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{
"cells": [
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{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Import supporting package"
]
},
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{
"cell_type": "code",
debug
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"execution_count": 1,
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"metadata": {},
"outputs": [],
"source": [
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"import xarray as xr\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import copy\n",
"\n",
"import glob\n",
"\n",
"import xrft\n",
"import finufft\n",
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"\n",
not finished
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"from uncertainties import ufloat\n",
"from uncertainties import unumpy as unp\n",
"from uncertainties import umath\n",
"\n",
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"from datetime import datetime\n",
"\n",
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"import matplotlib.pyplot as plt\n",
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"plt.rcParams['font.size'] = 18\n",
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"\n",
debug
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"from DataContainer.ReadData import read_hdf5_file, read_hdf5_global, read_hdf5_run_time, read_csv_file\n",
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"from Analyser.ImagingAnalyser import ImageAnalyser\n",
"from Analyser.FitAnalyser import FitAnalyser\n",
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"from Analyser.FitAnalyser import ThomasFermi2dModel, DensityProfileBEC2dModel, Polylog22dModel\n",
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"from Analyser.FFTAnalyser import fft, ifft, fft_nutou\n",
not finished
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"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",
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"\n",
"imageAnalyser = ImageAnalyser()"
]
},
debug
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{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Import supporting package"
]
},
{
"cell_type": "code",
"execution_count": 2,
"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()"
]
},
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{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Start a client for parallel computing"
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]
},
{
"cell_type": "code",
debug
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"execution_count": 3,
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"metadata": {},
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"outputs": [
debug
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{
"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 59239 instead\n",
debug
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" warnings.warn(\n"
]
},
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{
"data": {
"text/html": [
"<div>\n",
" <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
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" <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-72432ab8-fa4d-11ed-a2d0-80e82ce2fa8e</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://127.0.0.1:59239/status\" target=\"_blank\">http://127.0.0.1:59239/status</a>\n",
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" </td>\n",
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" </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",
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" </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;\">b4f1da7c</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://127.0.0.1:59239/status\" target=\"_blank\">http://127.0.0.1:59239/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-1c447eb9-c507-4810-aea3-3679987f2f3e</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://127.0.0.1:59240\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://127.0.0.1:59239/status\" target=\"_blank\">http://127.0.0.1:59239/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://127.0.0.1:59282\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://127.0.0.1:59283/status\" target=\"_blank\">http://127.0.0.1:59283/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://127.0.0.1:59243\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-nw1je4oy\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://127.0.0.1:59268\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://127.0.0.1:59271/status\" target=\"_blank\">http://127.0.0.1:59271/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://127.0.0.1:59244\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-o2y_ipun\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://127.0.0.1:59274\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://127.0.0.1:59277/status\" target=\"_blank\">http://127.0.0.1:59277/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://127.0.0.1:59245\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-vkyen84d\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://127.0.0.1:59275\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://127.0.0.1:59280/status\" target=\"_blank\">http://127.0.0.1:59280/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://127.0.0.1:59246\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-3iaj9i2x\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://127.0.0.1:59273\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://127.0.0.1:59276/status\" target=\"_blank\">http://127.0.0.1:59276/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://127.0.0.1:59247\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-pkpso9u6\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://127.0.0.1:59267\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://127.0.0.1:59269/status\" target=\"_blank\">http://127.0.0.1:59269/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://127.0.0.1:59248\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-s2xtac3t\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://127.0.0.1:59240' processes=6 threads=60, memory=55.88 GiB>"
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]
},
debug
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"execution_count": 3,
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"metadata": {},
"output_type": "execute_result"
}
],
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"source": [
"from dask.distributed import Client\n",
"client = Client(n_workers=6, threads_per_worker=10, processes=True, memory_limit='10GB')\n",
"client"
]
},
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{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Set global path for experiment"
]
},
{
"cell_type": "code",
debug
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"execution_count": 4,
finish reading from hdf5
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"metadata": {},
"outputs": [],
"source": [
first working version
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"# filepath = \"//DyLabNAS/Data/Evaporative_Cooling/2023/05/03/0043/*.h5\"\n",
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"# filepath = \"//DyLabNAS/Data/Evaporative_Cooling/2023/04/18/0003/2023-04-18_0003_Evaporative_Cooling_000.h5\"\n",
"\n",
not finished
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"# filepath = \"//DyLabNAS/Data/Repetition_scan/2023/04/21/0002/*.h5\"\n",
