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{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from PyQt5.QtCore import (Qt, pyqtSignal)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pymongo\n", "import xarray_mongodb\n", "import bson\n", "import datetime\n", "\n", "# datetime.datetime.utcnow()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "mongoClient = pymongo.MongoClient('mongodb://control:DyLab2021@127.0.0.1:27017/?authMechanism=DEFAULT')\n", "mongoDB = mongoClient.testDB\n", "mongoCollection = mongoDB.testCollection" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# mongoClient.admin.command(\n", "# 'createUser', 'control', \n", "# pwd='DyLab2021',\n", "# roles=[{'role': 'readWriteAnyDatabase', 'db': 'admin'}]\n", "# )" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# mongoClient.admin.command(\n", "# 'dropUser', 'contorl'\n", "# )" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# mongoClient.admin.command('usersInfo')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Task executing\n", "Task2 executing \n", "\n", "\n", "Task2 done\n", "\n", "Task done\n", "\n" ] } ], "source": [ "from time import sleep\n", "from multiprocessing.pool import ThreadPool\n", " \n", "# task executed in a worker thread\n", "def task():\n", " # report a message\n", " print(f'Task executing\\n')\n", " # block for a moment\n", " sleep(1)\n", " # report a message\n", " print(f'Task done\\n')\n", " \n", "def task2():\n", " # report a message\n", " print(f'Task2 executing \\n')\n", " # block for a moment\n", " sleep(1)\n", " # report a message\n", " print(f'Task2 done\\n')\n", " \n", "# protect the entry point\n", "if __name__ == '__main__':\n", " # create and configure the thread pool\n", " pool = ThreadPool()\n", " # issue tasks to the thread pool\n", " pool.apply_async(task)\n", " pool.apply_async(task2)\n", " # close the thread pool\n", " pool.close()\n", " # wait for all tasks to finish\n", " pool.join()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Import supporting package" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "import xarray as xr\n", "import pandas as pd\n", "import numpy as np\n", "import copy\n", "\n", "import glob\n", "\n", "import xrft\n", "import finufft\n", "\n", "from uncertainties import ufloat\n", "from uncertainties import unumpy as unp\n", "from uncertainties import umath\n", "\n", "from datetime import datetime\n", "\n", "import matplotlib.pyplot as plt\n", "plt.rcParams['font.size'] = 18\n", "\n", "from DataContainer.ReadData import read_hdf5_file, read_hdf5_global, read_hdf5_run_time, read_csv_file\n", "from Analyser.ImagingAnalyser import ImageAnalyser\n", "from Analyser.FitAnalyser import FitAnalyser\n", "from Analyser.FitAnalyser import ThomasFermi2dModel, DensityProfileBEC2dModel, Polylog22dModel\n", "from Analyser.FFTAnalyser import fft, ifft, fft_nutou\n", "from ToolFunction.ToolFunction import *\n", "\n", "from ToolFunction.HomeMadeXarrayFunction import errorbar, dataarray_plot_errorbar\n", "xr.plot.dataarray_plot.errorbar = errorbar\n", "xr.plot.accessor.DataArrayPlotAccessor.errorbar = dataarray_plot_errorbar\n", "\n", "imageAnalyser = ImageAnalyser()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Import supporting package" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "import xarray as xr\n", "import numpy as np\n", "\n", "from uncertainties import ufloat\n", "from uncertainties import unumpy as unp\n", "from uncertainties import umath\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "from DataContainer.ReadData import read_hdf5_file\n", "from Analyser.ImagingAnalyser import ImageAnalyser\n", "from Analyser.FitAnalyser import FitAnalyser\n", "from Analyser.FitAnalyser import ThomasFermi2dModel, DensityProfileBEC2dModel, Polylog22dModel\n", "from Analyser.FitAnalyser import NewFitModel\n", "from ToolFunction.ToolFunction import *\n", "\n", "from ToolFunction.HomeMadeXarrayFunction import errorbar, dataarray_plot_errorbar\n", "xr.plot.dataarray_plot.errorbar = errorbar\n", "xr.plot.accessor.DataArrayPlotAccessor.errorbar = dataarray_plot_errorbar\n", "\n", "imageAnalyser = ImageAnalyser()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1738.8478693742436\n" ] } ], "source": [ "sigma = 8.4743e-14 * 0.5\n", "M = 0.6827\n", "print(1/sigma*5.86e-6**2 / M**2)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "8.583565255602754e-06" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "5.86e-6/M" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Start a client for parallel computing" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", " <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n", " <div style=\"margin-left: 48px;\">\n", " <h3 style=\"margin-bottom: 0px;\">Client</h3>\n", " <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-e928dd09-0a9f-11ee-b50c-80e82ce2fa8e</p>\n", " <table style=\"width: 100%; text-align: left;\">\n", "\n", " <tr>\n", " \n", " <td style=\"text-align: left;\"><strong>Connection method:</strong> Cluster object</td>\n", " <td style=\"text-align: left;\"><strong>Cluster type:</strong> distributed.LocalCluster</td>\n", " \n", " </tr>\n", "\n", " \n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n", " </td>\n", " <td style=\"text-align: left;\"></td>\n", " </tr>\n", " \n", "\n", " </table>\n", "\n", " \n", "\n", " \n", " <details>\n", " <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Cluster Info</h3></summary>\n", " <div class=\"jp-RenderedHTMLCommon jp-RenderedHTML jp-mod-trusted jp-OutputArea-output\">\n", " <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\">\n", " </div>\n", " <div style=\"margin-left: 48px;\">\n", " <h3 style=\"margin-bottom: 0px; margin-top: 0px;\">LocalCluster</h3>\n", " <p style=\"color: #9D9D9D; margin-bottom: 0px;\">f62d3f1c</p>\n", " <table style=\"width: 100%; text-align: left;\">\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n", " </td>\n", " <td style=\"text-align: left;\">\n", " <strong>Workers:</strong> 6\n", " </td>\n", " </tr>\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Total threads:</strong> 60\n", " </td>\n", " <td style=\"text-align: left;\">\n", " <strong>Total memory:</strong> 55.88 GiB\n", " </td>\n", " </tr>\n", " \n", " <tr>\n", " <td style=\"text-align: left;\"><strong>Status:</strong> running</td>\n", " <td style=\"text-align: left;\"><strong>Using processes:</strong> True</td>\n", "</tr>\n", "\n", " \n", " </table>\n", "\n", " <details>\n", " <summary style=\"margin-bottom: 20px;\">\n", " <h3 style=\"display: inline;\">Scheduler Info</h3>\n", " </summary>\n", "\n", " <div style=\"\">\n", " <div>\n", " <div style=\"width: 24px; height: 24px; background-color: #FFF7E5; border: 3px solid #FF6132; border-radius: 5px; position: absolute;\"> </div>\n", " <div style=\"margin-left: 48px;\">\n", " <h3 style=\"margin-bottom: 0px;\">Scheduler</h3>\n", " <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-2b02de43-b3ad-400f-ae4c-0914b85d2b71</p>\n", " <table style=\"width: 100%; text-align: left;\">\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Comm:</strong> tcp://127.0.0.1:62759\n", " </td>\n", " <td style=\"text-align: left;\">\n", " <strong>Workers:</strong> 6\n", " </td>\n", " </tr>\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n", " </td>\n", " <td style=\"text-align: left;\">\n", " <strong>Total threads:</strong> 