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{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Import supporting package"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import xarray as xr\n",
"import numpy as np\n",
"\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 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",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"D:\\Program Files\\Python\\Python38\\Lib\\site-packages\\distributed\\node.py:182: UserWarning: Port 8787 is already in use.\n",
"Perhaps you already have a cluster running?\n",
"Hosting the HTTP server on port 51109 instead\n",
" 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",
" <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-224091e0-ec5e-11ed-addc-9c7bef43b4fb</p>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
"\n",
" <tr>\n",
" \n",
" <td style=\"text-align: left;\"><strong>Connection method:</strong> Cluster object</td>\n",
" <td style=\"text-align: left;\"><strong>Cluster type:</strong> distributed.LocalCluster</td>\n",
" \n",
" </tr>\n",
"\n",
" \n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:51109/status\" target=\"_blank\">http://127.0.0.1:51109/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" \n",
"\n",
" </table>\n",
"\n",
" \n",
" <button style=\"margin-bottom: 12px;\" data-commandlinker-command=\"dask:populate-and-launch-layout\" data-commandlinker-args='{\"url\": \"http://127.0.0.1:51109/status\" }'>\n",
" Launch dashboard in JupyterLab\n",
" </button>\n",
" \n",
"\n",
" \n",
" <details>\n",
" <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Cluster Info</h3></summary>\n",
" <div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n",
" <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\">\n",
" </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <h3 style=\"margin-bottom: 0px; margin-top: 0px;\">LocalCluster</h3>\n",
" <p style=\"color: #9D9D9D; margin-bottom: 0px;\">f360131e</p>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:51109/status\" target=\"_blank\">http://127.0.0.1:51109/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Workers:</strong> 6\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads:</strong> 60\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total memory:</strong> 55.88 GiB\n",
" </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <td style=\"text-align: left;\"><strong>Status:</strong> running</td>\n",
" <td style=\"text-align: left;\"><strong>Using processes:</strong> True</td>\n",
"</tr>\n",
"\n",
" \n",
" </table>\n",
"\n",
" <details>\n",
" <summary style=\"margin-bottom: 20px;\">\n",
" <h3 style=\"display: inline;\">Scheduler Info</h3>\n",
" </summary>\n",
"\n",
" <div style=\"\">\n",
" <div>\n",
" <div style=\"width: 24px; height: 24px; background-color: #FFF7E5; border: 3px solid #FF6132; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <h3 style=\"margin-bottom: 0px;\">Scheduler</h3>\n",
" <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-0e581a89-0733-49c5-8d25-60e26c6ac0bf</p>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Comm:</strong> tcp://127.0.0.1:51110\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Workers:</strong> 6\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:51109/status\" target=\"_blank\">http://127.0.0.1:51109/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads:</strong> 60\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Started:</strong> Just now\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total memory:</strong> 55.88 GiB\n",
" </td>\n",
" </tr>\n",
" </table>\n",
" </div>\n",
" </div>\n",
"\n",
" <details style=\"margin-left: 48px;\">\n",
" <summary style=\"margin-bottom: 20px;\">\n",
" <h3 style=\"display: inline;\">Workers</h3>\n",
" </summary>\n",
"\n",
" \n",
" <div style=\"margin-bottom: 20px;\">\n",
" <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <details>\n",
" <summary>\n",
" <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 0</h4>\n",
" </summary>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Comm: </strong> tcp://127.0.0.1:51150\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads: </strong> 10\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:51155/status\" target=\"_blank\">http://127.0.0.1:51155/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Memory: </strong> 9.31 GiB\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Nanny: </strong> tcp://127.0.0.1:51113\n",
" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
" <strong>Local directory: </strong> C:\\Users\\Jianshun Gao\\AppData\\Local\\Temp\\dask-worker-space\\worker-2b8gvagb\n",
" </td>\n",
" </tr>\n",
"\n",
" \n",
"\n",
" \n",
"\n",
" </table>\n",
" </details>\n",
" </div>\n",
" </div>\n",
" \n",
" <div style=\"margin-bottom: 20px;\">\n",
" <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <details>\n",
" <summary>\n",
" <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 1</h4>\n",
" </summary>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Comm: </strong> tcp://127.0.0.1:51141\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads: </strong> 10\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:51148/status\" target=\"_blank\">http://127.0.0.1:51148/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Memory: </strong> 9.31 GiB\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Nanny: </strong> tcp://127.0.0.1:51114\n",
" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
" <strong>Local directory: </strong> C:\\Users\\Jianshun Gao\\AppData\\Local\\Temp\\dask-worker-space\\worker-a2ff372r\n",
" </td>\n",
" </tr>\n",
"\n",
" \n",
"\n",
" \n",
"\n",
" </table>\n",
" </details>\n",
" </div>\n",
" </div>\n",
" \n",
" <div style=\"margin-bottom: 20px;\">\n",
" <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <details>\n",
" <summary>\n",
" <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 2</h4>\n",
" </summary>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Comm: </strong> tcp://127.0.0.1:51139\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads: </strong> 10\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:51145/status\" target=\"_blank\">http://127.0.0.1:51145/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Memory: </strong> 9.31 GiB\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Nanny: </strong> tcp://127.0.0.1:51115\n",
" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
" <strong>Local directory: </strong> C:\\Users\\Jianshun Gao\\AppData\\Local\\Temp\\dask-worker-space\\worker-r4y9shfe\n",
" </td>\n",
" </tr>\n",
"\n",
" \n",
"\n",
" \n",
"\n",
" </table>\n",
" </details>\n",
" </div>\n",
" </div>\n",
" \n",
" <div style=\"margin-bottom: 20px;\">\n",
" <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <details>\n",
" <summary>\n",
" <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 3</h4>\n",
" </summary>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Comm: </strong> tcp://127.0.0.1:51140\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads: </strong> 10\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:51144/status\" target=\"_blank\">http://127.0.0.1:51144/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Memory: </strong> 9.31 GiB\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Nanny: </strong> tcp://127.0.0.1:51116\n",
" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
" <strong>Local directory: </strong> C:\\Users\\Jianshun Gao\\AppData\\Local\\Temp\\dask-worker-space\\worker-jcbj2z99\n",
" </td>\n",
" </tr>\n",
"\n",
" \n",
"\n",
" \n",
"\n",
" </table>\n",
" </details>\n",
" </div>\n",
" </div>\n",
" \n",
" <div style=\"margin-bottom: 20px;\">\n",
" <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <details>\n",
" <summary>\n",
" <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 4</h4>\n",
" </summary>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Comm: </strong> tcp://127.0.0.1:51143\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads: </strong> 10\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:51153/status\" target=\"_blank\">http://127.0.0.1:51153/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Memory: </strong> 9.31 GiB\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Nanny: </strong> tcp://127.0.0.1:51117\n",
" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
" <strong>Local directory: </strong> C:\\Users\\Jianshun Gao\\AppData\\Local\\Temp\\dask-worker-space\\worker-3ol5ipx3\n",
" </td>\n",
" </tr>\n",
"\n",
" \n",
"\n",
" \n",
"\n",
" </table>\n",
" </details>\n",
" </div>\n",
" </div>\n",
" \n",
" <div style=\"margin-bottom: 20px;\">\n",
" <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <details>\n",
" <summary>\n",