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"\n",
debug
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"# filepath = r\"./testData/0002/*.h5\"\n",
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"\n",
debug and nwe functions
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"# 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",
not finished
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"# filepath = \"//DyLabNAS/Data/Evaporative_Cooling/2023/04/18/0003/*.h5\"\n",
"\n",
debug
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"# filepath = \"//DyLabNAS/Data/Evaporative_Cooling/2023/05/04/0000/*.h5\"\n",
"\n",
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"filepath = './result_from_experiment/2023-04-24/0013/2023-04-24_0013_Evaporative_Cooling_08.h5'"
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]
},
{
"cell_type": "code",
debug
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"execution_count": 5,
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"metadata": {},
The first working version
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"outputs": [],
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"source": [
finish reading from hdf5
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"groupList = [\n",
" \"images/MOT_3D_Camera/in_situ_absorption\",\n",
debug
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" \"images/ODT_1_Axis_Camera/in_situ_absorption\",\n",
" \"images/ODT_2_Axis_Camera/in_situ_absorption\",\n",
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"]\n",
"\n",
"dskey = {\n",
" \"images/MOT_3D_Camera/in_situ_absorption\": \"camera_1\",\n",
debug
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" \"images/ODT_1_Axis_Camera/in_situ_absorption\": \"camera_2\",\n",
" \"images/ODT_2_Axis_Camera/in_situ_absorption\": \"camera_3\",\n",
"}\n"
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]
},
finish reading from hdf5
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{
"cell_type": "code",
debug
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"execution_count": 6,
finish of fitting
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"metadata": {},
"outputs": [],
"source": [
debug
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"img_dir = '//DyLabNAS/Data/'\n",
"SequenceName = \"Evaporative_Cooling\" + \"/\"\n",
"folderPath = img_dir + SequenceName + '2023/05/23'# get_date()"
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]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
debug
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"# An example for one experimental run"
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]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
debug
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"## Load the data"
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]
},
{
"cell_type": "code",
debug
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"execution_count": 7,
finish reading from hdf5
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"metadata": {},
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"outputs": [
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{
"name": "stderr",
"output_type": "stream",
"text": [
"f:\\Jianshun\\analyseScript\\DataContainer\\ReadData.py:178: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
" if not key in datesetOfGlobal.scanAxis\n"
]
},
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
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"Dimensions: (y: 1200, x: 1920)\n",
debug
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"Dimensions without coordinates: y, x\n",
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"Data variables:\n",
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" atoms (y, x) uint16 dask.array&lt;chunksize=(1200, 1920), meta=np.ndarray&gt;\n",
" background (y, x) uint16 dask.array&lt;chunksize=(1200, 1920), meta=np.ndarray&gt;\n",
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" shotNum int64 0\n",
" OD (y, x) float64 dask.array&lt;chunksize=(1200, 1920), meta=np.ndarray&gt;\n",
"Attributes: (12/96)\n",
" TOF_free: 0.02\n",
add tool function
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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: nan\n",
" blink_on_time: nan\n",
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" ... ...\n",
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" y_offset: 0\n",
" y_offset_img: 0\n",
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" z_offset: 0.189\n",
" z_offset_img: 0.189\n",
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" scanAxis: []\n",
" scanAxisLength: []</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c6409470-b974-4710-947f-9c7221cd6785' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c6409470-b974-4710-947f-9c7221cd6785' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>y</span>: 1200</li><li><span>x</span>: 1920</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-83d6b4ad-9415-4693-acb9-22158ac310d7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-83d6b4ad-9415-4693-acb9-22158ac310d7' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-514c1139-1c51-492a-9bae-2578d4d55911' class='xr-section-summary-in' type='checkbox' checked><label for='section-514c1139-1c51-492a-9bae-2578d4d55911' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>atoms</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1200, 1920), meta=np.ndarray&gt;</div><input id='attrs-fbb285cd-1b5a-47d7-8cf3-38acc14f2812' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fbb285cd-1b5a-47d7-8cf3-38acc14f2812' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8960312-9826-4051-bd0a-5f234daa7481' class='xr-var-data-in' type='checkbox'><label for='data-d8960312-9826-4051-bd0a-5f234daa7481' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>IMAGE_SUBCLASS :</span></dt><dd>IMAGE_GRAYSCALE</dd><dt><span>IMAGE_VERSION :</span></dt><dd>1.2</dd><dt><span>IMAGE_WHITE_IS_ZERO :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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" <tr>\n",
" <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> 4.39 MiB </td>\n",
" <td> 4.39 MiB </td>\n",
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" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
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" <td> (1200, 1920) </td>\n",
" <td> (1200, 1920) </td>\n",
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" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
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" <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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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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" <svg width=\"170\" height=\"125\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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"\n",
" <!-- Horizontal lines -->\n",
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" <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
" <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" style=\"stroke-width:2\" />\n",
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"\n",
" <!-- Vertical lines -->\n",
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" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"75\" style=\"stroke-width:2\" />\n",
" <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"75\" style=\"stroke-width:2\" />\n",
