{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "71d00eeb",
   "metadata": {},
   "source": [
    "# Tutorial 2. Advanced capabilities of the MeasurementControl\n",
    "\n",
    "```{seealso}\n",
    "The complete source code of this tutorial can be found in\n",
    "\n",
    "{nb-download}`Tutorial 2. Advanced capabilities of the MeasurementControl.ipynb`\n",
    "```\n",
    "\n",
    "Following this Tutorial requires familiarity with the **core concepts** of Quantify, we **highly recommended** to consult the (short) {ref}`User guide` before proceeding (see Quantify documentation). If you have some difficulties following the tutorial it might be worth reviewing the {ref}`User guide`!\n",
    "\n",
    "We **highly recommended** beginning with {ref}`Tutorial 1. Controlling a basic experiment using MeasurementControl` before proceeding.\n",
    "\n",
    "In this tutorial, we will explore the more advanced features of Quantify. By the end of this tutorial, we will have covered:\n",
    "\n",
    "- Using hardware to drive experiments\n",
    "- Software averaging\n",
    "- Interrupting an experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f5bfac56",
   "metadata": {
    "mystnb": {
     "code_prompt_show": "Imports and auxiliary utilities"
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import signal\n",
    "import sys\n",
    "import time\n",
    "\n",
    "import numpy as np\n",
    "from lmfit import Model\n",
    "from qcodes import ManualParameter\n",
    "\n",
    "import quantify_core.visualization.pyqt_plotmon as pqm\n",
    "from quantify_core.data.handling import set_datadir, default_datadir\n",
    "from quantify_core.measurement.control import MeasurementControl\n",
    "from quantify_core.visualization.instrument_monitor import InstrumentMonitor\n",
    "\n",
    "rng = np.random.default_rng(seed=222222)  # random number generator"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75b88a56",
   "metadata": {},
   "source": [
    "**Before instantiating any instruments or starting a measurement** we change the\n",
    "directory in which the experiments are saved using the\n",
    "{meth}`~quantify_core.data.handling.set_datadir`\n",
    "\\[{meth}`~quantify_core.data.handling.get_datadir`\\] functions.\n",
    "\n",
    "----------------------------------------------------------------------------------------\n",
    "\n",
    "⚠️ **Warning!**\n",
    "\n",
    "We recommend always setting the directory at the start of the Python kernel and sticking to a single common data directory for all notebooks/experiments within your\n",
    "measurement setup/PC.\n",
    "\n",
    "The cell below sets a default data directory (`~/quantify-data` on Linux/macOS or\n",
    "`$env:USERPROFILE\\\\quantify-data` on Windows) for tutorial purposes. Change it to your\n",
    "desired data directory. The utilities to find/search/extract data only work if\n",
    "all the experiment containers are located within the same directory.\n",
    "\n",
    "----------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "21867a1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data will be saved in:\n",
      "/root/quantify-data\n"
     ]
    }
   ],
   "source": [
    "set_datadir(default_datadir())  # change me!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d236c77d",
   "metadata": {
    "mystnb": {
     "remove-output": true
    }
   },
   "outputs": [],
   "source": [
    "meas_ctrl = MeasurementControl(\"meas_ctrl\")\n",
    "plotmon = pqm.PlotMonitor_pyqt(\"plotmon_meas_ctrl\")\n",
    "meas_ctrl.instr_plotmon(plotmon.name)\n",
    "insmon = InstrumentMonitor(\"InstrumentMonitor\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75bac5f5",
   "metadata": {},
   "source": [
    "## A 1D Batched loop: Resonator Spectroscopy\n",
    "\n",
    "### Defining a simple model\n",
    "\n",
    "In this example, we want to find the resonance of some devices. We expect to find its resonance somewhere in the low 6 GHz range, but manufacturing imperfections make it impossible to know exactly without inspection.\n",
    "\n",
    "We first create `freq`: a {class}`.Settable` with a {class}`~qcodes.parameters.Parameter` to represent the frequency of the signal probing the resonator, followed by a custom {class}`.Gettable` to mock (i.e. emulate) the resonator.\n",
    "The {class}`!Resonator` will return a Lorentzian shape centered on the resonant frequency. Our {class}`.Gettable` will read the setpoints from `freq`, in this case a 1D array.\n",
    "\n",
    "```{note}\n",
    "The `Resonator` {class}`.Gettable` has a new attribute `.batched` set to `True`. This property informs the {class}`.MeasurementControl` that it will not be in charge of iterating over the setpoints, instead the `Resonator` manages its own data acquisition. Similarly, the `freq` {class}`.Settable` must have a `.batched=True` so that the {class}`.MeasurementControl` hands over the setpoints correctly.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5c4c4fe7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note that in an actual experimental setup `freq` will be a QCoDeS parameter\n",
    "# contained in a QCoDeS Instrument\n",
    "freq = ManualParameter(name=\"frequency\", unit=\"Hz\", label=\"Frequency\")\n",
    "freq.batched = True  # Tells meas_ctrl that the setpoints are to be passed in batches\n",
    "\n",
    "\n",
    "def lorenz(amplitude: float, fwhm: float, x: int, x_0: float):\n",
    "    \"\"\"Model of the frequency response.\"\"\"\n",
    "    return amplitude * ((fwhm / 2.0) ** 2) / ((x - x_0) ** 2 + (fwhm / 2.0) ** 2)\n",
    "\n",
    "\n",
    "class Resonator:\n",
    "    \"\"\"\n",
    "    Note that the Resonator is a valid Gettable not because of inheritance,\n",
    "    but because it has the expected attributes and methods.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self) -> None:\n",
    "        self.name = \"resonator\"\n",
    "        self.unit = \"V\"\n",
    "        self.label = \"Amplitude\"\n",
    "        self.batched = True\n",
    "        self.delay = 0.0\n",
    "\n",
    "        # hidden variables specifying the resonance\n",
    "        self._test_resonance = 6.0001048e9  # in Hz\n",
    "        self._test_width = 300  # FWHM in Hz\n",
    "\n",
    "    def get(self) -> float:\n",
    "        \"\"\"Emulation of the frequency response.\"\"\"\n",
    "        time.sleep(self.delay)\n",
    "        _lorenz = lambda x: lorenz(1, self._test_width, x, self._test_resonance)\n",
    "        return 1 - np.array(list(map(_lorenz, freq())))\n",
    "\n",
    "    def prepare(self) -> None:\n",
    "        \"\"\"Adding this print statement is not required but added for illustrative\n",
    "        purposes.\"\"\"\n",
    "        print(\"\\nPrepared Resonator...\")\n",