60\n", " </td>\n", " </tr>\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Started:</strong> Just now\n", " </td>\n", " <td style=\"text-align: left;\">\n", " <strong>Total memory:</strong> 55.88 GiB\n", " </td>\n", " </tr>\n", " </table>\n", " </div>\n", " </div>\n", "\n", " <details style=\"margin-left: 48px;\">\n", " <summary style=\"margin-bottom: 20px;\">\n", " <h3 style=\"display: inline;\">Workers</h3>\n", " </summary>\n", "\n", " \n", " <div style=\"margin-bottom: 20px;\">\n", " <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n", " <div style=\"margin-left: 48px;\">\n", " <details>\n", " <summary>\n", " <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 0</h4>\n", " </summary>\n", " <table style=\"width: 100%; text-align: left;\">\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Comm: </strong> tcp://127.0.0.1:62781\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://127.0.0.1:62787/status\" target=\"_blank\">http://127.0.0.1:62787/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://127.0.0.1:62762\n", " </td>\n", " <td style=\"text-align: left;\"></td>\n", " </tr>\n", " <tr>\n", " <td colspan=\"2\" style=\"text-align: left;\">\n", " <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-3gcis6xr\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://127.0.0.1:62795\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://127.0.0.1:62799/status\" target=\"_blank\">http://127.0.0.1:62799/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://127.0.0.1:62763\n", " </td>\n", " <td style=\"text-align: left;\"></td>\n", " </tr>\n", " <tr>\n", " <td colspan=\"2\" style=\"text-align: left;\">\n", " <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-z8bj7z95\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://127.0.0.1:62789\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://127.0.0.1:62791/status\" target=\"_blank\">http://127.0.0.1:62791/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://127.0.0.1:62764\n", " </td>\n", " <td style=\"text-align: left;\"></td>\n", " </tr>\n", " <tr>\n", " <td colspan=\"2\" style=\"text-align: left;\">\n", " <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-67f2wwr0\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://127.0.0.1:62797\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://127.0.0.1:62802/status\" target=\"_blank\">http://127.0.0.1:62802/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://127.0.0.1:62765\n", " </td>\n", " <td style=\"text-align: left;\"></td>\n", " </tr>\n", " <tr>\n", " <td colspan=\"2\" style=\"text-align: left;\">\n", " <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-g84sd6u2\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://127.0.0.1:62796\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://127.0.0.1:62798/status\" target=\"_blank\">http://127.0.0.1:62798/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://127.0.0.1:62766\n", " </td>\n", " <td style=\"text-align: left;\"></td>\n", " </tr>\n", " <tr>\n", " <td colspan=\"2\" style=\"text-align: left;\">\n", " <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-55nj59xw\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://127.0.0.1:62790\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://127.0.0.1:62793/status\" target=\"_blank\">http://127.0.0.1:62793/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://127.0.0.1:62767\n", " </td>\n", " <td style=\"text-align: left;\"></td>\n", " </tr>\n", " <tr>\n", " <td colspan=\"2\" style=\"text-align: left;\">\n", " <strong>Local directory: </strong> C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-s47mqrsf\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://127.0.0.1:62759' processes=6 threads=60, memory=55.88 GiB>" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from dask.distributed import Client\n", "client = Client(n_workers=6, threads_per_worker=10, processes=True, memory_limit='10GB')\n", "client" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Set global path for experiment" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# filepath = \"//DyLabNAS/Data/Evaporative_Cooling/2023/05/03/0043/*.h5\"\n", "# filepath = \"//DyLabNAS/Data/Evaporative_Cooling/2023/04/18/0003/2023-04-18_0003_Evaporative_Cooling_000.h5\"\n", "\n", "# filepath = \"//DyLabNAS/Data/Repetition_scan/2023/04/21/0002/*.h5\"\n", "\n", "# filepath = r\"./testData/0002/*.h5\"\n", "\n", "# filepath = r\"./testData/0002/2023-04-21_0002_Evaporative_Cooling_0.h5\"\n", "\n", "# filepath = r'd:/Jianshun Gao/Simulations/analyseScripts/testData/0002/2023-04-21_0002_Evaporative_Cooling_0.h5'\n", "\n", "# filepath = \"//DyLabNAS/Data/Evaporative_Cooling/2023/04/18/0003/*.h5\"\n", "\n", "# filepath = \"//DyLabNAS/Data/Evaporative_Cooling/2023/05/04/0000/*.h5\"\n", "\n", "filepath = './result_from_experiment/2023-04-24/0013/2023-04-24_0013_Evaporative_Cooling_08.h5'" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "groupList = [\n", " \"images/MOT_3D_Camera/in_situ_absorption\",\n", " \"images/ODT_1_Axis_Camera/in_situ_absorption\",\n", " \"images/ODT_2_Axis_Camera/in_situ_absorption\",\n", "]\n", "\n", "dskey = {\n", " \"images/MOT_3D_Camera/in_situ_absorption\": \"camera_1\",\n", " \"images/ODT_1_Axis_Camera/in_situ_absorption\": \"camera_2\",\n", " \"images/ODT_2_Axis_Camera/in_situ_absorption\": \"camera_3\",\n", "}\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "img_dir = '//DyLabNAS/Data/'\n", "SequenceName = \"Evaporative_Cooling\" + \"/\"\n", "folderPath = img_dir + SequenceName + '2023/05/23'# get_date()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# An example for one experimental run" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Load the data" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n", "<defs>\n", "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n", "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n", "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n", "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n", "</symbol>\n", "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n", "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 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var(--xr-background-color);\n", " padding-right: 10px;\n", "}\n", "\n", ".xr-attrs dd {\n", " grid-column: 2;\n", " white-space: pre-wrap;\n", " word-break: break-all;\n", "}\n", "\n", ".xr-icon-database,\n", ".xr-icon-file-text2,\n", ".xr-no-icon {\n", " display: inline-block;\n", " vertical-align: middle;\n", " width: 1em;\n", " height: 1.5em !important;\n", " stroke-width: 0;\n", " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", "</style><pre class='xr-text-repr-fallback'><xarray.Dataset>\n", "Dimensions: (compX_final_current: 11, runs: 3, y: 1200, x: 1920)\n", "Coordinates:\n", " * compX_final_current (compX_final_current) float64 0.0 0.002 ... 0.018 0.02\n", " * runs (runs) float64 0.0 1.0 2.0\n", "Dimensions without coordinates: y, x\n", "Data variables:\n", " atoms (compX_final_current, runs, y, x) uint16 dask.array<chunksize=(9, 3, 1200, 1920), meta=np.ndarray>\n", " background (compX_final_current, runs, y, x) uint16 dask.array<chunksize=(9, 3, 1200, 1920), meta=np.ndarray>\n", " dark (compX_final_current, runs, y, x) uint16 dask.array<chunksize=(9, 3, 1200, 1920), meta=np.ndarray>\n", " shotNum (compX_final_current, runs) <U2 dask.array<chunksize=(11, 3), meta=np.ndarray>\n", " OD (compX_final_current, runs, y, x) float64 dask.array<chunksize=(9, 3, 1200, 1920), meta=np.ndarray>\n", "Attributes: (12/120)\n", " TOF_free: 0.022\n", " abs_img_freq: 110.858\n", " absorption_imaging_flag: True\n", " backup_data: True\n", " blink_off_time: 0.001\n", " blink_on_time: 0.001\n", " ... ...