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"\n",
" <!-- Colored Rectangle -->\n",
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" <polygon points=\"0.0,0.0 120.0,0.0 120.0,75.0 0.0,75.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
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"\n",
" <!-- Text -->\n",
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" <text x=\"60.000000\" y=\"95.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1920</text>\n",
" <text x=\"140.000000\" y=\"37.500000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,37.500000)\">1200</text>\n",
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"</svg>\n",
" </td>\n",
" </tr>\n",
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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'>(y, x)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1200, 1920), meta=np.ndarray&gt;</div><input id='attrs-3dd92625-0949-40dc-8e31-cb51b242f3f8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3dd92625-0949-40dc-8e31-cb51b242f3f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62e7efad-494f-49ec-b557-aca60a8e58f2' class='xr-var-data-in' type='checkbox'><label for='data-62e7efad-494f-49ec-b557-aca60a8e58f2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>IMAGE_SUBCLASS :</span></dt><dd>IMAGE_GRAYSCALE</dd><dt><span>IMAGE_VERSION :</span></dt><dd>1.2</dd><dt><span>IMAGE_WHITE_IS_ZERO :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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" <tr>\n",
" <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> 4.39 MiB </td>\n",
" <td> 4.39 MiB </td>\n",
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" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
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" <td> (1200, 1920) </td>\n",
" <td> (1200, 1920) </td>\n",
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" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
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" <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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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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" <svg width=\"170\" height=\"125\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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"\n",
" <!-- Horizontal lines -->\n",
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" <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
" <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" style=\"stroke-width:2\" />\n",
add tool function
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"\n",
" <!-- Vertical lines -->\n",
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" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"75\" style=\"stroke-width:2\" />\n",
" <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"75\" style=\"stroke-width:2\" />\n",
add tool function
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"\n",
" <!-- Colored Rectangle -->\n",
regular backup
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" <polygon points=\"0.0,0.0 120.0,0.0 120.0,75.0 0.0,75.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
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"\n",
" <!-- Text -->\n",
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" <text x=\"60.000000\" y=\"95.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1920</text>\n",
" <text x=\"140.000000\" y=\"37.500000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,37.500000)\">1200</text>\n",
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"</svg>\n",
" </td>\n",
" </tr>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dark</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1200, 1920), meta=np.ndarray&gt;</div><input id='attrs-c771e47d-2a6d-4dfc-a6d7-c886ccb657f0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c771e47d-2a6d-4dfc-a6d7-c886ccb657f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89b302d3-c9f7-4325-8ca3-872d30a4b2e2' class='xr-var-data-in' type='checkbox'><label for='data-89b302d3-c9f7-4325-8ca3-872d30a4b2e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>IMAGE_SUBCLASS :</span></dt><dd>IMAGE_GRAYSCALE</dd><dt><span>IMAGE_VERSION :</span></dt><dd>1.2</dd><dt><span>IMAGE_WHITE_IS_ZERO :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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" <tr>\n",
" <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> 4.39 MiB </td>\n",
" <td> 4.39 MiB </td>\n",
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" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
regular backup
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" <td> (1200, 1920) </td>\n",
" <td> (1200, 1920) </td>\n",
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" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
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" <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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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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" <svg width=\"170\" height=\"125\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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"\n",
" <!-- Horizontal lines -->\n",
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" <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
" <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" style=\"stroke-width:2\" />\n",
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"\n",
" <!-- Vertical lines -->\n",
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" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"75\" style=\"stroke-width:2\" />\n",
" <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"75\" style=\"stroke-width:2\" />\n",
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"\n",
" <!-- Colored Rectangle -->\n",
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" <polygon points=\"0.0,0.0 120.0,0.0 120.0,75.0 0.0,75.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
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"\n",
" <!-- Text -->\n",
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" <text x=\"60.000000\" y=\"95.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1920</text>\n",
" <text x=\"140.000000\" y=\"37.500000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,37.500000)\">1200</text>\n",
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"</svg>\n",
" </td>\n",
" </tr>\n",
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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'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-fb0f62b1-bd1f-4126-9fa1-816e88474a57' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fb0f62b1-bd1f-4126-9fa1-816e88474a57' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4be39c24-e4d6-4de1-bb57-23a178a9aa56' class='xr-var-data-in' type='checkbox'><label for='data-4be39c24-e4d6-4de1-bb57-23a178a9aa56' 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, dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>OD</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1200, 1920), meta=np.ndarray&gt;</div><input id='attrs-37f08855-30e7-4226-b6a5-93d452d4743f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-37f08855-30e7-4226-b6a5-93d452d4743f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32e72f21-edc9-45dd-a58e-9ff5e858c05e' class='xr-var-data-in' type='checkbox'><label for='data-32e72f21-edc9-45dd-a58e-9ff5e858c05e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>IMAGE_SUBCLASS :</span></dt><dd>IMAGE_GRAYSCALE</dd><dt><span>IMAGE_VERSION :</span></dt><dd>1.2</dd><dt><span>IMAGE_WHITE_IS_ZERO :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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" <tr>\n",