    "\n",
    "    def finish(self) -> None:\n",
    "        \"\"\"Adding this print statement is not required but added for illustrative\n",
    "        purposes.\"\"\"\n",
    "        print(\"\\nFinished Resonator...\")\n",
    "\n",
    "\n",
    "gettable_res = Resonator()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3eba38ef",
   "metadata": {},
   "source": [
    "### Running the experiment\n",
    "\n",
    "Just like our Iterative 1D loop, our complete experiment is expressed in just four lines of code.\n",
    "\n",
    "The main difference is defining the `batched` property of our {class}`.Gettable` to `True`.\n",
    "The {class}`.MeasurementControl` will detect these settings and run in the appropriate mode.\n",
    "\n",
    "At this point the `freq` parameter is empty:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "98c44659",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "print(freq())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fbdd9d47",
   "metadata": {
    "mystnb": {
     "remove-output": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting batched measurement...\n",
      "Iterative settable(s) [outer loop(s)]:\n",
      "\t --- (None) --- \n",
      "Batched settable(s):\n",
      "\t frequency \n",
      "Batch size limit: 2000\n",
      "\n",
      "\n",
      "Prepared Resonator...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9a3b196789e1443888568a8f9aa76670",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Completed:   0%|           [ elapsed time: 00:00 | time left: ? ] it"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Finished Resonator...\n"
     ]
    }
   ],
   "source": [
    "meas_ctrl.settables(freq)\n",
    "meas_ctrl.setpoints(np.arange(6.0001e9, 6.00011e9, 5))\n",
    "meas_ctrl.gettables(gettable_res)\n",
    "dset = meas_ctrl.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ecc5e58d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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qtV8bGRmZkJBw/Phx4c1/+umnIUOGOFzb3NxsMBiEARgMhlOnTgnlTt1dG3Bwnwrgj5J02hAF22bjqGPet+MfBpPj8nzJNbW1Y+J3IPjqq69Onz4tfF1aWjpx4sTLLrtMo9Hk5ubu2bMnJCSktbU1PT1dWCTK8/zRo0eTk5N1Ol1VVVVdXV1dXd3PP/8sXD59+vTJkycT0bx587Zt21ZeXt7e3h4XF5eTk+NwbWtr65YtW3ieVygUFoslPT19woQJwpu4vDbgMDzvvLg8gM2ePbu4uFjuUQBIYNm/vt5/skGo/WUYpqMOl+e/firXd80fqvSmWX/a3dk1ibdP/IaolT+tXuCjDwV/w3FcU1NTWFiYd5W3TU1NarU6JCTE5as8z7e0tPA8HxYWJp5nduda/4e5XwA/JUz/8k7Tv499fMR3H/r4xyXkPO1LNHV4tO8+FPwNy7I6nc7rpSwRERE9hCLDMOHh4REREc6B2uu1/g+ZCuCnknRatcLFb6hPV9SUnDVeau0r4IlV0J9vyvTdhwIEDWQqgP/KGBop3DSKtlNlzDab7z7ReunNOyd+eU6pUKDVA4A7kKkA/uuvN08Retd3aajEMT5qUlilN4naJ3V+IkPT0OMXwD3IVAD/laTTskS8vSVr5z989Ej18Y9LeJ66TPwSsSwmfgHchUwF8GtqpYLE+5MTEfEVda53teyjMw32t+XtPX5VCiUmfgHchEwF8GtThrtYFWryzTZYJquLtw1VO271BQDdQaYC+LUQpcK5TMnQavbFZzWarB17j9sxjMkSkC3iAGSBTAXwa2vyMvqnTKmzQInvujyVzxoeJe0HAQQxZCqAX0vSaRX9UqbkokCJJ5ZlUKAE4D5kKoC/UykVDHU2VBJuIhn+yBmJOz8cPWvs/PLSylQiBgVKAO5DpgL4uynDdTzfde6XJ56X+JeXd9WSUOWqkRMAdAe/MAD+7rWlmZcqhzr/wfMSlw4576bBMBQbppb2UwCCGzIVwN8l6bQKhuEZ5tIqVYasnMQdCts5zqHol2HZMYmR0n4KQHBDpgIEAAXLMjwvvpfkeSlLf6v0JgXLdM7+MsQQcbxKwf5pSYZUHwEwEMiZqUaj8dSpU5WVlVar1Z3z6+rqysvLz5w509LS4uuxAfiVMDXrWPrLS1n6+/jHJVZOmEy2d/zlQ9VonQ/gGS+3x+u7oqKisrIylmU5jgsJCcnNzU1OTu7uZKPRuHPnzosXLwrnsyw7fvz4yy67jHHaWhIgKIWqlQaTtSNWhcyTtPT36FkjOT2f1arQQQnAM/JkamlpaVlZWU5OTmZmptls3rFjR0FBwbJly8LCwlyev2vXrvr6+oULFw4bNqy9vf3rr78uLS2NiYlJT0/v55EDyGJ0Yvg5g4mIJ/vfIyUt/eXF7yxgGDxMBfCUPHO/JSUl8fHxU6ZMYVlWq9XOmTPHarWWlZW5PNlms124cGHEiBHJyckMw6hUKuEO9dy5c/08bAC5rMnLYJxKfzUqyeZpQlRs52oaRuiAyBDhYSqAp2TIVIPB0NzcnJKSYj8SFRUVFRVVVVXl8nyFQhESEmIT7cNss9l4ng8NDfX1UAH8RJJOq1awDqW/JotknfTbLFxnB6WOdaoqJYuHqQCekiFTjUYjEUVFdWkiqtPpDAZDd5dMmzatoqLi+++/b25ubmho2L17t0ajycjAX6JhQGEYnmeE0l+GiMhqI0lKf6v0JouN6wxUhlgi4lkGiwIAPCbD81SLxUJEanWXteRqtVo47lJGRobNZtu3b19xcTERhYeHL1q0KDISD3tgAAlVsxYb01mTS8QQT/zvPj+6dnlOH9951RelHWnKdEz8Ek9aNTIVwGOy1f3yzl1bunfw4MHvvvtu4sSJycnJFoultLR006ZN1113XVxcnPPJ+fn5whfLly+XZqwAfiBUrdSbrMR3KVP6qUaCdWU/X2ju/NLe6ZdH0S+AF2TI1JCQECIym7tsAGk2mzUa1w9vGhsbv/vuuwkTJsycOVM4kpyc/OGHH/73v//9xS9+4Xw+ohSC0ujE8HNGk0OZkiSbk5us7c7bpqLoF8ALMkzvREdHE5Fe32VpnV6vF447q6+v53k+MTHRfkSpVA4aNKiurs6n4wTwK2vyMpx3pzGZpchUs02Y70XRL0AfyZCpERER0dHR5eXl9unf2trapqYmcc8Hi8Vib64k3L+KM5jneYPB0N19LUBQStJpVQqFuEaJeEaaX2Gm8z+dTZTUKnRQAvCGPGUIWVlZer2+qKiosbHxwoULu3bt0mq14gYO69at27x5s/B1QkKCTqcrKSk5fvx4Y2NjfX397t27jUbj6NGjZRk8gFyEDoXi5TQalQRvG6JiHTr9hqrxMBXAG/LUKKWlpTU3Nx8+fPj48eNEpNPpFi5cKDxndcay7MKFC4uLi/fu3SscUSqVWVlZU6dO7b8RA/iBVgvXeZ/a8fiz0WSrNrYl6by/p6zSmxpN7V0X0pDJIvFGcgADhGx1v5MnT54wYYJer1cqlTExMQ6v3nXXXeJvdTrdokWLzGZzY2OjUqmMjIxUKPD3aBhwfLGcBgtpACQkW6YSkUqlio+Pd//8kJAQl4tnAAYIYTkNIyyn6Xyk2sflNFhIAyAh/G0UIGCMTgx3Lv012/pU+mtut2EhDYBUkKkAAWNNXgZDjEPpr4rp0z2lUtiKXLSQhsVCGgBvIVMBAkaSTqvTKh3uU628rceLetHO8Q4LaXRhaiykAfAOMhUgkKiVjktUpblPFd6RIeJ4tQJ/LAB4Cb88AIGkneO6bB7OkDT3qZ03qcRQO+dBL24AEEOmAgQSjVLBd877dmx02rdN2TrvUy8tpAlR4o8FAC/hlwcgkPA8OeyiWtts9noX1Sq9qa7Z4nCfSjzT64UA4BIyFSCQmG02YhhxmRLH87/7/Kh377bqi1Kuo+12530qMWZbnyaTAQYyZCpAIBHmfh3KlLxu+/DzheaOO1Q7nsfcL4DX8MsDEEikbfsgavhwae4XDR8AvIZMBQgk0rZ9QMMHAGkhUwECidD2QarlNGj4ACAtZCpAgAlVK8VraYh4jcLLzTBClI47p6J7PkBfIFMBAsyw2FCHud/hcWHevdXw2FCHh6kpg8KlGifAAIRMBQgwJy408Qxz6VaVoR+qDd691Y81TUQkXkjzU02jNKMEGJCQqQABRqN0nJ4NYb2csFUrWCykAZAQfn8AAoww9yue/m1osXjRSqlKb2podWyihLlfgL5ApgIEGIZ4h1ZKVo7zopXSqi9K222cQ7NfIjRRAvAeMhUgwJzTt0nSSunnC83ODR/O1pulHS3AgIJMBQgwqfFhDJHDElUvWimZ2+23pJdqlEYlYO4XwHvIVIAAsyYvgxXX/RJ5t0S1Y3GqqEaJZXg0UQLoC2QqQIBJ0mknDYvq+xLVjsWpornfKSkxaKIE0BfIVIDAE6JkHOZ+GZ7z5o26NlFSo4cSQN8gUwECj1CmRHRp7vdsg8drac7p21CgBCCtIMzUwsLCwsJCuUcB4EODo7UOc79DY0M9fZMh0cI076UCpWGevwkAiHnZetufzZ07V+4hAPiWsERV/L33c7/2t+F5LE4F6KMgvE8FCHrC3K94+vd0Xaunb1JZb8LcL4C0kKkAgUeY+xVP/9Y1mT1qT1ilN9U2C+dj7hdAMshUgMDTMfcrmv61crxH7QlFjQk7Ye4XoM+QqQCB55y+jbrO/RLxpy56MP1bXtuCxoQAkvMyU3meb2lp4TivyiIAoG9S48OcW/56VPrbWfRLaEwIICEP6n45jtu/f39RUdHRo0dramo4jmNZNi4ubtKkSTNnzrz88stVKpXvBgoAdmvyMmb/qbBd/HdaL0p/+Y4LBSqW0JgQoI/cylSO4zZu3Pjvf/+7trY2NjZ23Lhx2dnZYWFhLS0tDQ0NR44cKSgoiI6Ovummm5YuXYpkBfC1JJ02PkJz3mgSH/So7UNnwwci4olniKW4SA0aEwL0kVuZ+tBDD1VWVi5atOjqq69OSUlxPqGqqqqgoOCzzz7bvHnz+vXrJR4jADgZnRh+zmgSnocKj0U9mrlNjQ87qxeev3bM/Y5JjPTJQAEGErcy9Zprrpk3b55Wq+3uhKFDh65YseK2227bsmWLdGMDgG49NC9t708X+Y46I2KIeWz+aA8vr7WX/TLEe3Q5ALjkVo3SokWLeghUO5VK9Ytf/KLPQwKA3r2x8wR/qQcS8USvFvzo4eVkL/r19HIAcMmtTG1sbLTZsHANwI+U17b0eS0NiYt+PbocAFxyK1OLiory8vLeeuutiooKH48HANzSxzb6orU0HdBECaDv3HqeOmrUqISEhPXr169fvz49Pf3aa6/Nzc0NC/N4D2QAkIoEbfTRQB9Aam7dp44dO/btt99et27dsmXLLly48Morr1x//fWrV6/+9ttveZ7v/XoAkJrQSknMq7U0aKIEICUPej6kpKTcf//9995774EDB7Zu3VpYWFhQUJCYmLhgwYJrr702MTHRd6MEAAep8WFn9K3UsY6GiKGhsb0XEtoNidac1beiiRKAtDzuTciy7IwZM1avXr1hw4ZHH300KioqPz9/6dKlL7/8si/GBwAuPTQvjaUuW9OUVRnd3JqmSm86ft4omjjGWhoAaXjfQz8iIiIvL+83v/nN1KlTeZ4/ffq0hMMCgJ51rKURPVJtbGt3c2uaVV+UNrbZsJYGQHIezP2K6fX6goKCrVu3lpeXsyybnZ39y1/+UtqRAUAPhMUwvGju1/3lNJ0LaQhraQCk5Vmmtre379+/f+vWrd98843NZhsyZMjdd9997bXXxsXF+Wh