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{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Import supporting package" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import xarray as xr\n", "import numpy as np\n", "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" ] }, { "attachments": {}, "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-f69f7e77-14be-11ee-a66c-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;\">cbd5084a</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-0b555d69-83fd-43e1-9215-9c67aeb6d78c</p>\n", " <table style=\"width: 100%; text-align: left;\">\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Comm:</strong> tcp://127.0.0.1:61752\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://127.0.0.1:61792\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://127.0.0.1:61794/status\" target=\"_blank\">http://127.0.0.1:61794/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://127.0.0.1:61755\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-vwm7x29k\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://127.0.0.1:61793\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://127.0.0.1:61798/status\" target=\"_blank\">http://127.0.0.1:61798/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://127.0.0.1:61756\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-dsm02jmm\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://127.0.0.1:61810\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://127.0.0.1:61811/status\" target=\"_blank\">http://127.0.0.1:61811/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://127.0.0.1:61757\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-kb6dkrm1\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://127.0.0.1:61775\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://127.0.0.1:61790/status\" target=\"_blank\">http://127.0.0.1:61790/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://127.0.0.1:61758\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-vtzwx9iy\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://127.0.0.1:61804\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://127.0.0.1:61806/status\" target=\"_blank\">http://127.0.0.1:61806/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://127.0.0.1:61759\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-a97vh11n\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://127.0.0.1:61796\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://127.0.0.1:61801/status\" target=\"_blank\">http://127.0.0.1:61801/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://127.0.0.1:61760\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-2jc_h5j9\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://127.0.0.1:61797\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://127.0.0.1:61800/status\" target=\"_blank\">http://127.0.0.1:61800/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://127.0.0.1:61761\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-mvwb15rr\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://127.0.0.1:61805\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://127.0.0.1:61808/status\" target=\"_blank\">http://127.0.0.1:61808/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://127.0.0.1:61762\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-co4d63m7\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://127.0.0.1:61752' 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" ] }, { "attachments": {}, "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')" ] }, { "attachments": {}, "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())" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Repetition Scans" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## scan MOT freq - Z Comp 0" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib notebook\n", "shotNum = \"0001\"\n", "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n", "\n", "dataSetDict = {\n", " dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n", " for i in range(len(groupList))\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, 825)\n", "imageAnalyser.span = (550, 1200)\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 = Ncount.mean(dim='runs')\n", "Ncount_std = Ncount.std(dim='runs')\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n", "plt.xlabel('MOT AOM Freq (MHz)')\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')" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## scan Push freq" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib notebook\n", "shotNum = \"0002\"\n", "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n", "\n", "dataSetDict = {\n", " dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n", " for i in range(len(groupList))\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, 825)\n", "imageAnalyser.span = (525, 1255)\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('Push AOM Freq (MHz)')\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')" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## scan Z comp current" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib notebook\n", "shotNum = \"0005\"\n", "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n", "\n", "dataSetDict = {\n", " dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n", " for i in range(len(groupList))\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 = (305, 875)\n", "imageAnalyser.span = (400, 400)\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('comp Z current (A)')\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')" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## scan cMOT final Amp" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "shotNum = \"0006\"\n", "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n", "\n", "dataSetDict = {\n", " dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n", " for i in range(len(groupList))\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 = (305, 875)\n", "imageAnalyser.span = (400, 400)\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('cMOT final Amp (%)')\n", "plt.ylabel('NCount')\n", "#plt.ylim([0, 25000])\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": [ "%matplotlib notebook\n", "shotNum = \"0011\"\n", "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n", "\n", "dataSetDict = {\n", " dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n", " for i in range(len(groupList))\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 = (305, 875)\n", "imageAnalyser.span = (400, 400)\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('comp Z current (A)')\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')" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Evaporative Cooling" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "img_dir = '//DyLabNAS/Data/'\n", "SequenceName = \"Evaporative_Cooling\"\n", "#folderPath = img_dir + SequenceName + \"/\" + get_date()\n", "folderPath = img_dir + SequenceName + \"/2023/06/20\"\n", "\n", "mongoDB = mongoClient[SequenceName]\n", "\n", "DB = MongoDB(mongoClient, mongoDB, date=get_date())" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Calibration of the magnetic fields" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Z Offset field = 0.119 A" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "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 = (160, 880)\n", "imageAnalyser.span = (250, 250)\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", "\n", "plt.ylabel('NCount')\n", "plt.tight_layout()\n", "#plt.ylim([0, 3500])\n", "plt.grid(visible=1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Ncount_mean_1 = Ncount_mean\n", "Ncount_std_1 = Ncount_std\n", "\n", "fitAnalyser_1 = FitAnalyser(\"Gaussian With Offset\", fitDim=1)\n", "# params = fitAnalyser.guess(Ncount_mean_1, x=scanAxis[0], guess_kwargs=dict(negative=True), dask=\"parallelized\")\n", "params = fitAnalyser_1.fitModel.make_params()\n", "params.add(name=\"amplitude\", value= -6000, max=np.inf, min=-np.inf, vary=True)\n", "params.add(name=\"center\", value= 2.9576, max=np.inf, min=-np.inf, vary=True)\n", "params.add(name=\"sigma\", value= 0.1, max=np.inf, min= 0, vary=True)\n", "params.add(name=\"offset\", value= 6000, max=np.inf, min=-np.inf, vary=True)\n", "\n", "fitResult_1 = fitAnalyser_1.fit(Ncount_mean_1, params, x=scanAxis[0]).load()\n", "freqdata = np.linspace(2.9445, 2.9601, 500)\n", "fitCurve_1 = fitAnalyser_1.eval(fitResult_1, x=freqdata, dask=\"parallelized\").load()\n", "fitCurve_1 = fitCurve_1.assign_coords({'x':np.array(freqdata)})\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "\n", "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n", "fitCurve_1.plot.errorbar(ax=ax, fmt='--g')\n", "plt.xlabel('Center Frequency (MHz)')\n", "plt.ylabel('NCount')\n", "#plt.ylim([0, 3500])\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f_1 = fitAnalyser_1.get_fit_value(fitResult_1).center\n", "df_1 = fitAnalyser_1.get_fit_std(fitResult_1).center\n", "\n", "print('f = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(f_1)* 1e3,df_1* 1e3]))\n", "\n", "s_1 = fitAnalyser_1.get_fit_value(fitResult_1).sigma\n", "ds_1 = fitAnalyser_1.get_fit_std(fitResult_1).sigma\n", "\n", "fwhm_1 = 2.3548200*s_1 * 1e3\n", "dfwhm_1 = 2.3548200*ds_1 * 1e3\n", "\n", "print('fwhm = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(fwhm_1),dfwhm_1]))" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Z Offset field = 0.189 A, 0.25 Vpp" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib notebook\n", "shotNum = \"0008\"\n", "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n", "\n", "dataSetDict = {\n", " dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i], maxFileNum = 100, 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 = (160, 880)\n", "imageAnalyser.span = (250, 250)\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", "\n", "plt.ylabel('NCount')\n", "plt.tight_layout()\n", "#plt.ylim([0, 3500])\n", "plt.grid(visible=1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Ncount_mean_1 = Ncount_mean\n", "Ncount_std_1 = Ncount_std\n", "\n", "fitAnalyser_1 = FitAnalyser(\"Gaussian With Offset\", fitDim=1)\n", "# params = fitAnalyser.guess(Ncount_mean_1, x=scanAxis[0], guess_kwargs=dict(negative=True), dask=\"parallelized\")\n", "params = fitAnalyser_1.fitModel.make_params()\n", "params.add(name=\"amplitude\", value= -6000, max=np.inf, min=-np.inf, vary=True)\n", "params.add(name=\"center\", value= 4.25, max=np.inf, min=-np.inf, vary=True)\n", "params.add(name=\"sigma\", value= 0.1, max=np.inf, min= 0, vary=True)\n", "params.add(name=\"offset\", value= 6000, max=np.inf, min=-np.inf, vary=True)\n", "\n", "fitResult_1 = fitAnalyser_1.fit(Ncount_mean_1, params, x=scanAxis[0]).load()\n", "freqdata = np.linspace(4.2375, 4.266, 500)\n", "fitCurve_1 = fitAnalyser_1.eval(fitResult_1, x=freqdata, dask=\"parallelized\").load()\n", "fitCurve_1 = fitCurve_1.assign_coords({'x':np.array(freqdata)})\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "\n", "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n", "fitCurve_1.plot.errorbar(ax=ax, fmt='--g')\n", "plt.xlabel('Center Frequency (MHz)')\n", "plt.ylabel('NCount')\n", "#plt.ylim([0, 3500])\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f_1 = fitAnalyser_1.get_fit_value(fitResult_1).center\n", "df_1 = fitAnalyser_1.get_fit_std(fitResult_1).center\n", "\n", "print('f = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(f_1)* 1e3,df_1* 1e3]))\n", "\n", "s_1 = fitAnalyser_1.get_fit_value(fitResult_1).sigma\n", "ds_1 = fitAnalyser_1.get_fit_std(fitResult_1).sigma\n", "\n", "fwhm_1 = 2.3548200*s_1 * 1e3\n", "dfwhm_1 = 2.3548200*ds_1 * 1e3\n", "\n", "print('fwhm = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(fwhm_1),dfwhm_1]))" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Z Offset field = 0.189 A, 0.5 Vpp" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib notebook\n", "shotNum = \"0009\"\n", "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n", "\n", "dataSetDict = {\n", " dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i], maxFileNum = 100, 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 = (160, 880)\n", "imageAnalyser.span = (250, 250)\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", "\n", "plt.ylabel('NCount')\n", "plt.tight_layout()\n", "#plt.ylim([0, 3500])\n", "plt.grid(visible=1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Ncount_mean_1 = Ncount_mean\n", "Ncount_std_1 = Ncount_std\n", "\n", "fitAnalyser_1 = FitAnalyser(\"Gaussian With Offset\", fitDim=1)\n", "# params = fitAnalyser.guess(Ncount_mean_1, x=scanAxis[0], guess_kwargs=dict(negative=True), dask=\"parallelized\")\n", "params = fitAnalyser_1.fitModel.make_params()\n", "params.add(name=\"amplitude\", value= -6000, max=np.inf, min=-np.inf, vary=True)\n", "params.add(name=\"center\", value= 4.25, max=np.inf, min=-np.inf, vary=True)\n", "params.add(name=\"sigma\", value= 0.1, max=np.inf, min= 0, vary=True)\n", "params.add(name=\"offset\", value= 6000, max=np.inf, min=-np.inf, vary=True)\n", "\n", "fitResult_1 = fitAnalyser_1.fit(Ncount_mean_1, params, x=scanAxis[0]).load()\n", "freqdata = np.linspace(4.23, 4.275, 500)\n", "fitCurve_1 = fitAnalyser_1.eval(fitResult_1, x=freqdata, dask=\"parallelized\").load()\n", "fitCurve_1 = fitCurve_1.assign_coords({'x':np.array(freqdata)})\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "\n", "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n", "fitCurve_1.plot.errorbar(ax=ax, fmt='--g')\n", "plt.xlabel('Center Frequency (MHz)')\n", "plt.ylabel('NCount')\n", "#plt.ylim([0, 3500])\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f_1 = fitAnalyser_1.get_fit_value(fitResult_1).center\n", "df_1 = fitAnalyser_1.get_fit_std(fitResult_1).center\n", "\n", "print('f = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(f_1)* 1e3,df_1* 1e3]))\n", "\n", "s_1 = fitAnalyser_1.get_fit_value(fitResult_1).sigma\n", "ds_1 = fitAnalyser_1.get_fit_std(fitResult_1).sigma\n", "\n", "fwhm_1 = 2.3548200*s_1 * 1e3\n", "dfwhm_1 = 2.3548200*ds_1 * 1e3\n", "\n", "print('fwhm = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(fwhm_1),dfwhm_1]))" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Z Offset field = 0.189 A, 1 Vpp" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib notebook\n", "shotNum = \"0010\"\n", "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n", "\n", "dataSetDict = {\n", " dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i], maxFileNum = 100, 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 = (160, 880)\n", "imageAnalyser.span = (250, 250)\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", "\n", "plt.ylabel('NCount')\n", "plt.tight_layout()\n", "#plt.ylim([0, 3500])\n", "plt.grid(visible=1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Ncount_mean_1 = Ncount_mean\n", "Ncount_std_1 = Ncount_std\n", "\n", "fitAnalyser_1 = FitAnalyser(\"Gaussian With Offset\", fitDim=1)\n", "# params = fitAnalyser.guess(Ncount_mean_1, x=scanAxis[0], guess_kwargs=dict(negative=True), dask=\"parallelized\")\n", "params = fitAnalyser_1.fitModel.make_params()\n", "params.add(name=\"amplitude\", value= -6000, max=np.inf, min=-np.inf, vary=True)\n", "params.add(name=\"center\", value= 4.25, max=np.inf, min=-np.inf, vary=True)\n", "params.add(name=\"sigma\", value= 0.1, max=np.inf, min= 0, vary=True)\n", "params.add(name=\"offset\", value= 6000, max=np.inf, min=-np.inf, vary=True)\n", "\n", "fitResult_1 = fitAnalyser_1.fit(Ncount_mean_1, params, x=scanAxis[0]).load()\n", "freqdata = np.linspace(4.23, 4.275, 500)\n", "fitCurve_1 = fitAnalyser_1.eval(fitResult_1, x=freqdata, dask=\"parallelized\").load()\n", "fitCurve_1 = fitCurve_1.assign_coords({'x':np.array(freqdata)})\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "\n", "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n", "fitCurve_1.plot.errorbar(ax=ax, fmt='--g')\n", "plt.xlabel('Center Frequency (MHz)')\n", "plt.ylabel('NCount')\n", "#plt.ylim([0, 3500])\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f_1 = fitAnalyser_1.get_fit_value(fitResult_1).center\n", "df_1 = fitAnalyser_1.get_fit_std(fitResult_1).center\n", "\n", "print('f = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(f_1)* 1e3,df_1* 1e3]))\n", "\n", "s_1 = fitAnalyser_1.get_fit_value(fitResult_1).sigma\n", "ds_1 = fitAnalyser_1.get_fit_std(fitResult_1).sigma\n", "\n", "fwhm_1 = 2.3548200*s_1 * 1e3\n", "dfwhm_1 = 2.3548200*ds_1 * 1e3\n", "\n", "print('fwhm = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(fwhm_1),dfwhm_1]))" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Z Offset field = 0.189 A, 3 Vpp" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib notebook\n", "shotNum = \"0011\"\n", "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n", "\n", "dataSetDict = {\n", " dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i], maxFileNum = 80, 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 = (160, 880)\n", "imageAnalyser.span = (250, 250)\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", "\n", "plt.ylabel('NCount')\n", "plt.tight_layout()\n", "#plt.ylim([0, 