\n", " z_offset: 0.189\n", " z_offset_img: 0.189\n", " compX_final_current: [0. 0.002 0.004 0.006 0.008 0.01 0...\n", " runs: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1...\n", " scanAxis: ['compX_final_current' 'runs']\n", " scanAxisLength: [33. 33.]</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-b9ae155a-cf09-4eb8-842a-5e3d68fb091f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b9ae155a-cf09-4eb8-842a-5e3d68fb091f' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>compX_final_current</span>: 11</li><li><span class='xr-has-index'>runs</span>: 3</li><li><span>y</span>: 1200</li><li><span>x</span>: 1920</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-17d8eed4-ed1b-4e5a-bcdc-3ad6bea77ff4' class='xr-section-summary-in' type='checkbox' checked><label for='section-17d8eed4-ed1b-4e5a-bcdc-3ad6bea77ff4' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>compX_final_current</span></div><div class='xr-var-dims'>(compX_final_current)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.002 0.004 ... 0.018 0.02</div><input id='attrs-863961cf-d72e-46e9-a866-67470bc1388c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-863961cf-d72e-46e9-a866-67470bc1388c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9344fd09-ba24-41d8-90ab-485ade4d6a91' class='xr-var-data-in' type='checkbox'><label for='data-9344fd09-ba24-41d8-90ab-485ade4d6a91' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0. , 0.002, 0.004, 0.006, 0.008, 0.01 , 0.012, 0.014, 0.016, 0.018,\n", " 0.02 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>runs</span></div><div class='xr-var-dims'>(runs)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0</div><input id='attrs-907ea110-b888-4cd7-a485-3ca7076a8dc3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-907ea110-b888-4cd7-a485-3ca7076a8dc3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6b9aa47-6f8c-484f-af8b-a8cafbff10be' class='xr-var-data-in' type='checkbox'><label for='data-b6b9aa47-6f8c-484f-af8b-a8cafbff10be' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0., 1., 2.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6906b7c8-0730-4704-8b36-2414b062c545' class='xr-section-summary-in' type='checkbox' checked><label for='section-6906b7c8-0730-4704-8b36-2414b062c545' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>atoms</span></div><div class='xr-var-dims'>(compX_final_current, runs, y, x)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(9, 3, 1200, 1920), meta=np.ndarray></div><input id='attrs-58fe1a57-5da5-4654-9b36-4d2bb4330969' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-58fe1a57-5da5-4654-9b36-4d2bb4330969' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-add8fc41-8deb-43d6-8742-8f88232b9f88' class='xr-var-data-in' type='checkbox'><label for='data-add8fc41-8deb-43d6-8742-8f88232b9f88' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>IMAGE_SUBCLASS :</span></dt><dd>IMAGE_GRAYSCALE</dd><dt><span>IMAGE_VERSION :</span></dt><dd>1.2</dd><dt><span>IMAGE_WHITE_IS_ZERO :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n", " <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> 145.02 MiB </td>\n", " <td> 118.65 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (11, 3, 1200, 1920) </td>\n", " <td> (9, 3, 1200, 1920) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 2 chunks in 104 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", " 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xlink:href='#icon-file-text2'></use></svg></label><input id='data-97971573-5ecd-4996-b065-4771111ac722' class='xr-var-data-in' type='checkbox'><label for='data-97971573-5ecd-4996-b065-4771111ac722' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>IMAGE_SUBCLASS :</span></dt><dd>IMAGE_GRAYSCALE</dd><dt><span>IMAGE_VERSION :</span></dt><dd>1.2</dd><dt><span>IMAGE_WHITE_IS_ZERO :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n", " <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> 145.02 MiB </td>\n", " <td> 118.65 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (11, 3, 1200, 1920) </td>\n", " <td> (9, 3, 1200, 1920) </td>\n", " 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meta=np.ndarray></div><input id='attrs-03aad486-7c52-4d72-88c1-26a5ce472582' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03aad486-7c52-4d72-88c1-26a5ce472582' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dfe5474a-18e1-4e4a-8e5f-30cd418675c1' class='xr-var-data-in' type='checkbox'><label for='data-dfe5474a-18e1-4e4a-8e5f-30cd418675c1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>IMAGE_SUBCLASS :</span></dt><dd>IMAGE_GRAYSCALE</dd><dt><span>IMAGE_VERSION :</span></dt><dd>1.2</dd><dt><span>IMAGE_WHITE_IS_ZERO :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n", " <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> 145.02 MiB </td>\n", " <td> 118.65 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (11, 3, 1200, 1920) </td>\n", " <td> (9, 3, 1200, 1920) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 2 chunks in 104 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> uint16 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"374\" height=\"139\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", " <line x1=\"20\" y1=\"0\" x2=\"20\" y2=\"25\" />\n", " <line x1=\"25\" y1=\"0\" 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x1=\"109\" y1=\"14\" x2=\"109\" y2=\"89\" style=\"stroke-width:2\" />\n", " <line x1=\"229\" y1=\"14\" x2=\"229\" y2=\"89\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"109.9485979497544,14.948597949754403 229.9485979497544,14.948597949754403 229.9485979497544,89.9485979497544 109.9485979497544,89.9485979497544\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"169.948598\" y=\"109.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1920</text>\n", " <text x=\"249.948598\" y=\"52.448598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,249.948598,52.448598)\">1200</text>\n", " <text x=\"92.474299\" y=\"102.474299\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,92.474299,102.474299)\">3</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>shotNum</span></div><div class='xr-var-dims'>(compX_final_current, runs)</div><div class='xr-var-dtype'><U2</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(11, 3), meta=np.ndarray></div><input id='attrs-0fe8bb8f-ab0e-401f-a5aa-a4b4ada06ef2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0fe8bb8f-ab0e-401f-a5aa-a4b4ada06ef2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5dcb4741-d547-41e8-8edd-ccc10624b80a' class='xr-var-data-in' type='checkbox'><label for='data-5dcb4741-d547-41e8-8edd-ccc10624b80a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n", " <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> 264 B </td>\n", " <td> 264 B </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (11, 3) </td>\n", " <td> (11, 3) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 1 graph layer </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> <U2 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"93\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"43\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"120\" x2=\"43\" y2=\"120\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n", " <line