8AOCS8DyV4YWNVInIg+U0nQ9Tu8DzVIC+czdTT5w4sXXr1q+++spoNGo0mvnz5y9cuDAzM9OngwOA7vR1axpsSgPgA25l6o4dO/74xz8S0YQJE+69994rr7wyNBTLwwHk1JetabApDYCPuJWpoaGhN99888KFC5OTk309IABwk9db02BTGgAfcavud/r06ffdd587gWq1Wvs8JABwi7Cchog83ZrmoXlpWEgD4AtuZep999334YcftrS09HBOW1vb559/fuutt7r/2Uaj8dSpU5WVle4n8cWLF0+ePFlZWdnaihpFGOi83poGm9IA+Ihbc7+33nrr3/72t3fffXfWrFlZWVljxoyJjY0NCwszmUz19fU//fTTkSNHioqKQkND77nnHjc/uKioqKysjGVZjuNCQkJyc3N7vg+ur6/fuXNnQ0OD/cjixYuHDBni5scBBB/RkpgOnq+l8exCAOiZW5k6Z86cmTNnbt++/Ysvvti9e7fzCSNGjLj33nuvvfZad7ZZJaLS0tKysrKcnJzMzEyz2bxjx46CgoJly5Z115e/ubl548aNWq32uuuuS0hIMJvNFy5cQBN/GOA6ltOQ189T0ZgQQGLurqVRq9WLFy9evHhxTU3NsWPHampqmpubw8PDExISJk6cmJSU5NGnlpSUxMfHT5kyhYi0Wu2cOXPWr19fVlaWnZ3t8vwDBw60t7cvWrQoPDyciFQqlfAFwED20Ly0vT9d5Inx4nnq3p9q7dtf4HkqgFQ87qOUmJjYx3b5BoOhubk5PT3dfiQqKioqKqqqqsplptpstlOnTqWmpgo5ynEcy3rfUhEgaLh8nrp2eY57F5J9IQ3Pu3shAPTMy96EfWE0GokoKipKfFCn09XU1Lg8v6GhwWazxcTE7Nmz58SJE+3t7TExMdOmTRs5cmR/DBfAX/X5eSoaEwJITIZMtVgsRKRWq8UH1Wq1cNyZyWQioiNHjoSHh19xxRVEdOzYsR07dlxzzTUjRoxwPj8/P1/4Yvny5dKOHMCveL3dm3NvQjxPBZCEDJkq8HQzc57nFy9erNFoiCglJeWDDz44fPiwy0xFlMIA0fE8lSd7y19hu7ckXU8dkewbveF5KoDkZHgwGRISQkRms1l80Gw2C3nZ3flJSUn2E9Rq9ZAhQ+rq6jjO7e6mAEHHu+3esDXisE8AACAASURBVNEbgO/IkKnR0dFEpNfrxQf1er1w3OX5DMM41CUpFAry/GYXIJg4P08lNx6pYqM3AN+RIVMjIiKio6PLy8vtiVhbW9vU1CTu+WCxWOzNldRqdVJS0sWLF202m3CE47iamhqdTickK8DAlBrvYol2r09GvbsKANzhTaZyHHf8+PGdO3f+8MMPwhGbzebRLWNWVpZery8qKmpsbLxw4cKuXbu0Wq14dc26des2b95s/3bq1Kmtra27du1qaGhoaGjYvXt3Y2MjdpoDIPtj0a7/AABZeFyj9MMPP/zhD3+oqqoiohtvvHHcuHE2my0vL++mm266+eab3XyTtLS05ubmw4cPHz9+nIh0Ot3ChQuF56YuDRky5Morr9y3b9+pU6eISKlUTp8+XZzBAAPQmryMq14rbjZbPGr70NHwgb+0eWqkRoHNUwEk4VmmGo3Gxx9/PDk5+eGHH96yZYtwUKFQ5ObmFhcXu5+pRDR58uQJEybo9XqlUhkTE+Pw6l133eVwZPTo0ampqQ0NDTzPx8TEKJWyVSwD+IkknXb8kMhvyuvtR9xp+9DR8EFYf8MzxNKEoZHYPBVAEp7N/e7cuZNl2VdffXX69Oni7oAjR448e/asp5+tUqni4+OdA7U7CoUiLi4uPj4egQogOKc3ORzxokbpbL25pwsAwG2eZer58+fT0tKcm9eHhYU1NTVJNyoAcEtqfJjD81QvapRQoAQgFc8yNTIy8uLFi87HT548GRsbK9GQAMBdXmxLjg3JAXzHs0ydPn36mTNnNmzYID546tSpTz/99LLLLpN0YADQOy+2JceG5AC+49mDyTFjxuTl5b3yyisFBQWNjY0ajeapp576+uuvY2Ji0BEQoP950UYfDfQBfMfjYp9HH300LS3t008/rays5Hn+7Nmz8+fP//Wvf+1+qREASKVzd/FL3Hmeigb6AD7CeN3ez2q1WiyW0NBQhmF6P7u/zJ49u7i4WO5RAPSTaqPpqteKm8xWYSI3UqPY+ficnhfGVBtNV/+5qLGtXfjWnUsAwE1u3ae2tLR0F70tLR1TTwqFQqt1a58pAJCKjSOG4YjsDRwYW2/7SnSe0LE41Z1LAMBNbmXqbbfdVltb2/M5GRkZb731lhRDAgB3CZvM2L8V9qXpuefDqi9KG9va7c9T3bkEANzkVqauWLGitbXjAcyJEye2b98+derUcePGabXa8+fP79mzJzIycsmSJb4cJwC40IcaJQ8uAQA3uZWpixYtEr64cOHC2rVr16xZM2vWLPurDzzwwAMPPOBFHyUA6CPUKAH4FY97EyYnJ4sDlYjCw8N/9atfffnll5IODAB6tyYvIyJEZW+lpFQwj81P6/mSh+alRWou/WUaDfQBJORZphoMBpfFSjzPGwwGiYYEAO4SapTUwu8xQ+02/rZ3DlYb27o7v0pvWpF/kIjsPR9QowQgIc8yddSoUT/++OPevXvFB41G40cffTRq1ChJBwYAvRNqlKyiUDSYrL/7/GgP5xtM7c41Sv0xVoABwLOeD7m5uRs3bnz66aczMzPHjh2r0WhqamqKiora29tfe+01Hw0RALrjXHBEPdYceXo+AHjEs0xVKBSvvfbahx9+uH379pKSEiIKDQ2dOnXqihUrUlNTfTNCAOiWc8ER9Vhz5On5AOAR7/soWSwWq9WKPkoAMqo2mha8XmzjyM1WSkITJZPFZuU6fvGjtMqCx65AHyUASXj2PFVMrVaHhYX5VaACDDRJOu17y7Pdb6UkvGTleKFGSaVg1909HYEKIBXPMtVms5m6YTabfTREAOjBGzt/dm6l1N3JnU2USKhRsto4bPQGICHPnqfu2bPnueeec/kSehMCyMKjvkhoogTgU55lalpa2v333y8+UldXt3fvXo1Gk5eXJ+nAAMAtHvVFQhMlAJ/yLFOTk5OTk5MdDq5cufLee+81Go3SjQoA3LUmL8Nhu7ce+iKtyctw2OgNTZQAJOR9jZKdRqO5/vrr169f3/e3AgBPebTdm2ijNzRRApCeBJlKRCqVqqGhQZK3AgCPuNzurceT0UQJwFckyNTKysqPPvpo5MiRfX8rSRQWFhYWFso9CoB+gholAP/h2fPUffv2vfzyy+IjwkIajUbzyiuvSDow782dO1fuIQD0H9QoAfgPzzI1Pj5+zpw54iMajSYpKWnu3Lk6nU7KcQGAe1CjBOA/PMvUYcOG3XLLLfHx8Q7Hm5qaamtr4+LipBsYALjF2xolhtheTgYAT3n2PHX//v2//e1vnY9//vnnzz77rERDAgAPoEYJwH9IU/fb3t7OstK8FQB4BDVKAP7D3bnflpYWnufb2to4jmtubha/ZDAYDh8+7DwhDAD9ADVKAP7D3UxdsmRJS0vH33AXLFjg8CrLsrfffruU4wIA96zJy1jwerHBZBW+jdIqe65Rcv9kAPCUu5m6cuVKi8Vy8uTJ7777bunSpfbjDMOEhoZOmjRp+PDhvhkhAPTCxnW2ReKp2WK70NjW/f6pbZxoy2TO2+2TAcAlD+5TiejEiRNjxowRZyoAyEuo+1WzZOWIGGq38be9c3DbI5cn6RxjtUpvWpF/kIhQ9wvgI54VFqWlpSFQAfyKUPdrFUWjwWR1Wc276otSg6kddb8AvuPWfWpTUxPHcTqdzmq1tra6rhJUKpVhYWGSjg0AeudcykvdVPO6fyYAeMetTL3jjjvq6+v37t1bVFSEPckB/IpzKS91U83r/pkA4B23MnXlypVtbW1ENGbMmEceecTlOYMGDZJyXADgHqGUl4h6reZ1/0wA8A7DB1fh3+zZs4uLi+UeBUC/qja2VRvb7n7/YEOzNSZC/f7yaRlDolye+d0Zw0vbf/ymvE6jVE5JiXr9pszuKoQBwAtofgQQ8GwcvyL/QEOLlRhqaLbc9s7BamOb82lVetOK/APflNcTMW3ttuPnGlH0CyAttzK1qanJ2Bt7RwgA6GdCQa/9257rfns9DQC85m6NUm1tbc/noEYJQC5udvFFs18AX3O3Rqm7JTR2qFECkIubXXzR7BfA11CjBBDwqo0mh23Jdz4+x7n4qNpoctiQ3OVpAOA1z/YkF9TW1u7atau8vNxqtcbFxWVnZ0+dOlXykQGAm9zclhwbkgP4msf3qZs3b/7zn/9ssVg0Go1GozEajTzPZ2dnr169OjQ01EejdB/uU2EAuuO9g3t/7lLxMGfMoLXLc7w7DQC85tl96okTJ15++eXs7Oz7779/xIgRRGQymTZt2vTWW2+9+eabv/3tb30zSAAAgADg2frUoqKi2NjYF198UQhUItJqtUuXLr3lllsKCwt9MDwA6B3qfgH8hGeZarVaR4wYoVKpHI6PHj3aarVKNyoA8EBqvOP2Fd3V/bpzGgB4zbNMnTx58k8//dTU1ORw/Ntvv83KyvL0s41G46lTpyorKz3K45aWFoPBYDabPf04gGC1Ji8jIkRFRMQTEUVqFN31+43UXHrc091pAOA1z56nTps2LTc39+GHH16+fPnYsWM1Gk1NTc3mzZsPHDjw0ksvWSwW4TSVSsUwTM9vVVRUVFZWxrIsx3EhISG5ubnJycm9DqClpeX//b//ZzabZ8yYkZmZ6dHgAYIV6n4B/IRnmVpYWPjZZ58R0VNPPeXw0q233mr/+m9/+9vEiRN7eJ/S0tKysrKcnJzMzEyz2bxjx46CgoJly5b1ugNrUVFRaGgoblIBHLCiv8Wy3f+NlmXIviF5D6cBgHc8y9TU1NS7776719MSExN7PqGkpCQ+Pn7KlClEpNVq58yZs379+rKysuzs7B6uOnHiRHV1dW5u7pYtWzwaNkBwc9nI13mRjJunAYDXPMvUlJSUlJSUPn6kwWBobm5OT0+3H4mKioqKiqqqquohU00m0759+2bMmOEPq2AB/ArqfgH8hAx7vRmNRiKKiuqyv6NOpzMYDD1cJSzjGTdunG8HBxCAUPcL4Cc87k1YW1v7ySef/Pzzz3q9XtyDafTo0U8//bQ77yCUMqnVavFBtVptL3FyVl5efubMmaVLl3o6WoCBYE1exoLXiw2mjvr5KK2yu7pfd04DAK95lql1dXV333230WgcN27c4MGDxS/Fx8d79Fbu90Q0m81FRUVTp07V6XTunJ+fny98sXz5co+GBBCgknTa95Zn/2n7DwdONWjUivTBOhvn4vfLxtF7y7Pvfv9gQ7M1NkK9dvk0NNAHkJZnmbpz506TyfT+++8PHz7c648MCQkhIofaXbPZrNG4/vU+dOgQESUkJFRXV1Pn1HFTU1N1dfWgQYOcG1AgSmGgqdKbVuQfICJiqM1q23+qfsHrxdseuTxJpxGfs+gvRURkMLUTQ/XNltveOehwDgD0kWeZajAY0tLS+hKoRBQdHU1Eer1efFCv1wvHnbW0tJhMpg0bNogPfv/9999///2NN96IfVsBHAp6yVVNrzvnAEAfeZapEydO3LZtm9Vqdb47dF9ERER0dHR5efm0adOE1hC1tbVNTU3jx4+3n2OxWBiGET5l9uzZ06dPt7+k1+u3bds2efLkcePGRUREeD0MgKDhXNBLTjW97pwDAH3kWd3vjBkzsrOzX3jhhdra2t7P7l5WVpZery8qKmpsbLxw4cKuXbu0Wq14dc26des2b94sfB0aGqoTCQ8PJyKNRqPT6VhWhrplAH/jXNBLTjW97pwDAH3k2X0qwzBLly793//937y8vNDQUPHdanp6+ksvveTm+6SlpTU3Nx8+fPj48eNEpNPpFi5cKDxnBQBPCQW9RNRDTa875wBAH3m2J/mZM2fuuusuhUKRnZ0dFRUlbuo7ZMgQT9e6WK1WvV6vVCpjYmI8urAH2JMcBqZqY1u1sU2o6Y2JUL+/fFrGkCiHc747Y3hp+4/flNdplMopKVGv35SJul8AaXlc96tUKv/zn//Exsb2/bNVKpWnK3AAwCUbx6/IPyDU9Da4qukVaoMNpnYipq3ddvxcIxroA0jOm/1TJQlUAJCQy16+Hp0AAH3nWaZmZWVVVla2tqJWEMC/9NrLF81+AfqBZ5k6ZcqUa6+99sknnywrK2tpabGIeLSvOABIq9devmj2C9APPKtR2rVr13PPPefypYyMjLfeekuaQfUBapRgYKo2mhx6+RY8doW4BKnXEwCg7zzL1IqKir1797p8KSEh4ZprrpFoVN5DpsKAVW1s+93nR/f8WDd3XPyflmQ452WvJwBAH8mwfyoA+IKN43meISKe57vpod/LCQDQR5L1IWpoaJDqrQDAU0KL/L0/1xJDe36qXfB6cbWxzaMTAKDv+pqpLS0tGzZs+PWvf/3MM89IMiAA8ALW0gD4A4/3JBfwPH/kyJEtW7bs3bvXbDbHxcVhw3AAGWEtDYA/8DhTa2pqtm7dum3btpqaGiIaNWrUww8/PGnSJHGfQgDoZ6nxYWf1XTLSeS1NzycAQN+5O/drNpsLCgoefvjhpUuXvv/++8nJyb///e8zMzNHjhyZmZmJQAWQ15q8jCjtpT0tXPbQ7/kEAOg7tzL14MGD119//erVq/V6/b333vvZZ5+9+uqr8+fPV6vVvh4fALgjSad9b3l2TJiKeIoJV6+7O8dhqYz4hFhXJwBA37mVqRcvXmxpaUlPT//Nb35z8803Dxo0yNfDAgCPCC3yG1qs9h76znW/9hPqXZ0AAH3nVqZOnTp1yZIlVVVV999//69+9au1a9dWV1f7emQA4D7U/QL4A7dqlBITEx955JEHHnigqKho69at+fn577333sSJE2tra6OiHPdoBID+h7pfAH/gQd2vSqWaN2/evHnzamtrhdLf8+fP19bWms3m+fPnz5w5U6VS9f4uAOADqPsF8Aee9fsV43n+6NGjW7Zs2bNnT1tb25QpU9544w1pB+cF9PuFgQk99AH8gfeZatfa2rpr166KiooHH3xQkjH1BTIVBiz00AeQnQT9fkNDQ6+77jp/CFSAgczG8X/8xURi6OSF5ic+PVZtNIlfrdKb0EMfwNckuE/1K7hPhYFJaJFPRPbi3iitatsjlyfpNL2+CgBSkWxfGgCQkbBUprvVMj2/CgBSQaYCBAPnpTIkWi3T86sAIBVkKkAwSI0Pcz5oXy3T86sAIJUgzNTCwsLCwkK5RwHQr4QW+d11ye/5VQCQCmqUAIKE0L+3u9UyPb8KAJIIwvtUgIEpSafpYbWMjeOf/OzYnh/r5oyNe/GGCQhUAF/AfSpAkBAWzPSwlgYLaQB8DfepAEGih51nsCkNQP9ApgIEiR52nsGmNAD9A5kKECScF8z0sJYGC2kAfAGZChAkhAUz9m+d19K4fAkAJIRMBQgSSTrte8uzY8JUxFNMuHrd3Tn24l7xS7FdXwIACSFTAYJEld60Iv9AQ4uVGGpottz2zkFhTarDS/VdXwIACSFTAQAApIFMBQgSWEsDIDtkKkCQwFoaANkhUwGCBNbSAMgOmQoQJLCWBkB2yFSAIIG1NACyQ6YCBAmspQGQHTIVIEig7hdAdshUgCCBul8A2SFTAYIE6n4BZIdMBQAAkAYyFSBIYC0NgOyQqQBBQlgwkzMyhnjSqBTpg3U2jhdesnGEtTQA/YDheV7uMUhp9uzZxcXFco8CQAZVetOivxQRkb3EN0qr2vbI5TaOd3k8SYdYBZCYUu4BAIA0HBbMUOeaGZ5nXB5fuzynfwcIEPzkzFSj0VhXV6dUKgcPHqxSqXo+uaWlpaGhwWKxREZGDho0iGGY/hkkQKBwXjBD3a+ZwVoaAF+QLVOLiorKyspYluU4LiQkJDc3Nzk52eWZzc3N27dvr62ttR+JiYmZM2dOQkJCfw0WIACkxoed1Tsm5aiEcJ7nXR7vr3EBDCDy1CiVlpaWlZXl5OSsXLnyzjvvjImJKSgoaGlx8bdsImpra1OpVLm5ubfffvtdd911zTXXtLS0bN261WKx9POwAfyZUNzrXN/b3XE5xggQ5OTJ1JKSkvj4+ClTprAsq9Vq58yZY7Vay8rKXJ4cGxt7/fXXp6WlhYWFqdXqESNGTJs2ra2traqqqp+HDeDPknTabY9cvu2Ry+eMGUQ8zR0bX/DYFfERmu6Oyz1egCAkQ6YaDIbm5uaUlBT7kaioqKioqO4y0vnRaXh4OBFxHOezMQIEpCSdxsbxPM8QEc/zorU0/JOfHdvzY92csXEv3jABgQrgIzJkqtFoJKKoqCjxQZ1OZzAY3HyHkydPsiyblJQk/eAAApmwnGbvz7XE0J6fahe8XlxtbHN5UO6RAgQnGTJVeA6qVqvFB9VqtZvPR0+ePHny5MnJkyeHhTm2MAUY4FzuP4NNaQD6jWx1v971mjh37tzu3btTUlKmTZvW3Tn5+fnCF8uXL/dycACByc39Z7CQBsBHZMjUkJAQIjKbzeKDZrNZo+nlGU91dfW2bduSkpKuuuqqHtanIkphwHJeTuNyLQ0W0gD4iAxzv9HR0USk1+vFB/V6vXC8OzU1NVu2bImPj1+wYIFCofDtEAECk8te+WigD9BvZMjUiIiI6Ojo8vJy+/RvbW1tU1OTuOeDxWKxWq32by9cuLB58+ZBgwYtWLBAqUQ/RQDXhGUzLtfSYCENQD+QJ5+ysrJ27txZVFQ0efJkk8lUWFio1WrT09PtJ6xbty4mJuaGG24gosbGxs2bN/M8P3LkyJMnT9rPiY+Pj42NlWH0AH6su7U0zgcBQHLyZGpaWlpzc/Phw4ePHz9ORDqdbuHChcJzVmeNjY1CSfB///tf8fEZM2YgUwHEhGUzBlO7fdmMfV8ah4PYlAbAF+Tc681qter1eqVSGRMTI9V7Yq83GMjueO/g3p9rxUfmjBnE84zzQWxKA+ALcj6bVKlU8fHxMg4AIMhgLQ2AvOTp9wsAvpAa79gIZVRCuMuD/TUigIEFmQoQPLCWBkBeyFSA4JGk0763PDsmTEU8xYSr192dI6ylsR+M7Two90gBghMyFSB4VOlNK/IPNLRYiaGGZstt7xwUeujbD9Z3HpR7pADBCZkKEDzQQx9AXuhJBBA8UPcLIC/cpwIED9T9AsgLmQoQPFD3CyAvZCpA8EAPfQB5IVMBgkq1sc1k5Yjo61P1j3xUUm00VelN1ca2Y1VGIjpWZbjQiKJfAF+Rs9+vL6DfLwxkQg99IrIX+kaEqBiGYxnGfiRKq0IPfQAfwX0qQPAQls2IV840ma2NbTaspQHoH8hUgODhvJaGXM1DYS0NgI8gUwGCh/OyGWJcnIa1NAA+gkwFCB7CshnxyplIjSJSo8RaGoD+gUwFCB7CshlxG/0PVk5fuyJnbFIkEa9RKmaOisVaGgDfQW9CgKBi4/gV+QcMpnahjf7Nbx9kGK6xzUbEtLXbjp9rtHFyDxEgeOE+FSCoOHTMF+p+7d+i6BfAp5CpAEHFsfTXqe4XRb8AvoNMBQgqjqW/TnW/KPoF8B1kKkBQceiYL9T92r9F0S+ATyFTAYJKkk7rXPcrfBsbrl53dw6KfgF8B/1+AYKK0PLXod+vvUwJzX4BfAr3qQBBBXW/ADIKwkwtLCwsLCyUexQA8kDdL4CMgrDnw9y5c+UeAoBsUuPDzupFqYm6X4B+FIT3qQAD2Zq8jIgQFVHHHWp4CBsRcumvzpEaBep+AXwHmQoQVGwcMQxHZL9DZXniiXjihSMMehMC+A4yFSCorPqiVFyU1GxubzbbiBhiiHhqbGtHjRKA7yBTAYIKapQAZIRMBQgq6E0IICNkKkBQEWqUOu5OeWJZClMr7K+iRgnAp5CpAEFFqFFSs0REPEOcjW+xtKNGCaB/IFMBgopQo2S1V/4yDBGDGiWA/oFMBQgqXWqUXDXzRo0SgO8gUwGCSpcaJacCJUKNEoAvIVMBgoqwf2pnKyXe4VYV+6cC+BQyFSDY2DhqNluIiBfuU0WxygXX3o4A/gaZChBUknTa8UMieaE+SShNYshe9zthaCT2JAfwHWQqQLA5pzd1fHXpprSj7vdsvVmeMQEMDMhUgGCTGh/WEaZoogTQv5CpAMHmoXlpLPEkTPiKnp8yxD82f7RswwIYAJCpAMHmjZ0nujxP7XyYyhO9WvCj3KMDCGbIVIBgc6ntg30KmBG+ZtDwAcCnkKkAwQbPUwHkgkwFCDZ4ngogF2QqQLDB81QAuSBTAYJNeW0LTx03qkSE56kA/QaZChBsBkdrGZ4nnifGsRPhsNhQWYYEMEAo5R6AB4xGY11dnVKpHDx4sEqlkns4AH6KIZ6Yzla/vKhSieeJbLINC2AACJhMLSoqKisrY1mW47iQkJDc3Nzk5GS5BwXgj87p23gihidi7M9TGWFOCr0JAXwqMOZ+S0tLy8rKcnJyVq5ceeedd8bExBQUFLS0tPR+JcDAkxofJlQodbr0PBVraQB8KjAytaSkJD4+fsqUKSzLarXaOXPmWK3WsrIyuccF4I/W5GUwxHRsnip6osoSj81TAXwqADLVYDA0NzenpKTYj0RFRUVFRVVVVck3KAD/laTThqqVHRO/orU0YVoVNnoD8KkAyFSj0UhEUVFR4oM6nc5gMMg0IgB/Z7JaO+5TiTrmfjm+pa1d3lEBBL0AqFGyWCxEpFarxQfVarVw3Nns2bPF3xYXF/tubAD+ieeZzueplwqUeOdehQAgqQDIVAHPO6606w5CFEAXqjS0WohnOmaAeSKeosKwAg3AtwJg7jckJISIzOYuawDMZrNGgydDAK6NS4ro/NK+RJVPv3QQAHwiADI1OjqaiPR6vfigXq8XjgOAs9eWZkZqVErFpcneSK3qzzdlyjgkgIEgADI1IiIiOjq6vLzcPv1bW1vb1NSEng8A3UnSaXc8ekXxb6+cMyZuWEzo3DHxOx+fg6JfAF8LjOepWVlZO3fuLCoqmjx5sslkKiws1Gq16enpco8LwH8l6TREtHZ5jtwDARhAAiNT09LSmpubDx8+fPz4cSLS6XQLFy4UnrMCAAD4icDIVCKaPHnyhAkT9Hq9UqmMiYmRezgAAACOAiZTiUilUsXHx8s9CgAAANcCoEYJAAAgICBTAQAApIFMBQAAkAYyFQAAQBrIVEcOLfgBACA49MMf78hUAAAAaSBToU/y8/PlHkLAwM/KffhZuQ8/K7+CTA0MhYWFcg8hkODH5T78rNyHn5X7BuzPCpkKAAAgDcb9vb4DAiqMAADAd4qLi3t4NdgyFQAAQC6Y+wUAAJAGMhUAAEAayFQAAABpIFMBAACkgUwFAACQBjIVAABAGshUAAAAaSBTAQAApIFMBQAAkAYyFQAAQBrIVAAAAGkgUwEAAKSBTAUAAJAGMhUAAEAayFQAAABpIFMBAACkgUwFAACQBjIVAABAGshUAAAAaSBTAQAApIFMBQAAkAYyFQAAQBrIVAAAAGkgUwEAAKSBTAUAAJCGUu4BAAQYjuPOnj3rcFCpVA4ZMkSW8fiPXbt2nT179s4773Q4XllZeebMmba2tqioqNGjR+t0OvtLwg9Tp9NFRUWJL2lsbNTr9YMHD1apVD18otVqfe211xYvXjxu3Djp/j0AvIdMBfCMwWC49dZbHQ7GxcV9/vnnsozHTxiNxldfffWxxx4THzx06NCbb755+vRp+xGWZbOzs++7776RI0dS5w/zV7/61QMPPCC+cOPGjf/85z/ff/994bTuqFSqkJCQP//5z//85z8ZhpH0XwjAG8hUAG/Mnz9/yZIl9m/VarWMg/EHH330UVhY2JVXXmk/snv37ueffz4lJeXFF1+cPHlyaGhofX39wYMHP/rooz179vQclu675ZZbfvnLX+7bt2/27NmSvCFAXyBTAbwRGxs7fvx4h4NGo5HjuOjo6IsXL544cSIhIWHUqFFExPP8iRMnampqIiMjx48f7zCf2d7eXlpa2traOnbs2NjY2Pr6+pCQkPDwcCJqaWlpa2uLjY21n2wymZqbmwcNGiS+LTt//nx5eblSqRw/onymbQAACZJJREFUfnxERITL8fz888+DBg1KS0tTKBQOw66pqSkvL2dZdvjw4UlJSURUV1cXEhIifivh3Ww2W0xMjPNPw2q1btq06Re/+AXLsvaT/+///m/YsGH/+Mc/tFqtcDAuLm7hwoVXX331mTNn3PkhCywWi9FodDioVCqjo6OF95w8efIXX3yBTAV/gEwFkMwf/vAHo9GYk5Ozbt06nucXLFjw1FNPnT59+rnnnisvL1er1RaLZdCgQc8///zEiROFS86cOfPEE0+cO3dOo9FYrdYHH3wwPz9/3rx5jz76KBG9++67BQUFmzdvtn/Epk2b3nzzzW3bttlD94UXXiguLlapVDabTaVS3X///Xl5eeLxzJ49+7333iMijuPGjx//8ssv28OysbFxzZo1+/btY1lWoVBYrdYlS5Y88sgjf/zjH+vr69etW2f/XKvVesstt+Tk5DzzzDPO/+KHDx82Go0zZsywH9mxY4fJZFqxYoU9UO2USqVHN6kHDhx46qmnHA6OGjUqPz9f+HrmzJl//etfDQaDw0NZgP6HTAXwhsFgOHHihP3bhISEyMhIIjp9+jTDMO+++25ycnJjY6PRaHz00UdjY2OFR4N1dXWrV6/+3e9+t379+sjISI7jVq1aZTab33777XHjxp06dWrVqlXNzc3uD+P3v//9Tz/99NJLL+Xk5Fgslnfeeef1119PSUmZMmWKcMLp06dDQ0M/+OCDwYMH79u375lnnlm/fv0999wjvLpq1aoff/zxySefnDdvnlqtrqioEMqvbrjhhqeffvrYsWP27N+7d6/RaFy8eLHLYRw5ckSpVI4ePdp+5NixY0Q0bdo0d/4tjEZjeXm5+Iher7d/PX36dPHfKg4dOvSHP/whMzPTfmT8+PE8z5eUlMyZM8edjwPwHWQqgDe2b9++fft2+7dPPfXUggULiKi9vf3ZZ58dOnQoEcXFxa1bt66hoeGtt94aPHgwEQ0aNOiZZ57Jy8v76quvlixZcujQoYqKiqefflooW01NTX3ggQec78m68/333x88ePCpp54SbhA1Gs3999//zTfffP755/ZM5Tju2WefFWaPL7/88qysrG+//VZ4qaSkpKSk5J577lm4cKFwZOTIkcId5KxZswYNGrRhwwZ7pm7cuHH48OGTJk1yOZIzZ87ExMSI57QbGhocZo8rKipOnTolfB0eHp6Tk2N/adu2bdu2bevuX1OlUtlLhU+fPv3KK69Mmzbtf/7nf+wnCPPVlZWVvf7EAHwNmQrgjWuuuWbp0qX2bxMSEoQv4uLihEAVlJSUREZGHj169OjRo/aDoaGhQinsDz/8QF1v5rKzs90fw5EjR4jIaDSKA8n+5oLBgweLH8cOHjx4//79wtfCkObOnev8zgqF4rrrrvvggw8efvjhyMjIqqqqI0eOPPjgg92NxGg0Ojx8ZVmW4zjxkb17977zzjvC1yNGjBBn6lVXXXXjjTeKT96+fftnn33m8CkNDQ1PPPFEYmLi6tWrxU+FhRkC52euAP0PmQrgjaioqLS0NJfHxd82NjaazWaHeBg6dKhQX9PS0uJwSUhIiPMDyO40NTUR0VdffeWwjES8Utbh3RQKhc1mE74WPl2cuGLXXXfdv//97+3bty9dunTjxo1qtfqaa67pbiQhISGNjY3iI3FxcVartaGhwV7TdMcdd9xxxx1EtHz5cvsYBDExMWPHjhUfOXz4sMNHmM3mJ598sr29/aWXXgoNDXV4SRhDd8MD6DfIVAAfioyMDAsLs9+fOb9KRA0NDYMGDRKOtLW1mUwm+wlKpbK9vV18iZCj4sufe+65YcOGeTE2IcsvXrw4fPhw51fj4uIuu+yyjRs33nDDDdu2bZs7d67wcS7FxsaePHlSfGTKlCk7d+7cv3//okWLvBibA57nV69effr06b/97W/x8fEOrwp3qC4LkgH6GXoTAvjQ1KlT6+vr7Y8wHWRkZBDRvn377EeKiorEJ8THx7e0tNTX19uPiG/gpk6dSkQ7duzwbmxZWVlEJH4q7OCGG26orKz8y1/+YjAYrr/++h7eKj093Wg01tXV2Y/Mnz8/JibmvffeEw/ea3//+9+Li4uff/55cRmUnRDnzkubAPof7lMBfOj666/fuHHj888/f999902ePFmpVJ47d2737t3z5s2bOHHipEmTxo0b9/bbb0dHR0+aNOmHH374xz/+Ia70ycnJYVn2lVdeuffee20222effVZRUWF/dezYsfPmzfvPf/7DMExubq5Op7t48eKBAwfCw8NvuOGGXsc2ZsyYK664Yv369SEhIbm5uRqN5uTJk7W1tdddd51wQlZW1tChQ7/88ssRI0YI8d8d4THw999/b6+81Wg0zz333BNPPHHXXXctW7YsMzMzLCzMYDCUlJRUV1cLFVtu2r179/r16xctWhQTE/Pjjz/a3z8lJUX4+tixY5GRkWPGjHH/PQF8BJkK4EOhoaF/+ctfXnnllTVr1vA8T0QMw4wdO1ZYQsowzAsvvLBq1aqnn36aiDQazRNPPPHGG2/YLx82bNhDDz3017/+VbiXveKKK26++ea3337bfsKqVatiY2M//PDDtWvXCkcGDx583333uTm8Z5555o033li7du27775LRCqV6vbbb7e/yjDM4sWL33rrrZ5vUoVxTpo0aefOneLVLJMnT/7nP//5j3/84+9//7v9AWp4ePj8+fOFB6tuEqqFN2/eLF5RY1+fynHc7t27FyxY4NzLAqD/McLvOQD4lNForKqqUqvViYmJDiWyRFRZWdna2pqSkqLVahctWmTv+SBobW09c+ZMVFRUYmKiyzc3m82VlZU2my0uLs7+aNZ9LS0tlZWVKpVqyJAhDuU/b7755oYNG7788kuhxUQP9u3b9/vf//6TTz5xLnpqbW09d+6c2WzW6XRDhgyx91qSxNdff71q1aoPPvhAWFEDIC/cpwL0B51OJ96PxYHLKiG70NBQh7JYByEhIS4fNLopLCwsPT3d+XhdXd2WLVuuvvrqXgOViGbNmpWenr5u3bpHHnnE4aXQ0FCXNdKSyM/Pz8vLQ6CCn0CmAoCj8+fPP/fcc2fOnFEqlc57t3VH6Gjoy3E5am9vf+yxx+wPVgFkh7lfAP/y6aefDh8+3M2ufj5iNBo3bdoUERExe/ZsrFEBcB8yFQAAQBpYnwoAACANZCoAAIA0kKkAAADSQKYCAABIA5kKAAAgDWQqAACANJCpAAAA0kCmAgAASAOZCgAAIA1kKgAAgDT+P16cl0gDjk3cAAAAAElFTkSuQmCC",
      "text/plain": [
       "<quantify_core.visualization.pyqt_plotmon.QtPlotObjForJupyter at 0x7f7601b17820>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotmon.main_QtPlot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f04a96a0",
   "metadata": {},
   "source": [
    "As expected, we find a Lorentzian spike in the readout at the resonant frequency, finding the peak of which is trivial.\n",
    "\n",
    "#### Memory-limited Settables/Gettables\n",
    "\n",
    "Instruments (either physical or virtual) operating in `batched` mode have an upper limit on how many datapoints can be processed at once.\n",
    "When an experiment is comprised of more datapoints than the instrument can handle, the {class}`.MeasurementControl` takes care of fulfilling the measurement of all the requested setpoints by running and an internal loop.\n",
    "\n",
    "By default the {class}`.MeasurementControl` assumes no limitations and passes all setpoints to the `batched` settable. However, as a best practice, the instrument limitation must be reflected by the `.batch_size` attribute of the `batched` settables. This is illustrated below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "11f48755",
   "metadata": {
    "mystnb": {
     "remove-output": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting batched measurement...\n",
      "Iterative settable(s) [outer loop(s)]:\n",
      "\t --- (None) --- \n",
      "Batched settable(s):\n",
      "\t frequency \n",
      "Batch size limit: 256\n",
      "\n",
      "\n",
      "Prepared Resonator...\n",
      "\n",
      "Prepared Resonator...\n",
      "\n",
      "Prepared Resonator...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "86f21c6ffe3d4c07a59fa36068604ac4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Completed:   0%|           [ elapsed time: 00:00 | time left: ? ] it"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Prepared Resonator...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Prepared Resonator...\n",
      "\n",
      "Prepared Resonator...\n",
      "\n",
      "Prepared Resonator...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Prepared Resonator...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Finished Resonator...\n"
     ]
    }
   ],
   "source": [
    "# Tells meas_ctrl that only 256 datapoints can be processed at once\n",
    "freq.batch_size = 256\n",
    "\n",
    "gettable_res.delay = 0.05  # short delay for plotting\n",
    "meas_ctrl.settables(freq)\n",
    "meas_ctrl.setpoints(np.arange(6.0001e9, 6.00011e9, 5))\n",
    "meas_ctrl.gettables(gettable_res)\n",
    "dset = meas_ctrl.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bd0cbdc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<quantify_core.visualization.pyqt_plotmon.QtPlotObjForJupyter at 0x7f7601b17820>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotmon.main_QtPlot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1a6c5b0",
   "metadata": {},
   "source": [
    "## Software Averaging: T1 Experiment\n",
    "\n",
    "In many cases it is desirable to run an experiment many times and average the result, such as when filtering noise on instruments or measuring probability.\n",
    "For this purpose, the {meth}`.MeasurementControl.run` provides the `soft_avg` argument.\n",
    "If set to *x*, the experiment will run *x* times whilst performing a running average over each setpoint.\n",
    "\n",
    "In this example, we want to find the relaxation time (aka T1) of a Qubit. As before, we define a {class}`.Settable` and {class}`.Gettable`, representing the varying timescales we will probe through and a mock Qubit emulated in software.\n",
    "The mock Qubit returns the expected decay sweep but with a small amount of noise (simulating the variable qubit characteristics). We set the qubit's T1 to 60 ms - obviously in a real experiment we would be trying to determine this, but for this illustration purposes in this tutorial we set it to a known value to verify our fit later on.\n",
    "\n",
    "Note that in this example meas_ctrl is still running in Batched mode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a8fd9739",
   "metadata": {},
   "outputs": [],
   "source": [
    "def decay(t, tau):\n",
    "    \"\"\"T1 experiment decay model.\"\"\"\n",
    "    return np.exp(-t / tau)\n",
    "\n",
    "\n",
    "time_par = ManualParameter(name=\"time\", unit=\"s\", label=\"Measurement Time\")\n",
    "# Tells meas_ctrl that the setpoints are to be passed in batches\n",
    "time_par.batched = True\n",
    "\n",
    "\n",
    "class MockQubit:\n",
    "    \"\"\"A mock qubit.\"\"\"\n",
    "\n",
    "    def __init__(self):\n",
    "        self.name = \"qubit\"\n",
    "        self.unit = \"%\"\n",
    "        self.label = \"High V\"\n",
    "        self.batched = True\n",
    "\n",
    "        self.delay = 0.01  # sleep time in secs\n",
    "        self.test_relaxation_time = 60e-6\n",
    "\n",
    "    def get(self):\n",
    "        \"\"\"Adds a delay to be able to appreciate the data acquisition.\"\"\"\n",
