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{ "cells": [ { "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", "import copy\n", "\n", "from uncertainties import ufloat\n", "from uncertainties import unumpy as unp\n", "from uncertainties import umath\n", "import random\n", "import matplotlib.pyplot as plt\n", "plt.rcParams['font.size'] = 12\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 NewFitModel, DensityProfileBEC2dModel\n", "from ToolFunction.ToolFunction import *\n", "\n", "from scipy.optimize import curve_fit\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()\n", "\n", "# %matplotlib notebook" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Start a client for parallel computing" ] }, { "cell_type": "code", "execution_count": 2, "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-a5e5cea6-2fbd-11ee-8430-80e82ce2fa8e</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;\">49266936</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> 8\n", " </td>\n", " </tr>\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Total threads:</strong> 128\n", " </td>\n", " <td style=\"text-align: left;\">\n", " <strong>Total memory:</strong> 149.01 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-6db1fb2d-d8be-4f76-9fcf-6a7cc9ed86ee</p>\n", " <table style=\"width: 100%; text-align: left;\">\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Comm:</strong> tcp://127.0.0.1:63903\n", " </td>\n", " <td style=\"text-align: left;\">\n", " <strong>Workers:</strong> 8\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> 128\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> 149.01 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://127.0.0.1:63943\n", " </td>\n", " <td style=\"text-align: left;\">\n", " <strong>Total threads: </strong> 16\n", " </td>\n", " </tr>\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:63946/status\" target=\"_blank\">http://127.0.0.1:63946/status</a>\n", " </td>\n", " <td style=\"text-align: left;\">\n", " <strong>Memory: </strong> 18.63 GiB\n", " </td>\n", " </tr>\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Nanny: </strong> tcp://127.0.0.1:63906\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-sctehcfb\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://127.0.0.1:63957\n", " </td>\n", " <td style=\"text-align: left;\">\n", " <strong>Total threads: </strong> 16\n", " </td>\n", " </tr>\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:63959/status\" target=\"_blank\">http://127.0.0.1:63959/status</a>\n", " </td>\n", " <td style=\"text-align: left;\">\n", " <strong>Memory: </strong> 18.63 GiB\n", " </td>\n", " </tr>\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Nanny: </strong> tcp://127.0.0.1:63907\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-b1kdawd2\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://127.0.0.1:63948\n", " </td>\n", " <td style=\"text-align: left;\">\n", " <strong>Total threads: </strong> 16\n", " </td>\n", " </tr>\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:63951/status\" target=\"_blank\">http://127.0.0.1:63951/status</a>\n", " </td>\n", " <td style=\"text-align: left;\">\n", " <strong>Memory: </strong> 18.63 GiB\n", " </td>\n", " </tr>\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Nanny: </strong> tcp://127.0.0.1:63908\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-845evwbp\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://127.0.0.1:63942\n", " </td>\n", " <td style=\"text-align: left;\">\n", " <strong>Total threads: </strong> 16\n", " </td>\n", " </tr>\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:63944/status\" target=\"_blank\">http://127.0.0.1:63944/status</a>\n", " </td>\n", " <td style=\"text-align: left;\">\n", " <strong>Memory: </strong> 18.63 GiB\n", " </td>\n", " </tr>\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Nanny: </strong> tcp://127.0.0.1:63909\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-xneexydx\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://127.0.0.1:63949\n", " </td>\n", " <td style=\"text-align: left;\">\n", " <strong>Total threads: </strong> 16\n", " </td>\n", " </tr>\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:63953/status\" target=\"_blank\">http://127.0.0.1:63953/status</a>\n", " </td>\n", " <td style=\"text-align: left;\">\n", " <strong>Memory: </strong> 18.63 GiB\n", " </td>\n", " </tr>\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Nanny: </strong> tcp://127.0.0.1:63910\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-zb64b79v\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://127.0.0.1:63950\n", " </td>\n", " <td style=\"text-align: left;\">\n", " <strong>Total threads: </strong> 16\n", " </td>\n", " </tr>\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:63954/status\" target=\"_blank\">http://127.0.0.1:63954/status</a>\n", " </td>\n", " <td style=\"text-align: left;\">\n", " <strong>Memory: </strong> 18.63 GiB\n", " </td>\n", " </tr>\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Nanny: </strong> tcp://127.0.0.1:63911\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-q6lyanmx\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: 6</h4>\n", " </summary>\n", " <table style=\"width: 100%; text-align: left;\">\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Comm: </strong> tcp://127.0.0.1:63925\n", " </td>\n", " <td style=\"text-align: left;\">\n", " <strong>Total threads: </strong> 16\n", " </td>\n", " </tr>\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:63934/status\" target=\"_blank\">http://127.0.0.1:63934/status</a>\n", " </td>\n", " <td style=\"text-align: left;\">\n", " <strong>Memory: </strong> 18.63 GiB\n", " </td>\n", " </tr>\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Nanny: </strong> tcp://127.0.0.1:63912\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-1cvfk3q5\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: 7</h4>\n", " </summary>\n", " <table style=\"width: 100%; text-align: left;\">\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Comm: </strong> tcp://127.0.0.1:63958\n", " </td>\n", " <td style=\"text-align: left;\">\n", " <strong>Total threads: </strong> 16\n", " </td>\n", " </tr>\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:63961/status\" target=\"_blank\">http://127.0.0.1:63961/status</a>\n", " </td>\n", " <td style=\"text-align: left;\">\n", " <strong>Memory: </strong> 18.63 GiB\n", " </td>\n", " </tr>\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Nanny: </strong> tcp://127.0.0.1:63913\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-0agsu53e\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://127.0.0.1:63903' processes=8 threads=128, memory=149.01 GiB>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from dask.distributed import Client\n", "client = Client(n_workers=8, threads_per_worker=16, processes=True, memory_limit='20GB')\n", "client" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Start a client for Mongo DB" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import pymongo\n", "import xarray_mongodb\n", "\n", "from DataContainer.MongoDB import MongoDB\n", "\n", "mongoClient = pymongo.MongoClient('mongodb://control:DyLab2021@127.0.0.1:27017/?authMechanism=DEFAULT')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set global path for experiment" ] }, { "cell_type": "code", "execution_count": 4, "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_0\",\n", " \"images/ODT_1_Axis_Camera/in_situ_absorption\": \"camera_1\",\n", " \"images/ODT_2_Axis_Camera/in_situ_absorption\": \"camera_2\",\n", "}\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "img_dir = 'C:/Users/control/DyLab/Experiments/DyBEC/'\n", "SequenceName = \"Repetition_scan\"\n", "folderPath = img_dir + SequenceName + \"/\" + get_date()\n", "\n", "mongoDB = mongoClient[SequenceName]\n", "\n", "DB = MongoDB(mongoClient, mongoDB, date=get_date())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Repetition Scans" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## scan MOT freq" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The detected scaning axes and values are: \n", "\n", "{'initial_horz_freq': array([102.75, 102.8 , 102.85, 102.9 , 102.95, 103. , 103.05]), 'initial_vert_freq': array([101.8 , 101.85, 101.9 , 101.95, 102. , 102.05, 102.1 , 102.15,\n", " 102.2 , 102.25])}\n" ] }, { "data": { "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') 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], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib notebook\n", "shotNum = \"0014\"\n", "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n", "\n", "dataSetDict = {\n", " dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n", " for i in [0,1]\n", "}\n", "\n", "dataSet = dataSetDict[\"camera_1\"]\n", "\n", "print_scanAxis(dataSet)\n", "\n", "scanAxis = get_scanAxis(dataSet)\n", "\n", "dataSet = auto_rechunk(dataSet)\n", "\n", "dataSet = imageAnalyser.get_absorption_images(dataSet)\n", "\n", "imageAnalyser.center = (310, 815)\n", "imageAnalyser.span = (550, 1275)\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", "Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n", "Ncount_mean = calculate_mean(Ncount)\n", "Ncount_std = calculate_std(Ncount)\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "Ncount_mean.plot.pcolormesh(ax=ax, vmin=0, cmap='jet', cbar_kwargs = dict(label='NCount'))\n", "plt.xlabel('Vert AOM Frequency')\n", "plt.ylabel('Horz AOM Frequency')\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "# DB.create_global(shotNum, dataSet)\n", "# DB.add_data(shotNum, dataSet_cropOD, engine='xarray')" ] }, { "cell_type": "code", "execution_count": 14, "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 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".xr-array-in:checked ~ .xr-array-preview {\n", " display: none;\n", "}\n", "\n", ".xr-array-in:checked ~ .xr-array-data,\n", ".xr-array-preview {\n", " display: inline-block;\n", "}\n", "\n", ".xr-dim-list {\n", " display: inline-block !important;\n", " list-style: none;\n", " padding: 0 !important;\n", " margin: 0;\n", "}\n", "\n", ".xr-dim-list li {\n", " display: inline-block;\n", " padding: 0;\n", " margin: 0;\n", "}\n", "\n", ".xr-dim-list:before {\n", " content: '(';\n", "}\n", "\n", ".xr-dim-list:after {\n", " content: ')';\n", "}\n", "\n", ".xr-dim-list li:not(:last-child):after {\n", " content: ',';\n", " padding-right: 5px;\n", "}\n", "\n", ".xr-has-index {\n", " font-weight: bold;\n", "}\n", "\n", ".xr-var-list,\n", ".xr-var-item {\n", " display: contents;\n", "}\n", "\n", ".xr-var-item > div,\n", ".xr-var-item label,\n", ".xr-var-item > .xr-var-name span {\n", " background-color: var(--xr-background-color-row-even);\n", " margin-bottom: 0;\n", "}\n", "\n", ".xr-var-item > .xr-var-name:hover span {\n", " padding-right: 5px;\n", "}\n", "\n", ".xr-var-list > li:nth-child(odd) > div,\n", ".xr-var-list > li:nth-child(odd) > label,\n", ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n", " background-color: var(--xr-background-color-row-odd);\n", "}\n", "\n", ".xr-var-name {\n", " grid-column: 1;\n", "}\n", "\n", ".xr-var-dims {\n", " grid-column: 2;\n", "}\n", "\n", ".xr-var-dtype {\n", " grid-column: 3;\n", " text-align: right;\n", " color: var(--xr-font-color2);\n", "}\n", "\n", ".xr-var-preview {\n", " grid-column: 4;\n", "}\n", "\n", ".xr-index-preview {\n", " grid-column: 2 / 5;\n", " color: var(--xr-font-color2);\n", "}\n", "\n", ".xr-var-name,\n", ".xr-var-dims,\n", ".xr-var-dtype,\n", ".xr-preview,\n", ".xr-attrs dt {\n", " white-space: nowrap;\n", " overflow: hidden;\n", " text-overflow: ellipsis;\n", " padding-right: 10px;\n", "}\n", "\n", ".xr-var-name:hover,\n", ".xr-var-dims:hover,\n", ".xr-var-dtype:hover,\n", ".xr-attrs dt:hover {\n", " overflow: visible;\n", " width: auto;\n", " z-index: 1;\n", "}\n", "\n", ".xr-var-attrs,\n", ".xr-var-data,\n", ".xr-index-data {\n", " display: none;\n", " background-color: var(--xr-background-color) !important;\n", " padding-bottom: 5px !important;\n", "}\n", "\n", ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n", ".xr-var-data-in:checked ~ .xr-var-data,\n", ".xr-index-data-in:checked ~ .xr-index-data {\n", " display: block;\n", "}\n", "\n", ".xr-var-data > table {\n", " float: right;\n", "}\n", "\n", ".xr-var-name span,\n", ".xr-var-data,\n", ".xr-index-name div,\n", ".xr-index-data,\n", ".xr-attrs {\n", " padding-left: 25px !important;\n", "}\n", "\n", ".xr-attrs,\n", ".xr-var-attrs,\n", ".xr-var-data,\n", ".xr-index-data {\n", " grid-column: 1 / -1;\n", "}\n", "\n", "dl.xr-attrs {\n", " padding: 0;\n", " margin: 0;\n", " display: grid;\n", " grid-template-columns: 125px auto;\n", "}\n", "\n", ".xr-attrs dt,\n", ".xr-attrs dd {\n", " padding: 0;\n", " margin: 0;\n", " float: left;\n", " padding-right: 10px;\n", " width: auto;\n", "}\n", "\n", ".xr-attrs dt {\n", " font-weight: normal;\n", " grid-column: 1;\n", "}\n", "\n", ".xr-attrs dt:hover span {\n", " display: inline-block;\n", " background: 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.DataArray 'OD' (initial_horz_freq: 1, initial_vert_freq: 1)>\n", "array([[65351.30239154]])\n", "Coordinates:\n", " * initial_horz_freq (initial_horz_freq) float64 103.0\n", " * initial_vert_freq (initial_vert_freq) float64 101.8</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'OD'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>initial_horz_freq</span>: 1</li><li><span class='xr-has-index'>initial_vert_freq</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-1a84307d-0552-4450-aa56-34b422ac9c57' class='xr-array-in' type='checkbox' checked><label for='section-1a84307d-0552-4450-aa56-34b422ac9c57' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>6.535e+04</span></div><div class='xr-array-data'><pre>array([[65351.30239154]])</pre></div></div></li><li class='xr-section-item'><input id='section-fc5fcc47-c424-4595-8493-b6fb33ab058a' class='xr-section-summary-in' type='checkbox' checked><label for='section-fc5fcc47-c424-4595-8493-b6fb33ab058a' 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'>initial_horz_freq</span></div><div class='xr-var-dims'>(initial_horz_freq)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>103.0</div><input id='attrs-2cb7430e-3fbe-4518-a5b0-c4ffa0684653' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2cb7430e-3fbe-4518-a5b0-c4ffa0684653' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-601e3bd6-edb0-4887-b832-a015a068cfbd' class='xr-var-data-in' type='checkbox'><label