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2023-07-01 09:21:45 +02:00
{
"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",
"\n",
"from uncertainties import ufloat\n",
"from uncertainties import unumpy as unp\n",
"from uncertainties import umath\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from DataContainer.ReadData import read_hdf5_file\n",
"from Analyser.ImagingAnalyser import ImageAnalyser\n",
"from Analyser.FitAnalyser import FitAnalyser\n",
"from Analyser.FitAnalyser import ThomasFermi2dModel, DensityProfileBEC2dModel, Polylog22dModel\n",
"from Analyser.FitAnalyser import NewFitModel\n",
"from ToolFunction.ToolFunction import *\n",
"\n",
"from ToolFunction.HomeMadeXarrayFunction import errorbar, dataarray_plot_errorbar\n",
"xr.plot.dataarray_plot.errorbar = errorbar\n",
"xr.plot.accessor.DataArrayPlotAccessor.errorbar = dataarray_plot_errorbar\n",
"\n",
"imageAnalyser = ImageAnalyser()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"from matplotlib.colors import ListedColormap, LinearSegmentedColormap\n",
"\n",
"import matplotlib.pyplot as plt\n",
"plt.rcParams[\"font.family\"] = \"arial\""
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def Ncount_to_atoms():\n",
" return 1 / 8.4743e-14 / 0.3725 * 5.86e-6**2 / 0.6606**2"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"import csv\n",
"\n",
"colormap = np.zeros((1024, 3))\n",
"\n",
"with open('smooth-cool-warm-table-float-1024.csv', newline='') as csvfile:\n",
" spamreader = csv.reader(csvfile, delimiter=' ', quotechar='|')\n",
" i = 0\n",
" for row in spamreader:\n",
" try:\n",
" a = row[0].split(',')\n",
" colormap[i, 0] = float(a[1])\n",
" colormap[i, 1] = float(a[2])\n",
" colormap[i, 2] = float(a[3])\n",
" i = i + 1\n",
" except:\n",
" pass\n",
"\n",
"colormap = ListedColormap(colormap)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"data_colors = colormap(np.linspace(0, 1, 7))\n",
"plot_blue = data_colors[0]\n",
"plot_red = data_colors[-1]\n",
"plot_red_alpha = 1"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Start a client for parallel computing"
]
},
{
"cell_type": "code",
"execution_count": 12,
"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-eb14457e-1720-11ee-8c04-80e82ce2fa8e</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;\">c071fe85</p>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Workers:</strong> 6\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads:</strong> 60\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total memory:</strong> 55.88 GiB\n",
" </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <td style=\"text-align: left;\"><strong>Status:</strong> running</td>\n",
" <td style=\"text-align: left;\"><strong>Using processes:</strong> True</td>\n",
"</tr>\n",
"\n",
" \n",
" </table>\n",
"\n",
" <details>\n",
" <summary style=\"margin-bottom: 20px;\">\n",
" <h3 style=\"display: inline;\">Scheduler Info</h3>\n",
" </summary>\n",
"\n",
" <div style=\"\">\n",
" <div>\n",
" <div style=\"width: 24px; height: 24px; background-color: #FFF7E5; border: 3px solid #FF6132; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <h3 style=\"margin-bottom: 0px;\">Scheduler</h3>\n",
" <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-d47e28b4-5276-45c0-964e-87f3987b2160</p>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Comm:</strong> tcp://127.0.0.1:54927\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Workers:</strong> 6\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads:</strong> 60\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Started:</strong> Just now\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total memory:</strong> 55.88 GiB\n",
" </td>\n",
" </tr>\n",
" </table>\n",
" </div>\n",
" </div>\n",
"\n",
" <details style=\"margin-left: 48px;\">\n",
" <summary style=\"margin-bottom: 20px;\">\n",
" <h3 style=\"display: inline;\">Workers</h3>\n",
" </summary>\n",
"\n",
" \n",
" <div style=\"margin-bottom: 20px;\">\n",
" <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <details>\n",
" <summary>\n",
" <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 0</h4>\n",
