|
|
{ "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-23571205-fef2-11ed-8994-80e82ce2fa8e</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;\">3caf8456</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-7358e8cb-f1c0-435a-b9a6-1ae89646703d</p>\n", " <table style=\"width: 100%; text-align: left;\">\n", " <tr>\n", " <td style=\"text-align: left;\">\n", " <strong>Comm:</strong> tcp://127.0.0.1:52558\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://127.0.0.1:52603\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://127.0.0.1:52607/status\" target=\"_blank\">http://127.0.0.1:52607/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://127.0.0.1:52561\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-_emdmb6_\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://127.0.0.1:52604\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://127.0.0.1:52606/status\" target=\"_blank\">http://127.0.0.1:52606/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://127.0.0.1:52562\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-ztl68fct\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://127.0.0.1:52590\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://127.0.0.1:52595/status\" target=\"_blank\">http://127.0.0.1:52595/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://127.0.0.1:52563\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-55i03qxs\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://127.0.0.1:52597\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://127.0.0.1:52599/status\" target=\"_blank\">http://127.0.0.1:52599/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://127.0.0.1:52564\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-a9s2v84x\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://127.0.0.1:52605\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://127.0.0.1:52610/status\" target=\"_blank\">http://127.0.0.1:52610/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://127.0.0.1:52565\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-jb353de7\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://127.0.0.1:52617\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://127.0.0.1:52618/status\" target=\"_blank\">http://127.0.0.1:52618/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://127.0.0.1:52566\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-g3juqn2t\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://127.0.0.1:52612\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://127.0.0.1:52613/status\" target=\"_blank\">http://127.0.0.1:52613/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://127.0.0.1:52567\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-xu67jkqc\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://127.0.0.1:52598\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://127.0.0.1:52601/status\" target=\"_blank\">http://127.0.0.1:52601/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://127.0.0.1:52568\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-kdvp5zu9\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://127.0.0.1:52558' 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": [ "## Set global path for experiment" ] }, { "cell_type": "code", "execution_count": 3, "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": 4, "metadata": {}, "outputs": [], "source": [ "img_dir = '//DyLabNAS/Data/'\n", "SequenceName = \"Repetition_scan\" + \"/\"\n", "folderPath = img_dir + SequenceName + 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": { "scrolled": false }, "outputs": [], "source": [ "shotNum = \"0000\"\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()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## scan Push freq" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "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('Push AOM Freq (MHz)')\n", "plt.ylabel('NCount')\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "plt.show()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## scan cMOT final Amp" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "shotNum = \"0003\"\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 = (306, 874)\n", "imageAnalyser.span = (550, 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 = 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('cMOT final Amp (%)')\n", "plt.ylabel('NCount')\n", "#plt.ylim([0, 25000])\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "plt.show()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## scan Z comp current" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "shotNum = \"0004\"\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('comp Z current (A)')\n", "plt.ylabel('NCount')\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "plt.show()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Evaporative Cooling" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "img_dir = '//DyLabNAS/Data/'\n", "SequenceName = \"Evaporative_Cooling\" + \"/\"\n", "folderPath = img_dir + SequenceName + get_date()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Check our BEC" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "shotNum = \"0001\"\n", "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n", "\n", "# datesetOfGlobal = xr.Dataset(\n", "# data_vars={}\n", "# )\n", "\n", "# datesetOfGlobal.attrs['scanAxis'] = ['sin_mod_freq']\n", "# datesetOfGlobal.attrs['sin_mod_freq'] = [40.0, 40.5, 41.0, 41.5, 42.0, 42.5, 43.0, 43.5, 44.0, 44.5, 45.0, 45.5, 46.0, 46.5, 47.0, 47.5, 48.0, 48.5, 49.0, 49.5, 50.0, 50.5, 51.0, 51.5, 52.0, 52.5, 53.0, 53.5, 54.0, 54.5, 55.0, 55.5, 56.0, 56.5, 57.0, 57.5, 58.0, 58.5, 59.0, 59.5, 60.0]\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 = (884, 953)\n", "imageAnalyser.span = (100, 100)\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", "\n", "Ncount_mean.plot.errorbar(ax=ax, yerr = None, fmt='ob')\n", "\n", "plt.ylabel('NCount')\n", "plt.tight_layout()\n", "#plt.ylim([0, 500])\n", "plt.grid(visible=1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dataSet_cropOD.plot.pcolormesh(cmap='jet', vmin=0, col=scanAxis[0], row=scanAxis[1])\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "fitModel = DensityProfileBEC2dModel()\n", "fitAnalyser = FitAnalyser(fitModel, fitDim=2)\n", "params = fitAnalyser.guess(dataSet_cropOD, guess_kwargs=dict(pureBECThreshold=0.3), dask=\"parallelized\")\n", "# params = fitAnalyser_1.fitModel.make_params()\n", "# params.add(name=\"amplitude\", value= -4000, max=np.inf, min=-np.inf, vary=True)\n", "# params.add(name=\"center\", value= 0.435, max=np.inf, min=-np.inf, vary=True)\n", "# params.add(name=\"sigma\", value= 0.01, max=np.inf, min= 0, vary=True)\n", "# params.add(name=\"offset\", value= 1000, max=np.inf, min=-np.inf, vary=True)\n", "\n", "fitResult = fitAnalyser.fit(dataSet_cropOD, params).load()\n", "\n", "val = fitAnalyser.get_fit_value(fitResult)\n", "std = fitAnalyser.get_fit_std(fitResult)\n", "\n", "fitCurve = fitAnalyser.eval(fitResult, x=np.arange(100), y=np.arange(100), dask=\"parallelized\").load()\n", "\n", "fitCurve.plot.pcolormesh(cmap='jet', vmin=0, col=scanAxis[0], row=scanAxis[1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "val" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Measure trap frequency at the end of evap. 3, blink off time = 275 µs" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The detected scaning axes and values are: \n", "\n", "{'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, 0.0204, 0.0211,\n", " 0.0218, 0.0225, 0.0232, 0.0239, 0.0246, 0.0253, 0.026 , 0.0267,\n", " 0.0274, 0.0281, 0.0288, 0.0295, 0.0302, 0.0309, 0.0316, 0.0323,\n", " 0.033 , 0.0337, 0.0344, 0.0351, 0.0358, 0.0365, 0.0372, 0.0379,\n", " 0.0386, 0.0393, 0.04 ]), 'runs': array([0., 1., 2.])}\n" ] }, { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAm4AAAHPCAYAAADqAFbFAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABUCElEQVR4nO3deXxU1f3/8feQhAlblCQoIQkGUaEIYkUjsVqCyiJLAxFboSjGQmqFArWoLKEkFVAULRSRigs7iNhIXQO0BIsLLT/71UK/tvVbQTGANGGJAglJuL8/xhkY5mZllntnXs/Hg8eQM2fuPfPJTfKZc8/iMAzDEAAAACyvWagbAAAAgIYhcQMAALAJEjcAAACbIHEDAACwCRI3AAAAmyBxAwAAsAkSNwAAAJuIDnUDrOz06dPav3+/2rRpI4fDEermAACAMGUYhr7++mt16NBBzZrV3q9G4laH/fv3KzU1NdTNAAAAEWLfvn1KSUmp9XkStzq0adNGkiuIcXFxATlHVVWVNm/erP79+ysmJiYg57Aj4mKOuJgjLrUjNuaIizniYi4YcSkvL1dqaqon96gNiVsd3LdH4+LiApq4tWzZUnFxcfyQnIW4mCMu5ohL7YiNOeJijriYC2Zc6huaxeQEAAAAmyBxAwAAsAkSNwAAAJsgcQMAALAJEjcAAACbIHEDAACwCRI3AAAAmyBxAwAAsAkSNwAAAJsgcQMAALAJtrwCAABho6ZG2r5dOnBASkqSbrpJiooKdav8h8QNAACEhcJCadIk6csvz5SlpEgLF0rZ2aFrlz9xqxQAANheYaE0YoR30iZJJSWu8sLC0LTL30jcAACArdXUuHraDMP3OXfZ5MmuenZH4gYAAGxt+3bfnrazGYa0b5+rnt2RuAEAAFs7cMC/9ayMxA0AANhaUpJ/61kZiRsAALC1m25yzR51OMyfdzik1FRXPbsjcQMAALYWFeVa8kPyTd7cXy9YEB7ruZG4AQAA28vOll55RUpO9i5PSXGVh8s6bizACwAAwkJ2tpSVxc4JAAAAthAVJWVmhroVgcOtUgAAAJsgcQMAALAJEjcAAACbIHEDAACwCRI3AAAAmwh54vbRRx9p8ODB6tixo1q0aKH4+HhlZGRo9erVXvUMw9Bzzz2nXr16KS4uTgkJCerTp4/efPNN0+MuWrRIXbt2ldPpVKdOnVRQUKCqqqpgvCUAAICACHnidvToUaWmpmru3Ll66623tHLlSqWlpemuu+7S7NmzPfVmzZql3Nxcpaen6/e//72WL18up9OpIUOGqLCw0OuYc+bM0aRJk5Sdna1Nmzbp/vvv19y5czV+/Phgvz0AAAC/Cfk6bpmZmco8Z8GVIUOGaM+ePVq6dKny8vIkSS+++KJuvPFGLVmyxFOvX79+at++vVasWKHsb5dELisr0+zZszVu3DjNnTvXc46qqirl5eVp8uTJ6tatW3DeHAAAgB+FvMetNomJiYqOPpNXxsTE6IILLvCqExsb6/nnVlRUpIqKCuXk5HjVzcnJkWEY2rhxY0DbDQAAECgh73FzO336tE6fPq0jR45ow4YN2rRpk55++mnP85MmTdKUKVP0wgsvKDs7WxUVFXriiSd07NgxTZw40VNv9+7dkqQePXp4HT8pKUmJiYme581UVlaqsrLS83V5ebkkqaqqKmDj49zHZfydN+JijriYIy61IzbmiIs54mIuGHFp6LEdhmEYAWtFI9x333169tlnJUnNmzfXggUL9LOf/cyrzrPPPqtJkyZ5kqv4+HitX79et956q6dObm6uVq5cqYqKCp9zdOnSRWlpadq0aZNpG/Lz81VQUOBTvnbtWrVs2bLJ7w0AAKAuJ06c0KhRo3Ts2DHFxcXVWs8yPW7Tp0/X2LFjdejQIb3++uuaMGGCjh8/rilTpkiSli1bpkmTJmnChAm67bbbdOrUKa1cuVJZWVkqLCzUgAEDPMdyOBy1nqeu56ZNm6YHHnjA83V5eblSU1PVv3//OoN4PqqqqrRlyxb169dPMTExATmHHREXc8TFHHGpHbExR1zMERdzwYiL+y5ffSyTuHXs2FEdO3aUJA0aNEiSK5EaM2aMoqOjNX78eI0dO1bz58/3vOa2225TZmam7rvvPu3Zs0eSlJCQoIqKCp04ccKnl+zw4cPq1atXrW1wOp1yOp0+5TExMQG/gINxDjsiLuaIizniUjtiY464mCMu5gIZl4Ye17KTE9LT01VdXa3PPvtM//rXv3Ty5Eldd911PvWuvfZa7d27V998842kM2Pbdu3a5VXv4MGDKi0tVffu3QPfeAAAgACwbOJWXFysZs2a6dJLL1WHDh0kSTt27PCqYxiGduzYobZt26pVq1aSpIEDByo2NlbLly/3qrt8+XI5HA4NGzYsGM0HAADwu5DfKs3NzVVcXJzS09N18cUXq7S0VBs2bND69ev14IMPql27dpKk7OxsLV26VE6nU4MGDVJlZaVWrFih9957T4888ohn7Fp8fLzy8vI0c+ZMxcfHq3///tq5c6fy8/M1duxY1nADAAC2FfLELSMjQ8uWLdOKFSt09OhRtW7dWj179tSqVas0evRoT701a9bo6aef1qpVq/Tiiy8qJiZGV1xxhVavXq1Ro0Z5HXPGjBlq06aNFi9erPnz56t9+/aaOnWqZsyYEey3BwAA4DchT9xycnJ8Fss1ExsbqylTpnhmmdZn4sSJXuu7AQAA2J1lx7gBAADAG4kbAACATZC4AQAA2ASJGwAAgE2QuAEAANgEiRsAAIBNkLgBAADYRMjXcUNw1dRI27dLBw5ISUnSTTdJUVGhbhUAAGgIErcIUlgoTZokffnlmbKUFGnhQik7O3TtAuyADz0ArIBbpRGisFAaMcI7aZOkkhJXeWFhaNoF2EFhoZSWJvXtK40a5XpMS+PnBkDwkbhFgJoaV0+bYfg+5y6bPNlVD4A3PvQAsBIStwiwfbvvH52zGYa0b5+rHoAz+NADwGpI3CLAgQP+rQdECj70ALAaErcIkJTk33pApOBDDwCrIXGLADfd5Jo96nCYP+9wSKmprnoAzuBDDwCrIXGLAFFRriU/JN/kzf31ggUsbQCciw89AKyGxC1CZGdLr7wiJSd7l6ekuMpZxw1NUVMjbdsmrVvnegy3Qfp86AFgNSRuESQ7W9q7VyoultaudT3u2UPShqaJlLXN+NADwErYOSHCREVJmZmhbgXszr222bnLZLjXNgu3hCY7W8rKYucEAKFH4gagUepb28zhcK1tlpUVXokNH3oAWAG3SgE0CmubAUDokLgBaBTWNgOA0CFxA9AorG0GAKFD4gagUVjbDABCh8QNQKOwthkAhA6JG4BGY20zAAgNlgMB0CSsbQYAwUfiBqDJGrK2WU0NyR0A+AuJG4CAKSx0LdZ79rpvKSmuMXLcTgWAxmOMG4CAcG+Lde5ive5tscJtT1MACAYSNwB+V9+2WJJrW6yamqA2CwBsj8QNYaumRtq2TVq3zvVIkhA8bIsFAIHBGDeEJcZWhRbbYgFAYNDjhrDD2KrQY1ssAAgMEjeEFcZWWQPbYgFAYJC4Iawwtsoa2BYLAAKDxA1hhbFV1sG2WADgf0xOQFhhbJW1sC0WAPgXiRvCintsVUmJ+Tg3h8P1PGOrgqch22IBABqGW6UIK4ytAgCEMxI3hB3GVgEAwhW3ShGWGFsFAAhHJG4IW4ytAgCEG26VAgAA2ASJGwAAgE2EPHH76KOPNHjwYHXs2FEtWrRQfHy8MjIytHr1ap+6VVVVeuqpp9SjRw+1aNFCF154oW644Qa9//77PnUXLVqkrl27yul0qlOnTiooKFBVVVUw3hIAAEBAhHyM29GjR5WamqqRI0cqOTlZx48f15o1a3TXXXdp7969ysvLkyTV1NRo+PDhevfdd/XQQw/phhtu0PHjx/Xhhx/q+PHjXsecM2eOZs6cqalTp6p///7auXOn8vLyVFJSoqVLl4bibQIAAJy3kCdumZmZyjxnBPmQIUO0Z88eLV261JO4LVq0SG+//bbee+899e7d21N38ODBXq8tKyvT7NmzNW7cOM2 "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], maxFileNum=153, 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 = (180, 958)\n", "imageAnalyser.span = (100, 100)\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", "\n", "Ncount_mean.plot.errorbar(ax=ax, yerr = None, fmt='ob')\n", "\n", "plt.ylabel('NCount')\n", "plt.tight_layout()\n", "#plt.ylim([0, 500])\n", "plt.grid(visible=1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "dataSet_cropOD.plot.pcolormesh(cmap='jet', vmin=0, col=scanAxis[0], row=scanAxis[1])\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "scrolled": false }, "outputs": [], "source": [ "fitAnalyser = FitAnalyser(\"Gaussian-2D\", fitDim=2)\n", "params = fitAnalyser.guess(dataSet_cropOD, dask=\"parallelized\")\n", "# params = fitAnalyser_1.fitModel.make_params()\n", "# params.add(name=\"amplitude\", value= -4000, max=np.inf, min=-np.inf, vary=True)\n", "# params.add(name=\"center\", value= 0.435, max=np.inf, min=-np.inf, vary=True)\n", "# params.add(name=\"sigma\", value= 0.01, max=np.inf, min= 0, vary=True)\n", "# params.add(name=\"offset\", value= 1000, max=np.inf, min=-np.inf, vary=True)\n", "\n", "fitResult = fitAnalyser.fit(dataSet_cropOD, params).load()\n", "\n", "val = fitAnalyser.get_fit_value(fitResult)\n", "std = fitAnalyser.get_fit_std(fitResult)\n", "\n", "fitCurve = fitAnalyser.eval(fitResult, x=np.arange(100), y=np.arange(100), dask=\"parallelized\").load()\n", "\n", "# fitCurve.plot.pcolormesh(cmap='jet', vmin=0, col=scanAxis[0], row=scanAxis[1])" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "image/png": "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 "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "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", "fig = plt.figure()\n", "ax = fig.gca()\n", "\n", "val_mean.plot.errorbar(ax=ax, yerr=std_mean, fmt='--ob')\n", "\n", "plt.grid()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "def dampingOscillation(x, center=0, amplitude=1.0, frequency=1.0, decay=1.0, slope=1.0, intercept=0.0):\n", " return amplitude * np.exp(-decay*x)*np.sin(2*np.pi*frequency*(x-center)) + (slope * x + intercept)\n", "\n", "fitModel = NewFitModel(dampingOscillation)\n", "\n", "fitAnalyser = FitAnalyser(fitModel, fitDim=1)\n", "\n", "params = fitAnalyser.fitModel.make_params()\n", "params.add(name=\"center\", value= 