    "        time.sleep(self.delay)\n",
    "        rel_time = self.test_relaxation_time\n",
    "        _func = lambda x: decay(x, rel_time) + rng.uniform(-0.1, 0.1)\n",
    "        return np.array(list(map(_func, time_par())))"
   ]
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  {
   "cell_type": "markdown",
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   "source": [
    "We will then sweep through 0 to 300 ms, getting our data from the mock Qubit. Let's first observe what a single run looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a04da706",
   "metadata": {
    "mystnb": "remove-output:true"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting batched measurement...\n",
      "Iterative settable(s) [outer loop(s)]:\n",
      "\t --- (None) --- \n",
      "Batched settable(s):\n",
      "\t time \n",
      "Batch size limit: 300\n",
      "\n"
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       "Completed:   0%|           [ elapsed time: 00:00 | time left: ? ] it"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (dim_0: 300)\n",
       "Coordinates:\n",
       "    x0       (dim_0) float64 0.0 1.003e-06 2.007e-06 ... 0.000299 0.0003\n",
       "Dimensions without coordinates: dim_0\n",
       "Data variables:\n",
       "    y0       (dim_0) float64 0.9972 1.063 1.065 ... -0.04871 0.07343 0.01665\n",
       "Attributes:\n",
       "    tuid:                             20231215-162637-782-b90b85\n",
       "    name:                             noisy\n",
       "    grid_2d:                          False\n",
       "    grid_2d_uniformly_spaced:         False\n",
       "    1d_2_settables_uniformly_spaced:  False</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-c97353dd-1b34-41bf-afc2-c54644a20d17' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c97353dd-1b34-41bf-afc2-c54644a20d17' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>dim_0</span>: 300</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-04590c88-870a-45fc-bdcc-22b1e71f814b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-04590c88-870a-45fc-bdcc-22b1e71f814b' 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>x0</span></div><div class='xr-var-dims'>(dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.003e-06 ... 0.000299 0.0003</div><input id='attrs-08495787-3b8f-468a-b14e-e879c12159e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-08495787-3b8f-468a-b14e-e879c12159e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4db8026e-cc64-488c-83eb-fcdda28a371b' class='xr-var-data-in' type='checkbox'><label for='data-4db8026e-cc64-488c-83eb-fcdda28a371b' 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'><dt><span>name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Measurement Time</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>batched :</span></dt><dd>True</dd></dl></div><div class='xr-var-data'><pre>array([0.00000000e+00, 1.00334448e-06, 2.00668896e-06, 3.01003344e-06,\n",
       "       4.01337793e-06, 5.01672241e-06, 6.02006689e-06, 7.02341137e-06,\n",
       "       8.02675585e-06, 9.03010033e-06, 1.00334448e-05, 1.10367893e-05,\n",
       "       1.20401338e-05, 1.30434783e-05, 1.40468227e-05, 1.50501672e-05,\n",
       "       1.60535117e-05, 1.70568562e-05, 1.80602007e-05, 1.90635452e-05,\n",
       "       2.00668896e-05, 2.10702341e-05, 2.20735786e-05, 2.30769231e-05,\n",
       "       2.40802676e-05, 2.50836120e-05, 2.60869565e-05, 2.70903010e-05,\n",
       "       2.80936455e-05, 2.90969900e-05, 3.01003344e-05, 3.11036789e-05,\n",
       "       3.21070234e-05, 3.31103679e-05, 3.41137124e-05, 3.51170569e-05,\n",
       "       3.61204013e-05, 3.71237458e-05, 3.81270903e-05, 3.91304348e-05,\n",
       "       4.01337793e-05, 4.11371237e-05, 4.21404682e-05, 4.31438127e-05,\n",
       "       4.41471572e-05, 4.51505017e-05, 4.61538462e-05, 4.71571906e-05,\n",
       "       4.81605351e-05, 4.91638796e-05, 5.01672241e-05, 5.11705686e-05,\n",
       "       5.21739130e-05, 5.31772575e-05, 5.41806020e-05, 5.51839465e-05,\n",
       "       5.61872910e-05, 5.71906355e-05, 5.81939799e-05, 5.91973244e-05,\n",
       "       6.02006689e-05, 6.12040134e-05, 6.22073579e-05, 6.32107023e-05,\n",
       "       6.42140468e-05, 6.52173913e-05, 6.62207358e-05, 6.72240803e-05,\n",
       "       6.82274247e-05, 6.92307692e-05, 7.02341137e-05, 7.12374582e-05,\n",
       "       7.22408027e-05, 7.32441472e-05, 7.42474916e-05, 7.52508361e-05,\n",
       "       7.62541806e-05, 7.72575251e-05, 7.82608696e-05, 7.92642140e-05,\n",
       "...\n",
       "       2.20735786e-04, 2.21739130e-04, 2.22742475e-04, 2.23745819e-04,\n",
       "       2.24749164e-04, 2.25752508e-04, 2.26755853e-04, 2.27759197e-04,\n",
       "       2.28762542e-04, 2.29765886e-04, 2.30769231e-04, 2.31772575e-04,\n",
       "       2.32775920e-04, 2.33779264e-04, 2.34782609e-04, 2.35785953e-04,\n",
       "       2.36789298e-04, 2.37792642e-04, 2.38795987e-04, 2.39799331e-04,\n",
       "       2.40802676e-04, 2.41806020e-04, 2.42809365e-04, 2.43812709e-04,\n",
       "       2.44816054e-04, 2.45819398e-04, 2.46822742e-04, 2.47826087e-04,\n",
       "       2.48829431e-04, 2.49832776e-04, 2.50836120e-04, 2.51839465e-04,\n",
       "       2.52842809e-04, 2.53846154e-04, 2.54849498e-04, 2.55852843e-04,\n",
       "       2.56856187e-04, 2.57859532e-04, 2.58862876e-04, 2.59866221e-04,\n",
       "       2.60869565e-04, 2.61872910e-04, 2.62876254e-04, 2.63879599e-04,\n",
       "       2.64882943e-04, 2.65886288e-04, 2.66889632e-04, 2.67892977e-04,\n",
       "       2.68896321e-04, 2.69899666e-04, 2.70903010e-04, 2.71906355e-04,\n",
       "       2.72909699e-04, 2.73913043e-04, 2.74916388e-04, 2.75919732e-04,\n",
       "       2.76923077e-04, 2.77926421e-04, 2.78929766e-04, 2.79933110e-04,\n",
       "       2.80936455e-04, 2.81939799e-04, 2.82943144e-04, 2.83946488e-04,\n",
       "       2.84949833e-04, 2.85953177e-04, 2.86956522e-04, 2.87959866e-04,\n",
       "       2.88963211e-04, 2.89966555e-04, 2.90969900e-04, 2.91973244e-04,\n",
       "       2.92976589e-04, 2.93979933e-04, 2.94983278e-04, 2.95986622e-04,\n",
       "       2.96989967e-04, 2.97993311e-04, 2.98996656e-04, 3.00000000e-04])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5e8a4491-191e-422b-a6a1-eb2e9c6cadd2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5e8a4491-191e-422b-a6a1-eb2e9c6cadd2' class='xr-section-summary' >Data variables: <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>y0</span></div><div class='xr-var-dims'>(dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9972 1.063 ... 0.07343 0.01665</div><input id='attrs-6e4b8f2f-9ba1-4155-8f35-0c09c3027823' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6e4b8f2f-9ba1-4155-8f35-0c09c3027823' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a911a9e4-9447-4477-be98-d7fc7f0fe35d' class='xr-var-data-in' type='checkbox'><label for='data-a911a9e4-9447-4477-be98-d7fc7f0fe35d' 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'><dt><span>name :</span></dt><dd>qubit</dd><dt><span>long_name :</span></dt><dd>High V</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>batched :</span></dt><dd>True</dd></dl></div><div class='xr-var-data'><pre>array([ 9.97233014e-01,  1.06318945e+00,  1.06455158e+00,  8.83793899e-01,\n",