for='data-601e3bd6-edb0-4887-b832-a015a068cfbd' 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([103.05])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>initial_vert_freq</span></div><div class='xr-var-dims'>(initial_vert_freq)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>101.8</div><input id='attrs-ceb926a9-c49d-45f6-b305-c3026f758a01' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ceb926a9-c49d-45f6-b305-c3026f758a01' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cdbc3fd8-77d8-4bda-8dc6-706f8c67e989' class='xr-var-data-in' type='checkbox'><label for='data-cdbc3fd8-77d8-4bda-8dc6-706f8c67e989' 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([101.85])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-510b5835-0192-48da-a654-eee9ac81726a' class='xr-section-summary-in' type='checkbox' ><label for='section-510b5835-0192-48da-a654-eee9ac81726a' 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>initial_horz_freq</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7f10b146-9ec5-442b-9b19-2080607460c7' class='xr-index-data-in' type='checkbox'/><label for='index-7f10b146-9ec5-442b-9b19-2080607460c7' 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([103.05], dtype='float64', name='initial_horz_freq'))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>initial_vert_freq</div></div><div class='xr ], "text/plain": [ "<xarray.DataArray 'OD' (initial_horz_freq: 1, initial_vert_freq: 1)>\n", "array([[65351.30239154]])\n", "Coordinates:\n", " * initial_horz_freq (initial_horz_freq) float64 103.0\n", " * initial_vert_freq (initial_vert_freq) float64 101.8" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Ncount_mean.where(Ncount_mean==Ncount_mean.max(), drop=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## scan MOT amp" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The detected scaning axes and values are: \n", "\n", "{'initial_horz_amp': array([0.45, 0.49, 0.53, 0.57, 0.61, 0.65, 0.69, 0.73, 0.77, 0.81]), 'initial_vert_amp': array([0.35, 0.39, 0.43, 0.47, 0.51, 0.55, 0.59, 0.63, 0.67, 0.71])}\n" ] }, { "data": { "application/javascript": "/* Put everything 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' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) 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], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib notebook\n", "shotNum = \"0015\"\n", "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n", "\n", "dataSetDict = {\n", " dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n", " for i in [0,1]\n", "}\n", "\n", "dataSet = dataSetDict[\"camera_1\"]\n", "\n", "print_scanAxis(dataSet)\n", "\n", "scanAxis = get_scanAxis(dataSet)\n", "\n", "dataSet = auto_rechunk(dataSet)\n", "\n", "dataSet = imageAnalyser.get_absorption_images(dataSet)\n", "\n", "imageAnalyser.center = (310, 815)\n", "imageAnalyser.span = (550, 1275)\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", "Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n", "Ncount_mean = calculate_mean(Ncount)\n", "Ncount_std = calculate_std(Ncount)\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "Ncount_mean.plot.pcolormesh(ax=ax, vmin=0, cmap='jet', cbar_kwargs = dict(label='NCount'))\n", "plt.xlabel('Vert AOM Frequency')\n", "plt.ylabel('Horz AOM Frequency')\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "# DB.create_global(shotNum, dataSet)\n", "# DB.add_data(shotNum, dataSet_cropOD, engine='xarray')" ] }, { "cell_type": "code", "execution_count": 16, "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 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left;\n", " padding-right: 10px;\n", " width: auto;\n", "}\n", "\n", ".xr-attrs dt {\n", " font-weight: normal;\n", " grid-column: 1;\n", "}\n", "\n", ".xr-attrs dt:hover span {\n", " display: inline-block;\n", " background: 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.DataArray 'OD' (initial_horz_amp: 1, initial_vert_amp: 1)>\n", "array([[70751.40524034]])\n", "Coordinates:\n", " * initial_horz_amp (initial_horz_amp) float64 0.81\n", " * initial_vert_amp (initial_vert_amp) float64 0.59</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'OD'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>initial_horz_amp</span>: 1</li><li><span class='xr-has-index'>initial_vert_amp</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-177509d9-805b-470b-a14d-b81705fd4a60' class='xr-array-in' type='checkbox' checked><label for='section-177509d9-805b-470b-a14d-b81705fd4a60' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>7.075e+04</span></div><div class='xr-array-data'><pre>array([[70751.40524034]])</pre></div></div></li><li class='xr-section-item'><input id='section-76e14a22-fe3b-447b-b2dd-2bb3ba9aabbf' class='xr-section-summary-in' type='checkbox' checked><label for='section-76e14a22-fe3b-447b-b2dd-2bb3ba9aabbf' 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'>initial_horz_amp</span></div><div class='xr-var-dims'>(initial_horz_amp)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.81</div><input id='attrs-94907e0a-18ce-4351-bfaf-d564533bd62f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-94907e0a-18ce-4351-bfaf-d564533bd62f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e22171f2-c0a0-45f8-84b3-b4e8ef1bb135' class='xr-var-data-in' type='checkbox'><label for='data-e22171f2-c0a0-45f8-84b3-b4e8ef1bb135' 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([0.81])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>initial_vert_amp</span></div><div class='xr-var-dims'>(initial_vert_amp)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.59</div><input id='attrs-8ec82677-4202-4a42-869f-e24cda492fcb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8ec82677-4202-4a42-869f-e24cda492fcb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d4611850-9260-41d7-b5c7-4bf79a2b8bba' class='xr-var-data-in' type='checkbox'><label for='data-d4611850-9260-41d7-b5c7-4bf79a2b8bba' 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([0.59])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4f941a64-d6be-490e-8286-4af1fee95797' class='xr-section-summary-in' type='checkbox' ><label for='section-4f941a64-d6be-490e-8286-4af1fee95797' 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>initial_horz_amp</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7335d05f-250d-4928-af8b-4bf9e657a1f1' class='xr-index-data-in' type='checkbox'/><label for='index-7335d05f-250d-4928-af8b-4bf9e657a1f1' 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([0.81], dtype='float64', name='initial_horz_amp'))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>initial_vert_amp</div></div><div class='xr-index-preview'>Pand ], "text/plain": [ "<xarray.DataArray 'OD' (initial_horz_amp: 1, initial_vert_amp: 1)>\n", "array([[70751.40524034]])\n", "Coordinates:\n", " * initial_horz_amp (initial_horz_amp) float64 0.81\n", " * initial_vert_amp (initial_vert_amp) float64 0.59" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Ncount_mean.where(Ncount_mean==Ncount_mean.max(), drop=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Scan final Z Comp Current" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The detected scaning axes and values are: \n", "\n", "{'compZ_final_current': array([0.233, 0.234, 0.235, 0.236, 0.237, 0.238, 0.239, 0.24 , 0.241,\n", " 0.242]), 'runs': array([0., 1., 2.])}\n" ] }, { "data": { "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = 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' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') 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], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib notebook\n", "shotNum = \"0018\"\n", "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n", "\n", "dataSetDict = {\n", " dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n", " for i in [0, 1]\n", "}\n", "\n", "dataSet = dataSetDict[\"camera_1\"]\n", "\n", "print_scanAxis(dataSet)\n", "\n", "scanAxis = get_scanAxis(dataSet)\n", "\n", "dataSet = auto_rechunk(dataSet)\n", "\n", "dataSet = imageAnalyser.get_absorption_images(dataSet)\n", "\n", "imageAnalyser.center = (325, 875)\n", "imageAnalyser.span = (500, 500)\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", "Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n", "Ncount_mean = calculate_mean(Ncount)\n", "Ncount_std = calculate_std(Ncount)\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n", "#plt.xlabel('')\n", "plt.ylabel('NCount')\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "plt.show()\n", "\n", "# DB.create_global(shotNum, dataSet)\n", "# DB.add_data(shotNum, dataSet_cropOD, engine='xarray')" ] }, { "cell_type": "code", "execution_count": 26, "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 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".xr-var-attrs,\n", ".xr-var-data,\n", ".xr-index-data {\n", " display: none;\n", " background-color: var(--xr-background-color) !important;\n", " padding-bottom: 5px !important;\n", "}\n", "\n", ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n", ".xr-var-data-in:checked ~ .xr-var-data,\n", ".xr-index-data-in:checked ~ .xr-index-data {\n", " display: block;\n", "}\n", "\n", ".xr-var-data > table {\n", " float: right;\n", "}\n", "\n", ".xr-var-name span,\n", ".xr-var-data,\n", ".xr-index-name div,\n", ".xr-index-data,\n", ".xr-attrs {\n", " padding-left: 25px !important;\n", "}\n", "\n", ".xr-attrs,\n", ".xr-var-attrs,\n", ".xr-var-data,\n", ".xr-index-data {\n", " grid-column: 1 / -1;\n", "}\n", "\n", "dl.xr-attrs {\n", " padding: 0;\n", " margin: 0;\n", " display: grid;\n", " grid-template-columns: 125px auto;\n", "}\n", "\n", ".xr-attrs dt,\n", ".xr-attrs dd {\n", " padding: 0;\n", " margin: 0;\n", " float: left;\n", " padding-right: 10px;\n", " width: auto;\n", "}\n", "\n", ".xr-attrs dt {\n", " font-weight: normal;\n", " grid-column: 1;\n", "}\n", "\n", ".xr-attrs dt:hover span {\n", " display: inline-block;\n", " background: 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.DataArray 'OD' (compZ_final_current: 1)>\n", "array([26642.0066982])\n", "Coordinates:\n", " * compZ_final_current (compZ_final_current) float64 0.238</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'OD'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>compZ_final_current</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-c81e0e81-6867-48a8-817b-0270bf8320f5' class='xr-array-in' type='checkbox' checked><label for='section-c81e0e81-6867-48a8-817b-0270bf8320f5' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>2.664e+04</span></div><div class='xr-array-data'><pre>array([26642.0066982])</pre></div></div></li><li class='xr-section-item'><input id='section-213a9602-e7c9-4a8d-872a-0a9a224c9c77' class='xr-section-summary-in' type='checkbox' checked><label for='section-213a9602-e7c9-4a8d-872a-0a9a224c9c77' 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'>compZ_final_current</span></div><div class='xr-var-dims'>(compZ_final_current)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.238</div><input id='attrs-97b5d183-12a2-43d2-bc55-2ce7784bfe38' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-97b5d183-12a2-43d2-bc55-2ce7784bfe38' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac436865-f3d0-4ffc-8f65-960c2f4ccf32' class='xr-var-data-in' type='checkbox'><label for='data-ac436865-f3d0-4ffc-8f65-960c2f4ccf32' 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([0.238])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-084aa2db-fadf-4f9c-9080-822a3683ad63' class='xr-section-summary-in' type='checkbox' ><label for='section-084aa2db-fadf-4f9c-9080-822a3683ad63' 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>compZ_final_current</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8f148761-2dbb-435d-b8e1-c7a944c677c6' class='xr-index-data-in' type='checkbox'/><label for='index-8f148761-2dbb-435d-b8e1-c7a944c677c6' 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([0.238], dtype='float64', name='compZ_final_current'))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e55ccffc-8a2c-4845-bb7e-8ecd0686637c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e55ccffc-8a2c-4845-bb7e-8ecd0686637c' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>" ], "text/plain": [ "<xarray.DataArray 'OD' (compZ_final_current: 1)>\n", "array([26642.0066982])\n", "Coordinates:\n", " * compZ_final_current (compZ_final_current) float64 0.238" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Ncount_mean.where(Ncount_mean==Ncount_mean.max(), drop=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Scan final horz amp" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The detected scaning axes and values are: \n", "\n", "{'final_horz_amp': array([5.0e-05, 7.0e-05, 9.0e-05, 1.1e-04, 1.3e-04, 1.5e-04, 1.7e-04,\n", " 1.9e-04, 2.1e-04, 2.3e-04, 2.5e-04]), 'runs': array([0., 1., 2.])}\n" ] }, { "data": { "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. 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{\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) 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], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib notebook\n", "shotNum = \"0019\"\n", "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n", "\n", "dataSetDict = {\n", " dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n", " for i in [0, 1]\n", "}\n", "\n", "dataSet = dataSetDict[\"camera_1\"]\n", "\n", "print_scanAxis(dataSet)\n", "\n", "scanAxis = get_scanAxis(dataSet)\n", "\n", "dataSet = auto_rechunk(dataSet)\n", "\n", "dataSet = imageAnalyser.get_absorption_images(dataSet)\n", "\n", "imageAnalyser.center = (325, 875)\n", "imageAnalyser.span = (500, 500)\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", "Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n", "Ncount_mean = calculate_mean(Ncount)\n", "Ncount_std = calculate_std(Ncount)\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n", "#plt.xlabel('')\n", "plt.ylabel('NCount')\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "plt.show()\n", "\n", "# DB.create_global(shotNum, dataSet)\n", "# DB.add_data(shotNum, dataSet_cropOD, engine='xarray')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Ncount_mean.where(Ncount_mean==Ncount_mean.max(), drop=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Scan final horz freq" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The detected scaning axes and values are: \n", "\n", "{'final_horz_freq': array([104.05 , 104.055, 104.06 , 104.065, 104.07 , 104.075, 104.08 ,\n", " 104.085, 104.09 , 104.095, 104.1 ]), 'runs': array([0., 1., 2.])