x1=\"43\" y1=\"0\" x2=\"43\" y2=\"120\" 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title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43315b10-c41f-435d-afd2-182f7bb50ddb' class='xr-var-data-in' type='checkbox'><label for='data-43315b10-c41f-435d-afd2-182f7bb50ddb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>IMAGE_SUBCLASS :</span></dt><dd>IMAGE_GRAYSCALE</dd><dt><span>IMAGE_VERSION :</span></dt><dd>1.2</dd><dt><span>IMAGE_WHITE_IS_ZERO :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 580.08 MiB </td>\n", " <td> 474.61 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (11, 3, 1200, 1920) </td>\n", " <td> (9, 3, 1200, 1920) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 2 chunks in 322 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float64 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"374\" height=\"139\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", " <line x1=\"20\" y1=\"0\" x2=\"20\" y2=\"25\" />\n", " <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"0.0,0.0 25.412616514582485,0.0 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for='section-a63ebf49-9886-4681-ba1a-6e4718d49545' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>compX_final_current</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-67fac2f8-b2f6-4fb9-8d02-3c04ac6d24f9' class='xr-index-data-in' type='checkbox'/><label for='index-67fac2f8-b2f6-4fb9-8d02-3c04ac6d24f9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([0.0, 0.002, 0.004, 0.006, 0.008, 0.01, 0.012, 0.014, 0.016,\n", " 0.018, 0.02],\n", " dtype='float64', name='compX_final_current'))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>runs</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fea26cbe-902d-436a-9a27-54039881eb60' class='xr-index-data-in' type='checkbox'/><label for='index-fea26cbe-902d-436a-9a27-54039881eb60' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([0.0, 1.0, 2.0], dtype='float64', name='runs'))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5d03d618-2e62-4ba2-9cc6-da9ff33c423f' class='xr-section-summary-in' type='checkbox' ><label for='section-5d03d618-2e62-4ba2-9cc6-da9ff33c423f' class='xr-section-summary' >Attributes: <span>(120)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>TOF_free :</span></dt><dd>0.022</dd><dt><span>abs_img_freq :</span></dt><dd>110.858</dd><dt><span>absorption_imaging_flag :</span></dt><dd>True</dd><dt><span>backup_data :</span></dt><dd>True</dd><dt><span>blink_off_time :</span></dt><dd>0.001</dd><dt><span>blink_on_time :</span></dt><dd>0.001</dd><dt><span>c_duration :</span></dt><dd>0.2</dd><dt><span>carrier_amp :</span></dt><dd>3</dd><dt><span>carrier_freq :</span></dt><dd>0.85</dd><dt><span>carrier_offset :</span></dt><dd>0</dd><dt><span>carrier_phase :</span></dt><dd>0</dd><dt><span>channel_in_use :</span></dt><dd>1</dd><dt><span>cmot_final_current :</span></dt><dd>0.65</dd><dt><span>cmot_hold :</span></dt><dd>0.06</dd><dt><span>cmot_initial_current :</span></dt><dd>0.18</dd><dt><span>compX_current :</span></dt><dd>0</dd><dt><span>compX_current_sg :</span></dt><dd>0.0</dd><dt><span>compX_initial_current :</span></dt><dd>0</dd><dt><span>compY_current :</span></dt><dd>0</dd><dt><span>compY_current_sg :</span></dt><dd>0.0</dd><dt><span>compY_final_current :</span></dt><dd>0</dd><dt><span>compY_initial_current :</span></dt><dd>0</dd><dt><span>compZ_current :</span></dt><dd>0</dd><dt><span>compZ_current_sg :</span></dt><dd>0.189</dd><dt><span>compZ_final_current :</span></dt><dd>0.264</dd><dt><span>compZ_initial_current :</span></dt><dd>0</dd><dt><span>default_camera :</span></dt><dd>0</dd><dt><span>deltaf :</span></dt><dd>0.161</dd><dt><span>evap_1_arm_1_final_pow :</span></dt><dd>0.35</dd><dt><span>evap_1_arm_1_mod_depth_final :</span></dt><dd>0</dd><dt><span>evap_1_arm_1_mod_depth_initial :</span></dt><dd>1.0</dd><dt><span>evap_1_arm_1_mod_ramp_duration :</span></dt><dd>1.15</dd><dt><span>evap_1_arm_1_pow_ramp_duration :</span></dt><dd>1.65</dd><dt><span>evap_1_arm_1_start_pow :</span></dt><dd>7</dd><dt><span>evap_1_arm_2_final_pow :</span></dt><dd>5</dd><dt><span>evap_1_arm_2_ramp_duration :</span></dt><dd>0.5</dd><dt><span>evap_1_arm_2_start_pow :</span></dt><dd>0</dd><dt><span>evap_1_mod_ramp_trunc_value :</span></dt><dd>1</dd><dt><span>evap_1_pow_ramp_trunc_value :</span></dt><dd>1.0</dd><dt><span>evap_1_rate_constant_1 :</span></dt><dd>0.525</dd><dt><span>evap_1_rate_constant_2 :</span></dt><dd>0.51</dd><dt><span>evap_2_arm_1_final_pow :</span></dt><dd>0.037</dd><dt><span>evap_2_arm_1_start_pow :</span></dt><dd>0.35</dd><dt><span>evap_2_arm_2_final_pow :</span></dt><dd>0.09</dd><dt><span>evap_2_arm_2_start_pow :</span></dt><dd>5</dd><dt><span>evap_2_ramp_duration :</span></dt><dd>1.0</dd><dt><span>evap_2_ramp_trunc_value :</span></dt><dd>1.0</dd><dt><span>evap_2_rate_constant_1 :</span></dt><dd>0.37</dd><dt><span>evap_2_rate_constant_2 :</span></dt><dd>0.71</dd><dt><span>evap_3_arm_1_final_pow :</span></dt><dd>0.1038</dd><dt><span>evap_3_arm_1_mod_depth_final :</span></dt><dd>0.43</dd><dt><span>evap_3_arm_1_mod_depth_initial :</span></dt><dd>0</dd><dt><span>evap_3_arm_1_start_pow :</span></dt><dd>0.037</dd><dt><span>evap_3_ramp_duration :</span></dt><dd>0.1</dd><dt><span>evap_3_ramp_trunc_value :</span></dt><dd>1.0</dd><dt><span>evap_3_rate_constant_1 :</sp " 0.002 0.004 0.006 0.008 0.01 0.012 0.014 0.016 0.018 0.02 0. 0.002\n", " 0.004 0.006 0.008 0.01 0.012 0.014 0.016 0.018 0.02 ]</dd><dt><span>runs :</span></dt><dd>[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 2. 2.\n", " 2. 2. 2. 2. 2. 2. 2. 2. 2.]</dd><dt><span>scanAxis :</span></dt><dd>['compX_final_current' 'runs']</dd><dt><span>scanAxisLength :</span></dt><dd>[33. 33.]</dd></dl></div></li></ul></div></div>" ], "text/plain": [ "<xarray.Dataset>\n", "Dimensions: (compX_final_current: 11, runs: 3, y: 1200, x: 1920)\n", "Coordinates:\n", " * compX_final_current (compX_final_current) float64 0.0 0.002 ... 0.018 0.02\n", " * runs (runs) float64 0.0 1.0 2.0\n", "Dimensions without coordinates: y, x\n", "Data variables:\n", " atoms (compX_final_current, runs, y, x) uint16 dask.array<chunksize=(9, 3, 1200, 1920), meta=np.ndarray>\n", " background (compX_final_current, runs, y, x) uint16 dask.array<chunksize=(9, 3, 1200, 1920), meta=np.ndarray>\n", " dark (compX_final_current, runs, y, x) uint16 dask.array<chunksize=(9, 3, 1200, 1920), meta=np.ndarray>\n", " shotNum (compX_final_current, runs) <U2 dask.array<chunksize=(11, 3), meta=np.ndarray>\n", " OD (compX_final_current, runs, y, x) float64 dask.array<chunksize=(9, 3, 1200, 1920), meta=np.ndarray>\n", "Attributes: (12/120)\n", " TOF_free: 0.022\n", " abs_img_freq: 110.858\n", " absorption_imaging_flag: True\n", " backup_data: True\n", " blink_off_time: 0.001\n", " blink_on_time: 0.001\n", " ... ...\n", " z_offset: 0.189\n", " z_offset_img: 0.189\n", " compX_final_current: [0. 0.002 0.004 0.006 0.008 0.01 0...\n", " runs: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1...\n", " scanAxis: ['compX_final_current' 'runs']\n", " scanAxisLength: [33. 33.]