" </summary>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Comm: </strong> tcp://127.0.0.1:54966\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads: </strong> 10\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:54967/status\" target=\"_blank\">http://127.0.0.1:54967/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Memory: </strong> 9.31 GiB\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Nanny: </strong> tcp://127.0.0.1:54930\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-cvobjmxg\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://127.0.0.1:54960\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads: </strong> 10\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:54963/status\" target=\"_blank\">http://127.0.0.1:54963/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Memory: </strong> 9.31 GiB\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Nanny: </strong> tcp://127.0.0.1:54931\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-r0l_epl5\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://127.0.0.1:54965\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads: </strong> 10\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:54968/status\" target=\"_blank\">http://127.0.0.1:54968/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Memory: </strong> 9.31 GiB\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Nanny: </strong> tcp://127.0.0.1:54932\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-mnfl9i_m\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://127.0.0.1:54954\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads: </strong> 10\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:54955/status\" target=\"_blank\">http://127.0.0.1:54955/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Memory: </strong> 9.31 GiB\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Nanny: </strong> tcp://127.0.0.1:54933\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-qkxkq62s\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://127.0.0.1:54958\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads: </strong> 10\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:54961/status\" target=\"_blank\">http://127.0.0.1:54961/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Memory: </strong> 9.31 GiB\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Nanny: </strong> tcp://127.0.0.1:54934\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-zk2j62q_\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://127.0.0.1:54971\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads: </strong> 10\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:54972/status\" target=\"_blank\">http://127.0.0.1:54972/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Memory: </strong> 9.31 GiB\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Nanny: </strong> tcp://127.0.0.1:54935\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-641vd42d\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://127.0.0.1:54927' processes=6 threads=60, memory=55.88 GiB>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from dask.distributed import Client\n",
"client = Client(n_workers=6, threads_per_worker=10, processes=True, memory_limit='10GB')\n",
"client"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Set global path for experiment"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"groupList = [\n",
" \"images/MOT_3D_Camera/in_situ_absorption\",\n",
" \"images/ODT_1_Axis_Camera/in_situ_absorption\",\n",
" \"images/ODT_2_Axis_Camera/in_situ_absorption\",\n",
"]\n",
"\n",
"dskey = {\n",
" \"images/MOT_3D_Camera/in_situ_absorption\": \"camera_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"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Old Loading Rate"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"img_dir = '//DyLabNAS/Data/'\n",
"SequenceName = \"MOT_3D_Imaging\" + \"/\""
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## With red push"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The detected scaning axes and values are: \n",
"\n",
"{'final_amp': array([0.16, 0.24, 0.32, 0.4 , 0.48, 0.56, 0.64, 0.72, 0.8 , 0.88, 0.96]), 'mot_load_duration': array([ 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. , 6. ,\n",
" 7. , 8. , 9. , 10. ]), 'runs': array([0., 1., 2.])}\n"
]
}
],
"source": [
"folderPath = img_dir + SequenceName + '2022/10/14'# get_date()\n",
"\n",
"shotNum = \"0012\"\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 = (600, 1150)\n",
"imageAnalyser.span = (1100, 1260)\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)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure()\n",
"# ax = fig.gca()\n",
"Ncount_mean.plot.errorbar(hue='final_amp')\n",
"# plt.xlabel('MOT AOM Frequency (MHz)')\n",
"# plt.ylabel('MOT Gradient Coil Current (A)')\n",
"plt.tight_layout()\n",
"# plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data = Ncount_mean.sel(final_amp=0.88)\n",
"data_std = Ncount_std.sel(final_amp=0.88)\n",
"fig = plt.figure()\n",
"# ax = fig.gca()\n",
"data.plot.errorbar()\n",
"# plt.xlabel('MOT AOM Frequency (MHz)')\n",
"# plt.ylabel('MOT Gradient Coil Current (A)')\n",
"plt.tight_layout()\n",
"# plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"def factor_from_Ncounts_to_Natom():\n",