0, max=np.inf, min=-np.inf, vary=True)\n", "params.add(name=\"amplitude\", value= 1, max=np.inf, min=-np.inf, vary=True)\n", "params.add(name=\"frequency\", value= 260, max=np.inf, min=-np.inf, vary=True)\n", "params.add(name=\"decay\", value= 0.1, max=np.inf, min=-np.inf, vary=True)\n", "params.add(name=\"slope\", value= 30, max=np.inf, min=-np.inf, vary=True)\n", "params.add(name=\"intercept\", value= 5.5, max=np.inf, min=-np.inf, vary=True)\n", "\n", "fitResult = fitAnalyser.fit(val_mean, params, x=scanAxis[0]).load()\n", "\n", "x = np.linspace(val_mean[scanAxis[0]][0], val_mean[scanAxis[0]][-1], 500)\n", "fitCurve = fitAnalyser.eval(fitResult, x=x, dask=\"parallelized\").load()" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "image/png": "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 "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "f_y = 126.5119 ± 0.7136 kHz\n" ] } ], "source": [ "fig = plt.figure()\n", "ax = fig.gca()\n", "\n", "val_mean.plot.errorbar(ax=ax, yerr=std_mean, fmt='or')\n", "fitCurve.plot.errorbar(ax=ax, yerr=None, fmt='--g')\n", "\n", "plt.grid()\n", "\n", "plt.xlabel('hold time after switch on the trap (s)')\n", "plt.ylabel('y center (pixels)')\n", "\n", "plt.show()\n", "\n", "val = fitAnalyser.get_fit_full_result(fitResult)\n", "f = val.frequency.item() / 2\n", "\n", "print('f_y = %.4f \\u00B1 %.4f kHz'% tuple([f.n,f.s]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from lmfit.models import LinearModel\n", "from Analyser.FitAnalyser import DampingOscillationModel\n", "\n", "fitModel = LinearModel() * DampingOscillationModel()\n", "\n", "fitAnalyser = FitAnalyser(fitModel, fitDim=1)\n", "\n", "params = fitAnalyser.fitModel.make_params()\n", "params.add(name=\"slope\", value= 30, max=np.inf, min=-np.inf, vary=True)\n", "params.add(name=\"intercept\", value= 5.5, max=np.inf, min=-np.inf, vary=True)\n", "params.add(name=\"center\", value= 0, max=np.inf, min=-np.inf, vary=True)\n", "params.add(name=\"amplitude\", value= 1, max=np.inf, min=-np.inf, vary=True)\n", "params.add(name=\"frequency\", value= 260, max=np.inf, min=-np.inf, vary=True)\n", "params.add(name=\"decay\", value= 0.1, max=np.inf, min=-np.inf, vary=True)\n", "params.add(name=\"offset\", value= 0, max=np.inf, min=-np.inf, vary=False)\n", "\n", "fitResult = fitAnalyser.fit(val_mean, params, x=scanAxis[0]).load()\n", "\n", "fitCurve = fitAnalyser.eval(fitResult, x=np.arange(100), y=np.arange(100), dask=\"parallelized\").load()\n", "\n", "# fitCurve.plot.pcolormesh(cmap='jet', vmin=0, col=scanAxis[0], row=scanAxis[1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "val_mean = val[dataKey].mean(dim='runs')\n", "std_mean = val[dataKey].std(dim='runs')\n", "val" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "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", "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", "# fitted_qtys_1 = np.delete(fitted_qtys_1, 27)\n", "# fitted_qtys_err_1 = np.delete(fitted_qtys_err_1, 27)\n", "# scan_para = np.delete(scan_para, 27)\n", "\n", "plt.figure()\n", "popt_x, pcov_x = curve_fit(damp_osci, scan_para, fitted_qtys_1, np.array([0, 7.5, 42.5, 1.3e2, 0.1]))\n", "freqdata = np.linspace(0.005, 40e-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('y center (pixels)')\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "#plt.ylim([0,750])\n", "#plt.xlim([90, 121])\n", "#plt.legend(prop={'size': 14})\n", "plt.show()\n", "\n", "f_x = popt_x[3] / 1000\n", "df_x = pcov_x[3][3]**0.5 / 1000\n", "\n", "print('f_y = %.4f \\u00B1 %.4f kHz'% tuple([np.abs(f_x),df_x]))" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Measure trap frequency at the end of evap. 3, blink off time = 250 µs" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "%matplotlib notebook\n", "shotNum = \"0015\"\n", "filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n", "\n", "dataSetDict = {\n", " dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i], maxFileNum=102, 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 = (180, 958)\n", "imageAnalyser.span = (100, 100)\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", "\n", "Ncount_mean.plot.errorbar(ax=ax, yerr = None, fmt='ob')\n", "\n", "plt.ylabel('NCount')\n", "plt.tight_layout()\n", "#plt.ylim([0, 500])\n", "plt.grid(visible=1)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "dataSet_cropOD.plot.pcolormesh(cmap='jet', vmin=0, col=scanAxis[0], row=scanAxis[1])\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "fitAnalyser = FitAnalyser(\"Gaussian-2D\", fitDim=2)\n", "params = fitAnalyser.guess(dataSet_cropOD, dask=\"parallelized\")\n", "# params = fitAnalyser_1.fitModel.make_params()\n", "# params.add(name=\"amplitude\", value= -4000, max=np.inf, min=-np.inf, vary=True)\n", "# params.add(name=\"center\", value= 0.435, max=np.inf, min=-np.inf, vary=True)\n", "# params.add(name=\"sigma\", value= 0.01, max=np.inf, min= 0, vary=True)\n", "# params.add(name=\"offset\", value= 1000, max=np.inf, min=-np.inf, vary=True)\n", "\n", "fitResult = fitAnalyser.fit(dataSet_cropOD, params).load()\n", "\n", "val = fitAnalyser.get_fit_value(fitResult)\n", "std = fitAnalyser.get_fit_std(fitResult)\n", "\n", "fitCurve = fitAnalyser.eval(fitResult, x=np.arange(100), y=np.arange(100), dask=\"parallelized\").load()\n", "\n", "# fitCurve.plot.pcolormesh(cmap='jet', vmin=0, col=scanAxis[0], row=scanAxis[1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# 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", "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": { "scrolled": false }, "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", "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", "# fitted_qtys_1 = np.delete(fitted_qtys_1, 27)\n", "# fitted_qtys_err_1 = np.delete(fitted_qtys_err_1, 27)\n", "# scan_para = np.delete(scan_para, 27)\n", "\n", "plt.figure()\n", "popt_x, pcov_x = curve_fit(damp_osci, scan_para, fitted_qtys_1, np.array([0, 7.5, 42.5, 1.3e2, 0.1]))\n", "freqdata = np.linspace(0.005, 40e-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('y center (pixels)')\n", "plt.tight_layout()\n", "plt.grid(visible=1)\n", "#plt.ylim([0,750])\n", "#plt.xlim([90, 121])\n", "#plt.legend(prop={'size': 14})\n", "plt.show()\n", "\n", "f_x = popt_x[3] / 1000\n", "df_x = pcov_x[3][3]**0.5 / 1000\n", "\n", "print('f_y = %.4f \\u00B1 %.4f kHz'% 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": [ "np.sqrt(0.091**2 + 0.334**2 + 0.447**2)" ] }, { "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 * 579.12 * 1e3\n", "\n", "Bz = (Delta*hbar) / (muB*gJ)\n", "print(Bz * 1e4)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "(fitted_SlopeInkHz * 0.5) + fitted_offsetInkHz" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "l = list(np.arange(0.005, 0.04, 0.0007))\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)" ] }, { "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": "3.9.13" }, "vscode": { "interpreter": { "hash": "c05913ad4f24fdc6b2418069394dc5835b1981849b107c9ba6df693aafd66650" } } }, "nbformat": 4, "nbformat_minor": 2}
|
