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" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:53497/status\" target=\"_blank\">http://127.0.0.1:53497/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",
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" <p style=\"color: #9D9D9D; margin-bottom: 0px;\">68a73a45</p>\n",
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" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
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" <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:53497/status\" target=\"_blank\">http://127.0.0.1:53497/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",
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" <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-cea99f1c-444d-4f12-bd6b-5a8725df82cd</p>\n",
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" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
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" <strong>Comm:</strong> tcp://127.0.0.1:53498\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",
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" <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:53497/status\" target=\"_blank\">http://127.0.0.1:53497/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",
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" <strong>Comm: </strong> tcp://127.0.0.1:53530\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",
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" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:53533/status\" target=\"_blank\">http://127.0.0.1:53533/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",
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" <strong>Nanny: </strong> tcp://127.0.0.1:53502\n",
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" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
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" <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-ph8qm9gw\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",
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" <strong>Comm: </strong> tcp://127.0.0.1:53527\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",
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" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:53528/status\" target=\"_blank\">http://127.0.0.1:53528/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",
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" <strong>Nanny: </strong> tcp://127.0.0.1:53503\n",
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" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
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" <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-x4snfqwt\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",
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" <strong>Comm: </strong> tcp://127.0.0.1:53539\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",
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" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:53540/status\" target=\"_blank\">http://127.0.0.1:53540/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",
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" <strong>Nanny: </strong> tcp://127.0.0.1:53504\n",
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" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
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" <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-yw4z2wi3\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",
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" <strong>Comm: </strong> tcp://127.0.0.1:53542\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",
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" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:53543/status\" target=\"_blank\">http://127.0.0.1:53543/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",
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" <strong>Nanny: </strong> tcp://127.0.0.1:53505\n",
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" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
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" <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-jpmm633i\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",
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" <strong>Comm: </strong> tcp://127.0.0.1:53531\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",
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" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:53532/status\" target=\"_blank\">http://127.0.0.1:53532/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",
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" <strong>Nanny: </strong> tcp://127.0.0.1:53506\n",
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" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
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" <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-_4j15kw6\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",
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" <strong>Comm: </strong> tcp://127.0.0.1:53536\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",
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" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:53537/status\" target=\"_blank\">http://127.0.0.1:53537/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",
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" <strong>Nanny: </strong> tcp://127.0.0.1:53507\n",
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" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
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" <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-bmbg4f7j\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": [
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"<Client: 'tcp://127.0.0.1:53498' processes=6 threads=60, memory=55.88 GiB>"
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]
},
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"execution_count": 29,
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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": 30,
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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": 31,
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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": 32,
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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()"
]
},
{
"cell_type": "code",
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"execution_count": 56,
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"metadata": {},
"outputs": [
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" Start (fileIndex, index) float64 nan nan nan nan ... nan nan nan nan\n",
" Increment (fileIndex, index) float64 nan nan nan nan ... nan nan nan nan\n",
" Unnamed: 5 (fileIndex, index) float64 nan nan nan nan ... nan nan nan nan</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-ab18782d-a842-4c21-a6b3-4f13566e3bb2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ab18782d-a842-4c21-a6b3-4f13566e3bb2' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>fileIndex</span>: 1</li><li><span class='xr-has-index'>index</span>: 1201</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c96b7ec7-482e-4441-b539-edde63b29062' class='xr-section-summary-in' type='checkbox' checked><label for='section-c96b7ec7-482e-4441-b539-edde63b29062' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>index</span></div><div class='xr-var-dims'>(index)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 1197 1198 1199 1200</div><input id='attrs-383cd32e-845a-4467-8d41-928ed8609827' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-383cd32e-845a-4467-8d41-928ed8609827' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf3f5910-78fd-4a93-9ee9-defd7ef8445d' class='xr-var-data-in' type='checkbox'><label for='data-bf3f5910-78fd-4a93-9ee9-defd7ef8445d' 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, ..., 1198, 1199, 1200], dtype=int64)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6044feb7-4f52-4887-90a7-1a68618d0360' class='xr-section-summary-in' type='checkbox' checked><label for='section-6044feb7-4f52-4887-90a7-1a68618d0360' class='xr-section-summary' >Data variables: <span>(6)</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>X</span></div><div class='xr-var-dims'>(fileIndex, index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan 0.0 1.0 ... 1.198e+03 1.199e+03</div><input id='attrs-1ae77e14-8e0f-49d7-8a0f-f4743612f057' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1ae77e14-8e0f-49d7-8a0f-f4743612f057' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a161b292-66bd-4d8b-9238-0e6ad0c4c618' class='xr-var-data-in' type='checkbox'><label for='data-a161b292-66bd-4d8b-9238-0e6ad0c4c618' 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([[ nan, 0.000e+00, 1.000e+00, ..., 1.197e+03, 1.198e+03,\n",