" <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> 17.58 MiB </td>\n",
" <td> 17.58 MiB </td>\n",
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" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
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" <td> (1200, 1920) </td>\n",
" <td> (1200, 1920) </td>\n",
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" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
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" <td colspan=\"2\"> 1 chunks in 16 graph layers </td>\n",
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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=\"170\" height=\"125\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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"\n",
" <!-- Horizontal lines -->\n",
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" <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
" <line x1=\"0\" y1=\"75\" x2=\"120\" y2=\"75\" style=\"stroke-width:2\" />\n",
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"\n",
" <!-- Vertical lines -->\n",
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" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"75\" style=\"stroke-width:2\" />\n",
" <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"75\" style=\"stroke-width:2\" />\n",
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"\n",
" <!-- Colored Rectangle -->\n",
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" <polygon points=\"0.0,0.0 120.0,0.0 120.0,75.0 0.0,75.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
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"\n",
" <!-- Text -->\n",
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" <text x=\"60.000000\" y=\"95.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1920</text>\n",
" <text x=\"140.000000\" y=\"37.500000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,37.500000)\">1200</text>\n",
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"</svg>\n",
" </td>\n",
" </tr>\n",
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"</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-0af79019-0843-4663-9a2e-6cd3a9028e04' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0af79019-0843-4663-9a2e-6cd3a9028e04' class='xr-section-summary' title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-9db6a54c-573c-47c9-b6a3-c845a2466e40' class='xr-section-summary-in' type='checkbox' ><label for='section-9db6a54c-573c-47c9-b6a3-c845a2466e40' class='xr-section-summary' >Attributes: <span>(96)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>TOF_free :</span></dt><dd>0.02</dd><dt><span>abs_img_freq :</span></dt><dd>110.858</dd><dt><span>absorption_imaging_flag :</span></dt><dd>True</dd><dt><span>backup_data :</span></dt><dd>True</dd><dt><span>blink_off_time :</span></dt><dd>nan</dd><dt><span>blink_on_time :</span></dt><dd>nan</dd><dt><span>c_duration :</span></dt><dd>0.2</dd><dt><span>cmot_final_current :</span></dt><dd>0.65</dd><dt><span>cmot_hold :</span></dt><dd>0.06</dd><dt><span>cmot_initial_current :</span></dt><dd>0.18</dd><dt><span>compX_current :</span></dt><dd>0.005</dd><dt><span>compX_current_sg :</span></dt><dd>0</dd><dt><span>compX_final_current :</span></dt><dd>0.005</dd><dt><span>compX_initial_current :</span></dt><dd>0.005</dd><dt><span>compY_current :</span></dt><dd>0</dd><dt><span>compY_current_sg :</span></dt><dd>0</dd><dt><span>compY_final_current :</span></dt><dd>0.0</dd><dt><span>compY_initial_current :</span></dt><dd>0</dd><dt><span>compZ_current :</span></dt><dd>0</dd><dt><span>compZ_current_sg :</span></dt><dd>0.189</dd><dt><span>compZ_final_current :</span></dt><dd>0.2812</dd><dt><span>compZ_initial_current :</span></dt><dd>0</dd><dt><span>default_camera :</span></dt><dd>0</dd><dt><span>evap_1_arm_1_final_pow :</span></dt><dd>0.35</dd><dt><span>evap_1_arm_1_mod_depth_final :</span></dt><dd>0</dd><dt><span>evap_1_arm_1_mod_depth_initial :</span></dt><dd>1.0</dd><dt><span>evap_1_arm_1_mod_ramp_duration :</span></dt><dd>1.15</dd><dt><span>evap_1_arm_1_pow_ramp_duration :</span></dt><dd>1.65</dd><dt><span>evap_1_arm_1_start_pow :</span></dt><dd>7</dd><dt><span>evap_1_arm_2_final_pow :</span></dt><dd>5</dd><dt><span>evap_1_arm_2_ramp_duration :</span></dt><dd>0.5</dd><dt><span>evap_1_arm_2_start_pow :</span></dt><dd>0</dd><dt><span>evap_1_mod_ramp_trunc_value :</span></dt><dd>1</dd><dt><span>evap_1_pow_ramp_trunc_value :</span></dt><dd>1.0</dd><dt><span>evap_1_rate_constant_1 :</span></dt><dd>0.525</dd><dt><span>evap_1_rate_constant_2 :</span></dt><dd>0.51</dd><dt><span>evap_2_arm_1_final_pow :</span></dt><dd>0.037</dd><dt><span>evap_2_arm_1_start_pow :</span></dt><dd>0.35</dd><dt><span>evap_2_arm_2_final_pow :</span></dt><dd>0.09</dd><dt><span>evap_2_arm_2_start_pow :</span></dt><dd>5</dd><dt><span>evap_2_ramp_duration :</span></dt><dd>1.0</dd><dt><span>evap_2_ramp_trunc_value :</span></dt><dd>0.7</dd><dt><span>evap_2_rate_constant_1 :</span></dt><dd>0.37</dd><dt><span>evap_2_rate_constant_2 :</span></dt><dd>0.71</dd><dt><span>evap_3_arm_1_final_pow :</span></dt><dd>0.1038</dd><dt><span>evap_3_arm_1_mod_depth_final :</span></dt><dd>0.43</dd><dt><span>evap_3_arm_1_mod_depth_initial :</span></dt><dd>0</dd><dt><span>evap_3_arm_1_start_pow :</span></dt><dd>0.037</dd><dt><span>evap_3_ramp_duration :</span></dt><dd>0.1</dd><dt><span>evap_3_ramp_trunc_value :</span></dt><dd>1</dd><dt><span>evap_3_rate_constant_1 :</span></dt><dd>-0.879</dd><dt><span>evap_3_rate_constant_2 :</span></dt><dd>-0.297</dd><dt><span>final_amp :</span></dt><dd>8e-05</dd><dt><span>final_freq :</span></dt><dd>104.0</dd><dt><span>gradCoil_current :</span></dt><dd>0.18</dd><dt><span>gradCoil_current_sg :</span></dt><dd>0</dd><dt><span>imaging_method :</span></dt><dd>in_situ_absorption</dd><dt><span>imaging_pulse_duration :</span></dt><dd>2.5e-05</dd><dt><span>imaging_wav
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],
"text/plain": [
"<xarray.Dataset>\n",
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"Dimensions: (y: 1200, x: 1920)\n",
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"Dimensions without coordinates: y, x\n",
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"Data variables:\n",
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" atoms (y, x) uint16 dask.array<chunksize=(1200, 1920), meta=np.ndarray>\n",
" background (y, x) uint16 dask.array<chunksize=(1200, 1920), meta=np.ndarray>\n",
" dark (y, x) uint16 dask.array<chunksize=(1200, 1920), meta=np.ndarray>\n",
" shotNum int64 0\n",
" OD (y, x) float64 dask.array<chunksize=(1200, 1920), meta=np.ndarray>\n",
"Attributes: (12/96)\n",
" TOF_free: 0.02\n",
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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: nan\n",
" blink_on_time: nan\n",
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" ... ...\n",
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" y_offset: 0\n",
" y_offset_img: 0\n",
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" z_offset: 0.189\n",
" z_offset_img: 0.189\n",
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" scanAxis: []\n",
" scanAxisLength: []"