" return 1 / (8.474337362524987e-14 * 0.3725) * 5.86e-6**2 / 0.438**2"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"def mot_loading(x, A, tau):\n",
" return A * (1 - np.exp(-x / tau)) * np.heaviside(x, 0)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"data = data * factor_from_Ncounts_to_Natom()\n",
"data_std = data_std * factor_from_Ncounts_to_Natom()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fitModel = NewFitModel(mot_loading)\n",
"fitAnalyser = FitAnalyser(fitModel, fitDim=1)\n",
"\n",
"params = fitAnalyser.fitModel.make_params()\n",
"params.add(name=\"A\", value=1e8, max=np.inf, min=-np.inf, vary=True)\n",
"params.add(name=\"tau\", value=1, max=np.inf, min=-np.inf, vary=True)\n",
"\n",
"fitResult = fitAnalyser.fit(data, params, x='mot_load_duration').load()\n",
"freqdata = np.linspace(0, 10, 500)\n",
"fitCurve = fitAnalyser.eval(fitResult, x=freqdata, dask=\"parallelized\").load()\n",
"fitCurve = fitCurve.assign_coords({'x':np.array(freqdata)})\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"data.plot.errorbar(ax=ax, yerr = data_std, fmt='ob')\n",
"fitCurve.plot.errorbar(ax=ax, fmt='--g')\n",
"plt.xlabel('Push AOM Freq (MHz)')\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
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" height: 1.5em !important;\n",
" stroke-width: 0;\n",
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: ()\n",
"Coordinates:\n",
" final_amp float64 0.88\n",
"Data variables:\n",
" A float64 3.548e+08\n",
" tau float64 2.918</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-306565fe-c794-4807-9d12-42b0a94b6e46' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-306565fe-c794-4807-9d12-42b0a94b6e46' 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-df5adb4c-20c8-4566-8e8d-a86a1f0f8f23' class='xr-section-summary-in' type='checkbox' checked><label for='section-df5adb4c-20c8-4566-8e8d-a86a1f0f8f23' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>final_amp</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.88</div><input id='attrs-46e0a84f-98ec-4515-8ae3-f47284f3bb80' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-46e0a84f-98ec-4515-8ae3-f47284f3bb80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-728f29aa-4c6d-4813-bfb4-f25e88ee19b1' class='xr-var-data-in' type='checkbox'><label for='data-728f29aa-4c6d-4813-bfb4-f25e88ee19b1' 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(0.88)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bcc4a974-46bb-4072-8c23-89fa6a78d769' class='xr-section-summary-in' type='checkbox' checked><label for='section-bcc4a974-46bb-4072-8c23-89fa6a78d769' class='xr-section-summary' >Data variables: <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>A</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.548e+08</div><input id='attrs-e952e42d-4ebd-4c1a-8370-88315a4e1039' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e952e42d-4ebd-4c1a-8370-88315a4e1039' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae3e1a27-1db7-44c3-8223-74f1032c0006' class='xr-var-data-in' type='checkbox'><label for='data-ae3e1a27-1db7-44c3-8223-74f1032c0006' 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(3.54756438e+08)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tau</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.918</div><input id='attrs-173f8442-39e3-4477-b976-54be113fe968' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-173f8442-39e3-4477-b976-54be113fe968' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-16a309d6-1e1c-4753-8138-a305ff297155' class='xr-var-data-in' type='checkbox'><label for='data-16a309d6-1e1c-4753-8138-a305ff297155' 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(2.91809259)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0a1d5298-df4e-4299-8163-fbaa9fb327b6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0a1d5298-df4e-4299-8163-fbaa9fb327b6'
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: ()\n",
"Coordinates:\n",
" final_amp float64 0.88\n",
"Data variables:\n",
" A float64 3.548e+08\n",
" tau float64 2.918"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"val = fitAnalyser.get_fit_value(fitResult)\n",
"val"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: ()\n",
"Coordinates:\n",
" final_amp float64 0.88\n",
"Data variables:\n",
" A float64 2.776e+06\n",