3500])\n", "plt.grid(visible=1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Ncount_mean_1 = Ncount_mean\n", "Ncount_std_1 = Ncount_std\n", "\n", "fitAnalyser_1 = FitAnalyser(\"Gaussian With Offset\", fitDim=1)\n", "# params = fitAnalyser.guess(Ncount_mean_1, x=scanAxis[0], guess_kwargs=dict(negative=True), dask=\"parallelized\")\n", "params = fitAnalyser_1.fitModel.make_params()\n", "params.add(name=\"amplitude\", value= -6000, max=np.inf, min=-np.inf, vary=True)\n", "params.add(name=\"center\", value= 4.25, max=np.inf, min=-np.inf, vary=True)\n", "params.add(name=\"sigma\", value= 0.1, max=np.inf, min= 0, vary=True)\n", "params.add(name=\"offset\", value= 6000, max=np.inf, min=-np.inf, vary=True)\n", "\n", "fitResult_1 = fitAnalyser_1.fit(Ncount_mean_1, params, x=scanAxis[0]).load()\n", "freqdata = np.linspace(4.22, 4.275, 500)\n", "fitCurve_1 = fitAnalyser_1.eval(fitResult_1, x=freqdata, dask=\"parallelized\").load()\n", "fitCurve_1 = fitCurve_1.assign_coords({'x':np.array(freqdata)})\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "\n", "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n", "fitCurve_1.plot.errorbar(ax=ax, fmt='--g')\n", "plt.xlabel('Center Frequency (MHz)')\n", "plt.ylabel('NCount')\n", "#plt.ylim([0, 3500])\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f_1 = fitAnalyser_1.get_fit_value(fitResult_1).center\n", "df_1 = fitAnalyser_1.get_fit_std(fitResult_1).center\n", "\n", "print('f = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(f_1)* 1e3,df_1* 1e3]))\n", "\n", "s_1 = fitAnalyser_1.get_fit_value(fitResult_1).sigma\n", "ds_1 = fitAnalyser_1.get_fit_std(fitResult_1).sigma\n", "\n", "fwhm_1 = 2.3548200*s_1 * 1e3\n", "dfwhm_1 = 2.3548200*ds_1 * 1e3\n", "\n", "print('fwhm = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(fwhm_1),dfwhm_1]))" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Scan Z offset field during evaporation" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The detected scaning axes and values are: \n", "\n", "{'compZ_current_sg': array([0.18 , 0.182, 0.184, 0.186, 0.188, 0.19 , 0.192, 0.194, 0.196,\n", " 0.198, 0.2 , 0.202, 0.204, 0.206, 0.208, 0.21 , 0.212, 0.214,\n", " 0.216, 0.218, 0.22 ]), 'runs': array([0., 1., 2.])}\n" ] }, { "data": { "image/png": "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 "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "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]\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 = (890, 880)\n", "imageAnalyser.span = (150, 150)\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('comp Z current (A)')\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": 10, "metadata": {}, "outputs": [ { "data": { "image/png": "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 "text/plain": [ "<Figure size 640x480 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "\n", "data = dataSet_cropOD.sel(compZ_current_sg = 0.198, runs=0)\n", "data = data.assign_coords(x=data.x * 2.352 * 5.86)\n", "data = data.assign_coords(y=data.y * 2.352 * 5.86)\n", "\n", "data.plot.pcolormesh(cmap='jet', vmin=0, vmax=2)\n", "\n", "plt.title('')\n", "plt.xlabel('x ($\\mu m$)')\n", "plt.ylabel('y ($\\mu m$)')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "f:\\Jianshun\\analyseScript\\Analyser\\FitAnalyser.py:86: RuntimeWarning: invalid value encountered in power\n", " res = (1- ((x-centerx)/(sigmax))**2 - ((y-centery)/(sigmay))**2)**(3 / 2)\n" ] } ], "source": [ "data = dataSet_cropOD.sel(compZ_current_sg = 0.198, runs=0)\n", "\n", "fitModel = DensityProfileBEC2dModel()\n", "fitAnalyser_1 = FitAnalyser(fitModel, fitDim=2)\n", "\n", "params = fitAnalyser_1.guess(data, dask=\"parallelized\")\n", "\n", "fitResult_1 = fitAnalyser_1.fit(data, params).load()\n", "\n", "# x = np.linspace(2.7725, 2.822, 500)\n", "# y = np.linspace(2.7725, 2.822, 500)\n", "# fitCurve_1 = fitAnalyser_1.eval(fitResult_1, x=x, y=y, dask=\"parallelized\").load()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\data\\AppData\\Roaming\\Python\\Python39\\site-packages\\numpy\\lib\\function_base.py:2246: RuntimeWarning: invalid value encountered in _get_fit_full_result_single (vectorized)\n", " outputs = ufunc(*inputs)\n" ] }, { "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 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"\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.Dataset>\n", "Dimensions: ()\n", "Coordinates:\n", " compZ_current_sg float64 0.198\n", " runs float64 0.0\n", "Data variables:\n", " BEC_amplitude object 792.7896024042066+/-nan\n", " thermal_amplitude object 0.0+/-nan\n", " BEC_centerx object 72.03322637975705+/-nan\n", " BEC_centery object 74.15709273093088+/-nan\n", " thermal_centerx object 73.1219837873983+/-nan\n", " thermal_centery object 75.0675362391377+/-nan\n", " BEC_sigmax object 25.92336716503899+/-nan\n", " BEC_sigmay object 10.643305951751195+/-nan\n", " thermal_sigmax object 17.18976684635309+/-nan\n", " thermal_sigmay object 14.449859669689275+/-nan\n", " thermalAspectRatio object 0.8406082408706373+/-nan\n", " condensate_fraction object 1.0+/-nan\n", "Attributes:\n", " IMAGE_SUBCLASS: IMAGE_GRAYSCALE\n", " IMAGE_VERSION: 1.2\n", " IMAGE_WHITE_IS_ZERO: 0\n", " x_start: 815\n", " x_end: 965\n", " y_end: 955\n", " y_start: 805\n", " x_center: 890\n", " y_center: 880\n", " x_span: 150\n", " y_span: 150</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-ee805def-8782-4ff7-aaa5-0307542091fe' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ee805def-8782-4ff7-aaa5-0307542091fe' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-bbeb3fd8-9d26-42ce-a0f6-9e9fccaac797' class='xr-section-summary-in' type='checkbox' checked><label