" <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 5</h4>\n",
" </summary>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Comm: </strong> tcp://127.0.0.1:51142\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads: </strong> 10\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:51151/status\" target=\"_blank\">http://127.0.0.1:51151/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Memory: </strong> 9.31 GiB\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Nanny: </strong> tcp://127.0.0.1:51118\n",
" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
" <strong>Local directory: </strong> C:\\Users\\Jianshun Gao\\AppData\\Local\\Temp\\dask-worker-space\\worker-0u_6_n9d\n",
" </td>\n",
" </tr>\n",
"\n",
" \n",
"\n",
" \n",
"\n",
" </table>\n",
" </details>\n",
" </div>\n",
" </div>\n",
" \n",
"\n",
" </details>\n",
"</div>\n",
"\n",
" </details>\n",
" </div>\n",
"</div>\n",
" </details>\n",
" \n",
"\n",
" </div>\n",
"</div>"
],
"text/plain": [
"<Client: 'tcp://127.0.0.1:51110' processes=6 threads=60, memory=55.88 GiB>"
]
},
"execution_count": 2,
"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": [
"# Read data"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Set file path"
]
},
{
"cell_type": "code",
"execution_count": 3,
"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\""
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'unlimited_dims': set(), 'source': 'd:\\\\Jianshun Gao\\\\Simulations\\\\analyseScripts\\\\testData\\\\0002\\\\2023-04-21_0002_Evaporative_Cooling_0.h5'}\n"
]
}
],
"source": [
"groupList = [\n",
" \"images/MOT_3D_Camera/in_situ_absorption\",\n",
" # \"images/ODT_1_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",
"}\n",
"\n",
"dataSetDict = {\n",
" dskey[groupList[i]]: read_hdf5_file(filepath, groupList[i])\n",
" for i in range(len(groupList))\n",
"}"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Rechunk the data for parallel computing"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"dataSet = dataSetDict[\"camera_1\"]\n",
"\n",
"scanAxis = dataSet.scanAxis\n",
"\n",
"# dataSet = dataSet.chunk(\n",
"# {\n",
"# # \"compZ_current_sg\": \"auto\",\n",
"# \"sin_mod_freq\": \"auto\",\n",
"# \"runs\": 2,\n",
"# \"x\": \"auto\",\n",
"# \"y\": \"auto\",\n",
"# }\n",
"# )"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Calculate absorption imaging"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## get OD images"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (runs: 3, x: 1200, y: 1920)\n",
"Coordinates:\n",
" * runs (runs) float64 0.0 1.0 2.0\n",
"Dimensions without coordinates: x, y\n",
"Data variables:\n",
" atoms (runs, x, y) uint16 dask.array&lt;chunksize=(1, 1200, 1920), meta=np.ndarray&gt;\n",
" background (runs, x, y) uint16 dask.array&lt;chunksize=(1, 1200, 1920), meta=np.ndarray&gt;\n",
" dark (runs, x, y) uint16 dask.array&lt;chunksize=(1, 1200, 1920), meta=np.ndarray&gt;\n",
" shotNum (runs) int64 0 1 2\n",
" OD (runs, x, y) float64 dask.array&lt;chunksize=(1, 1200, 1920), meta=np.ndarray&gt;\n",
"Attributes: (12/96)\n",
" TOF_free: 0.02\n",
" abs_img_freq: 110.858\n",
" absorption_imaging_flag: True\n",
" backup_data: True\n",
" blink_off_time: nan\n",
" blink_on_time: nan\n",
" ... ...\n",
" y_offset_img: 0\n",
" z_offset: 0.189\n",
" z_offset_img: 0.189\n",
" runs: [0. 1. 2.]\n",
" scanAxis: [&#x27;runs&#x27;]\n",
" scanAxisLength: [3.]</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-562506ba-5de6-455a-868a-eb6a8fd249fd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-562506ba-5de6-455a-868a-eb6a8fd249fd' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>runs</span>: 3</li><li><span>x</span>: 1200</li><li><span>y</span>: 1920</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f0c323de-b626-49c2-a725-b9d6ebfcc6ea' class='xr-section-summary-in' type='checkbox' checked><label for='section-f0c323de-b626-49c2-a725-b9d6ebfcc6ea' 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'>runs</span></div><div class='xr-var-dims'>(runs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0</div><input id='attrs-b3cad557-e427-4ac5-814c-f06ff668fe16' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b3cad557-e427-4ac5-814c-f06ff668fe16' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22ea35b3-1774-4e1e-8471-6be1acf96d7a' class='xr-var-data-in' type='checkbox'><label for='data-22ea35b3-1774-4e1e-8471-6be1acf96d7a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0., 1., 2.