}\n" ] }, { "data": { "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) 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], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib notebook\n", "shotNum = \"0020\"\n", "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n", "\n", "dataSetDict = {\n", " dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n", " for i in [0, 1]\n", "}\n", "\n", "dataSet = dataSetDict[\"camera_1\"]\n", "\n", "print_scanAxis(dataSet)\n", "\n", "scanAxis = get_scanAxis(dataSet)\n", "\n", "dataSet = auto_rechunk(dataSet)\n", "\n", "dataSet = imageAnalyser.get_absorption_images(dataSet)\n", "\n", "imageAnalyser.center = (325, 875)\n", "imageAnalyser.span = (500, 500)\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", "Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n", "Ncount_mean = calculate_mean(Ncount)\n", "Ncount_std = calculate_std(Ncount)\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n", "#plt.xlabel('')\n", "plt.ylabel('NCount')\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "plt.show()\n", "\n", "# DB.create_global(shotNum, dataSet)\n", "# DB.add_data(shotNum, dataSet_cropOD, engine='xarray')" ] }, { "cell_type": "code", "execution_count": 29, "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 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".xr-wrap {\n", " display: block !important;\n", " min-width: 300px;\n", " max-width: 700px;\n", "}\n", "\n", ".xr-text-repr-fallback {\n", " /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n", " display: none;\n", "}\n", "\n", ".xr-header {\n", " padding-top: 6px;\n", " padding-bottom: 6px;\n", " margin-bottom: 4px;\n", " border-bottom: solid 1px var(--xr-border-color);\n", "}\n", "\n", ".xr-header > div,\n", ".xr-header > ul {\n", " display: inline;\n", " margin-top: 0;\n", " margin-bottom: 0;\n", "}\n", "\n", ".xr-obj-type,\n", ".xr-array-name {\n", " margin-left: 2px;\n", " margin-right: 10px;\n", "}\n", "\n", ".xr-obj-type {\n", " color: var(--xr-font-color2);\n", "}\n", "\n", ".xr-sections {\n", " padding-left: 0 !important;\n", " display: grid;\n", " grid-template-columns: 150px auto auto 1fr 20px 20px;\n", "}\n", "\n", ".xr-section-item {\n", " display: contents;\n", "}\n", "\n", ".xr-section-item input {\n", " display: none;\n", "}\n", "\n", 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".xr-section-summary-in:checked + label > span {\n", " display: none;\n", "}\n", "\n", ".xr-section-summary,\n", ".xr-section-inline-details {\n", " padding-top: 4px;\n", " padding-bottom: 4px;\n", "}\n", "\n", ".xr-section-inline-details {\n", " grid-column: 2 / -1;\n", "}\n", "\n", ".xr-section-details {\n", " display: none;\n", " grid-column: 1 / -1;\n", " margin-bottom: 5px;\n", "}\n", "\n", ".xr-section-summary-in:checked ~ .xr-section-details {\n", " display: contents;\n", "}\n", "\n", ".xr-array-wrap {\n", " grid-column: 1 / -1;\n", " display: grid;\n", " grid-template-columns: 20px auto;\n", "}\n", "\n", ".xr-array-wrap > label {\n", " grid-column: 1;\n", " vertical-align: top;\n", "}\n", "\n", ".xr-preview {\n", " color: var(--xr-font-color3);\n", "}\n", "\n", ".xr-array-preview,\n", ".xr-array-data {\n", " padding: 0 5px !important;\n", " grid-column: 2;\n", "}\n", "\n", ".xr-array-data,\n", ".xr-array-in:checked ~ .xr-array-preview {\n", " display: none;\n", "}\n", "\n", ".xr-array-in:checked ~ .xr-array-data,\n", ".xr-array-preview {\n", " display: inline-block;\n", "}\n", "\n", ".xr-dim-list {\n", " display: inline-block !important;\n", " list-style: none;\n", " padding: 0 !important;\n", " margin: 0;\n", "}\n", "\n", ".xr-dim-list li {\n", " display: inline-block;\n", " padding: 0;\n", " margin: 0;\n", "}\n", "\n", ".xr-dim-list:before {\n", " content: '(';\n", "}\n", "\n", ".xr-dim-list:after {\n", " content: ')';\n", "}\n", "\n", ".xr-dim-list li:not(:last-child):after {\n", " content: ',';\n", " padding-right: 5px;\n", "}\n", "\n", ".xr-has-index {\n", " font-weight: bold;\n", "}\n", "\n", ".xr-var-list,\n", ".xr-var-item {\n", " display: contents;\n", "}\n", "\n", ".xr-var-item > div,\n", ".xr-var-item label,\n", ".xr-var-item > .xr-var-name span {\n", " background-color: var(--xr-background-color-row-even);\n", " margin-bottom: 0;\n", "}\n", "\n", ".xr-var-item > .xr-var-name:hover span {\n", " padding-right: 5px;\n", "}\n", "\n", ".xr-var-list > li:nth-child(odd) > div,\n", ".xr-var-list > li:nth-child(odd) > label,\n", ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n", " background-color: var(--xr-background-color-row-odd);\n", "}\n", "\n", ".xr-var-name {\n", " grid-column: 1;\n", "}\n", "\n", ".xr-var-dims {\n", " grid-column: 2;\n", "}\n", "\n", ".xr-var-dtype {\n", " grid-column: 3;\n", " text-align: right;\n", " color: var(--xr-font-color2);\n", "}\n", "\n", ".xr-var-preview {\n", " grid-column: 4;\n", "}\n", "\n", ".xr-index-preview {\n", " grid-column: 2 / 5;\n", " color: var(--xr-font-color2);\n", "}\n", "\n", ".xr-var-name,\n", ".xr-var-dims,\n", ".xr-var-dtype,\n", ".xr-preview,\n", ".xr-attrs dt {\n", " white-space: nowrap;\n", " overflow: hidden;\n", " text-overflow: ellipsis;\n", " padding-right: 10px;\n", "}\n", "\n", ".xr-var-name:hover,\n", ".xr-var-dims:hover,\n", ".xr-var-dtype:hover,\n", ".xr-attrs dt:hover {\n", " overflow: visible;\n", " width: auto;\n", " z-index: 1;\n", "}\n", "\n", ".xr-var-attrs,\n", ".xr-var-data,\n", ".xr-index-data {\n", " display: none;\n", " background-color: var(--xr-background-color) !important;\n", " padding-bottom: 5px !important;\n", "}\n", "\n", ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n", ".xr-var-data-in:checked ~ .xr-var-data,\n", ".xr-index-data-in:checked ~ .xr-index-data {\n", " display: block;\n", "}\n", "\n", ".xr-var-data > table {\n", " float: right;\n", "}\n", "\n", ".xr-var-name span,\n", ".xr-var-data,\n", ".xr-index-name div,\n", ".xr-index-data,\n", ".xr-attrs {\n", " padding-left: 25px !important;\n", "}\n", "\n", ".xr-attrs,\n", ".xr-var-attrs,\n", ".xr-var-data,\n", ".xr-index-data {\n", " grid-column: 1 / -1;\n", "}\n", "\n", "dl.xr-attrs {\n", " padding: 0;\n", " margin: 0;\n", " display: grid;\n", " grid-template-columns: 125px auto;\n", "}\n", "\n", ".xr-attrs dt,\n", ".xr-attrs dd {\n", " padding: 0;\n", " margin: 0;\n", " float: left;\n", " padding-right: 10px;\n", " width: auto;\n", "}\n", "\n", ".xr-attrs dt {\n", " font-weight: normal;\n", " grid-column: 1;\n", "}\n", "\n", ".xr-attrs dt:hover span {\n", " display: inline-block;\n", " background: 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.DataArray 'OD' (final_horz_freq: 1)>\n", "array([28952.76637914])\n", "Coordinates:\n", " * final_horz_freq (final_horz_freq) float64 104.1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'OD'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>final_horz_freq</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-ed0434fd-e803-4855-844d-70b0c3e842c8' class='xr-array-in' type='checkbox' checked><label for='section-ed0434fd-e803-4855-844d-70b0c3e842c8' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>2.895e+04</span></div><div class='xr-array-data'><pre>array([28952.76637914])</pre></div></div></li><li class='xr-section-item'><input id='section-d2a4729c-d36d-44fa-a8c1-4885a8e7593b' class='xr-section-summary-in' type='checkbox' checked><label for='section-d2a4729c-d36d-44fa-a8c1-4885a8e7593b' 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'>final_horz_freq</span></div><div class='xr-var-dims'>(final_horz_freq)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>104.1</div><input id='attrs-6f10bdef-1163-4796-8266-ab9332d89ea4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6f10bdef-1163-4796-8266-ab9332d89ea4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-343afb05-ab34-4141-92a8-e56c8c309e66' class='xr-var-data-in' type='checkbox'><label for='data-343afb05-ab34-4141-92a8-e56c8c309e66' 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([104.06])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d4d0971f-b5b7-4977-91e5-3ef6807a4b2f' class='xr-section-summary-in' type='checkbox' ><label for='section-d4d0971f-b5b7-4977-91e5-3ef6807a4b2f' 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>final_horz_freq</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-81425834-189f-4154-bdb4-8c5e8e7207d1' class='xr-index-data-in' type='checkbox'/><label for='index-81425834-189f-4154-bdb4-8c5e8e7207d1' 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([104.06], dtype='float64', name='final_horz_freq'))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-813756c4-49be-4230-a482-541103019f9c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-813756c4-49be-4230-a482-541103019f9c' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>" ], "text/plain": [ "<xarray.DataArray 'OD' (final_horz_freq: 1)>\n", "array([28952.76637914])\n", "Coordinates:\n", " * final_horz_freq (final_horz_freq) float64 104.1" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Ncount_mean.where(Ncount_mean==Ncount_mean.max(), drop=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Scan final vert amp" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The detected scaning axes and values are: \n", "\n", "{'final_vert_amp': array([0.035, 0.037, 0.039, 0.041, 0.043, 0.045, 0.047, 0.049, 0.051,\n", " 0.053, 0.055, 0.057, 0.059, 0.061, 0.063]), 'runs': array([0., 1., 2.])}\n" ] }, { "data": { 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' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') 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], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib notebook\n", "shotNum = \"0021\"\n", "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n", "\n", "dataSetDict = {\n", " dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n", " for i in [0, 1]\n", "}\n", "\n", "dataSet = dataSetDict[\"camera_1\"]\n", "\n", "print_scanAxis(dataSet)\n", "\n", "scanAxis = get_scanAxis(dataSet)\n", "\n", "dataSet = auto_rechunk(dataSet)\n", "\n", "dataSet = imageAnalyser.get_absorption_images(dataSet)\n", "\n", "imageAnalyser.center = (325, 875)\n", "imageAnalyser.span = (500, 500)\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", "Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n", "Ncount_mean = calculate_mean(Ncount)\n", "Ncount_std = calculate_std(Ncount)\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n", "#plt.xlabel('')\n", "plt.ylabel('NCount')\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "plt.show()\n", "\n", "# DB.create_global(shotNum, dataSet)\n", "# DB.add_data(shotNum, dataSet_cropOD, engine='xarray')" ] }, { "cell_type": "code", "execution_count": 31, "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 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".xr-var-attrs,\n", ".xr-var-data,\n", ".xr-index-data {\n", " display: none;\n", " background-color: var(--xr-background-color) !important;\n", " padding-bottom: 5px !important;\n", "}\n", "\n", ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n", ".xr-var-data-in:checked ~ .xr-var-data,\n", ".xr-index-data-in:checked ~ .xr-index-data {\n", " display: block;\n", "}\n", "\n", ".xr-var-data > table {\n", " float: right;\n", "}\n", "\n", ".xr-var-name span,\n", ".xr-var-data,\n", ".xr-index-name div,\n", ".xr-index-data,\n", ".xr-attrs {\n", " padding-left: 25px !important;\n", "}\n", "\n", ".xr-attrs,\n", ".xr-var-attrs,\n", ".xr-var-data,\n", ".xr-index-data {\n", " grid-column: 1 / -1;\n", "}\n", "\n", "dl.xr-attrs {\n", " padding: 0;\n", " margin: 0;\n", " display: grid;\n", " grid-template-columns: 125px auto;\n", "}\n", "\n", ".xr-attrs dt,\n", ".xr-attrs dd {\n", " padding: 0;\n", " margin: 0;\n", " float: left;\n", " padding-right: 10px;\n", " width: auto;\n", "}\n", "\n", ".xr-attrs dt {\n", " font-weight: normal;\n", " grid-column: 1;\n", "}\n", "\n", ".xr-attrs dt:hover span {\n", " display: inline-block;\n", " background: 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.DataArray 'OD' (final_vert_amp: 1)>\n", "array([29676.45060443])\n", "Coordinates:\n", " * final_vert_amp (final_vert_amp) float64 0.045</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'OD'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>final_vert_amp</span>: 1</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-afcf31cf-2ab4-4ec0-bbe4-f6342cf64b8b' class='xr-array-in' type='checkbox' checked><label for='section-afcf31cf-2ab4-4ec0-bbe4-f6342cf64b8b' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>2.968e+04</span></div><div class='xr-array-data'><pre>array([29676.45060443])</pre></div></div></li><li class='xr-section-item'><input id='section-3064ec8b-148e-48c2-a58b-98b70a9fbf8e' class='xr-section-summary-in' type='checkbox' checked><label for='section-3064ec8b-148e-48c2-a58b-98b70a9fbf8e' 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'>final_vert_amp</span></div><div class='xr-var-dims'>(final_vert_amp)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.045</div><input id='attrs-6fa74f1c-255e-4f85-9eda-30c219bff7d4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6fa74f1c-255e-4f85-9eda-30c219bff7d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a737f7e3-553a-4474-9fc0-5c8fa128a3ec' class='xr-var-data-in' type='checkbox'><label for='data-a737f7e3-553a-4474-9fc0-5c8fa128a3ec' 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([0.045])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4343ca55-551c-45a4-a744-a92bbc669a71' class='xr-section-summary-in' type='checkbox' ><label for='section-4343ca55-551c-45a4-a744-a92bbc669a71' 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>final_vert_amp</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e63c7b75-53c2-4eec-8e4c-e6725f45889a' class='xr-index-data-in' type='checkbox'/><label for='index-e63c7b75-53c2-4eec-8e4c-e6725f45889a' 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([0.045], dtype='float64', name='final_vert_amp'))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e98f96d0-b3a2-4063-9b37-a341ae427357' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e98f96d0-b3a2-4063-9b37-a341ae427357' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>" ], "text/plain": [ "<xarray.DataArray 'OD' (final_vert_amp: 1)>\n", "array([29676.45060443])\n", "Coordinates:\n", " * final_vert_amp (final_vert_amp) float64 0.045" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Ncount_mean.where(Ncount_mean==Ncount_mean.max(), drop=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Evaporative Cooling" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "img_dir = 'C:/Users/control/DyLab/Experiments/DyBEC/'\n", "# img_dir = '//DyLabNAS/Data/'\n", "SequenceName = \"Evaporative_Cooling\"\n", "folderPath = img_dir + SequenceName + \"/\" + get_date()\n", "\n", "mongoDB = mongoClient[SequenceName]\n", "\n", "DB = MongoDB(mongoClient, mongoDB, date=get_date())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Measure trap frequency at various points of evap 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Horz TF due to arm 1: blink on modulation = 0, blink_off_modulation = 0.35 in 700 µs, hold of 1 s" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Truncation: 0.5" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The detected scaning axes and values are: \n", "\n", "{'mod_blink_on_time': array([0.005, 0.006, 0.007, 0.008, 0.009, 0.01 , 0.011, 0.012, 0.013,\n", " 0.014, 0.015, 0.016, 0.017, 0.018, 0.019]), 'runs': array([0., 1., 2., 3.])