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "shotNum = \"0069\"\n", "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n", "# filePath = \"//DyLabNAS/Data/Evaporative_Cooling/2023/05/12/0065/*.h5\"\n", "# filePath = './result_from_experiment/2023-04-24/0013/2023-04-24_0013_Evaporative_Cooling_08.h5'\n", "\n", "dataSetDict = {\n", " dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n", " for i in [0] # range(len(groupList))\n", "}\n", "\n", "dataSet = dataSetDict[\"camera_1\"]\n", "dataSet = swap_xy(dataSet)\n", "\n", "scanAxis = get_scanAxis(dataSet)\n", "\n", "dataSet = auto_rechunk(dataSet)\n", "\n", "dataSet = imageAnalyser.get_absorption_images(dataSet)\n", "\n", "dataSet" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Calculate an plot OD images" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": "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 "text/plain": [ "<Figure size 3400x900 with 34 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# imageAnalyser.center = (960, 1040)\n", "# imageAnalyser.span = (100, 100)\n", "# imageAnalyser.fraction = (0.1, 0.1)\n", "\n", "imageAnalyser.center = (960, 875)\n", "imageAnalyser.span = (300, 300)\n", "imageAnalyser.fraction = (0.1, 0.1)\n", "\n", "dataSet_cropOD = imageAnalyser.crop_image(dataSet.OD)\n", "dataSet_cropOD = imageAnalyser.substract_offset(dataSet_cropOD).load()\n", "\n", "dataSet_cropOD.plot.pcolormesh(cmap='jet', vmin=0, col=scanAxis[0], row=scanAxis[1])\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "from DataContainer.MongoDB import MongoDB\n", "\n", "DB = MongoDB(mongoClient, mongoDB, date='2023/05/23')\n", "\n", "DB.create_global('0069', dataSet)\n", "\n", "DB.read_global_all('0069')\n", "\n", "DB.add_data('0069', dataSet_cropOD, engine='xarray')" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'TOF_free': 0.022,\n", " 'abs_img_freq': 110.858,\n", " 'absorption_imaging_flag': True,\n", " 'backup_data': True,\n", " 'blink_off_time': 0.001,\n", " 'blink_on_time': 0.001,\n", " 'c_duration': 0.2,\n", " 'carrier_amp': 3,\n", " 'carrier_freq': 0.85,\n", " 'carrier_offset': 0,\n", " 'carrier_phase': 0,\n", " 'channel_in_use': 1,\n", " 'cmot_final_current': 0.65,\n", " 'cmot_hold': 0.06,\n", " 'cmot_initial_current': 0.18,\n", " 'compX_current': 0,\n", " 'compX_current_sg': 0.0,\n", " 'compX_initial_current': 0,\n", " 'compY_current': 0,\n", " 'compY_current_sg': 0.0,\n", " 'compY_final_current': 0,\n", " 'compY_initial_current': 0,\n", " 'compZ_current': 0,\n", " 'compZ_current_sg': 0.189,\n", " 'compZ_final_current': 0.264,\n", " 'compZ_initial_current': 0,\n", " 'default_camera': 0,\n", " 'deltaf': 0.161,\n", " 'evap_1_arm_1_final_pow': 0.35,\n", " 'evap_1_arm_1_mod_depth_final': 0,\n", " 'evap_1_arm_1_mod_depth_initial': 1.0,\n", " 'evap_1_arm_1_mod_ramp_duration': 1.15,\n", " 'evap_1_arm_1_pow_ramp_duration': 1.65,\n", " 'evap_1_arm_1_start_pow': 7,\n", " 'evap_1_arm_2_final_pow': 5,\n", " 'evap_1_arm_2_ramp_duration': 0.5,\n", " 'evap_1_arm_2_start_pow': 0,\n", " 'evap_1_mod_ramp_trunc_value': 1,\n", " 'evap_1_pow_ramp_trunc_value': 1.0,\n", " 'evap_1_rate_constant_1': 0.525,\n", " 'evap_1_rate_constant_2': 0.51,\n", " 'evap_2_arm_1_final_pow': 0.037,\n", " 'evap_2_arm_1_start_pow': 0.35,\n", " 'evap_2_arm_2_final_pow': 0.09,\n", " 'evap_2_arm_2_start_pow': 5,\n", " 'evap_2_ramp_duration': 1.0,\n", " 'evap_2_ramp_trunc_value': 1.0,\n", " 'evap_2_rate_constant_1': 0.37,\n", " 'evap_2_rate_constant_2': 0.71,\n", " 'evap_3_arm_1_final_pow': 0.1038,\n", " 'evap_3_arm_1_mod_depth_final': 0.43,\n", " 'evap_3_arm_1_mod_depth_initial': 0,\n", " 'evap_3_arm_1_start_pow': 0.037,\n", " 'evap_3_ramp_duration': 0.1,\n", " 'evap_3_ramp_trunc_value': 1.0,\n", " 'evap_3_rate_constant_1': -0.879,\n", " 'evap_3_rate_constant_2': -0.297,\n", " 'final_amp': 0.00012,\n", " 'final_freq': 104.0,\n", " 'final_pow_1': 0.1038,\n", " 'final_pow_2': 0.09,\n", " 'gradCoil_current': 0.18,\n", " 'gradCoil_current_sg': 0,\n", " 'imaging_method': 'in_situ_absorption',\n", " 'imaging_pulse_duration': 2.5e-05,\n", " 'imaging_wavelength': 4.21291e-07,\n", " 'initial_amp': 0.62,\n", " 'initial_freq': 102.13,\n", " 'mod_depth_fin': 0.43,\n", " 'mod_depth_ini': 0,\n", " 'mod_depth_initial': 1.0,\n", " 'mot_3d_amp': 0.62,\n", " 'mot_3d_camera_trigger_duration': 0.00025,\n", " 'mot_3d_freq': 102.13,\n", " 'mot_load_duration': 2,\n", " 'odt_axis_camera_trigger_duration': 0.002,\n", " 'odt_hold_time_1': 0.01,\n", " 'odt_hold_time_2': 0.1,\n", " 'odt_hold_time_3': 0.1,\n", " 'odt_hold_time_4': 1.0,\n", " 'odt_hold_time_5': 0.01,\n", " 'operation_mode': 'SWEEP',\n", " 'pow_arm_1': 7,\n", " 'pow_arm_2': 0,\n", " 'pulse_delay': 8e-05,\n", " 'pulse_width': 0.01,\n", " 'push_amp': 0.16,\n", " 'push_freq': 102.66,\n", " 'ramp_duration': 1,\n", " 'recomp_ramp_duration': 0.5,\n", " 'recomp_ramp_pow_fin_arm_1': 0.1038,\n", " 'recomp_ramp_pow_fin_arm_2': 0.09,\n", " 'recomp_ramp_pow_ini_arm_1': 0.1038,\n", " 'recomp_ramp_pow_ini_arm_2': 0.09,\n", " 'save_results': False,\n", " 'sin_mod_amplitude': 0.00519,\n", " 'sin_mod_dc_offset': 0.1038,\n", " 'sin_mod_duration': nan,\n", " 'sin_mod_freq': nan,\n", " 'sin_mod_phase': 0.0,\n", " 'start_pow_1': 0.037,\n", " 'start_pow_2': 0.09,\n", " 'stern_gerlach_duration': 0.001,\n", " 'sweep_duration': 0.4,\n", " 'sweep_start_freq': 0.7695,\n", " 'sweep_stop_freq': 0.9305,\n", " 'tf_meas_ramp_duration': 0.1,\n", " 'wait_after_2dmot_off': 0,\n", " 'wait_time_between_images': 0.22,\n", " 'wavetype': 'SINE',\n", " 'x_offset': 0.0,\n", " 'x_offset_img': 0,\n", " 'y_offset': 0.0,\n", " 'y_offset_img': 0,\n", " 'z_offset': 0.189,\n", " 'z_offset_img': 0.189,\n", " 'compX_final_current': array([0. , 0.002, 0.004, 0.006, 0.008, 0.01 , 0.012, 0.014, 0.016,\n", " 0.018, 0.02 , 0. , 0.002, 0.004, 0.006, 0.008, 0.01 , 0.012,\n", " 0.014, 0.016, 0.018, 0.02 , 0. , 0.002, 0.004, 0.006, 0.008,\n", " 0.01 , 0.012, 0.014, 0.016, 0.018, 0.02 ]),\n", " 'runs': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1.,\n", " 1., 1., 1., 1., 1., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.]),\n", " 'scanAxis': ['compX_final_current', 'runs'],\n", " 'scanAxisLength': array([33., 33.])}" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "DB.read_global_all('0069')" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'OD': <xarray.DataArray 'OD' (y: 300, x: 300)>\n", " array([[-0.16156055, -0.05236126, -0.06646893, ..., -0.00193041,\n", " -0.04918329, -0.00193041],\n", " [-0.05072057, 0.01406993, -0.00193041, ..., 0.04252135,\n", " -0.03091795, 0.10034844],\n", " [-0.069753 , -0.06862178, -0.12908558, ..., 0.04187221,\n", " -0.00193041, -0.07390391],\n", " ...,\n", " [ 0.12868977, -0.02390931, -0.00193041, ..., 0.01487671,\n", " 0.07562783, 0.10343011],\n", " [ 0.09570806, 0.11247994, -0.04739278, ..., 0.01288468,\n", " -0.04845042, 0.02981829],\n", " [ 0.20392165, 0.07039025, -0.04955846, ..., 0.02792256,\n", " -0.12976378, -0.03367911]])\n", " Coordinates:\n", " compX_final_current float64 0.002\n", " runs float64 0.0\n", " Dimensions without coordinates: y, x\n", " Attributes:\n", " IMAGE_SUBCLASS: IMAGE_GRAYSCALE\n", " IMAGE_VERSION: 1.2\n", " IMAGE_WHITE_IS_ZERO: 0\n", " x_start: 810\n", " x_end: 1110\n", " y_end: 1025\n", " y_start: 725\n", " x_center: 960\n", " y_center: 875\n", " x_span: 300\n", " y_span: 300}" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res = DB.load_data_single('0069', 1, field={'OD':None})\n", "res" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from collections import OrderedDict\n", "\n", "import numpy as np\n", "import pymongo\n", "import