       "        8.54829507e-01,  9.88824590e-01,  8.59848089e-01,  8.63993368e-01,\n",
       "        8.66778567e-01,  8.37008024e-01,  8.06025533e-01,  7.41484630e-01,\n",
       "        8.46138450e-01,  8.81818090e-01,  8.21757097e-01,  6.85518511e-01,\n",
       "        6.77180395e-01,  7.58038769e-01,  8.32173346e-01,  8.09036911e-01,\n",
       "        8.08151483e-01,  7.30401084e-01,  6.79787321e-01,  6.35216418e-01,\n",
       "        6.21462118e-01,  6.74769273e-01,  6.85184552e-01,  6.75744828e-01,\n",
       "        7.04377623e-01,  6.19177287e-01,  5.97234026e-01,  5.07610440e-01,\n",
       "        6.50901787e-01,  5.09120535e-01,  5.83673947e-01,  4.57201858e-01,\n",
       "        5.84567728e-01,  5.77278894e-01,  4.73231736e-01,  4.27799388e-01,\n",
       "        5.78058588e-01,  5.51680750e-01,  5.78029923e-01,  4.18137096e-01,\n",
       "        4.11911450e-01,  4.03883917e-01,  4.48900719e-01,  5.28552840e-01,\n",
       "        4.73869815e-01,  3.70490854e-01,  5.22947828e-01,  3.61286103e-01,\n",
       "        5.14261530e-01,  4.98071218e-01,  4.50247260e-01,  4.80092186e-01,\n",
       "        4.49971181e-01,  2.94803112e-01,  4.24198776e-01,  3.66737841e-01,\n",
       "        3.41820967e-01,  3.04630949e-01,  3.82957211e-01,  3.82565819e-01,\n",
       "        3.21770660e-01,  2.44865232e-01,  2.37730253e-01,  3.49009836e-01,\n",
       "        2.76030232e-01,  3.13826895e-01,  2.12045390e-01,  3.69873311e-01,\n",
       "        2.21918296e-01,  3.52387047e-01,  2.36441581e-01,  2.91823407e-01,\n",
       "        2.03581456e-01,  3.37557190e-01,  2.36679497e-01,  3.43210183e-01,\n",
       "...\n",
       "       -4.61657876e-02, -6.83193110e-02, -6.93639006e-02,  2.18082367e-02,\n",
       "       -6.81647069e-02, -7.64109642e-02,  4.16576630e-02,  2.35153147e-02,\n",
       "       -5.69305264e-02,  9.79578354e-02, -3.31327779e-03,  7.28614896e-02,\n",
       "        7.61512947e-03,  7.18063579e-02,  2.44945400e-02, -3.43585728e-02,\n",
       "       -2.65995496e-02, -4.39182302e-02,  1.12579746e-01,  1.00128339e-01,\n",
       "        7.66104633e-02, -2.65458351e-03,  1.06969853e-01, -3.54225244e-02,\n",
       "       -2.69185161e-02, -5.81867142e-02,  3.68301790e-02,  6.57066623e-02,\n",
       "        3.67318576e-02,  3.53448321e-02,  1.05504322e-01,  1.06860498e-01,\n",
       "        1.54140346e-02,  5.44167177e-02,  1.11359192e-01, -6.79992311e-02,\n",
       "       -6.95409544e-02, -2.13099346e-02, -1.21397148e-02, -7.37210756e-02,\n",
       "        1.12387545e-01,  1.05818471e-01,  3.82974502e-02, -5.29576181e-02,\n",
       "       -1.97436354e-02,  4.10315200e-02, -8.30792914e-02, -2.56467620e-02,\n",
       "       -5.27878109e-02,  9.54954450e-02,  2.30129755e-02, -2.17952174e-02,\n",
       "       -1.78294604e-02, -4.52961073e-02, -2.87813661e-02, -7.93521559e-02,\n",
       "       -6.80826340e-02, -3.70492161e-02,  4.45122126e-02,  2.68997858e-02,\n",
       "        7.70401831e-02, -2.50338925e-02,  7.87982867e-02,  6.46034769e-02,\n",
       "        3.14973938e-02,  1.05457720e-01, -1.96987589e-02, -7.21829777e-02,\n",
       "        1.22799884e-02,  1.69386660e-02, -6.04139585e-02,  4.03635435e-02,\n",
       "       -2.40656312e-02, -8.53643450e-02,  9.97848250e-02, -3.39919183e-02,\n",
       "       -3.49335894e-02, -4.87097756e-02,  7.34301353e-02,  1.66534679e-02])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-19675892-c8dd-4f51-b227-b8620e154d31' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-19675892-c8dd-4f51-b227-b8620e154d31' 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-cba7d790-43e4-4a9d-80b3-d00431f220e6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cba7d790-43e4-4a9d-80b3-d00431f220e6' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>tuid :</span></dt><dd>20231215-162637-782-b90b85</dd><dt><span>name :</span></dt><dd>noisy</dd><dt><span>grid_2d :</span></dt><dd>False</dd><dt><span>grid_2d_uniformly_spaced :</span></dt><dd>False</dd><dt><span>1d_2_settables_uniformly_spaced :</span></dt><dd>False</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (dim_0: 300)\n",
       "Coordinates:\n",
       "    x0       (dim_0) float64 0.0 1.003e-06 2.007e-06 ... 0.000299 0.0003\n",
       "Dimensions without coordinates: dim_0\n",
       "Data variables:\n",
       "    y0       (dim_0) float64 0.9972 1.063 1.065 ... -0.04871 0.07343 0.01665\n",
       "Attributes:\n",
       "    tuid:                             20231215-162637-782-b90b85\n",
       "    name:                             noisy\n",
       "    grid_2d:                          False\n",
       "    grid_2d_uniformly_spaced:         False\n",
       "    1d_2_settables_uniformly_spaced:  False"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meas_ctrl.settables(time_par)\n",
    "meas_ctrl.setpoints(np.linspace(0.0, 300.0e-6, 300))\n",
    "meas_ctrl.gettables(MockQubit())\n",
    "meas_ctrl.run(\"noisy\")  # by default `.run` uses `soft_avg=1`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5e34df94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<quantify_core.visualization.pyqt_plotmon.QtPlotObjForJupyter at 0x7f7601b17820>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotmon.main_QtPlot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cd212ae",
   "metadata": {},
   "source": [
    "Alas, the noise in the signal has made this result unusable! Let's set the `soft_avg` argument of the {meth}`.MeasurementControl.run` to 100, averaging the results and hopefully filtering out the noise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cace9ef8",
   "metadata": {
    "mystnb": {
     "remove-output": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting batched measurement...\n",
      "Iterative settable(s) [outer loop(s)]:\n",
      "\t --- (None) --- \n",
      "Batched settable(s):\n",
      "\t time \n",
      "Batch size limit: 300\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d1b1ed45c90940d885ebfabb4ee42aa2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Completed:   0%|           [ elapsed time: 00:00 | time left: ? ] it"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dset = meas_ctrl.run(\"averaged\", soft_avg=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5aa93cbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<quantify_core.visualization.pyqt_plotmon.QtPlotObjForJupyter at 0x7f7601b17820>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotmon.main_QtPlot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7ffa625",