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]
},
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"execution_count": 7,
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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",
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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",
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"dataSet = imageAnalyser.get_absorption_images(dataSet)\n",
"\n",
"dataSet"
]
},
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{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
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"## Calculate an plot OD images"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
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"image/png": "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"text/plain": [
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"<Figure size 640x480 with 2 Axes>"
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
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"# 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",
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"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"
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]
},
{
"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"f:\\Jianshun\\analyseScript\\Analyser\\FitAnalyser.py:84: RuntimeWarning: invalid value encountered in power\n",
" res = (1- ((x-centerx)/(sigmax))**2 - ((y-centery)/(sigmay))**2)**(3 / 2)\n"
]
}
],
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"source": [
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"from Analyser.FitAnalyser import ThomasFermi2dModel, DensityProfileBEC2dModel, polylog2_2d\n",
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"\n",
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"fitModel = DensityProfileBEC2dModel()\n",
"# fitModel = ThomasFermi2dModel()\n",
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"\n",
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"fitAnalyser = FitAnalyser(fitModel, fitDim=2)\n",
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"\n",
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"# fitAnalyser = FitAnalyser(\"Gaussian-2D\", fitDim=2)\n",
"\n",
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"# dataSet_cropOD = dataSet_cropOD.chunk((1,1,100,100))\n",
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"\n",
"params = fitAnalyser.guess(dataSet_cropOD, guess_kwargs=dict(pureBECThreshold=0.3), dask=\"parallelized\")\n",
"fitResult = fitAnalyser.fit(dataSet_cropOD, params).load()"
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]
},
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{
"cell_type": "code",
The first working version
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"execution_count": 10,
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"metadata": {},
"outputs": [
{
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"data": {
"text/html": [
"<table><tr><th> name </th><th> value </th><th> initial value </th><th> min </th><th> max </th><th> vary </th><th> expression </th></tr><tr><td> BEC_amplitude </td><td> 0.00000000 </td><td> None </td><td> 0.00000000 </td><td> inf </td><td> True </td><td> </td></tr><tr><td> thermal_amplitude </td><td> 3073.52821 </td><td> None </td><td> 0.00000000 </td><td> inf </td><td> True </td><td> </td></tr><tr><td> BEC_centerx </td><td> 146.943010 </td><td> None </td><td> -inf </td><td> inf </td><td> True </td><td> </td></tr><tr><td> BEC_centery </td><td> 147.472246 </td><td> None </td><td> -inf </td><td> inf </td><td> True </td><td> </td></tr><tr><td> thermal_centerx </td><td> 120.557038 </td><td> None </td><td> -inf </td><td> inf </td><td> True </td><td> </td></tr><tr><td> thermal_centery </td><td> 179.364624 </td><td> None </td><td> -inf </td><td> inf </td><td> True </td><td> </td></tr><tr><td> BEC_sigmax </td><td> 17.1554887 </td><td> None </td><td> 0.00000000 </td><td> inf </td><td> True </td><td> </td></tr><tr><td> BEC_sigmay </td><td> 18.3156015 </td><td> None </td><td> 0.00000000 </td><td> inf </td><td> True </td><td> </td></tr><tr><td> thermal_sigmax </td><td> 71.8465440 </td><td> None </td><td> 0.00000000 </td><td> inf </td><td> True </td><td> </td></tr><tr><td> thermal_sigmay </td><td> 86.2158528 </td><td> None </td><td> -inf </td><td> inf </td><td> False </td><td> thermalAspectRatio * thermal_sigmax </td></tr><tr><td> thermalAspectRatio </td><td> 1.20000000 </td><td> None </td><td> 0.80000000 </td><td> 1.20000000 </td><td> True </td><td> </td></tr><tr><td> condensate_fraction </td><td> 0.00000000 </td><td> None </td><td> -inf </td><td> inf </td><td> False </td><td> BEC_amplitude / (BEC_amplitude + thermal_amplitude) </td></tr></table>"
],
"text/plain": [
"Parameters([('BEC_amplitude', <Parameter 'BEC_amplitude', value=0, bounds=[0:inf]>), ('thermal_amplitude', <Parameter 'thermal_amplitude', value=3073.528205527723, bounds=[0:inf]>), ('BEC_centerx', <Parameter 'BEC_centerx', value=146.94301032591366, bounds=[-inf:inf]>), ('BEC_centery', <Parameter 'BEC_centery', value=147.47224593536436, bounds=[-inf:inf]>), ('thermal_centerx', <Parameter 'thermal_centerx', value=120.55703835420424, bounds=[-inf:inf]>), ('thermal_centery', <Parameter 'thermal_centery', value=179.3646237177809, bounds=[-inf:inf]>), ('BEC_sigmax', <Parameter 'BEC_sigmax', value=17.155488681677085, bounds=[0:inf]>), ('BEC_sigmay', <Parameter 'BEC_sigmay', value=18.315601451967396, bounds=[0:inf]>), ('thermal_sigmax', <Parameter 'thermal_sigmax', value=71.84654400127174, bounds=[0:inf]>), ('thermal_sigmay', <Parameter 'thermal_sigmay', value=86.21585280152608, bounds=[-inf:inf], expr='thermalAspectRatio * thermal_sigmax'>), ('thermalAspectRatio', <Parameter 'thermalAspectRatio', value=1.2, bounds=[0.8:1.2]>), ('condensate_fraction', <Parameter 'condensate_fraction', value=0.0, bounds=[-inf:inf], expr='BEC_amplitude / (BEC_amplitude + thermal_amplitude)'>)])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"params.compute().item()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.QuadMesh at 0x1bdd890dfa0>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
add tool function
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}
],
finish of fitting
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"source": [
regular backup
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"fitCurve = fitAnalyser.eval(fitResult, x=np.arange(300), y=np.arange(300), dask=\"parallelized\").load()\n",
debug
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"\n",
"fitCurve.plot.pcolormesh(cmap='jet', vmin=0, col=scanAxis[0], row=scanAxis[1])"
finish of fitting
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]
},
{
debug
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"cell_type": "code",
regular backup
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"execution_count": 12,
finish of fitting
2023-05-04 13:47:33 +02:00
"metadata": {},
debug
2023-05-24 16:54:29 +02:00
"outputs": [],
finish of fitting
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"source": [
debug
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"fitModel2 = Polylog22dModel(prefix='thermal_')\n",
"fitAnalyser2 = FitAnalyser(fitModel2, fitDim=2)\n",
regular backup
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"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()"
finish of fitting
2023-05-04 13:47:33 +02:00
]
},
{
"cell_type": "code",