xarray_mongodb\n", "import bson\n", "import builtins\n", "\n", "import xarray as xr\n", "\n", "from ToolFunction.ToolFunction import get_date\n", "\n", "\n", "npTypeDict = {v: getattr(builtins, k) for k, v in np.sctypeDict.items() if k in vars(builtins)}\n", "npArrayType = type(np.array([0]))\n", "\n", "\n", "class MongoDB:\n", " \n", " def __init__(self, mongoClient, mongoDB, date=None) -> None:\n", " self.mongoClient = mongoClient\n", " self.mongoDB = mongoDB\n", " self.xdb = xarray_mongodb.XarrayMongoDB(mongoDB)\n", " \n", " if date is None:\n", " date= get_date()\n", " self.set_date(date)\n", " \n", " def _convert_numpy_type(self, data):\n", " for key in data:\n", " typeKey = type(data[key])\n", " if typeKey in npTypeDict:\n", " data[key] = data[key].item()\n", " elif typeKey == npArrayType:\n", " data[key] = data[key].tolist()\n", " else:\n", " try:\n", " data[key] = data[key].item()\n", " except:\n", " pass\n", " return data\n", " \n", " def _break_dataSet(self, dataSet, scanAxis=None):\n", " \n", " if scanAxis is None:\n", " scanAxis = dataSet.attrs['scanAxis'] \n", " \n", " dataArray = dataSet.shotNum\n", " \n", " stackedDataArray = dataArray.stack(_scanAxis=tuple(scanAxis)) \n", "\n", " return stackedDataArray\n", " \n", " def set_date(self, date):\n", " date = date.split(\"/\")\n", " self.year = int(date[0])\n", " self.month = int(date[1])\n", " self.day = int(date[2])\n", " \n", " def create_global(self, shotNum, dataSet=None, date=None):\n", " if not date is None:\n", " self.set_date(date)\n", " \n", " data = {\n", " 'year': self.year,\n", " 'month': self.month,\n", " 'day': self.day,\n", " 'shotNum': shotNum,\n", " }\n", "\n", " self.mongoDB['global'].delete_many(data)\n", " \n", " data = {\n", " 'year': self.year,\n", " 'month': self.month,\n", " 'day': self.day,\n", " 'shotNum': shotNum,\n", " 'runNum': 0,\n", " 'global_parameters' : {},\n", " }\n", " \n", " global_parameters = self._convert_numpy_type(dataSet.attrs)\n", " \n", " if not dataSet is None:\n", " data['global_parameters'].update(global_parameters)\n", " \n", " data = self._convert_numpy_type(data)\n", " \n", " if 'scanAxis' in dataSet.attrs:\n", " \n", " del data['global_parameters']['scanAxis']\n", " del data['global_parameters']['scanAxisLength']\n", " \n", " scanAxis = dataSet.attrs['scanAxis']\n", " data['global_parameters'].update(\n", " {\n", " key:0\n", " for key in scanAxis\n", " }\n", " )\n", " \n", " stackedDataArray = self._break_dataSet(dataSet)\n", " try:\n", " stackedDataArray.load()\n", " except:\n", " pass\n", " \n", " stackedDataArray = stackedDataArray.groupby('_scanAxis')\n", " \n", " for i in stackedDataArray:\n", " \n", " stackedDataArray_single = i[1]\n", " \n", " data.update(\n", " {\n", " 'runNum': int(stackedDataArray_single.item())\n", " }\n", " )\n", " \n", " data['global_parameters'].update(\n", " {\n", " key: stackedDataArray_single[key].item()\n", " for key in scanAxis\n", " }\n", " )\n", " \n", " if '_id' in data:\n", " del data['_id']\n", " \n", " self.mongoDB['global'].insert_one(data)\n", " \n", " else:\n", " self.mongoDB['global'].insert_one(data)\n", " \n", " def _add_data_normal(self, shotNum, runNum, data):\n", " \n", " if runNum is None:\n", " runNum = 0\n", " \n", " filter = { \n", " 'year': self.year,\n", " 'month': self.month,\n", " 'day': self.day,\n", " 'shotNum': shotNum,\n", " 'runNum': runNum,\n", " }\n", " \n", " self.mongoDB['global'].update_one(filter, {\"$set\": data}, upsert=False)\n", " \n", " def _add_data_xarray_dataArray(self, shotNum, dataArray, name=None, scanAxis=None):\n", " \n", " if scanAxis is None:\n", " scanAxis = list(dataArray.coords)\n", " \n", " dataArray.attrs = self._convert_numpy_type(dataArray.attrs)\n", " \n", " stackedDataArray = dataArray.stack(_scanAxis=tuple(scanAxis))\n", " stackedDataArray = stackedDataArray.groupby('_scanAxis')\n", " \n", " filter = { \n", " 'year': self.year,\n", " 'month': self.month,\n", " 'day': self.day,\n", " 'shotNum': shotNum,\n", " }\n", " \n", " for i in stackedDataArray:\n", " \n", " stackedDataArray_single = i[1].drop('_scanAxis')\n", " \n", " global_parameters = {\n", " 'global_parameters.' + key: stackedDataArray_single[key].item()\n", " for key in scanAxis\n", " }\n", "\n", " filter.update(global_parameters)\n", " \n", " mongoID, _ = self.xdb.put(stackedDataArray_single)\n", " \n", " data_label = {\n", " dataArray.name: \n", " {\n", " 'name': dataArray.name,\n", " 'mongoID': mongoID,\n", " 'engine': 'xarray',\n", " 'dtype': 'dataArray',\n", " }\n", " }\n", " \n", " self.mongoDB['global'].update_one(filter, {\"$set\": data_label}, upsert=False)\n", " \n", " def _add_data_xarray_dataSet(self, shotNum, dataSet, name, scanAxis=None):\n", " \n", " if scanAxis is None:\n", " scanAxis = list(dataSet.coords)\n", " \n", " dataSet.attrs = self._convert_numpy_type(dataSet.attrs)\n", " \n", " for key in list(dataSet.data_vars):\n", " dataSet[key].attrs = self._convert_numpy_type(dataSet[key].attrs)\n", " \n", " stackedDataSet = dataSet.stack(_scanAxis=tuple(scanAxis))\n", " stackedDataSet = stackedDataSet.groupby('_scanAxis')\n", " \n", " filter = { \n", " 'year': self.year,\n", " 'month': self.month,\n", " 'day': self.day,\n", " 'shotNum': shotNum,\n", " }\n", " \n", " for i in stackedDataSet:\n", " \n", " stackedDataSet_single = i[1].drop('_scanAxis')\n", " \n", " global_parameters = {\n", " 'global_parameters.' + key: stackedDataSet_single[key].item()\n", " for key in scanAxis\n", " }\n", "\n", " filter.update(global_parameters)\n", " \n", " mongoID, _ = self.xdb.put(dataSet)\n", " \n", " data_label = {\n", " name: \n", " {\n", " 'name': name,\n", " 'mongoID': mongoID,\n", " 'engine': 'xarray',\n", " 'dtype': 'dataSet',\n", " }\n", " }\n", " \n", " self.mongoDB['global'].update_one(filter, {\"$set\": data_label}, upsert=False)\n", " \n", " def _add_data_additional(self, shotNum, runNum, data, name):\n", " \n", " if runNum is None:\n", " runNum = 0\n", " \n", " filter = { \n", " 'year': self.year,\n", " 'month': self.month,\n", " 'day': self.day,\n", " 'shotNum': shotNum,\n", " 'runNum': runNum,\n", " }\n", " \n", " mongoID = self.mongoDB.additional.insert_one(data).inserted_id\n", " \n", " data_label = {\n", " name: \n", " {\n", " 'name': name,\n", " 'mongoID': mongoID,\n", " 'engine': 'additional',\n", " 'dtype': 'dict',\n", " }\n", " }\n", " \n", " self.mongoDB['global'].update_one(filter, {\"$set\": data_label}, upsert=False)\n", " \n", " def add_data(self, shotNum, data, runNum=None, date=None, name=None, engine='normal'):\n", " if not date is None:\n", " self.set_date(date)\n", " \n", " if engine == 'normal':\n", " self._add_data_normal(shotNum=shotNum, runNum=runNum, data=data)\n", " elif engine == 'xarray':\n", " if isinstance(data, type(xr.Dataset())):\n", " self._add_data_xarray_dataSet(shotNum=shotNum, dataSet=data, name=name)\n", " else:\n", " self._add_data_xarray_dataArray(shotNum=shotNum, dataArray=data, name=name)\n", " elif engine == 'additional':\n", " self._add_data_additional(shotNum=shotNum, runNum=runNum, data=data, name=name)\n", " \n", " def read_global_single(self, shotNum, runNum, date=None):\n", " \n", " if not date is None:\n", " self.set_date(date)\n", " \n", " filter = { \n", " 'year': self.year,\n", " 'month': self.month,\n", " 'day': self.day,\n", " 'shotNum': shotNum,\n", " 'runNum': runNum,\n", " }\n", " \n", " return self.mongoDB['global'].find_one(filter)\n", " \n", " def