{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import supporting package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'DataContainer'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32mf:\\Jianshun\\analyseScript\\backupScript\\2D-MOT power.ipynb Cell 2\u001b[0m in \u001b[0;36m1\n\u001b[0;32m      <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/backupScript/2D-MOT%20power.ipynb#W1sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39muncertainties\u001b[39;00m \u001b[39mimport\u001b[39;00m umath\n\u001b[0;32m      <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/backupScript/2D-MOT%20power.ipynb#W1sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mmatplotlib\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mpyplot\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mplt\u001b[39;00m\n\u001b[1;32m---> <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/backupScript/2D-MOT%20power.ipynb#W1sZmlsZQ%3D%3D?line=9'>10</a>\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mDataContainer\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mReadData\u001b[39;00m \u001b[39mimport\u001b[39;00m read_hdf5_file\n\u001b[0;32m     <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/backupScript/2D-MOT%20power.ipynb#W1sZmlsZQ%3D%3D?line=10'>11</a>\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mAnalyser\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mImagingAnalyser\u001b[39;00m \u001b[39mimport\u001b[39;00m ImageAnalyser\n\u001b[0;32m     <a href='vscode-notebook-cell:/f%3A/Jianshun/analyseScript/backupScript/2D-MOT%20power.ipynb#W1sZmlsZQ%3D%3D?line=11'>12</a>\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mAnalyser\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mFitAnalyser\u001b[39;00m \u001b[39mimport\u001b[39;00m FitAnalyser\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'DataContainer'"
     ]
    }
   ],
   "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": 4,
   "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": 5,
   "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": "[encoded data, 90052 characters]",
      "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": "[encoded data, 30800 characters]",
      "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": {
      "image/png": "[encoded data, 34656 characters]",
      "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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       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
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       "\n",
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
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       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
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       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
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       "  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' 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-5d1f8b9a-d597-4d37-8d23-c0d374053b2a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5d1f8b9a-d597-4d37-8d23-c0d374053b2a' 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",
       "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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       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
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       "  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 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-afb5-1b3ba4661daa' 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-32fc181f-3e6c-47c5-afac-ebe7f3cb8933' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-32fc181f-3e6c-47c5-afac-ebe7f3cb8933' 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",
       "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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       "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"
   ]
  },
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   "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": "[encoded data, 31444 characters]",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
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    }
   ],
   "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": "[encoded data, 31444 characters]",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
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    }
   ],
   "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": {
      "image/png": "[encoded data, 30592 characters]",
      "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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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
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       "\n",
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       "\n",
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       "\n",
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       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
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       "  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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       "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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       "\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>"
      ],
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       "<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": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_dir = '//DyLabNAS/Data/'\n",