" 1.199e+03]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CH1</span></div><div class='xr-var-dims'>(fileIndex, index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan -0.08 -0.08 ... 3.28 -5.52 9.68</div><input id='attrs-d61ac07e-5f98-4e4f-a48b-b929c97a8143' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d61ac07e-5f98-4e4f-a48b-b929c97a8143' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4cf86e6-5eac-4b59-b0c6-07041dfface3' class='xr-var-data-in' type='checkbox'><label for='data-c4cf86e6-5eac-4b59-b0c6-07041dfface3' 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([[ nan, -0.08, -0.08, ..., 3.28, -5.52, 9.68]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>CH2</span></div><div class='xr-var-dims'>(fileIndex, index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan 0.0 0.0 ... 0.008 -0.276 -0.128</div><input id='attrs-224f50c0-1174-4ce6-9c0e-0f907c6bd5fa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-224f50c0-1174-4ce6-9c0e-0f907c6bd5fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-721707eb-b76e-4ec3-be7c-e2d944b10c63' class='xr-var-data-in' type='checkbox'><label for='data-721707eb-b76e-4ec3-be7c-e2d944b10c63' 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([[ nan, 0. , 0. , ..., 0.008, -0.276, -0.128]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Start</span></div><div class='xr-var-dims'>(fileIndex, index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-996d5f33-c693-40cc-b5af-28e8a588ba9b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-996d5f33-c693-40cc-b5af-28e8a588ba9b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c043ab07-d337-4099-994c-a9d9011214ee' class='xr-var-data-in' type='checkbox'><label for='data-c043ab07-d337-4099-994c-a9d9011214ee' 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([[nan, nan, nan, ..., nan, nan, nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Increment</span></div><div class='xr-var-dims'>(fileIndex, index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-54fc1344-e6ad-4333-bc19-f559f9dd9ced' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-54fc1344-e6ad-4333-bc19-f559f9dd9ced' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-191146ed-a804-4ebe-aadf-e796f61c5a48' class='xr-var-data-in' type='checkbox'><label for='data-191146ed-a804-4ebe-aadf-e796f61c5a48' 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([[nan, nan, nan, ..., nan, nan, nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Unnamed: 5</span></div><div class='xr-var-dims'>(fileIndex, index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-a2e77687-a64b-4ed
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],
"text/plain": [
"<xarray.Dataset>\n",
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"Dimensions: (fileIndex: 1, index: 1201)\n",
"Coordinates:\n",
" * index (index) int64 0 1 2 3 4 5 6 ... 1195 1196 1197 1198 1199 1200\n",
"Dimensions without coordinates: fileIndex\n",
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"Data variables:\n",
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" X (fileIndex, index) float64 nan 0.0 1.0 ... 1.198e+03 1.199e+03\n",
" CH1 (fileIndex, index) float64 nan -0.08 -0.08 ... 3.28 -5.52 9.68\n",
" CH2 (fileIndex, index) float64 nan 0.0 0.0 ... 0.008 -0.276 -0.128\n",
" Start (fileIndex, index) float64 nan nan nan nan ... nan nan nan nan\n",
" Increment (fileIndex, index) float64 nan nan nan nan ... nan nan nan nan\n",
" Unnamed: 5 (fileIndex, index) float64 nan nan nan nan ... nan nan nan nan"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"filePath = './NewFile.csv'\n",
"data = read_csv_file(filePath)\n",
"remove_bad_shots(data, index=0)\n",
"data = data.astype(float)\n",
"data"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x2b6f565f730>]"
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]
},
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"execution_count": 57,
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"metadata": {},
"output_type": "execute_result"
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},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
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}
],
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"source": [
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"data.isel(fileIndex=0).CH2.plot(ax=ax)\n",
"plt.xlim()\n",
"plt.xlim()\n",
"\n",
"plt.show()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# An example for one experimental run"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load the data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
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"source": [
"shotNum = \"0069\"\n",
"filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n",
"# filePath = \"//DyLabNAS/Data/Evaporative_Cooling/2023/05/12/0065/*.h5\"\n",
"filePath = './result_from_experiment/2023-04-24/0013/2023-04-24_0013_Evaporative_Cooling_08.h5'\n",
"\n",
"dataSetDict = {\n",
" dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n",
" for i in [0] # range(len(groupList))\n",
"}\n",
"\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"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Calculate an plot OD images"
]
},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
"# 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()"
]
},
{
"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",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"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",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
"params.compute().item()"
]
},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
"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",
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"execution_count": null,
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"metadata": {},
"outputs": [],
"source": [
"fitModel2 = Polylog22dModel(prefix='thermal_')\n",
"fitAnalyser2 = FitAnalyser(fitModel2, fitDim=2)\n",
"fitCurve2 = fitAnalyser2.eval(fitResult, x=np.arange(100), y=np.arange(100), dask=\"parallelized\").load()\n",
"\n",
"fitModel3 = ThomasFermi2dModel(prefix='BEC_')\n",
"fitAnalyser3 = FitAnalyser(fitModel3, fitDim=2)\n",
"fitCurve3 = fitAnalyser3.eval(fitResult, x=np.arange(100), y=np.arange(100), dask=\"parallelized\").load()"
]
},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
"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",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
"value = fitAnalyser.get_fit_full_result(fitResult)"
]
},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
"source": [
"value"
]
},
{
"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": [
"_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": [],
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"source": [
"xdb.get(_id)"
]
},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"c = bson.objectid.ObjectId('646e4919802812f029b385d7')\n",
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"c"
]
},
{
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
"xdb.get(c)"
]
},
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{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import datetime\n",
"post = {\"author\": \"Mike\",\n",
" \"data_id\": _id,\n",
" \"tags\": [\"mongodb\", \"python\", \"pymongo\"],\n",
" \"date\": datetime.datetime.utcnow()}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"posts = mongoCollection\n",
"post_id = posts.insert_one(post).inserted_id\n",
"post_id"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
" for i in posts.find({'_id': bson.objectid.ObjectId('646e45a4802812f029b385d6')}):\n",
" print(i)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import gridfs\n",
"\n",
"fs = gridfs.GridFS(mongoDB, 'xarray')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fs.put(b\"hello world\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"c = bson.objectid.ObjectId('646e4919802812f029b385d7')\n",
"\n",
"fs.get(_id)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"np.sqrt(np.sum([0.061**2, 0.334**2, 0.447**2]))"
]
},
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{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "py39",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.13"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}