read_global_all(self, shotNum, date=None):\n", " \n", " from xarray.core.utils import equivalent\n", " \n", " if not date is None:\n", " self.set_date(date)\n", " \n", " filter = { \n", " 'year': self.year,\n", " 'month': self.month,\n", " 'day': self.day,\n", " 'shotNum': shotNum,\n", " }\n", " \n", " result = {}\n", " dropped_attrs = OrderedDict()\n", " \n", " docs = self.mongoDB['global'].find(filter).sort('runNum')\n", " docs = [doc['global_parameters'] for doc in docs]\n", " \n", " for doc in docs:\n", " \n", " global_parameters = doc\n", " \n", " result.update(\n", " {\n", " key: value\n", " for key, value in global_parameters.items()\n", " if key not in result and key not in dropped_attrs.keys()\n", " }\n", " )\n", " \n", " result = {\n", " key: value\n", " for key, value in result.items()\n", " if key not in global_parameters or equivalent(global_parameters[key], value)\n", " }\n", " \n", " dropped_attrs.update(\n", " {\n", " key: []\n", " for key in global_parameters if key not in result \n", " }\n", " )\n", " \n", " for doc in docs:\n", " \n", " global_parameters = doc\n", " \n", " dropped_attrs.update(\n", " {\n", " key: np.append(dropped_attrs[key], global_parameters[key])\n", " for key in dropped_attrs.keys()\n", " }\n", " ) \n", " \n", " scan_attrs = OrderedDict()\n", " scan_length = []\n", " \n", " for attrs_key in dropped_attrs.keys():\n", " flag = True\n", " for key in scan_attrs.keys():\n", " if equivalent(scan_attrs[key], dropped_attrs[attrs_key]):\n", " flag = False\n", " result.update({attrs_key: key})\n", " break\n", " if flag:\n", " scan_attrs.update({\n", " attrs_key: dropped_attrs[attrs_key]\n", " })\n", " scan_length = np.append(scan_length, len(dropped_attrs[attrs_key]))\n", "\n", " result.update(\n", " {\n", " key: value\n", " for key, value in scan_attrs.items()\n", " }\n", " )\n", "\n", " result.update(\n", " {\n", " \"scanAxis\": list(scan_attrs.keys()),\n", " \"scanAxisLength\": scan_length,\n", " }\n", " )\n", "\n", " return result\n", " \n", " def _load_data_single(self, mongoID, engine):\n", " if engine == 'xarray':\n", " return self.xdb.get(mongoID)\n", " if engine == 'additional':\n", " return self.mongoDB.additional.find_one({'_id': mongoID})\n", " \n", " def load_data_single(self, shotNum=None, runNum=None, globalDict=None, date=None, field=None):\n", " \n", " if not date is None:\n", " self.set_date(date)\n", " \n", " if globalDict is None:\n", " globalDict = self.read_global_single(shotNum=shotNum, runNum=runNum)\n", " \n", " if field is None:\n", " field = globalDict\n", " \n", " res = {}\n", " \n", " for key in field:\n", " if isinstance(globalDict[key], dict) and ('mongoID' in globalDict[key]):\n", " mongoID = globalDict[key]['mongoID']\n", " engine = globalDict[key]['engine']\n", " \n", " res.update(\n", " {\n", " key: self._load_data_single(mongoID=mongoID, engine=engine)\n", " }\n", " )\n", " \n", " return res\n", " \n", " def load_data(self, shotNum=None, globalDict=None, date=None, field=None):\n", " pass\n", " \n", "DB = MongoDB(mongoClient, mongoDB, date='2023/05/23')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "res = DB.load_data_single(shotNum='0069', field=['test'])\n", "res" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "DB.add_data('0069', dataSet_cropOD, engine='xarray')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data = {\n", " 'test':100\n", "}\n", "DB._add_data_additional(shotNum, data, name='test')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "DB._add_data_xarray_dataArray(shotNum, dataSet_cropOD)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "test = DB._convert_numpy_type(dataSet.OD.attrs)\n", "\n", "type(test['IMAGE_WHITE_IS_ZERO'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "type(dataSet.OD.attrs['IMAGE_WHITE_IS_ZERO'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "DB._add_data_xarray_dataSet(shotNum, dataSet, 'aaa')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "xdb.put(dataSet_cropOD)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "DB.create_global(shotNum, dataSet)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'TOF_free': 0.022,\n", " 'abs_img_freq': 110.858,\n", " 'absorption_imaging_flag': True,\n", " 'backup_data': True,\n", " 'blink_off_time': 0.001,\n", " 'blink_on_time': 0.001,\n", " 'c_duration': 0.2,\n", " 'carrier_amp': 3,\n", " 'carrier_freq': 0.85,\n", " 'carrier_offset': 0,\n", " 'carrier_phase': 0,\n", " 'channel_in_use': 1,\n", " 'cmot_final_current': 0.65,\n", " 'cmot_hold': 0.06,\n", " 'cmot_initial_current': 0.18,\n", " 'compX_current': 0,\n", " 'compX_current_sg': 0.0,\n", " 'compX_initial_current': 0,\n", " 'compY_current': 0,\n", " 'compY_current_sg': 0.0,\n", " 'compY_final_current': 0,\n", " 'compY_initial_current': 0,\n", " 'compZ_current': 0,\n", " 'compZ_current_sg': 0.189,\n", " 'compZ_final_current': 0.264,\n", " 'compZ_initial_current': 0,\n", " 'default_camera': 0,\n", " 'deltaf': 0.161,\n", " 'evap_1_arm_1_final_pow': 0.35,\n", " 'evap_1_arm_1_mod_depth_final': 0,\n", " 'evap_1_arm_1_mod_depth_initial': 1.0,\n", " 'evap_1_arm_1_mod_ramp_duration': 1.15,\n", " 'evap_1_arm_1_pow_ramp_duration': 1.65,\n", " 'evap_1_arm_1_start_pow': 7,\n", " 'evap_1_arm_2_final_pow': 5,\n", " 'evap_1_arm_2_ramp_duration': 0.5,\n", " 'evap_1_arm_2_start_pow': 0,\n", " 'evap_1_mod_ramp_trunc_value': 1,\n", " 'evap_1_pow_ramp_trunc_value': 1.0,\n", " 'evap_1_rate_constant_1': 0.525,\n", " 'evap_1_rate_constant_2': 0.51,\n", " 'evap_2_arm_1_final_pow': 0.037,\n", " 'evap_2_arm_1_start_pow': 0.35,\n", " 'evap_2_arm_2_final_pow': 0.09,\n", " 'evap_2_arm_2_start_pow': 5,\n", " 'evap_2_ramp_duration': 1.0,\n", " 'evap_2_ramp_trunc_value': 1.0,\n", " 'evap_2_rate_constant_1': 0.37,\n", " 'evap_2_rate_constant_2': 0.71,\n", " 'evap_3_arm_1_final_pow': 0.1038,\n", " 'evap_3_arm_1_mod_depth_final': 0.43,\n", " 'evap_3_arm_1_mod_depth_initial': 0,\n", " 'evap_3_arm_1_start_pow': 0.037,\n", " 'evap_3_ramp_duration': 0.1,\n", " 'evap_3_ramp_trunc_value': 1.0,\n", " 'evap_3_rate_constant_1': -0.879,\n", " 'evap_3_rate_constant_2': -0.297,\n", " 'final_amp': 0.00012,\n", " 'final_freq': 104.0,\n", " 'final_pow_1': 0.1038,\n", " 'final_pow_2': 0.09,\n", " 'gradCoil_current': 0.18,\n", " 'gradCoil_current_sg': 0,\n", " 'imaging_method': 'in_situ_absorption',\n", " 'imaging_pulse_duration': 2.5e-05,\n", " 'imaging_wavelength': 4.21291e-07,\n", " 'initial_amp': 0.62,\n", " 'initial_freq': 102.13,\n", " 'mod_depth_fin': 0.43,\n", " 'mod_depth_ini': 0,\n", " 'mod_depth_initial': 1.0,\n", " 'mot_3d_amp': 0.62,\n", " 'mot_3d_camera_trigger_duration': 0.00025,\n", " 'mot_3d_freq': 102.13,\n", " 'mot_load_duration': 2,\n", " 'odt_axis_camera_trigger_duration': 0.002,\n", " 'odt_hold_time_1': 0.01,\n", " 'odt_hold_time_2': 0.1,\n", " 'odt_hold_time_3': 0.1,\n", " 'odt_hold_time_4': 1.0,\n", " 'odt_hold_time_5': 0.01,\n", " 'operation_mode': 'SWEEP',\n", " 'pow_arm_1': 7,\n", " 'pow_arm_2': 0,\n", " 'pulse_delay': 8e-05,\n", " 'pulse_width': 0.01,\n", " 'push_amp': 0.16,\n", " 'push_freq': 102.66,\n", " 'ramp_duration': 1,\n", " 'recomp_ramp_duration': 0.5,\n", " 'recomp_ramp_pow_fin_arm_1': 0.1038,\n", " 'recomp_ramp_pow_fin_arm_2': 0.09,\n", " 'recomp_ramp_pow_ini_arm_1': 0.1038,\n", " 'recomp_ramp_pow_ini_arm_2': 0.09,\n", " 'save_results': False,\n", " 'sin_mod_amplitude': 0.00519,\n", " 'sin_mod_dc_offset': 0.1038,\n", " 'sin_mod_duration': nan,\n", " 'sin_mod_freq': nan,\n", " 'sin_mod_phase': 0.0,\n", " 'start_pow_1': 0.037,\n", " 'start_pow_2': 0.09,\n", " 'stern_gerlach_duration': 0.001,\n", " 'sweep_duration': 0.4,\n", " 'sweep_start_freq': 0.7695,\n", " 'sweep_stop_freq': 0.9305,\n", " 'tf_meas_ramp_duration': 0.1,\n", " 'wait_after_2dmot_off': 0,\n", " 'wait_time_between_images': 0.22,\n", " 'wavetype': 'SINE',\n", " 'x_offset': 0.0,\n", " 'x_offset_img': 0,\n", " 'y_offset': 0.0,\n", " 'y_offset_img': 0,\n", " 'z_offset': 0.189,\n", " 'z_offset_img': 0.189,\n", " 'compX_final_current': array([0. , 0.002, 0.004, 0.006, 0.008, 0.01 , 0.012, 0.014, 0.016,\n", " 0.018, 0.02 , 0. , 0.002, 0.004, 0.006, 0.008, 0.01 , 0.012,\n", " 0.014, 0.016, 0.018, 0.02 , 0. , 0.002, 0.004, 0.006, 0.008,\n", " 0.01 , 0.012, 0.014, 0.016, 0.018, 0.02 ]),\n", " 'runs': array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 1., 1., 1., 1.,\n", " 1., 1., 1., 1., 1., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2., 2.]),\n", " 'scanAxis': ['compX_final_current', 'runs'],\n", " 'scanAxisLength': array([33., 33.])}" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "DB.read_global_all('0069')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "DB.load_data_single(shotNum='0069', runNum=0).keys()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Do a 2D two-peak gaussian fit to the OD images" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "### Do the fit" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from Analyser.FitAnalyser import ThomasFermi2dModel, DensityProfileBEC2dModel, polylog2_2d\n", "\n", "fitModel = DensityProfileBEC2dModel()\n", "# fitModel = ThomasFermi2dModel()\n", "\n", "fitAnalyser = FitAnalyser(fitModel, fitDim=2)\n", "\n", "# fitAnalyser = FitAnalyser(\"Gaussian-2D\", fitDim=2)\n", "\n", "# dataSet_cropOD = dataSet_cropOD.chunk((1,1,100,100))\n", "\n", "params = fitAnalyser.guess(dataSet_cropOD, guess_kwargs=dict(pureBECThreshold=0.3), dask=\"parallelized\")\n", "fitResult = fitAnalyser.fit(dataSet_cropOD, params).load()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "params.compute().item()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fitCurve = fitAnalyser.eval(fitResult, x=np.arange(300), y=np.arange(300), dask=\"parallelized\").load()\n", "\n", "fitCurve.plot.pcolormesh(cmap='jet', vmin=0, col=scanAxis[0], row=scanAxis[1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fitModel2 = Polylog22dModel(prefix='thermal_')\n", "fitAnalyser2 = FitAnalyser(fitModel2, fitDim=2)\n", "fitCurve2 = fitAnalyser2.eval(fitResult, x=np.arange(300), y=np.arange(300), dask=\"parallelized\").load()\n", "\n", "fitModel3 = ThomasFermi2dModel(prefix='BEC_')\n", "fitAnalyser3 = FitAnalyser(fitModel3, fitDim=2)\n", "fitCurve3 = fitAnalyser3.eval(fitResult, x=np.arange(300), y=np.arange(300), dask=\"parallelized\").load()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig = plt.figure()\n", "ax = fig.gca()\n", "\n", "dataSet_cropOD.sum(dim='x').plot(ax=ax, col=scanAxis[0], row=scanAxis[1])\n", "fitCurve.sum(dim='x').plot(ax=ax, col=scanAxis[0], row=scanAxis[1])\n", "fitCurve2.sum(dim='x').plot(ax=ax, col=scanAxis[0], row=scanAxis[1])\n", "fitCurve3.sum(dim='x').plot(ax=ax, col=scanAxis[0], row=scanAxis[1])\n", "\n", "plt.show()\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "\n", "dataSet_cropOD.sum(dim='y').plot(ax=ax, col=scanAxis[0], row=scanAxis[1])\n", "fitCurve.sum(dim='y').plot(ax=ax, col=scanAxis[0], row=scanAxis[1])\n", "fitCurve2.sum(dim='y').plot(ax=ax, col=scanAxis[0], row=scanAxis[1])\n", "fitCurve3.sum(dim='y').plot(ax=ax, col=scanAxis[0], row=scanAxis[1])\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "value = fitAnalyser.get_fit_value(fitResult)\n", "std = fitAnalyser.get_fit_std(fitResult)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fitResult.item()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "value" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# upload data to MongoDB" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "xdb = xarray_mongodb.XarrayMongoDB(mongoDB)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "value = fitAnalyser.get_fit_value(fitResult)\n", "value" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dataSet_cropOD" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dataSet_cropOD.attrs['name'] = 'name'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dataSet_cropOD.attrs['IMAGE_WHITE_IS_ZERO']" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dataSet_cropOD" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "type(dataSet_cropOD.attrs['absorption_imaging_flag'].item())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "del dataSet_cropOD.attrs['IMAGE_WHITE_IS_ZERO']" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import builtins\n", "npTypeDict = {v: getattr(builtins, k) for k, v in np.typeDict.items() if k in vars(builtins)}\n", "\n", "for key in dataSet_cropOD.attrs:\n", " typeKey = type(dataSet_cropOD.attrs[key])\n", " if typeKey in npTypeDict:\n", " dataSet_cropOD.attrs[key] = dataSet_cropOD.attrs[key].item()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import builtins\n", "a = {v: getattr(builtins, k) for k, v in np.typeDict.items() if k in vars(builtins)}\n", "a\n", "a[type(dataSet_cropOD.attrs['absorption_imaging_flag'])]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "_id, _ = xdb.put(dataSet_cropOD)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "_id" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# _id = '646e3cbbdb91e17db4b4cbd2'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "xdb.get(_id)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "c = bson.objectid.ObjectId('646e4919802812f029b385d7')\n", "c" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "xdb.get(c)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import datetime\n", "post = {\"author\": \"Mike\",\n", " \"data_id\": _id,\n", " \"tags\": [\"mongodb\", \"python\", \"pymongo\"],\n", " \"date\": datetime.datetime.utcnow(),\n", " }\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "posts = mongoCollection\n", "post_id = posts.insert_one(post).inserted_id\n", "post_id" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "posts.find_one({'_id': bson.objectid.ObjectId('648318ae129ec5a1a24a7353')})" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for i in posts.find_one({'_id': bson.objectid.ObjectId('648318ae129ec5a1a24a7353')}):\n", " bb = i\n", " \n", "bb['test']" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import gridfs\n", "\n", "fs = gridfs.GridFS(mongoDB, 'xarray')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f = dataSet_cropOD.to_dict()\n", "f" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(ff)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import json\n", "ff = json.dumps(f) \n", "fs.put(ff, encoding='utf-8')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fs.put(b\"hello world\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "c = bson.objectid.ObjectId('6482f4a3129ec5a1a24a7342')\n", "\n", "fs.get(c)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(fs.get(c))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.sqrt(np.sum([0.061**2, 0.334**2, 0.447**2]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "py39", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2}
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