{ "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": [ "
\n", "
\n", "
\n", "

Client

\n", "

Client-23571205-fef2-11ed-8994-80e82ce2fa8e

\n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "
Connection method: Cluster objectCluster type: distributed.LocalCluster
\n", " Dashboard: http://127.0.0.1:8787/status\n", "
\n", "\n", " \n", "\n", " \n", "
\n", "

Cluster Info

\n", "
\n", "
\n", "
\n", "
\n", "

LocalCluster

\n", "

3caf8456

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "\n", " \n", "
\n", " Dashboard: http://127.0.0.1:8787/status\n", " \n", " Workers: 8\n", "
\n", " Total threads: 128\n", " \n", " Total memory: 149.01 GiB\n", "
Status: runningUsing processes: True
\n", "\n", "
\n", " \n", "

Scheduler Info

\n", " \n", "\n", "
\n", "
\n", "
\n", "
\n", "

Scheduler

\n", "

Scheduler-7358e8cb-f1c0-435a-b9a6-1ae89646703d

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
\n", " Comm: tcp://127.0.0.1:52558\n", " \n", " Workers: 8\n", "
\n", " Dashboard: http://127.0.0.1:8787/status\n", " \n", " Total threads: 128\n", "
\n", " Started: Just now\n", " \n", " Total memory: 149.01 GiB\n", "
\n", "
\n", "
\n", "\n", "
\n", " \n", "

Workers

\n", " \n", "\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 0

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:52603\n", " \n", " Total threads: 16\n", "
\n", " Dashboard: http://127.0.0.1:52607/status\n", " \n", " Memory: 18.63 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:52561\n", "
\n", " Local directory: C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-_emdmb6_\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 1

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:52604\n", " \n", " Total threads: 16\n", "
\n", " Dashboard: http://127.0.0.1:52606/status\n", " \n", " Memory: 18.63 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:52562\n", "
\n", " Local directory: C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-ztl68fct\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 2

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:52590\n", " \n", " Total threads: 16\n", "
\n", " Dashboard: http://127.0.0.1:52595/status\n", " \n", " Memory: 18.63 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:52563\n", "
\n", " Local directory: C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-55i03qxs\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 3

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:52597\n", " \n", " Total threads: 16\n", "
\n", " Dashboard: http://127.0.0.1:52599/status\n", " \n", " Memory: 18.63 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:52564\n", "
\n", " Local directory: C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-a9s2v84x\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 4

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:52605\n", " \n", " Total threads: 16\n", "
\n", " Dashboard: http://127.0.0.1:52610/status\n", " \n", " Memory: 18.63 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:52565\n", "
\n", " Local directory: C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-jb353de7\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 5

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:52617\n", " \n", " Total threads: 16\n", "
\n", " Dashboard: http://127.0.0.1:52618/status\n", " \n", " Memory: 18.63 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:52566\n", "
\n", " Local directory: C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-g3juqn2t\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 6

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:52612\n", " \n", " Total threads: 16\n", "
\n", " Dashboard: http://127.0.0.1:52613/status\n", " \n", " Memory: 18.63 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:52567\n", "
\n", " Local directory: C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-xu67jkqc\n", "
\n", "
\n", "
\n", "
\n", " \n", "
\n", "
\n", "
\n", "
\n", " \n", "

Worker: 7

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", " \n", "\n", " \n", "\n", "
\n", " Comm: tcp://127.0.0.1:52598\n", " \n", " Total threads: 16\n", "
\n", " Dashboard: http://127.0.0.1:52601/status\n", " \n", " Memory: 18.63 GiB\n", "
\n", " Nanny: tcp://127.0.0.1:52568\n", "
\n", " Local directory: C:\\Users\\data\\AppData\\Local\\Temp\\dask-worker-space\\worker-kdvp5zu9\n", "
\n", "
\n", "
\n", "
\n", " \n", "\n", "
\n", "
\n", "\n", "
\n", "
\n", "
\n", "
\n", " \n", "\n", "
\n", "
" ], "text/plain": [ "" ] }, "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": "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", "text/plain": [ "
" ] }, "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": [ "
" ] }, "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": [ "
" ] }, "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 }