}\n" ] }, { "data": { "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if 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{\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) 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src=\"data:image/png;base64,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 ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib notebook\n", "shotNum = \"0004\"\n", "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n", "\n", "dataSetDict = {\n", " dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i], excludeAxis = ['sweep_start_freq', 'sweep_stop_freq'])\n", " for i in [0]\n", "}\n", "\n", "dataSet = dataSetDict[\"camera_0\"]\n", "\n", "print_scanAxis(dataSet)\n", "\n", "scanAxis = get_scanAxis(dataSet)\n", "\n", "dataSet = auto_rechunk(dataSet)\n", "\n", "dataSet = imageAnalyser.get_absorption_images(dataSet)\n", "\n", "imageAnalyser.center = (800, 900)\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", "Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n", "Ncount_mean = calculate_mean(Ncount)\n", "Ncount_std = calculate_std(Ncount)\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "Ncount_mean.plot.errorbar(ax=ax, yerr = None, fmt='ob')\n", "plt.ylabel('NCount')\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [], "source": [ "dataSet_cropOD_chunk = dataSet_cropOD.chunk((1, 300, 300))\n", "fitAnalyser = FitAnalyser(\"Gaussian-2D\", fitDim=2)\n", "params = fitAnalyser.guess(dataSet_cropOD_chunk, dask=\"parallelized\")\n", "fitResult = fitAnalyser.fit(dataSet_cropOD_chunk, params, dask=\"parallelized\").load()" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "val = fitAnalyser.get_fit_value(fitResult)\n", "std = fitAnalyser.get_fit_std(fitResult)\n", "\n", "fitCurve = fitAnalyser.eval(fitResult, x=np.arange(300), y=np.arange(300), dask=\"parallelized\").load()\n", "\n", "# dataKey = 'sigmax'\n", "# dataKey = 'centerx'\n", "# dataKey = 'sigmay'\n", "dataKey = 'centery'\n", "\n", "# val_mean = val[dataKey].mean(dim='runs')\n", "# std_mean = val[dataKey].std(dim='runs')\n", "\n", "val_mean = calculate_mean(val[dataKey])\n", "std_mean = calculate_std(val[dataKey])\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "\n", "val_mean.plot.errorbar(yerr=std_mean, fmt='--ob')\n", "\n", "plt.grid()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "f = 303.5930 ± 6.9704 Hz\n" ] } ], "source": [ "def damp_osci(t, t0, A, B, nu, gamma):\n", " return A * np.exp(-gamma*t)*np.sin(2*np.pi*nu*(t-t0)) + B\n", "\n", "#yvals = val_mean.sel(blink_on_time=slice(0.005, 0.025))\n", "#yvals_std = std_mean.sel(blink_on_time=slice(0.005, 0.025))\n", "#xvals = dataSet_cropOD[scanAxis[0]].sel(blink_on_time=slice(0.005, 0.025))\n", "\n", "fitted_qtys_1 = val_mean.to_numpy()\n", "fitted_qtys_err_1 = std_mean.to_numpy()\n", "scan_para = dataSet_cropOD[scanAxis[0]].to_numpy()\n", "\n", "plt.figure()\n", "popt_x, pcov_x = curve_fit(damp_osci, scan_para, fitted_qtys_1, np.array([0, 1, 148, 1e2, 0.1]))\n", "freqdata = np.linspace(0.005, 20e-3, 500)\n", "plt.plot(freqdata, damp_osci(freqdata, *popt_x), 'g--',label='fit: t0=%5.3f, A=%5.3f, B=%5.3f, nu=%5.3f, Gamma=%5.3f' % tuple(popt_x))\n", "plt.errorbar(scan_para, fitted_qtys_1, yerr=fitted_qtys_err_1, fmt='or')\n", "plt.xlabel('hold time after switch on the trap (s)')\n", "plt.ylabel('Center along gravity direction (pixels)')\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "#plt.ylim([0,750])\n", "#plt.xlim([0.004, 0.025])\n", "#plt.legend(prop={'size': 14})\n", "plt.show()\n", "\n", "f_x = popt_x[3]\n", "df_x = pcov_x[3][3]**0.5\n", "\n", "print('f = %.4f \\u00B1 %.4f Hz'% tuple([np.abs(f_x),df_x]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Truncation: 0.55" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The detected scaning axes and values are: \n", "\n", "{'mod_blink_on_time': array([0.005, 0.006, 0.007, 0.008, 0.009, 0.01 , 0.011, 0.012, 0.013,\n", " 0.014, 0.015, 0.016, 0.017, 0.018, 0.019]), 'runs': array([0., 1., 2., 3.])}\n" ] }, { "data": { "application/javascript": "/* Put everything inside the 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' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib notebook\n", "shotNum = \"0005\"\n", "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n", "\n", "dataSetDict = {\n", " dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i], excludeAxis = ['sweep_start_freq', 'sweep_stop_freq'])\n", " for i in [0]\n", "}\n", "\n", "dataSet = dataSetDict[\"camera_0\"]\n", "\n", "print_scanAxis(dataSet)\n", "\n", "scanAxis = get_scanAxis(dataSet)\n", "\n", "dataSet = auto_rechunk(dataSet)\n", "\n", "dataSet = imageAnalyser.get_absorption_images(dataSet)\n", "\n", "imageAnalyser.center = (800, 900)\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", "Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n", "Ncount_mean = calculate_mean(Ncount)\n", "Ncount_std = calculate_std(Ncount)\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n", "plt.ylabel('NCount')\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [], "source": [ "dataSet_cropOD_chunk = dataSet_cropOD.chunk((1, 1, 300, 300))\n", "fitAnalyser = FitAnalyser(\"Gaussian-2D\", fitDim=2)\n", "params = fitAnalyser.guess(dataSet_cropOD_chunk, dask=\"parallelized\")\n", "fitResult = fitAnalyser.fit(dataSet_cropOD_chunk, params, dask=\"parallelized\").load()" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "val = fitAnalyser.get_fit_value(fitResult)\n", "std = fitAnalyser.get_fit_std(fitResult)\n", "\n", "fitCurve = fitAnalyser.eval(fitResult, x=np.arange(300), y=np.arange(300), dask=\"parallelized\").load()\n", "\n", "# dataKey = 'sigmax'\n", "# dataKey = 'centerx'\n", "# dataKey = 'sigmay'\n", "dataKey = 'centery'\n", "\n", "# val_mean = val[dataKey].mean(dim='runs')\n", "# std_mean = val[dataKey].std(dim='runs')\n", "\n", "val_mean = calculate_mean(val[dataKey])\n", "std_mean = calculate_std(val[dataKey])\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "\n", "val_mean.plot.errorbar(yerr=std_mean, fmt='--ob')\n", "\n", "plt.grid()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "f = 286.0152 ± 2.5433 Hz\n" ] } ], "source": [ "def damp_osci(t, t0, A, B, nu, gamma):\n", " return A * np.exp(-gamma*t)*np.sin(2*np.pi*nu*(t-t0)) + B\n", "\n", "yvals = val_mean#.sel(blink_on_time=slice(0.005, 0.025))\n", "yvals_std = std_mean#.sel(blink_on_time=slice(0.005, 0.025))\n", "xvals = dataSet_cropOD[scanAxis[0]]#.sel(blink_on_time=slice(0.005, 0.025))\n", "\n", "fitted_qtys_1 = yvals.to_numpy()\n", "fitted_qtys_err_1 = yvals_std.to_numpy()\n", "scan_para = xvals.to_numpy()\n", "\n", "plt.figure()\n", "popt_x, pcov_x = curve_fit(damp_osci, scan_para, fitted_qtys_1, np.array([0, 3, 145, 1e2, 0.1]))\n", "freqdata = np.linspace(0.005,19e-3, 500)\n", "plt.plot(freqdata, damp_osci(freqdata, *popt_x), 'g--',label='fit: t0=%5.3f, A=%5.3f, B=%5.3f, nu=%5.3f, Gamma=%5.3f' % tuple(popt_x))\n", "plt.errorbar(scan_para, fitted_qtys_1, yerr=fitted_qtys_err_1, fmt='or')\n", "plt.xlabel('hold time after switch on the trap (s)')\n", "plt.ylabel('Center along gravity direction (pixels)')\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "#plt.ylim([0,750])\n", "#plt.xlim([0.004, 0.025])\n", "#plt.legend(prop={'size': 14})\n", "plt.show()\n", "\n", "f_x = popt_x[3]\n", "df_x = pcov_x[3][3]**0.5\n", "\n", "print('f = %.4f \\u00B1 %.4f Hz'% tuple([np.abs(f_x),df_x]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Truncation: 0.6" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The detected scaning axes and values are: \n", "\n", "{'mod_blink_on_time': array([0.005 , 0.0057, 0.0064, 0.0071, 0.0078, 0.0085, 0.0092, 0.0099,\n", " 0.0106, 0.0113, 0.012 , 0.0127, 0.0134, 0.0141, 0.0148, 0.0155,\n", " 0.0162, 0.0169, 0.0176, 0.0183, 0.019 , 0.0197]), 'runs': array([0., 1., 2., 3.])}\n" ] }, { "data": { 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' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib notebook\n", "shotNum = \"0008\"\n", "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n", "\n", "dataSetDict = {\n", " dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i], excludeAxis = ['sweep_start_freq', 'sweep_stop_freq'])\n", " for i in [0]\n", "}\n", "\n", "dataSet = dataSetDict[\"camera_0\"]\n", "\n", "print_scanAxis(dataSet)\n", "\n", "scanAxis = get_scanAxis(dataSet)\n", "\n", "dataSet = auto_rechunk(dataSet)\n", "\n", "dataSet = imageAnalyser.get_absorption_images(dataSet)\n", "\n", "imageAnalyser.center = (800, 900)\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", "Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n", "Ncount_mean = calculate_mean(Ncount)\n", "Ncount_std = calculate_std(Ncount)\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "Ncount_mean.plot.errorbar(ax=ax, yerr = None, fmt='ob')\n", "plt.ylabel('NCount')\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [], "source": [ "dataSet_cropOD_chunk = dataSet_cropOD.chunk((1, 1, 300, 300))\n", "fitAnalyser = FitAnalyser(\"Gaussian-2D\", fitDim=2)\n", "params = fitAnalyser.guess(dataSet_cropOD_chunk, dask=\"parallelized\")\n", "fitResult = fitAnalyser.fit(dataSet_cropOD_chunk, params, dask=\"parallelized\").load()" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "val = fitAnalyser.get_fit_value(fitResult)\n", "std = fitAnalyser.get_fit_std(fitResult)\n", "\n", "fitCurve = fitAnalyser.eval(fitResult, x=np.arange(300), y=np.arange(300), dask=\"parallelized\").load()\n", "\n", "# dataKey = 'sigmax'\n", "# dataKey = 'centerx'\n", "# dataKey = 'sigmay'\n", "dataKey = 'centery'\n", "\n", "# val_mean = val[dataKey].mean(dim='runs')\n", "# std_mean = val[dataKey].std(dim='runs')\n", "\n", "val_mean = calculate_mean(val[dataKey])\n", "std_mean = calculate_std(val[dataKey])\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "\n", "val_mean.plot.errorbar(yerr=std_mean, fmt='--ob')\n", "\n", "plt.grid()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "data": { "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "f = 272.6675 ± 0.6739 Hz\n" ] } ], "source": [ "def damp_osci(t, t0, A, B, nu, gamma):\n", " return A * np.exp(-gamma*t)*np.sin(2*np.pi*nu*(t-t0)) + B\n", "\n", "yvals = val_mean#.sel(blink_on_time=slice(0.005, 0.025))\n", "yvals_std = std_mean#.sel(blink_on_time=slice(0.005, 0.025))\n", "xvals = dataSet_cropOD[scanAxis[0]]#.sel(blink_on_time=slice(0.005, 0.025))\n", "\n", "fitted_qtys_1 = yvals.to_numpy()\n", "fitted_qtys_err_1 = yvals_std.to_numpy()\n", "scan_para = xvals.to_numpy()\n", "\n", "plt.figure()\n", "popt_x, pcov_x = curve_fit(damp_osci, scan_para, fitted_qtys_1, np.array([0, 3, 145, 1e2, 0.1]))\n", "freqdata = np.linspace(0.005, 20e-3, 500)\n", "plt.plot(freqdata, damp_osci(freqdata, *popt_x), 'g--',label='fit: t0=%5.3f, A=%5.3f, B=%5.3f, nu=%5.3f, Gamma=%5.3f' % tuple(popt_x))\n", "plt.errorbar(scan_para, fitted_qtys_1, yerr=fitted_qtys_err_1, fmt='or')\n", "plt.xlabel('hold time after switch on the trap (s)')\n", "plt.ylabel('Center along gravity direction (pixels)')\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "#plt.ylim([0,750])\n", "#plt.xlim([0.004, 0.025])\n", "#plt.legend(prop={'size': 14})\n", "plt.show()\n", "\n", "f_x = popt_x[3]\n", "df_x = pcov_x[3][3]**0.5\n", "\n", "print('f = %.4f \\u00B1 %.4f Hz'% tuple([np.abs(f_x),df_x]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Truncation: 0.65" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The detected scaning axes and values are: \n", "\n", "{'mod_blink_on_time': array([0.005 , 0.0057, 0.0064, 0.0071, 0.0078, 0.0085, 0.0092, 0.0099,\n", " 0.0106, 0.0113, 0.012 , 0.0127, 0.0134, 0.0141, 0.0148, 0.0155,\n", " 0.0162, 0.0169, 0.0176, 0.0183, 0.019 , 0.0197]), 'runs': array([0., 1., 2., 3.])