regular backup
2023-05-24 19:59:04 +02:00
"execution_count": 13,
finish of fitting
2023-05-04 13:47:33 +02:00
"metadata": {},
regular backup
2023-05-24 19:59:04 +02:00
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
finish of fitting
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"source": [
regular backup
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"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()"
finish of fitting
2023-05-04 13:47:33 +02:00
]
},
{
"cell_type": "code",
regular backup
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"execution_count": 14,
finish of fitting
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"metadata": {},
regular backup
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"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\data\\AppData\\Roaming\\Python\\Python39\\site-packages\\numpy\\lib\\function_base.py:2246: RuntimeWarning: invalid value encountered in _get_fit_full_result_single (vectorized)\n",
" outputs = ufunc(*inputs)\n"
]
}
],
finish reading from hdf5
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"source": [
regular backup
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"value = fitAnalyser.get_fit_full_result(fitResult)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
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"\n",
".xr-attrs dt:hover span {\n",
" display: inline-block;\n",
" background: var(--xr-background-color);\n",
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"\n",
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"\n",
".xr-icon-database,\n",
".xr-icon-file-text2,\n",
".xr-no-icon {\n",
" display: inline-block;\n",
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: ()\n",
"Data variables:\n",
" BEC_amplitude object 0.0+/-nan\n",
" thermal_amplitude object 3066.9094836204504+/-nan\n",
" BEC_centerx object 146.94301032591366+/-nan\n",
" BEC_centery object 147.47224593536436+/-nan\n",
" thermal_centerx object 146.23870549779576+/-nan\n",
" thermal_centery object 148.7788885260284+/-nan\n",
" BEC_sigmax object 17.155488681677085+/-nan\n",
" BEC_sigmay object 18.315601451967396+/-nan\n",
" thermal_sigmax object 54.3744707945104+/-nan\n",
" thermal_sigmay object 65.24936495341248+/-nan\n",
" thermalAspectRatio object 1.2+/-nan\n",
" condensate_fraction object 0.0+/-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-778068f2-9916-45b1-88c5-daebb1468087' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-778068f2-9916-45b1-88c5-daebb1468087' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e9afc879-eb5f-4b18-b43f-e02b503a70da' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e9afc879-eb5f-4b18-b43f-e02b503a70da' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-95ca6adb-bf26-4d9f-9f6d-7a09056f366d' class='xr-section-summary-in' type='checkbox' checked><label for='section-95ca6adb-bf26-4d9f-9f6d-7a09056f366d' class='xr-section-summary' >Data variables: <span>(12)</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>BEC_amplitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>0.0+/-nan</div><input id='attrs-6c7a6cca-6c03-4e0f-8256-d2aa49b32534' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6c7a6cca-6c03-4e0f-8256-d2aa49b32534' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2326020-1d64-492a-8fcd-84df392942a4' class='xr-var-data-in' type='checkbox'><label for='data-f2326020-1d64-492a-8fcd-84df392942a4' 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+/-nan, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thermal_amplitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>3066.9094836204504+/-nan</div><input id='attrs-7a553c97-7292-405d-8f12-0fc16ed06209' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7a553c97-7292-405d-8f12-0fc16ed06209' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c2d7a07-027a-4b41-bf82-bfa4018d5acf' class='xr-var-data-in' type='checkbox'><label for='data-6c2d7a07-027a-4b41-bf82-bfa4018d5acf' 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(3066.9094836204504+/-nan, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>BEC_centerx</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>146.94301032591366+/-nan</div><input id='attrs-e3a1da0b-6e8b-4623-a1e5-e1a81819ab2e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e3a1da0b-6e8b-4623-a1e5-e1a81819ab2e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26391797-36df-49da-8213-8262d45bdfbb' class='xr-var-data-in' type='checkbox'><label for='data-26391797-36df-49da-8213-8262d45bdfbb' 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(146.94301032591366+/-nan, dtype=object)</pre></div></li><li class='xr-var-item'><div clas
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: ()\n",
"Data variables:\n",
" BEC_amplitude object 0.0+/-nan\n",
" thermal_amplitude object 3066.9094836204504+/-nan\n",
" BEC_centerx object 146.94301032591366+/-nan\n",
" BEC_centery object 147.47224593536436+/-nan\n",
" thermal_centerx object 146.23870549779576+/-nan\n",
" thermal_centery object 148.7788885260284+/-nan\n",
" BEC_sigmax object 17.155488681677085+/-nan\n",
" BEC_sigmay object 18.315601451967396+/-nan\n",
" thermal_sigmax object 54.3744707945104+/-nan\n",
" thermal_sigmay object 65.24936495341248+/-nan\n",
" thermalAspectRatio object 1.2+/-nan\n",
" condensate_fraction object 0.0+/-nan"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"value"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "unable to infer dtype on variable 'OD'; xarray cannot serialize arbitrary Python objects",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32mf:\\Jianshun\\analyseScript\\test.ipynb Cell 25\u001b[0m in \u001b[0;36m1\n\u001b[1;32m----> <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/test.ipynb#Y216sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m fitResult\u001b[39m.\u001b[39;49mto_netcdf(\u001b[39m\"\u001b[39;49m\u001b[39msaved_on_disk.nc\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n",
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\core\\dataarray.py:3959\u001b[0m, in \u001b[0;36mDataArray.to_netcdf\u001b[1;34m(self, path, mode, format, group, engine, encoding, unlimited_dims, compute, invalid_netcdf)\u001b[0m\n\u001b[0;32m 3955\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 3956\u001b[0m \u001b[39m# No problems with the name - so we're fine!\u001b[39;00m\n\u001b[0;32m 3957\u001b[0m dataset \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mto_dataset()\n\u001b[1;32m-> 3959\u001b[0m \u001b[39mreturn\u001b[39;00m to_netcdf( \u001b[39m# type: ignore # mypy cannot resolve the overloads:(\u001b[39;49;00m\n\u001b[0;32m 3960\u001b[0m dataset,\n\u001b[0;32m 3961\u001b[0m path,\n\u001b[0;32m 3962\u001b[0m mode\u001b[39m=\u001b[39;49mmode,\n\u001b[0;32m 3963\u001b[0m \u001b[39mformat\u001b[39;49m\u001b[39m=\u001b[39;49m\u001b[39mformat\u001b[39;49m,\n\u001b[0;32m 3964\u001b[0m group\u001b[39m=\u001b[39;49mgroup,\n\u001b[0;32m 3965\u001b[0m engine\u001b[39m=\u001b[39;49mengine,\n\u001b[0;32m 3966\u001b[0m encoding\u001b[39m=\u001b[39;49mencoding,\n\u001b[0;32m 3967\u001b[0m unlimited_dims\u001b[39m=\u001b[39;49munlimited_dims,\n\u001b[0;32m 3968\u001b[0m compute\u001b[39m=\u001b[39;49mcompute,\n\u001b[0;32m 3969\u001b[0m multifile\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[0;32m 3970\u001b[0m invalid_netcdf\u001b[39m=\u001b[39;49minvalid_netcdf,\n\u001b[0;32m 3971\u001b[0m )\n",