" tau float64 0.05867</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-11c5cd5b-f73f-4855-a7a6-c2f7d26b992b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-11c5cd5b-f73f-4855-a7a6-c2f7d26b992b' 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-9070dfd9-5691-4d56-afbe-ea45a13da857' class='xr-section-summary-in' type='checkbox' checked><label for='section-9070dfd9-5691-4d56-afbe-ea45a13da857' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>final_amp</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.88</div><input id='attrs-5e6f08c9-a13b-414c-8f0e-f69366ce99ab' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5e6f08c9-a13b-414c-8f0e-f69366ce99ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a61baea4-47a3-4bb0-abae-0f21c5b981e7' class='xr-var-data-in' type='checkbox'><label for='data-a61baea4-47a3-4bb0-abae-0f21c5b981e7' 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(0.88)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-29c4a383-1ce7-4a2e-a4a8-445563d27ef6' class='xr-section-summary-in' type='checkbox' checked><label for='section-29c4a383-1ce7-4a2e-a4a8-445563d27ef6' class='xr-section-summary' >Data variables: <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>A</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.776e+06</div><input id='attrs-667a112d-177c-4a07-ad4d-8ff370431006' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-667a112d-177c-4a07-ad4d-8ff370431006' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77ad3d5f-cffb-47d3-97fc-ae92496f9a2f' class='xr-var-data-in' type='checkbox'><label for='data-77ad3d5f-cffb-47d3-97fc-ae92496f9a2f' 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(2776453.58029856)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tau</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.05867</div><input id='attrs-7eddaa69-a7ac-4e0e-9dcc-bc1faf26f824' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7eddaa69-a7ac-4e0e-9dcc-bc1faf26f824' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3f9db1e-7125-465b-b8b5-3864b935dc4a' class='xr-var-data-in' type='checkbox'><label for='data-b3f9db1e-7125-465b-b8b5-3864b935dc4a' 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(0.05867137)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bdb7c108-f4fe-4ec3-afb5-1b3ba4661daa' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bdb7c108-f4fe-4ec3-afb5-1b3ba466
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: ()\n",
"Coordinates:\n",
" final_amp float64 0.88\n",
"Data variables:\n",
" A float64 2.776e+06\n",
" tau float64 0.05867"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"std = fitAnalyser.get_fit_std(fitResult)\n",
"std"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
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"\n",
".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"}\n",
"\n",
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" padding-right: 10px;\n",
" width: auto;\n",
"}\n",
"\n",
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" font-weight: normal;\n",
" grid-column: 1;\n",
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"\n",
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"\n",
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"\n",
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"}\n",
"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray ()&gt;\n",
"array(1.2157134383410748+/-0.02622973148346536, dtype=object)\n",
"Coordinates:\n",
" final_amp float64 0.88</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-5f93dfa3-52ec-495e-92cf-d730bb50a676' class='xr-array-in' type='checkbox' checked><label for='section-5f93dfa3-52ec-495e-92cf-d730bb50a676' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>1.216+/-0.026</span></div><div class='xr-array-data'><pre>array(1.2157134383410748+/-0.02622973148346536, dtype=object)</pre></div></div></li><li class='xr-section-item'><input id='section-fa458166-a05a-4e93-858d-3935c8899d5f' class='xr-section-summary-in' type='checkbox' checked><label for='section-fa458166-a05a-4e93-858d-3935c8899d5f' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>final_amp</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.88</div><input id='attrs-3a363972-1acf-477b-87b0-7dd33ed4bbcb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3a363972-1acf-477b-87b0-7dd33ed4bbcb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f6589cbf-4c1c-40d0-9976-2b9ae8db4d3b' class='xr-var-data-in' type='checkbox'><label for='data-f6589cbf-4c1c-40d0-9976-2b9ae8db4d3b' 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(0.88)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-41ec6a75-61b8-47c6-80f4-7d80b3e49d40' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-41ec6a75-61b8-47c6-80f4-7d80b3e49d40' class='xr-section-summary' title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-c2222dfd-faf3-4012-ae97-78b5ac9ee3da' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c2222dfd-faf3-4012-ae97-78b5ac9ee3da' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray ()>\n",
"array(1.2157134383410748+/-0.02622973148346536, dtype=object)\n",