}\n" ] }, { "data": { 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' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib notebook\n", "shotNum = \"0009\"\n", "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n", "\n", "dataSetDict = {\n", " dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i], excludeAxis = ['sweep_start_freq', 'sweep_stop_freq'])\n", " for i in [0]\n", "}\n", "\n", "dataSet = dataSetDict[\"camera_0\"]\n", "\n", "print_scanAxis(dataSet)\n", "\n", "scanAxis = get_scanAxis(dataSet)\n", "\n", "dataSet = auto_rechunk(dataSet)\n", "\n", "dataSet = imageAnalyser.get_absorption_images(dataSet)\n", "\n", "imageAnalyser.center = (800, 900)\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", "Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n", "Ncount_mean = calculate_mean(Ncount)\n", "Ncount_std = calculate_std(Ncount)\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "Ncount_mean.plot.errorbar(ax=ax, yerr = None, fmt='ob')\n", "plt.ylabel('NCount')\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [], "source": [ "dataSet_cropOD_chunk = dataSet_cropOD.chunk((1, 1, 300, 300))\n", "fitAnalyser = FitAnalyser(\"Gaussian-2D\", fitDim=2)\n", "params = fitAnalyser.guess(dataSet_cropOD_chunk, dask=\"parallelized\")\n", "fitResult = fitAnalyser.fit(dataSet_cropOD_chunk, params, dask=\"parallelized\").load()" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "data": { "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "val = fitAnalyser.get_fit_value(fitResult)\n", "std = fitAnalyser.get_fit_std(fitResult)\n", "\n", "fitCurve = fitAnalyser.eval(fitResult, x=np.arange(300), y=np.arange(300), dask=\"parallelized\").load()\n", "\n", "# dataKey = 'sigmax'\n", "# dataKey = 'centerx'\n", "# dataKey = 'sigmay'\n", "dataKey = 'centery'\n", "\n", "# val_mean = val[dataKey].mean(dim='runs')\n", "# std_mean = val[dataKey].std(dim='runs')\n", "\n", "val_mean = calculate_mean(val[dataKey])\n", "std_mean = calculate_std(val[dataKey])\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "\n", "val_mean.plot.errorbar(yerr=std_mean, fmt='--ob')\n", "\n", "plt.grid()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "data": { "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "f = 253.6669 ± 0.5183 Hz\n" ] } ], "source": [ "def damp_osci(t, t0, A, B, nu, gamma):\n", " return A * np.exp(-gamma*t)*np.sin(2*np.pi*nu*(t-t0)) + B\n", "\n", "yvals = val_mean#.sel(blink_on_time=slice(0.005, 0.025))\n", "yvals_std = std_mean#.sel(blink_on_time=slice(0.005, 0.025))\n", "xvals = dataSet_cropOD[scanAxis[0]]#.sel(blink_on_time=slice(0.005, 0.025))\n", "\n", "fitted_qtys_1 = yvals.to_numpy()\n", "fitted_qtys_err_1 = yvals_std.to_numpy()\n", "scan_para = xvals.to_numpy()\n", "\n", "plt.figure()\n", "popt_x, pcov_x = curve_fit(damp_osci, scan_para, fitted_qtys_1, np.array([0, 3, 145, 1e2, 0.1]))\n", "freqdata = np.linspace(0.005, 20e-3, 500)\n", "plt.plot(freqdata, damp_osci(freqdata, *popt_x), 'g--',label='fit: t0=%5.3f, A=%5.3f, B=%5.3f, nu=%5.3f, Gamma=%5.3f' % tuple(popt_x))\n", "plt.errorbar(scan_para, fitted_qtys_1, yerr=fitted_qtys_err_1, fmt='or')\n", "plt.xlabel('hold time after switch on the trap (s)')\n", "plt.ylabel('Center along gravity direction (pixels)')\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "#plt.ylim([0,750])\n", "#plt.xlim([0.004, 0.025])\n", "#plt.legend(prop={'size': 14})\n", "plt.show()\n", "\n", "f_x = popt_x[3]\n", "df_x = pcov_x[3][3]**0.5\n", "\n", "print('f = %.4f \\u00B1 %.4f Hz'% tuple([np.abs(f_x),df_x]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Truncation: 0.7" ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The detected scaning axes and values are: \n", "\n", "{'mod_blink_on_time': array([0.005 , 0.0057, 0.0064, 0.0071, 0.0078, 0.0085, 0.0092, 0.0099,\n", " 0.0106, 0.0113, 0.012 , 0.0127, 0.0134, 0.0141, 0.0148, 0.0155,\n", " 0.0162, 0.0169, 0.0176, 0.0183, 0.019 , 0.0197]), 'runs': array([0., 1., 2., 3.])}\n" ] }, { "data": { 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' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib notebook\n", "shotNum = \"0010\"\n", "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n", "\n", "dataSetDict = {\n", " dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i], excludeAxis = ['sweep_start_freq', 'sweep_stop_freq'])\n", " for i in [0]\n", "}\n", "\n", "dataSet = dataSetDict[\"camera_0\"]\n", "\n", "print_scanAxis(dataSet)\n", "\n", "scanAxis = get_scanAxis(dataSet)\n", "\n", "dataSet = auto_rechunk(dataSet)\n", "\n", "dataSet = imageAnalyser.get_absorption_images(dataSet)\n", "\n", "imageAnalyser.center = (800, 900)\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", "Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n", "Ncount_mean = calculate_mean(Ncount)\n", "Ncount_std = calculate_std(Ncount)\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "Ncount_mean.plot.errorbar(ax=ax, yerr = None, fmt='ob')\n", "plt.ylabel('NCount')\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [], "source": [ "dataSet_cropOD_chunk = dataSet_cropOD.chunk((1, 1, 300, 300))\n", "fitAnalyser = FitAnalyser(\"Gaussian-2D\", fitDim=2)\n", "params = fitAnalyser.guess(dataSet_cropOD_chunk, dask=\"parallelized\")\n", "fitResult = fitAnalyser.fit(dataSet_cropOD_chunk, params, dask=\"parallelized\").load()" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [ { "data": { "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "val = fitAnalyser.get_fit_value(fitResult)\n", "std = fitAnalyser.get_fit_std(fitResult)\n", "\n", "fitCurve = fitAnalyser.eval(fitResult, x=np.arange(300), y=np.arange(300), dask=\"parallelized\").load()\n", "\n", "# dataKey = 'sigmax'\n", "# dataKey = 'centerx'\n", "# dataKey = 'sigmay'\n", "dataKey = 'centery'\n", "\n", "# val_mean = val[dataKey].mean(dim='runs')\n", "# std_mean = val[dataKey].std(dim='runs')\n", "\n", "val_mean = calculate_mean(val[dataKey])\n", "std_mean = calculate_std(val[dataKey])\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "\n", "val_mean.plot.errorbar(yerr=std_mean, fmt='--ob')\n", "\n", "plt.grid()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [ { "data": { "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "f = 238.8665 ± 0.3901 Hz\n" ] } ], "source": [ "def damp_osci(t, t0, A, B, nu, gamma):\n", " return A * np.exp(-gamma*t)*np.sin(2*np.pi*nu*(t-t0)) + B\n", "\n", "yvals = val_mean#.sel(blink_on_time=slice(0.005, 0.025))\n", "yvals_std = std_mean#.sel(blink_on_time=slice(0.005, 0.025))\n", "xvals = dataSet_cropOD[scanAxis[0]]#.sel(blink_on_time=slice(0.005, 0.025))\n", "\n", "fitted_qtys_1 = yvals.to_numpy()\n", "scan_para = xvals.to_numpy()\n", "fitted_qtys_err_1 = yvals_std.to_numpy()\n", "\n", "\n", "plt.figure()\n", "popt_x, pcov_x = curve_fit(damp_osci, scan_para, fitted_qtys_1, np.array([0, 3, 147, 3e2, 0.1]))\n", "freqdata = np.linspace(0.005, 20e-3, 500)\n", "plt.plot(freqdata, damp_osci(freqdata, *popt_x), 'g--',label='fit: t0=%5.3f, A=%5.3f, B=%5.3f, nu=%5.3f, Gamma=%5.3f' % tuple(popt_x))\n", "plt.errorbar(scan_para, fitted_qtys_1, yerr=fitted_qtys_err_1, fmt='or')\n", "plt.xlabel('hold time after switch on the trap (s)')\n", "plt.ylabel('Center along gravity direction (pixels)')\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "#plt.ylim([0,750])\n", "#plt.xlim([0.004, 0.025])\n", "#plt.legend(prop={'size': 14})\n", "plt.show()\n", "\n", "f_x = popt_x[3]\n", "df_x = pcov_x[3][3]**0.5\n", "\n", "print('f = %.4f \\u00B1 %.4f Hz'% tuple([np.abs(f_x),df_x]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Truncation: 0.725" ] }, { "cell_type": "code", "execution_count": 93, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The detected scaning axes and values are: \n", "\n", "{'mod_blink_on_time': array([0.005 , 0.0057, 0.0064, 0.0071, 0.0078, 0.0085, 0.0092, 0.0099,\n", " 0.0106, 0.0113, 0.012 , 0.0127, 0.0134, 0.0141, 0.0148, 0.0155,\n", " 0.0162, 0.0169, 0.0176, 0.0183, 0.019 , 0.0197]), 'runs': array([0., 1., 2., 3.])}\n" ] }, { "data": { "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib notebook\n", "shotNum = \"0011\"\n", "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n", "\n", "dataSetDict = {\n", " dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i], excludeAxis = ['sweep_start_freq', 'sweep_stop_freq'])\n", " for i in [0]\n", "}\n", "\n", "dataSet = dataSetDict[\"camera_0\"]\n", "\n", "print_scanAxis(dataSet)\n", "\n", "scanAxis = get_scanAxis(dataSet)\n", "\n", "dataSet = auto_rechunk(dataSet)\n", "\n", "dataSet = imageAnalyser.get_absorption_images(dataSet)\n", "\n", "imageAnalyser.center = (800, 900)\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", "Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n", "Ncount_mean = calculate_mean(Ncount)\n", "Ncount_std = calculate_std(Ncount)\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "Ncount_mean.plot.errorbar(ax=ax, yerr = None, fmt='ob')\n", "plt.ylabel('NCount')\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [], "source": [ "dataSet_cropOD_chunk = dataSet_cropOD.chunk((1, 1, 300, 300))\n", "fitAnalyser = FitAnalyser(\"Gaussian-2D\", fitDim=2)\n", "params = fitAnalyser.guess(dataSet_cropOD_chunk, dask=\"parallelized\")\n", "fitResult = fitAnalyser.fit(dataSet_cropOD_chunk, params, dask=\"parallelized\").load()" ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [ { "data": { "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "val = fitAnalyser.get_fit_value(fitResult)\n", "std = fitAnalyser.get_fit_std(fitResult)\n", "\n", "fitCurve = fitAnalyser.eval(fitResult, x=np.arange(300), y=np.arange(300), dask=\"parallelized\").load()\n", "\n", "# dataKey = 'sigmax'\n", "# dataKey = 'centerx'\n", "# dataKey = 'sigmay'\n", "dataKey = 'centery'\n", "\n", "# val_mean = val[dataKey].mean(dim='runs')\n", "# std_mean = val[dataKey].std(dim='runs')\n", "\n", "val_mean = calculate_mean(val[dataKey])\n", "std_mean = calculate_std(val[dataKey])\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "\n", "val_mean.plot.errorbar(yerr=std_mean, fmt='--ob')\n", "\n", "plt.grid()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 98, "metadata": {}, "outputs": [ { "data": { "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "f = 232.4375 ± 0.3994 Hz\n" ] } ], "source": [ "def damp_osci(t, t0, A, B, nu, gamma):\n", " return A * np.exp(-gamma*t)*np.sin(2*np.pi*nu*(t-t0)) + B\n", "\n", "yvals = val_mean#.sel(blink_on_time=slice(0.005, 0.025))\n", "yvals_std = std_mean#.sel(blink_on_time=slice(0.005, 0.025))\n", "xvals = dataSet_cropOD[scanAxis[0]]#.sel(blink_on_time=slice(0.005, 0.025))\n", "\n", "fitted_qtys_1 = yvals.to_numpy()\n", "scan_para = xvals.to_numpy()\n", "fitted_qtys_err_1 = yvals_std.to_numpy()\n", "\n", "\n", "plt.figure()\n", "popt_x, pcov_x = curve_fit(damp_osci, scan_para, fitted_qtys_1, np.array([0, 3, 147, 3e2, 0.1]))\n", "freqdata = np.linspace(0.005, 20e-3, 500)\n", "plt.plot(freqdata, damp_osci(freqdata, *popt_x), 'g--',label='fit: t0=%5.3f, A=%5.3f, B=%5.3f, nu=%5.3f, Gamma=%5.3f' % tuple(popt_x))\n", "plt.errorbar(scan_para, fitted_qtys_1, yerr=fitted_qtys_err_1, fmt='or')\n", "plt.xlabel('hold time after switch on the trap (s)')\n", "plt.ylabel('Center along gravity direction (pixels)')\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "#plt.ylim([0,750])\n", "#plt.xlim([0.004, 0.025])\n", "#plt.legend(prop={'size': 14})\n", "plt.show()\n", "\n", "f_x = popt_x[3]\n", "df_x = pcov_x[3][3]**0.5\n", "\n", "print('f = %.4f \\u00B1 %.4f Hz'% tuple([np.abs(f_x),df_x]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Truncation: 0.75" ] }, { "cell_type": "code", "execution_count": 99, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The detected scaning axes and values are: \n", "\n", "{'mod_blink_on_time': array([0.005 , 0.0057, 0.0064, 0.0071, 0.0078, 0.0085, 0.0092, 0.0099,\n", " 0.0106, 0.0113, 0.012 , 0.0127, 0.0134, 0.0141, 0.0148, 0.0155,\n", " 0.0162, 0.0169, 0.0176, 0.0183, 0.019 , 0.0197]), 'runs': array([0., 1., 2., 3.])}\n" ] }, { "data": { "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib notebook\n", "shotNum = \"0012\"\n", "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n", "\n", "dataSetDict = {\n", " dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i], excludeAxis = ['sweep_start_freq', 'sweep_stop_freq'])\n", " for i in [0]\n", "}\n", "\n", "dataSet = dataSetDict[\"camera_0\"]\n", "\n", "print_scanAxis(dataSet)\n", "\n", "scanAxis = get_scanAxis(dataSet)\n", "\n", "dataSet = auto_rechunk(dataSet)\n", "\n", "dataSet = imageAnalyser.get_absorption_images(dataSet)\n", "\n", "imageAnalyser.center = (800, 900)\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", "Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n", "Ncount_mean = calculate_mean(Ncount)\n", "Ncount_std = calculate_std(Ncount)\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "Ncount_mean.plot.errorbar(ax=ax, yerr = None, fmt='ob')\n", "plt.ylabel('NCount')\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 100, "metadata": {}, "outputs": [], "source": [ "dataSet_cropOD_chunk = dataSet_cropOD.chunk((1, 1, 300, 300))\n", "fitAnalyser = FitAnalyser(\"Gaussian-2D\", fitDim=2)\n", "params = fitAnalyser.guess(dataSet_cropOD_chunk, dask=\"parallelized\")\n", "fitResult = fitAnalyser.fit(dataSet_cropOD_chunk, params, dask=\"parallelized\").load()" ] }, { "cell_type": "code", "execution_count": 101, "metadata": {}, "outputs": [ { "data": { "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "val = fitAnalyser.get_fit_value(fitResult)\n", "std = fitAnalyser.get_fit_std(fitResult)\n", "\n", "fitCurve = fitAnalyser.eval(fitResult, x=np.arange(300), y=np.arange(300), dask=\"parallelized\").load()\n", "\n", "# dataKey = 'sigmax'\n", "# dataKey = 'centerx'\n", "# dataKey = 'sigmay'\n", "dataKey = 'centery'\n", "\n", "# val_mean = val[dataKey].mean(dim='runs')\n", "# std_mean = val[dataKey].std(dim='runs')\n", "\n", "val_mean = calculate_mean(val[dataKey])\n", "std_mean = calculate_std(val[dataKey])\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "\n", "val_mean.plot.errorbar(yerr=std_mean, fmt='--ob')\n", "\n", "plt.grid()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 103, "metadata": {}, "outputs": [ { "data": { "application/javascript": "/* Put everything inside the global mpl namespace */\n/* global mpl */\nwindow.mpl = {};\n\nmpl.get_websocket_type = function () {\n if (typeof WebSocket !== 'undefined') {\n return WebSocket;\n } else if (typeof MozWebSocket !== 'undefined') {\n return MozWebSocket;\n } else {\n alert(\n 'Your browser does not have WebSocket support. ' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. ' +\n 'Performance may be slow.';\n }\n }\n\n this.imageObj = new Image();\n\n this.context = undefined;\n this.message = undefined;\n this.canvas = undefined;\n this.rubberband_canvas = undefined;\n this.rubberband_context = undefined;\n this.format_dropdown = undefined;\n\n this.image_mode = 'full';\n\n this.root = document.createElement('div');\n this.root.setAttribute('style', 'display: inline-block');\n this._root_extra_style(this.root);\n\n parent_element.appendChild(this.root);\n\n this._init_header(this);\n this._init_canvas(this);\n this._init_toolbar(this);\n\n var fig = this;\n\n this.waiting = false;\n\n this.ws.onopen = function () {\n fig.send_message('supports_binary', { value: fig.supports_binary });\n fig.send_message('send_image_mode', {});\n if (fig.ratio !