for='section-bbeb3fd8-9d26-42ce-a0f6-9e9fccaac797' 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>compZ_current_sg</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.198</div><input id='attrs-290bd221-ede4-4895-b984-6361188a7a61' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-290bd221-ede4-4895-b984-6361188a7a61' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0fc86fa5-f9e1-436b-96fd-ee93ac5da45c' class='xr-var-data-in' type='checkbox'><label for='data-0fc86fa5-f9e1-436b-96fd-ee93ac5da45c' 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(0.198)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>runs</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0</div><input id='attrs-0c115949-5d9f-4808-a73d-8486b09d9ef7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0c115949-5d9f-4808-a73d-8486b09d9ef7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7240579c-8983-46b8-8df4-f5586e5c5c9d' class='xr-var-data-in' type='checkbox'><label for='data-7240579c-8983-46b8-8df4-f5586e5c5c9d' 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.)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1b80cf78-e245-4bdf-b9d3-38af0d71eae7' class='xr-section-summary-in' type='checkbox' checked><label for='section-1b80cf78-e245-4bdf-b9d3-38af0d71eae7' class='xr-section-summary' >Data variables: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>BEC_amplitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>792.7896024042066+/-nan</div><input id='attrs-829f02b0-3ab6-4c29-a25b-a21789ce5b42' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-829f02b0-3ab6-4c29-a25b-a21789ce5b42' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ef932b3-726d-43a3-a5de-0dcf8f5a42f6' class='xr-var-data-in' type='checkbox'><label for='data-1ef932b3-726d-43a3-a5de-0dcf8f5a42f6' 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(792.7896024042066+/-nan, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thermal_amplitude</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div clas ], "text/plain": [ "<xarray.Dataset>\n", "Dimensions: ()\n", "Coordinates:\n", " compZ_current_sg float64 0.198\n", " runs float64 0.0\n", "Data variables:\n", " BEC_amplitude object 792.7896024042066+/-nan\n", " thermal_amplitude object 0.0+/-nan\n", " BEC_centerx object 72.03322637975705+/-nan\n", " BEC_centery object 74.15709273093088+/-nan\n", " thermal_centerx object 73.1219837873983+/-nan\n", " thermal_centery object 75.0675362391377+/-nan\n", " BEC_sigmax object 25.92336716503899+/-nan\n", " BEC_sigmay object 10.643305951751195+/-nan\n", " thermal_sigmax object 17.18976684635309+/-nan\n", " thermal_sigmay object 14.449859669689275+/-nan\n", " thermalAspectRatio object 0.8406082408706373+/-nan\n", " condensate_fraction object 1.0+/-nan\n", "Attributes:\n", " IMAGE_SUBCLASS: IMAGE_GRAYSCALE\n", " IMAGE_VERSION: 1.2\n", " IMAGE_WHITE_IS_ZERO: 0\n", " x_start: 815\n", " x_end: 965\n", " y_end: 955\n", " y_start: 805\n", " x_center: 890\n", " y_center: 880\n", " x_span: 150\n", " y_span: 150" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fitAnalyser_1.get_fit_full_result(fitResult_1)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.1654007155341837" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "792.7896024042066 * 147 /1e5" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Calibration of the magnetic fields - after connecting the Z coils to the HiPPS" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Z Offset field = 0.119 A" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib notebook\n", "shotNum = \"0035\"\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 = (160, 880)\n", "imageAnalyser.span = (250, 250)\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", "\n", "plt.ylabel('NCount')\n", "plt.tight_layout()\n", "#plt.ylim([0, 3500])\n", "plt.grid(visible=1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Ncount_mean_1 = Ncount_mean\n", "Ncount_std_1 = Ncount_std\n", "\n", "fitAnalyser_1 = FitAnalyser(\"Gaussian With Offset\", fitDim=1)\n", "# params = fitAnalyser.guess(Ncount_mean_1, x=scanAxis[0], guess_kwargs=dict(negative=True), dask=\"parallelized\")\n", "params = fitAnalyser_1.fitModel.make_params()\n", "params.add(name=\"amplitude\", value= -5000, max=np.inf, min=-np.inf, vary=True)\n", "params.add(name=\"center\", value= 2.8, max=np.inf, min=-np.inf, vary=True)\n", "params.add(name=\"sigma\", value= 0.1, max=np.inf, min= 0, vary=True)\n", "params.add(name=\"offset\", value= 5000, max=np.inf, min=-np.inf, vary=True)\n", "\n", "fitResult_1 = fitAnalyser_1.fit(Ncount_mean_1, params, x=scanAxis[0]).load()\n", "freqdata = np.linspace(2.7725, 2.822, 500)\n", "fitCurve_1 = fitAnalyser_1.eval(fitResult_1, x=freqdata, dask=\"parallelized\").load()\n", "fitCurve_1 = fitCurve_1.assign_coords({'x':np.array(freqdata)})\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "\n", "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n", "fitCurve_1.plot.errorbar(ax=ax, fmt='--g')\n", "plt.xlabel('Center Frequency (MHz)')\n", "plt.ylabel('NCount')\n", "#plt.xlim([2.7828, 2.81625])\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f_1 = fitAnalyser_1.get_fit_value(fitResult_1).center\n", "df_1 = fitAnalyser_1.get_fit_std(fitResult_1).center\n", "\n", "print('f = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(f_1)* 1e3,df_1* 1e3]))\n", "\n", "s_1 = fitAnalyser_1.get_fit_value(fitResult_1).sigma\n", "ds_1 = fitAnalyser_1.get_fit_std(fitResult_1).sigma\n", "\n", "fwhm_1 = 2.3548200*s_1 * 1e3\n", "dfwhm_1 = 2.3548200*ds_1 * 1e3\n", "\n", "print('fwhm = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(fwhm_1),dfwhm_1]))" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Z Offset field = 0.140 A" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib notebook\n", "shotNum = \"0044\"\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 = (160, 880)\n", "imageAnalyser.span = (250, 250)\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", "\n", "plt.ylabel('NCount')\n", "plt.tight_layout()\n", "#plt.ylim([0, 3500])\n", "plt.grid(visible=1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Ncount_mean_1 = Ncount_mean\n", "Ncount_std_1 = Ncount_std\n", "\n", "fitAnalyser_1 = FitAnalyser(\"Gaussian With Offset\", fitDim=1)\n", "# params = fitAnalyser.guess(Ncount_mean_1, x=scanAxis[0], guess_kwargs=dict(negative=True), dask=\"parallelized\")\n", "params = fitAnalyser_1.fitModel.make_params()\n", "params.add(name=\"amplitude\", value= -2500, max=np.inf, min=-np.inf, vary=True)\n", "params.add(name=\"center\", value= 3.177, max=np.inf, min=-np.inf, vary=True)\n", "params.add(name=\"sigma\", value= 0.1, max=np.inf, min= 0, vary=True)\n", "params.add(name=\"offset\", value= 2500, max=np.inf, min=-np.inf, vary=True)\n", "\n", "fitResult_1 = fitAnalyser_1.fit(Ncount_mean_1, params, x=scanAxis[0]).load()\n", "freqdata = np.linspace(3.158, 3.198, 500)\n", "fitCurve_1 = fitAnalyser_1.eval(fitResult_1, x=freqdata, dask=\"parallelized\").load()\n", "fitCurve_1 = fitCurve_1.assign_coords({'x':np.array(freqdata)})\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "\n", "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n", "fitCurve_1.plot.errorbar(ax=ax, fmt='--g')\n", "plt.xlabel('Center Frequency (MHz)')\n", "plt.ylabel('NCount')\n", "#plt.xlim([2.7828, 2.81625])\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f_1 = fitAnalyser_1.get_fit_value(fitResult_1).center\n", "df_1 = fitAnalyser_1.get_fit_std(fitResult_1).center\n", "\n", "print('f = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(f_1)* 1e3,df_1* 1e3]))\n", "\n", "s_1 = fitAnalyser_1.get_fit_value(fitResult_1).sigma\n", "ds_1 = fitAnalyser_1.get_fit_std(fitResult_1).sigma\n", "\n", "fwhm_1 = 2.3548200*s_1 * 1e3\n", "dfwhm_1 = 2.3548200*ds_1 * 1e3\n", "\n", "print('fwhm = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(fwhm_1),dfwhm_1]))" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Z Offset field = 0.200 A" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib notebook\n", "shotNum = \"0048\"\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 = (160, 880)\n", "imageAnalyser.span = (250, 250)\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", "\n", "plt.ylabel('NCount')\n", "plt.tight_layout()\n", "#plt.ylim([0, 3500])\n", "plt.grid(visible=1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Ncount_mean_1 = Ncount_mean\n", "Ncount_std_1 = Ncount_std\n", "\n", "fitAnalyser_1 = FitAnalyser(\"Gaussian With Offset\", fitDim=1)\n", "# params = fitAnalyser.guess(Ncount_mean_1, x=scanAxis[0], guess_kwargs=dict(negative=True), dask=\"parallelized\")\n", "params = fitAnalyser_1.fitModel.make_params()\n", "params.add(name=\"amplitude\", value= -4500, max=np.inf, min=-np.inf, vary=True)\n", "params.add(name=\"center\", value= 4.275, max=np.inf, min=-np.inf, vary=True)\n", "params.add(name=\"sigma\", value= 0.1, max=np.inf, min= 0, vary=True)\n", "params.add(name=\"offset\", value= 4500, max=np.inf, min=-np.inf, vary=True)\n", "\n", "fitResult_1 = fitAnalyser_1.fit(Ncount_mean_1, params, x=scanAxis[0]).load()\n", "freqdata = np.linspace(4.260, 4.289, 500)\n", "fitCurve_1 = fitAnalyser_1.eval(fitResult_1, x=freqdata, dask=\"parallelized\").load()\n", "fitCurve_1 = fitCurve_1.assign_coords({'x':np.array(freqdata)})\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "\n", "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n", "fitCurve_1.plot.errorbar(ax=ax, fmt='--g')\n", "plt.xlabel('Center Frequency (MHz)')\n", "plt.ylabel('NCount')\n", "#plt.xlim([2.7828, 2.81625])\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f_1 = fitAnalyser_1.get_fit_value(fitResult_1).center\n", "df_1 = fitAnalyser_1.get_fit_std(fitResult_1).center\n", "\n", "print('f = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(f_1)* 1e3,df_1* 1e3]))\n", "\n", "s_1 = fitAnalyser_1.get_fit_value(fitResult_1).sigma\n", "ds_1 = fitAnalyser_1.get_fit_std(fitResult_1).sigma\n", "\n", "fwhm_1 = 2.3548200*s_1 * 1e3\n", "dfwhm_1 = 2.3548200*ds_1 * 1e3\n", "\n", "print('fwhm = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(fwhm_1),dfwhm_1]))" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Z Offset field = 0.259 A" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib notebook\n", "shotNum = \"0056\"\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 = (160, 880)\n", "imageAnalyser.span = (250, 250)\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", "\n", "plt.ylabel('NCount')\n", "plt.tight_layout()\n", "#plt.ylim([0, 3500])\n", "plt.grid(visible=1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Ncount_mean_1 = Ncount_mean\n", "Ncount_std_1 = Ncount_std\n", "\n", "fitAnalyser_1 = FitAnalyser(\"Gaussian With Offset\", fitDim=1)\n", "# params = fitAnalyser.guess(Ncount_mean_1, x=scanAxis[0], guess_kwargs=dict(negative=True), dask=\"parallelized\")\n", "params = fitAnalyser_1.fitModel.make_params()\n", "params.add(name=\"amplitude\", value= -4500, max=np.inf, min=-np.inf, vary=True)\n", "params.add(name=\"center\", value= 5.3, max=np.inf, min=-np.inf, vary=True)\n", "params.add(name=\"sigma\", value= 0.1, max=np.inf, min= 0, vary=True)\n", "params.add(name=\"offset\", value= 4500, max=np.inf, min=-np.inf, vary=True)\n", "\n", "fitResult_1 = fitAnalyser_1.fit(Ncount_mean_1, params, x=scanAxis[0]).load()\n", "freqdata = np.linspace(5.34, 5.364, 500)\n", "fitCurve_1 = fitAnalyser_1.eval(fitResult_1, x=freqdata, dask=\"parallelized\").load()\n", "fitCurve_1 = fitCurve_1.assign_coords({'x':np.array(freqdata)})\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "\n", "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n", "fitCurve_1.plot.errorbar(ax=ax, fmt='--g')\n", "plt.xlabel('Center Frequency (MHz)')\n", "plt.ylabel('NCount')\n", "#plt.xlim([2.7828, 2.81625])\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f_1 = fitAnalyser_1.get_fit_value(fitResult_1).center\n", "df_1 = fitAnalyser_1.get_fit_std(fitResult_1).center\n", "\n", "print('f = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(f_1)* 1e3,df_1* 1e3]))\n", "\n", "s_1 = fitAnalyser_1.get_fit_value(fitResult_1).sigma\n", "ds_1 = fitAnalyser_1.get_fit_std(fitResult_1).sigma\n", "\n", "fwhm_1 = 2.3548200*s_1 * 1e3\n", "dfwhm_1 = 2.3548200*ds_1 * 1e3\n", "\n", "print('fwhm = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(fwhm_1),dfwhm_1]))" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Z Offset field = 0.329 A" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib notebook\n", "shotNum = \"0059\"\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 = (160, 880)\n", "imageAnalyser.span = (250, 250)\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", "\n", "plt.ylabel('NCount')\n", "plt.tight_layout()\n", "#plt.ylim([0, 3500])\n", "plt.grid(visible=1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Ncount_mean_1 = Ncount_mean\n", "Ncount_std_1 = Ncount_std\n", "\n", "fitAnalyser_1 = FitAnalyser(\"Gaussian With Offset\", fitDim=1)\n", "# params = fitAnalyser.guess(Ncount_mean_1, x=scanAxis[0], guess_kwargs=dict(negative=True), dask=\"parallelized\")\n", "params = fitAnalyser_1.fitModel.make_params()\n", "params.add(name=\"amplitude\", value= -4500, max=np.inf, min=-np.inf, vary=True)\n", "params.add(name=\"center\", value= 6.636, max=np.inf, min=-np.inf, vary=True)\n", "params.add(name=\"sigma\", value= 0.1, max=np.inf, min= 0, vary=True)\n", "params.add(name=\"offset\", value= 4500, max=np.inf, min=-np.inf, vary=True)\n", "\n", "fitResult_1 = fitAnalyser_1.fit(Ncount_mean_1, params, x=scanAxis[0]).load()\n", "freqdata = np.linspace(6.62, 6.655, 500)\n", "fitCurve_1 = fitAnalyser_1.eval(fitResult_1, x=freqdata, dask=\"parallelized\").load()\n", "fitCurve_1 = fitCurve_1.assign_coords({'x':np.array(freqdata)})\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "\n", "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n", "fitCurve_1.plot.errorbar(ax=ax, fmt='--g')\n", "plt.xlabel('Center Frequency (MHz)')\n", "plt.ylabel('NCount')\n", "#plt.xlim([2.7828, 2.81625])\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f_1 = fitAnalyser_1.get_fit_value(fitResult_1).center\n", "df_1 = fitAnalyser_1.get_fit_std(fitResult_1).center\n", "\n", "print('f = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(f_1)* 1e3,df_1* 1e3]))\n", "\n", "s_1 = fitAnalyser_1.get_fit_value(fitResult_1).sigma\n", "ds_1 = fitAnalyser_1.get_fit_std(fitResult_1).sigma\n", "\n", "fwhm_1 = 2.3548200*s_1 * 1e3\n", "dfwhm_1 = 2.3548200*ds_1 * 1e3\n", "\n", "print('fwhm = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(fwhm_1),dfwhm_1]))" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Z Offset field = 0.419 A" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib notebook\n", "shotNum = \"0063\"\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 = (160, 880)\n", "imageAnalyser.span = (250, 250)\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", "\n", "plt.ylabel('NCount')\n", "plt.tight_layout()\n", "#plt.ylim([0, 3500])\n", "plt.grid(visible=1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Ncount_mean_1 = Ncount_mean\n", "Ncount_std_1 = Ncount_std\n", "\n", "fitAnalyser_1 = FitAnalyser(\"Gaussian With Offset\", fitDim=1)\n", "# params = fitAnalyser.guess(Ncount_mean_1, x=scanAxis[0], guess_kwargs=dict(negative=True), dask=\"parallelized\")\n", "params = fitAnalyser_1.fitModel.make_params()\n", "params.add(name=\"amplitude\", value= -1500, max=np.inf, min=-np.inf, vary=True)\n", "params.add(name=\"center\", value= 8.286, max=np.inf, min=-np.inf, vary=True)\n", "params.add(name=\"sigma\", value= 0.1, max=np.inf, min= 0, vary=True)\n", "params.add(name=\"offset\", value= 1500, max=np.inf, min=-np.inf, vary=True)\n", "\n", "fitResult_1 = fitAnalyser_1.fit(Ncount_mean_1, params, x=scanAxis[0]).load()\n", "freqdata = np.linspace(8.27, 8.305, 500)\n", "fitCurve_1 = fitAnalyser_1.eval(fitResult_1, x=freqdata, dask=\"parallelized\").load()\n", "fitCurve_1 = fitCurve_1.assign_coords({'x':np.array(freqdata)})\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "\n", "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n", "fitCurve_1.plot.errorbar(ax=ax, fmt='--g')\n", "plt.xlabel('Center Frequency (MHz)')\n", "plt.ylabel('NCount')\n", "#plt.xlim([2.7828, 2.81625])\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f_1 = fitAnalyser_1.get_fit_value(fitResult_1).center\n", "df_1 = fitAnalyser_1.get_fit_std(fitResult_1).center\n", "\n", "print('f = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(f_1)* 1e3,df_1* 1e3]))\n", "\n", "s_1 = fitAnalyser_1.get_fit_value(fitResult_1).sigma\n", "ds_1 = fitAnalyser_1.get_fit_std(fitResult_1).sigma\n", "\n", "fwhm_1 = 2.3548200*s_1 * 1e3\n", "dfwhm_1 = 2.3548200*ds_1 * 1e3\n", "\n", "print('fwhm = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(fwhm_1),dfwhm_1]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "img_dir = 'C:/Users/control/DyLab/Experiments/DyBEC/'\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())" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Z