"Coordinates:\n",
" final_amp float64 0.88"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"res = fitAnalyser.get_fit_full_result(fitResult)\n",
"res.A / res.tau / 1e8"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Without push"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The detected scaning axes and values are: \n",
"\n",
"{'mot_load_duration': array([ 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. , 5.5,\n",
" 6. , 6.5, 7. , 8. , 9. , 10. , 11. , 12. , 13. , 14. , 15. ,\n",
" 16. ]), 'runs': array([0., 1.])}\n"
]
}
],
"source": [
"folderPath = img_dir + SequenceName + '2022/10/11'# get_date()\n",
"\n",
"shotNum = \"0018\"\n",
"filePath = folderPath + \"/\" + shotNum + \"/*.h5\"\n",
"\n",
"dataSetDict = {\n",
" dskey[groupList[i]]: read_hdf5_file(filePath, groupList[i])\n",
" for i in [0]\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 = (600, 1125)\n",
"imageAnalyser.span = (1100, 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 = calculate_mean(Ncount)\n",
"Ncount_std = calculate_std(Ncount)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure()\n",
"# ax = fig.gca()\n",
"Ncount_mean.plot.errorbar()\n",
"# plt.xlabel('MOT AOM Frequency (MHz)')\n",
"# plt.ylabel('MOT Gradient Coil Current (A)')\n",
"plt.tight_layout()\n",
"# plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data = Ncount_mean\n",
"data_std = Ncount_std\n",
"fig = plt.figure()\n",
"# ax = fig.gca()\n",
"data.plot.errorbar()\n",
"# plt.xlabel('MOT AOM Frequency (MHz)')\n",
"# plt.ylabel('MOT Gradient Coil Current (A)')\n",
"plt.tight_layout()\n",
"# plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"data = data * factor_from_Ncounts_to_Natom()\n",
"data_std = data_std * factor_from_Ncounts_to_Natom()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fitModel = NewFitModel(mot_loading)\n",
"fitAnalyser = FitAnalyser(fitModel, fitDim=1)\n",
"\n",
"params = fitAnalyser.fitModel.make_params()\n",
"params.add(name=\"A\", value=1e8, max=np.inf, min=-np.inf, vary=True)\n",
"params.add(name=\"tau\", value=1, max=np.inf, min=-np.inf, vary=True)\n",
"\n",
"fitResult = fitAnalyser.fit(data, params, x='mot_load_duration').load()\n",
"freqdata = np.linspace(0, 10, 500)\n",
"fitCurve = fitAnalyser.eval(fitResult, x=freqdata, dask=\"parallelized\").load()\n",
"fitCurve = fitCurve.assign_coords({'x':np.array(freqdata)})\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca()\n",
"\n",
"data.plot.errorbar(ax=ax, yerr = data_std, fmt='ob')\n",
"fitCurve.plot.errorbar(ax=ax, fmt='--g')\n",
"plt.xlabel('Push AOM Freq (MHz)')\n",
"plt.ylabel('NCount')\n",
"plt.tight_layout()\n",
"plt.grid(visible=1)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
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" 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'>&lt;xarray.Dataset&gt;\n",
"Dimensions: ()\n",
"Data variables:\n",
" A float64 1.193e+08\n",
" tau float64 4.666</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-3e8b8706-d701-41ad-8200-e1caf324ff4f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3e8b8706-d701-41ad-8200-e1caf324ff4f' 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-48c4bb83-68b0-49be-94ac-b96b366e2a21' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-48c4bb83-68b0-49be-94ac-b96b366e2a21' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-c2d074f7-2a4c-4728-8200-18cb7daecd27' class='xr-section-summary-in' type='checkbox' checked><label for='section-c2d074f7-2a4c-4728-8200-18cb7daecd27' class='xr-section-summary' >Data variables: <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>A</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.193e+08</div><input id='attrs-d11e30e4-f834-4f05-9d5f-d03a106fad86' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d11e30e4-f834-4f05-9d5f-d03a106fad86' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bdb6f408-a6e6-4edb-8486-2c3ccaaed511' class='xr-var-data-in' type='checkbox'><label for='data-bdb6f408-a6e6-4edb-8486-2c3ccaaed511' 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(1.19289449e+08)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tau</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.666</div><input id='attrs-8730b129-dbfe-44dc-87d2-e8279113e974' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8730b129-dbfe-44dc-87d2-e8279113e974' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e38aadd-38e6-4f9d-a670-31f988ae90aa' class='xr-var-data-in' type='checkbox'><label for='data-6e38aadd-38e6-4f9d-a670-31f988ae90aa' 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(4.66646868)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0e6a306c-69c4-4e2d-a06e-f91190fd01d3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0e6a306c-69c4-4e2d-a06e-f91190fd01d3' class='xr-section-summary' title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-7c126d51-7bce-44d7-a8e5-f8285ece4e46' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7c126d51-7bce-44d7-a8e5-f8285ece4e46' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: ()\n",