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\backends\\api.py:1216\u001b[0m, in \u001b[0;36mto_netcdf\u001b[1;34m(dataset, path_or_file, mode, format, group, engine, encoding, unlimited_dims, compute, multifile, invalid_netcdf)\u001b[0m\n\u001b[0;32m 1211\u001b[0m \u001b[39m# TODO: figure out how to refactor this logic (here and in save_mfdataset)\u001b[39;00m\n\u001b[0;32m 1212\u001b[0m \u001b[39m# to avoid this mess of conditionals\u001b[39;00m\n\u001b[0;32m 1213\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m 1214\u001b[0m \u001b[39m# TODO: allow this work (setting up the file for writing array data)\u001b[39;00m\n\u001b[0;32m 1215\u001b[0m \u001b[39m# to be parallelized with dask\u001b[39;00m\n\u001b[1;32m-> 1216\u001b[0m dump_to_store(\n\u001b[0;32m 1217\u001b[0m dataset, store, writer, encoding\u001b[39m=\u001b[39;49mencoding, unlimited_dims\u001b[39m=\u001b[39;49munlimited_dims\n\u001b[0;32m 1218\u001b[0m )\n\u001b[0;32m 1219\u001b[0m \u001b[39mif\u001b[39;00m autoclose:\n\u001b[0;32m 1220\u001b[0m store\u001b[39m.\u001b[39mclose()\n",
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\backends\\api.py:1263\u001b[0m, in \u001b[0;36mdump_to_store\u001b[1;34m(dataset, store, writer, encoder, encoding, unlimited_dims)\u001b[0m\n\u001b[0;32m 1260\u001b[0m \u001b[39mif\u001b[39;00m encoder:\n\u001b[0;32m 1261\u001b[0m variables, attrs \u001b[39m=\u001b[39m encoder(variables, attrs)\n\u001b[1;32m-> 1263\u001b[0m store\u001b[39m.\u001b[39;49mstore(variables, attrs, check_encoding, writer, unlimited_dims\u001b[39m=\u001b[39;49munlimited_dims)\n",
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\backends\\common.py:269\u001b[0m, in \u001b[0;36mAbstractWritableDataStore.store\u001b[1;34m(self, variables, attributes, check_encoding_set, writer, unlimited_dims)\u001b[0m\n\u001b[0;32m 266\u001b[0m \u001b[39mif\u001b[39;00m writer \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 267\u001b[0m writer \u001b[39m=\u001b[39m ArrayWriter()\n\u001b[1;32m--> 269\u001b[0m variables, attributes \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mencode(variables, attributes)\n\u001b[0;32m 271\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mset_attributes(attributes)\n\u001b[0;32m 272\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mset_dimensions(variables, unlimited_dims\u001b[39m=\u001b[39munlimited_dims)\n",
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\backends\\common.py:358\u001b[0m, in \u001b[0;36mWritableCFDataStore.encode\u001b[1;34m(self, variables, attributes)\u001b[0m\n\u001b[0;32m 355\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mencode\u001b[39m(\u001b[39mself\u001b[39m, variables, attributes):\n\u001b[0;32m 356\u001b[0m \u001b[39m# All NetCDF files get CF encoded by default, without this attempting\u001b[39;00m\n\u001b[0;32m 357\u001b[0m \u001b[39m# to write times, for example, would fail.\u001b[39;00m\n\u001b[1;32m--> 358\u001b[0m variables, attributes \u001b[39m=\u001b[39m cf_encoder(variables, attributes)\n\u001b[0;32m 359\u001b[0m variables \u001b[39m=\u001b[39m {k: \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mencode_variable(v) \u001b[39mfor\u001b[39;00m k, v \u001b[39min\u001b[39;00m variables\u001b[39m.\u001b[39mitems()}\n\u001b[0;32m 360\u001b[0m attributes \u001b[39m=\u001b[39m {k: \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mencode_attribute(v) \u001b[39mfor\u001b[39;00m k, v \u001b[39min\u001b[39;00m attributes\u001b[39m.\u001b[39mitems()}\n",
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\conventions.py:775\u001b[0m, in \u001b[0;36mcf_encoder\u001b[1;34m(variables, attributes)\u001b[0m\n\u001b[0;32m 772\u001b[0m \u001b[39m# add encoding for time bounds variables if present.\u001b[39;00m\n\u001b[0;32m 773\u001b[0m _update_bounds_encoding(variables)\n\u001b[1;32m--> 775\u001b[0m new_vars \u001b[39m=\u001b[39m {k: encode_cf_variable(v, name\u001b[39m=\u001b[39mk) \u001b[39mfor\u001b[39;00m k, v \u001b[39min\u001b[39;00m variables\u001b[39m.\u001b[39mitems()}\n\u001b[0;32m 777\u001b[0m \u001b[39m# Remove attrs from bounds variables (issue #2921)\u001b[39;00m\n\u001b[0;32m 778\u001b[0m \u001b[39mfor\u001b[39;00m var \u001b[39min\u001b[39;00m new_vars\u001b[39m.\u001b[39mvalues():\n",
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\conventions.py:775\u001b[0m, in \u001b[0;36m<dictcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m 772\u001b[0m \u001b[39m# add encoding for time bounds variables if present.\u001b[39;00m\n\u001b[0;32m 773\u001b[0m _update_bounds_encoding(variables)\n\u001b[1;32m--> 775\u001b[0m new_vars \u001b[39m=\u001b[39m {k: encode_cf_variable(v, name\u001b[39m=\u001b[39;49mk) \u001b[39mfor\u001b[39;00m k, v \u001b[39min\u001b[39;00m variables\u001b[39m.\u001b[39mitems()}\n\u001b[0;32m 777\u001b[0m \u001b[39m# Remove attrs from bounds variables (issue #2921)\u001b[39;00m\n\u001b[0;32m 778\u001b[0m \u001b[39mfor\u001b[39;00m var \u001b[39min\u001b[39;00m new_vars\u001b[39m.\u001b[39mvalues():\n",
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\conventions.py:189\u001b[0m, in \u001b[0;36mencode_cf_variable\u001b[1;34m(var, needs_copy, name)\u001b[0m\n\u001b[0;32m 186\u001b[0m var \u001b[39m=\u001b[39m coder\u001b[39m.\u001b[39mencode(var, name\u001b[39m=\u001b[39mname)\n\u001b[0;32m 188\u001b[0m \u001b[39m# TODO(kmuehlbauer): check if ensure_dtype_not_object can be moved to backends:\u001b[39;00m\n\u001b[1;32m--> 189\u001b[0m var \u001b[39m=\u001b[39m ensure_dtype_not_object(var, name\u001b[39m=\u001b[39;49mname)\n\u001b[0;32m 191\u001b[0m \u001b[39mfor\u001b[39;00m attr_name \u001b[39min\u001b[39;00m CF_RELATED_DATA:\n\u001b[0;32m 192\u001b[0m pop_to(var\u001b[39m.\u001b[39mencoding, var\u001b[39m.\u001b[39mattrs, attr_name)\n",
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\conventions.py:145\u001b[0m, in \u001b[0;36mensure_dtype_not_object\u001b[1;34m(var, name)\u001b[0m\n\u001b[0;32m 143\u001b[0m data[missing] \u001b[39m=\u001b[39m fill_value\n\u001b[0;32m 144\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> 145\u001b[0m data \u001b[39m=\u001b[39m _copy_with_dtype(data, dtype\u001b[39m=\u001b[39m_infer_dtype(data, name))\n\u001b[0;32m 147\u001b[0m \u001b[39massert\u001b[39;00m data\u001b[39m.\u001b[39mdtype\u001b[39m.\u001b[39mkind \u001b[39m!=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mO\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mor\u001b[39;00m data\u001b[39m.\u001b[39mdtype\u001b[39m.\u001b[39mmetadata\n\u001b[0;32m 148\u001b[0m var \u001b[39m=\u001b[39m Variable(dims, data, attrs, encoding, fastpath\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n",
"File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\xarray\\conventions.py:77\u001b[0m, in \u001b[0;36m_infer_dtype\u001b[1;34m(array, name)\u001b[0m\n\u001b[0;32m 74\u001b[0m \u001b[39mif\u001b[39;00m dtype\u001b[39m.\u001b[39mkind \u001b[39m!=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mO\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[0;32m 75\u001b[0m \u001b[39mreturn\u001b[39;00m dtype\n\u001b[1;32m---> 77\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m 78\u001b[0m \u001b[39m\"\u001b[39m\u001b[39munable to infer dtype on variable \u001b[39m\u001b[39m{!r}\u001b[39;00m\u001b[39m; xarray \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 79\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mcannot serialize arbitrary Python objects\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(name)\n\u001b[0;32m 80\u001b[0m )\n",
"\u001b[1;31mValueError\u001b[0m: unable to infer dtype on variable 'OD'; xarray cannot serialize arbitrary Python objects"
]
}
],
"source": [