Offset field = 0.489 A" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib notebook\n", "shotNum = \"0002\"\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 = (160, 880)\n", "imageAnalyser.span = (250, 250)\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", "\n", "plt.ylabel('NCount')\n", "plt.tight_layout()\n", "#plt.ylim([0, 3500])\n", "plt.grid(visible=1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Ncount_mean_1 = Ncount_mean\n", "Ncount_std_1 = Ncount_std\n", "\n", "fitAnalyser_1 = FitAnalyser(\"Gaussian With Offset\", fitDim=1)\n", "# params = fitAnalyser.guess(Ncount_mean_1, x=scanAxis[0], guess_kwargs=dict(negative=True), dask=\"parallelized\")\n", "params = fitAnalyser_1.fitModel.make_params()\n", "params.add(name=\"amplitude\", value= -2500, max=np.inf, min=-np.inf, vary=True)\n", "params.add(name=\"center\", value= 9.575, max=np.inf, min=-np.inf, vary=True)\n", "params.add(name=\"sigma\", value= 0.2, max=np.inf, min= 0, vary=True)\n", "params.add(name=\"offset\", value= 2500, max=np.inf, min=-np.inf, vary=True)\n", "\n", "fitResult_1 = fitAnalyser_1.fit(Ncount_mean_1, params, x=scanAxis[0]).load()\n", "freqdata = np.linspace(9.555, 9.595, 500)\n", "fitCurve_1 = fitAnalyser_1.eval(fitResult_1, x=freqdata, dask=\"parallelized\").load()\n", "fitCurve_1 = fitCurve_1.assign_coords({'x':np.array(freqdata)})\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "\n", "Ncount_mean.plot.errorbar(ax=ax, yerr = Ncount_std, fmt='ob')\n", "fitCurve_1.plot.errorbar(ax=ax, fmt='--g')\n", "plt.xlabel('Center Frequency (MHz)')\n", "plt.ylabel('NCount')\n", "#plt.xlim([2.7828, 2.81625])\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f_1 = fitAnalyser_1.get_fit_value(fitResult_1).center\n", "df_1 = fitAnalyser_1.get_fit_std(fitResult_1).center\n", "\n", "print('f = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(f_1)* 1e3,df_1* 1e3]))\n", "\n", "s_1 = fitAnalyser_1.get_fit_value(fitResult_1).sigma\n", "ds_1 = fitAnalyser_1.get_fit_std(fitResult_1).sigma\n", "\n", "fwhm_1 = 2.3548200*s_1 * 1e3\n", "dfwhm_1 = 2.3548200*ds_1 * 1e3\n", "\n", "print('fwhm = %.5f \\u00B1 %.5f kHz'% tuple([np.abs(fwhm_1),dfwhm_1]))" ] }, { "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": [ "f = [2798.24971, 3178.49790, 4275.39905, 5352.17283, 6637.80418, 8288.35264, 9573.59333]\n", "df = [0.36873, 0.29413, 0.20667, 0.20818, 0.21978, 0.20285, 0.18495]\n", "z_offset_current = [0.119, 0.140, 0.2, 0.259, 0.329, 0.419, 0.489]\n", "\n", "f_fit = f\n", "df_fit = df\n", "z_offset_current_fit = z_offset_current\n", "\n", "\n", "x = np.array(z_offset_current_fit)\n", "y = np.array(f_fit)\n", "\n", "# Degree of the fitting polynomial\n", "deg = 1\n", "# Parameters from the fit of the polynomial\n", "p = np.polyfit(x, y, deg)\n", "m = p[0] # Gradient\n", "c = p[1] # y-intercept\n", "\n", "#print(f'The fitted straight line has equation y = {m:.1f}x {c:=+6.1f}')\n", "\n", "# Model the data using the parameters of the fitted straight line\n", "y_model = np.polyval(p, x)\n", "\n", "# Create the linear (1 degree polynomial) model\n", "model = np.poly1d(p)\n", "# Fit the model\n", "y_model = model(x)\n", "\n", "# Mean\n", "y_bar = np.mean(y)\n", "# Coefficient of determination, R²\n", "R2 = np.sum((y_model - y_bar)**2) / np.sum((y - y_bar)**2)\n", "\n", "#print(f'R² = {R2:.2f}')\n", "\n", "fitted_SlopeInkHz = m\n", "fitted_offsetInkHz = c\n", "muB = 9.274e-24\n", "hbar = 6.626e-34 / (2 * np.pi)\n", "gJ = 1.24\n", "Slope = (((2 * np.pi * fitted_SlopeInkHz * 1e3)*hbar) / (muB*gJ)) * 1e4\n", "Offset = (((2 * np.pi * fitted_offsetInkHz * 1e3)*hbar) / (muB*gJ)) * 1e4\n", "\n", "def calib_fit(x, B):\n", " alpha = ((2 * np.pi * fitted_SlopeInkHz * 1e3)*hbar) / (muB*gJ)\n", " beta = ((2 * np.pi * fitted_offsetInkHz * 1e3)*hbar) / (muB*gJ)\n", " delta_nu = ((muB * gJ) / hbar) * np.sqrt((B**2-beta**2) + ((alpha * x) + beta)**2)\n", " return delta_nu / (2 * np.pi * 1e3)\n", "\n", "\n", "popt, pcov = curve_fit(calib_fit, z_offset_current, f, np.array([0.1*1e-4]))\n", "Boffset = popt[0] * 1e4\n", "dBoffset = pcov[0][0]**0.5 * 1e4\n", "\n", "fig = plt.figure()\n", "ax = fig.gca()\n", "plt.clf\n", "#plt.scatter(z_offset_current, f, c='gray', marker='o', edgecolors='k', s=30)\n", "plt.errorbar(z_offset_current, f, yerr=df, fmt='o')\n", "xvals = np.linspace(0, 0.5, 500)\n", "plt.plot(np.array(xvals), p[1] + p[0] * np.array(xvals), label=f'Line Fit')\n", "plt.plot(xvals, calib_fit(xvals, *popt), label=f'Curve Fit')\n", "plt.text(0.25, 2200, f'Line Slope = {Slope:.3f} G/A', fontsize=12)\n", "plt.text(0.25, 1500, f'Line Offset = {Offset:=.3f} G', fontsize=12)\n", "plt.text(0.25, 800, f'Bo= {Boffset:=.3f} +/- {dBoffset:=.3f} G', fontsize=12)\n", "plt.xlabel('Z Offset Coil Current (A)', fontsize=12)\n", "plt.ylabel('Resonance Frequency (kHz)', fontsize=12)\n", "plt.xticks(fontsize=12)\n", "plt.yticks(fontsize=12)\n", "plt.legend(fontsize=12)\n", "#plt.xlim(-0.01, 0.04)\n", "#plt.ylim(0, 2000)\n", "plt.grid(visible=1)\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "l = list(np.arange(9.555, 9.595, 0.002))\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)\n" ] }, { "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", "Delta = 2 * np.pi * 9573.59333 * 1e3\n", "\n", "Bz = (Delta*hbar) / (muB*gJ)\n", "print(Bz * 1e4)" ] }, { "attachments": {}, "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 = 0.0587\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))" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## ODT 2 Power Calibration" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "v = 0.7607\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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