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e632acb3-d149-4033-a241-5a8d01e14622' class='xr-section-summary-in' type='checkbox' checked><label for='section-e632acb3-d149-4033-a241-5a8d01e14622' 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'>(runs, x, y)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1200, 1920), meta=np.ndarray&gt;</div><input id='attrs-934b2987-a0aa-436c-94b9-7830e49b89a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-934b2987-a0aa-436c-94b9-7830e49b89a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8edce64-be70-4419-9721-78491f3a2f58' class='xr-var-data-in' type='checkbox'><label for='data-e8edce64-be70-4419-9721-78491f3a2f58' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>IMAGE_SUBCLASS :</span></dt><dd>IMAGE_GRAYSCALE</dd><dt><span>IMAGE_VERSION :</span></dt><dd>1.2</dd><dt><span>IMAGE_WHITE_IS_ZERO :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
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" <thead>\n",
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" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 13.18 MiB </td>\n",
" <td> 4.39 MiB </td>\n",
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" \n",
" <tr>\n",
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" <tr>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>background</span></div><div class='xr-var-dims'>(runs, x, y)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1200, 1920), meta=np.ndarray&gt;</div><input id='attrs-07d4d7cc-da37-4ae1-a441-45f3fad38a94' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-07d4d7cc-da37-4ae1-a441-45f3fad38a94' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-215a7593-22c0-40da-aa4d-26c86f328ecb' class='xr-var-data-in' type='checkbox'><label for='data-215a7593-22c0-40da-aa4d-26c86f328ecb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>IMAGE_SUBCLASS :</span></dt><dd>IMAGE_GRAYSCALE</dd><dt><span>IMAGE_VERSION :</span></dt><dd>1.2</dd><dt><span>IMAGE_WHITE_IS_ZERO :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table style=\"border-collapse: collapse;\">\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 13.18 MiB </td>\n",
" <td> 4.39 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (3, 1200, 1920) </td>\n",
" <td> (1, 1200, 1920) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 3 chunks in 10 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> uint16 numpy.ndarray </td>\n",
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" </table>\n",
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" <td>\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'>(runs, x, y)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1200, 1920), meta=np.ndarray&gt;</div><input id='attrs-35b57504-4fcd-4871-a3f2-3e90e75c41a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-35b57504-4fcd-4871-a3f2-3e90e75c41a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19b83311-9d82-4eff-b47a-c280a871a909' class='xr-var-data-in' type='checkbox'><label for='data-19b83311-9d82-4eff-b47a-c280a871a909' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>IMAGE_SUBCLASS :</span></dt><dd>IMAGE_GRAYSCALE</dd><dt><span>IMAGE_VERSION :</span></dt><dd>1.2</dd><dt><span>IMAGE_WHITE_IS_ZERO :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table style=\"border-collapse: collapse;\">\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 13.18 MiB </td>\n",
" <td> 4.39 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (3, 1200, 1920) </td>\n",
" <td> (1, 1200, 1920) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 3 chunks in 10 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> uint16 numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
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"\n",
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"\n",