"fitResult.to_netcdf(\"saved_on_disk.nc\")"
finish of fitting
2023-05-04 13:47:33 +02:00
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
debug
2023-05-24 16:54:29 +02:00
"# Get the Ncount"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"Ncount = dataSet_crop.OD.sum(dim=(scanAxis[0], 'x', 'y'))"
finish reading from hdf5
2023-04-24 13:03:23 +02:00
]
},
{
"cell_type": "code",
The first working version
2023-05-07 23:41:31 +02:00
"execution_count": null,
finish reading from hdf5
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"metadata": {},
"outputs": [],
"source": [
debug
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"Ncount.load()\n",
not finished
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"\n",
debug
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"fig = plt.figure()\n",
"ax = fig.gca()\n",
"Ncount.plot(ax=ax)"
not finished
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]
},
{
"cell_type": "code",
The first working version
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"execution_count": null,
not finished
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"metadata": {},
"outputs": [],
"source": [
debug
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"fitAnalyser = FitAnalyser(\"Lorentzian With Offset\")\n",
"params = fitAnalyser.guess(Ncount, x='runs', dask=\"parallelized\", guess_kwargs=dict(negative=True))"
not finished
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]
},
{
"cell_type": "code",
The first working version
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"execution_count": null,
not finished
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"metadata": {},
"outputs": [],
"source": [
debug
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"fitResult = fitAnalyser.fit(Ncount, params, x='runs', dask=\"parallelized\")\n",
"fitCurve = fitAnalyser.eval(fitResult, x=np.arange(40), dask=\"parallelized\").load()"
finish of fitting
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]
},
{
"cell_type": "code",
The first working version
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"execution_count": null,
finish of fitting
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"metadata": {},
The first working version
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"outputs": [],
not finish get_fit_result
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"source": [
debug
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"fig = plt.figure()\n",
"ax = fig.gca()\n",
"plt.errorbar([1], [1], yerr=[1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fitCurve.plot.errorbar(yerr=fitCurve)"
not finish get_fit_result
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]
},
{
"cell_type": "code",
The first working version
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"execution_count": null,
debug and nwe functions
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"metadata": {},
"outputs": [],
"source": [
debug
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"np.ufunc(fitCurve)"
debug and nwe functions
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]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
debug
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"# Read CSV"
debug and nwe functions
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]
},
{
"cell_type": "code",
The first working version
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"execution_count": null,
debug and nwe functions
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"metadata": {},
"outputs": [],
"source": [
debug
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"# filePath = 'Z:/Dy_Lab/Data/Measurements/Experiments/DyBEC/BEC Stability Check/20230509-0007/*.csv'\n",
"\n",
"# filePath = np.sort(glob.glob(filePath))\n",
"\n",
"# read_csv_file(filePath, maxFileNum=5, csvEngine='pandas', csvKwargs=dict(header=[0,1], na_filter=False, index_col=0))\n",
"# read_csv_file(filePath, csvEngine='dask')"
debug and nwe functions
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]
},
{
"cell_type": "code",
The first working version
2023-05-07 23:41:31 +02:00
"execution_count": null,
not finish get_fit_result
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"metadata": {},
The first working version
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"outputs": [],
finish of fitting
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"source": [
debug
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"filePath = 'Z:/Dy_Lab/Data/Measurements/Experiments/DyBEC/BEC Stability Check/20230509-0007/*.csv'\n",
not finished
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"\n",
debug
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"filePath = np.sort(glob.glob(filePath))\n",
"\n",
"data = np.empty(filePath.shape,dtype=object)\n",
"\n",
"i = 0\n",
"for fp in filePath:\n",
" data_single = pd.read_csv(fp)\n",
" data_single = xr.Dataset.from_dataframe(data_single)\n",
" data_single = data_single.drop_isel(index=0)\n",
" # data_single = data_single.expand_dims(dim='runs')\n",
" data[i] = data_single\n",
" i = i + 1\n",
"\n",
"data = xr.concat(data, 'runs')\n",
"\n",
"data = data.assign_coords(dict(index=data.Time.isel(runs=0).astype(float))).rename(dict(index='time')).astype(float)"
not finished
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]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
debug
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"data"
not finished
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]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
debug
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"arm2_mean = data['Channel A'].mean(dim='runs')\n",
"arm2_std = data['Channel A'].std(dim='runs')"
not finished
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]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
debug
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"arm2_mean.plot.errorbar(yerr=arm2_std, fmt='ob')"
not finished
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]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
debug
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"arm2_std.plot.errorbar(fmt='ob')"
not finished
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]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
debug
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"data['Channel A'].sel(time=4.55, method='nearest').plot.errorbar(fmt='ob')\n",
"\n",
"plt.ylim([0, 0.15])\n",
"plt.show()"
finish reading from hdf5
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]
},
debug
2023-05-24 16:54:29 +02:00
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
finish reading from hdf5
2023-04-24 13:03:23 +02:00
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
finish of fitting
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"display_name": "env",
finish reading from hdf5
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"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",
regular backup
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"version": "3.9.13"
finish reading from hdf5
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},
finish of fitting
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"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "c05913ad4f24fdc6b2418069394dc5835b1981849b107c9ba6df693aafd66650"
}
}
finish reading from hdf5
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},
"nbformat": 4,
"nbformat_minor": 2
}
Reference in New Issue Copy Permalink