"Data variables:\n",
" A float64 1.193e+08\n",
" tau float64 4.666"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"val = fitAnalyser.get_fit_value(fitResult)\n",
"val"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
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"}\n",
"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: ()\n",
"Data variables:\n",
" A float64 1.591e+06\n",
" tau float64 0.1586</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-38c68664-9f21-47e9-ae7b-e2791ad41bc4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-38c68664-9f21-47e9-ae7b-e2791ad41bc4' 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-15885cae-358f-44e6-b2c7-152bbe3484eb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-15885cae-358f-44e6-b2c7-152bbe3484eb' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-8e6f4849-f4ef-4966-8549-1e26327a3a91' class='xr-section-summary-in' type='checkbox' checked><label for='section-8e6f4849-f4ef-4966-8549-1e26327a3a91' class='xr-section-summary' >Data variables: <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>A</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.591e+06</div><input id='attrs-f987e1c1-8bbd-4837-aea6-81f1f3631bb5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f987e1c1-8bbd-4837-aea6-81f1f3631bb5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2cea5ed4-cd11-4731-92f1-a9f83e88f20a' class='xr-var-data-in' type='checkbox'><label for='data-2cea5ed4-cd11-4731-92f1-a9f83e88f20a' 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(1591103.66003124)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tau</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1586</div><input id='attrs-e050af12-da09-4017-b33d-295dda743e20' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e050af12-da09-4017-b33d-295dda743e20' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-413af6b2-77de-4114-ab8c-180f8e3f6dad' class='xr-var-data-in' type='checkbox'><label for='data-413af6b2-77de-4114-ab8c-180f8e3f6dad' 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(0.15857749)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-636e37ff-da1b-49df-af26-ab46778e7ee3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-636e37ff-da1b-49df-af26-ab46778e7ee3' class='xr-section-summary' title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-84a633aa-a33c-4e99-ba4d-8c38d3dadea4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-84a633aa-a33c-4e99-ba4d-8c38d3dadea4' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: ()\n",
"Data variables:\n",
" A float64 1.591e+06\n",
" tau float64 0.1586"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"std = fitAnalyser.get_fit_std(fitResult)\n",
"std"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
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"\n",
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"\n",
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"\n",
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"\n",
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray ()&gt;\n",
"array(0.2556310933363963+/-0.009332131323758952, dtype=object)</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-0f74fa61-81d9-43b3-8dfa-efeee1be9233' class='xr-array-in' type='checkbox' checked><label for='section-0f74fa61-81d9-43b3-8dfa-efeee1be9233' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.256+/-0.009</span></div><div class='xr-array-data'><pre>array(0.2556310933363963+/-0.009332131323758952, dtype=object)</pre></div></div></li><li class='xr-section-item'><input id='section-c5825fc5-54fe-44a0-94bf-b6406a837c7f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c5825fc5-54fe-44a0-94bf-b6406a837c7f' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-596126cf-96e8-460c-b70b-6f0af94bbaef' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-596126cf-96e8-460c-b70b-6f0af94bbaef' class='xr-section-summary' title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-8562ca1f-db6a-4c04-acc5-429ba0b0bb3a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8562ca1f-db6a-4c04-acc5-429ba0b0bb3a' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray ()>\n",
"array(0.2556310933363963+/-0.009332131323758952, dtype=object)"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"res = fitAnalyser.get_fit_full_result(fitResult)\n",