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" <text x=\"7.474299\" y=\"102.474299\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,102.474299)\">3</text>\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'>(runs)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2</div><input id='attrs-ebbdac90-8f1c-4e10-92d4-918979563e58' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ebbdac90-8f1c-4e10-92d4-918979563e58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c447a455-2cb8-4f89-affd-5c72e71fbd58' class='xr-var-data-in' type='checkbox'><label for='data-c447a455-2cb8-4f89-affd-5c72e71fbd58' 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], dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>OD</span></div><div class='xr-var-dims'>(runs, x, y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1200, 1920), meta=np.ndarray&gt;</div><input id='attrs-6a481dde-c013-4bc6-afab-441110ae80bb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6a481dde-c013-4bc6-afab-441110ae80bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d538438c-c886-4f97-9401-b4026ba0a3e2' class='xr-var-data-in' type='checkbox'><label for='data-d538438c-c886-4f97-9401-b4026ba0a3e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>IMAGE_SUBCLASS :</span></dt><dd>IMAGE_GRAYSCALE</dd><dt><span>IMAGE_VERSION :</span></dt><dd>1.2</dd><dt><span>IMAGE_WHITE_IS_ZERO :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table style=\"border-collapse: collapse;\">\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 52.73 MiB </td>\n",
" <td> 17.58 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (3, 1200, 1920) </td>\n",
" <td> (1, 1200, 1920) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 3 chunks in 40 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
" </td>\n",
" <td>\n",
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"\n",
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" <text x=\"7.474299\" y=\"102.474299\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,102.474299)\">3</text>\n",
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"</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-17ebeb8c-a7a0-4037-ac44-fe69655ac6ed' class='xr-section-summary-in' type='checkbox' ><label for='section-17ebeb8c-a7a0-4037-ac44-fe69655ac6ed' class='xr-section-summary' >Indexes: <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-index-name'><div>runs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f41ec41f-2dea-477e-b07f-a7cfde093ae5' class='xr-index-data-in' type='checkbox'/><label for='index-f41ec41f-2dea-477e-b07f-a7cfde093ae5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([0.0, 1.0, 2.0], dtype=&#x27;float64&#x27;, name=&#x27;runs&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3e608210-b9a0-4e86-8bbc-ae7b8780226b' class='xr-section-summary-in' type='checkbox' ><label for='section-3e608210-b9a0-4e86-8bbc-ae7b8780226b' 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>0.002</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</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>0.283</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>1.0</dd><dt><span>evap_2_rate_constant_1 :</span></dt><dd>0.37</dd><dt><span>evap_2_rate_constant_2 :</span></dt><dd>0.71</dd><dt><span>evap_3_arm_1_final_pow :</span></dt><dd>0.1038</dd><dt><span>evap_3_arm_1_mod_depth_final :</span></dt><dd>0.43</dd><dt><span>evap_3_arm_1_mod_depth_initial :</span></dt><dd>0</dd><dt><span>evap_3_arm_1_start_pow :</span></dt><dd>0.037</dd><dt><span>evap_3_ramp_duration :</span></dt><dd>0.1</dd><dt><span>evap_3_ramp_trunc_value :</span></dt><dd>1.0</dd><dt><span>evap_3_rate_constant_1 :</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</dd><dt><span>gradCoil_current :</span></dt><dd>0.18</dd><dt><span>gradCoil_current_sg :</span></dt><dd>0</dd><dt><span>imaging_method :</span></dt><dd>in_situ_absorption</dd><dt><span>imaging_pulse_duration :</span></dt><dd>2.5e-05</dd><dt><span>imaging_wavelength :</span></dt><dd>4.21291e-07</dd><dt><span>initial_amp :</span></dt><dd>0.62</dd><dt><span>initial_freq :</span></dt><dd>102.13</dd><dt><span>mod_depth_initial :</span></dt><dd>1.0</dd><dt><span>mot_3d_amp :</span></dt><dd>0.62</dd><dt><span>mot_3d_camera_exposure_time :</span></dt><dd>0.002</dd><dt><span>mot_3d_camera_trigger_duration :</span></dt><dd>0.00025</dd><dt><span>mot_3d_freq :</span></dt><dd>102.13</dd><dt><span>mot_load_duration :</span></dt><dd>4</dd><dt><span>odt_axis_camera_trigger_duration :</span></dt><dd>0.002</dd><dt><span>odt_hold_time_1 :</span></dt><dd>0.01</dd><dt><span>odt_hold_time_2 :</span></dt><dd>0.1</dd><dt><span>odt_hold_time_3 :</span></dt><dd>0.1</dd><dt><span>odt_hold_time_4 :</span></dt><dd>1</dd><dt><span>pow_arm_1 :</span></dt><dd>7</dd><dt><span>pow_arm_2 :</span></dt><dd>0</dd><dt><span>pulse_delay :</span></dt><dd>8e-05</dd><dt><span>push_amp :</span></dt><dd>0.16</dd><dt><span>push_freq :</span></dt><dd>102</dd><dt><span>ramp_duration :</span></dt><dd>1</dd><dt><span>recomp_ramp_duration :</span></dt><dd>0.5</dd><dt><span>recomp_ramp_pow_fin_arm_1 :</span></dt><dd>0.1038</dd><dt><span>recomp_ramp_pow_fin_arm_2 :</span></dt><dd>0.09</dd><dt><span>recomp_ramp_pow_ini_arm_1 :</span></dt><dd>0.1038</dd><dt><span>recomp_ramp_pow_ini_arm_2 :</span></dt><dd>0.09</dd><dt><span>save_results :</span></dt><dd>False</dd><dt><span>stern_gerlach_duration :</span></dt><dd>0.001</dd><dt><span>wait_after_2dmot_off :</span></dt><dd>0</dd><dt><span>wait_time_between_images :</span></dt><dd>0.22</dd><dt><span>x_offset :</span></dt><dd>0</dd><dt><span>x_offset_img :</span></dt><dd>0</dd><dt><span>y_offset :</span></dt><dd>0</dd><dt><span>y_offset_img :</span></dt><dd>0</dd><dt><span>z_offset :</span></dt><dd>0.189</dd><dt><span>z_offset_img :</span></dt><dd>0.189</dd><dt><span>runs :</span></dt><dd>[0. 1. 2.]</dd><dt><span>scanAxis :</span></dt><dd>[&#x27;runs&#x27;]</dd><dt><span>scanAxisLength :</span></dt><dd>[3.]</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (runs: 3, x: 1200, y: 1920)\n",
"Coordinates:\n",
" * runs (runs) float64 0.0 1.0 2.0\n",
"Dimensions without coordinates: x, y\n",
"Data variables:\n",
" atoms (runs, x, y) uint16 dask.array<chunksize=(1, 1200, 1920), meta=np.ndarray>\n",
" background (runs, x, y) uint16 dask.array<chunksize=(1, 1200, 1920), meta=np.ndarray>\n",
" dark (runs, x, y) uint16 dask.array<chunksize=(1, 1200, 1920), meta=np.ndarray>\n",
" shotNum (runs) int64 0 1 2\n",
" OD (runs, x, y) float64 dask.array<chunksize=(1, 1200, 1920), meta=np.ndarray>\n",
"Attributes: (12/96)\n",
" TOF_free: 0.02\n",
" abs_img_freq: 110.858\n",
" absorption_imaging_flag: True\n",
" backup_data: True\n",
" blink_off_time: nan\n",
" blink_on_time: nan\n",
" ... ...\n",
" y_offset_img: 0\n",
" z_offset: 0.189\n",
" z_offset_img: 0.189\n",
" runs: [0. 1. 2.]\n",
" scanAxis: ['runs']\n",
" scanAxisLength: [3.]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataSet = imageAnalyser.get_absorption_images(dataSet)\n",
"\n",
"dataSet"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Select region of interests"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# imageAnalyser.center = (529, 962)\n",
"# imageAnalyser.span = (100,100)\n",
"# imageAnalyser.fraction = (0.1, 0.1)\n",
"\n",
"# imageAnalyser.center = (890, 1150)\n",
"# imageAnalyser.span = (600,600)\n",
"# imageAnalyser.fraction = (0.1, 0.1)\n",
"\n",
"imageAnalyser.center = (890, 950)\n",
"imageAnalyser.span = (100,100)\n",
"imageAnalyser.fraction = (0.1, 0.1)\n",
"\n",
"dataSet_crop = imageAnalyser.crop_image(dataSet)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.plot.facetgrid.FacetGrid at 0x274c65832e0>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 720x216 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# dataSet_crop.OD.isel(runs=[0]).plot.pcolormesh(cmap='jet', vmin=0, col=scanAxis[0], row=scanAxis[1])\n",
"dataSet_crop.OD.plot.pcolormesh(cmap='jet', vmin=0, col=scanAxis[0])"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Remove the background"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"dataSet_crop['OD'] = dataSet_crop['OD'] + 500"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"dataSet_crop['OD'] = imageAnalyser.substract_offset(dataSet_crop['OD'])"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Test Fit"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"fitAnalyser = FitAnalyser(\"Two Gaussian-2D\", fitDim=2)\n",
"\n",
"params = fitAnalyser.guess(dataSet_crop.OD, dask=\"parallelized\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"fitResult = fitAnalyser.fit(dataSet_crop.OD, params).load()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"fitCurve = fitAnalyser.eval(fitResult, x=np.arange(100), y=np.arange(100), dask=\"parallelized\").load()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.plot.facetgrid.FacetGrid at 0x274c888b340>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 720x216 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# fitCurve.isel(**{scanAxis[0]:np.arange(30), 'runs':range(dataSet_crop.OD['runs'].size)}).plot.pcolormesh(cmap='jet', vmin=0, col=scanAxis[0], row=scanAxis[1])\n",