== 1) {\n fig.send_message('set_device_pixel_ratio', {\n device_pixel_ratio: fig.ratio,\n });\n }\n fig.send_message('refresh', {});\n };\n\n this.imageObj.onload = function () {\n if (fig.image_mode === 'full') {\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._root_extra_style = function (_canvas_div) {};\n\nmpl.figure.prototype._init_canvas = function () {\n var fig = this;\n\n var canvas_div = (this.canvas_div = document.createElement('div'));\n canvas_div.setAttribute(\n 'style',\n 'border: 1px solid #ddd;' +\n 'box-sizing: content-box;' +\n 'clear: both;' +\n 'min-height: 1px;' +\n 'min-width: 1px;' +\n 'outline: 0;' +\n 'overflow: hidden;' +\n 'position: relative;' +\n 'resize: both;'\n );\n\n function on_keyboard_event_closure(name) {\n return function (event) {\n return fig.key "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "f = 224.9906 ± 0.4995 Hz\n" ] } ], "source": [ "def damp_osci(t, t0, A, B, nu, gamma):\n", " return A * np.exp(-gamma*t)*np.sin(2*np.pi*nu*(t-t0)) + B\n", "\n", "yvals = val_mean#.sel(blink_on_time=slice(0.005, 0.025))\n", "yvals_std = std_mean#.sel(blink_on_time=slice(0.005, 0.025))\n", "xvals = dataSet_cropOD[scanAxis[0]]#.sel(blink_on_time=slice(0.005, 0.025))\n", "\n", "fitted_qtys_1 = yvals.to_numpy()\n", "scan_para = xvals.to_numpy()\n", "fitted_qtys_err_1 = yvals_std.to_numpy()\n", "\n", "\n", "plt.figure()\n", "popt_x, pcov_x = curve_fit(damp_osci, scan_para, fitted_qtys_1, np.array([0, 3, 147, 3e2, 0.1]))\n", "freqdata = np.linspace(0.005, 20e-3, 500)\n", "plt.plot(freqdata, damp_osci(freqdata, *popt_x), 'g--',label='fit: t0=%5.3f, A=%5.3f, B=%5.3f, nu=%5.3f, Gamma=%5.3f' % tuple(popt_x))\n", "plt.errorbar(scan_para, fitted_qtys_1, yerr=fitted_qtys_err_1, fmt='or')\n", "plt.xlabel('hold time after switch on the trap (s)')\n", "plt.ylabel('Center along gravity direction (pixels)')\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "#plt.ylim([0,750])\n", "#plt.xlim([0.004, 0.025])\n", "#plt.legend(prop={'size': 14})\n", "plt.show()\n", "\n", "f_x = popt_x[3]\n", "df_x = pcov_x[3][3]**0.5\n", "\n", "print('f = %.4f \\u00B1 %.4f Hz'% tuple([np.abs(f_x),df_x]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Truncation: 0.775" ] }, { "cell_type": "code", "execution_count": 128, "metadata": {}, "outputs": [ { "ename": "OSError", "evalue": "[Errno group not found: images] 'images'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mOSError\u001b[0m Traceback (most recent call last)", "Input \u001b[1;32mIn [128]\u001b[0m, in \u001b[0;36m<cell line: 5>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 2\u001b[0m shotNum \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0013\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 3\u001b[0m filePath \u001b[38;5;241m=\u001b[39m folderPath \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m shotNum \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/*.h5\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m----> 5\u001b[0m dataSetDict \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m 6\u001b[0m dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i], excludeAxis \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msweep_start_freq\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msweep_stop_freq\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m 7\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m 8\u001b[0m }\n\u001b[0;32m 10\u001b[0m dataSet \u001b[38;5;241m=\u001b[39m dataSetDict[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcamera_0\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m 12\u001b[0m print_scanAxis(dataSet)\n", "Input \u001b[1;32mIn [128]\u001b[0m, in \u001b[0;36m<dictcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m 2\u001b[0m shotNum \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m0013\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 3\u001b[0m filePath \u001b[38;5;241m=\u001b[39m folderPath \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m shotNum \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/*.h5\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 5\u001b[0m dataSetDict \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m----> 6\u001b[0m dskey[groupList[i]]: \u001b[43mread_hdf5_file\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilePath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgroupList\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexcludeAxis\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msweep_start_freq\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msweep_stop_freq\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 7\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m 8\u001b[0m }\n\u001b[0;32m 10\u001b[0m dataSet \u001b[38;5;241m=\u001b[39m dataSetDict[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcamera_0\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m 12\u001b[0m print_scanAxis(dataSet)\n", "File \u001b[1;32mZ:\\Dy_Lab\\Data\\Analysis\\2023\\07\\31\\DataContainer\\ReadData.py:170\u001b[0m, in \u001b[0;36mread_hdf5_file\u001b[1;34m(filePath, group, datesetOfGlobal, preprocess, join, parallel, engine, phony_dims, excludeAxis, maxFileNum, **kwargs)\u001b[0m\n\u001b[0;32m 167\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 168\u001b[0m kwargs\u001b[38;5;241m.\u001b[39mupdate({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpreprocess\u001b[39m\u001b[38;5;124m'\u001b[39m:preprocess})\n\u001b[1;32m--> 170\u001b[0m ds \u001b[38;5;241m=\u001b[39m xr\u001b[38;5;241m.\u001b[39mopen_mfdataset(fullFilePath, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 172\u001b[0m newDimKey \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mappend([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mz\u001b[39m\u001b[38;5;124m'\u001b[39m], [ \u001b[38;5;28mchr\u001b[39m(i) \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m97\u001b[39m, \u001b[38;5;241m97\u001b[39m\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m23\u001b[39m)])\n\u001b[0;32m 174\u001b[0m oldDimKey \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39msort(\n\u001b[0;32m 175\u001b[0m [\n\u001b[0;32m 176\u001b[0m key \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 179\u001b[0m ]\n\u001b[0;32m 180\u001b[0m )\n", "File \u001b[1;32m~\\anaconda3\\envs\\py39\\lib\\site-packages\\xarray\\backends\\api.py:990\u001b[0m, in \u001b[0;36mopen_mfdataset\u001b[1;34m(paths, chunks, concat_dim, compat, preprocess, engine, data_vars, coords, combine, parallel, join, attrs_file, combine_attrs, **kwargs)\u001b[0m\n\u001b[0;32m 985\u001b[0m datasets \u001b[38;5;241m=\u001b[39m [preprocess(ds) \u001b[38;5;28;01mfor\u001b[39;00m ds \u001b[38;5;129;01min\u001b[39;00m datasets]\n\u001b[0;32m 987\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m parallel:\n\u001b[0;32m 988\u001b[0m \u001b[38;5;66;03m# calling compute here will return the datasets/file_objs lists,\u001b[39;00m\n\u001b[0;32m 989\u001b[0m \u001b[38;5;66;03m# the underlying datasets will still be stored as dask arrays\u001b[39;00m\n\u001b[1;32m--> 990\u001b[0m datasets, closers \u001b[38;5;241m=\u001b[39m \u001b[43mdask\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdatasets\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mclosers\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 992\u001b[0m \u001b[38;5;66;03m# Combine all datasets, closing them in case of a ValueError\u001b[39;00m\n\u001b[0;32m 993\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n", "File \u001b[1;32m~\\anaconda3\\envs\\py39\\lib\\site-packages\\dask\\base.py:599\u001b[0m, in \u001b[0;36mcompute\u001b[1;34m(traverse, optimize_graph, scheduler, get, *args, **kwargs)\u001b[0m\n\u001b[0;32m 596\u001b[0m keys\u001b[38;5;241m.\u001b[39mappend(x\u001b[38;5;241m.\u001b[39m__dask_keys__())\n\u001b[0;32m 597\u001b[0m postcomputes\u001b[38;5;241m.\u001b[39mappend(x\u001b[38;5;241m.\u001b[39m__dask_postcompute__())\n\u001b[1;32m--> 599\u001b[0m results \u001b[38;5;241m=\u001b[39m schedule(dsk, keys, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 600\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m repack([f(r, \u001b[38;5;241m*\u001b[39ma) \u001b[38;5;28;01mfor\u001b[39;00m r, (f, a) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(results, postcomputes)])\n", "File \u001b[1;32m~\\anaconda3\\envs\\py39\\lib\\site-packages\\distributed\\client.py:3224\u001b[0m, in \u001b[0;36mClient.get\u001b[1;34m(self, dsk, keys, workers, allow_other_workers, resources, sync, asynchronous, direct, retries, priority, fifo_timeout, actors, **kwargs)\u001b[0m\n\u001b[0;32m 3222\u001b[0m should_rejoin \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m 3223\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3224\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgather\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpacked\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43masynchronous\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43masynchronous\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdirect\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdirect\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 3225\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m 3226\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m f \u001b[38;5;129;01min\u001b[39;00m futures\u001b[38;5;241m.\u001b[39mvalues():\n", "File \u001b[1;32m~\\anaconda3\\envs\\py39\\lib\\site-packages\\distributed\\client.py:2359\u001b[0m, in \u001b[0;36mClient.gather\u001b[1;34m(self, futures, errors, direct, asynchronous)\u001b[0m\n\u001b[0;32m 2357\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 2358\u001b[0m local_worker \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m-> 2359\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msync\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 2360\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_gather\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2361\u001b[0m \u001b[43m \u001b[49m\u001b[43mfutures\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2362\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2363\u001b[0m \u001b[43m \u001b[49m\u001b[43mdirect\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdirect\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2364\u001b[0m \u001b[43m \u001b[49m\u001b[43mlocal_worker\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlocal_worker\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2365\u001b[0m \u001b[43m \u001b[49m\u001b[43masynchronous\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43masynchronous\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2366\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[1;32m~\\anaconda3\\envs\\py39\\lib\\site-packages\\distributed\\utils.py:351\u001b[0m, in \u001b[0;36mSyncMethodMixin.sync\u001b[1;34m(self, func, asynchronous, callback_timeout, *args, **kwargs)\u001b[0m\n\u001b[0;32m 349\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m future\n\u001b[0;32m 350\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 351\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m sync(\n\u001b[0;32m 352\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloop, func, \u001b[38;5;241m*\u001b[39margs, callback_timeout\u001b[38;5;241m=\u001b[39mcallback_timeout, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[0;32m 353\u001b[0m )\n", "File \u001b[1;32m~\\anaconda3\\envs\\py39\\lib\\site-packages\\distributed\\utils.py:418\u001b[0m, in \u001b[0;36msync\u001b[1;34m(loop, func, callback_timeout, *args, **kwargs)\u001b[0m\n\u001b[0;32m 416\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m error:\n\u001b[0;32m 417\u001b[0m typ, exc, tb \u001b[38;5;241m=\u001b[39m error\n\u001b[1;32m--> 418\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exc\u001b[38;5;241m.\u001b[39mwith_traceback(tb)\n\u001b[0;32m 419\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 420\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", "File \u001b[1;32m~\\anaconda3\\envs\\py39\\lib\\site-packages\\distributed\\utils.py:391\u001b[0m, in \u001b[0;36msync.<locals>.f\u001b[1;34m()\u001b[0m\n\u001b[0;32m 389\u001b[0m future \u001b[38;5;241m=\u001b[39m wait_for(future, callback_timeout)\n\u001b[0;32m 390\u001b[0m future \u001b[38;5;241m=\u001b[39m asyncio\u001b[38;5;241m.\u001b[39mensure_future(future)\n\u001b[1;32m--> 391\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01myield\u001b[39;00m future\n\u001b[0;32m 392\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[0;32m 393\u001b[0m error \u001b[38;5;241m=\u001b[39m sys\u001b[38;5;241m.\u001b[39mexc_info()\n", "File \u001b[1;32m~\\anaconda3\\envs\\py39\\lib\\site-packages\\tornado\\gen.py:762\u001b[0m, in \u001b[0;36mRunner.run\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 759\u001b[0m exc_info \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 761\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 762\u001b[0m value \u001b[38;5;241m=\u001b[39m \u001b[43mfuture\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 763\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[0;32m 764\u001b[0m exc_info \u001b[38;5;241m=\u001b[39m sys\u001b[38;5;241m.\u001b[39mexc_info()\n", "File \u001b[1;32m~\\anaconda3\\envs\\py39\\lib\\site-packages\\distributed\\client.py:2222\u001b[0m, in \u001b[0;36mClient._gather\u001b[1;34m(self, futures, errors, direct, local_worker)\u001b[0m\n\u001b[0;32m 2220\u001b[0m exc \u001b[38;5;241m=\u001b[39m CancelledError(key)\n\u001b[0;32m 2221\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 2222\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exception\u001b[38;5;241m.\u001b[39mwith_traceback(traceback)\n\u001b[0;32m 2223\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exc\n\u001b[0;32m 2224\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mskip\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n", "File \u001b[1;32m~\\anaconda3\\envs\\py39\\lib\\site-packages\\dask\\utils.py:73\u001b[0m, in \u001b[0;36mapply\u001b[1;34m()\u001b[0m\n\u001b[0;32m 42\u001b[0m \u001b[38;5;124;03m\"\"\"Apply a function given its positional and keyword arguments.\u001b[39;00m\n\u001b[0;32m 43\u001b[0m \n\u001b[0;32m 44\u001b[0m \u001b[38;5;124;03mEquivalent to ``func(*args, **kwargs)``\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 70\u001b[0m \u001b[38;5;124;03m>>> dsk = {'task-name': task} # adds the task to a low level Dask task graph\u001b[39;00m\n\u001b[0;32m 71\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 72\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m kwargs:\n\u001b[1;32m---> 73\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 74\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 75\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m func(\u001b[38;5;241m*\u001b[39margs)\n", "File \u001b[1;32m~\\anaconda3\\envs\\py39\\lib\\site-packages\\xarray\\backends\\api.py:525\u001b[0m, in \u001b[0;36mopen_dataset\u001b[1;34m()\u001b[0m\n\u001b[0;32m 513\u001b[0m decoders \u001b[38;5;241m=\u001b[39m _resolve_decoders_kwargs(\n\u001b[0;32m 514\u001b[0m decode_cf,\n\u001b[0;32m 515\u001b[0m open_backend_dataset_parameters\u001b[38;5;241m=\u001b[39mbackend\u001b[38;5;241m.