"res.A / res.tau / 1e8"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Loading rate"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"img_dir = '//DyLabNAS/Data/'\n",
"SequenceName = \"Repetition_scan\" + \"/\""
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"powers = [150, 200, 250, 300, 350, 400]\n",
"ncounts_withpush = np.array([5072.7, 9171.8, 16721.7, 23160.8, 27965.0, 32395.8]) * Ncount_to_atoms()\n",
"ncount_withpush_errors = np.array([80.7, 146.9, 142.1, 514.9, 433.6, 631.2]) * Ncount_to_atoms()\n",
"\n",
"ncounts_withoutpush = np.array([629.0, 1567.6, 3063.7, 4426.5, 4755.4, 4920.9]) * Ncount_to_atoms()\n",
"ncount_withoutpush_errors = np.array([22.9, 53.8, 79.9, 63.7, 191.5, 190.9]) * Ncount_to_atoms()\n",
"\n",
"sat_ncount_withpush = np.array([23921.7, 39196.8, 60078.6, 75443.0, 84752.0, 91294.0]) * Ncount_to_atoms()\n",
"sat_ncount_withpush_errors = np.array([88.3, 138.8, 89.0, 278.0, 210.1, 355.1]) * Ncount_to_atoms()\n",
"\n",
"sat_ncount_withoutpush = np.array([4224.1, 9672.3, 17949.6, 24080.9, 25218.2, 26968.5]) * Ncount_to_atoms()\n",
"sat_ncount_withoutpush_errors = np.array([37.5, 77.2, 105.9, 75.3, 217.6, 265.0]) * Ncount_to_atoms()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1.50000000e+02, 2.00000000e+02, 2.50000000e+02, 3.00000000e+02,\n",
" 3.50000000e+02, 4.00000000e+02],\n",
" [1.26452425e+07, 2.28634918e+07, 4.16839061e+07, 5.77353148e+07,\n",
" 6.97112396e+07, 8.07563517e+07],\n",
" [1.26452425e+07, 2.28634918e+07, 4.16839061e+07, 5.77353148e+07,\n",
" 6.97112396e+07, 8.07563517e+07]])"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.array([powers, ncounts_withpush, ncounts_withpush])"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"np.savetxt('loading_rate_withpush.txt', \n",
" np.array([powers, ncounts_withpush, ncount_withpush_errors]),\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"np.savetxt('loading_rate_withoutpush.txt', \n",
" np.array([powers, ncounts_withoutpush, ncount_withoutpush_errors]),\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"np.savetxt('sat_ncount_withpush.txt', \n",
" np.array([powers, sat_ncount_withpush, sat_ncount_withpush_errors]),\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"np.savetxt('sat_ncount_withoutpush.txt', \n",
" np.array([powers, sat_ncount_withoutpush, sat_ncount_withoutpush_errors]),\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"np.savetxt('old_loading_rate_withpush.txt', \n",
" np.array([[430], [1.2157134383410748e8], [0.02622973148346536e8]]),\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"np.savetxt('old_loading_rate_withoutpush.txt', \n",
" np.array([[430], [0.2556310933363963e8], [0.009332131323758952e8]]),\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"np.savetxt('old_sat_ncount_withpush.txt', \n",
" np.array([[430], [3.54756438e8], [2776453.58029856]]),\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"np.savetxt('old_sat_ncount_withoutpush.txt', \n",
" np.array([[430], [1.19289449e8], [1591103.66003124]]),\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plot_axes.errorbar([430], [1.2157134383410748e8], yerr=[0.02622973148346536e8], color=plot_red, marker='^', **plot_kwarg)\n",
"plot_axes.errorbar([430], [0.2556310933363963e8], yerr=[0.009332131323758952e8], color='k', marker='*', markersize=15)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plot_axes.errorbar([430], [3.54756438], yerr=[2776453.58029856/1e8], color=plot_red, marker='^', **plot_kwarg)\n",
"plot_axes.errorbar([430], [1.19289449], yerr=[1591103.66003124/1e8], color=\"k\", marker='*', markersize=15)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 816x1615.68 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(6.8, 6.8/4*3*2*(1 + 16/50)), dpi=120)\n",
"grid = fig.add_gridspec(4, 1, height_ratios=[2, 50, 10, 50], wspace=0.4, hspace=0.1)\n",
"\n",
"######################\n",
"# fig.a\n",
"######################\n",
"\n",
"plot_axes = plt.subplot(grid[1, 0])\n",
"\n",
"plot_kwarg = {\n",
" # 'fmt': 'o',\n",
" \n",
" # Style of line\n",
" 'linestyle': 'None',\n",
" 'linewidth': 2,\n",
" \n",
" # Style of markder\n",
" # 'marker': '.',\n",
" 'markersize': 5,\n",
" # 'markerfacecolor': plot_blue,\n",
" # 'markeredgecolor': plot_blue,\n",
" 'markeredgewidth': 4,\n",
" \n",
" # Style of errorbar\n",
" 'capsize': 0,\n",
" # 'ecolor': plot_blue, # color of errorbar line\n",
" 'capthick': 1.5,\n",
" 'elinewidth': 3,\n",
" \n",
" # text for legend\n",
" 'label': 'Experiment',\n",
" }\n",
"\n",
"\n",
"# 1.2157134383410748+/-0.02622973148346536\n",
"# 0.2556310933363963+/-0.009332131323758952\n",
"\n",
"plot_axes.errorbar(powers, ncounts_withpush, yerr=ncount_withpush_errors, color=plot_red, marker='o', **plot_kwarg)\n",