"\n",
"fitCurve.plot.pcolormesh(cmap='jet', vmin=0, col=scanAxis[0])"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"a = fitAnalyser.get_fit_value(fitResult)\n",
"b = fitAnalyser.get_fit_std(fitResult)\n",
"data = combine_uncertainty(a, b)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Get the Ncount"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"Ncount = dataSet_crop.OD.sum(dim=(scanAxis[0], 'x', 'y'))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "No numeric data to plot.",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32md:\\Jianshun Gao\\Simulations\\analyseScripts\\test.ipynb Cell 29\u001b[0m in \u001b[0;36m<cell line: 5>\u001b[1;34m()\u001b[0m\n\u001b[0;32m <a href='vscode-notebook-cell://127.0.0.1:8080/d%3A/Jianshun%20Gao/Simulations/analyseScripts/test.ipynb#X40sdnNjb2RlLXJlbW90ZQ%3D%3D?line=2'>3</a>\u001b[0m fig \u001b[39m=\u001b[39m plt\u001b[39m.\u001b[39mfigure()\n\u001b[0;32m <a href='vscode-notebook-cell://127.0.0.1:8080/d%3A/Jianshun%20Gao/Simulations/analyseScripts/test.ipynb#X40sdnNjb2RlLXJlbW90ZQ%3D%3D?line=3'>4</a>\u001b[0m ax \u001b[39m=\u001b[39m fig\u001b[39m.\u001b[39mgca()\n\u001b[1;32m----> <a href='vscode-notebook-cell://127.0.0.1:8080/d%3A/Jianshun%20Gao/Simulations/analyseScripts/test.ipynb#X40sdnNjb2RlLXJlbW90ZQ%3D%3D?line=4'>5</a>\u001b[0m Ncount\u001b[39m.\u001b[39;49mplot(ax\u001b[39m=\u001b[39;49max)\n",
"File \u001b[1;32mD:\\Program Files\\Python\\Python38\\Lib\\site-packages\\xarray\\plot\\accessor.py:46\u001b[0m, in \u001b[0;36mDataArrayPlotAccessor.__call__\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m 44\u001b[0m \u001b[39m@functools\u001b[39m\u001b[39m.\u001b[39mwraps(dataarray_plot\u001b[39m.\u001b[39mplot, assigned\u001b[39m=\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39m__doc__\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m__annotations__\u001b[39m\u001b[39m\"\u001b[39m))\n\u001b[0;32m 45\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__call__\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Any:\n\u001b[1;32m---> 46\u001b[0m \u001b[39mreturn\u001b[39;00m dataarray_plot\u001b[39m.\u001b[39;49mplot(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_da, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
"File \u001b[1;32mD:\\Program Files\\Python\\Python38\\Lib\\site-packages\\xarray\\plot\\dataarray_plot.py:285\u001b[0m, in \u001b[0;36mplot\u001b[1;34m(darray, row, col, col_wrap, ax, hue, subplot_kws, **kwargs)\u001b[0m\n\u001b[0;32m 282\u001b[0m plotfunc: Callable\n\u001b[0;32m 284\u001b[0m \u001b[39mif\u001b[39;00m ndims \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m \u001b[39mor\u001b[39;00m darray\u001b[39m.\u001b[39msize \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[1;32m--> 285\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mTypeError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mNo numeric data to plot.\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m 286\u001b[0m \u001b[39mif\u001b[39;00m ndims \u001b[39min\u001b[39;00m (\u001b[39m1\u001b[39m, \u001b[39m2\u001b[39m):\n\u001b[0;32m 287\u001b[0m \u001b[39mif\u001b[39;00m row \u001b[39mor\u001b[39;00m col:\n",
"\u001b[1;31mTypeError\u001b[0m: No numeric data to plot."
]
},
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"Ncount.load()\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"Ncount.plot(ax=ax)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fitAnalyser = FitAnalyser(\"Lorentzian With Offset\")\n",
"params = fitAnalyser.guess(Ncount, x='sin_mod_freq', dask=\"parallelized\", guess_kwargs=dict(negative=True))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fitResult = fitAnalyser.fit(Ncount, params, x='sin_mod_freq', dask=\"parallelized\")\n",
"fitCurve = fitAnalyser.eval(fitResult, x=np.arange(40), dask=\"parallelized\").load()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"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)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"np.ufunc(fitCurve)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "env",
"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": "3.8.10"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "c05913ad4f24fdc6b2418069394dc5835b1981849b107c9ba6df693aafd66650"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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