   "metadata": {},
   "source": [
    "Success! We now have a smooth decay curve based on the characteristics of our qubit. All that remains is to run a fit against the expected values and we can solve for T1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bd8301b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'tau': 5.994542423605272e-05}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = Model(decay, independent_vars=[\"t\"])\n",
    "fit_res = model.fit(dset[\"y0\"].values, t=dset[\"x0\"].values, tau=1)\n",
    "\n",
    "fit_res.plot_fit(show_init=True)\n",
    "fit_res.values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3a36dec",
   "metadata": {},
   "source": [
    "## Interrupting\n",
    "Sometimes experiments, unfortunately, do not go as planned and it is desirable to interrupt and restart them with new parameters. In the following example, we have a long-running experiment where our Gettable is taking a long time to return data (maybe due to misconfiguration).\n",
    "Rather than waiting for this experiment to complete, instead we can interrupt any {class}`.MeasurementControl` loop using the standard interrupt signal.\n",
    "In a terminal environment this is usually achieved with a `ctrl` + `c` press on the keyboard or equivalent, whilst in a Jupyter environment interrupting the kernel (stop button) will cause the same result.\n",
    "\n",
    "When the {class}`.MeasurementControl` is interrupted, it will wait to obtain the results of the current iteration (or batch) and perform a final save of the data it has gathered, calling the `finish()` method on Settables & Gettables (if it exists) and return the partially completed dataset.\n",
    "\n",
    "```{note}\n",
    "The exact means of triggering an interrupt will differ depending on your platform and environment; the important part is to cause a `KeyboardInterrupt` exception to be raised in the Python process.\n",
    "```\n",
    "\n",
    "In case the current iteration is taking too long to complete (e.g. instruments not responding), you may force the execution of any python code to stop by signaling the same interrupt 5 times (e.g. pressing 5 times `ctrl` + `c`). Mind that performing this too fast might result in the `KeyboardInterrupt` not being properly handled and corrupting the dataset!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e1b46c8b",
   "metadata": {
    "tags": [
     "raises-exception"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting iterative measurement...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4a0fe23dac404b6ebf17f1c66d0818ac",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Completed:   0%|           [ elapsed time: 00:00 | time left: ? ] it"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "[!!!] 1 interruption(s) signaled. Stopping after this iteration/batch.\n",
      "[Send 4 more interruptions to forcestop (not safe!)].\n",
      "\n",
      "\n",
      "Interrupt signaled, exiting gracefully...\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "Measurement interrupted",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[16], line 27\u001b[0m\n\u001b[1;32m     25\u001b[0m meas_ctrl\u001b[38;5;241m.\u001b[39mgettables(SlowGettable())\n\u001b[1;32m     26\u001b[0m \u001b[38;5;66;03m# Try interrupting me!\u001b[39;00m\n\u001b[0;32m---> 27\u001b[0m dset \u001b[38;5;241m=\u001b[39m \u001b[43mmeas_ctrl\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mslow\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/quantify_core/measurement/control.py:388\u001b[0m, in \u001b[0;36mMeasurementControl.run\u001b[0;34m(self, name, soft_avg, lazy_set, save_data)\u001b[0m\n\u001b[1;32m    385\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_finish()\n\u001b[1;32m    386\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset_post()\n\u001b[0;32m--> 388\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_check_interrupt\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# Propagate interruption\u001b[39;00m\n\u001b[1;32m    390\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset\n",
      "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/quantify_core/measurement/control.py:666\u001b[0m, in \u001b[0;36mMeasurementControl._check_interrupt\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    658\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    659\u001b[0m \u001b[38;5;124;03mVerifies if the user has signaled the interruption of the experiment.\u001b[39;00m\n\u001b[1;32m    660\u001b[0m \n\u001b[1;32m    661\u001b[0m \u001b[38;5;124;03mIntended to be used after each iteration or after each batch of data.\u001b[39;00m\n\u001b[1;32m    662\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    663\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_thread_data\u001b[38;5;241m.\u001b[39mevents_num \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m    664\u001b[0m     \u001b[38;5;66;03m# It is safe to raise the KeyboardInterrupt here because we are guaranteed\u001b[39;00m\n\u001b[1;32m    665\u001b[0m     \u001b[38;5;66;03m# To be running MC code. The exception can be handled in a try-except\u001b[39;00m\n\u001b[0;32m--> 666\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMeasurement interrupted\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: Measurement interrupted"
     ]
    }
   ],
   "source": [
    "class SlowGettable:\n",
    "    \"\"\"A mock slow gettables.\"\"\"\n",
    "\n",
    "    def __init__(self):\n",
    "        self.name = \"slow\"\n",
    "        self.label = \"Amplitude\"\n",
    "        self.unit = \"V\"\n",
    "\n",
    "    def get(self):\n",
    "        \"\"\"Get method.\"\"\"\n",
    "        time.sleep(1.0)\n",
    "        if time_par() == 4:\n",
    "            # This same exception rises when pressing `ctrl` + `c`\n",
    "            # or the \"Stop kernel\" button is pressed in a Jupyter(Lab) notebook\n",
    "            if sys.platform == \"win32\":\n",
    "                # Emulating the kernel interrupt on windows might have side effects\n",
    "                raise KeyboardInterrupt\n",
    "            os.kill(os.getpid(), signal.SIGINT)\n",
    "        return time_par()\n",
    "\n",
    "\n",
    "time_par.batched = False\n",
    "meas_ctrl.settables(time_par)\n",
    "meas_ctrl.setpoints(np.arange(10))\n",
    "meas_ctrl.gettables(SlowGettable())\n",
    "# Try interrupting me!\n",
    "dset = meas_ctrl.run(\"slow\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a33155c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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