"plot_axes.errorbar(powers, ncounts_withoutpush, yerr=ncount_withoutpush_errors, color=\"k\", marker='s', **plot_kwarg)\n",
"plot_axes.errorbar([430], [1.2157134383410748e8], yerr=[0.02622973148346536e8], color=plot_red, marker='^', **plot_kwarg)\n",
"plot_axes.errorbar([430], [0.2556310933363963e8], yerr=[0.009332131323758952e8], color='k', marker='*', markersize=15)\n",
"\n",
"plot_axes.set_xlabel(\"2D-MOT power $P_\\mathrm{2D}$ (mW)\", fontsize=20)\n",
"plot_axes.set_ylabel(\"Loading rate $\\Phi_\\mathrm{3D}$ (atoms/s)\", fontsize=20)\n",
"\n",
"plot_axes.tick_params(axis='both', which='major', labelsize=20)\n",
"plot_axes.tick_params(axis='both', which='minor', labelsize=16)\n",
"plot_axes.xaxis.offsetText.set_fontsize(20)\n",
"plot_axes.yaxis.offsetText.set_fontsize(20)\n",
"\n",
"plt.setp(plot_axes.spines.values(), linewidth=3)\n",
"plot_axes.xaxis.set_tick_params(width=3)\n",
"plot_axes.yaxis.set_tick_params(width=3)\n",
"plot_axes.tick_params(direction='in', length=10)\n",
"\n",
"if np.max(plot_axes.get_xticks()) < 1000:\n",
" plot_axes.ticklabel_format(scilimits=(0, 0), axis='x', style='plain', useMathText=True)\n",
"else:\n",
" plot_axes.ticklabel_format(scilimits=(0, 0), axis='x', style='sci', useMathText=True)\n",
"if np.max(plot_axes.get_yticks()) < 1000:\n",
" plot_axes.ticklabel_format(scilimits=(0, 0), axis='y', style='plain', useMathText=True)\n",
"else:\n",
" plot_axes.ticklabel_format(scilimits=(0, 0), axis='y', style='sci', useMathText=True)\n",
"\n",
"# plot_axes.set_ylim([-0.1e8, 9e7])\n",
"\n",
"######################\n",
"\n",
"plot_axes.legend( [\"with red push\", \"without push\"],\n",
" fontsize=20, loc = 'lower right', bbox_to_anchor=(0.1, 0.7, 0.5, 0.5), shadow=False, \n",
" facecolor='white', framealpha=1, edgecolor='gray')\n",
"\n",
"######################\n",
"\n",
"######################\n",
"# fig.b\n",
"######################\n",
"\n",
"plot_axes = plt.subplot(grid[3, 0])\n",
"\n",
"plot_kwarg = {\n",
" # 'fmt': 'o',\n",
" \n",
" # Style of line\n",
" 'linestyle': 'None',\n",
" 'linewidth': 2,\n",
" \n",
" # Style of markder\n",
" # 'marker': '.',\n",
" 'markersize': 5,\n",
" # 'markerfacecolor': plot_blue,\n",
" # 'markeredgecolor': plot_blue,\n",
" 'markeredgewidth': 4,\n",
" \n",
" # Style of errorbar\n",
" 'capsize': 0,\n",
" # 'ecolor': plot_blue, # color of errorbar line\n",
" 'capthick': 1.5,\n",
" 'elinewidth': 3,\n",
" \n",
" # text for legend\n",
" # 'label': 'Experiment',\n",
" }\n",
"\n",
"plot_axes.errorbar([430], [3.54756438], yerr=[2776453.58029856/1e8], color=plot_red, marker='^', **plot_kwarg)\n",
"plot_axes.errorbar([430], [1.19289449], yerr=[1591103.66003124/1e8], color=\"k\", marker='*', markersize=15)\n",
"\n",
"plot_axes.errorbar(powers, sat_ncount_withpush / 1e8, yerr=sat_ncount_withpush_errors / 1e8, color=plot_red, marker='o', **plot_kwarg)\n",
"plot_axes.errorbar(powers, sat_ncount_withoutpush / 1e8, yerr=sat_ncount_withoutpush_errors / 1e8, color=\"k\", marker='s', **plot_kwarg)\n",
"\n",
"plot_axes.set_xlabel(\"2D-MOT power $P_\\mathrm{2D}$ (mW)\", fontsize=20)\n",
"plot_axes.set_ylabel(\"Saturation atom $N_\\mathrm{sat}$ $(10^8)$\", fontsize=20)\n",
"\n",
"plot_axes.tick_params(axis='both', which='major', labelsize=20)\n",
"plot_axes.tick_params(axis='both', which='minor', labelsize=16)\n",
"plot_axes.xaxis.offsetText.set_fontsize(20)\n",
"plot_axes.yaxis.offsetText.set_fontsize(20)\n",
"\n",
"plt.setp(plot_axes.spines.values(), linewidth=3)\n",
"plot_axes.xaxis.set_tick_params(width=3)\n",
"plot_axes.yaxis.set_tick_params(width=3)\n",
"plot_axes.tick_params(direction='in', length=10)\n",
"\n",
"if np.max(plot_axes.get_xticks()) < 1000:\n",
" plot_axes.ticklabel_format(scilimits=(0, 0), axis='x', style='plain', useMathText=True)\n",
"else:\n",
" plot_axes.ticklabel_format(scilimits=(0, 0), axis='x', style='sci', useMathText=True)\n",
"if np.max(plot_axes.get_yticks()) < 1000:\n",
" plot_axes.ticklabel_format(scilimits=(0, 0), axis='y', style='plain', useMathText=True)\n",
"else:\n",
" plot_axes.ticklabel_format(scilimits=(0, 0), axis='y', style='sci', useMathText=True)\n",
"\n",
"plot_axes.set_ylim([0, 4])\n",
"\n",
"plt.setp(plot_axes.get_yticklabels()[0], visible=False)\n",
"\n",
"######################################\n",
"\n",
"plot_axes = plt.subplot(grid[0, 0])\n",
"plot_axes.text(-0.17, 0.8, '(a)', va='bottom', fontsize=20)\n",
"plot_axes.set_axis_off()\n",
"\n",
"plot_axes = plt.subplot(grid[2, 0])\n",
"plot_axes.text(-0.17, 0.1, '(b)', va='bottom', fontsize=20)\n",
"plot_axes.set_axis_off()\n",
"\n",
"fig.savefig('figS1_v2.pdf', bbox_inches = \"tight\")\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.12"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
Reference in New Issue Copy Permalink