\u001b[39mopen_dataset_parameters,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 521\u001b[0m decode_coords\u001b[38;5;241m=\u001b[39mdecode_coords,\n\u001b[0;32m 522\u001b[0m )\n\u001b[0;32m 524\u001b[0m overwrite_encoded_chunks \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moverwrite_encoded_chunks\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m--> 525\u001b[0m backend_ds \u001b[38;5;241m=\u001b[39m backend\u001b[38;5;241m.\u001b[39mopen_dataset(\n\u001b[0;32m 526\u001b[0m filename_or_obj,\n\u001b[0;32m 527\u001b[0m drop_variables\u001b[38;5;241m=\u001b[39mdrop_variables,\n\u001b[0;32m 528\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mdecoders,\n\u001b[0;32m 529\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m 530\u001b[0m )\n\u001b[0;32m 531\u001b[0m ds \u001b[38;5;241m=\u001b[39m _dataset_from_backend_dataset(\n\u001b[0;32m 532\u001b[0m backend_ds,\n\u001b[0;32m 533\u001b[0m filename_or_obj,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 541\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m 542\u001b[0m )\n\u001b[0;32m 543\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ds\n", "File \u001b[1;32m~\\anaconda3\\envs\\py39\\lib\\site-packages\\xarray\\backends\\h5netcdf_.py:413\u001b[0m, in \u001b[0;36mopen_dataset\u001b[1;34m()\u001b[0m\n\u001b[0;32m 394\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mopen_dataset\u001b[39m( \u001b[38;5;66;03m# type: ignore[override] # allow LSP violation, not supporting **kwargs\u001b[39;00m\n\u001b[0;32m 395\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m 396\u001b[0m filename_or_obj: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m|\u001b[39m os\u001b[38;5;241m.\u001b[39mPathLike[Any] \u001b[38;5;241m|\u001b[39m BufferedIOBase \u001b[38;5;241m|\u001b[39m AbstractDataStore,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 410\u001b[0m decode_vlen_strings\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m 411\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Dataset:\n\u001b[0;32m 412\u001b[0m filename_or_obj \u001b[38;5;241m=\u001b[39m _normalize_path(filename_or_obj)\n\u001b[1;32m--> 413\u001b[0m store \u001b[38;5;241m=\u001b[39m H5NetCDFStore\u001b[38;5;241m.\u001b[39mopen(\n\u001b[0;32m 414\u001b[0m filename_or_obj,\n\u001b[0;32m 415\u001b[0m \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mformat\u001b[39m,\n\u001b[0;32m 416\u001b[0m group\u001b[38;5;241m=\u001b[39mgroup,\n\u001b[0;32m 417\u001b[0m lock\u001b[38;5;241m=\u001b[39mlock,\n\u001b[0;32m 418\u001b[0m invalid_netcdf\u001b[38;5;241m=\u001b[39minvalid_netcdf,\n\u001b[0;32m 419\u001b[0m phony_dims\u001b[38;5;241m=\u001b[39mphony_dims,\n\u001b[0;32m 420\u001b[0m decode_vlen_strings\u001b[38;5;241m=\u001b[39mdecode_vlen_strings,\n\u001b[0;32m 421\u001b[0m )\n\u001b[0;32m 423\u001b[0m store_entrypoint \u001b[38;5;241m=\u001b[39m StoreBackendEntrypoint()\n\u001b[0;32m 425\u001b[0m ds \u001b[38;5;241m=\u001b[39m store_entrypoint\u001b[38;5;241m.\u001b[39mopen_dataset(\n\u001b[0;32m 426\u001b[0m store,\n\u001b[0;32m 427\u001b[0m mask_and_scale\u001b[38;5;241m=\u001b[39mmask_and_scale,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 433\u001b[0m decode_timedelta\u001b[38;5;241m=\u001b[39mdecode_timedelta,\n\u001b[0;32m 434\u001b[0m )\n", "File \u001b[1;32m~\\anaconda3\\envs\\py39\\lib\\site-packages\\xarray\\backends\\h5netcdf_.py:176\u001b[0m, in \u001b[0;36mopen\u001b[1;34m()\u001b[0m\n\u001b[0;32m 173\u001b[0m lock \u001b[38;5;241m=\u001b[39m combine_locks([HDF5_LOCK, get_write_lock(filename)])\n\u001b[0;32m 175\u001b[0m manager \u001b[38;5;241m=\u001b[39m CachingFileManager(h5netcdf\u001b[38;5;241m.\u001b[39mFile, filename, mode\u001b[38;5;241m=\u001b[39mmode, kwargs\u001b[38;5;241m=\u001b[39mkwargs)\n\u001b[1;32m--> 176\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mcls\u001b[39m(manager, group\u001b[38;5;241m=\u001b[39mgroup, mode\u001b[38;5;241m=\u001b[39mmode, lock\u001b[38;5;241m=\u001b[39mlock, autoclose\u001b[38;5;241m=\u001b[39mautoclose)\n", "File \u001b[1;32m~\\anaconda3\\envs\\py39\\lib\\site-packages\\xarray\\backends\\h5netcdf_.py:127\u001b[0m, in \u001b[0;36m__init__\u001b[1;34m()\u001b[0m\n\u001b[0;32m 124\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mformat \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 125\u001b[0m \u001b[38;5;66;03m# todo: utilizing find_root_and_group seems a bit clunky\u001b[39;00m\n\u001b[0;32m 126\u001b[0m \u001b[38;5;66;03m# making filename available on h5netcdf.Group seems better\u001b[39;00m\n\u001b[1;32m--> 127\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_filename \u001b[38;5;241m=\u001b[39m find_root_and_group(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mds)[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mfilename\n\u001b[0;32m 128\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mis_remote \u001b[38;5;241m=\u001b[39m is_remote_uri(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_filename)\n\u001b[0;32m 129\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlock \u001b[38;5;241m=\u001b[39m ensure_lock(lock)\n", "File \u001b[1;32m~\\anaconda3\\envs\\py39\\lib\\site-packages\\xarray\\backends\\h5netcdf_.py:187\u001b[0m, in \u001b[0;36mds\u001b[1;34m()\u001b[0m\n\u001b[0;32m 185\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[0;32m 186\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mds\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m--> 187\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_acquire()\n", "File \u001b[1;32m~\\anaconda3\\envs\\py39\\lib\\site-packages\\xarray\\backends\\h5netcdf_.py:180\u001b[0m, in \u001b[0;36m_acquire\u001b[1;34m()\u001b[0m\n\u001b[0;32m 178\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_acquire\u001b[39m(\u001b[38;5;28mself\u001b[39m, needs_lock\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m 179\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_manager\u001b[38;5;241m.\u001b[39macquire_context(needs_lock) \u001b[38;5;28;01mas\u001b[39;00m root:\n\u001b[1;32m--> 180\u001b[0m ds \u001b[38;5;241m=\u001b[39m _nc4_require_group(\n\u001b[0;32m 181\u001b[0m root, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_group, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode, create_group\u001b[38;5;241m=\u001b[39m_h5netcdf_create_group\n\u001b[0;32m 182\u001b[0m )\n\u001b[0;32m 183\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ds\n", "File \u001b[1;32m~\\anaconda3\\envs\\py39\\lib\\site-packages\\xarray\\backends\\netCDF4_.py:191\u001b[0m, in \u001b[0;36m_nc4_require_group\u001b[1;34m()\u001b[0m\n\u001b[0;32m 188\u001b[0m ds \u001b[38;5;241m=\u001b[39m create_group(ds, key)\n\u001b[0;32m 189\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 190\u001b[0m \u001b[38;5;66;03m# wrap error to provide slightly more helpful message\u001b[39;00m\n\u001b[1;32m--> 191\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgroup not found: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, e)\n\u001b[0;32m 192\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ds\n", "\u001b[1;31mOSError\u001b[0m: [Errno group not found: images] 'images'" ] } ], "source": [ "%matplotlib notebook\n", "shotNum = \"0013\"\n", "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n", "\n", "dataSetDict = {\n", " dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i], excludeAxis = ['sweep_start_freq', 'sweep_stop_freq'])\n", " for i in [0]\n", "}\n", "\n", "dataSet = dataSetDict[\"camera_0\"]\n", "\n", "print_scanAxis(dataSet)\n", "\n", "scanAxis = get_scanAxis(dataSet)\n", "\n", "dataSet = auto_rechunk(dataSet)\n", "\n", "dataSet = imageAnalyser.get_absorption_images(dataSet)\n", "\n", "imageAnalyser.center = (800, 900)\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", "Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n", "Ncount_mean = calculate_mean(Ncount)\n", "Ncount_std = calculate_std(Ncount)\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "Ncount_mean.plot.errorbar(ax=ax, yerr = None, fmt='ob')\n", "plt.ylabel('NCount')\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dataSet_cropOD_chunk = dataSet_cropOD.chunk((1, 1, 300, 300))\n", "fitAnalyser = FitAnalyser(\"Gaussian-2D\", fitDim=2)\n", "params = fitAnalyser.guess(dataSet_cropOD_chunk, dask=\"parallelized\")\n", "fitResult = fitAnalyser.fit(dataSet_cropOD_chunk, params, dask=\"parallelized\").load()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "val = fitAnalyser.get_fit_value(fitResult)\n", "std = fitAnalyser.get_fit_std(fitResult)\n", "\n", "fitCurve = fitAnalyser.eval(fitResult, x=np.arange(300), y=np.arange(300), dask=\"parallelized\").load()\n", "\n", "# dataKey = 'sigmax'\n", "# dataKey = 'centerx'\n", "# dataKey = 'sigmay'\n", "dataKey = 'centery'\n", "\n", "# val_mean = val[dataKey].mean(dim='runs')\n", "# std_mean = val[dataKey].std(dim='runs')\n", "\n", "val_mean = calculate_mean(val[dataKey])\n", "std_mean = calculate_std(val[dataKey])\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "\n", "val_mean.plot.errorbar(yerr=std_mean, fmt='--ob')\n", "\n", "plt.grid()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 129, "metadata": {}, "outputs": [ { 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' +\n 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n 'Firefox 4 and 5 are also supported but you ' +\n 'have to enable WebSockets in about:config.'\n );\n }\n};\n\nmpl.figure = function (figure_id, websocket, ondownload, parent_element) {\n this.id = figure_id;\n\n this.ws = websocket;\n\n this.supports_binary = this.ws.binaryType !== undefined;\n\n if (!this.supports_binary) {\n var warnings = document.getElementById('mpl-warnings');\n if (warnings) {\n warnings.style.display = 'block';\n warnings.textContent =\n 'This browser does not support binary websocket messages. 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{\n // Full images could contain transparency (where diff images\n // almost always do), so we need to clear the canvas so that\n // there is no ghosting.\n fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n }\n fig.context.drawImage(fig.imageObj, 0, 0);\n };\n\n this.imageObj.onunload = function () {\n fig.ws.close();\n };\n\n this.ws.onmessage = this._make_on_message_function(this);\n\n this.ondownload = ondownload;\n};\n\nmpl.figure.prototype._init_header = function () {\n var titlebar = document.createElement('div');\n titlebar.classList =\n 'ui-dialog-titlebar ui-widget-header ui-corner-all ui-helper-clearfix';\n var titletext = document.createElement('div');\n titletext.classList = 'ui-dialog-title';\n titletext.setAttribute(\n 'style',\n 'width: 100%; text-align: center; padding: 3px;'\n );\n titlebar.appendChild(titletext);\n this.root.appendChild(titlebar);\n this.header = titletext;\n};\n\nmpl.figure.prototype._canvas_extra_style = function (_canvas_div) 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], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "f = 224.9906 ± 0.4995 Hz\n" ] } ], "source": [ "def damp_osci(t, t0, A, B, nu, gamma):\n", " return A * np.exp(-gamma*t)*np.sin(2*np.pi*nu*(t-t0)) + B\n", "\n", "yvals = val_mean#.sel(blink_on_time=slice(0.005, 0.025))\n", "yvals_std = std_mean#.sel(blink_on_time=slice(0.005, 0.025))\n", "xvals = dataSet_cropOD[scanAxis[0]]#.sel(blink_on_time=slice(0.005, 0.025))\n", "\n", "fitted_qtys_1 = yvals.to_numpy()\n", "scan_para = xvals.to_numpy()\n", "fitted_qtys_err_1 = yvals_std.to_numpy()\n", "\n", "\n", "plt.figure()\n", "popt_x, pcov_x = curve_fit(damp_osci, scan_para, fitted_qtys_1, np.array([0, 3, 147, 3e2, 0.1]))\n", "freqdata = np.linspace(0.005, 19e-3, 500)\n", "plt.plot(freqdata, damp_osci(freqdata, *popt_x), 'g--',label='fit: t0=%5.3f, A=%5.3f, B=%5.3f, nu=%5.3f, Gamma=%5.3f' % tuple(popt_x))\n", "plt.errorbar(scan_para, fitted_qtys_1, yerr=fitted_qtys_err_1, fmt='or')\n", "plt.xlabel('hold time after switch on the trap (s)')\n", "plt.ylabel('Center along gravity direction (pixels)')\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "#plt.ylim([0,750])\n", "#plt.xlim([0.004, 0.025])\n", "#plt.legend(prop={'size': 14})\n", "plt.show()\n", "\n", "f_x = popt_x[3]\n", "df_x = pcov_x[3][3]**0.5\n", "\n", "print('f = %.4f \\u00B1 %.4f Hz'% tuple([np.abs(f_x),df_x]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Truncation: 0.8" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib notebook\n", "shotNum = \"0036\"\n", "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n", "\n", "dataSetDict = {\n", " dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i], excludeAxis = ['sweep_start_freq', 'sweep_stop_freq'])\n", " for i in [0]\n", "}\n", "\n", "dataSet = dataSetDict[\"camera_0\"]\n", "\n", "print_scanAxis(dataSet)\n", "\n", "scanAxis = get_scanAxis(dataSet)\n", "\n", "dataSet = auto_rechunk(dataSet)\n", "\n", "dataSet = imageAnalyser.get_absorption_images(dataSet)\n", "\n", "imageAnalyser.center = (800, 900)\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", "Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n", "Ncount_mean = calculate_mean(Ncount)\n", "Ncount_std = calculate_std(Ncount)\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "Ncount_mean.plot.errorbar(ax=ax, yerr = None, fmt='ob')\n", "plt.ylabel('NCount')\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dataSet_cropOD_chunk = dataSet_cropOD.chunk((1, 1, 300, 300))\n", "fitAnalyser = FitAnalyser(\"Gaussian-2D\", fitDim=2)\n", "params = fitAnalyser.guess(dataSet_cropOD_chunk, dask=\"parallelized\")\n", "fitResult = fitAnalyser.fit(dataSet_cropOD_chunk, params, dask=\"parallelized\").load()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "val = fitAnalyser.get_fit_value(fitResult)\n", "std = fitAnalyser.get_fit_std(fitResult)\n", "\n", "fitCurve = fitAnalyser.eval(fitResult, x=np.arange(300), y=np.arange(300), dask=\"parallelized\").load()\n", "\n", "# dataKey = 'sigmax'\n", "# dataKey = 'centerx'\n", "# dataKey = 'sigmay'\n", "dataKey = 'centery'\n", "\n", "# val_mean = val[dataKey].mean(dim='runs')\n", "# std_mean = val[dataKey].std(dim='runs')\n", "\n", "val_mean = calculate_mean(val[dataKey])\n", "std_mean = calculate_std(val[dataKey])\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "\n", "val_mean.plot.errorbar(yerr=std_mean, fmt='--ob')\n", "\n", "plt.grid()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def damp_osci(t, t0, A, B, nu, gamma):\n", " return A * np.exp(-gamma*t)*np.sin(2*np.pi*nu*(t-t0)) + B\n", "\n", "yvals = val_mean#.sel(blink_on_time=slice(0.005, 0.025))\n", "yvals_std = std_mean#.sel(blink_on_time=slice(0.005, 0.025))\n", "xvals = dataSet_cropOD[scanAxis[0]]#.sel(blink_on_time=slice(0.005, 0.025))\n", "\n", "fitted_qtys_1 = yvals.to_numpy()\n", "scan_para = xvals.to_numpy()\n", "fitted_qtys_err_1 = yvals_std.to_numpy()\n", "\n", "\n", "plt.figure()\n", "popt_x, pcov_x = curve_fit(damp_osci, scan_para, fitted_qtys_1, np.array([0, 3, 147, 2e2, 0.1]))\n", "freqdata = np.linspace(0.005, 19e-3, 500)\n", "plt.plot(freqdata, damp_osci(freqdata, *popt_x), 'g--',label='fit: t0=%5.3f, A=%5.3f, B=%5.3f, nu=%5.3f, Gamma=%5.3f' % tuple(popt_x))\n", "plt.errorbar(scan_para, fitted_qtys_1, yerr=fitted_qtys_err_1, fmt='or')\n", "plt.xlabel('hold time after switch on the trap (s)')\n", "plt.ylabel('Center along gravity direction (pixels)')\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "#plt.ylim([0,750])\n", "#plt.xlim([0.004, 0.025])\n", "#plt.legend(prop={'size': 14})\n", "plt.show()\n", "\n", "f_x = popt_x[3]\n", "df_x = pcov_x[3][3]**0.5\n", "\n", "print('f = %.4f \\u00B1 %.4f Hz'% tuple([np.abs(f_x),df_x]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Truncation: 0.85" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib notebook\n", "shotNum = \"0038\"\n", "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n", "\n", "dataSetDict = {\n", " dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i], excludeAxis = ['sweep_start_freq', 'sweep_stop_freq'])\n", " for i in [0]\n", "}\n", "\n", "dataSet = dataSetDict[\"camera_0\"]\n", "\n", "print_scanAxis(dataSet)\n", "\n", "scanAxis = get_scanAxis(dataSet)\n", "\n", "dataSet = auto_rechunk(dataSet)\n", "\n", "dataSet = imageAnalyser.get_absorption_images(dataSet)\n", "\n", "imageAnalyser.center = (800, 900)\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", "Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n", "Ncount_mean = calculate_mean(Ncount)\n", "Ncount_std = calculate_std(Ncount)\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "Ncount_mean.plot.errorbar(ax=ax, yerr = None, fmt='ob')\n", "plt.ylabel('NCount')\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dataSet_cropOD_chunk = dataSet_cropOD.chunk((1, 1, 300, 300))\n", "fitAnalyser = FitAnalyser(\"Gaussian-2D\", fitDim=2)\n", "params = fitAnalyser.guess(dataSet_cropOD_chunk, dask=\"parallelized\")\n", "fitResult = fitAnalyser.fit(dataSet_cropOD_chunk, params, dask=\"parallelized\").load()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "val = fitAnalyser.get_fit_value(fitResult)\n", "std = fitAnalyser.get_fit_std(fitResult)\n", "\n", "fitCurve = fitAnalyser.eval(fitResult, x=np.arange(300), y=np.arange(300), dask=\"parallelized\").load()\n", "\n", "# dataKey = 'sigmax'\n", "# dataKey = 'centerx'\n", "# dataKey = 'sigmay'\n", "dataKey = 'centery'\n", "\n", "# val_mean = val[dataKey].mean(dim='runs')\n", "# std_mean = val[dataKey].std(dim='runs')\n", "\n", "val_mean = calculate_mean(val[dataKey])\n", "std_mean = calculate_std(val[dataKey])\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "\n", "val_mean.plot.errorbar(yerr=std_mean, fmt='--ob')\n", "\n", "plt.grid()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def damp_osci(t, t0, A, B, nu, gamma):\n", " return A * np.exp(-gamma*t)*np.sin(2*np.pi*nu*(t-t0)) + B\n", "\n", "yvals = val_mean#.sel(blink_on_time=slice(0.005, 0.025))\n", "yvals_std = std_mean#.sel(blink_on_time=slice(0.005, 0.025))\n", "xvals = dataSet_cropOD[scanAxis[0]]#.sel(blink_on_time=slice(0.005, 0.025))\n", "\n", "fitted_qtys_1 = yvals.to_numpy()\n", "scan_para = xvals.to_numpy()\n", "fitted_qtys_err_1 = yvals_std.to_numpy()\n", "\n", "\n", "plt.figure()\n", "popt_x, pcov_x = curve_fit(damp_osci, scan_para, fitted_qtys_1, np.array([0, 3, 147, 2e2, 0.1]))\n", "freqdata = np.linspace(0.005, 19e-3, 500)\n", "plt.plot(freqdata, damp_osci(freqdata, *popt_x), 'g--',label='fit: t0=%5.3f, A=%5.3f, B=%5.3f, nu=%5.3f, Gamma=%5.3f' % tuple(popt_x))\n", "plt.errorbar(scan_para, fitted_qtys_1, yerr=fitted_qtys_err_1, fmt='or')\n", "plt.xlabel('hold time after switch on the trap (s)')\n", "plt.ylabel('Center along gravity direction (pixels)')\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "#plt.ylim([0,750])\n", "#plt.xlim([0.004, 0.025])\n", "#plt.legend(prop={'size': 14})\n", "plt.show()\n", "\n", "f_x = popt_x[3]\n", "df_x = pcov_x[3][3]**0.5\n", "\n", "print('f = %.4f \\u00B1 %.4f Hz'% tuple([np.abs(f_x),df_x]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Truncation: 0.9" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib notebook\n", "shotNum = \"0040\"\n", "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n", "\n", "dataSetDict = {\n", " dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i], excludeAxis = ['sweep_start_freq', 'sweep_stop_freq'])\n", " for i in [0]\n", "}\n", "\n", "dataSet = dataSetDict[\"camera_0\"]\n", "\n", "print_scanAxis(dataSet)\n", "\n", "scanAxis = get_scanAxis(dataSet)\n", "\n", "dataSet = auto_rechunk(dataSet)\n", "\n", "dataSet = imageAnalyser.get_absorption_images(dataSet)\n", "\n", "imageAnalyser.center = (800, 900)\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", "Ncount = imageAnalyser.get_Ncount(dataSet_cropOD)\n", "Ncount_mean = calculate_mean(Ncount)\n", "Ncount_std = calculate_std(Ncount)\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "Ncount_mean.plot.errorbar(ax=ax, yerr = None, fmt='ob')\n", "plt.ylabel('NCount')\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dataSet_cropOD_chunk = dataSet_cropOD.chunk((1, 1, 300, 300))\n", "fitAnalyser = FitAnalyser(\"Gaussian-2D\", fitDim=2)\n", "params = fitAnalyser.guess(dataSet_cropOD_chunk, dask=\"parallelized\")\n", "fitResult = fitAnalyser.fit(dataSet_cropOD_chunk, params, dask=\"parallelized\").load()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "val = fitAnalyser.get_fit_value(fitResult)\n", "std = fitAnalyser.get_fit_std(fitResult)\n", "\n", "fitCurve = fitAnalyser.eval(fitResult, x=np.arange(300), y=np.arange(300), dask=\"parallelized\").load()\n", "\n", "# dataKey = 'sigmax'\n", "# dataKey = 'centerx'\n", "# dataKey = 'sigmay'\n", "dataKey = 'centery'\n", "\n", "# val_mean = val[dataKey].mean(dim='runs')\n", "# std_mean = val[dataKey].std(dim='runs')\n", "\n", "val_mean = calculate_mean(val[dataKey])\n", "std_mean = calculate_std(val[dataKey])\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "\n", "val_mean.plot.errorbar(yerr=std_mean, fmt='--ob')\n", "\n", "plt.grid()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def damp_osci(t, t0, A, B, nu, gamma):\n", " return A * np.exp(-gamma*t)*np.sin(2*np.pi*nu*(t-t0)) + B\n", "\n", "yvals = val_mean#.sel(blink_on_time=slice(0.005, 0.025))\n", "yvals_std = std_mean#.sel(blink_on_time=slice(0.005, 0.025))\n", "xvals = dataSet_cropOD[scanAxis[0]]#.sel(blink_on_time=slice(0.005, 0.025))\n", "\n", "fitted_qtys_1 = yvals.to_numpy()\n", "scan_para = xvals.to_numpy()\n", "fitted_qtys_err_1 = yvals_std.to_numpy()\n", "\n", "\n", "plt.figure()\n", "popt_x, pcov_x = curve_fit(damp_osci, scan_para, fitted_qtys_1, np.array([0, 3, 147, 2e2, 0.1]))\n", "freqdata = np.linspace(0.005, 19e-3, 500)\n", "plt.plot(freqdata, damp_osci(freqdata, *popt_x), 'g--',label='fit: t0=%5.3f, A=%5.3f, B=%5.3f, nu=%5.3f, Gamma=%5.3f' % tuple(popt_x))\n", "plt.errorbar(scan_para, fitted_qtys_1, yerr=fitted_qtys_err_1, fmt='or')\n", "plt.xlabel('hold time after switch on the trap (s)')\n", "plt.ylabel('Center along gravity direction (pixels)')\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "#plt.ylim([0,750])\n", "#plt.xlim([0.004, 0.025])\n", "#plt.legend(prop={'size': 14})\n", "plt.show()\n", "\n", "f_x = popt_x[3]\n", "df_x = pcov_x[3][3]**0.5\n", "\n", "print('f = %.4f \\u00B1 %.4f Hz'% tuple([np.abs(f_x),df_x]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tf_z = [299.3777, 288.1736, 272.4744, 259.5129, 241.8841, 233.8030, 229.2665, 219.5115, 211.4388, 199.3276, 184.9726]\n", "dtf_z = [3.7588, 3.5503,1.2193, 0.7946, 1.7848, 1.4811, 0.7289, 1.0038, 0.7646, 1.0321, 1.0431]\n", "trunc = [0.5, 0.55, 0.6, 0.65, 0.7, 0.725, 0.75, 0.775, 0.8, 0.85, 0.9]\n", "p_1 = [0.507, 0.449, 0.398, 0.354, 0.315, 0.298, 0.282, 0.266, 0.252, 0.226, 0.204]\n", "p_2 = [3.88, 3.38, 2.911, 2.474, 2.066, 1.873, 1.687, 1.507, 1.33, 1.003, 0.696]\n", " \n", "plt.figure()\n", "plt.errorbar(p_1, tf_z, yerr = dtf_z, fmt = 'ob')\n", "plt.xlabel('Power of Arm 1')\n", "plt.ylabel('Vertical TF (Hz)')\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "\n", "tf_y = [49.3970, 47.4535, 43.9127, 40.8258, 36.6638, 35.6111, 34.3650, 32.0196, 29.9294, 26.5413, 22.4257]\n", "dtf_y = [1.5574, 0.5536, 0.3721, 0.4734, 0.9428, 0.7725, 0.3264, 0.2862, 0.1988, 0.2477, 0.3690]\n", "trunc = [0.5, 0.55, 0.6, 0.65, 0.7, 0.725, 0.75, 0.775, 0.8, 0.85, 0.9]\n", " \n", "plt.figure()\n", "plt.errorbar(p_2, tf_y, yerr = dtf_y, fmt = 'oy')\n", "plt.xlabel('Power of Arm 2')\n", "plt.ylabel('Axial TF (Hz)')\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "\n", "tf_x = [303.5930, 286.0152, 272.6675, 253.6669, 238.8665, 232.4375, 224.9906, 218.7975, 210.9468, 196.7401, 180.0950]\n", "dtf_x = [6.9704, 2.5433, 0.6739, 0.5183, 0.3901, 0.3994, 0.4995, 0.3777, 0.3864, 0.3636, 0.4790]\n", "trunc = [0.5, 0.55, 0.6, 0.65, 0.7, 0.725, 0.75, 0.775, 0.8, 0.85, 0.9]\n", " \n", "plt.figure()\n", "plt.errorbar(p_1, tf_x, yerr = dtf_x, fmt = 'og')\n", "plt.xlabel('Power of Arm 1')\n", "plt.ylabel('Horz TF (Hz)')\n", "plt.tight_layout()\n", "plt.grid(visible=1)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "v_x = 27451.697 Hz\n" ] } ], "source": [ "import numpy as np\n", "from astropy import units as u, constants as ac\n", "\n", "DY_POLARIZABILITY = 184.4 # in a.u, most precise measured value of Dy polarizability\n", "DY_MASS = 164*u.u\n", "\n", "def horz_tf(x, w_x, w_z):\n", " w_x = w_x*u.um\n", " w_z = w_z*u.um\n", " x = x * u.W\n", " alpha = DY_POLARIZABILITY\n", " m = DY_MASS\n", " TrapDepth = 2*x/(np.pi*w_x*w_z) * (1 / (2 * ac.eps0 * ac.c)) * alpha * (4 * np.pi * ac.eps0 * ac.a0**3)\n", " ret = ((1/(2 * np.pi)) * np.sqrt(4 * TrapDepth / (m*w_x**2))).decompose()\n", " return ret.value\n", " \n", "def vert_tf(x, w_x, w_z):\n", " w_x = w_x*u.um\n", " w_z = w_z*u.um\n", " x = x * u.W\n", " alpha = DY_POLARIZABILITY\n", " m = DY_MASS\n", " TrapDepth = 2*x/(np.pi*w_x*w_z) * (1 / (2 * ac.eps0 * ac.c)) * alpha * (4 * np.pi * ac.eps0 * ac.a0**3)\n", " ret = ((1/(2 * np.pi)) * np.sqrt(4 * TrapDepth / (m*w_z**2))).decompose()\n", " return ret.value\n", "\n", "\n", "v = horz_tf(30, 30, 0.2)\n", "print('v_x = %.3f Hz' %(v))" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<h2> Model</h2> Model(horz_tf) <h2>Fit Statistics</h2><table><tr><td>fitting method</td><td>leastsq</td><td></td></tr><tr><td># function evals</td><td>26</td><td></td></tr><tr><td># data points</td><td>9</td><td></td></tr><tr><td># variables</td><td>2</td><td></td></tr><tr><td>chi-square</td><td> 79.9011718</td><td></td></tr><tr><td>reduced chi-square</td><td> 11.4144531</td><td></td></tr><tr><td>Akaike info crit.</td><td> 23.6520935</td><td></td></tr><tr><td>Bayesian info crit.</td><td> 24.0465426</td><td></td></tr></table><h2>Variables</h2><table><tr><th> name </th><th> value </th><th> standard error </th><th> relative error </th><th> initial value </th><th> min </th><th> max </th><th> vary </th></tr><tr><td> w_x </td><td> 24.1519450 </td><td> 292150.064 </td><td> (1209633.69%) </td><td> 25 </td><td> 0.00000000 </td><td> 100.000000 </td><td> True </td></tr><tr><td> w_z </td><td> 52.4613985 </td><td> 1903772.36 </td><td> (3628901.28%) </td><td> 35 </td><td> 0.00000000 </td><td> 100.000000 </td><td> True </td></tr></table><h2>Correlations (unreported correlations are < 0.100)</h2><table><tr><td>w_x</td><td>w_z</td><td>-1.0000</td></tr></table>" ], "text/plain": [ "<lmfit.model.ModelResult at 0x1b68d072d60>" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from lmfit import Model\n", "\n", "fitModel = Model(horz_tf, independent_vars=['x'], param_names=['w_x', 'w_z'], nan_policy='raise')\n", "params = fitModel.make_params()\n", "params['w_x'].set(value=25, min=0, max=100)\n", "params['w_z'].set(value=35, min=0, max=100)\n", "fitResult = fitModel.fit(data=np.array(tf_z[:-2]), params=params, x=np.array(p_1[:-2]))\n", "\n", "plt.figure()\n", "plt.errorbar(p_1, tf_x, yerr = dtf_x, fmt = 'og')\n", "plt.errorbar(p_1[:-2], fitResult.best_fit, fmt = 'k--')\n", "plt.xlabel('Power of Arm 1')\n", "plt.ylabel('Horz TF (Hz)')\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "\n", "fitResult" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 68, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.005, 0.0057, 0.0064, 0.0071, 0.0078, 0.0085, 0.0092, 0.0099, 0.0106, 0.0113, 0.012, 0.0127, 0.0134, 0.0141, 0.0148, 0.0155, 0.0162, 0.0169, 0.0176, 0.0183, 0.019, 0.0197]\n", "22\n" ] }, { "data": { "text/plain": [ "0.01235" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "l = list(np.arange(0.005, 0.02, 7e-4))\n", "# l = np.logspace(np.log10(250e-6), np.log10(500e-3), num=15)\n", "\n", "l = [round(item, 7) for item in l]\n", "#random.shuffle(l)\n", "\n", "print(l)\n", "print(len(l))\n", "np.mean(l)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pixel = 5.86e-6\n", "M = 0.6827\n", "F = (1/(0.3725*8.4743e-14)) * (pixel / M)**2\n", "NCount = 85000\n", "AtomNumber = NCount * F / 1e8\n", "print(AtomNumber)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "muB = 9.274e-24\n", "hbar = 6.626e-34 / (2 * np.pi)\n", "gJ = 1.24\n", "\n", "f = ufloat(10108.46982, 0.26282) \n", "Delta = 2 * np.pi * f * 1e3\n", "\n", "Bz = (Delta*hbar) / (muB*gJ)\n", "print(Bz * 1e4)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "muB = 9.274e-24\n", "hbar = 6.626e-34 / (2 * np.pi)\n", "gJ = 1.24\n", "Bz = 5.8854 * 1e-4\n", "(Bz*muB*gJ/hbar) / (2 * np.pi * 1e6)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ODT 1 Calibration" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "v_high = 2.7\n", "\"\"\"High Power\"\"\"\n", "P_arm1_high = 5.776 * v_high - 0.683\n", "\n", "v_mid = 0.2076\n", "\"\"\"Intermediate Power\"\"\"\n", "P_arm1_mid = 5.815 * v_mid - 0.03651\n", "\n", "v_low = 0.062\n", "\"\"\"Low Power\"\"\"\n", "P_arm1_low = 5271 * v_low - 27.5\n", "\n", "print(round(P_arm1_high, 3))\n", "print(round(P_arm1_mid, 3))\n", "print(round(P_arm1_low, 3))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ODT 2 Power Calibration" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "v = 0.842\n", "P_arm2 = 2.302 * v - 0.06452\n", "print(round(P_arm2, 3))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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" }, "vscode": { "interpreter": { "hash": "c05913ad4f24fdc6b2418069394dc5835b1981849b107c9ba6df693aafd66650" } } }, "nbformat": 4, "nbformat_minor": 2}
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