    "SequenceName = \"Repetition_scan\" + \"/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2492.803132748206"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ncount_to_atoms()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 9,
   "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": 9,
     "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": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## V1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "[encoded data, 113676 characters]",
      "text/plain": [
       "<Figure size 816x1615.68 with 4 Axes>"
      ]
     },
     "metadata": {},
     "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": []
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## V2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def curve(A, P0, Psat, x):\n",
    "    return A*(1-np.exp(-(x-P0)/Psat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "[encoded data, 87744 characters]",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 6), dpi=120)\n",
    "\n",
    "plot_axes = fig.gca()\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",
    "Nmax = np.max(ncounts_withpush)\n",
    "plot_axes.errorbar(powers, ncounts_withpush / Nmax, yerr=ncount_withpush_errors / Nmax, color=plot_red, marker='^', **plot_kwarg)\n",
    "\n",
    "Nmax = np.max(sat_ncount_withpush)\n",
    "plot_axes.errorbar(powers, sat_ncount_withpush / Nmax, yerr=sat_ncount_withpush_errors / Nmax, color=plot_blue, marker='o', **plot_kwarg)\n",
    "\n",
    "x = np.linspace(100, 550, 1000)\n",
    "fit_line = curve(1.422, 105.9, 234.2, x)\n",
    "plot_axes.errorbar(x, fit_line, color=plot_blue, fmt='--')\n",
    "\n",
    "x = np.linspace(100, 550, 1000)\n",
    "fit_line = curve(3.843, 117.4, 923.4, x)\n",
    "plot_axes.errorbar(x, fit_line, color=plot_red, fmt='--')\n",
    "\n",
    "plot_axes.set_xlim([50, 550])\n",
    "plot_axes.set_ylim([-0, 1.6])\n",
    "\n",
    "plot_axes.set_xlabel(\"2D-MOT power $P_\\mathrm{2D}$ (mW)\", fontsize=20)\n",
    "plot_axes.set_ylabel(\"Relative performance\", 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",
    "plt.setp(plot_axes.get_xticklabels()[0], visible=False)\n",
    "plt.setp(plot_axes.get_yticklabels()[0], visible=False)\n",
    "\n",
    "plot_axes.legend( [\"$\\Phi_\\mathrm{3D}/\\Phi_\\mathrm{3D}^{400}$\", \"$N_\\mathrm{sat}/N_\\mathrm{sat}^{400}$\"],\n",
    "    fontsize=20, loc = 'lower right', bbox_to_anchor=(-0.07, 0.7, 0.5, 0.5), shadow=False, \n",
    "    facecolor='white', framealpha=1, edgecolor='gray')\n",
    "\n",
    "fig.savefig('figS1_v3.pdf', bbox_inches = \"tight\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.26452425, 2.28634918, 4.16839061, 5.77353148, 6.97112396,\n",
       "       8.07563517])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ncounts_withpush/1e7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.02011692, 0.03661928, 0.03542273, 0.12835443, 0.10808794,\n",
       "       0.15734573])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ncount_withpush_errors/1e7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.59632089, 0.97709906, 1.49764122, 1.88064547, 2.11270051,\n",
       "       2.27577969])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sat_ncount_withpush/1e8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00220115, 0.00346001, 0.00221859, 0.00692999, 0.00523738,\n",
       "       0.00885194])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sat_ncount_withpush_errors/1e8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## V3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def curve(A, P0, Psat, x):\n",
    "    return A*(1-np.exp(-(x-P0)/Psat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "[encoded data, 91820 characters]",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 6), dpi=120)\n",
    "\n",
    "plot_axes = fig.gca()\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",
    "Nmax = np.max(ncounts_withpush)\n",
    "plot_axes.errorbar(powers, ncounts_withpush / Nmax, yerr=ncount_withpush_errors / Nmax, color=plot_red, marker='^', **plot_kwarg)\n",
    "\n",
    "Nmax = np.max(sat_ncount_withpush)\n",
    "plot_axes.errorbar(powers, sat_ncount_withpush / Nmax, yerr=sat_ncount_withpush_errors / Nmax, color=plot_blue, marker='o', **plot_kwarg)\n",
    "\n",
    "x = np.linspace(100, 450, 1000)\n",
    "fit_line = curve(1.422, 105.9, 234.2, x)\n",
    "plot_axes.errorbar(x, fit_line, color=plot_blue, fmt='--')\n",
    "\n",
    "x = np.linspace(100, 450, 1000)\n",
    "fit_line = curve(3.843, 117.4, 923.4, x)\n",
    "plot_axes.errorbar(x, fit_line, color=plot_red, fmt='--')\n",
    "\n",
    "plot_axes.set_xlim([100, 450])\n",
    "plot_axes.set_ylim([-0, 1.2])\n",
    "\n",
    "plot_axes.set_xlabel(\"2D-MOT power $P_\\mathrm{2D}$ (mW)\", fontsize=20)\n",
    "plot_axes.set_ylabel(\"Relative performance\", 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",
    "plt.setp(plot_axes.get_xticklabels()[0], visible=False)\n",
    "plt.setp(plot_axes.get_yticklabels()[0], visible=False)\n",
    "\n",
    "plot_axes.legend( [\"$\\Phi_\\mathrm{3D}/\\Phi_\\mathrm{3D}^{400}$\", \"$N_\\mathrm{sat}/N_\\mathrm{sat}^{400}$\"],\n",
    "    fontsize=20, loc = 'lower right', bbox_to_anchor=(-0.07, 0.7, 0.5, 0.5), shadow=False, \n",
    "    facecolor='white', framealpha=1, edgecolor='gray')\n",
    "\n",
    "fig.savefig('figS1_v4.pdf', bbox_inches = \"tight\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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